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Chu T, Si X, Xie H, Ma H, Shi Y, Yao W, Xing D, Zhao F, Dong F, Gai Q, Che K, Guo Y, Chen D, Ming D, Mao N. Regional structural-functional connectivity coupling in major depressive disorder is associated with neurotransmitter and genetic profiles. Biol Psychiatry 2024:S0006-3223(24)01555-5. [PMID: 39218135 DOI: 10.1016/j.biopsych.2024.08.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 08/03/2024] [Accepted: 08/22/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND Abnormalities in structural-functional connectivity (SC-FC) coupling have been identified globally in patients with major depressive disorder (MDD). However, investigations have neglected the variability and hierarchical distribution of these abnormalities across different brain regions. Furthermore, the biological mechanisms underlying regional SC-FC coupling patterns are not well understood. METHODS We enrolled 182 patients with MDD and 157 healthy control (HC) subjects, quantifying the intergroup differences in regional SC-FC coupling. The extreme gradient boosting (XGBoost), support vector machines (SVM) and random forest (RF) models were constructed to assess the potential of SC-FC coupling as biomarkers for MDD diagnosis and symptom prediction. Then, we examined the link between changes in regional SC-FC coupling in patients with MDD, neurotransmitter distributions, and gene expression. RESULTS We observed increased regional SC-FC coupling in default mode network (T = 3.233) and decreased coupling in frontoparietal network (T = -3.471) in MDD relative to HC. XGBoost (AUC = 0.853), SVM (AUC = 0.832) and RF (p < 0.05) models exhibited good prediction performance. The alterations in regional SC-FC coupling in patients with MDD were correlated with the distributions of four neurotransmitters (p < 0.05) and expression maps of specific genes. These genes were strongly enriched in genes implicated in excitatory neurons, inhibitory neurons, cellular metabolism, synapse function, and immune signaling. These findings were replicated on two brain atlases. CONCLUSIONS This work enhances our understanding of MDD and pave the way for the development of additional targeted therapeutic interventions.
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Affiliation(s)
- Tongpeng Chu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China, 300072; Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, China, 300072; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China, 300392; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin University, Tianjin, China, 300072; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital); Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China, 300072; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, China, 300072; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China, 300392; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin University, Tianjin, China, 300072
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Wei Yao
- Department of Neurology, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, Shandong, P. R. China, 253000
| | - Dong Xing
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, P. R. China, 264000
| | - Fanghui Dong
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Qun Gai
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Yuting Guo
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, P. R. China, 264000
| | - Danni Chen
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, P. R. China, 264000
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China, 300072; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, China, 300072; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China, 300392; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin University, Tianjin, China, 300072
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital); Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000.
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Sipes BS, Nagarajan SS, Raj A. Integrative, segregative, and degenerate harmonics of the structural connectome. Commun Biol 2024; 7:986. [PMID: 39143303 PMCID: PMC11324790 DOI: 10.1038/s42003-024-06669-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 08/01/2024] [Indexed: 08/16/2024] Open
Abstract
Unifying integration and segregation in the brain has been a fundamental puzzle in neuroscience ever since the conception of the "binding problem." Here, we introduce a framework that places integration and segregation within a continuum based on a fundamental property of the brain-its structural connectivity graph Laplacian harmonics and a new feature we term the gap-spectrum. This framework organizes harmonics into three regimes-integrative, segregative, and degenerate-that together account for various group-level properties. Integrative and segregative harmonics occupy the ends of the continuum, and they share properties such as reproducibility across individuals, stability to perturbation, and involve "bottom-up" sensory networks. Degenerate harmonics are in the middle of the continuum, and they are subject-specific, flexible, and involve "top-down" networks. The proposed framework accommodates inter-subject variation, sensitivity to changes, and structure-function coupling in ways that offer promising avenues for studying cognition and consciousness in the brain.
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Affiliation(s)
- Benjamin S Sipes
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
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3
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Fotiadis P, Parkes L, Davis KA, Satterthwaite TD, Shinohara RT, Bassett DS. Structure-function coupling in macroscale human brain networks. Nat Rev Neurosci 2024:10.1038/s41583-024-00846-6. [PMID: 39103609 DOI: 10.1038/s41583-024-00846-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2024] [Indexed: 08/07/2024]
Abstract
Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.
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Affiliation(s)
- Panagiotis Fotiadis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Anaesthesiology, University of Michigan, Ann Arbor, MI, USA.
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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4
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Zhang H, Cao P, Mak HKF, Hui ES. The structural-functional-connectivity coupling of the aging brain. GeroScience 2024; 46:3875-3887. [PMID: 38443539 PMCID: PMC11226573 DOI: 10.1007/s11357-024-01106-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/30/2024] [Indexed: 03/07/2024] Open
Abstract
Aging primarily affects memory and executive functions, a relationship that may be underpinned by the fact that almost all adults over 60 years old develop small vessel disease (SVD). The fact that a wide range of neuropathologies could only explain up to 43% of the variation in age-related cognitive impairment suggests that other factors, such as cognitive reserve, may play a role in the brain's resilience against aging-related cognitive decline. This study aims to examine the relationship between structural-functional-connectivity coupling (SFC), and aging, cognitive abilities and reserve, and SVD-related neuropathologies using a cohort of n = 176 healthy elders from the Harvard Aging Brain Study. The SFC is a recently proposed biomarker that reflects the extent to which anatomical brain connections can predict coordinated neural activity. After controlling for the effect of age, sex, and years of education, global SFC, as well as the intra-network SFC of the dorsolateral somatomotor and dorsal attention networks, and the inter-network SFC between dorsolateral somatomotor and frontoparietal networks decreased with age. The global SFC decreased with total cognitive score. There were significant interaction effects between years of education versus white matter hyperintensities and between years of education versus cerebral microbleeds on inter-network SFC. Enlarged perivascular space in basal ganglia was associated with higher inter-network SFC. Our results suggest that cognitive ability is associated with brain coupling at the global level and cognitive reserve with brain coupling at the inter-functional-brain-cluster level with interaction effect from white matter hyperintensities and cerebral microbleed in a cohort of healthy elderlies.
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Affiliation(s)
- Hui Zhang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong, China
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
| | - Peng Cao
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Henry K F Mak
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
- Alzheimer's Disease Research Network, The University of Hong Kong, Hong Kong, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
| | - Edward S Hui
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China.
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China.
- CU Lab for AI in Radiology (CLAIR), The Chinese University of Hong Kong, Hong Kong, China.
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5
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Zarkali A, Thomas GEC, Zetterberg H, Weil RS. Neuroimaging and fluid biomarkers in Parkinson's disease in an era of targeted interventions. Nat Commun 2024; 15:5661. [PMID: 38969680 PMCID: PMC11226684 DOI: 10.1038/s41467-024-49949-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 06/19/2024] [Indexed: 07/07/2024] Open
Abstract
A major challenge in Parkinson's disease is the variability in symptoms and rates of progression, underpinned by heterogeneity of pathological processes. Biomarkers are urgently needed for accurate diagnosis, patient stratification, monitoring disease progression and precise treatment. These were previously lacking, but recently, novel imaging and fluid biomarkers have been developed. Here, we consider new imaging approaches showing sensitivity to brain tissue composition, and examine novel fluid biomarkers showing specificity for pathological processes, including seed amplification assays and extracellular vesicles. We reflect on these biomarkers in the context of new biological staging systems, and on emerging techniques currently in development.
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Affiliation(s)
- Angeliki Zarkali
- Dementia Research Centre, Institute of Neurology, UCL, London, UK.
| | | | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Rimona S Weil
- Dementia Research Centre, Institute of Neurology, UCL, London, UK
- Department of Advanced Neuroimaging, UCL, London, UK
- Movement Disorders Centre, UCL, London, UK
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6
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Zarkali A, Hannaway N, McColgan P, Heslegrave AJ, Veleva E, Laban R, Zetterberg H, Lees AJ, Fox NC, Weil RS. Neuroimaging and plasma evidence of early white matter loss in Parkinson's disease with poor outcomes. Brain Commun 2024; 6:fcae130. [PMID: 38715714 PMCID: PMC11073930 DOI: 10.1093/braincomms/fcae130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/26/2024] [Accepted: 04/23/2024] [Indexed: 06/30/2024] Open
Abstract
Parkinson's disease is a common and debilitating neurodegenerative disorder, with over half of patients progressing to postural instability, dementia or death within 10 years of diagnosis. However, the onset and rate of progression to poor outcomes is highly variable, underpinned by heterogeneity in underlying pathological processes. Quantitative and sensitive measures predicting poor outcomes will be critical for targeted treatment, but most studies to date have been limited to a single modality or assessed patients with established cognitive impairment. Here, we used multimodal neuroimaging and plasma measures in 98 patients with Parkinson's disease and 28 age-matched controls followed up over 3 years. We examined: grey matter (cortical thickness and subcortical volume), white matter (fibre cross-section, a measure of macrostructure; and fibre density, a measure of microstructure) at whole-brain and tract level; structural and functional connectivity; and plasma levels of neurofilament light chain and phosphorylated tau 181. We evaluated relationships with subsequent poor outcomes, defined as development of mild cognitive impairment, dementia, frailty or death at any time during follow-up, in people with Parkinson's disease. We show that extensive white matter macrostructural changes are already evident at baseline assessment in people with Parkinson's disease who progress to poor outcomes (n = 31): with up to 19% reduction in fibre cross-section in multiple tracts, and a subnetwork of reduced structural connectivity strength, particularly involving connections between right frontoparietal and left frontal, right frontoparietal and left parietal and right temporo-occipital and left parietal modules. In contrast, grey matter volumes and functional connectivity were preserved in people with Parkinson's disease with poor outcomes. Neurofilament light chain, but not phosphorylated tau 181 levels were increased in people with Parkinson's disease with poor outcomes, and correlated with white matter loss. These findings suggest that imaging sensitive to white matter macrostructure and plasma neurofilament light chain may be useful early markers of poor outcomes in Parkinson's disease. As new targeted treatments for neurodegenerative disease are emerging, these measures show important potential to aid patient selection for treatment and improve stratification for clinical trials.
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Affiliation(s)
- Angeliki Zarkali
- Dementia Research Centre, Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Naomi Hannaway
- Dementia Research Centre, Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Peter McColgan
- Huntington’s Disease Centre, Institute of Neurology, University College London, London WC1B 5EH, UK
| | - Amanda J Heslegrave
- UK DRI Fluid Biomarker Lab and Biomarker Factory, University College London, London WC1E 6BT, UK
| | - Elena Veleva
- UK DRI Fluid Biomarker Lab and Biomarker Factory, University College London, London WC1E 6BT, UK
| | - Rhiannon Laban
- UK DRI Fluid Biomarker Lab and Biomarker Factory, University College London, London WC1E 6BT, UK
| | - Henrik Zetterberg
- Dementia Research Centre, Institute of Neurology, University College London, London WC1N 3AR, UK
- UK DRI Fluid Biomarker Lab and Biomarker Factory, University College London, London WC1E 6BT, UK
| | - Andrew J Lees
- Reta Lila Weston Institute of Neurological Studies, University College London, London WC1N 1PJ, UK
| | - Nick C Fox
- Dementia Research Centre, Institute of Neurology, University College London, London WC1N 3AR, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals, London WC1N 3BG, UK
| | - Rimona S Weil
- Dementia Research Centre, Institute of Neurology, University College London, London WC1N 3AR, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals, London WC1N 3BG, UK
- Movement Disorders Centre, University College London, London WC1N 3BG, UK
- The Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3AR, UK
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7
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Latifi S, Carmichael ST. The emergence of multiscale connectomics-based approaches in stroke recovery. Trends Neurosci 2024; 47:303-318. [PMID: 38402008 DOI: 10.1016/j.tins.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 12/31/2023] [Accepted: 01/21/2024] [Indexed: 02/26/2024]
Abstract
Stroke is a leading cause of adult disability. Understanding stroke damage and recovery requires deciphering changes in complex brain networks across different spatiotemporal scales. While recent developments in brain readout technologies and progress in complex network modeling have revolutionized current understanding of the effects of stroke on brain networks at a macroscale, reorganization of smaller scale brain networks remains incompletely understood. In this review, we use a conceptual framework of graph theory to define brain networks from nano- to macroscales. Highlighting stroke-related brain connectivity studies at multiple scales, we argue that multiscale connectomics-based approaches may provide new routes to better evaluate brain structural and functional remapping after stroke and during recovery.
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Affiliation(s)
- Shahrzad Latifi
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA; Department of Neuroscience, Rockefeller Neuroscience Institute, West Virginia University, Morgantown, WV 26506, USA
| | - S Thomas Carmichael
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA.
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8
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Chen P, Yang H, Zheng X, Jia H, Hao J, Xu X, Li C, He X, Chen R, Okubo TS, Cui Z. Group-common and individual-specific effects of structure-function coupling in human brain networks with graph neural networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.22.568257. [PMID: 38045396 PMCID: PMC10690242 DOI: 10.1101/2023.11.22.568257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
The human cerebral cortex is organized into functionally segregated but synchronized regions bridged by the structural connectivity of white matter pathways. While structure-function coupling has been implicated in cognitive development and neuropsychiatric disorders, studies yield inconsistent findings. The extent to which the structure-function coupling reflects reliable individual differences or primarily group-common characteristics remains unclear, at both the global and regional brain levels. By leveraging two independent, high-quality datasets, we found that the graph neural network accurately predicted unseen individuals' functional connectivity from structural connectivity, reflecting a strong structure-function coupling. This coupling was primarily driven by network topology and was substantially stronger than that of the linear models. Moreover, we observed that structure-function coupling was dominated by group-common effects, with subtle yet significant individual-specific effects. The regional group and individual effects of coupling were hierarchically organized across the cortex along a sensorimotor-association axis, with lower group and higher individual effects in association cortices. These findings emphasize the importance of considering both group and individual effects in understanding cortical structure-function coupling, suggesting insights into interpreting individual differences of the coupling and informing connectivity-guided therapeutics.
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9
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Dong X, Li Q, Wang X, He Y, Zeng D, Chu L, Zhao K, Li S. How brain structure-function decoupling supports individual cognition and its molecular mechanism. Hum Brain Mapp 2024; 45:e26575. [PMID: 38339909 PMCID: PMC10826895 DOI: 10.1002/hbm.26575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 12/06/2023] [Accepted: 12/12/2023] [Indexed: 02/12/2024] Open
Abstract
Functional signals emerge from the structural network, supporting multiple cognitive processes through underlying molecular mechanism. The link between human brain structure and function is region-specific and hierarchical across the neocortex. However, the relationship between hierarchical structure-function decoupling and the manifestation of individual behavior and cognition, along with the significance of the functional systems involved, and the specific molecular mechanism underlying structure-function decoupling remain incompletely characterized. Here, we used the structural-decoupling index (SDI) to quantify the dependency of functional signals on the structural connectome using a significantly larger cohort of healthy subjects. Canonical correlation analysis (CCA) was utilized to assess the general multivariate correlation pattern between region-specific SDIs across the whole brain and multiple cognitive traits. Then, we predicted five composite cognitive scores resulting from multivariate analysis using SDIs in primary networks, association networks, and all networks, respectively. Finally, we explored the molecular mechanism related to SDI by investigating its genetic factors and relationship with neurotransmitter receptors/transporters. We demonstrated that structure-function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. We revealed better performance in cognition prediction is achieved by using high-level hierarchical SDIs, with varying significance of different brain regions in predicting cognitive processes. We found that the SDIs were associated with the gene expression level of several receptor-related terms, and we also found the spatial distributions of four receptors/transporters significantly correlated with SDIs, namely D2, NET, MOR, and mGluR5, which play an important role in the flexibility of neuronal function. Collectively, our findings corroborate the association between hierarchical macroscale structure-function decoupling and individual cognition and provide implications for comprehending the molecular mechanism of structure-function decoupling. PRACTITIONER POINTS: Structure-function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. High-level hierarchical structure-function decoupling contributes much more than low-level decoupling to individual cognition. Structure-function decoupling could be regulated by genes associated with pivotal receptors that are crucial for neuronal function flexibility.
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Affiliation(s)
- Xiaoxi Dong
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
- IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Xuetong Wang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Yirong He
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Debin Zeng
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical EngineeringBeihang UniversityBeijingChina
| | - Lei Chu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical EngineeringBeihang UniversityBeijingChina
| | - Kun Zhao
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
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10
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Tan JB, Müller EJ, Orlando IF, Taylor NL, Margulies DS, Szeto J, Lewis SJG, Shine JM, O'Callaghan C. Abnormal higher-order network interactions in Parkinson's disease visual hallucinations. Brain 2024; 147:458-471. [PMID: 37677056 DOI: 10.1093/brain/awad305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/14/2023] [Accepted: 08/11/2023] [Indexed: 09/09/2023] Open
Abstract
Visual hallucinations in Parkinson's disease can be viewed from a systems-level perspective, whereby dysfunctional communication between brain networks responsible for perception predisposes a person to hallucinate. To this end, abnormal functional interactions between higher-order and primary sensory networks have been implicated in the pathophysiology of visual hallucinations in Parkinson's disease, however the precise signatures remain to be determined. Dimensionality reduction techniques offer a novel means for simplifying the interpretation of multidimensional brain imaging data, identifying hierarchical patterns in the data that are driven by both within- and between-functional network changes. Here, we applied two complementary non-linear dimensionality reduction techniques-diffusion-map embedding and t-distributed stochastic neighbour embedding (t-SNE)-to resting state functional MRI data, in order to characterize the altered functional hierarchy associated with susceptibility to visual hallucinations. Our study involved 77 people with Parkinson's disease (31 with hallucinations; 46 without hallucinations) and 19 age-matched healthy control subjects. In patients with visual hallucinations, we found compression of the unimodal-heteromodal gradient consistent with increased functional integration between sensory and higher order networks. This was mirrored in a traditional functional connectivity analysis, which showed increased connectivity between the visual and default mode networks in the hallucinating group. Together, these results suggest a route by which higher-order regions may have excessive influence over earlier sensory processes, as proposed by theoretical models of hallucinations across disorders. By contrast, the t-SNE analysis identified distinct alterations in prefrontal regions, suggesting an additional layer of complexity in the functional brain network abnormalities implicated in hallucinations, which was not apparent in traditional functional connectivity analyses. Together, the results confirm abnormal brain organization associated with the hallucinating phenotype in Parkinson's disease and highlight the utility of applying convergent dimensionality reduction techniques to investigate complex clinical symptoms. In addition, the patterns we describe in Parkinson's disease converge with those seen in other conditions, suggesting that reduced hierarchical differentiation across sensory-perceptual systems may be a common transdiagnostic vulnerability in neuropsychiatric disorders with perceptual disturbances.
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Affiliation(s)
- Joshua B Tan
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Eli J Müller
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
- Centre for Complex Systems, School of Physics, University of Sydney, Sydney 2050, Australia
| | - Isabella F Orlando
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Natasha L Taylor
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center, Center National de la Recherche Scientifique (CNRS) and Université de Paris, 75006 Paris, France
| | - Jennifer Szeto
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Simon J G Lewis
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - James M Shine
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
- Centre for Complex Systems, School of Physics, University of Sydney, Sydney 2050, Australia
| | - Claire O'Callaghan
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
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11
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Hahn L, Eickhoff SB, Mueller K, Schilbach L, Barthel H, Fassbender K, Fliessbach K, Kornhuber J, Prudlo J, Synofzik M, Wiltfang J, Diehl-Schmid J, Otto M, Dukart J, Schroeter ML. Resting-state alterations in behavioral variant frontotemporal dementia are related to the distribution of monoamine and GABA neurotransmitter systems. eLife 2024; 13:e86085. [PMID: 38224473 PMCID: PMC10789488 DOI: 10.7554/elife.86085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 12/14/2023] [Indexed: 01/16/2024] Open
Abstract
Background Aside to clinical changes, behavioral variant frontotemporal dementia (bvFTD) is characterized by progressive structural and functional alterations in frontal and temporal regions. We examined if there is a selective vulnerability of specific neurotransmitter systems in bvFTD by evaluating the link between disease-related functional alterations and the spatial distribution of specific neurotransmitter systems and their underlying gene expression levels. Methods Maps of fractional amplitude of low-frequency fluctuations (fALFF) were derived as a measure of local activity from resting-state functional magnetic resonance imaging for 52 bvFTD patients (mean age = 61.5 ± 10.0 years; 14 females) and 22 healthy controls (HC) (mean age = 63.6 ± 11.9 years; 13 females). We tested if alterations of fALFF in patients co-localize with the non-pathological distribution of specific neurotransmitter systems and their coding mRNA gene expression. Furthermore, we evaluated if the strength of co-localization is associated with the observed clinical symptoms. Results Patients displayed significantly reduced fALFF in frontotemporal and frontoparietal regions. These alterations co-localized with the distribution of serotonin (5-HT1b and 5-HT2a) and γ-aminobutyric acid type A (GABAa) receptors, the norepinephrine transporter (NET), and their encoding mRNA gene expression. The strength of co-localization with NET was associated with cognitive symptoms and disease severity of bvFTD. Conclusions Local brain functional activity reductions in bvFTD followed the distribution of specific neurotransmitter systems indicating a selective vulnerability. These findings provide novel insight into the disease mechanisms underlying functional alterations. Our data-driven method opens the road to generate new hypotheses for pharmacological interventions in neurodegenerative diseases even beyond bvFTD. Funding This study has been supported by the German Consortium for Frontotemporal Lobar Degeneration, funded by the German Federal Ministry of Education and Research (BMBF; grant no. FKZ01GI1007A).
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Affiliation(s)
- Lisa Hahn
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre JülichJülichGermany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University DüsseldorfDüsseldorfGermany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre JülichJülichGermany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University DüsseldorfDüsseldorfGermany
| | - Karsten Mueller
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Leonhard Schilbach
- LVR-Klinikum DüsseldorfDüsseldorfGermany
- Medical Faculty, Ludwig-Maximilians-UniversitätMünchenGermany
| | - Henryk Barthel
- Department for Nuclear Medicine, University Hospital LeipzigLeipzigGermany
| | - Klaus Fassbender
- Department of Neurology, Saarland University HospitalHomburgGermany
| | - Klaus Fliessbach
- Department of Psychiatry and Psychotherapy, University Hospital BonnBonnGermany
- German Center for Neurodegenerative Diseases (DZNE)BonnGermany
| | - Johannes Kornhuber
- Department of Psychiatry and Psychotherapy, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-NurembergErlangenGermany
| | - Johannes Prudlo
- German Center for Neurodegenerative Diseases (DZNE)BonnGermany
- Department of Neurology, University Medicine RostockRostockGermany
| | - Matthis Synofzik
- German Center for Neurodegenerative Diseases (DZNE)BonnGermany
- Department of Neurodegenerative Diseases, Center of Neurology, Hertie Institute for Clinical Brain ResearchTübingenGermany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE)BonnGermany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Medical University GöttingenGöttingenGermany
- Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of AveiroAveiroPortugal
| | - Janine Diehl-Schmid
- Department of Psychiatry and Psychotherapy, Technical University of MunichMunichGermany
- kbo-Inn-Salzach-Klinikum, Clinical Center for Psychiatry, Psychotherapy, Psychosomatic Medicine, Geriatrics and NeurologyWasserburg/InnGermany
| | | | - Markus Otto
- Department of Neurology, Ulm UniversityUlmGermany
- Department of Neurology, Martin-Luther-University Halle-WittenbergHalleGermany
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre JülichJülichGermany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University DüsseldorfDüsseldorfGermany
| | - Matthias L Schroeter
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Clinic for Cognitive Neurology, University Hospital LeipzigLeipzigGermany
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12
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Zang Z, Zhang X, Song T, Li J, Nie B, Mei S, Hu Z, Zhang Y, Lu J. Association between gene expression and functional-metabolic architecture in Parkinson's disease. Hum Brain Mapp 2023; 44:5387-5401. [PMID: 37605831 PMCID: PMC10543112 DOI: 10.1002/hbm.26443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 06/02/2023] [Accepted: 07/23/2023] [Indexed: 08/23/2023] Open
Abstract
Gene expression plays a critical role in the pathogenesis of Parkinson's disease (PD). How gene expression profiles are correlated with functional-metabolic architecture remains obscure. We enrolled 34 PD patients and 25 age-and-sex-matched healthy controls for simultaneous 18 F-FDG-PET/functional MRI scanning during resting state. We investigated the functional gradients and the ratio of standard uptake value. Principal component analysis was used to further combine the functional gradients and glucose metabolism into functional-metabolic architecture. Using partial least squares (PLS) regression, we introduced the transcriptomic data from the Allen Institute of Brain Sciences to identify gene expression patterns underlying the affected functional-metabolic architecture in PD. Between-group comparisons revealed significantly higher gradient variation in the visual, somatomotor, dorsal attention, frontoparietal, default mode, and subcortical network (pFDR < .048) in PD. Increased FDG-uptake was found in the somatomotor and ventral attention network while decreased FDG-uptake was found in the visual network (pFDR < .008). Spatial correlation analysis showed consistently affected patterns of functional gradients and metabolism (p = 2.47 × 10-8 ). PLS analysis and gene ontological analyses further revealed that genes were mainly enriched for metabolic, catabolic, cellular response to ions, and regulation of DNA transcription and RNA biosynthesis. In conclusion, our study provided genetic pathological mechanism to explain imaging-defined brain functional-metabolic architecture of PD.
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Affiliation(s)
- Zhenxiang Zang
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
| | - Xiaolong Zhang
- Department of Physiology, College of Basic Medical SciencesArmy Medical UniversityChongqingChina
| | - Tianbin Song
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
| | - Jiping Li
- Beijing Institute of Functional NeurosurgeryXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Binbin Nie
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy PhysicsChinese Academy of SciencesBeijingChina
| | - Shanshan Mei
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Zhi'an Hu
- Department of Physiology, College of Basic Medical SciencesArmy Medical UniversityChongqingChina
| | - Yuqing Zhang
- Beijing Institute of Functional NeurosurgeryXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
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13
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Fotiadis P, Cieslak M, He X, Caciagli L, Ouellet M, Satterthwaite TD, Shinohara RT, Bassett DS. Myelination and excitation-inhibition balance synergistically shape structure-function coupling across the human cortex. Nat Commun 2023; 14:6115. [PMID: 37777569 PMCID: PMC10542365 DOI: 10.1038/s41467-023-41686-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 09/08/2023] [Indexed: 10/02/2023] Open
Abstract
Recent work has demonstrated that the relationship between structural and functional connectivity varies regionally across the human brain, with reduced coupling emerging along the sensory-association cortical hierarchy. The biological underpinnings driving this expression, however, remain largely unknown. Here, we postulate that intracortical myelination and excitation-inhibition (EI) balance mediate the heterogeneous expression of structure-function coupling (SFC) and its temporal variance across the cortical hierarchy. We employ atlas- and voxel-based connectivity approaches to analyze neuroimaging data acquired from two groups of healthy participants. Our findings are consistent across six complementary processing pipelines: 1) SFC and its temporal variance respectively decrease and increase across the unimodal-transmodal and granular-agranular gradients; 2) increased myelination and lower EI-ratio are associated with more rigid SFC and restricted moment-to-moment SFC fluctuations; 3) a gradual shift from EI-ratio to myelination as the principal predictor of SFC occurs when traversing from granular to agranular cortical regions. Collectively, our work delivers a framework to conceptualize structure-function relationships in the human brain, paving the way for an improved understanding of how demyelination and/or EI-imbalances induce reorganization in brain disorders.
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Affiliation(s)
- Panagiotis Fotiadis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Matthew Cieslak
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Xiaosong He
- Department of Psychology, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mathieu Ouellet
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Santa Fe Institute, Santa Fe, NM, 87501, USA.
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14
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Khan AF, Adewale Q, Lin SJ, Baumeister TR, Zeighami Y, Carbonell F, Palomero-Gallagher N, Iturria-Medina Y. Patient-specific models link neurotransmitter receptor mechanisms with motor and visuospatial axes of Parkinson's disease. Nat Commun 2023; 14:6009. [PMID: 37752107 PMCID: PMC10522603 DOI: 10.1038/s41467-023-41677-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 09/08/2023] [Indexed: 09/28/2023] Open
Abstract
Parkinson's disease involves multiple neurotransmitter systems beyond the classical dopaminergic circuit, but their influence on structural and functional alterations is not well understood. Here, we use patient-specific causal brain modeling to identify latent neurotransmitter receptor-mediated mechanisms contributing to Parkinson's disease progression. Combining the spatial distribution of 15 receptors from post-mortem autoradiography with 6 neuroimaging-derived pathological factors, we detect a diverse set of receptors influencing gray matter atrophy, functional activity dysregulation, microstructural degeneration, and dendrite and dopaminergic transporter loss. Inter-individual variability in receptor mechanisms correlates with symptom severity along two distinct axes, representing motor and psychomotor symptoms with large GABAergic and glutamatergic contributions, and cholinergically-dominant visuospatial, psychiatric and memory dysfunction. Our work demonstrates that receptor architecture helps explain multi-factorial brain re-organization, and suggests that distinct, co-existing receptor-mediated processes underlie Parkinson's disease.
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Affiliation(s)
- Ahmed Faraz Khan
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada
| | - Quadri Adewale
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada
| | - Sue-Jin Lin
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada
| | - Tobias R Baumeister
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada
| | - Yashar Zeighami
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | | | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Cécile and Oskar Vogt Institute of Brain Research, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Medical Faculty, RWTH Aachen, and JARA - Translational Brain Medicine, Aachen, Germany
| | - Yasser Iturria-Medina
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada.
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada.
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15
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Wu H, Wu C, Qin J, Zhou C, Tan S, DuanMu X, Guan X, Bai X, Guo T, Wu J, Chen J, Wen J, Cao Z, Gao T, Gu L, Huang P, Zhang B, Xu X, Zhang M. Functional connectome predicting individual gait function and its relationship with molecular architecture in Parkinson's disease. Neurobiol Dis 2023:106216. [PMID: 37385459 DOI: 10.1016/j.nbd.2023.106216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 06/18/2023] [Accepted: 06/26/2023] [Indexed: 07/01/2023] Open
Abstract
Gait impairment is a common symptom of Parkinson's disease (PD), but its neural signature remains unclear due to the interindividual variability of gait performance. Identifying a robust gait-brain correlation at the individual level would provide insight into a generalizable neural basis of gait impairment. In this context, this study aimed to detect connectome that can predict individual gait function of PD, and follow-up analyses assess the molecular architecture underlying the connectome by relating it to the neurotransmitter-receptor/transporter density maps. Resting-state functional magnetic resonance imaging was used to detect the functional connectome, and gait function was assessed via a 10 m-walking test. The functional connectome was first detected within drug-naive patients (N = 48) by using connectome-based predictive modeling following cross-validation and then successfully validated within drug-managed patients (N = 30). The results showed that the motor, subcortical, and visual networks played an important role in predicting gait function. The connectome generated from patients failed to predict the gait function of 33 normal controls (NCs) and had distinct connection patterns compared to NCs. The negative connections (connection negatively correlated with 10 m-walking-time) pattern of the PD connectome was associated with the density of the D2 receptor and VAChT transporter. These findings suggested that gait-associated functional alteration induced by PD pathology differed from that induced by aging degeneration. The brain dysfunction related to gait impairment was more commonly found in regions expressing more dopaminergic and cholinergic neurotransmitters, which may aid in developing targeted treatments.
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Affiliation(s)
- Haoting Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Chenqing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Jianmei Qin
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Sijia Tan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Xiaojie DuanMu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Xueqin Bai
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Jingwen Chen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Jiaqi Wen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Zhengye Cao
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Ting Gao
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Luyan Gu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China.
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16
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Chen H, Hu Z, Ke Z, Xu Y, Bai F, Liu Z. Aberrant Multimodal Connectivity Pattern Involved in Default Mode Network and Limbic Network in Amyotrophic Lateral Sclerosis. Brain Sci 2023; 13:brainsci13050803. [PMID: 37239275 DOI: 10.3390/brainsci13050803] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/07/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder that progressively affects bulbar and limb function. Despite increasing recognition of the disease as a multinetwork disorder characterized by aberrant structural and functional connectivity, its integrity agreement and its predictive value for disease diagnosis remain to be fully elucidated. In this study, we recruited 37 ALS patients and 25 healthy controls (HCs). High-resolution 3D T1-weighted imaging and resting-state functional magnetic resonance imaging were, respectively, applied to construct multimodal connectomes. Following strict neuroimaging selection criteria, 18 ALS and 25 HC patients were included. Network-based statistic (NBS) and the coupling of grey matter structural-functional connectivity (SC-FC coupling) were performed. Finally, the support vector machine (SVM) method was used to distinguish the ALS patients from HCs. Results showed that, compared with HCs, ALS individuals exhibited a significantly increased functional network, predominantly encompassing the connections between the default mode network (DMN) and the frontoparietal network (FPN). The increased structural connections predominantly involved the inter-regional connections between the limbic network (LN) and the DMN, the salience/ventral attention network (SVAN) and FPN, while the decreased structural connections mainly involved connections between the LN and the subcortical network (SN). We also found increased SC-FC coupling in DMN-related brain regions and decoupling in LN-related brain regions in ALS, which could differentiate ALS from HCs with promising capacity based on SVM. Our findings highlight that DMN and LN may play a vital role in the pathophysiological mechanism of ALS. Additionally, SC-FC coupling could be regarded as a promising neuroimaging biomarker for ALS and shows important clinical potential for early recognition of ALS individuals.
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Affiliation(s)
- Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
| | - Zheqi Hu
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
- Medical School of Nanjing University, Nanjing University, Nanjing 210093, China
| | - Zhihong Ke
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
- Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 211166, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
| | - Zhuo Liu
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
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17
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Taylor NL, Shine JM. A whole new world: embracing the systems-level to understand the indirect impact of pathology in neurodegenerative disorders. J Neurol 2023; 270:1969-1975. [PMID: 36577819 DOI: 10.1007/s00415-022-11550-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/23/2022] [Indexed: 12/29/2022]
Abstract
The direct link between neuropathology and the symptoms that emerge from damage to the brain is often difficult to discern. In this perspective, we argue that a satisfying account of neurodegenerative symptoms most naturally emerges from the consideration of the brain from the systems-level. Specifically, we will highlight the role of the neuromodulatory arousal system, which is uniquely positioned to coordinate the brain's ability to flexibly integrate the otherwise segregated structures required to support higher cognitive functions. Importantly, the neuromodulatory arousal system is highly heterogeneous, encompassing structures that are common sites of neurodegeneration across Alzheimer's and Parkinson's disease. We will review studies that implicate the dysfunctional interactions amongst distributed brain regions as a side-effect of pathological involvement of the neuromodulatory arousal system in these neurodegenerative disorders. From this perspective, we will argue that future work in clinical neuroscience should attempt to consider the inherent complexity in the brain and employ analytic techniques that do not solely focus on regional functional impairments, but rather captures the brain as an inherently dynamic, distributed, multi-scale system. Through this lens, we hope that we will devise new and improved diagnostic markers and interventional approaches to aid in the treatment of neurodegenerative disorders.
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Affiliation(s)
- Natasha L Taylor
- Brain and Mind Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - James M Shine
- Brain and Mind Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
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18
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Wu D, Wang X, Lin S, Xu G, Tian J, Ma X. Predicting insomnia severity using structure-function coupling in female chronic insomnia patients. Behav Brain Res 2023; 441:114283. [PMID: 36621579 DOI: 10.1016/j.bbr.2023.114283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/13/2022] [Accepted: 01/02/2023] [Indexed: 01/07/2023]
Abstract
Functional connectivity between brain regions is constrained by the underlying structural pathways. However, how this structure-function coupling is disrupted in female patients with insomnia disorder is unclear. This study examines if the whole-brain pattern of structure-function coupling could be used to predict unseen female patients' insomnia severity index. Resting-state functional MRI and diffusion-weighted imaging were performed in 82 female participants with chronic insomnia. Structure-function coupling was computed using the Spearman rank correlations between structural and functional connectivity profiles. Using relevance vector regression approach and 10-fold cross-validation, we predicted the individuals' insomnia severity index using the pattern of whole-brain structure-function coupling. Finally, we extracted the contribution of each regional coupling to the prediction model. The pattern of structure-function coupling could be used to significantly predict unseen individuals' insomnia severity index scores (r = 0.29, permutation P < 0.001; mean absolute error (MAE) = 4.59, permutation P < 0.001). Moreover, the brain regions with high functional hierarchy, including regions in the default mode network, mainly displayed negative contribution weights, while the regions with lower functional hierarchy, including occipital regions and the precentral gyrus, mainly displayed positive contribution weights. This is the first study to demonstrate an association between structure-function coupling and the insomnia severity index in females with insomnia disorder. Importantly, our data suggest that insomnia severity is associated with a reduction in structure-function coupling in higher-order brain regions and an increase in structure-function coupling in lower-order brain regions.
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Affiliation(s)
- Dongyan Wu
- Department of Neurology, China-Japan Friendship Hospital, Beijing, PR China
| | - Xinzhi Wang
- Department of Radiology, Guangdong Second Provincial General Hospital, Guangzhou, PR China
| | - Shiqi Lin
- Department of Radiology, Guangdong Second Provincial General Hospital, Guangzhou, PR China
| | - Guang Xu
- Department of Neurology, Guangdong Second Provincial General Hospital, Guangzhou, PR China
| | - Junzhang Tian
- Department of Radiology, Guangdong Second Provincial General Hospital, Guangzhou, PR China
| | - Xiaofen Ma
- Department of Radiology, Guangdong Second Provincial General Hospital, Guangzhou, PR China.
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19
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Liu Y, Li F, Shang S, Wang P, Yin X, Krishnan Muthaiah VP, Lu L, Chen YC. Functional-structural large-scale brain networks are correlated with neurocognitive impairment in acute mild traumatic brain injury. Quant Imaging Med Surg 2023; 13:631-644. [PMID: 36819289 PMCID: PMC9929413 DOI: 10.21037/qims-22-450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 11/07/2022] [Indexed: 12/02/2022]
Abstract
Background This study was conducted to investigate topological changes in large-scale functional connectivity (FC) and structural connectivity (SC) networks in acute mild traumatic brain injury (mTBI) and determine their potential relevance to cognitive impairment. Methods Seventy-one patients with acute mTBI (29 males, 42 females, mean age 43.54 years) from Nanjing First Hospital and 57 matched healthy controls (HC) (33 males, 24 females, mean age 46.16 years) from the local community were recruited in this prospective study. Resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were acquired within 14 days (mean 3.29 days) after the onset of mTBI. Then, large-scale FC and SC networks with 116 regions from the automated anatomical labeling (AAL) brain atlas were constructed. Graph theory analysis was used to analyze global and nodal metrics. Finally, correlations were assessed between topological properties and neurocognitive performances evaluated by the Montreal Cognitive Assessment (MoCA). Bonferroni correction was performed out for multiple comparisons in all involved analyses. Results Compared with HC, acute mTBI patients had a higher normalized clustering coefficient (γ) for FC (Cohen's d=4.076), and higher γ and small worldness (σ) for SC (Cohen's d=0.390 and Cohen's d=0.395). The mTBI group showed aberrant nodal degree (Dc), nodal efficiency (Ne), and nodal local efficiency (Nloc) for FC and aberrant Dc, nodal betweenness (Bc), nodal clustering coefficient (NCp) and Ne for SC mainly in the frontal and temporal, cerebellum, and subcortical areas. Acute mTBI patients also had higher functional-structural coupling strength at both the group and individual levels (Cohen's d=0.415). These aberrant global and nodal topological properties at functional and structural levels were associated with attention, orientation, memory, and naming performances (all P<0.05). Conclusions Our findings suggested that large-scale FC and SC network changes, higher correlation between FC and SC and cognitive impairment can be detected in the acute stage of mTBI. These network aberrances may be a compensatory mechanism for cognitive impairment in acute mTBI patients.
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Affiliation(s)
- Yin Liu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Fengfang Li
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Song’an Shang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Peng Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Vijaya Prakash Krishnan Muthaiah
- Department of Rehabilitation Sciences, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, NY, USA
| | - Liyan Lu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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20
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Liu X, Qiu S, Wang X, Chen H, Tang Y, Qin Y. Aberrant dynamic Functional-Structural connectivity coupling of Large-scale brain networks in poststroke motor dysfunction. Neuroimage Clin 2023; 37:103332. [PMID: 36708666 PMCID: PMC10037213 DOI: 10.1016/j.nicl.2023.103332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 01/11/2023] [Accepted: 01/19/2023] [Indexed: 01/24/2023]
Abstract
BACKGROUND AND PURPOSE Stroke may lead to widespread functional and structural reorganization in the brain. Several studies have reported a potential correlation between functional network changes and structural network changes after stroke. However, it is unclear how functional-structural relationships change dynamically over the course of one resting-state fMRI scan in patients following a stroke; furthermore, we know little about their relationships with the severity of motor dysfunction. Therefore, this study aimed to investigate dynamic functional and structural connectivity (FC-SC) coupling and its relationship with motor function in subcortical stroke from the perspective of network dynamics. METHODS Resting-state functional magnetic resonance imaging and diffusion tensor imaging were obtained from 39 S patients (19 severe and 20 moderate) and 22 healthy controls (HCs). Brain structural networks were constructed by tracking fiber tracts in diffusion tensor imaging, and structural network topology metrics were calculated using a graph-theoretic approach. Independent component analysis, the sliding window method, and k-means clustering were used to calculate dynamic functional connectivity and to estimate different dynamic connectivity states. The temporal patterns and intergroup differences of FC-SC coupling were analyzed within each state. We also calculated dynamic FC-SC coupling and its relationship with functional network efficiency. In addition, the correlation between FC-SC coupling and the Fugl-Meyer assessment scale was analyzed. RESULTS For SC, stroke patients showed lower global efficiency than HCs (all P < 0.05), and severely affected patients had a higher characteristic path length (P = 0.003). For FC and FC-SC coupling, stroke patients predominantly showed lower local efficiency and reduced FC-SC coupling than HCs in state 2 (all P < 0.05). Furthermore, severely affected patients also showed lower local efficiency (P = 0.031) and reduced FC-SC coupling (P = 0.043) in state 3, which was markedly linked to the severity of motor dysfunction after stroke. In addition, FC-SC coupling was correlated with functional network efficiency in state 2 in moderately affected patients (r = 0.631, P = 0.004) but not significantly in severely affected patients. CONCLUSIONS Stroke patients show abnormal dynamic FC-SC coupling characteristics, especially in individuals with severe injuries. These findings may contribute to a better understanding of the anatomical functional interactions underlying motor deficits in stroke patients and provide useful information for personalized rehabilitation strategies.
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Affiliation(s)
- Xiaoying Liu
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China
| | - Shuting Qiu
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China
| | - Xiaoyang Wang
- Department of the Fujian Key Laboratory of Functional Imaging, Department of Radiology, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou 350025, China
| | - Hui Chen
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China
| | - Yuting Tang
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China
| | - Yin Qin
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China; Department of Rehabilitation Medicine, Fuzhou General Hospital (Dongfang Hospital), Xiamen University, Fuzhou 350025, China.
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21
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Chen Z, Fan B, Pang L, Wei M, Lv C, Zheng J. Longitudinal alterations of cortical structural-functional coupling in temporal lobe epilepsy. J Neuroimaging 2023; 33:156-166. [PMID: 36085558 DOI: 10.1111/jon.13046] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/16/2022] [Accepted: 08/25/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND AND PURPOSE To investigate the longitudinal alterations of cortical structural-functional coupling (SF coupling) in patients with temporal lobe epilepsy (TLE) over a 2-year follow-up, thereby exploring the neuropathophysiological mechanisms of TLE. METHODS Twenty-eight TLE patients and 42 age- and gender-matched healthy controls (HCs) were recruited. We used resting-state functional MRI and diffusion-weighted imaging to estimate and compare SF coupling at the multiscale network level (whole-brain, modular, and regional levels). Then, we analyzed the relationships between the spatial patterns of SF coupling, the principal functional connectivity (FC) gradient, and the functional participation coefficient (PC). Finally, we related regional SF coupling changes between baseline and follow-up to the expression of regional TLE-specific genes. RESULTS Compared with HCs, TLE patients showed higher baseline SF couplings within the whole-brain, limbic, and default-mode modules. SF couplings within visual and dorsal attention modules were increased at follow-up compared to baseline. In all three groups, the spatial patterns of SF coupling aligned with the principal FC gradient and the functional PC. The longitudinal change in regional SF coupling in TLE patients was significantly positively correlated with the expression of the CUX2 gene. CONCLUSIONS Aberrant SF coupling was revealed in TLE and related to macroscale cortical hierarchies, functional segregation, and TLE-specific gene expression; these data help increase our understanding of the neuropathophysiological mechanisms underlying TLE.
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Affiliation(s)
- Zexiang Chen
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Binglin Fan
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Linlin Pang
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Minda Wei
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Caitiao Lv
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jinou Zheng
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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22
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Guo J, Chen Y, Huang L, Liu W, Hu D, Lv Y, Kang H, Li N, Peng Y. Local structural-functional connectivity decoupling of caudate nucleus in infantile esotropia. Front Neurosci 2022; 16:1098735. [PMID: 36620443 PMCID: PMC9815444 DOI: 10.3389/fnins.2022.1098735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022] Open
Abstract
Abnormal brain structural and functional properties were demonstrated in patients with infantile esotropia (IE). However, few studies have investigated the interaction between structural and functional connectivity (SC-FC) in patients with IE. Structural network was generated with diffusion tensor imaging and functional network was constructed with resting-state functional magnetic resonance imaging for 18 patients with IE as well as 20 age- and gender- matched healthy subjects. The SC-FC coupling for global connectome, short connectome and long connectome were examined in IE patients and compared with those of healthy subjects. A linear mixed effects model was employed to examine the group-age interaction in terms of the coupling metrics. The Pearson correlation between coupling measures and strabismus degree was evaluated in IE patients, on which the regulatory effect of age was also investigated through hierarchical regression analysis. Significantly decreased SC-FC coupling score for short connections was observed in left caudate nucleus (CAU) in IE patients, whereas no brain regions exhibited altered coupling metrics for global connections or long connections. The group-age interaction was also evident in local coupling metrics of left CAU. The age-related regulatory effect on coupling-degree association was distinguishing between brain regions implicated in visual processing and cognition-related brain areas in IE patients. Local SC-FC decoupling in CAU was evident in patients with IE and was initiated in their early postnatal period, possibly interfering the visual cortico-striatal loop and subcortical optokinetic pathway subserving visual processing and nasalward optokinesis during neurodevelopment, which provides new insight into underlying neuropathological mechanism of IE.
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Affiliation(s)
- Jianlin Guo
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Yuanyuan Chen
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Lijuan Huang
- Department of Ophthalmology, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China,Department of Ophthalmology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Wen Liu
- Department of Ophthalmology, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Di Hu
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Yanqiu Lv
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Huiying Kang
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Ningdong Li
- Department of Ophthalmology, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China,Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing, China,*Correspondence: Ningdong Li,
| | - Yun Peng
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China,Yun Peng,
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23
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Jung K, Florin E, Patil KR, Caspers J, Rubbert C, Eickhoff SB, Popovych OV. Whole-brain dynamical modelling for classification of Parkinson's disease. Brain Commun 2022; 5:fcac331. [PMID: 36601625 PMCID: PMC9798283 DOI: 10.1093/braincomms/fcac331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/29/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Simulated whole-brain connectomes demonstrate enhanced inter-individual variability depending on the data processing and modelling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinson's disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model validation against behavioural data, such as subject classes, which we refer to as behavioural model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine learning reported in this study also demonstrated that the performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that the temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improved prediction performance. Based on our findings, we suggest that combining the simulation results with empirical data is effective for inter-individual research and its clinical application.
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Affiliation(s)
- Kyesam Jung
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52425 Jülich, Germany,Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52425 Jülich, Germany,Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Dusseldorf, 40225 Düsseldorf, Germany
| | - Christian Rubbert
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Dusseldorf, 40225 Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52425 Jülich, Germany,Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Oleksandr V Popovych
- Correspondence to: Oleksandr V. Popovych Institute of Neuroscience and Medicine Brain and Behaviour (INM-7) Research Centre Jülich, 52425 Jülich, Germany E-mail:
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24
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Thomas GEC, Zeidman P, Sultana T, Zarkali A, Razi A, Weil RS. Changes in both top-down and bottom-up effective connectivity drive visual hallucinations in Parkinson's disease. Brain Commun 2022; 5:fcac329. [PMID: 36601626 PMCID: PMC9798302 DOI: 10.1093/braincomms/fcac329] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/13/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022] Open
Abstract
Visual hallucinations are common in Parkinson's disease and are associated with a poorer quality of life and a higher risk of dementia. An important and influential model that is widely accepted as an explanation for the mechanism of visual hallucinations in Parkinson's disease and other Lewy body diseases is that these arise due to aberrant hierarchical processing, with impaired bottom-up integration of sensory information and overweighting of top-down perceptual priors within the visual system. This hypothesis has been driven by behavioural data and supported indirectly by observations derived from regional activation and correlational measures using neuroimaging. However, until now, there was no evidence from neuroimaging for differences in causal influences between brain regions measured in patients with Parkinson's hallucinations. This is in part because previous resting-state studies focused on functional connectivity, which is inherently undirected in nature and cannot test hypotheses about the directionality of connectivity. Spectral dynamic causal modelling is a Bayesian framework that allows the inference of effective connectivity-defined as the directed (causal) influence that one region exerts on another region-from resting-state functional MRI data. In the current study, we utilize spectral dynamic causal modelling to estimate effective connectivity within the resting-state visual network in our cohort of 15 Parkinson's disease visual hallucinators and 75 Parkinson's disease non-visual hallucinators. We find that visual hallucinators display decreased bottom-up effective connectivity from the lateral geniculate nucleus to primary visual cortex and increased top-down effective connectivity from the left prefrontal cortex to primary visual cortex and the medial thalamus, as compared with non-visual hallucinators. Importantly, we find that the pattern of effective connectivity is predictive of the presence of visual hallucinations and associated with their severity within the hallucinating group. This is the first study to provide evidence, using resting-state effective connectivity, to support a model of aberrant hierarchical predictive processing as the mechanism for visual hallucinations in Parkinson's disease.
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Affiliation(s)
- George E C Thomas
- Dementia Research Centre, UCL Institute of Neurology, WC1N 3AR London, UK
| | - Peter Zeidman
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, WC1N 3AR London, UK
| | - Tajwar Sultana
- Department of Computer and Information Systems Engineering, NED University of Engineering & Technology, Karachi 75270, Pakistan
- Department of Biomedical Engineering, NED University of Engineering & Technology, Karachi 74800, Pakistan
- Neurocomputation Laboratory, NCAI Computer and Information Systems Department, NED University of Engineering and Technology, Karachi 75270, Pakistan
| | - Angeliki Zarkali
- Dementia Research Centre, UCL Institute of Neurology, WC1N 3AR London, UK
| | - Adeel Razi
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, WC1N 3AR London, UK
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia
- CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, ON M5G 1M1, Canada
| | - Rimona S Weil
- Dementia Research Centre, UCL Institute of Neurology, WC1N 3AR London, UK
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, WC1N 3AR London, UK
- Movement Disorders Consortium, UCL, London, UK
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25
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Hui ES. Advanced Diffusion
MRI
of Stroke Recovery. J Magn Reson Imaging 2022; 57:1312-1319. [PMID: 36378071 DOI: 10.1002/jmri.28523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 11/16/2022] Open
Abstract
There is an urgent need for ways to improve our understanding of poststroke recovery to inform the development of novel rehabilitative interventions, and improve the clinical management of stroke patients. Supported by the notion that predictive information on poststroke recovery is embedded not only in the individual brain regions, but also the connections throughout the brain, majority of previous investigations have focused on the relationship between brain functional connections and post-stroke deficit and recovery. However, considering the fact that it is the static anatomical brain connections that constrain and facilitate the dynamic functional brain connections, the microstructures and structural connections of the brain may potentially be better alternatives to the functional MRI-based biomarkers of stroke recovery. This review, therefore, seeks to provide an overview of the basic concept and applications of two recently proposed advanced diffusion MRI techniques, namely lesion network mapping and fixel-based morphometry, that may be useful for the investigation of stroke recovery at the local and global levels of the brain. This review will also highlight the application of some of other emerging advanced diffusion MRI techniques that warrant further investigation in the context of stroke recovery research.
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Affiliation(s)
- Edward S. Hui
- Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Shatin Hong Kong China
- Department of Psychiatry The Chinese University of Hong Kong Shatin Hong Kong China
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26
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Zarkali A, Luppi AI, Stamatakis EA, Reeves S, McColgan P, Leyland LA, Lees AJ, Weil RS. Changes in dynamic transitions between integrated and segregated states underlie visual hallucinations in Parkinson's disease. Commun Biol 2022; 5:928. [PMID: 36075964 PMCID: PMC9458713 DOI: 10.1038/s42003-022-03903-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 08/25/2022] [Indexed: 11/09/2022] Open
Abstract
Hallucinations are a core feature of psychosis and common in Parkinson's. Their transient, unexpected nature suggests a change in dynamic brain states, but underlying causes are unknown. Here, we examine temporal dynamics and underlying structural connectivity in Parkinson's-hallucinations using a combination of functional and structural MRI, network control theory, neurotransmitter density and genetic analyses. We show that Parkinson's-hallucinators spent more time in a predominantly Segregated functional state with fewer between-state transitions. The transition from integrated-to-segregated state had lower energy cost in Parkinson's-hallucinators; and was therefore potentially preferable. The regional energy needed for this transition was correlated with regional neurotransmitter density and gene expression for serotoninergic, GABAergic, noradrenergic and cholinergic, but not dopaminergic, receptors. We show how the combination of neurochemistry and brain structure jointly shape functional brain dynamics leading to hallucinations and highlight potential therapeutic targets by linking these changes to neurotransmitter systems involved in early sensory and complex visual processing.
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Affiliation(s)
- Angeliki Zarkali
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK.
| | - Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Suzanne Reeves
- Division of Psychiatry, University College London, 149 Tottenham Court Rd, London, W1T 7BN, UK
| | - Peter McColgan
- Huntington's Disease Centre, University College London, Russell Square House, London, WC1B 5EH, UK
| | - Louise-Ann Leyland
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK
| | - Andrew J Lees
- Reta Lila Weston Institute of Neurological Studies, University College London, 1 Wakefield Street, London, WC1N 1PJ, UK
| | - Rimona S Weil
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3AR, UK
- Movement Disorders Consortium, University College London, London, WC1N 3BG, UK
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27
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Sun L, Wang X, Wang X, Cui X, Li G, Wang L, Wang L, Song M, Yu L. Genome-wide DNA methylation profiles of autism spectrum disorder. Psychiatr Genet 2022; 32:131-145. [PMID: 35353793 DOI: 10.1097/ypg.0000000000000314] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
OBJECTIVES We aimed to identify differentially methylated genes and related signaling pathways in autism spectrum disorder (ASD). METHODS First, the DNA methylation profile in the brain samples (GSE131706 and GSE80017) and peripheral blood samples (GSE109905) was downloaded from the Gene Expression Omnibus database (GEO) dataset, followed by identification of differentially methylated genes and functional analysis. Second, the GSE109905 data set was used to further validate the methylation state and test the ability to diagnose disease of identified differentially methylated genes. Third, expression measurement of selected differentially methylated genes was performed in whole blood from an independent sample. Finally, protein-protein interaction (PPI) network of core differentially methylated genes was constructed. RESULTS Totally, 74 differentially methylated genes were identified in ASD, including 38 hypermethylated genes and 36 hypomethylated genes. 15 differentially methylated genes were further identified after validation in the GSE109905 data set. Among these, major histocompatibility complex (HLA)-DQA1 was involved in the molecular function of myosin heavy chain class II receptor activity; HLA-DRB5 was involved in the signaling pathways of cell adhesion molecules, Epstein-Barr virus infection and antigen processing and presentation. In the PPI analysis, the interaction pairs of HLA-DQA1 and HLA-DRB5, FMN2 and ACTR3, and CALCOCO2 and BAZ2B were identified. Interestingly, FMN2, BAZ2B, HLA-DRB5, CALCOCO2 and DUSP22 had a potential diagnostic value for patients with ASD. The expression result of four differentially methylated genes (HLA-DRB5, NTM, IL16 and COL5A3) in the independent sample was consistent with the integrated analysis. CONCLUSIONS Identified differentially methylated genes and enriched signaling pathway could be associated with ASD.
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Affiliation(s)
- Ling Sun
- Mental Health Center, The First Hospital of Hebei Medical University
- Medical Department
| | - Xueyi Wang
- Mental Health Center, The First Hospital of Hebei Medical University
| | - Xia Wang
- Child Health Department (Psychological Behavior Department)
| | | | | | - Le Wang
- Institute of Pediatric Research, Children's Hospital of Hebei Province, China
| | - Lan Wang
- Mental Health Center, The First Hospital of Hebei Medical University
| | - Mei Song
- Mental Health Center, The First Hospital of Hebei Medical University
| | - Lulu Yu
- Mental Health Center, The First Hospital of Hebei Medical University
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28
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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: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [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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Schalkamp AK, Rahman N, Monzón-Sandoval J, Sandor C. Deep phenotyping for precision medicine in Parkinson's disease. Dis Model Mech 2022; 15:dmm049376. [PMID: 35647913 PMCID: PMC9178512 DOI: 10.1242/dmm.049376] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
A major challenge in medical genomics is to understand why individuals with the same disorder have different clinical symptoms and why those who carry the same mutation may be affected by different disorders. In every complex disorder, identifying the contribution of different genetic and non-genetic risk factors is a key obstacle to understanding disease mechanisms. Genetic studies rely on precise phenotypes and are unable to uncover the genetic contributions to a disorder when phenotypes are imprecise. To address this challenge, deeply phenotyped cohorts have been developed for which detailed, fine-grained data have been collected. These cohorts help us to investigate the underlying biological pathways and risk factors to identify treatment targets, and thus to advance precision medicine. The neurodegenerative disorder Parkinson's disease has a diverse phenotypical presentation and modest heritability, and its underlying disease mechanisms are still being debated. As such, considerable efforts have been made to develop deeply phenotyped cohorts for this disorder. Here, we focus on Parkinson's disease and explore how deep phenotyping can help address the challenges raised by genetic and phenotypic heterogeneity. We also discuss recent methods for data collection and computation, as well as methodological challenges that have to be overcome.
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Affiliation(s)
| | | | | | - Cynthia Sandor
- UK Dementia Research Institute at Cardiff University,Division of Psychological Medicine and Clinical Neuroscience, Haydn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK
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30
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Bohnen NI, Yarnall AJ, Weil RS, Moro E, Moehle MS, Borghammer P, Bedard MA, Albin RL. Cholinergic system changes in Parkinson's disease: emerging therapeutic approaches. Lancet Neurol 2022; 21:381-392. [PMID: 35131038 PMCID: PMC8985079 DOI: 10.1016/s1474-4422(21)00377-x] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/30/2021] [Accepted: 10/20/2021] [Indexed: 01/16/2023]
Abstract
In patients with Parkinson's disease, heterogeneous cholinergic system changes can occur in different brain regions. These changes correlate with a range of clinical features, both motor and non-motor, that are refractory to dopaminergic therapy, and can be conceptualised within a systems-level framework in which nodal deficits can produce circuit dysfunctions. The topographies of cholinergic changes overlap with neural circuitries involved in sleep and cognitive, motor, visuo-auditory perceptual, and autonomic functions. Cholinergic deficits within cognition network hubs predict cognitive deficits better than do total brain cholinergic changes. Postural instability and gait difficulties are associated with cholinergic system changes in thalamic, caudate, limbic, neocortical, and cerebellar nodes. Cholinergic system deficits can involve also peripheral organs. Hypercholinergic activity of mesopontine cholinergic neurons in people with isolated rapid eye movement (REM) sleep behaviour disorder, as well as in the hippocampi of cognitively normal patients with Parkinson's disease, suggests early compensation during the prodromal and early stages of Parkinson's disease. Novel pharmacological and neurostimulation approaches could target the cholinergic system to treat motor and non-motor features of Parkinson's disease.
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Affiliation(s)
- Nicolaas I Bohnen
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA; Department of Neurology, University of Michigan, Ann Arbor, MI, USA; Neurology Service, Ann Arbor, MI, USA; VA Geriatric Research Education and Clinical Center, Ann Arbor, MI, USA; Ann Arbor VAMC, Ann Arbor, MI, USA.
| | - Alison J Yarnall
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Rimona S Weil
- Dementia Research Centre, University College London, London, UK
| | - Elena Moro
- Division of Neurology, CHU of Grenoble, Grenoble, France; Grenoble Alpes University, and INSERM u1216, Grenoble, France
| | - Mark S Moehle
- Department of Pharmacology and Therapeutics, University of Florida, Gainesville, FL, USA
| | - Per Borghammer
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Marc-André Bedard
- Cognitive Pharmacology Research Unit, UQAM, Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada; Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada; Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Roger L Albin
- VA Geriatric Research Education and Clinical Center, Ann Arbor, MI, USA; Department of Neurology, University of Michigan, Ann Arbor, MI, USA
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31
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Zhang S, Xu X, Li Q, Chen J, Liu S, Zhao W, Cai H, Zhu J, Yu Y. Brain Network Topology and Structural–Functional Connectivity Coupling Mediate the Association Between Gut Microbiota and Cognition. Front Neurosci 2022; 16:814477. [PMID: 35422686 PMCID: PMC9002058 DOI: 10.3389/fnins.2022.814477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 02/07/2022] [Indexed: 11/13/2022] Open
Abstract
Increasing evidence indicates that gut microbiota can influence cognition via the gut–brain axis, and brain networks play a critical role during the process. However, little is known about how brain network topology and structural–functional connectivity (SC–FC) coupling contribute to gut microbiota-related cognition. Fecal samples were collected from 157 healthy young adults, and 16S amplicon sequencing was used to assess gut diversity and enterotypes. Topological properties of brain structural and functional networks were acquired by diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI data), and SC–FC coupling was further calculated. 3-Back, digit span, and Go/No-Go tasks were employed to assess cognition. Then, we tested for potential associations between gut microbiota, complex brain networks, and cognition. The results showed that gut microbiota could affect the global and regional topological properties of structural networks as well as node properties of functional networks. It is worthy of note that causal mediation analysis further validated that gut microbial diversity and enterotypes indirectly influence cognitive performance by mediating the small-worldness (Gamma and Sigma) of structural networks and some nodal metrics of functional networks (mainly distributed in the cingulate gyri and temporal lobe). Moreover, gut microbes could affect the degree of SC–FC coupling in the inferior occipital gyrus, fusiform gyrus, and medial superior frontal gyrus, which in turn influence cognition. Our findings revealed novel insights, which are essential to provide the foundation for previously unexplored network mechanisms in understanding cognitive impairment, particularly with respect to how brain connectivity participates in the complex crosstalk between gut microbiota and cognition.
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Affiliation(s)
- Shujun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Xiaotao Xu
- Department of Radiology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Qian Li
- Department of Radiology, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Jingyao Chen
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Siyu Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Wenming Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- *Correspondence: Jiajia Zhu,
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Department of Radiology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Radiology, Chaohu Hospital of Anhui Medical University, Hefei, China
- Yongqiang Yu,
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Basaia S, Agosta F, Diez I, Bueichekú E, d'Oleire Uquillas F, Delgado-Alvarado M, Caballero-Gaudes C, Rodriguez-Oroz M, Stojkovic T, Kostic VS, Filippi M, Sepulcre J. Neurogenetic traits outline vulnerability to cortical disruption in Parkinson's disease. Neuroimage Clin 2022; 33:102941. [PMID: 35091253 PMCID: PMC8800137 DOI: 10.1016/j.nicl.2022.102941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/03/2021] [Accepted: 01/10/2022] [Indexed: 01/18/2023]
Abstract
The genetic traits that underlie vulnerability to neuronal damage across specific brain circuits in Parkinson's disease (PD) remain to be elucidated. In this study, we characterized the brain topological intersection between propagating connectivity networks in controls and PD participants and gene expression patterns across the human cortex - such as the SNCA gene. We observed that brain connectivity originated from PD-related pathology epicenters in the brainstem recapitulated the anatomical distribution of alpha-synuclein histopathology in postmortem data. We also discovered that the gene set most related to cortical propagation patterns of PD-related pathology was primarily involved in microtubule cellular components. Thus, this study sheds light on new avenues for enhancing detection of PD neuronal vulnerability via an evaluation of in vivo connectivity trajectories across the human brain and successful integration of neuroimaging-genetic strategies.
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Affiliation(s)
- Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Federica Agosta
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy
| | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Elisenda Bueichekú
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Federico d'Oleire Uquillas
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Manuel Delgado-Alvarado
- Neurology Department, Sierrallana Hospital, Torrelavega, Spain; IDIVAL, Valdecilla Biomedical Research Institute, Santander, Spain; Biomedical Research Networking Center for Mental Health (CIBERSAM), Madrid, Spain
| | | | - MariCruz Rodriguez-Oroz
- Neurology Department, Clínica Universidad de Navarra, Neuroscience Unit, CIMA Universidad de Navarra, Spain
| | - Tanja Stojkovic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Vladimir S Kostic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
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Relationship between Amyloid-β Deposition and the Coupling between Structural and Functional Brain Networks in Patients with Mild Cognitive Impairment and Alzheimer's Disease. Brain Sci 2021; 11:brainsci11111535. [PMID: 34827535 PMCID: PMC8615711 DOI: 10.3390/brainsci11111535] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/17/2021] [Accepted: 11/17/2021] [Indexed: 01/02/2023] Open
Abstract
Previous studies have demonstrated that the accumulation of amyloid-β (Aβ) pathologies has distinctive stage-specific effects on the structural and functional brain networks along the Alzheimer's disease (AD) continuum. A more comprehensive account of both types of brain network may provide a better characterization of the stage-specific effects of Aβ pathologies. A potential candidate for this joint characterization is the coupling between the structural and functional brain networks (SC-FC coupling). We therefore investigated the effect of Aβ accumulation on global SC-FC coupling in patients with mild cognitive impairment (MCI), AD, and healthy controls. Patients with MCI were dichotomized according to their level of Aβ pathology seen in 18F-flutemetamol PET-CT scans-namely, Aβ-negative and Aβ-positive. Our results show that there was no difference in global SC-FC coupling between different cohorts. During the prodromal AD stage, there was a significant negative correlation between the level of Aβ pathology and the global SC-FC coupling of MCI patients with positive Aβ, but no significant correlation for MCI patients with negative Aβ. During the AD dementia stage, the correlation between Aβ pathology and global SC-FC coupling in patients with AD was positive. Our results suggest that Aβ pathology has distinctive stage-specific effects on global coupling between the structural and functional brain networks along the AD continuum.
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