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Ptak R, Doganci N, Bourgeois A. From Action to Cognition: Neural Reuse, Network Theory and the Emergence of Higher Cognitive Functions. Brain Sci 2021; 11:1652. [PMID: 34942954 PMCID: PMC8699577 DOI: 10.3390/brainsci11121652] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 12/13/2021] [Accepted: 12/15/2021] [Indexed: 11/16/2022] Open
Abstract
The aim of this article is to discuss the logic and assumptions behind the concept of neural reuse, to explore its biological advantages and to discuss the implications for the cognition of a brain that reuses existing circuits and resources. We first address the requirements that must be fulfilled for neural reuse to be a biologically plausible mechanism. Neural reuse theories generally take a developmental approach and model the brain as a dynamic system composed of highly flexible neural networks. They often argue against domain-specificity and for a distributed, embodied representation of knowledge, which sets them apart from modular theories of mental processes. We provide an example of reuse by proposing how a phylogenetically more modern mental capacity (mental rotation) may appear through the reuse and recombination of existing resources from an older capacity (motor planning). We conclude by putting arguments into context regarding functional modularity, embodied representation, and the current ontology of mental processes.
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Affiliation(s)
- Radek Ptak
- Division of Neurorehabilitation, University Hospitals Geneva, 1205 Geneva, Switzerland
- Laboratory of Cognitive Neurorehabilitation, Faculty of Medicine, University of Geneva, 1205 Geneva, Switzerland; (N.D.); (A.B.)
| | - Naz Doganci
- Laboratory of Cognitive Neurorehabilitation, Faculty of Medicine, University of Geneva, 1205 Geneva, Switzerland; (N.D.); (A.B.)
| | - Alexia Bourgeois
- Laboratory of Cognitive Neurorehabilitation, Faculty of Medicine, University of Geneva, 1205 Geneva, Switzerland; (N.D.); (A.B.)
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52
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Missing links: The functional unification of language and memory (L∪M). Neurosci Biobehav Rev 2021; 133:104489. [PMID: 34929226 DOI: 10.1016/j.neubiorev.2021.12.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 11/14/2021] [Accepted: 12/07/2021] [Indexed: 10/19/2022]
Abstract
The field of neurocognition is currently undergoing a significant change of perspective. Traditional neurocognitive models evolved into an integrative and dynamic vision of cognitive functioning. Dynamic integration assumes an interaction between cognitive domains traditionally considered to be distinct. Language and declarative memory are regarded as separate functions supported by different neural systems. However, they also share anatomical structures (notably, the inferior frontal gyrus, the supplementary motor area, the superior and middle temporal gyrus, and the hippocampal complex) and cognitive processes (such as semantic and working memory) that merge to endorse our quintessential daily lives. We propose a new model, "L∪M" (i.e., Language/union/Memory), that considers these two functions interactively. We fractionated language and declarative memory into three fundamental dimensions or systems ("Receiver-Transmitter", "Controller-Manager" and "Transformer-Associative" Systems), that communicate reciprocally. We formalized their interactions at the brain level with a connectivity-based approach. This new taxonomy overcomes the modular view of cognitive functioning and reconciles functional specialization with plasticity in neurological disorders.
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53
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Pang J, Guo H, Tang X, Fu Y, Yang Z, Li Y, An N, Luo J, Yao Z, Hu B. Uncovering the global task-modulated brain network in chunk decomposition with Chinese characters. Neuroimage 2021; 247:118826. [PMID: 34923135 DOI: 10.1016/j.neuroimage.2021.118826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 12/14/2021] [Accepted: 12/15/2021] [Indexed: 11/17/2022] Open
Abstract
Chunk decomposition, which requires the mental representation transformation in accordance with behavioral goals, is of vital importance to problem solving and creative thinking. Previous studies have identified that the frontal, parietal, and occipital cortex in the cognitive control network selectively activated in response to chunk tightness, however, functional localization strategy may overlook the interaction brain regions. Based on the notion of a global brain network, we proposed that multiple specialized regions have to be interconnected to maintain goal representation during the course of chunk decomposition. Therefore, the present study applied a beta-series correlation method to investigate interregional functional connectivity in the event-related design of chunk decomposition tasks using Chinese characters, which would highlight critical nodes irrespective to chunk tightness. The results reveal a network of functional hubs with highly within or between module connections, including the orbitofrontal cortex, superior/inferior parietal lobule, hippocampus, and thalamus. We speculate that the thalamus integrates information across modular as an integrative hub while the orbitofrontal cortex tracks the mental states of chunk decomposition on a moment-to-moment basis. The superior and inferior parietal lobule collaborate to manipulate the mental representation of chunk decomposition and the hippocampus associates the relationship between elements in the question and solution phase. Furthermore, the tightness of chunks is not only associated with different processors in visual systems but also leads to increased intermodular connections in right superior frontal gyrus and left precentral gyrus. To summary up, the present study first reveals the task-modulated brain network of chunk decomposition in addition to the tightness-related nodes in the frontal and occipital cortex.
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Affiliation(s)
- Jiaoyan Pang
- School of Government, Shanghai University of Political Science and Law, Shanghai, China
| | - Hanning Guo
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Gansu 730000, China.
| | - Xiaochen Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Yu Fu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Gansu 730000, China.
| | - Zhengwu Yang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Gansu 730000, China
| | - Yongchao Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Gansu 730000, China
| | - Na An
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Gansu 730000, China
| | - Jing Luo
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Gansu 730000, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Gansu 730000, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University and Institute of Semiconductors, Chinese Academy of Sciences, China; Ministry of Education, Open Source Software and Real-Time System Lanzhou University, Lanzhou, China.
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54
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Li T, Yang Y, Krueger F, Feng C, Wang J. Static and Dynamic Topological Organizations of the Costly Punishment Network Predict Individual Differences in Punishment Propensity. Cereb Cortex 2021; 32:4012-4024. [PMID: 34905766 DOI: 10.1093/cercor/bhab462] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 11/12/2021] [Accepted: 11/13/2021] [Indexed: 12/17/2022] Open
Abstract
Human costly punishment plays a vital role in maintaining social norms. Recently, a brain network model is conceptually proposed indicating that the implement of costly punishment depends on a subset of nodes in three high-level networks. This model, however, has not yet been empirically examined from an integrated perspective of large-scale brain networks. Here, we conducted comprehensive graph-based network analyses of resting-state functional magnetic resonance imaging data to explore system-level characteristics of intrinsic functional connectivity among 18 regions related to costly punishment. Nontrivial organizations (small-worldness, connector hubs, and high flexibility) were found that were qualitatively stable across participants and over time but quantitatively exhibited low test-retest reliability. The organizations were predictive of individual costly punishment propensities, which was reproducible on independent samples and robust against different analytical strategies and parameter settings. Moreover, the prediction was specific to system-level network organizations (rather than interregional functional connectivity) derived from positive (rather than negative or combined) connections among the specific (rather than randomly chosen) subset of regions from the three high-order (rather than primary) networks. Collectively, these findings suggest that human costly punishment emerges from integrative behaviors among specific regions in certain functional networks, lending support to the brain network model for costly punishment.
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Affiliation(s)
- Ting Li
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China
| | - Yuping Yang
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China
| | - Frank Krueger
- School of Systems Biology, George Mason University, Fairfax, 22030 VA, USA.,Department of Psychology, George Mason University, Fairfax, 22030 VA, USA
| | - Chunliang Feng
- School of Psychology, South China Normal University, 510631 Guangzhou, China.,Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631 Guangzhou, China.,Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China.,Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631 Guangzhou, China.,Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
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55
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Franciotti R, Moretti DV, Benussi A, Ferri L, Russo M, Carrarini C, Barbone F, Arnaldi D, Falasca NW, Koch G, Cagnin A, Nobili FM, Babiloni C, Borroni B, Padovani A, Onofrj M, Bonanni L. Cortical network modularity changes along the course of frontotemporal and Alzheimer's dementing diseases. Neurobiol Aging 2021; 110:37-46. [PMID: 34847523 DOI: 10.1016/j.neurobiolaging.2021.10.016] [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: 06/18/2021] [Revised: 10/07/2021] [Accepted: 10/26/2021] [Indexed: 12/14/2022]
Abstract
Cortical network modularity underpins cognitive functions, so we hypothesized its progressive derangement along the course of frontotemporal (FTD) and Alzheimer's (AD) dementing diseases. EEG was recorded in 18 FTD, 18 AD, and 20 healthy controls (HC). In the FTD and AD patients, the EEG recordings were performed at the prodromal stage of dementia, at the onset of dementia, and three years after the onset of dementia. HC underwent three EEG recordings at 2-3-year time interval. Information flows underlying EEG activity recorded at electrode pairs were estimated by means of Mutual Information (MI) analysis. The functional organization of the cortical network was modelled by means of the Graph theory analysis on MI adjacency matrices. Graph theory analysis showed that the main hub of HC (Parietal area) was lost in FTD patients at onset of dementia, substituted by provincial hubs in frontal leads. No changes in global network organization were found in AD. Despite a progressive cognitive impairment during the FTD and AD progression, only the FTD patients showed a derangement in the cortical network modularity, possibly due to dysfunctions in frontal functional connectivity.
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Affiliation(s)
- Raffaella Franciotti
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Davide V Moretti
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Alberto Benussi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Laura Ferri
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Mirella Russo
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Claudia Carrarini
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Filomena Barbone
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Dario Arnaldi
- Dipartimento di Neuroscienze (DINOGMI), University of Genova, Genoa, Italy; U.O. Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Nicola W Falasca
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Giacomo Koch
- Non Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy; Stroke Unit, Department of Neuroscience, Tor Vergata Policlinic, Rome, Italy
| | | | - Flavio M Nobili
- Dipartimento di Neuroscienze (DINOGMI), University of Genova, Genoa, Italy; U.O. Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "V. Erspamer," Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino (FR), Cassino, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Marco Onofrj
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy.
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56
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Lim JS, Lee JJ, Woo CW. Post-Stroke Cognitive Impairment: Pathophysiological Insights into Brain Disconnectome from Advanced Neuroimaging Analysis Techniques. J Stroke 2021; 23:297-311. [PMID: 34649376 PMCID: PMC8521255 DOI: 10.5853/jos.2021.02376] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 09/17/2021] [Indexed: 12/24/2022] Open
Abstract
The neurological symptoms of stroke have traditionally provided the foundation for functional mapping of the brain. However, there are many unresolved aspects in our understanding of cerebral activity, especially regarding high-level cognitive functions. This review provides a comprehensive look at the pathophysiology of post-stroke cognitive impairment in light of recent findings from advanced imaging techniques. Combining network neuroscience and clinical neurology, our research focuses on how changes in brain networks correlate with post-stroke cognitive prognosis. More specifically, we first discuss the general consequences of stroke lesions due to damage of canonical resting-state large-scale networks or changes in the composition of the entire brain. We also review emerging methods, such as lesion-network mapping and gradient analysis, used to study the aforementioned events caused by stroke lesions. Lastly, we examine other patient vulnerabilities, such as superimposed amyloid pathology and blood-brain barrier leakage, which potentially lead to different outcomes for the brain network compositions even in the presence of similar stroke lesions. This knowledge will allow a better understanding of the pathophysiology of post-stroke cognitive impairment and provide a theoretical basis for the development of new treatments, such as neuromodulation.
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Affiliation(s)
- Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jae-Joong Lee
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea
| | - Choong-Wan Woo
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea.,Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea
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57
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Hwang K, Shine JM, Bruss J, Tranel D, Boes A. Neuropsychological evidence of multi-domain network hubs in the human thalamus. eLife 2021; 10:69480. [PMID: 34622776 PMCID: PMC8526062 DOI: 10.7554/elife.69480] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 09/30/2021] [Indexed: 12/23/2022] Open
Abstract
Hubs in the human brain support behaviors that arise from brain network interactions. Previous studies have identified hub regions in the human thalamus that are connected with multiple functional networks. However, the behavioral significance of thalamic hubs has yet to be established. Our framework predicts that thalamic subregions with strong hub properties are broadly involved in functions across multiple cognitive domains. To test this prediction, we studied human patients with focal thalamic lesions in conjunction with network analyses of the human thalamocortical functional connectome. In support of our prediction, lesions to thalamic subregions with stronger hub properties were associated with widespread deficits in executive, language, and memory functions, whereas lesions to thalamic subregions with weaker hub properties were associated with more limited deficits. These results highlight how a large-scale network model can broaden our understanding of thalamic function for human cognition.
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Affiliation(s)
- Kai Hwang
- Department of Psychological and Brain Sciences, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Cognitive Control Collaborative, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Iowa Neuroscience Institute, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Psychiatry, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States
| | - James M Shine
- Brain and Mind Center, The University of Sydney, Sydney, Australia
| | - Joel Bruss
- Iowa Neuroscience Institute, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Neurology, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Pediatrics, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States
| | - Daniel Tranel
- Department of Psychological and Brain Sciences, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Iowa Neuroscience Institute, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Neurology, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States
| | - Aaron Boes
- Iowa Neuroscience Institute, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Psychiatry, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Neurology, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Pediatrics, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States
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58
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Golkowski D, Willnecker R, Rösler J, Ranft A, Schneider G, Jordan D, Ilg R. Dynamic Patterns of Global Brain Communication Differentiate Conscious From Unconscious Patients After Severe Brain Injury. Front Syst Neurosci 2021; 15:625919. [PMID: 34566586 PMCID: PMC8458756 DOI: 10.3389/fnsys.2021.625919] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 08/17/2021] [Indexed: 11/26/2022] Open
Abstract
The neurophysiology of the subjective sensation of being conscious is elusive; therefore, it remains controversial how consciousness can be recognized in patients who are not responsive but seemingly awake. During general anesthesia, a model for the transition between consciousness and unconsciousness, specific covariance matrices between the activity of brain regions that we call patterns of global brain communication reliably disappear when people lose consciousness. This functional magnetic imaging study investigates how patterns of global brain communication relate to consciousness and unconsciousness in a heterogeneous sample during general anesthesia and after brain injury. First, we describe specific patterns of global brain communication during wakefulness that disappear during propofol (n = 11) and sevoflurane (n = 14) general anesthesia. Second, we search for these patterns in a cohort of unresponsive wakeful patients (n = 18) and unmatched healthy controls (n = 20) in order to evaluate their potential use in clinical practice. We found that patterns of global brain communication characterized by high covariance in sensory and motor areas or low overall covariance and their dynamic change were strictly associated with intact consciousness in this cohort. In addition, we show that the occurrence of these two patterns is significantly related to activity within the frontoparietal network of the brain, a network known to play a crucial role in conscious perception. We propose that this approach potentially recognizes consciousness in the clinical routine setting.
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Affiliation(s)
- Daniel Golkowski
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany.,Neurologische Klinik und Poliklinik, University of Heidelberg, Heidelberg, Germany
| | - Rebecca Willnecker
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Jennifer Rösler
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Andreas Ranft
- Department of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Munich, Germany
| | - Gerhard Schneider
- Department of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Munich, Germany
| | - Denis Jordan
- Department of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Munich, Germany
| | - Rüdiger Ilg
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany.,Asklepios Clinic, Department of Neurology, Bad Tölz, Germany
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59
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Allegra M, Favaretto C, Metcalf N, Corbetta M, Brovelli A. Stroke-related alterations in inter-areal communication. NEUROIMAGE-CLINICAL 2021; 32:102812. [PMID: 34544032 PMCID: PMC8453222 DOI: 10.1016/j.nicl.2021.102812] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 08/02/2021] [Accepted: 08/29/2021] [Indexed: 01/03/2023]
Abstract
We used covariance-based Granger Causality on resting-state fMRI of stroke patients. Stroke determines an overall decrease of homotopic Granger causality (GC) Stroke determines a decrease of GC from and within the lesioned hemisphere. Stroke causes imbalances in GC between the lesioned and the healthy hemisphere. GC anomalies correlate with impaired performance in several behavioral domains.
Beyond causing local ischemia and cell damage at the site of injury, stroke strongly affects long-range anatomical connections, perturbing the functional organization of brain networks. Several studies reported functional connectivity abnormalities parallelling both behavioral deficits and functional recovery across different cognitive domains. FC alterations suggest that long-range communication in the brain is altered after stroke. However, standard FC analyses cannot reveal the directionality and time scale of inter-areal information transfer. We used resting-state fMRI and covariance-based Granger causality analysis to quantify network-level information transfer and its alteration in stroke. Two main large-scale anomalies were observed in stroke patients. First, inter-hemispheric information transfer was significantly decreased with respect to healthy controls. Second, stroke caused inter-hemispheric asymmetries, as information transfer within the affected hemisphere and from the affected to the intact hemisphere was significantly reduced. Both anomalies were more prominent in resting-state networks related to attention and language, and they correlated with impaired performance in several behavioral domains. Overall, our findings support the hypothesis that stroke provokes asymmetries between the affected and spared hemisphere, with different functional consequences depending on which hemisphere is lesioned.
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Affiliation(s)
- Michele Allegra
- Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, Marseille 13005, France.
| | - Chiara Favaretto
- Department of Neuroscience, Neurological Clinic, University of Padua, Padua, Italy; Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Nicholas Metcalf
- Department of Neurology, Radiology, and Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
| | - Maurizio Corbetta
- Department of Neuroscience, Neurological Clinic, University of Padua, Padua, Italy; Padova Neuroscience Center, University of Padua, Padua, Italy; Department of Neurology, Radiology, and Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, Marseille 13005, France.
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60
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Zhang J, Kucyi A, Raya J, Nielsen AN, Nomi JS, Damoiseaux JS, Greene DJ, Horovitz SG, Uddin LQ, Whitfield-Gabrieli S. What have we really learned from functional connectivity in clinical populations? Neuroimage 2021; 242:118466. [PMID: 34389443 DOI: 10.1016/j.neuroimage.2021.118466] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/06/2021] [Accepted: 08/09/2021] [Indexed: 02/09/2023] Open
Abstract
Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts.
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Affiliation(s)
- Jiahe Zhang
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA.
| | - Aaron Kucyi
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
| | - Jovicarole Raya
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Jason S Nomi
- Department of Psychology, University of Miami, Miami, FL 33124, USA
| | - Jessica S Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI 48202, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093, USA
| | | | - Lucina Q Uddin
- Department of Psychology, University of Miami, Miami, FL 33124, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
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61
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Shenoy Handiru V, Alivar A, Hoxha A, Saleh S, Suviseshamuthu ES, Yue GH, Allexandre D. Graph-theoretical analysis of EEG functional connectivity during balance perturbation in traumatic brain injury: A pilot study. Hum Brain Mapp 2021; 42:4427-4447. [PMID: 34312933 PMCID: PMC8410544 DOI: 10.1002/hbm.25554] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 05/08/2021] [Accepted: 05/27/2021] [Indexed: 12/13/2022] Open
Abstract
Traumatic brain injury (TBI) often results in balance impairment, increasing the risk of falls, and the chances of further injuries. However, the underlying neural mechanisms of postural control after TBI are not well understood. To this end, we conducted a pilot study to explore the neural mechanisms of unpredictable balance perturbations in 17 chronic TBI participants and 15 matched healthy controls (HC) using the EEG, MRI, and diffusion tensor imaging (DTI) data. As quantitative measures of the functional integration and segregation of the brain networks during the postural task, we computed the global graph-theoretic network measures (global efficiency and modularity) of brain functional connectivity derived from source-space EEG in different frequency bands. We observed that the TBI group showed a lower balance performance as measured by the center of pressure displacement during the task, and the Berg Balance Scale (BBS). They also showed reduced brain activation and connectivity during the balance task. Furthermore, the decrease in brain network segregation in alpha-band from baseline to task was smaller in TBI than HC. The DTI findings revealed widespread structural damage. In terms of the neural correlates, we observed a distinct role played by different frequency bands: theta-band modularity during the task was negatively correlated with the BBS in the TBI group; lower beta-band network connectivity was associated with the reduction in white matter structural integrity. Our future studies will focus on how postural training will modulate the functional brain networks in TBI.
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Affiliation(s)
- Vikram Shenoy Handiru
- Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, New Jersey, USA.,Department of Physical Medicine and Rehabilitation, Rutgers University New Jersey Medical School, Newark, New Jersey, USA
| | - Alaleh Alivar
- Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, New Jersey, USA.,Department of Physical Medicine and Rehabilitation, Rutgers University New Jersey Medical School, Newark, New Jersey, USA
| | - Armand Hoxha
- Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, New Jersey, USA
| | - Soha Saleh
- Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, New Jersey, USA.,Department of Physical Medicine and Rehabilitation, Rutgers University New Jersey Medical School, Newark, New Jersey, USA
| | - Easter S Suviseshamuthu
- Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, New Jersey, USA.,Department of Physical Medicine and Rehabilitation, Rutgers University New Jersey Medical School, Newark, New Jersey, USA
| | - Guang H Yue
- Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, New Jersey, USA.,Department of Physical Medicine and Rehabilitation, Rutgers University New Jersey Medical School, Newark, New Jersey, USA
| | - Didier Allexandre
- Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, New Jersey, USA.,Department of Physical Medicine and Rehabilitation, Rutgers University New Jersey Medical School, Newark, New Jersey, USA
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62
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Yang D, Zhu X, Yan C, Peng Z, Bagonis M, Laurienti PJ, Styner M, Wu G. Joint hub identification for brain networks by multivariate graph inference. Med Image Anal 2021; 73:102162. [PMID: 34274691 DOI: 10.1016/j.media.2021.102162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/01/2021] [Accepted: 07/02/2021] [Indexed: 11/19/2022]
Abstract
Recent developments in neuroimaging allow us to investigate the structural and functional connectivity between brain regions in vivo. Mounting evidence suggests that hub nodes play a central role in brain communication and neural integration. Such high centrality, however, makes hub nodes particularly susceptible to pathological network alterations and the identification of hub nodes from brain networks has attracted much attention in neuroimaging. Current popular hub identification methods often work in a univariate manner, i.e., selecting the hub nodes one after another based on either heuristic of the connectivity profile at each node or predefined settings of network modules. Since the topological information of the entire network (such as network modules) is not fully utilized, current methods have limited power to identify hubs that link multiple modules (connector hubs) and are biased toward identifying hubs having many connections within the same module (provincial hubs). To address this challenge, we propose a novel multivariate hub identification method. Our method identifies connector hubs as those that partition the network into disconnected components when they are removed from the network. Furthermore, we extend our hub identification method to find the population-based hub nodes from a group of network data. We have compared our hub identification method with existing methods on both simulated and human brain network data. Our proposed method achieves more accurate and replicable discovery of hub nodes and exhibits enhanced statistical power in identifying network alterations related to neurological disorders such as Alzheimer's disease and obsessive-compulsive disorder.
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Affiliation(s)
- Defu Yang
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China; Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | - Xiaofeng Zhu
- School of Natural and Computational Science, Massey University, Auckland, New Zealand
| | - Chenggang Yan
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Ziwen Peng
- Department of Child Psychiatry, Shenzhen Kangning Hospital, Shenzhen, China
| | - Maria Bagonis
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | | | - Martin Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA; Department of Computer Science, University of North Carolina at Chapel Hill, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA; Department of Computer Science, University of North Carolina at Chapel Hill, USA.
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63
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Kwak S, Kim H, Kim H, Youm Y, Chey J. Distributed functional connectivity predicts neuropsychological test performance among older adults. Hum Brain Mapp 2021; 42:3305-3325. [PMID: 33960591 PMCID: PMC8193511 DOI: 10.1002/hbm.25436] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 01/30/2023] Open
Abstract
Neuropsychological test is an essential tool in assessing cognitive and functional changes associated with late-life neurocognitive disorders. Despite the utility of the neuropsychological test, the brain-wide neural basis of the test performance remains unclear. Using the predictive modeling approach, we aimed to identify the optimal combination of functional connectivities that predicts neuropsychological test scores of novel individuals. Resting-state functional connectivity and neuropsychological tests included in the OASIS-3 dataset (n = 428) were used to train the predictive models, and the identified models were iteratively applied to the holdout internal test set (n = 216) and external test set (KSHAP, n = 151). We found that the connectivity-based predicted score tracked the actual behavioral test scores (r = 0.08-0.44). The predictive models utilizing most of the connectivity features showed better accuracy than those composed of focal connectivity features, suggesting that its neural basis is largely distributed across multiple brain systems. The discriminant and clinical validity of the predictive models were further assessed. Our results suggest that late-life neuropsychological test performance can be formally characterized with distributed connectome-based predictive models, and further translational evidence is needed when developing theoretically valid and clinically incremental predictive models.
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Affiliation(s)
- Seyul Kwak
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
| | - Hairin Kim
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
| | - Hoyoung Kim
- Department of PsychologyChonbuk National UniversityJeonjuRepublic of Korea
| | - Yoosik Youm
- Department of SociologyYonsei UniversitySeoulRepublic of Korea
| | - Jeanyung Chey
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
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64
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Pappas I, Hector H, Haws K, Curran B, Kayser AS, D'Esposito M. Improved normalization of lesioned brains via cohort-specific templates. Hum Brain Mapp 2021; 42:4187-4204. [PMID: 34143540 PMCID: PMC8356997 DOI: 10.1002/hbm.25474] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 04/24/2021] [Accepted: 04/26/2021] [Indexed: 12/16/2022] Open
Abstract
In MRI studies, spatial normalization is required to infer results at the group level. In the presence of a brain lesion, such as in stroke patients, the normalization process can be affected by tissue loss, spatial deformations, signal intensity changes, and other stroke sequelae that introduce confounds into the group analysis results. Previously, most neuroimaging studies with lesioned brains have used normalization methods optimized for intact brains, raising potential concerns about the accuracy of the resulting transformations and, in turn, their reported group level results. In this study, we demonstrate the benefits of creating an intermediate, cohort‐specific template in conjunction with diffeomorphism‐based methods to normalize structural MRI images in stroke patients. We show that including this cohort‐specific template improves accuracy compared to standard methods for normalizing lesioned brains. Critically, this method reduces overall differences in normalization accuracy between stroke patients and healthy controls, and may improve the localization and connectivity of BOLD signal in functional neuroimaging data.
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Affiliation(s)
- Ioannis Pappas
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA.,Department of Neurology, VA Northern California Health Care System, Martinez, California, USA
| | - Henrik Hector
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA.,Department of Neurology, VA Northern California Health Care System, Martinez, California, USA
| | - Kari Haws
- Department of Neurology, VA Northern California Health Care System, Martinez, California, USA
| | - Brian Curran
- Department of Neurology, VA Northern California Health Care System, Martinez, California, USA
| | - Andrew S Kayser
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA.,Department of Neurology, VA Northern California Health Care System, Martinez, California, USA.,Department of Neurology, University of California, San Francisco, San Francisco, California, USA
| | - Mark D'Esposito
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA.,Department of Neurology, VA Northern California Health Care System, Martinez, California, USA.,Department of Psychology, University of California, Berkeley, Berkeley, California, USA
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65
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Barnett AJ, Reilly W, Dimsdale-Zucker HR, Mizrak E, Reagh Z, Ranganath C. Intrinsic connectivity reveals functionally distinct cortico-hippocampal networks in the human brain. PLoS Biol 2021; 19:e3001275. [PMID: 34077415 PMCID: PMC8202937 DOI: 10.1371/journal.pbio.3001275] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 06/14/2021] [Accepted: 05/07/2021] [Indexed: 12/13/2022] Open
Abstract
Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. Here, using data-driven analyses of resting-state functional magnetic resonance imaging (fMRI) data, we identified the networks that interact with the hippocampus-the default mode network (DMN) and a "medial temporal network" (MTN) that included regions in the medial temporal lobe (MTL) and precuneus. We observed that the MTN plays a critical role in connecting the visual network to the DMN and hippocampus. The DMN could be further divided into 3 subnetworks: a "posterior medial" (PM) subnetwork comprised of posterior cingulate and lateral parietal cortices; an "anterior temporal" (AT) subnetwork comprised of regions in the temporopolar and dorsomedial prefrontal cortex; and a "medial prefrontal" (MP) subnetwork comprised of regions primarily in the medial prefrontal cortex (mPFC). These networks vary in their functional connectivity (FC) along the hippocampal long axis and represent different kinds of information during memory-guided decision-making. Finally, a Neurosynth meta-analysis of fMRI studies suggests new hypotheses regarding the functions of the MTN and DMN subnetworks, providing a framework to guide future research on the neural architecture of episodic memory.
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Affiliation(s)
- Alexander J. Barnett
- Center for Neuroscience, University of California at Davis, Davis, California, United States of America
| | - Walter Reilly
- Center for Neuroscience, University of California at Davis, Davis, California, United States of America
| | | | - Eda Mizrak
- Center for Neuroscience, University of California at Davis, Davis, California, United States of America
- Department of Psychology, University of Zurich, Zürich, Switzerland
| | - Zachariah Reagh
- Center for Neuroscience, University of California at Davis, Davis, California, United States of America
- Department of Neurology, University of California at Davis, Sacramento, California, United States of America
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Charan Ranganath
- Center for Neuroscience, University of California at Davis, Davis, California, United States of America
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66
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Deng X, Liu Z, Kang Q, Lu L, Zhu Y, Xu R. Cortical Structural Connectivity Alterations and Potential Pathogenesis in Mid-Stage Sporadic Parkinson's Disease. Front Aging Neurosci 2021; 13:650371. [PMID: 34135748 PMCID: PMC8200851 DOI: 10.3389/fnagi.2021.650371] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 03/08/2021] [Indexed: 11/13/2022] Open
Abstract
Many clinical symptoms of sporadic Parkinson's disease (sPD) cannot be completely explained by a lesion of the simple typical extrapyramidal circuit between the striatum and substantia nigra. Therefore, this study aimed to explore the new potential damaged pathogenesis of other brain regions associated with the multiple and complex clinical symptoms of sPD through magnetic resonance imaging (MRI). A total of 65 patients with mid-stage sPD and 35 healthy controls were recruited in this study. Cortical structural connectivity was assessed by seed-based analysis using the vertex-based morphology of MRI. Seven different clusters in the brain regions of cortical thickness thinning derived from the regression analysis using brain size as covariates between sPD and control were selected as seeds. Results showed that the significant alteration of cortical structural connectivity mainly occurred in the bilateral frontal orbital, opercular, triangular, precentral, rectus, supplementary-motor, temporal pole, angular, Heschl, parietal, supramarginal, postcentral, precuneus, occipital, lingual, cuneus, Rolandic-opercular, cingulum, parahippocampal, calcarine, olfactory, insula, paracentral-lobule, and fusiform regions at the mid-stage of sPD. These findings suggested that the extensive alteration of cortical structural connectivity is one of possible pathogenesis resulting in the multiple and complex clinical symptoms in sPD.
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Affiliation(s)
- Xia Deng
- Department of Neurology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zheng Liu
- Department of Neurology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Qin Kang
- Department of Neurology, Jiangxi Provincial People’s Hospital, The Affiliated People’s Hospital of Nanchang University, Nanchang, China
| | - Lin Lu
- Department of Neurology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yu Zhu
- Department of Neurology, Jiangxi Provincial People’s Hospital, The Affiliated People’s Hospital of Nanchang University, Nanchang, China
| | - Renshi Xu
- Department of Neurology, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Department of Neurology, Jiangxi Provincial People’s Hospital, The Affiliated People’s Hospital of Nanchang University, Nanchang, China
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67
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Effect of Multishell Diffusion MRI Acquisition Strategy and Parcellation Scale on Rich-Club Organization of Human Brain Structural Networks. Diagnostics (Basel) 2021; 11:diagnostics11060970. [PMID: 34072192 PMCID: PMC8227047 DOI: 10.3390/diagnostics11060970] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/07/2021] [Accepted: 05/26/2021] [Indexed: 12/15/2022] Open
Abstract
The majority of network studies of human brain structural connectivity are based on single-shell diffusion-weighted imaging (DWI) data. Recent advances in imaging hardware and software capabilities have made it possible to acquire multishell (b-values) high-quality data required for better characterization of white-matter crossing-fiber microstructures. The purpose of this study was to investigate the extent to which brain structural organization and network topology are affected by the choice of diffusion magnetic resonance imaging (MRI) acquisition strategy and parcellation scale. We performed graph-theoretical network analysis using DWI data from 35 Human Connectome Project subjects. Our study compared four single-shell (b = 1000, 3000, 5000, 10,000 s/mm2) and multishell sampling schemes and six parcellation scales (68, 200, 400, 600, 800, 1000 nodes) using five graph metrics, including small-worldness, clustering coefficient, characteristic path length, modularity and global efficiency. Rich-club analysis was also performed to explore the rich-club organization of brain structural networks. Our results showed that the parcellation scale and imaging protocol have significant effects on the network attributes, with the parcellation scale having a substantially larger effect. Regardless of the parcellation scale, the brain structural networks exhibited a rich-club organization with similar cortical distributions across the parcellation scales involving at least 400 nodes. Compared to single b-value diffusion acquisitions, the deterministic tractography using multishell diffusion imaging data consisting of shells with b-values higher than 5000 s/mm2 resulted in significantly improved fiber-tracking results at the locations where fiber bundles cross each other. Brain structural networks constructed using the multishell acquisition scheme including high b-values also exhibited significantly shorter characteristic path lengths, higher global efficiency and lower modularity. Our results showed that both parcellation scale and sampling protocol can significantly impact the rich-club organization of brain structural networks. Therefore, caution should be taken concerning the reproducibility of connectivity results with regard to the parcellation scale and sampling scheme.
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68
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Tu W, Ma Z, Zhang N. Brain network reorganization after targeted attack at a hub region. Neuroimage 2021; 237:118219. [PMID: 34052466 PMCID: PMC8289586 DOI: 10.1016/j.neuroimage.2021.118219] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 04/14/2021] [Accepted: 05/26/2021] [Indexed: 01/01/2023] Open
Abstract
The architecture of brain networks has been extensively studied in multiple species. However, exactly how the brain network reconfigures when a local region, particularly a hub region, stops functioning remains elusive. By combining chemogenetics and resting-state functional magnetic resonance imaging (rsfMRI) in an awake rodent model, we investigated the causal impact of acutely inactivating a hub region (i.e. the dorsal anterior cingulate cortex) on brain network properties. We found that suppressing neural activity in a hub could have a ripple effect that went beyond the hub-related connections and propagated to other neural connections across multiple brain systems. In addition, hub dysfunction affected the topological architecture of the whole-brain network in terms of the network resilience and segregation. Selectively inhibiting excitatory neurons in the hub further changed network integration. None of these changes were observed in sham rats or when a non-hub region (i.e. the primary visual cortex) was perturbed. This study has established a system that allows for mechanistically dissecting the relationship between local regions and brain network properties. Our data provide direct evidence supporting the hypothesis that acute dysfunction of a brain hub can cause large-scale network changes. These results also provide a comprehensive framework documenting the differential impact of hub versus non-hub nodes on network dynamics.
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Affiliation(s)
- Wenyu Tu
- Neuroscience Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Zilu Ma
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Nanyin Zhang
- Neuroscience Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA; Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
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69
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Probabilistic mapping of human functional brain networks identifies regions of high group consensus. Neuroimage 2021; 237:118164. [PMID: 34000397 PMCID: PMC8296467 DOI: 10.1016/j.neuroimage.2021.118164] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 05/11/2021] [Indexed: 11/21/2022] Open
Abstract
Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain's large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show "core" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.
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70
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Aben HP, De Munter L, Reijmer YD, Spikman JM, Visser-Meily JMA, Biessels GJ, De Kort PLM. Prediction of Cognitive Recovery After Stroke: The Value of Diffusion-Weighted Imaging-Based Measures of Brain Connectivity. Stroke 2021; 52:1983-1992. [PMID: 33966494 DOI: 10.1161/strokeaha.120.032033] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE Prediction of long-term recovery of a poststroke cognitive disorder (PSCD) is currently inaccurate. We assessed whether diffusion-weighted imaging (DWI)-based measures of brain connectivity predict cognitive recovery 1 year after stroke in patients with PSCD in addition to conventional clinical, neuropsychological, and imaging variables. METHODS This prospective monocenter cohort study included 217 consecutive patients with a clinical diagnosis of ischemic stroke, aged ≥50 years, and Montreal Cognitive Assessment score below 26 during hospitalization. Five weeks after stroke, patients underwent DWI magnetic resonance imaging. Neuropsychological assessment was performed 5 weeks and 1 year after stroke and was used to classify PSCD as absent, modest, or marked. Cognitive recovery was operationalized as a shift to a better PSCD category over time. We evaluated 4 DWI-based measures of brain connectivity: global network efficiency and mean connectivity strength, both weighted for mean diffusivity and fractional anisotropy. Conventional predictors were age, sex, level of education, clinical stroke characteristics, neuropsychological variables, and magnetic resonance imaging findings (eg, infarct size). DWI-based measures of brain connectivity were added to a multivariable model to assess additive predictive value. RESULTS Of 135 patients (mean age, 71 years; 95 men [70%]) with PSCD 5 weeks after ischemic stroke, 41 (30%) showed cognitive recovery. Three of 4 brain connectivity measures met the predefined threshold of P<0.1 in univariable regression analysis. There was no added value of these measures to a multivariable model that included level of education and infarct size as significant predictors of cognitive recovery. CONCLUSIONS Current DWI-based measures of brain connectivity appear to predict recovery of PSCD but at present have no added value over conventional predictors.
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Affiliation(s)
- Hugo P Aben
- Department of Neurology (H.P.A., P.L.M.D.K.), Elisabeth-Tweesteden Hospital, Tilburg, the Netherlands.,Department of Neurology and Neurosurgery (H.P.A., Y.D.R., G.J.B.), UMC Utrecht Brain Center, the Netherlands
| | - Leonie De Munter
- Department of Trauma TopCare (L.D.M.), Elisabeth-Tweesteden Hospital, Tilburg, the Netherlands
| | - Yael D Reijmer
- Department of Neurology and Neurosurgery (H.P.A., Y.D.R., G.J.B.), UMC Utrecht Brain Center, the Netherlands
| | - Jacoba M Spikman
- Department of Clinical Neuropsychology, University of Groningen, University Medical Center Groningen, the Netherlands (J.M.S.)
| | - Johanna M A Visser-Meily
- Department of Rehabilitation, Physical Therapy Science and Sports (J.M.A.V.-M.), UMC Utrecht Brain Center, the Netherlands
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery (H.P.A., Y.D.R., G.J.B.), UMC Utrecht Brain Center, the Netherlands
| | - Paul L M De Kort
- Department of Neurology (H.P.A., P.L.M.D.K.), Elisabeth-Tweesteden Hospital, Tilburg, the Netherlands
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Cognitive impairment after focal brain lesions is better predicted by damage to structural than functional network hubs. Proc Natl Acad Sci U S A 2021; 118:2018784118. [PMID: 33941692 DOI: 10.1073/pnas.2018784118] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Hubs are highly connected brain regions important for coordinating processing in brain networks. It is unclear, however, which measures of network "hubness" are most useful in identifying brain regions critical to human cognition. We tested how closely two measures of hubness-edge density and participation coefficient, derived from white and gray matter, respectively-were associated with general cognitive impairment after brain damage in two large cohorts of patients with focal brain lesions (N = 402 and 102, respectively) using cognitive tests spanning multiple cognitive domains. Lesions disrupting white matter regions with high edge density were associated with cognitive impairment, whereas lesions damaging gray matter regions with high participation coefficient had a weaker, less consistent association with cognitive outcomes. Similar results were observed with six other gray matter hubness measures. This suggests that damage to densely connected white matter regions is more cognitively impairing than similar damage to gray matter hubs, helping to explain interindividual differences in cognitive outcomes after brain damage.
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72
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Zhou WR, Wang M, Zheng H, Wang MJ, Dong GH. Altered modular segregation of brain networks during the cue-craving task contributes to the disrupted executive functions in internet gaming disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021; 107:110256. [PMID: 33503493 DOI: 10.1016/j.pnpbp.2021.110256] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 12/11/2020] [Accepted: 01/16/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Previous studies have shown that gaming-related cues could induce gaming cravings and bring about changes in brain activities in subjects with Internet gaming disorder (IGD). However, little is known about the brain network organizations in IGD subjects during a cue-craving task and the relationship between this network organization and IGD severity. METHODS Sixty-one IGD subjects and 61 matched recreational game users (RGUs) were scanned while performing a cue-craving task. We calculated and compared the participation coefficient (PC) among brain network modules between IGD subjects and RGUs. Based on the results, further group comparison analyses were performed to explain the PC changes and to explore the relationship between PCs and IGD severity. RESULTS While performing a cue-craving task, compared with RGUs, IGD subjects showed significantly decreased PCs in the default-mode network (DMN) and the frontal-parietal network (FPN). Specifically, the number of connections between nodes in the ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex and other nodes in the DMN of IGD subjects was much larger than that in RGUs. Correlation results showed that the number of DMN intra-modular connections was positively correlated with addiction severity and craving degree. CONCLUSIONS These results provide neural evidence that can explain why cognitive control, emotion, attention and other functions are impaired in IGD subjects in the face of gaming cues, which leads to compulsive behavior toward games. These findings extend our understanding of the neural mechanism of IGD and have important implications for developing effective interventions to treat IGD subjects.
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Affiliation(s)
- Wei-Ran Zhou
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China; Institutes of Psychological Sciences, Hangzhou Normal University, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
| | - Min Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China; Institutes of Psychological Sciences, Hangzhou Normal University, China
| | - Hui Zheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - Meng-Jing Wang
- Southeast University, Monash University Joint Graduate School, China
| | - Guang-Heng Dong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China; Institutes of Psychological Sciences, Hangzhou Normal University, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China.
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Herbet G. Should Complex Cognitive Functions Be Mapped With Direct Electrostimulation in Wide-Awake Surgery? A Network Perspective. Front Neurol 2021; 12:635439. [PMID: 33912124 PMCID: PMC8072013 DOI: 10.3389/fneur.2021.635439] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/17/2021] [Indexed: 12/18/2022] Open
Affiliation(s)
- Guillaume Herbet
- Institute of Functional Genomics, INSERM, CNRS, University of Montpellier, Montpellier, France.,Gui de Chauliac Hospital, Montpellier University Medical Center, Montpellier, France
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74
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Howard JD, Kahnt T. Causal investigations into orbitofrontal control of human decision making. Curr Opin Behav Sci 2021; 38:14-19. [PMID: 32864400 PMCID: PMC7448682 DOI: 10.1016/j.cobeha.2020.06.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Although it is widely accepted that the orbitofrontal cortex (OFC) is important for decision making, its precise contribution to behavior remains a topic of debate. While many loss of function experiments have been conducted in animals, causal studies of human OFC function are relatively scarce. This review discusses recent causal investigations into the human OFC, with an emphasis on advances in network-based brain stimulation approaches to indirectly perturb OFC function. Findings show that disruption of human OFC impairs decisions that require mental simulation of outcomes. Taken together, these results support the idea that human OFC contributes to decision making by representing a cognitive map of the task environment, facilitating inference of outcomes not yet experienced. Future work may utilize similar non-invasive approaches in clinical settings to mitigate decision making deficits in neuropsychiatric disorders.
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Affiliation(s)
- James D. Howard
- Department of Neurology, Northwestern University Feinberg School of Medicine
| | - Thorsten Kahnt
- Department of Neurology, Northwestern University Feinberg School of Medicine
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Feinberg School of Medicine
- Department of Psychology, Northwestern University, Weinberg College of Arts and Sciences
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75
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Nee DE. Integrative frontal-parietal dynamics supporting cognitive control. eLife 2021; 10:e57244. [PMID: 33650966 PMCID: PMC7963482 DOI: 10.7554/elife.57244] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 03/01/2021] [Indexed: 02/06/2023] Open
Abstract
Coordinating among the demands of the external environment and internal plans requires cognitive control supported by a fronto-parietal control network (FPCN). Evidence suggests that multiple control systems span the FPCN whose operations are poorly understood. Previously (Nee and D'Esposito, 2016; 2017), we detailed frontal dynamics that support control processing, but left open their role in broader cortical function. Here, I show that the FPCN consists of an external/present-oriented to internal/future-oriented cortical gradient extending outwardly from sensory-motor cortices. Areas at the ends of this gradient act in a segregative manner, exciting areas at the same level, but suppressing areas at different levels. By contrast, areas in the middle of the gradient excite areas at all levels, promoting integration of control processing. Individual differences in integrative dynamics predict higher level cognitive ability and amenability to neuromodulation. These data suggest that an intermediary zone within the FPCN underlies integrative processing that supports cognitive control.
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Affiliation(s)
- Derek Evan Nee
- Department of Psychology, Florida State UniversityTallahasseeUnited States
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76
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Jin L, Li C, Zhang Y, Yuan T, Ying J, Zuo Z, Gui S. The Functional Reorganization of Language Network Modules in Glioma Patients: New Insights From Resting State fMRI Study. Front Oncol 2021; 11:617179. [PMID: 33718172 PMCID: PMC7953055 DOI: 10.3389/fonc.2021.617179] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 01/12/2021] [Indexed: 12/11/2022] Open
Abstract
Background Prior investigations of language functions have focused on the response profiles of particular brain regions. However, the specialized and static view of language processing does not explain numerous observations of functional recovery following brain surgery. To investigate the dynamic alterations of functional connectivity (FC) within language network (LN) in glioma patients, we explored a new flexible model based on the neuroscientific hypothesis of core-periphery organization in LN. Methods Group-level LN mapping was determined from 109 glioma patients and forty-two healthy controls (HCs) using independent component analysis (ICA). FC and mean network connectivity (mNC: l/rFCw, FCb, and FCg) were compared between patients and HCs. Correlations between mNC and tumor volume (TV) were calculated. Results We identified ten separate LN modules from ICA. Compared to HCs, glioma patients showed a significant reduction in language network functional connectivity (LNFC), with a distinct pattern modulated by tumor position. Left hemisphere gliomas had a broader impact on FC than right hemisphere gliomas, with more reduced edges away from tumor sites (p=0.011). mNC analysis revealed a significant reduction in all indicators of FC except for lFCw in right hemisphere gliomas. These alterations were associated with TV in a double correlative relationship depending on the tumor position across hemispheres. Conclusion Our findings emphasize the importance of considering the modulatory effects of core-periphery mechanisms from a network perspective. Preoperative evaluation of changes in LN caused by gliomas could provide the surgeon a reference to optimize resection while maintaining functional balance.
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Affiliation(s)
- Lu Jin
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chuzhong Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yazhuo Zhang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Taoyang Yuan
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jianyou Ying
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Zhentao Zuo
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Songbai Gui
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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77
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NetDI: Methodology Elucidating the Role of Power and Dynamical Brain Network Features That Underpin Word Production. eNeuro 2021; 8:ENEURO.0177-20.2020. [PMID: 33293456 PMCID: PMC7890525 DOI: 10.1523/eneuro.0177-20.2020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 11/13/2020] [Accepted: 11/19/2020] [Indexed: 12/03/2022] Open
Abstract
Canonical language models describe eloquent function as the product of a series of cognitive processes, typically characterized by the independent activation profiles of focal brain regions. In contrast, more recent work has suggested that the interactions between these regions, the cortical networks of language, are critical for understanding speech production. We investigated the cortical basis of picture naming (PN) with human intracranial electrocorticography (ECoG) recordings and direct cortical stimulation (DCS), adjudicating between two competing hypotheses: are task-specific cognitive functions discretely computed within well-localized brain regions or rather by distributed networks? The time resolution of ECoG allows direct comparison of intraregional activation measures [high gamma (hγ) power] with graph theoretic measures of interregional dynamics. We developed an analysis framework, network dynamics using directed information (NetDI), using information and graph theoretic tools to reveal spatiotemporal dynamics at multiple scales: coarse, intermediate, and fine. Our analysis found novel relationships between the power profiles and network measures during the task. Furthermore, validation using DCS indicates that such network parameters combined with hγ power are more predictive than hγ power alone, for identifying critical language regions in the brain. NetDI reveals a high-dimensional space of network dynamics supporting cortical language function, and to account for disruptions to language function observed after neurosurgical resection, traumatic injury, and degenerative disease.
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78
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Tao Y, Rapp B. Investigating the network consequences of focal brain lesions through comparisons of real and simulated lesions. Sci Rep 2021; 11:2213. [PMID: 33500494 PMCID: PMC7838400 DOI: 10.1038/s41598-021-81107-9] [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: 03/09/2020] [Accepted: 01/04/2021] [Indexed: 11/12/2022] Open
Abstract
Given the increased interest in the functional human connectome, a number of computer simulation studies have sought to develop a better quantitative understanding of the effects of focal lesions on the brain’s functional network organization. However, there has been little work evaluating the predictions of this simulation work vis a vis real lesioned connectomes. One of the few relevant studies reported findings from real chronic focal lesions that only partially confirmed simulation predictions. We hypothesize that these discrepancies arose because although the effects of focal lesions likely consist of two components: short-term node subtraction and long-term network re-organization, previous simulation studies have primarily modeled only the short-term consequences of the subtraction of lesioned nodes and their connections. To evaluate this hypothesis, we compared network properties (modularity, participation coefficient, within-module degree) between real functional connectomes obtained from chronic stroke participants and “pseudo-lesioned” functional connectomes generated by subtracting the same sets of lesioned nodes/connections from healthy control connectomes. We found that, as we hypothesized, the network properties of real-lesioned connectomes in chronic stroke differed from those of the pseudo-lesioned connectomes which instantiated only the short-term consequences of node subtraction. Reflecting the long-term consequences of focal lesions, we found re-organization of the neurotopography of global and local hubs in the real but not the pseudo-lesioned connectomes. We conclude that the long-term network re-organization that occurs in response to focal lesions involves changes in functional connectivity within the remaining intact neural tissue that go well beyond the short-term consequences of node subtraction.
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Affiliation(s)
- Yuan Tao
- Department of Cognitive Science, Johns Hopkins University, Baltimore, USA.
| | - Brenda Rapp
- Department of Cognitive Science, Johns Hopkins University, Baltimore, USA
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79
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Hall SA, Lalee Z, Bell RP, Towe SL, Meade CS. Synergistic effects of HIV and marijuana use on functional brain network organization. Prog Neuropsychopharmacol Biol Psychiatry 2021; 104:110040. [PMID: 32687963 PMCID: PMC7685308 DOI: 10.1016/j.pnpbp.2020.110040] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 06/23/2020] [Accepted: 07/12/2020] [Indexed: 11/25/2022]
Abstract
HIV is associated with disruptions in cognition and brain function. Marijuana use is highly prevalent in HIV but its effects on resting brain function in HIV are unknown. Brain function can be characterized by brain activity that is correlated between regions over time, called functional connectivity. Neuropsychiatric disorders are increasingly being characterized by disruptions in such connectivity. We examined the synergistic effects of HIV and marijuana use on functional whole-brain network organization during resting state. Our sample included 78 adults who differed on HIV and marijuana status (19 with co-occurring HIV and marijuana use, 20 HIV-only, 17 marijuana-only, and 22 controls). We examined differences in local and long-range brain network organization using eight graph theoretical metrics: transitivity, local efficiency, within-module degree, modularity, global efficiency, strength, betweenness, and participation coefficient. Local and long-range connectivity were similar between the co-occurring HIV and marijuana use and control groups. In contrast, the HIV-only and marijuana-only groups were both associated with disruptions in brain network organization. These results suggest that marijuana use in HIV may normalize disruptions in brain network organization observed in persons with HIV. However, future work is needed to determine whether this normalization is suggestive of a beneficial or detrimental effect of marijuana on cognitive functioning in HIV.
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Affiliation(s)
- Shana A Hall
- Duke University School of Medicine, Department of Psychiatry & Behavioral Sciences, Durham, NC 27708, USA.
| | - Zahra Lalee
- Duke University School of Medicine, Department of Psychiatry & Behavioral Sciences, Durham, NC 27708, USA
| | - Ryan P Bell
- Duke University School of Medicine, Department of Psychiatry & Behavioral Sciences, Durham, NC 27708, USA
| | - Sheri L Towe
- Duke University School of Medicine, Department of Psychiatry & Behavioral Sciences, Durham, NC 27708, USA
| | - Christina S Meade
- Duke University School of Medicine, Department of Psychiatry & Behavioral Sciences, Durham, NC 27708, USA; Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27708, USA
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80
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Network-level macroscale structural connectivity predicts propagation of transcranial magnetic stimulation. Neuroimage 2020; 229:117698. [PMID: 33385561 PMCID: PMC9094638 DOI: 10.1016/j.neuroimage.2020.117698] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 12/09/2020] [Accepted: 12/18/2020] [Indexed: 12/25/2022] Open
Abstract
Information processing in the brain is mediated by structural white matter pathways and is highly dependent on topological brain properties. Here we combined transcranial magnetic stimulation (TMS) with high-density electroencephalography (EEG) and Diffusion Weighted Imaging (DWI), specifically looking at macroscale connectivity to understand whether regional, network-level or whole-brain structural properties are more responsible for stimulus propagation. Neuronavigated TMS pulses were delivered over two individually defined nodes of the default mode (DMN) and dorsal attention (DAN) networks in a group of healthy subjects, with test-retest reliability assessed 1-month apart. TMS-evoked activity was predicted by the modularity and structural integrity of the stimulated network rather than the targeted region(s) or the whole-brain connectivity, suggesting network-level structural connectivity as more relevant than local and global brain properties in shaping TMS signal propagation. The importance of network structural connectome was unveiled only by evoked activity, but not resting-state data. Future clinicals interventions might enhance target engagement by adopting DWI-guided, network-focused TMS.
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81
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DuBois JM, Mathotaarachchi S, Rousset OG, Sziklas V, Sepulcre J, Guiot MC, Hall JA, Massarweh G, Soucy JP, Rosa-Neto P, Kobayashi E. Large-scale mGluR5 network abnormalities linked to epilepsy duration in focal cortical dysplasia. Neuroimage Clin 2020; 29:102552. [PMID: 33401137 PMCID: PMC7787952 DOI: 10.1016/j.nicl.2020.102552] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 12/19/2020] [Accepted: 12/21/2020] [Indexed: 12/03/2022]
Abstract
To determine the extent of metabotropic glutamate receptor type 5 (mGluR5) network abnormalities associated with focal cortical dysplasia (FCD), we performed graph theoretical analysis of [11C]ABP688 PET binding potentials (BPND), which allows for quantification of mGluR5 availability. Undirected graphs were constructed for the entire cortex in 17 FCD patients and 33 healthy controls using inter-regional similarity of [11C]ABP688 BPND. We assessed group differences in network integration between healthy controls and the ipsilateral and contralateral hemispheres of FCD patients. Compared to healthy controls, FCD patients showed reduced network efficiency and reduced small-world connectivity. The mGluR5 network of FCD patients was also less resilient to targeted removal of high centrality nodes, suggesting a less integrated network organization. In highly efficient hub nodes of FCD patients, we observed a significant negative correlation between local efficiency and duration of epilepsy only in the contralateral hemisphere, suggesting that some nodes may be more vulnerable to persistent epileptic activity. Our study provides the first in vivo evidence for a widespread reduction in cortical mGluR5 network integration in FCD patients. In addition, we find that ongoing epileptic activity may alter chemoarchitectural brain organization resulting in reduced efficiency in distant regions that are essential for network integration.
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Affiliation(s)
- Jonathan M DuBois
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Sulantha Mathotaarachchi
- Translational Neuroimaging Laboratory, McGill Center for Studies in Aging, Douglas Mental Health University Institute, McGill University, Montreal, Canada
| | - Olivier G Rousset
- Division of Nuclear Medicine and Molecular Imaging, Johns Hopkins University, Baltimore, United States
| | - Viviane Sziklas
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Marie-Christine Guiot
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada; Department of Pathology, McGill University, Montreal, Canada
| | - Jeffery A Hall
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Gassan Massarweh
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Jean-Paul Soucy
- PET Unit, McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada; Bio-Imaging Group, PERFORM Centre, Concordia University, Montreal, Canada
| | - Pedro Rosa-Neto
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada; Translational Neuroimaging Laboratory, McGill Center for Studies in Aging, Douglas Mental Health University Institute, McGill University, Montreal, Canada; PET Unit, McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Eliane Kobayashi
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada.
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82
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Wang Y, Wang C, Miao P, Liu J, Wei Y, Wu L, Wang K, Cheng J. An imbalance between functional segregation and integration in patients with pontine stroke: A dynamic functional network connectivity study. NEUROIMAGE-CLINICAL 2020; 28:102507. [PMID: 33395996 PMCID: PMC7714678 DOI: 10.1016/j.nicl.2020.102507] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/24/2020] [Accepted: 11/12/2020] [Indexed: 11/04/2022]
Abstract
Pontine stroke patients show abnormal time-varying brain activity. Pontine stroke patients exist aberrant functional segregation and integration. The alterations of dFNC may lead to a poor functional recovery after stroke.
Background Previous studies on brain functional connectivity have revealed the neural physiopathology in patients with pontine stroke (PS). However, those studies focused only on the static features of intrinsic fluctuations, rather than on the time-varying effects throughout the entire scan. In the present study, we sought to explore the underlying mechanism of PS using the dynamic functional network connectivity (dFNC) method. Methods Resting-state functional magnetic resonance imaging (fMRI) data were collected from 58 patients with PS and 52 healthy controls (HC). Independent component analysis (ICA), the sliding window method, and k-means clustering analysis were performed to extract different functional networks, to calculate dFNC matrices, and to estimate distinct dynamic connectivity states. Additionally, temporal features were compared between the two groups in each state to explore the brain’s preference for different dynamic connectivity states in PS, and global and local efficiency were compared among states to explore the differences of topologic organization across different dFNC states. The correlations between clinical scales and the temporal features that differed between the two groups also were calculated. Results The dFNC analyses suggested four recurring states; in two of these states, the PS group showed a different duration from that of the HC group. Patients with PS spent significantly more time in a sparsely connected state (State 1), which was characterized by relatively low levels of connectivity within and between all brain networks. In contrast, patients with PS spent significantly less time in a highly segregated state (State 2), which was characterized by high levels of positive connectivities within primary perceptional domains and within higher cognitive control domains, and by high levels of negative inter-functional connectivities (inter-FCs) among primary perceptional and higher cognitive control domains. Additionally, the dwell time in State 2 was positively correlated with HC group’s long-term memory scores in the Rey Auditory Verbal Learning Test (RAVLT-L), whereas there was no correlation between the State-2 dwell time and RAVLT-L scores in the PS group. Furthermore, the sparsely connected state and the highly segregated state mentioned above had the highest global efficiency and the highest local efficiency among the four states, respectively. Conclusions In summary, we observed a preference in the aberrant brain for dynamic connectivity states with different network topologic organization in patients with PS, indicating abnormal functional segregation and integration of the whole brain and confirming the imperfection of functional network connectivity in patients with PS. These findings provide new evidence for the dynamic neural mechanisms underlying clinical symptoms in patients with PS.
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Affiliation(s)
- Yingying Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Caihong Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Peifang Miao
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingchun Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Ying Wei
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Luobing Wu
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kaiyu Wang
- GE Healthcare MR Research, Beijing, China
| | - Jingliang Cheng
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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83
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Patankar SP, Kim JZ, Pasqualetti F, Bassett DS. Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks. Netw Neurosci 2020; 4:1091-1121. [PMID: 33195950 PMCID: PMC7655114 DOI: 10.1162/netn_a_00157] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 07/15/2020] [Indexed: 01/03/2023] Open
Abstract
The human brain displays rich communication dynamics that are thought to be particularly well-reflected in its marked community structure. Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well understood. In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step toward the ability to manipulate the brain's large-scale dynamical activity in a targeted manner. We investigate the role of community structure in the controllability of structural brain networks. At the region level, we find that certain network measures of community structure are sometimes statistically correlated with measures of linear controllability. However, we then demonstrate that this relationship depends on the distribution of network edge weights. We highlight the complexity of the relationship between community structure and controllability by performing numerical simulations using canonical graph models with varying mesoscale architectures and edge weight distributions. Finally, we demonstrate that weighted subgraph centrality, a measure rooted in the graph spectrum, and which captures higher order graph architecture, is a stronger and more consistent predictor of controllability. Our study contributes to an understanding of how the brain's diverse mesoscale structure supports transient communication dynamics.
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Affiliation(s)
| | - Jason Z. Kim
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA USA
| | - Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
- Santa Fe Institute, Santa Fe, NM USA
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84
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Bagarinao E, Watanabe H, Maesawa S, Mori D, Hara K, Kawabata K, Ohdake R, Masuda M, Ogura A, Kato T, Koyama S, Katsuno M, Wakabayashi T, Kuzuya M, Hoshiyama M, Isoda H, Naganawa S, Ozaki N, Sobue G. Identifying the brain's connector hubs at the voxel level using functional connectivity overlap ratio. Neuroimage 2020; 222:117241. [DOI: 10.1016/j.neuroimage.2020.117241] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 08/07/2020] [Indexed: 01/06/2023] Open
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85
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Sadaghiani S, Dombert PL, Løvstad M, Funderud I, Meling TR, Endestad T, Knight RT, Solbakk AK, D'Esposito M. Lesions to the Fronto-Parietal Network Impact Alpha-Band Phase Synchrony and Cognitive Control. Cereb Cortex 2020; 29:4143-4153. [PMID: 30535068 DOI: 10.1093/cercor/bhy296] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 10/11/2018] [Accepted: 11/05/2018] [Indexed: 01/08/2023] Open
Abstract
Long-range phase synchrony in the α-oscillation band (near 10 Hz) has been proposed to facilitate information integration across anatomically segregated regions. Which areas may top-down regulate such cross-regional integration is largely unknown. We previously found that the moment-to-moment strength of high-α band (10-12 Hz) phase synchrony co-varies with activity in a fronto-parietal (FP) network. This network is critical for adaptive cognitive control functions such as cognitive flexibility required during set-shifting. Using electroencephalography (EEG) in 23 patients with focal frontal lobe lesions (resected tumors), we tested the hypothesis that the FP network is necessary for modulation of high-α band phase synchrony. Global phase-synchrony was measured using an adaptation of the phase-locking value (PLV) in a sliding window procedure, which allowed for measurement of changes in EEG-based resting-state functional connectivity across time. As hypothesized, the temporal modulation (range and standard deviation) of high-α phase synchrony was reduced as a function of FP network lesion extent, mostly due to dorsolateral prefrontal cortex (dlPFC) lesions. Furthermore, patients with dlPFC lesions exhibited reduced cognitive flexibility as measured by the Trail-Making Test (set-shifting). Our findings provide evidence that the FP network is necessary for modulatory control of high-α band long-range phase synchrony, and linked to cognitive flexibility.
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Affiliation(s)
- Sepideh Sadaghiani
- Psychology Department, University of Illinois at Urbana-Champaign, 61801 Urbana, IL, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at 61801 Urbana-Champaign, Urbana, IL, USA.,Department of Psychology, Helen Wills Neuroscience Institute, University of California, 94720 Berkeley, CA, USA
| | - Pascasie L Dombert
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, 94720 Berkeley, CA, USA.,Psychology and Neuroscience Programme, Maastricht Universirty, 6229 ER Maastricht, The Netherlands
| | - Marianne Løvstad
- Department of Psychology, University of Oslo, 0373 Oslo, Norway.,Department of Research, Sunnaas Rehabilitation Hospital, 1453 Nesodden, Norway
| | - Ingrid Funderud
- Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Torstein R Meling
- Department of Neurosurgery, Oslo University Hospital, Rikshospitalet, 0373 Oslo, Norway
| | - Tor Endestad
- Department of Psychology, University of Oslo, 0373 Oslo, Norway.,Department of Neuropsychology, Helgeland Hospital, 8657 Mosjøen, Norway
| | - Robert T Knight
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, 94720 Berkeley, CA, USA
| | - Anne-Kristin Solbakk
- Department of Psychology, University of Oslo, 0373 Oslo, Norway.,Department of Neurosurgery, Oslo University Hospital, Rikshospitalet, 0373 Oslo, Norway.,Department of Neuropsychology, Helgeland Hospital, 8657 Mosjøen, Norway
| | - Mark D'Esposito
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, 94720 Berkeley, CA, USA
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86
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Lynch CJ, Breeden AL, Gordon EM, Cherry JBC, Turkeltaub PE, Vaidya CJ. Precision Inhibitory Stimulation of Individual-Specific Cortical Hubs Disrupts Information Processing in Humans. Cereb Cortex 2020; 29:3912-3921. [PMID: 30364937 DOI: 10.1093/cercor/bhy270] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 09/20/2018] [Indexed: 12/14/2022] Open
Abstract
Noninvasive brain stimulation (NIBS) is a promising treatment for psychiatric and neurologic conditions, but outcomes are variable across treated individuals. In principle, precise targeting of individual-specific features of functional brain networks could improve the efficacy of NIBS interventions. Network theory predicts that the role of a node in a network can be inferred from its connections; as such, we hypothesized that targeting individual-specific "hub" brain areas with NIBS should impact cognition more than nonhub brain areas. Here, we first demonstrate that the spatial positioning of hubs is variable across individuals but reproducible within individuals upon repeated imaging. We then tested our hypothesis in healthy individuals using a prospective, within-subject, double-blind design. Inhibition of a hub with continuous theta burst stimulation disrupted information processing during working-memory more than inhibition of a nonhub area, despite targets being separated by only a few centimeters on the right middle frontal gyrus of each subject. Based upon these findings, we conclude that individual-specific brain network features are functionally relevant and could leveraged as stimulation sites in future NIBS interventions.
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Affiliation(s)
- Charles J Lynch
- Department of Psychology, Georgetown University, Washington, DC, USA.,Brain and Mind Research Institute, Weill Cornell Medicine, New York, USA
| | - Andrew L Breeden
- Department of Psychology, Georgetown University, Washington, DC, USA
| | - Evan M Gordon
- VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, Texas, USA.,Department of Psychology and Neuroscience, Baylor University, Waco, Texas, USA.,Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | - Joseph B C Cherry
- Department of Psychology, Georgetown University, Washington, DC, USA
| | - Peter E Turkeltaub
- Neurology Department, Georgetown University Medical Center, Washington, DC, USA.,Research Division, MedStar National Rehabilitation Hospital, Washington, DC, USA
| | - Chandan J Vaidya
- Department of Psychology, Georgetown University, Washington, DC, USA.,Children's National Health System, Washington DC
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87
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Bertolero MA, Bassett DS. On the Nature of Explanations Offered by Network Science: A Perspective From and for Practicing Neuroscientists. Top Cogn Sci 2020; 12:1272-1293. [PMID: 32441854 PMCID: PMC7687232 DOI: 10.1111/tops.12504] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/16/2020] [Accepted: 04/16/2020] [Indexed: 12/31/2022]
Abstract
Network neuroscience represents the brain as a collection of regions and inter-regional connections. Given its ability to formalize systems-level models, network neuroscience has generated unique explanations of neural function and behavior. The mechanistic status of these explanations and how they can contribute to and fit within the field of neuroscience as a whole has received careful treatment from philosophers. However, these philosophical contributions have not yet reached many neuroscientists. Here we complement formal philosophical efforts by providing an applied perspective from and for neuroscientists. We discuss the mechanistic status of the explanations offered by network neuroscience and how they contribute to, enhance, and interdigitate with other types of explanations in neuroscience. In doing so, we rely on philosophical work concerning the role of causality, scale, and mechanisms in scientific explanations. In particular, we make the distinction between an explanation and the evidence supporting that explanation, and we argue for a scale-free nature of mechanistic explanations. In the course of these discussions, we hope to provide a useful applied framework in which network neuroscience explanations can be exercised across scales and combined with other fields of neuroscience to gain deeper insights into the brain and behavior.
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Affiliation(s)
- Maxwell A Bertolero
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania
- Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania
- Santa Fe Institute
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88
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Dynamic Reconfiguration of Functional Topology in Human Brain Networks: From Resting to Task States. Neural Plast 2020; 2020:8837615. [PMID: 32963519 PMCID: PMC7495231 DOI: 10.1155/2020/8837615] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 07/23/2020] [Accepted: 08/26/2020] [Indexed: 11/24/2022] Open
Abstract
Task demands evoke an intrinsic functional network and flexibly engage multiple distributed networks. However, it is unclear how functional topologies dynamically reconfigure during task performance. Here, we selected the resting- and task-state (emotion and working-memory) functional connectivity data of 81 health subjects from the high-quality HCP data. We used the network-based statistic (NBS) toolbox and the Brain Connectivity Toolbox (BCT) to compute the topological features of functional networks for the resting and task states. Graph-theoretic analysis indicated that under high threshold, a small number of long-distance connections dominated functional networks of emotion and working memory that exhibit distinct long connectivity patterns. Correspondently, task-relevant functional nodes shifted their roles from within-module to between-module: the number of connector hubs (mainly in emotional networks) and kinless hubs (mainly in working-memory networks) increased while provincial hubs disappeared. Moreover, the global properties of assortativity, global efficiency, and transitivity decreased, suggesting that task demands break the intrinsic balance between local and global couplings among brain regions and cause functional networks which tend to be more separated than the resting state. These results characterize dynamic reconfiguration of large-scale distributed networks from resting state to task state and provide evidence for the understanding of the organization principle behind the functional architecture of task-state networks.
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89
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Griffis JC, Metcalf NV, Corbetta M, Shulman GL. Structural Disconnections Explain Brain Network Dysfunction after Stroke. Cell Rep 2020; 28:2527-2540.e9. [PMID: 31484066 DOI: 10.1016/j.celrep.2019.07.100] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 05/29/2019] [Accepted: 07/26/2019] [Indexed: 12/29/2022] Open
Abstract
Stroke causes focal brain lesions that disrupt functional connectivity (FC), a measure of activity synchronization, throughout distributed brain networks. It is often assumed that FC disruptions reflect damage to specific cortical regions. However, an alternative explanation is that they reflect the structural disconnection (SDC) of white matter pathways. Here, we compare these explanations using data from 114 stroke patients. Across multiple analyses, we find that SDC measures outperform focal damage measures, including damage to putative critical cortical regions, for explaining FC disruptions associated with stroke. We also identify a core mode of structure-function covariation that links the severity of interhemispheric SDCs to widespread FC disruptions across patients and that correlates with deficits in multiple behavioral domains. We conclude that a lesion's impact on the structural connectome is what determines its impact on FC and that interhemispheric SDCs may play a particularly important role in mediating FC disruptions after stroke.
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Affiliation(s)
- Joseph C Griffis
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Nicholas V Metcalf
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Maurizio Corbetta
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Bioengineering, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Neuroscience, University of Padua, Padua, Italy; Padua Neuroscience Center, Padua, Italy
| | - Gordon L Shulman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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90
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Garcia JO, Battelli L, Plow E, Cattaneo Z, Vettel J, Grossman ED. Understanding diaschisis models of attention dysfunction with rTMS. Sci Rep 2020; 10:14890. [PMID: 32913263 PMCID: PMC7483730 DOI: 10.1038/s41598-020-71692-6] [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: 11/21/2019] [Accepted: 07/27/2020] [Indexed: 01/18/2023] Open
Abstract
Visual attentive tracking requires a balance of excitation and inhibition across large-scale frontoparietal cortical networks. Using methods borrowed from network science, we characterize the induced changes in network dynamics following low frequency (1 Hz) repetitive transcranial magnetic stimulation (rTMS) as an inhibitory noninvasive brain stimulation protocol delivered over the intraparietal sulcus. When participants engaged in visual tracking, we observed a highly stable network configuration of six distinct communities, each with characteristic properties in node dynamics. Stimulation to parietal cortex had no significant impact on the dynamics of the parietal community, which already exhibited increased flexibility and promiscuity relative to the other communities. The impact of rTMS, however, was apparent distal from the stimulation site in lateral prefrontal cortex. rTMS temporarily induced stronger allegiance within and between nodal motifs (increased recruitment and integration) in dorsolateral and ventrolateral prefrontal cortex, which returned to baseline levels within 15 min. These findings illustrate the distributed nature by which inhibitory rTMS perturbs network communities and is preliminary evidence for downstream cortical interactions when using noninvasive brain stimulation for behavioral augmentations.
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Affiliation(s)
- Javier O Garcia
- US CCDC Army Research Laboratory, 459 Mulberry Pt Rd., Aberdeen Proving Ground, MD, 21005, USA. .,University of Pennsylvania, Philadelphia, PA, USA.
| | - Lorella Battelli
- Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di Tecnologia, Via Bettini 31, 38068, Rovereto, TN, Italy.,Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Ela Plow
- Department of Biomedical Engineering and Department of Physical Medicine and Rehabilitation, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Zaira Cattaneo
- Department of Psychology, University of Milano-Bicocca, 20126, Milan, Italy.,IRCCS Mondino Foundation, Pavia, Italy
| | - Jean Vettel
- US CCDC Army Research Laboratory, 459 Mulberry Pt Rd., Aberdeen Proving Ground, MD, 21005, USA.,University of Pennsylvania, Philadelphia, PA, USA.,University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Emily D Grossman
- Department of Cognitive Sciences, University of California Irvine, Irvine, CA, 92697, USA
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91
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Tao Y, Ficek B, Rapp B, Tsapkini K. Different patterns of functional network reorganization across the variants of primary progressive aphasia: a graph-theoretic analysis. Neurobiol Aging 2020; 96:184-196. [PMID: 33031971 DOI: 10.1016/j.neurobiolaging.2020.09.007] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 08/26/2020] [Accepted: 09/01/2020] [Indexed: 01/17/2023]
Abstract
Primary progressive aphasia (PPA) is a neurodegenerative syndrome with three main variants (nonfluent, logopenic, semantic) that are identified primarily based on language deficit profiles and are associated with neurotopographically distinct atrophic patterns. We used a graph-theoretic analytic approach to examine changes in functional network properties measured with resting-state fMRI in all three PPA variants compared with age-matched healthy controls. All three variants showed a more segregated network organization than controls. To better understand the changes underlying the increased segregation, we examined the distribution of functional "hubs". We found that while all variants lost hubs in the left superior frontal and parietal regions, new hubs were recruited in different areas across the variants. In particular, both logopenic and semantic variants recruited significant numbers of hubs in the right hemisphere. Importantly, these functional characteristics could not be fully explained by local volume changes. These findings indicate that patterns of functional connectivity can serve as further evidence to distinguish the PPA variants, and provide a basis for longitudinal studies and for investigating treatment effects. This study also highlights the utility of graph-theoretic approaches in understanding the brain's functional reorganization in response to neurodegenerative disease.
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Affiliation(s)
- Yuan Tao
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA.
| | - Bronte Ficek
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Brenda Rapp
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA; Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA; Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kyrana Tsapkini
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
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92
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Ptak R, Bourgeois A, Cavelti S, Doganci N, Schnider A, Iannotti GR. Discrete Patterns of Cross-Hemispheric Functional Connectivity Underlie Impairments of Spatial Cognition after Stroke. J Neurosci 2020; 40:6638-6648. [PMID: 32709694 PMCID: PMC7486659 DOI: 10.1523/jneurosci.0625-20.2020] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 06/06/2020] [Accepted: 07/04/2020] [Indexed: 12/17/2022] Open
Abstract
Despite intense research, the neural correlates of stroke-induced deficits of spatial cognition remain controversial. For example, several cortical regions and white-matter tracts have been designated as possible anatomic predictors of spatial neglect. However, many studies focused on local anatomy, an approach that does not harmonize with the notion that brain-behavior relationships are flexible and may involve interactions among distant regions. We studied in humans of either sex resting-state fMRI connectivity associated with performance in line bisection, reading and visual search, tasks commonly used for he clinical diagnosis of neglect. We defined left and right frontal, parietal, and temporal areas as seeds (or regions of interest, ROIs), and measured whole-brain seed-based functional connectivity (FC) and ROI-to-ROI connectivity in subacute right-hemisphere stroke patients. Performance on the line bisection task was associated with decreased FC between the right fusiform gyrus and left superior occipital cortex. Complementary increases and decreases of connectivity between both temporal and occipital lobes predicted reading errors. In addition, visual search deficits were associated with modifications of FC between left and right inferior parietal lobes and right insular cortex. These distinct connectivity patterns were substantiated by analyses of FC between left- and right-hemispheric ROIs, which revealed that decreased interhemispheric and right intrahemispheric FC was associated with higher levels of impairment. Together, these findings indicate that intrahemispheric and interhemispheric cooperation between brain regions lying outside the damaged area contributes to spatial deficits in a way that depends on the different cognitive components recruited during reading, spatial judgments, and visual exploration.SIGNIFICANCE STATEMENT Focal damage to the right cerebral hemisphere may result in a variety of deficits, often affecting the domain of spatial cognition. The neural correlates of these disorders have traditionally been studied with lesion-symptom mapping, but this method fails to capture the network dynamics that underlie cognitive performance. We studied functional connectivity in patients with right-hemisphere stroke and found a pattern of correlations between the left and right temporo-occipital, inferior parietal, and right insular cortex that were distinctively predictive of deficits in reading, spatial judgment, and visual exploration. This finding reveals the importance of interhemispheric interactions and network adaptations for the manifestation of spatial deficits after damage to the right hemisphere.
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Affiliation(s)
- Radek Ptak
- Laboratory of Cognitive Neurorehabilitation, Department of Clinical Neurosciences, Faculty of Medicine, University of Geneva, Geneva, 1206, Switzerland
- Division of Neurorehabilitation, University Hospitals of Geneva, Geneva, 1206, Switzerland
- Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, 1205, Switzerland
| | - Alexia Bourgeois
- Laboratory of Cognitive Neurorehabilitation, Department of Clinical Neurosciences, Faculty of Medicine, University of Geneva, Geneva, 1206, Switzerland
| | - Silvia Cavelti
- Laboratory of Cognitive Neurorehabilitation, Department of Clinical Neurosciences, Faculty of Medicine, University of Geneva, Geneva, 1206, Switzerland
| | - Naz Doganci
- Laboratory of Cognitive Neurorehabilitation, Department of Clinical Neurosciences, Faculty of Medicine, University of Geneva, Geneva, 1206, Switzerland
| | - Armin Schnider
- Laboratory of Cognitive Neurorehabilitation, Department of Clinical Neurosciences, Faculty of Medicine, University of Geneva, Geneva, 1206, Switzerland
- Division of Neurorehabilitation, University Hospitals of Geneva, Geneva, 1206, Switzerland
| | - Giannina Rita Iannotti
- Laboratory of Cognitive Neurorehabilitation, Department of Clinical Neurosciences, Faculty of Medicine, University of Geneva, Geneva, 1206, Switzerland
- Swiss Foundation for Innovation and Training in Surgery, University Hospitals of Geneva, Geneva, 1206, Switzerland
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93
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Tao Y, Rapp B. How functional network connectivity changes as a result of lesion and recovery: An investigation of the network phenotype of stroke. Cortex 2020; 131:17-41. [PMID: 32781259 DOI: 10.1016/j.cortex.2020.06.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 03/15/2020] [Accepted: 06/02/2020] [Indexed: 11/28/2022]
Abstract
This study, through a series of univariate and multivariate (classification) analyses, investigated fMRI task-based functional connectivity (FC) at pre- and post-treatment time-points in 18 individuals with chronic post-stroke dysgraphia. The investigation examined the effects of lesion and treatment-based recovery on functional organization, focusing on both inter-hemispheric (homotopic) and intra-hemispheric connectivity. The work confirmed, in the chronic stage, the "network phenotype of stroke injury" proposed by Siegel et al. (2016) consisting of abnormally low inter-hemispheric connectivity as well as abnormally high intra-hemispheric (ipsilesional) connectivity. In terms of recovery-based changes in FC, this study found overall hyper-normalization of these abnormal inter and intra-hemispheric connectivity patterns, suggestive of over-correction. Specifically, treatment-related homotopic FC increases were observed between left and right dorsal frontal-parietal regions. With regard to intra-hemispheric connections, recovery was dominated by increased ipsilateral connectivity between frontal and parietal regions along with decreased connectivity between the frontal regions and posterior parietal-occipital-temporal areas. Both inter and intra-hemispheric changes were associated with treatment-driven improvements in spelling performance. We suggest an interpretation according to which, with treatment, as posterior orthographic processing areas become more effective, executive control from frontal-parietal networks becomes less necessary.
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Affiliation(s)
- Yuan Tao
- Department of Cognitive Science, Johns Hopkins University, USA.
| | - Brenda Rapp
- Department of Cognitive Science, Johns Hopkins University, USA; Department of Neuroscience, Johns Hopkins University, USA; Department of Psychological and Brain Sciences, Johns Hopkins University, USA
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94
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Gratton C, Kraus BT, Greene DJ, Gordon EM, Laumann TO, Nelson SM, Dosenbach NUF, Petersen SE. Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry. Biol Psychiatry 2020; 88:28-39. [PMID: 31916942 PMCID: PMC7203002 DOI: 10.1016/j.biopsych.2019.10.026] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 10/07/2019] [Accepted: 10/25/2019] [Indexed: 12/28/2022]
Abstract
Studies comparing diverse groups have shown that many psychiatric diseases involve disruptions across distributed large-scale networks of the brain. There is hope that functional magnetic resonance imaging (fMRI) functional connectivity techniques will shed light on these disruptions, providing prognostic and diagnostic biomarkers as well as targets for therapeutic interventions. However, to date, progress on clinical translation of fMRI methods has been limited. Here, we argue that this limited translation is driven by a combination of intersubject heterogeneity and the relatively low reliability of standard fMRI techniques at the individual level. We review a potential solution to these limitations: the use of new "precision" fMRI approaches that shift the focus of analysis from groups to single individuals through the use of extended data acquisition strategies. We begin by discussing the potential advantages of fMRI functional connectivity methods for improving our understanding of functional neuroanatomy and disruptions in psychiatric disorders. We then discuss the budding field of precision fMRI and findings garnered from this work. We demonstrate that precision fMRI can improve the reliability of functional connectivity measures, while showing high stability and sensitivity to individual differences. We close by discussing the application of these approaches to clinical settings.
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Affiliation(s)
- Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, Illinois; Department of Neurology, Northwestern University, Evanston, Illinois.
| | - Brian T Kraus
- Department of Psychology, Northwestern University, Evanston, Illinois
| | - Deanna J Greene
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri; Department of Radiology, Washington University in St. Louis, St. Louis, Missouri
| | - Evan M Gordon
- VISN Center of Excellence for Research on Returning War Veterans, Waco, Texas; Department of Psychology and Neuroscience, Baylor University, Waco, Texas; Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
| | - Steven M Nelson
- VISN Center of Excellence for Research on Returning War Veterans, Waco, Texas; Department of Psychology and Neuroscience, Baylor University, Waco, Texas; Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas; Department of Psychiatry and Behavioral Science, Texas A&M Health Science Center, College of Medicine, Bryan, Texas
| | - Nico U F Dosenbach
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri; Department of Neurology, Washington University in St. Louis, St. Louis, Missouri; Department of Pediatrics, Washington University in St. Louis, St. Louis, Missouri; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri
| | - Steven E Petersen
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri; Department of Radiology, Washington University in St. Louis, St. Louis, Missouri; Department of Neurology, Washington University in St. Louis, St. Louis, Missouri; Department of Neuroscience, Washington University in St. Louis, St. Louis, Missouri; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri
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95
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Kim DJ, Min BK. Rich-club in the brain's macrostructure: Insights from graph theoretical analysis. Comput Struct Biotechnol J 2020; 18:1761-1773. [PMID: 32695269 PMCID: PMC7355726 DOI: 10.1016/j.csbj.2020.06.039] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 06/19/2020] [Accepted: 06/22/2020] [Indexed: 02/07/2023] Open
Abstract
The brain is a complex network. Growing evidence supports the critical roles of a set of brain regions within the brain network, known as the brain’s cores or hubs. These regions require high energy cost but possess highly efficient neural information transfer in the brain’s network and are termed the rich-club. The rich-club of the brain network is essential as it directly regulates functional integration across multiple segregated regions and helps to optimize cognitive processes. Here, we review the recent advances in rich-club organization to address the fundamental roles of the rich-club in the brain and discuss how these core brain regions affect brain development and disorders. We describe the concepts of the rich-club behind network construction in the brain using graph theoretical analysis. We also highlight novel insights based on animal studies related to the rich-club and illustrate how human studies using neuroimaging techniques for brain development and psychiatric/neurological disorders may be relevant to the rich-club phenomenon in the brain network.
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Key Words
- AD, Alzheimer’s disease
- ADHD, attention deficit hyperactivity disorder
- ASD, autism spectrum disorder
- BD, bipolar disorder
- Brain connectivity
- Brain network
- DTI, diffusion tensor imaging
- EEG, electroencephalography
- Graph theory
- MDD, major depressive disorder
- MEG, magnetoencephalography
- MRI, magnetic resonance imaging
- Neuroimaging
- Rich-club
- TBI, traumatic brain injury
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Affiliation(s)
- Dae-Jin Kim
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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96
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Higgins JP, Elliott JM, Parrish TB. Brain Network Disruption in Whiplash. AJNR Am J Neuroradiol 2020; 41:994-1000. [PMID: 32499250 DOI: 10.3174/ajnr.a6569] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 04/14/2020] [Indexed: 01/02/2023]
Abstract
BACKGROUND AND PURPOSE Whiplash-associated disorders frequently develop following motor vehicle collisions and often involve a range of cognitive and affective symptoms, though the neural correlates of the disorder are largely unknown. In this study, a sample of participants with chronic whiplash injuries were scanned by using resting-state fMRI to assess brain network changes associated with long-term outcome metrics. MATERIALS AND METHODS Resting-state fMRI was collected for 23 participants and used to calculate network modularity, a quantitative measure of the functional segregation of brain region communities. This was analyzed for associations with whiplash-associated disorder outcome metrics, including scales of neck disability, traumatic distress, depression, and pain. In addition to these clinical scales, cervical muscle fat infiltration was quantified by using Dixon fat-water imaging, which has shown promise as a biomarker for assessing disorder severity and predicting recovery in chronic whiplash. RESULTS An association was found between brain network structure and muscle fat infiltration, wherein lower network modularity was associated with larger amounts of cervical muscle fat infiltration after controlling for age, sex, body mass index, and scan motion (t = -4.02, partial R 2 = 0.49, P < .001). CONCLUSIONS This work contributes to the existing whiplash literature by examining a sample of participants with whiplash-associated disorder by using resting-state fMRI. Less modular brain networks were found to be associated with greater amounts of cervical muscle fat infiltration suggesting a connection between disorder severity and neurologic changes, and a potential role for neuroimaging in understanding the pathophysiology of chronic whiplash-associated disorders.
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Affiliation(s)
- J P Higgins
- From the Departments of Radiology (J.P.H., T.B.P.)
| | - J M Elliott
- Physical Therapy and Human Movement Sciences (J.M.E.), Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- Discipline of Physiotherapy, Faculty of Health Sciences (J.M.E.), The University of Sydney and the Northern Sydney Local Health District; and The Kolling Research Institute, St. Leonards, NSW, Australia
| | - T B Parrish
- From the Departments of Radiology (J.P.H., T.B.P.)
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97
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Bonkhoff AK, Espinoza FA, Gazula H, Vergara VM, Hensel L, Michely J, Paul T, Rehme AK, Volz LJ, Fink GR, Calhoun VD, Grefkes C. Acute ischaemic stroke alters the brain's preference for distinct dynamic connectivity states. Brain 2020; 143:1525-1540. [PMID: 32357220 PMCID: PMC7241954 DOI: 10.1093/brain/awaa101] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/26/2020] [Accepted: 02/16/2020] [Indexed: 01/01/2023] Open
Abstract
Acute ischaemic stroke disturbs healthy brain organization, prompting subsequent plasticity and reorganization to compensate for the loss of specialized neural tissue and function. Static resting state functional MRI studies have already furthered our understanding of cerebral reorganization by estimating stroke-induced changes in network connectivity aggregated over the duration of several minutes. In this study, we used dynamic resting state functional MRI analyses to increase temporal resolution to seconds and explore transient configurations of motor network connectivity in acute stroke. To this end, we collected resting state functional MRI data of 31 patients with acute ischaemic stroke and 17 age-matched healthy control subjects. Stroke patients presented with moderate to severe hand motor deficits. By estimating dynamic functional connectivity within a sliding window framework, we identified three distinct connectivity configurations of motor-related networks. Motor networks were organized into three regional domains, i.e. a cortical, subcortical and cerebellar domain. The dynamic connectivity patterns of stroke patients diverged from those of healthy controls depending on the severity of the initial motor impairment. Moderately affected patients (n = 18) spent significantly more time in a weakly connected configuration that was characterized by low levels of connectivity, both locally as well as between distant regions. In contrast, severely affected patients (n = 13) showed a significant preference for transitions into a spatially segregated connectivity configuration. This configuration featured particularly high levels of local connectivity within the three regional domains as well as anti-correlated connectivity between distant networks across domains. A third connectivity configuration represented an intermediate connectivity pattern compared to the preceding two, and predominantly encompassed decreased interhemispheric connectivity between cortical motor networks independent of individual deficit severity. Alterations within this third configuration thus closely resembled previously reported ones originating from static resting state functional MRI studies post-stroke. In summary, acute ischaemic stroke not only prompted changes in connectivity between distinct networks, but it also caused characteristic changes in temporal properties of large-scale network interactions depending on the severity of the individual deficit. These findings offer new vistas on the dynamic neural mechanisms underlying acute neurological symptoms, cortical reorganization and treatment effects in stroke patients.
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Affiliation(s)
- Anna K Bonkhoff
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Queen Square Institute of Neurology, University College London, London, UK
| | | | - Harshvardhan Gazula
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Victor M Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Lukas Hensel
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
| | - Jochen Michely
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
| | - Theresa Paul
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
| | - Anne K Rehme
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
| | - Lukas J Volz
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
| | - Gereon R Fink
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Christian Grefkes
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
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98
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Changes in Whole Brain Dynamics and Connectivity Patterns during Sevoflurane- and Propofol-induced Unconsciousness Identified by Functional Magnetic Resonance Imaging. Anesthesiology 2020; 130:898-911. [PMID: 31045899 DOI: 10.1097/aln.0000000000002704] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND A key feature of the human brain is its capability to adapt flexibly to changing external stimuli. This capability can be eliminated by general anesthesia, a state characterized by unresponsiveness, amnesia, and (most likely) unconsciousness. Previous studies demonstrated decreased connectivity within the thalamus, frontoparietal, and default mode networks during general anesthesia. We hypothesized that these alterations within specific brain networks lead to a change of communication between networks and their temporal dynamics. METHODS We conducted a pooled spatial independent component analysis of resting-state functional magnetic resonance imaging data obtained from 16 volunteers during propofol and 14 volunteers during sevoflurane general anesthesia that have been previously published. Similar to previous studies, mean z-scores of the resulting spatial maps served as a measure of the activity within a network. Additionally, correlations of associated time courses served as a measure of the connectivity between networks. To analyze the temporal dynamics of between-network connectivity, we computed the correlation matrices during sliding windows of 1 min and applied k-means clustering to the matrices during both general anesthesia and wakefulness. RESULTS Within-network activity was decreased in the default mode, attentional, and salience networks during general anesthesia (P < 0.001, range of median changes: -0.34, -0.13). Average between-network connectivity was reduced during general anesthesia (P < 0.001, median change: -0.031). Distinct between-network connectivity patterns for both wakefulness and general anesthesia were observed irrespective of the anesthetic agent (P < 0.001), and there were fewer transitions in between-network connectivity patterns during general anesthesia (P < 0.001, median number of transitions during wakefulness: 4 and during general anesthesia: 0). CONCLUSIONS These results suggest that (1) higher-order brain regions play a crucial role in the generation of specific between-network connectivity patterns and their dynamics, and (2) the capability to interact with external stimuli is represented by complex between-network connectivity patterns.
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99
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Raut RV, Mitra A, Marek S, Ortega M, Snyder AZ, Tanenbaum A, Laumann TO, Dosenbach NUF, Raichle ME. Organization of Propagated Intrinsic Brain Activity in Individual Humans. Cereb Cortex 2020; 30:1716-1734. [PMID: 31504262 PMCID: PMC7132930 DOI: 10.1093/cercor/bhz198] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 07/09/2019] [Accepted: 08/07/2019] [Indexed: 12/11/2022] Open
Abstract
Spontaneous infra-slow (<0.1 Hz) fluctuations in functional magnetic resonance imaging (fMRI) signals are temporally correlated within large-scale functional brain networks, motivating their use for mapping systems-level brain organization. However, recent electrophysiological and hemodynamic evidence suggest state-dependent propagation of infra-slow fluctuations, implying a functional role for ongoing infra-slow activity. Crucially, the study of infra-slow temporal lag structure has thus far been limited to large groups, as analyzing propagation delays requires extensive data averaging to overcome sampling variability. Here, we use resting-state fMRI data from 11 extensively-sampled individuals to characterize lag structure at the individual level. In addition to stable individual-specific features, we find spatiotemporal topographies in each subject similar to the group average. Notably, we find a set of early regions that are common to all individuals, are preferentially positioned proximal to multiple functional networks, and overlap with brain regions known to respond to diverse behavioral tasks-altogether consistent with a hypothesized ability to broadly influence cortical excitability. Our findings suggest that, like correlation structure, temporal lag structure is a fundamental organizational property of resting-state infra-slow activity.
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Affiliation(s)
- Ryan V Raut
- Department of Radiology, Washington University, St. Louis, MO 63110, USA
| | - Anish Mitra
- Department of Radiology, Washington University, St. Louis, MO 63110, USA
| | - Scott Marek
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA
| | - Mario Ortega
- Department of Neurology, Washington University, St. Louis, MO 63110, USA
| | - Abraham Z Snyder
- Department of Radiology, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, Washington University, St. Louis, MO 63110, USA
| | - Aaron Tanenbaum
- Department of Neurology, Washington University, St. Louis, MO 63110, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University, St. Louis, MO 63110, USA
| | - Nico U F Dosenbach
- Department of Radiology, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, Washington University, St. Louis, MO 63110, USA
- Department of Pediatrics, Washington University, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University, St. Louis, MO 63110, USA
- Department of Occupational Therapy, Washington University, St. Louis, MO 63110, USA
| | - Marcus E Raichle
- Department of Radiology, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, Washington University, St. Louis, MO 63110, USA
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100
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Herbet G, Duffau H. Revisiting the Functional Anatomy of the Human Brain: Toward a Meta-Networking Theory of Cerebral Functions. Physiol Rev 2020; 100:1181-1228. [PMID: 32078778 DOI: 10.1152/physrev.00033.2019] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
For more than one century, brain processing was mainly thought in a localizationist framework, in which one given function was underpinned by a discrete, isolated cortical area, and with a similar cerebral organization across individuals. However, advances in brain mapping techniques in humans have provided new insights into the organizational principles of anatomo-functional architecture. Here, we review recent findings gained from neuroimaging, electrophysiological, as well as lesion studies. Based on these recent data on brain connectome, we challenge the traditional, outdated localizationist view and propose an alternative meta-networking theory. This model holds that complex cognitions and behaviors arise from the spatiotemporal integration of distributed but relatively specialized networks underlying conation and cognition (e.g., language, spatial cognition). Dynamic interactions between such circuits result in a perpetual succession of new equilibrium states, opening the door to considerable interindividual behavioral variability and to neuroplastic phenomena. Indeed, a meta-networking organization underlies the uniquely human propensity to learn complex abilities, and also explains how postlesional reshaping can lead to some degrees of functional compensation in brain-damaged patients. We discuss the major implications of this approach in fundamental neurosciences as well as for clinical developments, especially in neurology, psychiatry, neurorehabilitation, and restorative neurosurgery.
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Affiliation(s)
- Guillaume Herbet
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, Montpellier, France; Team "Plasticity of Central Nervous System, Stem Cells and Glial Tumors," INSERM U1191, Institute of Functional Genomics, Montpellier, France; and University of Montpellier, Montpellier, France
| | - Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, Montpellier, France; Team "Plasticity of Central Nervous System, Stem Cells and Glial Tumors," INSERM U1191, Institute of Functional Genomics, Montpellier, France; and University of Montpellier, Montpellier, France
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