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CHANGES IN NETWORK FUNCTIONAL CONNECTIVITY AFTER TOTAL KNEE REPLACEMENT SURGERY WITH GENERAL ANESTHESIA: AN FMRI STUDY
By
HUA HUANG
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2018
© 2018 Hua Huang
To my Mother, and Father
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ACKNOWLEDGMENTS
I would like to express my special appreciation and thanks to my advisor
Professor Mingzhou Ding at this moment. Thank you for accepting me to start my PhD
research in neuroimaging and neuroscience. Thank you for your support when I was in
helpless situation, when support was needed to focus on my research work, and when I
got lost. Thank you for your precious funding support and your forgiveness when
inconvenience was raised up because of me. It is an impossible mission to reach this
stage without your guidance in my study and my life. It is my best luck to be your
student.
I would especially like to thank my committee members, Professor Hans van
Oostrom, Professor Jonathan Li, and Professor Jorg Peters for serving as my
committee members during my PhD study. I also want to thank you for your comments,
suggestions, and your support, especially suggestions from Professor Peters in graph
theory. Thank you all for reserving your precious time to attend my final defense.
I would also like to thank Professor Catherine Price, Dr. Jared Tanner, and Dr.
Hari Parvataneni for allowing me to join this research project and this big research
group to accomplish my study. Thanks for providing all the collected precious data and
sharing the cutting-edge research in this field.
I would especially like to thank Professor Roger Howe at Stanford University for
precious suggestions so I can persist and move forward with confidence. I would like to
give my special appreciation to Professor Wesley Bolch, and Debra Anderson for their
selfless help.
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I would also thank other researchers and all students in our lab for the help
during my study. Especially thank Dr. Qing Zhao, Dr. Abhijit Rajan, and Bijurika Nandi
for providing help and suggestions for my study.
A special appreciation to my family. I would like to express my thanks to my
Mother, and my Father. Thank you all for supporting me under any circumstances in so
many years. To my beloved daughter, thank you for making my life meaningful.
Thank you all who provided support throughout my study at University of Florida.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 8
LIST OF FIGURES .......................................................................................................... 9
LIST OF ABBREVIATIONS ........................................................................................... 11
ABSTRACT ................................................................................................................... 13
CHAPTER
1 INTRODUCTION .................................................................................................... 15
2 CHANGES IN INTRA-NETWORK CONNECTIVITY OF RESTING STATE NETWORKS FOLLOWING SURGERY .................................................................. 18
2.1 Introduction ....................................................................................................... 18 2.2 Methods ............................................................................................................ 20
2.2.1 Participant ............................................................................................... 20 2.2.2 Procedures .............................................................................................. 21
2.2.3 Anesthesia and Surgery Protocol ............................................................ 23 2.2.4 Neuroimaging .......................................................................................... 24
2.2.5 Functional MRI Data Preprocessing ........................................................ 24 2.2.6 FMRI Regions of Interest Selection ......................................................... 25
2.2.7 Variables for Regression ......................................................................... 26 2.2.8 Functional Connectivity Analysis ............................................................. 27 2.2.9 Statistical Analysis ................................................................................... 29
2.3 Results .............................................................................................................. 30 2.3.1 Intra-network Connectivity ....................................................................... 30 2.3.2 Node Strength of Intra-network Connectivity ........................................... 33
2.3.3 MCI versus Non-MCI ............................................................................... 34 2.4 Discussion ........................................................................................................ 35
3 CHANGES IN INTER-NETWORK CONNECTIVITY OF RESTING STATE NETWORKS FOLLOWING SURGERY .................................................................. 55
3.1 Introduction ....................................................................................................... 55 3.2 Methods ............................................................................................................ 57
3.2.1 FMRI Regions of Interest Selection ......................................................... 57
3.2.2 Functional Connectivity Analysis ............................................................. 57 3.3 Results .............................................................................................................. 59
3.3.1 Inter-network Connectivity ....................................................................... 59
3.3.2 Node Strength in Inter-network Connectivity ........................................... 62
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3.3.3 Correlation between Changes in Intra-network Connectivity and in Inter-network Connectivity ............................................................................. 64
3.4 Discussion ........................................................................................................ 65
4 CHANGES IN FUNCTIONAL BRAIN CONNECTOME FOLLOWING SURGERY .. 93
4.1 Introduction ....................................................................................................... 93 4.2 Methods ............................................................................................................ 95
4.2.1 FMRI Regions of Brain Areas .................................................................. 95
4.2.2 Functional Connectivity Analysis ............................................................. 95 4.2.3 Graph Theoretical Analysis ..................................................................... 95
4.3 Results .............................................................................................................. 98 4.3.1 Changes in Global Network Properties .................................................... 98
4.3.2 Resilience Analysis of the Whole Brain Network ..................................... 98 4.3.3 Connection Density and Mean Functional Connectivity ......................... 100 4.3.4 Brain Areas with Connectivity Changes ................................................. 101
4.4 Discussion ...................................................................................................... 103
5 CONCLUSIONS ................................................................................................... 117
LIST OF REFERENCES ............................................................................................. 119
BIOGRAPHICAL SKETCH .......................................................................................... 129
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LIST OF TABLES
Table page 2-1 The MNI Coordinates of the Regions of Interest (ROI) ....................................... 42
2-2 Participant Characteristics: Surgery Group versus Non-Surgery Group. ............ 43
2-3 Participant Characteristics: MCI versus Non-MCI in Different Groups. ............... 44
2-4 Mixed Repeated ANOVA between Surgery and Non-Surgery ............................ 45
2-5 Changes in Node Strength in Surgery Group ..................................................... 46
2-6 MCI versus Non-MCI in Surgery Group .............................................................. 47
2-7 MCI versus Non-MCI in Non-Surgery Group ...................................................... 48
3-1 The MNI Coordinates of the Regions of Interest (ROI) ....................................... 70
3-2 Inter-network Pearson correlation coefficients of DMN and SN in surgery group including MCI and non-MCI subtypes ....................................................... 71
3-3 Inter-network Pearson correlation coefficients of DMN and CEN in surgery group including MCI and non-MCI subtypes ....................................................... 74
3-4 Inter-network Pearson correlation coefficients of CEN and SN in surgery group including MCI and non-MCI subtypes ....................................................... 77
3-5 Node strength of DMN and SN of the inter-network in surgery group including MCI and non-MCI subtypes ................................................................................ 80
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LIST OF FIGURES
Figure page 2-1 Schematic design of parallel surgery and non-surgery participant timelines ...... 49
2-2 TKA surgery group mean edge functional connectivity changes from pre to post surgery time points ..................................................................................... 51
2-3 The connectivity of pre and post-surgery in four resting state network networks ............................................................................................................. 52
2-4 The comparison between MCI and non-MCI surgery groups ............................. 52
2-5 The comparison between MCI and non-MCI non-surgery groups ...................... 53
2-6 Changes in node strength of functional connectivity pre and post-surgery ......... 53
2-7 Node strength changes in different groups ......................................................... 54
2-8 Nod strength changes in MCI and non-MCI ....................................................... 54
3-1 Schematic diagram of the functional interactions between three resting state networks: DMN, CEN and SN............................................................................. 83
3-2 The inter-network correlation between DMN and SN ......................................... 84
3-3 The inter-network correlation between DMN and CEN ....................................... 85
3-4 The inter-network correlation between CEN and SN .......................................... 86
3-5 The comparison between MCI and non-MCI groups in inter-network correlation ........................................................................................................... 87
3-6 The node strength of DMN in inter-network correlation between DMN and SN .. 88
3-7 The node strength of SN in inter-network correlation between DMN and SN ..... 89
3-8 The comparison of node strength between MCI and non-MCI in inter-network correlation of DMN-SN ....................................................................................... 90
3-9 The correlation between intra-network connectivity changes of SN pre-post surgery and inter-network connectivity pre-post surgery of DMN-SN ................. 91
3-10 The correlation between intra-network connectivity and inter-network connectivity. ........................................................................................................ 92
4-1 Schematic diagram for the whole brain network analysis ................................. 107
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4-2 The topological properties of the brain networks in surgery group and non-surgery group before and after surgery ............................................................ 108
4-3 Resilience analysis: global efficiency was calculated after removing the nodes with descending order ............................................................................ 109
4-4 Comparison between pre-surgery and post-surgery in descending order range from 181 to 220 ...................................................................................... 110
4-5 Connection density and mean functional connectivity calculated by removing the nodes with descending order ...................................................................... 111
4-6 Comparison between pre-surgery and post-surgery in descending order range from 181 to 220 ...................................................................................... 112
4-7 Adjacency matrix keeping top 40% of functional connectivity ........................... 113
4-8 Brain area showing changes in connectivity ..................................................... 114
4-9 Areas with increased or decreased functional connectivity following surgery (positive adjacency matrix) ............................................................................... 115
4-10 Brain areas with increased or decreased functional connectivity following surgery (negative adjacency matrix) ................................................................. 116
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LIST OF ABBREVIATIONS
ACC Anterior cingulate cortex
AG Angular gyrus
AI Anterior insula
BA Brodmann area
BOLD Blood oxygen level dependent
CEN Central executive network
CFNB Continuous femoral nerve blocks
D-KEFS Delis-Kaplan executive function system
dLPFC Dorsolateral prefrontal cortex
DMN Default mode network
ExC Extrastriate visual cortex in the central fields
ExP Extrastriate visual cortex in the peripheral fields
FA Fractional anisotropy
FDR False discovery rate
FIR Finite impulse response
fMRI Functional magnetic resonance
IN Insula
IPL Inferior parietal lobule
lAI Left anterior insula
MCI Mild cognitive impairment
MED Morphine equivalent dosage
mPFC Medial prefrontal cortex
Non-MCI Non mild cognitive impairment
PCC Posterior cingulate cortex
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POCD Postoperative cognitive dysfunction
rAI Right anterior insula
ROI Regions of interest
rsfMRI Resting state functional magnetic resonance
RSN Resting state network
SN Salience network
SST Stop signal task
TKA Total knee arthroplasty
V1C Central visual cortex
V1P Peripheral visual cortex
vmPFC Ventromedial prefrontal cortex
VN Visual network
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
CHANGES IN NETWORK FUNCTIONAL CONNECTIVITY AFTER TOTAL KNEE REPLACEMENT SURGERY WITH GENERAL ANESTHESIA: AN FMRI STUDY
By
Hua Huang
December 2018
Chair: Mingzhou Ding Major: Biomedical Engineering
The brain is a large network comprised of many subnetworks. Brain functional
connectivity reflects the organization and coordination of different brain areas to achieve
normal activities. Functional magnetic resonance imaging (fMRI) is an important
noninvasive method to look into these functional connections. The brain functions have
different properties at different stages of the whole life span. Older adults may have
weakened brain functions compared to young adults. Major surgery as a perturbation on
the brain may induce acute or chronic functional changes in older adults.
This dissertation examined the brain functional changes caused by the total knee
arthroplasty (TKA) in older adults. Three important resting state networks were analyzed
first to examine the changes of intra-network connectivity. The interactions between
networks were inspected next to evaluate the changes in the coordination between
these three networks in maintaining the normal brain activity. The complex whole brain
functional connectome defined by the standard mask with 234 regions was analyzed
using graph theory to evaluate the changes in the whole brain functional connectivity
following TKA surgery.
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The main findings were as follows. (1) The intra-network connectivity in DMN,
SN, and CEN had significant decline after surgery. No significant changes were found in
non-surgery group. MCI surgery group was more susceptible to the injury caused by
surgery and had more functional decline compared to non-MCI surgery group.
Furthermore, the node strength in DMN and SN had significant decline. (2) In inter-
network connectivity between three networks, the anti-correlated connections of DMN-
SN declined significantly after surgery. Increased connectivity of DMN-CEN was found,
but there were no significant changes in SN-CEN. MCI patients had more pronounced
DMN-SN functional decline. The intra-network connectivity and inter-network
connectivity had significant linear relationship. (3) The whole brain network resilience,
connection density, and mean functional connectivity had significant increase in brain
areas with low functional connections. The decreased connectivity were identified in
brain areas of bilateral insular, amygdala, and putamen, and increased connectivity
were found in precuneus, fusiform, and occipital cortex. This dissertation thus
suggested that the surgery had significant and acute impacts on brain functional
network organization in older adults.
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CHAPTER 1 INTRODUCTION
The human brain is a complex system which finely controls the body and the
mind. The brain consists of various anatomic areas which are in charge of different
functions and coordinate with each other to perform different tasks. The independent
and visible anatomic structures of the brain are closely connected to each other
functionally, which is invisible (Bullmore & Sporns, 2009; Sporns, 2013). The brain
functional connectivity is well maintained to coordinate different brain functions to
achieve the normal activities (Power et al., 2011). Functional magnetic resonance
imaging (fMRI) is one of the most important and noninvasive techniques to look into
these functional connections by extracting the functional information from the signals
generated during brain activities while it brings no interference to the brain. How to
understand the brain activities and its functions obtained from fMRI is an important
topic. The organization of the brain connectivity can be thought of as a network in which
every component needs to work cooperatively with each other to achieve specific
functions. Many methods based on network theory (Andrews-Hanna et al., 2007;
Koshimori et al., 2016; Menon, 2011; Schiff et al., 2005; Xia & He, 2017), signal
analysis (Anderson et al., 2011; Murphy et al., 2009; Murphy & Fox, 2017; Wen et al.,
2012; Zhang et al., 2016), and data mining (De Schutter, 2018; Floren et al., 2015;
Glaser et al., 2017; Vu et al., 2018) have been proposed to reconstruct the nature of the
brain according to our knowledge and understand the whole picture of the brain
functions.
The brain networks are not static but dynamic. Brain functions have different
properties within different ranges of normal aging (Andrews-Hanna et al., 2007;
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Krajcovicova et al., 2014). Older adults develop weakened or strengthened brain
functions compared to young adults. Many interferences from inside or outside of the
brain may be harmful to this delicate but susceptible system. Major surgery as a
perturbation to the brain may induce some rapid or long-term changes in the human
brain in older adults (Browndyke et al., 2017). The anesthesia applied during the
surgery may also cause some acute response or chronic injury to the brain functions
(Huang et al., 2018; Ramani, 2017).
Resting state networks (RSN) as important human brain networks can be
identified using resting state functional magnetic resonance imaging (rsfMRI) (Boveroux
et al., 2010; Li et al., 2017; Rombouts et al., 2005). The rsfMRI can be used in brain
mapping to evaluate the interactions between different brain regions when no task is
performed and the brain region shows spontaneous fluctuations in BOLD (Blood-
oxygen-level dependent) signals. Among the resting state networks, three important
networks are closely related to cognitive functions. They are default mode network
(DMN), central executive network (CEN), and salience network (SN).
Many intrinsic and external situations, such as trauma and brain disorders, can
induce changes in these three brain networks, which may lead to pathological
coordination between brain regions and the cognitive impairment. The postoperative
cognitive dysfunction (POCD) is the symptoms associated with the decline in cognitive
functions after the patients receive major surgery; memory functions and executive
functions are particularly vulnerable (Deiner & Silverstein, 2009). These effects can
cause long-term disorder which may last for several years and even a lifetime.
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This dissertation examined the brain functional changes to evaluate the acute
injury caused by the total knee replacement surgery in older adults. This research
included three aims:
1. Three important resting state brain networks defined using standard
coordinates were analyzed for the intra-network connectivity to examine the changes
within each network after surgery.
2. The connectivity between networks were analyzed to evaluate the interactions
among the three networks in coordinating the brain activity. The relationship between
inter-network connectivity and intra-network connectivity was examined to test their
relationship.
3. The whole brain connectivity was analyzed based on the standard mask
including 234 brain regions. The complex network properties were analyzed using the
graph theory to evaluate the properties of the brain network including resilience of the
network to perturbation or injury. The decreased and increased whole brain functional
connections in each brain area were examined to evaluate the changes in brain
networks caused by surgery or the anesthesia in older adults.
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CHAPTER 2 CHANGES IN INTRA-NETWORK CONNECTIVITY OF RESTING STATE NETWORKS
FOLLOWING SURGERY
2.1 Introduction
Total knee arthroplasty (TKA) is one of the major surgeries which are normally
performed in older adults. This surgery also comes with side effects which cannot be
neglected which includes delirium and POCD (Moller et al., 1998; Rasmussen et al.,
2003). The mechanism of the POCD remains unknown and further investigation needs
to be conducted. Three important resting state networks including DMN, CEN, and SN
are closely related to cognitive functions. We hypothesized that examining these three
networks and their changes after surgery may shed light on the mechanisms of POCD.
The total knee replacement surgery with general anesthesia was reported to
show the acute cognitive decline within 48 hours after surgery in old adults (Boveroux et
al., 2010; Huang et al., 2018; Hudetz, 2012; Ramani, 2017). Important resting state
networks such as DMN were examined and all these networks show significant decline
in terms of the intra-network functional connectivity. The cognitive integrity was used to
predict the changes of the functional connectivity showing that patients with lower
cognitive integrity had more decline in DMN.
The cause of POCD is not well known so far, but several factors causing the
disruption of brain networks including anesthesia are thought to play a role. This effect
is pronounced in the older group of subjects when they receive major surgery. DMN,
CEN, and SN show significant decline of connectivity after surgery. However, whether
the declined functional connectivity are evenly distributed across all pairs of ROIs in the
network remains unknown (Liu et al., 2012; Xie et al., 2011; Ramani, 2017). Each ROI
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may be susceptible to the interference to different degrees, which needs to be looked
into.
According to the classification of the neuropsychological testing, patients can be
divided into two categories: MCI (mild cognitive impairment) patients and non-MCI
patients. The integrity of the brain functional network plays an important role in
maintaining the normal cognitive activities. The disorder of the cognitive status may
increase the susceptibility of brain networks to the trauma of surgery or anesthesia
(Huang et al., 2018; Browndyke et al., 2017; Sperling et al., 2011). This is another issue
that needed to be studied.
This chapter will focus on the intra-network connectivity in three cognitive
networks, DMN, CEN, and SN, before and after surgery to evaluate the changes
caused by the surgery. This may provide information on the prediction of long term
outcomes in the older adult group undergoing major surgery. First, we examined the
intra-network connectivity between each pair of ROIs in each of the three networks to
calculate the changes at the level of whole brain network and determine which network
suffers more injury after surgery. Second, we looked into the interactions between each
pair of ROIs within each network to find which pair of ROIs plays the most important role
in its network. Third, the importance of each ROI was examined using node strength in
each network to evaluate the susceptibility of each ROI having connectivity with the rest
of the other ROIs within each network. Last, the subtypes of MCI patients and non-MCI
patients in surgery group and non-surgery group were also examined for the
compromised functional connectivity to provide the evaluation of the insult or side
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effects of the surgery trauma for each group according to the classification of the
neuropsychological tests.
2.2 Methods
2.2.1 Participant
All participants who received the total knee arthroplasty (TKA) were recruited
through University of Florida orthopedic clinics; they were screened for dementia via a
telephone interview (Welsh et al., 1993; Cook et al., 2009), and enrolled between 2013
and 2016. This is part of the federally funded investigation. All participants in the non-
surgery group were recruited through University of Florida orthopedic clinics, community
mailings, and locally posted fliers. Non-surgery participants were selected as the control
group through a yoked review process to match individual surgery participant on age,
education, sex, and ethnicity or race. Non-surgery participants had to receive no
surgery for at least one year prior to enrollment. Both groups were recruited over the
same timeline and both were tested and scanned at the same time intervals. All
participants were selected to meet the following inclusion and exclusion criteria. 1) age
is 60 or older, 2) English is the primary language, 3) participants have osteoarthritis or
comparable joint pain, 4) participants have intact activities of daily living, and 5)
participants have baseline neuropsychological testing unsupportive for dementia criteria
per Diagnostic and Statistical Manual of Mental Disorders – Fifth Edition (American
Psychiatric Association, 2013). Additional exclusion criteria included as following: 1) any
other major surgery within the study timeline, 2) history of head
trauma/neurodegenerative illness, 3) documented learning or seizure disorder, 4) less
than a sixth-grade education, 5) substance abuse in the last year, 6) major cardiac
disease, 7) chronic medical illness known to induce encephalopathy, 8) implantable
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device precluding an MRI, and 9) an unwillingness to complete the MRI. Two
neuropsychologists reviewed the baseline data to confirm that test scores met the
expected ranges for non-demented individuals.
A total of 116 patients out of 232 surgery patients referred by the study surgeon
(HP) and contacted for study inclusion agreed to join this study with 73 subjects meeting
inclusion and exclusion criteria and completing baseline neuropsychological
assessment and MRI scanning. Four surgery participants were excluded from the data
analysis due to presence of pre-existing silent strokes (2 participant) and MRI post-
surgery scanner complications (2 participants). For the surgery group, the final dataset
included 69 surgery participants who completed baseline assessment, pre surgery and
post-surgery rsfMRI. For the non-surgery control group, a total of 68 participants out of
104 participants were enrolled. Two subjects were excluded due to a learning disorder
in neuropsychological testing and one was excluded for missing rsfMRI. The final
dataset included 65 non-surgery participants who completed the baseline assessment,
pre surgery and post pseudo-surgery rsfMRI.
This research was approved by the University of Florida Institutional Review
Board in Gainesville, Florida. The research was conducted in accordance to principles
of the Declaration of Helsinki. All participants were informed appropriately and signed
the consents.
2.2.2 Procedures
A schematic diagram was shown in Figure 2-1 to illustrate the whole procedures
of this study. Participants completed a phone cognitive screening (Cook et al., 2009)
and a comprehensive history and systematic interview to confirm inclusion and
exclusion criteria and the following tests were conducted: an in-person comorbidity
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rating (Charlson et al., 1987) of activities of daily living (Lawton & Brody, 1969),
neuropsychological assessment, and the first brain image scanning of MRI. All
participants in the surgery group received surgery of TKA. At the same time, the
participants in the control group were assigned a date for pseudo-surgery. All
participants received the post-surgery brain image scanning of MRI within 48 hours after
the surgery for patients and pseudo-surgery for controls. The same examiner completed
all tests for the surgery group and the non-surgery group. Trained raters who are blind
to participant’s condition scored the data and the data were double entered.
All participants including the surgery group and non-surgery group were
classified as MCI or non-MCI according to the comprehensive criteria determined by
Jak and Bondi and colleagues (Jak et al., 2009). An individual was classified as the
MCI, when the individual’s scores fall below one standard deviation in at least two
measures within any one domain defined by MCI criteria. The comprehensive criteria
were not only used to classify MCI but also used for the classification of the subtypes of
MCI including amnestic and non-amnestic. This method was recommend instead of
conservative criteria because it has the stable balance between sensitivity and
specificity for testing impairment and has the advantage of less false negatives (Jak et
al., 2009). MCI patients was classified with impairment domains listed as following: 1)
Attention Domain - Part A of the Trail Making Test (Corrigan & Hinkeldey, 1987), and
the letter number sequencing and digit span forward subtests of the WAIS-III (David
Wechsler, 1997); 2) Executive Domain: Part B of the Trail making test (Corrigan &
Hinkeldey, 1987), the total achievement score from the Tower Test from the Delis-
Kaplan Executive Function System (D-KEFS) test (Dean C. Delis, Edith Kaplan, 2001),
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and the Stroop Color Word test (Stroop, 1935); 3) Language Domain: Animal (Lezak,
2012) and letter fluency (Spreen & Strauss, 1998) and the Boston Naming Task
(Kaplan, 1983); 4) Visuospatial Domain: Copy portion of the Rey-Osterrieth Complex
Figure Design (Rey, 1941; Osterrieth, 1944), the matrix-reasoning portion of the
Weschler Adult Intelligence Scale – third edition (David Wechsler, 1997), and the
Judgment of Line Orientation (Benton, 1983); 5) Memory Domain: Hopkins Verbal
Learning Task (Brandt, 1991), the Logical Memory Delay portion of the Wechsler
Memory Scale – third edition (David Wechsler, 1997), and the delay portion of the Rey-
Osterrieth Complex Figure Design (Rey, 1941; Osterrieth, 1944).
2.2.3 Anesthesia and Surgery Protocol
The procedures for anesthesia and surgery followed the standardized steps. All
the TKA surgeries were done by the same surgeon for all surgery patients according to
the standard protocol. Surgery patients received the intravenous midazolam (1–4 mg)
for reducing anxiety followed by continuous femoral nerve blocks (CFNB) and single-
injection subgluteal sciatic nerve blocks with 20ml and 30ml of 0.5% ropivacaine as a
bolus injection, respectively. The CFNB was combined with 0.2% ropivacaine at the
infusion rate of 10 ml/hour during the surgery. Propofol, fentanyl, and rocuronium were
used for anesthesia induction and intubation but no opioids were used. Surgery patients
were ventilated with the mixture of air and oxygen to maintain the end-tidal carbon
dioxide at 35±5 mm. The anesthesia was carefully maintained using both inhaled
isoflurane and intravenous fentanyl and rocuronium. A tourniquet was elevated to
250mmHg before incision and deflated when the surgery was close to closure. Bony
preparation used the intramedullary instrumentation for the femoral side and the
extramedullary instrumentation for the tibial side, respectively. Both the anterior and
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posterior cruciate ligaments were removed and replaced by the artificial implants
attached to the bone using bone cement for all surgery patients. All the TKA surgeries
lasted 2-3 hours in operating room.
2.2.4 Neuroimaging
Structural and resting state functional MRI was collected before surgery and
within 48 hours after surgery for the surgery group and the non-surgery group (pseudo
surgery), while the delirium was assessed within 24 hours after surgery using the
Confusion Assessment Method (Inouye et al., 1990). In order to avoid the difficulty of
focusing on the cross during the MRI data collection especially rsfMRI after surgery, the
eye closed condition was chosen for resting state fMRI recording.
All participants including the surgery group and the non-surgery group took the
MRI for T1 weighted image, rsfMRI, task fMRI, and DTI (3T Siemens Verio; 8 channel
head coil). T1 weighted images were acquired using the following parameters: TR:
2500ms; TE: 3.77ms; 176 sagittal 1mm3 slices, 1 mm isotropic resolution; 256x256x176
matrix, 7/8 phase partial Fourier, total acquisition time: 9:22 minutes. Resting state fMRI
were acquired while the participant’s eyes were closed using the following parameters:
TR: 2000ms; TE: 30ms; 36 transverse slices; 3.5 mm3 isotropic voxel size,
225x225x126 matrix, GRAPPA, total acquisition time: 7:38 minutes.
2.2.5 Functional MRI Data Preprocessing
The resting state fMRI data of all subjects were preprocessed firstly according to
the methods described as follows before data analysis. The first six functional scans of
each patient were removed to eliminate transients during the MRI acquisition. The
remaining fMRI slices were preprocessed using the method of SPM provided by
www.fil.ion.ucl.ac.uk/spm. The slice timing correction was conducted to eliminate
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acquisition delays across slices. The motion artifacts were corrected by realigning all
functional MR images to the reference image after timing correction. Following the
motion correction, all the functional MR images were co-registered to the T1 structural
image, which were normalized to the standard MNI152 T1 template, and these slices
were resampled at the resolution of 3mm×3mm×3mm (X, Y, and Z, respectively).
Functional images in the MNI space were then smoothed with an 8mm full width at half
maximum (FWHM) isotropic Gaussian kernel for data analysis.
2.2.6 FMRI Regions of Interest Selection
Four resting state networks (RSNs): default mode network (DMN), central
executive network (CEN), salience network (SN), and visual network (VN) were chosen
according to the standard coordinates defined by Power and colleagues (Power et al.,
2011) and Yeo and colleagues (Thomas Yeo et al., 2011). The regions of interest
(ROIs) for the four RSNs were defined as following. DMN consists of 6 ROIs: medial
prefrontal cortex (mPFC), posterior cingulate cortex (PCC), bilateral angular gyrus (AG),
and bilateral temporal (LT); CEN includes bilateral dorsolateral prefrontal cortex
(DLPFC) and bilateral inferior parietal lobule (IPL); SN consists of dorsal anterior
cingulate cortex (ACC) and bilateral anterior insula (IN); and the VN consists of bilateral
central visual cortex (V1C), bilateral peripheral visual cortex (V1P), bilateral extrastriate
visual cortex in the central fields (ExC), and bilateral extrastriate visual cortex in the
peripheral fields (ExP). The ROIs representing each brain region were defined using a
5mm sphere in radius centered at the coordinates of that region to extract the BOLD
signals for further analysis. Table 2-1 listed coordinates of these regions. Here the main
interest is in the changes of the three major cognitive networks DMN, SN and CEN. VN
is mainly a sensory network and is included here as a control network.
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2.2.7 Variables for Regression
Many factors may contribute to the magnitude of the BOLD signals besides the
brain activities themselves. Cognitive reserve similar to brain reserve may result in the
discrepancy in brain integrity and cognitive functioning. Cognitive reserve is more about
the capability of the brain based on previous cognitive abilities to protect the brain
functions from pathological attacks to maintain the normal cognitive activities (Stern,
2002). The amount of cognitive reserve can prevent the pathological progression from
developing dementia (Valenzuela et al., 2006). Especially, cognitive reserve has shown
the possibility to modulate functional connectivity in patients with MCI (Bozzali et al.,
2015). Years of education is widely used for cognitive reserve evaluation (Valenzuela et
al., 2006). Thus, it was included as one variable for differentiating the cognitive reserve
for surgery groups and non-surgery groups.
Some variables such as morphine (Khalili-Mahani et al., 2012) and pain (Loggia
et al., 2013; Kuner & Flor, 2017) were also considered related to the magnitude of
functional connectivity. Morphine equivalent dosages (MED) were adopted to evaluate
the effects on postoperative surgery group using a published conversion algorithm
(Dowell, Haegerich, & Chou, 2016). The MED was potentially active if the dose was
administered within six hours before the MRI scanning after surgery. Pain assessment
ratings (0-100; 100=worst) were acquired before and after surgery prior to the MRI
scanning. If participants missed pain assessment ratings, a pain rating was imputed
based on the average of 10 imputed scores. Imputations were calibrated in SPSS using
a regression only including other participants who shared the same group for both
surgery and non-surgery patients.
27
Age and gender can also show the interaction with functional connectivity in
resting state networks (Goldstone et al., 2016). Thus, age, gender, pain, MED, and
education were taken as covariates in functional connectivity. To exclude the effects
caused by these variables on functional connectivity coefficients, the linear regression
was applied to regress out pain and education in both pre surgery group and non-
surgery group; pain, education, and active MED in post-surgery group; pain and
education in post non surgery group as well as age and gender for all groups. The
residual after linear regression plus mean value was used to replace the original
functional connectivity for all groups.
The linear regression formula is described as follows:
𝑌 = 𝛽0 + 𝛽1𝑋1 + 𝛽2𝑋2 + ⋯ + 휀 (2-1)
Where Y is dependent variable, β0 is intercept, β1, β2 are slope coefficients, X1, X2, …
are independent variable, ε is the residual term.
2.2.8 Functional Connectivity Analysis
The rsfMRI functional connectivity was evaluated based on the cross correlation
between the time series of BOLD signals extracted from the brain regions defined as
the ROIs. Nine nuisance signals were regressed out including 6 movement variables
and 3 averaged signals of white matter, cerebrospinal fluid, and global signal. The time
series were then filtered with a finite impulse response (FIR) band-pass filter (between
0.01 and 0.1 Hz). The filtered BOLD signals were averaged across all voxels to obtain
one mean signal representing each ROI. Motion scrubbing procedure for motion
censoring (Power et al., 2012) was conducted on the BOLD signals to reduce the
potential adverse effects of abrupt movements, no matter how small, on functional
connectivity. The functional connectivity between each pair of ROIs within each resting
28
state network was quantified using the Pearson cross correlation between each pair of
BOLD signals. Age, gender, pain, MED, and education (see above) were regressed out
from the functional connectivity between each pair of ROIs.
1. The mean functional connectivity of each RSN at the network level was
calculated by averaging the corrected cross correlation values of all pairs of ROIs within
each RSN for each subject (Fornito et al., 2016; Rubinov & Sporns, 2010). Both
preoperative and postoperative resting state functional connectivity of each subject
were calculated to evaluate the connectivity changes related to surgery.
The Pearson correlation coefficient is defined as follows:
𝜌𝑋,𝑌 =𝐸[(𝑋−𝜇𝑋)(𝑌−𝜇𝑌)]
𝜎𝑋𝜎𝑌 (2-2)
Where E is the expectation, µX is the mean of X, µY is the mean of Y, σX is the standard
deviation of X, σY is the standard deviation of Y.
The internal connectivity Cs is defined as follows:
𝐶𝑠 =∑ 𝜀𝑖,𝑗𝑖,𝑗∈𝑠
𝑁𝑠×(𝑁𝑠−1) (2-3)
Where Ns is the number of nodes within a RSN s, and εi,j is the existing edge within
module s, i≠j.
2. At node level, the functional connectivity between each pair of nodes in each
individual network were calculated to compare the difference between pre surgery and
post-surgery.
3. The node strength of each ROI which is the sum of the functional connectivity
between the ROI and the rest of other ROIs in each network was also calculated to
evaluate the importance of the ROI in the network at node level.
29
4. The subtypes of the MCI and non-MCI surgery groups were also analyzed at
node level and at the network level to examine the significant effects of the surgery on
the subtypes.
The node strength Si is defined as follows:
𝑆𝑖 = ∑ 𝑤𝑖𝑗𝑁𝑗=1 (2-4)
Where N is the number of nodes, wij is the weighted connectivity.
2.2.9 Statistical Analysis
An independent samples t-test was applied to compare surgery patients to non-
surgery participants on demographic variables. A one-way ANOVA was used to
examine demographic differences of MCI group between surgery and non-surgery
participants (MCI surgery group vs. non-MCI surgery group vs. MCI non-surgery group
vs. non-MCI non-surgery group). Bonferroni corrected Post Hoc analyses were
conducted on significant interactions between groups to find the group difference.
The difference of average connectivity in all RSNs between surgery group and
non-surgery group were examined using a mixed repeated measures analysis of
variance (ANOVA). All significant interactions were assessed using pairwise
comparisons, Bonferroni corrected. The difference in node strength within each network
between groups (surgery and non-surgery) were tested. To compare the difference
between pre-surgery and post-surgery for each group, paired sample t-test were applied
between pre and post time points with correction for multiple comparison using
Bonferroni correction. The statistic Cohen’s D was calculated (Faul et al., 2009; Faul et
al, 2007) to compare the differences between groups.
The Cohen’s D is determined as follows:
30
𝐷 = 𝑀2−𝑀1
√((𝑆𝐷1)2+(𝑆𝐷2)2)/2 (2-5)
Where M1 and M2 are the mean values, SD1 and SD2 are the standard deviation.
For the comparison between MCI group and non-MCI group in average
connectivity (pre-surgery and post-surgery), a paired sample t-test was applied to
compare pre and post time points for MCI surgery, non-MCI surgery, MCI non-surgery,
non-MCI non-surgery. The statistic Cohen’s D was also calculated to compare the
difference between groups.
2.3 Results
2.3.1 Intra-network Connectivity
Comparison of participant characteristics. Independent sample t-test was
examined to show that the surgery and non-surgery groups had no difference on age,
education, gender, race, ventricular volume, head size, pre-surgery pain at the time of
rsfMRI, and the interval days between pre-surgery and post-surgery rsfMRI. Although
non-surgery participants were matched with surgery participants demographically
through a thorough screening process, the surgery group had significantly lower
cognitive scores than the non-surgery group. The surgery group, however, had
significantly more pain at the time of post-surgery rsfMRI, which is expected. Four
surgery participants were identified as having delirium lasting less than one day after
surgery, but no participants had evidence of delirium at the time of the post-surgery
rsfMRI. Table 2-2 included the demographic characteristics for surgery and non-surgery
participants.
Intra-network changes after surgery. The effects of the surgery on the intra-
network connectivity were examined using the mixed repeated measures ANOVA for
31
significance in RSNs including DMN, CEN, SN and VN. Figure 2-2 included all four
networks for comparison between pre-surgery and post-surgery. For the connectivity in
DMN, the comparison between pre-surgery and post-surgery for surgery group and
non-surgery group were shown in Figure 2-2 and Figure 2-3 A. At network level, the
Pearson correlation in post-surgery group has significant decline in the intra-network
connectivity compared to the pre-surgery in the surgery group, as shown in Figure 2-3 A
(p<0.05). The non-surgery did not show significant changes between pre-pseudo
surgery and post-pseudo surgery in Figure 2-3 A (p>0.05). In order to look into the
effects of surgery on each pair of the connectivity in the DMN, the comparison were also
tested for each subject at node level shown in Figure 2-2. Several pairs of connectivity
including the ROIs associated with mPFC and PCC have significant decline in surgery
group (p<0.05). In non-surgery group, no significant declined was found across all pairs
of ROIs in DMN. All the statistic results were included in Table 2-4 for surgery and non-
surgery groups. Table 2-5 included the comparison of each pair of ROIs in surgery
group.
The comparison of CEN between pre-surgery and post-surgery for surgery group
and non-surgery group were shown in Figure 2-2 and Figure 2-3 B. At network level, the
Pearson correlation in post-surgery group has significant decline in the intra-network
connectivity compared to the pre-surgery in surgery group shown in Figure2-3 B
(p<0.05). The non-surgery did not show significant changes between pre-pseudo
surgery and post-pseudo surgery in Figure 2-3 B (p>0.05). In order to look into the
effects of surgery on each pair of the connectivity in the CEN, the comparison were also
examined for each subject at node level in Figure 2-2. Several pairs of connectivity have
32
significant decline in surgery group (p<0.05), including lIPL-rIPL. In the non-surgery
group, no significant declined was found across all pairs of ROIs in CEN. The statistic
results were listed in Table 2-4 for surgery and non-surgery groups. Table 2-5 included
the comparison of ROIs before and after surgery.
The comparison of SN between pre-surgery and post-surgery for surgery group
and non-surgery group were shown in Figure 2-2 and Figure 2-3 C. At network level, the
Pearson correlation in post-surgery group has significant decline in the intra-network
connectivity compared to the pre-surgery in surgery group shown in Figure 2-3 C
(p<0.05). The non-surgery group did not show significant changes between pre-pseudo
surgery and post-pseudo surgery in Figure 2-3 C (p>0.05). To examine the effects of
surgery on each pair of the connectivity in the SN, the comparison were also tested for
each subject at node level shown in Figure 2-2. Two pairs of connectivity associated
with dACC have significant decline in surgery group (p<0.05) including dACC-lIN and
dACC-rIN. In non-surgery group, no significant declined was found across all pairs of
ROIs in SN. The results were shown in Table 2-4 for surgery and non-surgery groups, in
Table 2-5 for surgery comparison.
For the control network VN, the comparison between pre-surgery and post-
surgery for surgery group and non-surgery group were examined in Figure 2-2 and
Figure 2-3 D. At network level, the Pearson correlation post-surgery did not show
significant changes in connectivity compared to pre-surgery in surgery group, as shown
in Figure 2-3 D (p>0.05). The non-surgery group had no significant changes between
pre-pseudo surgery and post-pseudo surgery in Figure 2-3 D (p>0.05). At node level,
the comparison were also tested for each subject to check the effects of surgery on
33
each pair of the connectivity in the VN in Figure 2-2. No pairs of connectivity have
significant decline in surgery group (p>0.05). In non-surgery group, no significant
declined was found among all pairs of ROIs in VN. It means that surgery has no
significant effects on VN. All the statistical results were listed in Table 2-4 for surgery
and non-surgery groups, and in table 2-5 for surgery comparison.
2.3.2 Node Strength of Intra-network Connectivity
The node vulnerabilities to surgery was calculated to evaluate the changes of
each node’s functional connectivity after surgery compared to pre surgery. The sum of
the functional connectivity between a ROI and the other ROIs within the same network
was calculated. The results were shown in Figure 2-6, Figure 2-7 and Table 2-5 for
graphical representation and numerical values.
For the connectivity in DMN, all the ROIs showed significant decline in node
strength after surgery in surgery group (p<0.05). In the non-surgery group, the node
strength did not show any significant changes between pre and post pseudo surgery
(p>0.05).
For the connectivity in CEN, some ROIs but not all had significant decline in node
strength after surgery (p<0.05). In the non-surgery group, the node strength did not
show any significant changes between pre and post pseudo surgery (p>0.05).
For the connectivity in SN, all the ROIs showed significant decline in node
strength after surgery in surgery group (p<0.05). In the non-surgery group, the node
strength did not show any obvious changes between pre and post pseudo surgery
(p>0.05).
For the control network VN, no ROIs showed significant changes in node
strength after surgery in both surgery group and non-surgery group (p>0.05).
34
2.3.3 MCI versus Non-MCI
Comparison of participant characteristics. The surgery group (69 patients)
included 13 MCI patients and 56 non-MCI patients who were cognitively normal
according to the criteria for MCI. The non-surgery group (65 controls) consisted of 10
MCI participants and 55 non-MCI participants.
One-way ANOVA results indicated that all groups including MCI and non-MCI in
the surgery group, and MCI and non-MCI in the non-surgery group showed no
difference in age, sex, race, head size, baseline pre-surgery pain level at the time of the
rsfMRI, days between baseline pre-surgery rsfMRI and post-surgery rsfMRI. These
groups, however, significantly differed on education, general cognitive screener, and
post-surgery rsfMRI pain level at the time of the rsfMRI. The analyses showed that the
MCI surgery group had significantly less education years and lower scores on the
cognitive screener than the non-MCI surgery and non-MCI non-surgery group. Because
medication levels were only calculated for the TKA surgery group, an independent
sample t-test was analyzed for the MCI surgery group and non-MCI surgery group. The
results showed no differences between morphine equivalent dose after surgery
(p=0.331). The surgery and non-surgery patient characteristics for MCI and non-MCI
groups were listed in Table 2-3.
Intra-network changes of MCI in surgery. The connectivity of the network in
pre-surgery and post-surgery were compared for the MCI surgery group (13 patients)
and non-MCI surgery group (56 patients). The MCI surgery group showed significant
decline in DMN and SN after surgery examined by paired t test (p<0.05). The non-MCI
surgery group also showed significant drop after surgery (p<0.05). The MCI surgery
group had large Cohen’s D values compared to the non-MCI surgery group. However,
35
there were no significant changes observed in CEN and VN for MCI and non-MCI
groups. The MCI results were included in Figure 2-4 and Table 2-6. The node strength
were also compared between MCI and non-MCI for surgery group and non-surgery
group in Figure 2-8. The MCI group showed more decline and big effect sizes compared
to non-MCI in DMN, SN, CEN, and VN. In DMN and SN, the node strength in MCI group
had big Cohen’s D values and maintained the significance in most of nodes even the
sample size in MCI was relatively small. CEN, however, did not show significance
between pre and post-groups in MCI and non-MCI except lIPL.
The same analyses were also applied to MCI non-surgery group and non-MCI
non-surgery group. No significant changes between pre-surgery and post-surgery were
identified in MCI non-surgery group and non-MCI non-surgery group. The results were
also shown in Figure 2-5 and Table 2-7.
2.4 Discussion
The functional connectivity has an acute decline in three major RSNs including
DMN, CEN and SN after the patients received the surgery of total knee replacement
surgery within 48 hours in older adults. No significant changes, however, in functional
connectivity after surgery were identified in VN. These results indicated that the
changes related to TKA surgery are selective at the network level with the major
cognitive networks being more vulnerable. At the node level, the changes were not
evenly distributed across all pairs of connectivity between ROIs in each network. These
findings also suggested that the effects of the surgery on connectivity were selective
and these changes showed different patterns on different networks. The changes in
node strength provided information on the vulnerability of each ROI within the network.
In DMN, the posterior nodes had more decline than the anterior nodes. In SN, the
36
dorsal anterior cingulate cortex had the most significant decline among all ROIs. The
significant changes in connectivity, however, were not observed in CEN and VN.
When participants were classified according to the cognitive criteria, the
connectivity changes were also quantified in MCI and non-MCI groups to evaluate the
postoperative effects in cognitive subtypes. The connectivity changes in MCI group
were more pronounced in DMN and SN compared to the non-MCI group.
Default Mode Network. The DMN is active during rest or internal thought
processes and deactivated during external tasks (Mason et al., 2007; Spreng et al.,
2009). It consisted of several brain areas which can be separated into anterior and
posterior subsections. The mPFC can represent the anterior subsection associated with
the self-referential mental thought and PCC the posterior brain subsection related to
episodic memory retrieval and semantic memory (Damoiseaux et al., 2008; Sestieri et
al., 2011; Xu et al., 2016).
In this chapter, we showed that the connectivity in DMN had significant drop after
TKA surgery. This phenomenon has been reported previously in other studies on the
decline of functional connectivity in surgery and diseases (Browndyke et al., 2017;
Huang et al., 2018; Ramani, 2017). In addition to these previous findings, the changes
in connectivity between nodes after surgery were examined within each network as well
as the mean connectivity at the network level. The PCC and bilateral AG in the posterior
subsystem are more vulnerable to the surgery than other ROIs in DMN. In surgery, the
anesthesia can disrupt the connectivity from lower levels to high levels and thus result in
the impairment of the integration of network. Previous studies revealed that the light
sedation of the anesthesia was related to the disruption of the communication between
37
anterior and posterior subsystems by decreasing the connectivity between PCC and the
other ROIs and causes the loss of consciousness (X. Liu et al., 2012; Ramani, 2017;
Xie et al., 2011).
Aging may be another reason which is associated with the decline of the
functional connectivity after surgery. In older adults, the cognitive performance is
relatively low because of the reduction of functional connectivity between anterior and
posterior subsystems in DMN (Andrews-Hanna et al., 2007). The aged brain structures
and functions may suffer more from anesthesia after surgery within 48 hours. This effect
may be acute or last longer. Our results suggested the disconnection between anterior
and posterior may last at least 48 hours after the restored consciousness, while this
disconnection is desirable for unconsciousness during the surgery for the purpose of
anesthesia. This decoupling side effect may be more obvious or serious for these
patients who have cognitive deficits or impairments. In DMN, the AG is mainly
associated with cognitive processes, especially lAG which is involved in semantic
processing, concept integration, and comprehension (Seghier, 2013). lAG showed the
largest decline among all ROIs in DMN after surgery. According to the functions of PCC
and AG in cognitive processing, the decline of the functional connectivity in these areas
may be considered as harbingers of the POCD (Browndyke et al., 2017).
Central Executive Network. The CEN is closely associated with the executive
tasks including working memory, problem solving, and decision making (Dosenbach et
al., 2006; Fang et al., 2016; Menon, 2011). Different from DMN, CEN shows increases
in activation during external tasks. In this study, the mean connectivity of CEN showed
significant drop after surgery which is in agreement with our previous findings (Huang et
38
al., 2018). The connectivity between each pair of ROIs in CEN, however, did not show
significant changes except for the pair lIPL-rIPL, even though there is a trend of decline
in connectivity in other pairs. These declines in CEN connectivity after surgery may
result in low cognitive performance including working memory.
Salience Network. Salience network is important in both internal and external
attention processing. It is critical for detecting behavioral stimuli and plays an important
role in coordinating the networks dynamically, especially the interactions between DMN
and CEN, in response to these events (Menon, 2011; Menon & Uddin, 2010; Seeley et
al., 2007). After surgery, the dACC had the significant decline compared to the other
two ROIs including lIN and rIN. The reduced intra-network connectivity may indicate the
compromised ability of dACC in attention or cognitive processing.
The SN is in charge of the switching of the engagement between the DMN and
the CEN according to the cognitive tasks. The DMN and CEN are anti-correlated and
alternatively response to the internal attention or external attention, respectively. In
response to the internal cognitive tasks, the DMN is activated and CEN is deactivated to
process these activities such as memory retrieval. When the response to the external
events is required, the DMN is deactivated and the SN is activated to be involved in the
task processing. The decreased connectivity in SN may inhibit the coordination between
DMN and CEN to respond to different tasks properly and quickly. DMN as the important
resting state network also showed the reduced intra-network connectivity as well as the
CEN. The coordination between DMN and SN is especially important not only in resting
state but also in task related performance. The connectivity strength between PCC in
DMN and dACC in SN is strongly associated with the task performance such as during
39
working memory tasks. When all these three networks showed the reduced intra-
network connectivity at the same time, the coordination between these three networks
was impeded and this side effects at least lasted for 48 hours after the surgery. These
weakened correlation and anti-correlation may be used to predict the susceptibility of
the network and the declined cognitive performance (Chen et al., 2016; Deiner et al.,
2009; He et al., 2014; Price et al., 2008). The inter-network connectivity analysis among
these three networks is included in Chapter 3.
Effects of Mild Cognitive Impairment. The participants included in this
dissertation are all older adults. Some participants developed mild impairment in brain
functions due to normal aging. According to the cognitive criteria for cognitive
impairment, all the participants including the surgery group and non-surgery can be
classified into two categories: MCI and non-MCI. In surgery group, the functional
connectivity in MCI subtype had more declined connectivity than the connectivity in the
non-MCI subtype within surgery patients. This phenomenon was clearest in DMN and
SN among the surgery patients with MCI.
The DMN in MCI group had impaired functional integrity, which was reported by
previous reports. The connectivity within DMN can be weakened and the deactivation
during the tasks such as visual coding and working memory reduced (Lee et al., 2016;
Rombouts et al., 2005). The weakened connectivity is also shown in the progression to
AD (Wu et al., 2011). Opposite In other cases, the increased connectivity in DMN can
also be observed which can be taken as the compensation for the dysfunction (Gardini
et al., 2015; Li et al., 2017). The abnormal connectivity has also been identified in SN
and the interaction between SN and CEN in MCI (He et al., 2014) as well as in AD
40
(Badhwar et al., 2017; Krajcovicova et al., 2014). The decline in functional connectivity
in MCI group may result in more serious cognitive dysfunctions than normal older
adults. In this chapter, the further weakened functional connectivity has been verified in
MCI group after the surgery and the surgery itself or anesthesia may exaggerate the
symptoms of cognitive impairment. The changes in connectivity can be quantified as a
biomarker to predict the postoperative cognitive changes in surgery with general
anesthesia.
Effects of General Anesthesia. General anesthesia may cause changes of the
functional connectivity of the brain. Our findings in this chapter is in agreement with the
previous reports. During general anesthesia, the breakdown or the rapid fragmentation
of the brain networks can be observed within the network or between networks during
unconsciousness (Boveroux et al., 2010; Lewis et al., 2012). Lewis found that during the
anesthesia, the communications between cortical networks with 2cm or greater distance
were impaired and the networks were disconnected consequently. This study was
conducted using the electrodes implanted in the temporal lobes. Boveroux reported that
the propofol-induced decrease in consciousness is linearly related to the decreased
DMN and CEN connectivity. The connectivity in low-level cortices, however, such as
auditory and visual networks was preserved during the sedation stages. These findings
strongly suggested that the general anesthesia can induce the acute disruption of the
brain networks. This disruption can last at least 48 hours after the surgery due to the
insult of the anesthesia. This effect may be exaggerated and the recovery from the
anesthesia may be impeded in MCI subtype.
41
This chapter mainly focused on evaluating the changes after surgery in the intra-
network connectivity among the three important resting state networks. These findings
open several important directions for future research. The declined connectivity may
lead to decline in cognitive performance. The task related fMRI study besides the
resting state fMRI can be conducted in the future to evaluate the effects of surgery on
behavior and the task activity of related networks. Due to the limited resources and time
span, the number of the MCI participants is relatively small in our study. Further study
will recruit more MCI participants to expand the sample pool and enhance statistical
power. Beside the acute effects of the surgery accomplished in this study, the long-term
effects of the postoperative cognition is important to evaluate the risk of the surgery or
the anesthesia in older adults and to guide the development of pre surgery intervention
to reduce the side effects on brain functions.
42
Table 2-1. The MNI Coordinates of the Regions of Interest (ROI)
NETWORK ROI MNI
BA X Y Z
DMN PCC 1 -51 29 23
mPFC -1 61 22 10 lAG -48 -66 34 39
lLT -65 -23 -9 21
rAG 53 -61 35 39
rLT 61 -21 -12 21
SN ACC -1 10 46 6 lIN -38 14 5 13
rIN 37 18 5 13
CEN lDLPFC -44 27 33 9
lIPL -53 -50 39 39
rDLPFC 46 28 31 9 rIPL 54 -44 43 40
VN lV1C -13 -100 -8 18
lV1P -16 -74 7 17
lExC -32 -89 -1 18
lExP -3 -74 23 18
rV1C 13 -100 -8 17
rV1P 16 -74 7 17
rExC 32 -89 -1 18
rExP 3 -74 23 18
43
Table 2-2. Participant Characteristics: Surgery Group versus Non-Surgery Group.
Demographic TKA (n = 69) NS (n = 65)
Mean ± SD Mean ± SD
Age 69.35±7.12 (range: 60–85) 68.37±5.50 (range: 60–83)
Education 15.23±2.83 (range: 10–23) 16.11±2.64 (range: 12–24)
Sex (M:F) 33:36 28:37
Race (W:NW) 61:8 61:4
TICS 36.71±4.23 (range: 26–47)* 38.55±3.25 (range: 30–44)
PreMRI Pain 12.57±19.87 (range: 0–75) 7.67±14.72 (range: 0–70)
PostMRI Pain 40.10±22.98 (range: 0–100)* 7.05±10.61 (range: 0–40)
PrePost MRI day span
8.77±5.91 (range: 3-41) 7.36±3.16 (range: 2-21)
MED 11.57±10.87 (range: 0–37.50) --------------------------------
Note. TKA = surgery group; NS = non-surgery
44
Table 2-3. Participant Characteristics: MCI versus Non-MCI in Different Groups.
Demographic
MCI-TKA (n =
13) TKA (n = 56)
MCI-NS (n =
10)
CN-NS (n =
55)
Mean ± SD Mean ± SD Mean ± SD Mean ± SD
Age 72.38±8.22
(range: 60–85)
68.64±6.73
(range: 60–85)
66.6±5.72
(range: 61–81)
68.9±5.61
(range: 60–83)
Education 13.08±2.36*
(range: 10–17)
15.76±2.73
(range: 12–23)
14.85±3.06
(range:12–22)
16.30±2.55
(range: 9–24)
Sex
(M:F) 7:6 26:30 5:5 22:33
Race
(W:NW) 12:1 49:7 8:2 53:2
TICS 33.38±3.57*
(range: 26–38)
37.48±4.01
(range: 27–47)
35.90±2.38
(range: 32–40)
38.84±3.13
(range: 30–44)
(range:
1.37x106–
1.87x106)
(range:
1.30x106–
1.89x106)
(range:
1.29x106–
1.67x106)
(range:
1.27x106–
1.89x106)
PreMRI Pain 14.62±22.50
(range: 0–75)
12.09±19.40
(range: 0–75)
8.30±11.84
(range: 0–30)
7.56±15.27
(range: 0–70)
PostMRI
Pain
46.69±23.09*
(range: 0–80)
38.57±22.89
(range: 2-100)
10.90±13.76
(range: 0–40)
6.34±9.2
(range: 0–40)
PrePost MRI
day span
8.54±5.38
(range: 3-21)
8.82±6.07
(range: 3-41)
6.90±2.38
(range: 3-11)
7.45±3.29
(range: 2–21)
MED 14.23±12.05
(range: 0–30)
10.96±10.60
(range: 0–
37.50)
--------------------- -------------------
Note. * denotes significant differences, where MCI-TKA group is significantly lower on education and TICS and higher on PostMRI pain. MCI-TKA = MCI in surgery group; TKA = non-MCI in surgery group; MCI-NS = MCI in non-surgery group; CN-NS = non-MCI in non-surgery group.
45
Table 2-4. Mixed Repeated ANOVA between Surgery and Non-Surgery
df F p partial η2
DMN
Time point 1,132 9.288 0.003** 0.066
Group* Time point 1,132 20.856 <.001*** 0.136
CEN
Time point 1,132 1.146 0.286 0.009
Group* Time point 1,132 6.851 0.010* 0.049
SN
Time point 1,132 6.906 0.010* 0.05
Group* Time point 1,132 15.3 <.001*** 0.104
VN
Time point 1,132 1.298 0.257 0.01
Group* Time point 1,132 0.183 0.67 0.001 Note. * = p<.05, ** = p<.01, *** = p<.001
46
Table 2-5. Changes in Node Strength in Surgery Group
Mean Sum Correlation
(SD)
RSN ROI Pre Post t(df) p CI d
DMN
PCC 1.587(.626) 1.142(.722) 4.442(68) <.000*** .25-.65 .66
rAG 1.530(.662) 1.187(.657) 3.912(68) <.000*** .17-.52 .52
lAG 1.790(.549) 1.323(.641) 5.551(68) <.000*** .30-.64 .78
mPFC 1.056(.878) .547(.814) 4.440(68) <.000*** .28-.74 .62
rLT 1.031(.619) .702(.677) 3.608(68) .001** .09-.15 .51
lLT 1.034(.579) .797(.661) 2.929(68) .005** .08-.08 .38
CEN
lDLPFC .753(.443) .663(.411) 1.645(68) .105 -.02-.20 .21
rDLPFC .844(.446) .716(.416) 2.251(68) .028 .01-.24 .30
lIPL .822(.438) .693(.421) 2.479(68) .016 .03-.23 .30
rIPL .934(.402) .794(.445) 2.340(68) .022 .02-.26 .33
SN
ACC .587(.406) .347(.341) 4.647(68) <.000*** .14-.34 .64
lAI .776(.291) .639(.277) 3.540(68) .001*** .06-.1 .48
rAI .789(.283) .648(.273) 3.444(68) .001*** .06-.22 .51 Note. ** = p<.01, *** = p<.001, d=Cohen's D, CI = Confidence Interval
47
Table 2-6. MCI versus Non-MCI in Surgery Group
MCI (n=13)
Mean Correlation (SD)
Pre Post t(df) P CI d
DMN .286(.136) .165(.100) 2.902(12) 0.013* .03-.21 1.017
CEN .310(.145) .234(.133) 2.155(12) 0.052 -.00-.15 0.550
SN .384(.147) .249(.110) 3.871(12) .002** .06-.21 1.037
VN .263(.094) .196(.083) 1.924(12) 0.078 -.01-.14 0.751
Non-MCI (n=56)
Mean Correlation (SD)
Pre Post t(df) P CI DMN .263(.095) .195(.118) 4.358(55) <.001** .04-.10 0.629
CEN .272(.121) .240(.125) 1.869(55) 0.067 -0-0.07 0.261
SN .353(.153) .278(.140) 3.227(55) .002** .03-.12 0.510
VN .254(.124) .249(.094) .309(55) 0.758 -.03-.04 0.045 Note. * = p<.05, ** = p<.01, *** = p<.001, d=Cohen's D, CI = Confidence Interval
48
Table 2-7. MCI versus Non-MCI in Non-Surgery Group
MCI (n=10)
Mean Correlation (SD)
Pre Post t(df) p CI
DMN .217(.095) .241(.114) -.579(9) 0.467 -.10-.05
CEN .267(.111) .287(.099) -.537(9) 0.604 -.10-.06
SN .363(.127) .346(.136) .599(9) 0.59 -.05-.09
VN .369(.143) .328(.129) .702(9) 0.501 -.09-.17
Non-MCI (n=55)
Mean Correlation (SD)
Pre Post t(df) p CI
DMN .271(.110) .285(.083) -.904(54) 0.37 -.04-.02
CEN .275(.124) .292(.109) -.956(54) 0.343 -.05-.02
SN .394(.133) .417(.133) -1.155(54) 0.253 -.06-.02
VN .278(.102) .277(.111) .101(54) 0.92 -.02-.03 Note. * = p<.05, ** = p<.01, *** = p<.001, CI = Confidence Interval
49
Figure 2-1. Schematic design of parallel surgery and non-surgery participant timelines. TKA = total knee arthroplasty.
50
A
51
B
Figure 2-2. TKA surgery group mean edge functional connectivity changes from pre to post surgery time points. A. Line thickness between nodes is weighted by node-to-node correlation. Lowercase “r” and “l” denote right and left brain hemispheres, respectively. B. The tables of edges for DMN, CEN and SN.
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Figure 2-3. The connectivity of pre and post-surgery in four resting state network networks. A. The connectivity change in DMN. B. The connectivity change in CEN. C. The connectivity change in SN. D. The connectivity change in VN. Note: * p<0.05. Control=Non-Surgery.
Figure 2-4. The comparison between MCI and non-MCI surgery groups for connectivity of pre and post-surgery in four resting state network networks. A. The connectivity change in DMN. B. The connectivity change in CEN. C. The connectivity change in SN. D. The connectivity change in VN. Note: * p<0.05
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Figure 2-5. The comparison between MCI and non-MCI non-surgery groups for connectivity of pre and post-surgery in four resting state network networks. A. The connectivity change in DMN. B. The connectivity change in CEN. C. The connectivity change in SN. D. The connectivity change in VN. Note: * p<0.05
Figure 2-6. Changes in node strength of functional connectivity pre and post-surgery in four resting state network networks. A. The node strength change in DMN. B. The node strength change in CEN. C. The node strength change in SN. D. The node strength change in VN.
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Figure 2-7. Node strength changes in different groups. A. The node strength change in DMN. B. The node strength change in CEN. C. The node strength change in SN. D. The node strength change in VN. Note: * p<0.05.
Figure 2-8. Nod strength changes in MCI and non-MCI. A. The node strength change in DMN. B. The node strength change in CEN. C. The node strength change in SN. D. The node strength change in VN. Note: * p<0.05
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CHAPTER 3 CHANGES IN INTER-NETWORK CONNECTIVITY OF RESTING STATE NETWORKS
FOLLOWING SURGERY
3.1 Introduction
In Chapter 2 we analyzed the interactions between different nodes within resting
state networks. The interactions between networks in a complex system is another
important property in view of the graph theory. The resting state networks of human
brain include several important networks which can be separated by the Group ICA
(Independent Component Analysis) and these networks can interact with each other
during the resting state as well as the task related activities. Many factors can affect the
interactions between these networks including drug addiction, morphine, pain, diseases,
etc. (Chen et al., 2016; Iadipaolo et al., 2018; Manoliu et al., 2014; Marusak et al.,
2018). Major surgeries such as total knee arthroplasty (TKA) may induce the post-
operative cognitive dysfunction (POCD) caused by surgery itself or anesthesia. The
functional connectivity of the network in surgery group can be impaired by the surgery,
anesthesia, or other risk factors. The latter may provide a neural basis for the former.
The role of inter-network interactions has not been studied.
The three important networks of DMN, CEN and SN as the major resting state
networks have been examined for the declines in intra-network connectivity within 48
hours after surgery compared to pre-surgery. To examine the changes of the interaction
between networks, in this chapter we extended our study to the inter-network
connection to evaluate the changes in connectivity after surgery between each pair of
individual networks among DMN, CEN, and SN at the level of whole network and at the
level of pairs of ROIs.
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Past work has shown that SN is in charge of coordinating DMN and CEN to
perform tasks by dynamically switching between the two networks (Goulden et al.,
2014). During the resting state, DMN is activated. During tasks, however, the SN is
activated and the DMN is deactivated. The SN and DMN are anti-correlated to perform
different tasks accordingly. These dynamic changes can be interrupted by external
interferences such as major surgery or anesthesia. Among the inter-network
interactions, the interaction between DMN and SN is more important during the task and
resting state. Bonnelle found that the Integrity of the SN can predict the behavior of
DMN after traumatic brain injury (Bonnelle et al., 2012). Jilka also examined the
interactions between SN and DMN by testing both the motor switching ability and SST
(Jilka et al., 2014). The disrupted interactions between SN and DMN can also be found
in patients with cocaine addiction (Liang et al., 2015). In their study, the alterations of
the decreased connections between bilateral insula and DMN were observed as well as
reduced connections between posterior cingulate and CEN; connection strength
between rostral anterior cingulate and SN and MTL (medial temporal lobe) was also
reduced in addiction patients.
To study the properties of the inter-network connectivity among the DMN, CEN,
and SN before and after surgery, we first examined the inter-network connectivity
between each pair of three networks to see the changes at the level of whole network.
Second, we looked into the interactions for each pair of the ROIs between two networks
to find which ROI plays the most important role in the inter-network relationship that can
maintain the stability of the network, and which ROI is more susceptible to the insult of
the major surgery or the anesthesia. Third, the node strength for each network was
57
examined to evaluate the changes of each ROI bearing the connectivity with the rest of
the other ROIs between two networks. Last, the correlation between the connectivity of
the inter-network and connectivity of the intra-network was calculated to provide the
prediction of the changes of network properties before and after surgery for surgery
patients. MCI patients and non-MCI patients in surgery group and non-surgery groups
were also examined to provide the evaluation of the side effects of the surgery trauma
for groups of different cognitive status.
3.2 Methods
3.2.1 FMRI Regions of Interest Selection
The inter-network analysis included three resting state networks (RSNs): default
mode network (DMN), central executive network (CEN), and salience network (SN);
standard coordinates defined by Power and colleagues (Power et al., 2011) and Yeo
and colleagues (Thomas Yeo et al., 2011) were used for these networks. The regions of
interest (ROIs) for the three RSNs were defined as following. DMN consists of six ROIs:
medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), bilateral angular
gyrus (AG), and bilateral temporal (LT); CEN includes bilateral dorsolateral prefrontal
cortex (DLPFC) and bilateral inferior parietal lobule (IPL); and SN consists of dorsal
anterior cingulate cortex (ACC) and bilateral anterior insula (IN). The ROIs representing
each brain region were defined using a 5mm sphere in radius centered at the
coordinates of that region and BOLD signals were extracted from each ROI for further
analysis. Table 3-1 listed coordinates of these regions.
3.2.2 Functional Connectivity Analysis
The inter-network functional connectivity between each pair of ROIs was
quantified using the Pearson cross correlation between each pair of BOLD signals. Age,
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gender, pain, MED, and education were regressed out from the functional connectivity
of each pair of ROIs between networks.
1. At the network level, the BOLD signals were averaged across all pairs of ROIs
in each network. Then the mean functional connectivity between each pair of RSNs was
calculated by calculating the cross correlation between the mean BOLD signals
representing each individual network for each subject. Both preoperative and
postoperative resting state functional connectivity of each subject were calculated to
evaluate the connectivity changes related to surgery.
2. At the node level, the functional connectivity of each pair of nodes between
each pair of networks was calculated to compare the difference between pre-surgery
and post-surgery.
3. The node strength of each ROI which is the sum of the functional connectivity
between this ROI and the rest of other ROIs in each pair of networks was also
calculated at node level to evaluate the importance of the ROI in the communication
between each pair of networks.
4. MCI and non-MCI surgery groups were also analyzed at node level and at the
network level to examine the significant effects of the surgery on cognitive subtypes.
5. The relation between the intra-network connectivity studied in Chapter 2 and
the inter-network connectivity studied in this chapter were examined.
The Pearson correlation coefficient is defined as follows:
𝜌𝑋,𝑌 =𝐸[(𝑋−𝜇𝑋)(𝑌−𝜇𝑌)]
𝜎𝑋𝜎𝑌 (3-1)
Where E is the expectation, µX is the mean of X, µY is the mean of Y, σX is the standard
deviation of X, σY is the standard deviation of Y.
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The inter-network connectivity Cs,t is defined as follows:
𝐶𝑠,𝑡 =∑ 𝜀𝑖,𝑗𝑖∊𝑠,𝑗∈𝑡
𝑁𝑠×𝑁𝑡 (3-2)
Where Ns is the number of nodes within module s, whereas Nt is the number of nodes
within module t, and εi,j is the existing edge between module s and module t. Here,
Ns=Nt=i=j=1 for the inter-network at the network level.
The node strength Si is defined as follows:
𝑆𝑖 = ∑ 𝑤𝑖𝑗𝑁𝑗=1 (3-3)
Where N is the number of nodes, and wij is the weighted connectivity, i∊ s, j∊ t.
3.3 Results
3.3.1 Inter-network Connectivity
The inter-network correlation was calculated for different pairwise combinations
of the three networks: 1) DMN and SN; 2) DMN and CEN; and 3) CEN and SN, as
shown in Figure 3-1. The inter-network correlation between DMN and SN was shown in
Figure 3-2. The comparisons between pre-surgery and post-surgery, surgery group and
non-surgery group were plotted in Figure 3-2 A. At network level, by comparing mean
values of Pearson correlation of pre-surgery and post-surgery data in surgery group, the
post-surgery group had significant decline (absolute value) in functional connectivity
compared to the pre-surgery group (p<0.05); see Figure 3-2 B. The non-surgery group,
as expected, did not show significant changes between pre-pseudo surgery and post-
pseudo surgery data (p>0.05). At the nodal level, the correlation matrix in Figure 3-2 C
displayed each pair of ROIs between DMN and SN in pre-surgery group, post-surgery
group, and the difference between pre and post-surgery groups, with statistical
significance indicated (p<0.05). In the surgery group, several pairs of ROIs had
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significant declines in functional connectivity (p<0.05), such as PCC-dACC, mPFC-
dACC, etc. In the non-surgery group, there was no any significant changes observed in
any pairs of ROIs. The Cohen’s D (absolute values) in surgery group is also larger than
in non-surgery group. Statistic results of all pairs of ROIs were listed in Table 3-2 for
surgery group including functional connectivity of pre-surgery, functional connectivity of
post-surgery, p values of paired t test, FDR corrected p values, and Cohen’s D values.
Inter-network Pearson correlation coefficients of DMN and SN in surgery group also
included MCI and non-MCI subtypes. The MCI group showed more decline in functional
connectivity compared to non-MCI group in surgery group after patients received
surgery. The pairs of ROIs which showed significant drop in MCI group were also
different from the pairs in non-MCI group; the small number of samples is a limitation.
No significant changes were observed in MCI and non-MCI subtypes in non-surgery
group. The detailed information about MCI and non-MCI subtypes in surgery group was
listed in Figure 3-5 A and Table 3-2.
The inter-network correlation between DMN and CEN was shown in Figure 3-3.
The comparison between pre-surgery and post-surgery for surgery group and for non-
surgery group was plotted in Figure 3-3 A. At network level, by comparing mean values
of Pearson correlation of pre-surgery and post-surgery in surgery group, post-surgery
group had significant increase in functional connectivity compared to the pre-surgery
group which is indicated in Figure 3-3 B (p<0.05). Non-surgery group, however, did not
show significant changes between pre-pseudo surgery and post-pseudo surgery
(p>0.05). At nodal level, the correlation matrix in Figure 3-3 C displayed each pair of
ROIs between DMN and CEN in pre-surgery group, post-surgery group, and the
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difference between them, with statistical significance indicated (p<0.05). Several pairs of
ROIs had significant increase (p<0.05, FDR corrected) in surgery group, such as PCC-
lIPL and mPFC-rdLPFC. In non-surgery group, there was no any significant changes
observed in any pairs of ROIs. The Cohen’s D (absolute values) in surgery group was
also bigger than in non-surgery group. The statistic results of all pairs of ROIs were
listed in Table 3-3 for surgery group including functional connectivity of pre-surgery,
functional connectivity of post-surgery, p values of paired t test, FDR corrected p values,
and Cohen’s D values. Inter-network Pearson correlation coefficients of DMN and CEN
in surgery group also included MCI and non-MCI subtypes. Different from correlation
between DMN and SN, the MCI group in inter-network of DMN and CEN showed no
significant changes in functional connectivity compared to non-MCI group in surgery
group. The pairs of ROIs which showed significant changes (increase and decrease in
absolute values) in non-MCI group did not show significant changes in MCI group; again
small sample size may play a role in this. No significant changes were observed in MCI
and non-MCI subtypes in non-surgery group. The detailed information about MCI and
non-MCI subtypes in surgery group was listed in Figure 3-5 B and Table 3-3.
The inter-network correlation between CEN and SN was shown in Figure 3-4.
The comparison between pre-surgery and post-surgery in surgery group and in non-
surgery group were plotted in Figure 3-4 A. At network level, by comparing mean
values of Pearson correlation of pre surgery and post-surgery in surgery group, post-
surgery group had increase in functional connectivity compared to the pre-surgery
group but it’s not significant, which is indicated in Figure 3-4 B (p>0.05). Non-surgery
group, however, did not show changes between pre-pseudo surgery and post-pseudo
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surgery (p>0.05). At nodal level, the correlation matrix in Figure 3-4 C displayed each
pair of ROIs between DMN and CEN in pre-surgery group, post-surgery group, and the
difference between pre and post-surgery. No pairs of ROIs had significant changes
(p<0.05) in surgery group. In non-surgery group, there is no any significant changes
observed in any pairs of ROIs. The Cohen’s D (absolute values) in surgery group has
no obvious difference compared to non-surgery group. The statistic results of all pairs of
ROIs were listed in Table 3-4 for surgery group including functional connectivity of pre
surgery, functional connectivity of post-surgery, p values of paired t test, FDR corrected
p values, and Cohen’s D values. Inter-network Pearson correlation coefficients of CEN
and SN in surgery group was also analyzed for MCI and non-MCI subtypes. Different
from correlation between DMN and SN, the MCI group in inter-network of CEN and SN
showed no significant changes in functional connectivity compared to non-MCI group in
surgery group. The pairs of ROIs which showed no significant changes (increase and
decrease in absolute values) in non-MCI group did not show significance in MCI group.
No significant changes were observed in MCI and non-MCI subtypes in non-surgery
group. The detailed information about MCI and non-MCI subtypes in surgery group was
listed in Figure 3-5 C and Table 3-4.
3.3.2 Node Strength in Inter-network Connectivity
The previous analysis of functional connectivity between pairs of ROIs was about
the change of edge weight according to the network analysis. For each ROI, the node
strength can be used to evaluate the importance of the ROI in each individual network
or pairs of networks. In the inter-network analysis, the node strength of one ROI can be
calculated by summing all functional connectivity between this ROI and all the ROIs in
the other network. Here we carried out such an analysis for DMN and SN in which the
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node strength for each ROI in DMN and SN was calculated before and after surgery
and their values were compared. After surgery, the node strength in DMN showed
significant decline compared to pre-surgery, as shown in Figure 3-6 A. The non-surgery
group did not show significant changes. The statistical analysis was conducted to test
the significance for each ROI in DMN. All ROIs except rLT showed significant decline
after surgery (p<0.05) in Figure 3-6 B, and no significance was observed in non-surgery
group. The same analysis was done in SN too. After surgery, the node strength in SN
also showed significant decline compared to pre-surgery, as shown in Figure 3-7 A. The
non-surgery group did not show significant changes. The statistical analysis was
conducted to test the significance for each ROI in SN. All three ROIs in SN showed
significant decline after surgery (p<0.05) in Figure 3-7 B, and no significant changes
were observed in non-surgery group. The statistic results of all pairs of ROIs were listed
in Table 3-5 for surgery group including node strength of pre-surgery, node strength of
post-surgery, p values of paired t test, FDR corrected p values, and Cohen’s D values.
Inter-network node strength of DMN and SN also included MCI and non-MCI subtypes.
The MCI group showed more decline in node strength compared to non-MCI group in
surgery group. The ROIs which showed significant drop in MCI group are the same as
the ROIs in non-MCI group except rAG in DMN and dACC in SN. No significant
changes were observed in MCI and non-MCI subtypes in non-surgery group. The
detailed information about MCI and non-MCI subtypes in surgery group was listed in
Table 3-5.
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3.3.3 Correlation between Changes in Intra-network Connectivity and in Inter-network Connectivity
The results in Chapter 2 provided us information about the changes of the intra-
network connectivity in each network; the changes of the inter-network connectivity
were analyzed in this chapter. Their relationship is considered next. We found the
following results. First, pre to post DMN-SN connectivity change and SN connectivity
change had a positive correlation for the whole group, MCI subtypes and non-MCI
subtypes in non-surgery group, as shown in Figure 3-9. It means more drop in SN intra-
network connectivity, more anti-correlation inter-network between DMN and SN. In
surgery group, no such significant correlation was found in these groups. Second, pre to
post DMN-SN connectivity change and DMN connectivity change had a negative
correlation for the whole group, MCI group and non-MCI group in both surgery group
and non-surgery group, except the MCI non-surgery group. It means that more drop in
DMN intra-network connectivity, less anti-correlation between DMN and SN, as shown
in Figure 3-10 A. This correlation is especially significant in MCI surgery group. It
indicated that DMN intra-network connectivity decrease might be related to lost
interaction between DMN and SN. Third, in the relation between pre to post DMN-SN
connectivity change and DMN pre-surgery, the negative correlation had significance for
the whole group, the MCI group and the non-MCI group in surgery group and non-
surgery group, except the MCI non-surgery group. This correlation is also especially
significant in the MCI surgery group with no significance in MCI non-surgery group. It
means more intra-network connectivity in DMN before surgery, more decline in anti-
correlation between DMN and SN after surgery, as shown in Figure 3-10 B. In other
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words, the DMN-SN interaction is more susceptible to anesthesia and/or surgery when
DMN connectivity is high especially in the MCI group pre surgery.
3.4 Discussion
The main finding of this chapter is that inter-network functional connectivity
showed acute changes within 48 hours following the TKA surgery. Significant decline
was observed in inter-network connectivity of DMN-SN. Significant increase was seen in
inter-network connectivity of DMN-CEN. No significant changes were found in inter-
network connectivity of CEN-SN. This indicated that the surgery-related changes in
inter-network functional connectivity were selective and the effects may be opposite at
network level. At the node level, the changes were not evenly distributed among all
pairs of the inter-network connectivity. These results also indicated that the effects of
the surgery were specific to certain connectivity and the patterns of changes were
different from network to network. In DMN-SN, the mean value of the anti-correlation
became less negatively in post-surgery group and this trend was also shown in each
pair of the connectivity. In DMN-CEN, the mean post-surgery correlation increased to a
more positive value than the one in pre-surgery. However, this trend was not observed
in all pairs of the DMN-CEN connectivity. Some pairs of connectivity positively
correlated and others anti-correlated. The connectivity in CEN-SN did not show
significant changes in mean values of all pairs of ROIs, and also no significant changes
were found in any pair of the ROIs.
Inter-network Changes in DMN-SN. The connectivity between DMN and SN
are important among all resting state network related to the cognitive functions. The
anti-correlated connectivity between these two networks are important to coordinate the
normal functions during cognitive tasks as well as during resting state. Although SN is
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more activated and in charge of coordinating different networks to perform tasks, the SN
cannot control the whole process without the facilitation of DMN. The anti-correlated
coupling between DMN and SN has been demonstrated in predicting the cognition
performance in healthy aging and Parkinson’s disease (Putcha et al., 2016). Low
performance is related to more positive DMN-SN coupling. In the stop-signal task
(SST), the efficient inhibition of inappropriate responses is required to perform well in
the task, which needs the rapid deactivation of DMN. If impairment of inhibitory control
occurs, the SST performance is affected significantly. This abnormality of DMN function
was predicted by the amount of the white matter damage in the SN fiber tracts
(Bonnelle et al., 2012). It has been proposed that the SST performance is captured by
the horse race model: a race between an excitatory and an inhibitory process. In terms
of the brain network, the efficient stopping is associated with activation within a right
lateral part of the SN and the deactivation within the DMN.
Besides inhibition, task switching also needs the coordination between DMN and
SN. The switching ability during motor tasks is weaker if FA (fractional anisotropy) is
lower in the connection between rAI and dACC, which leads to longer reaction time. The
coupling between the rAI and DMN is enhanced with increased cognitive control. The
impairments in SN especially rAI inhibit the dynamic network interactions (Jilka et al.,
2014). Interactions between DMN and SN can also be disrupted in patients with cocaine
addiction. At the module level, the intermodule connectivity decreased between DMN
and SN in cocaine dependent patients. At the node level, the bilateral insula has
decreased connections with DMN (Liang et al., 2015).
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In this chapter, we found that the anti-correlation became less negative after
surgery, which indicated that the surgery or anesthesia weakened the interactions
between DMN and SN. The decline in this inter-network connection may inhibit the
ability to response to tasks quickly and accurately, such as memory tasks. This impaired
symptom can be observed in major depressive disorders with DMN-SN changed to less
negative correlation (Manoliu et al., 2014). Considering the age of the surgery group,
this impaired connection can take longer time to recover from this dysfunction, which
could be a neural mechanism of POCD.
Inter-network Changes in SN-CEN and DMN-CEN. The SN is in charge of
dynamically switching between DMN and CEN (Goulden et al., 2014). The coupling
between SN and CEN or DMN and CEN in resting state network can be impaired in
many cases. The connectivity between SN and CEN is normally positive. In the hepatic
encephalopathy, the connections between aberrant SN and CEN are significantly
different among the healthy controls, patients without minimal hepatic encephalopathy,
and patients with minimal hepatic encephalopathy (Chen et al., 2016). The dynamic
connectivity of SN and CEN can be altered in mindfulness, which was observed in
children and adolescents (Marusak et al., 2018). This finding may suggest possible
interventions that may be applied before surgery to enhance the ability to resist the
detrimental effects of surgery.
While the connectivity between SN and DMN is negatively correlated, the CEN
and DMN does not necessarily hold negative correlations with each other, despite some
suggestions of the literature (Iadipaolo et al., 2018). The DMN-CEN (right side)
connection is more positively correlated in Parkinson’s disease compared to healthy
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control (Putchaa et al., 2015).The aberrant inter-network connectivity can be found in
major depressive disorders. The insular dysfunction within the salience network is
associated with severe symptoms and the DMN-CEN connection shifted from positive
value to negative value or from negative value to less negative value in major
depression disorders (Manoliu et al., 2014). This chapter reported increased positive
connectivity between DMN-CEN after surgery in older adults; this could reflect a
compensatory response. This interaction between DMN and CEN may also be impaired
by the dysfunction of activation in DMN or modulation of SN, as discussed below.
Relationship between intra-network connectivity and inter-network
connectivity. The connectivity within the network and the connection between networks
are not completely independent. Each ROI has two roles in the network: it needs to
coordinate the interactions between networks as well as it holds the interactions inside
the network. The impairment of the network itself may also affect the interactions
between networks. SN has a causal influence on activity within DMN in cognitive tasks
(Chiong et al., 2013; Jilka et al., 2014; Sridharan et al., 2008). This indicates that the SN
is able to impact the modulation of DMN activity through inter-network interactions. In
Parkinson’s disease, SN dysfunction due to striatal disruptions may alter the
interactions between DMN and SN (Putcha et al., 2016). In this chapter, it was found
that the significant correlation between the changes of SN (pre-post surgery) and the
changes of DMN-SN (pre-post surgery) in non-surgery group was compromised in
surgery group. It indicated that the role of SN in interaction between DMN and SN could
be significantly reduced by surgery and/or anesthesia with the weakened internal
connectivity in SN, which leads to less interactions.
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To summarize, this study concerned the interactions between networks and how
surgery affected such interactions. The anti-correlation between DMN and SN is
important in coordinating the resting state functions and task processing. The declined
anti-correlation can significantly reduce the brain’s ability to carry out such coordination,
thus leading to impaired task performance, especially when activation of dACC in SN
and deactivation of PCC in DMN are required simultaneously. This impairment can be
further exaggerated in older adults with MCI. The dysfunction and the functional
changes beyond the 48 hours after surgery may need to be investigated in future
studies to evaluate the longer term impact on brain connectivity change and related
cognitive impairment.
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Table 3-1. The MNI Coordinates of the Regions of Interest (ROI)
NETWORK ROI MNI
BA X Y Z
DMN PCC 1 -51 29 23
mPFC -1 61 22 10 lAG -48 -66 34 39
lLT -65 -23 -9 21
rAG 53 -61 35 39
rLT 61 -21 -12 21
SN ACC -1 10 46 6 lIN -38 14 5 13
rIN 37 18 5 13
CEN lDLPFC -44 27 33 9
lIPL -53 -50 39 39
rDLPFC 46 28 31 9
rIPL 54 -44 43 40
71
Table 3-2. Inter-network Pearson correlation coefficients of DMN and SN in surgery group including MCI and non-MCI subtypes
Patient Group (n=69)
Node pair Pre Post p FDR
corrected p
Cohen's D
PCC-dACC -0.2406 -0.1332 0.0007 0.0017 ** -0.5310
PCC-lIN -0.2417 -0.0465 1.1E-07 1.46E-06 *** -0.9499
PCC-rIN -0.3055 -0.1108 1.6E-07 1.46E-06 *** -0.9748
rAG-dACC -0.1690 -0.1399 0.3410 0.3610 -0.1492
rAG-lIN -0.0886 -0.0029 0.0046 0.0104 * -0.4295
rAG-rIN -0.1366 -0.0257 0.0005 0.0015 ** -0.5526
lAG-dACC -0.2040 -0.1597 0.2289 0.2575 -0.2277
lAG-lIN -0.1404 -0.0223 0.0004 0.0015 ** -0.5494
lAG-rIN -0.2422 -0.1035 0.0001 0.0004 *** -0.6855
mPFC-dACC -0.1206 0.0160 0.0001 0.0003 *** -0.5695
mPFC-lIN -0.1226 -0.0851 0.2138 0.2568 -0.1931
mPFC-rIN -0.1934 -0.1059 0.0062 0.0123 * -0.4272
rLT-dACC -0.1916 -0.1357 0.0635 0.1040 -0.3017
rLT-lIN -0.0896 -0.0997 0.7032 0.7032 0.0581
rLT-rIN -0.1254 -0.0630 0.0725 0.1088 -0.3371
lLT-dACC -0.1398 -0.1063 0.2140 0.2568 -0.2050
lLT-lIN -0.1144 -0.0595 0.0605 0.1040 -0.3069
lLT-rIN -0.1271 -0.0781 0.0940 0.1302 -0.2812 Note: *p<0.05, **p<0.01, ***p<0.001
72
Table 3-2. Continued
MCI Group (n=13)
Node pair Pre Post p FDR
corrected p
Cohen's D
PCC-dACC -0.2896 -0.1985 0.1842 0.3316 -0.3740
PCC-lIN -0.2390 -0.0850 0.0022 0.0097 ** -0.9255
PCC-rIN -0.3793 -0.1400 0.0002 0.0033 ** -1.5817
rAG-dACC -0.2481 -0.1781 0.4025 0.4385 -0.2800
rAG-lIN -0.1517 -0.0624 0.2909 0.3741 -0.4617
rAG-rIN -0.2166 -0.0367 0.0357 0.0919 -0.8470
lAG-dACC -0.2746 -0.1457 0.3163 0.3796 -0.4744
lAG-lIN -0.1994 0.0122 0.0143 0.0430 * -0.9319
lAG-rIN -0.3731 -0.0666 0.0007 0.0039 ** -1.3995
mPFC-dACC -0.1588 0.0629 0.0042 0.0150 * -0.8314
mPFC-lIN -0.1652 -0.1144 0.4385 0.4385 -0.2575
mPFC-rIN -0.3221 -0.1281 0.0004 0.0039 ** -1.4093
rLT-dACC -0.2527 -0.1157 0.1146 0.2292 -0.6160
rLT-lIN -0.1205 -0.0801 0.4233 0.4385 -0.2528
rLT-rIN -0.1920 -0.0142 0.0425 0.0956 -1.0371
lLT-dACC -0.1591 -0.0932 0.2201 0.3376 -0.4732
lLT-lIN -0.0776 -0.0483 0.2656 0.3677 -0.1896
lLT-rIN -0.1239 -0.0522 0.2250 0.3376 -0.4349
73
Table 3-2. Continued
Non-MCI Group (n=56)
Node pair Pre Post p FDR corrected p
Cohen's D
PCC-dACC -0.2293 -0.1180 0.0019 0.0087 ** -0.5805
PCC-lIN -0.2423 -0.0375 3.1E-06 5.65E-05 *** -0.9552
PCC-rIN -0.2883 -0.1041 2.2E-05 0.0002 *** -0.8847
rAG-dACC -0.1507 -0.1311 0.5501 0.5501 -0.1092
rAG-lIN -0.0739 0.0109 0.0087 0.0195 * -0.4244
rAG-rIN -0.1180 -0.0231 0.0055 0.0195 * -0.4804
lAG-dACC -0.1876 -0.1630 0.4835 0.5120 -0.1424
lAG-lIN -0.1267 -0.0303 0.0082 0.0195 * -0.4533
lAG-rIN -0.2118 -0.1121 0.0073 0.0195 * -0.5154
mPFC-dACC -0.1117 0.0051 0.0019 0.0087 ** -0.4978
mPFC-lIN -0.1127 -0.0782 0.3158 0.4373 -0.1771
mPFC-rIN -0.1636 -0.1008 0.0892 0.1606 -0.2951
rLT-dACC -0.1774 -0.1404 0.2404 0.3607 -0.2103
rLT-lIN -0.0825 -0.1043 0.4759 0.5120 0.1230
rLT-rIN -0.1099 -0.0744 0.3465 0.4455 -0.1902
lLT-dACC -0.1354 -0.1094 0.4028 0.4833 -0.1533
lLT-lIN -0.1230 -0.0621 0.0878 0.1606 -0.3293
lLT-rIN -0.1279 -0.0842 0.1939 0.3173 -0.2464
74
Table 3-3. Inter-network Pearson correlation coefficients of DMN and CEN in surgery group including MCI and non-MCI subtypes
Patient Group (n=69)
Node pair Pre Post p FDR corrected p
Cohen's D
PCC-ldLPFC -0.0390 -0.0290 0.6964 0.8452 -0.0542
PCC-lIPL 0.0391 0.1386 0.0006 0.0076 ** -0.4335
PCC-rdLPFC -0.0522 -0.0669 0.6334 0.8452 0.0677
PCC-rIPL -0.1250 -0.0269 0.0006 0.0076 ** -0.4535
rAG-ldLPFC 0.0593 0.0514 0.7889 0.8452 0.0391
rAG-lIPL 0.3130 0.3344 0.4409 0.8140 -0.1051
rAG-rdLPFC 0.1611 0.1073 0.0757 0.2020 0.2536
rAG-rIPL 0.2534 0.2461 0.7988 0.8452 0.0345
lAG-ldLPFC 0.0800 0.0712 0.7513 0.8452 0.0374
lAG-lIPL 0.2724 0.3595 0.0017 0.0138 * -0.3954
lAG-rdLPFC 0.0024 0.0099 0.8100 0.8452 -0.0371
lAG-rIPL 0.0073 0.0191 0.7014 0.8452 -0.0517
mPFC-ldLPFC
-0.0319 0.0507 0.0184 0.0632 -0.3981
mPFC-lIPL 0.0304 -0.0473 0.0084 0.0338 * 0.3860
mPFC-rdLPFC
-0.0290 0.0692 0.0045 0.0216 * -0.4033
mPFC-rIPL -0.1286 -0.0543 0.0273 0.0818 -0.3574
rLT-ldLPFC -0.0866 -0.0084 0.0035 0.0210 * -0.4320
rLT-lIPL 0.1026 0.0733 0.3478 0.7989 0.1489
rLT-rdLPFC -0.0007 0.0165 0.5766 0.8452 -0.0902
rLT-rIPL 0.0379 0.0360 0.9466 0.9466 0.0091
lLT-ldLPFC 0.0000 -0.0179 0.5132 0.8452 0.0957
lLT-lIPL 0.0591 0.0365 0.3902 0.7989 0.1066
lLT-rdLPFC -0.0010 -0.0114 0.7174 0.8452 0.0555
lLT-rIPL -0.0527 -0.0300 0.3994 0.7989 -0.1177 Note: *=p<0.05, **=p<0.01, ***=p<0.001
75
Table 3-3. Continued
MCI Group (n=13)
Node pair Pre Post p FDR corrected p
Cohen's D
PCC-ldLPFC 0.0231 -0.0627 0.1767 0.4711 0.4011
PCC-lIPL -0.0600 0.0856 0.0345 0.2953 -0.6151
PCC-rdLPFC 0.0174 -0.1134 0.0615 0.2953 0.5198
PCC-rIPL -0.1265 -0.0205 0.1099 0.3298 -0.4680
rAG-ldLPFC 0.0886 -0.0173 0.0774 0.3018 0.4665
rAG-lIPL 0.2406 0.3087 0.3883 0.6557 -0.3143
rAG-rdLPFC 0.1686 0.0896 0.0880 0.3018 0.3107
rAG-rIPL 0.2059 0.1834 0.6845 0.8646 0.1112
lAG-ldLPFC 0.0819 0.0696 0.8392 0.8757 0.0409
lAG-lIPL 0.1571 0.3444 0.0520 0.2953 -0.7077
lAG-rdLPFC 0.0146 0.0309 0.8127 0.8757 -0.0620
lAG-rIPL 0.0184 -0.0272 0.5867 0.7822 0.1767
mPFC-ldLPFC 0.0101 0.1149 0.2937 0.6408 -0.4408
mPFC-lIPL -0.0693 -0.0632 0.9196 0.9196 -0.0328
mPFC-rdLPFC -0.0198 0.1521 0.0484 0.2953 -0.7648
mPFC-rIPL -0.1347 -0.0718 0.4098 0.6557 -0.2689
rLT-ldLPFC -0.0711 -0.0553 0.7889 0.8757 -0.0874
rLT-lIPL 0.0417 0.1075 0.3978 0.6557 -0.3244
rLT-rdLPFC 0.0566 -0.0377 0.0469 0.2953 0.5801
rLT-rIPL 0.0241 0.0415 0.7421 0.8757 -0.0782
lLT-ldLPFC 0.0622 0.0017 0.2257 0.5416 0.3828
lLT-lIPL 0.0077 0.0551 0.5264 0.7432 -0.2039
lLT-rdLPFC 0.0171 -0.0378 0.3811 0.6557 0.3328
lLT-rIPL -0.0562 -0.0083 0.5113 0.7432 -0.2052
76
Table 3-3. Continued
Non-MCI Group (n=56)
Node pair Pre Post p FDR corrected p
Cohen's D
PCC-ldLPFC -0.0534 -0.0212 0.2488 0.4594 -0.1819
PCC-lIPL 0.0621 0.1509 0.0057 0.0341 * -0.3930
PCC-rdLPFC -0.0684 -0.0562 0.7221 0.9501 -0.0584
PCC-rIPL -0.1247 -0.0283 0.0028 0.0308 * -0.4457
rAG-ldLPFC 0.0526 0.0673 0.6634 0.9501 -0.0743
rAG-lIPL 0.3299 0.3404 0.7198 0.9501 -0.0526
rAG-rdLPFC 0.1593 0.1114 0.1823 0.3978 0.2354
rAG-rIPL 0.2644 0.2607 0.9105 0.9501 0.0175
lAG-ldLPFC 0.0795 0.0715 0.8002 0.9501 0.0362
lAG-lIPL 0.2991 0.3630 0.0154 0.0739 -0.3088
lAG-rdLPFC -0.0004 0.0051 0.8771 0.9501 -0.0290
lAG-rIPL 0.0047 0.0298 0.4470 0.7664 -0.1128
mPFC-ldLPFC -0.0416 0.0359 0.0374 0.1282 -0.3870
mPFC-lIPL 0.0536 -0.0437 0.0039 0.0308 * 0.4794
mPFC-rdLPFC -0.0311 0.0500 0.0323 0.1282 -0.3276
mPFC-rIPL -0.1272 -0.0502 0.0428 0.1284 -0.3779
rLT-ldLPFC -0.0902 0.0024 0.0021 0.0308 * -0.5103
rLT-lIPL 0.1168 0.0653 0.1328 0.3541 0.2627
rLT-rdLPFC -0.0140 0.0290 0.2323 0.4594 -0.2204
rLT-rIPL 0.0411 0.0348 0.8426 0.9501 0.0317
lLT-ldLPFC -0.0144 -0.0225 0.8007 0.9501 0.0417
lLT-lIPL 0.0711 0.0322 0.1604 0.3849 0.1869
lLT-rdLPFC -0.0052 -0.0053 0.9982 0.9982 0.0004
lLT-rIPL -0.0520 -0.0350 0.5617 0.8987 -0.0914
77
Table 3-4. Inter-network Pearson correlation coefficients of CEN and SN in surgery group including MCI and non-MCI subtypes
Patient Group (n=69)
Node pair Pre Post p FDR corrected p
Cohen's D
ldLPFC-dACC 0.1403 0.2024 0.0806 0.4253 -0.2803
ldLPFC-lIN 0.0657 0.0421 0.4272 0.8059 0.1274
ldLPFC-rIN 0.0214 -0.0048 0.3476 0.8059 0.1435
lIPL-dACC 0.0818 0.0470 0.2534 0.7601 0.1818
lIPL-lIN 0.1753 0.1880 0.6365 0.8714 -0.0747
lIPL-rIN 0.0888 0.0919 0.9138 0.9138 -0.0171
rdLPFC-dACC 0.1032 0.1627 0.0627 0.4253 -0.2469
rdLPFC-lIN -0.0151 -0.0016 0.6535 0.8714 -0.0738
rdLPFC-rIN 0.0635 0.0705 0.8054 0.8868 -0.0371
rIPL-dACC 0.1170 0.1233 0.8129 0.8868 -0.0324
rIPL-lIN 0.0975 0.1414 0.1063 0.4253 -0.2418
rIPL-rIN 0.1684 0.1902 0.4701 0.8059 -0.1087 Note: *=p<0.05, **=p<0.01, ***=p<0.001
78
Table 3-4. Continued
MCI Group (n=13)
Node pair Pre Post p FDR corrected p
Cohen's D
ldLPFC-dACC 0.0753 0.2590 0.1091 0.5395 -0.6690
ldLPFC-lIN 0.0601 0.1065 0.3494 0.8387 -0.2565
ldLPFC-rIN 0.0534 0.0414 0.8630 0.9672 0.0590
lIPL-dACC 0.1320 0.0127 0.1349 0.5395 0.4964
lIPL-lIN 0.1954 0.1897 0.9294 0.9672 0.0237
lIPL-rIN 0.0951 0.1106 0.7969 0.9672 -0.0663
rdLPFC-dACC -0.0224 0.2262 0.0034 0.0409 * -0.9795
rdLPFC-lIN 0.0155 0.0120 0.9651 0.9672 0.0187
rdLPFC-rIN 0.0824 0.0639 0.8137 0.9672 0.1011
rIPL-dACC 0.1137 0.1661 0.3200 0.8387 -0.2118
rIPL-lIN 0.1072 0.1017 0.9270 0.9672 0.0269
rIPL-rIN 0.2038 0.2004 0.9672 0.9672 0.0157
79
Table 3-4. Continued
Non-MCI Group (n=56)
Node pair Pre Post p FDR corrected p
Cohen's D
ldLPFC-dACC 0.1554 0.1892 0.3364 0.8078 -0.1630
ldLPFC-lIN 0.0670 0.0272 0.2527 0.8078 0.2140
ldLPFC-rIN 0.0139 -0.0155 0.3379 0.8078 0.1659
lIPL-dACC 0.0702 0.0550 0.6445 0.8078 0.0847
lIPL-lIN 0.1707 0.1876 0.5714 0.8078 -0.1116
lIPL-rIN 0.0873 0.0876 0.9926 0.9926 -0.0018
rdLPFC-dACC 0.1324 0.1479 0.6378 0.8078 -0.0665
rdLPFC-lIN -0.0222 -0.0048 0.5938 0.8078 -0.0952
rdLPFC-rIN 0.0591 0.0721 0.6732 0.8078 -0.0673
rIPL-dACC 0.1178 0.1133 0.8843 0.9647 0.0245
rIPL-lIN 0.0953 0.1506 0.0716 0.8078 -0.3125
rIPL-rIN 0.1602 0.1878 0.3981 0.8078 -0.1387
80
Table 3-5. Node strength of DMN and SN of the inter-network in surgery group including MCI and non-MCI subtypes
Patient Group (n=69)
Node Strength of DMN
Node pair Pre Post p FDR corrected p
Cohen's D
PCC -0.7878 -0.2905 2.39E-08 1.43E-07 *** -1.0325
rAG -0.3942 -0.1685 0.0015 0.0023 ** -0.4654
lAG -0.5866 -0.2855 0.0004 0.0008 *** -0.6170
mPFC -0.4366 -0.1750 0.0002 0.0007 *** -0.5176
rLT -0.4066 -0.2985 0.1208 0.1208 -0.2609
lLT -0.3814 -0.2440 0.0202 0.0242 * -0.3814
Patient Group (n=69)
Node Strength of SN
Node pair Pre Post p FDR corrected p
Cohen's D
dACC -1.0657 -0.6589 0.0013 0.0013 ** -0.5310
lIN -0.7973 -0.3160 1.75E-05 2.63E-05 *** -0.6920
rIN -1.1302 -0.4871 4.14E-06 1.24E-05 *** -0.8466
Note: *=p<0.05, **=p<0.01, ***=p<0.001
81
Table 3-5. Continued
MCI Group (n=13)
Node Strength of DMN
Node pair Pre Post p FDR corrected p
Cohen's D
PCC -0.9079 -0.4235 0.0002 0.0006 *** -1.2855
rAG -0.6163 -0.2772 0.1013 0.1013 -0.6090
lAG -0.8470 -0.2000 0.0141 0.0282 * -1.1309
mPFC -0.6461 -0.1795 0.0002 0.0006 *** -1.1621
rLT -0.5652 -0.2100 0.0521 0.0782 -0.7819
lLT -0.3605 -0.1937 0.0770 0.0924 -0.4862
MCI Group (n=13)
Node Strength of SN
Node pair Pre Post p FDR corrected p
Cohen's D
dACC -1.3828 -0.6683 0.0741 0.0741 -0.7269
lIN -0.9533 -0.3779 0.0035 0.0053 ** -0.8908
rIN -1.6070 -0.4377 0.0004 0.0013 ** -1.6761
82
Table 3-5. Continued
Non-MCI Group (n=56)
Node Strength of DMN
Node pair Pre Post p FDR corrected p
Cohen's D
PCC -0.7600 -0.2596 2.42E-06 1.45E-05 *** -0.9982
rAG -0.3426 -0.1433 0.0077 0.0145 * -0.4307
lAG -0.5261 -0.3054 0.0097 0.0145 * -0.4795
mPFC -0.3880 -0.1739 0.0092 0.0145 * -0.4087
rLT -0.3698 -0.3190 0.4974 0.4974 -0.1259
lLT -0.3862 -0.2556 0.0621 0.0745 -0.3562
Non-MCI Group (n=56)
Node Strength of SN
Node pair Pre Post p FDR corrected p
Cohen's D
dACC -0.9921 -0.6567 0.0085 0.0085 ** -0.4742
lIN -0.7611 -0.3016 0.0005 0.0009 *** -0.6478
rIN -1.0196 -0.4986 0.0006 0.0009 *** -0.6878
83
Figure 3-1. Schematic diagram of the functional interactions between three resting state networks: DMN, CEN and SN.
84
Figure 3-2. The inter-network correlation between DMN and SN. A) Pre-surgery patient
group; post-surgery patient group; pre-surgery control group; post-surgery control group. B) Mean values of Pearson correlation of pre-surgery and post-surgery in surgery group; Mean values of Pearson correlation of pre-surgery and post-surgery in non-surgery group. C) Correlation matrix of each pair of ROIs between DMN and SN in pre and post-surgery group; Correlation matrix of each pair of ROIs between DMN and SN in pre and post non-surgery group. Note: *p<0.05
85
Figure 3-3. The inter-network correlation between DMN and CEN. A) Pre-surgery
patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. B) Mean values of Pearson correlation of pre-surgery and post-surgery in surgery group; Mean values of Pearson correlation of pre-surgery and post-surgery in non-surgery group. C) Correlation matrix of each pair of ROIs between DMN and CEN in pre and post-surgery group; Correlation matrix of each pair of ROIs between DMN and CEN in pre and post non-surgery group. Note: *p<0.05
86
Figure 3-4. The inter-network correlation between CEN and SN. A) Pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. B) Mean values of Pearson correlation of pre-surgery and post-surgery in surgery group; Mean values of Pearson correlation of pre-surgery and post-surgery in non-surgery group. C) Correlation matrix of each pair of ROIs between CEN and SN in pre and post-surgery group; Correlation matrix of each pair of ROIs between CEN and SN in pre and post non-surgery group. Note: *p<0.05
87
Figure 3-5. The comparison between MCI and non-MCI groups in inter-network correlation. A: The DMN-SN correlation of MCI and non-MCI in surgery and non-surgery groups. B: The DMN-CEN correlation of MCI and non-MCI in surgery and non-surgery groups. C: The CEN-SN correlation of MCI and non-MCI in surgery and non-surgery groups. Note: *=p<0.05
88
Figure 3-6. The node strength of DMN in inter-network correlation between DMN and SN. A) The changes of node strength of each ROI in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. B) Node strength of Pearson correlation of pre-surgery and post-surgery in surgery group; pre-surgery and post-surgery in non-surgery group. Note: *p<0.05
89
Figure 3-7. The node strength of SN in inter-network correlation between DMN and SN. A) The changes of node strength of each ROI in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. B) Node strength of Pearson correlation of pre-surgery and post-surgery in surgery group; pre-surgery and post-surgery in non-surgery group. Note: *p<0.05
90
Figure 3-8. The comparison of node strength between MCI and non-MCI in inter-network correlation of DMN-SN. A: The DMN node strength of MCI and non-MCI in surgery and non-surgery groups. B: The SN strength of MCI and non-MCI in surgery and non-surgery groups. Note: *p<0.05
91
Figure 3-9. The correlation between intra-network connectivity changes of SN pre-post surgery and inter-network connectivity pre-post surgery of DMN-SN in whole group, MCI, and non-MCI groups: MCI surgery patient group; non-MCI surgery patient group; MCI non-surgery control group; MCI non-surgery control group.
92
Figure 3-10. The correlation between intra-network connectivity and inter-network connectivity. A: DMN-SN pre-post surgery and DMN pre surgery. B: DMN-SN pre-post surgery and DMN pre surgery in whole group, MCI, and non-MCI group: MCI surgery patient group; non-MCI surgery patient group; MCI non-surgery control group; MCI non-surgery control group.
93
CHAPTER 4 CHANGES IN FUNCTIONAL BRAIN CONNECTOME FOLLOWING SURGERY
4.1 Introduction
Brain connectome analysis is the analysis in which the whole brain is treated as
one large network. Each individual brain area needs to coordinate with other brain areas
to maintain the normal functions for various tasks. The whole brain analysis can provide
us the view about the interactions or changes in interactions across the whole brain
functionally and structurally (Sha et al., 2017; Sporns, 2013; Xia & He, 2017; T. Xu et
al., 2016; Zhang et al., 2016). When the analysis comes to the whole brain, there is
increased complexity. For the purpose of this chapter, from the perspective of network
analysis, each brain area is treated as a node in a network. The whole brain can be
taken as a network consisting of hundreds of functional units with each being a brain
area with unique functions. Recent work has shown that graph theory can be used as
an effective tool to analyze the complex connections in the human brain (Achard et al.,
2006; Bullmore & Sporns, 2009; Lo et al., 2015).
In the human brain, the functional network is finely balanced to conduct
complicated and delicate functions. In order to look into the complex brain system as a
graph, several parameters were developed to evaluate the properties of the network,
including degree, clustering coefficient, global coefficient, modularity, small world,
robustness or resilience (Aerts et al., 2016; Bassett & Bullmore, 2006; Latora &
Marchiori, 2001; Watts & Strogatz, 1998; Rubinov & Sporns, 2010). How do these
parameters change after the brain undergoes a traumatic event? Much remains to be
learned.
94
Many diseases can induce changes in brain networks. Significant randomization
of the brain network was demonstrated after disease onset. Some have suggested that
such randomization might be protective. In addition, hubs of the network are more
resilient to the targeted attacks; this has been shown in patients with schizophrenia,
which is a disorder of brain organization and network dysfunction (Lo et al., 2015).
Because the hubs of the brain functional networks are important to form the main
backbone of the networks and process all the communications across all brain areas,
understanding how they are reorganized after traumatic events is important. Past work
has shown that these hubs can be reorganized in some situations such as comatose
patients (Achard et al., 2012). Whether randomization and hub reorganization occurs
after major surgery has not been demonstrated. Resilience analysis at the network level
is a way to get at this issue.
This chapter analyzed the whole brain’s functional connections using graph
theory analysis and resilience analysis to examine pre and post changes in patients
undergoing TKA surgery. First, the whole brain was parcellated into 234 components.
The general properties of the network were analyzed to compare the difference
between pre-surgery and post-surgery. Second, the resilience or robustness of the
network was evaluated to examine how the network is resistant to the insult of the
surgery. The targeted attack and randomized attack of the network were compared to
find the importance of different brain areas according to the node degree. Third, the
significant differences between pre-surgery and post-surgery in node strength was
analyzed for each brain area to compare surgery group and non-surgery group, and the
brain areas which were significantly impacted by the surgery were identified.
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4.2 Methods
4.2.1 FMRI Regions of Brain Areas
The whole brain was divided into 234 brain areas (ROIs) using the standard
mask created by Hagmann and colleagues (Hagmann et al., 2008). The BOLD signals
were extracted from each ROI by averaging all the signals from the voxels in the ROI.
4.2.2 Functional Connectivity Analysis
The functional connectivity between each pair of ROIs was quantified using the
Pearson cross correlation. Age, gender, pain, MED, and education were regressed out
from the functional connectivity. After regression, 234*234 connectivity matrices were
created for further analysis. Both preoperative and postoperative resting state functional
connectivity of each subject were calculated to evaluate changes in the whole brain
functional connectome related to surgery.
4.2.3 Graph Theoretical Analysis
A schematic diagram was shown in Figure 4-1 to illustrate the analytical
framework of this chapter. The functional connectivity matrix was created for each
subject in Figure 4-1 A. The adjacency matrix was generated using the 40% threshold
to remove the weak connectivity and keep the top 40% strong connectivity (positive
correlation) Figure 4-1 B.
The resilience or the robustness of the network was analyzed with simulating the
attack to the network by removing the node from the network one at a time to check the
changes of the network properties. Two attacks were simulated: targeted attack and
random attack. In the targeted attack, the global efficiency of the network was
calculated by removing the node in the network according to the descending order of
the node degree one at a time. In the random attack, the global efficiency of the network
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was calculated by randomly removing the node in the same network one at a time
without considering the order of the node degree. The continuous changes of the global
efficiency of the network was represented by the dynamic curves and compared
between pre-surgery and post-surgery.
We defined the parameters used to characterize a network next (Fornito et al.,
2016). The network properties were calculated using the functions provided by Brain
Connectivity Toolbox (Rubinov & Sporns, 2010). Global efficiency of a network is the
inverse of the mean of the shortest path length between each pair of nodes within the
network. It is a measure of integrated network topology. It is calculated according to:
𝐸𝑔𝑙𝑜𝑏 =1
𝑁(𝑁−1)∑ ∑
1
𝐿𝑚𝑖𝑛(𝑖,𝑗)
𝑁𝑗≠𝑖
𝑁𝑖=1 (4-1)
where Lmin denotes the shortest path length between node i and node j, N is the number
of the nodes.
The degree of a node i is the number of connection it has
𝑘𝑖 = ∑ 𝑎𝑖𝑗𝑁𝑗=1 (4-2)
where N is the number of connections, aij is the connection between i and j.
The node strength Si is defined as follows:
𝑆𝑖 = ∑ 𝑤𝑖𝑗𝑁𝑗=1 (4-3)
Where N is the number of connections, wij is the weighted connectivity between i and j.
Connection density was calculated to examine changes of network density when
the network was attacked. Connection density was analyzed by removing the node
according to the descending order of the node degree one at a time when the network
had the targeted attack. The mean functional connectivity was also calculated using the
same strategy as connection density. The mean functional connectivity was calculated
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by dividing the sum of the connectivity by the number of all possible connections in the
network. Connection density is defined as follows:
𝐾𝑑𝑒𝑛𝑠 =𝐾
(𝑁2−𝑁)/2 (4-4)
where N is the number of vertices, K is the number of edges.
The mean functional connectivity is defined as:
𝐶𝑚𝑒𝑎𝑛 =1
𝑁(𝑁−1)∑ ∑ 𝐶𝑖𝑗
𝑁𝑗≠𝑖
𝑁𝑖=1 (4-5)
where N is the number of nodes, Cij is the connectivity of node i and node j.
The clustering coefficients is defined as:
𝐶𝑙𝑤(𝑖) =2
𝑘𝑖(𝑘𝑖−1)∑ (𝑤𝑖𝑗𝑤𝑗ℎ𝑤ℎ𝑖)
1/3𝑗,ℎ (4-6)
where ki is the degree of node i, wij, wjh, and whi are the normalized edge weights, j and
h denote node j and node h.
The modularity is defined as:
𝑄 =1
2𝑚∑ [𝐴𝑖𝑗 −
𝑘𝑖𝑘𝑗
2𝑚]𝑖𝑗 𝛿(𝑐𝑖, 𝑐𝑗) (4-7)
where Aij denotes the weight of the edge connecting nodes i and j, ki and kj are the total
connectivity (node strength) for node i and j, the δ(ci,cj) is 1 only when ci=cj, and
m=(1/2)∑ 𝐴𝑖𝑗𝑖𝑗 is the sum of all edge weights.
Curve difference between pre-surgery and post-surgery was calculated by first
selecting the range of the nodes according to the descending order of the node degree
and then applying paired t test to calculate the p values for significance (p<0.05). The
surgery group and non-surgery group before and after surgery were analyzed for
resilience, connection density, and functional connectivity as well as the curve
difference.
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To evaluate changes in connectivity for each ROI, the node strength was
calculated based on the positive adjacency matrix and negative adjacency matrix in
Figure 4-1 C. The node strength is the sum of the connectivity between this node and
the rest of the 233 nodes in the whole brain to evaluate the importance of the node in
the entire network. The difference between pre-surgery and post-surgery was calculated
by node strength of pre-surgery minus that of post-surgery for each subject. The
significance was compared between the difference of surgery group (pre minus post)
and non-surgery group (pre minus post) using two sample t test. The brain areas with
significant difference between pre-surgery and post-surgery for the surgery group
compared to the non-surgery group were mapped onto the brain and the corresponding
anatomical structures in Figure 4-1 D.
4.3 Results
4.3.1 Changes in Global Network Properties
Modularity, clustering coefficient, global efficiency, and connectivity were
calculated for pre-surgery and post-surgery data. Comparison between pre-surgery and
post-surgery groups revealed that there were no significant changes in these global
network parameters both in the surgery group and the non-surgery group, as shown in
Figure 4-2.
4.3.2 Resilience Analysis of the Whole Brain Network
The resilience or the robustness of the network against two types of simulated
attacks: targeted attack and random attack was analyzed for pre-surgery group, post-
surgery group, pre-pseudo surgery group, and post-pseudo surgery group and the
results were shown in Figure 4-3 A and B, respectively. In targeted attack, the global
efficiency of the network showed no significant difference between pre-surgery group
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and post-surgery before the descending node order 150. The post-surgery group
showed postponed drop in global efficiency between the descending node order 150
and 234 compared to the pre-surgery group. In non-surgery group, there is no
difference between pre-pseudo surgery and post-pseudo surgery across all descending
node order. Only the post-surgery group showed postponed global efficiency when all
four groups were compared together in Figure 4-3 A.
The same network was used in random attack analysis for each subject. There
were no changes in global efficiency in surgery group and non-surgery group when the
node was removed from the network without considering the descending order of the
node degree as shown in Figure 4-3 B.
The changes in the global efficiency curves were statistically analyzed using
paired t test for significance across all subjects in each subtype group with the
descending order of the node degree from 181 to 220 as shown in Figure 4-4 A. When
the removed nodes were in the range of descending order 181-220, the surgery group
showed significant changes between pre-surgery group and post-surgery (p=0.0476).
When the removed nodes were in the range of descending order 201-220, the surgery
group also showed significant changes between pre-surgery group and post-surgery
(p=0.0291). When the removed nodes are in the range of descending order 220-230,
the surgery group did not show significant changes between pre-surgery and post-
surgery groups. These changes in global efficiency or resilience were only observed in
targeted attacks. There was no significant difference found in random attacks in Figure
4-4 B.
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4.3.3 Connection Density and Mean Functional Connectivity
Connection density was analyzed for pre-surgery and post-surgery data (Figure
4-5 A). The connection density showed changes in post-surgery group compared to the
pre-surgery group starting from the descending node order 150. No changes were seen
in non-surgery group. In surgery group, there were changes in post-surgery group
compared to the pre-surgery around node order 200. The non-surgery group, however,
showed no changes between pre-pseudo surgery and post-pseudo surgery. Again, the
surgery group only showed the changes related to targeted attacks after the patients
underwent surgery.
As shown in Figure 4-6, when the removed nodes were in the range of
descending order 181-220, the surgery group showed significant increase between pre-
surgery group and post-surgery (p=0.04930). When the removed nodes are in the range
of descending order 201-220 and 211-220, the surgery group also showed significant
increase between pre-surgery group and post-surgery (p=0.0437 and p=0.0405,
respectively). There is no significant difference found in the non-surgery group.
The mean functional connectivity, indicating overall how strong the connections
are in the defined network, was analyzed for comparison between pre-surgery and post-
surgery in Figure 4-5 B. The surgery group showed changes in post-surgery group
compared to the pre-surgery group starting from the descending node order 150. No
changes were seen in non-surgery group, as shown in Figure 4-5 B. In surgery group,
there were changes in post-surgery group compared to the pre-surgery around node
order 200. The non-surgery group, however, showed no difference between pre-pseudo
surgery and post-pseudo surgery in Figure 4-5 B. The surgery group only showed
changes related to targeted attack after the patients received the surgery.
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Statistically, when the removed nodes were in the range of descending order
181-220 and 201-220, the surgery group did not show significant difference between
pre-surgery group and post-surgery (p>0.05). When the removed nodes were in the
range of descending order 211-220, the surgery group showed significant increase
between pre-surgery group and post-surgery (p=0.0474). There is no significant
difference found in non-surgery group.
4.3.4 Brain Areas with Connectivity Changes
The adjacency matrix of the functional connectivity created using top 40%
connections was shown in Figure 4-7. For the node strength analysis, the adjacency
matrix was separated into positive connectivity matrix for pre-surgery group and post-
surgery group in Figure 4-7 A and B, respectively. Figure 4-7 C showed the difference
of post-surgery minus pre-surgery matrix in surgery group. Figure 4-7 D showed the
difference of post-surgery minus pre-surgery matrix in non-surgery group. The negative
connectivity matrix also included pre-surgery group and post-surgery group in Figure 4-
7 E and F, respectively. Figure 4-7 G showed the difference of post-surgery minus pre-
surgery matrix in surgery group. Figure 4-7 H showed the difference of post-surgery
minus pre-surgery matrix in non-surgery group.
The node strength was analyzed for both positive connectivity matrix and
negative connectivity matrix created from the adjacency matrix. The difference was
calculated between post-surgery and pre-surgery for surgery group and non-surgery
group. The nodes in surgery group with significant changes compared to non-surgery
group were mapped onto the brain with corresponding anatomical structures identified.
For positive connectivity in adjacency matrix, the nodes with significant
decreased node strength were shown in Figure 4-8 A in blue. The nodes with increased
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node strength were shown in Figure 4-8 A in red. For negative connectivity (converted
to absolute values) in adjacency matrix, the nodes with significant decreased node
strength were shown in Figure 4-8 B in blue. The nodes with increased node strength
were shown in Figure 4-8 B in red. The brain areas with decreased node strength and
increased node strength were listed in the table shown in Figure 4-8 for both positive
matrix and negative matrix. In positive connectivity, decreased connectivity were
observed in brain areas related to cognition and memory such as insula, amygdala and
putamen. The increased connectivity were mainly in precuneus, fusiform and parietal. In
negative connectivity, decreased connectivity were observed in brain areas such as
precuneus and parietal. The increased connectivity were mainly in lateral-occipital and
putamen.
The brain areas with the corresponding brain anatomic structures indicated were
displayed in Figure 4-9 for positive connectivity and Figure 4-10 for negative
connectivity. Figure 4-9 A-D showed the brain areas with decreased node strength in
blue. Figure 4-9 E-H show the brain areas with increased node strength in red. Different
brain slices were shown in Figure 4-9 I and J for decreased node strength and
increased node strength, respectively. For negative connectivity, the brain areas with
decreased node strength and increased node strength were shown in the brain
anatomic structures in Figure 4-10. Figure 4-10 A-D show the brain areas with
decreased node strength. Figure 4-10 E-H show the brain areas with increased node
strength. The slices for the brain map also were shown in Figure 4-10 I and J for
decreased node strength and increased node strength, respectively.
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4.4 Discussion
The whole brain connectome analysis was carried out to examine the pattern of
the connectivity matrix change within 48 hours after surgery. The global properties of
the brain network did not show significant changes, which means in general, the whole
brain still maintained a relatively stable and normal functioning after surgery. This is
reasonable when patients can still preform their normal daily tasks after surgery. While
some function networks have become impaired as shown in Chapters 2 and 3, overall,
the properties of the global brain network remain unchanged. Previous work has shown
that the global properties of the brain networks were homeostatically conserved even in
comatose patients (Achard et al., 2012).
The resilience of the whole brain connectome. The resilience analysis
examined the performance of the network by evaluating the global efficiency when the
brain areas were removed one by one according to their importance in connections. The
resilience analysis revealed that the functions of the network have rearranged across all
brain areas to resist the insult of surgery/anesthesia. This reorganization mainly
happened in local brain networks with low node connections or node degree instead of
on the whole brain network scale. The global efficiency in low degree areas after
surgery was relatively high compared to the global efficiency before surgery. This
change indicated that the more randomized organization was formed after surgery and
the importance was distributed to lower levels of the network (Joyce et al., 2013; Lo et
al., 2015). When we tested the resilience in randomized network, the resilience did not
show significant changes in any groups, which means that the reorganization especially
the increased functions was not random and may reflect neural compensation (Lo et al.,
2015).
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The changes in connection density and mean functional connectivity were also
found in low hub ranges of brain areas. This indicated that the brain areas with low
connections became more connected after surgery and the strength of the connections
was also significantly increased; this may be another form of neural compensation to
maintain normal functions.
Brain areas with connectivity changes. The connectivity of each brain area
was compared between pre-surgery and post-surgery groups. Strong connectivity of the
ROI is often interpreted to mean that the brain area is more involved in brain functions
and vice versa. The previous results of intra-network connectivity changes and inter-
network connectivity changes in Chapters 2 and 3 suggested that in well-defined
cognitive networks, connectivity is decreased both within and between brain networks
after surgery. In this chapter, we showed that the connectivity of the brain connectome
also showed changes after surgery within 48 hours. Some had less connectivity when
the sum of the connections was compared between these nodes and the rest of the
other nodes in terms of node strength. These areas included bilateral insula, putamen,
temporal pole, amygdala, and superiortemporal cortex. The functions of these brain
areas are directly related to working memory, modulation of memory consolidation,
sensory processing, homeostasis, executive function, and movement. Less connectivity
after surgery may be interpreted to indicate that the brain activity was impaired in these
brain areas. It is known that general anesthesia has significant effects on many brain
areas including reduced and increased connectivity (Hudetz, 2012). The impaired brain
areas in our study match the findings on impaired brain areas as a result of general
anesthesia, especially reduced connectivity in default mode network, sensory cortex,
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and insula (Hudetz, 2012; Martuzzi et al., 2010). Given that in our study the impact of
general anesthesia and surgery itself is difficult to separate, the similarity in brain areas
suffering injuries after surgery suggests a major role of general anesthesia in the
changes of these brain networks.
Increased connectivity was also found in some brain areas including precuneus,
occipital cortex, fusiform, inferior temporal gyrus, and parahippocampal. These brain
areas were involved in diverse processes including working memory, episodic memory
retrieval, attention, visuospatial activity, consciousness, and object recognition. The
increased connectivity in these brain areas may indicate that more activity was needed
to compensate for the lost functions of other brains in order to perform normal brain
functions after surgery. Similar findings have been reported in mild traumatic brain injury
and attributed to neural plasticity (Iraji et al., 2016). In this study the areas showing
increased connectivity after injury included frontal-occipital functional connectivity, PCC
and association areas. In other words, brain regions related to working memory,
executive functions were affected (Hillary et al., 2011, 2014; Nakamura et al., 2009;
Pandit et al., 2013).
Brain connectivity is evaluated using Pearson’s correlation. Correlation can be
positive and negative. The meaning of negative correlation remains debated. Across the
whole brain there are far fewer negative correlation than positive correlations. We found
that there was reduced anti-correlation in precuneus, and superiorparietal but increased
anti-correlation in bilateral putamen, and lateraloccipital, and right fusiform. Further
studies are needed to understand the meaning of these findings.
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In this chapter we considered the properties of the whole brain network. It
represents a natural logical evolution from Chapters 2 and 3. The resilience or
robustness analysis was performed for pre-surgery and post-surgery. In post-surgery,
the resilience was improved so that the network was more robust to further injuries or
perturbations after surgery. Moreover, pre-surgery hub nodes became less hub-like in
the network after surgery, whereas the nodes ranked low in hub-ness pre-surgery
became more important compared to pre-surgery. This phenomenon was also observed
in connection density and mean functional connectivity. More connection was found in
less important nodes and the connectivity also increased for these nodes after surgery,
which may indicate that brain regions with less significance pre-surgery took on
additional functions to compensate for the lost functions of brain networks that suffered
impairment due to injury. It is important to note that, nodes with significant changes in
strength appeared to be located in brain areas related to memory, such as bilateral
amygdala, bilateral insula, and bilateral lateral temporal lobes. Given that memory
dysfunction is a major symptom of POCD, examining the long term consequences of
the observed connectivity changes is an important future task.
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Figure 4-1. Schematic diagram for the whole brain network analysis. A) The mask used
to divide the brain into 234 ROI. B) Adjacency matrix of functional connectivity used for network analysis. C) Network properties calculated include: resilience, node strength, node degree, and clustering coefficient, etc. D: Brain areas showing decreased (blue) and increased (red) connectivity after surgery.
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Figure 4-2. The topological properties of the brain networks in surgery group and non-surgery group before and after surgery. A: comparison of surgery group between pre and post-surgery in functional connectivity, global efficiency, clustering, and modularity. B: comparison of non-surgery group between pre and post-surgery in functional connectivity, global efficiency, clustering, and modularity. *p<0.05
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Figure 4-3. Resilience analysis: global efficiency was calculated after removing the nodes with descending order. A: Targeted attack in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. B: Random attack in pre-surgery patient; post-surgery patient group; pre-surgery control group; post-surgery control group.
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Figure 4-4. Comparison between pre-surgery and post-surgery in descending order range from 181 to 220. A: Targeted attack in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. B: Random attack in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. *p<0.05
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Figure 4-5. Connection density and mean functional connectivity calculated by removing the nodes with descending order. A: Connection density in pre-surgery patien group; post-surgery patient group; pre-surgery control group; post-surgery control group. B: Mean functional connectivity in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group.
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Figure 4-6. Comparison between pre-surgery and post-surgery in descending order range from 181 to 220. A: Connection density in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group. B: Mean functional connectivity in pre-surgery patient group; post-surgery patient group; pre-surgery control group; post-surgery control group.
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Figure 4-7. Adjacency matrix keeping top 40% of functional connectivity. Positive connectivity matrix (A, B) and negative connectivity matrix (E, F). A: Connectivity matrix of pre surgery patient group; B: connectivity matrix of post-surgery patient group; C: the difference of post-surgery and pre-surgery connectivity matrix in surgery group; D: the difference of post-surgery and pre-surgery matrix in non-surgery group; E: connectivity matrix of pre-surgery patient group; F: connectivity matrix of post-surgery patient group; G: the difference of post-surgery and pre-surgery matrix in surgery group; H: the difference of post-surgery and pre-surgery matrix in non-surgery group.
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Figure 4-8. Brain area showing changes in connectivity. A. Positive connectivity: the brain areas with decreased or less positive node strength shown in blue. The brain areas with increased or more positive node strength shown in red. B. Negative connectivity: the brain areas with decreased or less negative node strength shown in blue. The brain areas with increased node or more negative strength shown in red.
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Figure 4-9. Areas with increased or decreased functional connectivity following surgery (positive adjacency matrix). Brain areas with decreased node strength were shown in A, B, C, and D. Brain areas with increased node strength were shown in E, F, G, H. Threshold at p < 0.05.
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Figure 4-10. Brain areas with increased or decreased functional connectivity following surgery (negative adjacency matrix). Brain areas with decreased node strength were shown in A, B, C, and D. Brain areas with increased node strength were shown in E, F, G, H. Threshold: p<0.05.
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CHAPTER 5 CONCLUSIONS
In this dissertation we carried out systematic research to look into the changes of
functional brain networks in older adults who underwent TKA surgery using the medical
imaging technique of fMRI. The main focus was to evaluate the acute effects of major
surgery (within 48 hours), which could form the foundation for studying long-term
changes in the human brain and related cognitive side effects such as POCD. This
dissertation treated three topics: intra-network connectivity changes following surgery,
inter-network connectivity changes following surgery, and whole brain functional
connectome changes following surgery. Our main findings can be summarized as
follow:
1. Intra-network analysis. (1) The connectivity in three important resting state
networks DMN, SN, and CEN had significant decline after surgery. No significant
changes were found in non-surgery group. (2) MCI surgery group was more susceptible
to the surgery-related functional injury and had more decline in connectivity compared
to non-MCI surgery group. (3) All the nodes in DMN and SN had significant decline in
node strength; a subset of the nodes in CEN had significant functional connectivity
decline. (4) No change was found in VN, indicating that the injury was selective, and the
cognitive networks were more vulnerable.
2. Inter-network analysis. The three cognitive networks were subjected to inter-
network functional connectivity analysis. (1) The anti-correlated functional connectivity
between DMN and SN declined significantly after surgery in surgery patient group.
Significant increased connectivity between DMN and CEN was found, but there was no
significant changes in SN-CEN inter-network functional connectivity. There were no
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significant changes in the inter-network functional connectivity in control group. (2)
Patients with MCI had more pronounced DMN-SN functional decline compared to
patients without MCI. (3) The inter-network connectivity of DMN-SN pre-surgery had
significant correlation with surgery-related changes in DMN or SN intra-network
connectivity; this finding may help with the development of predictive neural markers for
intra-network connectivity change following surgery.
3. Whole brain functional connectome analysis. (1) The resilience of the brain
network had significant increase in brain areas with low functional connections pre-
surgery. (2) The connection density and mean functional connectivity were increased in
brain areas with low functional connections pre-surgery. (3) Brain areas with
significantly decreased connectivity included bilateral insula, putamen, amygdala, and
temporapole, and temporal lobe. Significantly increased connectivity were found in
precuneus, fusiform, parahippocampal, and lateral occipital cortex.
Our findings demonstrated unequivocally that major surgeries such as TKA with
general anesthesia have a significant impact on brain functional network organization in
older adults. There is clear evidence of neural injury to key brain networks such as
DMN, SN, and CEN and to key brain regions such as amygdala, putamen, and insula.
These changes underlie postoperative cognitive decline (POCD) although much more
research is required to draw firm conclusions. Further studies recruiting more subjects
and with longer-term follow up to will provide the needed information about how acute
changes observed here can lead to long-term disability and impairments.
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BIOGRAPHICAL SKETCH
Hua Huang was born in Anhui, China. He received his bachelor’s degree in
electrical engineering in 2002. He graduated from Chongqing University and received
his master’s degree in biomedical engineering focusing on cell mechanics and
hemorheology in 2006. In 2010, he graduated from Fudan University with Ph.D. degree
in microelectronics and solid state electronics focusing on microelectromechanical
systems (MEMS), optics and acoustics at microscale, and lab on a chip. He joined
Ph.D. program in 2013 at University of Florida in biomedical engineering focusing on
neuroimaging and neuroscience and he will graduate in 2018.