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

Lopez, Emahnuel Troisi; Minino, Roberta; Liparoti, Marianna; Polverino, Arianna; Romano, Antonella; Lucidi, Fabio; Tessitore, Alessandro; Amico, Enrico; Sorrentino, Giuseppe; Jirsa, Viktor K.; Sorrentino, Pierpaolo

doi:10.1101/2022.02.03.22270343

Cited by 6 publications

(6 citation statements)

References 57 publications

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“…Another study proposed the identifiability score and the general brain fingerprinting framework as a way to define clinical connectome fingerprints relevant to cognitive decline (Sorrentino, Rucco, Lardone, et al, 2021). In another study, the authors proposed the clinical connectome fingerprint (CCF) approach where they showed a reduction of the identifiability score in the cohort with Parkinson's disease in beta band which was also proportional to the motor impairment (Lopez et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Multiplexity of human brain oscillations as a personal brain signature

Dimitriadis,

Routley,

Linden

et al. 2023

Human Brain Mapping

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Human individuality is likely underpinned by the constitution of functional brain networks that ensure consistency of each person's cognitive and behavioral profile. These functional networks should, in principle, be detectable by noninvasive neurophysiology. We use a method that enables the detection of dominant frequencies of the interaction between every pair of brain areas at every temporal segment of the recording period, the dominant coupling modes (DoCM). We apply this method to brain oscillations, measured with magnetoencephalography (MEG) at rest in two independent datasets, and show that the spatiotemporal evolution of DoCMs constitutes an individualized brain fingerprint. Based on this successful fingerprinting we suggest that DoCMs are important targets for the investigation of neural correlates of individual psychological parameters and can provide mechanistic insight into the underlying neurophysiological processes, as well as their disturbance in brain diseases.

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Section: Discussionmentioning

confidence: 99%

Multiplexity of human brain oscillations as a personal brain signature

Dimitriadis,

Routley,

Linden

et al. 2023

Human Brain Mapping

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“…Several recent neuroimaging studies have shown that ongoing brain activity at rest, without performing a specific task, defines a neurophysiological profile unique to each person. Unlike hand fingerprints, these brain-fingerprints are associated with individual cognitive traits and are altered by pathology (1)(2)(3)(4)(5)(6)(7)(8)(9), forming a distinct personal neurophysiological profile. Whether a person's genotype is associated with their neurophysiological profile is currently unknown.…”

Section: Main Textmentioning

confidence: 99%

Genetic Foundations of Neurophysiological and Behavioural Variability Across the Lifespan

da Silva Castanheira,

Poli,

Hansen

et al. 2024

Preprint

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Neurophysiological brain activity underpins cognitive functions and behavioural traits. Here, we sought to establish to what extent individual neurophysiological traits spontaneously expressed in ongoing brain activity are primarily driven by genetic variation. We also investigated whether changes in such neurophysiological features observed across the lifespan are supported by longitudinal changes in cortical gene expression. We studied the heritability of neurophysiological traits from task-free brain activity of monozygotic and dizygotic twins as well as non-related individuals recorded with magnetoencephalography. We found that these traits were more similar between monozygotic twins compared to dizygotic twins, and that these heritable core dynamical properties of brain activity are predominantly influenced by genes involved in neurotransmission processes. These genes are expressed in the cortex along a topographical gradient aligned with the distribution of major cognitive functions and psychological processes. Our data also show that the impact of these genetic determinants on cognitive and psychological traits increases with age. These findings collectively highlight the persistent genetic influence across the lifespan on neurophysiological brain activity that supports individual cognitive and behavioural traits.

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“…MEG acquisition, preprocessing, source reconstruction and synchrony estimation have been performed according to our previous works 23,66,69 .…”

Section: Meg Acquisition Preprocessing and Source Reconstructionmentioning

confidence: 99%

“…Such an approach has been previously tested in both health and disease, revealing that people suffering from neurological diseases show reduced identifiability with respect to healthy subjects. The reduced identifiability was able in predicting individual clinical features, giving birth to the concept of the Clinical Connectome Fingerprint (CCF) [23][24][25][26] . In healthy conditions, the same methodology may help outlining brain-behavior relations, by highlighting connectivity patterns variation, as a function of physiological modifications.…”

Section: Introductionmentioning

confidence: 99%

Brain fingerprint changes across the menstrual cycle correlate with emotional state

Cipriano¹,

Liparoti

Lopez³

et al. 2023

Preprint

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Background: Menstrual cycle (MC) is the cyclical phenomenon with the greatest impact on women's mood and behavior. To date, little is known about the potential mechanism and neuroanatomical correlates of behavioral and emotional fluctuations across the MC. Brain connectome fingerprinting, a recently introduced technique in the field of brain network analysis, represents a valid approach in assessing the subject-specific connectivity and in predicting clinical impairment in several neurological diseases. Nevertheless, its performance, and clinical utility, in healthy individuals has not yet been investigated. Methods: We conducted the Clinical Connectome Fingerprint (CCF) analysis on source-reconstructed magnetoencephalography signals in a cohort of 24 women across the MC. Results: All the parameters of identifiability did not differ according to the MC phases. The periovulatory and mid-luteal phases showed a less stable, more variable over time, brain connectome compared to the early follicular phase. This difference in brain connectome stability (especially in the posterior brain regions) was able to significantly predict self-esteem, wellbeing, and mood. Conclusion: These results confirm the high reliability of the CCF and its independence from the MC phases and, at the same time, provide neuroanatomical correlates of the emotional and mood aspects that change across the MC.

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

Cited by 6 publications

References 57 publications

Multiplexity of human brain oscillations as a personal brain signature

Multiplexity of human brain oscillations as a personal brain signature

Genetic Foundations of Neurophysiological and Behavioural Variability Across the Lifespan

Brain fingerprint changes across the menstrual cycle correlate with emotional state

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