1
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Smith NR, Ameen S, Miller SN, Kasper JM, Schwarz JM, Hommel JD, Borzou A. The neuroanatomical organization of the hypothalamus is driven by spatial and topological efficiency. Front Syst Neurosci 2024; 18:1417346. [PMID: 39165582 PMCID: PMC11334159 DOI: 10.3389/fnsys.2024.1417346] [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: 04/14/2024] [Accepted: 07/18/2024] [Indexed: 08/22/2024] Open
Abstract
The hypothalamus in the mammalian brain is responsible for regulating functions associated with survival and reproduction representing a complex set of highly interconnected, yet anatomically and functionally distinct, sub-regions. It remains unclear what factors drive the spatial organization of sub-regions within the hypothalamus. One potential factor may be structural connectivity of the network that promotes efficient function with well-connected sub-regions placed closer together geometrically, i.e., the strongest axonal signal transferred through the shortest geometrical distance. To empirically test for such efficiency, we use hypothalamic data derived from the Allen Mouse Brain Connectivity Atlas, which provides a structural connectivity map of mouse brain regions derived from a series of viral tracing experiments. Using both cost function minimization and comparison with a weighted, sphere-packing ensemble, we demonstrate that the sum of the distances between hypothalamic sub-regions are not close to the minimum possible distance, consistent with prior whole brain studies. However, if such distances are weighted by the inverse of the magnitude of the connectivity, their sum is among the lowest possible values. Specifically, the hypothalamus appears within the top 94th percentile of neural efficiencies of randomly packed configurations and within one standard deviation of the median efficiency when packings are optimized for maximal neural efficiency. Our results, therefore, indicate that a combination of geometrical and topological constraints help govern the structure of the hypothalamus.
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Affiliation(s)
- Nathan R. Smith
- Center for Addiction Sciences and Therapeutics, Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, United States
| | - Shabeeb Ameen
- Physics Department and BioInspired Institute, Syracuse University, Syracuse, NY, United States
| | - Sierra N. Miller
- Center for Addiction Sciences and Therapeutics, Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, United States
| | - James M. Kasper
- Center for Addiction Sciences and Therapeutics, Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, United States
| | - Jennifer M. Schwarz
- Physics Department and BioInspired Institute, Syracuse University, Syracuse, NY, United States
- Indian Creek Farm, Ithaca, NY, United States
| | - Jonathan D. Hommel
- Center for Addiction Sciences and Therapeutics, Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, United States
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2
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Ganglberger F, Kargl D, Töpfer M, Hernandez-Lallement J, Lawless N, Fernandez-Albert F, Haubensak W, Bühler K. BrainTACO: an explorable multi-scale multi-modal brain transcriptomic and connectivity data resource. Commun Biol 2024; 7:730. [PMID: 38877144 PMCID: PMC11178817 DOI: 10.1038/s42003-024-06355-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 05/20/2024] [Indexed: 06/16/2024] Open
Abstract
Exploring the relationships between genes and brain circuitry can be accelerated by joint analysis of heterogeneous datasets from 3D imaging data, anatomical data, as well as brain networks at varying scales, resolutions, and modalities. Generating an integrated view, beyond the individual resources' original purpose, requires the fusion of these data to a common space, and a visualization that bridges the gap across scales. However, despite ever expanding datasets, few platforms for integration and exploration of this heterogeneous data exist. To this end, we present the BrainTACO (Brain Transcriptomic And Connectivity Data) resource, a selection of heterogeneous, and multi-scale neurobiological data spatially mapped onto a common, hierarchical reference space, combined via a holistic data integration scheme. To access BrainTACO, we extended BrainTrawler, a web-based visual analytics framework for spatial neurobiological data, with comparative visualizations of multiple resources. This enables gene expression dissection of brain networks with, to the best of our knowledge, an unprecedented coverage and allows for the identification of potential genetic drivers of connectivity in both mice and humans that may contribute to the discovery of dysconnectivity phenotypes. Hence, BrainTACO reduces the need for time-consuming manual data aggregation often required for computational analyses in script-based toolboxes, and supports neuroscientists by directly leveraging the data instead of preparing it.
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Affiliation(s)
- Florian Ganglberger
- Biomedical Image Informatics, VRVis Research Center, Vienna, Austria
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma, Biberach an der Riss, Germany
| | - Dominic Kargl
- Department of Neuronal Cell Biology, Vienna Medical University, Vienna, Austria
| | - Markus Töpfer
- Biomedical Image Informatics, VRVis Research Center, Vienna, Austria
| | - Julien Hernandez-Lallement
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma, Biberach an der Riss, Germany
| | - Nathan Lawless
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma, Biberach an der Riss, Germany
| | - Francesc Fernandez-Albert
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma, Biberach an der Riss, Germany
| | - Wulf Haubensak
- Department of Neuronal Cell Biology, Vienna Medical University, Vienna, Austria
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria
| | - Katja Bühler
- Biomedical Image Informatics, VRVis Research Center, Vienna, Austria.
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3
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Liang X, Sun L, Liao X, Lei T, Xia M, Duan D, Zeng Z, Li Q, Xu Z, Men W, Wang Y, Tan S, Gao JH, Qin S, Tao S, Dong Q, Zhao T, He Y. Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence. Nat Commun 2024; 15:784. [PMID: 38278807 PMCID: PMC10817914 DOI: 10.1038/s41467-024-44863-6] [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: 05/15/2023] [Accepted: 01/08/2024] [Indexed: 01/28/2024] Open
Abstract
Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Here, we show cortical thinning patterns primarily located in the lateral frontal and parietal heteromodal nodes during childhood and adolescence, which are structurally constrained by white matter network architecture and are particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible in another independent dataset. These findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.
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Affiliation(s)
- Xinyuan Liang
- State 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
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Lianglong Sun
- State 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
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Tianyuan Lei
- State 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
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Mingrui Xia
- State 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
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Dingna Duan
- State 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
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Zilong Zeng
- State 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
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Qiongling Li
- State 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
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Zhilei Xu
- State 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
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, 100096, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, China
| | - Shaozheng Qin
- State 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
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Tengda Zhao
- State 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.
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
| | - Yong He
- State 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.
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
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4
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Campelo dos Santos AL, DeGiorgio M, Assis R. Predicting evolutionary targets and parameters of gene deletion from expression data. BIOINFORMATICS ADVANCES 2024; 4:vbae002. [PMID: 38282974 PMCID: PMC10812876 DOI: 10.1093/bioadv/vbae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 12/08/2023] [Accepted: 01/04/2024] [Indexed: 01/30/2024]
Abstract
Motivation Gene deletion is traditionally thought of as a nonadaptive process that removes functional redundancy from genomes, such that it generally receives less attention than duplication in evolutionary turnover studies. Yet, mounting evidence suggests that deletion may promote adaptation via the "less-is-more" evolutionary hypothesis, as it often targets genes harboring unique sequences, expression profiles, and molecular functions. Hence, predicting the relative prevalence of redundant and unique functions among genes targeted by deletion, as well as the parameters underlying their evolution, can shed light on the role of gene deletion in adaptation. Results Here, we present CLOUDe, a suite of machine learning methods for predicting evolutionary targets of gene deletion events from expression data. Specifically, CLOUDe models expression evolution as an Ornstein-Uhlenbeck process, and uses multi-layer neural network, extreme gradient boosting, random forest, and support vector machine architectures to predict whether deleted genes are "redundant" or "unique", as well as several parameters underlying their evolution. We show that CLOUDe boasts high power and accuracy in differentiating between classes, and high accuracy and precision in estimating evolutionary parameters, with optimal performance achieved by its neural network architecture. Application of CLOUDe to empirical data from Drosophila suggests that deletion primarily targets genes with unique functions, with further analysis showing these functions to be enriched for protein deubiquitination. Thus, CLOUDe represents a key advance in learning about the role of gene deletion in functional evolution and adaptation. Availability and implementation CLOUDe is freely available on GitHub (https://github.com/anddssan/CLOUDe).
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Affiliation(s)
- Andre Luiz Campelo dos Santos
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, United States
| | - Michael DeGiorgio
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, United States
| | - Raquel Assis
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, United States
- Institute for Human Health and Disease Intervention, Florida Atlantic University, Boca Raton, FL 33431, United States
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5
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Yee Y, Ellegood J, French L, Lerch JP. Organization of thalamocortical structural covariance and a corresponding 3D atlas of the mouse thalamus. Neuroimage 2024; 285:120453. [PMID: 37979895 DOI: 10.1016/j.neuroimage.2023.120453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 10/29/2023] [Accepted: 11/08/2023] [Indexed: 11/20/2023] Open
Abstract
For information from sensory organs to be processed by the brain, it is usually passed to appropriate areas of the cerebral cortex. Almost all of this information passes through the thalamus, a relay structure that reciprocally connects to the vast majority of the cortex. The thalamus facilitates this information transfer through a set of thalamocortical connections that vary in cellular structure, molecular profiles, innervation patterns, and firing rates. Additionally, corticothalamic connections allow for intracortical information transfer through the thalamus. These efferent and afferent connections between the thalamus and cortex have been the focus of many studies, and the importance of cortical connectivity in defining thalamus anatomy is demonstrated by multiple studies that parcellate the thalamus based on cortical connectivity profiles. Here, we examine correlated morphological variation between the thalamus and cortex, or thalamocortical structural covariance. For each voxel in the thalamus as a seed, we construct a cortical structural covariance map that represents correlated cortical volume variation, and examine whether high structural covariance is observed in cortical areas that are functionally relevant to the seed. Then, using these cortical structural covariance maps as features, we subdivide the thalamus into six non-overlapping regions (clusters of voxels), and assess whether cortical structural covariance is associated with cortical connectivity that specifically originates from these regions. We show that cortical structural covariance is high in areas of the cortex that are functionally related to the seed voxel, cortical structural covariance varies along cortical depth, and sharp transitions in cortical structural covariance profiles are observed when varying seed locations in the thalamus. Subdividing the thalamus based on structural covariance, we additionally demonstrate that the six thalamic clusters of voxels stratify cortical structural covariance along the dorsal-ventral, medial-lateral, and anterior-posterior axes. These cluster-associated structural covariance patterns are prominently detected in cortical regions innervated by fibers projecting out of their related thalamic subdivisions. Together, these results advance our understanding of how the thalamus and the cortex couple in their volumes. Our results indicate that these volume correlations reflect functional organization and structural connectivity, and further provides a novel segmentation of the mouse thalamus that can be used to examine thalamic structural variation and thalamocortical structural covariation in disease models.
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Affiliation(s)
- Yohan Yee
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada.
| | - Jacob Ellegood
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Canada
| | - Leon French
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Jason P Lerch
- Department of Medical Biophysics, University of Toronto, Toronto, Canada; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
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6
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Feng G, Chen R, Zhao R, Li Y, Ma L, Wang Y, Men W, Gao J, Tan S, Cheng J, He Y, Qin S, Dong Q, Tao S, Shu N. Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture. Commun Biol 2023; 6:1257. [PMID: 38087047 PMCID: PMC10716168 DOI: 10.1038/s42003-023-05647-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.
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Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Rui Zhao
- College of Life Sciences, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Gene Resource and Molecular Development, Beijing, China
| | - Yuanyuan Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jiahong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
- BABRI Centre, Beijing Normal University, Beijing, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
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7
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Bazinet V, Hansen JY, Misic B. Towards a biologically annotated brain connectome. Nat Rev Neurosci 2023; 24:747-760. [PMID: 37848663 DOI: 10.1038/s41583-023-00752-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2023] [Indexed: 10/19/2023]
Abstract
The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.
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Affiliation(s)
- Vincent Bazinet
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Justine Y Hansen
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada.
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8
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Saberi A, Paquola C, Wagstyl K, Hettwer MD, Bernhardt BC, Eickhoff SB, Valk SL. The regional variation of laminar thickness in the human isocortex is related to cortical hierarchy and interregional connectivity. PLoS Biol 2023; 21:e3002365. [PMID: 37943873 PMCID: PMC10684102 DOI: 10.1371/journal.pbio.3002365] [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: 03/28/2023] [Revised: 11/28/2023] [Accepted: 10/06/2023] [Indexed: 11/12/2023] Open
Abstract
The human isocortex consists of tangentially organized layers with unique cytoarchitectural properties. These layers show spatial variations in thickness and cytoarchitecture across the neocortex, which is thought to support function through enabling targeted corticocortical connections. Here, leveraging maps of the 6 cortical layers based on 3D human brain histology, we aimed to quantitatively characterize the systematic covariation of laminar structure in the cortex and its functional consequences. After correcting for the effect of cortical curvature, we identified a spatial pattern of changes in laminar thickness covariance from lateral frontal to posterior occipital regions, which differentiated the dominance of infra- versus supragranular layer thickness. Corresponding to the laminar regularities of cortical connections along cortical hierarchy, the infragranular-dominant pattern of laminar thickness was associated with higher hierarchical positions of regions, mapped based on resting-state effective connectivity in humans and tract-tracing of structural connections in macaques. Moreover, we show that regions with similar laminar thickness patterns have a higher likelihood of structural connections and strength of functional connections. In sum, here we characterize the organization of laminar thickness in the human isocortex and its association with cortico-cortical connectivity, illustrating how laminar organization may provide a foundational principle of cortical function.
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Affiliation(s)
- Amin Saberi
- Otto Hahn Research Group for Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Casey Paquola
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Konrad Wagstyl
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
| | - Meike D. Hettwer
- Otto Hahn Research Group for Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Max Planck School of Cognition, Leipzig, Germany
| | - Boris C. Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Simon B. Eickhoff
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sofie L. Valk
- Otto Hahn Research Group for Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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9
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Sun S, Torok J, Mezias C, Ma D, Raj A. Spatial cell-type enrichment predicts mouse brain connectivity. Cell Rep 2023; 42:113258. [PMID: 37858469 DOI: 10.1016/j.celrep.2023.113258] [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: 12/12/2022] [Revised: 06/07/2023] [Accepted: 09/28/2023] [Indexed: 10/21/2023] Open
Abstract
A fundamental neuroscience topic is the link between the brain's molecular, cellular, and cytoarchitectonic properties and structural connectivity. Recent studies relate inter-regional connectivity to gene expression, but the relationship to regional cell-type distributions remains understudied. Here, we utilize whole-brain mapping of neuronal and non-neuronal subtypes via the matrix inversion and subset selection algorithm to model inter-regional connectivity as a function of regional cell-type composition with machine learning. We deployed random forest algorithms for predicting connectivity from cell-type densities, demonstrating surprisingly strong prediction accuracy of cell types in general, and particular non-neuronal cells such as oligodendrocytes. We found evidence of a strong distance dependency in the cell connectivity relationship, with layer-specific excitatory neurons contributing the most for long-range connectivity, while vascular and astroglia were salient for short-range connections. Our results demonstrate a link between cell types and connectivity, providing a roadmap for examining this relationship in other species, including humans.
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Affiliation(s)
- Shenghuan Sun
- Department of Radiology, University of California, San Francisco, San Francisco, CA, USA
| | - Justin Torok
- Department of Radiology, University of California, San Francisco, San Francisco, CA, USA
| | | | - Daren Ma
- Department of Radiology, University of California, San Francisco, San Francisco, CA, USA
| | - Ashish Raj
- Department of Radiology, University of California, San Francisco, San Francisco, CA, USA.
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10
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Walker BL, Nie Q. NeST: nested hierarchical structure identification in spatial transcriptomic data. Nat Commun 2023; 14:6554. [PMID: 37848426 PMCID: PMC10582109 DOI: 10.1038/s41467-023-42343-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 10/06/2023] [Indexed: 10/19/2023] Open
Abstract
Spatial gene expression in tissue is characterized by regions in which particular genes are enriched or depleted. Frequently, these regions contain nested inside them subregions with distinct expression patterns. Segmentation methods in spatial transcriptomic (ST) data extract disjoint regions maximizing similarity over the greatest number of genes, typically on a particular spatial scale, thus lacking the ability to find region-within-region structure. We present NeST, which extracts spatial structure through coexpression hotspots-regions exhibiting localized spatial coexpression of some set of genes. Coexpression hotspots identify structure on any spatial scale, over any possible subset of genes, and are highly explainable. NeST also performs spatial analysis of cell-cell interactions via ligand-receptor, identifying active areas de novo without restriction of cell type or other groupings, in both two and three dimensions. Through application on ST datasets of varying type and resolution, we demonstrate the ability of NeST to reveal a new level of biological structure.
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Affiliation(s)
- Benjamin L Walker
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA, 92627, USA
- Department of Mathematics, University of California Irvine, Irvine, CA, 92627, USA
| | - Qing Nie
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA, 92627, USA.
- Department of Mathematics, University of California Irvine, Irvine, CA, 92627, USA.
- Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA, 92627, USA.
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11
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Sebenius I, Seidlitz J, Warrier V, Bethlehem RAI, Alexander-Bloch A, Mallard TT, Garcia RR, Bullmore ET, Morgan SE. Robust estimation of cortical similarity networks from brain MRI. Nat Neurosci 2023; 26:1461-1471. [PMID: 37460809 PMCID: PMC10400419 DOI: 10.1038/s41593-023-01376-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 06/08/2023] [Indexed: 08/05/2023]
Abstract
Structural similarity is a growing focus for magnetic resonance imaging (MRI) of connectomes. Here we propose Morphometric INverse Divergence (MIND), a new method to estimate within-subject similarity between cortical areas based on the divergence between their multivariate distributions of multiple MRI features. Compared to the prior approach of morphometric similarity networks (MSNs) on n > 11,000 scans spanning three human datasets and one macaque dataset, MIND networks were more reliable, more consistent with cortical cytoarchitectonics and symmetry and more correlated with tract-tracing measures of axonal connectivity. MIND networks derived from human T1-weighted MRI were more sensitive to age-related changes than MSNs or networks derived by tractography of diffusion-weighted MRI. Gene co-expression between cortical areas was more strongly coupled to MIND networks than to MSNs or tractography. MIND network phenotypes were also more heritable, especially edges between structurally differentiated areas. MIND network analysis provides a biologically validated lens for cortical connectomics using readily available MRI data.
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Affiliation(s)
- Isaac Sebenius
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK.
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Varun Warrier
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Richard A I Bethlehem
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Aaron Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Travis T Mallard
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Rafael Romero Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Dpto. de Fisiología Médica y Biofísica, Barcelona, Spain
| | | | - Sarah E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
- Alan Turing Institute, London, UK
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12
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Bazinet V, Hansen JY, Vos de Wael R, Bernhardt BC, van den Heuvel MP, Misic B. Assortative mixing in micro-architecturally annotated brain connectomes. Nat Commun 2023; 14:2850. [PMID: 37202416 DOI: 10.1038/s41467-023-38585-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 05/08/2023] [Indexed: 05/20/2023] Open
Abstract
The wiring of the brain connects micro-architecturally diverse neuronal populations, but the conventional graph model, which encodes macroscale brain connectivity as a network of nodes and edges, abstracts away the rich biological detail of each regional node. Here, we annotate connectomes with multiple biological attributes and formally study assortative mixing in annotated connectomes. Namely, we quantify the tendency for regions to be connected based on the similarity of their micro-architectural attributes. We perform all experiments using four cortico-cortical connectome datasets from three different species, and consider a range of molecular, cellular, and laminar annotations. We show that mixing between micro-architecturally diverse neuronal populations is supported by long-distance connections and find that the arrangement of connections with respect to biological annotations is associated to patterns of regional functional specialization. By bridging scales of cortical organization, from microscale attributes to macroscale connectivity, this work lays the foundation for next-generation annotated connectomics.
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Affiliation(s)
- Vincent Bazinet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Justine Y Hansen
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Martijn P van den Heuvel
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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13
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Vaikakkara Chithran A, Allan DW, O'Connor TP. Adult expression of Semaphorins and Plexins is essential for motor neuron survival. Sci Rep 2023; 13:5894. [PMID: 37041188 PMCID: PMC10090137 DOI: 10.1038/s41598-023-32943-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 04/05/2023] [Indexed: 04/13/2023] Open
Abstract
Axon guidance cues direct the growth and steering of neuronal growth cones, thus guiding the axons to their targets during development. Nonetheless, after axons have reached their targets and established functional circuits, many mature neurons continue to express these developmental cues. The role of axon guidance cues in the adult nervous system has not been fully elucidated. Using the expression pattern data available on FlyBase, we found that more than 96% of the guidance genes that are expressed in the Drosophila melanogaster embryo continue to be expressed in adults. We utilized the GeneSwitch and TARGET systems to spatiotemporally knockdown the expression of these guidance genes selectively in the adult neurons, once the development was completed. We performed an RNA interference (RNAi) screen against 44 guidance genes in the adult Drosophila nervous system and identified 14 genes that are required for adult survival and normal motility. Additionally, we show that adult expression of Semaphorins and Plexins in motor neurons is necessary for neuronal survival, indicating that guidance genes have critical functions in the mature nervous system.
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Affiliation(s)
- Aarya Vaikakkara Chithran
- Graduate Program in Neuroscience, University of British Columbia, 3402-2215 Wesbrook Mall, Vancouver, BC, V6T 1Z3, Canada
- Department of Cellular and Physiological Sciences, University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada
| | - Douglas W Allan
- Department of Cellular and Physiological Sciences, University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC, V6T 1Z3, Canada
| | - Timothy P O'Connor
- Department of Cellular and Physiological Sciences, University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada.
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC, V6T 1Z3, Canada.
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14
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Arnatkeviciute A, Markello RD, Fulcher BD, Misic B, Fornito A. Toward Best Practices for Imaging Transcriptomics of the Human Brain. Biol Psychiatry 2023; 93:391-404. [PMID: 36725139 DOI: 10.1016/j.biopsych.2022.10.016] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 10/03/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022]
Abstract
Modern brainwide transcriptional atlases provide unprecedented opportunities for investigating the molecular correlates of brain organization, as quantified using noninvasive neuroimaging. However, integrating neuroimaging data with transcriptomic measures is not straightforward, and careful consideration is required to make valid inferences. In this article, we review recent work exploring how various methodological choices affect 3 main phases of imaging transcriptomic analyses, including 1) processing of transcriptional atlas data; 2) relating transcriptional measures to independently derived neuroimaging phenotypes; and 3) evaluating the functional implications of identified associations through gene enrichment analyses. Our aim is to facilitate the development of standardized and reproducible approaches for this rapidly growing field. We identify sources of methodological variability, key choices that can affect findings, and considerations for mitigating false positive and/or spurious results. Finally, we provide an overview of freely available open-source toolboxes implementing current best-practice procedures across all 3 analysis phases.
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Affiliation(s)
- Aurina Arnatkeviciute
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia.
| | - Ross D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Ben D Fulcher
- School of Physics, The University of Sydney, Sydney, New South Wales, Australia
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
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15
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Where the genome meets the connectome: Understanding how genes shape human brain connectivity. Neuroimage 2021; 244:118570. [PMID: 34508898 DOI: 10.1016/j.neuroimage.2021.118570] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/10/2021] [Accepted: 09/07/2021] [Indexed: 02/07/2023] Open
Abstract
The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organization of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.
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16
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Song Y, Zhou D, Li S. Maximum Entropy Principle Underlies Wiring Length Distribution in Brain Networks. Cereb Cortex 2021; 31:4628-4641. [PMID: 33999124 DOI: 10.1093/cercor/bhab110] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/16/2021] [Accepted: 03/17/2021] [Indexed: 11/14/2022] Open
Abstract
A brain network comprises a substantial amount of short-range connections with an admixture of long-range connections. The portion of long-range connections in brain networks is observed to be quantitatively dissimilar across species. It is hypothesized that the length of connections is constrained by the spatial embedding of brain networks, yet fundamental principles that underlie the wiring length distribution remain unclear. By quantifying the structural diversity of a brain network using Shannon's entropy, here we show that the wiring length distribution across multiple species-including Drosophila, mouse, macaque, human, and C. elegans-follows the maximum entropy principle (MAP) under the constraints of limited wiring material and the spatial locations of brain areas or neurons. In addition, by considering stochastic axonal growth, we propose a network formation process capable of reproducing wiring length distributions of the 5 species, thereby implementing MAP in a biologically plausible manner. We further develop a generative model incorporating MAP, and show that, for the 5 species, the generated network exhibits high similarity to the real network. Our work indicates that the brain connectivity evolves to be structurally diversified by maximizing entropy to support efficient interareal communication, providing a potential organizational principle of brain networks.
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Affiliation(s)
- Yuru Song
- Neuroscience Graduate Program, University of California, San Diego, CA, USA
| | - Douglas Zhou
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.,Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.,Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Songting Li
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.,Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.,Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai 200240, China
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17
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Fulcher BD, Arnatkeviciute A, Fornito A. Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data. Nat Commun 2021; 12:2669. [PMID: 33976144 PMCID: PMC8113439 DOI: 10.1038/s41467-021-22862-1] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 03/31/2021] [Indexed: 02/03/2023] Open
Abstract
Transcriptomic atlases have improved our understanding of the correlations between gene-expression patterns and spatially varying properties of brain structure and function. Gene-category enrichment analysis (GCEA) is a common method to identify functional gene categories that drive these associations, using gene-to-category annotation systems like the Gene Ontology (GO). Here, we show that applying standard GCEA methodology to spatial transcriptomic data is affected by substantial false-positive bias, with GO categories displaying an over 500-fold average inflation of false-positive associations with random neural phenotypes in mouse and human. The estimated false-positive rate of a GO category is associated with its rate of being reported as significantly enriched in the literature, suggesting that published reports are affected by this false-positive bias. We show that within-category gene-gene coexpression and spatial autocorrelation are key drivers of the false-positive bias and introduce flexible ensemble-based null models that can account for these effects, made available as a software toolbox.
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Affiliation(s)
- Ben D Fulcher
- School of Physics, The University of Sydney, Camperdown, NSW, Australia.
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia.
| | - Aurina Arnatkeviciute
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia
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18
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Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain. Neuroinformatics 2021; 19:649-667. [PMID: 33704701 PMCID: PMC8566442 DOI: 10.1007/s12021-021-09511-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/06/2021] [Indexed: 11/16/2022]
Abstract
Finding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis. We identified representative and literature-validated cortico-midbrain and cortico-striatal projections, whose gene subsets were enriched with annotations for neuronal and synaptic function and related developmental and metabolic processes. The results were highly reproducible when including all available projections, as well as consistent with factorisations obtained using the Dictionary Learning and Sparse Coding technique. Hence, Linked ICA yielded reproducible independent components that were preserved under increasing data variance. Taken together, we have developed and validated a novel paradigm for linking gene expression and structural projection patterns in the mouse mesoconnectome, which can power future studies aiming to relate genes to brain function.
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19
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DeGiorgio M, Assis R. Learning Retention Mechanisms and Evolutionary Parameters of Duplicate Genes from Their Expression Data. Mol Biol Evol 2021; 38:1209-1224. [PMID: 33045078 PMCID: PMC7947822 DOI: 10.1093/molbev/msaa267] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Learning about the roles that duplicate genes play in the origins of novel phenotypes requires an understanding of how their functions evolve. A previous method for achieving this goal, CDROM, employs gene expression distances as proxies for functional divergence and then classifies the evolutionary mechanisms retaining duplicate genes from comparisons of these distances in a decision tree framework. However, CDROM does not account for stochastic shifts in gene expression or leverage advances in contemporary statistical learning for performing classification, nor is it capable of predicting the parameters driving duplicate gene evolution. Thus, here we develop CLOUD, a multi-layer neural network built on a model of gene expression evolution that can both classify duplicate gene retention mechanisms and predict their underlying evolutionary parameters. We show that not only is the CLOUD classifier substantially more powerful and accurate than CDROM, but that it also yields accurate parameter predictions, enabling a better understanding of the specific forces driving the evolution and long-term retention of duplicate genes. Further, application of the CLOUD classifier and predictor to empirical data from Drosophila recapitulates many previous findings about gene duplication in this lineage, showing that new functions often emerge rapidly and asymmetrically in younger duplicate gene copies, and that functional divergence is driven by strong natural selection. Hence, CLOUD represents a major advancement in classifying retention mechanisms and predicting evolutionary parameters of duplicate genes, thereby highlighting the utility of incorporating sophisticated statistical learning techniques to address long-standing questions about evolution after gene duplication.
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Affiliation(s)
- Michael DeGiorgio
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431
- Institute for Human Health and Disease Intervention, Florida Atlantic University, Boca Raton, FL 33431
| | - Raquel Assis
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431
- Institute for Human Health and Disease Intervention, Florida Atlantic University, Boca Raton, FL 33431
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20
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Lau HYG, Fornito A, Fulcher BD. Scaling of gene transcriptional gradients with brain size across mouse development. Neuroimage 2021; 224:117395. [PMID: 32979525 DOI: 10.1016/j.neuroimage.2020.117395] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 01/25/2023] Open
Abstract
The structure of the adult brain is the result of complex physical mechanisms acting in three-dimensional space through development. Consequently, the brain's spatial embedding plays a key role in its organization, including the gradient-like patterning of gene expression that encodes the molecular underpinning of functional specialization. However, we do not yet understand how changes in brain shape and size that occur across development influence the brain's transcriptional architecture. Here we investigate the spatial embedding of transcriptional patterns of over 1800 genes across seven time points through mouse-brain development using data from the Allen Developing Mouse Brain Atlas. We find that transcriptional similarity decreases exponentially with separation distance across all developmental time points, with a correlation length scale that follows a power-law scaling relationship with a linear dimension of brain size. This scaling suggests that the mouse brain achieves a characteristic balance between local molecular similarity (homogeneous gene expression within a specialized brain area) and longer-range diversity (between functionally specialized brain areas) throughout its development. Extrapolating this mouse developmental scaling relationship to the human cortex yields a prediction consistent with the value measured from microarray data. We introduce a simple model of brain growth as spatially autocorrelated gene-expression gradients that expand through development, which captures key features of the mouse developmental data. Complementing the well-known exponential distance rule for structural connectivity, our findings characterize an analogous exponential distance rule for transcriptional gradients that scales across mouse brain development, providing new understanding of spatial constraints on the brain's molecular patterning.
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Affiliation(s)
- Hoi Yan Gladys Lau
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia; School of Physics, The University of Sydney, NSW 2006, Australia; Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Ben D Fulcher
- School of Physics, The University of Sydney, NSW 2006, Australia.
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21
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Timonidis N, Bakker R, Tiesinga P. Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data. Neuroinformatics 2020; 18:611-626. [PMID: 32448958 PMCID: PMC7498447 DOI: 10.1007/s12021-020-09471-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational models to learn and predict cell-class and layer-specific axonal projections using gene expression data. Predictions were done in two ways, namely predicting projection strengths using the expression of individual genes and using the co-expression of genes organized in spatial modules, as well as predicting binary forms of projection. For predicting the strength of projections, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using a dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction workflow that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.
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Affiliation(s)
- Nestor Timonidis
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525, AJ, Nijmegen, the Netherlands.
| | - Rembrandt Bakker
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525, AJ, Nijmegen, the Netherlands.,Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Wilhelm-Johnen-Strasse, 52425, Jülich, Germany
| | - Paul Tiesinga
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525, AJ, Nijmegen, the Netherlands
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22
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Li Y, Huang H, Chen H, Liu T. Deep Neural Networks for In Situ Hybridization Grid Completion and Clustering. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:536-546. [PMID: 30106689 PMCID: PMC7199204 DOI: 10.1109/tcbb.2018.2864262] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Transcriptome in brain plays a crucial role in understanding the cortical organization and the development of brain structure and function. Two challenges, incomplete data and high dimensionality of transcriptome, remain unsolved. Here, we present a novel training scheme that successfully adapts the U-net architecture to the problem of volume recovery. By analogy to denoising autoencoder, we hide a portion of each training sample so that the network can learn to recover missing voxels from context. Then on the completed volumes, we show that Restricted Boltzmann Machines (RBMs) can be used to infer co-occurrences among voxels, providing foundations for dividing the cortex into discrete subregions. As we stack multiple RBMs to form a deep belief network (DBN), we progressively map the high-dimensional raw input into abstract representations and create a hierarchy of transcriptome architecture. A coarse to fine organization emerges from the network layers. This organization incidentally corresponds to the anatomical structures, suggesting a close link between structures and the genetic underpinnings. Thus, we demonstrate a new way of learning transcriptome-based hierarchical organization using RBM and DBN.
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23
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Woo YJ, Roussos P, Haroutunian V, Katsel P, Gandy S, Schadt EE, Zhu J. Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer's disease. BMC Med 2020; 18:23. [PMID: 32024511 PMCID: PMC7003435 DOI: 10.1186/s12916-019-1488-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 12/24/2019] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND The human brain is complex and interconnected structurally. Brain connectome change is associated with Alzheimer's disease (AD) and other neurodegenerative diseases. Genetics and genomics studies have identified molecular changes in AD; however, the results are often limited to isolated brain regions and are difficult to interpret its findings in respect to brain connectome. The mechanisms of how one brain region impacts the molecular pathways in other regions have not been systematically studied. And how the brain regions susceptible to AD pathology interact with each other at the transcriptome level and how these interactions relate to brain connectome change are unclear. METHODS Here, we compared structural brain connectomes defined by probabilistic tracts using diffusion magnetic resonance imaging data in Alzheimer's Disease Neuroimaging Initiative database and a brain transcriptome dataset covering 17 brain regions. RESULTS We observed that the changes in diffusion measures associated with AD diagnosis status and the associations were replicated in an independent cohort. The result suggests that disease associated white matter changes are focal. Analysis of the brain connectome by genomic data, tissue-tissue transcriptional synchronization between 17 brain regions, indicates that the regions connected by AD-associated tracts were likely connected at the transcriptome level with high number of tissue-to-tissue correlated (TTC) gene pairs (P = 0.03). And genes involved in TTC gene pairs between white matter tract connected brain regions were enriched in signaling pathways (P = 6.08 × 10-9). Further pathway interaction analysis identified ionotropic glutamate receptor pathway and Toll receptor signaling pathways to be important for tissue-tissue synchronization at the transcriptome level. Transcript profile entailing Toll receptor signaling in the blood was significantly associated with diffusion properties of white matter tracts, notable association between fractional anisotropy and bilateral cingulum angular bundles (Ppermutation = 1.0 × 10-2 and 4.9 × 10-4 for left and right respectively). CONCLUSIONS In summary, our study suggests that brain connectomes defined by MRI and transcriptome data overlap with each other.
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Affiliation(s)
- Young Jae Woo
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Panos Roussos
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Vahram Haroutunian
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Pavel Katsel
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Samuel Gandy
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Sema4, Stamford, CT, 06902, USA
| | - Jun Zhu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Sema4, Stamford, CT, 06902, USA.
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24
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Kong XZ, Tzourio-Mazoyer N, Joliot M, Fedorenko E, Liu J, Fisher SE, Francks C. Gene Expression Correlates of the Cortical Network Underlying Sentence Processing. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2020; 1:77-103. [PMID: 36794006 PMCID: PMC9923707 DOI: 10.1162/nol_a_00004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 12/19/2019] [Indexed: 04/16/2023]
Abstract
A pivotal question in modern neuroscience is which genes regulate brain circuits that underlie cognitive functions. However, the field is still in its infancy. Here we report an integrated investigation of the high-level language network (i.e., sentence-processing network) in the human cerebral cortex, combining regional gene expression profiles, task fMRI, large-scale neuroimaging meta-analysis, and resting-state functional network approaches. We revealed reliable gene expression-functional network correlations using three different network definition strategies, and identified a consensus set of genes related to connectivity within the sentence-processing network. The genes involved showed enrichment for neural development and actin-related functions, as well as association signals with autism, which can involve disrupted language functioning. Our findings help elucidate the molecular basis of the brain's infrastructure for language. The integrative approach described here will be useful for studying other complex cognitive traits.
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Affiliation(s)
| | - Nathalie Tzourio-Mazoyer
- University of Bordeaux, GIN, IMN, UMR 5293, Bordeaux, France
- CNRS, GIN, IMN, UMR 5293, Bordeaux, France
- CEA, GIN, IMN, UMR 5293, Bordeaux, France
| | - Marc Joliot
- University of Bordeaux, GIN, IMN, UMR 5293, Bordeaux, France
- CNRS, GIN, IMN, UMR 5293, Bordeaux, France
- CEA, GIN, IMN, UMR 5293, Bordeaux, France
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, MIT, Cambridge, MA, 02139, USA
| | - Jia Liu
- Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Simon E. Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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25
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Suetterlin P, Hurley S, Mohan C, Riegman KLH, Pagani M, Caruso A, Ellegood J, Galbusera A, Crespo-Enriquez I, Michetti C, Yee Y, Ellingford R, Brock O, Delogu A, Francis-West P, Lerch JP, Scattoni ML, Gozzi A, Fernandes C, Basson MA. Altered Neocortical Gene Expression, Brain Overgrowth and Functional Over-Connectivity in Chd8 Haploinsufficient Mice. Cereb Cortex 2019; 28:2192-2206. [PMID: 29668850 PMCID: PMC6018918 DOI: 10.1093/cercor/bhy058] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Indexed: 12/13/2022] Open
Abstract
Truncating CHD8 mutations are amongst the highest confidence risk factors for autism spectrum disorder (ASD) identified to date. Here, we report that Chd8 heterozygous mice display increased brain size, motor delay, hypertelorism, pronounced hypoactivity, and anomalous responses to social stimuli. Whereas gene expression in the neocortex is only mildly affected at midgestation, over 600 genes are differentially expressed in the early postnatal neocortex. Genes involved in cell adhesion and axon guidance are particularly prominent amongst the downregulated transcripts. Resting-state functional MRI identified increased synchronized activity in cortico-hippocampal and auditory-parietal networks in Chd8 heterozygous mutant mice, implicating altered connectivity as a potential mechanism underlying the behavioral phenotypes. Together, these data suggest that altered brain growth and diminished expression of important neurodevelopmental genes that regulate long-range brain wiring are followed by distinctive anomalies in functional brain connectivity in Chd8+/- mice. Human imaging studies have reported altered functional connectivity in ASD patients, with long-range under-connectivity seemingly more frequent. Our data suggest that CHD8 haploinsufficiency represents a specific subtype of ASD where neuropsychiatric symptoms are underpinned by long-range over-connectivity.
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Affiliation(s)
- Philipp Suetterlin
- Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, UK
| | - Shaun Hurley
- Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, UK
| | - Conor Mohan
- Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, UK
| | - Kimberley L H Riegman
- Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, UK
| | - Marco Pagani
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, 38068 Rovereto, TN, Italy
| | - Angela Caruso
- Research Coordination and Support Service, Istituto Superiore di Sanità, 00161 Rome, Italy
| | - Jacob Ellegood
- Department of Medical Biophysics, University of Toronto, Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada M5T 3H7
| | - Alberto Galbusera
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, 38068 Rovereto, TN, Italy
| | - Ivan Crespo-Enriquez
- Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, UK
| | - Caterina Michetti
- Center for Synaptic Neuroscience and Technology, Istituto Italiano di Tecnologia, 16132 Genova, Italy
| | - Yohan Yee
- Department of Medical Biophysics, University of Toronto, Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada M5T 3H7
| | - Robert Ellingford
- Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, UK
| | - Olivier Brock
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 9NU, UK
| | - Alessio Delogu
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 9NU, UK
| | - Philippa Francis-West
- Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, UK
| | - Jason P Lerch
- Department of Medical Biophysics, University of Toronto, Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada M5T 3H7
| | - Maria Luisa Scattoni
- Research Coordination and Support Service, Istituto Superiore di Sanità, 00161 Rome, Italy
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, 38068 Rovereto, TN, Italy
| | - Cathy Fernandes
- MRC Social, Genetic & Developmental Psychiatry Centre, PO82, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK
| | - M Albert Basson
- Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK
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26
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Yuan Y, Liu J, Zhao P, Xing F, Huo H, Fang T. Structural Insights Into the Dynamic Evolution of Neuronal Networks as Synaptic Density Decreases. Front Neurosci 2019; 13:892. [PMID: 31507365 PMCID: PMC6714520 DOI: 10.3389/fnins.2019.00892] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Accepted: 08/08/2019] [Indexed: 11/13/2022] Open
Abstract
The human brain is thought to be an extremely complex but efficient computing engine, processing vast amounts of information from a changing world. The decline in the synaptic density of neuronal networks is one of the most important characteristics of brain development, which is closely related to synaptic pruning, synaptic growth, synaptic plasticity, and energy metabolism. However, because of technical limitations in observing large-scale neuronal networks dynamically connected through synapses, how neuronal networks are organized and evolve as their synaptic density declines remains unclear. Here, by establishing a biologically reasonable neuronal network model, we show that despite a decline in the synaptic density, the connectivity, and efficiency of neuronal networks can be improved. Importantly, by analyzing the degree distribution, we also find that both the scale-free characteristic of neuronal networks and the emergence of hub neurons rely on the spatial distance between neurons. These findings may promote our understanding of neuronal networks in the brain and have guiding significance for the design of neuronal network models.
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Affiliation(s)
- Ye Yuan
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Jian Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Peng Zhao
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Fu Xing
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Hong Huo
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Tao Fang
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
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27
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Arnatkevičiūtė A, Fulcher BD, Fornito A. Uncovering the Transcriptional Correlates of Hub Connectivity in Neural Networks. Front Neural Circuits 2019; 13:47. [PMID: 31379515 PMCID: PMC6659348 DOI: 10.3389/fncir.2019.00047] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 07/04/2019] [Indexed: 12/04/2022] Open
Abstract
Connections in nervous systems are disproportionately concentrated on a small subset of neural elements that act as network hubs. Hubs have been found across different species and scales ranging from C. elegans to mouse, rat, cat, macaque, and human, suggesting a role for genetic influences. The recent availability of brain-wide gene expression atlases provides new opportunities for mapping the transcriptional correlates of large-scale network-level phenotypes. Here we review studies that use these atlases to investigate gene expression patterns associated with hub connectivity in neural networks and present evidence that some of these patterns are conserved across species and scales.
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Affiliation(s)
- Aurina Arnatkevičiūtė
- Monash Biomedical Imaging, School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
| | - Ben D. Fulcher
- Monash Biomedical Imaging, School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Alex Fornito
- Monash Biomedical Imaging, School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
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28
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Cauda F, Nani A, Manuello J, Premi E, Palermo S, Tatu K, Duca S, Fox PT, Costa T. Brain structural alterations are distributed following functional, anatomic and genetic connectivity. Brain 2019; 141:3211-3232. [PMID: 30346490 DOI: 10.1093/brain/awy252] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 08/22/2018] [Indexed: 12/18/2022] Open
Abstract
The pathological brain is characterized by distributed morphological or structural alterations in the grey matter, which tend to follow identifiable network-like patterns. We analysed the patterns formed by these alterations (increased and decreased grey matter values detected with the voxel-based morphometry technique) conducting an extensive transdiagnostic search of voxel-based morphometry studies in a large variety of brain disorders. We devised an innovative method to construct the networks formed by the structurally co-altered brain areas, which can be considered as pathological structural co-alteration patterns, and to compare these patterns with three associated types of connectivity profiles (functional, anatomical, and genetic). Our study provides transdiagnostical evidence that structural co-alterations are influenced by connectivity constraints rather than being randomly distributed. Analyses show that although all the three types of connectivity taken together can account for and predict with good statistical accuracy, the shape and temporal development of the co-alteration patterns, functional connectivity offers the better account of the structural co-alteration, followed by anatomic and genetic connectivity. These results shed new light on the possible mechanisms at the root of neuropathological processes and open exciting prospects in the quest for a better understanding of brain disorders.
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Affiliation(s)
- Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Enrico Premi
- Stroke Unit, Azienda Socio Sanitaria Territoriale Spedali Civili, Spedali Civili Hospital, Brescia, Italy.,Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Sara Palermo
- Department of Neuroscience, University of Turin, Turin, Italy
| | - Karina Tatu
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, Texas, USA.,South Texas Veterans Health Care System, San Antonio, Texas, USA
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
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29
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Iyamu ID, Lv W, Malik N, Mishra RK, Schiltz GE. Development of Tetrahydroindazole-Based Potent and Selective Sigma-2 Receptor Ligands. ChemMedChem 2019; 14:1248-1256. [PMID: 31071238 PMCID: PMC6613831 DOI: 10.1002/cmdc.201900203] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 05/08/2019] [Indexed: 11/08/2022]
Abstract
The sigma-2 receptor has been shown to play important roles in a number of important diseases, including central nervous system (CNS) disorders and cancer. However, mechanisms by which sigma-2 contributes to these diseases remain unclear. The development of new sigma-2 ligands that can be used to probe the function of this protein and potentially as drug discovery leads is therefore of great importance. Herein we report the development of a series of tetrahydroindazole compounds that are highly potent and selective for sigma-2. Structure-activity relationship data were used to generate a pharmacophore model that summarizes the common features present in the potent ligands. Assays for solubility and microsomal stability showed that several members of this compound series possess promising characteristics for further development of useful chemical probes or drug discovery leads.
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Affiliation(s)
- Iredia D Iyamu
- Center for Molecular Innovation and Drug Discovery, Northwestern University, Evanston, IL, 60208, USA
| | - Wei Lv
- Center for Molecular Innovation and Drug Discovery, Northwestern University, Evanston, IL, 60208, USA
| | - Neha Malik
- Center for Molecular Innovation and Drug Discovery, Northwestern University, Evanston, IL, 60208, USA
| | - Rama K Mishra
- Center for Molecular Innovation and Drug Discovery, Northwestern University, Evanston, IL, 60208, USA
- Department of Pharmacology, Northwestern University, Chicago, IL, 60611, USA
| | - Gary E Schiltz
- Center for Molecular Innovation and Drug Discovery, Northwestern University, Evanston, IL, 60208, USA
- Department of Pharmacology, Northwestern University, Chicago, IL, 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
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30
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Human visual cortex is organized along two genetically opposed hierarchical gradients with unique developmental and evolutionary origins. PLoS Biol 2019; 17:e3000362. [PMID: 31269028 PMCID: PMC6634416 DOI: 10.1371/journal.pbio.3000362] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 07/16/2019] [Accepted: 06/25/2019] [Indexed: 01/24/2023] Open
Abstract
Human visual cortex is organized with striking consistency across individuals. While recent findings demonstrate an unexpected coupling between functional and cytoarchitectonic regions relative to the folding of human visual cortex, a unifying principle linking these anatomical and functional features of the cortex remains elusive. To fill this gap in knowledge, we combined independent and ground truth measurements of cytoarchitectonic regions and genetic tissue characterization within human occipitotemporal cortex. Using a data-driven approach, we examined whether differential gene expression among cytoarchitectonic areas could contribute to the arealization of occipitotemporal cortex into a hierarchy based on transcriptomics. This approach revealed two opposing gene expression gradients: one that contains a series of genes with expression magnitudes that ascend from posterior (e.g., areas human occipital [hOc]1, hOc2, hOc3, etc.) to anterior cytoarchitectonic areas (e.g., areas fusiform gyrus [FG]1–FG4) and another that contains a separate series of genes that show a descending gradient from posterior to anterior areas. Using data from the living human brain, we show that each of these gradients correlates strongly with variations in measures related to either thickness or myelination of cortex, respectively. We further reveal that these genetic gradients emerge along unique trajectories in human development: the ascending gradient is present at 10–12 gestational weeks, while the descending gradient emerges later (19–24 gestational weeks). Interestingly, it is not until early childhood (before 5 years of age) that the two expression gradients achieve their adult-like mean expression values. Additional analyses in nonhuman primates (NHPs) reveal that homologous genes do not generate the same ascending and descending expression gradients as in humans. We discuss these findings relative to previously proposed hierarchies based on functional and cytoarchitectonic features of visual cortex. Altogether, these findings bridge macroscopic features of human cytoarchitectonic areas in visual cortex with microscopic features of cellular organization and genetic expression, which, despite the complexity of this multiscale correspondence, can be described by a sparse subset (approximately 200) of genes. These findings help pinpoint the genes contributing to healthy cortical development and explicate the cortical biology distinguishing humans from other primates, as well as establishing essential groundwork for understanding future work linking genetic mutations with the function and development of the human brain. The expression of a sparse subset of human genes forms two opposed gradients that capture the processing hierarchy of visual cortex; these transcription gradients emerge at different points during human development and distinguish human from nonhuman primates.
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31
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Roberti I, Lovino M, Di Cataldo S, Ficarra E, Urgese G. Exploiting Gene Expression Profiles for the Automated Prediction of Connectivity between Brain Regions. Int J Mol Sci 2019; 20:E2035. [PMID: 31027180 PMCID: PMC6514667 DOI: 10.3390/ijms20082035] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 04/17/2019] [Accepted: 04/20/2019] [Indexed: 12/27/2022] Open
Abstract
The brain comprises a complex system of neurons interconnected by an intricate network of anatomical links. While recent studies demonstrated the correlation between anatomical connectivity patterns and gene expression of neurons, using transcriptomic information to automatically predict such patterns is still an open challenge. In this work, we present a completely data-driven approach relying on machine learning (i.e., neural networks) to learn the anatomical connection directly from a training set of gene expression data. To do so, we combined gene expression and connectivity data from the Allen Mouse Brain Atlas to generate thousands of gene expression profile pairs from different brain regions. To each pair, we assigned a label describing the physical connection between the corresponding brain regions. Then, we exploited these data to train neural networks, designed to predict brain area connectivity. We assessed our solution on two prediction problems (with three and two connectivity class categories) involving cortical and cerebellum regions. As demonstrated by our results, we distinguish between connected and unconnected regions with 85% prediction accuracy and good balance of precision and recall. In our future work we may extend the analysis to more complex brain structures and consider RNA-Seq data as additional input to our model.
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Affiliation(s)
- Ilaria Roberti
- Politecnico di Torino (DAUIN), Department of Control and Computer Engineering, Corso Duca Degli Abruzzi 24, 10129 Torino, Italy.
| | - Marta Lovino
- Politecnico di Torino (DAUIN), Department of Control and Computer Engineering, Corso Duca Degli Abruzzi 24, 10129 Torino, Italy.
| | - Santa Di Cataldo
- Politecnico di Torino (DAUIN), Department of Control and Computer Engineering, Corso Duca Degli Abruzzi 24, 10129 Torino, Italy.
| | - Elisa Ficarra
- Politecnico di Torino (DAUIN), Department of Control and Computer Engineering, Corso Duca Degli Abruzzi 24, 10129 Torino, Italy.
| | - Gianvito Urgese
- Politecnico di Torino (DAUIN), Department of Control and Computer Engineering, Corso Duca Degli Abruzzi 24, 10129 Torino, Italy.
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32
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Abstract
Visual cortex forms the basis of visual processing and plays important roles in visual encoding. By using the recently published Allen Brain Observatory dataset consisting of large-scale calcium imaging of mouse V1 activities under visual stimuli, we were able to obtain high-quality data capturing simultaneous neuronal activities at multiple sub-areas and cortical depths of V1. Using prediction models, we analyzed the activity profiles related to static and drifting grating stimuli. We conducted a comprehensive survey of the coding ability of multiple cortical locations toward different stimulus attributes. Specifically, we focused on orientations and spatial frequencies (for static stimuli), as well as moving directions and speed (for drifting stimuli). By using results produced from a prediction model, we quantified the decoding performance profile at different sub-areas and layers of V1. In addition, we analyzed the interactions and interference between different stimulus attributes. The insights obtained from these discoveries would contribute to more precise and quantitative understanding of V1 coding mechanisms.
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33
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Assis R. Lineage-Specific Expression Divergence in Grasses Is Associated with Male Reproduction, Host-Pathogen Defense, and Domestication. Genome Biol Evol 2019; 11:207-219. [PMID: 30398650 PMCID: PMC6331041 DOI: 10.1093/gbe/evy245] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2018] [Indexed: 02/02/2023] Open
Abstract
Poaceae (grasses) is an agriculturally important and widely distributed family of plants with extraordinary phenotypic diversity, much of which was generated under recent lineage-specific evolution. Yet, little is known about the genes and functional modules involved in the lineage-specific divergence of grasses. Here, I address this question on a genome-wide scale by applying a novel branch-based statistic of lineage-specific expression divergence, LED, to RNA-seq data from nine tissues of the wild grass Brachypodium distachyon and its domesticated relatives Oryza sativa japonica (rice) and Sorghum bicolor (sorghum). I find that LED is generally smallest in B. distachyon and largest in O. sativa japonica, which underwent domestication earlier than S. bicolor, supporting the hypothesis that domestication may increase the rate of lineage-specific expression divergence in grasses. Moreover, in all three species, LED is positively correlated with protein-coding sequence divergence and tissue specificity, and negatively correlated with network connectivity. Further analysis reveals that genes with large LED are often primarily expressed in anther, implicating lineage-specific expression divergence in the evolution of male reproductive phenotypes. Gene ontology enrichment analysis also identifies an overrepresentation of terms related to male reproduction in the two domesticated grasses, as well as to those involved in host-pathogen defense in all three species. Last, examinations of genes with the largest LED reveal that their lineage-specific expression divergence may have contributed to antimicrobial functions in B. distachyon, to enhanced adaptation and yield during domestication in O. sativa japonica, and to defense against a widespread and devastating fungal pathogen in S. bicolor. Together, these findings suggest that lineage-specific expression divergence in grasses may increase under domestication and preferentially target rapidly evolving genes involved in male reproduction, host-pathogen defense, and the origin of domesticated phenotypes.
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Affiliation(s)
- Raquel Assis
- Department of Biology, Pennsylvania State University, University Park
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34
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Fornito A, Arnatkevičiūtė A, Fulcher BD. Bridging the Gap between Connectome and Transcriptome. Trends Cogn Sci 2019; 23:34-50. [DOI: 10.1016/j.tics.2018.10.005] [Citation(s) in RCA: 156] [Impact Index Per Article: 31.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 10/10/2018] [Accepted: 10/23/2018] [Indexed: 11/24/2022]
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35
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Stiso J, Bassett DS. Spatial Embedding Imposes Constraints on Neuronal Network Architectures. Trends Cogn Sci 2018; 22:1127-1142. [PMID: 30449318 DOI: 10.1016/j.tics.2018.09.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 09/20/2018] [Accepted: 09/24/2018] [Indexed: 10/28/2022]
Abstract
Recent progress towards understanding circuit function has capitalized on tools from network science to parsimoniously describe the spatiotemporal architecture of neural systems. Such tools often address systems topology divorced from its physical instantiation. Nevertheless, for embedded systems such as the brain, physical laws directly constrain the processes of network growth, development, and function. We review here the rules imposed by the space and volume of the brain on the development of neuronal networks, and show that these rules give rise to a specific set of complex topologies. These rules also affect the repertoire of neural dynamics that can emerge from the system, and thereby inform our understanding of network dysfunction in disease. We close by discussing new tools and models to delineate the effects of spatial embedding.
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Affiliation(s)
- Jennifer Stiso
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA; Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA.
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36
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Sobolewski M, Singh G, Schneider JS, Cory-Slechta DA. Different Behavioral Experiences Produce Distinctive Parallel Changes in, and Correlate With, Frontal Cortex and Hippocampal Global Post-translational Histone Levels. Front Integr Neurosci 2018; 12:29. [PMID: 30072878 PMCID: PMC6060276 DOI: 10.3389/fnint.2018.00029] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 06/29/2018] [Indexed: 12/29/2022] Open
Abstract
While it is clear that behavioral experience modulates epigenetic profiles, it is less evident how the nature of that experience influences outcomes and whether epigenetic/genetic "biomarkers" could be extracted to classify different types of behavioral experience. To begin to address this question, male and female mice were subjected to either a Fixed Interval (FI) schedule of food reward, or a single episode of forced swim followed by restraint stress, or no explicit behavioral experience after which global expression levels of two activating (H3K9ac and H3K4me3) and two repressive (H3K9me2 and H3k27me3) post-translational histone modifications (PTHMs), were measured in hippocampus (HIPP) and frontal cortex (FC). The specific nature of the behavioral experience differentiated profiles of PTHMs in a sex- and brain region-dependent manner, with all 4 PTHMs changing in parallel in response to different behavioral experiences. These different behavioral experiences also modified the pattern of correlations of PTHMs both within and across FC and HIPP. Unexpectedly, highly robust correlations were found between global PTHM levels and behavioral performances, suggesting that global PTHMs may provide a higher-order pattern recognition function. Further efforts are needed to determine the generality of such findings and what characteristics of behavioral experience are critical for modulating PTHM responses.
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Affiliation(s)
- Marissa Sobolewski
- Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY, United States
| | - Garima Singh
- Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Jay S. Schneider
- Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Deborah A. Cory-Slechta
- Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY, United States
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37
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Yee Y, Fernandes DJ, French L, Ellegood J, Cahill LS, Vousden DA, Spencer Noakes L, Scholz J, van Eede MC, Nieman BJ, Sled JG, Lerch JP. Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity. Neuroimage 2018; 179:357-372. [PMID: 29782994 DOI: 10.1016/j.neuroimage.2018.05.028] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 04/13/2018] [Accepted: 05/10/2018] [Indexed: 12/14/2022] Open
Abstract
An organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings. Here, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed. We found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the visceral area. Additionally, this pattern of explained variation differed spatially across the brain, with transcriptomic similarity playing a larger role in the cortex than subcortex, while connectivity explains structural covariance best in parts of the cortex, midbrain, and hindbrain. These results suggest that both gene expression and connectivity underlie structural volume covariance, albeit to different extents depending on brain region, and this relationship is modulated by distance.
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Affiliation(s)
- Yohan Yee
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada.
| | - Darren J Fernandes
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Leon French
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Jacob Ellegood
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Lindsay S Cahill
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Dulcie A Vousden
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | | | - Jan Scholz
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Matthijs C van Eede
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Brian J Nieman
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - John G Sled
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Jason P Lerch
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
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38
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Scholtens LH, van den Heuvel MP. Multimodal Connectomics in Psychiatry: Bridging Scales From Micro to Macro. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:767-776. [PMID: 29779726 DOI: 10.1016/j.bpsc.2018.03.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 02/28/2018] [Accepted: 03/16/2018] [Indexed: 01/21/2023]
Abstract
The human brain is a highly complex system, with a large variety of microscale cellular morphologies and macroscale global properties. Working at multiple scales, it forms an efficient system for processing and integration of multimodal information. Studies have repeatedly demonstrated strong associations between modalities of both microscales and macroscales of brain organization. These consistent observations point toward potential common organization principles where regions with a microscale architecture supportive of a larger computational load have more and stronger connections in the brain network on the macroscale. Conversely, disruptions observed on one organizational scale could modulate the other. First neuropsychiatric micro-macro comparisons in, among other conditions, Alzheimer's disease and schizophrenia, have, for example, shown overlapping alterations across both scales. We give an overview of recent findings on associations between microscale and macroscale organization observed in the healthy brain, followed by a summary of microscale and macroscale findings reported in the context of brain disorders. We conclude with suggestions for future multiscale connectome comparisons linking multiple scales and modalities of organization and suggest how such comparisons could contribute to a more complete fundamental understanding of brain organization and associated disease-related alterations.
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Affiliation(s)
- Lianne H Scholtens
- Connectome Lab, Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research, VU Amsterdam, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Connectome Lab, Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research, VU Amsterdam, Amsterdam, The Netherlands; Department of Clinical Genetics, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands.
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39
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Anderson KM, Krienen FM, Choi EY, Reinen JM, Yeo BTT, Holmes AJ. Gene expression links functional networks across cortex and striatum. Nat Commun 2018; 9:1428. [PMID: 29651138 PMCID: PMC5897339 DOI: 10.1038/s41467-018-03811-x] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 03/14/2018] [Indexed: 12/12/2022] Open
Abstract
The human brain is comprised of a complex web of functional networks that link anatomically distinct regions. However, the biological mechanisms supporting network organization remain elusive, particularly across cortical and subcortical territories with vastly divergent cellular and molecular properties. Here, using human and primate brain transcriptional atlases, we demonstrate that spatial patterns of gene expression show strong correspondence with limbic and somato/motor cortico-striatal functional networks. Network-associated expression is consistent across independent human datasets and evolutionarily conserved in non-human primates. Genes preferentially expressed within the limbic network (encompassing nucleus accumbens, orbital/ventromedial prefrontal cortex, and temporal pole) relate to risk for psychiatric illness, chloride channel complexes, and markers of somatostatin neurons. Somato/motor associated genes are enriched for oligodendrocytes and markers of parvalbumin neurons. These analyses indicate that parallel cortico-striatal processing channels possess dissociable genetic signatures that recapitulate distributed functional networks, and nominate molecular mechanisms supporting cortico-striatal circuitry in health and disease. The functional connectivity of brain regions can be reflected in a shared molecular architecture. This cross-modal study demonstrates correspondence of spatial patterns of gene expression to limbic and somato/motor cortico-striatal networks in human and non-human primates.
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Affiliation(s)
- Kevin M Anderson
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - Fenna M Krienen
- Department of Genetics, Harvard Medical School, Boston, MA, 02114, USA
| | - Eun Young Choi
- Department of Neurosurgery, Stanford University, Stanford, CA, 94305, USA
| | - Jenna M Reinen
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Network Programme, National University of Singapore, Singapore, 117456, Singapore.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Charlestown, MA, 02129, USA
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT, 06520, USA. .,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Charlestown, MA, 02129, USA. .,Department of Psychiatry, Yale University, New Haven, CT, 06520, USA. .,Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114, USA.
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40
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Arnatkevic̆iūtė A, Fulcher BD, Pocock R, Fornito A. Hub connectivity, neuronal diversity, and gene expression in the Caenorhabditis elegans connectome. PLoS Comput Biol 2018; 14:e1005989. [PMID: 29432412 PMCID: PMC5825174 DOI: 10.1371/journal.pcbi.1005989] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 02/23/2018] [Accepted: 01/19/2018] [Indexed: 11/18/2022] Open
Abstract
Studies of nervous system connectivity, in a wide variety of species and at different scales of resolution, have identified several highly conserved motifs of network organization. One such motif is a heterogeneous distribution of connectivity across neural elements, such that some elements act as highly connected and functionally important network hubs. These brain network hubs are also densely interconnected, forming a so-called rich club. Recent work in mouse has identified a distinctive transcriptional signature of neural hubs, characterized by tightly coupled expression of oxidative metabolism genes, with similar genes characterizing macroscale inter-modular hub regions of the human cortex. Here, we sought to determine whether hubs of the neuronal C. elegans connectome also show tightly coupled gene expression. Using open data on the chemical and electrical connectivity of 279 C. elegans neurons, and binary gene expression data for each neuron across 948 genes, we computed a correlated gene expression score for each pair of neurons, providing a measure of their gene expression similarity. We demonstrate that connections between hub neurons are the most similar in their gene expression while connections between nonhubs are the least similar. Genes with the greatest contribution to this effect are involved in glutamatergic and cholinergic signaling, and other communication processes. We further show that coupled expression between hub neurons cannot be explained by their neuronal subtype (i.e., sensory, motor, or interneuron), separation distance, chemically secreted neurotransmitter, birth time, pairwise lineage distance, or their topological module affiliation. Instead, this coupling is intrinsically linked to the identity of most hubs as command interneurons, a specific class of interneurons that regulates locomotion. Our results suggest that neural hubs may possess a distinctive transcriptional signature, preserved across scales and species, that is related to the involvement of hubs in regulating the higher-order behaviors of a given organism.
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Affiliation(s)
- Aurina Arnatkevic̆iūtė
- Brain and Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Clayton, VIC, Australia
| | - Ben D. Fulcher
- Brain and Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Clayton, VIC, Australia
| | - Roger Pocock
- Development and Stem Cells Program, Monash Biomedicine Discovery Institute and Department of Anatomy and Developmental Biology, Monash University, Clayton, VIC, Australia
| | - Alex Fornito
- Brain and Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Clayton, VIC, Australia
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41
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Manuello J, Nani A, Premi E, Borroni B, Costa T, Tatu K, Liloia D, Duca S, Cauda F. The Pathoconnectivity Profile of Alzheimer's Disease: A Morphometric Coalteration Network Analysis. Front Neurol 2018; 8:739. [PMID: 29472885 PMCID: PMC5810291 DOI: 10.3389/fneur.2017.00739] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 12/21/2017] [Indexed: 01/18/2023] Open
Abstract
Gray matter alterations are typical features of brain disorders. However, they do not impact on the brain randomly. Indeed, it has been suggested that neuropathological processes can selectively affect certain assemblies of neurons, which typically are at the center of crucial functional networks. Because of their topological centrality, these areas form a core set that is more likely to be affected by neuropathological processes. In order to identify and study the pattern formed by brain alterations in patients’ with Alzheimer’s disease (AD), we devised an innovative meta-analytic method for analyzing voxel-based morphometry data. This methodology enabled us to discover that in AD gray matter alterations do not occur randomly across the brain but, on the contrary, follow identifiable patterns of distribution. This alteration pattern exhibits a network-like structure composed of coaltered areas that can be defined as coatrophy network. Within the coatrophy network of AD, we were able to further identify a core subnetwork of coaltered areas that includes the left hippocampus, left and right amygdalae, right parahippocampal gyrus, and right temporal inferior gyrus. In virtue of their network centrality, these brain areas can be thought of as pathoconnectivity hubs.
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Affiliation(s)
- Jordi Manuello
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Andrea Nani
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy.,Michael Trimble Neuropsychiatry Research Group, Birmingham and Solihull Mental Health NHS Foundation Trust, Birmingham, United Kingdom
| | - Enrico Premi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Tommaso Costa
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Karina Tatu
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Donato Liloia
- FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Sergio Duca
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
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42
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Spencer M, Takahashi N, Chakraborty S, Miles J, Shyu CR. Heritable genotype contrast mining reveals novel gene associations specific to autism subgroups. J Biomed Inform 2018; 77:50-61. [PMID: 29197649 PMCID: PMC5788310 DOI: 10.1016/j.jbi.2017.11.016] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 11/15/2017] [Accepted: 11/28/2017] [Indexed: 12/11/2022]
Abstract
Though the genetic etiology of autism is complex, our understanding can be improved by identifying genes and gene-gene interactions that contribute to the development of specific autism subtypes. Identifying such gene groupings will allow individuals to be diagnosed and treated according to their precise characteristics. To this end, we developed a method to associate gene combinations with groups with shared autism traits, targeting genetic elements that distinguish patient populations with opposing phenotypes. Our computational method prioritizes genetic variants for genome-wide association, then utilizes Frequent Pattern Mining to highlight potential interactions between variants. We introduce a novel genotype assessment metric, the Unique Inherited Combination support, which accounts for inheritance patterns observed in the nuclear family while estimating the impact of genetic variation on phenotype manifestation at the individual level. High-contrast variant combinations are tested for significant subgroup associations. We apply this method by contrasting autism subgroups defined by severe or mild manifestations of a phenotype. Significant associations connected 286 genes to the subgroups, including 193 novel autism candidates. 71 pairs of genes have joint associations with subgroups, presenting opportunities to investigate interacting functions. This study analyzed 12 autism subgroups, but our informatics method can explore other meaningful divisions of autism patients, and can further be applied to reveal precise genetic associations within other phenotypically heterogeneous disorders, such as Alzheimer's disease.
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Affiliation(s)
- Matt Spencer
- Informatics Institute, University of Missouri, 241 Naka Hall, Columbia, MO 65211, USA.
| | - Nicole Takahashi
- Thompson Center for Autism & Neurodevelopmental Disorders, University of Missouri, 205 Portland St, Columbia, MO 65211, USA.
| | - Sounak Chakraborty
- Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO 65211, USA.
| | - Judith Miles
- Thompson Center for Autism & Neurodevelopmental Disorders, University of Missouri, 205 Portland St, Columbia, MO 65211, USA; Department of Child Health, School of Medicine, MA204 Medical Sciences Building, University of Missouri, Columbia, MO 65212, USA.
| | - Chi-Ren Shyu
- Informatics Institute, University of Missouri, 241 Naka Hall, Columbia, MO 65211, USA; Department of Electrical Engineering and Computer Science, University of Missouri, 201 Naka Hall, Columbia, MO 65211, USA; School of Medicine, University of Missouri, MA204 Medical Sciences Building, Columbia, MO 65212, USA.
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43
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Jiang X, Assis R. Natural Selection Drives Rapid Functional Evolution of Young Drosophila Duplicate Genes. Mol Biol Evol 2017; 34:3089-3098. [PMID: 28961791 PMCID: PMC5850746 DOI: 10.1093/molbev/msx230] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Gene duplication is thought to play a major role in phenotypic evolution. Yet the forces involved in the functional divergence of young duplicate genes remain unclear. Here, we use population-genetic inference to elucidate the role of natural selection in the functional evolution of young duplicate genes in Drosophila melanogaster. We find that negative selection acts on young duplicates with ancestral functions, and positive selection on those with novel functions, suggesting that natural selection may determine whether and how young duplicate genes are retained. Moreover, evidence of natural selection is strongest in protein-coding regions and 3' UTRs of young duplicates, indicating that selection may primarily target encoded proteins and regulatory sequences specific to 3' UTRs. Further analysis reveals that natural selection acts immediately after duplication and weakens over time, possibly explaining the observed bias toward the acquisition of new functions by young, rather than old, duplicate gene copies. Last, we find an enrichment of testis-related functions in young duplicates that underwent recent positive selection, but not in young duplicates that did not undergo recent positive selection, or in old duplicates that either did or did not undergo recent positive selection. Thus, our findings reveal that natural selection is a key player in the functional evolution of young duplicate genes, acts rapidly and in a region-specific manner, and may underlie the origin of novel testis-specific phenotypes in Drosophila.
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Affiliation(s)
- Xueyuan Jiang
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA
| | - Raquel Assis
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA
- Department of Biology, Pennsylvania State University, University Park, PA
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44
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Vértes PE, Rittman T, Whitaker KJ, Romero-Garcia R, Váša F, Kitzbichler MG, Wagstyl K, Fonagy P, Dolan RJ, Jones PB, Goodyer IM, Bullmore ET. Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks. Philos Trans R Soc Lond B Biol Sci 2017; 371:rstb.2015.0362. [PMID: 27574314 PMCID: PMC5003862 DOI: 10.1098/rstb.2015.0362] [Citation(s) in RCA: 131] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2016] [Indexed: 12/22/2022] Open
Abstract
Human functional magnetic resonance imaging (fMRI) brain networks have a complex topology comprising integrative components, e.g. long-distance inter-modular edges, that are theoretically associated with higher biological cost. Here, we estimated intra-modular degree, inter-modular degree and connection distance for each of 285 cortical nodes in multi-echo fMRI data from 38 healthy adults. We used the multivariate technique of partial least squares (PLS) to reduce the dimensionality of the relationships between these three nodal network parameters and prior microarray data on regional expression of 20 737 genes. The first PLS component defined a transcriptional profile associated with high intra-modular degree and short connection distance, whereas the second PLS component was associated with high inter-modular degree and long connection distance. Nodes in superior and lateral cortex with high inter-modular degree and long connection distance had local transcriptional profiles enriched for oxidative metabolism and mitochondria, and for genes specific to supragranular layers of human cortex. In contrast, primary and secondary sensory cortical nodes in posterior cortex with high intra-modular degree and short connection distance had transcriptional profiles enriched for RNA translation and nuclear components. We conclude that, as predicted, topologically integrative hubs, mediating long-distance connections between modules, are more costly in terms of mitochondrial glucose metabolism.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.
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Affiliation(s)
- Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Kirstie J Whitaker
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | | | - František Váša
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | | | - Konrad Wagstyl
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Peter Fonagy
- Research Department of Clinical, Educational and Health Psychology, University College London, London WC1E 6BT, UK
| | - Raymond J Dolan
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London WC1N 3BG, UK Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK
| | - Peter B Jones
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | - Ian M Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | | | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK MRC/Wellcome Trust Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge CB2 0SZ, UK Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK Immuno-Psychiatry, Immuno-Inflammation Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage SG1 2NY, UK
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45
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Ganglberger F, Kaczanowska J, Penninger JM, Hess A, Bühler K, Haubensak W. Predicting functional neuroanatomical maps from fusing brain networks with genetic information. Neuroimage 2017; 170:113-120. [PMID: 28877513 DOI: 10.1016/j.neuroimage.2017.08.070] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Revised: 08/21/2017] [Accepted: 08/24/2017] [Indexed: 02/07/2023] Open
Abstract
Functional neuroanatomical maps provide a mesoscale reference framework for studies from molecular to systems neuroscience and psychiatry. The underlying structure-function relationships are typically derived from functional manipulations or imaging approaches. Although highly informative, these are experimentally costly. The increasing amount of publicly available brain and genetic data offers a rich source that could be mined to address this problem computationally. Here, we developed an algorithm that fuses gene expression and connectivity data with functional genetic meta data and exploits cumulative effects to derive neuroanatomical maps related to multi-genic functions. We validated the approach by using public available mouse and human data. The generated neuroanatomical maps recapture known functional anatomical annotations from literature and functional MRI data. When applied to multi-genic meta data from mouse quantitative trait loci (QTL) studies and human neuropsychiatric databases, this method predicted known functional maps underlying behavioral or psychiatric traits. Taken together, genetically weighted connectivity analysis (GWCA) allows for high throughput functional exploration of brain anatomy in silico. It maps functional genetic associations onto brain circuitry for refining functional neuroanatomy, or identifying trait-associated brain circuitry, from genetic data.
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Affiliation(s)
| | - Joanna Kaczanowska
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, 1030, Vienna, Austria
| | - Josef M Penninger
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), 1030, Vienna, Austria
| | - Andreas Hess
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander University Erlangen-Nuremberg, Fahrstrasse 17, 91054, Erlangen, Germany
| | - Katja Bühler
- VRVis Research Center, Donau-City Strasse 11, 1220, Vienna, Austria.
| | - Wulf Haubensak
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, 1030, Vienna, Austria.
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46
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Pantazatos SP, Li X. Commentary: BRAIN NETWORKS. Correlated Gene Expression Supports Synchronous Activity in Brain Networks. Science 348, 1241-4. Front Neurosci 2017; 11:412. [PMID: 28769750 PMCID: PMC5513927 DOI: 10.3389/fnins.2017.00412] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Accepted: 06/30/2017] [Indexed: 11/13/2022] Open
Abstract
A recent report claims that functional brain networks defined with resting-state functional magnetic resonance imaging (fMRI) can be recapitulated with correlated gene expression (i.e., high within-network tissue-tissue "strength fraction," SF) (Richiardi et al., 2015). However, the authors do not adequately control for spatial proximity. We replicated their main analysis, performed a more effective adjustment for spatial proximity, and tested whether "null networks" (i.e., clusters with center coordinates randomly placed throughout cortex) also exhibit high SF. Removing proximal tissue-tissue correlations by Euclidean distance, as opposed to removing correlations within arbitrary tissue labels as in Richiardi et al. (2015), reduces within-network SF to no greater than null. Moreover, randomly placed clusters also have significantly high SF, indicating that high within-network SF is entirely attributable to proximity and is unrelated to functional brain networks defined by resting-state fMRI. We discuss why additional validations in the original article are invalid and/or misleading and suggest future directions.
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Affiliation(s)
- Spiro P Pantazatos
- Department of Psychiatry, Columbia UniversityNew York, NY, United States.,Molecular Imaging and Neuropathology Division, New York State Psychiatric InstituteNew York, NY, United States
| | - Xinyi Li
- Biomedical Informatics, Columbia UniversityNew York, NY, United States
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47
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Yi B, Sahn JJ, Ardestani PM, Evans AK, Scott LL, Chan JZ, Iyer S, Crisp A, Zuniga G, Pierce JT, Martin SF, Shamloo M. Small molecule modulator of sigma 2 receptor is neuroprotective and reduces cognitive deficits and neuroinflammation in experimental models of Alzheimer's disease. J Neurochem 2017; 140:561-575. [PMID: 27926996 DOI: 10.1111/jnc.13917] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 11/23/2016] [Accepted: 12/01/2016] [Indexed: 12/29/2022]
Abstract
Accumulating evidence suggests that modulating the sigma 2 receptor (Sig2R) can provide beneficial effects for neurodegenerative diseases. Herein, we report the identification of a novel class of Sig2R ligands and their cellular and in vivo activity in experimental models of Alzheimer's disease (AD). We report that SAS-0132 and DKR-1051, selective ligands of Sig2R, modulate intracellular Ca2+ levels in human SK-N-SH neuroblastoma cells. The Sig2R ligands SAS-0132 and JVW-1009 are neuroprotective in a C. elegans model of amyloid precursor protein-mediated neurodegeneration. Since this neuroprotective effect is replicated by genetic knockdown and knockout of vem-1, the ortholog of progesterone receptor membrane component-1 (PGRMC1), these results suggest that Sig2R ligands modulate a PGRMC1-related pathway. Last, we demonstrate that SAS-0132 improves cognitive performance both in the Thy-1 hAPPLond/Swe+ transgenic mouse model of AD and in healthy wild-type mice. These results demonstrate that Sig2R is a promising therapeutic target for neurocognitive disorders including AD.
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Affiliation(s)
- Bitna Yi
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, California, USA
| | - James J Sahn
- Department of Chemistry, The University of Texas at Austin, Austin, Texas, USA
| | - Pooneh Memar Ardestani
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, California, USA
| | - Andrew K Evans
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, California, USA
| | - Luisa L Scott
- Waggoner Center for Alcohol and Addiction Research, Institute of Neuroscience, Center for Learning and Memory, Center for Brain, Behavior and Evolution and Department of Neuroscience, The University of Texas at Austin, Austin, Texas, USA
| | - Jessica Z Chan
- Department of Chemistry, The University of Texas at Austin, Austin, Texas, USA
| | - Sangeetha Iyer
- Waggoner Center for Alcohol and Addiction Research, Institute of Neuroscience, Center for Learning and Memory, Center for Brain, Behavior and Evolution and Department of Neuroscience, The University of Texas at Austin, Austin, Texas, USA
| | - Ashley Crisp
- Waggoner Center for Alcohol and Addiction Research, Institute of Neuroscience, Center for Learning and Memory, Center for Brain, Behavior and Evolution and Department of Neuroscience, The University of Texas at Austin, Austin, Texas, USA
| | - Gabriella Zuniga
- Waggoner Center for Alcohol and Addiction Research, Institute of Neuroscience, Center for Learning and Memory, Center for Brain, Behavior and Evolution and Department of Neuroscience, The University of Texas at Austin, Austin, Texas, USA
| | - Jonathan T Pierce
- Waggoner Center for Alcohol and Addiction Research, Institute of Neuroscience, Center for Learning and Memory, Center for Brain, Behavior and Evolution and Department of Neuroscience, The University of Texas at Austin, Austin, Texas, USA
| | - Stephen F Martin
- Department of Chemistry, The University of Texas at Austin, Austin, Texas, USA
| | - Mehrdad Shamloo
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, California, USA
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48
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Lein ES, Belgard TG, Hawrylycz M, Molnár Z. Transcriptomic Perspectives on Neocortical Structure, Development, Evolution, and Disease. Annu Rev Neurosci 2017; 40:629-652. [PMID: 28661727 DOI: 10.1146/annurev-neuro-070815-013858] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The cerebral cortex is the source of our most complex cognitive capabilities and a vulnerable target of many neurological and neuropsychiatric disorders. Transcriptomics offers a new approach to understanding the cortex at the level of its underlying genetic code, and rapid technological advances have propelled this field to the high-throughput study of the complete set of transcribed genes at increasingly fine resolution to the level of individual cells. These tools have revealed features of the genetic architecture of adult cortical areas, layers, and cell types, as well as spatiotemporal patterning during development. This has allowed a fresh look at comparative anatomy as well, illustrating surprisingly large differences between mammals while at the same time revealing conservation of some features from avians to mammals. Finally, transcriptomics is fueling progress in understanding the causes of neurodevelopmental diseases such as autism, linking genetic association studies to specific molecular pathways and affected brain regions.
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Affiliation(s)
- Ed S Lein
- Allen Institute for Brain Science, Seattle, Washington 98103; ,
| | | | | | - Zoltán Molnár
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, United Kingdom;
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49
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Forest M, Iturria‐Medina Y, Goldman JS, Kleinman CL, Lovato A, Oros Klein K, Evans A, Ciampi A, Labbe A, Greenwood CM. Gene networks show associations with seed region connectivity. Hum Brain Mapp 2017; 38:3126-3140. [PMID: 28321948 PMCID: PMC6866840 DOI: 10.1002/hbm.23579] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Revised: 02/17/2017] [Accepted: 03/10/2017] [Indexed: 12/25/2022] Open
Abstract
Primary patterns in adult brain connectivity are established during development by coordinated networks of transiently expressed genes; however, neural networks remain malleable throughout life. The present study hypothesizes that structural connectivity from key seed regions may induce effects on their connected targets, which are reflected in gene expression at those targeted regions. To test this hypothesis, analyses were performed on data from two brains from the Allen Human Brain Atlas, for which both gene expression and DW-MRI were available. Structural connectivity was estimated from the DW-MRI data and an approach motivated by network topology, that is, weighted gene coexpression network analysis (WGCNA), was used to cluster genes with similar patterns of expression across the brain. Group exponential lasso models were then used to predict gene cluster expression summaries as a function of seed region structural connectivity patterns. In several gene clusters, brain regions located in the brain stem, diencephalon, and hippocampal formation were identified that have significant predictive power for these expression summaries. These connectivity-associated clusters are enriched in genes associated with synaptic signaling and brain plasticity. Furthermore, using seed region based connectivity provides a novel perspective in understanding relationships between gene expression and connectivity. Hum Brain Mapp 38:3126-3140, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Marie Forest
- Lady Davis Institute, Jewish General HospitalMontrealQuebecCanada
- Department of Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealQuebecCanada
- Ludmer Centre for Neuroinformatics and Mental Health, McGill UniversityMontrealQuebecCanada
| | - Yasser Iturria‐Medina
- Ludmer Centre for Neuroinformatics and Mental Health, McGill UniversityMontrealQuebecCanada
- Montreal Neurological Institute, McGill UniversityMontrealQuebecCanada
- Department of Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
| | - Jennifer S. Goldman
- Ludmer Centre for Neuroinformatics and Mental Health, McGill UniversityMontrealQuebecCanada
- Montreal Neurological Institute, McGill UniversityMontrealQuebecCanada
- Department of Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
| | - Claudia L. Kleinman
- Lady Davis Institute, Jewish General HospitalMontrealQuebecCanada
- Ludmer Centre for Neuroinformatics and Mental Health, McGill UniversityMontrealQuebecCanada
- Department of Human GeneticsMcGill UniversityMontrealQuebecCanada
| | - Amanda Lovato
- Lady Davis Institute, Jewish General HospitalMontrealQuebecCanada
| | - Kathleen Oros Klein
- Lady Davis Institute, Jewish General HospitalMontrealQuebecCanada
- Ludmer Centre for Neuroinformatics and Mental Health, McGill UniversityMontrealQuebecCanada
| | - Alan Evans
- Ludmer Centre for Neuroinformatics and Mental Health, McGill UniversityMontrealQuebecCanada
- Montreal Neurological Institute, McGill UniversityMontrealQuebecCanada
- Department of Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
| | - Antonio Ciampi
- Department of Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealQuebecCanada
- Department of Human GeneticsMcGill UniversityMontrealQuebecCanada
| | - Aurélie Labbe
- Département De Sciences De La DécisionHECMontrealQuebecCanada
| | - Celia M.T. Greenwood
- Lady Davis Institute, Jewish General HospitalMontrealQuebecCanada
- Department of Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealQuebecCanada
- Ludmer Centre for Neuroinformatics and Mental Health, McGill UniversityMontrealQuebecCanada
- Department of Human GeneticsMcGill UniversityMontrealQuebecCanada
- Department of OncologyMcGill UniversityMontrealQuebecCanada
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50
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Ball G, Beare R, Seal ML. Network component analysis reveals developmental trajectories of structural connectivity and specific alterations in autism spectrum disorder. Hum Brain Mapp 2017; 38:4169-4184. [PMID: 28560746 DOI: 10.1002/hbm.23656] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2016] [Revised: 05/10/2017] [Accepted: 05/12/2017] [Indexed: 11/06/2022] Open
Abstract
The structural organization of the brain can be characterized as a hierarchical ensemble of segregated modules linked by densely interconnected hub regions that facilitate distributed functional interactions. Disturbances to this network may be an important marker of abnormal development. Recently, several neurodevelopmental disorders, including autism spectrum disorder (ASD), have been framed as disorders of connectivity but the full nature and timing of these disturbances remain unclear. In this study, we use non-negative matrix factorization, a data-driven, multivariate approach, to model the structural network architecture of the brain as a set of superposed subnetworks, or network components. In an openly available dataset of 196 subjects scanned between 5 and 85 years we identify a set of robust and reliable subnetworks that develop in tandem with age and reflect both anatomically local and long-range, network hub connections. In a second experiment, we compare network components in a cohort of 51 high-functioning ASD adolescents to a group of age-matched controls. We identify a specific subnetwork representing an increase in local connection strength in the cingulate cortex in ASD (t = 3.44, P < 0.001). This work highlights possible long-term implications of alterations to the developmental trajectories of specific cortical subnetworks. Hum Brain Mapp 38:4169-4184, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Richard Beare
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Marc L Seal
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
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