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Rolls ET. Two what, two where, visual cortical streams in humans. Neurosci Biobehav Rev 2024; 160:105650. [PMID: 38574782 DOI: 10.1016/j.neubiorev.2024.105650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/25/2024] [Accepted: 03/31/2024] [Indexed: 04/06/2024]
Abstract
ROLLS, E. T. Two What, Two Where, Visual Cortical Streams in Humans. NEUROSCI BIOBEHAV REV 2024. Recent cortical connectivity investigations lead to new concepts about 'What' and 'Where' visual cortical streams in humans, and how they connect to other cortical systems. A ventrolateral 'What' visual stream leads to the inferior temporal visual cortex for object and face identity, and provides 'What' information to the hippocampal episodic memory system, the anterior temporal lobe semantic system, and the orbitofrontal cortex emotion system. A superior temporal sulcus (STS) 'What' visual stream utilising connectivity from the temporal and parietal visual cortex responds to moving objects and faces, and face expression, and connects to the orbitofrontal cortex for emotion and social behaviour. A ventromedial 'Where' visual stream builds feature combinations for scenes, and provides 'Where' inputs via the parahippocampal scene area to the hippocampal episodic memory system that are also useful for landmark-based navigation. The dorsal 'Where' visual pathway to the parietal cortex provides for actions in space, but also provides coordinate transforms to provide inputs to the parahippocampal scene area for self-motion update of locations in scenes in the dark or when the view is obscured.
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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.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Zhang B, Rolls ET, Wang X, Xie C, Cheng W, Feng J. Roles of the medial and lateral orbitofrontal cortex in major depression and its treatment. Mol Psychiatry 2024:10.1038/s41380-023-02380-w. [PMID: 38212376 DOI: 10.1038/s41380-023-02380-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 12/06/2023] [Accepted: 12/13/2023] [Indexed: 01/13/2024]
Abstract
We describe evidence for dissociable roles of the medial and lateral orbitofrontal cortex (OFC) in major depressive disorder (MDD) from structure, functional activation, functional connectivity, metabolism, and neurochemical systems. The reward-related medial orbitofrontal cortex has lower connectivity and less reward sensitivity in MDD associated with anhedonia symptoms; and the non-reward related lateral OFC has higher functional connectivity and more sensitivity to non-reward/aversive stimuli in MDD associated with negative bias symptoms. Importantly, we propose that conventional antidepressants act to normalize the hyperactive lateral (but not medial) OFC to reduce negative bias in MDD; while other treatments are needed to operate on the medial OFC to reduce anhedonia, with emerging evidence suggesting that ketamine may act in this way. The orbitofrontal cortex is the key cortical region in emotion and reward, and the current review presents much new evidence about the different ways that the medial and lateral OFC are involved in MDD.
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Affiliation(s)
- Bei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, PR China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China.
- Oxford Centre for Computational Neuroscience, Oxford, UK.
- Department of Computer Science, University of Warwick, Coventry, UK.
| | - Xiang Wang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, PR China
- Medical Psychological Institute, Central South University, Changsha, PR China
- China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, PR China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, PR China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, PR China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, PR China.
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, PR China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, PR China.
- Department of Computer Science, University of Warwick, Coventry, UK.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, PR China.
- Zhangjiang Fudan International Innovation Center, Shanghai, PR China.
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Li Z, Ma Q, Deng Y, Rolls ET, Shen C, Li Y, Zhang W, Xiang S, Langley C, Sahakian BJ, Robbins TW, Yu JT, Feng J, Cheng W. Irritable Bowel Syndrome Is Associated With Brain Health by Neuroimaging, Behavioral, Biochemical, and Genetic Analyses. Biol Psychiatry 2024:S0006-3223(24)00027-1. [PMID: 38199582 DOI: 10.1016/j.biopsych.2023.12.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/14/2023] [Accepted: 12/13/2023] [Indexed: 01/12/2024]
Abstract
BACKGROUND Irritable bowel syndrome (IBS) interacts with psychopathology in a complex way; however, little is known about the underlying brain, biochemical, and genetic mechanisms. METHODS To clarify the phenotypic and genetic associations between IBS and brain health, we performed a comprehensive retrospective cohort study on a large population. Our study included 171,104 participants from the UK Biobank who underwent a thorough assessment of IBS, with the majority also providing neuroimaging, behavioral, biochemical, and genetic information. Multistage linked analyses were conducted, including phenome-wide association analysis, polygenic risk score calculation, and 2-sample Mendelian randomization analysis. RESULTS The phenome-wide association analysis showed that IBS was linked to brain health problems, including anxiety and depression, and poor cognitive performance. Significantly lower brain volumes associated with more severe IBS were found in key areas related to emotional regulation and higher-order cognition, including the medial orbitofrontal cortex/ventromedial prefrontal cortex, anterior insula, anterior and mid-cingulate cortices, dorsolateral prefrontal cortex, and hippocampus. Higher triglycerides, lower high-intensity lipoprotein, and lower platelets were also related (p < 1 × 10-10) to more severe IBS. Finally, Mendelian randomization analyses demonstrated potential causal relationships between IBS and brain health and indicated possible mediating effects of dyslipidemia and inflammation. CONCLUSIONS For the first time, this study provides a comprehensive understanding of the relationship between IBS and brain health phenotypes, integrating perspectives from neuroimaging, behavioral performance, biochemical factors, and genetics, which is of great significance for clinical applications to potentially address brain health impairments in patients with IBS.
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Affiliation(s)
- Zeyu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Qing Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yueting Deng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, United Kingdom; Oxford Centre for Computational Neuroscience, Oxford, United Kingdom.
| | - Chun Shen
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yuzhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Christelle Langley
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Barbara J Sahakian
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Trevor W Robbins
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Jin-Tai Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, United Kingdom; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
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Zhang R, Rolls ET, Cheng W, Feng J. Different cortical connectivities in human females and males relate to differences in strength and body composition, reward and emotional systems, and memory. Brain Struct Funct 2024; 229:47-61. [PMID: 37861743 PMCID: PMC10827883 DOI: 10.1007/s00429-023-02720-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023]
Abstract
Sex differences in human brain structure and function are important, partly because they are likely to be relevant to the male-female differences in behavior and in mental health. To analyse sex differences in cortical function, functional connectivity was measured in 36,531 participants (53% female) in the UK Biobank (mean age 69) using the Human Connectome Project multimodal parcellation atlas with 360 well-specified cortical regions. Most of the functional connectivities were lower in females (Bonferroni corrected), with the mean Cohen's d = - 0.18. Removing these as covariates reduced the difference of functional connectivities for females-males from d = - 0.18 to - 0.06. The lower functional connectivities in females were especially of somatosensory/premotor regions including the insula, opercular cortex, paracentral lobule and mid-cingulate cortex, and were correlated with lower maximum workload (r = 0.17), and with higher whole body fat mass (r = - 0.17). But some functional connectivities were higher in females, involving especially the ventromedial prefrontal cortex and posterior cingulate cortex, and these were correlated with higher liking for some rewards such as sweet foods, higher happiness/subjective well-being, and with better memory-related functions. The main findings were replicated in 1000 individuals (532 females, mean age 29) from the Human Connectome Project. This investigation shows the cortical systems with different functional connectivity between females and males, and also provides for the first time a foundation for understanding the implications for behavior of these differences between females and males.
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Affiliation(s)
- Ruohan Zhang
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK
| | - Edmund T Rolls
- 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.
- Oxford Centre for Computational Neuroscience, Oxford, UK.
| | - Wei Cheng
- 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, UK
- 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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Shen C, Rolls ET, Xiang S, Langley C, Sahakian BJ, Cheng W, Feng J. Brain and molecular mechanisms underlying the nonlinear association between close friendships, mental health, and cognition in children. eLife 2023; 12:e84072. [PMID: 37399053 DOI: 10.7554/elife.84072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 05/31/2023] [Indexed: 07/04/2023] Open
Abstract
Close friendships are important for mental health and cognition in late childhood. However, whether the more close friends the better, and the underlying neurobiological mechanisms are unknown. Using the Adolescent Brain Cognitive Developmental study, we identified nonlinear associations between the number of close friends, mental health, cognition, and brain structure. Although few close friends were associated with poor mental health, low cognitive functions, and small areas of the social brain (e.g., the orbitofrontal cortex, the anterior cingulate cortex, the anterior insula, and the temporoparietal junction), increasing the number of close friends beyond a level (around 5) was no longer associated with better mental health and larger cortical areas, and was even related to lower cognition. In children having no more than five close friends, the cortical areas related to the number of close friends revealed correlations with the density of μ-opioid receptors and the expression of OPRM1 and OPRK1 genes, and could partly mediate the association between the number of close friends, attention-deficit/hyperactivity disorder (ADHD) symptoms, and crystalized intelligence. Longitudinal analyses showed that both too few and too many close friends at baseline were associated with more ADHD symptoms and lower crystalized intelligence 2 y later. Additionally, we found that friendship network size was nonlinearly associated with well-being and academic performance in an independent social network dataset of middle-school students. These findings challenge the traditional idea of 'the more, the better,' and provide insights into potential brain and molecular mechanisms.
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Affiliation(s)
- Chun Shen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science (Fudan University), Ministry of Education, Shanghai, China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Christelle Langley
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
| | - Barbara J Sahakian
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science (Fudan University), Ministry of Education, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
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Rolls ET. Emotion, motivation, decision-making, the orbitofrontal cortex, anterior cingulate cortex, and the amygdala. Brain Struct Funct 2023:10.1007/s00429-023-02644-9. [PMID: 37178232 DOI: 10.1007/s00429-023-02644-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/12/2023] [Indexed: 05/15/2023]
Abstract
The orbitofrontal cortex and amygdala are involved in emotion and in motivation, but the relationship between these functions performed by these brain structures is not clear. To address this, a unified theory of emotion and motivation is described in which motivational states are states in which instrumental goal-directed actions are performed to obtain rewards or avoid punishers, and emotional states are states that are elicited when the reward or punisher is or is not received. This greatly simplifies our understanding of emotion and motivation, for the same set of genes and associated brain systems can define the primary or unlearned rewards and punishers such as sweet taste or pain. Recent evidence on the connectivity of human brain systems involved in emotion and motivation indicates that the orbitofrontal cortex is involved in reward value and experienced emotion with outputs to cortical regions including those involved in language, and is a key brain region involved in depression and the associated changes in motivation. The amygdala has weak effective connectivity back to the cortex in humans, and is implicated in brainstem-mediated responses to stimuli such as freezing and autonomic activity, rather than in declarative emotion. The anterior cingulate cortex is involved in learning actions to obtain rewards, and with the orbitofrontal cortex and ventromedial prefrontal cortex in providing the goals for navigation and in reward-related effects on memory consolidation mediated partly via the cholinergic 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, UK.
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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: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Rolls ET. Hippocampal spatial view cells for memory and navigation, and their underlying connectivity in humans. Hippocampus 2023; 33:533-572. [PMID: 36070199 PMCID: PMC10946493 DOI: 10.1002/hipo.23467] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 08/16/2022] [Accepted: 08/16/2022] [Indexed: 01/08/2023]
Abstract
Hippocampal and parahippocampal gyrus spatial view neurons in primates respond to the spatial location being looked at. The representation is allocentric, in that the responses are to locations "out there" in the world, and are relatively invariant with respect to retinal position, eye position, head direction, and the place where the individual is located. The underlying connectivity in humans is from ventromedial visual cortical regions to the parahippocampal scene area, leading to the theory that spatial view cells are formed by combinations of overlapping feature inputs self-organized based on their closeness in space. Thus, although spatial view cells represent "where" for episodic memory and navigation, they are formed by ventral visual stream feature inputs in the parahippocampal gyrus in what is the parahippocampal scene area. A second "where" driver of spatial view cells are parietal inputs, which it is proposed provide the idiothetic update for spatial view cells, used for memory recall and navigation when the spatial view details are obscured. Inferior temporal object "what" inputs and orbitofrontal cortex reward inputs connect to the human hippocampal system, and in macaques can be associated in the hippocampus with spatial view cell "where" representations to implement episodic memory. Hippocampal spatial view cells also provide a basis for navigation to a series of viewed landmarks, with the orbitofrontal cortex reward inputs to the hippocampus providing the goals for navigation, which can then be implemented by hippocampal connectivity in humans to parietal cortex regions involved in visuomotor actions in space. The presence of foveate vision and the highly developed temporal lobe for object and scene processing in primates including humans provide a basis for hippocampal spatial view cells to be key to understanding episodic memory in the primate and human hippocampus, and the roles of this system in primate including human navigation.
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Affiliation(s)
- Edmund T. Rolls
- Oxford Centre for Computational NeuroscienceOxfordUK
- Department of Computer ScienceUniversity of WarwickCoventryUK
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11
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Linli Z, Rolls ET, Zhao W, Kang J, Feng J, Guo S. Smoking is associated with lower brain volume and cognitive differences: A large population analysis based on the UK Biobank. Prog Neuropsychopharmacol Biol Psychiatry 2023; 123:110698. [PMID: 36528239 DOI: 10.1016/j.pnpbp.2022.110698] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 11/25/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
The evidence about the association of smoking with both brain structure and cognitive functions remains inconsistent. Using structural magnetic resonance imaging from the UK Biobank (n = 33,293), we examined the relationships between smoking status, dosage, and abstinence with total and 166 regional brain gray matter volumes (GMV). The relationships between the smoking parameters with cognitive function, and whether this relationship was mediated by brain structure, were then investigated. Smoking was associated with lower total and regional GMV, with the extent depending on the frequency of smoking and on whether smoking had ceased: active regular smokers had the lowest GMV (Cohen's d = -0.362), and former light smokers had a slightly smaller GMV (Cohen's d = -0.060). The smaller GMV in smokers was most evident in the thalamus. Higher lifetime exposure (i.e., pack-years) was associated with lower total GMV (β = -311.84, p = 8.35 × 10-36). In those who ceased smoking, the duration of abstinence was associated with a larger total GMV (β = 139.57, p = 2.36 × 10-08). It was further found that reduced cognitive function was associated with smoker parameters and that the associations were partially mediated by brain structure. This is the largest scale investigation we know of smoking and brain structure, and these results are likely to be robust. The findings are of associations between brain structure and smoking, and in the future, it will be important to assess whether brain structure influences smoking status, or whether smoking influences brain structure, or both.
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Affiliation(s)
- Zeqiang Linli
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China; School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou, PR China.
| | - Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK; Department of Computer Science, University of Warwick, Coventry, UK
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China
| | - Jujiao Kang
- Centre for Computational Systems Biology, Fudan University, Shanghai, PR China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry, UK; Centre for Computational Systems Biology, Fudan University, Shanghai, PR China.
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China.
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12
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Rolls ET, Feng R, Feng J. Lifestyle risks associated with brain functional connectivity and structure. Hum Brain Mapp 2023; 44:2479-2492. [PMID: 36799566 PMCID: PMC10028639 DOI: 10.1002/hbm.26225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 01/16/2023] [Accepted: 01/23/2023] [Indexed: 02/18/2023] Open
Abstract
Some lifestyle factors are related to health and brain function and structure, but the brain systems involved are incompletely understood. A general linear model was used to test the associations of the combined and separate lifestyle risk measures of alcohol use, smoking, diet, amounts of physical activity, leisure activity, and mobile phone use, with brain functional connectivity with the high resolution Human Connectome Project (HCP) atlas in 19,415 participants aged 45-78 from the UK Biobank, with replication with HCP data. Higher combined lifestyle risk scores were associated with lower functional connectivity across the whole brain, but especially of three brain systems. Low physical, and leisure and social, activity were associated with low connectivities of the somatosensory/motor cortical regions and of hippocampal memory-related regions. Low mobile phone use, perhaps indicative of poor social communication channels, was associated with low functional connectivity of brain regions in and related to the superior temporal sulcus that are involved in social behavior and face processing. Smoking was associated with lower functional connectivity of especially frontal regions involved in attention. Lower cortical thickness in some of these regions, and also lower subcortical volume of the hippocampus, amygdala, and globus pallidus, were also associated with the sum of the poor lifestyle scores. This very large scale analysis emphasizes how the lifestyle of humans relates to their brain structure and function, and provides a foundation for understanding the causalities that relate to the differences found here in the brains of different individuals.
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Affiliation(s)
- Edmund T Rolls
- Department of Computer Science, University of Warwick, Coventry, UK
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Ruiqing Feng
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry, UK
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
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13
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Rolls ET, Wirth S. Hippocampal system neurons encoding views in different species: Introduction to the Special Issue of Hippocampus 2023. Hippocampus 2023; 33:445-447. [PMID: 37042547 DOI: 10.1002/hipo.23538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 03/25/2023] [Indexed: 04/13/2023]
Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Sylvia Wirth
- Institute of Cognitive Science Marc Jeannerod, Centre National de la Recherche Scientifique UMR5229, and University of Lyon 1, Bron, France
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Rolls ET. Hippocampal spatial view cells, place cells, and concept cells: View representations. Hippocampus 2023; 33:667-687. [PMID: 37035903 DOI: 10.1002/hipo.23536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 03/21/2023] [Accepted: 03/23/2023] [Indexed: 04/11/2023]
Abstract
A commentary is provided on issues raised in the Special Issue of Hippocampus (2023) on hippocampal system view representations. First, the evidence for hippocampal and parahippocampal spatial view cells in primates including humans shows that the allocentric representations provided by at least some of these cells are very useful for human memory in that where objects and rewards are seen in the world "out there" is a key component of episodic memory and navigation. Spatial view cell representations provide for memory and navigation to be independent of the place where the individual is currently located and of the egocentric coordinates of the viewed location and the facing direction of the individual. Second, memory and navigation in humans are normally related to the visual cues encoded by spatial view cells that define a location "out there" such as a building, hill, and so forth, not to an unmarked place without local cues and identified only by distant environmental/room cues. Third, "mixed" representations, for example of particular combinations of spatial view and place, can arise if the training has been for only some combinations of place and view, for that is what can then be learned by the hippocampus. Fourth, rodents, with their much less good visual acuity (~1 cycle/° in rats, compared with ~60 cycles/° for the human fovea), and rodents' very wide viewing angle for the world (~270°) might be expected, when using the same computational mechanisms as in primates, to use widely spaced environmental cues to define a place where the rodent is located, supported by inputs about place using local olfactory and tactile cues. Fifth, it is shown how view-point dependent allocentric representations could form a view-point independent allocentric representation for memory and navigation. Sixth, concept cells in humans and primates with connectivity to the hippocampus are compared.
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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
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Rolls ET, Deco G, Huang CC, Feng J. Prefrontal and somatosensory-motor cortex effective connectivity in humans. Cereb Cortex 2023; 33:4939-4963. [PMID: 36227217 DOI: 10.1093/cercor/bhac1496] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/07/2022] [Accepted: 09/08/2022] [Indexed: 05/27/2023] 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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Zhang J, Yao Y, Wu JS, Rolls ET, Sun CC, Bu LH, Lu JF, Lin CP, Feng JF, Mao Y, Zhou LF. The cortical regions and white matter tracts underlying auditory comprehension in patients with primary brain tumor. Hum Brain Mapp 2023; 44:1603-1616. [PMID: 36515634 PMCID: PMC9921237 DOI: 10.1002/hbm.26161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 11/07/2022] [Accepted: 11/11/2022] [Indexed: 12/15/2022] Open
Abstract
The comprehension of spoken language is one of the most essential language functions in humans. However, the neurological underpinnings of auditory comprehension remain under debate. Here we used multi-modal neuroimaging analyses on a group of patients with low-grade gliomas to localize cortical regions and white matter tracts responsible for auditory language comprehension. Region-of-interests and voxel-level whole-brain analyses showed that cortical areas in the posterior temporal lobe are crucial for language comprehension. The fiber integrity assessed with diffusion tensor imaging of the arcuate fasciculus and the inferior longitudinal fasciculus was strongly correlated with both auditory comprehension and the grey matter volume of the inferior temporal and middle temporal gyri. Together, our findings provide direct evidence for an integrated network of auditory comprehension whereby the superior temporal gyrus and sulcus, the posterior parts of the middle and inferior temporal gyri serve as auditory comprehension cortex, and the arcuate fasciculus and the inferior longitudinal fasciculus subserve as crucial structural connectivity. These findings provide critical evidence on the neural underpinnings of language comprehension.
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Affiliation(s)
- Jie Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Shanghai, China.,Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China.,Neurosurgical Institute of Fudan University, Shanghai, China.,Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
| | - Ye Yao
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China.,National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.,Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China
| | - Jin-Song Wu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Shanghai, China.,Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China.,Neurosurgical Institute of Fudan University, Shanghai, China.,Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
| | - Edmund T Rolls
- Department of Computer Science, University of Warwick, Coventry, UK.,Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China.,Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Ce-Chen Sun
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China
| | - Ling-Hao Bu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Shanghai, China.,Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China.,Neurosurgical Institute of Fudan University, Shanghai, China.,Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
| | - Jun-Feng Lu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Shanghai, China.,Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China.,Neurosurgical Institute of Fudan University, Shanghai, China.,Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
| | - Ching-Po Lin
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Department of Computer Science, University of Warwick, Coventry, UK.,Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Shanghai, China.,Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China.,Neurosurgical Institute of Fudan University, Shanghai, China.,Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
| | - Liang-Fu Zhou
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.,National Center for Neurological Disorders, Shanghai, China.,Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China.,Neurosurgical Institute of Fudan University, Shanghai, China.,Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
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18
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Rolls ET, Feng R, Cheng W, Feng J. Orbitofrontal cortex connectivity is associated with food reward and body weight in humans. Soc Cogn Affect Neurosci 2023; 18:nsab083. [PMID: 34189586 PMCID: PMC10498940 DOI: 10.1093/scan/nsab083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 06/10/2021] [Accepted: 06/29/2021] [Indexed: 11/12/2022] Open
Abstract
The aim was to investigate with very large-scale analyses whether there are underlying functional connectivity differences between humans that relate to food reward and whether these in turn are associated with being overweight. In 37 286 humans from the UK Biobank, resting-state functional connectivities of the orbitofrontal cortex (OFC), especially with the anterior cingulate cortex, were positively correlated with the liking for sweet foods (False Discovery Rate (FDR) P < 0.05). They were also positively correlated with the body mass index (BMI) (FDR P < 0.05). Moreover, in a sample of 502 492 people, the 'liking for sweet foods' was correlated with their BMI (r = 0.06, P < 10-125). In a cross-validation with 545 participants from the Human Connectome Project, a higher functional connectivity involving the OFC relative to other brain areas was associated with a high BMI (≥30) compared to a mid-BMI group (22-25; P = 6 × 10-5), and low OFC functional connectivity was associated with a low BMI (≤20.5; P < 0.024). It is proposed that a high BMI relates to increased efficacy of OFC food reward systems and a low BMI to decreased efficacy. This was found with no stimulation by food, so may be an underlying individual difference in brain connectivity that is related to food reward and BMI.
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Affiliation(s)
- Edmund T Rolls
- 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
- Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Ruiqing Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, 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
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
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Rolls ET. The orbitofrontal cortex, food reward, body weight and obesity. Soc Cogn Affect Neurosci 2023; 18:6217585. [PMID: 33830272 PMCID: PMC9997078 DOI: 10.1093/scan/nsab044] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/17/2021] [Accepted: 04/08/2021] [Indexed: 12/23/2022] Open
Abstract
In primates including humans, the orbitofrontal cortex is the key brain region representing the reward value and subjective pleasantness of the sight, smell, taste and texture of food. At stages of processing before this, in the insular taste cortex and inferior temporal visual cortex, the identity of the food is represented, but not its affective value. In rodents, the whole organisation of reward systems appears to be different, with reward value reflected earlier in processing systems. In primates and humans, the amygdala is overshadowed by the great development of the orbitofrontal cortex. Social and cognitive factors exert a top-down influence on the orbitofrontal cortex, to modulate the reward value of food that is represented in the orbitofrontal cortex. Recent evidence shows that even in the resting state, with no food present as a stimulus, the liking for food, and probably as a consequence of that body mass index, is correlated with the functional connectivity of the orbitofrontal cortex and ventromedial prefrontal cortex. This suggests that individual differences in these orbitofrontal cortex reward systems contribute to individual differences in food pleasantness and obesity. Implications of how these reward systems in the brain operate for understanding, preventing and treating obesity are described.
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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
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20
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Verhagen JV, Baker KL, Vasan G, Pieribone VA, Rolls ET. Odor encoding by signals in the olfactory bulb. J Neurophysiol 2023; 129:431-444. [PMID: 36598147 PMCID: PMC9925169 DOI: 10.1152/jn.00449.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 01/05/2023] Open
Abstract
To understand the operation of the olfactory system, it is essential to know how information is encoded in the olfactory bulb. We applied Shannon information theoretic methods to address this, with signals from up to 57 glomeruli simultaneously optically imaged from presynaptic inputs in glomeruli in the mouse dorsal (dOB) and lateral (lOB) olfactory bulb, in response to six exemplar pure chemical odors. We discovered that, first, the tuning of these signals from glomeruli to a set of odors is remarkably broad, with a mean sparseness of 0.83 and a mean signal correlation of 0.64. Second, both of these factors contribute to the low information that is available from the responses of even populations of many tens of glomeruli, which was only 1.35 bits across 33 glomeruli on average, compared with the 2.58 bits required to perfectly encode these six odors. Third, although there is considerable interest in the possibility of temporal encoding of stimulus including odor identity, the amount of information in the temporal aspects of the presynaptic glomerular responses was low (mean 0.11 bits) and, importantly, was redundant with respect to the information available from the rates. Fourth, the information from simultaneously recorded glomeruli asymptotes very gradually and nonlinearly, showing that glomeruli do not have independent responses. Fifth, the information from a population became available quite rapidly, within 100 ms of sniff onset, and the peak of the glomerular response was at 200 ms. Sixth, the information from the lOB was not additive with that of the dOB.NEW & NOTEWORTHY We report broad tuning and low odor information available across the lateral and dorsal bulb populations of glomeruli. Even though response latencies can be significantly predictive of stimulus identity, such contained very little information and none that was not redundant with information based on rate coding alone. Last, in line with the emerging notion of the important role of earliest stages of responses ("primacy"), we report a very rapid rise in information after each inhalation.
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Affiliation(s)
- Justus V Verhagen
- The John B. Pierce Laboratory, New Haven, Connecticut
- Department of Neuroscience, Yale University, New Haven, Connecticut
| | - Keeley L Baker
- The John B. Pierce Laboratory, New Haven, Connecticut
- Department of Neuroscience, Yale University, New Haven, Connecticut
| | - Ganesh Vasan
- The John B. Pierce Laboratory, New Haven, Connecticut
- Department of Neuroscience, Yale University, New Haven, Connecticut
| | - Vincent A Pieribone
- The John B. Pierce Laboratory, New Haven, Connecticut
- Department of Neuroscience, Yale University, New Haven, Connecticut
- Department of Cellular and Molecular Physiology, Yale School of Medicine, New Haven, Connecticut
| | - Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom
- University of Warwick, Coventry, United Kingdom
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Wang HF, Zhang W, Rolls ET, Li Y, Wang L, Ma YH, Kang J, Feng J, Yu JT, Cheng W. Hearing impairment is associated with cognitive decline, brain atrophy and tau pathology. EBioMedicine 2022; 86:104336. [PMID: 36356475 PMCID: PMC9649369 DOI: 10.1016/j.ebiom.2022.104336] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/01/2022] [Accepted: 10/17/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Hearing impairment was recently identified as the most prominent risk factor for dementia. However, the mechanisms underlying the link between hearing impairment and dementia are still unclear. METHODS We investigated the association of hearing performance with cognitive function, brain structure and cerebrospinal fluid (CSF) proteins in cross-sectional, longitudinal, mediation and genetic association analyses across the UK Biobank (N = 165,550), the Chinese Alzheimer's Biomarker and Lifestyle (CABLE, N = 863) study, and the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 1770) database. FINDINGS Poor hearing performance was associated with worse cognitive function in the UK Biobank and in the CABLE study. Hearing impairment was significantly related to lower volume of temporal cortex, hippocampus, inferior parietal lobe, precuneus, etc., and to lower integrity of white matter (WM) tracts. Furthermore, a higher polygenic risk score (PRS) for hearing impairment was strongly associated with lower cognitive function, lower volume of gray matter, and lower integrity of WM tracts. Moreover, hearing impairment was correlated with a high level of CSF tau protein in the CABLE study and in the ADNI database. Finally, mediation analyses showed that brain atrophy and tau pathology partly mediated the association between hearing impairment and cognitive decline. INTERPRETATION Hearing impairment is associated with cognitive decline, brain atrophy and tau pathology, and hearing impairment may reflect the risk for cognitive decline and dementia as it is related to bran atrophy and tau accumulation in brain. However, it is necessary to assess the mechanism in future animal studies. FUNDING A full list of funding bodies that supported this study can be found in the Acknowledgements section.
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Affiliation(s)
- Hui-Fu Wang
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Zhang
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Edmund T Rolls
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK; Oxford Centre for Computational Neuroscience, Oxford, UK
| | | | - Yuzhu Li
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Linbo Wang
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Ya-Hui Ma
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jujiao Kang
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; Zhangjiang Fudan International Innovation Center, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China
| | - Jin-Tai Yu
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
| | - Wei Cheng
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
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25
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Ma Q, Wang H, Rolls ET, Xiang S, Li J, Li Y, Zhou Q, Cheng W, Li F. Lower gestational age is associated with lower cortical volume and cognitive and educational performance in adolescence. BMC Med 2022; 20:424. [PMID: 36329481 PMCID: PMC9635194 DOI: 10.1186/s12916-022-02627-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Gestational age (GA) is associated with later cognition and behavior. However, it is unclear how specific cognitive domains and brain structural development varies with the stepwise change of gestational duration. METHODS This large-scale longitudinal cohort study analyzed 11,878 early adolescents' brain volume maps at 9-10 years (baseline) and 5685 at 11-12 years (a 2-year follow-up) from the Adolescent Brain Cognitive Development (ABCD) study. According to gestational age, adolescents were divided into five categorical groups: ≤ 33 weeks, 34-35 weeks, 36 weeks, 37-39 weeks, and ≥ 40 weeks. The NIH Toolbox was used to estimate neurocognitive performance, including crystallized and fluid intelligence, which was measured for 11,878 adolescents at baseline with crystallized intelligence and relevant subscales obtained at 2-year follow-up (with participant numbers ranging from 6185 to 6310 depending on the cognitive domain). An additional large population-based cohort of 618,070 middle adolescents at ninth-grade (15-16 years) from the Danish national register was utilized to validate the association between gestational age and academic achievements. A linear mixed model was used to examine the group differences between gestational age and neurocognitive performance, school achievements, and grey matter volume. A mediation analysis was performed to examine whether brain structural volumes mediated the association between GA and neurocognition, followed with a longitudinal analysis to track the changes. RESULTS Significant group differences were found in all neurocognitive scores, school achievements, and twenty-five cortical regional volumes (P < 0.05, Bonferroni corrected). Specifically, lower gestational ages were associated with graded lower cognition and school achievements and with smaller brain volumes of the fronto-parieto-temporal, fusiform, cingulate, insula, postcentral, hippocampal, thalamic, and pallidal regions. These lower brain volumes mediated the association between gestational age and cognitive function (P = 1 × 10-8, β = 0.017, 95% CI: 0.007-0.028). Longitudinal analysis showed that compared to full term adolescents, preterm adolescents still had smaller brain volumes and crystallized intelligence scores at 11-12 years. CONCLUSIONS These results emphasize the relationships between gestational age at birth and adolescents' lower brain volume, and lower cognitive and educational performance, measured many years later when 9-10 and 11-12 years old. The study indicates the importance of early screening and close follow-up for neurocognitive and behavioral development for children and adolescents born with gestational ages that are even a little lower than full term.
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Affiliation(s)
- Qing Ma
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China
| | - Hui Wang
- Department of Developmental and Behavioral Pediatric & Child Primary Care/MOE-Shanghai Key Laboratory of Children's Environmental Health, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200082, China
| | - Edmund T Rolls
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, Conventry, UK.,Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Shitong Xiang
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China
| | - Jiong Li
- Department of Clinical Medicine, Aarhus University, Aarhus, 8000, Denmark
| | - Yuzhu Li
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China
| | - Qiongjie Zhou
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200011, China.,Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, 200011, China
| | - Wei Cheng
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China. .,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China. .,Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, 321004, China. .,Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, 200032, China.
| | - Fei Li
- Department of Developmental and Behavioral Pediatric & Child Primary Care/MOE-Shanghai Key Laboratory of Children's Environmental Health, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200082, China.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Huang SY, Li YZ, Zhang YR, Huang YY, Wu BS, Zhang W, Deng YT, Chen SD, He XY, Chen SF, Dong Q, Zhang C, Chen RJ, Suckling J, Rolls ET, Feng JF, Cheng W, Yu JT. Sleep, physical activity, sedentary behavior, and risk of incident dementia: a prospective cohort study of 431,924 UK Biobank participants. Mol Psychiatry 2022; 27:4343-4354. [PMID: 35701596 DOI: 10.1038/s41380-022-01655-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/17/2022] [Accepted: 05/31/2022] [Indexed: 02/07/2023]
Abstract
Although sleep, physical activity and sedentary behavior have been found to be associated with dementia risk, findings are inconsistent and their joint relationship remains unclear. This study aimed to investigate independent and joint associations of these three modifiable behaviors with dementia risks. A total of 431,924 participants (median follow-up 9.0 years) without dementia from UK Biobank were included. Multiple Cox regressions were used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). Models fitted with restricted cubic spline were conducted to test for linear and nonlinear shapes of each association. Sleep duration, leisure-time physical activity (LTPA), and screen-based sedentary behavior individually associated with dementia risks in different non-linear patterns. Sleep duration associated with dementia in a U-shape with a nadir at 7 h/day. LTPA revealed a curvilinear relationship with dementia in diminishing tendency, while sedentary behavior revealed a J-shaped relationship. The dementia risk was 17% lower in the high LTPA group (HR[95%CI]: 0.83[0.76-0.91]) and 22% higher in the high sedentary behavior group (1.22[1.10-1.35]) compared to the corresponding low-level group, respectively. A combination of seven-hour/day sleep, moderate-to-high LTPA, and low-to-moderate sedentary behavior showed the lowest dementia risk (0.59[0.50-0.69]) compared to the referent group (longer or shorter sleep/low LTPA/high sedentary behavior). Notably, each behavior was non-linearly associated with brain structures in a pattern similar to its association with dementia, suggesting they may affect dementia risk by affecting brain structures. Our findings highlight the potential to change these three daily behaviors individually and simultaneously to reduce the risk of dementia.
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Affiliation(s)
- Shu-Yi Huang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Zhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Yuan Huang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Yue-Ting Deng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiao-Yu He
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shu-Fen Chen
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Can Zhang
- Genetics and Aging Research Unit, McCance Center for Brain Health, Mass General Institute for Neurodegenerative Diseases (MIND), Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Ren-Jie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai, China
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Oxford Centre for Computational Neuroscience, Oxford, UK
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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Chen SD, Zhang W, Li YZ, Yang L, Huang YY, Deng YT, Wu BS, Suckling J, Rolls ET, Feng JF, Cheng W, Dong Q, Yu JT. A Phenome-wide Association and Mendelian Randomization Study for Alzheimer's Disease: A Prospective Cohort Study of 502,493 Participants From the UK Biobank. Biol Psychiatry 2022; 93:790-801. [PMID: 36788058 DOI: 10.1016/j.biopsych.2022.08.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 06/15/2022] [Accepted: 08/05/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Considerable uncertainty remains regarding associations of multiple risk factors with Alzheimer's disease (AD). We aimed to systematically screen and validate a wide range of potential risk factors for AD. METHODS Among 502,493 participants from the UK Biobank, baseline data were extracted for 4171 factors spanning 10 different categories. Phenome-wide association analyses and time-to-event analyses were conducted to identify factors associated with both polygenic risk scores for AD and AD diagnosis at follow-up. We performed two-sample Mendelian randomization analysis to further assess their potential causal relationships with AD and imaging association analysis to discover underlying mechanisms. RESULTS We identified 39 factors significantly associated with both AD polygenic risk scores and risk of incident AD, where higher levels of education, body size, basal metabolic rate, fat-free mass, computer use, and cognitive functions were associated with a decreased risk of developing AD, and selective food intake and more outdoor exposures were associated with an increased risk of developing AD. The identified factors were also associated with AD-related brain structures, including the hippocampus, entorhinal cortex, and inferior/middle temporal cortex, and 21 of these factors were further supported by Mendelian randomization evidence. CONCLUSIONS To our knowledge, this is the first study to comprehensively and rigorously assess the effects of wide-ranging risk factors on AD. Strong evidence was found for fat-free body mass, basal metabolic rate, computer use, selective food intake, and outdoor exposures as new risk factors for AD. Integration of genetic, clinical, and neuroimaging information may help prioritize risk factors and prevention targets for AD.
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Affiliation(s)
- Shi-Dong Chen
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Yu-Zhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Liu Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yu-Yuan Huang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yue-Ting Deng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Oxford Centre for Computational Neuroscience, Oxford, United Kingdom; Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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Rolls ET. The hippocampus, ventromedial prefrontal cortex, and episodic and semantic memory. Prog Neurobiol 2022; 217:102334. [PMID: 35870682 DOI: 10.1016/j.pneurobio.2022.102334] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/07/2022] [Accepted: 07/19/2022] [Indexed: 11/24/2022]
Abstract
The human ventromedial prefrontal cortex (vmPFC)/anterior cingulate cortex is implicated in reward and emotion, but also in memory. It is shown how the human orbitofrontal cortex connecting with the vmPFC and anterior cingulate cortex provide a route to the hippocampus for reward and emotional value to be incorporated into episodic memory, enabling memory of where a reward was seen. It is proposed that this value component results in primarily episodic memories with some value component to be repeatedly recalled from the hippocampus so that they are more likely to become incorporated into neocortical semantic and autobiographical memories. The same orbitofrontal and anterior cingulate regions also connect in humans to the septal and basal forebrain cholinergic nuclei, thereby helping to consolidate memory, and helping to account for why damage to the vMPFC impairs memory. The human hippocampus and vmPFC thus contribute in complementary ways to forming episodic and semantic memories.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK; University of Warwick, Department of Computer Science, Coventry, UK.
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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: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Shen C, Rolls ET, Cheng W, Kang J, Dong G, Xie C, Zhao XM, Sahakian BJ, Feng J. Associations of Social Isolation and Loneliness With Later Dementia. Neurology 2022; 99:e164-e175. [PMID: 35676089 DOI: 10.1212/wnl.0000000000200583] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 03/08/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES To investigate the independent associations of social isolation and loneliness with incident dementia and to explore the potential neurobiological mechanisms. METHODS We utilized the UK Biobank cohort to establish Cox proportional hazard models with social isolation and loneliness as separate exposures. Demographic (sex, age, and ethnicity), socioeconomic (education level, household income, and Townsend deprivation index), biological (body mass index, APOE genotype, diabetes, cancer, cardiovascular disease, and other), cognitive (speed of processing and visual memory), behavioral (current smoker, alcohol intake, and physical activity), and psychological (social isolation or loneliness, depressive symptoms, and neuroticism) factors measured at baseline were adjusted. Then, voxel-wise brainwide association analyses were used to identify gray matter volumes (GMVs) associated with social isolation and with loneliness. Partial least squares regression was performed to test the spatial correlation of GMV differences and gene expression using the Allen Human Brain Atlas. RESULTS We included 462,619 participants (mean age at baseline 57.0 years [SD 8.1]). With a mean follow-up of 11.7 years (SD 1.7), 4,998 developed all-cause dementia. Social isolation was associated with a 1.26-fold increased risk of dementia (95% CI, 1.15-1.37) independently of various risk factors including loneliness and depression (i.e., full adjustment). However, the fully adjusted hazard ratio for dementia related to loneliness was 1.04 (95% CI, 0.94-1.16) and 75% of this relationship was attributable to depressive symptoms. Structural MRI data were obtained from 32,263 participants (mean age 63.5 years [SD 7.5]). Socially isolated individuals had lower GMVs in temporal, frontal, and other (e.g., hippocampal) regions. Mediation analysis showed that the identified GMVs partly mediated the association between social isolation at baseline and cognitive function at follow-up. Social isolation-related lower GMVs were related to underexpression of genes that are downregulated in Alzheimer disease and to genes that are involved in mitochondrial dysfunction and oxidative phosphorylation. DISCUSSION Social isolation is a risk factor for dementia that is independent of loneliness and many other covariates. Social isolation-related brain structural differences coupled with different molecular functions also support the associations of social isolation with cognition and dementia. Social isolation may thus be an early indicator of an increased risk of dementia.
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Affiliation(s)
- Chun Shen
- From the Institute of Science and Technology for Brain-Inspired Intelligence (C.S., W.C., J.K., G.D., C.X., X.-M.Z., B.S., J.F.), Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education (C.S., G.D., C.X., X.-M.Z., J.F.), Shanghai Center for Mathematical Sciences (J.K.), and MOE Frontiers Center for Brain Science (X.-M.Z., J.F.), Fudan University, Shanghai, China; Department of Computer Science (E.R., J.F.), University of Warwick, Coventry; Oxford Centre for Computational Neuroscience (E.R.); Behavioural and Clinical Neuroscience Institute (B.S.) and Department of Psychiatry (B.S.), University of Cambridge, UK; and Zhangjiang Fudan International Innovation Center (J.F.), Shanghai, China
| | - Edmund T Rolls
- From the Institute of Science and Technology for Brain-Inspired Intelligence (C.S., W.C., J.K., G.D., C.X., X.-M.Z., B.S., J.F.), Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education (C.S., G.D., C.X., X.-M.Z., J.F.), Shanghai Center for Mathematical Sciences (J.K.), and MOE Frontiers Center for Brain Science (X.-M.Z., J.F.), Fudan University, Shanghai, China; Department of Computer Science (E.R., J.F.), University of Warwick, Coventry; Oxford Centre for Computational Neuroscience (E.R.); Behavioural and Clinical Neuroscience Institute (B.S.) and Department of Psychiatry (B.S.), University of Cambridge, UK; and Zhangjiang Fudan International Innovation Center (J.F.), Shanghai, China
| | - Wei Cheng
- From the Institute of Science and Technology for Brain-Inspired Intelligence (C.S., W.C., J.K., G.D., C.X., X.-M.Z., B.S., J.F.), Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education (C.S., G.D., C.X., X.-M.Z., J.F.), Shanghai Center for Mathematical Sciences (J.K.), and MOE Frontiers Center for Brain Science (X.-M.Z., J.F.), Fudan University, Shanghai, China; Department of Computer Science (E.R., J.F.), University of Warwick, Coventry; Oxford Centre for Computational Neuroscience (E.R.); Behavioural and Clinical Neuroscience Institute (B.S.) and Department of Psychiatry (B.S.), University of Cambridge, UK; and Zhangjiang Fudan International Innovation Center (J.F.), Shanghai, China
| | - Jujiao Kang
- From the Institute of Science and Technology for Brain-Inspired Intelligence (C.S., W.C., J.K., G.D., C.X., X.-M.Z., B.S., J.F.), Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education (C.S., G.D., C.X., X.-M.Z., J.F.), Shanghai Center for Mathematical Sciences (J.K.), and MOE Frontiers Center for Brain Science (X.-M.Z., J.F.), Fudan University, Shanghai, China; Department of Computer Science (E.R., J.F.), University of Warwick, Coventry; Oxford Centre for Computational Neuroscience (E.R.); Behavioural and Clinical Neuroscience Institute (B.S.) and Department of Psychiatry (B.S.), University of Cambridge, UK; and Zhangjiang Fudan International Innovation Center (J.F.), Shanghai, China
| | - Guiying Dong
- From the Institute of Science and Technology for Brain-Inspired Intelligence (C.S., W.C., J.K., G.D., C.X., X.-M.Z., B.S., J.F.), Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education (C.S., G.D., C.X., X.-M.Z., J.F.), Shanghai Center for Mathematical Sciences (J.K.), and MOE Frontiers Center for Brain Science (X.-M.Z., J.F.), Fudan University, Shanghai, China; Department of Computer Science (E.R., J.F.), University of Warwick, Coventry; Oxford Centre for Computational Neuroscience (E.R.); Behavioural and Clinical Neuroscience Institute (B.S.) and Department of Psychiatry (B.S.), University of Cambridge, UK; and Zhangjiang Fudan International Innovation Center (J.F.), Shanghai, China
| | - Chao Xie
- From the Institute of Science and Technology for Brain-Inspired Intelligence (C.S., W.C., J.K., G.D., C.X., X.-M.Z., B.S., J.F.), Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education (C.S., G.D., C.X., X.-M.Z., J.F.), Shanghai Center for Mathematical Sciences (J.K.), and MOE Frontiers Center for Brain Science (X.-M.Z., J.F.), Fudan University, Shanghai, China; Department of Computer Science (E.R., J.F.), University of Warwick, Coventry; Oxford Centre for Computational Neuroscience (E.R.); Behavioural and Clinical Neuroscience Institute (B.S.) and Department of Psychiatry (B.S.), University of Cambridge, UK; and Zhangjiang Fudan International Innovation Center (J.F.), Shanghai, China
| | - Xing-Ming Zhao
- From the Institute of Science and Technology for Brain-Inspired Intelligence (C.S., W.C., J.K., G.D., C.X., X.-M.Z., B.S., J.F.), Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education (C.S., G.D., C.X., X.-M.Z., J.F.), Shanghai Center for Mathematical Sciences (J.K.), and MOE Frontiers Center for Brain Science (X.-M.Z., J.F.), Fudan University, Shanghai, China; Department of Computer Science (E.R., J.F.), University of Warwick, Coventry; Oxford Centre for Computational Neuroscience (E.R.); Behavioural and Clinical Neuroscience Institute (B.S.) and Department of Psychiatry (B.S.), University of Cambridge, UK; and Zhangjiang Fudan International Innovation Center (J.F.), Shanghai, China
| | - Barbara J Sahakian
- From the Institute of Science and Technology for Brain-Inspired Intelligence (C.S., W.C., J.K., G.D., C.X., X.-M.Z., B.S., J.F.), Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education (C.S., G.D., C.X., X.-M.Z., J.F.), Shanghai Center for Mathematical Sciences (J.K.), and MOE Frontiers Center for Brain Science (X.-M.Z., J.F.), Fudan University, Shanghai, China; Department of Computer Science (E.R., J.F.), University of Warwick, Coventry; Oxford Centre for Computational Neuroscience (E.R.); Behavioural and Clinical Neuroscience Institute (B.S.) and Department of Psychiatry (B.S.), University of Cambridge, UK; and Zhangjiang Fudan International Innovation Center (J.F.), Shanghai, China
| | - Jianfeng Feng
- From the Institute of Science and Technology for Brain-Inspired Intelligence (C.S., W.C., J.K., G.D., C.X., X.-M.Z., B.S., J.F.), Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education (C.S., G.D., C.X., X.-M.Z., J.F.), Shanghai Center for Mathematical Sciences (J.K.), and MOE Frontiers Center for Brain Science (X.-M.Z., J.F.), Fudan University, Shanghai, China; Department of Computer Science (E.R., J.F.), University of Warwick, Coventry; Oxford Centre for Computational Neuroscience (E.R.); Behavioural and Clinical Neuroscience Institute (B.S.) and Department of Psychiatry (B.S.), University of Cambridge, UK; and Zhangjiang Fudan International Innovation Center (J.F.), Shanghai, China.
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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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Yang A, Rolls ET, Dong G, Du J, Li Y, Feng J, Cheng W, Zhao XM. Longer screen time utilization is associated with the polygenic risk for Attention-deficit/hyperactivity disorder with mediation by brain white matter microstructure. EBioMedicine 2022; 80:104039. [PMID: 35509143 PMCID: PMC9079003 DOI: 10.1016/j.ebiom.2022.104039] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 04/06/2022] [Accepted: 04/14/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) has been reported to be associated with longer screen time utilization (STU) at the behavioral level. However, whether there are shared neural links between ADHD symptoms and prolonged STU is not clear and has not been explored in a single large-scale dataset. METHODS Leveraging the genetics, neuroimaging and behavioral data of 11,000+ children aged 9-11 from the Adolescent Brain Cognitive Development cohort, this study investigates the associations between the polygenic risk and trait for ADHD, STU, and white matter microstructure through cross-sectionally and longitudinal analyses. FINDINGS Children with higher polygenic risk scores for ADHD tend to have longer STU and more severe ADHD symptoms. Fractional anisotropy (FA) values in several white matter tracts are negatively correlated with both the ADHD polygenic risk score and STU, including the inferior frontal-striatal tract, inferior frontal-occipital fasciculus, superior longitudinal fasciculus and corpus callosum. Most of these tracts are linked to visual-related functions. Longitudinal analyses indicate a directional effect of white matter microstructure on the ADHD scale, and a bi-directional effect between the ADHD scale and STU. Furthermore, reduction of FA in several white matter tracts mediates the association between the ADHD polygenic risk score and STU. INTERPRETATION These findings shed new light on the shared neural overlaps between ADHD symptoms and prolonged STU, and provide evidence that the polygenic risk for ADHD is related, via white matter microstructure and the ADHD trait, to STU. FUNDING This study was mainly supported by NSFC and National Key R&D Program of China.
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Affiliation(s)
- Anyi Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, UK; Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Guiying Dong
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai, China
| | - Jingnan Du
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai, China
| | - Yuzhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, UK; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Rolls ET, Wan Z, Cheng W, Feng J. Risk-taking in humans and the medial orbitofrontal cortex reward system. Neuroimage 2022; 249:118893. [PMID: 35007715 DOI: 10.1016/j.neuroimage.2022.118893] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 12/28/2021] [Accepted: 01/06/2022] [Indexed: 11/29/2022] Open
Abstract
Risk-taking differs between humans, and is associated with the personality measures of impulsivity and sensation-seeking. To analyse the brain systems involved, self-report risk-taking, resting state functional connectivity, and related behavioral measures were analyzed in 18,740 participants of both sexes from the UK Biobank. Functional connectivities of the medial orbitofrontal cortex, ventromedial prefrontal cortex (VMPFC), and the parahippocampal areas were significantly higher in the risk-taking group (p < 0.001, FDR corrected). The risk-taking measure was validated in that it was significantly associated with alcohol drinking amount (r = 0.08, p = 5.1×10-28), cannabis use (r = 0.12, p = 6.0×10-66), and anxious feelings (r = -0.12, p = 7.6×-98). The functional connectivity findings were cross-validated in two independent datasets. The higher functional connectivity of the medial orbitofrontal cortex and VMPFC included higher connectivity with the anterior cingulate cortex, which provides a route for these reward-related regions to have a greater influence on action in risk-taking individuals. In conclusion, the medial orbitofrontal cortex, which is involved in reward value and pleasure, was found to be related to risk-taking, which is associated with impulsivity. An implication is that risk-taking is driven by specific orbitofrontal cortex reward systems, and is different for different rewards which are represented differently in the brains of different individuals. This is an advance in understanding the bases and mechanisms of risk-taking in humans, given that the orbitofrontal cortex, VMPFC and anterior cingulate cortex are highly developed in humans, and that risk-taking can be reported in humans.
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Affiliation(s)
- Edmund T Rolls
- 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; Oxford Centre for Computational Neuroscience, Oxford, UK.
| | - Zhuo Wan
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, 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; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China
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36
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Ma Q, Rolls ET, Huang CC, Cheng W, Feng J. Extensive cortical functional connectivity of the human hippocampal memory system. Cortex 2021; 147:83-101. [PMID: 35026557 DOI: 10.1016/j.cortex.2021.11.014] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 10/12/2021] [Accepted: 11/22/2021] [Indexed: 01/09/2023]
Abstract
The cortical connections of the human hippocampal memory system are fundamental to understanding its operation in health and disease, especially in the context of the great development of the human cortex. The functional connectivity of the human hippocampal system was analyzed in 172 participants imaged at 7T in the Human Connectome Project. The human hippocampus has high functional connectivity not only with the entorhinal cortex, but also with areas that are more distant in the ventral 'what' stream including the perirhinal cortex and temporal cortical visual areas. Parahippocampal gyrus TF in humans has connectivity with this ventral 'what' subsystem. Correspondingly for the dorsal stream, the hippocampus has high functional connectivity not only with the presubiculum, but also with areas more distant, the medial parahippocampal cortex TH which includes the parahippocampal place or scene area, the posterior cingulate including retrosplenial cortex, and the parietal cortex. Further, there is considerable cross connectivity between the ventral and dorsal streams with the hippocampus. The findings are supported by anatomical connections, which together provide an unprecedented and quantitative overview of the extensive cortical connectivity of the human hippocampal system that goes beyond hierarchically organised and segregated pathways connecting the hippocampus and neocortex, and leads to new concepts on the operation of the hippocampal memory system in humans.
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Affiliation(s)
- Qing Ma
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, UK; Oxford Centre for Computational Neuroscience, Oxford, UK.
| | - 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
| | - Wei Cheng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, UK; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
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Wang L, Zhou C, Cheng W, Rolls ET, Huang P, Ma N, Liu Y, Zhang Y, Guan X, Guo T, Wu J, Gao T, Xuan M, Gu Q, Xu X, Zhang B, Gong W, Du J, Zhang W, Feng J, Zhang M. Dopamine depletion and subcortical dysfunction disrupt cortical synchronization and metastability affecting cognitive function in Parkinson's disease. Hum Brain Mapp 2021; 43:1598-1610. [PMID: 34904766 PMCID: PMC8886656 DOI: 10.1002/hbm.25745] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/28/2021] [Accepted: 11/29/2021] [Indexed: 12/14/2022] Open
Abstract
Parkinson's disease (PD) is primarily characterized by the loss of dopaminergic cells and atrophy in subcortical regions. However, the impact of these pathological changes on large-scale dynamic integration and segregation of the cortex are not well understood. In this study, we investigated the effect of subcortical dysfunction on cortical dynamics and cognition in PD. Spatiotemporal dynamics of the phase interactions of resting-state blood-oxygen-level-dependent signals in 159 PD patients and 152 normal control (NC) individuals were estimated. The relationships between subcortical atrophy, subcortical-cortical fiber connectivity impairment, cortical synchronization/metastability, and cognitive performance were then assessed. We found that cortical synchronization and metastability in PD patients were significantly decreased. To examine whether this is an effect of dopamine depletion, we investigated 45 PD patients both ON and OFF dopamine replacement therapy, and found that cortical synchronization and metastability are significantly increased in the ON state. The extent of cortical synchronization and metastability in the OFF state reflected cognitive performance and mediates the difference in cognitive performance between the PD and NC groups. Furthermore, both the thalamic volume and thalamocortical fiber connectivity had positive relationships with cortical synchronization and metastability in the dopaminergic OFF state, and mediate the difference in cortical synchronization between the PD and NC groups. In addition, thalamic volume also reflected cognitive performance, and cortical synchronization/metastability mediated the relationship between thalamic volume and cognitive performance in PD patients. Together, these results highlight that subcortical dysfunction and reduced dopamine levels are responsible for decreased cortical synchronization and metastability, further affecting cognitive performance in PD. This might lead to biomarkers being identified that can predict if a patient is at risk of developing dementia.
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Affiliation(s)
- Linbo Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ningning Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Yuchen Liu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Yajuan Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ting Gao
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Min Xuan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Quanquan Gu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weikang Gong
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Jingnan Du
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Rolls ET. On pattern separation in the primate, including human, hippocampus. Trends Cogn Sci 2021; 25:920-922. [PMID: 34598879 DOI: 10.1016/j.tics.2021.07.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 07/04/2021] [Accepted: 07/08/2021] [Indexed: 11/26/2022]
Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK; Department of Computer Science, University of Warwick, Coventry, UK.
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Huang CC, Rolls ET, Hsu CCH, Feng J, Lin CP. Extensive Cortical Connectivity of the Human Hippocampal Memory System: Beyond the "What" and "Where" Dual Stream Model. Cereb Cortex 2021; 31:4652-4669. [PMID: 34013342 PMCID: PMC8866812 DOI: 10.1093/cercor/bhab113] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 04/09/2021] [Accepted: 04/10/2021] [Indexed: 10/06/2023] Open
Abstract
The human hippocampus is involved in forming new memories: damage impairs memory. The dual stream model suggests that object "what" representations from ventral stream temporal cortex project to the hippocampus via the perirhinal and then lateral entorhinal cortex, and spatial "where" representations from the dorsal parietal stream via the parahippocampal gyrus and then medial entorhinal cortex. The hippocampus can then associate these inputs to form episodic memories of what happened where. Diffusion tractography was used to reveal the direct connections of hippocampal system areas in humans. This provides evidence that the human hippocampus has extensive direct cortical connections, with connections that bypass the entorhinal cortex to connect with the perirhinal and parahippocampal cortex, with the temporal pole, with the posterior and retrosplenial cingulate cortex, and even with early sensory cortical areas. The connections are less hierarchical and segregated than in the dual stream model. This provides a foundation for a conceptualization for how the hippocampal memory system connects with the cerebral cortex and operates in humans. One implication is that prehippocampal cortical areas such as the parahippocampal TF and TH subregions and perirhinal cortices may implement specialized computations that can benefit from inputs from the dorsal and ventral streams.
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Affiliation(s)
- Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
- Shanghai Changning Mental Health Center, Shanghai 200335, China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
- Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Chih-Chin Heather Hsu
- Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei 11217, Taiwan
- Institute of Neuroscience, National Yang-Ming Chiao Tung University, Taipei 11217, Taiwan
| | - Jianfeng Feng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Ching-Po Lin
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China
- Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei 11217, Taiwan
- Brain Research Center, National Yang-Ming Chiao Tung University, Taipei 11217, Taiwan
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40
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Abstract
Emotions can be defined as states elicited by rewards or punishments, and indeed the neurology of emotional disorders can be understood in terms of this foundation. The orbitofrontal cortex in humans and other primates is a critical area in emotion processing, determining the value of stimuli and whether they are rewarding or nonrewarding. The cortical processing that occurs before the orbitofrontal cortex primarily involves defining the identity of stimuli, i.e., "what" is present and not reward value. There is evidence that this holds true for taste, visual, somatosensory, and olfactory stimuli. The human medial orbitofrontal cortex is important in processing many different types of reward, and the lateral orbitofrontal cortex in processing nonreward and punishment. Humans with damage to the orbitofrontal cortex have an impaired ability to identify facial and voice expressions of emotions, and impaired subjective experience of emotion. They can have an altered personality and be impulsive because they are impaired at processing failures to receive expected rewards and at processing punishments. In humans, the role of the amygdala in the processing of emotions is reduced because of the great evolutionary development of the orbitofrontal cortex: amygdala damage has much less effect on emotion than does orbitofrontal cortex damage. The orbitofrontal cortex projects reward value information to the anterior cingulate cortex, which is involved in learning those actions required to obtain rewards and avoid punishments. The cingulate cortex thus provides an output route for emotional behavior. In depression, the medial orbitofrontal cortex has decreased connectivity and sensitivity to reward, and the lateral orbitofrontal cortex has increased connectivity and sensitivity to nonreward. The orbitofrontal cortex has major projections to the anterior cingulate cortex, including its subcommissural region, and the anterior cingulate cortex is also implicated in depression.
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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, United Kingdom.
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Rolls ET. The connections of neocortical pyramidal cells can implement the learning of new categories, attractor memory, and top-down recall and attention. Brain Struct Funct 2021; 226:2523-2536. [PMID: 34347165 PMCID: PMC8448704 DOI: 10.1007/s00429-021-02347-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/19/2021] [Indexed: 11/17/2022]
Abstract
Neocortical pyramidal cells have three key classes of excitatory input: forward inputs from the previous cortical area (or thalamus); recurrent collateral synapses from nearby pyramidal cells; and backprojection inputs from the following cortical area. The neocortex performs three major types of computation: (1) unsupervised learning of new categories, by allocating neurons to respond to combinations of inputs from the preceding cortical stage, which can be performed using competitive learning; (2) short-term memory, which can be performed by an attractor network using the recurrent collaterals; and (3) recall of what has been learned by top–down backprojections from the following cortical area. There is only one type of excitatory neuron involved, pyramidal cells, with these three types of input. It is proposed, and tested by simulations of a neuronal network model, that pyramidal cells can implement all three types of learning simultaneously, and can subsequently usefully categorise the forward inputs; keep them active in short-term memory; and later recall the representations using the backprojection input. This provides a new approach to understanding how one type of excitatory neuron in the neocortex can implement these three major types of computation, and provides a conceptual advance in understanding how the cerebral neocortex may work.
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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.
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Rolls ET. Learning Invariant Object and Spatial View Representations in the Brain Using Slow Unsupervised Learning. Front Comput Neurosci 2021; 15:686239. [PMID: 34366818 PMCID: PMC8335547 DOI: 10.3389/fncom.2021.686239] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 06/29/2021] [Indexed: 11/13/2022] Open
Abstract
First, neurophysiological evidence for the learning of invariant representations in the inferior temporal visual cortex is described. This includes object and face representations with invariance for position, size, lighting, view and morphological transforms in the temporal lobe visual cortex; global object motion in the cortex in the superior temporal sulcus; and spatial view representations in the hippocampus that are invariant with respect to eye position, head direction, and place. Second, computational mechanisms that enable the brain to learn these invariant representations are proposed. For the ventral visual system, one key adaptation is the use of information available in the statistics of the environment in slow unsupervised learning to learn transform-invariant representations of objects. This contrasts with deep supervised learning in artificial neural networks, which uses training with thousands of exemplars forced into different categories by neuronal teachers. Similar slow learning principles apply to the learning of global object motion in the dorsal visual system leading to the cortex in the superior temporal sulcus. The learning rule that has been explored in VisNet is an associative rule with a short-term memory trace. The feed-forward architecture has four stages, with convergence from stage to stage. This type of slow learning is implemented in the brain in hierarchically organized competitive neuronal networks with convergence from stage to stage, with only 4-5 stages in the hierarchy. Slow learning is also shown to help the learning of coordinate transforms using gain modulation in the dorsal visual system extending into the parietal cortex and retrosplenial cortex. Representations are learned that are in allocentric spatial view coordinates of locations in the world and that are independent of eye position, head direction, and the place where the individual is located. This enables hippocampal spatial view cells to use idiothetic, self-motion, signals for navigation when the view details are obscured for short periods.
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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, United Kingdom
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Abstract
A neuroscience-based approach has recently been proposed for the relation between the mind and the brain. The proposal is that events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental state, with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: causality can best be described in brains as operating within but not between levels. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. Here, some implications are developed. It is proposed that causality, at least as it applies to the brain, should satisfy three conditions. First, interventionist tests for causality must be satisfied. Second, the causally related events should be at the same level of explanation. Third, a temporal order condition must be satisfied, with a suitable time scale in the order of 10 ms (to exclude application to quantum physics; and a cause cannot follow an effect). Next, although it may be useful for different purposes to describe causality involving the mind and brain at the mental level, or at the brain level, it is argued that the brain level may sometimes be more accurate, for sometimes causal accounts at the mental level may arise from confabulation by the mentalee, whereas understanding exactly what computations have occurred in the brain that result in a choice or action will provide the correct causal account for why a choice or action was made. Next, it is argued that possible cases of "downward causation" can be accounted for by a within-levels-of-explanation account of causality. This computational neuroscience approach provides an opportunity to proceed beyond Cartesian dualism and physical reductionism in considering the relations between the mind and the brain.
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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, United Kingdom
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Gong W, Rolls ET, Du J, Feng J, Cheng W. Brain structure is linked to the association between family environment and behavioral problems in children in the ABCD study. Nat Commun 2021; 12:3769. [PMID: 34145259 PMCID: PMC8213719 DOI: 10.1038/s41467-021-23994-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 05/26/2021] [Indexed: 12/02/2022] Open
Abstract
Children’s behavioral problems have been associated with their family environments. Here, we investigate whether specific features of brain structures could relate to this link. Using structural magnetic resonance imaging of 8756 children aged 9-11 from the Adolescent Brain Cognitive Developmental study, we show that high family conflict and low parental monitoring scores are associated with children’s behavioral problems, as well as with smaller cortical areas of the orbitofrontal cortex, anterior cingulate cortex, and middle temporal gyrus. A longitudinal analysis indicates that psychiatric problems scores are associated with increased family conflict and decreased parental monitoring 1 year later, and mediate associations between the reduced cortical areas and family conflict, and parental monitoring scores. These results emphasize the relationships between the brain structure of children, their family environments, and their behavioral problems. Child behavior has been associated with parenting behavior. Here, the authors investigate associations between child behavior, parental behavior, and structural MRI using the Adolescent Brain Cognitive Developmental (ABCD) study dataset.
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Affiliation(s)
- Weikang Gong
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK.,Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Jingnan Du
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK.,Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China. .,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China. .,Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Rolls ET, Cheng W, Du J, Wei D, Qiu J, Dai D, Zhou Q, Xie P, Feng J. Functional connectivity of the right inferior frontal gyrus and orbitofrontal cortex in depression. Soc Cogn Affect Neurosci 2021; 15:75-86. [PMID: 31993660 PMCID: PMC7171374 DOI: 10.1093/scan/nsaa014] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 12/30/2019] [Accepted: 01/20/2020] [Indexed: 01/13/2023] Open
Abstract
The orbitofrontal cortex extends into the laterally adjacent inferior frontal gyrus. We analyzed how voxel-level functional connectivity of the inferior frontal gyrus and orbitofrontal cortex is related to depression in 282 people with major depressive disorder (125 were unmedicated) and 254 controls, using FDR correction P < 0.05 for pairs of voxels. In the unmedicated group, higher functional connectivity was found of the right inferior frontal gyrus with voxels in the lateral and medial orbitofrontal cortex, cingulate cortex, temporal lobe, angular gyrus, precuneus, hippocampus and frontal gyri. In medicated patients, these functional connectivities were lower and toward those in controls. Functional connectivities between the lateral orbitofrontal cortex and the precuneus, posterior cingulate cortex, inferior frontal gyrus, ventromedial prefrontal cortex and the angular and middle frontal gyri were higher in unmedicated patients, and closer to controls in medicated patients. Medial orbitofrontal cortex voxels had lower functional connectivity with temporal cortex areas, the parahippocampal gyrus and fusiform gyrus, and medication did not result in these being closer to controls. These findings are consistent with the hypothesis that the orbitofrontal cortex is involved in depression, and can influence mood and behavior via the right inferior frontal gyrus, which projects to premotor cortical areas.
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Affiliation(s)
- Edmund T Rolls
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
- Department of Computer Science, University of Warwick, CV4 7AL, Coventry, UK
- Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
- Correspondence should be addressed to: Wei Cheng. E-mail:
| | - Jingnan Du
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
| | - Dongtao Wei
- Department of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- Department of Psychology, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
| | - Dan Dai
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
| | - Qunjie Zhou
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
| | - Peng Xie
- Institute of Neuroscience, Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Neurobiology, Chongqing, China
- Department of Neurology, Yongchuan Hospital of Chongqing Medical University, 402160, Chongqing, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
- Department of Computer Science, University of Warwick, CV4 7AL, Coventry, UK
- School of Mathematical Sciences, School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, 200433, Shanghai, China
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He L, Wei D, Yang F, Zhang J, Cheng W, Feng J, Yang W, Zhuang K, Chen Q, Ren Z, Li Y, Wang X, Mao Y, Chen Z, Liao M, Cui H, Li C, He Q, Lei X, Feng T, Chen H, Xie P, Rolls ET, Su L, Li L, Qiu J. Functional Connectome Prediction of Anxiety Related to the COVID-19 Pandemic. Am J Psychiatry 2021; 178:530-540. [PMID: 33900813 DOI: 10.1176/appi.ajp.2020.20070979] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
OBJECTIVE Increased anxiety in response to the COVID-19 pandemic has been widely noted. The purpose of this study was to test whether the prepandemic functional connectome predicted individual anxiety induced by the pandemic. METHODS Anxiety scores from healthy undergraduate students were collected during the severe and remission periods of the pandemic (first survey, February 22-28, 2020, N=589; second survey, April 24 to May 1, 2020, N=486). Brain imaging data and baseline (daily) anxiety ratings were acquired before the pandemic. The predictive performance of the functional connectome on individual anxiety was examined using machine learning and was validated in two external undergraduate student samples (N=149 and N=474). The clinical relevance of the findings was further explored by applying the connectome-based neuromarkers of pandemic-related anxiety to distinguish between individuals with specific mental disorders and matched healthy control subjects (generalized anxiety disorder, N=43; major depression, N=536; schizophrenia, N=72). RESULTS Anxiety scores increased from the prepandemic baseline to the severe stage of the pandemic and remained high in the remission stage. The prepandemic functional connectome predicted pandemic-related anxiety and generalized to the external sample but showed poor performance for predicting daily anxiety. The connectome-based neuromarkers of pandemic-related anxiety further distinguished between participants with generalized anxiety and healthy control subjects but were not useful for diagnostic classification in major depression and schizophrenia. CONCLUSIONS These findings demonstrate the feasibility of using the functional connectome to predict individual anxiety induced by major stressful events (e.g., the current global health crisis), which advances our understanding of the neurobiological basis of anxiety susceptibility and may have implications for developing targeted psychological and clinical interventions that promote the reduction of stress and anxiety.
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Affiliation(s)
- Li He
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Fan Yang
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Jie Zhang
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Wei Cheng
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Jianfeng Feng
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Wenjing Yang
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Kaixiang Zhuang
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Zhiting Ren
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Yu Li
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Yu Mao
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Zhiyi Chen
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Mei Liao
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Huiru Cui
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Chunbo Li
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Qinghua He
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Xu Lei
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Tingyong Feng
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Hong Chen
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Peng Xie
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Edmund T Rolls
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Linyan Su
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Lingjiang Li
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China (He, Wei, W. Yang, Zhuang, Q. Chen, Ren, Y. Li, Wang, Mao, Z. Chen, Q. He, Lei, T. Feng, H. Chen, Qiu); Guangdong Mental Health Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (F. Yang); Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Zhang, Cheng, J. Feng); Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, China (Liao, Su, L. Li,); Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Cui, C. Li); Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China (Xie); Oxford Center for Computational Neuroscience, Oxford, U.K. (Rolls); Department of Computer Science, University of Warwick, Coventry, U.K. (Rolls); and Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China (Qiu)
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48
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Abstract
The local recurrent collateral connections between cortical neurons provide a basis for attractor neural networks for memory, attention, decision-making, and thereby for many aspects of human behavior. In schizophrenia, a reduction of the firing rates of cortical neurons, caused for example by reduced NMDA receptor function or reduced spines on neurons, can lead to instability of the high firing rate attractor states that normally implement short-term memory and attention in the prefrontal cortex, contributing to the cognitive symptoms. Reduced NMDA receptor function in the orbitofrontal cortex by reducing firing rates may produce negative symptoms, by reducing reward, motivation, and emotion. Reduced functional connectivity between some brain regions increases the temporal variability of the functional connectivity, contributing to the reduced stability and more loosely associative thoughts. Further, the forward projections have decreased functional connectivity relative to the back projections in schizophrenia, and this may reduce the effects of external bottom-up inputs from the world relative to internal top-down thought processes. Reduced cortical inhibition, caused by a reduction of GABA neurotransmission, can lead to instability of the spontaneous firing states of cortical networks, leading to a noise-induced jump to a high firing rate attractor state even in the absence of external inputs, contributing to the positive symptoms of schizophrenia. In depression, the lateral orbitofrontal cortex non-reward attractor network system is over-connected and has increased sensitivity to non-reward, providing a new approach to understanding depression. This is complemented by under-sensitivity and under-connectedness of the medial orbitofrontal cortex reward system in depression.
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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.
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49
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Abstract
The relation between mental states and brain states is important in computational neuroscience, and in psychiatry in which interventions with medication are made on brain states to alter mental states. The relation between the brain and the mind has puzzled philosophers for centuries. Here a neuroscience approach is proposed in which events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental state, with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: causality can best be described in brains as operating within but not between levels. This allows the supervenient (e.g., mental) properties to be emergent, though once understood at the mechanistic levels they may seem less emergent, and expected. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. This approach may provide a way of thinking about brains and minds that is different from dualism and from reductive physicalism, and which is rooted in the computational processes that are fundamental to understanding brain and mental events, and that mean that the mental and mechanistic levels are linked by the computational process being performed. Explanations at the different levels of operation may be useful in different ways. For example, if we wish to understand how arithmetic is performed in the brain, description at the mental level of the algorithm being computed will be useful. But if the brain operates to result in mental disorders, then understanding the mechanism at the neural processing level may be more useful, in for example, the treatment of psychiatric disorders.
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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, United Kingdom
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50
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Rolls ET. Neurons including hippocampal spatial view cells, and navigation in primates including humans. Hippocampus 2021; 31:593-611. [PMID: 33760309 DOI: 10.1002/hipo.23324] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/01/2021] [Accepted: 03/13/2021] [Indexed: 01/11/2023]
Abstract
A new theory is proposed of mechanisms of navigation in primates including humans in which spatial view cells found in the primate hippocampus and parahippocampal gyrus are used to guide the individual from landmark to landmark. The navigation involves approach to each landmark in turn (taxis), using spatial view cells to identify the next landmark in the sequence, and does not require a topological map. Two other cell types found in primates, whole body motion cells, and head direction cells, can be utilized in the spatial view cell navigational mechanism, but are not essential. If the landmarks become obscured, then the spatial view representations can be updated by self-motion (idiothetic) path integration using spatial coordinate transform mechanisms in the primate dorsal visual system to transform from egocentric to allocentric spatial view coordinates. A continuous attractor network or time cells or working memory is used in this approach to navigation to encode and recall the spatial view sequences involved. I also propose how navigation can be performed using a further type of neuron found in primates, allocentric-bearing-to-a-landmark neurons, in which changes of direction are made when a landmark reaches a particular allocentric bearing. This is useful if a landmark cannot be approached. The theories are made explicit in models of navigation, which are then illustrated by computer simulations. These types of navigation are contrasted with triangulation, which requires a topological map. It is proposed that the first strategy utilizing spatial view cells is used frequently in humans, and is relatively simple because primates have spatial view neurons that respond allocentrically to locations in spatial scenes. An advantage of this approach to navigation is that hippocampal spatial view neurons are also useful for episodic memory, and for imagery.
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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
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