Dataset Information

Functional connectome fingerprinting and stability in multiple sclerosis

ABSTRACT:

Background

Functional connectome fingerprinting can identify individuals based on their functional connectome. Previous studies relied mostly on short intervals between fMRI acquisitions.

Objective

This cohort study aimed to determine the stability of connectome-based identification and their underlying signatures in patients with multiple sclerosis and healthy individuals with long follow-up intervals.

Methods

We acquired resting-state fMRI in 70 patients with multiple sclerosis and 273 healthy individuals with long follow-up times (up to 4 and 9 years, respectively). Using functional connectome fingerprinting, we examined the stability of the connectome and additionally investigated which regions, connections and networks supported individual identification. Finally, we predicted cognitive and behavioural outcome based on functional connectivity.

Results

Multiple sclerosis patients showed connectome stability and identification accuracies similar to healthy individuals, with longer time delays between imaging sessions being associated with accuracies dropping from 89% to 76%. Lesion load, brain atrophy or cognitive impairment did not affect identification accuracies within the range of disease severity studied. Connections from the fronto-parietal and default mode network were consistently most distinctive, i.e., informative of identity. The functional connectivity also allowed the prediction of individual cognitive performances.

Conclusion

Our results demonstrate that discriminatory signatures in the functional connectome are stable over extended periods of time in multiple sclerosis, resulting in similar identification accuracies and distinctive long-lasting functional connectome fingerprinting signatures in patients and healthy individuals.

SUBMITTER:

PROVIDER: S-EPMC10460476 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundChanges in large-scale brain networks have been reported in migraine patients, but it remains unclear how these manifest in the various phases of the migraine cycle. Case-control fMRI studies spanning the entire migraine cycle are lacking, precluding a complete assessment of brain functional connectivity in migraine. Such studies are essential for understanding the inherent changes in the brain of migraine patients as well as transient changes along the cycle. Here, we leverage the concept of functional connectome (FC) fingerprinting, whereby individual subjects may be identified based on their FC, to investigate changes in FC and its stability across different phases of the migraine cycle.MethodsWe employ a case-control longitudinal design to study a group of 10 patients with episodic menstrual or menstrual-related migraine without aura, in the 4 phases of their spontaneous migraine cycle (preictal, ictal, postictal, interictal), and a group of 14 healthy controls in corresponding phases of the menstrual cycle, using resting-state functional magnetic resonance imaging (fMRI). We propose a novel multilevel clinical connectome fingerprinting approach to analyse the FC identifiability not only within-subject, but also within-session and within-group.ResultsThis approach allowed us to obtain individual FC fingerprints by reconstructing the data using the first 19 principal components to maximize identifiability at all levels. We found decreased FC identifiability for patients in the preictal phase relative to controls, which increased with the progression of the attack and became comparable to controls in the interictal phase. Using Network-Based Statistic analysis, we found increased FC strength across several brain networks for patients in the ictal and postictal phases relative to controls.ConclusionOur novel multilevel clinical connectome fingerprinting approach captured FC variations along the migraine cycle in a case-control longitudinal study, bringing new insights into the cyclic nature of the disorder.