The neurophysiological brain-fingerprint of Parkinson's disease

Abstract

Background: Research in healthy young adults shows that characteristic patterns of brain activity define individual "brain-fingerprints" that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson's disease (PD).

Methods: We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s.

Findings: The arrhythmic spectral components of cortical activity in patients with Parkinson's disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson's brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson's symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology.

Interpretation: The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson's disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets.

Funding: Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).

Keywords: Arrhythmic brain activity; Brain-fingerprinting; Magnetoencephalography; Movement disorders; Neural dynamics; Oscillations; Parkinson’s disease.

Fig. 1

Brain-fingerprinting pipeline and study design. (a) For each participant, the power spectral density of MEG source time series for each cortical parcel of the Desikan-Killiany atlas is estimated from two data segments (1 and 2), each approximately 4 minutes long. The resulting power spectra across all cortical parcels generate one brain-fingerprint for each data segment (b-fp1 and b-fp2). The similarity between two brain-fingerprints, measured with cross-correlation statistics, produces an inter-individual confusion matrix. The diagonal elements of this matrix represent the self-similarity (Iself) between each participant’ two consecutive brain-fingerprints, while the off-diagonal elements represent the other-similarity (Iother) of a participant’s brain-fingerprint with those from the other participants in the cohort. (b) We tested the brain-fingerprinting approach in three inter-individual differentiation experiments: i) between healthy controls, ii) between patients with Parkinson’s Disease (PD), and iii) between patients and healthy controls. (c) We derived an individual differentiability score for each participant based on the self-similarity of their two consecutive brain-fingerprints in relation to their other-similarity with the rest of the participants. The score of individual differentiability is calculated by z-scoring the self-similarity score against the other-similarity scores.

Fig. 2

Differentiating patients with Parkinson’s disease from healthy controls using spectral brain-fingerprints. (a) Accuracy in distinguishing participants from their brain-fingerprints derived from full, arrhythmic, and rhythmic brain-fingerprints, estimated from 4-minute (bar plots) and 30-second (scatter plots) data segments. The scatter plots indicate the differentiation accuracy for all brain-fingerprint pairs derived from all possible contiguous 30-second segments from the original 4-minute data segments. Grey segments at the base of the bar plots represent control differentiation performances based on empty-room MEG recordings collected during each participant’s visit (see Supplemental Fig. S1). Error bars represent bootstrapped 95% confidence intervals. (b) Self-similarity statistics within participants for full spectral, rhythmic, and arrhythmic brain-fingerprints. The plots show the empirical density of self-similarity statistics between two consecutive brain-fingerprints in control and PD cohorts. Thehe PD group displays a wider distribution, suggesting more variability in patients for full spectral and arrhythmic features (1/f). (c) Self-similarity of brain-fingerprints from brief (30-second) data segments across full spectral, rhythmic, and arrhythmic features across all frequency bands. Patients with PD show lower self-similarity with increased gap durations between data segments used to derive brain-fingerprints (y-intercept shift downwards). The self-similarity of patient full spectrum brain-fingerprints decreases more rapidly with the gap duration between recordings. In contrast, the self-similarity of patient brain-fingerprints from rhythmic components was more self-similar than controls at short gap durations, and became comparable at longer durations. Shaded regions indicate the standard error on the mean. See Supplemental Fig. S5 for a narrow-band description of the observed rhythmic effects.

Fig. 3

Comparative analysis of brain-fingerprint differentiation in Parkinson’s disease and control groups. (a) Cortical maps comparing ICC scores for differentiating between patients and controls. Orange areas indicate regions where differentiation of individual patients is more effective than in controls. We replicated this finding in two independent samples of healthy controls: the PREVENT-AD dataset (top panel) and the Cam-CAN dataset (bottom panel). (b) Differentiation accuracy from brain-fingerprints defined by top features for differentiating patients (left, cortical areas shown in orange) and top features for differentiating controls (right, cortical areas shown in purple).

Fig. 4

Decoding Parkinson’s disease symptom laterality from brain-fingerprints. (a) Cortical topography of decoding accuracies for Parkinson’s symptom laterality (based on binarized Hoehn & Yahr scores). On the right, power spectra of resting-state neurophysiological activity in the right postcentral gyrus, the cortical region with the highest accuracy for decoding symptom laterality. The plots represent the average power spectrum for each group: healthy controls, individuals with unilateral symptoms, and individuals with bilateral symptoms, with shaded areas indicating standard errors across groups. (b) Scatter plot showing how the decoding accuracy of binarized Hoehn & Yahr scores from brain-fingerprint features of each cortical parcel correlates with the saliency of each parcel, as determined by its ΔICC score.

Fig. 5

Correlation of spectral brain-fingerprints with cortical functional hierarchy and neurotransmitter systems. (a) Top: Cortical map illustrating the first unimodal-to-transmodal functional gradient, sourced from neuromaps. Bottom: Linear association between the weights of cortical regions in this functional gradient (sourced from neuromaps) and their prominence in the PD brain-fingerprint (Fig. 3a, top). (b) Top: Bayes factor analysis of the topographical alignment between PD brain-fingerprint features (from Fig. 3a) and atlases of various cortical neurochemical systems, highlighting strong correlations particularly with serotonin, cannabinoid, mu-opioid, and norepinephrine systems. Each row represents data from different control samples. Bottom: Selected neurochemical cortical atlases, as obtained from neuromaps.