Incorporating individual brain differences into DCMs for M/EEG

Develop a principled methodology to incorporate individual brain differences (subject-level heterogeneity) into dynamic causal models for magneto/electroencephalography (M/EEG) data so that effective connectivity can be inferred while accounting for variability across subjects.

Background

Dynamic causal modeling (DCM) provides a framework to infer effective connectivity among brain regions using neural mass models fit to M/EEG data. However, human brains exhibit substantial inter-subject variability even when subjects are matched on demographics, complicating group analyses.

The paper highlights that accounting for this heterogeneity within DCM frameworks is unclear and challenging, motivating hierarchical or mixed-effects approaches. Addressing this gap would enable more reliable group comparisons and better characterization of disease-related differences.

References

It is unclear how to take individual brain differences into account in a dynamic causal model with M/EEG data.

— Differential Dynamic Causal Nets: Model Construction, Identification and Group Comparisons  (2601.21478 - You et al., 29 Jan 2026) in Introduction, paragraph beginning “The fourth challenge is about heterogeneity of human brains…”