The progressive loss of brain network fingerprints in Amyotrophic Lateral Sclerosis predicts clinical impairment

Abstract

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by functional connectivity alterations in both motor and extra-motor brain regions. In network analysis, fingerprint represent a valid approach able to assess the subject-specific connectivity features of a given population. Here, we applied the Clinical Connectome Fingerprint (CCF) analysis to source-reconstructed magnetoencephalography (MEG) signals in a cohort of seventy-eight subjects: thirty-nine ALS patients and thirty-nine healthy controls. We set out to develop an identifiability matrix to assess the extent to which each subject was recognisable based on his/her connectome. The analysis was performed in all five canonical frequency bands. Then we built a multilinear regression model to test the ability of clinical fingerprint to predict the clinical evolution of disease assessed by the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-r), the King s disease staging system and the Milano-Torino Staging (MiToS) disease staging system. We found a drop in patients identifiability in the alpha band compared to the healthy controls. Furthermore, the clinical fingerprint was predictive of ALSFRS-r (p=0.0397; beta;=32.8), King s (p=0.0001; beta;= -7.40) and MiToS (p=0.0025; beta;= -4.9) scores and negatively correlated with King s and MiToS scales according to Spearman s correlation. Our results demonstrated the ability of the CCF approach in assessing the individual motor condition and its relationship with ALS disease. Thanks to the subject-specific characteristic of this approach, we hope that further exploration related to its clinical application may help improve the management of disease.

Task matters: individual MEG signatures from naturalistic and neurophysiological brain states

“…We found that the spatial signatures of the most identifiable edges for individual differentiation were modified across brain states (Fig. 4-5a for alpha (8)(9)(10)(11)(12)(13) and beta frequencies (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), other frequency bands reported in Supplementary Fig. 3-5).…”

“…Only very recently has the fingerprinting field started to capitalize on the spatiotemporal complexity of fast neurophysiological signals recorded from magnetoencephalography (MEG) in order to investigate neural features of individual differentiation 16,[19][20][21][22] . There are several reasons for doing so, since recorded MEG signals contain extremely rich information 23,24 .…”

“…Second, the measured signals oscillate at multiple frequencies that allow for band-specific interpretations; and third, oscillations that resonate at different frequencies have a biological meaning that is related to cognitive functioning. Indeed, recent studies taking advantage of spontaneous electrophysiological recordings have provided new insights into the neurophysiological nature of brain fingerprints in healthy 16,19 and clinical populations [20][21][22] . So far, these studies have only focused on characterizing individual MEG signatures from task-free conditions, under which individuals are not engaged in any particular task 25,26 .…”

Topological changes of fast large-scale brain dynamics in Mild Cognitive Impairment predict the decay of the hippocampal memory

“…Moreover, a Principal Component Analysis (PCA) was carried out to reduce environmental noise. Finally, we performed a supervised Independent Component Analysis (ICA) to remove physiological artefacts from the ECG (one component per participant) and the EOG (no component per participant, rarely one) (Romano et al, 2022; Rucco et al, 2022)…”

Altered spreading of neuronal avalanches in temporal lobe epilepsy relates to cognitive performance: a resting-state hdEEG study

“…More recently, avalanche transition matrices have been applied to magneto/electroencephalography (M/EEG) data, in order to encapsulate the spatio-temporal spreading of neuronal avalanches on the large scale. These studies highlighted that neuronal avalanches spread preferentially across the white-matter bundles (Sorrentino et al, 2021), and that they are drastically altered in neurodegenerative diseases (Polverino et al, 2022; Romano et al, 2022, Sorrentino et al, 2021). In the context of epilepsy, the criticality framework has been used to investigate global brain dynamics during ictal and interictal epileptic activity (Osorio et al, 2009, Meisel et al, 2012, Arviv et al, 2016) showing a deviation of the system from the critical state in both conditions.…”