1
|
Troisi Lopez E, Minino R, Liparoti M, Polverino A, Romano A, De Micco R, Lucidi F, Tessitore A, Amico E, Sorrentino G, Jirsa V, Sorrentino P. Fading of brain network fingerprint in Parkinson's disease predicts motor clinical impairment. Hum Brain Mapp 2022; 44:1239-1250. [PMID: 36413043 PMCID: PMC9875937 DOI: 10.1002/hbm.26156] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/18/2022] [Accepted: 11/10/2022] [Indexed: 11/23/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Emahnuel Troisi Lopez
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Roberta Minino
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Marianna Liparoti
- Department of Developmental and Social PsychologyUniversity "La Sapienza" of RomeRomeItaly
| | - Arianna Polverino
- Institute for Diagnosis and Treatment Hermitage CapodimonteNaplesItaly
| | - Antonella Romano
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Rosa De Micco
- Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Fabio Lucidi
- Department of Developmental and Social PsychologyUniversity "La Sapienza" of RomeRomeItaly
| | - Alessandro Tessitore
- Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, EPFLGenevaSwitzerland,Department of Radiology and Medical InformaticsUniversity of Geneva (UNIGE)GenevaSwitzerland
| | - Giuseppe Sorrentino
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly,Institute for Diagnosis and Treatment Hermitage CapodimonteNaplesItaly,Institute of Applied Sciences and Intelligent Systems, National Research CouncilNaplesItaly
| | - Viktor Jirsa
- Institut de Neurosciences des SystèmesAix‐Marseille UniversitéMarseilleFrance
| | | |
Collapse
|
2
|
Troisi Lopez E, Colonnello V, Liparoti M, Castaldi M, Alivernini F, Russo PM, Sorrentino G, Lucidi F, Mandolesi L, Sorrentino P. Brain network topology and personality traits: A source level magnetoencephalographic study. Scand J Psychol 2022; 63:495-503. [PMID: 35674278 PMCID: PMC9796445 DOI: 10.1111/sjop.12835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 04/08/2022] [Accepted: 05/04/2022] [Indexed: 01/01/2023]
Abstract
Personality neuroscience is focusing on the correlation between individual differences and the efficiency of large-scale networks from the perspective of the brain as an interconnected network. A suitable technique to explore this relationship is the magnetoencephalography (MEG), but not many MEG studies are aimed at investigating topological properties correlated to personality traits. By using MEG, the present study aims to evaluate how individual differences described in Cloninger's psychobiological model are correlated with specific cerebral structures. Fifty healthy individuals (20 males, 30 females, mean age: 27.4 ± 4.8 years) underwent Temperament and Character Inventory examination and MEG recording during a resting state condition. High harm avoidance scores were associated with a reduced centrality of the left caudate nucleus and this negative correlation was maintained in females when we analyzed gender differences. Our data suggest that the caudate nucleus plays a key role in adaptive behavior and could be a critical node in insular salience network. The clear difference between males and females allows us to suggest that topological organization correlated to personality is highly dependent on gender. Our findings provide new insights to evaluate the mutual influences of topological and functional connectivity in neural communication efficiency and disruption as biomarkers of psychopathological traits.
Collapse
Affiliation(s)
- Emahnuel Troisi Lopez
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Valentina Colonnello
- Department of Experimental, Diagnostic and Specialty Medicine, Alma Mater StudiorumUniversity of Bologna, Policlinico S. Orsola‐MalpighiBolognaItaly
| | - Marianna Liparoti
- Department of Social and Developmental Psychology, Faculty of Medicine and PsychologyUniversity of Roma “Sapienza”RomeItaly
| | - Mauro Castaldi
- Institute for Diagnosis and Cure Hermitage CapodimonteNaplesItaly
| | - Fabio Alivernini
- Department of Social and Developmental Psychology, Faculty of Medicine and PsychologyUniversity of Roma “Sapienza”RomeItaly
| | - Paolo Maria Russo
- Department of Experimental, Diagnostic and Specialty Medicine, Alma Mater StudiorumUniversity of Bologna, Policlinico S. Orsola‐MalpighiBolognaItaly
| | - Giuseppe Sorrentino
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly,Institute for Diagnosis and Cure Hermitage CapodimonteNaplesItaly
| | - Fabio Lucidi
- Department of Social and Developmental Psychology, Faculty of Medicine and PsychologyUniversity of Roma “Sapienza”RomeItaly
| | - Laura Mandolesi
- Department of HumanitiesUniversity of Naples “Federico II”NaplesItaly
| | | |
Collapse
|
3
|
Sareen E, Zahar S, Ville DVD, Gupta A, Griffa A, Amico E. Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations. Neuroimage 2021; 240:118331. [PMID: 34237444 DOI: 10.1016/j.neuroimage.2021.118331] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/22/2021] [Accepted: 07/01/2021] [Indexed: 12/16/2022] Open
Abstract
Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprinting performances in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identifiability offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.
Collapse
Affiliation(s)
- Ekansh Sareen
- Signal Processing and Biomedical Imaging, Dept. of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India
| | - Sélima Zahar
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Anubha Gupta
- Signal Processing and Biomedical Imaging, Dept. of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India
| | - Alessandra Griffa
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
| |
Collapse
|
4
|
Liparoti M, Troisi Lopez E, Sarno L, Rucco R, Minino R, Pesoli M, Perruolo G, Formisano P, Lucidi F, Sorrentino G, Sorrentino P. Functional brain network topology across the menstrual cycle is estradiol dependent and correlates with individual well-being. J Neurosci Res 2021; 99:2271-2286. [PMID: 34110041 PMCID: PMC8453714 DOI: 10.1002/jnr.24898] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 05/11/2021] [Accepted: 05/15/2021] [Indexed: 12/16/2022]
Abstract
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.
Collapse
Affiliation(s)
- Marianna Liparoti
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy
| | - Emahnuel Troisi Lopez
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy
| | - Laura Sarno
- Department of Neurosciences, Reproductive Science and Dentistry, University of Naples "Federico II", Naples, Italy
| | - Rosaria Rucco
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy.,Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli, Italy
| | - Roberta Minino
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy
| | - Matteo Pesoli
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy
| | - Giuseppe Perruolo
- Department of Translational Medicine, University of Naples "Federico II", Naples, Italy.,URT "Genomic of Diabetes" of Institute of Experimental Endocrinology and Oncology, National Council of Research, CNR, Naples, Italy
| | - Pietro Formisano
- Department of Translational Medicine, University of Naples "Federico II", Naples, Italy.,URT "Genomic of Diabetes" of Institute of Experimental Endocrinology and Oncology, National Council of Research, CNR, Naples, Italy
| | - Fabio Lucidi
- Department of Developmental and Social Psychology, University of Rome "Sapienza", Rome, Italy
| | - Giuseppe Sorrentino
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy.,Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli, Italy.,Hermitage Capodimonte Clinic, Naples, Italy
| | - Pierpaolo Sorrentino
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli, Italy.,Institut de Neurosciences des Systèmes, Faculty of Medicine, Aix-Marseille Université, Marseille, France
| |
Collapse
|