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  • Neuroinformatics
  • 2017-2021
  • Publications
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Kurt G. Schilling; Chantal M. W. Tax; Francois Rheault; Bennett A. Landman; +3 Authors

    AbstractCharacterizing and understanding the limitations of diffusion MRI fiber tractography is a prerequisite for methodological advances and innovations which will allow these techniques to accurately map the connections of the human brain. The so‐called “crossing fiber problem” has received tremendous attention and has continuously triggered the community to develop novel approaches for disentangling distinctly oriented fiber populations. Perhaps an even greater challenge occurs when multiple white matter bundles converge within a single voxel, or throughout a single brain region, and share the same parallel orientation, before diverging and continuing towards their final cortical or sub‐cortical terminations. These so‐called “bottleneck” regions contribute to the ill‐posed nature of the tractography process, and lead to both false positive and false negative estimated connections. Yet, as opposed to the extent of crossing fibers, a thorough characterization of bottleneck regions has not been performed. The aim of this study is to quantify the prevalence of bottleneck regions. To do this, we use diffusion tractography to segment known white matter bundles of the brain, and assign each bundle to voxels they pass through and to specific orientations within those voxels (i.e. fixels). We demonstrate that bottlenecks occur in greater than 50‐70% of fixels in the white matter of the human brain. We find that all projection, association, and commissural fibers contribute to, and are affected by, this phenomenon, and show that even regions traditionally considered “single fiber voxels” often contain multiple fiber populations. Together, this study shows that a majority of white matter presents bottlenecks for tractography which may lead to incorrect or erroneous estimates of brain connectivity or quantitative tractography (i.e., tractometry), and underscores the need for a paradigm shift in the process of tractography and bundle segmentation for studying the fiber pathways of the human brain.

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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Human Brain Mapping
    Article
    License: CC BY
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    Human Brain Mapping
    Article . 2021 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Human Brain Mappingarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Human Brain Mapping
      Article
      License: CC BY
      Data sources: UnpayWall
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Human Brain Mapping
      Article . 2021 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Denis A. Engemann; Apolline Mellot; Richard Höchenberger; Hubert Banville; +4 Authors

    AbstractPopulation-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.Highlights- We provide systematic reusable benchmarks for brain age from M/EEG signals- The benchmarks were carried out on M/EEG from four countries > 2500 recordings- We compared machine learning pipelines capable of handling the non-linear regression task of relating biomedical outcomes to M/EEG dynamics, based on classical machine learning and deep learning- Next to data-driven methods we benchmarked template-based source localization as a practical tool for generating features less affected by electromagnetic field spread- The benchmarks are built on top of the MNE ecosystem and the braindecode package and can be applied on any M/EEG dataset presented in the BIDS format

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    MPG.PuRe
    Article . 2022
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    https://www.biorxiv.org/conten...
    Preprint
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    NeuroImage
    Article . 2022 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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    Article . 2022
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    NeuroImage
    Article . 2021
    DOAJ
    Article . 2022
    Data sources: DOAJ
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ MPG.PuRearrow_drop_down
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      NeuroImage
      Article . 2022 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      NeuroImage
      Article . 2021
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Théodon, Léo; Eremina, Tatyana; Dia, Kassem; Lamadie, Fabrice; +2 Authors

    International audience; This paper presents a new method for estimating the parameters of a stochastic geometric model for multiphase flow image processing using local measures. Local measures differ from global measures in that they are only based on a small part of a binary image and consequently provide different information of certain properties such as area and perimeter. Since local measures have been shown to be helpful in estimating the typical grain elongation ratio of a homogeneous Boolean model, the objective of this study was to use these local measures to statistically infer the parameters of a more complex non-Boolean model from a sample of observations. An optimization algorithm is used to minimize a cost function based on the likelihood of a probability density of local measurements. The performance of the model is analysed using numerical experiments and real observations. The errors relative to real images of most of the properties of the model-generated images are less than 2%. The covariance and particle size distribution are also calculated and compared.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Image Analysis and S...arrow_drop_down
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    Image Analysis and Stereology
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Image Analysis and Stereology
    Article . 2021 . Peer-reviewed
    License: CC BY NC
    Data sources: Crossref
    DOAJ
    Article . 2021
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    Other literature type . 2021
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Image Analysis and S...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Image Analysis and Stereology
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      Image Analysis and Stereology
      Article . 2021 . Peer-reviewed
      License: CC BY NC
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      DOAJ
      Article . 2021
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      Other literature type . 2021
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: GALLET, Quentin; BOUTELOUP, Vincent; LOCATELLI, Maxime; HABERT, Marie Odile; +20 Authors

    International audience; Recent evidence suggests an association between benzodiazepines (BZDs) use and lower brain amyloid load, a hallmark of AD pathophysiology. Other AD-related markers include hippocampal atrophy, but the effect of BZDs on hippocampal volume remains unclear. We aimed at 1) replicating findings on BZDs use and brain amyloid load and 2) investigating associations between BZDs use and hippocampal volume, in the MEMENTO clinical cohort of nondemented older adults with isolated memory complaint or light cognitive impairment at baseline. Total Standardized Uptake Value Ratio (SUVR) of brain amyloid load and hippocampal volume (HV) were obtained, respectively, from F-18 Florbetapir positron emission tomography (PET) and magnetic resonance imaging (MRI), and compared between BZD chronic users and nonusers using multiple linear regressions adjusted for age, sex, educational level, ApoE epsilon 4 genotype, cognitive and neuropsychiatric assessments, history of major depressive episodes and antidepressant intake. BZD users were more likely to manifest symptoms of depression, anxiety and apathy. In the MRI subgroup, BZD users were also more frequently females with low education and greater clinical impairments as assessed with the clinical dementia rating scale. Short- versus long-acting BZDs, Z-drugs versus non-Z-drugs BZDs, as well as dose and duration of BZD use, were also considered in the analyses. Total SUVR and HV were significantly lower and larger, respectively, in BZD users (n = 38 in the PET subgroup and n = 331 in the MRI subgroup) than in nonusers (n = 251 in the PET subgroup and n = 1840 in the MRI subgroup), with a medium (Cohen's d = -0.43) and low (Cohen's d = 0.10) effect size, respectively. Short-acting BZDs and Z-drugs were more significantly associated with larger HV. We found no effect of dose and duration of BZD use. Our results support the involvement of the GABAergic system as a potential target for blocking AD-related pathophysiology, possibly via reduction in neuronal activity and neuroinflammation. Future longitudinal studies may confirm the causal effect of BZDs to block amyloid accumulation and hippocampal atrophy.

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    Europe PubMed Central
    Other literature type . 2021
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    Oskar Bordeaux
    Article . 2021
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    Neuropsychopharmacology
    Article . 2021 . Peer-reviewed
    License: Springer TDM
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    Hal-Diderot
    Article . 2021
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Foucault, Cédric; Meyniel, Florent;

    International audience; From decision making to perception to language, predicting what is coming next is crucial. It is also challenging in stochastic, changing, and structured environments; yet the brain makes accurate predictions in many situations. What computational architecture could enable this feat? Bayesian inference makes optimal predictions but is prohibitively difficult to compute. Here, we show that a specific recurrent neural network architecture enables simple and accurate solutions in several environments. This architecture relies on three mechanisms: gating, lateral connections, and recurrent weight training. Like the optimal solution and the human brain, such networks develop internal representations of their changing environment (including estimates of the environment's latent variables and the precision of these estimates), leverage multiple levels of latent structure, and adapt their effective learning rate to changes without changing their connection weights. Being ubiquitous in the brain, gated recurrence could therefore serve as a generic building block to predict in real-life environments. Editor's evaluation There has been a longstanding interest in developing normative models of how humans handle latent information in stochastic and volatile environments. This study examines recurrent neural network models trained on sequence-prediction tasks analogous to those used in human cognitive studies. The results demonstrate that such models lead to highly accurate predictions for challenging sequences in which the statistics are non-stationary and change at random times. These novel and remarkable results open up new avenues for cognitive modelling.

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    eLife
    Other literature type . Article . 2021 . Peer-reviewed
    License: CC BY
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    DOAJ-Articles
    Article . 2021
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      eLife
      Other literature type . Article . 2021 . Peer-reviewed
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    Authors: David Hassanein Berro; Jean-Michel Lemée; Louis-Marie Leiber; Evelyne Emery; +2 Authors

    Abstract Background Pre-surgical mapping of language using functional MRI aimed principally to determine the dominant hemisphere. This mapping is currently performed using covert linguistic task in way to avoid motion artefacts potentially biasing the results. However, overt task is closer to natural speaking, allows a control on the performance of the task, and may be easier to perform for stressed patients and children. However, overt task, by activating phonological areas on both hemispheres and areas involved in pitch prosody control in the non-dominant hemisphere, is expected to modify the determination of the dominant hemisphere by the calculation of the lateralization index (LI). Objective Here, we analyzed the modifications in the LI and the interactions between cognitive networks during covert and overt speech task. Methods Thirty-three volunteers participated in this study, all but four were right-handed. They performed three functional sessions consisting of (1) covert and (2) overt generation of a short sentence semantically linked with an audibly presented word, from which we estimated the “Covert” and “Overt” contrasts, and a (3) resting-state session. The resting-state session was submitted to spatial independent component analysis to identify language network at rest (LANG), cingulo-opercular network (CO), and ventral attention network (VAN). The LI was calculated using the bootstrapping method. Results The LI of the LANG was the most left-lateralized (0.66 ± 0.38). The LI shifted from a moderate leftward lateralization for the Covert contrast (0.32 ± 0.38) to a right lateralization for the Overt contrast (− 0.13 ± 0.30). The LI significantly differed from each other. This rightward shift was due to the recruitment of right hemispheric temporal areas together with the nodes of the CO. Conclusion Analyzing the overt speech by fMRI allowed improvement in the physiological knowledge regarding the coordinated activity of the intrinsic connectivity networks. However, the rightward shift of the LI in this condition did not provide the basic information on the hemispheric language dominance. Overt linguistic task cannot be recommended for clinical purpose when determining hemispheric dominance for language.

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    BMC Neuroscience
    Article . 2021 . Peer-reviewed
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    BMC Neuroscience
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      BMC Neuroscience
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Samuel Planton; Stanislas Dehaene;

    Abstract The ability to detect the abstract pattern underlying a temporal sequence of events is crucial to many human activities, including language and mathematics, but its cortical correlates remain poorly understood. It is also unclear whether repeated exposure to the same sequence of sensory stimuli is sufficient to induce the encoding of an abstract amodal representation of the pattern. Using functional MRI, we probed the existence of such abstract codes for sequential patterns, their localization in the human brain, and their relation to existing language and math-responsive networks. We used a passive sequence violation paradigm, in which a given sequence is repeatedly presented before rare deviant sequences are introduced. We presented two binary patterns, AABB and ABAB, in four presentation formats, either visual or auditory, and either cued by the identity of the stimuli or by their spatial location. Regardless of the presentation format, a habituation to the repeated pattern and a response to pattern violations were seen in a set of inferior frontal, intraparietal and temporal areas. Within language areas, such pattern-violation responses were only found in the inferior frontal gyrus (IFG), whereas all math-responsive regions responded to pattern changes. Most of these regions also responded whenever the modality or the cue changed, suggesting a general sensitivity to violation detection. Thus, the representation of sequence patterns appears to be distributed, yet to include a core set of abstract amodal regions, particularly the IFG.

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    Cortex
    Article . 2021 . Peer-reviewed
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      Cortex
      Article . 2021 . Peer-reviewed
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    Authors: Mignacco, Francesca; Krzakala, Florent; Urbani, Pierfrancesco; Zdeborová, Lenka;

    We analyze in a closed form the learning dynamics of stochastic gradient descent (SGD) for a single-layer neural network classifying a high-dimensional Gaussian mixture where each cluster is assigned one of two labels. This problem provides a prototype of a non-convex loss landscape with interpolating regimes and a large generalization gap. We define a particular stochastic process for which SGD can be extended to a continuous-time limit that we call stochastic gradient flow. In the full-batch limit, we recover the standard gradient flow. We apply dynamical mean-field theory from statistical physics to track the dynamics of the algorithm in the high-dimensional limit via a self-consistent stochastic process. We explore the performance of the algorithm as a function of the control parameters shedding light on how it navigates the loss landscape. Comment: 8 pages + appendix, 4 figures

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    https://doi.org/10.48550/arxiv...
    Article . 2020
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      https://doi.org/10.48550/arxiv...
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    Authors: Prany Wantzen; Patrice Clochon; Franck Doidy; Fabrice Wallois; +8 Authors

    AbstractBackgroundAutism spectrum disorder (ASD) is associated with atypical neural activity in resting state. Most of the studies have focused on abnormalities in alpha frequency as a marker of ASD dysfunctions. However, few have explored alpha synchronization within a specific interest in resting-state networks, namely the default mode network (DMN), the sensorimotor network (SMN), and the dorsal attention network (DAN). These functional connectivity analyses provide relevant insight into the neurophysiological correlates of multimodal integration in ASD.MethodsUsing high temporal resolution EEG, the present study investigates the functional connectivity in the alpha band within and between the DMN, SMN, and the DAN. We examined eyes-closed EEG alpha lagged phase synchronization, using standardized low-resolution brain electromagnetic tomography (sLORETA) in 29 participants with ASD and 38 developing (TD) controls (age, sex, and IQ matched).ResultsWe observed reduced functional connectivity in the ASD group relative to TD controls, within and between the DMN, the SMN, and the DAN. We identified three hubs of dysconnectivity in ASD: the posterior cingulate cortex, the precuneus, and the medial frontal gyrus. These three regions also presented decreased current source density in the alpha band.ConclusionThese results shed light on possible multimodal integration impairments affecting the communication between bottom-up and top-down information. The observed hypoconnectivity between the DMN, SMN, and DAN could also be related to difficulties in switching between externally oriented attention and internally oriented thoughts.

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    Journal of Neurodevelopmental Disorders
    Article . 2022 . Peer-reviewed
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    Journal of Neurodevelopmental Disorders
    Article . 2022 . Peer-reviewed
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    https://doi.org/10.21203/rs.3....
    Preprint . 2021
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      Journal of Neurodevelopmental Disorders
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      Journal of Neurodevelopmental Disorders
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      https://doi.org/10.21203/rs.3....
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    Authors: Nicolas Traut; Katja Heuer; Guillaume Lemaitre; Anita Beggiato; +19 Authors

    AbstractMRI has been extensively used to identify anatomical and functional differences in Autism Spectrum Disorder (ASD). Yet, many of these findings have proven difficult to replicate because studies rely on small cohorts and are built on many complex, undisclosed, analytic choices. We conducted an international challenge to predict ASD diagnosis from MRI data, where we provided preprocessed anatomical and functional MRI data from > 2,000 individuals. Evaluation of the predictions was rigorously blinded. 146 challengers submitted prediction algorithms, which were evaluated at the end of the challenge using unseen data and an additional acquisition site. On the best algorithms, we studied the importance of MRI modalities, brain regions, and sample size. We found evidence that MRI could predict ASD diagnosis: the 10 best algorithms reliably predicted diagnosis with AUC∼0.80 – far superior to what can be currently obtained using genotyping data in cohorts 20-times larger. We observed that functional MRI was more important for prediction than anatomical MRI, and that increasing sample size steadily increased prediction accuracy, providing an efficient strategy to improve biomarkers. We also observed that despite a strong incentive to generalise to unseen data, model development on a given dataset faces the risk of overfitting: performing well in cross-validation on the data at hand, but not generalising. Finally, we were able to predict ASD diagnosis on an external sample added after the end of the challenge (EU-AIMS), although with a lower prediction accuracy (AUC=0.72). This indicates that despite being based on a large multisite cohort, our challenge still produced biomarkers fragile in the face of dataset shifts.

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    Article . 2022 . Peer-reviewed
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      NeuroImage
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Kurt G. Schilling; Chantal M. W. Tax; Francois Rheault; Bennett A. Landman; +3 Authors

    AbstractCharacterizing and understanding the limitations of diffusion MRI fiber tractography is a prerequisite for methodological advances and innovations which will allow these techniques to accurately map the connections of the human brain. The so‐called “crossing fiber problem” has received tremendous attention and has continuously triggered the community to develop novel approaches for disentangling distinctly oriented fiber populations. Perhaps an even greater challenge occurs when multiple white matter bundles converge within a single voxel, or throughout a single brain region, and share the same parallel orientation, before diverging and continuing towards their final cortical or sub‐cortical terminations. These so‐called “bottleneck” regions contribute to the ill‐posed nature of the tractography process, and lead to both false positive and false negative estimated connections. Yet, as opposed to the extent of crossing fibers, a thorough characterization of bottleneck regions has not been performed. The aim of this study is to quantify the prevalence of bottleneck regions. To do this, we use diffusion tractography to segment known white matter bundles of the brain, and assign each bundle to voxels they pass through and to specific orientations within those voxels (i.e. fixels). We demonstrate that bottlenecks occur in greater than 50‐70% of fixels in the white matter of the human brain. We find that all projection, association, and commissural fibers contribute to, and are affected by, this phenomenon, and show that even regions traditionally considered “single fiber voxels” often contain multiple fiber populations. Together, this study shows that a majority of white matter presents bottlenecks for tractography which may lead to incorrect or erroneous estimates of brain connectivity or quantitative tractography (i.e., tractometry), and underscores the need for a paradigm shift in the process of tractography and bundle segmentation for studying the fiber pathways of the human brain.

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    Human Brain Mapping
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    Human Brain Mapping
    Article . 2021 . Peer-reviewed
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      Human Brain Mapping
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      Human Brain Mapping
      Article . 2021 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Denis A. Engemann; Apolline Mellot; Richard Höchenberger; Hubert Banville; +4 Authors

    AbstractPopulation-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.Highlights- We provide systematic reusable benchmarks for brain age from M/EEG signals- The benchmarks were carried out on M/EEG from four countries > 2500 recordings- We compared machine learning pipelines capable of handling the non-linear regression task of relating biomedical outcomes to M/EEG dynamics, based on classical machine learning and deep learning- Next to data-driven methods we benchmarked template-based source localization as a practical tool for generating features less affected by electromagnetic field spread- The benchmarks are built on top of the MNE ecosystem and the braindecode package and can be applied on any M/EEG dataset presented in the BIDS format

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    https://www.biorxiv.org/conten...
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    NeuroImage
    Article . 2022 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Théodon, Léo; Eremina, Tatyana; Dia, Kassem; Lamadie, Fabrice; +2 Authors

    International audience; This paper presents a new method for estimating the parameters of a stochastic geometric model for multiphase flow image processing using local measures. Local measures differ from global measures in that they are only based on a small part of a binary image and consequently provide different information of certain properties such as area and perimeter. Since local measures have been shown to be helpful in estimating the typical grain elongation ratio of a homogeneous Boolean model, the objective of this study was to use these local measures to statistically infer the parameters of a more complex non-Boolean model from a sample of observations. An optimization algorithm is used to minimize a cost function based on the likelihood of a probability density of local measurements. The performance of the model is analysed using numerical experiments and real observations. The errors relative to real images of most of the properties of the model-generated images are less than 2%. The covariance and particle size distribution are also calculated and compared.

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    Authors: GALLET, Quentin; BOUTELOUP, Vincent; LOCATELLI, Maxime; HABERT, Marie Odile; +20 Authors

    International audience; Recent evidence suggests an association between benzodiazepines (BZDs) use and lower brain amyloid load, a hallmark of AD pathophysiology. Other AD-related markers include hippocampal atrophy, but the effect of BZDs on hippocampal volume remains unclear. We aimed at 1) replicating findings on BZDs use and brain amyloid load and 2) investigating associations between BZDs use and hippocampal volume, in the MEMENTO clinical cohort of nondemented older adults with isolated memory complaint or light cognitive impairment at baseline. Total Standardized Uptake Value Ratio (SUVR) of brain amyloid load and hippocampal volume (HV) were obtained, respectively, from F-18 Florbetapir positron emission tomography (PET) and magnetic resonance imaging (MRI), and compared between BZD chronic users and nonusers using multiple linear regressions adjusted for age, sex, educational level, ApoE epsilon 4 genotype, cognitive and neuropsychiatric assessments, history of major depressive episodes and antidepressant intake. BZD users were more likely to manifest symptoms of depression, anxiety and apathy. In the MRI subgroup, BZD users were also more frequently females with low education and greater clinical impairments as assessed with the clinical dementia rating scale. Short- versus long-acting BZDs, Z-drugs versus non-Z-drugs BZDs, as well as dose and duration of BZD use, were also considered in the analyses. Total SUVR and HV were significantly lower and larger, respectively, in BZD users (n = 38 in the PET subgroup and n = 331 in the MRI subgroup) than in nonusers (n = 251 in the PET subgroup and n = 1840 in the MRI subgroup), with a medium (Cohen's d = -0.43) and low (Cohen's d = 0.10) effect size, respectively. Short-acting BZDs and Z-drugs were more significantly associated with larger HV. We found no effect of dose and duration of BZD use. Our results support the involvement of the GABAergic system as a potential target for blocking AD-related pathophysiology, possibly via reduction in neuronal activity and neuroinflammation. Future longitudinal studies may confirm the causal effect of BZDs to block amyloid accumulation and hippocampal atrophy.

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    Europe PubMed Central
    Other literature type . 2021
    Data sources: PubMed Central
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    Oskar Bordeaux
    Article . 2021
    Data sources: Oskar Bordeaux
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    Neuropsychopharmacology
    Article . 2021 . Peer-reviewed
    License: Springer TDM
    Data sources: Crossref
    Hal-Diderot
    Article . 2021
    Data sources: Hal-Diderot
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    Authors: Foucault, Cédric; Meyniel, Florent;

    International audience; From decision making to perception to language, predicting what is coming next is crucial. It is also challenging in stochastic, changing, and structured environments; yet the brain makes accurate predictions in many situations. What computational architecture could enable this feat? Bayesian inference makes optimal predictions but is prohibitively difficult to compute. Here, we show that a specific recurrent neural network architecture enables simple and accurate solutions in several environments. This architecture relies on three mechanisms: gating, lateral connections, and recurrent weight training. Like the optimal solution and the human brain, such networks develop internal representations of their changing environment (including estimates of the environment's latent variables and the precision of these estimates), leverage multiple levels of latent structure, and adapt their effective learning rate to changes without changing their connection weights. Being ubiquitous in the brain, gated recurrence could therefore serve as a generic building block to predict in real-life environments. Editor's evaluation There has been a longstanding interest in developing normative models of how humans handle latent information in stochastic and volatile environments. This study examines recurrent neural network models trained on sequence-prediction tasks analogous to those used in human cognitive studies. The results demonstrate that such models lead to highly accurate predictions for challenging sequences in which the statistics are non-stationary and change at random times. These novel and remarkable results open up new avenues for cognitive modelling.

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    eLife
    Other literature type . Article . 2021 . Peer-reviewed
    License: CC BY
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    DOAJ-Articles
    Article . 2021
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    Article . 2021
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      eLife
      Other literature type . Article . 2021 . Peer-reviewed
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      DOAJ-Articles
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    Authors: David Hassanein Berro; Jean-Michel Lemée; Louis-Marie Leiber; Evelyne Emery; +2 Authors

    Abstract Background Pre-surgical mapping of language using functional MRI aimed principally to determine the dominant hemisphere. This mapping is currently performed using covert linguistic task in way to avoid motion artefacts potentially biasing the results. However, overt task is closer to natural speaking, allows a control on the performance of the task, and may be easier to perform for stressed patients and children. However, overt task, by activating phonological areas on both hemispheres and areas involved in pitch prosody control in the non-dominant hemisphere, is expected to modify the determination of the dominant hemisphere by the calculation of the lateralization index (LI). Objective Here, we analyzed the modifications in the LI and the interactions between cognitive networks during covert and overt speech task. Methods Thirty-three volunteers participated in this study, all but four were right-handed. They performed three functional sessions consisting of (1) covert and (2) overt generation of a short sentence semantically linked with an audibly presented word, from which we estimated the “Covert” and “Overt” contrasts, and a (3) resting-state session. The resting-state session was submitted to spatial independent component analysis to identify language network at rest (LANG), cingulo-opercular network (CO), and ventral attention network (VAN). The LI was calculated using the bootstrapping method. Results The LI of the LANG was the most left-lateralized (0.66 ± 0.38). The LI shifted from a moderate leftward lateralization for the Covert contrast (0.32 ± 0.38) to a right lateralization for the Overt contrast (− 0.13 ± 0.30). The LI significantly differed from each other. This rightward shift was due to the recruitment of right hemispheric temporal areas together with the nodes of the CO. Conclusion Analyzing the overt speech by fMRI allowed improvement in the physiological knowledge regarding the coordinated activity of the intrinsic connectivity networks. However, the rightward shift of the LI in this condition did not provide the basic information on the hemispheric language dominance. Overt linguistic task cannot be recommended for clinical purpose when determining hemispheric dominance for language.

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    BMC Neuroscience
    Article . 2021 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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    BMC Neuroscience
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      BMC Neuroscience
      Article . 2021 . Peer-reviewed
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      BMC Neuroscience
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    Authors: Samuel Planton; Stanislas Dehaene;

    Abstract The ability to detect the abstract pattern underlying a temporal sequence of events is crucial to many human activities, including language and mathematics, but its cortical correlates remain poorly understood. It is also unclear whether repeated exposure to the same sequence of sensory stimuli is sufficient to induce the encoding of an abstract amodal representation of the pattern. Using functional MRI, we probed the existence of such abstract codes for sequential patterns, their localization in the human brain, and their relation to existing language and math-responsive networks. We used a passive sequence violation paradigm, in which a given sequence is repeatedly presented before rare deviant sequences are introduced. We presented two binary patterns, AABB and ABAB, in four presentation formats, either visual or auditory, and either cued by the identity of the stimuli or by their spatial location. Regardless of the presentation format, a habituation to the repeated pattern and a response to pattern violations were seen in a set of inferior frontal, intraparietal and temporal areas. Within language areas, such pattern-violation responses were only found in the inferior frontal gyrus (IFG), whereas all math-responsive regions responded to pattern changes. Most of these regions also responded whenever the modality or the cue changed, suggesting a general sensitivity to violation detection. Thus, the representation of sequence patterns appears to be distributed, yet to include a core set of abstract amodal regions, particularly the IFG.

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    Cortex
    Article . 2021 . Peer-reviewed
    License: Elsevier TDM
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      Cortex
      Article . 2021 . Peer-reviewed
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    Authors: Mignacco, Francesca; Krzakala, Florent; Urbani, Pierfrancesco; Zdeborová, Lenka;

    We analyze in a closed form the learning dynamics of stochastic gradient descent (SGD) for a single-layer neural network classifying a high-dimensional Gaussian mixture where each cluster is assigned one of two labels. This problem provides a prototype of a non-convex loss landscape with interpolating regimes and a large generalization gap. We define a particular stochastic process for which SGD can be extended to a continuous-time limit that we call stochastic gradient flow. In the full-batch limit, we recover the standard gradient flow. We apply dynamical mean-field theory from statistical physics to track the dynamics of the algorithm in the high-dimensional limit via a self-consistent stochastic process. We explore the performance of the algorithm as a function of the control parameters shedding light on how it navigates the loss landscape. Comment: 8 pages + appendix, 4 figures

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    https://doi.org/10.48550/arxiv...
    Article . 2020
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      https://doi.org/10.48550/arxiv...
      Article . 2020
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    Authors: Prany Wantzen; Patrice Clochon; Franck Doidy; Fabrice Wallois; +8 Authors

    AbstractBackgroundAutism spectrum disorder (ASD) is associated with atypical neural activity in resting state. Most of the studies have focused on abnormalities in alpha frequency as a marker of ASD dysfunctions. However, few have explored alpha synchronization within a specific interest in resting-state networks, namely the default mode network (DMN), the sensorimotor network (SMN), and the dorsal attention network (DAN). These functional connectivity analyses provide relevant insight into the neurophysiological correlates of multimodal integration in ASD.MethodsUsing high temporal resolution EEG, the present study investigates the functional connectivity in the alpha band within and between the DMN, SMN, and the DAN. We examined eyes-closed EEG alpha lagged phase synchronization, using standardized low-resolution brain electromagnetic tomography (sLORETA) in 29 participants with ASD and 38 developing (TD) controls (age, sex, and IQ matched).ResultsWe observed reduced functional connectivity in the ASD group relative to TD controls, within and between the DMN, the SMN, and the DAN. We identified three hubs of dysconnectivity in ASD: the posterior cingulate cortex, the precuneus, and the medial frontal gyrus. These three regions also presented decreased current source density in the alpha band.ConclusionThese results shed light on possible multimodal integration impairments affecting the communication between bottom-up and top-down information. The observed hypoconnectivity between the DMN, SMN, and DAN could also be related to difficulties in switching between externally oriented attention and internally oriented thoughts.

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    Journal of Neurodevelopmental Disorders
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    Journal of Neurodevelopmental Disorders
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    https://doi.org/10.21203/rs.3....
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      Journal of Neurodevelopmental Disorders
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      Journal of Neurodevelopmental Disorders
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      https://doi.org/10.21203/rs.3....
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    Authors: Nicolas Traut; Katja Heuer; Guillaume Lemaitre; Anita Beggiato; +19 Authors

    AbstractMRI has been extensively used to identify anatomical and functional differences in Autism Spectrum Disorder (ASD). Yet, many of these findings have proven difficult to replicate because studies rely on small cohorts and are built on many complex, undisclosed, analytic choices. We conducted an international challenge to predict ASD diagnosis from MRI data, where we provided preprocessed anatomical and functional MRI data from > 2,000 individuals. Evaluation of the predictions was rigorously blinded. 146 challengers submitted prediction algorithms, which were evaluated at the end of the challenge using unseen data and an additional acquisition site. On the best algorithms, we studied the importance of MRI modalities, brain regions, and sample size. We found evidence that MRI could predict ASD diagnosis: the 10 best algorithms reliably predicted diagnosis with AUC∼0.80 – far superior to what can be currently obtained using genotyping data in cohorts 20-times larger. We observed that functional MRI was more important for prediction than anatomical MRI, and that increasing sample size steadily increased prediction accuracy, providing an efficient strategy to improve biomarkers. We also observed that despite a strong incentive to generalise to unseen data, model development on a given dataset faces the risk of overfitting: performing well in cross-validation on the data at hand, but not generalising. Finally, we were able to predict ASD diagnosis on an external sample added after the end of the challenge (EU-AIMS), although with a lower prediction accuracy (AUC=0.72). This indicates that despite being based on a large multisite cohort, our challenge still produced biomarkers fragile in the face of dataset shifts.

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