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Lang J, Yang LZ, Li H. Rest2Task: Modeling task-specific components in resting-state functional connectivity and applications. Brain Res 2024; 1845:149265. [PMID: 39393483 DOI: 10.1016/j.brainres.2024.149265] [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: 04/27/2024] [Revised: 08/04/2024] [Accepted: 10/03/2024] [Indexed: 10/13/2024]
Abstract
The networks observed in the brain during resting-state activity are not entirely "task-free." Instead, they hint at a hierarchical structure prepared for adaptive cognitive functions. Recent studies have increasingly demonstrated the potential of resting-state fMRI to predict local activations or global connectomes during task performance. However, uncertainties remain regarding the unique and shared task-specific components within resting-state brain networks, elucidating local activations and global connectome patterns. A coherent framework is also required to integrate these task-specific components to predict local activations and global connectome patterns. In this work, we introduce the Rest2Task model based on the partial least squares-based multivariate regression algorithm, which effectively integrates mappings from resting-state connectivity to local activations and global connectome patterns. By analyzing the coefficients of the regression model, we extracted task-specific resting-state components corresponding to brain local activation or global connectome of various tasks and applied them to the brain lateralization prediction and psychiatric disorders diagnostic. Our model effectively substitutes traditional whole-brain functional connectivity (FC) in predicting functional lateralization and diagnosing brain disorders. Our research represents the inaugural effort to quantify the contribution of patterns (components) within resting-state FC to different tasks, endowing these components with specific task-related contextual information. The task-specific resting-state components offer new insights into brain lateralization processing and disease diagnosis, potentially providing fresh perspectives on the adaptive transformation of brain networks in response to tasks.
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Affiliation(s)
- Jinwei Lang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China
| | - Li-Zhuang Yang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China; Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, China.
| | - Hai Li
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China; Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, China.
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2
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Santoro A, Battiston F, Lucas M, Petri G, Amico E. Higher-order connectomics of human brain function reveals local topological signatures of task decoding, individual identification, and behavior. Nat Commun 2024; 15:10244. [PMID: 39592571 PMCID: PMC11599762 DOI: 10.1038/s41467-024-54472-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 11/11/2024] [Indexed: 11/28/2024] Open
Abstract
Traditional models of human brain activity often represent it as a network of pairwise interactions between brain regions. Going beyond this limitation, recent approaches have been proposed to infer higher-order interactions from temporal brain signals involving three or more regions. However, to this day it remains unclear whether methods based on inferred higher-order interactions outperform traditional pairwise ones for the analysis of fMRI data. To address this question, we conducted a comprehensive analysis using fMRI time series of 100 unrelated subjects from the Human Connectome Project. We show that higher-order approaches greatly enhance our ability to decode dynamically between various tasks, to improve the individual identification of unimodal and transmodal functional subsystems, and to strengthen significantly the associations between brain activity and behavior. Overall, our approach sheds new light on the higher-order organization of fMRI time series, improving the characterization of dynamic group dependencies in rest and tasks, and revealing a vast space of unexplored structures within human functional brain data, which may remain hidden when using traditional pairwise approaches.
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Affiliation(s)
- Andrea Santoro
- Neuro-X Institute, EPFL, Geneva, Switzerland.
- CENTAI, Turin, Italy.
| | - Federico Battiston
- Department of Network and Data Science, Central European University, Vienna, Austria
| | - Maxime Lucas
- CENTAI, Turin, Italy
- Department of Mathematics & Namur Institute for Complex Systems (naXys), Université de Namur, Namur, Belgium
| | - Giovanni Petri
- CENTAI, Turin, Italy
- NPLab, Network Science Institute, Northeastern University London, London, UK
- Department of Physics, Northeastern University, Boston, MA, USA
| | - Enrico Amico
- Neuro-X Institute, EPFL, Geneva, Switzerland.
- School of Mathematics, University of Birmingham, Birmingham, UK.
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK.
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3
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Duong-Tran D, Nguyen N, Mu S, Chen J, Bao J, Xu F, Garai S, Cadena-Pico J, Kaplan AD, Chen T, Zhao Y, Shen L, Goñi J. A principled framework to assess the information-theoretic fitness of brain functional sub-circuits. ARXIV 2024:arXiv:2406.18531v2. [PMID: 38979488 PMCID: PMC11230349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects' functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods and provide insights for future research in individualized parcellations.
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Affiliation(s)
- Duy Duong-Tran
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Mathematics, United States Naval Academy, Annapolis, MD, USA
| | - Nghi Nguyen
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Shizhuo Mu
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiong Chen
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Frederick Xu
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sumita Garai
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jose Cadena-Pico
- Machine Learning Group, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Alan David Kaplan
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Tianlong Chen
- Department of Computer Science, The University of North Carolina at Chapel Hill
| | - Yize Zhao
- School of Public Health, Yale University, New Heaven, CT, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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4
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Peña-Casanova J, Sánchez-Benavides G, Sigg-Alonso J. Updating functional brain units: Insights far beyond Luria. Cortex 2024; 174:19-69. [PMID: 38492440 DOI: 10.1016/j.cortex.2024.02.004] [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: 09/28/2023] [Revised: 01/15/2024] [Accepted: 02/15/2024] [Indexed: 03/18/2024]
Abstract
This paper reviews Luria's model of the three functional units of the brain. To meet this objective, several issues were reviewed: the theory of functional systems and the contributions of phylogenesis and embryogenesis to the brain's functional organization. This review revealed several facts. In the first place, the relationship/integration of basic homeostatic needs with complex forms of behavior. Secondly, the multi-scale hierarchical and distributed organization of the brain and interactions between cells and systems. Thirdly, the phylogenetic role of exaptation, especially in basal ganglia and cerebellum expansion. Finally, the tripartite embryogenetic organization of the brain: rhinic, limbic/paralimbic, and supralimbic zones. Obviously, these principles of brain organization are in contradiction with attempts to establish separate functional brain units. The proposed new model is made up of two large integrated complexes: a primordial-limbic complex (Luria's Unit I) and a telencephalic-cortical complex (Luria's Units II and III). As a result, five functional units were delineated: Unit I. Primordial or preferential (brainstem), for life-support, behavioral modulation, and waking regulation; Unit II. Limbic and paralimbic systems, for emotions and hedonic evaluation (danger and relevance detection and contribution to reward/motivational processing) and the creation of cognitive maps (contextual memory, navigation, and generativity [imagination]); Unit III. Telencephalic-cortical, for sensorimotor and cognitive processing (gnosis, praxis, language, calculation, etc.), semantic and episodic (contextual) memory processing, and multimodal conscious agency; Unit IV. Basal ganglia systems, for behavior selection and reinforcement (reward-oriented behavior); Unit V. Cerebellar systems, for the prediction/anticipation (orthometric supervision) of the outcome of an action. The proposed brain units are nothing more than abstractions within the brain's simultaneous and distributed physiological processes. As function transcends anatomy, the model necessarily involves transition and overlap between structures. Beyond the classic approaches, this review includes information on recent systemic perspectives on functional brain organization. The limitations of this review are discussed.
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Affiliation(s)
- Jordi Peña-Casanova
- Integrative Pharmacology and Systems Neuroscience Research Group, Neuroscience Program, Hospital del Mar Medical Research Institute, Barcelona, Spain; Department of Psychiatry and Legal Medicine, Autonomous University of Barcelona, Bellaterra, Barcelona, Spain; Test Barcelona Services, Teià, Barcelona, Spain.
| | | | - Jorge Sigg-Alonso
- Department of Behavioral and Cognitive Neurobiology, Institute of Neurobiology, National Autonomous University of México (UNAM), Queretaro, Mexico
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Duong-Tran D, Kaufmann R, Chen J, Wang X, Garai S, Xu F, Bao J, Amico E, Kaplan AD, Petri G, Goni J, Zhao Y, Shen L. Homological landscape of human brain functional sub-circuits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.22.573062. [PMID: 38187668 PMCID: PMC10769445 DOI: 10.1101/2023.12.22.573062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Human whole-brain functional connectivity networks have been shown to exhibit both local/quasilocal (e.g., set of functional sub-circuits induced by node or edge attributes) and non-local (e.g., higher-order functional coordination patterns) properties. Nonetheless, the non-local properties of topological strata induced by local/quasilocal functional sub-circuits have yet to be addressed. To that end, we proposed a homological formalism that enables the quantification of higher-order characteristics of human brain functional sub-circuits. Our results indicated that each homological order uniquely unravels diverse, complementary properties of human brain functional sub-circuits. Noticeably, the H 1 homological distance between rest and motor task were observed at both whole-brain and sub-circuit consolidated level which suggested the self-similarity property of human brain functional connectivity unraveled by homological kernel. Furthermore, at the whole-brain level, the rest-task differentiation was found to be most prominent between rest and different tasks at different homological orders: i) Emotion task H 0 , ii) Motor task H 1 , and iii) Working memory task H 2 . At the functional sub-circuit level, the rest-task functional dichotomy of default mode network is found to be mostly prominent at the first and second homological scaffolds. Also at such scale, we found that the limbic network plays a significant role in homological reconfiguration across both task- and subject- domain which sheds light to subsequent Investigations on the complex neuro-physiological role of such network. From a wider perspective, our formalism can be applied, beyond brain connectomics, to study non-localized coordination patterns of localized structures stretching across complex network fibers.
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Affiliation(s)
- Duy Duong-Tran
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
- Department of Mathematics, United States Naval Academy, Annapolis, MD, USA
| | - Ralph Kaufmann
- Department of Mathematics, Purdue University, West Lafayette, IN, USA
| | - Jiong Chen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, PA, USA
| | - Xuan Wang
- Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA
| | - Sumita Garai
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Frederick Xu
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Enrico Amico
- Neuro-X Institute, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | - Alan David Kaplan
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Giovanni Petri
- CENTAI Institute, 10138 Torino, Italy
- NPLab, Network Science Institute, Northeastern University London, London, E1W 1LP, United Kingdom
- Networks Unit, IMT Lucca Institute, 55100 Lucca, Italy
| | - Joaquin Goni
- 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, US
| | - Yize Zhao
- School of Public Health, Yale University, New Heaven, CT, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
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6
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Abbas K, Liu M, Wang M, Duong-Tran D, Tipnis U, Amico E, Kaplan AD, Dzemidzic M, Kareken D, Ances BM, Harezlak J, Goñi J. Tangent functional connectomes uncover more unique phenotypic traits. iScience 2023; 26:107624. [PMID: 37694156 PMCID: PMC10483051 DOI: 10.1016/j.isci.2023.107624] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 07/31/2023] [Accepted: 08/10/2023] [Indexed: 09/12/2023] Open
Abstract
Functional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projections, resulting into tangent-FCs. Tangent-FCs have led to more accurate models predicting brain conditions or aging. Motivated by the fact that tangent-FCs seem to be better biomarkers than FCs, we hypothesized that tangent-FCs have also a higher fingerprint. We explored the effects of six factors: fMRI condition, scan length, parcellation granularity, reference matrix, main-diagonal regularization, and distance metric. Our results showed that identification rates are systematically higher when using tangent-FCs across the "fingerprint gradient" (here including test-retest, monozygotic and dizygotic twins). Highest identification rates were achieved when minimally (0.01) regularizing FCs while performing tangent space projection using Riemann reference matrix and using correlation distance to compare the resulting tangent-FCs. Such configuration was validated in a second dataset (resting-state).
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Affiliation(s)
- Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Mintao Liu
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Michael Wang
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Duy Duong-Tran
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Mathematics, United States Naval Academy, Annapolis, MD, USA
| | - Uttara Tipnis
- Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Alan D. Kaplan
- Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Mario Dzemidzic
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, Indianapolis, IN, USA
| | - David Kareken
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, Indianapolis, IN, USA
| | - Beau M. Ances
- Department of Neurology, Washington University in Saint Louis, School of Medicine, St Louis, MO, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, Bloomington, IN, USA
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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7
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Colenbier N, Sareen E, Del-Aguila Puntas T, Griffa A, Pellegrino G, Mantini D, Marinazzo D, Arcara G, Amico E. Task matters: Individual MEG signatures from naturalistic and neurophysiological brain states. Neuroimage 2023; 271:120021. [PMID: 36918139 DOI: 10.1016/j.neuroimage.2023.120021] [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: 11/17/2022] [Revised: 02/21/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
The discovery that human brain connectivity data can be used as a "fingerprint" to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have identified the possibility to extract these brain signatures from the temporal rich dynamics of resting-state magneto encephalography (MEG) recordings. Nevertheless, it is still uncertain to what extent MEG signatures can serve as an indicator of human identifiability during task-related conduct. Here, using MEG data from naturalistic and neurophysiological tasks, we show that identification improves in tasks relative to resting-state, providing compelling evidence for a task dependent axis of MEG signatures. Notably, improvements in identifiability were more prominent in strictly controlled tasks. Lastly, the brain regions contributing most towards individual identification were also modified when engaged in task activities. We hope that this investigation advances our understanding of the driving factors behind brain identification from MEG signals.
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Affiliation(s)
| | - Ekansh Sareen
- Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Tamara Del-Aguila Puntas
- Laboratorio de Psicobiologia, Departmento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Spain
| | - Alessandra Griffa
- Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Leenaards Memory Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium
| | | | - Enrico Amico
- Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland.
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8
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Williams D. Basal ganglia functional connectivity network analysis does not support the 'noisy signal' hypothesis of Parkinson's disease. Brain Commun 2023; 5:fcad123. [PMID: 37124947 PMCID: PMC10139445 DOI: 10.1093/braincomms/fcad123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/23/2023] [Accepted: 04/12/2023] [Indexed: 05/02/2023] Open
Abstract
The 'noisy signal' hypothesis of basal ganglia dysfunction in Parkinson's disease (PD) suggests that major motor symptoms of the disorder are caused by the development of abnormal basal ganglia activity patterns resulting in the propagation of 'noisy' signals to target systems. While such abnormal activity patterns might be useful biomarkers for the development of therapeutic interventions, correlation between specific changes in activity and PD symptoms has been inconsistently demonstrated, and raises questions concerning the accuracy of the hypothesis. Here, we tested this hypothesis by considering three nodes of the basal ganglia network, the subthalamus, globus pallidus interna, and cortex during self-paced and cued movements in patients with PD. Interactions between these regions were analyzed using measures that assess both linear and non-linear relationships. Marked changes in the network are observed with dopamine state. Specifically, we detected functional disconnection of the basal ganglia from the cortex and higher network variability in untreated PD, but various patterns of directed functional connectivity with lower network variability in treated PD. When we examine the system output, significant correlation is observed between variability in the cortico-basal ganglia network and muscle activity variability but only in the treated state. Rather than supporting a role of the basal ganglia in the transmission of noisy signals in patients with PD, these findings suggest that cortico-basal ganglia network interactions by fault or design, in the treated Parkinsonian state, are actually associated with improved cortical network output variability.
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Affiliation(s)
- David Williams
- Correspondence to: Dr David Williams. Department of Internal Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Khalifa Bin Zayed Street, Tawam, Next to Tawam Hospital, Al Ain, PO Box 15551, United Arab Emirates. E-mail:
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9
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Damiani S, Tarchi L, La-Torraca-Vittori P, Scalabrini A, Castellini G, Ricca V, Fusar-Poli P, Politi P. State-dependent reductions of local brain connectivity in schizophrenia and their relation to performance and symptoms: A functional magnetic resonance imaging study. Psychiatry Res Neuroimaging 2022; 326:111541. [PMID: 36122541 DOI: 10.1016/j.pscychresns.2022.111541] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 08/01/2022] [Accepted: 09/04/2022] [Indexed: 11/17/2022]
Abstract
State-dependent reallocation of cognitive resources is impaired in schizophrenia and may be underlined by alterations in brain local-connectivity. Increasing evidence suggests local connectivity reductions from rest to task in healthy individuals, while insufficient information is available for schizophrenia spectrum. Resting-state and stop-signal task fMRI scans of 107 healthy controls and 32 patients with DSM-IV-TR schizophrenia or schizoaffective disorder were analyzed. As primary aim we measured within-group shifts in local-connectivity from rest to task as voxel-wise Regional Homogeneity (ReHo-shift). Secondary aims were to test: i) Between-groups differences in ReHo-rest, ReHo-task and ReHo-shift; ii) ReHo covariations with task performance (=shorter reaction times) and severity of symptoms (SAPS/SANS scores). Age, sex, and education were accounted for as covariates. Motion, global-signal-regression, antipsychotic dosage and smoothing associations with ReHo were evaluated. Rest-to-task ReHo reductions occurred in both groups on a whole-brain level (False-Discovery-Rate p=0.05). Trends of greater ReHo reductions in patients versus controls were observed. Controls performed better than patients (p<0.001). ReHo negatively correlated with performance in both groups. ReHo-shift predicted worse performance in controls, but better performance in patients (uncorrected p=0.05). ReHo reductions correlated with severity of symptoms. State-dependent reconfigurations in local-connectivity provide new links between neurobiology and behavioral/clinical features of the schizophrenia spectrum.
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Affiliation(s)
- Stefano Damiani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy.
| | - Livio Tarchi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy; Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, FI, Italy
| | | | - Andrea Scalabrini
- Department of Human and Social Sciences, University of Bergamo, Bergamo, BG, Italy
| | - Giovanni Castellini
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, FI, Italy
| | - Valdo Ricca
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, FI, Italy
| | - Paolo Fusar-Poli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy; Department of Psychosis Studies, Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; National Institute for Health Research, Maudsley Biomedical Research Centre, London, UK
| | - Pierluigi Politi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy
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10
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Jiang Z, Dong L, Wu L, Liu Y. Quantifying navigation complexity in transportation networks. PNAS NEXUS 2022; 1:pgac126. [PMID: 36741457 PMCID: PMC9896943 DOI: 10.1093/pnasnexus/pgac126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 07/20/2022] [Indexed: 02/07/2023]
Abstract
The complexity of navigation in cities has increased with the expansion of urban areas, creating challenging transportation problems that drive many studies on the navigability of networks. However, due to the lack of individual mobility data, large-scale empirical analysis of the wayfinder's real-world navigation is rare. Here, using 225 million subway trips from three major cities in China, we quantify navigation difficulty from an information perspective. Our results reveal that (1) people conserve a small number of repeatedly used routes and (2) the navigation information in the subnetworks formed by those routes is much smaller than the theoretical value in the global network, suggesting that the decision cost for actual trips is significantly smaller than the theoretical upper limit found in previous studies. By modeling routing behaviors in growing networks, we show that while the global network becomes difficult to navigate, navigability can be improved in subnetworks. We further present a universal linear relationship between the empirical and theoretical search information, which allows the two metrics to predict each other. Our findings demonstrate how large-scale observations can quantify real-world navigation behaviors and aid in evaluating transportation planning.
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Affiliation(s)
- Zhuojun Jiang
- Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
| | - Lei Dong
- Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
| | - Lun Wu
- Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
| | - Yu Liu
- Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
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11
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Functional brain connectomes reflect acute and chronic cannabis use. Sci Rep 2022; 12:2449. [PMID: 35165360 PMCID: PMC8844352 DOI: 10.1038/s41598-022-06509-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/31/2022] [Indexed: 12/21/2022] Open
Abstract
AbstractResting state fMRI has been employed to identify alterations in functional connectivity within or between brain regions following acute and chronic exposure to Δ9-tetrahydrocannabinol (THC), the psychoactive component in cannabis. Most studies focused a priori on a limited number of local brain areas or circuits, without considering the impact of cannabis on whole-brain network organization. The present study attempted to identify changes in the whole-brain human functional connectome as assessed with ultra-high field (7T) resting state scans of cannabis users (N = 26) during placebo and following vaporization of cannabis. Two distinct data-driven methodologies, i.e. network-based statistics (NBS) and connICA, were used to identify changes in functional connectomes associated with acute cannabis intoxication and history of cannabis use. Both methodologies revealed a broad state of hyperconnectivity within the entire range of major brain networks in chronic cannabis users compared to occasional cannabis users, which might be reflective of an adaptive network reorganization following prolonged cannabis exposure. The connICA methodology also extracted a distinct spatial connectivity pattern of hypoconnectivity involving the dorsal attention, limbic, subcortical and cerebellum networks and of hyperconnectivity between the default mode and ventral attention network, that was associated with the feeling of subjective high during THC intoxication. Whole-brain network approaches identified spatial patterns in functional brain connectomes that distinguished acute from chronic cannabis use, and offer an important utility for probing the interplay between short and long-term alterations in functional brain dynamics when progressing from occasional to chronic use of cannabis.
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12
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Kawakita G, Kamiya S, Sasai S, Kitazono J, Oizumi M. Quantifying brain state transition cost via Schrödinger Bridge. Netw Neurosci 2022; 6:118-134. [PMID: 35356194 PMCID: PMC8959122 DOI: 10.1162/netn_a_00213] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 10/18/2021] [Indexed: 11/04/2022] Open
Abstract
Abstract
Quantifying brain state transition cost is a fundamental problem in systems neuroscience. Previous studies utilized network control theory to measure the cost by considering a neural system as a deterministic dynamical system. However, this approach does not capture the stochasticity of neural systems, which is important for accurately quantifying brain state transition cost. Here, we propose a novel framework based on optimal control in stochastic systems. In our framework, we quantify the transition cost as the Kullback-Leibler divergence from an uncontrolled transition path to the optimally controlled path, which is known as Schrödinger Bridge. To test its utility, we applied this framework to functional magnetic resonance imaging data from the Human Connectome Project and computed the brain state transition cost in cognitive tasks. We demonstrate correspondence between brain state transition cost and the difficulty of tasks. The results suggest that our framework provides a general theoretical tool for investigating cognitive functions from the viewpoint of transition cost.
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Affiliation(s)
- Genji Kawakita
- Graduate School of Arts and Sciences, University of Tokyo, Tokyo, Japan
| | - Shunsuke Kamiya
- Graduate School of Arts and Sciences, University of Tokyo, Tokyo, Japan
| | - Shuntaro Sasai
- Araya Inc., Tokyo, Japan
- University of Wisconsin–Madison, Madison, WI, USA
| | - Jun Kitazono
- Graduate School of Arts and Sciences, University of Tokyo, Tokyo, Japan
| | - Masafumi Oizumi
- Graduate School of Arts and Sciences, University of Tokyo, Tokyo, Japan
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13
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Duong-Tran D, Abbas K, Amico E, Corominas-Murtra B, Dzemidzic M, Kareken D, Ventresca M, Goñi J. A morphospace of functional configuration to assess configural breadth based on brain functional networks. Netw Neurosci 2021; 5:666-688. [PMID: 34746622 PMCID: PMC8567831 DOI: 10.1162/netn_a_00193] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 03/17/2021] [Indexed: 11/07/2022] Open
Abstract
The quantification of human brain functional (re)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: (a) network configural breadth, (b) task-to task transitional reconfiguration, and (c) within-task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, trapping efficiency (TE) and exit entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the network configural breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks, and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence, and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.
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Affiliation(s)
- Duy Duong-Tran
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
| | - Kausar Abbas
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
| | - Enrico Amico
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | | | - Mario Dzemidzic
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - David Kareken
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Mario Ventresca
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, IN, USA
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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14
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Amico E, Abbas K, Duong-Tran DA, Tipnis U, Rajapandian M, Chumin E, Ventresca M, Harezlak J, Goñi J. Toward an information theoretical description of communication in brain networks. Netw Neurosci 2021; 5:646-665. [PMID: 34746621 PMCID: PMC8567835 DOI: 10.1162/netn_a_00185] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 01/18/2021] [Indexed: 11/21/2022] Open
Abstract
Modeling communication dynamics in the brain is a key challenge in network neuroscience. We present here a framework that combines two measurements for any system where different communication processes are taking place on top of a fixed structural topology: path processing score (PPS) estimates how much the brain signal has changed or has been transformed between any two brain regions (source and target); path broadcasting strength (PBS) estimates the propagation of the signal through edges adjacent to the path being assessed. We use PPS and PBS to explore communication dynamics in large-scale brain networks. We show that brain communication dynamics can be divided into three main "communication regimes" of information transfer: absent communication (no communication happening); relay communication (information is being transferred almost intact); and transducted communication (the information is being transformed). We use PBS to categorize brain regions based on the way they broadcast information. Subcortical regions are mainly direct broadcasters to multiple receivers; Temporal and frontal nodes mainly operate as broadcast relay brain stations; visual and somatomotor cortices act as multichannel transducted broadcasters. This work paves the way toward the field of brain network information theory by providing a principled methodology to explore communication dynamics in large-scale brain networks.
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Affiliation(s)
- Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Duy Anh Duong-Tran
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Uttara Tipnis
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | | | - Evgeny Chumin
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Mario Ventresca
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, Bloomington, IN, USA
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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15
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Svaldi DO, Goñi J, Abbas K, Amico E, Clark DG, Muralidharan C, Dzemidzic M, West JD, Risacher SL, Saykin AJ, Apostolova LG. Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease. Hum Brain Mapp 2021; 42:3500-3516. [PMID: 33949732 PMCID: PMC8249900 DOI: 10.1002/hbm.25448] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/07/2021] [Accepted: 04/06/2021] [Indexed: 12/29/2022] Open
Abstract
Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.
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Affiliation(s)
| | - Joaquín Goñi
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
- Weldon School of Biomedical EngineeringPurdue UniversityWest LafayetteIndianaUSA
| | - Kausar Abbas
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
| | - Enrico Amico
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
| | - David G. Clark
- Indiana University School of MedicineIndianapolisIndianaUSA
| | | | | | - John D. West
- Indiana University School of MedicineIndianapolisIndianaUSA
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16
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Chiêm B, Crevecoeur F, Delvenne JC. Structure-informed functional connectivity driven by identifiable and state-specific control regions. Netw Neurosci 2021; 5:591-613. [PMID: 34189379 PMCID: PMC8233121 DOI: 10.1162/netn_a_00192] [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: 10/27/2020] [Accepted: 03/17/2021] [Indexed: 11/19/2022] Open
Abstract
Describing how the brain anatomical wiring contributes to the emergence of coordinated neural activity underlying complex behavior remains challenging. Indeed, patterns of remote coactivations that adjust with the ongoing task-demand do not systematically match direct, static anatomical links. Here, we propose that observed coactivation patterns, known as functional connectivity (FC), can be explained by a controllable linear diffusion dynamics defined on the brain architecture. Our model, termed structure-informed FC, is based on the hypothesis that different sets of brain regions controlling the information flow on the anatomical wiring produce state-specific functional patterns. We thus introduce a principled framework for the identification of potential control centers in the brain. We find that well-defined, sparse, and robust sets of control regions, partially overlapping across several tasks and resting state, produce FC patterns comparable to empirical ones. Our findings suggest that controllability is a fundamental feature allowing the brain to reach different states. Understanding how brain anatomy promotes particular patterns of coactivations among neural regions is a key challenge in neuroscience. This challenge can be addressed using network science and systems theory. Here, we propose that coactivations result from the diffusion of information through the network of anatomical links connecting brain regions, with certain regions controlling the dynamics. We translate this hypothesis into a model called structure-informed functional connectivity, and we introduce a framework for identifying control regions based on empirical data. We find that our model produces coactivation patterns comparable to empirical ones, and that distinct sets of control regions are associated with different functional states. These findings suggest that controllability is an important feature allowing the brain to reach different states.
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Affiliation(s)
- Benjamin Chiêm
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Frédéric Crevecoeur
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Jean-Charles Delvenne
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
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17
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Abbas K, Liu M, Venkatesh M, Amico E, Kaplan AD, Ventresca M, Pessoa L, Harezlak J, Goñi J. Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints. Brain Connect 2021; 11:333-348. [PMID: 33470164 PMCID: PMC8215418 DOI: 10.1089/brain.2020.0881] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Functional connectomes (FCs) have been shown to provide a reproducible individual fingerprint, which has opened the possibility of personalized medicine for neuro/psychiatric disorders. Thus, developing accurate ways to compare FCs is essential to establish associations with behavior and/or cognition at the individual level. Methods: Canonically, FCs are compared using Pearson's correlation coefficient of the entire functional connectivity profiles. Recently, it has been proposed that the use of geodesic distance is a more accurate way of comparing FCs, one which reflects the underlying non-Euclidean geometry of the data. Computing geodesic distance requires FCs to be positive-definite and hence invertible matrices. As this requirement depends on the functional magnetic resonance imaging scanning length and the parcellation used, it is not always attainable and sometimes a regularization procedure is required. Results: In the present work, we show that regularization is not only an algebraic operation for making FCs invertible, but also that an optimal magnitude of regularization leads to systematically higher fingerprints. We also show evidence that optimal regularization is data set-dependent and varies as a function of condition, parcellation, scanning length, and the number of frames used to compute the FCs. Discussion: We demonstrate that a universally fixed regularization does not fully uncover the potential of geodesic distance on individual fingerprinting and indeed could severely diminish it. Thus, an optimal regularization must be estimated on each data set to uncover the most differentiable across-subject and reproducible within-subject geodesic distances between FCs. The resulting pairwise geodesic distances at the optimal regularization level constitute a very reliable quantification of differences between subjects.
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Affiliation(s)
- Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Mintao Liu
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Manasij Venkatesh
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, USA
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | | | - Mario Ventresca
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Luiz Pessoa
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, USA
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, Maryland, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, Bloomington, Indiana, USA
| | - 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
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18
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Yin H, Clegg RG, Mondragón RJ. Simplification of networks by conserving path diversity and minimisation of the search information. Sci Rep 2020; 10:19150. [PMID: 33154403 PMCID: PMC7644697 DOI: 10.1038/s41598-020-75741-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 10/12/2020] [Indexed: 11/09/2022] Open
Abstract
Alternative paths in a network play an important role in its functionality as they can maintain the information flow under node/link failures. In this paper we explore the navigation of a network taking into account the alternative paths and in particular how can we describe this navigation in a concise way. Our approach is to simplify the network by aggregating into groups the nodes that do not contribute to alternative paths. We refer to these groups as super-nodes, and describe the post-aggregation network with super-nodes as the skeleton network. We present a method to describe with the least amount of information the paths in the super-nodes and skeleton network. Applying our method to several real networks we observed that there is scaling behaviour between the information required to describe all the paths in a network and the minimal information to describe the paths of its skeleton. We show how from this scaling we can evaluate the information of the paths for large networks with less computational cost.
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Affiliation(s)
- H Yin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.
| | - R G Clegg
- School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - R J Mondragón
- School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
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19
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Ito T, Hearne LJ, Cole MW. A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales. Neuroimage 2020; 221:117141. [PMID: 32663642 PMCID: PMC7779074 DOI: 10.1016/j.neuroimage.2020.117141] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 05/21/2020] [Accepted: 07/02/2020] [Indexed: 11/24/2022] Open
Abstract
Many studies have identified the role of localized and distributed cognitive functionality by mapping either local task-related activity or distributed functional connectivity (FC). However, few studies have directly explored the relationship between a brain region's localized task activity and its distributed task FC. Here we systematically evaluated the differential contributions of task-related activity and FC changes to identify a relationship between localized and distributed processes across the cortical hierarchy. We found that across multiple tasks, the magnitude of regional task-evoked activity was high in unimodal areas, but low in transmodal areas. In contrast, we found that task-state FC was significantly reduced in unimodal areas relative to transmodal areas. This revealed a strong negative relationship between localized task activity and distributed FC across cortical regions that was associated with the previously reported principal gradient of macroscale organization. Moreover, this dissociation corresponded to hierarchical cortical differences in the intrinsic timescale estimated from resting-state fMRI and region myelin content estimated from structural MRI. Together, our results contribute to a growing literature illustrating the differential contributions of a hierarchical cortical gradient representing localized and distributed cognitive processes.
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Affiliation(s)
- Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA; Behavioral and Neural Sciences PhD Program, Rutgers University, Newark, NJ, 07102, USA.
| | - Luke J Hearne
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
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20
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Rajapandian M, Amico E, Abbas K, Ventresca M, Goñi J. Uncovering differential identifiability in network properties of human brain functional connectomes. Netw Neurosci 2020; 4:698-713. [PMID: 32885122 PMCID: PMC7462422 DOI: 10.1162/netn_a_00140] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 03/30/2020] [Indexed: 01/05/2023] Open
Abstract
The identifiability framework (𝕀f) has been shown to improve differential identifiability (reliability across-sessions and -sites, and differentiability across-subjects) of functional connectomes for a variety of fMRI tasks. But having a robust single session/subject functional connectome is just the starting point to subsequently assess network properties for characterizing properties of integration, segregation, and communicability, among others. Naturally, one wonders whether uncovering identifiability at the connectome level also uncovers identifiability on the derived network properties. This also raises the question of where to apply the 𝕀f framework: on the connectivity data or directly on each network measurement? Our work answers these questions by exploring the differential identifiability profiles of network measures when 𝕀f is applied (a) on the functional connectomes, and (b) directly on derived network measurements. Results show that improving across-session reliability of functional connectomes (FCs) also improves reliability of derived network measures. We also find that, for specific network properties, application of 𝕀f directly on network properties is more effective. Finally, we discover that applying the framework, either way, increases task sensitivity of network properties. At a time when the neuroscientific community is focused on subject-level inferences, this framework is able to uncover FC fingerprints, which propagate to derived network properties.
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Affiliation(s)
| | - Enrico Amico
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute of Integrative Neuroscience, West Lafayette, IN, USA
| | - Kausar Abbas
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute of Integrative Neuroscience, West Lafayette, IN, USA
| | - Mario Ventresca
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute of Integrative Neuroscience, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, West Lafayette, IN, USA
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21
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Amico E, Dzemidzic M, Oberlin BG, Carron CR, Harezlak J, Goñi J, Kareken DA. The disengaging brain: Dynamic transitions from cognitive engagement and alcoholism risk. Neuroimage 2020; 209:116515. [PMID: 31904492 PMCID: PMC8496455 DOI: 10.1016/j.neuroimage.2020.116515] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 01/01/2020] [Indexed: 10/25/2022] Open
Abstract
Human functional brain connectivity is usually measured either at "rest" or during cognitive tasks, ignoring life's moments of mental transition. We propose a different approach to understanding brain network transitions. We applied a novel independent component analysis of functional connectivity during motor inhibition (stop signal task) and during the continuous transition to an immediately ensuing rest. A functional network reconfiguration process emerged that: (i) was most prominent in those without familial alcoholism risk, (ii) encompassed brain areas engaged by the task, yet (iii) appeared only transiently after task cessation. The pattern was not present in a pre-task rest scan or in the remaining minutes of post-task rest. Finally, this transient network reconfiguration related to a key behavioral trait of addiction risk: reward delay discounting. These novel findings illustrate how dynamic brain functional reconfiguration during normally unstudied periods of cognitive transition might reflect addiction vulnerability, and potentially other forms of brain dysfunction.
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Affiliation(s)
- Enrico Amico
- Purdue Institute for Integrative Neuroscience, Purdue University, USA; School of Industrial Engineering, Purdue University, USA
| | - Mario Dzemidzic
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, USA
| | - Brandon G Oberlin
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, USA; Department of Psychiatry, Indiana University School of Medicine, USA
| | - Claire R Carron
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, USA
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, USA; School of Industrial Engineering, Purdue University, USA; Weldon School of Biomedical Engineering, Purdue University, USA.
| | - David A Kareken
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, USA.
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22
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Hilger K, Fukushima M, Sporns O, Fiebach CJ. Temporal stability of functional brain modules associated with human intelligence. Hum Brain Mapp 2020; 41:362-372. [PMID: 31587450 PMCID: PMC7267930 DOI: 10.1002/hbm.24807] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 08/15/2019] [Accepted: 09/16/2019] [Indexed: 12/23/2022] Open
Abstract
Individual differences in general cognitive ability (i.e., intelligence) have been linked to individual variations in the modular organization of functional brain networks. However, these analyses have been limited to static (time-averaged) connectivity, and have not yet addressed whether dynamic changes in the configuration of brain networks relate to general intelligence. Here, we used multiband functional MRI resting-state data (N = 281) and estimated subject-specific time-varying functional connectivity networks. Modularity optimization was applied to determine individual time-variant module partitions and to assess fluctuations in modularity across time. We show that higher intelligence, indexed by an established composite measure, the Wechsler Abbreviated Scale of Intelligence (WASI), is associated with higher temporal stability (lower temporal variability) of brain network modularity. Post-hoc analyses reveal that subjects with higher intelligence scores engage in fewer periods of extremely high modularity - which are characterized by greater disconnection of task-positive from task-negative networks. Further, we show that brain regions of the dorsal attention network contribute most to the observed effect. In sum, our study suggests that investigating the temporal dynamics of functional brain network topology contributes to our understanding of the neural bases of general cognitive abilities.
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Affiliation(s)
- Kirsten Hilger
- Department of PsychologyGoethe University FrankfurtFrankfurt am MainGermany
- IDeA Center for Individual Development and Adaptive EducationFrankfurt am MainGermany
| | - Makoto Fukushima
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndiana
| | - Olaf Sporns
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndiana
| | - Christian J. Fiebach
- Department of PsychologyGoethe University FrankfurtFrankfurt am MainGermany
- IDeA Center for Individual Development and Adaptive EducationFrankfurt am MainGermany
- Brain Imaging CenterGoethe University FrankfurtFrankfurt am MainGermany
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