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Nenning KH, Fösleitner O, Schwartz E, Schwarz M, Schmidbauer V, Geisl G, Widmann C, Pirker S, Baumgartner C, Prayer D, Pataraia E, Bartha-Doering L, Langs G, Kasprian G, Bonelli SB. The impact of hippocampal impairment on task-positive and task-negative language networks in temporal lobe epilepsy. Clin Neurophysiol 2021; 132:404-411. [PMID: 33450563 DOI: 10.1016/j.clinph.2020.10.031] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 10/12/2020] [Accepted: 10/27/2020] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To study hippocampal integration within task-positive and task-negative language networks and the impact of a diseased left and right hippocampus on the language connectome in temporal lobe epilepsy (TLE). METHODS We used functional magnetic resonance imaging (fMRI) to study a homogenous group of 32 patients with TLE (17 left) and 14 healthy controls during a verb-generation task. We performed functional connectivity analysis and quantified alterations within the language connectome and evaluated disruptions of the functional dissociation along the anterior-posterior axis of the hippocampi. RESULTS Connectivity analysis revealed significant differences between left and right TLE compared to healthy controls. Left TLE showed widespread impairment of task-positive language networks, while right TLE showed less pronounced alterations. Particularly right TLE showed altered connectivity for cortical regions that were part of the default mode network (DMN). Left TLE showed a disturbed functional dissociation pattern along the left hippocampus to left and right inferior frontal language regions, while left and right TLE revealed an altered dissociation pattern along the right hippocampus to regions associated with the DMN. CONCLUSIONS Our results showed an impaired hippocampal integration into active language and the default mode networks, which both may contribute to language impairment in TLE. SIGNIFICANCE Our results emphasize the direct role of the left hippocampus in language processing, and the potential role of the right hippocampus as a modulator between DMN and task-positive networks.
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Affiliation(s)
- Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Olivia Fösleitner
- Department of Biomedical Imaging and Image-guided Therapy, Division of Neuroradiology and Musculoskeletal Radiology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Ernst Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Michelle Schwarz
- Department of Neurology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Victor Schmidbauer
- Department of Neurology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Gudrun Geisl
- Department of Neurology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Christian Widmann
- Department of Biomedical Imaging and Image-guided Therapy, Division of Neuroradiology and Musculoskeletal Radiology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Susanne Pirker
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Riedelgasse 5, 1130 Vienna, Austria; Department of Neurology, General Hospital Hietzing with Neurological Center Rosenhügel, Riedelgasse 5, 1130 Vienna, Austria
| | - Christoph Baumgartner
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Riedelgasse 5, 1130 Vienna, Austria; Department of Neurology, General Hospital Hietzing with Neurological Center Rosenhügel, Riedelgasse 5, 1130 Vienna, Austria
| | - Daniela Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Neuroradiology and Musculoskeletal Radiology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Ekaterina Pataraia
- Department of Neurology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Lisa Bartha-Doering
- Department of Paediatrics and Adolescent Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Gregor Kasprian
- Department of Biomedical Imaging and Image-guided Therapy, Division of Neuroradiology and Musculoskeletal Radiology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Silvia B Bonelli
- Department of Neurology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria.
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Deep learning identifies partially overlapping subnetworks in the human social brain. Commun Biol 2021; 4:65. [PMID: 33446815 PMCID: PMC7809473 DOI: 10.1038/s42003-020-01559-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 12/03/2020] [Indexed: 12/23/2022] Open
Abstract
Complex social interplay is a defining property of the human species. In social neuroscience, many experiments have sought to first define and then locate ‘perspective taking’, ‘empathy’, and other psychological concepts to specific brain circuits. Seldom, bottom-up studies were conducted to first identify explanatory patterns of brain variation, which are then related to psychological concepts; perhaps due to a lack of large population datasets. In this spirit, we performed a systematic de-construction of social brain morphology into its elementary building blocks, involving ~10,000 UK Biobank participants. We explored coherent representations of structural co-variation at population scale within a recent social brain atlas, by translating autoencoder neural networks from deep learning. The learned subnetworks revealed essential patterns of structural relationships between social brain regions, with the nucleus accumbens, medial prefrontal cortex, and temporoparietal junction embedded at the core. Some of the uncovered subnetworks contributed to predicting examined social traits in general, while other subnetworks helped predict specific facets of social functioning, such as the experience of social isolation. As a consequence of our population-level evidence, spatially overlapping subsystems of the social brain probably relate to interindividual differences in everyday social life. Kiesow et al. use deep learning to identify partially overlapping subnetworks in the human social brain at the population level. They also demonstrate that the learned subnetwork representations can be used to predict social traits.
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53
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He H, Cao H, Huang B, He M, Ma C, Yao D, Luo C, Yao G, Duan M. Functional abnormalities of striatum are related to the season-specific effect on schizophrenia. Brain Imaging Behav 2021; 15:2347-2355. [PMID: 33398777 DOI: 10.1007/s11682-020-00430-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/08/2020] [Indexed: 11/29/2022]
Abstract
Schizophrenia is a syndrome that is typically accompanied by delusions, hallucinations and cognitive impairments. Specifically, abundant evidences support the notion that more people diagnosed with schizophrenia are born during fall-winter than spring-summer. Although pathophysiological of schizophrenia might be associated with abnormal brain functional network, little is currently known the relationship between season and deficient brain functional network of schizophrenia. To investigate this issue, in this study 51 schizophrenic subjects and 72 healthy controls underwent MRI scanning to detect the brain functional mapping, each at spring-summer and fall-winter season throughout the year. The data-driven method was used to measure the blood oxygen metabolism variability (BOMV). Decreased BOMV in spring-summer while increased in fall-winter were observed within dopaminergic network of schizophrenic subjects, including striatum, thalamus, and hippocampus. The post hoc analysis exploring the coupling among changed BOMV regions, confirmed that a positive relationship, between pallidum and hippocampus existed in fall-winter healthy controls, but not in fall-winter schizophrenic subjects. These findings identified that seasonal effect on striatum might be associated with modulation of striatum-hippocampus. Our results provide a new insight into the role of season in understanding the pathophysiological of schizophrenia.
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Affiliation(s)
- Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Huan Cao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Binxin Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Manxi He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Chi Ma
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China. .,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
| | - Gang Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China.
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, People's Republic of China.
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Pironti VA, Vatansever D, Sahakian BJ. Shared alterations in resting-state brain connectivity in adults with attention-deficit/hyperactivity disorder and their unaffected first-degree relatives. Psychol Med 2021; 51:329-339. [PMID: 31769365 PMCID: PMC7893505 DOI: 10.1017/s0033291719003374] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 09/15/2019] [Accepted: 11/04/2019] [Indexed: 01/08/2023]
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) is a developmental condition that often persists into adulthood with extensive negative consequences on quality of life. Despite emerging evidence indicating the genetic basis of ADHD, investigations into the familial expression of latent neurocognitive traits remain limited. METHODS In a group of adult ADHD probands (n = 20), their unaffected first-degree relatives (n = 20) and typically developing control participants (n = 20), we assessed endophenotypic alterations in the default mode network (DMN) connectivity during resting-state functional magnetic resonance imaging in relation to cognitive performance and clinical symptoms. In an external validation step, we also examined the dimensional nature of this neurocognitive trait in a sample of unrelated healthy young adults (n = 100) from the Human Connectome Project (HCP). RESULTS The results illustrated reduced anti-correlations between the posterior cingulate cortex/precuneus and right middle frontal gyrus that was shared between adult ADHD probands and their first-degree relatives, but not with healthy controls. The observed connectivity alterations were linked to higher ADHD symptoms that was mediated by performance in a sustained attention task. Moreover, this brain-based neurocognitive trait dimensionally explained ADHD symptom variability in the HCP sample. CONCLUSIONS Alterations in the default mode connectivity may represent a dimensional endophenotype of ADHD, hence a significant aspect of the neuropathophysiology of this disorder. As such, brain network organisation can potentially be employed as an important neurocognitive trait to enhance statistical power of genetic studies in ADHD and as a surrogate efficacy endpoint in the development of novel pharmaceuticals.
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Affiliation(s)
- Valentino Antonio Pironti
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
- Suno Innova Ltd, Unit 6, 109 Cambridge Road Industrial Estate, Cambridge, UK
| | - Deniz Vatansever
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, PR China
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Psychology, University of York, Heslington, York, UK
| | - Barbara Jacquelyn Sahakian
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, PR China
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55
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Elman I, Upadhyay J, Lowen S, Karunakaran K, Albanese M, Borsook D. Mechanisms Underlying Unconscious Processing and Their Alterations in Post-traumatic Stress Disorder: Neuroimaging of Zero Monetary Outcomes Contextually Framed as "No Losses" vs. "No Gains". Front Neurosci 2020; 14:604867. [PMID: 33390889 PMCID: PMC7772193 DOI: 10.3389/fnins.2020.604867] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 11/24/2020] [Indexed: 11/26/2022] Open
Abstract
Although unconscious processing is a key element of mental operation, its neural correlates have not been established. Also, clinical observations suggest that unconscious processing may be involved in the pathophysiology of post-traumatic stress disorder (PTSD), but the neurobiological mechanisms underlying such impairments remain unknown. The purpose of the present study was to examine putative mechanisms underlying unconscious processing by healthy participants and to determine whether these mechanisms may be altered in PTSD patients. Twenty patients with PTSD and 27 healthy individuals were administered a validated wheel of fortune-type gambling task during functional magnetic resonance imaging (fMRI). Unconscious processing was elicited using unconscious contextual framing of the zero monetary outcomes as "no loss," "no gain" or as "neutral." Brief passive visual processing of the "no loss" vs. "no gain" contrast by healthy participants yielded bilateral frontal-, temporal- and insular cortices and striatal activations. Between-group comparison revealed smaller activity in the left anterior prefrontal-, left dorsolateral prefrontal-, right temporal- and right insular cortices and in bilateral striatum in PTSD patients with the left dorsolateral prefrontal cortex activity been more pronounced in those with greater PTSD severity. These observations implicate frontal-, temporal-, and insular cortices along with the striatum in the putative mechanisms underlying unconscious processing of the monetary outcomes. Additionally, our results support the hypothesis that PTSD is associated with primary cortical and subcortical alterations involved in the above processes and that these alterations may be related to some aspects of PTSD symptomatology.
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Affiliation(s)
- Igor Elman
- Center for Pain and the Brain, Department of Anesthesiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
- Cambridge Health Alliance, Harvard Medical School, Cambridge, MA, United States
| | - Jaymin Upadhyay
- Center for Pain and the Brain, Department of Anesthesiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, United States
| | | | - Keerthana Karunakaran
- Center for Pain and the Brain, Department of Anesthesiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Mark Albanese
- Cambridge Health Alliance, Harvard Medical School, Cambridge, MA, United States
| | - David Borsook
- Center for Pain and the Brain, Department of Anesthesiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, United States
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56
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Zhuang K, Yang W, Li Y, Zhang J, Chen Q, Meng J, Wei D, Sun J, He L, Mao Y, Wang X, Vatansever D, Qiu J. Connectome-based evidence for creative thinking as an emergent property of ordinary cognitive operations. Neuroimage 2020; 227:117632. [PMID: 33316392 DOI: 10.1016/j.neuroimage.2020.117632] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 11/02/2020] [Accepted: 12/05/2020] [Indexed: 01/23/2023] Open
Abstract
Creative thinking is a hallmark of human cognition, which enables us to generate novel and useful ideas. Nevertheless, its emergence within the macro-scale neurocognitive circuitry remains largely unknown. Using resting-state fMRI data from two large population samples (SWU: n = 931; HCP: n = 1001) and a novel "travelling pattern prediction analysis", here we identified the modularized functional connectivity patterns linked to creative thinking ability, which concurrently explained individual variability across ordinary cognitive abilities such as episodic memory, working memory and relational processing. Further interrogation of this neural pattern with graph theoretical tools revealed both hub-like brain structures and globally-efficient information transfer paths that together may facilitate higher creative thinking ability through the convergence of distinct cognitive operations. Collectively, our results provide reliable evidence for the hypothesized emergence of creative thinking from core cognitive components through neural integration, and thus allude to a significant theoretical advancement in the study of creativity.
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Affiliation(s)
- Kaixiang Zhuang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing 400715, China
| | - Wenjing Yang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing 400715, China
| | - Yu Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing 400715, China
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing 400715, China
| | - Jie Meng
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing 400715, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing 400715, China
| | - Jiangzhou Sun
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing 400715, China
| | - Li He
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing 400715, China
| | - Yu Mao
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing 400715, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing 400715, China
| | - Deniz Vatansever
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing 400715, China.
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57
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Smith JL, Allen JW, Haack C, Wehrmeyer K, Alden K, Lund MB, Mascaro JS. The Impact of App-Delivered Mindfulness Meditation on Functional Connectivity and Self-Reported Mindfulness Among Health Profession Trainees. Mindfulness (N Y) 2020; 12:92-106. [PMID: 33052251 PMCID: PMC7543678 DOI: 10.1007/s12671-020-01502-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/08/2020] [Indexed: 12/30/2022]
Abstract
Objectives Previous research indicates that mindfulness meditation reduces anxiety and depression and enhances well-being. We examined the impact of app-delivered mindfulness meditation on resting state functional MRI (fMRI) connectivity among physician assistant (PA) students and surgery residents. Methods PA students and residents were randomized to receive a popular meditation app or to wait-list control group. Before and after the 8-week meditation period, we acquired fMRI scans of participants’ resting state, and participants completed a self-report measure of mindfulness. We used a 2 × 2, within- and between-group factorial design and leveraged a whole-brain connectome approach to examine changes in within- and between-network connectivity across the entire brain, and to examine whether changes in connectivity were associated with app use or to changes in self-reported mindfulness. Results Meditation practitioners exhibited significantly stronger connectivity between the frontoparietal network and the left and right nucleus accumbens and between the default mode (DMN) and salience networks, among other regions. Mindfulness practice time was correlated with increased connectivity between the lateral parietal cortex and the supramarginal gyrus, which were also positively correlated with increased scores on the “Describing” subscale of the Five Facet Mindfulness Questionnaire between baseline and post-meditation. These findings are consistent with previous research indicating that mindfulness-based interventions alter functional connectivity within the DMN and between the DMN and other networks both during meditation and at rest, as well as increased connectivity in systems important for emotion and reward. Conclusions Recent commentaries call for healthcare provider and trainee wellness programs that are sustainable and preventive in nature rather than reactive; these data indicate that even brief sessions of app-delivered mindfulness practice are associated with functional connectivity changes in a dose-dependent manner. Electronic supplementary material The online version of this article (10.1007/s12671-020-01502-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jeremy L Smith
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA USA
| | - Jason W Allen
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA USA.,Department of Neurology, Emory University, Atlanta, GA USA
| | - Carla Haack
- Department of Surgery, Emory University, Atlanta, GA USA
| | - Kathryn Wehrmeyer
- Department of Family and Preventive Medicine, Emory University, 1841 Clifton Road NE, Suite 507, Atlanta, GA 30329 USA
| | - Kayley Alden
- Department of Family and Preventive Medicine, Emory University, 1841 Clifton Road NE, Suite 507, Atlanta, GA 30329 USA
| | - Maha B Lund
- Department of Family and Preventive Medicine, Physician Assistant Program, Emory University, Atlanta, GA USA
| | - Jennifer S Mascaro
- Department of Family and Preventive Medicine, Emory University, 1841 Clifton Road NE, Suite 507, Atlanta, GA 30329 USA
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Ashinoff BK, Mayhew SD, Mevorach C. The same, but different: Preserved distractor suppression in old age is implemented through an age-specific reactive ventral fronto-parietal network. Hum Brain Mapp 2020; 41:3938-3955. [PMID: 32573907 PMCID: PMC7469802 DOI: 10.1002/hbm.25097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/12/2020] [Accepted: 06/02/2020] [Indexed: 01/15/2023] Open
Abstract
Previous studies have shown age-related impairments in the ability to suppress salient distractors. One possibility is that this is mediated by age-related impairments in the recruitment of the left intraparietal sulcus (Left IPS), which has been shown to mediate the suppression of salient distractors in healthy, young participants. Alternatively, this effect may be due to a shift in engagement from proactive control to reactive control, possibly to compensate for age-related impairments in proactive control. Another possibility is that this is due to changes in the functional specificity of brain regions that mediate salience suppression, expressed in changes in spontaneous connectivity of these regions. We assessed these possibilities by having participants engage in a proactive distractor suppression task while in an fMRI scanner. Although we did not find any age-related differences in behavior, the young (N = 15) and older (N = 15) cohorts engaged qualitatively distinctive brain networks to complete the task. Younger participants engaged the predicted proactive control network, including the Left IPS. On the other hand, older participants simultaneously engaged both a proactive and a reactive network, but this was not a consequence of reduced network specificity as resting state functional connectivity was largely comparable in both age groups. Furthermore, improved behavioral performance for older adults was associated with increased resting state functional connectivity between these two networks. Overall, the results of this study suggest that age-related differences in the recruitment of a left lateralized ventral fronto-parietal network likely reflect the specific recruitment of reactive control mechanisms for distractor inhibition.
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Affiliation(s)
- Brandon K. Ashinoff
- Centre for Human Brain Health (CHBH), School of PsychologyUniversity of BirminghamEdgbastonUK
| | - Stephen D. Mayhew
- Centre for Human Brain Health (CHBH), School of PsychologyUniversity of BirminghamEdgbastonUK
| | - Carmel Mevorach
- Centre for Human Brain Health (CHBH), School of PsychologyUniversity of BirminghamEdgbastonUK
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Functional parcellation of the default mode network: a large-scale meta-analysis. Sci Rep 2020; 10:16096. [PMID: 32999307 PMCID: PMC7528067 DOI: 10.1038/s41598-020-72317-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 08/19/2020] [Indexed: 11/08/2022] Open
Abstract
The default mode network (DMN) consists of several regions that selectively interact to support distinct domains of cognition. Of the various sites that partake in DMN function, the posterior cingulate cortex (PCC), temporal parietal junction (TPJ), and medial prefrontal cortex (MPFC) are frequently identified as key contributors. Yet, it remains unclear whether these subcomponents of the DMN make unique contributions to specific cognitive processes and health conditions. To address this issue, we applied a meta-analytic parcellation approach used in prior work. This approach used the Neurosynth database and classification methods to quantify the association between PCC, TPJ, and MPFC activation and specific topics related to cognition and health (e.g., decision making and smoking). Our analyses replicated prior observations that the PCC, TPJ, and MPFC collectively support multiple cognitive functions such as decision making, memory, and awareness. To gain insight into the functional organization of each region, we parceled each region based on its coactivation pattern with the rest of the brain. This analysis indicated that each region could be further subdivided into functionally distinct subcomponents. Taken together, we further delineate DMN function by demonstrating the relative strengths of association among subcomponents across a range of cognitive processes and health conditions. A continued attentiveness to the specialization within the DMN allows future work to consider the nuances in sub-regional contributions necessary for healthy cognition, as well as create the potential for more targeted treatment protocols in various health conditions.
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60
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Yao Y, He H, Duan M, Li S, Li C, Chen X, Yao G, Chang X, Shu H, Wang H, Luo C. The Effects of Music Intervention on Pallidum-DMN Circuit of Schizophrenia. BIOMED RESEARCH INTERNATIONAL 2020; 2020:4107065. [PMID: 33015164 PMCID: PMC7525302 DOI: 10.1155/2020/4107065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2019] [Revised: 11/04/2019] [Accepted: 12/03/2019] [Indexed: 11/20/2022]
Abstract
Music intervention has been applied to improve symptoms of schizophrenic subjects as a complementary treatment in medicine. Although the psychiatric symptoms, especially for motivation and emotion, could be increased in schizophrenia, the underlying neural mechanisms remain poorly understood. We employed a longitudinal study to measure the alteration of striatum functional networks in schizophrenic subjects undergoing Mozart music listening using resting-state functional magnetic resonance imaging (fMRI). Forty-five schizophrenic inpatients were recruited and randomly assigned to two groups. Under the standard care with antipsychotic medication, one group received music intervention for 1 month and the other group is set as control. Both schizophrenic groups were compared to healthy subjects. Resting-state fMRI was acquired from schizophrenic subjects at baseline and after one-month music intervention and from healthy subjects at baseline. Striatum network was assessed through seed-based static and dynamic functional connectivity (FC) analyses. After music intervention, increased static FC was observed between pallidum and ventral hippocampus in schizophrenic subjects. Increased dynamic FCs were also found between pallidus and subregions of default mode network (DMN), including cerebellum crus and posterior cingulate cortex. Moreover, static pallidus-hippocampus FC increment was positively correlated with the improvement of negative symptoms in schizophrenic subjects. Together, these findings provided evidence that music intervention might have an effect on the FC of the striatum-DMN circuit and might be related to the remission of symptoms of schizophrenia.
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Affiliation(s)
- Yutong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shicai Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cheng Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xi Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Gang Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xin Chang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Haifeng Shu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hongming Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
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Xu T, Nenning KH, Schwartz E, Hong SJ, Vogelstein JT, Goulas A, Fair DA, Schroeder CE, Margulies DS, Smallwood J, Milham MP, Langs G. Cross-species functional alignment reveals evolutionary hierarchy within the connectome. Neuroimage 2020; 223:117346. [PMID: 32916286 PMCID: PMC7871099 DOI: 10.1016/j.neuroimage.2020.117346] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 08/04/2020] [Accepted: 08/31/2020] [Indexed: 11/22/2022] Open
Abstract
Evolution provides an important window into how cortical organization
shapes function and vice versa. The complex mosaic of changes in brain
morphology and functional organization that have shaped the mammalian cortex
during evolution, complicates attempts to chart cortical differences across
species. It limits our ability to fully appreciate how evolution has shaped our
brain, especially in systems associated with unique human cognitive capabilities
that lack anatomical homologues in other species. Here, we develop a
function-based method for cross-species alignment that enables the
quantification of homologous regions between humans and rhesus macaques, even
when their location is decoupled from anatomical landmarks. Critically, we find
cross-species similarity in functional organization reflects a gradient of
evolutionary change that decreases from unimodal systems and culminates with the
most pronounced changes in posterior regions of the default mode network
(angular gyrus, posterior cingulate and middle temporal cortices). Our findings
suggest that the establishment of the default mode network, as the apex of a
cognitive hierarchy, has changed in a complex manner during human evolution
– even within subnetworks.
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Affiliation(s)
- Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
| | - Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Ernst Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, MD, USA
| | - Alexandros Goulas
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Hamburg, Germany
| | - Damien A Fair
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
| | - Charles E Schroeder
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA; Departments of neurosurgery and Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) UMR 7225, Frontlab, Institut du Cerveau et de la Moelle Epinière, Paris, France
| | - Jonny Smallwood
- Department of Psychology, Queen's University, Kingston, Ontario, Canada; Psychology Department, University of York, York, UK
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
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62
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Chiou R, Humphreys GF, Lambon Ralph MA. Bipartite Functional Fractionation within the Default Network Supports Disparate Forms of Internally Oriented Cognition. Cereb Cortex 2020; 30:5484-5501. [PMID: 32494802 PMCID: PMC7472201 DOI: 10.1093/cercor/bhaa130] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 04/10/2020] [Accepted: 04/26/2020] [Indexed: 01/01/2023] Open
Abstract
Our understanding about the functionality of the brain's default network (DN) has significantly evolved over the past decade. Whereas traditional views define this network based on its suspension/disengagement during task-oriented behavior, contemporary accounts have characterized various situations wherein the DN actively contributes to task performance. However, it is unclear how different task-contexts drive componential regions of the DN to coalesce into a unitary network and fractionate into different subnetworks. Here we report a compendium of evidence that provides answers to these questions. Across multiple analyses, we found a striking dyadic structure within the DN in terms of the profiles of task-triggered fMRI response and effective connectivity, significantly extending beyond previous inferences based on meta-analysis and resting-state activities. In this dichotomy, one subset of DN regions prefers mental activities "interfacing with" perceptible events, while the other subset prefers activities "detached from" perceptible events. While both show a common "aversion" to sensory-motoric activities, their differential preferences manifest a subdivision that sheds light upon the taxonomy of the brain's memory systems. This dichotomy is consistent with proposals of a macroscale gradational structure spanning across the cerebrum. This gradient increases its representational complexity, from primitive sensory-motoric processing, through lexical-semantic representations, to elaborated self-generated thoughts.
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Affiliation(s)
- Rocco Chiou
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge UK
| | - Gina F Humphreys
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge UK
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63
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Bzdok D, Dunbar RIM. The Neurobiology of Social Distance. Trends Cogn Sci 2020; 24:717-733. [PMID: 32561254 PMCID: PMC7266757 DOI: 10.1016/j.tics.2020.05.016] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 05/20/2020] [Accepted: 05/29/2020] [Indexed: 12/11/2022]
Abstract
Never before have we experienced social isolation on such a massive scale as we have in response to coronavirus disease 2019 (COVID-19). However, we know that the social environment has a dramatic impact on our sense of life satisfaction and well-being. In times of distress, crisis, or disaster, human resilience depends on the richness and strength of social connections, as well as on active engagement in groups and communities. Over recent years, evidence emerging from various disciplines has made it abundantly clear: perceived social isolation (i.e., loneliness) may be the most potent threat to survival and longevity. We highlight the benefits of social bonds, the choreographies of bond creation and maintenance, as well as the neurocognitive basis of social isolation and its deep consequences for mental and physical health.
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Affiliation(s)
- Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada; Quebec Artificial Intelligence Institute (Mila), Montreal, Canada.
| | - Robin I M Dunbar
- Department of Experimental Psychology, University of Oxford, Oxford, UK.
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64
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Dohmatob E, Dumas G, Bzdok D. Dark control: The default mode network as a reinforcement learning agent. Hum Brain Mapp 2020; 41:3318-3341. [PMID: 32500968 PMCID: PMC7375062 DOI: 10.1002/hbm.25019] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 03/22/2020] [Accepted: 04/12/2020] [Indexed: 12/11/2022] Open
Abstract
The default mode network (DMN) is believed to subserve the baseline mental activity in humans. Its higher energy consumption compared to other brain networks and its intimate coupling with conscious awareness are both pointing to an unknown overarching function. Many research streams speak in favor of an evolutionarily adaptive role in envisioning experience to anticipate the future. In the present work, we propose a process model that tries to explain how the DMN may implement continuous evaluation and prediction of the environment to guide behavior. The main purpose of DMN activity, we argue, may be described by Markov decision processes that optimize action policies via value estimates through vicarious trial and error. Our formal perspective on DMN function naturally accommodates as special cases previous interpretations based on (a) predictive coding, (b) semantic associations, and (c) a sentinel role. Moreover, this process model for the neural optimization of complex behavior in the DMN offers parsimonious explanations for recent experimental findings in animals and humans.
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Affiliation(s)
- Elvis Dohmatob
- Criteo AI LabParisFrance
- INRIA, Parietal TeamSaclayFrance
- Neurospin, CEAGif‐sur‐YvetteFrance
| | - Guillaume Dumas
- Institut Pasteur, Human Genetics and Cognitive Functions UnitParisFrance
- CNRS UMR 3571 Genes, Synapses and Cognition, Institut PasteurParisFrance
- University Paris Diderot, Sorbonne Paris CitéParisFrance
- Centre de Bioinformatique, Biostatistique et Biologie IntégrativeParisFrance
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, School of Computer ScienceMcGill UniversityMontrealCanada
- Mila—Quebec Artificial Intelligence InstituteMontrealCanada
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65
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Romanella SM, Roe D, Paciorek R, Cappon D, Ruffini G, Menardi A, Rossi A, Rossi S, Santarnecchi E. Sleep, Noninvasive Brain Stimulation, and the Aging Brain: Challenges and Opportunities. Ageing Res Rev 2020; 61:101067. [PMID: 32380212 DOI: 10.1016/j.arr.2020.101067] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 02/26/2020] [Accepted: 04/04/2020] [Indexed: 02/06/2023]
Abstract
As we age, sleep patterns undergo severe modifications of their micro and macrostructure, with an overall lighter and more fragmented sleep structure. In general, interventions targeting sleep represent an excellent opportunity not only to maintain life quality in the healthy aging population, but also to enhance cognitive performance and, when pathology arises, to potentially prevent/slow down conversion from e.g. Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). Sleep abnormalities are, in fact, one of the earliest recognizable biomarkers of dementia, being also partially responsible for a cascade of cortical events that worsen dementia pathophysiology, including impaired clearance systems leading to build-up of extracellular amyloid-β (Aβ) peptide and intracellular hyperphosphorylated tau proteins. In this context, Noninvasive Brain Stimulation (NiBS) techniques, such as transcranial electrical stimulation (tES) and transcranial magnetic stimulation (TMS), may help investigate the neural substrates of sleep, identify sleep-related pathology biomarkers, and ultimately help patients and healthy elderly individuals to restore sleep quality and cognitive performance. However, brain stimulation applications during sleep have so far not been fully investigated in healthy elderly cohorts, nor tested in AD patients or other related dementias. The manuscript discusses the role of sleep in normal and pathological aging, reviewing available evidence of NiBS applications during both wakefulness and sleep in healthy elderly individuals as well as in MCI/AD patients. Rationale and details for potential future brain stimulation studies targeting sleep alterations in the aging brain are discussed, including enhancement of cognitive performance, overall quality of life as well as protein clearance.
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66
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Gordon EM, Laumann TO, Marek S, Raut RV, Gratton C, Newbold DJ, Greene DJ, Coalson RS, Snyder AZ, Schlaggar BL, Petersen SE, Dosenbach NUF, Nelson SM. Default-mode network streams for coupling to language and control systems. Proc Natl Acad Sci U S A 2020; 117:17308-17319. [PMID: 32632019 PMCID: PMC7382234 DOI: 10.1073/pnas.2005238117] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The human brain is organized into large-scale networks identifiable using resting-state functional connectivity (RSFC). These functional networks correspond with broad cognitive domains; for example, the Default-mode network (DMN) is engaged during internally oriented cognition. However, functional networks may contain hierarchical substructures corresponding with more specific cognitive functions. Here, we used individual-specific precision RSFC to test whether network substructures could be identified in 10 healthy human brains. Across all subjects and networks, individualized network subdivisions were more valid-more internally homogeneous and better matching spatial patterns of task activation-than canonical networks. These measures of validity were maximized at a hierarchical scale that contained ∼83 subnetworks across the brain. At this scale, nine DMN subnetworks exhibited topographical similarity across subjects, suggesting that this approach identifies homologous neurobiological circuits across individuals. Some DMN subnetworks matched known features of brain organization corresponding with cognitive functions. Other subnetworks represented separate streams by which DMN couples with other canonical large-scale networks, including language and control networks. Together, this work provides a detailed organizational framework for studying the DMN in individual humans.
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Affiliation(s)
- Evan M Gordon
- Veterans Integrated Service Network 17 Center of Excellence for Research on Returning War Veterans, US Department of Veterans Affairs, Waco, TX 76711;
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235
- Department of Psychology and Neuroscience, Baylor University, Waco, TX 76789
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Scott Marek
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Ryan V Raut
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL 60208
| | - Dillan J Newbold
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Deanna J Greene
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Rebecca S Coalson
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Bradley L Schlaggar
- Kennedy Krieger Institute, Baltimore, MD 21205
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205
- Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD 21205
| | - Steven E Petersen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110
- Department of Psychological & Brain Sciences, Washington University School of Medicine, St. Louis, MO 63110
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110
| | - Steven M Nelson
- Veterans Integrated Service Network 17 Center of Excellence for Research on Returning War Veterans, US Department of Veterans Affairs, Waco, TX 76711
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235
- Department of Psychology and Neuroscience, Baylor University, Waco, TX 76789
- Department of Psychiatry and Behavioral Science, Texas A&M Health Science Center, Bryan, TX 77807
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67
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Permutation inference for canonical correlation analysis. Neuroimage 2020; 220:117065. [PMID: 32603857 PMCID: PMC7573815 DOI: 10.1016/j.neuroimage.2020.117065] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/26/2020] [Accepted: 06/14/2020] [Indexed: 02/03/2023] Open
Abstract
Canonical correlation analysis (CCA) has become a key tool for population neuroimaging, allowing investigation of associations between many imaging and non-imaging measurements. As age, sex and other variables are often a source of variability not of direct interest, previous work has used CCA on residuals from a model that removes these effects, then proceeded directly to permutation inference. We show that a simple permutation test, as typically used to identify significant modes of shared variation on such data adjusted for nuisance variables, produces inflated error rates. The reason is that residualisation introduces dependencies among the observations that violate the exchangeability assumption. Even in the absence of nuisance variables, however, a simple permutation test for CCA also leads to excess error rates for all canonical correlations other than the first. The reason is that a simple permutation scheme does not ignore the variability already explained by previous canonical variables. Here we propose solutions for both problems: in the case of nuisance variables, we show that transforming the residuals to a lower dimensional basis where exchangeability holds results in a valid permutation test; for more general cases, with or without nuisance variables, we propose estimating the canonical correlations in a stepwise manner, removing at each iteration the variance already explained, while dealing with different number of variables in both sides. We also discuss how to address the multiplicity of tests, proposing an admissible test that is not conservative, and provide a complete algorithm for permutation inference for CCA.
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68
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Lanzoni L, Ravasio D, Thompson H, Vatansever D, Margulies D, Smallwood J, Jefferies E. The role of default mode network in semantic cue integration. Neuroimage 2020; 219:117019. [PMID: 32522664 PMCID: PMC7443705 DOI: 10.1016/j.neuroimage.2020.117019] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Revised: 05/29/2020] [Accepted: 06/02/2020] [Indexed: 11/14/2022] Open
Abstract
Recent accounts of large-scale cortical organisation suggest that the default mode network (DMN) is positioned at the top of a principal gradient, reflecting the separation between heteromodal and unimodal sensory-motor regions in patterns of connectivity and in geodesic distance along the cortical surface (Margulies et al., 2016). This isolation of DMN from external inputs might allow the integration of disparate sources of information that can constrain subsequent cognition. We tested this hypothesis by manipulating the degree to which semantic decisions for ambiguous words (e.g. jam) were constrained by preceding visual cues depicting relevant spatial contexts (e.g. supermarket or road) and/or facial emotions (e.g. happy vs. frustrated). We contrasted (i) the effects of a single preceding cue with a no-cue condition employing scrambled images, and (ii) convergent spatial and emotion cues with single cues. Single cues elicited stronger activation in the multiple demand network relative to no cues, consistent with the requirement to maintain information in working memory. The availability of two convergent cues elicited stronger activation within DMN regions (bilateral angular gyrus, middle temporal gyrus, medial prefrontal cortex, and posterior cingulate), even though behavioural performance was unchanged by cueing – consequently task difficulty is unlikely to account for the observed differences in brain activation. A regions-of-interest analysis along the unimodal-to-heteromodal principal gradient revealed maximal activation for the convergent cue condition at the heteromodal end, corresponding to the DMN. Our findings are consistent with the view that regions of DMN support states of information integration that constrain ongoing cognition and provide a framework for understanding the location of these effects at the heteromodal end of the principal gradient.
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Affiliation(s)
| | - Daniela Ravasio
- Department of Psychological Sciences, University of Bergamo, Italy
| | | | - Deniz Vatansever
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, PR China
| | - Daniel Margulies
- Institute Du Cerveau et de la Moelle épiniére (ICM), Paris, France
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69
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Williams AN, Ridgeway S, Postans M, Graham KS, Lawrence AD, Hodgetts CJ. The role of the pre-commissural fornix in episodic autobiographical memory and simulation. Neuropsychologia 2020; 142:107457. [PMID: 32259556 PMCID: PMC7322517 DOI: 10.1016/j.neuropsychologia.2020.107457] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 03/25/2020] [Accepted: 03/30/2020] [Indexed: 12/13/2022]
Abstract
Neuropsychological and functional magnetic resonance imaging evidence suggests that the ability to vividly remember our personal past, and imagine future scenarios, involves two closely connected regions: the hippocampus and ventromedial prefrontal cortex (vmPFC). Despite evidence of a direct anatomical connection from hippocampus to vmPFC, it is unknown whether hippocampal-vmPFC structural connectivity supports both past- and future-oriented episodic thinking. To address this, we applied a novel deterministic tractography protocol to diffusion-weighted magnetic resonance imaging (dMRI) data from a group of healthy young adult humans who undertook an adapted past-future autobiographical interview (portions of this data were published in Hodgetts et al., 2017a). This tractography protocol enabled distinct subdivisions of the fornix, detected previously in axonal tracer studies, to be reconstructed in vivo, namely the pre-commissural (connecting the hippocampus to vmPFC) and post-commissural (linking the hippocampus and medial diencephalon) fornix. As predicted, we found that inter-individual differences in pre-commissural - but not post-commissural - fornix microstructure (fractional anisotropy) were significantly correlated with the episodic richness of both past and future autobiographical narratives. Notably, these results held when controlling for non-episodic narrative content, verbal fluency, and grey matter volumes of the hippocampus and vmPFC. This study provides novel evidence that reconstructing events from one's personal past, and constructing possible future events, involves a distinct, structurally-instantiated hippocampal-vmPFC pathway.
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Affiliation(s)
- Angharad N Williams
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom; Max Planck Research Group Adaptive Memory, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103, Leipzig, Germany.
| | - Samuel Ridgeway
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
| | - Mark Postans
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
| | - Kim S Graham
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
| | - Andrew D Lawrence
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom.
| | - Carl J Hodgetts
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom; Department of Psychology, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, United Kingdom
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70
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Tompson SH, Kahn AE, Falk EB, Vettel JM, Bassett DS. Functional brain network architecture supporting the learning of social networks in humans. Neuroimage 2020; 210:116498. [PMID: 31917325 PMCID: PMC8740914 DOI: 10.1016/j.neuroimage.2019.116498] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 12/23/2019] [Accepted: 12/24/2019] [Indexed: 01/22/2023] Open
Abstract
Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. We found that participants learned the community structure of the networks, as evidenced by a slower reaction time when a trial moved between communities than when a trial moved within a community. Learning the community structure of social networks was also characterized by significantly greater functional connectivity of the hippocampus and temporoparietal junction when transitioning between communities than when transitioning within a community. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions for social networks than for non-social networks. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies.
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Affiliation(s)
- Steven H Tompson
- Human Sciences Campaign, U.S. Combat Capabilities Development Center Army Research Laboratory, Aberdeen, MD, 21005, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ari E Kahn
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Emily B Falk
- Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Marketing Department, Wharton School, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jean M Vettel
- Human Sciences Campaign, U.S. Combat Capabilities Development Center Army Research Laboratory, Aberdeen, MD, 21005, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA; Santa Fe Institute, Santa Fe, NM, 87501, USA.
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71
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Khan W, Amad A, Giampietro V, Werden E, De Simoni S, O'Muircheartaigh J, Westman E, O'Daly O, Williams SCR, Brodtmann A. The heterogeneous functional architecture of the posteromedial cortex is associated with selective functional connectivity differences in Alzheimer's disease. Hum Brain Mapp 2020; 41:1557-1572. [PMID: 31854490 PMCID: PMC7268042 DOI: 10.1002/hbm.24894] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 10/31/2019] [Accepted: 11/29/2019] [Indexed: 11/11/2022] Open
Abstract
The posteromedial cortex (PMC) is a key region involved in the development and progression of Alzheimer's disease (AD). Previous studies have demonstrated a heterogenous functional architecture of the region that is composed of discrete functional modules reflecting a complex pattern of functional connectivity. However, little is understood about the mechanisms underpinning this complex network architecture in neurodegenerative disease, and the differential vulnerability of connectivity-based subdivisions in the PMC to AD pathogenesis. Using a data-driven approach, we applied a constrained independent component analysis (ICA) on healthy adults from the Human Connectome Project to characterise the local functional connectivity patterns within the PMC, and its unique whole-brain functional connectivity. These distinct connectivity profiles were subsequently quantified in the Alzheimer's Disease Neuroimaging Initiative study, to examine functional connectivity differences in AD patients and cognitively normal (CN) participants, as well as the entire AD pathological spectrum. Our findings revealed decreased functional connectivity in the anterior precuneus, dorsal posterior cingulate cortex (PCC), and the central precuneus in AD patients compared to CN participants. Functional abnormalities in the dorsal PCC and central precuneus were also related to amyloid burden and volumetric hippocampal loss. Across the entire AD spectrum, functional connectivity of the central precuneus was associated with disease severity and specific deficits in memory and executive function. These findings provide new evidence showing that the PMC is selectively impacted in AD, with prominent network failures of the dorsal PCC and central precuneus underpinning the neurodegenerative and cognitive dysfunctions associated with the disease.
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Affiliation(s)
- Wasim Khan
- The Florey Institute for Neuroscience and Mental HealthUniversity of MelbourneMelbourneVictoriaAustralia
- Department of NeuroimagingInstitute of Psychiatry, Psychology, and Neuroscience (IoPPN), King's College LondonLondonUK
| | - Ali Amad
- Department of NeuroimagingInstitute of Psychiatry, Psychology, and Neuroscience (IoPPN), King's College LondonLondonUK
- Univ Lille Nord de France, CHRU de LilleLilleFrance
| | - Vincent Giampietro
- Department of NeuroimagingInstitute of Psychiatry, Psychology, and Neuroscience (IoPPN), King's College LondonLondonUK
| | - Emilio Werden
- The Florey Institute for Neuroscience and Mental HealthUniversity of MelbourneMelbourneVictoriaAustralia
| | - Sara De Simoni
- Computational, Cognitive and Clinical Neuroimaging LaboratoryImperial College London, Division of Brain Sciences, Hammersmith HospitalLondonUK
| | - Jonathan O'Muircheartaigh
- Department of NeuroimagingInstitute of Psychiatry, Psychology, and Neuroscience (IoPPN), King's College LondonLondonUK
- Department of Forensic and Neurodevelopmental SciencesInstitute of Psychiatry, Psychology, and Neuroscience (IoPPN), King's College LondonLondonUK
- Department of Perinatal Imaging and HealthSt. Thomas' Hospital, King's College LondonLondonUK
- MRC Centre for Neurodevelopmental DisordersKing's College LondonLondonUK
| | - Eric Westman
- Department of NeuroimagingInstitute of Psychiatry, Psychology, and Neuroscience (IoPPN), King's College LondonLondonUK
- Department of NeurobiologyCare Sciences and Society, Karolinska InstituteStockholmSweden
| | - Owen O'Daly
- Department of NeuroimagingInstitute of Psychiatry, Psychology, and Neuroscience (IoPPN), King's College LondonLondonUK
| | - Steve C. R. Williams
- Department of NeuroimagingInstitute of Psychiatry, Psychology, and Neuroscience (IoPPN), King's College LondonLondonUK
- NIHR Biomedical Research Centre for Mental HealthKing's College LondonLondonUK
- NIHR Biomedical Research Unit for DementiaKing's College LondonLondonUK
- MRC Centre for Neurodevelopmental DisordersKing's College LondonLondonUK
| | - Amy Brodtmann
- Austin Health, HeidelbergMelbourneVictoriaAustralia
- Eastern Clinical Research UnitMonash University, Box Hill HospitalMelbourneVictoriaAustralia
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72
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Wang HT, Smallwood J, Mourao-Miranda J, Xia CH, Satterthwaite TD, Bassett DS, Bzdok D. Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists. Neuroimage 2020; 216:116745. [PMID: 32278095 DOI: 10.1016/j.neuroimage.2020.116745] [Citation(s) in RCA: 111] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 02/12/2020] [Accepted: 03/12/2020] [Indexed: 12/12/2022] Open
Abstract
The 21st century marks the emergence of "big data" with a rapid increase in the availability of datasets with multiple measurements. In neuroscience, brain-imaging datasets are more commonly accompanied by dozens or hundreds of phenotypic subject descriptors on the behavioral, neural, and genomic level. The complexity of such "big data" repositories offer new opportunities and pose new challenges for systems neuroscience. Canonical correlation analysis (CCA) is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities. Importantly, CCA is well suited to describing relationships across multiple sets of data, such as in recently available big biomedical datasets. Our primer discusses the rationale, promises, and pitfalls of CCA.
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Affiliation(s)
- Hao-Ting Wang
- Department of Psychology, University of York, Heslington, York, United Kingdom; Sackler Center for Consciousness Science, University of Sussex, Brighton, United Kingdom.
| | - Jonathan Smallwood
- Department of Psychology, University of York, Heslington, York, United Kingdom
| | - Janaina Mourao-Miranda
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - Cedric Huchuan Xia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, 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; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Physics & Astronomy, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danilo Bzdok
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Germany; JARA-BRAIN, Jülich-Aachen Research Alliance, Germany; Parietal Team, INRIA, Neurospin, Bat 145, CEA Saclay, 91191, Gif-sur-Yvette, France; Department of Biomedical Engineering, Montreal Neurological Institute, Faculty of Medicine, McGill University, Montreal, Canada; Mila - Quebec Artificial Intelligence Institute, Canada.
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73
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Zillekens IC, Schliephake LM, Brandi ML, Schilbach L. A look at actions: direct gaze modulates functional connectivity of the right TPJ with an action control network. Soc Cogn Affect Neurosci 2020; 14:977-986. [PMID: 31593216 PMCID: PMC6917026 DOI: 10.1093/scan/nsz071] [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: 04/09/2019] [Revised: 06/11/2019] [Accepted: 08/19/2019] [Indexed: 11/12/2022] Open
Abstract
Social signals such as eye contact and motor actions are essential elements of social interactions. However, our knowledge about the interplay of gaze signals and the control of actions remains limited. In a group of 30 healthy participants, we investigated the effect of gaze (direct gaze vs averted) on behavioral and neural measures of action control as assessed by a spatial congruency task (spatially congruent vs incongruent button presses in response to gaze shifts). Behavioral results demonstrate that inter-individual differences in condition-specific incongruency costs were associated with autistic traits. While there was no interaction effect of gaze and action control on brain activation, in a context of incongruent responses to direct gaze shifts, a psychophysiological interaction analysis showed increased functional coupling between the right temporoparietal junction, a key region in gaze processing, and the inferior frontal gyri, which have been related to both social cognition and motor inhibition. Conversely, incongruency costs to averted gaze were reflected in increased connectivity with action control areas implicated in top-down attentional processes. Our findings indicate that direct gaze perception inter-individually modulates motor actions and enforces the functional integration of gaze-related social cognition and action control processes, thereby connecting functional elements of social interactions.
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Affiliation(s)
- Imme Christina Zillekens
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany.,International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | | | - Marie-Luise Brandi
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Leonhard Schilbach
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany.,International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany.,Department of Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany.,Outpatient and Day Clinic for Disorders of Social Interaction, Max Planck Institute of Psychiatry, Munich, Germany
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74
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Rodríguez-Cruces R, Bernhardt BC, Concha L. Multidimensional associations between cognition and connectome organization in temporal lobe epilepsy. Neuroimage 2020; 213:116706. [PMID: 32151761 DOI: 10.1016/j.neuroimage.2020.116706] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 01/14/2020] [Accepted: 03/03/2020] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVE Temporal lobe epilepsy (TLE) is known to affect large-scale structural networks and cognitive function in multiple domains. The study of complex relations between structural network organization and cognition requires comprehensive analytical methods and a shift towards multivariate techniques. Here, we sought to identify multidimensional associations between cognitive performance and structural network topology in TLE. METHODS We studied 34 drug-resistant adult TLE patients and 24 age- and sex-matched healthy controls. Participants underwent a comprehensive neurocognitive battery and multimodal MRI, allowing for large-scale connectomics, and morphological evaluation of subcortical and neocortical regions. Using canonical correlation analysis, we identified a multivariate mode that links cognitive performance to a brain structural network. Our approach was complemented by bootstrap-based hierarchical clustering to derive cognitive subtypes and associated patterns of macroscale connectome anomalies. RESULTS Both methodologies provided converging evidence for a close coupling between cognitive impairments across multiple domains and large-scale structural network compromise. Cognitive classes presented with an increasing gradient of abnormalities (increasing cortical and subcortical atrophy and less efficient white matter connectome organization in patients with increasing degrees of cognitive impairments). Notably, network topology characterized cognitive performance better than morphometric measures did. CONCLUSIONS Our multivariate approach emphasized a close coupling of cognitive dysfunction and large-scale network anomalies in TLE. Our findings contribute to understand the complexity of structural connectivity regulating the heterogeneous cognitive deficits found in epilepsy.
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Affiliation(s)
- Raúl Rodríguez-Cruces
- Universidad Nacional Autónoma de México, Instituto de Neurobiología, Querétaro, Querétaro, Mexico; MICA Laboratory, Montreal Neurological Institute and Hospital, Montreal, Canada.
| | - Boris C Bernhardt
- MICA Laboratory, Montreal Neurological Institute and Hospital, Montreal, Canada.
| | - Luis Concha
- Universidad Nacional Autónoma de México, Instituto de Neurobiología, Querétaro, Querétaro, Mexico.
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75
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Kiesow H, Dunbar RIM, Kable JW, Kalenscher T, Vogeley K, Schilbach L, Marquand AF, Wiecki TV, Bzdok D. 10,000 social brains: Sex differentiation in human brain anatomy. SCIENCE ADVANCES 2020; 6:eaaz1170. [PMID: 32206722 PMCID: PMC7080454 DOI: 10.1126/sciadv.aaz1170] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 12/18/2019] [Indexed: 06/07/2023]
Abstract
In human and nonhuman primates, sex differences typically explain much interindividual variability. Male and female behaviors may have played unique roles in the likely coevolution of increasing brain volume and more complex social dynamics. To explore possible divergence in social brain morphology between men and women living in different social environments, we applied probabilistic generative modeling to ~10,000 UK Biobank participants. We observed strong volume effects especially in the limbic system but also in regions of the sensory, intermediate, and higher association networks. Sex-specific brain volume effects in the limbic system were linked to the frequency and intensity of social contact, such as indexed by loneliness, household size, and social support. Across the processing hierarchy of neural networks, different conditions for social interplay may resonate in and be influenced by brain anatomy in sex-dependent ways.
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Affiliation(s)
- Hannah Kiesow
- Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | | | - Joseph W. Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Tobias Kalenscher
- Comparative Psychology, Institute of Experimental Psychology, Heinrich Heine University, Düsseldorf, Germany
| | - Kai Vogeley
- Department of Psychiatry, University Hospital Cologne, Cologne, Germany
- Institute for Neuroscience and Medicine—Cognitive Neuroscience (INM-3), Research Center Jülich, Wilhelm-Johnen Strasse, 52428 Jülich, Germany
| | - Leonhard Schilbach
- Independent Max Planck Research Group for Social Neuroscience, Max-Planck-Institute for Psychiatry, Munich, Germany
- Outpatient and Day Clinic for Disorders of Social Interaction, Max-Planck-Institute for Psychiatry, Munich, Germany
- Department of Psychiatry, Ludwig Maximilians Universität, Munich, Germany
| | - Andre F. Marquand
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, Netherlands
- Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London, De Crespigny Park, London, UK
| | | | - Danilo Bzdok
- Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany
- Translational Brain Medicine, Jülich Aachen Research Alliance (JARA), Aachen, Germany
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, McGill University, Montreal, Canada
- Mila-Quebec Artificial Intelligence Institute, Montreal, Canada
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76
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Clemens B, Derntl B, Smith E, Junger J, Neulen J, Mingoia G, Schneider F, Abel T, Bzdok D, Habel U. Predictive Pattern Classification Can Distinguish Gender Identity Subtypes from Behavior and Brain Imaging. Cereb Cortex 2020; 30:2755-2765. [PMID: 31999324 DOI: 10.1093/cercor/bhz272] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 08/02/2019] [Accepted: 09/12/2019] [Indexed: 11/13/2022] Open
Abstract
The exact neurobiological underpinnings of gender identity (i.e., the subjective perception of oneself belonging to a certain gender) still remain unknown. Combining both resting-state functional connectivity and behavioral data, we examined gender identity in cisgender and transgender persons using a data-driven machine learning strategy. Intrinsic functional connectivity and questionnaire data were obtained from cisgender (men/women) and transgender (trans men/trans women) individuals. Machine learning algorithms reliably detected gender identity with high prediction accuracy in each of the four groups based on connectivity signatures alone. The four normative gender groups were classified with accuracies ranging from 48% to 62% (exceeding chance level at 25%). These connectivity-based classification accuracies exceeded those obtained from a widely established behavioral instrument for gender identity. Using canonical correlation analyses, functional brain measurements and questionnaire data were then integrated to delineate nine canonical vectors (i.e., brain-gender axes), providing a multilevel window into the conventional sex dichotomy. Our dimensional gender perspective captures four distinguishable brain phenotypes for gender identity, advocating a biologically grounded reconceptualization of gender dimorphism. We hope to pave the way towards objective, data-driven diagnostic markers for gender identity and transgender, taking into account neurobiological and behavioral differences in an integrative modeling approach.
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Affiliation(s)
- Benjamin Clemens
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, 52074 Aachen, Germany.,Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Wilhelm-Johnen-Straße 52425 Jülich, Germany
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany.,Werner Reichardt Center for Integrative Neuroscience (CIN), University of Tübingen, Otfried-Müller-Str. 25, 72076 Tübingen, Germany.,LEAD Research Network, Walter-Simon-Straße 12, 72072 Tübingen, Germany
| | - Elke Smith
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, 52074 Aachen, Germany.,Department of Psychology, Biological Psychology, University of Cologne, Bernhard-Feilchenfeld-Str. 11, 50969 Cologne, Germany
| | - Jessica Junger
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, 52074 Aachen, Germany.,Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Wilhelm-Johnen-Straße 52425 Jülich, Germany
| | - Josef Neulen
- Department of Gynecological Endocrinology and Reproductive Medicine, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany
| | - Gianluca Mingoia
- Interdisciplinary Center for Clinical Research (IZKF), RWTH Aachen University, Faculty of Medicine, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Frank Schneider
- Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Wilhelm-Johnen-Straße 52425 Jülich, Germany.,University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Moorenstrasse 5, 40225 Düsseldorf, Germany
| | - Ted Abel
- Department of Biology, University of Pennsylvania, 433 South University Avenue, Philadelphia, PA 19104, United States.,Department of Neuroscience & Pharmacology, Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, 51 Newton Road 5-660 Bowen Science Building, Iowa City, IA 52242, United States
| | - Danilo Bzdok
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, 52074 Aachen, Germany.,Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Wilhelm-Johnen-Straße 52425 Jülich, Germany.,Parietal Team, INRIA/Neurospin Saclay, 1 rue Honoré d'Estienne d'Orves, Campus de l'École Polytechnique, 91120 Palaiseau, France.,Department of Biomedical Engineering, Faculty of Medicine, McGill University, 3775, rue University Montréal, QC H3A 2B4, Canada.,Montreal Institute for Learning Algorithms (MILA), 6666 St-Urbain, #200 Montreal, QC H2S 3H1, Canada
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, 52074 Aachen, Germany.,Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Wilhelm-Johnen-Straße 52425 Jülich, Germany
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77
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Wen X, Dong L, Chen J, Xiang J, Yang J, Li H, Liu X, Luo C, Yao D. Detecting the Information of Functional Connectivity Networks in Normal Aging Using Deep Learning From a Big Data Perspective. Front Neurosci 2020; 13:1435. [PMID: 32009894 PMCID: PMC6978665 DOI: 10.3389/fnins.2019.01435] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 12/19/2019] [Indexed: 01/09/2023] Open
Abstract
A resting-state functional connectivity (rsFC)-constructed functional network (FN) derived from functional magnetic resonance imaging (fMRI) data can effectively mine alterations in brain function during aging due to the non-invasive and effective advantages of fMRI. With global health research focusing on aging, several open fMRI datasets have been made available that combine deep learning with big data and are a new, promising trend and open issue for brain information detection in fMRI studies of brain aging. In this study, we proposed a new method based on deep learning from the perspective of big data, named Deep neural network (DNN) with Autoencoder (AE) pretrained Functional connectivity Analysis (DAFA), to deeply mine the important functional connectivity changes in fMRI during brain aging. First, using resting-state fMRI data from 421 subjects from the CamCAN dataset, functional connectivities were calculated using sliding window method, and the complex functional patterns were mined by an AE. Then, to increase the statistical power and reliability of the results, we used an AE-pretrained DNN to relabel the functional connectivities of each subject to classify them as belonging to the attributes of young or old individuals. A method called search-back analysis was performed to find alterations in brain function during aging according to the relabeled functional connectivities. Finally, behavioral data regarding fluid intelligence and response time were used to verify the revealed functional changes. Compared to traditional methods, DAFA revealed additional, important aged-related changes in FC patterns [e.g., FC connections within the default mode (DMN) and the sensorimotor and cingulo-opercular networks, as well as connections between the frontoparietal and cingulo-opercular networks, between the DMN and the frontoparietal/cingulo-opercular/sensorimotor/occipital/cerebellum networks, and between the sensorimotor and frontoparietal/cingulo-opercular networks], which were correlated to behavioral data. These findings demonstrated that the proposed DAFA method was superior to traditional FC-determining methods in discovering changes in brain functional connectivity during aging. In addition, it may be a promising method for exploring important information in other fMRI studies.
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Affiliation(s)
- Xin Wen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Yang
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hechun Li
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Liu
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
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78
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Lombardo MV, Eyler L, Moore A, Datko M, Carter Barnes C, Cha D, Courchesne E, Pierce K. Default mode-visual network hypoconnectivity in an autism subtype with pronounced social visual engagement difficulties. eLife 2019; 8:47427. [PMID: 31843053 PMCID: PMC6917498 DOI: 10.7554/elife.47427] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 11/08/2019] [Indexed: 12/17/2022] Open
Abstract
Social visual engagement difficulties are hallmark early signs of autism (ASD) and are easily quantified using eye tracking methods. However, it is unclear how these difficulties are linked to atypical early functional brain organization in ASD. With resting state fMRI data in a large sample of ASD toddlers and other non-ASD comparison groups, we find ASD-related functional hypoconnnectivity between ‘social brain’ circuitry such as the default mode network (DMN) and visual and attention networks. An eye tracking-identified ASD subtype with pronounced early social visual engagement difficulties (GeoPref ASD) is characterized by marked DMN-occipito-temporal cortex (OTC) hypoconnectivity. Increased DMN-OTC hypoconnectivity is also related to increased severity of social-communication difficulties, but only in GeoPref ASD. Early and pronounced social-visual circuit hypoconnectivity is a key underlying neurobiological feature describing GeoPref ASD and may be critical for future social-communicative development and represent new treatment targets for early intervention in these individuals. Many parents of children with autism spectrum disorder (ASD) spot the first signs when their child is still a toddler, by noticing that their child is less interested than other toddlers in people and in social play. These early differences in behavior can have long-term implications for brain development. The brains of toddlers with little interest in social stimuli will receive less social input than those of other toddlers. This will make it even harder for the brain to develop the circuits required to support social skills. But even among children with ASD, there are large differences in children's interest in the social world. One way of measuring these differences is to track eye movements. Lombardo et al. presented toddlers with and without ASD with images of moving colorful geometric shapes next to videos of dancing children. The majority of toddlers, including most of those with ASD, spent more time looking at the children than the shapes. But about 20% of the toddlers with ASD spent most of their time looking at the shapes. These toddlers also had the most severe social symptoms. To find out why, Lombardo et al. measured the toddlers' brain activity while they slept. During sleep, or when at rest, the brain shows stereotyped patterns of activity. Groups of brain regions that work together – such as those involved in vision – fire in synchrony. Lombardo et al. found that toddlers who preferred looking at shapes over people showed different patterns of brain activity while asleep compared to other children. In the toddlers who preferred shapes, brain networks involved in social skills were less likely to coordinate their activity with networks that support vision and attention. These findings suggest there may be multiple subtypes of ASD, with different symptoms resulting from different patterns of brain activity. At present, all children who receive a diagnosis of ASD receive much the same behavioral therapy. But in the future, studies of brain networks could allow children to receive more specific diagnoses. This could in turn lead to more effective and personalized treatments.
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Affiliation(s)
- Michael V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy.,Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Lisa Eyler
- Department of Psychiatry, University of California, San Diego, San Diego, United States.,VISN 22 Mental Illness Research, Education, and Clinical Center, VA San Diego Healthcare System, San Diego, United States
| | - Adrienne Moore
- Autism Center of Excellence, Department of Neuroscience, University of California, San Diego, San Diego, United States
| | - Michael Datko
- Autism Center of Excellence, Department of Neuroscience, University of California, San Diego, San Diego, United States
| | - Cynthia Carter Barnes
- Autism Center of Excellence, Department of Neuroscience, University of California, San Diego, San Diego, United States
| | - Debra Cha
- Autism Center of Excellence, Department of Neuroscience, University of California, San Diego, San Diego, United States
| | - Eric Courchesne
- Autism Center of Excellence, Department of Neuroscience, University of California, San Diego, San Diego, United States
| | - Karen Pierce
- Autism Center of Excellence, Department of Neuroscience, University of California, San Diego, San Diego, United States
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79
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Murphy C, Poerio G, Sormaz M, Wang HT, Vatansever D, Allen M, Margulies DS, Jefferies E, Smallwood J. Hello, is that me you are looking for? A re-examination of the role of the DMN in social and self relevant aspects of off-task thought. PLoS One 2019; 14:e0216182. [PMID: 31697677 PMCID: PMC6837379 DOI: 10.1371/journal.pone.0216182] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 10/15/2019] [Indexed: 11/21/2022] Open
Abstract
Neural activity within the default mode network (DMN) is widely assumed to relate to processing during off-task states, however it remains unclear whether this association emerges from a shared role in self or social content that is common in these conditions. In the current study, we examine the possibility that the role of the DMN in ongoing thought emerges from contributions to specific features of off-task experience such as self-relevant or social content. A group of participants described their experiences while performing a laboratory task over a period of days. In a different session, neural activity was measured while participants performed Self/Other judgements (e.g., Does the word ‘Honest’ apply to you (Self condition) or Barack Obama (Other condition)). Despite the prominence of social and personal content in off-task reports, there was no association with neural activity during off-task trait adjective judgements. Instead, during both Self and Other judgements we found recruitment of caudal posterior cingulate cortex—a core DMN hub—was above baseline for individuals whose laboratory experiences were characterised as detailed. These data provide little support for a role of the DMN in self or other content in the off-task state and instead suggest a role in how on-going thought is represented.
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Affiliation(s)
- Charlotte Murphy
- Department of Psychology, University of York, York England, United Kingdom
- * E-mail:
| | - Giulia Poerio
- Department of Psychology, The University of Sheffield, Sheffield, England, United Kingdom
| | - Mladen Sormaz
- Department of Psychology, University of York, York England, United Kingdom
| | - Hao-Ting Wang
- Department of Psychology, University of York, York England, United Kingdom
| | - Deniz Vatansever
- Department of Psychology, University of York, York England, United Kingdom
| | - Micah Allen
- Cambridge Psychiatry, Cambridge University, Cambridge, United Kingdom
| | | | | | - Jonathan Smallwood
- Department of Psychology, University of York, York England, United Kingdom
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80
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Ngo GH, Eickhoff SB, Nguyen M, Sevinc G, Fox PT, Spreng RN, Yeo BTT. Beyond consensus: Embracing heterogeneity in curated neuroimaging meta-analysis. Neuroimage 2019; 200:142-158. [PMID: 31229658 PMCID: PMC6703957 DOI: 10.1016/j.neuroimage.2019.06.037] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 05/17/2019] [Accepted: 06/17/2019] [Indexed: 01/08/2023] Open
Abstract
Coordinate-based meta-analysis can provide important insights into mind-brain relationships. A popular approach for curated small-scale meta-analysis is activation likelihood estimation (ALE), which identifies brain regions consistently activated across a selected set of experiments, such as within a functional domain or mental disorder. ALE can also be utilized in meta-analytic co-activation modeling (MACM) to identify brain regions consistently co-activated with a seed region. Therefore, ALE aims to find consensus across experiments, treating heterogeneity across experiments as noise. However, heterogeneity within an ALE analysis of a functional domain might indicate the presence of functional sub-domains. Similarly, heterogeneity within a MACM analysis might indicate the involvement of a seed region in multiple co-activation patterns that are dependent on task contexts. Here, we demonstrate the use of the author-topic model to automatically determine if heterogeneities within ALE-type meta-analyses can be robustly explained by a small number of latent patterns. In the first application, the author-topic modeling of experiments involving self-generated thought (N = 179) revealed cognitive components fractionating the default network. In the second application, the author-topic model revealed that the left inferior frontal junction (IFJ) participated in multiple task-dependent co-activation patterns (N = 323). Furthermore, the author-topic model estimates compared favorably with spatial independent component analysis in both simulation and real data. Overall, the results suggest that the author-topic model is a flexible tool for exploring heterogeneity in ALE-type meta-analyses that might arise from functional sub-domains, mental disorder subtypes or task-dependent co-activation patterns. Code for this study is publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/meta-analysis/Ngo2019_AuthorTopic).
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Affiliation(s)
- Gia H Ngo
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Minh Nguyen
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | - Gunes Sevinc
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; South Texas Veterans Health Care System, San Antonio, TX, USA
| | - R Nathan Spreng
- Laboratory of Brain and Cognition, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Departments of Psychiatry and Psychology, McGill University, Montreal, QC, Canada
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore; NUS Graduate School for Integrated Sciences and Engineering, National University of Singapore, Singapore.
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81
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Alves PN, Foulon C, Karolis V, Bzdok D, Margulies DS, Volle E, Thiebaut de Schotten M. An improved neuroanatomical model of the default-mode network reconciles previous neuroimaging and neuropathological findings. Commun Biol 2019; 2:370. [PMID: 31633061 PMCID: PMC6787009 DOI: 10.1038/s42003-019-0611-3] [Citation(s) in RCA: 170] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 09/16/2019] [Indexed: 12/16/2022] Open
Abstract
The brain is constituted of multiple networks of functionally correlated brain areas, out of which the default-mode network (DMN) is the largest. Most existing research into the DMN has taken a corticocentric approach. Despite its resemblance with the unitary model of the limbic system, the contribution of subcortical structures to the DMN may be underappreciated. Here, we propose a more comprehensive neuroanatomical model of the DMN including subcortical structures such as the basal forebrain, cholinergic nuclei, anterior and mediodorsal thalamic nuclei. Additionally, tractography of diffusion-weighted imaging was employed to explore the structural connectivity, which revealed that the thalamus and basal forebrain are of central importance for the functioning of the DMN. The contribution of these neurochemically diverse brain nuclei reconciles previous neuroimaging with neuropathological findings in diseased brains and offers the potential for identifying a conserved homologue of the DMN in other mammalian species.
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Affiliation(s)
- Pedro Nascimento Alves
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
- Department of Neurosciences and Mental Health, Neurology, Hospital de Santa Maria, CHULN, Lisbon, Portugal
- Language Research Laboratory, Faculty of Medicine, Universidade de Lisboa, Lisbon, Portugal
| | - Chris Foulon
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
- Computational Neuroimaging Laboratory, Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, Austin, TX USA
| | - Vyacheslav Karolis
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
- FMRIB centre, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Danilo Bzdok
- INRIA, Parietal Team, Saclay, France
- Neurospin, CEA, Gif-sur-Yvette, France
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany
| | - Daniel S. Margulies
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
| | - Emmanuelle Volle
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
- Centre de Neuroimagerie de Recherche CENIR, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
- Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France
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82
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Li J, Bolt T, Bzdok D, Nomi JS, Yeo BTT, Spreng RN, Uddin LQ. Topography and behavioral relevance of the global signal in the human brain. Sci Rep 2019; 9:14286. [PMID: 31582792 PMCID: PMC6776616 DOI: 10.1038/s41598-019-50750-8] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 09/18/2019] [Indexed: 11/09/2022] Open
Abstract
The global signal in resting-state functional MRI data is considered to be dominated by physiological noise and artifacts, yet a growing literature suggests that it also carries information about widespread neural activity. The biological relevance of the global signal remains poorly understood. Applying principal component analysis to a large neuroimaging dataset, we found that individual variation in global signal topography recapitulates well-established patterns of large-scale functional brain networks. Using canonical correlation analysis, we delineated relationships between individual differences in global signal topography and a battery of phenotypes. The first canonical variate of the global signal, resembling the frontoparietal control network, was significantly related to an axis of positive and negative life outcomes and psychological function. These results suggest that the global signal contains a rich source of information related to trait-level cognition and behavior. This work has significant implications for the contentious debate over artifact removal practices in neuroimaging.
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Affiliation(s)
- Jingwei Li
- ECE, CIRC, N.1 & MNP, National University of Singapore, Singapore, Singapore
| | - Taylor Bolt
- Data Science Division, Gallup, Atlanta, GA, USA
| | - Danilo Bzdok
- Department of Psychiatry, Psychotherapy and Psychosomatics, Aachen University, Aachen, Germany.,JARA, Translational Brain Medicine, Aachen, Germany.,Parietal Team, INRIA, Neurospin, bat 145, CEA Saclay, 91191, Gif-sur-Yvette, France
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - B T Thomas Yeo
- ECE, CIRC, N.1 & MNP, National University of Singapore, Singapore, Singapore
| | - R Nathan Spreng
- Laboratory of Brain and Cognition, Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada. .,Departments of Psychiatry and Psychology, McGill University, Montreal, QC, Canada.
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA. .,Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, USA.
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83
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The brain’s default network: updated anatomy, physiology and evolving insights. Nat Rev Neurosci 2019; 20:593-608. [DOI: 10.1038/s41583-019-0212-7] [Citation(s) in RCA: 421] [Impact Index Per Article: 84.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/31/2019] [Indexed: 12/15/2022]
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84
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Ho NSP, Wang X, Vatansever D, Margulies DS, Bernhardt B, Jefferies E, Smallwood J. Individual variation in patterns of task focused, and detailed, thought are uniquely associated within the architecture of the medial temporal lobe. Neuroimage 2019; 202:116045. [PMID: 31349068 DOI: 10.1016/j.neuroimage.2019.116045] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 06/26/2019] [Accepted: 07/21/2019] [Indexed: 11/29/2022] Open
Abstract
Understanding the neural processes that support different patterns of ongoing thought is an important goal of contemporary cognitive neuroscience. Early accounts assumed the default mode network (DMN) was especially important for conscious attention to task-irrelevant/personally relevant materials. However, simple task-negative accounts of the DMN are incompatible with more recent evidence that neural patterns within the system can be related to ongoing processing during active task states. To better characterise the contribution of the DMN to ongoing thought, we conducted a cross-sectional analysis of the relationship between the structural organisation of the brain, as indexed by cortical thickness, and patterns of experience, identified using experience sampling in the cognitive laboratory. In a sample of 181 healthy individuals (mean age 20 years, 117 females) we identified an association between cortical thickness in the anterior parahippocampus and patterns of task focused thought, as well as an adjacent posterior region in which cortical thickness was associated with experiences with higher levels of subjective detail. Both regions fell within regions of medial temporal lobe associated with the DMN, yet varied in their functional connectivity: the time series of signals in the 'on-task' region were more correlated with systems important for external task-relevant processing (as determined by meta-analysis) including the dorsal and ventral attention, and fronto-parietal networks. In contrast, connectivity within the region linked to subjective 'detail' was more correlated with the medial core of the DMN (posterior cingulate and the medial pre-frontal cortex) and regions of primary visual cortex. These results provide cross-sectional evidence that confirms a role of the DMN in how detailed experiences are and so provide further evidence that the role of this system in experience is not simply task-irrelevant. Our results also highlight processes within the medial temporal lobe, and their interactions with other regions of cortex, as important in determining multiple aspects of how human cognition unfolds.
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Affiliation(s)
| | - Xiuyi Wang
- Department of Psychology, University of York, England, UK
| | - Deniz Vatansever
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, PR China
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) UMR 7225, Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Boris Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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85
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Bzdok D, Nichols TE, Smith SM. Towards Algorithmic Analytics for Large-scale Datasets. NAT MACH INTELL 2019; 1:296-306. [PMID: 31701088 PMCID: PMC6837858 DOI: 10.1038/s42256-019-0069-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 06/05/2019] [Indexed: 11/09/2022]
Abstract
The traditional goals of quantitative analytics cherish simple, transparent models to generate explainable insights. Large-scale data acquisition, enabled for instance by brain scanning and genomic profiling with microarray-type techniques, has prompted a wave of statistical inventions and innovative applications. Modern analysis approaches 1) tame large variable arrays capitalizing on regularization and dimensionality-reduction strategies, 2) are increasingly backed up by empirical model validations rather than justified by mathematical proofs, 3) will compare against and build on open data and consortium repositories, as well as 4) often embrace more elaborate, less interpretable models in order to maximize prediction accuracy. Here we review these trends in learning from "big data" and illustrate examples from imaging neuroscience.
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Affiliation(s)
- Danilo Bzdok
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52072 Aachen, Germany
- JARA, Translational Brain Medicine, Aachen, Germany
- Parietal Team, INRIA, Neurospin, bat 145, CEA Saclay, 91191 Gif-sur-Yvette, France
| | - Thomas E Nichols
- Wellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK
- Big Data Institute, University of Oxford, Oxford, UK
| | - Stephen M Smith
- Wellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK
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86
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Consciousness in a multilevel architecture: What causes the lateralization of effective connectivity under resting state? Conscious Cogn 2019; 73:102755. [PMID: 31154020 DOI: 10.1016/j.concog.2019.05.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 05/13/2019] [Indexed: 11/22/2022]
Abstract
Here we present our answers to a critical commentary by Elkhonon Goldberg on our recent publication (Velichkovsky et al., 2018). To avoid discussions about novelty effects in the human brain activity and memory processes, we narrowed down this response to a reanalysis of our data along the lines proposed in the commentary, namely to comparing the effective links between symmetrical brain structures during the first and the last parts of a prolonged resting-state fMRI experiment. We also tested for sex differences in our results and checked for a stability of top-down interactions during the course of experiment because learning is often expressed in the weakening of upper level control over low-level mechanisms. Our attempts to test the predictions based on the novelty hypothesis has led to mixed results suggesting that the discovered right-to-left dominance of causal connections at rest may have a deeper origin than supposed in the Goldberg's commentary.
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87
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Braga RM, Van Dijk KRA, Polimeni JR, Eldaief MC, Buckner RL. Parallel distributed networks resolved at high resolution reveal close juxtaposition of distinct regions. J Neurophysiol 2019; 121:1513-1534. [PMID: 30785825 PMCID: PMC6485740 DOI: 10.1152/jn.00808.2018] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Examination of large-scale distributed networks within the individual reveals details of cortical network organization that are absent in group-averaged studies. One recent discovery is that a distributed transmodal network, often referred to as the “default network,” comprises two closely interdigitated networks, only one of which is coupled to posterior parahippocampal cortex. Not all studies of individuals have identified the same networks, and questions remain about the degree to which the two networks are separate, particularly within regions hypothesized to be interconnected hubs. In this study we replicate the observation of network separation across analytical (seed-based connectivity and parcellation) and data projection (volume and surface) methods in two individuals each scanned 31 times. Additionally, three individuals were examined with high-resolution (7T; 1.35 mm) functional magnetic resonance imaging to gain further insight into the anatomical details. The two networks were identified with separate regions localized to adjacent portions of the cortical ribbon, sometimes inside the same sulcus. Midline regions previously implicated as hubs revealed near complete spatial separation of the two networks, displaying a complex spatial topography in the posterior cingulate and precuneus. The network coupled to parahippocampal cortex also revealed a separate region directly within the hippocampus, at or near the subiculum. These collective results support that the default network is composed of at least two spatially juxtaposed networks. Fine spatial details and juxtapositions of the two networks can be identified within individuals at high resolution, providing insight into the network organization of association cortex and placing further constraints on interpretation of group-averaged neuroimaging data. NEW & NOTEWORTHY Recent evidence has emerged that canonical large-scale networks such as the “default network” fractionate into parallel distributed networks when defined within individuals. This research uses high-resolution imaging to show that the networks possess juxtapositions sometimes evident inside the same sulcus and within regions that have been previously hypothesized to be network hubs. Distinct circumscribed regions of one network were also resolved in the hippocampal formation, at or near the parahippocampal cortex and subiculum.
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Affiliation(s)
- Rodrigo M Braga
- Department of Psychology, Center for Brain Science, Harvard University , Cambridge, Massachusetts.,The Computational, Cognitive & Clinical Neuroimaging Laboratory, Hammersmith Hospital Campus, Imperial College London , London , United Kingdom.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Koene R A Van Dijk
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School , Boston, Massachusetts.,Division of Health Sciences and Technology, Massachusetts Institute of Technology , Cambridge, Massachusetts
| | - Mark C Eldaief
- Department of Psychology, Center for Brain Science, Harvard University , Cambridge, Massachusetts.,Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University , Cambridge, Massachusetts.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School , Boston, Massachusetts.,Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts
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88
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Leech R, Smallwood J. The posterior cingulate cortex: Insights from structure and function. HANDBOOK OF CLINICAL NEUROLOGY 2019; 166:73-85. [PMID: 31731926 DOI: 10.1016/b978-0-444-64196-0.00005-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
The posterior cingulate cortex (PCC) (Brodmann areas 23/31) is one of the least well-understood regions of the cortex. The PCC has very high levels of metabolic consumption, and network analyses of functional and structural data suggest it is a core hub in the human connectome; however, contemporary neuroscience lacks a clear account of its functional significance. Consequently, many studies over the last decade have focused on understanding the role this region plays in cognition, particularly given its apparent tendency to deactivate during demanding external tasks. Consistent with the cytoarchitecture, recent work, leveraging complex analytical approaches, highlight that the connections the PCC forms with other regions are heterogeneous, going beyond a single network, while recent studies of its function highlight a role in a wide range of complex forms of cognition including memory, navigation, and narrative comprehension. This constellation of observations highlights a role for PCC in a set of cognitive processes that are supported by internal representations but may lack a common type of representational content. Together, these structural and functional studies contribute to an emerging view of the PCC as contributing to how cognition unfolds rather than what it is focused on.
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Affiliation(s)
- Robert Leech
- Department of Neuroimaging, King's College London, London, United Kingdom.
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