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Zhao W, Su K, Zhu H, Kaiser M, Fan M, Zou Y, Li T, Yin D. Activity flow under the manipulation of cognitive load and training. Neuroimage 2024; 297:120761. [PMID: 39069226 DOI: 10.1016/j.neuroimage.2024.120761] [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: 03/04/2024] [Revised: 06/11/2024] [Accepted: 07/26/2024] [Indexed: 07/30/2024] Open
Abstract
Flexible cognitive functions, such as working memory (WM), usually require a balance between localized and distributed information processing. However, it is challenging to uncover how local and distributed processing specifically contributes to task-induced activity in a region. Although the recently proposed activity flow mapping approach revealed the relative contribution of distributed processing, few studies have explored the adaptive and plastic changes that underlie cognitive manipulation. In this study, we recruited 51 healthy volunteers (31 females) and investigated how the activity flow and brain activation of the frontoparietal systems was modulated by WM load and training. While the activation of both executive control network (ECN) and dorsal attention network (DAN) increased linearly with memory load at baseline, the relative contribution of distributed processing showed a linear response only in the DAN, which was prominently attributed to within-network activity flow. Importantly, adaptive training selectively induced an increase in the relative contribution of distributed processing in the ECN and also a linear response to memory load, which were predominantly due to between-network activity flow. Furthermore, we demonstrated a causal effect of activity flow prediction through training manipulation on connectivity and activity. In contrast with classic brain activation estimation, our findings suggest that the relative contribution of distributed processing revealed by activity flow prediction provides unique insights into neural processing of frontoparietal systems under the manipulation of cognitive load and training. This study offers a new methodological framework for exploring information integration versus segregation underlying cognitive processing.
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Affiliation(s)
- Wanyun Zhao
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
| | - Kaiqiang Su
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
| | - Hengcheng Zhu
- Division of Biostatistics, University of Minnesota, Minneapolis 55455, MN, USA
| | - Marcus Kaiser
- Precision Imaging Beacon, School of Medicine, University of Nottingham, Nottingham NG7 2UH, United Kingdom; School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Mingxia Fan
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China
| | - Yong Zou
- Institute of Theoretical Physics, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China
| | - Ting Li
- Shanghai Changning Mental Health Center, Shanghai 200335, China
| | - Dazhi Yin
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China; Shanghai Changning Mental Health Center, Shanghai 200335, China.
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2
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Calazans MAA, Ferreira FABS, Santos FAN, Madeiro F, Lima JB. Machine Learning and Graph Signal Processing Applied to Healthcare: A Review. Bioengineering (Basel) 2024; 11:671. [PMID: 39061753 PMCID: PMC11273494 DOI: 10.3390/bioengineering11070671] [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: 05/27/2024] [Revised: 06/20/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
Signal processing is a very useful field of study in the interpretation of signals in many everyday applications. In the case of applications with time-varying signals, one possibility is to consider them as graphs, so graph theory arises, which extends classical methods to the non-Euclidean domain. In addition, machine learning techniques have been widely used in pattern recognition activities in a wide variety of tasks, including health sciences. The objective of this work is to identify and analyze the papers in the literature that address the use of machine learning applied to graph signal processing in health sciences. A search was performed in four databases (Science Direct, IEEE Xplore, ACM, and MDPI), using search strings to identify papers that are in the scope of this review. Finally, 45 papers were included in the analysis, the first being published in 2015, which indicates an emerging area. Among the gaps found, we can mention the need for better clinical interpretability of the results obtained in the papers, that is not to restrict the results or conclusions simply to performance metrics. In addition, a possible research direction is the use of new transforms. It is also important to make new public datasets available that can be used to train the models.
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Affiliation(s)
| | - Felipe A. B. S. Ferreira
- Unidade Acadêmica do Cabo de Santo Agostinho, Universidade Federal Rural de Pernambuco, Cabo de Santo Agostinho 54518-430, Brazil;
| | - Fernando A. N. Santos
- Institute for Advanced Studies, Universiteit van Amsterdam, 1012 WP Amsterdam, The Netherlands;
| | - Francisco Madeiro
- Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, Brazil;
| | - Juliano B. Lima
- Centro de Tecnologia e Geociências, Universidade Federal de Pernambuco, Recife 50670-901, Brazil;
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3
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Dworetsky A, Seitzman BA, Adeyemo B, Nielsen AN, Hatoum AS, Smith DM, Nichols TE, Neta M, Petersen SE, Gratton C. Two common and distinct forms of variation in human functional brain networks. Nat Neurosci 2024; 27:1187-1198. [PMID: 38689142 PMCID: PMC11248096 DOI: 10.1038/s41593-024-01618-2] [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: 02/28/2023] [Accepted: 03/07/2024] [Indexed: 05/02/2024]
Abstract
The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While most previous work treats individual differences as linked to boundary shifts between the borders of regions, here we show that cortical 'variants' also occur at a distance from their typical position, forming ectopic intrusions. Both 'border' and 'ectopic' variants are common across individuals, but differ in their location, network associations, properties of subgroups of individuals, activations during tasks, and prediction of behavioral phenotypes. Border variants also track significantly more with shared genetics than ectopic variants, suggesting a closer link between ectopic variants and environmental influences. This work argues that these two dissociable forms of variation-border shifts and ectopic intrusions-must be separately accounted for in the analysis of individual differences in cortical systems across people.
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Affiliation(s)
- Ally Dworetsky
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Benjamin A Seitzman
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Alexander S Hatoum
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Derek M Smith
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Neurology, Division of Cognitive Neurology/Neuropsychology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Maital Neta
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Steven E Petersen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA.
- Department of Psychology, Northwestern University, Evanston, IL, USA.
- Neuroscience Program, Florida State University, Tallahassee, FL, USA.
- Department of Neurology, Northwestern University, Evanston, IL, USA.
- Interdepartmental Neuroscience Program, Northwestern University, Evanston, IL, USA.
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4
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van Nifterick AM, Scheijbeler EP, Gouw AA, de Haan W, Stam CJ. Local signal variability and functional connectivity: Sensitive measures of the excitation-inhibition ratio? Cogn Neurodyn 2024; 18:519-537. [PMID: 38699618 PMCID: PMC11061092 DOI: 10.1007/s11571-023-10003-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/08/2023] [Accepted: 08/13/2023] [Indexed: 05/05/2024] Open
Abstract
A novel network version of permutation entropy, the inverted joint permutation entropy (JPEinv), holds potential as non-invasive biomarker of abnormal excitation-inhibition (E-I) ratio in Alzheimer's disease (AD). In this computational modelling study, we test the hypotheses that this metric, and related measures of signal variability and functional connectivity, are sensitive to altered E-I ratios. The E-I ratio in each neural mass of a whole-brain computational network model was systematically varied. We evaluated whether JPEinv, local signal variability (by permutation entropy) and functional connectivity (by weighted symbolic mutual information (wsMI)) were related to E-I ratio, on whole-brain and regional level. The hub disruption index can identify regions primarily affected in terms of functional connectivity strength (or: degree) by the altered E-I ratios. Analyses were performed for a range of coupling strengths, filter and time-delay settings. On whole-brain level, higher E-I ratios were associated with higher functional connectivity (by JPEinv and wsMI) and lower local signal variability. These relationships were nonlinear and depended on the coupling strength, filter and time-delay settings. On regional level, hub-like regions showed a selective decrease in functional degree (by JPEinv and wsMI) upon a lower E-I ratio, and non-hub-like regions showed a selective increase in degree upon a higher E-I ratio. These results suggest that abnormal functional connectivity and signal variability, as previously reported in patients across the AD continuum, can inform us about altered E-I ratios. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-10003-x.
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Affiliation(s)
- Anne M. van Nifterick
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Elliz P. Scheijbeler
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Alida A. Gouw
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Willem de Haan
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
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5
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Liparoti M, Cipriano L, Troisi Lopez E, Polverino A, Minino R, Sarno L, Sorrentino G, Lucidi F, Sorrentino P. Brain flexibility increases during the peri-ovulatory phase as compared to early follicular phase of the menstrual cycle. Sci Rep 2024; 14:1976. [PMID: 38263324 PMCID: PMC10805777 DOI: 10.1038/s41598-023-49588-y] [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: 06/19/2023] [Accepted: 12/09/2023] [Indexed: 01/25/2024] Open
Abstract
The brain operates in a flexible dynamic regime, generating complex patterns of activity (i.e. neuronal avalanches). This study aimed at describing how brain dynamics change according to menstrual cycle (MC) phases. Brain activation patterns were estimated from resting-state magnetoencephalography (MEG) scans, acquired from women at early follicular (T1), peri-ovulatory (T2) and mid-luteal (T3) phases of the MC. We investigated the functional repertoire (number of brain configurations based on fast high-amplitude bursts of the brain signals) and the region-specific influence on large-scale dynamics across the MC. Finally, we assessed the relationship between sex hormones and changes in brain dynamics. A significantly larger number of visited configurations in T2 as compared to T1 was specifically observed in the beta frequency band. No relationship between changes in brain dynamics and sex hormones was evident. Finally, we showed that the left posterior cingulate gyrus and the right insula were recruited more often in the functional repertoire during T2 as compared to T1, while the right pallidum was more often part of the functional repertoires during T1 as compared to T2. In summary, we showed hormone-independent increased flexibility of the brain dynamics during the ovulatory phase. Moreover, we demonstrated that several specific brain regions play a key role in determining this change.
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Affiliation(s)
- Marianna Liparoti
- Department of Philosophical, Pedagogical and Quantitative-Economic Sciences, University of Chieti-Pescara "G. d'Annunzio", 66100, Chieti, Italy
| | - Lorenzo Cipriano
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", 80133, Naples, Italy
| | - Emahnuel Troisi Lopez
- Institute of Applied Sciences and Intelligent Systems, National Research Council, 80078, Pozzuoli, Italy
| | - Arianna Polverino
- Institute for Diagnosis and Cure Hermitage Capodimonte, 80131, Naples, Italy
| | - Roberta Minino
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", 80133, Naples, Italy
| | - Laura Sarno
- Department of Neurosciences, Reproductive Science and Dentistry, University of Naples "Federico II", 80131, Naples, Italy
| | - Giuseppe Sorrentino
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", 80133, Naples, Italy
- Institute of Applied Sciences and Intelligent Systems, National Research Council, 80078, Pozzuoli, Italy
- Institute for Diagnosis and Cure Hermitage Capodimonte, 80131, Naples, Italy
| | - Fabio Lucidi
- Department of Social and Developmental Psychology, "Sapienza" University of Rome, 00185, Rome, Italy
| | - Pierpaolo Sorrentino
- Institute of Applied Sciences and Intelligent Systems, National Research Council, 80078, Pozzuoli, Italy.
- Institut de Neurosciences Des Systèmes, Aix-Marseille Université, 13005, Marseille, France.
- Department of Biomedical Sciences, University of Sassari, 07100, Sassari, Italy.
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6
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Zhu H, Huang Z, Yang Y, Su K, Fan M, Zou Y, Li T, Yin D. Activity flow mapping over probabilistic functional connectivity. Hum Brain Mapp 2023; 44:341-361. [PMID: 36647263 PMCID: PMC9842909 DOI: 10.1002/hbm.26044] [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: 05/05/2022] [Revised: 07/01/2022] [Accepted: 07/28/2022] [Indexed: 01/25/2023] Open
Abstract
Emerging evidence indicates that activity flow over resting-state network topology allows the prediction of task activations. However, previous studies have mainly adopted static, linear functional connectivity (FC) estimates as activity flow routes. It is unclear whether an intrinsic network topology that captures the dynamic nature of FC can be a better representation of activity flow routes. Moreover, the effects of between- versus within-network connections and tight versus loose (using rest baseline) task contrasts on the prediction of task-evoked activity across brain systems remain largely unknown. In this study, we first propose a probabilistic FC estimation derived from a dynamic framework as a new activity flow route. Subsequently, activity flow mapping was tested using between- and within-network connections separately for each region as well as using a set of tight task contrasts. Our results showed that probabilistic FC routes substantially improved individual-level activity flow prediction. Although it provided better group-level prediction, the multiple regression approach was more dependent on the length of data points at the individual-level prediction. Regardless of FC type, we consistently observed that between-network connections showed a relatively higher prediction performance in higher-order cognitive control than in primary sensorimotor systems. Furthermore, cognitive control systems exhibit a remarkable increase in prediction accuracy with tight task contrasts and a decrease in sensorimotor systems. This work demonstrates that probabilistic FC estimates are promising routes for activity flow mapping and also uncovers divergent influences of connectional topology and task contrasts on activity flow prediction across brain systems with different functional hierarchies.
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Affiliation(s)
- Hengcheng Zhu
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina
| | - Ziyi Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina
| | - Yifeixue Yang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina
| | - Kaiqiang Su
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina
| | - Mingxia Fan
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic ScienceEast China Normal UniversityShanghaiChina
| | - Yong Zou
- Institute of Theoretical Physics, School of Physics and Electronic ScienceEast China Normal UniversityShanghaiChina
| | - Ting Li
- Shanghai Changning Mental Health CenterShanghaiChina
| | - Dazhi Yin
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina
- Shanghai Changning Mental Health CenterShanghaiChina
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7
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Duma GM, Danieli A, Mattar MG, Baggio M, Vettorel A, Bonanni P, Mento G. Resting state network dynamic reconfiguration and neuropsychological functioning in temporal lobe epilepsy: An HD-EEG investigation. Cortex 2022; 157:1-13. [PMID: 36257103 DOI: 10.1016/j.cortex.2022.08.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 07/07/2022] [Accepted: 08/24/2022] [Indexed: 12/15/2022]
Abstract
Temporal lobe epilepsy (TLE) is nowadays considered a network disorder impacting several cognitive domains. In this work we investigated dynamic network reconfiguration differences in patients with unilateral TLE compared to a healthy control group, focusing on two connectivity indices: flexibility and integration. We apply these indices for the first time to high-density EEG source-based functional connectivity. We observed that patients with TLE exhibited significantly lower flexibility than healthy controls in the Control, Default Mode and Attentive Dorsal networks, expressed in the delta, theta and alpha bands. In addition, patients with TLE displayed greater integration values across the majority of the resting state networks, especially in the delta, theta and gamma bands. Relevantly, a higher integration index in the Control, Attentive Dorsal and Visual networks in the delta band was correlated with lower performance in visual attention and executive functions. Moreover, a greater integration index in the gamma band of the Control, Somatomotor and Temporoparietal networks was related to lower long-term memory performance. These results suggest that patients with TLE display dysregulated network reconfiguration, with lower flexibility in the brain areas related to cognitive control and attention, together with excessive inter-network communication (integration index). Finally, the correlation between network integration and the reduced cognitive performance suggests a potential mechanism underlying specific alterations in neuropsychological profile of patients with TLE.
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Affiliation(s)
- Gian Marco Duma
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France; IRCCS E. Medea Scientific Institute, Epilepsy Unit, Conegliano, Treviso, Italy.
| | - Alberto Danieli
- IRCCS E. Medea Scientific Institute, Epilepsy Unit, Conegliano, Treviso, Italy
| | - Marcelo G Mattar
- Department of Cognitive Science, University of California, San Diego, San Diego, CA, USA
| | - Martina Baggio
- IRCCS E. Medea Scientific Institute, Epilepsy Unit, Conegliano, Treviso, Italy
| | - Airis Vettorel
- IRCCS E. Medea Scientific Institute, Epilepsy Unit, Conegliano, Treviso, Italy
| | - Paolo Bonanni
- IRCCS E. Medea Scientific Institute, Epilepsy Unit, Conegliano, Treviso, Italy
| | - Giovanni Mento
- Department of General Psychology, University of Padova, Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
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8
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Xiao J, Uddin LQ, Meng Y, Li L, Gao L, Shan X, Huang X, Liao W, Chen H, Duan X. A spatio-temporal decomposition framework for dynamic functional connectivity in the human brain. Neuroimage 2022; 263:119618. [PMID: 36087902 DOI: 10.1016/j.neuroimage.2022.119618] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 08/15/2022] [Accepted: 09/06/2022] [Indexed: 11/29/2022] Open
Abstract
Much recent attention has been directed toward investigating the spatial and temporal organization of brain dynamics, but the rules which constrain the variation of spatio-temporal organization in functional connectivity under different brain states remain unclear. Here, we developed a novel computational approach based on tensor decomposition and regularization to represent dynamic functional connectivity as a linear combination of dynamic modules and time-varying weights. In this approach, dynamic modules represent co-activating functional connectivity patterns, and time-varying weights represent the temporal expression of dynamic modules. We applied this dynamic decomposition model (DDM) on a resting-state fMRI dataset and found that whole-brain dynamic functional connectivity can be decomposed as a linear combination of eight dynamic modules which we summarize as 'high order modules' and 'primary-high order modules', according to their spatial attributes and correspondence with existing intrinsic functional brain networks. By clustering the time-varying weights, we identified five brain states including three major states and two minor states. We found that state transitions mainly occurred between the three major states, and that temporal variation of dynamic modules may contribute to brain state transitions. We then conceptualized the variability of weights as the flexibility of the corresponding dynamic modules and found that different dynamic modules exhibit different amounts of flexibility and contribute to different cognitive measures. Finally, we applied DDM to a schizophrenia resting-state fMRI dataset and found that atypical flexibility of dynamic modules correlates with impaired cognitive flexibility in schizophrenia. Overall, this work provides a quantitative framework that characterizes temporal variation in the topology of dynamic functional connectivity.
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Affiliation(s)
- Jinming Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro Information, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro Information, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Lei Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro Information, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Leying Gao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro Information, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiaolong Shan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro Information, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xinyue Huang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro Information, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro Information, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro Information, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro Information, University of Electronic Science and Technology of China, Chengdu 611731, China.
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9
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Lima Dias Pinto I, Rungratsameetaweemana N, Flaherty K, Periyannan A, Meghdadi A, Richard C, Berka C, Bansal K, Garcia JO. Intermittent brain network reconfigurations and the resistance to social media influence. Netw Neurosci 2022; 6:870-896. [PMID: 36605415 PMCID: PMC9810364 DOI: 10.1162/netn_a_00255] [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: 11/23/2021] [Accepted: 05/10/2022] [Indexed: 01/09/2023] Open
Abstract
Since its development, social media has grown as a source of information and has a significant impact on opinion formation. Individuals interact with others and content via social media platforms in a variety of ways, but it remains unclear how decision-making and associated neural processes are impacted by the online sharing of informational content, from factual to fabricated. Here, we use EEG to estimate dynamic reconfigurations of brain networks and probe the neural changes underlying opinion change (or formation) within individuals interacting with a simulated social media platform. Our findings indicate that the individuals who changed their opinions are characterized by less frequent network reconfigurations while those who did not change their opinions tend to have more flexible brain networks with frequent reconfigurations. The nature of these frequent network configurations suggests a fundamentally different thought process between intervals in which individuals are easily influenced by social media and those in which they are not. We also show that these reconfigurations are distinct to the brain dynamics during an in-person discussion with strangers on the same content. Together, these findings suggest that brain network reconfigurations may not only be diagnostic to the informational context but also the underlying opinion formation.
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Affiliation(s)
| | | | - Kristen Flaherty
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,Cornell Tech, New York, NY, USA
| | - Aditi Periyannan
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,Tufts University, Medford, MA, USA
| | | | | | - Chris Berka
- Advanced Brain Monitoring, Carlsbad, CA, USA
| | - Kanika Bansal
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,Department of Biomedical Engineering, Columbia University, New York, NY, USA,* Corresponding Authors: ;
| | - Javier Omar Garcia
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,* Corresponding Authors: ;
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Filho E, Husselman TA, Zugic L, Penna E, Taneva N. Performance Gains in an Open Skill Video-Game Task: The Role of Neural Efficiency and Neural Proficiency. Appl Psychophysiol Biofeedback 2022; 47:239-251. [PMID: 35688989 DOI: 10.1007/s10484-022-09553-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 05/10/2022] [Accepted: 05/30/2022] [Indexed: 11/25/2022]
Abstract
We examined whether practice in an open skill video-game task would lead to changes in performance, attention, motivation, perceived effort, and theta, alpha, and beta waves. Specifically, we were interested on whether potential performance gains from practice would be primarily explained by the neural efficiency (i.e., cortical idling) or the neural proficiency hypothesis (i.e., mix of heightened and reduced activation across the cortex). To this end, we asked 16 novice participants (8 males and 8 females; Mage = 23.13 years) to play a Nintendo Wii video-game shooting task, namely Link's Crossbow Training. Pre-test scores, which were followed by an acquisition phase, were compared to post-test scores. Performance and subjective data were recorded for each trial and EEG data was continuously recorded using the portable EEGO System. Our findings revealed that performance increased while attention decreased at post-test, thereby confirming that practice leads to performance gains and reduces attentional overload. No changes in motivation or perceived effort were observed, perhaps because effort is a gestalt multidimension construct and video-gaming is an inherently motivating activity. EEG frequency analysis revealed that, for the most part, performance gains were accompanied by increased cortical activity across frequencies bands, thus lending primary support to the neural proficiency hypothesis. Accordingly, neurofeedback interventions to aid motor learning should teach performers not only how to silence their brains (i.e., quiescence state linked to automaticity and "flow") but also how to amplify task-relevant brain networks.
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Affiliation(s)
- Edson Filho
- Wheelock College of Education & Human Development, Boston University, 2 Silber Way, 02215, Boston, MA, United States.
| | - Tammy-Ann Husselman
- School of Psychology and Computer Science, University of Central Lancashire, Preston, UK
| | - Luca Zugic
- School of Psychology and Computer Science, University of Central Lancashire, Preston, UK
| | - Eduardo Penna
- Faculty of Physical Education, Federal University of Para, Belém, Brazil
| | - Nadezhda Taneva
- School of Psychology and Computer Science, University of Central Lancashire, Preston, UK
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Scheijbeler EP, van Nifterick AM, Stam CJ, Hillebrand A, Gouw AA, de Haan W. Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer's disease? Netw Neurosci 2022; 6:382-400. [PMID: 35733433 PMCID: PMC9208018 DOI: 10.1162/netn_a_00224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/15/2021] [Indexed: 11/24/2022] Open
Abstract
Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer's disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Local activity was characterized by permutation entropy (PE). Network-level interactions were studied using the inverted joint permutation entropy (JPEinv), corrected for volume conduction. The JPEinv showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta band entropy. Between-group differences were widespread across brain regions. Receiver operating characteristic (ROC) analysis of classification of MCI versus SCD subjects revealed that a logistic regression model trained on JPEinv features (78.4% [62.5-93.3%]) slightly outperformed PE (76.9% [60.3-93.4%]) and relative theta power-based models (76.9% [60.4-93.3%]). Classification performance of theta JPEinv was at least as good as the relative theta power benchmark. The JPEinv is therefore a potential biomarker for early-stage AD that should be explored in larger studies.
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Affiliation(s)
- Elliz P. Scheijbeler
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Anne M. van Nifterick
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Alida A. Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Willem de Haan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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12
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Gao Z, Cheng L, Li J, Chen Q, Hao N. The dark side of creativity: Neural correlates of malevolent creative idea generation. Neuropsychologia 2022; 167:108164. [DOI: 10.1016/j.neuropsychologia.2022.108164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 10/19/2022]
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Maleki N, Sawyer KS, Levy S, Harris GJ, Oscar-Berman M. Intrinsic brain functional connectivity patterns in alcohol use disorder. Brain Commun 2022; 4:fcac290. [PMID: 36419966 PMCID: PMC9679426 DOI: 10.1093/braincomms/fcac290] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 08/28/2022] [Accepted: 11/03/2022] [Indexed: 11/06/2022] Open
Abstract
Alcohol use disorder is associated with damaging effects to the brain. This study aimed to examine differences in static and dynamic intrinsic functional connectivity patterns in individuals with a history of alcohol use disorder in comparison to those with no history of alcohol abuse. A total of 55 participants consisting of 23 patients and 32 control individuals underwent neuropsychological assessments and resting-state functional magnetic resonance imaging on a 3 Tesla MRI scanner. Differences in functional connectivity between the two groups were determined using static and dynamic independent component analysis. Differences in static functional connectivity between the two groups were identified in the default mode network, attention network, frontoparietal network, frontal cortical network and cerebellar network. Furthermore, the analyses revealed specific differences in the dynamic temporal characteristics of functional connectivity between the two groups of participants, in a cluster involving key regions in reward, sensorimotor and frontal cortical functional networks, with some connections correlating with the length of sobriety and some others with the severity of drinking. The findings altogether suggest dysregulation in the intrinsic connectivity of cortico-basal ganglia-thalamo-cortical loops that may reflect persistent alcohol use disorder-related network abnormalities, compensatory recovery-related processes whereby additional neural resources are recruited to achieve normal levels of performance, or a predisposition toward developing alcohol use disorder.
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Affiliation(s)
- Nasim Maleki
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA.,Psychology Research Service, VA Healthcare System, Jamaica Plain Campus, Boston, MA 02130, USA
| | - Kayle S Sawyer
- Psychology Research Service, VA Healthcare System, Jamaica Plain Campus, Boston, MA 02130, USA.,Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA.,Sawyer Scientific, LLC, Boston, MA 02130, USA
| | - Sarah Levy
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Gordon J Harris
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Marlene Oscar-Berman
- Psychology Research Service, VA Healthcare System, Jamaica Plain Campus, Boston, MA 02130, USA.,Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
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Varangis E, Qi W, Stern Y, Lee S. The role of neural flexibility in cognitive aging. Neuroimage 2021; 247:118784. [PMID: 34902547 DOI: 10.1016/j.neuroimage.2021.118784] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/09/2021] [Accepted: 12/04/2021] [Indexed: 11/28/2022] Open
Abstract
Studies assessing relationships between neural and cognitive changes in healthy aging have shown that a variety of aspects of brain structure and function explain a significant portion of the variability in cognitive outcomes throughout adulthood. Many studies assessing relationships between brain function and cognition have utilized time-averaged, or static functional connectivity methods to explore ways in which brain network organization may contribute to aspects of cognitive aging. However, recent studies in this field have suggested that time-varying, or dynamic measures of functional connectivity, which assess changes in functional connectivity over the course of a scan session, may play a stronger role in explaining cognitive outcomes in healthy young adults. Further, both static and dynamic functional connectivity studies suggest that there may be differences in patterns of brain-cognition relationships as a function of whether or not the participant is performing a task during the scan. Thus, the goals of the present study were threefold: (1) assess whether neural flexibility during both resting as well as task-based scans is related to participant age and cognitive performance in a lifespan aging sample, (2) determine whether neural flexibility moderates relationships between age and cognitive performance, and (3) explore differences in neural flexibility between rest and task. Participants in the study were 386 healthy adults between the ages of 20-80 who provided resting state and/or task-based (Matrix Reasoning) functional magnetic resonance imaging (fMRI) scan data as part of their participation in two ongoing studies of cognitive aging. Neural flexibility measures from both resting and task-based scans reflected the number of times each node changed network assignment, and were averaged both across the whole brain (global neural flexibility) as well as within ten somatosensory/cognitive networks. Results showed that neural flexibility was not related to participant age, and that task-based global neural flexibility, as well as task-based neural flexibility in several networks, tended to be negatively related to reaction times during the Matrix Reasoning task, however these effects did not survive strict multiple comparisons correction. Resting state neural flexibility was not significantly related to either participant age or cognitive performance. Additionally, no neural flexibility measures significantly moderated relationships between participant age and cognitive outcomes. Further, neural flexibility differed as a function of scan type, with resting state neural flexibility being significantly greater than task-based neural flexibility. Thus, neural flexibility measures computed during a cognitive task may be more meaningfully related to cognitive performance across the adult lifespan then resting state measures of neural flexibility.
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Affiliation(s)
- Eleanna Varangis
- Department of Neurology, Columbia University, New York, NY 10032, USA
| | - Weiwei Qi
- Mental Health Data Science, New York State Psychiatric Institute, 1051 Riverside Drive, Unit 48, New York, NY 10032, USA; Department of Biostatistics, Columbia University, New York, NY 10032, USA
| | - Yaakov Stern
- Department of Neurology, Columbia University, New York, NY 10032, USA; Department of Psychiatry, Columbia University, New York, NY 10032, USA
| | - Seonjoo Lee
- Mental Health Data Science, New York State Psychiatric Institute, 1051 Riverside Drive, Unit 48, New York, NY 10032, USA; Department of Psychiatry, Columbia University, New York, NY 10032, USA; Department of Biostatistics, Columbia University, New York, NY 10032, USA.
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