Phase Linearity Measurement: A Novel Index for Brain Functional Connectivity

Baselice, Fabio; Sorriso, Antonietta; Rucco, Rosaria; Sorrentino, Pierpaolo

doi:10.1109/tmi.2018.2873423

Cited by 41 publications

(66 citation statements)

References 33 publications

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“…With regard to the analysis of real data, we used sourcereconstructed MEG signals that have been recorded from healthy subjects from the MEG facility in Naples, Italy. As expected from our previous work, nearly in all cases the most prominent peak in the interferometric spectrum was the one occurring at 0 Hz [13] . This is not surprising, taking into account the importance of iso-frequency synchronization in the human brain [2].…”

Section: Discussionsupporting

confidence: 89%

“…The proposed metric is based on the analysis of the shape of the interferometric spectrum of the two signals. This approach is an evolution of a previously published metrics, the phase linearity measurement (PLM), that uses the interferometric spectrum to detect the presence of iso-frequency synchronization [13]. In brief, the proposed approach explores the interferometric spectrum, looking for the presence of power peaks at frequencies different from 0 Hz (related to the iso-frequency coupling).…”

Section: Discussionmentioning

confidence: 99%

“…The phase linearity measurement (PLM) is able to effectively measure the iso-frequency functional connectivity between brain areas by analyzing the signals synchronization [13]. Given the time series related to two brain regions acquired by an EEG or a MEG system, the PLM algorithm requires as first step the computation of their analytic signals, followed by the extraction of the phase difference.…”

Section: Methodsmentioning

confidence: 99%

“…The PLM measurement, as presented in [13], is effective in measuring brain connectivity in the iso-frequency case, i.e. when the two signals are within the same frequency bandwidth.…”

Section: Methodsmentioning

confidence: 99%

“…For example, when one deals with resting-state data, it is not known if n:m cross-frequency synchronization is occurring and, if it is, where and between which frequencies. In the current manuscript, we modify an iso-frequency metric designed by our group, the phase linearity measurement (PLM) [13], applying the same principles in order to determine, from broadband data, if any component from the signals is synchronized with others and, if so, the frequencies involved in the coupling. The underlying idea, as explained in the methods, lies in the study of the shape of the interferometric spectrum computed comparing the two signals.…”

Section: Introductionmentioning

confidence: 99%

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An extension of Phase Linearity Measurement for revealing cross frequency coupling among brain areas

Sorrentino¹,

Ambrosanio²,

Rucco³

et al. 2019

J NeuroEngineering Rehabil

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Background: Brain areas need to coordinate their activity in order to enable complex behavioral responses. Synchronization is one of the mechanisms neural ensembles use to communicate. While synchronization between signals operating at similar frequencies is fairly straightforward, the estimation of synchronization occurring between different frequencies of oscillations has proven harder to capture. One specifically hard challenge is to estimate cross-frequency synchronization between broadband signals when no a priori hypothesis is available about the frequencies involved in the synchronization. Methods: In the present manuscript, we expand upon the phase linearity measurement, an iso-frequency synchronization metrics previously developed by our group, in order to provide a conceptually similar approach able to detect the presence of cross-frequency synchronization between any components of the analyzed broadband signals. Results: The methodology has been tested on both synthetic and real data. We first exploited Gaussian process realizations in order to explore the properties of our new metrics in a synthetic case study. Subsequently, we analyze real source-reconstructed data acquired by a magnetoencephalographic system from healthy controls in a clinical setting to study the performance of our metrics in a realistic environment. Conclusions: In the present paper we provide an evolution of the PLM methodology able to reveal the presence of cross-frequency synchronization between broadband data.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The PLM measurement, as presented in [13], is effective in measuring brain connectivity in the iso-frequency case, i.e. when the two signals are within the same frequency bandwidth.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An extension of Phase Linearity Measurement for revealing cross frequency coupling among brain areas

Sorrentino¹,

Ambrosanio²,

Rucco³

et al. 2019

J NeuroEngineering Rehabil

Self Cite

View full text Add to dashboard Cite

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

Lopez

Minino

Liparoti

et al. 2022

Human Brain Mapping

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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.

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Functional brain network topology across the menstrual cycle is estradiol dependent and correlates with individual well‐being

Liparoti

Lopez

Sarno

et al. 2021

J of Neuroscience Research

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The menstrual cycle (MC) is a sex hormone‐related phenomenon that repeats itself cyclically during the woman's reproductive life. In this explorative study, we hypothesized that coordinated variations of multiple sex hormones may affect the large‐scale organization of the brain functional network and that, in turn, such changes might have psychological correlates, even in the absence of overt clinical signs of anxiety and/or depression. To test our hypothesis, we investigated longitudinally, across the MC, the relationship between the sex hormones and both brain network and psychological changes. We enrolled 24 naturally cycling women and, at the early‐follicular, peri‐ovulatory, and mid‐luteal phases of the MC, we performed: (a) sex hormone dosage, (b) magnetoencephalography recording to study the brain network topology, and (c) psychological questionnaires to quantify anxiety, depression, self‐esteem, and well‐being. We showed that during the peri‐ovulatory phase, in the alpha band, the leaf fraction and the tree hierarchy of the brain network were reduced, while the betweenness centrality (BC) of the right posterior cingulate gyrus (rPCG) was increased. Furthermore, the increase in BC was predicted by estradiol levels. Moreover, during the luteal phase, the variation of estradiol correlated positively with the variations of both the topological change and environmental mastery dimension of the well‐being test, which, in turn, was related to the increase in the BC of rPCG. Our results highlight the effects of sex hormones on the large‐scale brain network organization as well as on their possible relationship with the psychological state across the MC. Moreover, the fact that physiological changes in the brain topology occur throughout the MC has widespread implications for neuroimaging studies.

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Phase Linearity Measurement: A Novel Index for Brain Functional Connectivity

Cited by 41 publications

References 33 publications

An extension of Phase Linearity Measurement for revealing cross frequency coupling among brain areas

An extension of Phase Linearity Measurement for revealing cross frequency coupling among brain areas

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

Functional brain network topology across the menstrual cycle is estradiol dependent and correlates with individual well‐being

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