BOLD fMRI detectable alterations of brain activity in children and adolescents on the autism spectrum

Abstract The dissertation consists of three peer-reviewed publications and is related to the basic research of autism spectrum disorder (ASD), especially the assessment of changes in brain function using functional magnetic resonance imaging (fMRI). The purpose was to discover possible differences in cued and spontaneous brain activity in autistic child and adolescent participants compared to typically developing controls. We used blood oxygen level dependent (BOLD) fMRI imaging of the brain, with which the participants were examined at rest and while looking at facial expressions. The resting state (RS) fMRI data artifacts were reduced, and brain networks were identified using independent component analysis. In addition, the RS was analyzed 1) over the entire measurement period using the regional homogeneity (ReHo) method, which measures local connectivity, and 2) based on the states of different brain networks grouped into shorter periods using the co-activation patterns (CAP) method. Statistically significant differences between groups were found in RS, more clearly with the CAP method. Also, significant differences in brain activity were found between the groups regarding the observation of facial expressions. The dissertation increases the understanding of changes in brain networks related to the autism spectrum, strengthening and supplementing previous research results. Based on our results, analyses of brain networks grouped into similar activation phases of shorter duration are worth further development. The new information can help develop earlier and more accurate imaging diagnostics, tentatively recognizing possible intervention target brain networks and evaluating therapeutic effects.Tiivistelmä Väitöskirja koostuu kolmesta vertaisarvioidusta julkaisusta ja liittyy autismikirjon kehityshäiriön perustutkimukseen, erityisesti aivotoiminnan muutosten arviointiin toiminnallisen magneettikuvauksen (functional MRI, fMRI) avulla. Tutkimuksen tarkoituksena oli selvittää stimuloidun ja spontaanin aivotoiminnan mahdollisia eroavaisuuksia lasten ja nuorten autismikirjossa neurotyypillisiin verrokkeihin nähden. Tutkimusmenetelmänä käytettiin veren happipitoisuudesta riippuvaista aivojen fMRI-kuvausta, jolla osallistujia tutkittiin levossa sekä heidän katsellessaan kasvojen ilmeitä. Itsenäisten komponenttien analyysilla (ICA) vähennettiin lepotilan fMRI-datan häiriöitä ja tunnistettiin aivoverkostoja. Lisäksi lepotilaa analysoitiin 1) koko mittausjakson ajalta signaalien alueellista homogeenisuutta ts. aivojen paikallista kytkennällisyyttä mittaavalla regional homogeneity (ReHo) -menetelmällä ja 2) eri aivoverkostojen tilojen perusteella lyhyemmiksi ajanjaksoiksi ns. yhtäaikaisten aktivaatioiden kuvioihin (co-activation patterns; CAP) ryhmiteltyinä. Näissä löydettiin tilastollisesti merkittäviä ryhmien välisiä eroja, selkeämmin CAP-menetelmällä. Myös kasvojen ilmeiden tarkkailuun liittyen havaittiin tilastollisesti merkittäviä aivotoiminnan eroja ryhmien välillä. Väitöskirja lisää ymmärrystä autismikirjoon liittyvistä aivoverkostojen muutoksista vahvistaen ja täydentäen aiempia tutkimustuloksia. Sen perusteella samankaltaisiin lyhempikestoisiin aktivaatiovaiheisiin ryhmiteltyjen aivoverkostojen analyyseja kannattaa kehittää. Uusi tieto voi auttaa varhaisemman ja tarkemman kuvantamisdiagnostiikan kehittämisessä, tarvittaessa oikeisiin aivoverkostoihin kohdennetuissa interventioissa ja niiden vaikutusten arvioinnissa ja seurannassa

Co-activation pattern alterations in autism spectrum disorder:a volume-wise hierarchical clustering fMRI study

Abstract Introduction: There has been a growing effort to characterize the time-varying functional connectivity of resting state (RS) fMRI brain networks (RSNs). Although voxel-wise connectivity studies have examined different sliding window lengths, nonsequential volume-wise approaches have been less common. Methods: Inspired by earlier co-activation pattern (CAP) studies, we applied hierarchical clustering (HC) to classify the image volumes of the RS-fMRI data on 28 adolescents with autism spectrum disorder (ASD) and their 27 typically developing (TD) controls. We compared the distribution of the ASD and TD groups‘ volumes in CAPs as well as their voxel-wise means. For simplification purposes, we conducted a group independent component analysis to extract 14 major RSNs. The RSNs' average z-scores enabled us to meaningfully regroup the RSNs and estimate the percentage of voxels within each RSN for which there was a significant group difference. These results were jointly interpreted to find global group-specific patterns. Results: We found similar brain state proportions in 58 CAPs (clustering interval from 2 to 30). However, in many CAPs, the voxel-wise means differed significantly within a matrix of 14 RSNs. The rest-activated default mode-positive and default mode-negative brain state properties vary considerably in both groups over time. This division was seen clearly when the volumes were partitioned into two CAPs and then further examined along the HC dendrogram of the diversifying brain CAPs. The ASD group network activations followed a more heterogeneous distribution and some networks maintained higher baselines; throughout the brain deactivation state, the ASD participants had reduced deactivation in 12/14 networks. During default mode-negative CAPs, the ASD group showed simultaneous visual network and either dorsal attention or default mode network overactivation. Conclusion: Nonsequential volume gathering into CAPs and the comparison of voxel-wise signal changes provide a complementary perspective to connectivity and an alternative to sliding window analysis