Biology subjects

Schaer, M.

Publications and source records attributed to Schaer, M..

11 recordsLinked to original sources

bioRxiv2026

Heterogeneity of Brain Dynamics in Genetic and Psychiatric Conditions

Whether the heterogeneity of psychiatric conditions converges on shared neurophysiological alterations or translates into distinct signatures remains unclear. We assembled high-density electroencephalogram (hd-EEG) resting-state recordings from 4,812 individuals aged 5 months to 66 years across 11 psychiatric conditions, a broad spectrum of rare genetic variants, and typically developing (TD) individuals. We established normative developmental trajectories of source-space EEG across spectral organization, connectivity, and signal complexity. Psychiatric conditions showed small deviations, revealing a shared transdiagnostic profile. In contrast, single rare variants showed substantially larger, distinct and sometimes mirror-opposite signatures that collapsed toward the psychiatric profile when pooled. Autism Spectrum Disorder showed some of the smallest group-level effects yet the largest individual deviations, indicating substantial but directionally inconsistent alterations. EEG deviations followed a cortical gradient, with larger effects in sensorimotor regions. We demonstrate that sample sizes in the hundreds are required for robust associations with psychiatric diagnoses. This interactive open resource provides normative scores to benchmark future results.

neuroscience↗

bioRxiv2025

From Syllables to Words: EEG Evidence of Different Age Trajectories in Speech Tracking and Statistical Learning in Infants at High and Low Likelihood for Autism

Delayed onset of canonical babbling and first words is often reported in infants later diagnosed with autism spectrum disorder. Identifying the neural mechanisms underlying language acquisition in autism is therefore critical to inform early diagnosis, prognosis, and intervention strategies. In this study, we investigated two speech processing mechanisms previously identified as atypical in children and adults with autism: the neural ability to track syllables; and statistical learning, the capacity to detect speech regularities beneath surface variability. We recorded 83 longitudinal high-density EEGs from 44 infants (2.5-22.6 months) at high (HL) and low (LL) likelihood for autism and assessed their verbal outcomes at 20 months. Neural entrainment was measured at syllable and word frequencies during exposure to a multi-speaker stream of concatenated tri-syllabic words, followed by a word recognition test using ERP recording. Our findings revealed reduced tracking abilities at the syllabic level in HL infants, a measure that correlated with verbal outcomes. While HL infants did not exhibit deficits in statistical learning itself, they displayed reduced novelty orientation during the word recognition test, indicated by a reduced late ERP. By contrast, multi-talker variability temporarily disrupted word segmentation around 12 months in LL infants, but not in HL infants, potentially reflecting decreased sensitivity to human voices variability in the HL group. These results emphasize the importance of longitudinal protocols employing online, implicit measures to track the hierarchical stages of speech processing in both HL and LL infants.

neuroscience↗

bioRxiv2024

Atypical Audio-Visual Neural Synchrony and Speech Processing in children with Autism Spectrum Disorder

BackgroundChildren with Autism Spectrum Disorders (ASD) often exhibit communication difficulties that may stem from basic auditory temporal integration impairment but also be aggravated by an audio-visual integration deficit, resulting in a lack of interest in face-to-face communication. This study addresses whether speech processing anomalies in young (mean age 3.09-year-old) children with ASD are associated with alterations of audio-visual temporal integration. MethodsWe used high-density electroencephalography (HD-EEG) and eye tracking to record brain activity and gaze patterns in 31 children (6 females) with ASD and 33 typically developing (TD) children (11 females), while they watched cartoon videos. Neural responses to temporal audio-visual stimuli were analyzed using Temporal Response Functions model and phase analyses for audiovisual temporal coordination. ResultsThe reconstructability of speech signals from auditory responses was reduced in children with ASD compared to controls, but despite more restricted gaze patterns in ASD it was similar for visual responses in both groups. Speech reception was most strongly affected when visual speech information was also present, an interference that was not seen in TD children. These differences were associated with a broader phase angle distribution (exceeding pi/2) in the EEG theta range in autistic children, signaling reduced reliability of audio-visual temporal alignment. ConclusionThese findings show that speech processing anomalies in ASD do not stand alone and that they are associated already at a very early development stage with audio-visual imbalance with lousier auditory response encoding and disrupted audio-visual temporal coordination.

neuroscience↗

bioRxiv2021

Neural oscillation coupling selectively predicts speech reception in young children with Autism Spectrum Disorder

Communication difficulties in autism spectrum disorder (ASD) involve a speech reception deficit, whose biological causes are not yet identified. This deficit could denote atypical neuronal ensemble activity, as reflected by neural oscillations. Atypical cross-frequency oscillation coupling in particular could disrupt the possibility to jointly track and predict dynamic acoustic stimuli, a dual process that is essential for speech comprehension. Whether such oscillation anomalies can already be found in very young children with ASD, and with what specificity they relate to individual language reception capacity is unknown. In this study, neural activity was collected using EEG in 64 very young children with and without ASD (mean age 3) while they were exposed to naturalistic-continuous speech via an age-appropriate cartoon. EEG power typically associated with phrase-level chunking (delta, 1-3Hz), phonemic encoding (low-gamma, 25-35Hz) and top-down control (beta, 12-20Hz) was markedly reduced in ASD relative to typically developing (TD) children. Speech neural-tracking by delta and theta oscillations was also weaker in ASD than TD children. Critically, children with ASD exhibited slightly atypical theta/gamma coupling (PAC) involving a higher-than-normal gamma frequency, and markedly atypical beta/gamma PAC. Even though many oscillation features were atypical in our sample of 31 very young children with ASD, the beta/gamma coupling anomaly was the single best predictor of individual speech reception difficulties. These findings suggest that early interventions targeting the normalization of low-gamma and low-beta activity, might help young children with ASD to engage more in oral interactions. HighlightsVery young children diagnosed with autism spectrum disorder already present marked alterations of neural oscillatory activity in response to natural speech. The hierarchical processing of phonemic- and syllabic-range information (theta/gamma coupling) is atypical in young ASD children. Speech reception deficit in children with ASD is indexed by abnormal bottom-up (low-gamma) and top-down (low-beta) coordination.

neuroscience↗

bioRxiv2021

Distinct profiles of spatio-temporal brain dynamics along symptoms dimensions in autism

Autism Spectrum Disorders are accompanied by atypical brain activity and impairments in brain connectivity. In particular, dynamic functional connectivity approaches highlighted aberrant brain fluctuations at rest in individuals with autism compared to a group composed of typically developed individuals, matched in age and gender. However, the characterization of these variations remains unclear. Here, we quantified the spatio-temporal network dynamics using two novel dynamic group-based measures, namely system diversity and spatio-temporal diversity. Using the public database ABIDE 1, we explored the differences between individuals with autism and typically developed individuals. Our results show evidence that individuals with autism have atypical connectivity patterns over time characterized by a lower integration of heterogeneous cognitive processes and unstable functional activity, except for the default mode network presenting its own specific dynamic pattern. Within the autism group, we find this pattern of results to be stronger in more severely affected patients with a predominance of symptoms in the social affect domain. However, patients with prominently restricted and repetitive behaviours demonstrate a more conservative profile of brain dynamics characterized by a lower spatio-temporal diversity of the default mode network.

neuroscience↗

bioRxiv2020

Early alterations of large-scale brain networkstemporal dynamics in young children with autism.

Disruption of large-scale brain networks is associated with autism spectrum disorders (ASD). Recently, we found that directed functional connectivity alterations of social brain networks are a core component of atypical brain development at early developmental stages in ASD (Sperdin et al., 2018). Here, we investigated the spatio-temporal dynamics of whole-brain neuronal networks at a subsecond scale in 90 toddlers and preschoolers (47 with ASD) using an EEG microstate approach. Results revealed the presence of five microstate classes that best described the entire dataset (labeled as microstate classes A-E). Microstate class C related to the Default Mode Network (DMN) occurred less in children with ASD. Analysis of brain-behavioural relationships within the ASD group suggested that a compensatory mechanism from microstate C was associated with less severe symptoms and better adaptive skills. These results demonstrate that the temporal properties of some specific EEG microstates are altered in ASD at early developmental stages.

neuroscience↗

bioRxiv2020

Unraveling the Developmental Dynamic of Visual Exploration of Social Interactions in Autism

Atypical deployment of social gaze is present early on in toddlers with autism spectrum disorders (ASDs). Yet, studies characterizing the developmental dynamic behind it are scarce. Here we used a data-driven method to delineate the developmental change in visual exploration of social interaction over childhood years in autism. Longitudinal eye-tracking data were acquired as children with ASD and their typically developing (TD) peers freely explored a short cartoon movie. We found divergent moment-to-moment gaze patterns in children with ASD compared to their TD peers. This divergence was particularly evident in sequences that displayed social interactions between characters and even more so in children with lower developmental and functional levels. The basic visual properties of the animated scene did not account for the enhanced divergence. Over childhood years, these differences dramatically increased to become more idiosyncratic. These findings suggest that social attention should be targeted early in clinical treatments.

neuroscience↗

bioRxiv2019

Structural control energy of resting-state functional brain states reveals inefficient brain dynamics in psychosis vulnerability.

How the brains white-matter anatomy constrains brain activity is an open question that might give insights into the mechanisms that underlie mental disorders such as schizophrenia. Chromosome 22q11.2 deletion syndrome (22q11DS) is a neurodevelopmental disorder with an extremely high risk for psychosis providing a test case to study developmental aspects of schizophrenia. In this study, we used principles from network control theory to probe the implications of aberrant structural connectivity for the brains functional dynamics in 22q11DS. We retrieved brain states from resting-state functional magnetic resonance images of 78 patients with 22q11DS and 85 healthy controls. Then, we compared them in terms of persistence control energy; i.e., the control energy that would be required to persist in each of these states based on individual structural connectivity and a dynamic model. Persistence control energy was altered in a broad pattern of brain states including both energetically more demanding and less demanding brain states in 22q11DS. Further, we found a negative relationship between persistence control energy and resting-state activation time, which suggests that the brain reduces energy by spending less time in energetically demanding brain states. In patients with 22q11DS, this behavior was less pronounced, suggesting a dynamic inefficiency of brain function in the disease. In summary, our results provide initial insights into the dynamic implications of altered structural connectivity in 22q11DS, which might improve our understanding of the mechanisms underlying the disease.

neuroscience↗

bioRxiv2019

Large-scale brain network dynamics provide a measure of psychosis and anxiety in 22q11.2 deletion syndrome.

Prodromal positive psychotic symptoms and anxiety are two strong risk factors for schizophrenia in 22q11.2 deletion syndrome (22q11DS). The analysis of large-scale brain network dynamics during rest is promising to investigate aberrant brain function and identify potentially more reliable biomarkers. We retrieved and examined dynamics of large-scale functional brain networks using innovation-driven co-activation patterns (iCAPs) and probed into functional signatures of prodromal psychotic symptoms and anxiety. Patients with 22q11DS had shorter activation in cognitive brain networks and longer activation in emotion processing networks. Functional signatures of prodromal psychotic symptoms confirmed an implication of cingulo-prefrontal salience network activation duration and coupling. Functional signatures of anxiety un-covered an implication of amygdala activation and coupling, indicating differential roles of dorsal and ventral sub-divisions of anterior cingulate and medial prefrontal cortices. These results confirm that the dynamic nature of brain network activation contains essential function to develop clinically relevant imaging markers of psychosis vulnerability.

neuroscience↗

bioRxiv2017

Early alterations of social brain networks in young children with autism

Social impairments are a hallmark of Autism Spectrum Disorders (ASD), but empirical evidence for early brain network alterations in response to social stimuli is scant in ASD. Here, we recorded the gaze patterns and brain activity of toddlers and preschoolers with ASD and their typically developing (TD) peers while they explored dynamic social scenes. Source-space directed functional connectivity analyses revealed the presence of network alterations in the theta frequency band, manifesting as increased driving (hyper-activity) and stronger connections (hyper-connectivity) from key nodes of the social brain associated with autism. Further analyses of brain-behavioural relationships within the ASD group suggested that compensatory mechanisms from dorsomedial frontal, inferior temporal and insular cortical regions were associated with lower clinical impairment and less atypical gaze patterns. Our results provide strong evidence that directed functional connectivity alterations of social brain networks is a core component of atypical brain development at early stages of ASD.

neuroscience↗

bioRxiv2017

MRIQC: Predicting Quality in Manual MRI Assessment Protocols Using No-Reference Image Quality Measures

Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality assessments have been demonstrated on single-site datasets, it is unclear that solutions can generalize to unseen data acquired at new sites. Here, we introduce the MRI Quality Control tool (MRIQC), a tool for extracting quality measures and fitting a binary (accept/exclude) classifier. Our tool can be run both locally and as a free online service via the OpenNeuro.org portal. The classifier is trained on a publicly available, multi-site dataset (17 sites, N=1102). We perform model selection evaluating different normalization and feature exclusion approaches aimed at maximizing across-site generalization and estimate an accuracy of 76%{+/-}13% on new sites, using leave-one-site-out cross-validation. We confirm that result on a held-out dataset (2 sites, N=265) also obtaining a 76% accuracy. Even though the performance of the trained classifier is statistically above chance, we show that it is susceptible to site effects and unable to account for artifacts specific to new sites. MRIQC performs with high accuracy in intra-site prediction, but performance on unseen sites leaves space for improvement which might require more labeled data and new approaches to the between-site variability. Overcoming these limitations is crucial for a more objective quality assessment of neuroimaging data, and to enable the analysis of extremely large and multi-site samples.

bioinformatics↗