Toward precision psychiatry using HD-EEG and normative modeling

Introduction Electroencephalography (EEG) has been extensively studied for decades in psychiatric research. However, its integration into clinical practice as a diagnostic or prognostic tool remains unachieved. We hypothesize that a key reason for this is the underlying heterogeneity among patients, which is often overlooked in psychiatric EEG research that relies on a case-control approach. Objectives The main objective of this study is to quantify the electrophysiological heterogeneity of psychiatric disorders. Methods We combine HD-EEG with normative modeling to quantify this heterogeneity using two well-established and extensively investigated EEG characteristics—spectral power and functional connectivity—across a cohort of 1,674 patients with attention-deficit/hyperactivity disorder, autism spectrum disorder, learning disorder, or anxiety, and 560 matched controls, see figure 1. Results Normative models revealed that deviations from population norms among patients were highly heterogeneous and frequency-dependent. The spatial overlap of deviations among patients did not exceed 40% for spectral power and 24% for connectivity. Taking individual deviations into account significantly enhanced comparative analysis and the identification of patient-specific markers, which showed a correlation with clinical assessments. Image 1: Conclusions Our study underscores the necessity of moving EEG research in psychiatry beyond the group-level approach to achieve precision psychiatry. Disclosure of Interest None Declared

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Publication Details

Journal
European Psychiatry
Published
2025-04-01
DOI
https://doi.org/10.1192/j.eurpsy.2025.288
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
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article

Toward precision psychiatry using HD-EEG and normative modeling

Judie Tabbal, Borja Rodríguez‐Herreros, Aya Kabbara, Sahar Allouch et al.
European Psychiatry
EEG and Brain-Computer Interfaces
article

Toward precision psychiatry using HD-EEG and normative modeling

Judie Tabbal, Borja Rodríguez‐Herreros, Aya Kabbara, Sahar Allouch, Ahmad Mheich, Aline Lefebvre-Lepot, Guillaume Robert, Nadia Chabane, Abbas Ebadi, Anton Iftimovici, Mahmoud Hassan
article en

Abstract

Introduction Electroencephalography (EEG) has been extensively studied for decades in psychiatric research. However, its integration into clinical practice as a diagnostic or prognostic tool remains unachieved. We hypothesize that a key reason for this is the underlying heterogeneity among patients, which is often overlooked in psychiatric EEG research that relies on a case-control approach. Objectives The main objective of this study is to quantify the electrophysiological heterogeneity of psychiatric disorders. Methods We combine HD-EEG with normative modeling to quantify this heterogeneity using two well-established and extensively investigated EEG characteristics—spectral power and functional connectivity—across a cohort of 1,674 patients with attention-deficit/hyperactivity disorder, autism spectrum disorder, learning disorder, or anxiety, and 560 matched controls, see figure 1. Results Normative models revealed that deviations from population norms among patients were highly heterogeneous and frequency-dependent. The spatial overlap of deviations among patients did not exceed 40% for spectral power and 24% for connectivity. Taking individual deviations into account significantly enhanced comparative analysis and the identification of patient-specific markers, which showed a correlation with clinical assessments. Image 1: Conclusions Our study underscores the necessity of moving EEG research in psychiatry beyond the group-level approach to achieve precision psychiatry. Disclosure of Interest None Declared

European PsychiatryVol. 68(S1)
Reykjavík University (IS), Université Paris Cité (FR), Université Paris-Saclay (FR), Centre Hospitalier Universitaire Vaudois (CH), Institut de Recherche en Informatique et Systèmes Aléatoires (FR), FHU Neurovasc (FR)
Openalex Percentile: Top 76%
EEG and Brain-Computer Interfaces
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