652 research outputs found
An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data
AbstractLarge amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface
Lead-DBS v3.0: Mapping Deep Brain Stimulation Effects to Local Anatomy and Global Networks.
Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics
Normative vs. patient-specific brain connectivity in deep brain stimulation
Brain connectivity profiles seeding from deep brain stimulation (DBS) electrodes have emerged as informative tools to estimate outcome variability across DBS patients. Given the limitations of acquiring and processing patient-specific diffusion-weighted imaging data, a number of studies have employed normative atlases of the human connectome. To date, it remains unclear whether patient-specific connectivity information would strengthen the accuracy of such analyses. Here, we compared similarities and differences between patient-specific, disease-matched and normative structural connectivity data and estimation of clinical improvement that they may generate. Data from 33 patients suffering from Parkinson's Disease who underwent surgery at three different centers were retrospectively collected. Stimulation-dependent connectivity profiles seeding from active contacts were estimated using three modalities, namely either patient-specific diffusion-MRI data, disease-matched or normative group connectome data (acquired in healthy young subjects). Based on these profiles, models of optimal connectivity were constructed and used to estimate the clinical improvement in out of sample data. All three modalities resulted in highly similar optimal connectivity profiles that could largely reproduce findings from prior research based on a novel multi-center cohort. In a data-driven approach that estimated optimal whole-brain connectivity profiles, out-of-sample predictions of clinical improvements were calculated. Using either patient-specific connectivity (R = 0.43 at p = 0.001), an age- and disease-matched group connectome (R = 0.25, p = 0.048) and a normative connectome based on healthy/young subjects (R = 0.31 at p = 0.028), significant predictions could be made and underlying optimal connectivity profiles were highly similar. Our results of patient-specific connectivity and normative connectomes lead to similar main conclusions about which brain areas are associated with clinical improvement. Still, although results were not significantly different, they hint at the fact that patient-specific connectivity may bear the potential of estimating slightly more variance when compared to group connectomes. Furthermore, use of normative connectomes involves datasets with high signal-to-noise acquired on specialized MRI hardware, while clinical datasets as the ones used here may not exactly match their quality. Our findings support the role of DBS electrode connectivity profiles as a promising method to investigate DBS effects and to potentially guide DBS programming
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Methods for improved mapping of brain lesion connectivity
Recent advances over the past two decades in neuroimaging methods have enabled us to map the connectivity of the brain. In parallel, pathophysiological models of brain disease have shifted from an emphasis on understanding pathology in specific brain regions to characterizing disruptions to interconnected neural networks. Nevertheless, these recent methods for mapping brain connectivity are still under development. Every step of the mapping process becomes a potential source for additional error due to noise or artifacts that could impact final analyses. Segmentation, parcellation, registration, and tractography are some of the steps where this occurs. Moreover, mapping the connectivity in a brain lesion is even more susceptible to errors in these steps. In this body of work, I describe multiple new methods for improving the accuracy of mapping lesion connectivity by reducing errors at the tractography stage which is the most error prone stage. First, we develop an approach for directly normalizing streamlines into a template space that avoids performing tractography in the normalized template space, reducing the error of connectomes constructed in the template space with respect to the ground truth native space connectome. Second, we develop a rapid approach for performing shortest path tractography and constructing shortest path probability weighted connectomes which increases the connection specificity relative to local streamline tracking approaches. We then demonstrate how our shortest path tractography approach can be used construct a disconnectome, a connectivity map of the proportion of connections lost due to intersecting a lesion. We then develop a fast, greedy graph-theoretic algorithm that extracts the maximally disconnected subgraph containing brain regions with the greatest shared loss of connectivity. Finally, we demonstrate how combining methods from diffusion based image inpainting and optimal estimation can be used to restore or inpaint corrupted fiber diffusion models in lesioned white matter tissue, enabling tractography and the study of lesion connectivity and modeling of microstructural measures in the patientâs native space
Comparison of patient-specific and normative connectivity profiles in deep brain stimulation
Objective: Brain connectivity profiles seeding from deep brain stimulation (DBS) electrodes have emerged as informative tools to estimate outcome variability across DBS patients. Given the limitations of acquiring and processing patient-specific diffusion-weighted imaging data, a number of studies have employed normative atlases of the human connectome. To date, it remains unclear whether patient-specific connectivity information would strengthen the accuracy of such analyses. Here, we compared similarities and differences between patient-specific, disease-matched and normative structural connectivity data and estimated the clinical improvement that they may generate.
Methods: Data from 33 patients suffering from Parkinsonâs disease who underwent surgery at three different centers were retrospectively collected. Stimulation-dependent connectivity profiles seeding from active contacts were estimated using three modalities, namely, either patient-specific diffusion-MRI data, disease-matched or normative group connectome data (acquired in healthy young subjects). Based on these profiles, models of optimal connectivity were constructed and used to estimate the clinical improvement in out-of-sample data.
Results: All three modalities resulted in highly similar optimal connectivity profiles that could largely reproduce findings from prior research based on a novel multicenter cohort. In a data-driven approach that estimated optimal whole-brain connectivity profiles, out-of-sample predictions of clinical improvements were calculated. Using either patient-specific connectivity (R = 0.43 at p = 0.001), an age- and disease-matched group connectome (R = 0.25, p = 0.048) or a normative connectome based on healthy/young subjects (R = 0.31 at p = 0.028), significant predictions could be made, and the underlying optimal connectivity profiles were highly similar.
Conclusion: Our results of patient-specific connectivity and normative connectomes lead to similar main conclusions about which brain areas are associated with clinical improvement. Nevertheless, although the results were not significantly different, they hint at the fact that patient-specific connectivity has potential for estimating slightly more variance when compared to group connectomes. Furthermore, the use of normative connectomes involves datasets with high signal-to-noise acquired on specialized MRI hardware, while clinical datasets such as the ones used here may not exactly match their quality. Our findings support the role of DBS electrode connectivity profiles as a promising method to investigate DBS effects and to potentially guide DBS programming.Zielsetzung: KonnektivitĂ€tsprofile des Gehirns, die von Elektroden zur Tiefenhirnstimulation (THS) ausgehen, haben sich als informativ fĂŒr die SchĂ€tzung von VariabilitĂ€t im Behandlungserfolg bei THS-PatientInnen erwiesen. Angesichts von EinschrĂ€nkungen bei der Erhebung und Verarbeitung patientenspezifischer, diffusionsgewichteter Bilddaten wurden in einer Reihe von Studien normative Atlanten des menschlichen KonnektivitĂ€tsprofils verwendet. Bis heute ist unklar, ob patientenspezifische KonnektivitĂ€tsinformation die Genauigkeit solcher Analysen verbessern wĂŒrde. Ziel dieser Studie war der Vergleich zwischen Ăhnlichkeiten und Unterschieden patientenspezifischer, krankheits-gematchter und normativer, struktureller KonnektivitĂ€tsdaten, sowie der FĂ€higkeit dieser Methoden zur Vorhersage eines etwaigen klinischen Behandlungserfolges.
Methoden: Die Analysen basierten auf retrospektiven Daten von 33 Parkinson-PatientInnen, welche an drei verschiedenen Zentren operiert worden waren. StimulationsabhÀngige KonnektivitÀtsprofile mit Ursprung in aktiven DBS-Kontakten wurden mittels der drei ModalitÀten geschÀtzt, also entweder basierend auf patientenspezifischen, diffusionsgewichteten MRT-Daten, oder auf krankheits-gematchten sowie auf normativen GruppenkonnektivitÀtsdaten (erhoben an gesunden, jungen ProbandInnen). Auf Grundlage dieser Profile wurden Modelle optimaler KonnektivitÀt konstruiert und zur SchÀtzung des klinischen Behandlungserfolgs in unabhÀngigen Daten herangezogen.
Ergebnisse: Alle drei ModalitĂ€ten fĂŒhrten zu sehr Ă€hnlichen optimalen KonnektivitĂ€tsprofilen, mit Hilfe derer sich auf Grundlage einer neuartigen multizentrischen Kohorte vorherige Forschungsbefunde weitgehend reproduzieren lieĂen. In einem datengesteuerten Ansatz, bei dem optimale KonnektivitĂ€tsprofile ĂŒber das gesamte Gehirn hinweg geschĂ€tzt wurden, wurden Vorhersagen ĂŒber den klinischen Behandlungserfolg in unabhĂ€ngigen Daten berechnet. Unter Verwendung entweder der patientenspezifischen KonnektivitĂ€t (R = 0,43 bei p = 0,001), eines alters- und krankheits-gematchten GruppenkonnektivitĂ€tsprofils (R = 0,25, p = 0,048) oder eines normativen KonnektivitĂ€tsprofils basierend auf Daten gesunder/junger ProbandInnen (R = 0,31 bei p = 0,028) konnten signifikante Vorhersagen getroffen werden, wobei die zugrunde liegenden optimalen KonnektivitĂ€tsprofile groĂe Ăhnlichkeit aufwiesen.
Schlussfolgerung: Unsere Ergebnisse, welche patientenspezifische sowie normative KonnektivitĂ€tsprofile einbeziehen, fĂŒhren zu Ă€hnlichen Hauptschlussfolgerungen darĂŒber, welche Hirnareale mit klinischem Behandlungserfolg assoziiert sind. Obwohl sich die Ergebnisse nicht signifikant unterschieden, deuten sie dennoch darauf hin, dass patientenspezifische KonnektivitĂ€t ĂŒber Potenzial zur SchĂ€tzung geringfĂŒgig höherer Varianz im Vergleich zu gruppenbasierten KonnektivitĂ€sprofilen verfĂŒgt. DarĂŒber hinaus stĂŒtzen sich Analysen, welche auf normativen KonnektivitĂ€tsprofile basieren, auf DatensĂ€tze mit hohem Signal-Rausch-VerhĂ€ltnis, welche durch spezialisierte MRT-Technologie erfasst wurden, wĂ€hrend klinische DatensĂ€tze, wie sie auch in dieser Studie herangezogen wurden, diesen an QualitĂ€t möglicherweise nicht gleichkommen. Unsere Befunde stĂŒtzen die Rolle von KonnektivitĂ€tsprofilen, welche von THS-Elektroden ausgehen, als eine vielversprechende Methode zur Untersuchung von THS-Effekten und möglicherweise zur Verbesserung der THS-Programmierung
Critical scaling of whole-brain resting-state dynamics
The online version contains supplementary material available at https://doi.org/10.1038/s42003-023-05001-y.Scale invariance is a characteristic of neural activity. How this property emerges from neural interactions remains a fundamental question. Here, we studied the relation between scale-invariant brain dynamics and structural connectivity by analyzing human resting-state (rs-) fMRI signals, together with diffusion MRI (dMRI) connectivity and its approximation as an exponentially decaying function of the distance between brain regions. We analyzed the rs-fMRI dynamics using functional connectivity and a recently proposed phenomenological renormalization group (PRG) method that tracks the change of collective activity after successive coarse-graining at different scales. We found that brain dynamics display power-law correlations and power-law scaling as a function of PRG coarse-graining based on functional or structural connectivity. Moreover, we modeled the brain activity using a network of spins interacting through large-scale connectivity and presenting a phase transition between ordered and disordered phases. Within this simple model, we found that the observed scaling features were likely to emerge from critical dynamics and connections exponentially decaying with distance. In conclusion, our study tests the PRG method using large-scale brain activity and theoretical models and suggests that scaling of rs-fMRI activity relates to criticality.A.P.-A. was supported by a RamĂłn y Cajal fellowship (RYC2020-029117-I) from FSE/Agencia Estatal de InvestigaciĂłn (AEI), Spanish Ministry of Science and Innovation. A.P.-A. and G.D. were supported by the EU Fet Flagship Human Brain Project SGA3 (945539). G.D. was supported by the Spanish Research Project AWAKENING (PID2019-105772GB-I00/AEI/10.13039/501100011033), financed by the Spanish Ministry of Science, Innovation and Universities (MCIU), State Research Agency (AEI). M.L.K. is supported by the Centre for Eudaimonia and Human Flourishing (funded by the Pettit and Carlsberg Foundations) and Center for Music in the Brain (funded by the Danish National Research Foundation, DNRF117).Peer ReviewedPostprint (published version
Dealing with heterogeneity in the prediction of clinical diagnosis
Le diagnostic assisté par ordinateur est un domaine de recherche en émergence et se situe
Ă lâintersection de lâimagerie mĂ©dicale et de lâapprentissage machine. Les donnĂ©es mĂ©di-
cales sont de nature trĂšs hĂ©tĂ©rogĂšne et nĂ©cessitent une attention particuliĂšre lorsque lâon
veut entraĂźner des modĂšles de prĂ©diction. Dans cette thĂšse, jâai explorĂ© deux sources
dâhĂ©tĂ©rogĂ©nĂ©itĂ©, soit lâagrĂ©gation multisites et lâhĂ©tĂ©rogĂ©nĂ©itĂ© des Ă©tiquettes cliniques
dans le contexte de lâimagerie par rĂ©sonance magnĂ©tique (IRM) pour le diagnostic de la
maladie dâAlzheimer (MA). La premiĂšre partie de ce travail consiste en une introduction
gĂ©nĂ©rale sur la MA, lâIRM et les dĂ©fis de lâapprentissage machine en imagerie mĂ©dicale.
Dans la deuxiÚme partie de ce travail, je présente les trois articles composant la thÚse.
Enfin, la troisiĂšme partie porte sur une discussion des contributions et perspectives fu-
tures de ce travail de recherche. Le premier article de cette thĂšse montre que lâagrĂ©gation
des donnĂ©es sur plusieurs sites dâacquisition entraĂźne une certaine perte, comparative-
ment Ă lâanalyse sur un seul site, qui tend Ă diminuer plus la taille de lâĂ©chantillon aug-
mente. Le deuxiÚme article de cette thÚse examine la généralisabilité des modÚles de
prĂ©diction Ă lâaide de divers schĂ©mas de validation croisĂ©e. Les rĂ©sultats montrent que
la formation et les essais sur le mĂȘme ensemble de sites surestiment la prĂ©cision du
modĂšle, comparativement aux essais sur des nouveaux sites. Jâai Ă©galement montrĂ© que
lâentraĂźnement sur un grand nombre de sites amĂ©liore la prĂ©cision sur des nouveaux sites.
Le troisiĂšme et dernier article porte sur lâhĂ©tĂ©rogĂ©nĂ©itĂ© des Ă©tiquettes cliniques et pro-
pose un nouveau cadre dans lequel il est possible dâidentifier un sous-groupe dâindividus
qui partagent une signature homogÚne hautement prédictive de la démence liée à la MA.
Cette signature se retrouve également chez les patients présentant des symptÎmes mod-
érés. Les résultats montrent que 90% des sujets portant la signature ont progressé vers
la démence en trois ans. Les travaux de cette thÚse apportent ainsi de nouvelles con-
tributions Ă la maniĂšre dont nous approchons lâhĂ©tĂ©rogĂ©nĂ©itĂ© en diagnostic mĂ©dical et
proposent des pistes de solution pour tirer profit de cette hétérogénéité.Computer assisted diagnosis has emerged as a popular area of research at the intersection
of medical imaging and machine learning. Medical data are very heterogeneous in nature
and therefore require careful attention when one wants to train prediction models. In
this thesis, I explored two sources of heterogeneity, multisite aggregation and clinical
label heterogeneity, in an application of magnetic resonance imaging to the diagnosis
of Alzheimerâs disease. In the process, I learned about the feasibility of multisite data
aggregation and how to leverage that heterogeneity in order to improve generalizability
of prediction models. Part one of the document is a general context introduction to
Alzheimerâs disease, magnetic resonance imaging, and machine learning challenges in
medical imaging. In part two, I present my research through three articles (two published
and one in preparation). Finally, part three provides a discussion of my contributions
and hints to possible future developments. The first article shows that data aggregation
across multiple acquisition sites incurs some loss, compared to single site analysis, that
tends to diminish as the sample size increase. These results were obtained through semisynthetic
Monte-Carlo simulations based on real data. The second article investigates the
generalizability of prediction models with various cross-validation schemes. I showed
that training and testing on the same batch of sites over-estimates the accuracy of the
model, compared to testing on unseen sites. However, I also showed that training on a
large number of sites improves the accuracy on unseen sites. The third article, on clinical
label heterogeneity, proposes a new framework where we can identify a subgroup of
individuals that share a homogeneous signature highly predictive of AD dementia. That
signature could also be found in patients with mild symptoms, 90% of whom progressed
to dementia within three years. The thesis thus makes new contributions to dealing
with heterogeneity in medical diagnostic applications and proposes ways to leverage
that heterogeneity to our benefit
The impact of regional heterogeneity in whole-brain dynamics in the presence of oscillations
Large variability exists across brain regions in health and disease, considering their cellular and molecular composition, connectivity and function. Large-scale whole-brain models comprising coupled brain regions provide insights into the underlying dynamics that shape complex patterns of spontaneous brain activity. In particular, biophysically grounded mean-field whole-brain models in the asynchronous regime were used to demonstrate the dynamical consequences of including regional variability. Nevertheless, the role of heterogeneities when brain dynamics are supporting by synchronous oscillating state, which is a ubiquitous phenomenon in brain, remains poorly understood. Here, we implemented two models capable of presenting oscillatory behaviour with different levels of abstraction: a phenomenological Stuart Landau model and an exact mean-field model. The fit of these models informed by structural-to-functionalâweighted MRI signal (T1w/T2w) allowed to explore the implication of the inclusion of heterogeneities for modelling resting-state fMRI recordings from healthy participants. We found that disease-specific regional functional heterogeneity imposed dynamical consequences within the oscillatory regime in fMRI recordings from neurodegeneration with specific impacts in brain atrophy/structure (Alzheimer patients). Overall, we found that models with oscillations perform better when structural and functional regional heterogeneities are considered showing that phenomenological and biophysical models behave similarly at the brink of the Hopf bifurcation.Fil: Sanz Perl Hernandez, Yonatan. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; Argentina. Universidad de Buenos Aires; Argentina. Universidad de San AndrĂ©s; Argentina. Universitat Pompeu Fabra; EspañaFil: Zamora Lopez, Gorka. Universitat Pompeu Fabra; EspañaFil: MontbriĂł, Ernest. Universitat Pompeu Fabra; EspañaFil: Monge Asensio, MartĂ. Universitat Pompeu Fabra; EspañaFil: Vohryzek, Jakub. Universitat Pompeu Fabra; España. University of Oxford; Reino UnidoFil: Fittipaldi, MarĂa Sol. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; Argentina. Universidad de San AndrĂ©s; Argentina. University of California; Estados Unidos. Trinity College; IrlandaFil: Gonzalez Campo, Cecilia. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; Argentina. Universidad de San AndrĂ©s; ArgentinaFil: Moguilner, Sebastian Gabriel. University of California; Estados Unidos. Trinity College; Irlanda. Universidad Adolfo Ibañez; ChileFil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; Argentina. Universidad de San AndrĂ©s; Argentina. University of California; Estados Unidos. Trinity College; Irlanda. Universidad Adolfo Ibañez; ChileFil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; Argentina. Universidad de Buenos Aires; Argentina. Universidad de San AndrĂ©s; Argentina. Universidad Adolfo Ibañez; ChileFil: Yeo, B. T. Thomas. National University of Singapore; SingapurFil: Kringelbach, Morten L.. University of Oxford; Reino Unido. University Aarhus; Dinamarca. Universidade do Minho; PortugalFil: Deco, Gustavo. Universitat Pompeu Fabra; España. Max Planck Institute for Human Cognitive and Brain Sciences; Alemania. Monash University; Australi
Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data
The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h2 = 0.53â0.90, p < 10â 5), and were significantly correlated with the joint-analytical estimates from the ENIGMA cohort on the tract and voxel-wise levels. The similarity in regional heritability suggests that the additive genetic contribution to white matter microstructure is consistent across populations and imaging acquisition parameters. It also suggests that the overarching genetic influence provides an opportunity to define a common genetic search space for future gene-discovery studies. Uniquely, the measurements of additive genetic contribution performed in this study can be repeated using online genetic analysis tools provided by the HCP ConnectomeDB web application
The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism
Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)âa grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7â64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies
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