Clinical connectome fingerprints of cognitive decline

Brain connectome fingerprinting is rapidly rising as a novel influential field in brain network analysis. Yet, it is still unclear whether connectivity fingerprints could be effectively used for mapping and predicting disease progression from human brain data. We hypothesize that dysregulation of brain activity in disease would reflect in worse subject identification. We propose a novel framework, Clinical Connectome Fingerprinting , to detect individual connectome features from clinical populations. We show that clinical fingerprints can map individual variations between elderly healthy subjects and patients with mild cognitive impairment in functional connectomes extracted from magnetoencephalography data. We find that identifiability is reduced in patients as compared to controls, and show that these connectivity features are predictive of the individual Mini-Mental State Examination (MMSE) score in patients. We hope that the proposed methodology can help in bridging the gap between connectivity features and biomarkers of brain dysfunction in large-scale brain networks.

Automated summary

This study introduces Clinical Connectome Fingerprinting for detecting individual connectome features in clinical populations, showing differences in identifiability between patients and controls. The connectivity features are found to be predictive of cognitive scores in patients, bridging the gap between connectivity features and biomarkers of brain dysfunction in large-scale brain networks.