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Madden DJ, Merenstein JL, Mullin HA, Jain S, Rudolph MD, Cohen JR. Age-related differences in resting-state, task-related, and structural brain connectivity: graph theoretical analyses and visual search performance. Brain Struct Funct 2024:10.1007/s00429-024-02807-2. [PMID: 38856933 DOI: 10.1007/s00429-024-02807-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 05/13/2024] [Indexed: 06/11/2024]
Abstract
Previous magnetic resonance imaging (MRI) research suggests that aging is associated with a decrease in the functional interconnections within and between groups of locally organized brain regions (modules). Further, this age-related decrease in the segregation of modules appears to be more pronounced for a task, relative to a resting state, reflecting the integration of functional modules and attentional allocation necessary to support task performance. Here, using graph-theoretical analyses, we investigated age-related differences in a whole-brain measure of module connectivity, system segregation, for 68 healthy, community-dwelling individuals 18-78 years of age. We obtained resting-state, task-related (visual search), and structural (diffusion-weighted) MRI data. Using a parcellation of modules derived from the participants' resting-state functional MRI data, we demonstrated that the decrease in system segregation from rest to task (i.e., reconfiguration) increased with age, suggesting an age-related increase in the integration of modules required by the attentional demands of visual search. Structural system segregation increased with age, reflecting weaker connectivity both within and between modules. Functional and structural system segregation had qualitatively different influences on age-related decline in visual search performance. Functional system segregation (and reconfiguration) influenced age-related decline in the rate of visual evidence accumulation (drift rate), whereas structural system segregation contributed to age-related slowing of encoding and response processes (nondecision time). The age-related differences in the functional system segregation measures, however, were relatively independent of those associated with structural connectivity.
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Affiliation(s)
- David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA.
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, 27710, USA.
- Center for Cognitive Neuroscience, Duke University, Durham, NC, 27708, USA.
| | - Jenna L Merenstein
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA
| | - Hollie A Mullin
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA
- Department of Psychology, Pennsylvania State University, University Park, PA, 16802, USA
| | - Shivangi Jain
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA
- AdventHealth Research Institute, Neuroscience Institute, Orlando, FL, 32804, USA
| | - Marc D Rudolph
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
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2
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Corredor D, Segobin S, Hinault T, Eustache F, Dayan J, Guillery-Girard B, Naveau M. The multiscale topological organization of the functional brain network in adolescent PTSD. Cereb Cortex 2024; 34:bhae246. [PMID: 38864573 PMCID: PMC11167567 DOI: 10.1093/cercor/bhae246] [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: 01/04/2024] [Revised: 05/17/2024] [Accepted: 05/18/2024] [Indexed: 06/13/2024] Open
Abstract
The experience of an extremely aversive event can produce enduring deleterious behavioral, and neural consequences, among which posttraumatic stress disorder (PTSD) is a representative example. Although adolescence is a period of great exposure to potentially traumatic events, the effects of trauma during adolescence remain understudied in clinical neuroscience. In this exploratory work, we aim to study the whole-cortex functional organization of 14 adolescents with PTSD using a data-driven method tailored to our population of interest. To do so, we built on the network neuroscience framework and specifically on multilayer (multisubject) community analysis to study the functional connectivity of the brain. We show, across different topological scales (the number of communities composing the cortex), a hyper-colocalization between regions belonging to occipital and pericentral regions and hypo-colocalization in middle temporal, posterior-anterior medial, and frontal cortices in the adolescent PTSD group compared to a nontrauma exposed group of adolescents. These preliminary results raise the question of an altered large-scale cortical organization in adolescent PTSD, opening an interesting line of research for future investigations.
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Affiliation(s)
- David Corredor
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Shailendra Segobin
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Thomas Hinault
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Francis Eustache
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Jacques Dayan
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
- Pôle Hospitalo-Universitaire de Psychiatrie de l’Enfant et de l’Adolescent, Centre Hospitalier Guillaume Régnier, Université Rennes 1, Rennes 35700, France
| | - Bérengère Guillery-Girard
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Mikaël Naveau
- UNICAEN, CNRS, INSERM, CEA, UAR3408 CYCERON, Normandie Université, Caen 14000, France
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Fouladivanda M, Iraji A, Wu L, van Erp TG, Belger A, Hawamdeh F, Pearlson GD, Calhoun VD. A spatially constrained independent component analysis jointly informed by structural and functional network connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.13.553101. [PMID: 38853973 PMCID: PMC11160563 DOI: 10.1101/2023.08.13.553101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. Brain connectivity of different modalities provides insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multi-modal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multi-objective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multi-modal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared to the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.
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Affiliation(s)
- Mahshid Fouladivanda
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
| | - Armin Iraji
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
| | - Lei Wu
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Theodorus G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior School of Medicine, University of California, Irvine, CA, USA
| | - Aysenil Belger
- Department of Psychiatry Director, Neuroimaging Research in Psychiatry Director, Clinical Translational Core, UNC Intellectual and Developmental Disabilities Research Center, University of North Carolina, Chapel Hill, NC, USA
| | - Faris Hawamdeh
- Center for Disaster Informatics and Computational Epidemiology (DICE), Georgia State University, Atlanta, GA, USA
| | - Godfrey D. Pearlson
- Olin Neuropsychiatry Research Center, Department of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, USA
| | - Vince D. Calhoun
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
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Fornaro S, Menardi A, Vallesi A. Topological features of functional brain networks and subclinical impulsivity: an investigation in younger and older adults. Brain Struct Funct 2024; 229:865-877. [PMID: 38446245 PMCID: PMC11003924 DOI: 10.1007/s00429-023-02745-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/28/2023] [Indexed: 03/07/2024]
Abstract
Impulsive traits (i.e., the tendency to act without forethought regardless of negative outcomes) are frequently found in healthy populations. When exposed to risk factors, individuals may develop debilitating disorders of impulse control (addiction, substance abuse, gambling) characterized by behavioral and cognitive deficits, eventually leading to huge socioeconomic costs. With the far-reaching aim of preventing the onset of impulsive disorders, it is relevant to investigate the topological organization of functional brain networks associated with impulsivity in sub-clinical populations. Taking advantage of the open-source LEMON dataset, we investigated the topological features of resting-state functional brain networks associated with impulsivity in younger (n = 146, age: 20-35) and older (n = 61, age: 59-77) individuals, using a graph-theoretical approach. Specifically, we computed indices of segregation and integration at the level of specific circuits and nodes known to be involved in impulsivity (frontal, limbic, and striatal networks). In younger individuals, results revealed that impulsivity was associated with a more widespread, less clustered and less efficient functional organization, at all levels of analyses and in all selected networks. Conversely, impulsivity in older individuals was associated with reduced integration and increased segregation of striatal regions. Speculatively, such alterations of functional brain networks might underlie behavioral and cognitive abnormalities associated with impulsivity, a working hypothesis worth being tested in future research. Lastly, differences between younger and older individuals might reflect the implementation of age-specific adaptive strategies, possibly accounting for observed differences in behavioral manifestations. Potential interpretations, limitations and implications are discussed.
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Affiliation(s)
- Silvia Fornaro
- Department of Neuroscience (DNS), University of Padova, Padova, Italy.
- Padova Neuroscience Center, University of Padova, Padova, Italy.
| | - Arianna Menardi
- Department of Neuroscience (DNS), University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Antonino Vallesi
- Department of Neuroscience (DNS), University of Padova, Padova, Italy.
- Padova Neuroscience Center, University of Padova, Padova, Italy.
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5
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Han H, Jiang J, Gu L, Gan JQ, Wang H. Brain connectivity patterns derived from aging-related alterations in dynamic brain functional networks and their potential as features for brain age classification. J Neural Eng 2024; 21:026015. [PMID: 38479020 DOI: 10.1088/1741-2552/ad33b1] [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: 05/20/2023] [Accepted: 03/13/2024] [Indexed: 03/26/2024]
Abstract
Objective.Recent studies have demonstrated that the analysis of brain functional networks (BFNs) is a powerful tool for exploring brain aging and age-related neurodegenerative diseases. However, investigating the mechanism of brain aging associated with dynamic BFN is still limited. The purpose of this study is to develop a novel scheme to explore brain aging patterns by constructing dynamic BFN using resting-state functional magnetic resonance imaging data.Approach.A dynamic sliding-windowed non-negative block-diagonal representation (dNBDR) method is proposed for constructing dynamic BFN, based on which a collection of dynamic BFN measures are suggested for examining age-related differences at the group level and used as features for brain age classification at the individual level.Results.The experimental results reveal that the dNBDR method is superior to the sliding time window with Pearson correlation method in terms of dynamic network structure quality. Additionally, significant alterations in dynamic BFN structures exist across the human lifespan. Specifically, average node flexibility and integration coefficient increase with age, while the recruitment coefficient shows a decreased trend. The proposed feature extraction scheme based on dynamic BFN achieved the highest accuracy of 78.7% in classifying three brain age groups.Significance. These findings suggest that dynamic BFN measures, dynamic community structure metrics in particular, play an important role in quantitatively assessing brain aging.
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Affiliation(s)
- Hongfang Han
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, People's Republic of China
| | - Jiuchuan Jiang
- School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210003, Jiangsu, People's Republic of China
| | - Lingyun Gu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, People's Republic of China
| | - John Q Gan
- School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, People's Republic of China
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6
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Puxeddu MG, Faskowitz J, Seguin C, Yovel Y, Assaf Y, Betzel R, Sporns O. Relation of connectome topology to brain volume across 103 mammalian species. PLoS Biol 2024; 22:e3002489. [PMID: 38315722 PMCID: PMC10868790 DOI: 10.1371/journal.pbio.3002489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 02/15/2024] [Accepted: 01/08/2024] [Indexed: 02/07/2024] Open
Abstract
The brain connectome is an embedded network of anatomically interconnected brain regions, and the study of its topological organization in mammals has become of paramount importance due to its role in scaffolding brain function and behavior. Unlike many other observable networks, brain connections incur material and energetic cost, and their length and density are volumetrically constrained by the skull. Thus, an open question is how differences in brain volume impact connectome topology. We address this issue using the MaMI database, a diverse set of mammalian connectomes reconstructed from 201 animals, covering 103 species and 12 taxonomy orders, whose brain size varies over more than 4 orders of magnitude. Our analyses focus on relationships between volume and modular organization. After having identified modules through a multiresolution approach, we observed how connectivity features relate to the modular structure and how these relations vary across brain volume. We found that as the brain volume increases, modules become more spatially compact and dense, comprising more costly connections. Furthermore, we investigated how spatial embedding shapes network communication, finding that as brain volume increases, nodes' distance progressively impacts communication efficiency. We identified modes of variation in network communication policies, as smaller and bigger brains show higher efficiency in routing- and diffusion-based signaling, respectively. Finally, bridging network modularity and communication, we found that in larger brains, modular structure imposes stronger constraints on network signaling. Altogether, our results show that brain volume is systematically related to mammalian connectome topology and that spatial embedding imposes tighter restrictions on larger brains.
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Affiliation(s)
- Maria Grazia Puxeddu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
| | - Yossi Yovel
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Tel Aviv, Israel
| | - Yaniv Assaf
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Tel Aviv, Israel
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
- Program in Neuroscience, Indiana University, Bloomington, Indiana, United States of America
- Program in Cognitive Science, Indiana University, Bloomington, Indiana, United States of America
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
- Program in Neuroscience, Indiana University, Bloomington, Indiana, United States of America
- Program in Cognitive Science, Indiana University, Bloomington, Indiana, United States of America
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7
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Canal-Garcia A, Veréb D, Mijalkov M, Westman E, Volpe G, Pereira JB. Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer's disease. Cereb Cortex 2024; 34:bhad542. [PMID: 38212285 PMCID: PMC10839846 DOI: 10.1093/cercor/bhad542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/13/2024] Open
Abstract
Increasing evidence suggests that patients with Alzheimer's disease present alterations in functional connectivity but previous results have not always been consistent. One of the reasons that may account for this inconsistency is the lack of consideration of temporal dynamics. To address this limitation, here we studied the dynamic modular organization on resting-state functional magnetic resonance imaging across different stages of Alzheimer's disease using a novel multilayer brain network approach. Participants from preclinical and clinical Alzheimer's disease stages were included. Temporal multilayer networks were used to assess time-varying modular organization. Logistic regression models were employed for disease stage discrimination, and partial least squares analyses examined associations between dynamic measures with cognition and pathology. Temporal multilayer functional measures distinguished all groups, particularly preclinical stages, overcoming the discriminatory power of risk factors such as age, sex, and APOE ϵ4 carriership. Dynamic multilayer functional measures exhibited strong associations with cognition as well as amyloid and tau pathology. Dynamic multilayer functional connectivity shows promise as a functional imaging biomarker for both early- and late-stage Alzheimer's disease diagnosis.
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Affiliation(s)
- Anna Canal-Garcia
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm 17165, Sweden
| | - Dániel Veréb
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm 17165, Sweden
| | - Mite Mijalkov
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm 17165, Sweden
| | - Eric Westman
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm 17165, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg 40530, Sweden
| | - Joana B Pereira
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm 17165, Sweden
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Lavanga M, Stumme J, Yalcinkaya BH, Fousek J, Jockwitz C, Sheheitli H, Bittner N, Hashemi M, Petkoski S, Caspers S, Jirsa V. The virtual aging brain: Causal inference supports interhemispheric dedifferentiation in healthy aging. Neuroimage 2023; 283:120403. [PMID: 37865260 DOI: 10.1016/j.neuroimage.2023.120403] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 09/20/2023] [Accepted: 10/05/2023] [Indexed: 10/23/2023] Open
Abstract
The mechanisms of cognitive decline and its variability during healthy aging are not fully understood, but have been associated with reorganization of white matter tracts and functional brain networks. Here, we built a brain network modeling framework to infer the causal link between structural connectivity and functional architecture and the consequent cognitive decline in aging. By applying in-silico interhemispheric degradation of structural connectivity, we reproduced the process of functional dedifferentiation during aging. Thereby, we found the global modulation of brain dynamics by structural connectivity to increase with age, which was steeper in older adults with poor cognitive performance. We validated our causal hypothesis via a deep-learning Bayesian approach. Our results might be the first mechanistic demonstration of dedifferentiation during aging leading to cognitive decline.
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Affiliation(s)
- Mario Lavanga
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Johanna Stumme
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Bahar Hazal Yalcinkaya
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Jan Fousek
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Hiba Sheheitli
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Nora Bittner
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Meysam Hashemi
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Spase Petkoski
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix-Marseille University, Marseille 13005, France.
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Carozza S, Akarca D, Astle D. The adaptive stochasticity hypothesis: Modeling equifinality, multifinality, and adaptation to adversity. Proc Natl Acad Sci U S A 2023; 120:e2307508120. [PMID: 37816058 PMCID: PMC10589678 DOI: 10.1073/pnas.2307508120] [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: 05/04/2023] [Accepted: 08/25/2023] [Indexed: 10/12/2023] Open
Abstract
Neural phenotypes are the result of probabilistic developmental processes. This means that stochasticity is an intrinsic aspect of the brain as it self-organizes over a protracted period. In other words, while both genomic and environmental factors shape the developing nervous system, another significant-though often neglected-contributor is the randomness introduced by probability distributions. Using generative modeling of brain networks, we provide a framework for probing the contribution of stochasticity to neurodevelopmental diversity. To mimic the prenatal scaffold of brain structure set by activity-independent mechanisms, we start our simulations from the medio-posterior neonatal rich club (Developing Human Connectome Project, n = 630). From this initial starting point, models implementing Hebbian-like wiring processes generate variable yet consistently plausible brain network topologies. By analyzing repeated runs of the generative process (>107 simulations), we identify critical determinants and effects of stochasticity. Namely, we find that stochastic variation has a greater impact on brain organization when networks develop under weaker constraints. This heightened stochasticity makes brain networks more robust to random and targeted attacks, but more often results in non-normative phenotypic outcomes. To test our framework empirically, we evaluated whether stochasticity varies according to the experience of early-life deprivation using a cohort of neurodiverse children (Centre for Attention, Learning and Memory; n = 357). We show that low-socioeconomic status predicts more stochastic brain wiring. We conclude that stochasticity may be an unappreciated contributor to relevant developmental outcomes and make specific predictions for future research.
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Affiliation(s)
- Sofia Carozza
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, CambridgeCB2 7EF, United Kingdom
- Department of Neurology, Harvard Medical School, Boston, MA02115
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA02115
| | - Danyal Akarca
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, CambridgeCB2 7EF, United Kingdom
| | - Duncan Astle
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, CambridgeCB2 7EF, United Kingdom
- Department of Psychiatry, University of Cambridge, CambridgeCB2 0SZ, United Kingdom
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10
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Liu X, Tyler LK, Cam-Can, Davis SW, Rowe JB, Tsvetanov KA. Cognition's dependence on functional network integrity with age is conditional on structural network integrity. Neurobiol Aging 2023; 129:195-208. [PMID: 37392579 DOI: 10.1016/j.neurobiolaging.2023.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/29/2023] [Accepted: 06/02/2023] [Indexed: 07/03/2023]
Abstract
Maintaining good cognitive function is crucial for well-being across the lifespan. We proposed that the degree of cognitive maintenance is determined by the functional interactions within and between large-scale brain networks. Such connectivity can be represented by the white matter architecture of structural brain networks that shape intrinsic neuronal activity into integrated and distributed functional networks. We explored how the function-structure connectivity convergence, and the divergence of functional connectivity from structural connectivity, contribute to the maintenance of cognitive function across the adult lifespan. Multivariate analyses were used to investigate the relationship between function-structure connectivity convergence and divergence with multivariate cognitive profiles, respectively. Cognitive function was increasingly dependent on function-structure connectivity convergence as age increased. The dependency of cognitive function on connectivity was particularly strong for high-order cortical networks and subcortical networks. The results suggest that brain functional network integrity sustains cognitive functions in old age, as a function of the integrity of the brain's structural connectivity.
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Affiliation(s)
- Xulin Liu
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
| | - Lorraine K Tyler
- The Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, UK
| | - Cam-Can
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Simon W Davis
- Department of Neurology, Duke University, School of Medicine, Durham, NC, USA
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Kamen A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; The Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, UK
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11
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Kavčič A, Demšar J, Georgiev D, Bon J, Soltirovska-Šalamon A. Age related changes and sex related differences of functional brain networks in childhood: A high-density EEG study. Clin Neurophysiol 2023; 150:216-226. [PMID: 37104911 DOI: 10.1016/j.clinph.2023.03.357] [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: 07/11/2022] [Revised: 02/11/2023] [Accepted: 03/18/2023] [Indexed: 04/29/2023]
Abstract
OBJECTIVE The aim of this study was to explore functional network age-related changes and sex-related differences during the early lifespan with a high-density resting state electroencephalography (rs-EEG). METHODS We analyzed two data sets of high-density rs-EEG in healthy children and adolescents. We recorded a 64-channel EEG and calculated functional connectomes in 27 participants aged 5-18 years. To validate our results, we used publicly available data and calculated functional connectomes in another 86 participants aged 6-18 years from a 128-channel rs-EEG. We were primarily interested in alpha frequency band, but we also analyzed theta and beta frequency bands. RESULTS We observed age-related increase of characteristic path, clustering coefficient and interhemispheric strength in the alpha frequency band of both data sets and in the beta frequency band of the larger validation data set. Age-related increase of global efficiency was seen in the theta band of the validation data set and in the alpha band of the test data set. Increase in small worldness was observed only in the alpha frequency band of the test data set. We also observed an increase of individual peak alpha frequency with age in both data sets. Sex-related differences were only observed in the beta frequency band of the larger validation data set, with females having higher values than same aged males. CONCLUSIONS Functional brain networks show indices of higher segregation, but also increasing global integration with maturation. Age-related changes are most prominent in the alpha frequency band. SIGNIFICANCE To the best of our knowledge, our study was the first to analyze maturation related changes and sex-related differences of functional brain networks with a high-density EEG and to compare functional connectomes generated from two diverse high-density EEG data sets. Understanding the age-related changes and sex-related differences of functional brain networks in healthy children and adolescents is crucial for identifying network abnormalities in different neurologic and psychiatric conditions, with the aim to identify possible markers for prognosis and treatment.
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Affiliation(s)
- Alja Kavčič
- Division of Pediatrics, Department of Neonatology, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Jure Demšar
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia; Faculty of Computer and Information Sciences, University of Ljubljana, Ljubljana, Slovenia
| | - Dejan Georgiev
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Jurij Bon
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, Slovenia; University Psychiatric Clinic Ljubljana, Ljubljana, Slovenia
| | - Aneta Soltirovska-Šalamon
- Division of Pediatrics, Department of Neonatology, University Medical Centre Ljubljana, Ljubljana, Slovenia; Faculty of Medicine, University of Ljubljana, Slovenia.
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12
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Petkoski S, Ritter P, Jirsa VK. White-matter degradation and dynamical compensation support age-related functional alterations in human brain. Cereb Cortex 2023; 33:6241-6256. [PMID: 36611231 PMCID: PMC10183745 DOI: 10.1093/cercor/bhac500] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 11/02/2022] [Accepted: 11/03/2022] [Indexed: 01/09/2023] Open
Abstract
Structural connectivity of the brain at different ages is analyzed using diffusion-weighted magnetic resonance imaging (MRI) data. The largest decrease of streamlines is found in frontal regions and for long inter-hemispheric links. The average length of the tracts also decreases, but the clustering is unaffected. From functional MRI we identify age-related changes of dynamic functional connectivity (dFC) and spatial covariation features of functional connectivity (FC) links captured by metaconnectivity. They indicate more stable dFC, but wider range and variance of MC, whereas static features of FC did not show any significant differences with age. We implement individual connectivity in whole-brain models and test several hypotheses for the mechanisms of operation among underlying neural system. We demonstrate that age-related functional fingerprints are only supported if the model accounts for: (i) compensation of the individual brains for the overall loss of structural connectivity and (ii) decrease of propagation velocity due to the loss of myelination. We also show that with these 2 conditions, it is sufficient to decompose the time-delays as bimodal distribution that only distinguishes between intra- and inter-hemispheric delays, and that the same working point also captures the static FC the best, and produces the largest variability at slow time-scales.
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Affiliation(s)
- Spase Petkoski
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Petra Ritter
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.,Department of Neurology with Experimental Neurology, Brain Simulation Section, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,Bernstein Focus State Dependencies of Learning, Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Viktor K Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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13
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Deery HA, Di Paolo R, Moran C, Egan GF, Jamadar SD. The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging. Psychophysiology 2023; 60:e14159. [PMID: 36106762 PMCID: PMC10909558 DOI: 10.1111/psyp.14159] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 12/23/2022]
Abstract
The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.
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Affiliation(s)
- Hamish A. Deery
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Robert Di Paolo
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Chris Moran
- Peninsula Clinical School, Central Clinical SchoolMonash UniversityFrankstonVictoriaAustralia
- Department of Geriatric MedicinePeninsula HealthFrankstonVictoriaAustralia
| | - Gary F. Egan
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
| | - Sharna D. Jamadar
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
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14
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Han H, Ge S, Wang H. Prediction of brain age based on the community structure of functional networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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15
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Puxeddu MG, Faskowitz J, Sporns O, Astolfi L, Betzel RF. Multi-modal and multi-subject modular organization of human brain networks. Neuroimage 2022; 264:119673. [PMID: 36257489 DOI: 10.1016/j.neuroimage.2022.119673] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 09/22/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022] Open
Abstract
The human brain is a complex network of anatomically interconnected brain areas. Spontaneous neural activity is constrained by this architecture, giving rise to patterns of statistical dependencies between the activity of remote neural elements. The non-trivial relationship between structural and functional connectivity poses many unsolved challenges about cognition, disease, development, learning and aging. While numerous studies have focused on statistical relationships between edge weights in anatomical and functional networks, less is known about dependencies between their modules and communities. In this work, we investigate and characterize the relationship between anatomical and functional modular organization of the human brain, developing a novel multi-layer framework that expands the classical concept of multi-layer modularity. By simultaneously mapping anatomical and functional networks estimated from different subjects into communities, this approach allows us to carry out a multi-subject and multi-modal analysis of the brain's modular organization. Here, we investigate the relationship between anatomical and functional modules during resting state, finding unique and shared structures. The proposed framework constitutes a methodological advance in the context of multi-layer network analysis and paves the way to further investigate the relationship between structural and functional network organization in clinical cohorts, during cognitively demanding tasks, and in developmental or lifespan studies.
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Affiliation(s)
- Maria Grazia Puxeddu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405; Program in Neuroscience, Indiana University, Bloomington, IN 47405
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405; Cognitive Science Program, Indiana University, Bloomington, IN 47405; Program in Neuroscience, Indiana University, Bloomington, IN 47405; Network Science Institute, Indiana University, Bloomington, IN 47405
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering, University of Rome La Sapienza, Rome, 00185, Italy; IRCCS, Fondazione Santa Lucia, Rome, 00142, Italy
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405; Cognitive Science Program, Indiana University, Bloomington, IN 47405; Program in Neuroscience, Indiana University, Bloomington, IN 47405; Network Science Institute, Indiana University, Bloomington, IN 47405.
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16
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Jang YH, Kim H, Lee JY, Ahn JH, Chung AW, Lee HJ. Altered development of structural MRI connectome hubs at near-term age in very and moderately preterm infants. Cereb Cortex 2022; 33:5507-5523. [PMID: 36408630 DOI: 10.1093/cercor/bhac438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 10/04/2022] [Accepted: 10/05/2022] [Indexed: 11/22/2022] Open
Abstract
Abstract
Preterm infants may exhibit altered developmental patterns of the brain structural network by endogenous and exogenous stimuli, which are quantifiable through hub and modular network topologies that develop in the third trimester. Although preterm brain networks can compensate for white matter microstructural abnormalities of core connections, less is known about how the network developmental characteristics of preterm infants differ from those of full-term infants. We identified 13 hubs and 4 modules and revealed subtle differences in edgewise connectivity and local network properties between 134 preterm and 76 full-term infants, identifying specific developmental patterns of the brain structural network in preterm infants. The modules of preterm infants showed an imbalanced composition. The edgewise connectivity in preterm infants showed significantly decreased long- and short-range connections and local network properties in the dorsal superior frontal gyrus. In contrast, the fusiform gyrus and several nonhub regions showed significantly increased wiring of short-range connections and local network properties. Our results suggested that decreased local network in the frontal lobe and excessive development in the occipital lobe may contribute to the understanding of brain developmental deviances in preterm infants.
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Affiliation(s)
- Yong Hun Jang
- Hanyang University Graduate School of Biomedical Science and Engineering Department of Translational Medicine, , Seoul 04763 , Republic of Korea
| | - Hyuna Kim
- Hanyang University Graduate School of Biomedical Science and Engineering Department of Translational Medicine, , Seoul 04763 , Republic of Korea
| | - Joo Young Lee
- Hanyang University Graduate School of Biomedical Science and Engineering Department of Translational Medicine, , Seoul 04763 , Republic of Korea
| | - Ja-Hye Ahn
- Hanyang University College of Medicine Department of Pediatrics, Hanyang University Hospital, , Seoul 04763 , Republic of Korea
| | - Ai Wern Chung
- Harvard Medical School Fetal Neonatal-Neuroimaging and Developmental Science Center, Boston Children’s Hospital, , Boston, MA 02115 , USA
- Harvard Medical School Department of Pediatrics, Boston Children’s Hospital, , Boston, MA 02115 , USA
| | - Hyun Ju Lee
- Hanyang University College of Medicine Department of Pediatrics, Hanyang University Hospital, , Seoul 04763 , Republic of Korea
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17
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Wu Z, Xu J, Nürnberger A, Sabel BA. Global brain network modularity dynamics after local optic nerve damage following noninvasive brain stimulation: an EEG-tracking study. Cereb Cortex 2022; 33:4729-4739. [PMID: 36197322 DOI: 10.1093/cercor/bhac375] [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: 04/13/2022] [Revised: 08/25/2022] [Accepted: 08/26/2022] [Indexed: 11/13/2022] Open
Abstract
Tightly connected clusters of nodes, called communities, interact in a time-dependent manner in brain functional connectivity networks (FCN) to support complex cognitive functions. However, little is known if and how different nodes synchronize their neural interactions to form functional communities ("modules") during visual processing and if and how this modularity changes postlesion (progression or recovery) following neuromodulation. Using the damaged optic nerve as a paradigm, we now studied brain FCN modularity dynamics to better understand module interactions and dynamic reconfigurations before and after neuromodulation with noninvasive repetitive transorbital alternating current stimulation (rtACS). We found that in both patients and controls, local intermodule interactions correlated with visual performance. However, patients' recovery of vision after treatment with rtACS was associated with improved interaction strength of pathways linked to the attention module, and it improved global modularity and increased the stability of FCN. Our results show that temporal coordination of multiple cortical modules and intermodule interaction are functionally relevant for visual processing. This modularity can be neuromodulated with tACS, which induces a more optimal balanced and stable multilayer modular structure for visual processing by enhancing the interaction of neural pathways with the attention network module.
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Affiliation(s)
- Zheng Wu
- Institute of Medical Psychology, Medical Faculty, Otto-von-Guericke University of Magdeburg, Haus 65, Leipziger Strasse 44, Magdeburg 39120, Germany.,Data and Knowledge Engineering Group, Faculty of Computer Science, Otto-von-Guericke University of Magdeburg, Gebaeude 29, Universitaetsplatz 2, Magdeburg 39106, Germany
| | - Jiahua Xu
- Institute of Medical Psychology, Medical Faculty, Otto-von-Guericke University of Magdeburg, Haus 65, Leipziger Strasse 44, Magdeburg 39120, Germany.,Hertie Institute for Clinical Brain Research, Department Neurology and Stroke, Hoppe-Seyler-Strasse 3, Tübingen 72076, Germany
| | - Andreas Nürnberger
- Data and Knowledge Engineering Group, Faculty of Computer Science, Otto-von-Guericke University of Magdeburg, Gebaeude 29, Universitaetsplatz 2, Magdeburg 39106, Germany
| | - Bernhard A Sabel
- Institute of Medical Psychology, Medical Faculty, Otto-von-Guericke University of Magdeburg, Haus 65, Leipziger Strasse 44, Magdeburg 39120, Germany
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18
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Stumme J, Krämer C, Miller T, Schreiber J, Caspers S, Jockwitz C. Interrelating differences in structural and functional connectivity in the older adult's brain. Hum Brain Mapp 2022; 43:5543-5561. [PMID: 35916531 PMCID: PMC9704795 DOI: 10.1002/hbm.26030] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/11/2022] [Accepted: 07/15/2022] [Indexed: 01/15/2023] Open
Abstract
In the normal aging process, the functional connectome restructures and shows a shift from more segregated to more integrated brain networks, which manifests itself in highly different cognitive performances in older adults. Underpinnings of this reorganization are not fully understood, but may be related to age-related differences in structural connectivity, the underlying scaffold for information exchange between regions. The structure-function relationship might be a promising factor to understand the neurobiological sources of interindividual cognitive variability, but remain unclear in older adults. Here, we used diffusion weighted and resting-state functional magnetic resonance imaging as well as cognitive performance data of 573 older subjects from the 1000BRAINS cohort (55-85 years, 287 males) and performed a partial least square regression on 400 regional functional and structural connectivity (FC and SC, respectively) estimates comprising seven resting-state networks. Our aim was to identify FC and SC patterns that are, together with cognitive performance, characteristic of the older adults aging process. Results revealed three different aging profiles prevalent in older adults. FC was found to behave differently depending on the severity of age-related SC deteriorations. A functionally highly interconnected system is associated with a structural connectome that shows only minor age-related decreases. Because this connectivity profile was associated with the most severe age-related cognitive decline, a more interconnected FC system in older adults points to a process of dedifferentiation. Thus, functional network integration appears to increase primarily when SC begins to decline, but this does not appear to mitigate the decline in cognitive performance.
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Affiliation(s)
- Johanna Stumme
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany,Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Camilla Krämer
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany,Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Tatiana Miller
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany,Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Jan Schreiber
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany,Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany,Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
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19
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Seguin C, Mansour L S, Sporns O, Zalesky A, Calamante F. Network communication models narrow the gap between the modular organization of structural and functional brain networks. Neuroimage 2022; 257:119323. [PMID: 35605765 DOI: 10.1016/j.neuroimage.2022.119323] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/25/2022] [Accepted: 05/17/2022] [Indexed: 11/28/2022] Open
Abstract
Structural and functional brain networks are modular. Canonical functional systems, such as the default mode network, are well-known modules of the human brain and have been implicated in a large number of cognitive, behavioral and clinical processes. However, modules delineated in structural brain networks inferred from tractography generally do not recapitulate canonical functional systems. Neuroimaging evidence suggests that functional connectivity between regions in the same systems is not always underpinned by anatomical connections. As such, direct structural connectivity alone would be insufficient to characterize the functional modular organization of the brain. Here, we demonstrate that augmenting structural brain networks with models of indirect (polysynaptic) communication unveils a modular network architecture that more closely resembles the brain's established functional systems. We find that diffusion models of polysynaptic connectivity, particularly communicability, narrow the gap between the modular organization of structural and functional brain networks by 20-60%, whereas routing models based on single efficient paths do not improve mesoscopic structure-function correspondence. This suggests that functional modules emerge from the constraints imposed by local network structure that facilitates diffusive neural communication. Our work establishes the importance of modeling polysynaptic communication to understand the structural basis of functional systems.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, NSW, Australia; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States.
| | - Sina Mansour L
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States; Cognitive Science Program, Indiana University, Bloomington, IN, United States; Program in Neuroscience, Indiana University, Bloomington, IN, United States; Network Science Institute, Indiana University, Bloomington, IN, United States
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Fernando Calamante
- The University of Sydney, School of Biomedical Engineering, Sydney, NSW, Australia; Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Sydney Imaging, The University of Sydney, Sydney, NSW, Australia
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20
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Zamani Esfahlani F, Faskowitz J, Slack J, Mišić B, Betzel RF. Local structure-function relationships in human brain networks across the lifespan. Nat Commun 2022; 13:2053. [PMID: 35440659 PMCID: PMC9018911 DOI: 10.1038/s41467-022-29770-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 03/29/2022] [Indexed: 12/13/2022] Open
Abstract
A growing number of studies have used stylized network models of communication to predict brain function from structure. Most have focused on a small set of models applied globally. Here, we compare a large number of models at both global and regional levels. We find that globally most predictors perform poorly. At the regional level, performance improves but heterogeneously, both in terms of variance explained and the optimal model. Next, we expose synergies among predictors by using pairs to jointly predict FC. Finally, we assess age-related differences in global and regional coupling across the human lifespan. We find global decreases in the magnitude of structure-function coupling with age. We find that these decreases are driven by reduced coupling in sensorimotor regions, while higher-order cognitive systems preserve local coupling with age. Our results describe patterns of structure-function coupling across the cortex and how this may change with age.
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Affiliation(s)
- Farnaz Zamani Esfahlani
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA
| | - Jonah Slack
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA.
- Cognitive Science Program, Indiana University, Bloomington, IN, 47405, USA.
- Network Science Institute, Indiana University, Bloomington, IN, 47405, USA.
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21
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The overlapping modular organization of human brain functional networks across the adult lifespan. Neuroimage 2022; 253:119125. [PMID: 35331872 DOI: 10.1016/j.neuroimage.2022.119125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 03/02/2022] [Accepted: 03/19/2022] [Indexed: 01/06/2023] Open
Abstract
Previous studies have demonstrated that the brain functional modular organization, which is a fundamental feature of the human brain, would change along the adult lifespan. However, these studies assumed that each brain region belonged to a single functional module, although there has been convergent evidence supporting the existence of overlap among functional modules in the human brain. To reveal how age affects the overlapping functional modular organization, this study applied an overlapping module detection algorithm that requires no prior knowledge to the resting-state fMRI data of a healthy cohort (N = 570) aged from 18 to 88 years old. A series of measures were derived to delineate the characteristics of the overlapping modular structure and the set of overlapping nodes (brain regions participating in two or more modules) identified from each participant. Age-related regression analyses on these measures found linearly decreasing trends in the overlapping modularity and the modular similarity. The number of overlapping nodes was found increasing with age, but the increment was not even over the brain. In addition, across the adult lifespan and within each age group, the nodal overlapping probability consistently had positive correlations with both functional gradient and flexibility. Further, by correlation and mediation analyses, we showed that the influence of age on memory-related cognitive performance might be explained by the change in the overlapping functional modular organization. Together, our results revealed age-related decreased segregation from the brain functional overlapping modular organization perspective, which could provide new insight into the adult lifespan changes in brain function and the influence of such changes on cognitive performance.
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22
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Coelho A, Magalhães R, Moreira PS, Amorim L, Portugal-Nunes C, Castanho T, Santos NC, Sousa N, Fernandes HM. A novel method for estimating connectivity-based parcellation of the human brain from diffusion MRI: Application to an aging cohort. Hum Brain Mapp 2022; 43:2419-2443. [PMID: 35274787 PMCID: PMC9057102 DOI: 10.1002/hbm.25773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 12/20/2021] [Accepted: 12/27/2021] [Indexed: 11/18/2022] Open
Abstract
Connectivity‐based parcellation (CBP) methods are used to define homogenous and biologically meaningful parcels or nodes—the foundations of brain network fingerprinting—by grouping voxels with similar patterns of brain connectivity. However, we still lack a gold standard method and the use of CBPs to study the aging brain remains scarce. Our study proposes a novel CBP method from diffusion MRI data and shows its potential to produce a more accurate characterization of the longitudinal alterations in brain network topology occurring in aging. For this, we constructed whole‐brain connectivity maps from diffusion MRI data of two datasets: an aging cohort evaluated at two timepoints (mean interval time: 52.8 ± 7.24 months) and a normative adult cohort—MGH‐HCP. State‐of‐the‐art clustering techniques were used to identify the best performing technique. Furthermore, we developed a new metric (connectivity homogeneity fingerprint [CHF]) to evaluate the success of the final CBP in improving regional/global structural connectivity homogeneity. Our results show that our method successfully generates highly homogeneous parcels, as described by the significantly larger CHF score of the resulting parcellation, when compared to the original. Additionally, we demonstrated that the developed parcellation provides a robust anatomical framework to assess longitudinal changes in the aging brain. Our results reveal that aging is characterized by a reorganization of the brain's structural network involving the decrease of intra‐hemispheric, increase of inter‐hemispheric connectivity, and topological rearrangement. Overall, this study proposes a new methodology to perform accurate and robust evaluations of CBP of the human brain.
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Affiliation(s)
- Ana Coelho
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Ricardo Magalhães
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Pedro S Moreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Liliana Amorim
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Carlos Portugal-Nunes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Teresa Castanho
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Nadine Correia Santos
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Nuno Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Henrique M Fernandes
- Center for Music in the Brain (MIB), Aarhus University, Aarhus, Denmark.,Department of Psychiatry, University of Oxford, Oxford, UK
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23
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Chiêm B, Abbas K, Amico E, Duong-Tran DA, Crevecoeur F, Goñi J. Improving Functional Connectome Fingerprinting with Degree-Normalization. Brain Connect 2022; 12:180-192. [PMID: 34015966 PMCID: PMC8978572 DOI: 10.1089/brain.2020.0968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Functional connectivity quantifies the statistical dependencies between the activity of brain regions, measured using neuroimaging data such as functional magnetic resonance imaging (fMRI) blood-oxygenation-level dependent time series. The network representation of functional connectivity, called a functional connectome (FC), has been shown to contain an individual fingerprint allowing participants identification across consecutive testing sessions. Recently, researchers have focused on the extraction of these fingerprints, with potential applications in personalized medicine. Materials and Methods: In this study, we show that a mathematical operation denominated degree-normalization can improve the extraction of FC fingerprints. Degree-normalization has the effect of reducing the excessive influence of strongly connected brain areas in the whole-brain network. We adopt the differential identifiability framework and apply it to both original and degree-normalized FCs of 409 individuals from the Human Connectome Project, in resting-state and 7 fMRI tasks. Results: Our results indicate that degree-normalization systematically improves three fingerprinting metrics, namely differential identifiability, identification rate, and matching rate. Moreover, the results related to the matching rate metric suggest that individual fingerprints are embedded in a low-dimensional space. Discussion: The results suggest that low-dimensional functional fingerprints lie in part in weakly connected subnetworks of the brain and that degree-normalization helps uncovering them. This work introduces a simple mathematical operation that could lead to significant improvements in future FC fingerprinting studies.
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Affiliation(s)
- Benjamin Chiêm
- Institute of Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium.,Institute of Neurosciences, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA.,School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Duy Anh Duong-Tran
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA.,School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Frédéric Crevecoeur
- Institute of Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium.,Institute of Neurosciences, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA.,School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA.,Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA.,Address correspondence to: Joaquín Goñi, Purdue Institute for Integrative Neuroscience, Purdue University, 315 North Grant Street, West Lafayette, IN 47907, USA
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24
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Mutti C, Misirocchi F, Zilioli A, Rausa F, Pizzarotti S, Spallazzi M, Parrino L. Sleep and brain evolution across the human lifespan: A mutual embrace. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:938012. [PMID: 36926070 PMCID: PMC10013002 DOI: 10.3389/fnetp.2022.938012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022]
Abstract
Sleep can be considered a window to ascertain brain wellness: it dynamically changes with brain maturation and can even indicate the occurrence of concealed pathological processes. Starting from prenatal life, brain and sleep undergo an impressive developmental journey that accompanies human life throughout all its steps. A complex mutual influence rules this fascinating course and cannot be ignored while analysing its evolution. Basic knowledge on the significance and evolution of brain and sleep ontogenesis can improve the clinical understanding of patient's wellbeing in a more holistic perspective. In this review we summarized the main notions on the intermingled relationship between sleep and brain evolutionary processes across human lifespan, with a focus on sleep microstructure dynamics.
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Affiliation(s)
- Carlotta Mutti
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Francesco Misirocchi
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Alessandro Zilioli
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Francesco Rausa
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Silvia Pizzarotti
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Marco Spallazzi
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Liborio Parrino
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
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25
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Zamani Esfahlani F, Jo Y, Puxeddu MG, Merritt H, Tanner JC, Greenwell S, Patel R, Faskowitz J, Betzel RF. Modularity maximization as a flexible and generic framework for brain network exploratory analysis. Neuroimage 2021; 244:118607. [PMID: 34607022 DOI: 10.1016/j.neuroimage.2021.118607] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 09/03/2021] [Accepted: 09/20/2021] [Indexed: 11/28/2022] Open
Abstract
The modular structure of brain networks supports specialized information processing, complex dynamics, and cost-efficient spatial embedding. Inter-individual variation in modular structure has been linked to differences in performance, disease, and development. There exist many data-driven methods for detecting and comparing modular structure, the most popular of which is modularity maximization. Although modularity maximization is a general framework that can be modified and reparamaterized to address domain-specific research questions, its application to neuroscientific datasets has, thus far, been narrow. Here, we highlight several strategies in which the "out-of-the-box" version of modularity maximization can be extended to address questions specific to neuroscience. First, we present approaches for detecting "space-independent" modules and for applying modularity maximization to signed matrices. Next, we show that the modularity maximization frame is well-suited for detecting task- and condition-specific modules. Finally, we highlight the role of multi-layer models in detecting and tracking modules across time, tasks, subjects, and modalities. In summary, modularity maximization is a flexible and general framework that can be adapted to detect modular structure resulting from a wide range of hypotheses. This article highlights multiple frontiers for future research and applications.
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Affiliation(s)
- Farnaz Zamani Esfahlani
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Youngheun Jo
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Maria Grazia Puxeddu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States; Department of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Rome 00185, Italy; IRCCS Fondazione Santa Lucia, Rome 00179, Italy
| | - Haily Merritt
- Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47405, United States; Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
| | - Jacob C Tanner
- Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47405, United States; Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
| | - Sarah Greenwell
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Riya Patel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States; Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States; Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States; Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States; Network Science Institute, Indiana University, Bloomington, IN 47405, United States.
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26
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Connectivity modulations induced by reach&grasp movements: a multidimensional approach. Sci Rep 2021; 11:23097. [PMID: 34845265 PMCID: PMC8630117 DOI: 10.1038/s41598-021-02458-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 11/08/2021] [Indexed: 11/09/2022] Open
Abstract
Reach&grasp requires highly coordinated activation of different brain areas. We investigated whether reach&grasp kinematics is associated to EEG-based networks changes. We enrolled 10 healthy subjects. We analyzed the reach&grasp kinematics of 15 reach&grasp movements performed with each upper limb. Simultaneously, we obtained a 64-channel EEG, synchronized with the reach&grasp movement time points. We elaborated EEG signals with EEGLAB 12 in order to obtain event related synchronization/desynchronization (ERS/ERD) and lagged linear coherence between Brodmann areas. Finally, we evaluated network topology via sLORETA software, measuring network local and global efficiency (clustering and path length) and the overall balance (small-worldness). We observed a widespread ERD in α and β bands during reach&grasp, especially in the centro-parietal regions of the hemisphere contralateral to the movement. Regarding functional connectivity, we observed an α lagged linear coherence reduction among Brodmann areas contralateral to the arm involved in the reach&grasp movement. Interestingly, left arm movement determined widespread changes of α lagged linear coherence, specifically among right occipital regions, insular cortex and somatosensory cortex, while the right arm movement exerted a restricted contralateral sensory-motor cortex modulation. Finally, no change between rest and movement was found for clustering, path length and small-worldness. Through a synchronized acquisition, we explored the cortical correlates of the reach&grasp movement. Despite EEG perturbations, suggesting that the non-dominant reach&grasp network has a complex architecture probably linked to the necessity of a higher visual control, the pivotal topological measures of network local and global efficiency remained unaffected.
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27
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Puxeddu MG, Petti M, Astolfi L. Multi-layer analysis of multi-frequency brain networks as a new tool to study EEG topological organization. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:924-927. [PMID: 34891441 DOI: 10.1109/embc46164.2021.9630173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Oscillatory activity rising from the interaction among neurons is widely observed in the brain at different scales and is thought to encode distinctive properties of the neural processing. Classical investigations of neuroelectrical activity and connectivity usually focus on specific frequency bands, considered as separate aspects of brain functioning. However, this might not paint the whole picture, preventing to see the brain activity as a whole, as the result of an integrated process. This study aims to provide a new framework for the analysis of the functional interaction between brain regions across frequencies and different subjects. We ground our work on the latest advances in graph theory, exploiting multi-layer community detection. In our multi-layer network model, layers keep track of single frequencies, including all the information in a unique graph. Community detection is then applied by means of a multilayer formulation of modularity. As a proof-of-concept of our approach, we provide here an application to multi-frequency functional brain networks derived from resting state EEG collected in a group of healthy subjects. Our results indicate that α-band selectively characterizes an inter-individual common organization of EEG brain networks during open eyes resting state. Future applications of this new approach may include the extraction of subject-specific features able to capture selected properties of the brain processes, related to physiological or pathological conditions.
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28
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Krupnik R, Yovel Y, Assaf Y. Inner Hemispheric and Interhemispheric Connectivity Balance in the Human Brain. J Neurosci 2021; 41:8351-8361. [PMID: 34465598 PMCID: PMC8496194 DOI: 10.1523/jneurosci.1074-21.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 07/25/2021] [Accepted: 08/08/2021] [Indexed: 11/21/2022] Open
Abstract
The connectome of the brain has a great impact on the function of the brain as the structure of the connectome affects the speed and efficiency of information transfer. As a highly energy-consuming organ, an efficient network structure is essential. A previous study has shown consistent overall brain connectivity across a large variety of species. This connectivity conservation was explained by a balance between interhemispheric and intrahemispheric connections; that is, spices with highly connected hemispheres appear to have weaker interhemisphere connections. This study examines this connectivity trade-off in the human brain using diffusion-based tractography and network analysis in the Human Connectome Project (970 subjects, 527 female). We explore the biological origins of this phenomenon, heritability, and the effect on cognitive measures.The proportion of commissural fibers in the brain had a negative correlation to hemispheric efficiency, pointing to a trade-off between inner hemispheric and interhemispheric connectivity. Network hubs including anterior and middle cingulate cortex, superior frontal areas, medial occipital areas, the parahippocampal gyrus, post- and precentral gyri, and the precuneus had the strongest contribution to this phenomenon. Other results show a high heritability as well as a strong connection to crystalized intelligence. This work presents cohort-based network analysis research, spanning a large variety of samples and exploring the overall architecture of the human connectome. Our results show a connectivity conservation phenomenon at the base of the overall brain network architecture. This network structure may explain much of the functional, behavioral, and cognitive variability among different brains.SIGNIFICANCE STATEMENT The network structure of the brain is at the basis of every brain function as it dictates the characteristics of information transfer. Understanding the patterns and mechanisms that guide the connectome structure is crucial to understanding the brain itself. Here we unravel the mechanism at the base of the connectivity conservation phenomenon by exploring the interaction between hemispheric and commissural connectivity in a large-scale cohort-based connectivity study. We describe the trade-off between the two components and examine the origins of the trade-off and observe the effect on cognitive abilities and behavior.
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Affiliation(s)
- Ronnie Krupnik
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yossi Yovel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- School of Zoology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
- Steinhardt Museum of Natural History, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yaniv Assaf
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- School of Neurobiology, Biochemistry and Biophysics, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
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29
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Huang Q, Ren S, Zhang T, Li J, Jiang D, Xiao J, Hua F, Xie F, Guan Y. Aging-Related Modular Architectural Reorganization of the Metabolic Brain Network. Brain Connect 2021; 12:432-442. [PMID: 34210172 DOI: 10.1089/brain.2021.0054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Background: Modules in brain network represent groups of brain regions that are collectively involved in one or more cognitive domains. Exploring aging-related reorganization of the brain modular architecture using metabolic brain network could further our understanding about aging-related neuromechanism and neurodegenerations. Materials and Methods: In this study, 432 subjects who performed 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) were enrolled and divided into young and old adult groups, as well as female and male groups. The modular architecture was detected, and the connector and hub nodes were identified to explore the topological role of the brain regions based on the metabolic brain network. Results: This study revealed that human metabolic brain network was modular and could be clustered into three modules. The modular architecture was reorganized from young to old ages with regions related to sensorimotor function clustered into the same module; and the number of connector nodes was reduced and most connector nodes were localized in temporo-occipital areas related to visual and auditory functions in old ages. The major gender difference is that the metabolic brain network was delineated into four modules in old female group with the nodes related to sensorimotor function split into two modules. Discussion: Those findings suggest aging is associated with reorganized brain modular architecture. Clinical Trial Registration number: ChiCTR2000036842.
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Affiliation(s)
- Qi Huang
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Shuhua Ren
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Tianhao Zhang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Junpeng Li
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Donglang Jiang
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jianfei Xiao
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Fengchun Hua
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Fang Xie
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
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30
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Puxeddu MG, Petti M, Astolfi L. A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks. Front Syst Neurosci 2021; 15:624183. [PMID: 33732115 PMCID: PMC7956967 DOI: 10.3389/fnsys.2021.624183] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 01/21/2021] [Indexed: 12/21/2022] Open
Abstract
Modular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across time, subjects, frequency, or other domains. Identifying the modular structure in multilayer brain networks represents a gateway toward a deeper understanding of neural processes underlying cognition. Electroencephalographic (EEG) signals, thanks to their high temporal resolution, can give rise to multilayer networks able to follow the dynamics of brain activity. Despite this potential, the community organization has not yet been thoroughly investigated in brain networks estimated from EEG. Furthermore, at the state of the art, there is still no agreement about which algorithm is the most suitable to detect communities in multilayer brain networks, and a way to test and compare them all under a variety of conditions is lacking. In this work, we perform a comprehensive analysis of three algorithms at the state of the art for multilayer community detection (namely, genLouvain, DynMoga, and FacetNet) as compared with an approach based on the application of a single-layer clustering algorithm to each slice of the multilayer network. We test their ability to identify both steady and dynamic modular structures. We statistically evaluate their performances by means of ad hoc benchmark graphs characterized by properties covering a broad range of conditions in terms of graph density, number of clusters, noise level, and number of layers. The results of this simulation study aim to provide guidelines about the choice of the more appropriate algorithm according to the different properties of the brain network under examination. Finally, as a proof of concept, we show an application of the algorithms to real functional brain networks derived from EEG signals collected at rest with closed and open eyes. The test on real data provided results in agreement with the conclusions of the simulation study and confirmed the feasibility of multilayer analysis of EEG-based brain networks in both steady and dynamic conditions.
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Affiliation(s)
- Maria Grazia Puxeddu
- Department of Computer, Control and Management Engineering "Antonio Ruberti", University of Rome Sapienza, Rome, Italy.,IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Manuela Petti
- Department of Computer, Control and Management Engineering "Antonio Ruberti", University of Rome Sapienza, Rome, Italy.,IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering "Antonio Ruberti", University of Rome Sapienza, Rome, Italy.,IRCCS Fondazione Santa Lucia, Rome, Italy
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31
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Sahoo D, Satterthwaite TD, Davatzikos C. Hierarchical Extraction of Functional Connectivity Components in Human Brain Using Resting-State fMRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:940-950. [PMID: 33284752 DOI: 10.1109/tmi.2020.3042873] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The study of functional networks of the human brain has been of significant interest in cognitive neuroscience for over two decades, albeit they are typically extracted at a single scale using various methods, including decompositions like ICA. However, since numerous studies have suggested that the functional organization of the brain is hierarchical, analogous decompositions might better capture functional connectivity patterns. Moreover, hierarchical decompositions can efficiently reduce the very high dimensionality of functional connectivity data. This paper provides a novel method for the extraction of hierarchical connectivity components in the human brain using resting-state fMRI. The method builds upon prior work of Sparse Connectivity Patterns (SCPs) by introducing a hierarchy of sparse, potentially overlapping patterns. The components are estimated by cascaded factorization of correlation matrices generated from fMRI. The goal of the paper is to extract sparse interpretable hierarchically-organized patterns using correlation matrices where a low rank decomposition is formed by a linear combination of a higher rank decomposition. We formulate the decomposition as a non-convex optimization problem and solve it using gradient descent algorithms with adaptive step size. Along with the hierarchy, our method aims to capture the heterogeneity of the set of common patterns across individuals. We first validate our model through simulated experiments. We then demonstrate the effectiveness of the developed method on two different real-world datasets by showing that multi-scale hierarchical SCPs are reproducible between sub-samples and are more reproducible as compared to single scale patterns. We also compare our method with an existing hierarchical community detection approach.
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32
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Zhang Y, Wang Y, Chen N, Guo M, Wang X, Chen G, Li Y, Yang L, Li S, Yao Z, Hu B. Age-Associated Differences of Modules and Hubs in Brain Functional Networks. Front Aging Neurosci 2021; 12:607445. [PMID: 33536893 PMCID: PMC7848126 DOI: 10.3389/fnagi.2020.607445] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 12/21/2020] [Indexed: 01/07/2023] Open
Abstract
Healthy aging is usually accompanied by changes in the functional modular organization of the human brain, which may result in the decline of cognition and underlying brain dysfunction. However, the relationship between age-related brain functional modular structure differences and cognition remain debatable. In this study, we investigated the age-associated differences of modules and hubs from young, middle and old age groups, using resting-state fMRI data from a large cross-sectional adulthood sample. We first divided the subjects into three age groups and constructed an individual-level network for each subject. Subsequently, a module-guided group-level network construction method was applied to form a weighted network for each group from which functional modules were detected. The intra- and inter-modular connectivities were observed negatively correlated with age. According to the detected modules, we found the number of connector hubs in the young group was more than middle-age and old group, while the quantity of provincial hubs in middle-age group was discovered more than other two groups. Further ROI-wise analysis shows that different hubs have distinct age-associated trajectories of intra- and inter-modular connections, which suggests the different types of topological role transitions in functional networks across age groups. Our results indicated an inverse association between functional segregation/integration with age, which demonstrated age-associated differences in communication effeciency. This study provides a new perspective and useful information to better understand the normal aging of brain networks.
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Affiliation(s)
- Yinghui Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
- Guangyuan Mental Health Center, Guangyuan, China
| | - Yin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Nan Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Man Guo
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiuzhen Wang
- Guangyuan Mental Health Center, Guangyuan, China
| | | | - Yongchao Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Lin Yang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Shan Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, China
- Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Ministry of Education, Lanzhou, China
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33
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James A. The Why, What and How of Artificial General Intelligence Chip Development. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3069871] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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34
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Maallo AMS, Granovetter MC, Freud E, Kastner S, Pinsk MA, Glen D, Patterson C, Behrmann M. Large-scale resculpting of cortical circuits in children after surgical resection. Sci Rep 2020; 10:21589. [PMID: 33299002 PMCID: PMC7725819 DOI: 10.1038/s41598-020-78394-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 11/24/2020] [Indexed: 11/09/2022] Open
Abstract
Despite the relative successes in the surgical treatment of pharmacoresistant epilepsy, there is rather little research on the neural (re)organization that potentially subserves behavioral compensation. Here, we examined the post-surgical functional connectivity (FC) in children and adolescents who have undergone unilateral cortical resection and, yet, display remarkably normal behavior. Conventionally, FC has been investigated in terms of the mean correlation of the BOLD time courses extracted from different brain regions. Here, we demonstrated the value of segregating the voxel-wise relationships into mutually exclusive populations that were either positively or negatively correlated. While, relative to controls, the positive correlations were largely normal, negative correlations among networks were increased. Together, our results point to reorganization in the contralesional hemisphere, possibly suggesting competition for cortical territory due to the demand for representation of function. Conceivably, the ubiquitous negative correlations enable the differentiation of function in the reduced cortical volume following a unilateral resection.
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Affiliation(s)
- Anne Margarette S Maallo
- Department of Psychology and Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA
| | - Michael C Granovetter
- Department of Psychology and Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA.,School of Medicine, University of Pittsburgh, Pittsburgh, USA
| | - Erez Freud
- Department of Psychology, The Centre for Vision Research, York University, Toronto, Canada
| | - Sabine Kastner
- Princeton Neuroscience Institute, Princeton University, Princeton, USA.,Department of Psychology, Princeton University, Princeton, USA
| | - Mark A Pinsk
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Daniel Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, USA
| | | | - Marlene Behrmann
- Department of Psychology and Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA.
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Reh R, Williams LJ, Todd RM, Ward LM. Warped rhythms: Epileptic activity during critical periods disrupts the development of neural networks for human communication. Behav Brain Res 2020; 399:113016. [PMID: 33212087 DOI: 10.1016/j.bbr.2020.113016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 11/06/2020] [Accepted: 11/09/2020] [Indexed: 12/27/2022]
Abstract
It is well established that temporal lobe epilepsy-the most common and well-studied form of epilepsy-can impair communication by disrupting social-emotional and language functions. In pediatric epilepsy, where seizures co-occur with the development of critical brain networks, age of onset matters: The earlier in life seizures begin, the worse the disruption in network establishment, resulting in academic hardship and social isolation. Yet, little is known about the processes by which epileptic activity disrupts developing human brain networks. Here we take a synthetic perspective-reviewing a range of research spanning studies on molecular and oscillatory processes to those on the development of large-scale functional networks-in support of a novel model of how such networks can be disrupted by epilepsy. We seek to bridge the gap between research on molecular processes, on the development of human brain circuitry, and on clinical outcomes to propose a model of how epileptic activity disrupts brain development.
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Affiliation(s)
- Rebecca Reh
- University of British Columbia, Department of Psychology, 2136 West Mall, Vancouver BC V6T 1Z4, Canada
| | - Lynne J Williams
- BC Children's Hospital MRI Research Facility, 4480 Oak Street, Vancouver, BC V6H 0B3, Canada
| | - Rebecca M Todd
- University of British Columbia, Department of Psychology, 2136 West Mall, Vancouver BC V6T 1Z4, Canada; University of British Columbia, Djavad Mowafaghian Centre for Brain Health, 2215 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada.
| | - Lawrence M Ward
- University of British Columbia, Department of Psychology, 2136 West Mall, Vancouver BC V6T 1Z4, Canada; University of British Columbia, Djavad Mowafaghian Centre for Brain Health, 2215 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada
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Alteration of the Intra- and Inter-Lobe Connectivity of the Brain Structural Network in Normal Aging. ENTROPY 2020; 22:e22080826. [PMID: 33286597 PMCID: PMC7517412 DOI: 10.3390/e22080826] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/23/2020] [Accepted: 07/24/2020] [Indexed: 01/18/2023]
Abstract
The morphological changes in cortical parcellated regions during aging and whether these atrophies may cause brain structural network intra- and inter-lobe connectivity alterations are subjects that have been minimally explored. In this study, a novel fractal dimension-based structural network was proposed to measure atrophy of 68 parcellated cortical regions. Alterations of structural network parameters, including intra- and inter-lobe connectivity, were detected in a middle-aged group (30–45 years old) and an elderly group (50–65 years old). The elderly group exhibited significant lateralized atrophy in the left hemisphere, and most of these fractal dimension atrophied regions were included in the regions of the “last-in, first-out” model. Globally, the elderly group had lower modularity values, smaller component size modules, and fewer bilateral association fibers. They had lower intra-lobe connectivity in the frontal and parietal lobes, but higher intra-lobe connectivity in the temporal and occipital lobes. Both groups exhibited similar inter-lobe connecting pattern. The elderly group revealed separations, sparser long association fibers, commissural fibers, and lateral inter-lobe connectivity lost effect, mainly in the right hemisphere. New wiring and reconfiguring modules may have occurred within the brain structural network to compensate for connectivity, decreasing and preventing functional loss in cerebral intra- and inter-lobe connectivity.
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