Interpretable Decoding of Frequency-Resolved Functional Connectivity
- 1Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
Abstract
Whole-brain functional connectivity, estimated from magnetoencephalography (MEG) data, provides a compact representation of long-range neuronal communication, making it suitable for predictive biomarker discovery. In this work, we propose a deep learning framework (FC-CNN) for predicting brain states from frequency-resolved functional connectivity estimates derived from resting-state MEG recordings. We systematically compare the performance of FC-CNN to that of conventional regression methods using amplitude and phase-based functional connectivity in the well-studied age-prediction task on the Cam-CAN cohort (n=576). We show that FC-CNN outperforms conventional approaches, and that, compared to phase synchronization, amplitude envelope correlation consistently leads to higher prediction performance. Moreover, we present quantitative evidence that the weights of a trained deep learning model can enable neurophysiological interpretation of the activity patterns that inform successful predictions. Our work demonstrates that the proposed approach successfully decodes brain states from MEG functional connectivity and is promising for discovery of predictive biomarkers for brain disorders.
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BibTeX
@misc{ruuskanen2026interpretable,
title = {{Interpretable Decoding of Frequency-Resolved Functional Connectivity}},
author = {S. Ruuskanen and E. Saarro and C. M. Caivano and L. Parkkonen and I. Zubarev},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.20.745932},
url = {https://www.biorxiv.org/content/10.64898/2026.08.20.745932v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - Interpretable Decoding of Frequency-Resolved Functional Connectivity
AU - S. Ruuskanen
AU - E. Saarro
AU - C. M. Caivano
AU - L. Parkkonen
AU - I. Zubarev
PY - 2026
DA - 2026/08/20
PB - bioRxiv
DO - 10.64898/2026.08.20.745932
UR - https://www.biorxiv.org/content/10.64898/2026.08.20.745932v2
AB - Whole-brain functional connectivity, estimated from magnetoencephalography (MEG) data, provides a compact representation of long-range neuronal communication, making it suitable for predictive biomarker discovery. In this work, we propose a deep learning framework (FC-CNN) for predicting brain states from frequency-resolved functional connectivity estimates derived from resting-state MEG recordings. We systematically compare the performance of FC-CNN to that of conventional regression methods using amplitude and phase-based functional connectivity in the well-studied age-prediction task on the Cam-CAN cohort (n=576). We show that FC-CNN outperforms conventional approaches, and that, compared to phase synchronization, amplitude envelope correlation consistently leads to higher prediction performance. Moreover, we present quantitative evidence that the weights of a trained deep learning model can enable neurophysiological interpretation of the activity patterns that inform successful predictions. Our work demonstrates that the proposed approach successfully decodes brain states from MEG functional connectivity and is promising for discovery of predictive biomarkers for brain disorders.
ER -