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Soleimani N, Iraji A, Belger A, Calhoun VD. A method for estimating dynamic functional network connectivity gradients (dFNG) from ICA captures smooth inter-network modulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583731. [PMID: 38559041 PMCID: PMC10979844 DOI: 10.1101/2024.03.06.583731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Dynamic functional network connectivity (dFNC) analysis is a widely used approach for studying brain function and offering insight into how brain networks evolve over time. Typically, dFNC studies utilized fixed spatial maps and evaluate transient changes in coupling among time courses estimated from independent component analysis (ICA). This manuscript presents a complementary approach that relaxes this assumption by spatially reordering the components dynamically at each timepoint to optimize for a smooth gradient in the FNC (i.e., a smooth gradient among ICA connectivity values). Several methods are presented to summarize dynamic FNC gradients (dFNGs) over time, starting with static FNC gradients (sFNGs), then exploring the reordering properties as well as the dynamics of the gradients themselves. We then apply this approach to a dataset of schizophrenia (SZ) patients and healthy controls (HC). Functional dysconnectivity between different brain regions has been reported in schizophrenia, yet the neural mechanisms behind it remain elusive. Using resting state fMRI and ICA on a dataset consisting of 151 schizophrenia patients and 160 age and gender-matched healthy controls, we extracted 53 intrinsic connectivity networks (ICNs) for each subject using a fully automated spatially constrained ICA approach. We develop several summaries of our functional network connectivity gradient analysis, both in a static sense, computed as the Pearson correlation coefficient between full time series, and a dynamic sense, computed using a sliding window approach followed by reordering based on the computed gradient, and evaluate group differences. Static connectivity analysis revealed significantly stronger connectivity between subcortical (SC), auditory (AUD) and visual (VIS) networks in patients, as well as hypoconnectivity in sensorimotor (SM) network relative to controls. sFNG analysis highlighted distinctive clustering patterns in patients and HCs along cognitive control (CC)/ default mode network (DMN), SC/ AUD/ SM/ cerebellar (CB), and VIS gradients. Furthermore, we observed significant differences in the sFNGs between groups in SC and CB domains. dFNG analysis suggested that SZ patients spend significantly more time in a SC/ CB state based on the first gradient, while HCs favor the DMN state. For the second gradient, however, patients exhibited significantly higher activity in CB/ VIS domains, contrasting with HCs' DMN engagement. The gradient synchrony analysis conveyed more shifts between SM/ SC networks and transmodal CC/ DMN networks in patients. In addition, the dFNG coupling revealed distinct connectivity patterns between SC, SM and CB centroids in SZ patients compared to HCs. To recap, our results advance our understanding of brain network modulation by examining smooth connectivity trajectories. This provides a more complete spatiotemporal summary of the data, contributing to the growing body of current literature regarding the functional dysconnectivity in schizophrenia patients. By employing dFNG, we highlight a new perspective to capture large scale fluctuations across the brain while maintaining the convenience of brain networks and low dimensional summary measures.
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Affiliation(s)
- Najme Soleimani
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
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Duma GM, Pellegrino G, Rabuffo G, Danieli A, Antoniazzi L, Vitale V, Scotto Opipari R, Bonanni P, Sorrentino P. Altered spread of waves of activities at large scale is influenced by cortical thickness organization in temporal lobe epilepsy: a magnetic resonance imaging-high-density electroencephalography study. Brain Commun 2023; 6:fcad348. [PMID: 38162897 PMCID: PMC10754317 DOI: 10.1093/braincomms/fcad348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/11/2023] [Accepted: 12/13/2023] [Indexed: 01/03/2024] Open
Abstract
Temporal lobe epilepsy is a brain network disorder characterized by alterations at both the structural and the functional levels. It remains unclear how structure and function are related and whether this has any clinical relevance. In the present work, we adopted a novel methodological approach investigating how network structural features influence the large-scale dynamics. The functional network was defined by the spatio-temporal spreading of aperiodic bursts of activations (neuronal avalanches), as observed utilizing high-density electroencephalography in patients with temporal lobe epilepsy. The structural network was modelled as the region-based thickness covariance. Loosely speaking, we quantified the similarity of the cortical thickness of any two brain regions, both across groups and at the individual level, the latter utilizing a novel approach to define the subject-wise structural covariance network. In order to compare the structural and functional networks (at the nodal level), we studied the correlation between the probability that a wave of activity would propagate from a source to a target region and the similarity of the source region thickness as compared with other target brain regions. Building on the recent evidence that large-waves of activities pathologically spread through the epileptogenic network in temporal lobe epilepsy, also during resting state, we hypothesize that the structural cortical organization might influence such altered spatio-temporal dynamics. We observed a stable cluster of structure-function correlation in the bilateral limbic areas across subjects, highlighting group-specific features for left, right and bilateral temporal epilepsy. The involvement of contralateral areas was observed in unilateral temporal lobe epilepsy. We showed that in temporal lobe epilepsy, alterations of structural and functional networks pair in the regions where seizures propagate and are linked to disease severity. In this study, we leveraged on a well-defined model of neurological disease and pushed forward personalization approaches potentially useful in clinical practice. Finally, the methods developed here could be exploited to investigate the relationship between structure-function networks at subject level in other neurological conditions.
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Affiliation(s)
- Gian Marco Duma
- Epilepsy Unit, IRCCS E. Medea Scientific Institute, Conegliano 31015, Italy
| | - Giovanni Pellegrino
- Epilepsy Program, Schulich School of Medicine and Dentistry, Western University, London N6A5C1, Canada
| | - Giovanni Rabuffo
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille 13005, France
| | - Alberto Danieli
- Epilepsy Unit, IRCCS E. Medea Scientific Institute, Conegliano 31015, Italy
| | - Lisa Antoniazzi
- Epilepsy Unit, IRCCS E. Medea Scientific Institute, Conegliano 31015, Italy
| | - Valerio Vitale
- Department of Neuroscience, Neuroradiology Unit, San Bortolo Hospital, Vicenza 36100, Italy
| | | | - Paolo Bonanni
- Epilepsy Unit, IRCCS E. Medea Scientific Institute, Conegliano 31015, Italy
| | - Pierpaolo Sorrentino
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille 13005, France
- Department of Biomedical Sciences, University of Sassari, Sassari 07100, Italy
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Romano A, Troisi Lopez E, Cipriano L, Liparoti M, Minino R, Polverino A, Cavaliere C, Aiello M, Granata C, Sorrentino G, Sorrentino P. Topological changes of fast large-scale brain dynamics in mild cognitive impairment predict early memory impairment: a resting-state, source reconstructed, magnetoencephalography study. Neurobiol Aging 2023; 132:36-46. [PMID: 37717553 DOI: 10.1016/j.neurobiolaging.2023.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 09/19/2023]
Abstract
Functional connectivity has been used as a framework to investigate widespread brain interactions underlying cognitive deficits in mild cognitive impairment (MCI). However, many functional connectivity metrics focus on the average of the periodic activities, disregarding the aperiodic bursts of activity (i.e., the neuronal avalanches) characterizing the large-scale dynamic activities of the brain. Here, we apply the recently described avalanche transition matrix framework to source-reconstructed magnetoencephalography signals in a cohort of 32 MCI patients and 32 healthy controls to describe the spatio-temporal features of neuronal avalanches and explore their topological properties. Our results showed that MCI patients showed a more centralized network (as assessed by higher values of the degree divergence and leaf fraction) as compared to healthy controls. Furthermore, we found that the degree divergence (in the theta band) was predictive of hippocampal memory impairment. These findings highlight the role of the changes of aperiodic bursts in clinical conditions and may contribute to a more thorough phenotypical assessment of patients.
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Affiliation(s)
- Antonella Romano
- Department of Motor and Wellness Sciences, University of Naples "Parthenope", Naples, Italy
| | - Emahnuel Troisi Lopez
- Department of Motor and Wellness Sciences, University of Naples "Parthenope", Naples, Italy
| | - Lorenzo Cipriano
- Department of Motor and Wellness Sciences, University of Naples "Parthenope", Naples, Italy
| | - Marianna Liparoti
- Department of Developmental and Social Psychology, University of Rome "La Sapienza", Rome, Italy
| | - Roberta Minino
- Department of Motor and Wellness Sciences, University of Naples "Parthenope", Naples, Italy
| | - Arianna Polverino
- Institute of Diagnosis and Treatment, Hermitage Capodimonte, Naples, Italy
| | - Carlo Cavaliere
- IRCCS SYNLAB-SDN, Naples Via Emanuele Gianturco, Naples, Italy
| | - Marco Aiello
- IRCCS SYNLAB-SDN, Naples Via Emanuele Gianturco, Naples, Italy
| | - Carmine Granata
- Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy
| | - Giuseppe Sorrentino
- Department of Motor and Wellness Sciences, University of Naples "Parthenope", Naples, Italy; Institute of Diagnosis and Treatment, Hermitage Capodimonte, Naples, Italy; Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy.
| | - Pierpaolo Sorrentino
- Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy; Institut de Neurosciences des Systèmes, Inserm, INS, Aix-Marseille University, Marseille, France
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Betzel RF, Faskowitz J, Sporns O. Living on the edge: network neuroscience beyond nodes. Trends Cogn Sci 2023; 27:1068-1084. [PMID: 37716895 PMCID: PMC10592364 DOI: 10.1016/j.tics.2023.08.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/14/2023] [Accepted: 08/10/2023] [Indexed: 09/18/2023]
Abstract
Network neuroscience has emphasized the connectional properties of neural elements - cells, populations, and regions. This has come at the expense of the anatomical and functional connections that link these elements to one another. A new perspective - namely one that emphasizes 'edges' - may prove fruitful in addressing outstanding questions in network neuroscience. We highlight one recently proposed 'edge-centric' method and review its current applications, merits, and limitations. We also seek to establish conceptual and mathematical links between this method and previously proposed approaches in the network science and neuroimaging literature. We conclude by presenting several avenues for future work to extend and refine existing edge-centric analysis.
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Affiliation(s)
- Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA.
| | - Joshua Faskowitz
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA
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Sorrentino P, Lopez ET, Romano A, Granata C, Corsi MC, Sorrentino G, Jirsa V. Brain fingerprint is based on the aperiodic, scale-free, neuronal activity. Neuroimage 2023:120260. [PMID: 37392807 DOI: 10.1016/j.neuroimage.2023.120260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/13/2023] [Accepted: 06/28/2023] [Indexed: 07/03/2023] Open
Abstract
Subject differentiation bears the possibility to individualize brain analyses. However, the nature of the processes generating subject-specific features remains unknown. Most of the current literature uses techniques that assume stationarity (e.g., Pearson's correlation), which might fail to capture the non-linear nature of brain activity. We hypothesize that non-linear perturbations (defined as neuronal avalanches in the context of critical dynamics) spread across the brain and carry subject-specific information, contributing the most to differentiability. To test this hypothesis, we compute the avalanche transition matrix (ATM) from source-reconstructed magnetoencephalographic data, as to characterize subject-specific fast dynamics. We perform differentiability analysis based on the ATMs, and compare the performance to that obtained using Pearson's correlation (which assumes stationarity). We demonstrate that selecting the moments and places where neuronal avalanches spread improves differentiation (P < 0.0001, permutation testing), despite the fact that most of the data (i.e., the linear part) are discarded. Our results show that the non-linear part of the brain signals carries most of the subject-specific information, thereby clarifying the nature of the processes that underlie individual differentiation. Borrowing from statistical mechanics, we provide a principled way to link emergent large-scale personalized activations to non-observable, microscopic processes.
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Affiliation(s)
- Pierpaolo Sorrentino
- Institut de Neurosciences des Systèmes, Aix-Marseille Universitè, Marseille, France; Institute of Applied Sciences and Intelligent Systems, CNR, Naples, Italy.
| | - Emahnuel Troisi Lopez
- Institute of Applied Sciences and Intelligent Systems, CNR, Naples, Italy; Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy
| | - Antonella Romano
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy
| | - Carmine Granata
- Institute of Applied Sciences and Intelligent Systems, CNR, Naples, Italy
| | - Marie Constance Corsi
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France
| | - Giuseppe Sorrentino
- Institute of Applied Sciences and Intelligent Systems, CNR, Naples, Italy; Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy; Institute of Diagnosis and Treatment Hermitage Capodimonte, Naples, Italy
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Aix-Marseille Universitè, Marseille, France
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