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Wang Y, Li J, Jin S, Wang J, Lv Y, Zou Q, Wang J. Mapping morphological cortical networks with joint probability distributions from multiple morphological features. Neuroimage 2024; 296:120673. [PMID: 38851550 DOI: 10.1016/j.neuroimage.2024.120673] [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: 01/09/2024] [Revised: 06/05/2024] [Accepted: 06/06/2024] [Indexed: 06/10/2024] Open
Abstract
Morphological features sourced from structural magnetic resonance imaging can be used to infer human brain connectivity. Although integrating different morphological features may theoretically be beneficial for obtaining more precise morphological connectivity networks (MCNs), the empirical evidence to support this supposition is scarce. Moreover, the incorporation of different morphological features remains an open question. In this study, we proposed a method to construct cortical MCNs based on multiple morphological features. Specifically, we adopted a multi-dimensional kernel density estimation algorithm to fit regional joint probability distributions (PDs) from different combinations of four morphological features, and estimated inter-regional similarity in the joint PDs via Jensen-Shannon divergence. We evaluated the method by comparing the resultant MCNs with those built based on different single morphological features in terms of topological organization, test-retest reliability, biological plausibility, and behavioral and cognitive relevance. We found that, compared to MCNs built based on different single morphological features, MCNs derived from multiple morphological features displayed less segregated, but more integrated network architecture and different hubs, had higher test-retest reliability, encompassed larger proportions of inter-hemispheric edges and edges between brain regions within the same cytoarchitectonic class, and explained more inter-individual variance in behavior and cognition. These findings were largely reproducible when different brain atlases were used for cortical parcellation. Further analysis of macaque MCNs revealed weak, but significant correlations with axonal connectivity from tract-tracing, independent of the number of morphological features. Altogether, this paper proposes a new method for integrating different morphological features, which will be beneficial for constructing MCNs.
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Affiliation(s)
- Yuqi Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Suhui Jin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Jing Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Yating Lv
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Qihong Zou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China.
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Wang J, He Y. Toward individualized connectomes of brain morphology. Trends Neurosci 2024; 47:106-119. [PMID: 38142204 DOI: 10.1016/j.tins.2023.11.011] [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: 09/14/2023] [Revised: 11/16/2023] [Accepted: 11/30/2023] [Indexed: 12/25/2023]
Abstract
The morphological brain connectome (MBC) delineates the coordinated patterns of local morphological features (such as cortical thickness) across brain regions. While classically constructed using population-based approaches, there is a growing trend toward individualized modeling. Currently, the methods for individualized MBCs are varied, posing challenges for method selection and cross-study comparisons. Here, we summarize how individualized MBCs are modeled through low-order methods (correlation-, divergence-, distance-, and deviation-based methods) describing relations in brain morphology, as well as high-order methods capturing similarities in these low-order relations. We discuss the merits and limitations of different methods, examining them in the context of robustness, reproducibility, and reliability. We highlight the importance of elucidating the cellular and molecular mechanisms underlying the individualized connectome, and establishing normative benchmarks to assess individual variation in development, aging, and neuropsychiatric disorders.
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Affiliation(s)
- Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou 510631, China.
| | - Yong He
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.
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Niu J, Jiao Q, Cui D, Dou R, Guo Y, Yu G, Zhang X, Sun F, Qiu J, Dong L, Cao W. Age-associated cortical similarity networks correlate with cell type-specific transcriptional signatures. Cereb Cortex 2024; 34:bhad454. [PMID: 38037843 DOI: 10.1093/cercor/bhad454] [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: 09/25/2023] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
Human brain structure shows heterogeneous patterns of change across adults aging and is associated with cognition. However, the relationship between cortical structural changes during aging and gene transcription signatures remains unclear. Here, using structural magnetic resonance imaging data of two separate cohorts of healthy participants from the Cambridge Centre for Aging and Neuroscience (n = 454, 18-87 years) and Dallas Lifespan Brain Study (n = 304, 20-89 years) and a transcriptome dataset, we investigated the link between cortical morphometric similarity network and brain-wide gene transcription. In two cohorts, we found reproducible morphometric similarity network change patterns of decreased morphological similarity with age in cognitive related areas (mainly located in superior frontal and temporal cortices), and increased morphological similarity in sensorimotor related areas (postcentral and lateral occipital cortices). Changes in morphometric similarity network showed significant spatial correlation with the expression of age-related genes that enriched to synaptic-related biological processes, synaptic abnormalities likely accounting for cognitive decline. Transcription changes in astrocytes, microglia, and neuronal cells interpreted most of the age-related morphometric similarity network changes, which suggest potential intervention and therapeutic targets for cognitive decline. Taken together, by linking gene transcription signatures to cortical morphometric similarity network, our findings might provide molecular and cellular substrates for cortical structural changes related to cognitive decline across adults aging.
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Affiliation(s)
- Jinpeng Niu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Qing Jiao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Dong Cui
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Ruhai Dou
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Yongxin Guo
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Guanghui Yu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Xiaotong Zhang
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Fengzhu Sun
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Jianfeng Qiu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Weifang Cao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
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Yao G, Luo J, Zou T, Li J, Hu S, Yang L, Li X, Tian Y, Zhang Y, Feng K, Xu Y, Liu P. Transcriptional patterns of the cortical Morphometric Inverse Divergence in first-episode, treatment-naïve early-onset schizophrenia. Neuroimage 2024; 285:120493. [PMID: 38086496 DOI: 10.1016/j.neuroimage.2023.120493] [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: 09/15/2023] [Revised: 12/06/2023] [Accepted: 12/08/2023] [Indexed: 12/18/2023] Open
Abstract
Early-onset Schizophrenia (EOS) is a profoundly progressive psychiatric disorder characterized by both positive and negative symptoms, whose pathogenesis is influenced by genes, environment and brain structure development. In this study, the MIND (Morphometric Inverse Divergence) network was employed to explore the relationship between morphological similarity and specific transcriptional expression patterns in EOS patients. This study involved a cohort of 187 participants aged between 7 and 17 years, consisting of 97 EOS patients and 90 healthy controls (HC). Multiple morphological features were used to construct the MIND network for all participants. Furthermore, we explored the associations between MIND network and brain-wide gene expression in EOS patients through partial least squares (PLS) regression, shared genetic predispositions with other psychiatric disorders, functional enrichment of PLS weighted genes, as well as transcriptional signature assessment of cell types, cortical layers, and developmental stages. The MIND showed similarity differences in the orbitofrontal cortex, pericalcarine cortex, lingual gyrus, and multiple networks in EOS patients compared to HC. Moreover, our exploration revealed a significant overlap of PLS2 weighted genes linking to EOS-related MIND differences and the dysregulated genes reported in other psychiatric diseases. Interestingly, genes correlated with MIND changes (PLS2-) exhibited a significant enrichment not only in metabolism-related pathways, but also in specific astrocytes, cortical layers (specifically layer I and III), and posterior developmental stages (late infancy to young adulthood stages). However, PLS2+ genes were primarily enriched in synapses signaling-related pathways and early developmental stages (from early-mid fetal to neonatal early infancy) but not in special cell types or layers. These findings provide a novel perspective on the intricate relationship between macroscopic morphometric structural abnormalities and microscopic transcriptional patterns during the onset and progression of EOS.
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Affiliation(s)
- Guanqun Yao
- School of Medicine, Tsinghua University, Beijing 100084, China; Department of Psychiatry, Yuquan Hospital, Tsinghua University, Beijing 100040, China; Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Jing Luo
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Ting Zou
- School of Life Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jing Li
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China; School of Mental Health, Shanxi Medical University, Taiyuan 030001, China
| | - Shuang Hu
- Shanghai Mental Health Center, Shanghai 200030, China
| | - Langxiong Yang
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xinrong Li
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Yu Tian
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Yuqi Zhang
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Kun Feng
- School of Medicine, Tsinghua University, Beijing 100084, China; Department of Psychiatry, Yuquan Hospital, Tsinghua University, Beijing 100040, China; Institute for Precision Medicine, Tsinghua University, Beijing 100084, China.
| | - Yong Xu
- Department of Clinical Psychology, The Eighth Affiliated Hospital, Sun Yat-Sen University, No. 3025, Shennan Middle Road, Futian Street, Futian District, Shenzhen 518031, China.
| | - Pozi Liu
- School of Medicine, Tsinghua University, Beijing 100084, China; Department of Psychiatry, Yuquan Hospital, Tsinghua University, Beijing 100040, China.
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Huang XT, Lv X, Jiang H. The weight-adjusted-waist index and cognitive impairment among U.S. older adults: a population-based study. Front Endocrinol (Lausanne) 2023; 14:1276212. [PMID: 38027119 PMCID: PMC10663941 DOI: 10.3389/fendo.2023.1276212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 09/29/2023] [Indexed: 12/01/2023] Open
Abstract
Objectives Multiple research projects have provided evidence of the correlation between obesity and cognitive impairment. WWI, a novel metric for assessing obesity, has the potential to provide a more precise assessment of muscle and fat mass. This research aimed to investigate the association between WWI and cognitive functioning among elderly individuals residing in the United States. Methods This study utilized data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2011 and 2014. Weighted multiple linear regression models, smoothed fitted curves, and generalized weighted models were employed to examine the associations between WWI and cognitive function in linear and nonlinear contexts. Results The study included a cohort of 2,764 adult volunteers aged 60 years and older, all with complete data. Upon controlling for all potential confounding variables, our analysis revealed statistically significant negative associations between WWI and the Digit Symbol Substitution Test (DSST) score. Specifically, for each 1-unit increase in WWI, there was a corresponding loss of 3.57 points in the DSST score [-3.57 (-4.31, -2.82)]. The negative correlations between WWI with CERAD total word recall [-0.63 (-0.85, -0.40)], CERAD delayed recall [-0.19 (-0.30, -0.07)], and AFT [-0.65 (-0.94, -0.37)] were significant only in partially adjusted models. Conclusion Higher WWI was associated with poorer cognitive function.
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Affiliation(s)
| | - Xiang Lv
- Department of Anesthesiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hong Jiang
- Department of Anesthesiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Mallaroni P, Mason NL, Kloft L, Reckweg JT, van Oorsouw K, Ramaekers JG. Cortical structural differences following repeated ayahuasca use hold molecular signatures. Front Neurosci 2023; 17:1217079. [PMID: 37869513 PMCID: PMC10585114 DOI: 10.3389/fnins.2023.1217079] [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: 05/04/2023] [Accepted: 09/21/2023] [Indexed: 10/24/2023] Open
Abstract
Introduction Serotonergic psychedelics such as ayahuasca are reported to promote both structural and functional neural plasticity via partial 5-HT2A agonism. However, little is known about how these molecular mechanisms may extend to repeated psychedelic administration in humans, let alone neuroanatomy. While early evidence suggests localised changes to cortical thickness in long-term ayahuasca users, it is unknown how such findings may be reflected by large-scale anatomical brain networks comprising cytoarchitecturally complex regions. Methods Here, we examined the relationship between cortical gene expression markers of psychedelic action and brain morphometric change following repeated ayahuasca usage, using high-field 7 Tesla neuroimaging data derived from 24 members of an ayahuasca-using church (Santo Daime) and case-matched controls. Results Using a morphometric similarity network (MSN) analysis, repeated ayahuasca use was associated with a spatially distributed cortical patterning of both structural differentiation in sensorimotor areas and de-differentiation in transmodal areas. Cortical MSN remodelling was found to be spatially correlated with dysregulation of 5-HT2A gene expression as well as a broader set of genes encoding target receptors pertinent to ayahuasca's effects. Furthermore, these associations were similarly interrelated with altered gene expression of specific transcriptional factors and immediate early genes previously identified in preclinical assays as relevant to psychedelic-induced neuroplasticity. Conclusion Taken together, these findings provide preliminary evidence that the molecular mechanisms of psychedelic action may scale up to a macroscale level of brain organisation in vivo. Closer attention to the role of cortical transcriptomics in structural-functional coupling may help account for the behavioural differences observed in experienced psychedelic users.
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Affiliation(s)
- Pablo Mallaroni
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Natasha L. Mason
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Lilian Kloft
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Johannes T. Reckweg
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Kim van Oorsouw
- Department of Forensic Psychology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Johannes G. Ramaekers
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
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Griffiths-King D, Wood AG, Novak J. Predicting 'Brainage' in late childhood to adolescence (6-17yrs) using structural MRI, morphometric similarity, and machine learning. Sci Rep 2023; 13:15591. [PMID: 37730747 PMCID: PMC10511546 DOI: 10.1038/s41598-023-42414-5] [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: 02/13/2023] [Accepted: 09/10/2023] [Indexed: 09/22/2023] Open
Abstract
Brain development is regularly studied using structural MRI. Recently, studies have used a combination of statistical learning and large-scale imaging databases of healthy children to predict an individual's age from structural MRI. This data-driven, predicted 'Brainage' typically differs from the subjects chronological age, with this difference a potential measure of individual difference. Few studies have leveraged higher-order or connectomic representations of structural MRI data for this Brainage approach. We leveraged morphometric similarity as a network-level approach to structural MRI to generate predictive models of age. We benchmarked these novel Brainage approaches using morphometric similarity against more typical, single feature (i.e., cortical thickness) approaches. We showed that these novel methods did not outperform cortical thickness or cortical volume measures. All models were significantly biased by age, but robust to motion confounds. The main results show that, whilst morphometric similarity mapping may be a novel way to leverage additional information from a T1-weighted structural MRI beyond individual features, in the context of a Brainage framework, morphometric similarity does not provide more accurate predictions of age. Morphometric similarity as a network-level approach to structural MRI may be poorly positioned to study individual differences in brain development in healthy participants in this way.
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Affiliation(s)
- Daniel Griffiths-King
- Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK
| | - Amanda G Wood
- Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK
- School of Psychology, Faculty of Health, Melbourne Burwood Campus, Deakin University, Geelong, VIC, Australia
- Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Jan Novak
- Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK.
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