Beyond homogeneity: charting the landscape of heterogeneity in neurodevelopmental and psychiatric electroencephalography.

Researchers studied brain activity patterns (EEG) in over 1,600 children with ADHD, autism, learning difficulties, or anxiety. They found that each child's brain patterns were very different from others, even within the same condition. Only 24-40% of children shared similar brain pattern differences. This explains why EEG tests haven't become useful for diagnosing these conditions - each child's brain is unique.

The study suggests doctors need personalized approaches rather than looking for common patterns across all children.

This large-scale study examined EEG patterns in 1,674 patients with ADHD, autism, learning disorders, or anxiety, plus 560 controls. Using normative modeling, researchers found that EEG deviations from population norms were highly heterogeneous among patients - spatial overlap between patients was only 40% for spectral power and 24% for connectivity measures. This heterogeneity varied by frequency bands. The individual deviation approach enhanced comparative analysis and identified patient-specific markers that correlated with clinical assessments.

The findings suggest that traditional case-control EEG research may miss important individual differences, potentially explaining why EEG hasn't successfully transitioned to clinical diagnostic use despite decades of research.

Results suggest EEG research should move away from traditional group comparisons toward individualized approaches. This could improve diagnostic precision for neurodevelopmental conditions, but requires development of personalized normative frameworks. The findings may explain why decades of EEG research haven't translated to clinical practice.

Study type is not specified in the abstract. The abstract doesn't provide details about methodology, control for confounding variables, or validation of the normative modeling approach. Clinical correlation details are limited, and long-term follow-up data is not mentioned.

Electroencephalography (EEG) has been thoroughly studied for decades in neurodevelopmental and psychiatric research. Yet its integration into clinical practice as a diagnostic/prognostic tool remains unachieved. We hypothesize that a key reason is the underlying patient's heterogeneity, overlooked in EEG research relying on a case-control approach. We combine high-density 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 1674 patients with attention-deficit/hyperactivity disorder, autism spectrum disorder, learning disorder, or anxiety, and 560 matched controls.

Normative models showed that deviations from population norms among patients were highly heterogeneous and frequency-dependent. Deviation spatial overlap across patients did not exceed 40% and 24% for spectral and connectivity, respectively. Considering individual deviations in patients has significantly enhanced comparative analysis, and the identification of patient-specific markers has demonstrated a correlation with clinical assessments, representing a crucial step towards attaining precision psychiatry through EEG.