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Rolls ET, Yan X, Deco G, Zhang Y, Jousmaki V, Feng J. A ventromedial visual cortical 'Where' stream to the human hippocampus for spatial scenes revealed with magnetoencephalography. Commun Biol 2024; 7:1047. [PMID: 39183244 PMCID: PMC11345434 DOI: 10.1038/s42003-024-06719-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 08/12/2024] [Indexed: 08/27/2024] Open
Abstract
The primate including the human hippocampus implicated in episodic memory and navigation represents a spatial view, very different from the place representations in rodents. To understand this system in humans, and the computations performed, the pathway for this spatial view information to reach the hippocampus was analysed in humans. Whole-brain effective connectivity was measured with magnetoencephalography between 30 visual cortical regions and 150 other cortical regions using the HCP-MMP1 atlas in 21 participants while performing a 0-back scene memory task. In a ventromedial visual stream, V1-V4 connect to the ProStriate region where the retrosplenial scene area is located. The ProStriate region has connectivity to ventromedial visual regions VMV1-3 and VVC. These ventromedial regions connect to the medial parahippocampal region PHA1-3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal regions have effective connectivity to the entorhinal cortex, perirhinal cortex, and hippocampus. In contrast, when viewing faces, the effective connectivity was more through a ventrolateral visual cortical stream via the fusiform face cortex to the inferior temporal visual cortex regions TE2p and TE2a. A ventromedial visual cortical 'Where' stream to the hippocampus for spatial scenes was supported by diffusion topography in 171 HCP participants at 7 T.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK.
- Department of Computer Science, University of Warwick, Coventry, UK.
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.
| | - Xiaoqian Yan
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Gustavo Deco
- Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona, Spain
| | - Yi Zhang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Veikko Jousmaki
- Aalto NeuroImaging, Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry, UK
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
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Rolls ET, Deco G, Huang CC, Feng J. The connectivity of the human frontal pole cortex, and a theory of its involvement in exploit versus explore. Cereb Cortex 2024; 34:bhad416. [PMID: 37991264 DOI: 10.1093/cercor/bhad416] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/23/2023] Open
Abstract
The frontal pole is implicated in humans in whether to exploit resources versus explore alternatives. Effective connectivity, functional connectivity, and tractography were measured between six human frontal pole regions and for comparison 13 dorsolateral and dorsal prefrontal cortex regions, and the 360 cortical regions in the Human Connectome Project Multi-modal-parcellation atlas in 171 HCP participants. The frontal pole regions have effective connectivity with Dorsolateral Prefrontal Cortex regions, the Dorsal Prefrontal Cortex, both implicated in working memory; and with the orbitofrontal and anterior cingulate cortex reward/non-reward system. There is also connectivity with temporal lobe, inferior parietal, and posterior cingulate regions. Given this new connectivity evidence, and evidence from activations and damage, it is proposed that the frontal pole cortex contains autoassociation attractor networks that are normally stable in a short-term memory state, and maintain stability in the other prefrontal networks during stable exploitation of goals and strategies. However, if an input from the orbitofrontal or anterior cingulate cortex that expected reward, non-reward, or punishment is received, this destabilizes the frontal pole and thereby other prefrontal networks to enable exploration of competing alternative goals and strategies. The frontal pole connectivity with reward systems may be key in exploit versus explore.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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Rolls ET, Deco G, Zhang Y, Feng J. Hierarchical organization of the human ventral visual streams revealed with magnetoencephalography. Cereb Cortex 2023; 33:10686-10701. [PMID: 37689834 DOI: 10.1093/cercor/bhad318] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/17/2023] [Accepted: 08/17/2023] [Indexed: 09/11/2023] Open
Abstract
The hierarchical organization between 25 ventral stream visual cortical regions and 180 cortical regions was measured with magnetoencephalography using the Human Connectome Project Multimodal Parcellation atlas in 83 Human Connectome Project participants performing a visual memory task. The aim was to reveal the hierarchical organization using a whole-brain model based on generative effective connectivity with this fast neuroimaging method. V1-V4 formed a first group of interconnected regions. Especially V4 had connectivity to a ventrolateral visual stream: V8, the fusiform face cortex, and posterior inferior temporal cortex PIT. These regions in turn had effectivity connectivity to inferior temporal cortex visual regions TE2p and TE1p. TE2p and TE1p then have connectivity to anterior temporal lobe regions TE1a, TE1m, TE2a, and TGv, which are multimodal. In a ventromedial visual stream, V1-V4 connect to ventromedial regions VMV1-3 and VVC. VMV1-3 and VVC connect to the medial parahippocampal gyrus PHA1-3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal PHA1-3 regions have connectivity to the hippocampal system regions the perirhinal cortex, entorhinal cortex, and hippocampus. These effective connectivities of two ventral visual cortical streams measured with magnetoencephalography provide support to the hierarchical organization of brain systems measured with fMRI, and new evidence on directionality.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Yi Zhang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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Wu X, Guo Y, Xue J, Dong Y, Sun Y, Wang B, Xiang J, Liu Y. Abnormal and Changing Information Interaction in Adults with Attention-Deficit/Hyperactivity Disorder Based on Network Motifs. Brain Sci 2023; 13:1331. [PMID: 37759932 PMCID: PMC10526475 DOI: 10.3390/brainsci13091331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/27/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Network motif analysis approaches provide insights into the complexity of the brain's functional network. In recent years, attention-deficit/hyperactivity disorder (ADHD) has been reported to result in abnormal information interactions in macro- and micro-scale functional networks. However, most existing studies remain limited due to potentially ignoring meso-scale topology information. To address this gap, we aimed to investigate functional motif patterns in ADHD to unravel the underlying information flow and analyze motif-based node roles to characterize the different information interaction methods for identifying the abnormal and changing lesion sites of ADHD. The results showed that the interaction functions of the right hippocampus and the right amygdala were significantly increased, which could lead patients to develop mood disorders. The information interaction of the bilateral thalamus changed, influencing and modifying behavioral results. Notably, the capability of receiving information in the left inferior temporal and the right lingual gyrus decreased, which may cause difficulties for patients in processing visual information in a timely manner, resulting in inattention. This study revealed abnormal and changing information interactions based on network motifs, providing important evidence for understanding information interactions at the meso-scale level in ADHD patients.
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Affiliation(s)
- Xubin Wu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yuxiang Guo
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China;
| | - Jiayue Xue
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yanqing Dong
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yumeng Sun
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yi Liu
- Department of Anesthesiology, Shanxi Province Cancer Hospital, Taiyuan 030013, China
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Rolls ET, Rauschecker JP, Deco G, Huang CC, Feng J. Auditory cortical connectivity in humans. Cereb Cortex 2023; 33:6207-6227. [PMID: 36573464 PMCID: PMC10422925 DOI: 10.1093/cercor/bhac496] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/27/2022] [Accepted: 11/29/2022] [Indexed: 12/28/2022] Open
Abstract
To understand auditory cortical processing, the effective connectivity between 15 auditory cortical regions and 360 cortical regions was measured in 171 Human Connectome Project participants, and complemented with functional connectivity and diffusion tractography. 1. A hierarchy of auditory cortical processing was identified from Core regions (including A1) to Belt regions LBelt, MBelt, and 52; then to PBelt; and then to HCP A4. 2. A4 has connectivity to anterior temporal lobe TA2, and to HCP A5, which connects to dorsal-bank superior temporal sulcus (STS) regions STGa, STSda, and STSdp. These STS regions also receive visual inputs about moving faces and objects, which are combined with auditory information to help implement multimodal object identification, such as who is speaking, and what is being said. Consistent with this being a "what" ventral auditory stream, these STS regions then have effective connectivity to TPOJ1, STV, PSL, TGv, TGd, and PGi, which are language-related semantic regions connecting to Broca's area, especially BA45. 3. A4 and A5 also have effective connectivity to MT and MST, which connect to superior parietal regions forming a dorsal auditory "where" stream involved in actions in space. Connections of PBelt, A4, and A5 with BA44 may form a language-related dorsal stream.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China
| | - Josef P Rauschecker
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057, USA
- Institute for Advanced Study, Technical University, Munich, Germany
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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Bianco MG, Duggento A, Nigro S, Conti A, Toschi N, Passamonti L. Heritability of human "directed" functional connectome. Brain Behav 2023; 13:e2839. [PMID: 36989125 PMCID: PMC10175995 DOI: 10.1002/brb3.2839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 10/03/2022] [Accepted: 11/15/2022] [Indexed: 03/30/2023] Open
Abstract
INTRODUCTION The functional connectivity patterns in the brain are highly heritable; however, it is unclear how genetic factors influence the directionality of such "information flows." Studying the "directionality" of the brain functional connectivity and assessing how heritability modulates it can improve our understanding of the human connectome. METHODS Here, we investigated the heritability of "directed" functional connections using a state-space formulation of Granger causality (GC), in conjunction with blind deconvolution methods accounting for local variability in the hemodynamic response function. Such GC implementation is ideal to explore the directionality of functional interactions across a large number of networks. Resting-state functional magnetic resonance imaging data were drawn from the Human Connectome Project (total n = 898 participants). To add robustness to our findings, the dataset was randomly split into a "discovery" and a "replication" sample (each with n = 449 participants). The two cohorts were carefully matched in terms of demographic variables and other confounding factors (e.g., education). The effect of shared environment was also modeled. RESULTS The parieto- and prefronto-cerebellar, parieto-prefrontal, and posterior-cingulate to hippocampus connections showed the highest and most replicable heritability effects with little influence by shared environment. In contrast, shared environmental factors significantly affected the visuo-parietal and sensory-motor directed connectivity. CONCLUSION We suggest a robust role of heritability in influencing the directed connectivity of some cortico-subcortical circuits implicated in cognition. Further studies, for example using task-based fMRI and GC, are warranted to confirm the asymmetric effects of genetic factors on the functional connectivity within cognitive networks and their role in supporting executive functions and learning.
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Affiliation(s)
- Maria Giovanna Bianco
- Neuroscience Research Center, Department of Medical and Surgical Sciences, "Magna Graecia" University of Catanzaro, Italy
| | - Andrea Duggento
- Department of Biomedicine and Prevention, University "Tor Vergata", Rome, Italy
| | - Salvatore Nigro
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari 'Aldo Moro, "Pia Fondazione Cardinale G. Panico", Tricase, Italy
| | - Allegra Conti
- Department of Biomedicine and Prevention, University "Tor Vergata", Rome, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University "Tor Vergata", Rome, Italy
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, Boston, MA, 02129, USA
| | - Luca Passamonti
- Institute of Bioimaging and Molecular Physiology, National Research Council, Milan, Italy
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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Rolls ET, Deco G, Huang CC, Feng J. The human posterior parietal cortex: effective connectome, and its relation to function. Cereb Cortex 2023; 33:3142-3170. [PMID: 35834902 PMCID: PMC10401905 DOI: 10.1093/cercor/bhac266] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/10/2022] [Accepted: 06/11/2022] [Indexed: 01/04/2023] Open
Abstract
The effective connectivity between 21 regions in the human posterior parietal cortex, and 360 cortical regions was measured in 171 Human Connectome Project (HCP) participants using the HCP atlas, and complemented with functional connectivity and diffusion tractography. Intraparietal areas LIP, VIP, MIP, and AIP have connectivity from early cortical visual regions, and to visuomotor regions such as the frontal eye fields, consistent with functions in eye saccades and tracking. Five superior parietal area 7 regions receive from similar areas and from the intraparietal areas, but also receive somatosensory inputs and connect with premotor areas including area 6, consistent with functions in performing actions to reach for, grasp, and manipulate objects. In the anterior inferior parietal cortex, PFop, PFt, and PFcm are mainly somatosensory, and PF in addition receives visuo-motor and visual object information, and is implicated in multimodal shape and body image representations. In the posterior inferior parietal cortex, PFm and PGs combine visuo-motor, visual object, and reward input and connect with the hippocampal system. PGi in addition provides a route to motion-related superior temporal sulcus regions involved in social interactions. PGp has connectivity with intraparietal regions involved in coordinate transforms and may be involved in idiothetic update of hippocampal visual scene representations.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Gustavo Deco
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, Institute of Brain and Education Innovation, East China Normal University, Shanghai 200602, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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Rolls ET, Wirth S, Deco G, Huang C, Feng J. The human posterior cingulate, retrosplenial, and medial parietal cortex effective connectome, and implications for memory and navigation. Hum Brain Mapp 2023; 44:629-655. [PMID: 36178249 PMCID: PMC9842927 DOI: 10.1002/hbm.26089] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 09/05/2022] [Accepted: 09/07/2022] [Indexed: 01/25/2023] Open
Abstract
The human posterior cingulate, retrosplenial, and medial parietal cortex are involved in memory and navigation. The functional anatomy underlying these cognitive functions was investigated by measuring the effective connectivity of these Posterior Cingulate Division (PCD) regions in the Human Connectome Project-MMP1 atlas in 171 HCP participants, and complemented with functional connectivity and diffusion tractography. First, the postero-ventral parts of the PCD (31pd, 31pv, 7m, d23ab, and v23ab) have effective connectivity with the temporal pole, inferior temporal visual cortex, cortex in the superior temporal sulcus implicated in auditory and semantic processing, with the reward-related vmPFC and pregenual anterior cingulate cortex, with the inferior parietal cortex, and with the hippocampal system. This connectivity implicates it in hippocampal episodic memory, providing routes for "what," reward and semantic schema-related information to access the hippocampus. Second, the antero-dorsal parts of the PCD (especially 31a and 23d, PCV, and also RSC) have connectivity with early visual cortical areas including those that represent spatial scenes, with the superior parietal cortex, with the pregenual anterior cingulate cortex, and with the hippocampal system. This connectivity implicates it in the "where" component for hippocampal episodic memory and for spatial navigation. The dorsal-transitional-visual (DVT) and ProStriate regions where the retrosplenial scene area is located have connectivity from early visual cortical areas to the parahippocampal scene area, providing a ventromedial route for spatial scene information to reach the hippocampus. These connectivities provide important routes for "what," reward, and "where" scene-related information for human hippocampal episodic memory and navigation. The midcingulate cortex provides a route from the anterior dorsal parts of the PCD and the supracallosal part of the anterior cingulate cortex to premotor regions.
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Affiliation(s)
- Edmund T. Rolls
- Oxford Centre for Computational NeuroscienceOxfordUK
- Department of Computer ScienceUniversity of WarwickCoventryUK
- Institute of Science and Technology for Brain Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain Inspired IntelligenceFudan University, Ministry of EducationShanghaiChina
- Fudan ISTBI—ZJNU Algorithm Centre for Brain‐Inspired IntelligenceZhejiang Normal UniversityJinhuaChina
| | - Sylvia Wirth
- Institut des Sciences Cognitives Marc Jeannerod, UMR 5229CNRS and University of LyonBronFrance
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain
- Brain and CognitionPompeu Fabra UniversityBarcelonaSpain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA)Universitat Pompeu FabraBarcelonaSpain
| | - Chu‐Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina
| | - Jianfeng Feng
- Department of Computer ScienceUniversity of WarwickCoventryUK
- Institute of Science and Technology for Brain Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain Inspired IntelligenceFudan University, Ministry of EducationShanghaiChina
- Fudan ISTBI—ZJNU Algorithm Centre for Brain‐Inspired IntelligenceZhejiang Normal UniversityJinhuaChina
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Dobbertin M, Blair KS, Carollo E, Blair JR, Dominguez A, Bajaj S. Neuroimaging alterations of the suicidal brain and its relevance to practice: an updated review of MRI studies. Front Psychiatry 2023; 14:1083244. [PMID: 37181903 PMCID: PMC10174251 DOI: 10.3389/fpsyt.2023.1083244] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 04/04/2023] [Indexed: 05/16/2023] Open
Abstract
Suicide is a leading cause of death in the United States. Historically, scientific inquiry has focused on psychological theory. However, more recent studies have started to shed light on complex biosignatures using MRI techniques, including task-based and resting-state functional MRI, brain morphometry, and diffusion tensor imaging. Here, we review recent research across these modalities, with a focus on participants with depression and Suicidal Thoughts and Behavior (STB). A PubMed search identified 149 articles specific to our population of study, and this was further refined to rule out more diffuse pathologies such as psychotic disorders and organic brain injury and illness. This left 69 articles which are reviewed in the current study. The collated articles reviewed point to a complex impairment showing atypical functional activation in areas associated with perception of reward, social/affective stimuli, top-down control, and reward-based learning. This is broadly supported by the atypical morphometric and diffusion-weighted alterations and, most significantly, in the network-based resting-state functional connectivity data that extrapolates network functions from well validated psychological paradigms using functional MRI analysis. We see an emerging picture of cognitive dysfunction evident in task-based and resting state fMRI and network neuroscience studies, likely preceded by structural changes best demonstrated in morphometric and diffusion-weighted studies. We propose a clinically-oriented chronology of the diathesis-stress model of suicide and link other areas of research that may be useful to the practicing clinician, while helping to advance the translational study of the neurobiology of suicide.
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Affiliation(s)
- Matthew Dobbertin
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, NE, United States
- Child and Adolescent Psychiatric Inpatient Center, Boys Town National Research Hospital, Boys Town, NE, United States
- *Correspondence: Matthew Dobbertin,
| | - Karina S. Blair
- Program for Trauma and Anxiety in Children (PTAC), Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, NE, United States
| | - Erin Carollo
- Stritch School of Medicine, Loyola University Chicago, Chicago, IL, United States
| | - James R. Blair
- Child and Adolescent Mental Health Centre, Mental Health Services, Copenhagen, Denmark
| | - Ahria Dominguez
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, NE, United States
| | - Sahil Bajaj
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, NE, United States
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Rolls ET, Deco G, Huang CC, Feng J. Human amygdala compared to orbitofrontal cortex connectivity, and emotion. Prog Neurobiol 2023; 220:102385. [PMID: 36442728 DOI: 10.1016/j.pneurobio.2022.102385] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/14/2022] [Accepted: 11/24/2022] [Indexed: 11/26/2022]
Abstract
The amygdala and orbitofrontal cortex have been implicated in emotion. To understand these regions better in humans, their effective connectivity with 360 cortical regions was measured in 171 humans from the Human Connectome Project, and complemented with functional connectivity and diffusion tractography. The human amygdala has effective connectivity from few cortical regions compared to the orbitofrontal cortex: primarily from auditory cortex A5 and the related superior temporal gyrus and temporal pole regions; the piriform (olfactory) cortex; the lateral orbitofrontal cortex 47m; somatosensory cortex; the hippocampus, entorhinal cortex, perirhinal cortex, and parahippocampal TF; and from the cholinergic nucleus basalis. The amygdala has effective connectivity to the hippocampus, entorhinal and perirhinal cortex; to the temporal pole; and to the lateral orbitofrontal cortex. The orbitofrontal cortex has effective connectivity from gustatory, olfactory, and temporal visual, auditory and pole cortex, and to the pregenual anterior and posterior cingulate cortex, hippocampal system, and prefrontal cortex, and provides for rewards and punishers to be used in reported emotions, and memory and navigation to goals. Given the paucity of amygdalo-neocortical connectivity in humans, it is proposed that the human amygdala is involved primarily in autonomic and conditioned responses via brainstem connectivity, rather than in reported (declarative) emotion.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK; Department of Computer Science, University of Warwick, Coventry, UK; Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain Brain and Cognition, Pompeu Fabra University, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry, UK; Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
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11
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Rolls ET, Deco G, Huang CC, Feng J. The human orbitofrontal cortex, vmPFC, and anterior cingulate cortex effective connectome: emotion, memory, and action. Cereb Cortex 2022; 33:330-356. [PMID: 35233615 DOI: 10.1093/cercor/bhac070] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/02/2022] [Accepted: 02/03/2022] [Indexed: 01/17/2023] Open
Abstract
The human orbitofrontal cortex, ventromedial prefrontal cortex (vmPFC), and anterior cingulate cortex are involved in reward processing and thereby in emotion but are also implicated in episodic memory. To understand these regions better, the effective connectivity between 360 cortical regions and 24 subcortical regions was measured in 172 humans from the Human Connectome Project and complemented with functional connectivity and diffusion tractography. The orbitofrontal cortex has effective connectivity from gustatory, olfactory, and temporal visual, auditory, and pole cortical areas. The orbitofrontal cortex has connectivity to the pregenual anterior and posterior cingulate cortex and hippocampal system and provides for rewards to be used in memory and navigation to goals. The orbitofrontal and pregenual anterior cortex have connectivity to the supracallosal anterior cingulate cortex, which projects to midcingulate and other premotor cortical areas and provides for action-outcome learning including limb withdrawal or flight or fight to aversive and nonreward stimuli. The lateral orbitofrontal cortex has outputs to language systems in the inferior frontal gyrus. The medial orbitofrontal cortex connects to the nucleus basalis of Meynert and the pregenual cingulate to the septum, and damage to these cortical regions may contribute to memory impairments by disrupting cholinergic influences on the neocortex and hippocampus.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain.,Cognition, Pompeu Fabra University, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China
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12
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Rolls ET, Deco G, Huang CC, Feng J. Prefrontal and somatosensory-motor cortex effective connectivity in humans. Cereb Cortex 2022; 33:4939-4963. [PMID: 36227217 DOI: 10.1093/cercor/bhac391] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/07/2022] [Accepted: 09/08/2022] [Indexed: 11/12/2022] Open
Abstract
Effective connectivity, functional connectivity, and tractography were measured between 57 cortical frontal and somatosensory regions and the 360 cortical regions in the Human Connectome Project (HCP) multimodal parcellation atlas for 171 HCP participants. A ventral somatosensory stream connects from 3b and 3a via 1 and 2 and then via opercular and frontal opercular regions to the insula, which then connects to inferior parietal PF regions. This stream is implicated in "what"-related somatosensory processing of objects and of the body and in combining with visual inputs in PF. A dorsal "action" somatosensory stream connects from 3b and 3a via 1 and 2 to parietal area 5 and then 7. Inferior prefrontal regions have connectivity with the inferior temporal visual cortex and orbitofrontal cortex, are implicated in working memory for "what" processing streams, and provide connectivity to language systems, including 44, 45, 47l, TPOJ1, and superior temporal visual area. The dorsolateral prefrontal cortex regions that include area 46 have connectivity with parietal area 7 and somatosensory inferior parietal regions and are implicated in working memory for actions and planning. The dorsal prefrontal regions, including 8Ad and 8Av, have connectivity with visual regions of the inferior parietal cortex, including PGs and PGi, and are implicated in visual and auditory top-down attention.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Gustavo Deco
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain.,Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China.,Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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13
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Xue J, Yao R, Cui X, Wang B, Wei J, Wu X, Sun J, Yang Y, Xiang J, Liu Y. Abnormal information interaction in multilayer directed network based on cross-frequency integration of mild cognitive impairment and Alzheimer’s disease. Cereb Cortex 2022; 33:4230-4247. [PMID: 36104855 DOI: 10.1093/cercor/bhac339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/14/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Mild cognitive impairment (MCI) and Alzheimer’s disease (AD) have been reported to result in abnormal cross-frequency integration. However, previous studies have failed to consider specific abnormalities in receiving and outputting information among frequency bands during integration. Here, we investigated heterogeneity in receiving and outputting information during cross-frequency integration in patients. The results showed that during cross-frequency integration, information interaction first increased and then decreased, manifesting in the heterogeneous distribution of inter-frequency nodes for receiving information. A possible explanation was that due to damage to some inter-frequency hub nodes, intra-frequency nodes gradually became new inter-frequency nodes, whereas original inter-frequency nodes gradually became new inter-frequency hub nodes. Notably, damage to the brain regions that receive information between layers was often accompanied by a strengthened ability to output information and the emergence of hub nodes for outputting information. Moreover, an important compensatory mechanism assisted in the reception of information in the cingulo-opercular and auditory networks and in the outputting of information in the visual network. This study revealed specific abnormalities in information interaction and compensatory mechanism during cross-frequency integration, providing important evidence for understanding cross-frequency integration in patients with MCI and AD.
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Affiliation(s)
- Jiayue Xue
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Rong Yao
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jing Wei
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Xubin Wu
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jie Sun
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Yanli Yang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Yi Liu
- Department of Anesthesiology, Shanxi Province Cancer Hospital , No. 3, Workers New Street, Taiyuan, Shanxi, 030013 , China
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14
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Rolls ET, Deco G, Huang CC, Feng J. Multiple cortical visual streams in humans. Cereb Cortex 2022; 33:3319-3349. [PMID: 35834308 DOI: 10.1093/cercor/bhac276] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/16/2022] [Accepted: 06/17/2022] [Indexed: 11/14/2022] Open
Abstract
The effective connectivity between 55 visual cortical regions and 360 cortical regions was measured in 171 HCP participants using the HCP-MMP atlas, and complemented with functional connectivity and diffusion tractography. A Ventrolateral Visual "What" Stream for object and face recognition projects hierarchically to the inferior temporal visual cortex, which projects to the orbitofrontal cortex for reward value and emotion, and to the hippocampal memory system. A Ventromedial Visual "Where" Stream for scene representations connects to the parahippocampal gyrus and hippocampus. An Inferior STS (superior temporal sulcus) cortex Semantic Stream receives from the Ventrolateral Visual Stream, from visual inferior parietal PGi, and from the ventromedial-prefrontal reward system and connects to language systems. A Dorsal Visual Stream connects via V2 and V3A to MT+ Complex regions (including MT and MST), which connect to intraparietal regions (including LIP, VIP and MIP) involved in visual motion and actions in space. It performs coordinate transforms for idiothetic update of Ventromedial Stream scene representations. A Superior STS cortex Semantic Stream receives visual inputs from the Inferior STS Visual Stream, PGi, and STV, and auditory inputs from A5, is activated by face expression, motion and vocalization, and is important in social behaviour, and connects to language systems.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Gustavo Deco
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain.,Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China.,Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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15
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Rolls ET, Deco G, Huang CC, Feng J. The human language effective connectome. Neuroimage 2022; 258:119352. [PMID: 35659999 DOI: 10.1016/j.neuroimage.2022.119352] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 05/31/2022] [Indexed: 01/07/2023] Open
Abstract
To advance understanding of brain networks involved in language, the effective connectivity between 26 cortical regions implicated in language by a community analysis and 360 cortical regions was measured in 171 humans from the Human Connectome Project, and complemented with functional connectivity and diffusion tractography, all using the HCP multimodal parcellation atlas. A (semantic) network (Group 1) involving inferior cortical regions of the superior temporal sulcus cortex (STS) with the adjacent inferior temporal visual cortex TE1a and temporal pole TG, and the connected parietal PGi region, has effective connectivity with inferior temporal visual cortex (TE) regions; with parietal PFm which also has visual connectivity; with posterior cingulate cortex memory-related regions; with the frontal pole, orbitofrontal cortex, and medial prefrontal cortex; with the dorsolateral prefrontal cortex; and with 44 and 45 for output regions. It is proposed that this system can build in its temporal lobe (STS and TG) and parietal parts (PGi and PGs) semantic representations of objects incorporating especially their visual and reward properties. Another (semantic) network (Group 3) involving superior regions of the superior temporal sulcus cortex and more superior temporal lobe regions including STGa, auditory A5, TPOJ1, the STV and the Peri-Sylvian Language area (PSL) has effective connectivity with auditory areas (A1, A4, A5, Pbelt); with relatively early visual areas involved in motion, e.g., MT and MST, and faces/words (FFC); with somatosensory regions (frontal opercular FOP, insula and parietal PF); with other TPOJ regions; and with the inferior frontal gyrus regions (IFJa and IFSp). It is proposed that this system builds semantic representations specialising in auditory and related facial motion information useful in theory of mind and somatosensory / body image information, with outputs directed not only to regions 44 and 45, but also to premotor 55b and midcingulate premotor cortex. Both semantic networks (Groups 1 and 3) have access to the hippocampal episodic memory system via parahippocampal TF. A third largely frontal network (Group 2) (44, 45, 47l; 55b; the Superior Frontal Language region SFL; and including temporal pole TGv) receives effective connectivity from the two semantic systems, and is implicated in syntax and speech output.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China.
| | - Gustavo Deco
- Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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16
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Scharwächter L, Schmitt FJ, Pallast N, Fink GR, Aswendt M. Network analysis of neuroimaging in mice. Neuroimage 2022; 253:119110. [PMID: 35311664 DOI: 10.1016/j.neuroimage.2022.119110] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/01/2022] [Accepted: 03/15/2022] [Indexed: 10/18/2022] Open
Abstract
Graph theory allows assessing changes of neuronal connectivity and interactions of brain regions in response to local lesions, e.g., after stroke, and global perturbations, e.g., due to psychiatric dysfunctions or neurodegenerative disorders. Consequently, network analysis based on constructing graphs from structural and functional MRI connectivity matrices is increasingly used in clinical studies. In contrast, in mouse neuroimaging, the focus is mainly on basic connectivity parameters, i.e., the correlation coefficient or fiber counts, whereas more advanced network analyses remain rarely used. This review summarizes graph theoretical measures and their interpretation to describe networks derived from recent in vivo mouse brain studies. To facilitate the entry into the topic, we explain the related mathematical definitions, provide a dedicated software toolkit, and discuss practical considerations for the application to rs-fMRI and DTI. This way, we aim to foster cross-species comparisons and the application of standardized measures to classify and interpret network changes in translational brain disease studies.
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Affiliation(s)
- Leon Scharwächter
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany
| | - Felix J Schmitt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; University of Cologne, Institute of Zoology, Dept. of Computational Systems Neuroscience, Cologne, Germany
| | - Niklas Pallast
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany
| | - Gereon R Fink
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany
| | - Markus Aswendt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany.
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17
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Rolls ET, Deco G, Huang CC, Feng J. The Effective Connectivity of the Human Hippocampal Memory System. Cereb Cortex 2022; 32:3706-3725. [PMID: 35034120 DOI: 10.1093/cercor/bhab442] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 02/04/2023] Open
Abstract
Effective connectivity measurements in the human hippocampal memory system based on the resting-state blood oxygenation-level dependent signal were made in 172 participants in the Human Connectome Project to reveal the directionality and strength of the connectivity. A ventral "what" hippocampal stream involves the temporal lobe cortex, perirhinal and parahippocampal TF cortex, and entorhinal cortex. A dorsal "where" hippocampal stream connects parietal cortex with posterior and retrosplenial cingulate cortex, and with parahippocampal TH cortex, which, in turn, project to the presubiculum, which connects to the hippocampus. A third stream involves the orbitofrontal and ventromedial-prefrontal cortex with effective connectivity with the hippocampal, entorhinal, and perirhinal cortex. There is generally stronger forward connectivity to the hippocampus than backward. Thus separate "what," "where," and "reward" streams can converge in the hippocampus, from which back projections return to the sources. However, unlike the simple dual stream hippocampal model, there is a third stream related to reward value; there is some cross-connectivity between these systems before the hippocampus is reached; and the hippocampus has some effective connectivity with earlier stages of processing than the entorhinal cortex and presubiculum. These findings complement diffusion tractography and provide a foundation for new concepts on the operation of the human hippocampal memory system.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Gustavo Deco
- Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona 08018, Spain
- Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China
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18
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Guha A, Yee CM, Heller W, Miller GA. Alterations in the default mode-salience network circuit provide a potential mechanism supporting negativity bias in depression. Psychophysiology 2021; 58:e13918. [PMID: 34403515 DOI: 10.1111/psyp.13918] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 07/21/2021] [Accepted: 07/27/2021] [Indexed: 11/28/2022]
Abstract
Aberrant effective connectivity between default mode (DMN) and salience (SAL) networks may support the tendency of depressed individuals to find it difficult to disengage from self-focused, negatively-biased thinking and may contribute to the onset and maintenance of depression. Assessment of effective connectivity, which can statistically characterize the direction of influence between regions within neural circuits, may provide new insights into the nature of DMN-SAL connectivity disruptions in depression. Functional magnetic resonance imaging (fMRI) was collected from 38 individuals with a history of major depression and 50 healthy comparison participants during completion of an emotion-word Stroop task. Activation within DMN and SAL networks and effective connectivity between DMN and SAL, assessed via Granger causality, were examined. Individuals with a history of depression exhibited greater overall network activation, greater directed connectivity from DMN to SAL, and less directed connectivity from SAL to DMN than healthy comparison participants during negative-word trials. Among individuals with a history of depression, greater DMN-to-SAL connectivity was associated with lower overall network activation and worse task performance during positive-word trials; this pattern was not observed among healthy participants. Present findings indicate that greater network activation and, specifically, influence of DMN on SAL, support negativity bias among previously depressed individuals.
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Affiliation(s)
- Anika Guha
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA
| | - Cindy M Yee
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Wendy Heller
- Department of Psychology, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, USA
| | - Gregory A Miller
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA.,Department of Psychology, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, USA
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19
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Rolls ET, Cheng W, Gilson M, Gong W, Deco G, Lo CYZ, Yang AC, Tsai SJ, Liu ME, Lin CP, Feng J. Beyond the disconnectivity hypothesis of schizophrenia. Cereb Cortex 2021; 30:1213-1233. [PMID: 31381086 DOI: 10.1093/cercor/bhz161] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 06/24/2019] [Accepted: 06/24/2019] [Indexed: 01/01/2023] Open
Abstract
To go beyond the disconnectivity hypothesis of schizophrenia, directed (effective) connectivity was measured between 94 brain regions, to provide evidence on the source of the changes in schizophrenia and a mechanistic model. Effective connectivity (EC) was measured in 180 participants with schizophrenia and 208 controls. For the significantly different effective connectivities in schizophrenia, on average the forward (stronger) effective connectivities were smaller, whereas the backward connectivities tended to be larger. Further, higher EC in schizophrenia was found from the precuneus and posterior cingulate cortex (PCC) to areas such as the parahippocampal, hippocampal, temporal, fusiform, and occipital cortices. These are backward effective connectivities and were positively correlated with the positive symptoms of schizophrenia. Lower effective connectivities were found from temporal and other regions and were negatively correlated with the symptoms, especially the negative and general symptoms. Further, a signal variance parameter was increased for areas that included the parahippocampal gyrus and hippocampus, consistent with the hypothesis that hippocampal overactivity is involved in schizophrenia. This investigation goes beyond the disconnectivity hypothesis by drawing attention to differences in schizophrenia between backprojections and forward connections, with the backward connections from the precuneus and PCC implicated in memory stronger in schizophrenia.
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Affiliation(s)
- Edmund T Rolls
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Oxford Centre for Computational Neuroscience, Oxford OX1 4BH, UK
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433, China
| | - Matthieu Gilson
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona E-08018, Spain and Brain and Cognition, Pompeu Fabra University, Barcelona, Spain
| | - Weikang Gong
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX1 4BH, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona E-08018, Spain and Brain and Cognition, Pompeu Fabra University, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona, 08010, Spain
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China
| | - Albert C Yang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11267, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11267, Taiwan
| | - Mu-En Liu
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11267, Taiwan
| | - Ching-Po Lin
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Institute of Neuroscience, National Yang-Ming University, Taipei 11221, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei 11221, Taiwan
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,School of Mathematical Sciences, School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200433, PR China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433, China
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20
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Bajaj S, Raikes AC, Razi A, Miller MA, Killgore WDS. Blue-Light Therapy Strengthens Resting-State Effective Connectivity within Default-Mode Network after Mild TBI. J Cent Nerv Syst Dis 2021; 13:11795735211015076. [PMID: 34104033 PMCID: PMC8145607 DOI: 10.1177/11795735211015076] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 02/08/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Emerging evidence suggests that post concussive symptoms, including mood changes, may be improved through morning blue-wavelength light therapy (BLT). However, the neurobiological mechanisms underlying these effects remain unknown. We hypothesize that BLT may influence the effective brain connectivity (EC) patterns within the default-mode network (DMN), particularly involving the medial prefrontal cortex (MPFC), which may contribute to improvements in mood. METHODS Resting-state functional MRI data were collected from 41 healthy-controls (HCs) and 28 individuals with mild traumatic brain injury (mTBI). Individuals with mTBI also underwent a diffusion-weighted imaging scan and were randomly assigned to complete either 6 weeks of daily morning BLT (N = 14) or amber light therapy (ALT; N = 14). Advanced spectral dynamic causal modeling (sDCM) and diffusion MRI connectometry were used to estimate EC patterns and structural connectivity strength within the DMN, respectively. RESULTS The sDCM analysis showed dominant connectivity pattern following mTBI (pre-treatment) within the hemisphere contralateral to the one observed for HCs. BLT, but not ALT, resulted in improved directional information flow (ie, EC) from the left lateral parietal cortex (LLPC) to MPFC within the DMN. The improvement in EC from LLPC to MPFC was accompanied by stronger structural connectivity between the 2 areas. For the BLT group, the observed improvements in function and structure were correlated (at a trend level) with changes in self-reported happiness. CONCLUSIONS The current preliminary findings provide empirical evidence that morning short-wavelength light therapy could be used as a novel alternative rehabilitation technique for mTBI. TRIAL REGISTRY The research protocols were registered in the ClinicalTrials.gov database (CT Identifiers NCT01747811 and NCT01721356).
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Affiliation(s)
- Sahil Bajaj
- Social, Cognitive and Affective Neuroscience (SCAN) Laboratory, Department of Psychiatry, College of Medicine, University of Arizona, Tucson, AZ, USA
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Adam C Raikes
- Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging at Monash University, Clayton, VIC, Australia
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Michael A Miller
- Social, Cognitive and Affective Neuroscience (SCAN) Laboratory, Department of Psychiatry, College of Medicine, University of Arizona, Tucson, AZ, USA
| | - William DS Killgore
- Social, Cognitive and Affective Neuroscience (SCAN) Laboratory, Department of Psychiatry, College of Medicine, University of Arizona, Tucson, AZ, USA
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21
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Bajaj S, Killgore WDS. Association between emotional intelligence and effective brain connectome: A large-scale spectral DCM study. Neuroimage 2021; 229:117750. [PMID: 33454407 DOI: 10.1016/j.neuroimage.2021.117750] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 12/21/2020] [Accepted: 01/07/2021] [Indexed: 12/18/2022] Open
Abstract
INTRODUCTION Emotional Intelligence (EI) is a well-documented aspect of social and interpersonal functioning, but the underlying neural mechanisms for this capacity remain poorly understood. Here we used advanced brain connectivity techniques to explore the associations between EI and effective connectivity (EC) within four functional brain networks. METHODS The Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) was used to collect EI data from 55 healthy individuals (mean age = 30.56±8.3 years, 26 males). The MSCEIT comprises two area cores - experiential EI (T1) and strategic EI (T2). The T1 core included two sub-scales - perception of emotions (S1) and using emotions to facilitate thinking (S2), and the T2 core included two sub-scales - understanding of emotions (S3) and management of emotions (S4). All participants underwent structural and resting-state functional magnetic resonance imaging (rsfMRI) scans. The spectral dynamic causal modeling approach was implemented to estimate EC within four networks of interest - the default-mode network (DMN), dorsal attention network (DAN), control-execution network (CEN) and salience network (SN). The strength of EC within each network was correlated with the measures of EI, with correlations at pFDR < 0.05 considered as significant. RESULTS There was no significant association between any of the measures of EI and EC strength within the DMN and DAN. For CEN, however, we found that there were significant negative associations between EC strength from the right anterior prefrontal cortex (RAPFC) to the left anterior prefrontal cortex (LAPFC) and both S2 and T1, and significant positive associations between EC strength from LAPFC to RAPFC and S2. EC strength from the right superior parietal cortex (SPC) to RAPFC also showed significant negative association with S4 and T2. For the SN, S3 showed significant negative association with EC strength from the right insula to RAPFC and significant positive association with EC strength from the left insula to dorsal anterior cingulate cortex (DACC). CONCLUSIONS We provide evidence that the negative ECs within the right hemisphere, and from the right to left hemisphere, and positive ECs within the left hemisphere and from the left to right hemisphere of CEN (involving bilateral frontal and right parietal region) and SN (involving right frontal, anterior cingulate and bilateral insula) play a significant role in regulating and processing emotions. These findings also suggest that measures of EC can be utilized as important biomarkers to better understand the underlying neural mechanisms of EI.
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Affiliation(s)
- Sahil Bajaj
- Social, Cognitive and Affective Neuroscience Laboratory (SCAN Lab), Department of Psychiatry, College of Medicine, University of Arizona, Tucson, AZ, USA; Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE 68010, USA.
| | - William D S Killgore
- Social, Cognitive and Affective Neuroscience Laboratory (SCAN Lab), Department of Psychiatry, College of Medicine, University of Arizona, Tucson, AZ, USA
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22
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Almpanis E, Siettos C. Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach. AIMS Neurosci 2020; 7:66-88. [PMID: 32607412 PMCID: PMC7321769 DOI: 10.3934/neuroscience.2020005] [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: 01/06/2020] [Accepted: 03/25/2020] [Indexed: 11/29/2022] Open
Abstract
We propose a numerical-based approach extending the conditional MVAR Granger causality (MVGC) analysis for the construction of directed connectivity networks in the presence of both exogenous/stimuli and modulatory inputs. The performance of the proposed scheme is validated using both synthetic stochastic data considering also the influence of haemodynamics latencies and a benchmark fMRI dataset related to the role of attention in the perception of visual motion. The particular fMRI dataset has been used in many studies to evaluate alternative model hypotheses using the Dynamic Causal Modelling (DCM) approach. Based on the use of the Bayes factor, we show that the obtained GC connectivity network compares well to a reference model that has been selected through DCM analysis among other candidate models. Thus, our findings suggest that the proposed scheme can be successfully used as a stand-alone or complementary to DCM approach to find directed causal connectivity patterns in task-related fMRI studies.
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Affiliation(s)
- Evangelos Almpanis
- Section of Condensed Matter Physics, National and Kapodistrian University of Athens, Greece.,Institute of Nanoscience and Nanotechnology, NCSR "Demokritos," Athens, Greece
| | - Constantinos Siettos
- Dipartimento di Matematica e Applicazioni "Renato Caccioppoli", Università degli Studi di Napoli Federico II, Italy
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23
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Ursino M, Ricci G, Magosso E. Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models. Front Comput Neurosci 2020; 14:45. [PMID: 32581756 PMCID: PMC7292208 DOI: 10.3389/fncom.2020.00045] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 04/30/2020] [Indexed: 12/12/2022] Open
Abstract
Objective: Assessing brain connectivity from electrophysiological signals is of great relevance in neuroscience, but results are still debated and depend crucially on how connectivity is defined and on mathematical instruments utilized. Aim of this work is to assess the capacity of bivariate Transfer Entropy (TE) to evaluate connectivity, using data generated from simple neural mass models of connected Regions of Interest (ROIs). Approach: Signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. We investigated whether the presence of a statistically significant connection can be detected and if connection strength can be quantified. Main Results: Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions, and if the epoch lengths are longer than 10 s. In case of multivariate networks, some spurious connections can emerge (i.e., a statistically significant TE even in the absence of a true connection); however, quite a good correlation between TE and synaptic strength is still preserved. Moreover, TE appears more robust for distal regions (longer delays) compared with proximal regions (smaller delays): an approximate a priori knowledge on this delay can improve the procedure. Finally, non-linear phenomena affect the assessment of connectivity, since they may significantly reduce TE estimation: information transmission between two ROIs may be weak, due to non-linear phenomena, even if a strong causal connection is present. Significance: Changes in functional connectivity during different tasks or brain conditions, might not always reflect a true change in the connecting network, but rather a change in information transmission. A limitation of the work is the use of bivariate TE. In perspective, the use of multivariate TE can improve estimation and reduce some of the problems encountered in the present study.
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Affiliation(s)
- Mauro Ursino
- Department of Electrical, Electronic and Information Engineering, University of Bologna, Cesena, Italy
| | - Giulia Ricci
- Department of Electrical, Electronic and Information Engineering, University of Bologna, Cesena, Italy
| | - Elisa Magosso
- Department of Electrical, Electronic and Information Engineering, University of Bologna, Cesena, Italy
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24
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Adhikari BM, Jahanshad N, Shukla D, Turner J, Grotegerd D, Dannlowski U, Kugel H, Engelen J, Dietsche B, Krug A, Kircher T, Fieremans E, Veraart J, Novikov DS, Boedhoe PSW, van der Werf YD, van den Heuvel OA, Ipser J, Uhlmann A, Stein DJ, Dickie E, Voineskos AN, Malhotra AK, Pizzagalli F, Calhoun VD, Waller L, Veer IM, Walter H, Buchanan RW, Glahn DC, Hong LE, Thompson PM, Kochunov P. A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol. Brain Imaging Behav 2020; 13:1453-1467. [PMID: 30191514 DOI: 10.1007/s11682-018-9941-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.
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Affiliation(s)
- Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Neda Jahanshad
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
| | - Dinesh Shukla
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jessica Turner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | | | - Udo Dannlowski
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Harald Kugel
- Department of Clinical Radiology, University of Münster, Münster, Germany
| | - Jennifer Engelen
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Bruno Dietsche
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Premika S W Boedhoe
- Department of Psychiatry, Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam, Netherlands
| | - Ysbrand D van der Werf
- Department of Psychiatry, Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam, Netherlands
| | - Odile A van den Heuvel
- Department of Psychiatry, Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam, Netherlands
| | - Jonathan Ipser
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Anne Uhlmann
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Dan J Stein
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Erin Dickie
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Aristotle N Voineskos
- Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Anil K Malhotra
- Department of Psychiatry, The Zucker Hillside Hospital, Glen Oaks, New York, NY, USA
| | - Fabrizio Pizzagalli
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
| | - Vince D Calhoun
- The Mind Research Network & The University of New Mexico, Albuquerque, NM, USA
| | - Lea Waller
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Matte, Berlin, Germany
| | - Ilja M Veer
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Matte, Berlin, Germany
| | - Hernik Walter
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Matte, Berlin, Germany
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - David C Glahn
- Department of Psychiatry, Yale University, School of Medicine, New Haven, CT, USA
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Paul M Thompson
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
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25
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Ishida T, Dierks T, Strik W, Morishima Y. Converging Resting State Networks Unravels Potential Remote Effects of Transcranial Magnetic Stimulation for Major Depression. Front Psychiatry 2020; 11:836. [PMID: 32973580 PMCID: PMC7468386 DOI: 10.3389/fpsyt.2020.00836] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 07/31/2020] [Indexed: 12/20/2022] Open
Abstract
Despite being a commonly used protocol to treat major depressive disorder (MDD), the underlying mechanism of repetitive transcranial magnetic stimulation (rTMS) on dorsolateral prefrontal cortex (DLPFC) remains unclear. In the current study, we investigated the resting-state fMRI data of 100 healthy subjects by exploring three overlapping functional networks associated with the psychopathologically MDD-related areas (the nucleus accumbens, amygdala, and ventromedial prefrontal cortex). Our results showed that these networks converged at the bilateral DLPFC, which suggested that rTMS over DLPFC might improve MDD by remotely modulating the MDD-related areas synergistically. Additionally, they functionally converged at the DMPFC and bilateral insula which are known to be associated with MDD. These two areas could also be potential targets for rTMS treatment. Dynamic causal modelling (DCM) and Granger causality analysis (GCA) revealed that all pairwise connections among bilateral DLPFC, DMPFC, bilateral insula, and three psychopathologically MDD-related areas contained significant causality. The DCM results also suggested that most of the functional interactions between MDD-related areas and bilateral DLPFC, DMPFC, and bilateral insula can predominantly be explained by the effective connectivity from the psychopathologically MDD-related areas to the rTMS stimulation sites. Finally, we found the conventional functional connectivity to be a more representative measure to obtain connectivity parameters compared to GCA and DCM analysis. Our research helped inspecting the convergence of the functional networks related to a psychiatry disorder. The results identified potential targets for brain stimulation treatment and contributed to the optimization of patient-specific brain stimulation protocols.
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Affiliation(s)
- Takuya Ishida
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Meguro-ku, Japan.,Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Kimiidera, Japan.,Division of Systems Neuroscience of Psychopathology, Translational Research Centre, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Thomas Dierks
- Division of Systems Neuroscience of Psychopathology, Translational Research Centre, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Werner Strik
- University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Yosuke Morishima
- Division of Systems Neuroscience of Psychopathology, Translational Research Centre, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
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26
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Xue J, Guo H, Gao Y, Wang X, Cui H, Chen Z, Wang B, Xiang J. Altered Directed Functional Connectivity of the Hippocampus in Mild Cognitive Impairment and Alzheimer's Disease: A Resting-State fMRI Study. Front Aging Neurosci 2019; 11:326. [PMID: 31866850 PMCID: PMC6905409 DOI: 10.3389/fnagi.2019.00326] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 11/12/2019] [Indexed: 11/29/2022] Open
Abstract
The hippocampus is generally reported as one of the regions most impacted by Alzheimer's disease (AD) and is closely associated with memory function and orientation. Undirected functional connectivity (FC) alterations occur in patients with mild cognitive impairment (MCI) and AD, and these alterations have been the subject of many studies. However, abnormal patterns of directed FC remain poorly understood. In this study, to identify changes in directed FC between the hippocampus and other brain regions, Granger causality analysis (GCA) based on voxels was applied to resting-state functional magnetic resonance imaging (rs-fMRI) data from 29 AD, 65 MCI, and 30 normal control (NC) subjects. The results showed significant differences in the patterns of directed FC among the three groups. There were fewer brain regions showing changes in directed FC with the hippocampus in the MCI group than the NC group, and these regions were mainly located in the temporal lobe, frontal lobe, and cingulate cortex. However, regarding the abnormalities in directed FC in the AD group, the number of affected voxels was greater, the size of the clusters was larger, and the distribution was wider. Most of the abnormal connections were unidirectional and showed hemispheric asymmetry. In addition, we also investigated the correlations between the abnormal directional FCs and cognitive and clinical measurement scores in the three groups and found that some of them were significantly correlated. This study revealed abnormalities in the transmission and reception of information in the hippocampus of MCI and AD patients and offer insight into the neurophysiological mechanisms underlying MCI and AD.
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Affiliation(s)
| | | | | | | | | | | | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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27
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A randomized, double-blind, placebo-controlled trial of blue wavelength light exposure on sleep and recovery of brain structure, function, and cognition following mild traumatic brain injury. Neurobiol Dis 2019; 134:104679. [PMID: 31751607 DOI: 10.1016/j.nbd.2019.104679] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 10/20/2019] [Accepted: 11/15/2019] [Indexed: 01/17/2023] Open
Abstract
Sleep and circadian rhythms are among the most powerful but least understood contributors to cognitive performance and brain health. Here we capitalize on the circadian resetting effect of blue-wavelength light to phase shift the sleep patterns of adult patients (aged 18-48 years) recovering from mild traumatic brain injury (mTBI), with the aim of facilitating recovery of brain structure, connectivity, and cognitive performance. During a randomized, double-blind, placebo-controlled trial of 32 adults with a recent mTBI, we compared 6-weeks of daily 30-min pulses of blue light (peak λ = 469 nm) each morning versus amber placebo light (peak λ = 578 nm) on neurocognitive and neuroimaging outcomes, including gray matter volume (GMV), resting-state functional connectivity, directed connectivity using Granger causality, and white matter integrity using diffusion tensor imaging (DTI). Relative to placebo, morning blue light led to phase-advanced sleep timing, reduced daytime sleepiness, and improved executive functioning, and was associated with increased volume of the posterior thalamus (i.e., pulvinar), greater thalamo-cortical functional connectivity, and increased axonal integrity of these pathways. These findings provide insight into the contributions of the circadian and sleep systems in brain repair and lay the groundwork for interventions targeting the retinohypothalamic system to facilitate injury recovery.
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28
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Rolls ET, Zhou Y, Cheng W, Gilson M, Deco G, Feng J. Effective connectivity in autism. Autism Res 2019; 13:32-44. [PMID: 31657138 DOI: 10.1002/aur.2235] [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: 06/05/2019] [Revised: 10/02/2019] [Accepted: 10/03/2019] [Indexed: 11/06/2022]
Abstract
The aim was to go beyond functional connectivity, by measuring in the first large-scale study differences in effective, that is directed, connectivity between brain areas in autism compared to controls. Resting-state functional magnetic resonance imaging was analyzed from the Autism Brain Imaging Data Exchange (ABIDE) data set in 394 people with autism spectrum disorder and 473 controls, and effective connectivity (EC) was measured between 94 brain areas. First, in autism, the middle temporal gyrus and other temporal areas had lower effective connectivities to the precuneus and cuneus, and these were correlated with the Autism Diagnostic Observational Schedule total, communication, and social scores. This lower EC from areas implicated in face expression analysis and theory of mind to the precuneus and cuneus implicated in the sense of self may relate to the poor understanding of the implications of face expression inputs for oneself in autism, and to the reduced theory of mind. Second, the hippocampus and amygdala had higher EC to the middle temporal gyrus in autism, and these are thought to be back projections based on anatomical evidence and are weaker than in the other direction. This may be related to increased retrieval of recent and emotional memories in autism. Third, some prefrontal cortex areas had higher EC with each other and with the precuneus and cuneus. Fourth, there was decreased EC from the temporal pole to the ventromedial prefrontal cortex, and there was evidence for lower activity in the ventromedial prefrontal cortex, a brain area implicated in emotion-related decision-making. Autism Res 2020, 13: 32-44. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: To understand autism spectrum disorders better, it may be helpful to understand whether brain systems cause effects on each other differently in people with autism. In this first large-scale neuroimaging investigation of effective connectivity in people with autism, it is shown that parts of the temporal lobe involved in facial expression identification and theory of mind have weaker effects on the precuneus and cuneus implicated in the sense of self. This may relate to the poor understanding of the implications of face expression inputs for oneself in autism, and to the reduced theory of mind.
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Affiliation(s)
- Edmund T Rolls
- Department of Computer Science, University of Warwick, Coventry, UK.,Oxford Centre for Computational Neuroscience, Oxford, UK.,Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Yunyi Zhou
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Matthieu Gilson
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Barcelona, Spain
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry, UK.,Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
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29
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Dynamics of brain connectivity after stroke. Rev Neurosci 2019; 30:605-623. [DOI: 10.1515/revneuro-2018-0082] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 11/18/2018] [Indexed: 01/04/2023]
Abstract
Abstract
Recovery from a stroke is a dynamic time-dependent process, in which the central nervous system reorganises to accommodate for the impact of the injury. The purpose of this paper is to review recent longitudinal studies of changes in brain connectivity after stroke. A systematic review of research papers reporting functional or effective connectivity at two or more time points in stroke patients was conducted. Stroke leads to an early reduction of connectivity in the motor network. With recovery time, the connectivity increases and can reach the same levels as in healthy participants. The increase in connectivity is correlated with functional motor gains. A new, more randomised pattern of connectivity may then emerge in the longer term. In some instances, a pattern of increased connectivity even higher than in healthy controls can be observed, and is related either to a specific time point or to a specific neural structure. Rehabilitation interventions can help improve connectivity between specific regions. Moreover, motor network connectivity undergoes reorganisation during recovery from a stroke and can be related to behavioural recovery. A detailed analysis of changes in connectivity pattern may enable a better understanding of adaptation to a stroke and how compensatory mechanisms in the brain may be supported by rehabilitation.
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30
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Zaytseva Y, Fajnerová I, Dvořáček B, Bourama E, Stamou I, Šulcová K, Motýl J, Horáček J, Rodriguez M, Španiel F. Theoretical Modeling of Cognitive Dysfunction in Schizophrenia by Means of Errors and Corresponding Brain Networks. Front Psychol 2018; 9:1027. [PMID: 30026711 PMCID: PMC6042473 DOI: 10.3389/fpsyg.2018.01027] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Accepted: 05/31/2018] [Indexed: 01/22/2023] Open
Abstract
The current evidence of cognitive disturbances and brain alterations in schizophrenia does not provide the plausible explanation of the underlying mechanisms. Neuropsychological studies outlined the cognitive profile of patients with schizophrenia, that embodied the substantial disturbances in perceptual and motor processes, spatial functions, verbal and non-verbal memory, processing speed and executive functioning. Standardized scoring in the majority of the neurocognitive tests renders the index scores or the achievement indicating the severity of the cognitive impairment rather than the actual performance by means of errors. At the same time, the quantitative evaluation may lead to the situation when two patients with the same index score of the particular cognitive test, demonstrate qualitatively different performances. This may support the view why test paradigms that habitually incorporate different cognitive variables associate weakly, reflecting an ambiguity in the interpretation of noted cognitive constructs. With minor exceptions, cognitive functions are not attributed to the localized activity but eventuate from the coordinated activity in the generally dispersed brain networks. Functional neuroimaging has progressively explored the connectivity in the brain networks in the absence of the specific task and during the task processing. The spatio-temporal fluctuations of the activity of the brain areas detected in the resting state and being highly reproducible in numerous studies, resemble the activation and communication patterns during the task performance. Relatedly, the activation in the specific brain regions oftentimes is attributed to a number of cognitive processes. Given the complex organization of the cognitive functions, it becomes crucial to designate the roles of the brain networks in relation to the specific cognitive functions. One possible approach is to identify the commonalities of the deficits across the number of cognitive tests or, common errors in the various tests and identify their common "denominators" in the brain networks. The qualitative characterization of cognitive performance might be beneficial in addressing diffuse cognitive alterations presumably caused by the dysconnectivity of the distributed brain networks. Therefore, in the review, we use this approach in the description of standardized tests in the scope of potential errors in patients with schizophrenia with a subsequent reference to the brain networks.
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Affiliation(s)
- Yuliya Zaytseva
- National Institute of Mental Health, Klecany, Czechia
- 3rd Faculty of Medicine, Charles University in Prague, Prague, Czechia
| | | | | | - Eva Bourama
- 3rd Faculty of Medicine, Charles University in Prague, Prague, Czechia
| | - Ilektra Stamou
- 3rd Faculty of Medicine, Charles University in Prague, Prague, Czechia
| | - Kateřina Šulcová
- National Institute of Mental Health, Klecany, Czechia
- 3rd Faculty of Medicine, Charles University in Prague, Prague, Czechia
| | - Jiří Motýl
- National Institute of Mental Health, Klecany, Czechia
| | - Jiří Horáček
- National Institute of Mental Health, Klecany, Czechia
- 3rd Faculty of Medicine, Charles University in Prague, Prague, Czechia
| | | | - Filip Španiel
- National Institute of Mental Health, Klecany, Czechia
- 3rd Faculty of Medicine, Charles University in Prague, Prague, Czechia
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31
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Santangelo V. Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities. Front Integr Neurosci 2018. [PMID: 29535614 PMCID: PMC5835354 DOI: 10.3389/fnint.2018.00008] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory) in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory) in one spatial location. The analysis of the independent components (ICs) revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS) and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF) and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC). The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among brain networks implicated during divided attention across spatial locations and sensory modalities, pointing out the importance of investigating effective connectivity of large-scale brain networks supporting complex behavior.
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Affiliation(s)
- Valerio Santangelo
- Department of Philosophy, Social Sciences & Education, University of Perugia, Perugia, Italy.,Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy
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32
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Rolls ET, Cheng W, Gilson M, Qiu J, Hu Z, Ruan H, Li Y, Huang CC, Yang AC, Tsai SJ, Zhang X, Zhuang K, Lin CP, Deco G, Xie P, Feng J. Effective Connectivity in Depression. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017. [PMID: 29529414 DOI: 10.1016/j.bpsc.2017.10.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Resting-state functional connectivity reflects correlations in the activity between brain areas, whereas effective connectivity between different brain areas measures directed influences of brain regions on each other. Using the latter approach, we compare effective connectivity results in patients with major depressive disorder (MDD) and control subjects. METHODS We used a new approach to the measurement of effective connectivity, in which each brain area has a simple dynamical model, and known anatomical connectivity is used to provide constraints. This helps the approach to measure the effective connectivity between the 94 brain areas parceled in the automated anatomical labeling (AAL2) atlas, using resting-state functional magnetic resonance imaging. Moreover, we show how the approach can be used to measure the differences in effective connectivity between different groups of individuals, using as an example effective connectivity in the healthy brain and in individuals with depression. The first brainwide resting-state effective-connectivity neuroimaging analysis of depression, with 350 healthy individuals and 336 patients with major depressive disorder, is described. RESULTS Key findings are that the medial orbitofrontal cortex, implicated in reward and subjective pleasure, has reduced effective connectivity from temporal lobe input areas in depression; that the lateral orbitofrontal cortex, implicated in nonreward, has increased activity (variance) in depression, with decreased effective connectivity to and from cortical areas contralateral to language-related areas; and that the hippocampus, implicated in memory, has increased activity (variance) in depression and increased effective connectivity from the temporal pole. CONCLUSIONS Measurements of effective connectivity made using the new method provide a new approach to causal mechanisms in the brain in depression.
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Affiliation(s)
- Edmund T Rolls
- Department of Computer Science, University of Warwick, Coventry, United Kingdom; Oxford Centre for Computational Neuroscience, Oxford, United Kingdom.
| | - Wei Cheng
- Department of Computer Science, University of Warwick, Coventry, United Kingdom; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China
| | - Matthieu Gilson
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Southwest University, Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Zicheng Hu
- Institute of Neuroscience, Chongqing Medical University, Chongqing, China; Chongqing Key Laboratory of Neurobiology, Chongqing, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongtao Ruan
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China; School of Mathematical Sciences, Fudan University, Shanghai, PR China
| | - Yu Li
- Department of Psychology, Southwest University, Chongqing, China
| | - Chu-Chung Huang
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
| | - Albert C Yang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Xiaodong Zhang
- Institute of Neuroscience, Chongqing Medical University, Chongqing, China; Chongqing Key Laboratory of Neurobiology, Chongqing, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Kaixiang Zhuang
- Department of Psychology, Southwest University, Chongqing, China
| | - Ching-Po Lin
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China; Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan; Brain Research Center, National Yang-Ming University, Taipei, Taiwan.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats, Universitat Pompeu Fabra, Barcelona, Spain
| | - Peng Xie
- Institute of Neuroscience, Chongqing Medical University, Chongqing, China; Chongqing Key Laboratory of Neurobiology, Chongqing, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry, United Kingdom; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China; School of Mathematical Sciences, Fudan University, Shanghai, PR China; School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, PR China.
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33
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Chand GB, Wu J, Qiu D, Hajjar I. Racial Differences in Insular Connectivity and Thickness and Related Cognitive Impairment in Hypertension. Front Aging Neurosci 2017; 9:177. [PMID: 28620297 PMCID: PMC5449740 DOI: 10.3389/fnagi.2017.00177] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Accepted: 05/18/2017] [Indexed: 01/19/2023] Open
Abstract
Hypertensive African–Americans have a greater risk of cognitive impairment than hypertensive Caucasian–Americans. The neural basis of this increased risk is yet unknown. Neuroimaging investigations suggest that the normal neural activity comprises complex interactions between brain networks. Recent studies consistently demonstrate that the insula, part of the salience network, provides modulation effects (information flow) over the default-mode and central-executive networks in cognitively normal subjects, and argue that the modulation effect is declined in cognitive impairment. The purpose of this study is to examine the information flow at the nodes of three networks using resting state functional magnetic resonance imaging (MRI) data in cognitively impaired hypertensive individuals with the African–Americans and the Caucasian–Americans races, and to compare the thickness of impaired node between two racial groups. Granger causality methodology was used to calculate information flow between networks using resting state functional MRI data, and FreeSurfer was used to measure cortical thickness from T1-weighted structural images. We found that negative information flow of the insula in both African–Americans and Caucasian–Americans, which was in contrast with previously reported positive information flow in this region of normal individuals. Also, significantly greater negative information flow in insula was found in African–Americans than Caucasian–Americans (Wilcoxon rank sum; Z = 2.06; p < 0.05). Significantly, lower insula thickness was found in African–Americans compared with Caucasian–Americans (median = 2.797 mm vs. 2.897 mm) (Wilcoxon rank sum; Z = 2.09; p < 0.05). Finally, the insula thickness correlated with the global cognitive testing measured by Montreal cognitive assessment (Spearman’s correlation; r = 0.30; p < 0.05). These findings suggest that the insula is a potential biomarker for the racial disparity in cognitive impairment of hypertensive individuals.
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Affiliation(s)
- Ganesh B Chand
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Emory University School of Medicine, AtlantaGA, United States
| | - Junjie Wu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, AtlantaGA, United States
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, AtlantaGA, United States.,Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, AtlantaGA, United States
| | - Ihab Hajjar
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Emory University School of Medicine, AtlantaGA, United States.,Department of Neurology, Emory Alzheimer's Disease Research Center, Emory University School of Medicine, AtlantaGA, United States
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34
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Chand GB, Dhamala M. Interactions between the anterior cingulate-insula network and the fronto-parietal network during perceptual decision-making. Neuroimage 2017; 152:381-389. [PMID: 28284798 DOI: 10.1016/j.neuroimage.2017.03.014] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2016] [Revised: 03/03/2017] [Accepted: 03/07/2017] [Indexed: 12/19/2022] Open
Abstract
Information processing in the human brain during cognitively demanding goal-directed tasks is thought to involve several large-scale brain networks, including the anterior cingulate-insula network (aCIN) and the fronto-parietal network (FPN). Recent functional MRI (fMRI) studies have provided clues that the aCIN initiates activity changes in the FPN. However, when and how often these networks interact remains largely unknown to date. Here, we systematically examined the oscillatory interactions between the aCIN and the FPN by using the spectral Granger causality analysis of reconstructed brain source signals from the scalp electroencephalography (EEG) recorded from human participants performing a face-house perceptual categorization task. We investigated how the aCIN and the FPN interact, what the temporal sequence of events in these nodes is, and what frequency bands of information flow bind these nodes in networks. We found that beta band (13-30Hz) and gamma (30-100Hz) bands of interactions are involved between the aCIN and the FPN during decision-making tasks. In gamma band, the aCIN initiated the Granger causal control over the FPN in 25-225 ms timeframe. In beta band, the FPN achieved a control over the aCIN in 225-425 ms timeframe. These band-specific time-dependent Granger causal controls of the aCIN and the FPN were retained for behaviorally harder decision-making tasks. These findings of times and frequencies of oscillatory interactions in the aCIN and FPN provide us new insights into the general neural mechanisms for sensory information-guided, goal-directed behaviors, including perceptual decision-making processes.
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Affiliation(s)
- Ganesh B Chand
- Department of Physics and Astronomy, Georgia State University, Atlanta, GA 30303, USA; Department of Medicine, Emory University School of Medicine, Atlanta, GA 30329, USA.
| | - Mukesh Dhamala
- Neuroscience Institute, Georgia State University, Atlanta, GA 30303, USA; Center for Behavioral Neuroscience, Center for Nano-Optics, Center for Diagnostics and Therapeutics, GSU-GaTech Center for Advanced Brain Imaging, Georgia State University, Atlanta, GA 30303, USA
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