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Auditory Brainstem Response (ABR) and Cambridge Neuropsychological Test Automated Battery (CANTAB) as Objective Support in Diagnosing Childhood ADHD and ASD

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Auditory Brainstem Response

(ABR) and Cambridge Neuropsychological Test

Automated Battery (CANTAB) as Objective Support in

Diagnosing Childhood ADHD and ASD

Emma Claesdotter, MD

DOCTORAL DISSERTATIONby due permission of the Faculty of Medicine, Lund University, Sweden.To be defended at Psychiatry Lund, Baravägen 1, the 20 th of October,

09,00

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Faculty opponentProf. Tommy Lewander, MD, PhD

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OrganizationLUND UNIVERSITYFaculty of MedicineDepartment of Clinical sciences, LundPsychiatryLund, Sweden

Doctoral Dissertation

Date of issue 28/9-2017

Author: Emma Claesdotter)

Title and subtitle: Auditory Brainstem Response (ABR) and Cambridge Neuropsychological Test Automated Battery (CANTAB) as Objective Support in Diagnosing Childhood ADHD and ASDAbstractBackgroundThere is a need for easily administered objective tests in neurodevelopmental disorders. We investigated the clinical utility for child and adolescent psychiatry of Auditory Brainstem Response (ABR) and Cambridge Neuropsychological Test Automated Battery (CANTAB) as support in diagnosing attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). MethodsThe thesis consists of four studies based on ABR and CANTAB. The patients (7-17 years old) were recruited from child and adolescent psychiatry outpatient clinics in Lund and Eslöv, in southern Sweden.. The age and gender matched controls were recruited from schools in the same areas. All invited families agreed to participate.ABR: 63 females and 48 males with ADHD were compared to 26 females and 20 male controls. Furthermore 31 females and 27 males with ASD were compared to 24 females and 23 male controls. Patients were diagnosed according to the DSM-IV/DSM-5. The ABR consists of seven positive peaks (waves I–VII) that occur during 10 Ms following a sound stimulus recorded by five electrodes.CANTAB:112 children with ADHD and 95 controls performed five CANTAB tests: Stockings of Cambridge (SOC), Intra-Extra Dimensional Set Shift (IED), Spatial Working Memory (SWM), Stop Reaction Time (SRT), and Stop Signal Task (SST).ResultsWe found three aberrations in the young female ADHD group and three other aberrations in the young male ADHD group compared to controls, while one aberration was present in both genders. In the ASD study only one aberration was found compared to controls. CANTAB: Between-group differences were found in all CANTAB tests except one. With hierarchical multiple regression analysis a main effect of ADHD diagnosis without interaction were seen in all the tests except SOC where a significant interaction with age was found.ConclusionsThe results suggest that ABR and CANTAB can differentiate between typically developed children and children with ADHD or ASD. All our results are based on observed group differences and cannot at this stage be transferred to single individuals.

Key words ADHD, ASD, ABR, CANTAB, Child and adolescent psychiatry, Gender

Classification system and/or index terms (if any)

Supplementary bibliographical information Language Eng

ISSN 1652-8220 ISBN 978-91-7619-507-9

Recipient’s notes Number of pages Price

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Is this what you mean? This is how it appears on the CANTAB website.

Auditory Brainstem Response (ABR) and Cambridge

Neuropsychological Test Automated Battery (CANTAB)

as Objective Support in Diagnosing Childhood ADHD

and ASD

Emma Claesdotter, MD

Faculty OpponentProf. Tommy Lewander, MD, PhD

Department of Neuroscience, PsychiatryUppsala University, Sweden

Supervisor:Magnus Lindvall, MD, PhD, Associated Professor

Assisting Supervisor:Maria Råstam, MD, PhD, Senior Professor

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Coverphoto by: Helge Hybbinette Copyright Emma Claesdotter Faculty of MedicineDepartment of Clinical sciences, Lund PsychiatryLund, SwedenISBN 978-91-7619-507-9ISSN 1652-8220Lund University, Faculty of Medicine Doctoral Dissertation Series 2017:124 Printed in Sweden by Media-Tryck, Lund UniversityLund 2017

To my lovely kids Helge, Sonja, Caesar & Winston

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”Det finns en liten, liten sekund mellan tanke och handling. I den ligger människans hela frihet.” F.M Alexander

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Table of Contents

Abstract...............................................................................11Populärvetenskaplig sammanfattning på svenska..............12Abbreviations......................................................................14Original papers....................................................................15

Introduction.................................................................................17Background.........................................................................17ABR.....................................................................................22CANTAB...............................................................................27

Aims of the thesis........................................................................33Ethical considerations.................................................................35Methods.......................................................................................37

Subjects..............................................................................37Procedures..........................................................................40Measures.............................................................................42Statistics..............................................................................45

Results........................................................................................47Paper I.................................................................................47Paper II................................................................................50Paper III...............................................................................54Paper IV...............................................................................56

Discussion...................................................................................61Limitations...................................................................................67Future..........................................................................................69Acknowledgment.........................................................................71References..................................................................................73

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AbstractBackgroundThere is a need for easily administered objective tests in neurodevelopmental disorders. We investigated the clinical utility for child and adolescent psychiatry of Auditory Brainstem Response (ABR) and Cambridge Neuropsychological Test Automated Battery (CANTAB) as support in diagnosing attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD).

MethodsThe thesis consists of four studies based on ABR and CANTAB. The patients (7-17 years old) were recruited from child and adolescent psychiatry outpatient clinics in Lund and Eslöv, in southern Sweden. The age and gender matched controls were recruited from schools in the same areas. All invited families agreed to participate.ABR: 63 females and 48 males with ADHD were compared to 26 females and 20 male controls. Furthermore 31 females and 27 males with ASD were compared to 24 females and 23 male controls. Patients were diagnosed according to the DSM-IV/DSM-5. The ABR consists of seven positive peaks (waves I–VII) derived from electrical activity in the brain that occur during 10 Ms following a sound stimulus recorded by five electrodes.CANTAB:112 children with ADHD and 95 controls performed five CANTAB tests: Stockings of Cambridge (SOC), Intra-Extra Dimensional Set Shift (IED), Spatial Working Memory (SWM), Stop Reaction Time (SRT), and Stop Signal Task (SST).

ResultsABR: We found three aberrations in the young female ADHD group and three other aberrations in the young male ADHD group compared to controls, while one aberration was present in both genders. In the ASD study only one aberration was found compared to controls.

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CANTAB: Between-group differences were found in all CANTAB tests except one. With hierarchical multiple regression analysis a main effect of ADHD diagnosis without interaction with age were seen in all the tests except SOC where a significant interaction with age was found.

ConclusionsThe results suggest that ABR and CANTAB can differentiate between typically developed children and children with ADHD or ASD. All our results are based on observed group differences and cannot at this stage be transferred to single individuals.

Populärvetenskaplig sammanfattning på svenskaIntroduktion: Diagnostik av ADHD (attention deficit hyperactivity disorder) och ASD (autism spektrum tillstånd) inom barnpsykiatri baseras på anamnesupptagning, psykologtestning och skattningsformulär. Det finns få objektiva diagnosmetoder. I vårt projekt ville vi undersöka om ABR (Auditory Brainstem Response) och CANTAB (Cambridge Neuropsychological Test Automated Battery) kan fungera som objektivt stöd vid diagnostik av ADHD och ASD, två av barnpsykiatrins vanligaste diagnoser.Barnpsykiatriska diagnoser är idag definierade av en bestämd uppsättning beteendeavvikelser/symtom. Att enbart använda beteendeproblem/symtom i diagnostik är problematiskt eftersom symtom inte bara påverkas av avvikelser i hjärnans funktion relaterade till av den utvecklingsrelaterade diagnosen, utan även av exempelvis psykosociala faktorer. Trots att mycket tyder på att ADHD och ASD till stor del har biologiska orsaker, finns det fortfarande ingen enighet om specifika biologiska korrelat. ABR är en metod som visar hjärnans subkortikala neuronala aktivitet. Vi vill undersöka ABR hos barn och ungdomar med ADHD och ASD och jämföra dem med kontroller. Vår förhoppning är att hitta skillnader på gruppnivå som i framtida studier ska kunna specificeras och så småningom hjälpa oss att skilja ut patienter med ADHD/ASD från typiskt utvecklade barn (TD). ABR skulle

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därmed i framtiden kunna utgöra ett stöd i barn/ungdomspsykiatrisk diagnostik.Vi vill också studera barn med ADHD avseende exekutiva funktioner med hjälp av CANTAB, ett datoriserat neuropsykologiskt test. Metod: ABR är samlingsnamnet för undersökningar vid vilka man registrerar elektriska potentialer som alstras i hörselsystemet efter akustisk stimulering. ABR används idag på audiologiska avdelningar, huvudsakligen för hörtröskelmätningar, diagnostik av sensorineural hörselnedsättning samt diagnostik av hjärnstamslesioner. Vid en ABR-undersökning använder man sig av ytelektroder som fästs i pannan och bakom öronen. Akustiska stimuli presenteras via hörlurar. De elektriska signalerna från elektroderna förstärks och medelvärde bildas. Detta resulterar i en grafisk kurva bestående av toppar och dalar. Vid tolkning av kurvorna kan man titta på en rad olika mått och jämföra med standard ABR alternativt en 3500 Hz kurva. Vi har valt att studera latenstiderna, dvs. den tid som passerar från det att stimuli presenteras till att respektive toppar och dalar uppkommer samt interaurala skillnader (skillnaden i ankomsttid för ett ljud mellan höger och vänster öra), samt vågornas amplitud samt hur patienternas ABR stämmer överens med standard ABR alternativt 3500 Hz kurvan. ABR anses vara en icke-invasiv, objektiv metod som mäter ljudbanans funktion, från åttonde kraniala nerven till de första thalamo-kortikala strukturerna. Metoden har använts i klinisk praxis och för forskningsändamål under mer än trettio år. Sedan några decennier har arbete pågått med att försöka finna ett objektivt sätt att utvärdera barn och ungdomars neuropsykologiska problem genom att använda väl validerade datoriserade test med tillgängliga data för normalmaterial. Den mest kompletta uppsättningen av sådana datoriserade test som finns på marknaden idag har utvecklats av Cambridge Cognition: CANTAB. CANTAB innehåller ett testbatteri av totalt 22 tester (varav 20 oberoende av språk) som mäter bl.a. visuellt och rumsligt minne, exekutiva funktioner, uppmärksamhet, semantiskt och verbalt minne samt beslutsfattande och svarskontroll på olika svårighetsnivåer. CANTAB har många fördelar jämfört med traditionella papper- och penna test. Precision, noggrannhet i mätning av svar, pålitlighet och objektivt återförande av det uppmätta är några av dem.

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Vi har valt ut 5 olika CANTAB tester som testar barns exekutiva funktioner. Patienter till studierna rekryterades från barn- och ungdomspsykiatrins öppenvårdsmottagningar i Eslöv och Lund. Alla patienter har diagnostiserats enligt DSM-IV respektive DSM-5. Kontrollgrupperna i samtliga studier består av ålders- och könsmatchade barn/ungdomar från samma geografiska upptagningsområde. Resultat: ABR visade skillnader hos ADHD-gruppen jämfört med kontrollgruppen. I ADHD-gruppen hittade vi tre ABR skillnader i gruppen flickor med ADHD respektive tre ABR skillnader i gruppen pojkar med ADHD. Endast en av skillnaderna förekom i båda grupperna. Skillnaderna i ABR motsvarar anatomiskt thalamus, mellanhjärnan och hörselnerven.Hos ASD patienterna såg vi samma skillnad i ABR hos både hos pojkar och flickor. ABR avvikelsen vi hittade korresponderar anatomiskt till pons.CANTAB påvisade påtagligt sämre resultat avseende exekutiva funktioner i ADHD-gruppen jämfört med kontrollgruppen utan att detta kunde förklaras av ålder. Detta gäller alla test utom ett, där åldern verkar ha spelat störst roll för det försämrade resultatet.Slutsats: Våra resultat visar att både ABR och CANTAB skiljer ut ADHD respektive ASD-gruppen från normala kontroller på gruppnivå. Då vi har tittat på skillnader på gruppnivå, kan man inte dra slutsatser om den enskilda individen utifrån våra resultat.Det behövs mer forskning kring ABR och CANTAB på barn och ungdomar innan man kan använda dessa instrument som en del i en ADHD- eller en ASD-utredning.

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AbbreviationsABR Auditory brainstem responseADHD Attention deficient hyperactivity disorder ADI-R Autism Diagnostic Interview–RevisedADOS Autism Diagnostic Observation ScheduleAQ Autism-Spectrum QuotientASD Autism spectrum disorderCANTAB Cambridge Neuropsychological Test Automated BatteryCAP Child and adolescent psychiatry clinicCAPD Central auditory processing disorder DCD Developmental coordination disorderDSM Diagnostic and Statistical Manual of Mental DisordersECG ElectrocardiographyEEG ElectroencephalographyENT Ear, nose, and throat ERG ElectroretinographyERP Event-related potentialsFTF 5–15IED Intra-Extra Dimensional Set ShiftIPI Interpeak intervalIQ Intelligence quotientISI Interstimulus interval MS MillisecondNDD Neurodevelopmental disorderNIMH National Institute of Mental HealthRdoC Research Domain CriteriaSD Standard deviationSNAP-IV Swanson, Nolan and Pelham Teacher and Parent Rating Scale–IVSOC Stockings of Cambridge SPL Sound Pressure LevelSRT Stop Reaction Time SST Stop Signal Task SWM Spatial Working MemoryTD Typically developedTTL Transistor–transistor logicTR Traits

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WISC-IV Wechsler Intelligence Scale for Children–Fourth editionWAIS-IV Wechsler Adult Intelligence Scale–Fourth edition

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Original papersI. Abnormal brainstem auditory response in young females

with ADHDEmma Claesdotter-Hybbinettea*, Maryam Safdarzadeh-Haghighib, Maria Råstama, Magnus Lindvalla aDept. of Clinical Sciences Lund, Lund University, Sweden bDept. of Psychology, Lund University, Sweden Psychiatry Res. 2015 Oct 30;229(3):750–4. doi: 10.1016/j.psychres.2015.08.007. Epub 2015 Aug 6.

II. Gender specific differences in auditory brain stem response in young patients with ADHDEmma Claesdotter-Hybbinette*, Matti Cervin, Sofia Åkerlund, Maria Råstam, Magnus LindvallDept. of Clinical Sciences Lund, Lund University, Sweden J Neuropsychiatry. 2016;2(1):14. http://neuropsychiatry.imedpub.com/gender-specific-differences-in-auditory-brainstem-response-in-young-patients-with-adhd.php?aid=8913

III. Abnormal brainstem response in young patients with autismEmma Claesdotter-Hybbinette/Sofia Åkerlund, Matti Cervin, Maria Råstam, Magnus LindvallDept. of Clinical Sciences Lund, Lund University, Sweden Submitted

IV. The effects of ADHD on cognitive performanceEmma Claesdotter-Hybbinette, Sofia Åkerlund, Matti Cervin, Maria Råstam, Magnus LindvallDept. of Clinical Sciences Lund, Lund University, Sweden Submitted and under review

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Introduction

BackgroundThe neurodevelopmental disorders (NDDs) are a group of disorders with onset in the developmental period (Gillberg, 1995). The most common NDDs are attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), tic disorder, and developmental coordination disorders (DCDs). The prevalence of NDDs is estimated to be 5%-10% (Gillberg, 2010), ASD 1%–3% (Baxter et al., 2015; Kim et al., 2011; Lundström et al., 2012), DCD 4%–6% of children (Gillberg & Kadesjö, 2003; Zwicker et al., 2012), the prevalence for tic disorder being around 5% (Dooley, 2006), and ADHD with a prevalence around 7% (Thomas et al., 2015). NDDs are a group of disorders in which the development of the central nervous system is disturbed, manifesting itself as impaired motor function, or as problems of learning, language and non-verbal communication. The range of developmental deficits varies from very specific limitations of learning or control of executive functions to global impairments of social skills or intelligence. NDD characteristics and severity often vary during the lifespan due to, for example, changes in demands of the environment, life events, and co-existing disorders (Biederman et al., 2010). Over time, some individuals will not fulfill the criteria for the original diagnosis. Even if the diagnosis does not persist, impairment will often persist into adult life, putting these patients in great need of help from the society. Having greater risk of poor school performance and low academic achievement, grade retention, school dropout, aggression, conduct problems and delinquency, early substance experimentation, and difficulties in social relationships, marriage, and employment (Biederman, 2010; Kieling & Rohde, 2012; Perou et al., 2013). NDDs frequently co-occur (Gillberg et al., 2004; Halleröd et al., 2010). Approximately 50% of youngsters with

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ADHD also meet diagnostic criteria for oppositional defiance disorder (ODD) and/or conduct disorder (CD), adding significant impairment to the clinical picture (Kerekes et al., 2017; Kutcher et al., 2004). The clinical presentation of NDDs includes strengths as well as symptoms of deficit and delays in achieving expected milestones. In my thesis I have focused on ADHD and ASD in a Swedish child and adolescent population (7-17 years of age).

Categorical versus dimensional diagnoses In child and adolescent psychiatry, diagnoses are defined by a certain set of behavioral abnormalities/symptoms (American Psychiatric Association [APA], 1994, 2013). Using only behavioral problems/symptoms when establishing a diagnosis is problematic, because the symptoms are affected not only by abnormalities in function due to the NDD but also by psychosocial factors such as previous experiences and the current context of the child. There are several national and international guidelines for the assessment and treatment of NDDs. However, there is no consensus regarding what should be included in a neuropsychiatric examination.Today, clinical praxis is a categorical conceptualization of NDD diagnoses based on manuals like the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV [4th ed.]/DSM-5 [5th ed.]; APA, 1994, 2013) and the International Classification of Diseases ICD; World Health Organization [WHO], 1993). In the process of trying to understand more about mental health issues, researchers have questioned the practice of looking at mental diagnoses as something one either has or doesn’t have (categorical), not overlapping with other diagnoses (distinct), and the concept of a mental diagnosis as being something that is not associated with the probability of having another diagnosis (independent). As a result, the US National Institute of Mental Health (NIMH) called for “the development, for research purposes, of new ways of classifying psychopathology based on dimensions of observable behavior and neurobiological measures,” and in 2010 NIMH launched the framework of Research Domain Criteria (RDoC). The main aim was to break the previous strict classification of mental illnesses and instead create a framework

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for studying mental disorders that would take into consideration modern research such as genetics, neuroscience, and behavioral sciences. RDoC is a dimensional system built on “the span from normal to abnormal” (Insel, 2014) and consists of five research domains: Negative Valence Systems (fear, anxiety, loss, frustrative non-reward), Positive Valence Systems (reward learning, reward valuation, habits), Cognitive Systems (attention, perception, declarative memory, working memory, cognitive control), Systems for Social Processes (attachment formation, social communication, perception of self, perception of others), and Arousal/Modulatory Systems (arousal, circadian rhythm, sleep and wakefulness) (Insel et al., 2010). Each of these domains can be studied using seven different classes of variables: genes, molecules, cells, neural circuits, physiology, behaviors, and self-reports (Morris et al., 2012). In RDoC one starts with behavioral/brain relationships and then link them to clinical symptoms without trying to fit them into diagnostic categories. RDoC adds insights into genes, molecules, cells, circuits, and physiology. In the future, RDoC might be used in parallel with categorical manuals of mental diagnostics, such as the DSM, to create a new kind of taxonomy. With the RDoC dimensional approach in mind, looking at NDDs like ADHD and ASD, there are three key concepts of interest: developmental trajectory, sensitive period, and dynamic interaction of systems (Casey et al., 2014). Regarding developmental trajectories, one needs normative data in order to determine whether the deviation of interest is due to developmental delay, deviation, or regression in function. The concept of sensitive periods is based on the fact that environmental events impact brain development and that the brain is more or less malleable at different times in life (Keshavan et al., 2008). Regarding the dynamic interactions of systems across development, one must look at the relations between different RDoC domains of function to get the right understanding of the patient problem. Early in brain development different regions interact dynamically, and these interactions will have an impact on the course of development (Durstone & Casey, 2006; Geschwind & Levitt, 2007). Although it appears that various indicators of NDDs are continuously distributed, dimensional indicators can be symptoms of a categorical condition (similar to fever as an indicator of ear infection).

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The use of categorical diagnosing of NDDs is widespread and well accepted in both clinical and research settings. At present, using the DSM or ICD is a good way to make sure several aspects of the disorders are present, in order to arrive at a diagnosis. The positive aspect is that it protects people from being diagnosed too easily, and the negative aspect is that people who do have impaired everyday function due to symptoms of inattention, hyperactivity and/or deficits in social communication might not be diagnosed because they do not meet all criteria. The DSM and ICD are methods and well accepted across different professions and countries. However question of whether NDDs are categorical or continuous is both important and unresolved and still of great interest, with a vivid, ongoing discussion (Hengartner &Lehmann, 2017).

ADHDAttention deficit hyperactivity disorder was first mentioned in the literature in 1798 by Sir Alexander Crichton (Palmer & Finger, 2001). ADHD has been recognized as one of the most common NDDs in childhood (Kieling & Rohde, 2010). It is a heterogeneous condition with persistent symptoms of hyperactivity, inattention, and impulsiveness, which impair functioning in multiple settings (APA, 1994, 2013). ADHD is a highly heritable disorder, and twin studies have shown a heritability rate of 60%–80% (Faraone et al., 2006; Larsson et al., 2012; Larson et al., 2013). The estimated worldwide prevalence of ADHD in children is around 7% (Thomas et al., 2015). The symptoms, all or some, frequently persist into adulthood (Faraone et al., 2006) and are associated with functional limitations as well as psychiatric and somatic morbidity. ADHD is a considerable burden to the affected individual and to society (Kieling & Rohde, 2010; Larsson et al., 2007; Perou et al., 2013), resulting in reduced quality of life (Agarwal et al., 2012). ADHD affects both sexes, with a male to female ratio approaching 1:1 (Froehlich et al., 2007), in contrast to previous studies showing a gender ratio (girl:boy) ranging from 1:3 to 1:16. (Nøvik et al., 2006). Girls also tend to be diagnosed later in life (Mahone Wodka, 2008; Quinn & Madhoo, 2014). Researchers have been trying to find differences in symptoms to explain the gender differences in prevalence. It has been proposed that girls with ADHD may be more likely to have the inattentive type of ADHD and may suffer more from internalizing symptoms and inattention, while boys, on the other hand, have

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more hyperactive and aggressive symptoms (Barkley et al., 1990; Biederman et al., 2002, Biederman & Faraone 2004; Graetz et al., 2005; Levy et al., 2005; Newcorn et al., 2001). An ADHD diagnosis should be based on neurodevelopmental and clinical history (APA, 1994, 2013). In addition, neuropsychological testing and rating scales are often used as a supplement. Several environmental factors have been proposed as contributors to ADHD, including food additives/diet (Stevenson et al., 2010) lead contamination (Cho et al., 2010; Nicolescu et al., 2010; Nigg et al., 2010), maternal smoking during pregnancy (Kovess et al., 2015; Nomura et al., 2010), and low birth weight (Hatch et al., 2014). Neurobiological factors in ADHD have been well studied, and several different neurotransmitter systems are thought to be involved (Spencer et al., 2007). The core symptoms of ADHD are thought to be due to imbalances in dopaminergic and noradrenergic systems (Faraone et al., 2006 Li et al., 2006; Kooij et al., 2010). Many brain regions are candidates for impaired functioning in ADHD (Seidman et al., 2000). MRI-based structural imaging studies have lately found evidence of structural brain abnormalities among ADHD patients, with the most common findings being smaller volumes in frontal cortex, cerebellum, and subcortical structures (Castellanos, 2002; Hoogman, 2017). Studies with functional MRI have reported hypofunction in the dorsal anterior cingulate cortex (Bush et al., 2005) as well as in both the cerebellum (Berquin et al., 1998) and the corpus callosum (Castellanos et al., 2002; Hoogman et al., 2017) in patients with ADHD. Twin studies have showed ADHD to be highly genetic, with a mean heritability of 77% (Spencer et al., 2007); researchers have not found a specific ADHD gene, but rather several genes associated with a higher risk of ADHD symptoms (Fisher et al., 2002). Neither neuroimaging studies nor genetic studies have been able to identify a distinctive etiology for ADHD.

ASDAutism in children was first described by Leo Kanner in 1943, who systematically described what he called “autistic disturbance in affective contact” (Kanner, 1943). For many years childhood autism was grouped together with schizophrenia, and it was not until 1971 (Kolvin et al., 1971) that autism was separated from schizophrenia in diagnostic manuals and labeled childhood psychosis. In DSM-IV the term childhood autism/autistic disorder was first used and labeled “pervasive developmental disorder”

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(APA, 1994). ASD is a heterogeneous NDD characterized by social impairments and difficulties in communication and sensory processing, as well as repetitive/restricted behaviors (APA, 2013). Gillberg (1983a, 1983b) was the first to implicate that there was an autistic continuum, meaning that there were more children that could fit the criteria for autism than the type described by Kanner (1943). In line with this theory Lorna Wing and others proposed the term “autism spectrum” (Wing, 1991, 1997). Excitingly enough, in 1983 Gillberg also published an article where he looked at the same group of patients with ABR (Gillberg et al., 1983). Depending on assessment method and the age group in focus, large population-based studies report an ASD prevalence of 1% to 3% (Baron-Cohen et al., 2009; Baxter et al., 2015; Kim et al., 2011). The male to female ratio lies between 3 and 5:1, decreasing in later studies, most likely due to better knowledge of how autism is expressed in females (Van Wijngaarden-Cremers et al., 2014). The prevalence of ASD diagnoses has grown steadily over the last few years (Atladottir et al., 2015). Suggested explanations do not include an actual increase in phenotype, but rather an increased awareness, broadening of diagnostic criteria, changes in definitions, and improved screening/identification by providers (Johnson et al., 2007; Fombonne, 2009). This is supported by Lundström et al. (2012), who showed a stable prevalence of ASD in a Swedish population of 9- to 12-year-olds over the last 10 years.The ASD assessment process is generally long and demanding, both for the patient and for the mental health system. The gold standard includes different self-report scales, Autism Diagnostic Interview (ADI-R) (Rutter et al., 2003), and Autism Diagnostic Observation Schedule (ADOS) (Lord et al., 2000), which leaves a big part of the assessment to the clinician’s own ability to interpret results and discriminate autistic features. Genetics and epigenetics have been a large research area. Kooij et al. (2010) and Leblond et al. (2014) have pointed at SHANK genes (coding proteins located post-synaptic at the glutamatergic synapses) as high-risk genes for ASD. Scheid et al. (2013) have found that genes coding enzymes involved in myelin synthesis, FA2H, to be high-risk genes. Other researchers have found nearly 800 ASD-risk genes (Butler et al. 2015). These genes are as stated by Vissers et al. (2016) “not fully understood,” and the polygenesis in ASD might be as extreme as in schizophrenia (Loh et al.,

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2015), linking almost every human gene to schizophrenia. This would make it impossible to tie ASD to one or a few specific genes.ASD has an early onset (Tharpe et al., 2006; Volkmar et al., 2014) and is primarily characterized by impaired social interaction and communicative abilities. ASD is considered to impact the child’s ability to initiate social engagement, regulate social interactions, and share enjoyment and interests with others (APA, 2013). It also commonly manifests with delayed and aberrant speech and language development and an abnormal sensory perception (Baranek et al., 2006). The abnormal sensory perception in ASD can be expressed in several different ways, including being hyper- or hyporeactive to certain stimuli such as touch, smell, or sound. The abnormal perception of sound in ASD has been the subject of research in several studies, mostly using brain imaging, showing differences between patients with ASD and typically developed (TD) subjects in cortex activation. Many of the studies found a lower activation of the cortex in ASD (Boddaert et al., 2004; Orekhova et al., 2012). The reasons for the visual differences of cortex activation in ASD are yet to be explained. Several studies suggest that the explanation can be found in abnormal processing within subcortical brain structures like the brainstem (Orekhova et al., 2012). As early as in 1983 Edward M. Ornitz stated that autism was best explained by the term “sensory modulation disorder” and pointed towards the brainstem for explanations (Ornitz, 1983). Geva and Feldman (2008) proposed a neurobiological model for the effects of early brainstem functioning on the development of behavior and emotion regulation in infants. It has also been suggested that brainstem abnormalities may be partly responsible for deviant language development as well as cognitive and social development in children with ASD. In 2013 Jou et al. conducted a two-year-long study of the brainstem in young males with autism versus TD, which showed alterations in brain-growth trajectories suggesting brainstem abnormalities in autism primarily involving gray matter structures (Jou et al., 2013). In 2016 Waterhouse et al. pointed out that there is no biological validity in the ASD diagnosis: Individuals diagnosed with ASD do not share one common brain impairment (Waterhouse et al., 2016). Instead, they vary both in brain size/volume (Lefebvre et al., 2015; Riddle

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et al., 2017), in electrophysiology (Billeci et al., 2013), and in neurochemistry findings (Zürcher et al., 2015). To summarize, neither ADHD nor ASD can be explained by one single biological etiology. Biological causes must be looked at in a wider perspective, not focusing on categorical diagnoses but rather on well-defined subgroups based on symptoms and risk factors. A new paradigm in psychiatric diagnostics is in the making, moving from categorical towards dimensional diagnostics. In this paradigm shift finding new ways of looking at neurodevelopmental principles is an important task.

ABRElectrophysiology is the biomedical field regarding the study of the production in and the effects of electrical activity in the body: in the heart, electrocardiography (EKG); in the eyes, electroretinography (ERG); and in the brain, electroencephalography (EEG). The electrical potentials across cell membranes are created by the change of different concentrations of ions, for example, sodium (Na+) and potassium (K+), between the inside of the cell (intracellular liquid) and the outside (extracellular liquid). These ions diffuse across the cell membrane through specialized ion channels that can be open or closed. This creates an electrical charge. If the charge exceeds a certain threshold, an action potential is created. Action potentials are all-or-nothing events; the amplitude does not vary depending on the size of the proceeding stimulus, but the numbers of firing neurons do. The cascade that is elicited is what is called neural communication. Electrical properties of the brain can be measured in different ways: EEG, event-related potentials (ERP), and ABR. EEG measures the electrical properties of the brain by using extracranial electrodes (de Almeida, 2013). When recording neural activity that is limited to one specific, repeated stimulus (sensory, cognitive, or motor event), it is referred to as an ERP (Micera et al., 2006). These responses are identical in each repetition, making it possible to average them, and in that way to calculate the background noise contribution to the recorded ERP, that is, electromagnetic interference, brain background activity, or other biosignals.

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Please note that ”retinal” refers to testing for eye diseases not kidney diseases.

ABR was first described by Jewett and Williston in 1971 (Jewett & Williston, 1971). ABR is the collective name for the method, which records the electrical potentials generated in the auditory system for acoustic stimulation. ABR reflects the subcortical neuronal electrical activity in the auditory pathways, during10 milliseconds (Ms) after a brief auditory stimulus. The acoustic characteristics of the stimulation, the electrode placement and design, the studied time frame, the number of averaging signals, and the electrical filtering may be varied. The sound stimuli used may include, for example, unfiltered clicks, filtered clicks, or brief tone pulses. One can use single stimuli or more complex stimuli (e.g., forward masking). The ABR consists of a sequence of seven positive peaks (waves I–VII) that normally occur within 10 Ms following the onset of a stimulus recorded by surface electrodes on the mastoid processes of each ear and on the forehead. Waves I and II are produced by the peripheral part of the auditory nerve, whereas the subsequent peaks are due to the combined electrical activity of nuclei at gradually higher levels of the ascending auditory pathway in the brainstem. Wave III is believed to be generated in the cochlear nucleus. Waves IV and V have multiple origins, but superior olivary complex and lateral lemniscuses are two of the major sources (Klin, 1993; Møsller et al., 1981; Parkkonen et al., 2009). Waves VI and VII represent higher anatomic areas of the brain and are thought to have thalamic origin (Figure 1).

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Figure 1. Illustration of the standard ABR wave pattern and corresponding anatomical structures within the first 10 Ms after stimulation. Reprinted with permission of Sensodetect.

The brainstem maturation, towards an adult architecture starts in the more peripheral regions, with waves I, II, and III maturing at the age of 1–2 years. Wave V matures last and is considered to have developed an adult architecture at age 3–4 years (Burkard & Sims, 2001; Fujikawa-Brooks et al., 2010; Konrad-Martin et al., 2012; Moore & Linthicum, 2007). ABR is currently used in audiological departments, mainly for sensorineural hearing loss and lesions in the brainstem but also for newborn hearing screenings, estimation of auditory threshold, detecting auditory nerve lesions, assessing for the presence of demyelinating conditions, detecting brain death, and determining coma type and recovery prognosis.To measure ABR surface electrodes are attached to the forehead, behind the ears, and on the mastoid part of the temporal bones. Acoustic stimuli are presented through headphones. The electrical signals from the electrodes are amplified and averaged, resulting in a graph consisting of peaks and valleys (Figure 2).

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Figure 2. Median curves of all healthy females, ADHD females, healthy males, and ADHD males. Sound 1, left ears.

Analysis of the ABR wave patterns can be done for example by peak amplitudes; interpeak latency; waveform morphology, that is, correlation to a norm ABR; and interaural correlation (Musiek & Berge 1998; Musiek & Lee, 1995; Sand 1990). ABR is a method that does not require active patient participation. Complex stimuli (e.g., forward masking) were used in the present studies to increase the possibility of detecting deviances in comparison with matched healthy children. Complex stimuli may reveal aberrations, which may not be assessed by standard single-stimuli ABR procedures. In our studies, we were interested in the processing that occurs in the basic auditory pathways, situated in the brainstem. Therefore, electrodes were placed and calibrated so that the later responses from cerebral clusters of higher processing complexity

were left out. In our studies, only wanting brainstem activity, the available data extend to only 10 Ms post stimuli. This excludes the post-brainstem responses and therefore provides a clearer picture of the deeper cerebral functions. There have been studies of ABR up to half a second post stimuli, capturing the later, cognitive auditory processing, in this way letting the ABR be affected by a test subject’s conscious thoughts, memories, or preferences attached to the induced sound. This has been done to show differences in the brains of musicians and non-musicians (Okhrei et al., 2012). Gender has been discussed to be a confounder in ABR, since TD females have been seen to have shorter latencies in ABR (Aoyagi et al., 1990;

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Dehan & Jerger, 1990; Durrant et al., 1990; López-Escámez et al., 1998; Stuart & Yang, 2001). In studies of ABR where genders were not separated and where a substantial proportion of the TD group is females, this could result in a prolongation of waves due to too many females in the TD group shortening the latencies of the TD group. In ABR studies it is therefore important to control for gender (Russo et al., 2008). Auditory deviances are important clinical symptoms in many mental disorders, such as psychosis (DSM-IV/DSM-5). In both ASD and ADHD, sensory modification in hearing is a substantial clinical finding (DSM-IV/DSM-5).

ABR and ADHD The literature on ABR and ADHD is scarce. Studies have shown that children with ADHD have abnormalities in their pontine and thalamic regions (Cortese et al., 2013; Dickstein et al., 2006; Ivanov et al., 2010), not only structural abnormalities but also functional ones (Zhu et al., 2008). Could ABR be used to monitor these? Lahat et al. (1995) found prolonged latencies of waves I–III and I–V in 114 children with ADHD compared to normal controls. They also found a significant asymmetry of wave III latency between the ears in the study group, but not in the control group. In 2011 Vaney et al. (2011) published a study on 20 ADHD patients and 20 TD participants that found ABR differences in the ADHD group, but these were not statistically significant. In 1999 Ismail and Amin (1999) performed ABR on 30 children with ADHD and found no statistically significant differences between the children with ADHD and TD group regarding absolute latencies of waves I, III, and V as well as interpeak intervals (IPIs) I–III, III–V, and I–V. From this, Ismail and Amin concluded that disturbances in ADHD could occur at a brain level higher than the brainstem level and suggested thalamocortical projections. Schochat et al. (2002) looked at wave V and couldn’t find a statistically significant difference in the ABR of ADHD children. ADHD and ABR were studied in 2002 by Puente et al. (2002), who found that the latencies of waves III and V were significantly prolonged in children with ADHD. Furthermore, a significant difference was found between mean IPIs of I–III and I–V of children with ADHD compared with controls. In this study brainstem transmission was significantly longer in children with ADHD. Over the years ABR research has been more focused on schizophrenia (Källstrand et al., 2002, 2010) and ASD (Kwon et al., 2007; O’Hearn, 2013; Rosenhall et al., 2004; Tas et al., 2007).

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Please spell out ahead of the abbreviation.

There have also been ABR studies focusing on ADHD and P300 (auditory cognitive evoked potential) (Puente et al., 2001), most of them to evaluate whether P300 topography predicts treatment response with methylphenidate and/or atomoxetine in ADHD patients (Sanfins et al., 2017). P300 studies are a bit different from the above-described ABR, as P300 response is elicited with the “oddball paradigm” where an unexpected sound stimulus is embedded in a series of reoccurring stimuli. The P300 is named after a signal component peaking around a post-stimulus latency of 300 Ms. Hence, both brainstem and cortical structures are involved in the P300 results. The cortical structures involved in the P300 generation include the reticular cortex, the prefrontal cortex, the center-parietal cortex, the temporal cortex, the limbic system (hippocampus), and parts of the thalamus. These structures are also involved in the process of attention. Hence, P300 findings are interesting in the ABR–ADHD context. It was found that 31-electrode mean auditory P300 amplitude (AA) predicts response to atomoxetine (Sangal & Sangal, 2005, 2006), whereas AA at T8 (right mid-temporal) predicts response to methylphenidate (Sangal & Sangal, 2006). Sangal and Sangal (2006) also point to the fact that ADHD patients seem to vary in their P300 response and suggest that the explanation might be a heterogeneity in the patient group.Sanfins et al. (2017) pointed out that the scientific nomenclature regarding P300 is confusing and is a hindrance to literature reviews. The following terms are used interchangeably to relate to the same procedure: long latency auditory evoked potential, late auditory evoked potential, cognitive potential, P300, and auditory evoked potential related events.

ABR and ASDIn 1983 Ornitz proposed that since a main characteristic of ASD is over- and under-reactivity to sound, auditory brainstem processing could be affected in ASD (Ornitz, 1983). In 1993 Klin pointed out that there did not seem to be consistency regarding wich aspect of ABR that differed in ASD (Klin 1993). Noted is that in many of the studies conducted in 1980s and 1990s (Courchesne, 1985; Gillberg et al., 1983; Klin, 1993; Minshew, 1991) both the TD controls and the ASD patients were heterogeneous, consisting of patients with very different severity of ASD, hearing loss and not controlled for gender. This led to

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Is this correct? Otherwise, they are labeled ”patients” along with those with ADS.


Is this what you mean?

suggestions that the found differences were actually differences between the participants, not related to the ASD, and that there were no evident differences in ABR in a well-defined ASD population (Klin, 1993; Minshew, 1991). However, researchers continued to study ABR in ASD and found links between ABR and ASD: a prolongation of the waves themselves or IPIs (Kwon et al., 2007; Maziade et al., 2000; Rosenhall et al., 2003; Tas et al., 2007). The waves that were studied differed between studies but most studies of them found prolongation in wave V. Hence ABR studies focusing on children with ASD have found both brainstem abnormalities (Rosenblum et al., 1980; Roth et al., 2012) but also normal ABR in ASD children (Minshew, 1991; Russo et al., 2008). The inconsistent results regarding ABR and ASD may be due to the heterogeneous symptomatology of ASD that might in turn be related to different abnormalities in the brainstem (Bomba & Pang, 2004; Klin, 1993; Rosenhall, 2003). Despite the lack of consensus, many researches suggest potential for ABR to be a possible biomarker for ASD, providing insight into the disorder and ASD subgroups.

CANTAB CANTAB was developed in the 1980s and was originally designed for use in elderly patients with neurodegenerative diseases such as Parkinson’s and Alzheimer’s diseases (Barnett et al., 2015). CANTAB has been tested in animals with good results and is still in use with both monkeys and rats (Hvoslef-Eide et al., 2015, Weed et al., 1999). Since the 1980s the use of CANTAB has increased and broadened considerably to include disorders such as depression, schizophrenia, and epilepsy, and developmental disorders such as ADHD and ASD (Fried et al., 2015; Liotti et al., 2007; Palade & Benga, 2007; Pantelis et al., 1997; Rhodes et al., 2005; Roque et al., 2011; Sweeney et al., 2000; Torgersen et al., 2012). CANTAB is a test battery of a total of 25 tests (of which 20 are independent of language) that measure, among other things, visual and spatial memory, executive function, attention, semantic and verbal memory, and decision-making of various difficulty levels (Luciana & Nelson, 2002). Many of these tests have normalized data down to age 4 years. The CANTAB tests have additional important advantages over traditional

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tollare3, 05/03/17,

CANTAB tests

tollare3, 05/03/17,

25 tester numera


Do you mean ”not only”?

neuropsychological testing. Some of the benefits of CANTAB are that it requires no verbal response and is therefore ideal for use with small children. The tests do not require reading skills, since there is no verbal component of the test. The two advantages mentioned above indicate that the tests can be used in children of different cultural backgrounds. Children of today have a great deal of computer experience, and since CANTAB tests are like computer games, this is motivating for children. CANTAB tests have graded degrees of difficulty to avoid “floor and ceiling” effects. CANTAB uses a touchscreen method that minimizes the interaction with the tester and thus reduces test anxiety. Since CANTAB is easily administered and does not require a doctor or a psychologist, it can be used in places where services are limited. There is an extensive literature on CANTAB and ADHD in adults (Chamberlain et al., 2007; De Luca et al., 2003; Lowe & Rabbitt, 1998; Ni et al., 2013; Turner et al., 2005; Williams et al., 1999). CANTAB and ADHD in a younger population has not been as well studied (Liotti et al., 2007; Rohdes et al., 2005; Seidman et al., 2000), but the results have shown robust evidence of dysfunction in executive and cognitive functioning in children with ADHD, highly congruent with those reported in studies using traditional neuropsychological testing batteries, supporting the utility of CANTAB to assess neuropsychological deficits in children with ADHD in clinical and research settings.

CANTAB testsAll participants in study IV were administered five tests from the CANTAB set that target domains of executive functioning, shifting, verbal memory, visual memory, and planning: Intra-Extra Dimensional Set Shift (IED), Stockings of Cambridge (SOC), Spatial Working Memory (SWM), Stop Reaction Time (SRT), and Stop Signal Task (SST).At the start of our work these were the CANTAB tests of choice at that time. On 20 January 2015 CANTAB launched their new academic product, “CANTAB Connect Research” and changed their recommended test based on recent research. In these new recommendations SOC was not recommended for ADHD but for ASD, epilepsy, and Huntington’s disease.IED: Intra-Extra Dimensional Set Shift is a test of shifting and flexibility of attention. IED consists of color-filled shapes and

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white lines. Simple stimuli are made up of just one of these dimensions, whereas compound stimuli are made up of both. The participant starts with only simple stimuli and must learn which one is correct by touching it. Then the stimuli and/or rules are changed. Participants must first use feedback to learn a rule, and then reverse that rule when feedback implies that it has changed. These shifts are initially intradimensional (e.g., color-filled shapes remain the only relevant dimension), then later extradimensional (white lines become the only relevant dimension). The extradimensional shift stage of the task is sensitive to frontostriatal dysfunction, producing activations in prefrontal regions, including the left anterior prefrontal cortex and right dorsolateral prefrontal cortex in healthy volunteers (Lee et al., 2000). We evaluated stages completed (IED.SC) and total errors (IED.TEA).

Figure 3. IED

SOC: Stockings of Cambridge is a spatial planning test that gives a measure of frontal lobe function. SOC is a test of executive function, spatial planning, and working memory. SOC performance was found to activate a neural network of structures including the dorsolateral prefrontal cortex (Baker et al., 1996), explaining why patients with frontal lobe damage have been shown to perform poorly on the SOC test (Owen et al., 1990). In SOC the participant must use the balls in the lower display to

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Figure 3?

copy the pattern shown in the upper display. The time taken to complete the pattern and the number of moves required are taken as measures of the participants’ planning ability. We evaluated problem solved in minimum moves.

Figure 4. SOC

SWM: The Spatial Working Memory test evaluates working memory and executive functioning. The test requires executive functions and manipulation of visuospatial information. SWM begins with a number of colored boxes that are shown on the screen. By touching the boxes and using the process of elimination, the participant should find one blue “token” in each of a number of boxes and use them to fill up an empty column on the right-hand side of the screen. During the test, the number of boxes is gradually increased. We evaluated between-search errors (SWM.BE) and strategy (SWM.STRAT).

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Figure 5. SWM

SRT: Simple Reaction Time. This test assesses psychomotor processing speed. A square is shown on the screen, and the participant is instructed to press the button as soon as they see the square. The interval is variable between the trial response and the onset of the stimulus for the next trial. We evaluated median correct latency (SRT.MCL) and percentage of correct answers (SRT.PCT).

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tollare3, 05/03/17,

Om du går in på cantabs hemsida få har de en lagom lång beskrivning på engelska för var test

Figure 6. SRT

SST: The Stop Signal Test measures the ability to inhibit a response. SST performance is associated with integrity of the right inferior frontal gyrus (Aron et al., 2003), and SST reaction time has been shown to be significantly increased in children and adults with ADHD (Aron et al., 2003).The subject must respond to an arrow stimulus by touching either of two choices, depending on the direction in which the arrow points. If an audio tone is present, the subject is instructed to inhibit that response. At the end of every assessed block, a graphical representation of the subject’s performance is shown, and the subject is encouraged to go faster on the next block. SST performance is associated with integrity of the right inferior frontal gyrus (Aron et al., 2003). We evaluated direction errors (SST.DEOAGT) and stop signal reaction time (SST.SSRT).

Figure 7. SST

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This is stated at the bottom of the previous page. Do you wish to repeat it here?

Aims of the thesis

Diagnostics in child and adolescent psychiatry is based on symptom presentation, medical history, psychological testing and questionnaires. There are few objective diagnostic methods. We wanted to investigate whether ABR and/or CANTAB could play a supporting role in diagnosing ADHD and ASD, two of the most common diagnoses in child and adolescent psychiatry. Further, we wanted to, if possible, link the ABR and CANTAB results to the regions and neurological systems in the brain that are involved in ADHD and ASD. The main aim of this thesis was the evaluation of ABR and CANTAB, as supportive of diagnostics in child and adolescent psychiatry, especially ADHD and ASD.

The specific aims were1. to evaluate ABR on a group level in patients with ADHD

and ASD.2. to evaluate CANTAB on a group level in patients with

ADHD.

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Ethical considerations

Each of the four studies was approved by the regional ethics committee at Lund University (Dnr: 2010/210, Dnr: 2015/11). In all studies written informed consent was obtained from all participants and their parents/guardians. All participants were informed of their right to withdraw from the studies at any time without giving any reason.The measuring system (brainstem-evoked response audiometry) A1000, was borrowed from SensoDetect BERA. SensoDetect did not sponsor the studies in any way. All studies were research driven.

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Methods

SubjectsParticipants in all studies were recruited from the child and adolescent outpatient clinics (CAP clinics) and schools in Eslöv and Lund, two cities in the south of Sweden. All participants, both patients and controls, were drug free at the time of the testing. All patients were diagnosed according to the DSM-IV (Papers I, II, and IV) and DSM-5 (Paper III). Written informed consent was obtained from all the subjects and their parents/guardians.

Paper IThis study included a total of 43 females with ADHD (mean age 13.3 years, SD 3.0) and 21 female control subjects (mean age 13.6 years, SD 2.7). All patients were diagnosed according to agreed diagnostic procedures at the CAPs in Eslöv and Lund using semistructured diagnostic interviews, neurological examination, psychological evaluation, and self-report questionnaires: Autism-Spectrum Quotient (AQ), Swanson, Nolan and Pelham Teacher and Parent Rating Scale–IV (SNAP-IV), Five to Fifteen questionnaire (5–15). All diagnoses were confirmed by a senior psychiatrist. Patients with other psychiatric diagnoses at the time of the testing were excluded to avoid comorbidity. All subjects with hearing impairment were excluded from the study. This was verified by interviews with parents, and the subjects were not allowed to have a record at an ear, nose, and throat (ENT) clinic. The control subjects were not allowed to have a previous record of any psychiatric disorder. None of the participants had a known intellectual disability. All contacted families agreed to participate.Table 1. Demographic characteristics and descriptive statistics for ADHD and control participants.

Sex N Age(Mean) SD

Female (ADHD) 43 13.3 3.0

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As this questionnaire appears to have more than one common abbreviation, I suggest including them both.

Female (CONT) 21 13.6 2.7

Paper IIOur first study (Paper I) was considered a pilot study. In a larger study we added 20 female ADHD patients to the 43 patients from Study I. We also included male ADHS patients and their TD controls.The subjects in Study II were 63 young females with ADHD (mean age 13.8 years, SD 2.5) and 48 young males with ADHD (mean age 13.1 years, SD 1.8). The TD control group consisted of 26 female subjects (mean age 13.8 years, SD 2.7)(20 of these from Study I) and 20 male TD control subjects (mean age 12.8 years, SD 1.7). The female ADHD and control group consisted of the ADHD females and controls included in Paper I, but with an additional 20 female ADHD subjects and 5 additional female control subjects. All patients were diagnosed according to agreed procedures at the CAPs in Eslöv and Lund (semistructured diagnostic interview, neurological examination, psychological evaluation, and questionnaires: AQ, SNAP IV, 5–15). Patients with other psychiatric diagnoses at the time of the testing were excluded to avoid known comorbidity. The control subjects were not allowed to have a previous record of any psychiatric disorder. Excluded from all groups were subjects with hearing impairment either verified by interviews with parents, or a record at an ENT clinic. None of the participants had a known intellectual disability. All contacted families agreed to participate.Table 2. Demographic characteristics and descriptive statistics for ADHD and control participants.

Sex N Age(Mean) SD

Female (ADHD) 63 13.8 2.5

Female (CONT) 26 13.8 2.7

Male (ADHD) 48 13.1 1.8

Male (CONT) 20 12.8 1.7

Paper III

The subjects in Study III included 39 children with ASD and 34 TD

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See my comment on p. 1, para 2, above.

children. Twenty-one females with ASD (mean age 12.71 years, SD 3.36), 18 males with ASD (mean age 11.50 years, SD 3.09). The control group consisted of 24 females (mean age 13.1 years, SD 3.5) and 23 males (mean age 13.2 years, SD 3.2), participated in this study. Due to evoked potentials being of abnormal high voltage, typically triggered by patient movement, coughing or tension, 9 ASD males, 10 ASD females, 6 TD males and 7 TD females were excluded from the study. The children with ASD were assessed in clinical settings by trained psychologists using the Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview–Revised (ADI-R). To participate in the study, the ASD children were not allowed to have had any previous contact with the (ENT) clinic to exclude hearing impairment. All ASD patients had an IQ of 70 or above, as measured by either the Wechsler Intelligence Scale for Children–Fourth edition (WISC-IV) (Wechsler, 2003) or the Wechsler Adult Intelligence Scale–Fourth edition (WAIS-IV) (Wechsler, 2008). The ASD diagnoses were confirmed by a senior psychiatrist. To exclude TD participants with psychiatric diagnoses or hearing impairment, the controls were not allowed to have any previous record from a CAP nor a record at the ENT clinic. No TD had a known intellectual disability or other NDD (Table 3). All contacted families agreed to participate.

Table 3. Demographic characteristics and descriptive statistics for ASD and control participants

Sex N Age(Mean) SD

Female (ASD) 21 12.71 3.36

Female (TD) 24 13.12 3.47

Male (ASD) 18 11.50 3.09

Male (TD) 23 13.18 3.22

Paper IVThe subjects in Paper IV were 112 children with ADHD (aged 7–18 years; mean age 12.8 years, SD 3.07) and 95 control subjects (aged 7–18 years; mean age 13.5years, SD 2.47, 72% males).

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When comparing the two groups, there was a non-significant tendency towards a younger age in the ADHD group (p = 0.063) and a significantly greater percentage of males in the ADHD group (p = 0.014). All patients were diagnosed according to agreed procedures at the CAPs in Eslöv and Lund in clinical settings by trained psychologists and a child and adolescent psychiatrist (semistructured diagnostic interview, neurological examination, psychological evaluation, and questionnaires: AQ, SNAP IV, 5–15). The ADHD diagnoses were confirmed by a senior psychiatrist. Exclusion criteria for the ADHD subjects were other psychiatric diagnoses other than ADHD at the time of testing. Exclusion criteria for the control group were NDD or other psychiatric disorders at the time of testing. None of the participants had a known intellectual disability. All contacted families agreed to participate. Table 4. Demographic characteristics and descriptive statistics for ADHD and control participants.

ADHD SD Control SD P

N 112 95

Age 12.8 (3.07) 13.54 (2.47) 0.063

Gender % Male 72 (64.3) 44 (46.3) 0.014

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See my comment on p. 1, para 2, above.

ProceduresABRAll ABR tests were performed in a soundproof, slightly darkened room. Participants were comfortably seated in an armchair with a neck brace to make sure the neck was relaxed during the testing. Five surface electrodes were applied: two reference electrodes on the mastoid bone behind the left and right ear, respectively; two active electrodes; and one ground electrode placed on the forehead. To assure good transmission, the sites were washed with disinfectant, and abrasive paste was used to attach the electrodes. Absolute impedances and interelectrode impedances were measured before and after the experiments to verify that electrode contact was maintained. Earphones were fitted to cover both ears. The subjects were instructed to turn off their cellular phones and relax with their eyes closed and were permitted to fall asleep. The test required no active participation. Before the testing, written information had been sent home to the subjects’ parents or guardians as well as to the subjects. On site of the test session, subjects were again verbally informed of the nature of the experiments. The subjects were tested one at a time, and the duration of the testing procedure was approximately 30 minutes.

ABR apparatus and stimulusThe measuring system used was borrowed from SensoDetect BERA (brainstem-evoked response audiometry) A1000. The stimuli were presented via TDH-50P headphones with Model 51 cushions (Telephonics, Farmingdale, NY, USA). Presentations were made binaurally with the stimuli in phase over headphones. The click pulses were repeated until a total of 1024 accepted evoked potentials had been collected for each sound stimulus. Thus, each ABR waveform represents an average of the responses to 1024 stimulus presentations. Transistor–transistor logic (TTL) trigger pulses coordinated the sweeps with the auditory stimuli. A TTL pulse is the signal that tells the ABR system to measure. With a correctly timed TTL pulse, all ABR representations will be synchronized. Aberrant activity, such as extremely high amplitudes due to extraordinary movements, was

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rejected. Sound levels were calibrated using a Bruel & Kjaer 2203 sound level meter and Type 4152 artificial ear (Bruel & Kjaer S&V Measurement, Naerum, Denmark). The acoustic output from the earphones corresponded to Sound pressure level (SPL): 80 dB HL or 109 peSPL (peak equivalence Sound pressure level). A square-shaped click pulse was used as a probe in the auditory masking stimuli. The sound stimuli included square-shaped click pulses (0.136 Ms duration, including 0.023 mms rise and fall; 192 Ms interstimulus interval), high-pass filtered pulses (a Butterworth high-pass filtered square-shaped click pulse with a cutoff of 3000 Hz), forward masking (12.3 Ms gap from masker to click pulse), and backward masking (12.3 Ms from click pulse to the masker) stimuli, as previously described. A 1500 Hz Butterworth low-pass filtered white noise, with 15 Ms duration (including 0.4 Ms rise and fall times) was used as masker for both forward and backward masking stimuli. All stimuli were constructed using MATLAB Signal Processing Toolbox (The Math Works, Inc., Natick, MA, USA).In Papers I and II, three types of sound stimuli were used: a high pass filtered pulse in TR4, a backward masked standard click pulse in TR5, TR6 and TR14 and a combination of four stimuli in TR15 (standard click, high pass filtered pulse, forward masked standard click pulse, and backward masked standard click pulse combined).In Paper III the first test was using the forward masked standard click pulse as stimulus. The second test was using ABR curves from four stimuli, namely the standard square-shaped click, the high pass filtered pulse, the forward masking stimulus and the backward masking stimulus. Based on previous studies (Källstrand et al., 2002, 2010), we were interested in five time windows: 2.5–4.0 Ms, 3.3–4.3 Ms, 3.5–7.5 Ms, 4.0–7.5 Ms, and 6.0–7.0 Ms.We used a normed curve or a 3500 Hz curve as comparison; see Results.

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Please spell out: “sound pressure level”? “speech perception level”?

Figure 8. ABR testing. With permission from SensoDetect

CANTABWe used 5 CANTAB tests: IED, SOC, SWM, SRT, and SST. When testing, we used a computer with a touchscreen. In 2016 CANTAB launched iPad testing. The subjects were verbally instructed before each of the tests. Before the testing procedure started, the instructor showed a sample test on the screen. The CANTAB testing took approximately 45 minutes to perform. The tests were performed by either Emma Claesdotter (MD) or Magnus Lindvall (MD, PhD).

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tollare3, 05/03/17,

Et mellanslag för mycket mellan dessa ord

Figure 9. CANTAB tests. The figure shows screens from three different CANTAB tests: Stockings of Cambridge, Intra-Extra Dimensional Set Shift, and Spatial Working Memory.

MeasuresDiagnostic evaluation at the CAP clinic

Papers I–II and IVAll patients were assessed at the CAP clinic according to our standard procedures regarding ADHD; this included both neuropsychiatric and neuropsychological assessment (more fully described in the Subjects section). The ADHD diagnosis was confirmed by a senior psychiatrist. ADHD was referred to as described in DSM-IV.

Paper IIIAll patients were assessed at the CAP clinic according to standard procedures regarding ASD: ADOS and ADI. We used either WISC-IV (Wechsler, 2003) or WAIS-IV (Wechsler, 2008), depending on the age of the patient, to measure the patients’ IQ. All patients in our study had an IQ over 70. The ASD diagnoses were confirmed

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by a senior psychiatrist. ASD was referred to as described in DSM-5.

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ADOS(Paper III)The Autism Diagnostic Observation Schedule was created in 1989 (Reaven et al., 2008). ADOS is an instrument for diagnosing and assessing ASD. The protocol is a semistructured, play-based observational interview of children or adults suspected of presenting with an autism spectrum disorder. ADOS consists of a series of structured and semistructured tasks. ADOS takes 45 minutes to administer and involve social interaction between the examiner and the subject. The examiner observes the subject’s behaviors and assigns these to observational categories. Categorized observations are combined to produce quantitative scores for analysis. Research-determined cut-offs identify the potential diagnosis ASD, allowing a standardized assessment of autistic symptoms. ADOS requires a well-trained and ADOS-experienced tester to administer and score correctly. Sensitivities of the tool have been reported from 86% to 100%, and specificity with other developmental disabilities is 73% to 100%.

ADI-R(Paper III)The Autism Diagnostic Interview–Revised is a paired instrument with ADOS (Rutter et al., 2003). ADI-R is a semistructured interview conducted with the caregiver. It covers the developmental history of the subject over the age of 5 years: social reciprocity, communication, and repetitive behaviors, as well as onset. The usefulness of the ADOS and ADI-R has been well demonstrated in distinguishing between autism and delays of development. ADOS and ADI are considered to be the “gold standard” in diagnostic evaluations for ASD (Akshoomoff et al., 2006; de Bildt et al., 2004).

WISC/WAIS(Paper III)The Wechsler Intelligence Scale for Children and Wechsler Adult Intelligence Scale (WAIS) are individually administered intelligence tests for children aged 6–17 years (WISC) and adults over 17 years (WAIS). The tests were developed by David Wechsler in 1938 (WAIS) and 1949 (WISC) (Wechsler, 2003,

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2008). Both tests can be used without the subject having reading and writing skills and take about 60 minutes to complete. WISC/WAIS are the most widely used intelligence tests. WISC/WAIS have been translated into or adapted to over 25 languages. WISC/WAIS have gone through several editions. In our studies, we used WISC/WAIS IV. The latest edition, WISC/WAIS V, came out in 2014, but was not introduced at our CAP clinic until late 2015. WISC tests are divided into four groups, “verbal function,” “perceptual function,” “working memory,” and “speed.” The verbal function subtests measure aspects of vocabulary, linguistic analogies, and verbal reasoning. The perceptual function subtests examine domains like abstract logical ability. Working memory is tested with tasks whereby the subject is asked to repeat sequences of numbers and letters. Speed function is tested with subtests where the subject is asked to solve tasks as fast and accurate as possible.

AQ(Paper I, II, and IV.)The AQ is a 50‐item questionnaire for measuring autistic traits in individuals with average IQ or above (Baron‐Cohen et al., 2001). The cut-off is 26 for use in a clinical setting and 32 for use in a general population (Baron-Cohen et al., 2001; Woodbury-Smith et al., 2005). There are three different versions of AQ: AQ (adult self‐report), AQ‐Adolescent (parent-report), and AQ‐Child (parent report). The AQ items are divided into five subscales consisting of 10 items each. AQ assesses domains of cognitive strengths and difficulties related to ASD: communication, social skills, imagination, attention to detail, and attention switching. Each of the 50 questions is answered with one of four responses: “definitely agree,” “slightly agree,” “slightly disagree,” and “definitely disagree.” We used AQ-Adolescent (Baron-Cohen et al., 2006) or AQ-Child (Auyeung et al., 2008), depending on the subject’s age. The AQ-Child was employed when the child was younger than 11 years.

SNAP-IV(Paper I, II, and IV.)SNAP originally developed to evaluate ADHD symptoms according to DSM-III (APA, 1980) has been updated with every subsequent edition of DSM (Swanson, 1981; Swanson et al., 2012). SNAP-IV is

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Usually, in scaled responses this is expressed as “somewhat.”

a well-known and widely used test of ADHD in most CAPs in Sweden (Dunerfeldt et al., 2010). There are two forms of SNAP-IV; a long form of 90 questions that assesses not only ADHD but also ODD and all other forms of childhood disorders listed in DSM-IV and a short form of SNAP-IV, which we have used. This version, with 20 of the 90 questions, assesses core symptoms of ADHD and symptoms of ODD. Questions are answered (translated from Swedish to English): “Not at all”, “Just a little”, “Quite a bit” and “Very much” (Inte alls; Bara lite; En heldel and Väldigt mycket). SNAP-IV has normative data for ages 5–11 years, but in Sweden it is used up to age 18 years (Bussing et al., 2008; Collett et al., 2003). SNAP-IV is not gender-normed. Bussing et al. have found small to medium-size differences in gender discrepancies in parent and teacher ratings of boys and girls, where the ratings were higher for boys than girls (Bussing et al., 2008), but the differences have been too small to support the use of gender-specific norms for SNAP-IV. There is one parental SNAP-IV version and one teacher SNAP-IV version, which are to be used with the same patient (Dunerfeldt et al., 2010).

5–15(Paper I, II, and IV)5-15 is a parental questionnaire developed with the purpose of visualizing difficulties in children and adolescents aged 5 to 15 years (Kadesjö et al., 2004). The questionnaire has been tested in several clinical settings, showing great reliability and user satisfaction (Airaksinen et al., 2004; Bohlin & Janols, 2004; Bruce et al., 2006). 5-15 consists of 181 statements regarding the child’s skills and behaviors, followed by boxes marked “Does not apply,” “Applies sometimes/to some extent,” and “Applies.” The areas covered include motor function, attention/executive function, language, memory, learning, social skills, and internalizing and externalizing behavior problems (Korkman et al., 2004; Trillingsgaard, 2004).

StatisticsThe anonymous audiograms were analyzed by SENSODETECT, and the raw data files were handed over to us for statistic

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Do you mean 90?

analysis. The statistical analyses were undertaken using IBM SPSS Statistics (IBM Corporation, NY, USA) and R (www.r-project.org). The level of statistical significance was set to p = 0.05.

Comparison of means between two or more groups (Papers I, II, III, and IV)Independent sample t-testWhen groups of variables are normally distributed and have equal variance, one can use the t-test to compare mean scores of two independent groups. In Paper IV we used this method to compare the ADHD group to the control group. When using the t-test, the variables have to be continuous.Mann-Whitney U-test or Wilcoxon rank sum test This test is used when the two groups are not normally distributed. The test does, however, demand that compared data come from distributions of equal variance. Mann-Whitney U-test compares median instead of mean. When using Mann-Whitney U-test, the variables have to be continuous. In Papers I, II, and III the variables were not normally distributed; therefore, this test was used to compare the ADHD and the ASD groups with the control group.Analysis of variances (ANOVA)When comparing the means between more than two groups, ANOVA can be used. ANOVA can be used when the groups are normally distributed. If the groups are not normally distributed one shall use Kruskal-Wallis test instead. ANOVA tests the hypothesis that all means are the same. An ANOVA test compares the variance within the groups with the variances between the groups (F value). The greater the variances between the groups and the less the variances within the groups, the greater is (F). In Paper IV we compared model fit using an ANOVA, to assess whether the inclusion of polynomial terms improved the model fit. Once the best fitting model had been selected for each cognitive variable, we tested whether including diagnostic group, or an interaction term of group, improved the model fit.

Correlation analyses (Papers I, II, and III)

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Spearman’s non-parametric rank correlation (Spearman’s rho) is a test that calculates the relationship between two variables. The ranking stands for the strength of the relationship. This test was used in Papers I, II, and III to identify pathologies in the ABR related to a normative ABR. Values ranging from -1 to +1 were obtained using Spearman’s rho. High, positive values indicate similarity between the tested ABR and the normative ABR, that is, no pathology. Values around zero indicate no relation, and low values close to -1 indicate an inverse relationship. Then, all these values were ranked. Thus, the test subject’s most aberrant ABR region, when compared with the norm curve, got a high number, and vice versa.

Model fitting (Paper IV)Multiple hierarchical linear regressionLinear regression is used to evaluate the relationship between a numerical outcome and a numerical exposure. The method for examining an outcome with several exposure variables is called a multiple linear regression. Adding these variables in steps results in a “multiple hierarchical linear regression.” This method also provides a correlation coefficient that tells us something about the strength (closeness) of the linear association. In Paper IV we used hierarchical linear regression to test, across all subjects, which model best fitted age-related changes in the cognitive variables.

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Results

Paper IWe started our research by setting up a pilot study focusing on young females. Since we in our clinical work, had noticed a relative increase of young female ADHD patients. This tendency has also been noted in research; ADHD is considered to have a male to female ratio approaching 1:1 (Froehlich et al., 2007), contrasting previous studies showing a gender ratio (girl:boy) ranging from 1:3 to 1:16 (Nøvik et al., 2006).

ResultsWe compared the ABR of 43 girls with ADHD to 21 age-matched TD controls. ABR data of the two groups were compared to a normed curve and to a 3500 Hz curve. Based on previous studies (Källstrand et al., 2002, 2010), we were interested in five time windows: 2.5–4.0 Ms, 3.3–4.3 Ms, 3.5–7.5 Ms, 4.0–7.5 Ms and 6.0–7.0 Ms. We used ranked and non-ranked correlation. The ABR of the ADHD group differed from the control group in three locations, denominated Traits (TR). The trait numbers were not linked to the total numbers of traits but were used as identifiers.In Study I we found an aberrant curve profile in the ABR region 6.0–7.0 Ms when compared to a normed curve, TR6 (p = 0.0004). This region is considered to correspond to the thalamus (Figure 1). We also found a higher presence of 3500 Hz frequencies in the region 4.0–7.5 Ms (TR 14) (p = 0.0026), corresponding anatomically to an area ranging from the superior olivary complex to the thalamus. In the ABR region 3.3–4.3 Ms, we found an aberrant curve profile (TR15) (p = 0.001), corresponding anatomically to the lateral leminiscus/pons area (Table 5) (Figure 10a-c)

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Figure 10? (following on from the Methods section).

Table 5. ABR results for young females with ADHD compared to controls. Mann-Whitney U-test was used.

Trait ADHD group(N)

Control group(N)

ADHD mean(SD) Control mean(SD) p value Starting point (Ms)

Window size (Ms)

Type of norm Stimuli Ranked/Non-ranked

TR6 43 21 164.14 (61.91) 112.43 (38.83) 0.0004 6.0 1.0 Norm curve BM Ranked

TR14 43 21 3.73 (1.98) 2.05 (1.94) 0.0026 4.0 3.5 3500HZ BM Ranked

TR15 43 21 67.16 (49.74) 27.95 (23.76) 0.001 3.3 1.0 Norm curve Standard Ranked

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Figure 10 a-c. Trait 6, 14 and 15 and their p values.

Paper IIIn study II, we compared the ABR of 63 young females with ADHD to 26 age-and gender-matched control subjects. The clinical group consisted partly of the same group of ADHD females and controls included in Paper I, but with an additional 20 female ADHD subjects and 5 additional female control subjects. The enhanced ADHD group also included 48 males with ADHD and their age-matched 20 male TD. We used the same technique in this study as in Study I.

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ResultsAfter the inclusion of more female ADHD and control subjects, all three significant traits from Study I remained significant, with substantial reductions in the p-values for the different traits.TR6 (p = 0.000064), TR14 (p = 0.00059), and TR15 (p = 0.00035) to be compared with results from study I, TR6 (p = 0.0004), TR14 (p = 0.0026), and TR15 (p = 0.001). When the ABR from the 48 young males with ADHD were compared to the control subjects, we found three significant traits: TR4, TR5, and TR14.TR 4 is described as a lower correlation to a norm curve (p = 0.00105) in area 3.5–7.5 Ms, corresponding anatomically to the inferior colliculus and thalamic area. TR5 identifies aberrant curve profiles 2.5–4.0 Ms, corresponding anatomically to the nucleus cochlea (p = 0.00027). TR14 (p = 0.00013) shows, as mentioned in Paper I, a higher presence of 3500 Hz frequencies in the region 4.0–7.5 Ms, corresponding to the superior olivary complex and thalamus. One trait occurred in both the female and male groups. TR14 (Table 6) (Figure 11a-f).

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Table 6 ABR results for young patients with ADHD compared to controls. Mann-Whitney U-test was used.

Trait Female ADHD mean(SD) median

Female control mean(SD) median

Female p-values

Male ADHD mean(SD) median

Male control mean(SD) median

Male p-values

Starting point (Ms)

Window size (Ms)

Type of norm

Stimuli

Ranked/Non-ranked

TR4 0.62 (0.29); 071 0.73 (0.19); 0,81 0.084 0.57(0.26); 0.62 0.77 (0,18); 0.80 0.00105 3.5 2.0 Norm curve

HP Non-ranked

TR5 0.85 (0.14); 0.90 0.85 (0.22); 0.94 0.321 0.80 (0.18); 0.87 0.93 (0,06); 0.95 0.00027 2.5 1.5 Norm curve

BM Non-ranked

TR6 172 (56); 174 116 (49); 123 0.000064 157 (63); 156 139 (64); 122 0.208 6.0 1.0 Norm curve

BM Ranked

TR14 3.8 (1.8); 4 2.2 (1.9); 2 0.00059 4.5 (1.8); 5 2.3 (1.8); 2 0.00013 4.0 3.5 3500HZ BM Ranked

TR15 66 (47); 61 31 (24); 27 0.00035 57 (42); 52 47 (34); 39 0.518 3.3 1.0 Norm curve

Standard

Ranked

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Figure 11 a

Figure 11b.

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Figure 11c.

Figure 11d.

Figure 11e.

Figure 11 a-e: Trait 4, 5, 6, 14 and 15 and their p values. Mean and Standard Deviation are indicated in the figures.

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Paper IIIBased on the altered sensory and auditory processing associated with ASD (Baranek et al., 2006) and on the evidence that brainstem function might be abnormal in children with ASD (Geva & Feldman, 2008; Santos et al., 2017), we decided to evaluate ABR in children with ASD. Further, we studied the amplitude and interaural correlation in each of the seven ABR waves. We studied the region 2.5–4.0 Ms for amplitude and region 3.3–4.4 Ms for interaural differences.

ResultsThe ABRs in 39 children with ASD were compared with the ABRs of 34 TD children. When male and female ASD subjects were grouped together, we found that the amplitude of wave III deviated in the ASD group compared to the TD group (ABR region 2.5–4.0 Ms) (denoted deviation 1 (DV 1)) (Table 7) (Figure 12), with the ASD group exhibiting a higher wave amplitude than the control groups. From a neuroanatomical point of view, DV 1 corresponds to the pons region. Males and females were also studied separately. No differences in interaural correlation could be seen within the ASD female group compared to the TD females. However, the TD males did show a significantly lower degree of correlation between left and right ear compared to the ASD groups and the TD females (denoted deviation 2 (DV 2)) (Table 7) (Figure 13).

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Table 7. Deviation (DV) 1 and 2. ABR results for young patients with ASD compared to TD.

Sex DV Diagnos/TD N Mean Median SD P-value Starting point (Ms) Window size (Ms) Type of norm Stimuli Ranked/Non-ranked

Female DV 1 ASD 21 0.56 0,57 0.09 0.0002 2.5 1.5 Norm curve FM Non-ranked

Female DV 1 TD 17 0.42 0,43 0.12 0.0002 2.5 1.5 Norm curve FM Non-ranked

Male DV 1 ASD 18 0.52 0,53 0.08 0.02 2.5 1.5 Norm curve FM Non-ranked

Male DV 1 TD 17 0.45 0,47 0.09 0.02 2.5 1.5 Norm curve FM Non-ranked

Female DV 2 ASD 21 0.67 0,63 0.17 0.15 3.3 1.1 Norm curve Four stimuli Non-ranked

Female DV 2 TD 17 0.75 0,8 0.17 0.15 3.3 1.1 Norm curve Four stimuli Non-ranked

Male DV 2 ASD 18 0.73 0,77 0.19 0.006 3.3 1.1 Norm curve Four stimuli Non-ranked

Male DV 2 TD 17 0.52 0,61 0.26 0.006 3.3 1.1 Norm curve Four stimuli Non-ranked

DV 1 showing wave amplitude. DV 2 showing interaural correlation. Mann-Whitney U test was used. ABR: Auditory Brainstem Response.

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Figure 12. Deviation 1 (DV 1). Showing wave amplitude differences in Auditory Brainstem Response (ABR) in an ASD group versus a TD group separated by gender.

Figure 13. Deviation 2 (DV2). Showing interaural correlation between left and right ear in Auditory Brainstem Response (ABR) in an ASD group versus a TD group separated by gender.

Paper IV

The aim of the Study IV was to study the cognitive performance of young ADHD subjects compared to TD control subjects using CANTAB. In Paper IV we express the studied effect as a statistical interaction term in regression modeling.

ResultsWe compared a group of 112 drug-free young ADHD patients with a control group of 95 TD subjects. A non-significant tendency towards a younger age in the ADHD group could be seen. Simple t-tests showed a difference between the two groups for all cognitive measures except SOC (Table 8). All measures except SWM Strategy and SST Direction Errors showed a non-linear effect of age, with the inclusion of a quadratic term significantly improving model fit for all measures. SWM Strategy and SST Direction Errors showed a linear effect of age. There was a

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significant effect of ADHD diagnosis on performance for all CANTAB tests. We performed a hierarchical multiple regression analysis and found a main effect of ADHD diagnosis without interaction with age on all the tests except SOC where we found a significant interaction with age (Table 9, Table 10, Figure 14)

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Table 8: Demographic characteristics and descriptive statistics for ADHD and control participants.

ADHD SD Control SD P

112 95

Age 12.8 (3.07) 13.54 (2.47) 0.063

Gender % Male 72 (64.3) 44 (46.3) 0.014

IED Stages Completed 7.43 (1.41) 7.95 (0.97) 0.004

Total Errors 51.31 (31.59) 40.47 (20.74) 0.005

SOC Problems Solved in Minimum Moves 6.93 (1.85) 7.38 (1.95) 0.106

SWM Between Search Errors 48.54 (21.23) 34.85 (19.33) <0.001

Strategy 36.79 (4.73) 34.54 (4.92) 0.001

SRT Median Correct Latency 362.66 (159.55) 303.24 (88.68) 0.003

Percent Correct 93.8 (6.44) 97.01 (3.5) <0.001

SST Direction Errors 9.83 (13.24) 5.46 (7.86) 0.006

Stop Signal Reaction Time 254.3 (98.14) 216.32 (84.35) 0.004

IED=Inter-Extra Dimensional Set Shift, SOC=Stocking of Cambridge, SWM=Spatial Working Memory, SRT= Stop Reaction Time, SST=Stop Signal Task

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Table 9. Age model comparison ANOVA.

Res.Df RSS Df Sum of Sq F Pr(> F)

IED.TEA

1 196 149,530.50

2 195 145,483.30 1 4,047.27 5.42 0.02

IED.SC

1 196 308.84

2 195 300.89 1 7.95 5.15 0.02

SOC

1 187 585.17

2 186 552.61 1 32.55 10.96 0.001

SWM.BE

1 203 83,157.65

2 202 80,375.69 1 2,781.96 6.99 0.01

SWM.STRAT

1 203 4,848.26

2 202 4,847.05 1 1.21 0.05 0.82

SRT.MCL

1 187 3,042,973.0

2 186 2,939,837.0 1 103,135.10 6.53 0.01

SRT.PCT

1 187 4,843.41

2 186 4,520.95 1 322.46 13.27 0.0004

SST.DEOSAGT

1 200 25,107.15

2 199 25,069.51 1 37.64 0.30 0.59

SST.SSRT

1 199 1,364,661.0

2 198 1,252,423.0 1 112,238.00 17.74 0.0000

IED.TEA=Inter-Extra Dimensional Set Shift Total Errors, IED.SC= Inter-Extra Dimensional Set Shift Stdges Completed, SOC=Stocking of Cambridge, SWM.BE=Spatial Working Memory Between Errors, SWM.STRAT=Spatial Working Memory Strategy, SRT.MCL= Stop Reaction Time Median Correct Latency, SRT.PCT= Stop Reaction Time Percent Correct, SST.DEOSAGT=Stop Signal Task Direction Errors, SST.SSRT= Stop Signal Task Stop Signal Reaction Time. Res.Df=Residual degrees of freedom, RSS=Residual sum of Squares, Df=Degrees of freedom, Sum of Sq= Sum of Squares

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Table 10. ADHD model comparison ANOVA results.

Res.Df RSS Df Sum of Sq F Pr(> F)

IED.TEA

2 195 145,483.30

3 194 141,319.70 1 4,163.59 5.73 0.02

4 192 139,569.20 2 1,750.51 1.20 0.30

IED.SC

2 195 300.89

3 194 291.01 1 9.87 6.58 0.01

4 192 287.90 2 3.11 1.04 0.36

SOC

2 186 552.61

3 185 552.37 1 0.24 0.08 0.77

4 183 533.92 2 18.45 3.16 0.04

SWM.BE

2 202 80,375.69

3 201 74,674.15 1 5,701.54 15.43 0.0001

4 199 73,525.16 2 1,148.98 1.55 0.21

SWM.STRAT

2 203 4,848.26

3 202 4,635.97 1 212.29 9.21 0.003

4 201 4,630.70 1 5.27 0.23 0.63

SRT.MCL

2 186 2,939,837.0

3 185 2,859,400.0 1 80,437.64 5.20 0.02

4 183 2,831,293.0 2 28,106.75 0.91 0.41

SRT.PCT

2 186 4,520.95

3 185 4,376.64 1 144.31 6.07 0.01

4 183 4,349.56 2 27.08 0.57 0.57

SST.DEOSAGT

2 200 25,107.15

3 199 24,457.89 1 649.26 5.27 0.02

4 198 24,385.50 1 72.39 0.59 0.44

SST.SSRT

2 198 1,252,423.0

3 197 1,233,307.0 1 19,116.90 3.02 0.08

4 195 1,232,680.0 2 627.03 0.05 0.95

For abbreviations See Table 9.

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Figure 14.

Age VS CANTAB tests in ADHD and controls. Diagnosis: .

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Discussion

Studies I, II, and III are open studies. The aim was to identify possible ABR aberrancies (traits) associated with ADHD and ASD. In Study I and II we investigated possible ABR differences in a young ADHD patients. Our approach to this was that young females and young males differs in clinical ADHD symptoms. Previous research has proposed that young females with ADHD may be more likely to have the inattentive type of ADHD and more internalizing symptoms, while young males, on the other hand, have more hyperactive and aggressive symptoms (Biederman et al., 2002; Biederman & Faraone, 2004; Newcorn et al., 2001). Even though the ADHD prevalence is starting to even out between the genders, there is still a tendency towards a later diagnosis of young females compared to young males (Froehlich et al., 2007; Nøvik et al., 2006). Earlier research has suggested that there are neurodevelopmental differences between the genders (Charman et al., 2017; Graetz et al., 2005; Levy et al., 2005; Mahone & Wodka, 2008; Suades-González et al., 2017). Further, the diagnostic criteria and diagnostic procedures are defined and designed based on male prototype ADHD symptoms. Previous studies (Puente et al., 2002; Schochat et al., 2002; Vaney et al., 2011) on ABR in children with ADHD have not been able to find clear significant differences in amplitude, peak latencies, or interpeak latencies compared to TD. We decided to study ABR in young individuals with ADHD and compare them to a normed ABR curve, a 3500 Hz curve, and non-clinical individuals. Based on previous studies (Källstrand et al., 2002, 2010), we were interested in five time windows: 2.5–4.0 Ms, 3.3–4.3 Ms, 3.5–7.5 Ms, 4.0–7.5 Ms and 6.0–7.0 Ms. The reason for using a 3500 Hz curve as comparison is that studies on ADHD in children and adults have shown aberrant EEG frequencies when compared to healthy controls (Alba et al., 2015; Loo & Makeig, 2012).

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We started our research project with a study on young ADHD females, Study I, since our primary interest from the beginning was ADHD characteristics in young females. In Study II we were interested in whether our findings were gender specific or not.

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Comments on the main findings of Study I and IIWe found three aberrant traits in the young female ADHD group (TR6, TR14, TR15) and three traits in the young male ADHD group (TR4, TR5, TR14). TR14, which was present in both gender groups, was derived from a higher correlation, when compared to the TD group. The trait corresponds anatomically to the midbrain ranging from the superior olivary complex to the thalamus. TR15, only present in the young female ADHD group, corresponds to the pons area. The female trait TR6, one of the male traits TR4 as well as the co-occurring trait TR14 correlated neuro-anatomically to the thalamic area or in the near proximity. TR5, one of the male traits, corresponds anatomically to the cochlear nucleus, a more peripheral part of the brain.As stated, three of the traits (TR6, TR4, and TR14) corresponded anatomically to thalamic parts of the brainstem. In line with our results, it has previously been described that thalamic dysfunction is involved in symptoms of hyperactivity, inattention, and dysregulation of sleep and wakefulness, all of which are core symptoms of ADHD (Ivanov et al., 2010). TR14 corresponded to the midbrain ranging from the superior olivary complex to the thalamus. The superior olivary is the first major site where auditory information from both ears converges and hence an important locus for sound localization, something some ADHD children have problems with. This information is then forwarded to the thalamus, where the transformation of sound source acoustics (frequency, time, and amplitude) begins to form into perceptual features (i.e., acoustic features) (Bartlett, 2013). The central thalamus has been found to play a critical role in regulating arousal, attention, and goal-directed behavior (Mair et al., 2011). The thalamus receives projections from different arousal systems like the locus coeruleus and cholinergic innervation from the basal forebrain (Liu et al., 2015). Through projections from the thalamus, the overall excitability of the cortex during states of attention can be affected. How these crucial interactions are mediated, and the thalamus’s role in them, is still unclear. Wells et al. (2016) have recently identified a gene (PTCHD 1) that seems to modulate the amount of calcium-dependent potassium currents that are essential for the thalamic

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communication with the rest of the brain. Animal experiments with PTCHD1-deleted mice have shown that these mice made significantly more errors in concentration tests and were also easily distracted and showed hyperactivity, classic ADHD symptoms. Interestingly enough, these mice were also resistant to stimulant medication, that is, methylphenidate (Wells et al., 2016). One can assume that thalamus dysfunction is central in understanding ADHD symptoms and that traits located here might be linked to ADHD.The cochlear nucleus, which corresponded to the TR5 in boys with ADHD, is where parts of the auditory temporal processing occur and it is an important locus for sound detection in noisy environments and in selective auditory attention (Fortune & Lurie, 2009); deficits in these aspects of sound processing are frequent symptoms of ADHD (Strait & Krus, 2011; Söderlund et al., 2007). It would be interesting to see if one could link TR5 to a patient’s self-report questionnaire to see whether these patients report more problems in sound detection and selective auditory attention. Further, follow-up studies could investigate if boys with ADHD have more problems in regulating auditory stimuli, since the trait was only significant in the male ADHD group.TR15, only present in females with ADHD, corresponds to the pons area of the brainstem. This aligns with earlier studies that have found higher resting-state activity in MRIs of young patients with ADHD compared to controls, in the same part of the brain (Zang et al., 2007). The pons consists of several nuclei, among these, locus coeruleus. The locus coeruleus is central to the noradrenaline synthesis of the central nervous system. It is involved in attention regulation, sleep and wakefulness, and so forth. The locus coeruleus has an excitatory effect on the neurons it communicates with, that is, areas in the brainstem, cerebellum, hypothalamus, nuclei in the thalamus, and the cortex. Studies have shown increased regulation of locus coeruleus activity by methylphenidates and by atomoxetine (Bari & Aston-Jones, 2013; Devilbiss & Berridge, 2006; Howells et al., 2012), common ADHD medications.Taken together, the findings in Study I and II seem to be in line with earlier brain imaging studies showing that children with ADHD have abnormalities in their pontine and thalamic regions (Cortese et al., 2013; Ivanov et al., 2010; Dickstein, 2006). Two

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traits are only present among boys (TR4, TR5), and another two only among girls (TR6, TR15). No further conclusion can be drawn regarding this without verifying the results in further studies. One explanation that could account for some of the differences could be the simple fact that the brainstem is affected by a combination of hormonal and head-size differences (Dehan & Jerger, 1990). An early study (Rosenhall et al., 2003) considered the differences could be an effect partly due to the length of the cochlear nerve and the auditory pathway in the brainstem.

Comments on the main findings in Study III The aim of Study III was to study possible ABR alterations in a group of young subjects with ASD. Previous research has mainly focused on abnormal cortex activity in the perception of sound in ASD (Blasi et al., 2015; Hames et al., 2016; Orekhova et al., 2012), but there is preliminary evidence of abnormal brainstem processing responses to auditory stimuli in young patients with ASD (Russo et al., 2008). Further, Guinan (1996) suggested that brainstem abnormalities might be partly responsible for the difficulties with language and cognitive and social development in children with ASD. In line with this, the brainstem in children with ASD has been shown to be smaller and have a slower growth rate than in TD children (Jou et al., 2013). Based on earlier research (Dabbous, 2012; Hitoglou et al., 2010; Kwon et al., 2001; Maziade et al., 2000; Miron et al., 2016; Rosenhall et al., 2004; Roth et al., 2011), we decided to explore possible ABR differences in amplitude and interaural correlation in each of the seven waves. Interaural differences have for a long time been considered to represent brainstem pathology (Hall, 1984), although later studies show that healthy humans use the small interaural differences to locate sound (Undurraga et al., 2016). From earlier studies in cats, we knew that wave amplitude changes are often detected in neurological disorders (Buchwald, 1983).We found that the amplitude of wave III was higher in the ASD group compared to the TD group (DV 1). DV 1 corresponded to the pons region. A higher amplitude in ABR indicates that in response to an acoustic stimulus more neurons are firing in that particular area than in the control group (Buchwald, 1983). Autistic children can show difficulties within all the areas involved in the processing of sensory information in the pons region, that is, facial expression, chewing, swallowing, hearing, taste, facial

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sensation, touch, and pain (APA, 2013). Increased activity in the pons region in ASD is something that has been seen in earlier ASD research (Di Martino et al., 2011; Khalfa et al., 2001; Sajdel-Sulkowska et al., 2011; Suzuki et al., 2013). Binaural hearing is essential for directional hearing. Interaural differences, for example, time or level, provide us with the necessary information to locate the positions of sound sources. Auditory input from both ears is thought to fuse in the pons (the superior olivary complex). When the binaural fusion is incomplete, it may result in abnormal dichotic hearing and problems processing auditory input correctly. For example, Undurraga et al. (2016) found that a lower correlation of auditory processing between the left and right ear could indicate more functional processing of sound, since it has been shown that healthy humans use the small interaural differences to locate sound and to detect sound in background noise. When looking at the ABR of ASD patients, earlier studies have found a high correlation between the auditory processing of the left and right ears, which has been related to the difficulties in sorting out and managing everyday sounds that are often reported in children with ASD (Alcantara et al., 2004).In Study III, the TD males were the only group that differed in interaural correlation by having a lower correlation between the left and right ears in the ABR corresponding to the midbrain. The lower correlation between the left and right ear in the TD male group has to be confirmed in future studies. Interaural time difference has been observed in ASD patients but not in TD patients in studies where the ABR of the genders has not been separated (Rosenhall et al., 2003). Lateral ABR asymmetry of amplitudes and latency has been found in infants (Sininger et al., 1998) but not in adults. The mechanism is unknown, but Sininger et al. (1995) suggested that the left ear of neonates without any correlation to gender might receive greater suppression by contralateral noise. Lateralized function at the cortical level is well established; studies have visualized a more symmetrical processing of language by women and more lateralized processing by men (Shaywitz et al., 1995). The known asymmetry in auditory sensitivity mostly seen in adult men has long been attributed to non-biologic factors such as exposure to traumatizing noise. The finding of lateral asymmetry in ABR in newborns points to a biologic significance of the phenomenon.

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The anatomical or physiological basis for lateralized function at the brainstem level is not obvious and has not been stated, but one hypothesis could be that there is a difference in innervation density in the auditory pathway, converging in the midbrain. Could the lower correlation in the midbrain region in TD boys be linked to a later found male lateralization at a cortical level? Previous research as well as our findings is inconsistent and incomplete in this area, and there are no conclusions to be made from our finding. As in studies I and II, our results in Study III are based on differences between groups and need to be verified by more studies before one can draw the conclusion that ABR can be a valuable measurement of ASD in one single individual.

Comments on main findings in Study IV In Study IV we examined executive functions in young people with ADHD, expressed as a statistical interaction term in regression modeling. Our hypothesis was that children with ADHD would show deficits on neuropsychological test performance when compared to TD control subjects. This hypothesis was correct in all CANTAB measures except for the test SOC. Our findings are well in line with the fact that SOC has been removed from the recommended CANTAB test battery for ADHD; that change occurred in 2015, and SOC was instead recommended for ASD, epilepsy and Huntington’s disease (www.cambridgecognition.com). We found a main effect of ADHD with no significant interaction between ADHD and age in all tests except SOC. This would suggest that the between-group differences represent a deficit. Our results are consistent with findings that children with ADHD have a delayed or deviant brain development (Castellanos et al., 2002; Hoogman et al., 2017; Villemonteix et al., 2015; Wyciszkiewicz et al., 2017). Even though our data show that the ADHD group has a parallel (better with age) but significantly poorer performance on our CANTAB tests, one cannot rule out that the ADHD group would catch up between 17 and 25 years of age. Some researchers state that ADHD children have a brain that matures late but eventually catches up compared to healthy controls (Gogtay et al., 2002; Shaw et al., 2007). According to our CANTAB study, the so-called developmental deviation model maybe more valid, that is, that ADHD patients do not score normal at any age (Castellano et al., 2002; Halperin & Healey, 2011; Hoogman et al., 2017).

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The clinical utility of ABR and CANTABAll our results are based on observed group differences and cannot therefore, at this stage be transferred to single individuals. Further, to link these differences to neurobiological impairment, more research is needed. In the future RDoC might be a useful tool for identifying ABR aberrancies and linking them to different domains and symptoms, rather than striving to separate ADHD patients from healthy controls. Clearly there is a need for further research to substantiate our ABR findings including replication of our findings.Our findings show that the CANTAB could differentiate between ADHD patients and age-matched TD controls which seems to support the utility of CANTAB in children and adolescent with ADHD in clinical and research settings. CANTAB is already in use in many CAPs worldwide. Diagnostic procedures in CAPs are delicate and extensive work. CANTAB could be used as a complement-a piece in the great jigsaw puzzle of child and adolescent psychiatry diagnostics.

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Limitations

Most of the limitations of this thesis are listed in the papers included in the thesis. Here some of the general limitations are discussed.One limitation of Papers I, II, and III is the relatively small sample size. In addition to this, in Paper III around 30% of the total number of children participating (ASD and TD) were excluded in the first screening due to low quality ABR. Another limitation is that partly the same set of young females is used in Paper I and Paper II, facilitating possible errors being carried forward. One obvious limitation in the same Papers (I, II, and III) is that no clinical comparisons group was included. The first three studies should be seen as trait-defining studies in preparation for a double-blind follow-up study set up during 2017, trying to verify our results.When analyzing ABR, we have used a time-dependent technique, but there have been suggestions to add spectra analysis to contribute to further understanding of the ABR, especially analyzing latencies (Bywater, 2014. Studies show that the aging process is essentially a peripheral phenomenon that does not involve the central part of the acoustic pathways (Burkard & Sims, 2001; Konrad-Martin et al., 2012).

In Study I and II we did not correct for multiple comparisons. A strict application of Bonferroni, using all possible time window positions, would lead to dividing all p-values by 400. This, in turn, leads to only TR6 for females and TR14 for males passing as significant (Table 11). As the traits actually were identified individually, based on previous research and specific areas of interest in the ABR curves, such application of Bonferroni could be argued to be too strict. As the per-trait hypotheses, rather, had a maximum of 70 to 120 possible window starting positions,

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the per trait Bonferroni correction should be multiplying p-values by these instead of 400 (Table 11). The N for Bonferroni is calculated based on the fact that each Ms consists of 20 data points (the resolution of the ABR). With a time window of 2 Ms starting from 2 Ms to 9 Ms, N in Bonferroni will be will be 20 × 5 = 100. This less strict Bonferroni would lead to TR6, TR14, and TR15 for females and TR5 and TR14 for males to be significant, only disqualifying TR4 males from our original data.

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Table 11. TR4, TR5. TR6. TR14 and TR15 and their p-values before and after Bonferroni.

Trait Female p-values

Male p-values

Bonferroni (N)

Female Bonferroni

Male Bonferroni

Bonferroni (N)

Female Bonferroni (400)

Male Bonferroni (400)

TR4 0,084 0,00105 100 8.4 0,105 400 33.6 0.42

TR5 0,321 0,00027 110 35.31 0,0297 400 128.4 0.108

TR6 0,000064 0,208 120 0,00768 24.96 400 0.0256 83.2

TR14 0,00059 0,00013 70 0,0413 0,0091 400 0.236 0.052

TR15 0,00035 0,518 120 0,042 62.16 400 0.14 207.2

Another limitation is that only participants in one of our study-groups; the ASD-group in Study III were IQ-tested. In all the other studies there was no known intellectual disability but of the children were not IQ-tested.In Paper IV we have a larger sample size, but a limitation is the age span. Participant were 7–17 years of age, and drawing conclusions on developmental trajectories on this age group can be problematic due to the known fact that the brain matures up to the age of 25 years (Arain et al., 2013). Another important limitation is that we have no IQ values for neither of the groups. There are studies showing that testing executive functions is not valuable if not IQ is also taken into consideration (Salthouse, 2005). However, there are conflicting studies showing that tested IQ was not correlated to tested executive functions (Ardila, 2000; Dennis et al., 2009). Could the parallel developmental trajectories described in this study be a result of a possible lower IQ in the ADHD group?

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Future

ABRABR in psychiatric settings is a quite new technique, and the findings presented in this thesis are based on group differences and cannot at this time be applied to clinical use with individual patients. Future research of interest would be to see if the results are replicable. Further it would be of great interest to investigate whether ADHD medication would decrease the ABR differences between the group of children with ADHD and TD controls, or to see whether it is possible to predict choice of medical treatment after looking at the patient’s ABR. Another focus of interest would be to see whether different ABR traits can be tied to actual ADHD symptoms. That is, do ADHD patients with a deviant ABR response in TR6, corresponding anatomically to the cochlear nucleus, have more problems in the area of sound detection? Regarding ABR and ASD, oversensitivity in ASD patients is a challenge when using ABR. In Paper III we had an approximate exclusion rate of 30% in all groups, due to tensions and movement during testing. Quite a few ASD patients complained about the sound level and about having sensors attached to their foreheads during testing. To avoid such difficulties in the future and hence reduce exclusion rates, it is important to develop test procedures more suitable for individuals with sensory processing difficulties. Future research should also involve measures of different autistic features and symptom severity to explore possible relations to altered ABR in different areas of the brain. There is a need for further studies on ABR with child and adolescent control subjects with other psychiatric diagnoses to further substantiate our findings. We have initiated a double-blind study at the CAP in Eslöv, with the aim to investigate whether ABR could be used to distinguish patients with different diagnoses. My findings in studies I, II, and III have led us to a power analysis based on the two ABR

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aberrancies named DV1 and DV2 in ASD and TR14 and TR6 in ADHD. This has revealed that we need 27 young females and 35 young males with ADHD, 11 young females and 23 young males with ASD DV1, and 19 young males with ASD DV2 to give us an 80% power (p < 0.05). According to the diagnostic frequency of ADHD and ASD, we will need approximately 150 patients.

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CANTABCANTAB is already a widely used test instrument in both research and clinical practice. The demand for easily administered and accurate neuropsychological tests is rising, and CANTAB can play a role here. Our study proves that the four (SWM, IED, SRT, SST) of the five CANTAB tests we looked at are all valid in differentiating between an ADHD population and a control population.

Bayesian networkA Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. One could describe a Bayesian network as a statistical graphical model that represents probabilistic conditional relationships between different variables. The Bayesian network structure consists of nodes and edges between nodes that indicate probabilistic conditional dependency. When the joint probability of the nodes is maximized, the Bayesian network describes the data best. As joint probability varies depending on the direction of edges, causes and results may be inferred between the node of interest and directing nodes. Bayesian networks are used to show interdependencies of different parameters of a database of interest. The Bayesian network takes in all variable dependencies at the same time, rather than calculating on the variables in pairs. Bayesian networks have been extensively used in biomedical research (Chavalarias et al., 2016; Lucas et al., 2004), psychology (Field, 2016; Gopnik & Tenenbaum, 2007), and oncology (Montazerhodjat et al., 2017). They are useful tools in medicine to visualize how different symptoms are related to each other (De la Fuente, 2011). We want to look at the probabilistic relationships between ABR and/or CANTAB and/or 5-15 in ADHD. The variables we will use are the results of the series of tests mentioned above: ABR (TR5, TR6, TR14, and TR15), CANTAB tests (SOC, IED, SWM, SRT, and SST), and the questionnaire 5–15. The aim of the study is to evaluate a Bayesian network in comparison with normal clinical neuropsychiatric assessment

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based on neurodevelopmental testing and a clinical history. Results will be published in 2017.

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Acknowledgments

Magnus “the Godfather” Lindvall, my supervisor. Thank you for all your support. Thank you for everything you have taught me about research and for trying to slow things down a bit. Thank you for all the good laughs and all the wonderful texts and emails back and forth during these 5.5 years. Thank you for encouraging me to dig into the world of research. Yours truly, “Turbo”Maria Råstam, my co-supervisor. Thank you for all your invaluable help and support during this process. Thank you for outstanding knowledge in the field of Child Psychiatry and NDD in particular. Thank you for all interesting research input and for strengthening feminist discussions. You are an amazing role model in so many ways. Thank you also for the best quote ever “If you want a speech ask a man, if you want something done ask a woman” Sofia Åkerlund, my co-writer. Thank you for a great cooperation. Thank you for all your inestimable knowledge in the field of ASD. Thank you for a great co-writing process especially in paper III. It would not have happened without you.Matti Cervin, my co-writer. Thank you for all your excellent scientific input and for being such a visionary. Thank you for a great co-writing process. Thank you for all your hours of proofreading.Viveca Caspersen-Viklund. Thank you for all outstanding administrative support. I also want to thank my supervisors at BUP; Eva-Lena Palm, Therese Theander, Sophia Eberhard, Linda Welin and at VKP Åsa Westrin for making it possible to do research at Region Skåne and work clinically at the same time.A special thank you to Helge “GG” Löfberg for endless support and encouragement in all my career from being a student in your

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tutor group to calling ever so often discussion research policies and life in general. Du är bäst!Thank you to my wonderful family and amazing friends for bearing with my complaints about everything not going smooth and in 120 km as I want it to. Thank you for all the support and love you have given me during this process especially, now on the finishing line. When I needed you most you were always there.Thank you “The Rolling Stones” for encouraging support, for telling me on and on that everything is possible that I can do whatever I want. Last but not least...All my love to my four kids. I love you to the moon and back! Ta ingen skit!

Financial support for this thesis was given by Region Skåne and Stiftelsen Lindhaga.

VEDENSKABEN

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Do you mean "Swanson, J. M., Nolan, W., & Pelham, W. (1981, August). The SNAP Rating Scale for the Diagnosis of the Attention Deficit Disorder. Paper presented at the meeting of the American Psychological Association, Los Angeles"?

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Please confirm.

portal.research.lu.se · web viewbackground. there is a need for easily administered objective...

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