NeuroImage 217 (2020) 116873

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NeuroImage

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Graph theoretical quantification of white matter reorganization aftercortical stroke in mice

Niklas Pallast a, Frederique Wieters a, Marieke Nill a, Gereon R. Fink a,b, Markus Aswendt a,b,*

a University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Germanyb Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany

A R T I C L E I N F O

Keywords:Stroke recoveryNeuroimagingDTIConnectivityGraph theory

* Corresponding author. Cognitive Neuroscience,E-mail address: markus.aswendt@uk-koeln.de (M

https://doi.org/10.1016/j.neuroimage.2020.11687Received 6 January 2020; Received in revised formAvailable online 5 May 20201053-8119/© 2020 Published by Elsevier Inc. This

A B S T R A C T

Stroke is a devastating disease leading to cell death and disconnection between neurons both locally and remote,often resulting in severe long-term disability. Spontaneous reorganization of areas and pathways not primarilyaffected by ischemia is, however, associated with albeit limited recovery of function. Quantitative mapping ofwhole-brain changes of structural connectivity concerning the ischemia-induced sensorimotor deficit and re-covery thereof would help to target structural plasticity in order to improve rehabilitation. Currently, only in vivodiffusion MRI can extract the structural whole-brain connectome noninvasively. This approach is, however, usedprimarily in human studies. Here, we applied atlas-based MRI analysis and graph theory to DTI in wild-type micewith cortical stroke lesions. Using a DTI network approach and graph theory, we aimed at gaining insights into thedynamics of the spontaneous reorganization after stroke related to the recovery of function. We found evidencefor altered structural integrity of connections of specific brain regions, including the breakdown of connectionsbetween brain regions directly affected by stroke as well as long-range rerouting of intra- and transhemisphericconnections related to improved sensorimotor behavior.

1. Introduction

Diffusion-weighted MRI (dMRI) is sensitive to the directionality ofwater diffusion along cellular boundaries such as the myelin sheetenwrapping the axons in the central nervous system. Diffusion tensorimaging (DTI) is a specific type of dMRI that measures the three-dimensional displacement of tissue water and extracts the orientationof fiber tracts with quantitative scalars, e.g., fractional anisotropy (FA) –the amount of diffusional asymmetry in a voxel, which is used as amarker of axonal integrity (Tae et al., 2018). Previous animal and humanstudies applied DTI for the non-invasive detection of pathology-specificmicrostructural white matter changes such as demyelination and sec-ondary neurodegeneration, e.g., in multiple sclerosis, and stroke (Bor-etius et al., 2012; Granziera et al., 2007; Hübner et al., 2017; Dijkhuizenet al., 2012). Here, we will focus on neuroimaging of cerebral ischemia, afocal brain lesion resulting in dynamic brain-wide network changes(Silasi and Murphy, 2014). DTI was applied in several preclinical studiesusing rats as well as clinical studies to measure these changes, forexample white matter integrity, which correlated with the functionaldeficit and recovery, respectively (van Meer et al., 2012; Moura et al.,2019; Schaechter et al., 2009).

Institute of Neuroscience and Me. Aswendt).

311 April 2020; Accepted 21 Ap

is an open access article under t

We hypothesized that mechanisms of stroke pathophysiology andplasticity pass through the network to structurally connected regions andthat the connectivity change between specific regions is related to thefunctional deficit and the recovery thereof. This step required advancedDTI analysis using graph theory, a mathematical approach to describe apairwise relation between brain regions – nodes - that are connected byneuronal fibers – edges (Fornito et al., 2015). Altered graph theoreticalnetwork measures were found in human DTI studies of Alzheimer’sdisease, multiple sclerosis, epilepsy, and stroke (Crofts et al., 2011; Fili-ppi et al., 2019; Fischer et al., 2015). So far, there is no experimentalstroke study using DTI with a focus on graph theory network analysis thatrelates spontaneous network changes to functional improvements.

Here, we present a graph-theoretical approach for mapping the dis-ease spread after experimental stroke in mice with longitudinal in-vivoDTI registered to the Allen Mouse Brain Atlas (Lein et al., 2007). Weshow that the analysis provides a novel level of accuracy in the quanti-tative monitoring of connectivity changes between brain regions. Weprovide evidence that changes in the white matter close to the corticallesion correlate with the sensorimotor deficit and that the behavioralimprovement correlates with elevated structural connectivity in trans-hemispheric thalamocortical and cortico-cortical fiber tracts.

dicine (INM-3), Research Center Juelich, Germany.

ril 2020

he CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

N. Pallast et al. NeuroImage 217 (2020) 116873

2. Material and methods

2.1. Animals and experimental protocol

All procedures described here were performed according to theARRIVE (Kilkenny et al., 2010) and IMPROVE Guidelines (du Sert et al.,2017). The experiments were conducted in compliance with animal carelaws and institutional guidelines and were approved by the Landesamtfür Natur, Umwelt und Verbraucherschutz North Rhine-Westphalia -animal protocol number 84–02.04.2016.A461. Animals were housed inindividually ventilated cages under 12 h light/12 h darkness cycle withaccess to water and food ad libitum. All experimenters were blindedagainst the experimental group (stroke, sham) during the data recordingas well as the primary data analysis (such as the video evaluation of thebehavioral tests). The project and all related experimental data weremanaged using an electronic research database (Pallast et al., 2018). Atotal of N ¼ 22 C57Bl/6J mice (Charles River, Sulzfeld, Germany) with8 weeks of age were included in this study. Seven sham mice served ascontrol andwere co-housedwith stroke littermates for the duration of theexperiment, while 15 mice received photothrombosis. Animals werehabituated and trained to perform the behavioral tests three times theweek before the stroke. Three days before stroke surgery, the baselinebehavior was recorded. After stroke, repetitive behavioral testing andMRimaging were performed at days 1, 3, 7, 14, 21, and 28, followed byanimal perfusion and brain tissue preparation for histology. The animalsreceived 1 mg/ml Tramadol (#100040, Grünenthal, Germany) in theirdrinking water three days before and three days after the surgery. Inaddition, the animals received an i.p. injection of 4 mg/kg Caprofen(Rimadyl, Pfizer, Germany) during the surgery.

Cortical strokes were induced by photothrombosis, which leads to acortical lesion by local endothelial membrane damage, platelet aggre-gation, and thrombus formation, as reported previously (Pallast et al.,2019). Briefly, mice were anesthetized with 3–4% Isoflurane in 70/30N2/O2 and placed in a stereotactic frame (#504926, WPI, Germany). Thesurface of the head was disinfected with povidone-iodine (Betaisodona,Mundipharma, Germany) and an incision along the midline from the eyelevel to the neck (~1.5 cm) was made. The periosteumwas retracted, andthe surface of the skull was cleaned with PBS and a cotton swab. The laser(MGL–FN–561 nm, CNI, China) was adjusted to 35 mW at 561 nm, fixedon the stereotactic frame to the coordinates of the primary somatosen-sory cortex, forelimb region (M/L: 2.00 mm and A/P: 0.00 mm). Toinduce a small stroke, the animals received an intraperitoneal (i.p.) in-jection of 1000 μg of the photosensitive dye Rose Bengal (#A17053, AlfaAesar, Germany). After the dye was allowed to distribute for 5 min in thewhole organism, the laser was projected through the intact skull for 15min with a laser intensity of 35 mW. Laser intensity was calibrated beforethe experiment using a power meter (PM121D, ThorLabs, Germany). Thesham-operated animals underwent the same procedure, including theskin incision and the injection of the Rose Bengal, but no laser irradia-tion, so no ischemia was induced. Afterwards, the wound was sutured(4–0, Ethicon), and the animals were transferred to a preheated heat boxfor up to 1 h (V1200, MediHeat).

2.2. Behavioral tests

Sensorimotor behavior was examinedwith established tests that focuson spontaneous and voluntary movements without a training or reha-bilitation effect with one test before stroke, two tests in the first weekafter stroke followed by weekly tests until day 28. Experimentation andanalysis were done by a blinded experimenter according to standardoperating procedures to reduce the user-induced error.

2.2.1. Rotating BeamThe Rotating Beam test, which is a modification of the balance beam

(Carter et al., 2001), detects sensorimotor deficit in mice over severalweeks (Cheng et al., 2014). The beam is a 120 cm long rod with a

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diameter of 13mm, which rotates at a speed of 6 RPM and at the height of60 cm. To prevent the animal from suffering an injury when falling off,the floor was covered with bubble wrap. The animals were habituated tothe beam three times before the baseline measurement. All runs wererecorded on video (Brio 4k, Logitech, Switzerland). The time (cm/s) tocover the whole distance, how often the hind paws slipped away, andwhether the mouse fell off the Rotating Beam were evaluated. When themouse did not reach the whole distance of 120 cm or <5 cm/s, the runwas excluded. Each mouse performed 4 runs and was motivated by beingreset into its home cage.

2.2.2. Grid WalkThe Grid Walk (or foot fault test) is a sensorimotor test for long-term

dysfunction after stroke and was developed to evaluate the sensorimotorcoordination of the limbs in the rat model and was later modified formice by Baskin (Baskin et al., 2003). The Grid Walk consists of a metalsquare grid (mesh size 12.7 � 12.7 mm) with a diameter of 1.05 mm atthe height of 50 cm. A camera (Brio 4K, Logitech, Switzerland) wasplaced at a 45� angle below the grid, and the animals were allowed for 5min to move over the grid freely. For the analysis, the numbers of footfaults of the impaired limb and non-foot fault steps were counted. A stepis considered a foot fault if it is not providing support and the foot wentthrough the grid hole. Further, if an animal is resting with the grid at thelevel of the wrist, this is also considered a fault. If the grid is anywhereforward of the wrist area, then this is considered as a normal step. Thepercent of foot faults was calculated by: [#foot faults/(#foot faults þ#non-foot fault steps) * 100]. A ratio between foot faults and totals stepsis used to consider differences in the degree of locomotion between an-imals and trials.

2.2.3. Cylinder TestThe Cylinder Test with mice detects long-term sensorimotor

dysfunction after stroke and was developed by Hua (Hua et al., 2002).The Cylinder Test consists of a 15 cm high and 10 cm in diametertransparent cylinder. For habituation, animals were placed in the cylin-der for 5 min on days 7, 5, and 2 before the stroke. The cylinder wasplaced on a transparent plate with a camera (Brio 4K, Logitech,Switzerland) at a 90� angle below. The mice were placed into the cyl-inder, and the spontaneous movements were recorded for 5 min. Pawtouches and paw drags were quantified as described previously (Roomeand Vanderluit, 2015).

2.3. MRI data acquisition

The MRI data acquisition was performed at the Max Planck Institutefor Metabolism Research, Cologne, Germany, using an actively shielded94/20USR BioSpec, equipped with Avance III electronics, the water-cooled gradient system B-GA 12S2 featuring 440 mT/m, and thecryogenically-cooled 2ch TX/Rx coil (Bruker, BioSpin, Ettlingen, Ger-many). Each subject was fully anesthetized before and subsequentlyplaced on the animal carrier, which provides mouse fixation, monitoring,and supply with gases (70/30 N2/O2 with 2–3% Isoflurane). The fixationincludes a tooth bar and ear holders to minimize head motion. Breathingrate and temperature were recorded with a pressure-sensitive pad and arectal fiber optic probe (Small Animal Instruments Inc., New York, NY,USA). Heating and cooling were achieved with a water flow systemincluded in the mouse holder. Heating and Isoflurane anesthesia wereadjusted to keep each animal at 37 �C core body temperature and at abreathing rate of 90–100 per minute. All monitoring information wasassociated with the scanning progress and recorded using a custom-madedata acquisition system based on DASYLab (measX, M€onchengladbach,Germany).

Initial sequences (Localizer, Localizer multislice, and Calcshim) wereacquired to adjust the mouse position and to perform a two-step shimprocedure (first global shim, followed by local shim on the brain vol-ume). A high-resolution rapid acquisition with relaxation enhancement

Table 1List of brain regions inside or close to the stroke lesion (based on initial T2w-MRI1 day after stroke) with related structural/functional region class and child re-gions. These regions were derived from the Allen Brain Atlas ontology (Dong,2008). For the complete atlas ontology see http://atlas.brain-map.org/atlas?atlas¼602630314. The 13 selected brain regions (in bold) were used for the DTIanalysis.

Region class Acronyms Region names Next level relatedchild regions

Somato-motorareas

MOp Primary motor area MOp Layer 1, 2/3,5, 6a/b

MOs Secondary motor area MOs Layer 1, 2/3,5, 6a/b

Somatosensoryareas

SSp-ll Primary somatosensoryarea, lower limb

SSp-ll Layer 1, 2/3,4, 5, 6a/b

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(Turbo-RARE) scan was used for anatomical, T2-weighted MRI (T2w-MRI) with the following parameters: 256� 256matrix size with 28 slices(0.4 mm slice thickness, no gap), field of view (FOV)¼ 17.5� 17.5 mm2,repetition time TR ¼ 5500 ms, echo time TE ¼ 32.5 ms, flip angle 90�.With the same slice orientation, DTI was acquired using a 4-shot echo-planar imaging (EPI) sequence with the parameters: matrix size of 128� 128 with 20 slices (slice thickness ¼ 0.5 mm), FOV ¼ 18 � 18 mm,repetition time TR ¼ 3000 ms, echo time TE ¼ 17.5 ms, and flip angle90�, acquisition time ¼ 14 min. The b value was 670 s/mm2 (30 imagesin non-collinear directions), gradient duration ¼ 3.5 ms, gradient sepa-ration ¼ 8 ms. All MRI data were acquired using the software ParaVision6.0.1. and stored as raw data in the Bruker format. T2w-MRI and DTIscans were performed before (baseline) and after stroke induction (day 1,7, 14, 21, and 28).

SSp-ul Primary somatosensoryarea, upper limb

SSp-ul Layer 1, 2/3,4, 5, 6a/b

SSp-un Primary somatosensoryarea, unassigned

SSp-un Layer 1, 2/3, 4, 5, 6a/b

SSs Supplementalsomatosensory area

SSs Layer 1, 2/3, 4,5, 6a/b

Thalamus DORsm Thalamus, sensory-motorcortex related

VENT, SPF, SPA,PP, GENd

DORpm Thalamus, polymodalassociation cortex related

LAT, ATN, MED,MTN, ILM, RT,GENv, EPI

Fiber tracts cst Corticospinal tract Int, cpd, py, pydmfbs Medial forebrain bundle

systemmfbc, mfsbshy

cc Corpus callosum fa, ee, ccg, fp, ccb,ccs

Striatum STRd Striatum dorsal region CPsAMY Striatum-like amygdalar

nucleiAAA, BA, CEA, IA,MEA

2.4. MRI post-processing

In order to apply graph theory and to allow comparability acrossdifferent species, standard brain parcellation schemes should be applied(Kaiser, 2011). One approach is to register each individual dataset with astandardized atlas to compare the brain region between subjects whileleaving the raw data untouched. Here, we used our recently developedautomated Atlas-based Imaging Data Analysis pipeline – AIDAmri: https://github.com/maswendt/AIDAmri (Pallast et al., 2019). Thepre-processing using AIDAmri included skull stripping, biasfield-correction, and a two-step image registration with the Allen MouseBrain Reference Atlas – ARA (Lein et al., 2006).

The ischemic stroke lesion visible as T2 hyperintense region wassemi-automatically segmented using the 3D snake evolution tool of ITK-SNAP. The resulting stroke masks were transferred to the ARA and usedfor qualitative and quantitative lesion comparison across groups usingincidence maps and atlas-region wise calculation of ischemic tissue asdescribed previously (Pallast et al., 2019).

For DTI, we applied a slice-wise motion correction using FSLMCFLIRT and the brain was extracted by applying a binary mask to theoriginal DTI data set. Subsequently, a two-step registration procedure ofthe T2w-MRI data set and a template mouse brain provided accurateregistration, which was then applied to the DTI scan. DSI studio was usedfor whole-brain fiber tracking analysis of diffusion measures in theassociated ARA regions. We used a modified ARA with N ¼ 98 selectedregions (separated by the midline into 49 left and 49 right regions, seeTable 1) with the aim to include the most important anatomical andfunctional compartments such as the somatosensory cortical andsubcortical regions (SSp, SSs, MOp, MOs, DORsm, DORpm, etc.). Theparental atlas was described in more detail in one of our previous pub-lications (Pallast et al., 2019b) and the Matlab code to generate a customatlas based on the ARA ontology and template is available on Github(https://github.com/maswendt/AIDAmri). Tractography was based on30 gradient directions and the Jones30 DTI scheme (Skare et al., 2000)using DSI Studio (http://dsi-studio.labsolver.org). Fractional anisotropy(FA), radial diffusivity (RD), axial diffusivity (AD), and mean diffusivitiy(MD) were calculated in DSI Studio. A total number of 1,000,000 fiberswith moving distance in each tracking interval of 0.5 mm and a fiberlength of 0.5–12 mm was specified for the fiber tracking. The trackingwas terminated if the motion direction had a crossing angle of more than55�. For the region-based analysis, a list text file of ARA regions in thenative space was included, and each passing or ending track was counted.The result was an adjacency matrix for graph analysis, where each col-umn i and row j represent one region, and each value in the matrix ai;jcontains the weight of an edge. The value ai;j represents the fiber numbercalculated with DSI Studio.

2.5. Atlas-based connectivity analysis

Subsequent processing of the connectivity adjacency matrices

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(Supplementary Fig. S1) was performed using the graph and networkalgorithms for undirected graphs implemented in the Brain ConnectivityToolbox (Rubinov et al., 2009) for Matlab (Matlab Version R2018a, TheMathWorks Inc., Natick, USA). The adjacency matrices output ofAIDAmri, which are numeric matrices with N� Nof the graph analysisalong with the acronyms for the brain regions as node names for anin-house developed Matlab script. Each particular adjacency matrix AM;T

of all mice M at each time point T describes a graph GM;T by integerentries ai;jin the row i ¼ f1;…; ng and column j ¼ f1;…;ng, representingan edge from the node i to the node j or vice versa. The selected nodesrepresent n ¼ 13 regions, listed in Table 1. The volume of the associatednode region was reduced to a point with x,y,z-coordinates related to thecenter of gravity. The determined position is necessary to locate nodes ofthe given graphs relative to the ARA (Fig. 1A and B). For simplification,in the results section, brain regions were displayed in the plane and not inthree-dimensional space with posterior to anterior (tail to head) view.

For the subsequent evaluations, some further a priori definitions weremade: ipsilateral side with the stroke lesion was on the left (L) side andthe contralateral side on the right side (R) of the brain. The selectedsomatosensory grey and white matter regions were associated in fourregion classes (Table 1): somatomotor and somatosensory areas, thal-amus, fiber tracts, and striatum.

For the analysis, we focused on the network measures 1) edgestrength (in other contexts also termed edge weight), which is the valueof each edge ai;jbetween two nodes, shortest path between the nodes i

and j passing a the interim nodes ns ;and 2) the weighted degree kðiÞ ¼

PN

j¼1ai;j (in other contexts also termed node strength), with the number of

edges adjacent to the node i and edge strength ai;j between each node iand j, subdivided into ipsi- and contralateral intrahemispheric, andinterhemispheric node connectivity. The changes were determinedwithin a region class and between the region classes and related to thecontrol group. The edge strengths between two regions of the control

Fig. 1. Selected sensorimotor network with related cortical, subcortical, and white matter areas (A–B). Graphical visualization of the absolute position of nodes(centroid of the region volume) and edges in the atlas space for the selected brain areas related to the sensorimotor system: somatomotor cortex (MOp and MOs),somatosensory cortex (SSp-ll, SSp-ul, SSp-un, SSs), thalamus (DORsm, DORpm), striatum (STRd, sAMY), and fiber tracts (cst, mfbs, cc). Intrahemispheric connectionsare marked in blue and orange. Coronal section (view anterior to posterior and coronal/sagittal cross-section of the Allen Brain Reference Atlas as the backgroundimage (C–D). 3D volume rendering of the primary and secondary somatosensory region (SSp and SSs), Striatum dorsal region (STRd), and thalamus (DORsm andDORpm), generated with the © 2019 Allen Institute for Brain Science Brain Explorer 2. (C) Anterior to posterior view. (D) Rotated view from dorsal/anterior toventral/posterior.

N. Pallast et al. NeuroImage 217 (2020) 116873

group (Sham) were determined as normal connectivity between theselected regions. Significant differences in edge strength to this regularconnectivity were referred to as reduced and increased connectivity,respectively. For better orientation of the brain structures with relatednodes, the geometrical position and location of thalamus, striatum, andsomatosensory area were given in relation to the whole brain (Fig. 1Cand D). Based on the graphs of baseline and day 28 post stroke (Sup-plement Figure S2-3), we calculated the shortest path from source node ito target node j using a modified version of the Dijkstra’s algorithm basedon averaged connectivity of all animals in the stroke group. On theshortest path between the nodes i and j there are a certain number atinterim nodes ns that lead to a subgraph M ¼ fi; i þ 1; ::: i þ ns; jg. Theedge weights were inverted to account for the principle that for theshortest path, the sum of weights of the related edges is minimized. Thenormalized sum of the inverted weights as distances along the edges inthe diagram gives the mean edge strength dði; jÞ of the shortest path be-

tween the nodes i and j using dði; jÞ ¼ 1M

Pj

m¼i

1am;mþ1

.

2.6. Comparison to viral tracing

Viral tracing connectivity data were retrieved from the Allen MouseBrain Connectivity Atlas (Oh et al., 2014) using a custom Matlab scriptand visualized with Brain Explorer 2 ©2019 Allen Institute for BrainScience. We selected all experiments per atlas region (sometimes morethan one virus injection experiment per region) such as experiment113884251 (https://connectivity.brain-map.org/projection/experiment/113884251) with injection of virus into the DORsm (fluorescencedistribution in the injection size: 40% VAL -, 21% VPL -, 10% VPN, 22%RT, and 6% fiber tracts). The projection volume is defined as the fluo-rescence with the volume of the projection signal in the respective regionin mm3. The results of the experiments per region were averaged and the(fluorescence) projection density summarized for all traced brain regionsaccording to parental region structure of our modified atlas. For a qual-itative comparison, the projection volumes were correlated with the nodestrength for pre-selected regions (DORsm/pm, SSs, STRd and SSp-ul) asseeds in all stroke mice at baseline.

2.7. Histology

For the preparation of mouse brain tissue and immunohistochemistrya previously published protocol was used (Pallast et al., 2019b). Briefly,coronal tissue sections (20 μm) were from paraformaldehyde-fixed braintissue were used for immunohistochemistry with the antibodies GFAP(1:500; Agilent Cat# Z0334, RRID:AB_10013382) and Cy3 (1:500;Jackson ImmunoResearch Labs Cat# 711-165-152, RRID:AB_2307443).Cell nuclei were stained using DAPI (HP20.1, Carl-Roth). Whole brain

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slice stitch/merge images were acquired with the fluorescence micro-scopes Keyence BC8000 (10� objective) and Life Tech Evos (20�objective).

Quantification of GFAP-positive cells in brain atlas regions of striatalbrain sections (from n ¼ 6 randomly selected stroke brains) was per-formed using our tool AIDAhisto for the registration of microscopy withthe Allen Mouse Brain Atlas and automated cell counting (Pallast et al.,2019b).

2.8. Statistics

Statistical testing of the diffusion metrics FA, RD, AD, and MD wasperformed with the Statistics andMachine Learning Toolbox in Matlab. Arepeated measures model was applied with Tukey-Kramer-correction formultiple comparisons. Statistical testing of the lesion size/location,behavioral, and edge strength data were performed using Prism (macOSversion 8.2.1, www.graphpad.com). For the evaluation of lesion size, arepeated measures one-way ANOVA with the sphericity correctionmethod of Geisser and Greenhouse and Tukey correction for multiplecomparisons was used. For the evaluation of lesion location, a two-wayANOVA the sphericity correction method of Geisser and Greenhouseand Dunnett correction for multiple comparisons was used. For thebehavioral data, a mixed-effects analysis with the sphericity correctionmethod of Geisser and Greenhouse andwith Sidak correction for multiplecomparisons was applied. For the edge strength data, a mixed-effectsanalysis with the sphericity correction method of Geisser and Green-house and with the original false discovery rate (FDR) correction formultiple comparisons of Benjamin and Hochberg (Benjamini et al., 1995)was applied. Post-hoc multiple comparisons were reported in the resultssection only when there was a significant effect over time and/or group.For the full statistical report including F ratio, Geisser-Greenhouse’sepsilon, see Supplementary Table 1-22). The mixed model was chosen toadjust for the nonindependence of the dependent variable (e.g. edgestrength) in our study (Schober et al., 2018). Prism uses a compoundsymmetry covariance matrix, and the fit is done with Restricted (Resid-ual) Maximum Likelihood (REML) that provides unbiased estimates ofunknown variances. The cell counting results were compared using atwo-tailed t-test.

Additionally, the linear relationship between a selected edge and thebehavioral tests and the projection volume and the node strength weredetermined by Spearman’s linear partial correlation of both samples. Theinfluence of the tenmost substantial edges between the regions in Table 1were eliminated by including them as control variables. Hence, thepartial correlation determines the relationship between two observa-tions, without the influence of other edges. This approach avoidedspurious correlations and isolated the relationship between a selectededge and the behavior.

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The significance levels were displayed as * p < 0.05 (significantlevel), ** p< 0.01, and *** p< 0.001. The data were plotted as a box plot(5–95% percentile) with connecting lines generated by a Piecewise CubicHermite Interpolating Polynomial (PCHIP). For the spearman correla-tion, data were plotted as a scatter plot, and the coefficient rho withrelated p-value was reported.

3. Results

3.1. Sensorimotor deficit and spontaneous recovery

A battery of sensorimotor tests was used to assess the stroke-inducedbehavioral deficit and the spontaneous recovery over four weeks. For theRotating Beam Test (RBT), we measured speed and hindlimb drops.While the sham mice did not change significantly their speed on the RBTduring the 4 weeks observation period, stroke mice walked significantlyslower in the first two weeks compared to the sham group (p < 0.001, p< 0.001, p¼ 0.021) and their baseline (p< 0.001, p< 0.001, p¼ 0.042).We observed no difference in speed between the groups at three andweeks post stroke (Fig. 2A). A similar time course of functionalimprovement was measured in equalized number of hindlimb drops atfour weeks compared to baseline for the stroke group (Fig. 2B). Whereasstroke mice showed significantly more hindlimb drops only in the acutephase, at one day after stroke (p ¼ 0.014) compared to sham mice, thedeficit lasted until day 28 (p ¼ 0.005). With the Grid Walk Test, wecalculated the percentage of foot faults while the mice explored the gridfreely (Fig. 2C). Again, we detected a substantial effect over time in thestroke group only (p < 0.001). The stroke group did not recover back tobaseline levels (p < 0.001) and remained significantly different to thesham group at day 28 (p ¼ 0.012). Finally, we evaluated the asymmetryof touches on the cylinder wall and whether paw drags occurred duringthe touch (Fig. 2D). There was no significant effect over time for theasymmetry score (data not shown). However, the percentage of paw

Fig. 2. Results of the sensorimotor behavior tests. (A) Rotating Beam speed normnormalized to the total number of touches/steps, and (D) Cylinder Test percentage

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drags with the affected paw was significantly higher in the stroke vs.sham group in the acute phase, e.g. one day after stroke (p ¼ 0.003), andup to day 21 (p ¼ 0.043).

3.2. Lesion location and size

The lesion size and location were evaluated using lesion masks, whichwere semi-automatically drawn on T2-weighted MRI (T2w-MRI) for thedays 1, 7, and 14 with most prominent stroke lesion-related T2 hyper-intensity. Quantitative pixel-wise analysis across mice was summarized inincidence maps, in which the individual stroke masks were averaged andthe pixel color-coded to represent the number of animals (Fig. 3A). Quali-tatively, thisanalysis showedahomogenousdistributionof the stroke lesionacross the different time points, while the overall lesion size decreased overtime. In the quantitative analysis (Fig. 3B), we measured a significantdecrease in lesion size between the first and second week (p< 0.001). Thelocation of the lesion was analyzed quantitatively by comparing the rela-tionship of the affected volume normalized by the volume of the regionaveraged across mice (Fig. 3C). One day after stroke, the brain edema wasreflected in the hyperintense region on T2w-MRI comprising primary andsecondary sensorimotor areas as well as fiber tracts (cc and mfbs) withconsiderable variability. During the first two weeks, the hyperintense re-gion on T2w-MRI diminished. At twoweeks post stroke, the regionsMOp, LSSp-ll, L SSp-ul, and L SSp-un were most affected compared to the otherregions (p< 0.001) with a volume ratio of 13.4–45.2%.

The twomeasures lesion size and sensorimotor deficit, reflected in thebehavior Rotating Beam Test (decreased speed, increased number ofhindlimb drops), Cylinder Test (increased number of paw drags), andGrid Walk Test (increased foot faults), were correlated with each otherfor the first two weeks after stroke (Supplementary Material Fig. S4).There was a significant correlation only for the Rotating Beam Testmeasures speed (r2 ¼ 0.27 and p < 0.001) and hindlimb drops (r2 ¼ 0.71and p < 0.001).

alized to baseline, (B) Rotating Beam hindlimb drops, (C) Grid Walk foot faultsof paw drags per touch. Sham (blue) n ¼ 7, stroke (red) n ¼ 15.

Fig. 3. (A) Incidence map for the T2-MRI stroke masks with ARA template as background. The color code defines in how many mice a particular pixel was inside thestroke mask. B) Consolidation of the hyperintense stroke region based on the T2w-MRI during the first two weeks after stroke. C) Qualitative comparison of theaffected volume normalized to region volume. Data plotted for all stroke mice N ¼ 15.

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3.3. Fiber tracking in the somatosensory system

3.3.1. ValidationWe compared the fiber tracking for a selected striato-thalamocortical

pathway (DORsm, DORpm, SSp, SSs, STRd) to viral tracing data in theAllen Mouse Brain Connectivity atlas (Oh et al., 2014). We detected avery similar fiber pattern in the qualitative comparison of the fluores-cence projection volume derived from the viral tracing (Fig. 4A and B)with fiber tracking in representative sham and stroke animals at day 28post stroke (Fig. 4C–F). Stark differences to the viral tracing weredetected after stroke. Whereas subcortical thalamic and striatal pathwaysstill followed a similar pattern, intracortical connectivity between SSpand SSs was degenerated (Fig. 4C and D).

In addition to the qualitative comparison, we correlated the fluores-cence projection volume of the viral tracing experiments for a selection ofregions (DORpm/sm, SSs, STRd, SSp-ul) with the node strength derivedfrom the DTI data (Supplementary Fig. S5). Except for SSs, we detectedstrong positive correlations, indicating reliable fiber tracking ofanatomically relevant connections using our DTI protocol.

The DTI measures fractional anisotropy (FA), radial diffusivity (RD),axial diffusivity (AD), and mean diffusivitiy (MD) were calculated for alltime points in the ipsilesional regions SSp-ll/ul/un (SupplementaryFig. S6). These regions were selected based on the atlas-based analysis oflesion location, which marked these regions (next to MOp) as most long-term affected by stroke (Fig. 3). In SSp-ul and SSp-un, AD was decreasedafter stroke compared to sham at day 1 (p ¼ 0.024 and p < 0.001).Furthermore, FA and AD increased compared to sham from day 14 on inall three regions and continued to be significantly different in SSp-ul and

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SSp-un until day 28 post stroke (p< 0.001). The other measures, MD andRD showed no significant change over time nor between the stroke andsham group, except for the RD and MD of SSp-un at day 1 after stroke (p¼ 0.012 and p ¼ 0.001).

We quantified the gliosis as one potential confounding factor for thefiber tracking in one of the most stroke-affected regions SSp-ul byimmunohistochemistry (Supplement Fig. S7). Compared to the con-tralesional hemisphere, we detected an increased number of GFAP-positive cells, as a sign of gliosis, in the ipsilesional hemisphere (p ¼0.037).

3.3.2. Stroke-related structural connectivity changesFrom the whole-brain tractography with N � N elements in the

matrix per mouse and time point (Supplementary Fig. S1), we selectedin a first step n ¼ 13 grey and white matter regions associated with thesensorimotor network (Table 1, Fig. 1). Changes in the structural con-nectivity of these regions were expected to reflect changes observed inthe sensorimotor behavioral tests. In a second step, the tractographydata of the sensorimotor regions were analyzed using graph theory forthe degree kðiÞ of intrahemispheric and interhemispheric connectednodes (Supplementary Fig. S2-3). In a graphical overview, the nodesand edges were summarized. The edge strengths between two regions ofthe control group (Sham) were determined as regular connectivity.Significant differences in edge strength to this regular connectivity werereferred to normal (grey), reduced (red) or increased (green) connec-tivity (Fig. 5).

By comparing the overall changes for the selected regions in strokecompared to sham mice, we found reduced edge strength dði; jÞ in the

Fig. 4. Qualitative comparison betweenthe viral tracing and DTI fiber tracking.(A–B) Experiment 113884251 of the Brainconnectivity atlas generated with the © 2019Allen Institute for Brain Science. Brain Ex-plorer 2: viral tracer injected into the thal-amus (DORsm, DORpm). (C–D) Comparablefiber tracking by DSI Studio in a representa-tive stroke (C–D) and sham (E–F) animal,respectively. White arrows label the connec-tivity between SSs and SSp (SSp-ul, SSp-ll,SSp-un) and the thalamo-striatal-corticalfiber tract.

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long-term affected regions (L MOp, L SSp-ll, L SSp-ul, L SSp-un) inside thestroke lesion (ipsilateral, left hemisphere) and to L SSs. Here, the meanedge strength between L SSp-un and L SSs was 152.6 and had no interimnodesm at baseline on the shortest path. Since the edge strength L SSp-un<> L SSs was reduced over time, the shortest path crossed the interimnode L cc at day 28 and the mean edge strength decreases to 107.64.

The reduced edge strength in intracortical connections of strokelesion-associated regions propagated to large fiber tracts (L cc and Lmfbs) and contralateral subcortical areas (R STRd, R mfbs, R AMY).Notably, for regions outside the stroke lesion, such as MOs (affected onlyin the acute phase after stroke), intracortical connections, e.g., MOs toMOp, and subcortical connections, e.g., L SSs to L STRd and L cc,remained unchanged. Next to decreased connectivity strength, edgeweights increased for short-range and long-range transhemisphericconnections such as L MOs to R MOs.

In order to quantify the dynamic changes underlying the observedconnectivity changes between stroke and sham, we performed a DTInetwork analysis separately for the ipsilesional, contralesional, andtranshemispheric pathways, as well as the connectivity inside and be-tween the selected region classes (Table 1).

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3.3.3. Ipsilesional connectivityIn the ipsilateral hemisphere, the internal connectivity of the so-

matosensory areas (grouped according to Table 1) located in the center ofthe lesion was disturbed from day 7 to four weeks post stroke (Fig. 6).There was a significant drop of edge strength in the compounds L SSp-un– L SSp-ul, L SSp-ul – L SSp-ll, L SSs – L SSp-ul, which remained until threeor four weeks post stroke (p ¼ 0.004, p ¼ 0.003, p ¼ 0.036). Interest-ingly, the connection between L SSp-ul and L cst, with minimal edgestrength before stroke, increased by a factor of 15 at two weeks poststroke (p < 0.001) and further on until four weeks (p ¼ 0.010). Bycomparing the shortest path between L SSp-ul and L cst, we found a denovo direct connection, bypassing the nodes L STRd and L cc according toanatomical features in the sham group. However, since that new path hada lower edge strength than the original path via L STRd and L cc, theinterim nodes remained unchanged between baseline and P28 of thestroke group.

Although the thalamo-thalamic connectivity remained intact afterstroke, after the first week post stroke there was an increased edgestrength in the ipsilateral thalamocortical connections L DORpm to L SSp-ul and SSp-un, respectively, which remained until four weeks (p ¼ 0.006

Fig. 5. Schematic representation of the most prominent DTI network changes in the sensorimotor and associated white matter regions extracted by graphtheory over 4 weeks after stroke. The positions of the nodes on the ipsilateral (Ipsi, L) and contralateral (Contra, R) hemisphere were derived from the centroid ofeach region and transferred to the 2D coronal mouse brain layer (background) while keeping the proportional distance to surrounding edges. Nodes inside the strokelesion are marked bold. The edges between the nodes were color-coded: increase (green), decrease (red), or no change (grey) of the edge weight, compared to thecontrol group.

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and p ¼ 0.008). The overall edge strength for the large white mattertracts L cc and L mfbs was slightly reduced at day 28 post stroke (p ¼0.002).

3.3.4. Contralesional connectivityIn the right hemisphere, connectivity inside large fiber tracts such as

the corticospinal tract (cst) and corpus callosum (cc) remained intact anddid not change over time compared to sham (Fig. 7A). The connectivity ofthe striatum was only temporarily disturbed compared to sham. TheStriatum-like amygdala nuclei (sAMY) connected with the cst (R cst – RsAMY) decreased for example at day 14 (p ¼ 0.001). In contrast, theinter-thalamic and striatothalamic connectivity improved, mainly due toincreased edge strength between the functional components R DORsm - RDORpm, the adjacent fiber tracts (R DORsm – R cst), and the striatum (RDORsm – R STRd). For these connections, the edge strength increasedfrom two weeks post stroke on and remained significantly different tosham at four weeks post stroke (p ¼ 0.010, p < 0.001 and p ¼ 0.014).

3.3.5. Transhemispheric connectivityBetween the ischemic and the healthy hemisphere, we also measured

substantial connectivity changes (Fig. 7B). Similar to the effects on thecontralateral hemisphere, internal connections between the left and rightthalamus, the L DORpm - R DORpm, were not strongly improved (only atday 14 p ¼ 0.011). Somatomotor regions remained spared by the strokelesion. In this line, the ipsilesional inner area connectivity remainedunchanged. However, connectivity between the somatomotor areas andcortico-thalamic areas was increased: R MOs - L MOp (p¼ 0.002), R MOs– L MOs (p ¼ 0.005), and L DORpm - R MOs (p ¼ 0.006) at four weekspost stroke. Here, the mean edge strength between L MOs and RMOs was93.2 and had no interim nodes m at baseline on the shortest path and.Since the edge strength L MOs <> R MOs was increased over time, themean edge strength of the shortest path was increased to 223.14.Furthermore, the mean edge strength between L SSp-un and R SSs was66.52 and had the interim nodesM ¼ {L SSp-un, L SSs, L STRd, R STRd, RSSs} at baseline on the shortest path. Since the edge strength L DORsm

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<> R DORpm was increased over time, the shortest path crossed theinterim nodeM ¼ {L SSp-un, L cc, L STRd, L DORpm, R DORpm, R STRd,R SSs} at day 28 and the mean edge strength increased to 75.02.

Likewise, new transhemispheric striatocortical connections appeared,for example between L STRd and R MOs. At baseline, the shortest pathbetween these regions with a mean edge strength of 79.83 included theinterim nodes M ¼ {L STRd, R STRd, R cc, R MOs}. Since new striato-cortical connections appeared between L STRd <> R MOs, the shortestpath crossed no interim node at P28 and the mean edge strengthincreased to 180.79.

3.4. Correlation of connectivity changes and sensorimotor behavior

In order to identify changes in structural connectivity between re-gions that exert a direct impact on the sensorimotor behavior, the edgestrength for all sensorimotor regions from the ipsi- and contralesionalhemisphere, as well as transhemispheric connections, were comparedto the behavior using partial correlation to minimize false positive re-sults. There was no correlation of speed/hindlimb drop (Rotating Beamtest), paw drag (Cylinder Test), and foot fault (Grid Walk Test) to theedge strength for all sensorimotor regions in sham animals. In contrast,in the stroke group, we found the connectivity between L SSp-un to LSSp-ul, which was decreased in stroke vs. sham mice, to be negativelycorrelated with the speed (rho ¼ �0.536 and p < 0.001). The un-changed edge strength for R cst to R cc between sham and stroke micewas also negatively correlated to the speed (rho ¼ �0.286 and p ¼0.011). More correlations for selective interhemispheric connectionswere found for the measure hindlimb drop: the increased R cst to R cc(rho ¼ 0.276 and p ¼ 0.015) and the increased R DORpm to R STRd(rho ¼�0.252 and p ¼ 0.027) as well as the decreased R MOp to L mfbs(rho ¼ 0.367 and p ¼ 0.001) connection (compared to sham, Fig. 4). Incontrast, for the measures paw drag (Cylinder Test) and foot fault (GridWalk Test) only the decreased connection L SSp-un to L SSp-ul waspositively correlated (rho ¼ 0.32 and p-value ¼ 0.004, rho ¼ 0.379 andp-value <0.001).

Fig. 6. Ipsilesional connectivity changes. Changes in connectivity inside functional/structural areas shown as circles with color code for disturbed (red), intact(grey), and improved connections (green) compared to sham mice. The connectivity between these areas was compared over time after stroke to sham mice andsignificant changes are shown as color-coded lines for reduced (red), normal (grey), and increased (green) edge strength. Longitudinal measure of edge strength forselected brain region regions in sham (blue) and stroke (red) mice.

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4. Discussion

Quantitative mapping of whole-brain connectivity at different scalesis expected to reveal how brain function emerges from brain structureand how cellular damage propagates through the network leading tofunctional deficits. Network analysis using graph theory may provide ageneral framework to categorize connectivity changes and relate them toalterations caused by different neurological disorders (van den Heuveland Sporns, 2019). Here, we applied diffusion network analysis afterstroke in the mouse model as a complement to previous human studiesusing DTI as a novel biomarker for stroke-related functional deficitand/or recovery thereof (Koch et al., 2016). In contrast to the humanstudies, we provide the first longitudinal and quantitative mapping ofindividual cortical, subcortical, and fiber tract changes related to corticalstroke. We used region-based diffusion tractography and graph analysisto investigate the dynamic structural network changes over four weeksafter stroke, combined with repetitive behavioral testing.

4.1. Stroke lesion size and sensorimotor behavior

Cortical lesions were induced by photothrombosis, which leads to thedisruption of endothelial integrity, localized cell death, and a very smallpenumbra. It offers the advantages of a relatively small interindividualvariability of stroke size and low mortality, and has hence previouslybeen used for longitudinal MRI and plasticity studies (Carmichael, 2005;Chen et al., 2007; Clarkson et al., 2013). We characterized the lesion sizebased on the T2-weighted MRI, which according to a recentmeta-analysis, serves as a useful noninvasive assessment of infarct size

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during the first two weeks after the onset of ischemia (Milidonis et al.,2015). In the stroke mice, we detected with most of the behavioralmeasures a spontaneous functional improvement to the level ofpre-stroke (baseline). However, only the Rotating Beam test showed asignificant correlation to the lesion size. This finding might be due aminor sensorimotor deficit due to the relatively small lesion size and theset of cortical regions affected by the stroke at 14 days.

4.1.1. Diffusion network analysisFor the tractography, somatosensory and anatomically-related areas

such as the thalamus, striatum, and main fiber tracts were used for agroup comparison. When comparing edge strength differences over time,we found strongly reduced local connectivity between brain areasaffected by stroke. In contrast, regions such as MOs, which were onlytransiently affected by stroke, remained connected without a significantchange. The cortical ischemic territory was mainly in the somatosensoryareas, which showed the most substantial decline in edge strength.Different from the motor cortex, we measured a decreased edge strengthcompared to control in the primary to secondary somatosensory areas.These local changes in the connectivity of regions close to (or inside the)ischemic territory are in line with a previous report of reduced commu-nicability (a graph theoretical measure of how easy information cantravel through a network) in stroke patients (Crofts et al., 2011).

In contrast to that study, we did not detect decreased connectivity inhomologous contralesional regions. However, there was a decrease inconnectivity between the ipsilesional primary and secondary motorcortex and the contralesional striatum, which further propagated to theamygdala. Distant astroglial responses to photothrombosis in the rat

Fig. 7. Contralesional and transhemispheric connectivity changes. Changes in connectivity inside functional/structural areas shown as circles with color code fordisturbed (red), intact (grey) and improved connections (green) compared to sham mice. The connectivity between these areas was compared over time after stroke tosham mice and significant changes are shown as color-coded lines for reduced (red), normal (grey), increased (green) edge strength. Longitudinal measure of edgestrength for selected brain region regions in sham (blue) and stroke (red) mice for a) contralesional and b) transhemispheric connections.

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were described for the striatum up to four weeks post stroke and tran-siently for the amygdala, albeit for the ipsilesional hemisphere only(Nowicka et al., 2008). Therefore, the here detected decrease in crossedcortico-striatal connectivity could also be a structural consequence oftranshemispheric diaschisis, which is a frequent phenomenon forcortico-cortical pathways (Carrera and Tononi, 2014). Interestingly, wedetected enhanced interhemispheric connectivity between homotopicmotor areas, which is in line with previous human and animal studies offunctional connectivity (Rehme and Grefkes, 2013). Furthermore, weapplied a modified version of the Dijkstra’s algorithm to calculate theshortest and most efficient path including edges with high edge strengthbetween two regions. At baseline, the shortest path calculation resembledanatomically meaningful ipsilateral and crossed connections such as

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between the striatum and the motor cortex through the corpus callosum,contralateral striatum. In line with previous findings of plasticity incrossed cortico-striatal projections related to compensatory relearning ofmotor tasks after stroke (Balbinot et al., 2019), we detected an improvedshortest path between ipsilesional striatum and contralesional motorcortex.

However, the detected changes in structural connectivity did notcorrelate with all behavioral measures the same way. For example, theconnectivity between L SSp-un to L SSp-ul, which was decreased in strokevs. sham mice, was negatively correlated with the speed and positivelycorrelated with paw drags and foot faults. Hence, the degenerated fibertract was found in faster mice on the Rotating Beam, however, at thesame time, mice with less errors in paw placement in the Cylinder Test.

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These results are contradictory to the assumption that improvedstructural connectivity should result in better behavior, as it has beensuggested by a DTI study of healthy patients performing a bimanual co-ordination task better when the white matter integrity in the corpuscallosum was higher (Johansen-Berg et al., 2007). Such correlation wasnot detectable in our sham group, which might be related to the vari-ability of the DTI data or the type of behavioral assessment, whichrequire multiple components of the limbic and sensorimotor system andcannot be related to a single brain region.

Nevertheless, three significant correlations with the behavior weredetected for the SSp-un to L SSp-ul connection. According to a recentcomparison (Chon et al., 2019) of the Allen Mouse Brain atlas regions(Ding et al., 2017) and the Paxinos and Franklin’s mouse brain atlas(Paxinos, 2013), the unassigned region (SSp-un) relates to the primarysomatosensory dysgranular zone (S1DZ). In the rat brain, projectionsfrom barrel field S1DZ were characterized in detail by anterorgradetracing using biotinylated dextran amine (Lee and Kim, 2012). Thedysgranular zone terminates together with other somatosensory areas inthe striatum. However, in the thalamus and spinal cord the target regionis separated from other somatosensory regions and overlaps withmotor-related structures. In this line, early microstimulation studies havedemonstrated that movements, especially of the hindlimb, can be evokedfrom the dysgranular zone (Ebner and Kaas, 2015). While the correla-tions to behavior are not fully conclusive in the current study, the dys-granular zone showed an interesting pattern of decreased corticalconnectivity but increased connectivity to subcortical striatum, thal-amus, and corticospinal tract, compared to sham, which is of interest forfuture studies.

4.1.2. LimitationsWe applied a high-resolution DTI mouse protocol (141 μm in-plane

resolution), which limits SNR and the accuracy to distinguish crossingfibers in contrast to, for example, diffusion spectrum imaging (DSI)(Green et al., 2018). In order to verify the reliability of the DTI acquisi-tion and processing, we compared the fiber tracking before stroke forselected somatosensory regions with the well-established viral tracing forthe same brain regions derived from the database of the Allen MouseBrain Connectivity atlas (Oh et al., 2014). Although the two data setsorigin from completely different measurements, water diffusion proper-ties of the brain tissue vs. fluorescence intensity of a viral tracer, most ofthe pathways were strongly correlated. That is in line with previousstudies, comparing microscopic, viral tracer-based connectivity to mes-o-/macroscopic ex-vivo/in-vivo DTI of the mouse brain (Calabrese et al.,2015; Jiang and Johnson, 2011). Importantly, DTI constitutes only anindirect measure of axonal orientation with false positive/negativeconnections, and, in contrast to viral tracing, DTI lacks directionality(Maier-Hein et al., 2017). In order to compare the structural changesdetected by DTI, future studies would benefit from a correlation tocellular mapping of connectivity, for example with viral tracing and 3Dlight sheet microscopy (Goubran et al., 2019). Regarding the imageresolution, we also cannot dismiss that, for example, the cst to striatumand thalamus tracts derived from the fiber tracking are related toanatomically different fiber tracts, namely the ascending sensory anddescending motor pathway, which is passing in close distance (Zhanget al., 2017).

An important note of caution regarding the interpretation of thereliability of short-range fiber tracking results inside the stroke area aswell as the peri-infarct zone is because of gliosis. Activated astrocytesproliferate around the photothrombotic lesion core and create a glialscare, which is referred to gliosis (Li et al., 2014) and was detected in ourstudy as well. Related to that, we detected an increase in the diffusionmetrics of axonal injury and microstructural integrity, AD and FA, overtime in regions initially strongly affected by stroke such as the somato-sensory regions. However, the inverse measures of membrane densityand measure of myelination, MD and RD, did not change after stroke.Anisotropy is known to be increased in the presence of highly orientated

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fibers (Moura et al., 2019), which in case of the regions inside or close tothe lesion but not distant regions might be also due to gliosis as reportedpreviously in rats following traumatic brain injury (Budde et al., 2011).In contrast, in a different study using the large middle cerebral occlusionmodel in rats, increased FA levels in per-lesional regions were confirmedhistologically to be associated with high levels of myelin indicatingstructural reorganization (van Meer et al., 2012). FA values were alsoreported to increase in peri-lesional white matter and overshoot thebaseline levels, which was correlated with increased axonal density,suggesting axonal remodeling (van der Zijden et al., 2008). As wemeasured both, connectivity loss and increase, in these somatosensoryregions, it is difficult to restrict the interpretation to one underlyingbiological process.

Finally, the results presented here show for the first-time correlationsof structural changes and behavior for specific connections betweenbrain regions. This, however, does not imply causality and needs to besupported by future studies inhibiting or stimulating these connections toprovoke behavioral responses and including histological correlations forthe DTI-relevant microstructural changes.

CRediT authorship contribution statement

Niklas Pallast: Conceptualization, Methodology, Software, Investi-gation, Writing - original draft, Visualization. Frederique Wieters:Investigation, Writing - review & editing. Marieke Nill: Investigation.Gereon R. Fink: Conceptualization, Resources, Funding acquisition,Writing - review & editing. Markus Aswendt: Conceptualization,Methodology, Formal analysis, Supervision, Writing - review & editing,Visualization.

Acknowledgements

The authors acknowledge help with histology performed by OliviaK€asgen, Veronika Fritz, and Mich�ele Tegtmeier as well as with statisticsby Felix Schmitt. This work was supported by funding from the FriebeFoundation (T0498/28960/16).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.neuroimage.2020.116873.

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