Resting-state electroencephalography reveals four subphenotypes of early autism spectrum disorder
Poster No:
271
Submission Type:
Abstract Submission
Authors:
Ahmad MHEICH1,2, Sahar Allouch1, Mahmoud Hassan1, Nadia Chabane2, Borja Rodríguez- Herreros2
Institutions:
1MINDIG, Rennes, France, 2Service des Troubles du Spectre de l’Autisme et apparentés, Département de psychiatrie, CHUV, Lausanne, Switzerland
First Author:
Ahmad MHEICH
MINDIG|Service des Troubles du Spectre de l’Autisme et apparentés, Département de psychiatrie, CHUV
Rennes, France|Lausanne, Switzerland
MINDIG|Service des Troubles du Spectre de l’Autisme et apparentés, Département de psychiatrie, CHUV
Rennes, France|Lausanne, Switzerland
Co-Author(s):
Nadia Chabane
Service des Troubles du Spectre de l’Autisme et apparentés, Département de psychiatrie, CHUV
Lausanne, Switzerland
Service des Troubles du Spectre de l’Autisme et apparentés, Département de psychiatrie, CHUV
Lausanne, Switzerland
Borja Rodríguez- Herreros
Service des Troubles du Spectre de l’Autisme et apparentés, Département de psychiatrie, CHUV
Lausanne, Switzerland
Service des Troubles du Spectre de l’Autisme et apparentés, Département de psychiatrie, CHUV
Lausanne, Switzerland
Introduction:
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects social communication and interaction, as well as repetitive or restricted behaviors, interests, or activities that can vary in individuals along a continuum of severity (Lord, 2018). Clinical and biological manifestations are very heterogeneous among individuals with ASD and subgrouping was mainly performed using clinical parameters (Buch, 2023). However, these subgroups do not take into account underlying disease physiopathology, and were shown not entirely predictive of disease prognosis. Resting-state electroencephalography (EEG) is a powerful tool to identify abnormal patterns of cognitive and behavioral deficits in ASD. These disruptions have previously been identified across multiple frequency bands using cortical spectral power and functional connectivity using EEG recordings. Our goal in this study is to identify different subgroups of ASD patients based on their distinct electrophysiological profiles and to evaluate their clinical significance.
Methods:
Here, we used resting-state high-density (HD)-EEG recordings of 541 ASD patients, collected from different sites, to conduct a clustering analysis based on an unsupervised learning technique called Similarity Network Fusion (Wang, 2014). The data were aggregated from four distinct studies: the Healthy BrainNetwork Dataset (HBN)(https://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/index.html), Autism Biomarker Consortium for Clinical Trials Dataset (ABCCT) (https://nda.nih.gov/edit_collection.html?id=2288), Biomarkers of Developmental Trajectories and Treatment in ASD (BiomarkDev) and LausanneASD dataset. Subjects included in this study were aged between 2 and 18 years old (mean = 9.04 ±3.34; 54% M). High-density (128-channels) resting-state EEG data were recorded while participants had their eyes open.
Results:
We showed that ASD patients (N = 541) can be subgrouped into four phenotypes with distinct electrophysiological profiles. These clusters are characterised by different levels of disruptions in brain regions across several frequencies, which consistently correlate with clinical profiles. Our findings show that novel phenotyping using electric brain signal analysis can distinguish ASD subtypes based on different patterns of functional connectivity synchronization between brain regions. These patterns can reflect underlying disease neurobiology. The identification of ASD subtypes based on profiles of differential alterations in functional connectivity between brain regions has clear potential in patient's stratification for more accurate prognosis and better clinical trials. Innovative profiling in ASD can also support new therapeutic strategies that are brain-based and designed to modulate brain activity disruption.
Conclusions:
Here, we employed resting-state high-density electroencephalography (HD-EEG) and an unsupervised data-driven clustering approach to identify subtypes of ASD patients based on their electrophysiological profiles. Our analysis uncovered four distinct ASD subtypes that showed clinically relevant differences in their electrophysiological features. These findings provide new insights into the biological heterogeneity of ASD and may help inform the development of personalized treatment strategies for patients with this complex disorder.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Keywords:
Autism
Computational Neuroscience
Development
Electroencephaolography (EEG)
Phenotype-Genotype
Psychiatric Disorders
Statistical Methods
Other - Stratification
1|2Indicates the priority used for review
·The four ASD subtypes. a) number of subjects in each group, b) Site distribution between groups, c) sex distribution between groups, d) age distribution between groups.
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Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Wang, Bo (2014). Similarity Network Fusion for Aggregating Data Types on a Genomic Scale. Nature Methods 11 (3): 333‑37.
Buch, Amanda M. (2023). Molecular and Network-Level Mechanisms Explaining Individual Differences in Autism Spectrum Disorder. Nature Neuroscience 26 (4): 650‑63.