Fading of brain network fingerprint in Parkinson's disease predicts motor clinical impairment

Abstract

The clinical connectome fingerprint (CCF) was recently introduced as a way to assess brain dynamics. It is an approach able to recognize individuals, based on the brain network. It showed its applicability providing network features used to predict the cognitive decline in preclinical Alzheimer's disease. In this article, we explore the performance of CCF in 47 Parkinson's disease (PD) patients and 47 healthy controls, under the hypothesis that patients would show reduced identifiability as compared to controls, and that such reduction could be used to predict motor impairment. We used source‐reconstructed magnetoencephalography signals to build two functional connectomes for 47 patients with PD and 47 healthy controls. Then, exploiting the two connectomes per individual, we investigated the identifiability characteristics of each subject in each group. We observed reduced identifiability in patients compared to healthy individuals in the beta band. Furthermore, we found that the reduction in identifiability was proportional to the motor impairment, assessed through the Unified Parkinson's Disease Rating Scale, and, interestingly, able to predict it (at the subject level), through a cross‐validated regression model. Along with previous evidence, this article shows that CCF captures disrupted dynamics in neurodegenerative diseases and is particularly effective in predicting motor clinical impairment in PD.

The neurophysiological brain-fingerprint of Parkinson’s disease

“…This greater, moment-to-moment variability of brain activity was expected from previous observations of pronounced sequent variability of fMRI connectomes in individuals with or at risk of mental health disorders 27 , such as schizophrenia 28 , and in the brain-network fingerprints of patients with PD 31 . Our data extend these observations to spectral derivatives of electrophysiological brain activity.…”

“…Our study demonstrates the application and relevance of brain fingerprinting to Parkinson's disease (PD) research. We derived brain-fingerprints from task-free MEG recordings and first replicated the prior observation that the brain-fingerprints of patients with PD present increased variability over short periods of time compared to healthy controls 30,31 . We identified that this effect is due in large part to the enhanced temporal variability of the arrhythmic component of the neurophysiological brain activity of PD patients, making them less distinguishable from one another.…”

“…We first averaged the ΔICC values within each of the canonical frequency bands and then averaged these across all bands. This process involved averaging ΔICC within specified frequency ranges: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma . This resulted in six ΔICC maps, one for each frequency band, which were then averaged to obtain a broadband ΔICC map.…”

“…By extension, neurophysiological activity may also express greater temporal variability in patients with PD, especially in unimodal regions of the brain functional hierarchy where their coupling with hemodynamic signals is the strongest 30 . The individual differentiation of patients using connectome brain-fingerprints derived from neurophysiology is therefore expected to be particularly challenging, as previously observed with magnetoencephalography (MEG) 31 .…”

“…However, subsequent studies have indicated an increased variability in brain-fingerprints over time in PD 26,28,29 , which challenges the differentiation between patients on the basis of this neuroimaging phenotype. For example, a recent study with magnetoencephalography (MEG) showed that the differentiation accuracy between the brain-fingerprints of patients with PD, derived from connectomes, declines with the severity of their motor symptoms 30 . This also challenges the ability to distinguish PD patients from healthy controls based on brain-fingerprints, thereby raising questions about the relevance and effectiveness of these approaches in neurological disease research, or for testing novel therapeutic pathways.…”