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The Imbalanced Plasticity Hypothesis of Schizophrenia-Related Psychosis: A Predictive Perspective. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2021; 21:679-697. [PMID: 34050524 DOI: 10.3758/s13415-021-00911-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/28/2021] [Indexed: 02/06/2023]
Abstract
A considerable number of studies have attempted to account for the psychotic aspects of schizophrenia in terms of the influential predictive coding (PC) hypothesis. We argue that the prediction-oriented perspective on schizophrenia-related psychosis may benefit from a mechanistic model that: 1) gives due weight to the extent to which alterations in short- and long-term synaptic plasticity determine the degree and the direction of the functional disruption that occurs in psychosis; and 2) addresses the distinction between the two central syndromes of psychosis in schizophrenia: disorganization and reality-distortion. To accomplish these goals, we propose the Imbalanced Plasticity Hypothesis - IPH, and demonstrate that it: 1) accounts for commonalities and differences between disorganization and reality distortion in terms of excessive (hyper) or insufficient (hypo) neuroplasticity, respectively; 2) provides distinct predictions in the cognitive and electrophysiological domains; and 3) is able to reconcile conflicting PC-oriented accounts of psychosis.
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52
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Huang D, Liu Z, Cao H, Yang J, Wu Z, Long Y. Childhood trauma is linked to decreased temporal stability of functional brain networks in young adults. J Affect Disord 2021; 290:23-30. [PMID: 33991943 DOI: 10.1016/j.jad.2021.04.061] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 01/13/2021] [Accepted: 04/25/2021] [Indexed: 01/08/2023]
Abstract
BACKGROUND Both childhood trauma and disruptions in brain functional networks are implicated in the development of psychiatric disorders in early adulthood. However, the relationships between these two factors remain unclear. This study aimed to investigate whether and how childhood trauma would relate to changes of functional network dynamics in young adults. METHODS Resting-state functional magnetic resonance imaging data were collected from 53 young healthy adults, whose childhood trauma histories were assessed by the Childhood Trauma Questionnaire (CTQ). Network switching rate, a measure of stability of dynamic brain networks over time, was calculated at both global and local levels for each participant. Switching rates at both levels were compared between participants with and without childhood trauma, and further correlated with CTQ total score. RESULTS In the current sample, 19 (35.8%) participants reported a history of childhood trauma. At the global level, participants with childhood trauma showed significantly higher network switching rates than those without trauma (F = 10.021, p = 0.003). A significant positive correlation was found between network switching rates and CTQ scores in the entire sample (r = 0.378, p = 0.007). At the local level, these effects were mainly observed in the default-mode, fronto-parietal, cingulo-opercular, and occipital subnetworks. CONCLUSIONS Our study provides preliminary evidence for a possible long-term effect of childhood trauma on brain functional dynamism. These findings may have potential contributions to psychiatric disorders during adulthood.
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Affiliation(s)
- Danqing Huang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, Changsha, Hunan, China; China National Clinical Research Center on Mental Disorders, Changsha, Hunan, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, Changsha, Hunan, China; China National Clinical Research Center on Mental Disorders, Changsha, Hunan, China
| | - Hengyi Cao
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Hempstead, New York, United States; Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York, United States.
| | - Jie Yang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, Changsha, Hunan, China; China National Clinical Research Center on Mental Disorders, Changsha, Hunan, China
| | - Zhipeng Wu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, Changsha, Hunan, China; China National Clinical Research Center on Mental Disorders, Changsha, Hunan, China
| | - Yicheng Long
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, Changsha, Hunan, China; China National Clinical Research Center on Mental Disorders, Changsha, Hunan, China.
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53
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Dimitriadis SI, Messaritaki E, K Jones D. The impact of graph construction scheme and community detection algorithm on the repeatability of community and hub identification in structural brain networks. Hum Brain Mapp 2021; 42:4261-4280. [PMID: 34170066 PMCID: PMC8356981 DOI: 10.1002/hbm.25545] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 05/14/2021] [Indexed: 12/20/2022] Open
Abstract
A critical question in network neuroscience is how nodes cluster together to form communities, to form the mesoscale organisation of the brain. Various algorithms have been proposed for identifying such communities, each identifying different communities within the same network. Here, (using test–retest data from the Human Connectome Project), the repeatability of thirty‐three community detection algorithms, each paired with seven different graph construction schemes were assessed. Repeatability of community partition depended heavily on both the community detection algorithm and graph construction scheme. Hard community detection algorithms (in which each node is assigned to only one community) outperformed soft ones (in which each node can belong to more than one community). The highest repeatability was observed for the fast multi‐scale community detection algorithm paired with a graph construction scheme that combines nine white matter metrics. This pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs had higher repeatability than provincial hubs. Our results provide a workflow for repeatable identification of structural brain networks communities, based on the optimal pairing of community detection algorithm and graph construction scheme.
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Affiliation(s)
- Stavros I Dimitriadis
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.,School of Psychology, Cardiff University, Cardiff, UK.,Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK.,Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, UK
| | - Eirini Messaritaki
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.,School of Psychology, Cardiff University, Cardiff, UK.,BRAIN Biomedical Research Unit, Cardiff University, Cardiff, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.,School of Psychology, Cardiff University, Cardiff, UK
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54
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Chumin EJ, Risacher SL, West JD, Apostolova LG, Farlow MR, McDonald BC, Wu YC, Saykin AJ, Sporns O. Temporal stability of the ventral attention network and general cognition along the Alzheimer's disease spectrum. Neuroimage Clin 2021; 31:102726. [PMID: 34153687 PMCID: PMC8220588 DOI: 10.1016/j.nicl.2021.102726] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 05/24/2021] [Accepted: 06/09/2021] [Indexed: 02/01/2023]
Abstract
Understanding the interrelationships of clinical manifestations of Alzheimer's disease (AD) and functional connectivity (FC) as the disease progresses is necessary for use of FC as a potential neuroimaging biomarker. Degradation of resting-state networks in AD has been observed when FC is estimated over the entire scan, however, the temporal dynamics of these networks are less studied. We implemented a novel approach to investigate the modular structure of static (sFC) and time-varying (tvFC) connectivity along the AD spectrum in a two-sample Discovery/Validation design (n = 80 and 81, respectively). Cortical FC networks were estimated across 4 diagnostic groups (cognitively normal, subjective cognitive decline, mild cognitive impairment, and AD) for whole scan (sFC) and with sliding window correlation (tvFC). Modularity quality (across a range of spatial scales) did not differ in either sFC or tvFC. For tvFC, group differences in temporal stability within and between multiple resting state networks were observed; however, these differences were not consistent between samples. Correlation analyses identified a relationship between global cognition and temporal stability of the ventral attention network, which was reproduced in both samples. While the ventral attention system has been predominantly studied in task-evoked designs, the relationship between its intrinsic dynamics at-rest and general cognition along the AD spectrum highlights its relevance regarding clinical manifestation of the disease.
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Affiliation(s)
- Evgeny J. Chumin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA,Indiana University Network Science Institute, Bloomington, IN, USA,Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, USA,Corresponding author at: Psychology Building 308, 1101 E 10th St, Bloomington, IN 47405, USA.
| | - Shannon L. Risacher
- Indiana University Network Science Institute, Bloomington, IN, USA,Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA,Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, USA,Department of Neurology, IUSM, Indianapolis, IN, USA
| | - John D. West
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA,Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, USA
| | - Liana G. Apostolova
- Indiana University Network Science Institute, Bloomington, IN, USA,Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA,Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, USA,Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Martin R. Farlow
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, USA,Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Brenna C. McDonald
- Indiana University Network Science Institute, Bloomington, IN, USA,Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA,Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, USA,Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA,Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, USA
| | - Andrew J. Saykin
- Indiana University Network Science Institute, Bloomington, IN, USA,Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA,Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, USA,Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA,Indiana University Network Science Institute, Bloomington, IN, USA,Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA,Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, USA
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55
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Braun U, Harneit A, Pergola G, Menara T, Schäfer A, Betzel RF, Zang Z, Schweiger JI, Zhang X, Schwarz K, Chen J, Blasi G, Bertolino A, Durstewitz D, Pasqualetti F, Schwarz E, Meyer-Lindenberg A, Bassett DS, Tost H. Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia. Nat Commun 2021; 12:3478. [PMID: 34108456 PMCID: PMC8190281 DOI: 10.1038/s41467-021-23694-9] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Accepted: 04/27/2021] [Indexed: 02/06/2023] Open
Abstract
Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory performance entails brain-wide switching between activity states using a combination of functional magnetic resonance imaging in healthy controls and individuals with schizophrenia, pharmacological fMRI, genetic analyses and network control theory. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Individuals with schizophrenia show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations. Our results demonstrate the relevance of dopamine signaling for the steering of whole-brain network dynamics during working memory and link these processes to schizophrenia pathophysiology. Working memory requires the brain to switch between cognitive states and activity patterns. Here, the authors show that the steering of these neural network dynamics is influenced by dopamine D1- and D2-receptor function and altered in schizophrenia.
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Affiliation(s)
- Urs Braun
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany. .,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
| | - Anais Harneit
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Giulio Pergola
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Tommaso Menara
- Mechanical Engineering Department, University of California at Riverside, Riverside, CA, USA
| | - Axel Schäfer
- Bender Institute of Neuroimaging, Justus Liebig University Giessen, Gießen, Germany.,Center for Mind, Brain and Behavior, University of Marburg and Justus Liebig University Giessen, Gießen, Germany
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Zhenxiang Zang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Janina I Schweiger
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Xiaolong Zhang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Kristina Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Giuseppe Blasi
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Fabio Pasqualetti
- Mechanical Engineering Department, University of California at Riverside, Riverside, CA, USA
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.,Department of Psychiatry, University of Pennsylvania, Philadelphia, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, USA.,Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, USA.,Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, USA.,The Santa Fe Institute, Santa Fe, NM, USA
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
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56
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Early changes in brain network topology and activation of affective pathways predict persistent pain in the rat. Pain 2021; 162:45-55. [PMID: 32773593 DOI: 10.1097/j.pain.0000000000002010] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Adaptations in brain communication are associated with multiple pain disorders and are hypothesized to promote the transition from acute to chronic pain. Despite known increases in brain synaptic activity, it is unknown if and how changes in pathways and networks contribute to persistent pain. A tunable rat model that induces transient or persistent temporomandibular joint pain was used to characterize brain network and subcircuit changes when sensitivity is detected in both transient and persistent pain groups and later when sensitivity is present only for the persistent pain group. Brain activity was measured by F-FDG positron emission tomography imaging and used to construct intersubject correlation networks; network connectivity distributions, diagnostics, and community structure were assessed. Activation of subcircuits was tested by structural equation modeling. Findings reveal differences in the brain networks at day 7 between the persistent and transient pain groups, a time when peripheral sensitivity is detected in both groups, but spontaneous pain occurs only in the persistent pain group. At day 7, increased (P ≤ 0.01) clustering, node strength, network segregation, and activation of prefrontal-limbic pathways are observed only in the group that develops persistent pain. Later, increased clustering and node strength are more pronounced with persistent pain, particularly within the limbic system, and decrease when pain resolves. Pretreatment with intra-articular etanercept to attenuate pain confirms that these adaptations are associated with pain onset. Results suggest that early and sustained brain changes can differentiate persistent and transient pain, implying they could be useful as prognostic biomarkers for persistent pain and in identifying therapeutic targets.
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57
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Zöller D, Sandini C, Schaer M, Eliez S, Bassett DS, Van De Ville D. Structural control energy of resting-state functional brain states reveals less cost-effective brain dynamics in psychosis vulnerability. Hum Brain Mapp 2021; 42:2181-2200. [PMID: 33566395 PMCID: PMC8046160 DOI: 10.1002/hbm.25358] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 12/01/2020] [Accepted: 01/05/2021] [Indexed: 12/19/2022] Open
Abstract
How the brain's white-matter anatomy constrains brain activity is an open question that might give insights into the mechanisms that underlie mental disorders such as schizophrenia. Chromosome 22q11.2 deletion syndrome (22q11DS) is a neurodevelopmental disorder with an extremely high risk for psychosis providing a test case to study developmental aspects of schizophrenia. In this study, we used principles from network control theory to probe the implications of aberrant structural connectivity for the brain's functional dynamics in 22q11DS. We retrieved brain states from resting-state functional magnetic resonance images of 78 patients with 22q11DS and 85 healthy controls. Then, we compared them in terms of persistence control energy; that is, the control energy that would be required to persist in each of these states based on individual structural connectivity and a dynamic model. Persistence control energy was altered in a broad pattern of brain states including both energetically more demanding and less demanding brain states in 22q11DS. Further, we found a negative relationship between persistence control energy and resting-state activation time, which suggests that the brain reduces energy by spending less time in energetically demanding brain states. In patients with 22q11DS, this behavior was less pronounced, suggesting a deficiency in the ability to reduce energy through brain activation. In summary, our results provide initial insights into the functional implications of altered structural connectivity in 22q11DS, which might improve our understanding of the mechanisms underlying the disease.
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Affiliation(s)
- Daniela Zöller
- Medical Image Processing LaboratoryInstitute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
- Institute of Neuromodulation and NeurotechnologyUniversity of TübingenTübingenGermany
- Developmental Imaging an Psychopathology Laboratory, Department of PsychiatryUniversity of GenevaGenevaSwitzerland
| | - Corrado Sandini
- Institute of Neuromodulation and NeurotechnologyUniversity of TübingenTübingenGermany
| | - Marie Schaer
- Institute of Neuromodulation and NeurotechnologyUniversity of TübingenTübingenGermany
| | - Stephan Eliez
- Institute of Neuromodulation and NeurotechnologyUniversity of TübingenTübingenGermany
| | - Danielle S. Bassett
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Electrical & Systems EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Physics & AstronomyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Dimitri Van De Ville
- Medical Image Processing LaboratoryInstitute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
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58
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Yin D, Kaiser M. Understanding neural flexibility from a multifaceted definition. Neuroimage 2021; 235:118027. [PMID: 33836274 DOI: 10.1016/j.neuroimage.2021.118027] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 01/19/2021] [Accepted: 03/27/2021] [Indexed: 11/19/2022] Open
Abstract
Flexibility is a hallmark of human intelligence. Emerging studies have proposed several flexibility measurements at the level of individual regions, to produce a brain map of neural flexibility. However, flexibility is usually inferred from separate components of brain activity (i.e., intrinsic/task-evoked), and different definitions are used. Moreover, recent studies have argued that neural processing may be more than a task-driven and intrinsic dichotomy. Therefore, the understanding to neural flexibility is still incomplete. To address this issue, we propose a multifaceted definition of neural flexibility according to three key features: broad cognitive engagement, distributed connectivity, and adaptive connectome dynamics. For these three features, we first review the advances in computational approaches, their functional relevance, and their potential pitfalls. We then suggest a set of metrics that can help us assign a flexibility rating to each region. Subsequently, we present an emergent probabilistic view for further understanding the functional operation of individual regions in the unified framework of intrinsic and task-driven states. Finally, we highlight several areas related to the multifaceted definition of neural flexibility for future research. This review not only strengthens our understanding of flexible human brain, but also suggests that the measure of neural flexibility could bridge the gap between understanding intrinsic and task-driven brain function dynamics.
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Affiliation(s)
- Dazhi Yin
- Key Laboratory of Brain Functional Genomics (Ministry of Education and Shanghai), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China.
| | - Marcus Kaiser
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK; School of Medicine, University of Nottingham, Nottingham NG7 2UH, UK; Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
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59
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Abdallah M, Zahr NM, Saranathan M, Honnorat N, Farrugia N, Pfefferbaum A, Sullivan EV, Chanraud S. Altered Cerebro-Cerebellar Dynamic Functional Connectivity in Alcohol Use Disorder: a Resting-State fMRI Study. THE CEREBELLUM 2021; 20:823-835. [PMID: 33655376 PMCID: PMC8413394 DOI: 10.1007/s12311-021-01241-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/31/2021] [Indexed: 12/28/2022]
Abstract
Alcohol use disorder (AUD) is widely associated with cerebellar dysfunction and altered cerebro-cerebellar functional connectivity (FC) that lead to cognitive impairments. Evidence for this association comes from resting-state functional magnetic resonance imaging (rsfMRI) studies that assess time-averaged measures of FC across the duration of a typical scan. This approach, however, precludes the assessment of potentially FC dynamics happening at faster timescales. In this study, using rsfMRI data, we aim at exploring cerebro-cerebellar FC dynamics in AUD patients (N = 18) and age- and sex-matched controls (N = 18). In particular, we quantified group-level differences in the temporal variability of FC between the posterior cerebellum and large-scale cognitive systems, and we investigated the role of the cerebellum in large-scale brain dynamics in terms of the temporal flexibility and integration of its regions. We found that, relative to controls, the AUD group exhibited significantly greater FC variability between the cerebellum and both the frontoparietal executive control (F1,31 = 7.01, p(FDR) = 0.028) and ventral attention (F1,31 = 7.35, p(FDR) = 0.028) networks. Moreover, the AUD group exhibited significantly less flexibility (F1,31 = 8.61, p(FDR) = 0.028) and greater integration (F1,31 = 9.11, p(FDR) = 0.028) in the cerebellum. Finally, in an exploratory analysis, we found distributed changes in the dynamics of canonical large-scale networks in AUD. Overall, this study brings evidence of AUD-related alterations in dynamic FC within major cerebro-cerebellar networks. This pattern has implications for explaining the development and maintenance of this disorder and improving our understating of the cerebellum's involvement in addiction.
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Affiliation(s)
- Majd Abdallah
- Aquitaine Institute of Cognitive and Integrative Neuroscience, UMR CNRS 5287, University of Bordeaux, Bordeaux, France
| | - Natalie M Zahr
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305-5723, USA.,Center for Health Sciences, SRI International, Menlo Park, CA, 94025, USA
| | | | - Nicolas Honnorat
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305-5723, USA.,Center for Health Sciences, SRI International, Menlo Park, CA, 94025, USA
| | | | - Adolf Pfefferbaum
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305-5723, USA.,Center for Health Sciences, SRI International, Menlo Park, CA, 94025, USA
| | - Edith V Sullivan
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305-5723, USA.,Center for Health Sciences, SRI International, Menlo Park, CA, 94025, USA
| | - Sandra Chanraud
- Aquitaine Institute of Cognitive and Integrative Neuroscience, UMR CNRS 5287, University of Bordeaux, Bordeaux, France. .,Laboratory of Neuroimaging and Daily Life, EPHE, PSL, Research University, Bordeaux, France.
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Bianciardi B, Uhlhaas PJ. Do NMDA-R antagonists re-create patterns of spontaneous gamma-band activity in schizophrenia? A systematic review and perspective. Neurosci Biobehav Rev 2021; 124:308-323. [PMID: 33581223 DOI: 10.1016/j.neubiorev.2021.02.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/29/2021] [Accepted: 02/03/2021] [Indexed: 12/13/2022]
Abstract
NMDA-R hypofunctioninig is a core pathophysiological mechanism in schizophrenia. However, it is unclear whether the physiological changes observed following NMDA-R antagonist administration are consistent with gamma-band alterations in schizophrenia. This systematic review examined the effects of NMDA-R antagonists on the amplitude of spontaneous gamma-band activity and functional connectivity obtained from preclinical (n = 24) and human (n = 9) studies and compared these data to resting-state EEG/MEG-measurements in schizophrenia patients (n = 27). Overall, the majority of preclinical and human studies observed increased gamma-band power following acute administration of NMDA-R antagonists. However, the direction of gamma-band power alterations in schizophrenia were inconsistent, which involved upregulation (n = 10), decreases (n = 7), and no changes (n = 8) in spectral power. Five out of 6 preclinical studies observed increased connectivity, while in healthy controls receiving Ketamine and in schizophrenia patients the direction of connectivity results was also inconsistent. Accordingly, the effects of NMDA-R hypofunctioning on gamma-band oscillations are different than pathophysiological signatures observed in schizophrenia. The implications of these findings for current E/I balance models of schizophrenia are discussed.
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Affiliation(s)
- Bianca Bianciardi
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
| | - Peter J Uhlhaas
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK; Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany.
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61
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Mueller JM, Pritschet L, Santander T, Taylor CM, Grafton ST, Jacobs EG, Carlson JM. Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle. Netw Neurosci 2021; 5:125-144. [PMID: 33688609 PMCID: PMC7935041 DOI: 10.1162/netn_a_00169] [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: 06/27/2020] [Accepted: 09/07/2020] [Indexed: 12/20/2022] Open
Abstract
Sex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified, consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intranetwork increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain–hormone interactions with dynamic community detection suggests that hormonal changes during the menstrual cycle result in temporary, localized patterns of brain network reorganization. Sex steroid hormones influence the central nervous system across multiple spatiotemporal scales. Estrogen and progesterone concentrations rise and fall throughout the menstrual cycle, but it remains poorly understood whether day-to-day fluctuations in hormones shape human brain dynamics. Here, we assessed the structure and stability of resting-state brain network connectivity in concordance with serum hormone levels from a female who underwent fMRI and venipuncture for 30 consecutive days. Our results reveal that while network structure is largely stable over the course of a menstrual cycle, temporary reorganization of several large-scale functional brain networks occurs during the ovulatory window. In particular, a default mode subnetwork exhibits increased connectivity with itself and with nodes belonging to the temporoparietal and limbic networks, providing novel perspective into brain-hormone interactions.
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Affiliation(s)
- Joshua M Mueller
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Laura Pritschet
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Tyler Santander
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Caitlin M Taylor
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Scott T Grafton
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Emily Goard Jacobs
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Jean M Carlson
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
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62
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Boukhdhir A, Zhang Y, Mignotte M, Bellec P. Unraveling reproducible dynamic states of individual brain functional parcellation. Netw Neurosci 2021; 5:28-55. [PMID: 33688605 PMCID: PMC7935036 DOI: 10.1162/netn_a_00168] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 09/08/2020] [Indexed: 01/04/2023] Open
Abstract
Data-driven parcellations are widely used for exploring the functional organization of the brain, and also for reducing the high dimensionality of fMRI data. Despite the flurry of methods proposed in the literature, functional brain parcellations are not highly reproducible at the level of individual subjects, even with very long acquisitions. Some brain areas are also more difficult to parcellate than others, with association heteromodal cortices being the most challenging. An important limitation of classical parcellations is that they are static, that is, they neglect dynamic reconfigurations of brain networks. In this paper, we proposed a new method to identify dynamic states of parcellations, which we hypothesized would improve reproducibility over static parcellation approaches. For a series of seed voxels in the brain, we applied a cluster analysis to regroup short (3 min) time windows into "states" with highly similar seed parcels. We split individual time series of the Midnight scan club sample into two independent sets of 2.5 hr (test and retest). We found that average within-state parcellations, called stability maps, were highly reproducible (over 0.9 test-retest spatial correlation in many instances) and subject specific (fingerprinting accuracy over 70% on average) between test and retest. Consistent with our hypothesis, seeds in heteromodal cortices (posterior and anterior cingulate) showed a richer repertoire of states than unimodal (visual) cortex. Taken together, our results indicate that static functional parcellations are incorrectly averaging well-defined and distinct dynamic states of brain parcellations. This work calls to revisit previous methods based on static parcellations, which includes the majority of published network analyses of fMRI data. Our method may, thus, impact how researchers model the rich interactions between brain networks in health and disease.
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Affiliation(s)
- Amal Boukhdhir
- Centre de recherche de l’institut universitaire de gériatrie de Montréal, Montréal, Québec, Canada
- Département d’informatique et de recherche opérationnelle, Université de Montréal, Montréal, Québec, Canada
| | - Yu Zhang
- Centre de recherche de l’institut universitaire de gériatrie de Montréal, Montréal, Québec, Canada
- Département de psychologie, Université de Montréal, Montréal, Québec, Canada
| | - Max Mignotte
- Département de psychologie, Université de Montréal, Montréal, Québec, Canada
| | - Pierre Bellec
- Centre de recherche de l’institut universitaire de gériatrie de Montréal, Montréal, Québec, Canada
- Département de psychologie, Université de Montréal, Montréal, Québec, Canada
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63
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Gifford G, Crossley N, Morgan S, Kempton MJ, Dazzan P, Modinos G, Azis M, Samson C, Bonoldi I, Quinn B, Smart SE, Antoniades M, Bossong MG, Broome MR, Perez J, Howes OD, Stone JM, Allen P, Grace AA, McGuire P. Integrated metastate functional connectivity networks predict change in symptom severity in clinical high risk for psychosis. Hum Brain Mapp 2021; 42:439-451. [PMID: 33048435 PMCID: PMC7775992 DOI: 10.1002/hbm.25235] [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: 05/22/2020] [Revised: 08/28/2020] [Accepted: 09/29/2020] [Indexed: 01/22/2023] Open
Abstract
The ability to identify biomarkers of psychosis risk is essential in defining effective preventive measures to potentially circumvent the transition to psychosis. Using samples of people at clinical high risk for psychosis (CHR) and Healthy controls (HC) who were administered a task fMRI paradigm, we used a framework for labelling time windows of fMRI scans as 'integrated' FC networks to provide a granular representation of functional connectivity (FC). Periods of integration were defined using the 'cartographic profile' of time windows and k-means clustering, and sub-network discovery was carried out using Network Based Statistics (NBS). There were no network differences between CHR and HC groups. Within the CHR group, using integrated FC networks, we identified a sub-network negatively associated with longitudinal changes in the severity of psychotic symptoms. This sub-network comprised brain areas implicated in bottom-up sensory processing and in integration with motor control, suggesting it may be related to the demands of the fMRI task. These data suggest that extracting integrated FC networks may be useful in the investigation of biomarkers of psychosis risk.
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Affiliation(s)
- George Gifford
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Nicolas Crossley
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Sarah Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,The Alan Turing Institute, London, UK
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK
| | - Gemma Modinos
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Matilda Azis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Carly Samson
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ilaria Bonoldi
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK
| | - Beverly Quinn
- CAMEO Early Intervention in Psychosis Service, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Sophie E Smart
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Mathilde Antoniades
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Psychiatry, Icahn Medical School, Mt Sinai Hospital, New York, New York, USA
| | - Matthijs G Bossong
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Matthew R Broome
- Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Jesus Perez
- CAMEO Early Intervention in Psychosis Service, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Oliver D Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK
| | - James M Stone
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK.,Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Paul Allen
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Psychology, University of Roehampton, London, UK
| | - Anthony A Grace
- Departments of Neuroscience, Psychiatry and Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK
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64
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Yang Z, Telesford QK, Franco AR, Lim R, Gu S, Xu T, Ai L, Castellanos FX, Yan CG, Colcombe S, Milham MP. Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations. Neuroimage 2021; 225:117489. [PMID: 33130272 PMCID: PMC7829665 DOI: 10.1016/j.neuroimage.2020.117489] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 10/21/2020] [Indexed: 01/16/2023] Open
Abstract
Multilayer network models have been proposed as an effective means of capturing the dynamic configuration of distributed neural circuits and quantitatively describing how communities vary over time. Beyond general insights into brain function, a growing number of studies have begun to employ these methods for the study of individual differences. However, test-retest reliabilities for multilayer network measures have yet to be fully quantified or optimized, potentially limiting their utility for individual difference studies. Here, we systematically evaluated the impact of multilayer community detection algorithms, selection of network parameters, scan duration, and task condition on test-retest reliabilities of multilayer network measures (i.e., flexibility, integration, and recruitment). A key finding was that the default method used for community detection by the popular generalized Louvain algorithm can generate erroneous results. Although available, an updated algorithm addressing this issue is yet to be broadly adopted in the neuroimaging literature. Beyond the algorithm, the present work identified parameter selection as a key determinant of test-retest reliability; however, optimization of these parameters and expected reliabilities appeared to be dataset-specific. Once parameters were optimized, consistent with findings from the static functional connectivity literature, scan duration was a much stronger determinant of reliability than scan condition. When the parameters were optimized and scan duration was sufficient, both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks were reliable, although reliability in the movie watching condition was significantly higher than in the other three tasks. The minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, and 30 min for the other three tasks. Our results caution the field against the use of default parameters without optimization based on the specific datasets to be employed - a process likely to be limited for most due to the lack of test-retest samples to enable parameter optimization.
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Affiliation(s)
- Zhen Yang
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Psychiatry, NYU Grossman School of Medicine, 550 1st Avenue, New York, NY 10016, United States.
| | - Qawi K Telesford
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States
| | - Alexandre R Franco
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Psychiatry, NYU Grossman School of Medicine, 550 1st Avenue, New York, NY 10016, United States; Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Ryan Lim
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States
| | - Shi Gu
- University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Ting Xu
- Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Lei Ai
- Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Francisco X Castellanos
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, NY 10016, United States
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Stan Colcombe
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Psychiatry, NYU Grossman School of Medicine, 550 1st Avenue, New York, NY 10016, United States
| | - Michael P Milham
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States.
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65
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Patil AU, Ghate S, Madathil D, Tzeng OJL, Huang HW, Huang CM. Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition. Sci Rep 2021; 11:165. [PMID: 33420212 PMCID: PMC7794287 DOI: 10.1038/s41598-020-80293-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 12/08/2020] [Indexed: 01/29/2023] Open
Abstract
Creative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.
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Affiliation(s)
- Abhishek Uday Patil
- Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan
| | - Sejal Ghate
- Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Deepa Madathil
- Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Ovid J L Tzeng
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan
- Cognitive Neuroscience Laboratory, Institute of Linguistics, Academia Sinica, Taipei, Taiwan
- College of Humanities and Social Sciences, Taipei Medical University, Taipei, Taiwan
- Department of Educational Psychology and Counseling, National Taiwan Normal University, Taipei, Taiwan
- Hong Kong Institute for Advanced Study, City University of Hong Kong, Kowloon, Hong Kong
| | - Hsu-Wen Huang
- Department of Linguistics and Translation, City University of Hong Kong, Kowloon, Hong Kong
| | - Chih-Mao Huang
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan.
- Cognitive Neuroscience Laboratory, Institute of Linguistics, Academia Sinica, Taipei, Taiwan.
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66
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Huang X, Wu Z, Liu Z, Liu D, Huang D, Long Y. Acute Effect of Betel Quid Chewing on Brain Network Dynamics: A Resting-State Functional Magnetic Resonance Imaging Study. Front Psychiatry 2021; 12:701420. [PMID: 34504445 PMCID: PMC8421637 DOI: 10.3389/fpsyt.2021.701420] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/02/2021] [Indexed: 12/12/2022] Open
Abstract
Betel quid (BQ) is one of the most popular addictive substances in the world. However, the neurophysiological mechanism underlying BQ addiction remains unclear. This study aimed to investigate whether and how BQ chewing would affect brain function in the framework of a dynamic brain network model. Resting-state functional magnetic resonance imaging scans were collected from 24 male BQ-dependent individuals and 26 male non-addictive healthy individuals before and promptly after chewing BQ. Switching rate, a measure of temporal stability of functional brain networks, was calculated at both global and local levels for each scan. The results showed that BQ-dependent and healthy groups did not significantly differ on switching rate before BQ chewing (F = 0.784, p = 0.381, analysis of covariance controlling for age, education, and head motion). After chewing BQ, both BQ-dependent (t = 2.674, p = 0.014, paired t-test) and healthy (t = 2.313, p = 0.029, paired t-test) individuals showed a significantly increased global switching rate compared to those before chewing BQ. Significant corresponding local-level effects were observed within the occipital areas for both groups, and within the cingulo-opercular, fronto-parietal, and cerebellum regions for BQ-dependent individuals. Moreover, in BQ-dependent individuals, switching rate was significantly correlated with the severity of BQ addiction assessed by the Betel Quid Dependence Scale scores (Spearman's rho = 0.471, p = 0.020) before BQ chewing. Our study provides preliminary evidence for the acute effects of BQ chewing on brain functional dynamism. These findings may provide insights into the neural mechanisms of substance addictions.
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Affiliation(s)
- Xiaojun Huang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China.,Department of Clinical Psychology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, China
| | - Zhipeng Wu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Zhening Liu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Dayi Liu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Danqing Huang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yicheng Long
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
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67
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Parkes L, Satterthwaite TD, Bassett DS. Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment. Curr Opin Neurobiol 2020; 65:120-128. [PMID: 33242721 PMCID: PMC7770086 DOI: 10.1016/j.conb.2020.10.016] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 10/11/2020] [Accepted: 10/14/2020] [Indexed: 02/01/2023]
Abstract
Searching for biomarkers has been a chief pursuit of the field of psychiatry. Toward this end, studies have catalogued candidate resting-state biomarkers in nearly all forms of mental disorder. However, it is becoming increasingly clear that these biomarkers lack specificity, limiting their capacity to yield clinical impact. We discuss three avenues of research that are overcoming this limitation: (i) the adoption of transdiagnostic research designs, which involve studying and explicitly comparing multiple disorders from distinct diagnostic axes of psychiatry; (ii) dimensional models of psychopathology that map the full spectrum of symptomatology and that cut across traditional disorder boundaries; and (iii) modeling individuals' unique functional connectomes throughout development. We provide a framework for tying these subfields together that draws on tools from machine learning and network science.
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Affiliation(s)
- Linden Parkes
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA.
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68
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Malagurski B, Liem F, Oschwald J, Mérillat S, Jäncke L. Longitudinal functional brain network reconfiguration in healthy aging. Hum Brain Mapp 2020; 41:4829-4845. [PMID: 32857461 PMCID: PMC7643380 DOI: 10.1002/hbm.25161] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 07/12/2020] [Accepted: 07/19/2020] [Indexed: 12/17/2022] Open
Abstract
Healthy aging is associated with changes in cognitive performance and functional brain organization. In fact, cross-sectional studies imply lower modularity and significant heterogeneity in modular architecture across older subjects. Here, we used a longitudinal dataset consisting of four occasions of resting-state-fMRI and cognitive testing (spanning 4 years) in 150 healthy older adults. We applied a graph-theoretic analysis to investigate the time-evolving modular structure of the whole-brain network, by maximizing the multilayer modularity across four time points. Global flexibility, which reflects the tendency of brain nodes to switch between modules across time, was significantly higher in healthy elderly than in a temporal null model. Further, global flexibility, as well as network-specific flexibility of the default mode, frontoparietal control, and somatomotor networks, were significantly associated with age at baseline. These results indicate that older age is related to higher variability in modular organization. The temporal metrics were not associated with simultaneous changes in processing speed or learning performance in the context of memory encoding. Finally, this approach provides global indices for longitudinal change across a given time span and it may contribute to uncovering patterns of modular variability in healthy and clinical aging populations.
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Affiliation(s)
- Brigitta Malagurski
- University Research Priority Program “Dynamics of Healthy Aging”University of ZurichZurichSwitzerland
| | - Franziskus Liem
- University Research Priority Program “Dynamics of Healthy Aging”University of ZurichZurichSwitzerland
| | - Jessica Oschwald
- University Research Priority Program “Dynamics of Healthy Aging”University of ZurichZurichSwitzerland
| | - Susan Mérillat
- University Research Priority Program “Dynamics of Healthy Aging”University of ZurichZurichSwitzerland
| | - Lutz Jäncke
- University Research Priority Program “Dynamics of Healthy Aging”University of ZurichZurichSwitzerland
- Division of Neuropsychology, Institute of PsychologyUniversity of ZurichZurichSwitzerland
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69
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Jalilianhasanpour R, Ryan D, Agarwal S, Beheshtian E, Gujar SK, Pillai JJ, Sair HI. Dynamic Brain Connectivity in Resting State Functional MR Imaging. Neuroimaging Clin N Am 2020; 31:81-92. [PMID: 33220830 DOI: 10.1016/j.nic.2020.09.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Dynamic functional connectivity adds another dimension to resting-state functional MR imaging analysis. In recent years, dynamic functional connectivity has been increasingly used in resting-state functional MR imaging, and several studies have demonstrated that dynamic functional connectivity patterns correlate with different physiologic and pathologic brain states. In fact, evidence suggests that dynamic functional connectivity is a more sensitive marker than static functional connectivity; therefore, it might be a promising tool to add to clinical functional neuroimaging. This article provides a broad overview of dynamic functional connectivity and reviews its general principles, techniques, and potential clinical applications.
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Affiliation(s)
- Rozita Jalilianhasanpour
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA
| | - Daniel Ryan
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA
| | - Shruti Agarwal
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA
| | - Elham Beheshtian
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA
| | - Sachin K Gujar
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA
| | - Jay J Pillai
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA; Department of Neurosurgery, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, MD 21287, USA
| | - Haris I Sair
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA; The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
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Doss MK, May DG, Johnson MW, Clifton JM, Hedrick SL, Prisinzano TE, Griffiths RR, Barrett FS. The Acute Effects of the Atypical Dissociative Hallucinogen Salvinorin A on Functional Connectivity in the Human Brain. Sci Rep 2020; 10:16392. [PMID: 33009457 PMCID: PMC7532139 DOI: 10.1038/s41598-020-73216-8] [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: 02/14/2020] [Accepted: 09/11/2020] [Indexed: 12/23/2022] Open
Abstract
Salvinorin A (SA) is a κ-opioid receptor agonist and atypical dissociative hallucinogen found in Salvia divinorum. Despite the resurgence of hallucinogen studies, the effects of κ-opioid agonists on human brain function are not well-understood. This placebo-controlled, within-subject study used functional magnetic resonance imaging for the first time to explore the effects of inhaled SA on strength, variability, and entropy of functional connectivity (static, dynamic, and entropic functional connectivity, respectively, or sFC, dFC, and eFC). SA tended to decrease within-network sFC but increase between-network sFC, with the most prominent effect being attenuation of the default mode network (DMN) during the first half of a 20-min scan (i.e., during peak effects). SA reduced brainwide dFC but increased brainwide eFC, though only the former effect survived multiple comparison corrections. Finally, using connectome-based classification, most models trained on dFC network interactions could accurately classify the first half of SA scans. In contrast, few models trained on within- or between-network sFC and eFC performed above chance. Notably, models trained on within-DMN sFC and eFC performed better than models trained on other network interactions. This pattern of SA effects on human brain function is strikingly similar to that of other hallucinogens, necessitating studies of direct comparisons.
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Affiliation(s)
- Manoj K Doss
- Department of Psychiatry and Behavioral Sciences, Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, 5510 Nathan Shock Drive, Baltimore, MD, 21224, USA.
| | - Darrick G May
- Department of Psychiatry and Behavioral Sciences, Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, 5510 Nathan Shock Drive, Baltimore, MD, 21224, USA
| | - Matthew W Johnson
- Department of Psychiatry and Behavioral Sciences, Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, 5510 Nathan Shock Drive, Baltimore, MD, 21224, USA
| | - John M Clifton
- Department of Psychiatry and Behavioral Sciences, Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, 5510 Nathan Shock Drive, Baltimore, MD, 21224, USA
| | - Sidnee L Hedrick
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, Lexington, USA
| | - Thomas E Prisinzano
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, Lexington, USA
| | - Roland R Griffiths
- Department of Psychiatry and Behavioral Sciences, Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, 5510 Nathan Shock Drive, Baltimore, MD, 21224, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Frederick S Barrett
- Department of Psychiatry and Behavioral Sciences, Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, 5510 Nathan Shock Drive, Baltimore, MD, 21224, USA
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71
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Wen X, Wang R, Yin W, Lin W, Zhang H, Shen D. Development of Dynamic Functional Architecture during Early Infancy. Cereb Cortex 2020; 30:5626-5638. [PMID: 32537641 DOI: 10.1093/cercor/bhaa128] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Revised: 03/24/2020] [Accepted: 04/21/2020] [Indexed: 02/07/2023] Open
Abstract
Uncovering the moment-to-moment dynamics of functional connectivity (FC) in the human brain during early development is crucial for understanding emerging complex cognitive functions and behaviors. To this end, this paper leveraged a longitudinal resting-state functional magnetic resonance imaging dataset from 51 typically developing infants and, for the first time, thoroughly investigated how the temporal variability of the FC architecture develops at the "global" (entire brain), "mesoscale" (functional system), and "local" (brain region) levels in the first 2 years of age. Our results revealed that, in such a pivotal stage, 1) the whole-brain FC dynamic is linearly increased; 2) the high-order functional systems tend to display increased FC dynamics for both within- and between-network connections, while the primary systems show the opposite trajectories; and 3) many frontal regions have increasing FC dynamics despite large heterogeneity in developmental trajectories and velocities. All these findings indicate that the brain is gradually reconfigured toward a more flexible, dynamic, and adaptive system with globally increasing but locally heterogeneous trajectories in the first 2 postnatal years, explaining why infants have rapidly developing high-order cognitive functions and complex behaviors.
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Affiliation(s)
- Xuyun Wen
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rifeng Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Weiyan Yin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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72
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Shanmugan S, Cao W, Satterthwaite TD, Sammel MD, Ashourvan A, Bassett DS, Ruparel K, Gur RC, Epperson CN, Loughead J. Impact of childhood adversity on network reconfiguration dynamics during working memory in hypogonadal women. Psychoneuroendocrinology 2020; 119:104710. [PMID: 32563173 PMCID: PMC7745207 DOI: 10.1016/j.psyneuen.2020.104710] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 05/07/2020] [Accepted: 05/07/2020] [Indexed: 12/25/2022]
Abstract
Many women with no history of cognitive difficulties experience executive dysfunction during menopause. Significant adversity during childhood negatively impacts executive function into adulthood and may be an indicator of women at risk of a mid-life cognitive decline. Previous studies have indicated that alterations in functional network connectivity underlie these negative effects of childhood adversity. There is growing evidence that functional brain networks are not static during executive tasks; instead, such networks reconfigure over time. Optimal dynamics are necessary for efficient executive function; while too little reconfiguration is insufficient for peak performance, too much reconfiguration (supra-optimal reconfiguration) is also maladaptive and associated with poorer performance. Here we examined the impact of adverse childhood experiences (ACEs) on network flexibility, a measure of dynamic reconfiguration, during a letter n-back task within three networks that support executive function: frontoparietal, salience, and default mode networks. Several animal and human subject studies have suggested that childhood adversity exerts lasting effects on executive function via serotonergic mechanisms. Tryptophan depletion (TD) was used to examine whether serotonin function drives ACE effects on network flexibility. We hypothesized that ACE would be associated with higher flexibility (supra-optimal flexibility) and that TD would further increase this measure. Forty women underwent functional imaging at two time points in this double-blind, placebo controlled, crossover study. Participants also completed the Penn Conditional Exclusion Test, a task assessing abstraction and mental flexibility. The effects of ACE and TD were evaluated using generalized estimating equations. ACE was associated with higher flexibility across networks (frontoparietal β = 0.00748, D = 2.79, p = 0.005; salience β = 0.00679, D = 3.02, p = 0.003; and default mode β = 0.00910, D = 3.53, p = 0.0004). While there was no interaction between ACE and TD, active TD increased network flexibility in both ACE groups in comparison to sham depletion (frontoparietal β = 0.00489, D = 2.15, p = 0.03; salience β = 0.00393, D = 1.91, p = 0.06; default mode β = 0.00334, D = 1.73, p = 0.08). These results suggest that childhood adversity has lasting impacts on dynamic reconfiguration of functional brain networks supporting executive function and that decreasing serotonin levels may exacerbate these effects.
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Affiliation(s)
- Sheila Shanmugan
- Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Penn PROMOTES Research on Sex and Gender in Health, University of Pennsylvania, Philadelphia, PA, USA.
| | - Wen Cao
- Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Mary D Sammel
- Penn PROMOTES Research on Sex and Gender in Health, University of Pennsylvania, Philadelphia, PA, USA; Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Obstetrics and Gynecology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Arian Ashourvan
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Kosha Ruparel
- Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - C Neill Epperson
- Department of Psychiatry, Anschutz Medical Campus, University of Colorado, Aurora, CO USA
| | - James Loughead
- Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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73
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Wei Q, Zhao L, Zou Y, Wang J, Qiu Y, Niu M, Kang Z, Liu X, Tang Y, Li C, Zhang J, Fan X, Huang R, Han Z. The role of altered brain structural connectivity in resilience, vulnerability, and disease expression to schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2020; 101:109917. [PMID: 32169560 DOI: 10.1016/j.pnpbp.2020.109917] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 02/05/2020] [Accepted: 03/09/2020] [Indexed: 01/06/2023]
Abstract
BACKGROUND Schizophrenia (SCZ) is a highly heritable disorder associated with brain connectivity changes. Although the mechanism of disease expression and vulnerability of SCZ have been reported by previous studies, the mechanism of resilience to SCZ based on the brain structural connectivity is poorly understood. The goal of the present study was to identify the structural brain connectivity related with the resilience to SCZ, which is defined here as the capacity to avoid or delay the onset of SCZ in unaffected siblings of SCZ probands. METHOD We collected diffusion tensor imaging (DTI) data of 49 medication-naive, first-episode SCZ (FE-SCZ) patients, 56 unaffected siblings of SCZ probands (SIB-SCZ), and 90 healthy controls. Then we used graph theoretical approach to calculate the topological properties of the brain structural network, including global, subnetwork, and regional parameters. Finally, we compared the parameters between the three groups, and identified the brain structural network related to the resilience, vulnerability and disease expression to SCZ. RESULTS With respect to resilience, only the SIB-SCZ showed significantly increased connectivity in the subnetworks of the left cuneus-precuneus and left posterior cingulate gyrus-precuneus, and in brain areas of right supramarginal gyrus and right inferior temporal gyrus. With respect to vulnerability, both the FE-SCZ and SIB-SCZ had decreased cluster coefficients and local efficiency, and decreased nodal efficiency in the right medial superior frontal gyrus and right medial orbital superior frontal gyrus compared with the healthy controls. With respect to disease expression, only the FE-SCZ group showed decreased or increased global, subnetwork, and nodal connectivity in broader brain regions compared with the healthy controls. CONCLUSION Difference in the topological properties of brain structural connectivity not only reflect the underlying mechanism of vulnerability but also that of resilience to schizophrenia. Alteration in the brain structural connectivity associating with resilience and disease expression may contribute to the onset of SCZ.
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Affiliation(s)
- Qinling Wei
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province, China
| | - Ling Zhao
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, 55 Zhongshan Avenue West, Guangzhou, Guangdong Province, China; Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH University, Aachen, Germany
| | - Yan Zou
- Department of Radiology, the Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province, China
| | - Junjing Wang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, 55 Zhongshan Avenue West, Guangzhou, Guangdong Province, China; Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou 510006, China
| | - Yong Qiu
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province, China
| | - Meiqi Niu
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, 55 Zhongshan Avenue West, Guangzhou, Guangdong Province, China
| | - Zhuang Kang
- Department of Radiology, the Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province, China
| | - Xiaojin Liu
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, 55 Zhongshan Avenue West, Guangzhou, Guangdong Province, China
| | - Yanxia Tang
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province, China; Department of Neurology, Yiyang Central Hospital,118 Kangfu Road,Yiyang, Hunan Province 413000, China
| | - Changhong Li
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, 55 Zhongshan Avenue West, Guangzhou, Guangdong Province, China
| | - Jinbei Zhang
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province, China
| | - Xiaoduo Fan
- UMass Memorial Medical Center, University of Massachusetts Medical School, One Biotech, Suite 100, 365 Plantation Street, Worcester, MA 01605, United States
| | - Ruiwang Huang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, 55 Zhongshan Avenue West, Guangzhou, Guangdong Province, China.
| | - Zili Han
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province, China.
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74
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Tian S, Zhang S, Mo Z, Chattun MR, Wang Q, Wang L, Zhu R, Shao J, Wang X, Yao Z, Si T, Lu Q. Antidepressants normalize brain flexibility associated with multi-dimensional symptoms in major depressive patients. Prog Neuropsychopharmacol Biol Psychiatry 2020; 100:109866. [PMID: 31972187 DOI: 10.1016/j.pnpbp.2020.109866] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 01/06/2020] [Accepted: 01/15/2020] [Indexed: 11/29/2022]
Abstract
BACKGROUND The fundamental pathophysiology of major depressive disorder (MDD) could be characterized by functional brain networks which tightly and dynamically connect into groups as communities, making the flexible brain possible to external multifarious demands. We aim to scrutinize what brain dynamics go awry in MDD and antidepressants effects on multi-dimensional symptoms. METHODS Thirty-five patients and thirty-five controls underwent resting-state functional magnetic resonance imaging (MRI). Patients were scanned before and after 8 or 12 weeks of pharmacotherapy. Group independent component analysis decomposed resting-state images to instinct networks and networks' integrated flexibility was calculated. Network flexibility between patients at baseline and after therapy were compared. RESULTS All patients completed the clinical trial and MRI scans. Following antidepressants treatment, we found significant normalization of reduced network flexibility in default mode network (DMN) and cognitive control network (CCN) of MDD patients. Selectively significant correlations between network flexibility and multi-dimensional symptoms such as anxiety/somatization and hysteresis factor were also found. CONCLUSIONS "Hypoflexible" CCN may involve in anxiety syndrome. Low flexibility in DMN may be indicative of hysteresis. These suggest an important pathophysiology of depressive manifestation of MDD. The antidepressant-induced normalization of the "hypoflexibility" suggests a selective pathway through which antidepressants may alleviate symptoms in depression.
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Affiliation(s)
- Shui Tian
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Siqi Zhang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Zhaoqi Mo
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Mohammad Ridwan Chattun
- Department of Psychiatry,the Affiliated Nanjing Brain Hospital of Nanjing Medical University,Nanjing 210029, China
| | - Qiang Wang
- Nanjing Brain Hospital, Medical School of Nanjing University,Nanjing 210093, China
| | - Li Wang
- Peking University Institute of Mental Health & Sixth Hospital, Beijing 100191, China; National Clinical Research Center for Mental Disorder & The Key Laboratory of Mental Health, Ministry of Health, Ministry of Health (Peking University), Beijing 100191, China
| | - Rongxin Zhu
- Department of Psychiatry,the Affiliated Nanjing Brain Hospital of Nanjing Medical University,Nanjing 210029, China
| | - Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Xinyi Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Zhijian Yao
- Department of Psychiatry,the Affiliated Nanjing Brain Hospital of Nanjing Medical University,Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University,Nanjing 210093, China.
| | - Tianmei Si
- Peking University Institute of Mental Health & Sixth Hospital, Beijing 100191, China; National Clinical Research Center for Mental Disorder & The Key Laboratory of Mental Health, Ministry of Health, Ministry of Health (Peking University), Beijing 100191, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China.
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75
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Molina JL, Voytek B, Thomas ML, Joshi YB, Bhakta SG, Talledo JA, Swerdlow NR, Light GA. Memantine Effects on Electroencephalographic Measures of Putative Excitatory/Inhibitory Balance in Schizophrenia. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:562-568. [PMID: 32340927 PMCID: PMC7286803 DOI: 10.1016/j.bpsc.2020.02.004] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 02/07/2020] [Accepted: 02/11/2020] [Indexed: 12/20/2022]
Abstract
BACKGROUND Abnormalities in cortical excitation and inhibition (E/I) balance are thought to underlie sensory and information processing deficits in schizophrenia. Deficits in early auditory information processing mediate both neurocognitive and functional impairment and appear to be normalized by acute pharmacologic challenge with the NMDA antagonist memantine (MEM). METHODS Thirty-six subjects with a diagnosis of schizophrenia and 31 healthy control subjects underwent electroencephalographic recordings. Subjects ingested either placebo or MEM (10 or 20 mg) in a double-blind, within-subject, crossover, randomized design. The aperiodic, 1/f-like scaling property of the neural power spectra, which is thought to index relative E/I balance, was estimated using a robust linear regression algorithm. RESULTS Patients with schizophrenia had greater aperiodic components compared with healthy control subjects (p < .01, d = 0.64), which was normalized after 20 mg MEM. Analysis revealed a significant dose × diagnosis interaction (p < .0001, d = 0.82). Furthermore, the MEM effect (change in aperiodic component in MEM vs. placebo conditions) was associated with baseline attention and vigilance (r = .54, p < .05) and MEM-induced enhancements in gamma power (r = -.60, p < .01). CONCLUSIONS Findings confirmed E/I balance abnormalities in schizophrenia that were normalized with acute MEM administration and suggest that neurocognitive profiles may predict treatment response based on E/I sensitivity. These data provide proof-of-concept evidence for the utility of E/I balance indices as metrics of acute pharmacologic sensitivity for central nervous system therapeutics.
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Affiliation(s)
- Juan L Molina
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Bradley Voytek
- Department of Cognitive Sciences, Halicioğlu Data Science Institute, and Neurosciences Graduate Program, University of California, San Diego, La Jolla, California
| | - Michael L Thomas
- Department of Psychiatry, University of California, San Diego, La Jolla, California; Department of Psychology, Colorado State University, Fort Collins, Colorado
| | - Yash B Joshi
- Department of Psychiatry, University of California, San Diego, La Jolla, California; VA Desert Pacific Mental Illness Research, Education and Clinical Center, VA San Diego Healthcare System, San Diego, California
| | - Savita G Bhakta
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Jo A Talledo
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Neal R Swerdlow
- Department of Psychiatry, University of California, San Diego, La Jolla, California.
| | - Gregory A Light
- Department of Psychiatry, University of California, San Diego, La Jolla, California; VA Desert Pacific Mental Illness Research, Education and Clinical Center, VA San Diego Healthcare System, San Diego, California
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76
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MacDowell CJ, Buschman TJ. Low-Dimensional Spatiotemporal Dynamics Underlie Cortex-wide Neural Activity. Curr Biol 2020; 30:2665-2680.e8. [PMID: 32470366 DOI: 10.1016/j.cub.2020.04.090] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 04/07/2020] [Accepted: 04/30/2020] [Indexed: 01/04/2023]
Abstract
Cognition arises from the dynamic flow of neural activity through the brain. To capture these dynamics, we used mesoscale calcium imaging to record neural activity across the dorsal cortex of awake mice. We found that the large majority of variance in cortex-wide activity (∼75%) could be explained by a limited set of ∼14 "motifs" of neural activity. Each motif captured a unique spatiotemporal pattern of neural activity across the cortex. These motifs generalized across animals and were seen in multiple behavioral environments. Motif expression differed across behavioral states, and specific motifs were engaged by sensory processing, suggesting the motifs reflect core cortical computations. Together, our results show that cortex-wide neural activity is highly dynamic but that these dynamics are restricted to a low-dimensional set of motifs, potentially allowing for efficient control of behavior.
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Affiliation(s)
- Camden J MacDowell
- Princeton Neuroscience Institute, Princeton University, Washington Rd., Princeton, NJ 08540, USA; Department of Molecular Biology, Princeton University, Washington Rd., Princeton, NJ 08540, USA; Rutgers Robert Wood Johnson Medical School, 125 Paterson St., New Brunswick, NJ 08901, USA.
| | - Timothy J Buschman
- Princeton Neuroscience Institute, Princeton University, Washington Rd., Princeton, NJ 08540, USA; Department of Psychology, Princeton University, Washington Rd., Princeton, NJ 08540, USA.
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77
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Stability-driven non-negative matrix factorization-based approach for extracting dynamic network from resting-state EEG. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.01.071] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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78
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Xia CH, Ma Z, Cui Z, Bzdok D, Thirion B, Bassett DS, Satterthwaite TD, Shinohara RT, Witten DM. Multi-scale network regression for brain-phenotype associations. Hum Brain Mapp 2020; 41:2553-2566. [PMID: 32216125 PMCID: PMC7383128 DOI: 10.1002/hbm.24982] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/31/2020] [Accepted: 02/26/2020] [Indexed: 02/03/2023] Open
Abstract
Brain networks are increasingly characterized at different scales, including summary statistics, community connectivity, and individual edges. While research relating brain networks to behavioral measurements has yielded many insights into brain‐phenotype relationships, common analytical approaches only consider network information at a single scale. Here, we designed, implemented, and deployed Multi‐Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge‐ and community‐level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051), we demonstrate that MSNR recapitulates interpretable and statistically significant connectivity patterns associated with brain development, sex differences, and motion‐related artifacts. Compared to single‐scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge‐ and community‐level information, MSNR has the potential to yield novel insights into brain‐behavior relationships.
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Affiliation(s)
- Cedric Huchuan Xia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Zongming Ma
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Zaixu Cui
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Danilo Bzdok
- Department of Psychiatry, Psychopathology and Psychosomatics, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany.,Université Paris-Saclay, CEA, Inria, Gif-sur-Yvette, France.,Department of Bioengineering, McGill University, Montreal, Canada
| | | | - Danielle S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Physics and Astronomy, School of Arts and Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Santa Fe Institute, Santa Fe, New Mexico, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Russell T Shinohara
- Penn Statistics and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Center for Biomedical Imaging Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Daniela M Witten
- Department of Statistics, College of Arts and Science, University of Washington, Seattle, Washington, USA.,Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington, USA
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79
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Gifford G, Crossley N, Kempton MJ, Morgan S, Dazzan P, Young J, McGuire P. Resting state fMRI based multilayer network configuration in patients with schizophrenia. Neuroimage Clin 2020; 25:102169. [PMID: 32032819 PMCID: PMC7005505 DOI: 10.1016/j.nicl.2020.102169] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 12/18/2019] [Accepted: 01/10/2020] [Indexed: 02/06/2023]
Abstract
Novel methods for measuring large-scale dynamic brain organisation are needed to provide new biomarkers of schizophrenia. Using a method for modelling dynamic modular organisation (Mucha et al., 2010), evidence suggests higher 'flexibility' (switching between multilayer network communities) to be a feature of schizophrenia (Braun et al., 2016). The current study compared flexibility between 55 patients with schizophrenia and 72 controls (the COBRE Dataset). In addition, novel methods of 'between resting state network synchronisation' (BRSNS) and the probability of transition from one community to another were used to further describe group differences in dynamic community structure. There was significantly higher schizophrenia group flexibility scores in cerebellar (F (1124) = 9.33, p (FDR) = 0.017), subcortical (F (1124) = 13.14, p (FDR) = 0.005), and fronto-parietal task control (F (1124) = 7.19, p (FDR) = 0.033) resting state networks (RSNs), as well as in the left thalamus (MNI XYZ: -2, -13, 12; F(1, 124) = 17.1, p (FDR) < 0.001) and the right crus I (MNI XYZ: 35, -67, -34; F (1, 124) = 19.65, p (FDR) < 0.001). Flexibility in the left thalamus reflected transitions between communities covering default mode and sensory-somatomotor RSNs. BRSNS scores suggested altered dynamic inter-RSN modular configuration in schizophrenia. This study suggests less stable community structure in a schizophrenia group at an RSN and node level and provides novel methods of exploring dynamic community structure. Mediation of group differences by mean time window correlation did however suggest flexibility to be no better as a schizophrenia biomarker than simpler measures and a range of methodological choices affected results.
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Affiliation(s)
- George Gifford
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK.
| | - Nicolas Crossley
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK; Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Diagonal Paraguay 362, Santiago 8330077, Chile
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Sarah Morgan
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK; The Alan Turing Institute, London NW1 2DB, UK
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Jonathan Young
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
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80
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Zhang X, Braun U, Tost H, Bassett DS. Data-Driven Approaches to Neuroimaging Analysis to Enhance Psychiatric Diagnosis and Therapy. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:780-790. [PMID: 32127291 DOI: 10.1016/j.bpsc.2019.12.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2019] [Revised: 12/10/2019] [Accepted: 12/19/2019] [Indexed: 01/23/2023]
Abstract
Combining advanced neuroimaging with novel computational methods in network science and machine learning has led to increasingly meaningful descriptions of structure and function in both the normal and the abnormal brain, thereby contributing significantly to our understanding of psychiatric disorders as circuit dysfunctions. Despite its marked potential for psychiatric care, this approach has not yet extended beyond the research setting to any clinically useful applications. Here we review current developments in the study of neuroimaging data using network models and machine learning methods, with a focus on their promise in offering a framework for clinical translation. We discuss 3 potential contributions of these methods to psychiatric care: 1) a better understanding of psychopathology beyond current diagnostic boundaries; 2) individualized prediction of treatment response and prognosis; and 3) formal theories to guide the development of novel interventions. Finally, we highlight current obstacles and sketch a forward-looking perspective of how the application of machine learning and network modeling methods should proceed to accelerate their potential transformation of clinically useful tools.
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Affiliation(s)
- Xiaolong Zhang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Urs Braun
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany; Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania; Santa Fe Institute, Santa Fe, New Mexico.
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81
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Long Y, Chen C, Deng M, Huang X, Tan W, Zhang L, Fan Z, Liu Z. Psychological resilience negatively correlates with resting-state brain network flexibility in young healthy adults: a dynamic functional magnetic resonance imaging study. ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:809. [PMID: 32042825 DOI: 10.21037/atm.2019.12.45] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Psychological resilience is an important personality trait whose decrease is associated with many common psychiatric disorders, but the neural mechanisms underlying it remain largely unclear. In this study, we aimed to explore the neural correlates of psychological resilience in healthy adults by investigating its relationship with functional brain network flexibility, a fundamental dynamic feature of brain network defined by switching frequency of its modular community structures. Methods Resting-state functional magnetic resonance imaging (fMRI) scans were acquired from 41 healthy adults, whose psychological resilience was quantified by the Connor-Davidson Resilience Scale (CD-RISC). Dynamic functional brain network was constructed for each subject, whose flexibility was calculated at all the global, subnetwork and region-of-interest (ROI) levels. After that, the associations between CD-RISC score and brain network flexibility were assessed at all levels by partial correlations controlling for age, sex, education and head motion. Correlation was also tested between the CD-RISC score and modularity of conventional static brain network for comparative purposes. Results The CD-RISC score was significant negatively correlated with the brain network flexibility at global level (r=-0.533, P=0.001), and with flexibility of the visual subnetwork at subnetwork level (r=-0.576, corrected P=0.002). Moreover, significant (corrected P<0.05) or trends for (corrected P<0.10) negative correlations were found between the CD-RISC score and flexibilities of a number of visual and default-mode areas at ROI level. Meanwhile, the modularity of static brain network did not reveal significant correlation with CD-RISC score (P>0.05). Conclusions Our results suggest that excessive fluctuations of the functional brain community structures during rest may be indicative of a lower psychological resilience, and the visual and default-mode systems may play crucial roles in such relationship. These findings may provide important implications for improving our understanding of the psychological resilience.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China.,Mental Health Institute of Central South University, Changsha 410011, China
| | - Chujun Chen
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Mengjie Deng
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Xiaojun Huang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Wenjian Tan
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Li Zhang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Zebin Fan
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China.,Mental Health Institute of Central South University, Changsha 410011, China
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82
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Tian S, Sun Y, Shao J, Zhang S, Mo Z, Liu X, Wang Q, Wang L, Zhao P, Chattun MR, Yao Z, Si T, Lu Q. Predicting escitalopram monotherapy response in depression: The role of anterior cingulate cortex. Hum Brain Mapp 2019; 41:1249-1260. [PMID: 31758634 PMCID: PMC7268019 DOI: 10.1002/hbm.24872] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Revised: 11/04/2019] [Accepted: 11/12/2019] [Indexed: 12/22/2022] Open
Abstract
Neuroimaging biomarkers of treatment efficacy can be used to guide personalized treatment in major depressive disorder (MDD). Escitalopram is recommended as first-line therapy for MDD and severe depression. An interesting hypothesis suggests that the reconfiguration of dynamic brain networks might provide important insights into antidepressant mechanisms. The present study assesses whether the spatiotemporal modulation across functional brain networks could serve as a predictor of effective antidepressant treatment with escitalopram. A total of 106 first-episode, drug-naïve patients and 109 healthy controls from three different multicenters underwent resting-state functional magnetic resonance imaging. Patients were considered as responders if they had a reduction of at least 50% in Hamilton Rating Scale for Depression scores at endpoint (>2 weeks). Multilayer modularity framework was applied on the whole brain to construct features in relation to network dynamic characters that were used for multivariate pattern analysis. Linear soft-threshold support vector machine models were used to separate responders from nonresponders. The permutation tests demonstrated the robustness of discrimination performances. The discriminative regions formed a spatially distributed pattern with anterior cingulate cortex (ACC) as the hub in the default mode subnetwork. Interestingly, a significantly larger module allegiance of ACC was also found in treatment responders compared to nonresponders, suggesting high interactivities of ACC to other regions may be beneficial for the recovery after treatment. Consistent results across multicenters confirmed that ACC could serve as a predictor of escitalopram monotherapy treatment outcome, implying strong likelihood of replication in the future.
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Affiliation(s)
- Shui Tian
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Yurong Sun
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Junneng Shao
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Siqi Zhang
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhaoqi Mo
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Xiaoxue Liu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Qiang Wang
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Li Wang
- Peking University Institute of Mental Health & Sixth Hospital, Beijing, China.,National Clinical Research Center for Mental Disorder & The Key Laboratory of Mental Health, Ministry of Health, Ministry of Health (Peking University), Beijing, China
| | - Peng Zhao
- Department of Medical Psychology, Affiliated Nanjing Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Mohammad Ridwan Chattun
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Zhijian Yao
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Tianmei Si
- Peking University Institute of Mental Health & Sixth Hospital, Beijing, China.,National Clinical Research Center for Mental Disorder & The Key Laboratory of Mental Health, Ministry of Health, Ministry of Health (Peking University), Beijing, China
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
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83
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He L, Zhuang K, Li Y, Sun J, Meng J, Zhu W, Mao Y, Chen Q, Chen X, Qiu J. Brain flexibility associated with need for cognition contributes to creative achievement. Psychophysiology 2019; 56:e13464. [DOI: 10.1111/psyp.13464] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 06/10/2019] [Accepted: 07/24/2019] [Indexed: 12/18/2022]
Affiliation(s)
- Li He
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
- School of Education Chongqing Normal University Chongqing China
| | - Kaixiang Zhuang
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Yu Li
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Jiangzhou Sun
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Jie Meng
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Wenfeng Zhu
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Yu Mao
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Xiaoyi Chen
- School of Education Chongqing Normal University Chongqing China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality Beijing Normal University Beijing China
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84
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Khambhati AN, Kahn AE, Costantini J, Ezzyat Y, Solomon EA, Gross RE, Jobst BC, Sheth SA, Zaghloul KA, Worrell G, Seger S, Lega BC, Weiss S, Sperling MR, Gorniak R, Das SR, Stein JM, Rizzuto DS, Kahana MJ, Lucas TH, Davis KA, Tracy JI, Bassett DS. Functional control of electrophysiological network architecture using direct neurostimulation in humans. Netw Neurosci 2019; 3:848-877. [PMID: 31410383 PMCID: PMC6663306 DOI: 10.1162/netn_a_00089] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 04/14/2019] [Indexed: 01/30/2023] Open
Abstract
Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe and manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. By integrating multimodal intracranial recordings and diffusion-weighted imaging from patients with drug-resistant epilepsy, we test hypothesized structural and functional rules that predict altered patterns of synchronized local field potentials. We demonstrate the ability to predictably reconfigure functional interactions depending on stimulation strength and location. Stimulation of areas with structurally weak connections largely modulates the functional hubness of downstream areas and concurrently propels the brain towards more difficult-to-reach dynamical states. By using focal perturbations to bridge large-scale structure, function, and markers of behavior, our findings suggest that stimulation may be tuned to influence different scales of network interactions driving cognition. Brain stimulation devices capable of perturbing the physiological state of neural systems are rapidly gaining popularity for their potential to treat neurological and psychiatric disease. A root problem is that underlying dysfunction spans a large-scale network of brain regions, requiring the ability to control the complex interactions between multiple brain areas. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. We demonstrate the ability to predictably reconfigure patterns of interactions between functional brain areas by modulating the strength and location of stimulation. Our findings have high significance for designing stimulation protocols capable of modulating distributed neural circuits in the human brain.
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Affiliation(s)
- Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Ari E Kahn
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia Costantini
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Youssef Ezzyat
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ethan A Solomon
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert E Gross
- Department of Neurosurgery, Emory University Hospital, Atlanta, GA, USA
| | - Barbara C Jobst
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institutes of Health, Bethesda, MD, USA
| | | | - Sarah Seger
- Department of Neurosurgery, University of Texas, Southwestern Medical Center, Dallas, TX, USA
| | - Bradley C Lega
- Department of Neurosurgery, University of Texas, Southwestern Medical Center, Dallas, TX, USA
| | - Shennan Weiss
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Michael R Sperling
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Richard Gorniak
- Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Sandhitsu R Das
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Joel M Stein
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel S Rizzuto
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Timothy H Lucas
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Joseph I Tracy
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
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85
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Abstract
Psychiatric disorders are disturbances of cognitive and behavioral processes mediated by the brain. Emerging evidence suggests that accurate biomarkers for psychiatric disorders might benefit from incorporating information regarding multiple brain regions and their interactions with one another, rather than considering local perturbations in brain structure and function alone. Recent advances in the field of applied mathematics generally - and network science specifically - provide a language to capture the complexity of interacting brain regions, and the application of this language to fundamental questions in neuroscience forms the emerging field of network neuroscience. This chapter provides an overview of the use and utility of network neuroscience for building biomarkers in psychiatry. The chapter begins with an overview of the theoretical frameworks and tools that encompass network neuroscience before describing applications of network neuroscience to the study of schizophrenia and major depressive disorder. With reference to work on genetic, molecular, and environmental correlates of network neuroscience features, the promises and challenges of network neuroscience for providing tools that aid in the diagnosis and the evaluation of treatment response in psychiatric disorders are discussed.
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86
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Tian S, Chattun MR, Zhang S, Bi K, Tang H, Yan R, Wang Q, Yao Z, Lu Q. Dynamic community structure in major depressive disorder: A resting-state MEG study. Prog Neuropsychopharmacol Biol Psychiatry 2019; 92:39-47. [PMID: 30572002 DOI: 10.1016/j.pnpbp.2018.12.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 12/11/2018] [Accepted: 12/14/2018] [Indexed: 11/25/2022]
Abstract
BACKGROUND Major Depressive Disorder (MDD), characterized by depressed mood or anhedonia, is associated with altered functional connectivity (FC) within and between large scale networks such as the Default Mode Network (DMN), the Central Executive Network (CEN) and the Salience Network (SN). Since aberrant FC exhibits temporal variability and could give rise to distorted reconfiguration of functional brain networks, an in-depth analysis of the community structure could provide further insight into the synchrony of networks. We hypothesized that alterations in dynamic network community structure in MDD could be temporally accompanied by disrupted conscious states of these three networks. METHODS 26 MDD patients and 25 healthy controls were scanned using a whole-head resting-state Magnetoencephalography (MEG) machine. A novel multilayer modularity framework explored the functional modulation of these networks. Recruitment (R) and integration (I) provided the strength of interaction within networks or across networks, respectively. RESULTS The brain regions in the DMN, CEN and SN were transiently integrated and segmented in both patients and controls. R of CEN and I of SN were significantly greater in MDD compared to controls. CONCLUSION Intrinsic resting-state networks dynamically interact and reorganize into distinct functional modules in both patients and controls. However, the CEN "hyper-intertwines" with itself and SN "hyper-integrates" among the network of interest in depressed patients compared to controls. Network-level alterations in R and I revealed a more generalized system-level effect rather than a focal-wise effect from a neural dynamic perspective. This could potentially highlight an abnormal network-based mechanism in depression.
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Affiliation(s)
- Shui Tian
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Mohammad Ridwan Chattun
- Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Siqi Zhang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Kun Bi
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Hao Tang
- Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Rui Yan
- Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Qiang Wang
- Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China
| | - Zhijian Yao
- Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China.
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87
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Altered dynamic global signal topography in antipsychotic-naive adolescents with early-onset schizophrenia. Schizophr Res 2019; 208:308-316. [PMID: 30772067 DOI: 10.1016/j.schres.2019.01.035] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 01/14/2019] [Accepted: 01/26/2019] [Indexed: 01/11/2023]
Abstract
Schizophrenia (SCZ) is a severe neuropsychiatric disease associated with dysfunction of brain regions and networks. Recent, functional magnetic resonance imaging (fMRI) studies have determined that the global signal (GS) is an important source of the local neuronal activity. However, the dynamics of this effect in SCZ remains unknown. To address this issue, 39 drug-naive patients with early-onset schizophrenia (EOS) and 31 age-, gender- and education-matched healthy controls underwent resting-state fMRI scans. Dynamic functional connectivity (DFC) was employed to assess the dynamic patterns of the GS in EOS. Dynamic analysis demonstrated that the topography of the GS in EOS can be divided into five different states. In the state1, the GS mainly affected the sensory regions. In the state2, the GS mainly affected the default mode network (DMN). In the state3, the GS mainly affected the frontoparietal network and the cingulate-opercular network. In the state4, the GS mainly affected the sensory and subcortical regions. In the state5, the GS mainly affected the sensory regions and DMN. In particular, the changes in the cerebellum, putamen and supramarginal gyrus was inversely proportional to the clinical symptoms. Our findings demonstrate that the influence of the GS on brain networks is dynamic and changes of this relationship may associate with clinical behavior in SCZ.
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88
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Luo Y, Zhang J, Wang C, Zhao X, Chang Q, Wang H, Wang C. Discriminating schizophrenia disease progression using a P50 sensory gating task with dense-array EEG, clinical assessments, and cognitive tests. Expert Rev Neurother 2019; 19:459-470. [DOI: 10.1080/14737175.2019.1601558] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Yu Luo
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100083, China
- Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100083, China
- Hefei Innovation Research Institute, Beihang University, Hefei, 100083, Anhui, China
| | - Jicong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100083, China
- Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100083, China
- Hefei Innovation Research Institute, Beihang University, Hefei, 100083, Anhui, China
| | - Changming Wang
- Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Xiaohui Zhao
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100083, China
- Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100083, China
- Hefei Innovation Research Institute, Beihang University, Hefei, 100083, Anhui, China
| | - Qi Chang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Hua Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100083, China
- Hefei Innovation Research Institute, Beihang University, Hefei, 100083, Anhui, China
| | - Chuanyue Wang
- Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
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89
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Overlaps in brain dynamic functional connectivity between schizophrenia and autism spectrum disorder. SCIENTIFIC AFRICAN 2019. [DOI: 10.1016/j.sciaf.2018.e00019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
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90
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Harlalka V, Bapi RS, Vinod PK, Roy D. Atypical Flexibility in Dynamic Functional Connectivity Quantifies the Severity in Autism Spectrum Disorder. Front Hum Neurosci 2019; 13:6. [PMID: 30774589 PMCID: PMC6367662 DOI: 10.3389/fnhum.2019.00006] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 01/08/2019] [Indexed: 01/10/2023] Open
Abstract
Resting-state functional connectivity (FC) analyses have shown atypical connectivity in autism spectrum disorder (ASD) as compared to typically developing (TD). However, this view emerges from investigating static FC overlooking the whole brain transient connectivity patterns. In our study, we investigated how age and disease influence the dynamic changes in functional connectivity of TD and ASD. We used resting-state functional magnetic resonance imaging (rs-fMRI) data stratified into three cohorts: children (7-11 years), adolescents (12-17 years), and adults (18+ years) for the analysis. The dynamic variability in the connection strength and the modular organization in terms of measures such as flexiblity, cohesion strength, and disjointness were explored for each subject to characterize the differences between ASD and TD. In ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hyper-variability relates to the symptom severity in ASD. We also found that whole-brain flexibility correlates with static modularity only in TD. Further, we observed a core-periphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor, and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD.
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Affiliation(s)
- Vatika Harlalka
- Center for Computational Natural Sciences and Bioinformatics, IIIT Hyderabad, Hyderabad, India
| | - Raju S. Bapi
- Cognitive Science Lab, IIIT Hyderabad, Hyderabad, India
- School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India
| | - P. K. Vinod
- Center for Computational Natural Sciences and Bioinformatics, IIIT Hyderabad, Hyderabad, India
| | - Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, India
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91
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Lydon-Staley DM, Ciric R, Satterthwaite TD, Bassett DS. Evaluation of confound regression strategies for the mitigation of micromovement artifact in studies of dynamic resting-state functional connectivity and multilayer network modularity. Netw Neurosci 2019; 3:427-454. [PMID: 30793090 PMCID: PMC6370491 DOI: 10.1162/netn_a_00071] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Accepted: 09/19/2018] [Indexed: 01/13/2023] Open
Abstract
Dynamic functional connectivity reflects the spatiotemporal organization of spontaneous brain activity in health and disease. Dynamic functional connectivity may be susceptible to artifacts induced by participant motion. This report provides a systematic evaluation of 12 commonly used participant-level confound regression strategies designed to mitigate the effects of micromovements in a sample of 393 youths (ages 8-22 years). Each strategy was evaluated according to a number of benchmarks, including (a) the residual association between participant motion and edge dispersion, (b) distance-dependent effects of motion on edge dispersion, (c) the degree to which functional subnetworks could be identified by multilayer modularity maximization, and (d) measures of module reconfiguration, including node flexibility and node promiscuity. Results indicate variability in the effectiveness of the evaluated pipelines across benchmarks. Methods that included global signal regression were the most consistently effective de-noising strategies.
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Affiliation(s)
| | - Rastko Ciric
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S. Bassett
- Department of Bioengineering, 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
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92
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Regan MC, Zhu Z, Yuan H, Myers SJ, Menaldino DS, Tahirovic YA, Liotta DC, Traynelis SF, Furukawa H. Structural elements of a pH-sensitive inhibitor binding site in NMDA receptors. Nat Commun 2019; 10:321. [PMID: 30659174 PMCID: PMC6338780 DOI: 10.1038/s41467-019-08291-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 12/18/2018] [Indexed: 12/31/2022] Open
Abstract
Context-dependent inhibition of N-methyl-D-aspartate (NMDA) receptors has important therapeutic implications for the treatment of neurological diseases that are associated with altered neuronal firing and signaling. This is especially true in stroke, where the proton concentration in the afflicted area can increase by an order of magnitude. A class of allosteric inhibitors, the 93-series, shows greater potency against GluN1-GluN2B NMDA receptors in such low pH environments, allowing targeted therapy only within the ischemic region. Here we map the 93-series compound binding site in the GluN1-GluN2B NMDA receptor amino terminal domain and show that the interaction of the N-alkyl group with a hydrophobic cage of the binding site is critical for pH-dependent inhibition. Mutation of residues in the hydrophobic cage alters pH-dependent potency, and remarkably, can convert inhibitors into potentiators. Our study provides a foundation for the development of highly specific neuroprotective compounds for the treatment of neurological diseases. Context-dependent inhibition of NMDA receptors has important therapeutic implications for treatment of neurological diseases. Here, the authors use structural biology and biophysics to describe the basis for pH-dependent inhibition for a class of allosteric NMDAR inhibitors, called the 93-series.
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Affiliation(s)
- Michael C Regan
- WM Keck Structural Biology Laboratory, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Zongjian Zhu
- Department of Pharmacology, Emory University School of Medicine, Atlanta, GA, 30322, USA.,Department of Neonatology, First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Hongjie Yuan
- Department of Pharmacology, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Scott J Myers
- Department of Pharmacology, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Dave S Menaldino
- Department of Chemistry, Emory University, Atlanta, GA, 30322, USA
| | | | - Dennis C Liotta
- Department of Chemistry, Emory University, Atlanta, GA, 30322, USA
| | - Stephen F Traynelis
- Department of Pharmacology, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Hiro Furukawa
- WM Keck Structural Biology Laboratory, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.
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93
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Chen Y, Liu YN, Zhou P, Zhang X, Wu Q, Zhao X, Ming D. The Transitions Between Dynamic Micro-States Reveal Age-Related Functional Network Reorganization. Front Physiol 2019; 9:1852. [PMID: 30662409 PMCID: PMC6328489 DOI: 10.3389/fphys.2018.01852] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2017] [Accepted: 12/07/2018] [Indexed: 01/23/2023] Open
Abstract
Normal dynamic change in human brain occurs with age increasing, yet much remains unknown regarding how brain develops, matures, and ages. Functional connectivity analysis of the resting-state brain is a powerful method for revealing the intrinsic features of functional networks, and micro-states, which are the intrinsic patterns of functional connectivity in dynamic network courses, and are suggested to be more informative of brain functional changes. The aim of this study is to explore the age-related changes in these micro-states of dynamic functional network. Three healthy groups were included: the young (ages 21-32 years), the adult (age 41-54 years), and the old (age 60-86 years). Sliding window correlation method was used to construct the dynamic connectivity networks, and then the micro-states were individually identified with clustering analysis. The distribution of age-related connectivity variations in several intrinsic networks for each micro-state was analyzed then. The micro-states showed substantial age-related changes in the transitions between states but not in the dwelling time. Also there was no age-related reorganization observed within any micro-state. But there were reorganizations observed in the transition between them. These results suggested that the identified micro-states represented certain underlying connectivity patterns in functional brain system, which are similar to the intrinsic cognitive networks or resources. In addition, the dynamic transitions between these states were probable mechanisms of reorganization or compensation in functional brain networks with age increasing.
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Affiliation(s)
- Yuanyuan Chen
- College of Microelectronics, Tianjin University, Tianjin, China
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Ya-nan Liu
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Peng Zhou
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Xiong Zhang
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Qiong Wu
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Xin Zhao
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Dong Ming
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
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94
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Silston B, Bassett DS, Mobbs D. How Dynamic Brain Networks Tune Social Behavior in Real Time. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 2018; 27:413-421. [PMID: 31467465 DOI: 10.1177/0963721418773362] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
During social interaction, the brain has the enormous task of interpreting signals that are fleeting, subtle, contextual, abstract, and often ambiguous. Despite the signal complexity, the human brain has evolved to be highly successful in the social landscape. Here, we propose that the human brain makes sense of noisy dynamic signals through accumulation, integration, and prediction, resulting in a coherent representation of the social world. We propose that successful social interaction is critically dependent on a core set of highly connected hubs that dynamically accumulate and integrate complex social information and, in doing so, facilitate social tuning during moment-to-moment social discourse. Successful interactions, therefore, require adaptive flexibility generated by neural circuits composed of highly integrated hubs that coordinate context-appropriate responses. Adaptive properties of the neural substrate, including predictive and adaptive coding, and neural reuse, along with perceptual, inferential, and motivational inputs, provide the ingredients for pliable, hierarchical predictive models that guide our social interactions.
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Affiliation(s)
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania.,Department of Physics and Astronomy, University of Pennsylvania.,Department of Electrical and Systems Engineering, University of Pennsylvania.,Department of Neurology, University of Pennsylvania
| | - Dean Mobbs
- Division of the Humanities and Social Sciences, California Institute of Technology
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95
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Khambhati AN, Sizemore AE, Betzel RF, Bassett DS. Modeling and interpreting mesoscale network dynamics. Neuroimage 2018; 180:337-349. [PMID: 28645844 PMCID: PMC5738302 DOI: 10.1016/j.neuroimage.2017.06.029] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Revised: 06/12/2017] [Accepted: 06/14/2017] [Indexed: 11/28/2022] Open
Abstract
Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations. Here we review recent advances in a range of modeling approaches that embrace the temporally-evolving interconnected structure of the brain and summarize that structure in a dynamic graph. We describe recent efforts to model dynamic patterns of connectivity, dynamic patterns of activity, and patterns of activity atop connectivity. In the context of these models, we review important considerations in statistical testing, including parametric and non-parametric approaches. Finally, we offer thoughts on careful and accurate interpretation of dynamic graph architecture, and outline important future directions for method development.
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Affiliation(s)
- Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeautics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeautics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
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96
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Sizemore AE, Bassett DS. Dynamic graph metrics: Tutorial, toolbox, and tale. Neuroimage 2018; 180:417-427. [PMID: 28698107 PMCID: PMC5758445 DOI: 10.1016/j.neuroimage.2017.06.081] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 05/24/2017] [Accepted: 06/29/2017] [Indexed: 11/23/2022] Open
Abstract
The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.
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Affiliation(s)
- Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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97
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Abstract
The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.
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Affiliation(s)
- Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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98
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Barrett FS, Carbonaro TM, Hurwitz E, Johnson MW, Griffiths RR. Double-blind comparison of the two hallucinogens psilocybin and dextromethorphan: effects on cognition. Psychopharmacology (Berl) 2018; 235:2915-2927. [PMID: 30062577 PMCID: PMC6162157 DOI: 10.1007/s00213-018-4981-x] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 07/23/2018] [Indexed: 01/30/2023]
Abstract
OBJECTIVES Classic psychedelics (serotonin 2A receptor agonists) and dissociative hallucinogens (NMDA receptor antagonists), though differing in pharmacology, may share neuropsychological effects. These drugs, however, have undergone limited direct comparison. This report presents data from a double-blind, placebo-controlled within-subjects study comparing the neuropsychological effects of multiple doses of the classic psychedelic psilocybin with the effects of a single high dose of the dissociative hallucinogen dextromethorphan (DXM). METHODS Twenty hallucinogen users (11 females) completed neurocognitive assessments during five blinded drug administration sessions (10, 20, and 30 mg/70 kg psilocybin; 400 mg/70 kg DXM; and placebo) in which participants and study staff were informed that a large range of possible drug conditions may have been administered. RESULTS Global cognitive impairment, assessed using the Mini-Mental State Examination during peak drug effects, was not observed with psilocybin or DXM. Orderly and dose-dependent effects of psilocybin were observed on psychomotor performance, working memory, episodic memory, associative learning, and visual perception. Effects of DXM on psychomotor performance, visual perception, and associative learning were in the range of effects of a moderate to high dose (20 to 30 mg/70 kg) of psilocybin. CONCLUSIONS This was the first study of the dose effects of psilocybin on a large battery of neurocognitive assessments. Evidence of delirium or global cognitive impairment was not observed with either psilocybin or DXM. Psilocybin had greater effects than DXM on working memory. DXM had greater effects than all psilocybin doses on balance, episodic memory, response inhibition, and executive control.
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Affiliation(s)
- Frederick S Barrett
- Behavioral Pharmacology Research Unit, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 5510 Nathan Shock Dr., Baltimore, MD, 21224, USA.
| | - Theresa M Carbonaro
- Behavioral Pharmacology Research Unit, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 5510 Nathan Shock Dr., Baltimore, MD, 21224, USA
| | - Ethan Hurwitz
- Behavioral Pharmacology Research Unit, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 5510 Nathan Shock Dr., Baltimore, MD, 21224, USA
| | - Matthew W Johnson
- Behavioral Pharmacology Research Unit, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 5510 Nathan Shock Dr., Baltimore, MD, 21224, USA
| | - Roland R Griffiths
- Behavioral Pharmacology Research Unit, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 5510 Nathan Shock Dr., Baltimore, MD, 21224, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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99
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Abstract
Network theory provides an intuitively appealing framework for studying relationships among interconnected brain mechanisms and their relevance to behaviour. As the space of its applications grows, so does the diversity of meanings of the term network model. This diversity can cause confusion, complicate efforts to assess model validity and efficacy, and hamper interdisciplinary collaboration. In this Review, we examine the field of network neuroscience, focusing on organizing principles that can help overcome these challenges. First, we describe the fundamental goals in constructing network models. Second, we review the most common forms of network models, which can be described parsimoniously along the following three primary dimensions: from data representations to first-principles theory; from biophysical realism to functional phenomenology; and from elementary descriptions to coarse-grained approximations. Third, we draw on biology, philosophy and other disciplines to establish validation principles for these models. We close with a discussion of opportunities to bridge model types and point to exciting frontiers for future pursuits.
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Affiliation(s)
- Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics and Astronomy, 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.
| | - Perry Zurn
- Department of Philosophy, American University, Washington, DC, USA
| | - Joshua I Gold
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
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100
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Bassett DS, Xia CH, Satterthwaite TD. Understanding the Emergence of Neuropsychiatric Disorders With Network Neuroscience. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:742-753. [PMID: 29729890 PMCID: PMC6119485 DOI: 10.1016/j.bpsc.2018.03.015] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 03/28/2018] [Accepted: 03/29/2018] [Indexed: 11/23/2022]
Abstract
Major neuropsychiatric disorders such as psychosis are increasingly acknowledged to be disorders of brain connectivity. Yet tools to map, model, predict, and change connectivity are difficult to develop, largely because of the complex, dynamic, and multivariate nature of interactions between brain regions. Network neuroscience (NN) provides a theoretical framework and mathematical toolset to address these difficulties. Building on areas of mathematics such as graph theory, NN in its simplest form summarizes neuroimaging data by treating brain regions as nodes in a graph and by treating interactions or connections between nodes as edges in the graph. Network metrics can then be used to quantitatively describe the architecture of the graph, which in turn reflects the network's function. We review evidence supporting the utility of NN in understanding psychiatric disorders, with a focus on normative brain network development and abnormalities associated with psychosis. We also emphasize relevant methodological challenges, such as motion artifact correction, which are particularly important to consider when applying network tools to developmental neuroimaging data. We close with a discussion of several emerging frontiers of NN in psychiatry, including generative network modeling and network control theory. We aim to offer an accessible introduction to this emerging field and motivate further work that uses NN to better understand the normative development of brain networks and alterations in that development that accompany or foreshadow psychiatric disease.
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Affiliation(s)
- Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Cedric Huchuan Xia
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
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