1
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Klug S, Murgaš M, Godbersen GM, Hacker M, Lanzenberger R, Hahn A. Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain. Neuroimage 2024; 295:120658. [PMID: 38810891 DOI: 10.1016/j.neuroimage.2024.120658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 04/22/2024] [Accepted: 05/27/2024] [Indexed: 05/31/2024] Open
Abstract
PURPOSE The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach. METHODS 51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions. RESULTS Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning. CONCLUSIONS Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.
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Affiliation(s)
- Sebastian Klug
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Godber M Godbersen
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria.
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2
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Sanchez-Rodriguez LM, Khan AF, Adewale Q, Bezgin G, Therriault J, Fernandez-Arias J, Servaes S, Rahmouni N, Tissot C, Stevenson J, Jiang H, Chai X, Carbonell F, Rosa-Neto P, Iturria-Medina Y. In-vivo neuronal dysfunction by Aβ and tau overlaps with brain-wide inflammatory mechanisms in Alzheimer's disease. Front Aging Neurosci 2024; 16:1383163. [PMID: 38966801 PMCID: PMC11223503 DOI: 10.3389/fnagi.2024.1383163] [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: 02/07/2024] [Accepted: 05/09/2024] [Indexed: 07/06/2024] Open
Abstract
The molecular mechanisms underlying neuronal dysfunction in Alzheimer's disease (AD) remain uncharacterized. Here, we identify genes, molecular pathways and cellular components associated with whole-brain dysregulation caused by amyloid-beta (Aβ) and tau deposits in the living human brain. We obtained in-vivo resting-state functional MRI (rs-fMRI), Aβ- and tau-PET for 47 cognitively unimpaired and 16 AD participants from the Translational Biomarkers in Aging and Dementia cohort. Adverse neuronal activity impacts by Aβ and tau were quantified with personalized dynamical models by fitting pathology-mediated computational signals to the participant's real rs-fMRIs. Then, we detected robust brain-wide associations between the spatial profiles of Aβ-tau impacts and gene expression in the neurotypical transcriptome (Allen Human Brain Atlas). Within the obtained distinctive signature of in-vivo neuronal dysfunction, several genes have prominent roles in microglial activation and in interactions with Aβ and tau. Moreover, cellular vulnerability estimations revealed strong association of microglial expression patterns with Aβ and tau's synergistic impact on neuronal activity (q < 0.001). These results further support the central role of the immune system and neuroinflammatory pathways in AD pathogenesis. Neuronal dysregulation by AD pathologies also associated with neurotypical synaptic and developmental processes. In addition, we identified drug candidates from the vast LINCS library to halt or reduce the observed Aβ-tau effects on neuronal activity. Top-ranked pharmacological interventions target inflammatory, cancer and cardiovascular pathways, including specific medications undergoing clinical evaluation in AD. Our findings, based on the examination of molecular-pathological-functional interactions in humans, may accelerate the process of bringing effective therapies into clinical practice.
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Affiliation(s)
- Lazaro M. Sanchez-Rodriguez
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, QC, Canada
| | - Ahmed F. Khan
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, QC, Canada
| | - Quadri Adewale
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, QC, Canada
| | - Gleb Bezgin
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Joseph Therriault
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Jaime Fernandez-Arias
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Stijn Servaes
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Nesrine Rahmouni
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Cécile Tissot
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
- Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Jenna Stevenson
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Hongxiu Jiang
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
| | - Xiaoqian Chai
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
| | | | - Pedro Rosa-Neto
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Yasser Iturria-Medina
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, QC, Canada
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3
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Podvalny E, Sanchez-Romero R, Cole MW. Functionality of arousal-regulating brain circuitry at rest predicts human cognitive abilities. Cereb Cortex 2024; 34:bhae192. [PMID: 38745558 DOI: 10.1093/cercor/bhae192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 05/16/2024] Open
Abstract
Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which, in turn, modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (n = 149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects' cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.
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Affiliation(s)
- Ella Podvalny
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07102, United States
| | - Ruben Sanchez-Romero
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07102, United States
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07102, United States
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4
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King L, Weiner KS. Transcriptomic contributions to a modern cytoarchitectonic parcellation of the human cerebral cortex. Brain Struct Funct 2024; 229:919-936. [PMID: 38492042 DOI: 10.1007/s00429-023-02754-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 12/19/2023] [Indexed: 03/18/2024]
Abstract
Transcriptomic contributions to the anatomical, functional, and network layout of the human cerebral cortex (HCC) have become a major interest in cognitive and systems neuroscience. Here, we tested if transcriptomic differences support a modern, algorithmic cytoarchitectonic parcellation of HCC. Using a data-driven approach, we identified a sparse subset of genes that differentially contributed to the cytoarchitectonic parcellation of HCC. A combined metric of cortical thickness and myelination (CT/M ratio), as well as cell density, correlated with gene expression. Enrichment analyses showed that genes specific to the cytoarchitectonic parcellation of the HCC were related to molecular functions such as transmembrane transport and ion channel activity. Together, the relationship between transcriptomics and cytoarchitecture bridges the gap among (i) gradients at the macro-scale (including thickness and myelination), (ii) areas at the meso-scale, and (iii) cell density at the microscale, as well as supports the recently proposed cortical spectrum theory and structural model.
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Affiliation(s)
- Leana King
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, 94720, USA.
- Department of Neuroscience, University of California Berkeley, Berkeley, CA, 94720, USA.
| | - Kevin S Weiner
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, 94720, USA
- Department of Neuroscience, University of California Berkeley, Berkeley, CA, 94720, USA
- Department of Psychology, University of California Berkeley, Berkeley, CA, 94720, USA
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5
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McManus E, Muhlert N, Duncan NW. InSpectro-Gadget: A Tool for Estimating Neurotransmitter and Neuromodulator Receptor Distributions for MRS Voxels. Neuroinformatics 2024; 22:135-145. [PMID: 38386228 DOI: 10.1007/s12021-024-09654-w] [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] [Accepted: 12/21/2023] [Indexed: 02/23/2024]
Abstract
Magnetic resonance spectroscopy (MRS) is widely used to estimate concentrations of glutamate and γ -aminobutyric acid (GABA) in specific regions of the living human brain. As cytoarchitectural properties differ across the brain, interpreting these measurements can be assisted by having knowledge of such properties for the MRS region(s) studied. In particular, some knowledge of likely local neurotransmitter receptor patterns can potentially give insights into the mechanistic environment GABA- and glutamatergic neurons are functioning in. This may be of particular utility when comparing two or more regions, given that the receptor populations may differ substantially across them. At the same time, when studying MRS data from multiple participants or timepoints, the homogeneity of the sample becomes relevant, as measurements taken from areas with different cytoarchitecture may be difficult to compare. To provide insights into the likely cytoarchitectural environment of user-defined regions-of-interest, we produced an easy to use tool - InSpectro-Gadget - that interfaces with receptor mRNA expression information from the Allen Human Brain Atlas. This Python tool allows users to input masks and automatically obtain a graphical overview of the receptor population likely to be found within. This includes comparison between multiple masks or participants where relevant. The receptors and receptor subunit genes featured include GABA- and glutamatergic classes, along with a wide range of neuromodulators. The functionality of the tool is explained here and its use is demonstrated through a set of example analyses. The tool is available at https://github.com/lizmcmanus/Inspectro-Gadget .
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Affiliation(s)
| | - Nils Muhlert
- School of Health Sciences, University of Manchester, Manchester, UK
| | - Niall W Duncan
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.
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6
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Pak V, Adewale Q, Bzdok D, Dadar M, Zeighami Y, Iturria-Medina Y. Distinctive whole-brain cell types predict tissue damage patterns in thirteen neurodegenerative conditions. eLife 2024; 12:RP89368. [PMID: 38512130 PMCID: PMC10957173 DOI: 10.7554/elife.89368] [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] [Indexed: 03/22/2024] Open
Abstract
For over a century, brain research narrative has mainly centered on neuron cells. Accordingly, most neurodegenerative studies focus on neuronal dysfunction and their selective vulnerability, while we lack comprehensive analyses of other major cell types' contribution. By unifying spatial gene expression, structural MRI, and cell deconvolution, here we describe how the human brain distribution of canonical cell types extensively predicts tissue damage in 13 neurodegenerative conditions, including early- and late-onset Alzheimer's disease, Parkinson's disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, mutations in presenilin-1, and 3 clinical variants of frontotemporal lobar degeneration (behavioral variant, semantic and non-fluent primary progressive aphasia) along with associated three-repeat and four-repeat tauopathies and TDP43 proteinopathies types A and C. We reconstructed comprehensive whole-brain reference maps of cellular abundance for six major cell types and identified characteristic axes of spatial overlapping with atrophy. Our results support the strong mediating role of non-neuronal cells, primarily microglia and astrocytes, in spatial vulnerability to tissue loss in neurodegeneration, with distinct and shared across-disorder pathomechanisms. These observations provide critical insights into the multicellular pathophysiology underlying spatiotemporal advance in neurodegeneration. Notably, they also emphasize the need to exceed the current neuro-centric view of brain diseases, supporting the imperative for cell-specific therapeutic targets in neurodegeneration.
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Affiliation(s)
- Veronika Pak
- Department of Neurology and Neurosurgery, McGill UniversityMontrealCanada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontrealCanada
- Ludmer Centre for Neuroinformatics & Mental HealthMontrealCanada
| | - Quadri Adewale
- Department of Neurology and Neurosurgery, McGill UniversityMontrealCanada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontrealCanada
- Ludmer Centre for Neuroinformatics & Mental HealthMontrealCanada
| | - Danilo Bzdok
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontrealCanada
- Department of Biomedical Engineering, McGill UniversityMontrealCanada
- School of Computer Science, McGill UniversityMontrealCanada
- Mila – Quebec Artificial Intelligence InstituteMontrealCanada
| | | | | | - Yasser Iturria-Medina
- Department of Neurology and Neurosurgery, McGill UniversityMontrealCanada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontrealCanada
- Ludmer Centre for Neuroinformatics & Mental HealthMontrealCanada
- Department of Biomedical Engineering, McGill UniversityMontrealCanada
- McGill Centre for Studies in AgingMontrealCanada
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7
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Wagstyl K, Adler S, Seidlitz J, Vandekar S, Mallard TT, Dear R, DeCasien AR, Satterthwaite TD, Liu S, Vértes PE, Shinohara RT, Alexander-Bloch A, Geschwind DH, Raznahan A. Transcriptional cartography integrates multiscale biology of the human cortex. eLife 2024; 12:RP86933. [PMID: 38324465 PMCID: PMC10945526 DOI: 10.7554/elife.86933] [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] [Indexed: 02/09/2024] Open
Abstract
The cerebral cortex underlies many of our unique strengths and vulnerabilities, but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate, and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression. These maps allow spatially unbiased discovery of cortical zones with extreme transcriptional profiles or unusually rapid transcriptional change which index distinct microstructure and predict neuroimaging measures of cortical folding and functional activation. Modules of spatially coexpressed genes define a family of canonical expression maps that integrate diverse spatial scales and temporal epochs of human brain organization - ranging from protein-protein interactions to large-scale systems for cognitive processing. These module maps also parse neuropsychiatric risk genes into subsets which tag distinct cyto-laminar features and differentially predict the location of altered cortical anatomy and gene expression in patients. Taken together, the methods, resources, and findings described here advance our understanding of human cortical organization and offer flexible bridges to connect scientific fields operating at different spatial scales of human brain research.
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Affiliation(s)
- Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College LondonLondonUnited Kingdom
| | - Sophie Adler
- UCL Great Ormond Street Institute for Child HealthHolbornUnited Kingdom
| | - Jakob Seidlitz
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt UniversityNashvilleUnited States
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General HospitalBostonUnited States
- Department of Psychiatry, Harvard Medical SchoolBostonUnited States
| | - Richard Dear
- Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - Alex R DeCasien
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of MedicinePhiladelphiaUnited States
| | - Siyuan Liu
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
| | - Petra E Vértes
- Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Daniel H Geschwind
- Center for Autism Research and Treatment, Semel Institute, Program in Neurogenetics, Department of Neurology and Department of Human Genetics, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
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8
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Zheng L, Rubinski A, Denecke J, Luan Y, Smith R, Strandberg O, Stomrud E, Ossenkoppele R, Svaldi DO, Higgins IA, Shcherbinin S, Pontecorvo MJ, Hansson O, Franzmeier N, Ewers M. Combined Connectomics, MAPT Gene Expression, and Amyloid Deposition to Explain Regional Tau Deposition in Alzheimer Disease. Ann Neurol 2024; 95:274-287. [PMID: 37837382 DOI: 10.1002/ana.26818] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/07/2023] [Accepted: 10/03/2023] [Indexed: 10/16/2023]
Abstract
OBJECTIVE We aimed to test whether region-specific factors, including spatial expression patterns of the tau-encoding gene MAPT and regional levels of amyloid positron emission tomography (PET), enhance connectivity-based modeling of the spatial variability in tau-PET deposition in the Alzheimer disease (AD) spectrum. METHODS We included 685 participants (395 amyloid-positive participants within AD spectrum and 290 amyloid-negative controls) with tau-PET and amyloid-PET from 3 studies (Alzheimer's Disease Neuroimaging Initiative, 18 F-AV-1451-A05, and BioFINDER-1). Resting-state functional magnetic resonance imaging was obtained in healthy controls (n = 1,000) from the Human Connectome Project, and MAPT gene expression from the Allen Human Brain Atlas. Based on a brain-parcellation atlas superimposed onto all modalities, we obtained region of interest (ROI)-to-ROI functional connectivity, ROI-level PET values, and MAPT gene expression. In stepwise regression analyses, we tested connectivity, MAPT gene expression, and amyloid-PET as predictors of group-averaged and individual tau-PET ROI values in amyloid-positive participants. RESULTS Connectivity alone explained 21.8 to 39.2% (range across 3 studies) of the variance in tau-PET ROI values averaged across amyloid-positive participants. Stepwise addition of MAPT gene expression and amyloid-PET increased the proportion of explained variance to 30.2 to 46.0% and 45.0 to 49.9%, respectively. Similarly, for the prediction of patient-level tau-PET ROI values, combining all 3 predictors significantly improved the variability explained (mean adjusted R2 range across studies = 0.118-0.148, 0.156-0.196, and 0.251-0.333 for connectivity alone, connectivity plus MAPT expression, and all 3 modalities combined, respectively). INTERPRETATION Across 3 study samples, combining the functional connectome and molecular properties substantially enhanced the explanatory power compared to single modalities, providing a valuable tool to explain regional susceptibility to tau deposition in AD. ANN NEUROL 2024;95:274-287.
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Affiliation(s)
- Lukai Zheng
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Anna Rubinski
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Jannis Denecke
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Ying Luan
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands
| | | | | | | | - Michael J Pontecorvo
- Eli Lilly and Company, Indianapolis, IN, USA
- Avid Radiopharmaceuticals, Philadelphia, PA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- Munich Cluster for Systems Neurology, Munich, Germany
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Michael Ewers
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
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9
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Huang Z, Mashour GA, Hudetz AG. Propofol Disrupts the Functional Core-Matrix Architecture of the Thalamus in Humans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576934. [PMID: 38328136 PMCID: PMC10849566 DOI: 10.1101/2024.01.23.576934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Research into the role of thalamocortical circuits in anesthesia-induced unconsciousness is difficult due to anatomical and functional complexity. Prior neuroimaging studies have examined either the thalamus as a whole or focused on specific subregions, overlooking the distinct neuronal subtypes like core and matrix cells. We conducted a study of heathy volunteers and functional magnetic resonance imaging during conscious baseline, deep sedation, and recovery. We advanced the functional gradient mapping technique to delineate the functional geometry of thalamocortical circuits, within a framework of the unimodal-transmodal functional axis of the cortex. We observed a significant shift in this geometry during unconsciousness, marked by the dominance of unimodal over transmodal geometry. This alteration was closely linked to the spatial variations in the density of matrix cells within the thalamus. This research bridges cellular and systems-level understanding, highlighting the crucial role of thalamic core-matrix functional architecture in understanding the neural mechanisms of states of consciousness.
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Affiliation(s)
- Zirui Huang
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - George A Mashour
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Anthony G Hudetz
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
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10
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Yamamoto Y, Takahata K, Kubota M, Takeuchi H, Moriguchi S, Sasaki T, Seki C, Endo H, Matsuoka K, Tagai K, Kimura Y, Kurose S, Mimura M, Kawamura K, Zhang MR, Higuchi M. Association of protein distribution and gene expression revealed by positron emission tomography and postmortem gene expression in the dopaminergic system of the human brain. Eur J Nucl Med Mol Imaging 2023; 50:3928-3936. [PMID: 37581725 DOI: 10.1007/s00259-023-06390-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 08/08/2023] [Indexed: 08/16/2023]
Abstract
PURPOSE The topological distribution of dopamine-related proteins is determined by gene transcription and subsequent regulations. Recent research strategies integrating positron emission tomography with a transcriptome atlas have opened new opportunities to understand the influence of regulation after transcription on protein distribution. Previous studies have reported that messenger (m)-RNA expression levels spatially correlate with the density maps of serotonin receptors but not with those of transporters. This discrepancy may be due to differences in regulation after transcription between presynaptic and postsynaptic proteins, which have not been studied in the dopaminergic system. Here, we focused on dopamine D1 and D2/D3 receptors and dopamine transporters and investigated their region-wise relationship between mRNA expression and protein distribution. METHODS We examined the region-wise correlation between regional binding potentials of the target region relative to that of non-displaceable tissue (BPND) values of 11C-SCH-23390 and mRNA expression levels of dopamine D1 receptors (D1R); regional BPND values of 11C-FLB-457 and mRNA expression levels of dopamine D2/D3 receptors (D2/D3R); and regional total distribution volume (VT) values of 18F-FE-PE2I and mRNA expression levels of dopamine transporters (DAT) using Spearman's rank correlation. RESULTS We found significant positive correlations between regional BPND values of 11C-SCH-23390 and the mRNA expression levels of D1R (r = 0.769, p = 0.0021). Similar to D1R, regional BPND values of 11C-FLB-457 positively correlated with the mRNA expression levels of D2R (r = 0.809, p = 0.0151) but not with those of D3R (r = 0.413, p = 0.3095). In contrast to D1R and D2R, no significant correlation between VT values of 18F-FE-PE2I and mRNA expression levels of DAT was observed (r = -0.5934, p = 0.140). CONCLUSION We found a region-wise correlation between the mRNA expression levels of dopamine D1 and D2 receptors and their respective protein distributions. However, we found no region-wise correlation between the mRNA expression levels of dopamine transporters and their protein distributions, indicating different regulatory mechanisms for the localization of pre- and postsynaptic proteins. These results provide a broader understanding of the application of the transcriptome atlas to neuroimaging studies of the dopaminergic nervous system.
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Affiliation(s)
- Yasuharu Yamamoto
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba, 263-8555, Japan
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan
| | - Keisuke Takahata
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba, 263-8555, Japan.
| | - Manabu Kubota
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba, 263-8555, Japan
- Department of Psychiatry, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Hiroyoshi Takeuchi
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan
| | - Sho Moriguchi
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba, 263-8555, Japan
| | - Takeshi Sasaki
- Department of Psychiatry, Tokyo Metropolitan Bokutoh Hospital, 4-23-15 Kotobashi, Sumida-Ku, Tokyo, 130-8575, Japan
| | - Chie Seki
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba, 263-8555, Japan
| | - Hironobu Endo
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba, 263-8555, Japan
| | - Kiwamu Matsuoka
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba, 263-8555, Japan
| | - Kenji Tagai
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba, 263-8555, Japan
| | - Yasuyuki Kimura
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba, 263-8555, Japan
- Department of Clinical and Experimental Neuroimaging, Center for Development of Advanced Medicine for Dementia, National Center for Geriatrics and Gerontology, 7-430 Morioka, Obu, Aichi, 474-8511, Japan
| | - Shin Kurose
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba, 263-8555, Japan
| | - Masaru Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan
| | - Kazunori Kawamura
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba, Chiba, 263-8555, Japan
| | - Ming-Rong Zhang
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba, Chiba, 263-8555, Japan
| | - Makoto Higuchi
- Department of Functional Brain Imaging, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba, 263-8555, Japan
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Oldham S, Ball G. A phylogenetically-conserved axis of thalamocortical connectivity in the human brain. Nat Commun 2023; 14:6032. [PMID: 37758726 PMCID: PMC10533558 DOI: 10.1038/s41467-023-41722-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
The thalamus enables key sensory, motor, emotive, and cognitive processes via connections to the cortex. These projection patterns are traditionally considered to originate from discrete thalamic nuclei, however recent work showing gradients of molecular and connectivity features in the thalamus suggests the organisation of thalamocortical connections occurs along a continuous dimension. By performing a joint decomposition of densely sampled gene expression and non-invasive diffusion tractography in the adult human thalamus, we define a principal axis of genetic and connectomic variation along a medial-lateral thalamic gradient. Projections along this axis correspond to an anterior-posterior cortical pattern and are aligned with electrophysiological properties of the cortex. The medial-lateral axis demonstrates phylogenetic conservation, reflects transitions in neuronal subtypes, and shows associations with neurodevelopment and common brain disorders. This study provides evidence for a supra-nuclear axis of thalamocortical organisation characterised by a graded transition in molecular properties and anatomical connectivity.
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Affiliation(s)
- Stuart Oldham
- Developmental Imaging, Murdoch Children's Research Institute, Parkville, VIC, Australia.
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia.
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Parkville, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
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12
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Lawn T, Howard MA, Turkheimer F, Misic B, Deco G, Martins D, Dipasquale O. From Neurotransmitters to Networks: Transcending Organisational Hierarchies with Molecular-informed Functional Imaging. Neurosci Biobehav Rev 2023; 150:105193. [PMID: 37086932 PMCID: PMC10390343 DOI: 10.1016/j.neubiorev.2023.105193] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 04/01/2023] [Accepted: 04/19/2023] [Indexed: 04/24/2023]
Abstract
The human brain exhibits complex interactions across micro, meso-, and macro-scale organisational principles. Recent synergistic multi-modal approaches have begun to link micro-scale information to systems level dynamics, transcending organisational hierarchies and offering novel perspectives into the brain's function and dysfunction. Specifically, the distribution of micro-scale properties (such as receptor density or gene expression) can be mapped onto macro-scale measures from functional MRI to provide novel neurobiological insights. Methodological approaches to enrich functional imaging analyses with molecular information are rapidly evolving, with several streams of research having developed relatively independently, each offering unique potential to explore the trans-hierarchical functioning of the brain. Here, we address the three principal streams of research - spatial correlation, molecular-enriched network, and in-silico whole brain modelling analyses - to provide a critical overview of the different sources of molecular information, how this information can be utilised within analyses of fMRI data, the merits and pitfalls of each methodology, and, through the use of key examples, highlight their promise to shed new light on key domains of neuroscientific inquiry.
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Affiliation(s)
- Timothy Lawn
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Matthew A Howard
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Bratislav Misic
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec, Canada.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, Barcelona 08005, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia.
| | - Daniel Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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13
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Ferrari-Souza JP, Lussier FZ, Leffa DT, Therriault J, Tissot C, Bellaver B, Ferreira PC, Malpetti M, Wang YT, Povala G, Benedet AL, Ashton NJ, Chamoun M, Servaes S, Bezgin G, Kang MS, Stevenson J, Rahmouni N, Pallen V, Poltronetti NM, O’Brien JT, Rowe JB, Cohen AD, Lopez OL, Tudorascu DL, Karikari TK, Klunk WE, Villemagne VL, Soucy JP, Gauthier S, Souza DO, Zetterberg H, Blennow K, Zimmer ER, Rosa-Neto P, Pascoal TA. APOEε4 associates with microglial activation independently of Aβ plaques and tau tangles. SCIENCE ADVANCES 2023; 9:eade1474. [PMID: 37018391 PMCID: PMC10075966 DOI: 10.1126/sciadv.ade1474] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 03/02/2023] [Indexed: 06/01/2023]
Abstract
Animal studies suggest that the apolipoprotein E ε4 (APOEε4) allele is a culprit of early microglial activation in Alzheimer's disease (AD). Here, we tested the association between APOEε4 status and microglial activation in living individuals across the aging and AD spectrum. We studied 118 individuals with positron emission tomography for amyloid-β (Aβ; [18F]AZD4694), tau ([18F]MK6240), and microglial activation ([11C]PBR28). We found that APOEε4 carriers presented increased microglial activation relative to noncarriers in early Braak stage regions within the medial temporal cortex accounting for Aβ and tau deposition. Furthermore, microglial activation mediated the Aβ-independent effects of APOEε4 on tau accumulation, which was further associated with neurodegeneration and clinical impairment. The physiological distribution of APOE mRNA expression predicted the patterns of APOEε4-related microglial activation in our population, suggesting that APOE gene expression may regulate the local vulnerability to neuroinflammation. Our results support that the APOEε4 genotype exerts Aβ-independent effects on AD pathogenesis by activating microglia in brain regions associated with early tau deposition.
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Affiliation(s)
- João Pedro Ferrari-Souza
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Firoza Z. Lussier
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Douglas T. Leffa
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- ADHD Outpatient Program and Development Psychiatry Program, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Joseph Therriault
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Cécile Tissot
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Bruna Bellaver
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | | | - Maura Malpetti
- Department of Clinical Neurosciences, Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Yi-Ting Wang
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Guilherme Povala
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Andréa L. Benedet
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Nicholas J. Ashton
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Mira Chamoun
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Stijn Servaes
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Gleb Bezgin
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Min Su Kang
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
- Artificial Intelligence and Computational Neurosciences lab, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Jenna Stevenson
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Nesrine Rahmouni
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Vanessa Pallen
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Nina Margherita Poltronetti
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - John T. O’Brien
- Department of Clinical Neurosciences, Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - James B. Rowe
- Department of Clinical Neurosciences, Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Ann D. Cohen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Oscar L. Lopez
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Dana L. Tudorascu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Thomas K. Karikari
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - William E. Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Jean-Paul Soucy
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Diogo O. Souza
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Eduardo R. Zimmer
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Department of Pharmacology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Graduate Program in Biological Sciences: Pharmacology and Therapeuctis, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Tharick A. Pascoal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
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14
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Okkels N, Horsager J, Labrador-Espinosa MA, Hansen FO, Andersen KB, Just MK, Fedorova TD, Skjærbæk C, Munk OL, Hansen KV, Gottrup H, Hansen AK, Grothe MJ, Borghammer P. Distribution of cholinergic nerve terminals in the aged human brain measured with [ 18F]FEOBV PET and its correlation with histological data. Neuroimage 2023; 269:119908. [PMID: 36720436 DOI: 10.1016/j.neuroimage.2023.119908] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/10/2023] [Accepted: 01/27/2023] [Indexed: 01/30/2023] Open
Abstract
INTRODUCTION [18F]fluoroetoxybenzovesamicol ([18F]FEOBV) is a positron emission topography (PET) tracer for the vesicular acetylcholine transporter (VAChT), a protein located predominantly in synaptic vesicles in cholinergic nerve terminals. We aimed to use [18F]FEOBV PET to study the cholinergic topography of the healthy human brain. MATERIALS AND METHODS [18F]FEOBV PET brain data volumes of healthy elderly humans were normalized to standard space and intensity-normalized to the white matter. Stereotactic atlases of regions of interest were superimposed to describe and quantify tracer distribution. The spatial distribution of [18F]FEOBV PET uptake was compared with histological and gene expression data. RESULTS Twenty participants of both sexes and a mean age of 73.9 ± 6.0 years, age-range [64; 86], were recruited. Highest tracer binding was present in the striatum, some thalamic nuclei, and the basal forebrain. Intermediate binding was found in most nuclei of the brainstem, thalamus, and hypothalamus; the vermis and flocculonodular lobe; and the hippocampus, amygdala, insula, cingulate, olfactory cortex, and Heschl's gyrus. Lowest binding was present in most areas of the cerebral cortex, and in the cerebellar nuclei and hemispheres. The spatial distribution of tracer correlated with immunohistochemical post-mortem data, as well as with regional expression levels of SLC18A3, the VAChT coding gene. DISCUSSION Our in vivo findings confirm the regional cholinergic distribution in specific brain structures as described post-mortem. A positive spatial correlation between tracer distribution and regional gene expression levels further corroborates [18F]FEOBV PET as a validated tool for in vivo cholinergic imaging. The study represents an advancement in the continued efforts to delineate the spatial topography of the human cholinergic system in vivo.
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Affiliation(s)
- Niels Okkels
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Neurology, Aarhus University Hospital, Aarhus, Denmark.
| | - Jacob Horsager
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus, Denmark
| | - Miguel A Labrador-Espinosa
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain; Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Frederik O Hansen
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Katrine B Andersen
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Mie Kristine Just
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Tatyana D Fedorova
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Casper Skjærbæk
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Ole L Munk
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Kim V Hansen
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus, Denmark
| | - Hanne Gottrup
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Allan K Hansen
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus, Denmark; Department of Nuclear Medicine, Aalborg University Hospital, Aalborg, Denmark
| | - Michel J Grothe
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain; Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Per Borghammer
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus, Denmark
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15
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Pisani S, Gunasekera B, Lu Y, Vignando M, Ffytche D, Aarsland D, Chaudhuri KR, Ballard C, Lee JY, Kim YK, Velayudhan L, Bhattacharyya S. Grey matter volume loss in Parkinson's disease psychosis and its relationship with serotonergic gene expression: A meta-analysis. Neurosci Biobehav Rev 2023; 147:105081. [PMID: 36775084 DOI: 10.1016/j.neubiorev.2023.105081] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/14/2023] [Accepted: 02/05/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND Neuroanatomical alterations underlying psychosis in Parkinson's Disease (PDP) remain unclear. We carried out a meta-analysis of MRI studies investigating the neural correlates of PDP and examined its relation with dopaminergic and serotonergic receptor gene expression. METHODS PubMed, Web of Science and Embase were searched for MRI studies (k studies = 10) of PDP compared to PD patients without psychosis (PDnP). Seed-based d Mapping with Permutation of Subject Images and multiple linear regression analyses was used to examine the relationship between pooled estimates of grey matter volume (GMV) loss in PDP and D1/D2 and 5-HT1a/5-HT2a receptor gene expression estimates from Allen Human Brain Atlas. RESULTS We observed lower grey matter volume in parietal-temporo-occipital regions (PDP n = 211, PDnP, n = 298). GMV loss in PDP was associated with local expression of 5-HT1a (b = 0.109, p = 0.012) and 5-HT2a receptors (b= -0.106, p = 0.002) but not dopaminergic receptors. CONCLUSION Widespread GMV loss in the parieto-temporo-occipital regions may underlie PDP. Association between grey matter volume and local expression of serotonergic receptor genes may suggest a role for serotonergic receptors in PDP.
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Affiliation(s)
- Sara Pisani
- Division of Academic Psychiatry, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, United Kingdom.
| | - Brandon Gunasekera
- Division of Academic Psychiatry, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, United Kingdom.
| | - Yining Lu
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, United Kingdom.
| | - Miriam Vignando
- Centre for Neuroimaging Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, United Kingdom.
| | - Dominic Ffytche
- Division of Academic Psychiatry, Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, United Kingdom.
| | - Dag Aarsland
- Division of Academic Psychiatry, Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, United Kingdom; Centre for Age-Related Medicine (SESAM), Stavanger University Hospital, Stavanger, Norway.
| | - K Ray Chaudhuri
- Department of Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, and Parkinson's Foundation Centre of Excellence, King's College Hospital, London, United Kingdom.
| | - Clive Ballard
- Medical School, Medical School Building, St Luke's Campus, Magdalen Road, University of Exeter, Exeter EX1 2LU, United Kingdom.
| | - Jee-Young Lee
- Department of Neurology, Seoul National University-Seoul Metropolitan Government, Boramae Medical Center, 20, Boramae-ro 5-gil, Dongjak-gu, Seoul 07061, Republic of Korea.
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, Seoul National University-Seoul Metropolitan Government, Boramae Medical Center, 20, Boramae-ro 5-gil, Dongjak-gu, Seoul 07061, Republic of Korea.
| | - Latha Velayudhan
- Division of Academic Psychiatry, Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, United Kingdom; Department of Population Health Sciences, University of Leicester, United Kingdom.
| | - Sagnik Bhattacharyya
- Division of Academic Psychiatry, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, United Kingdom.
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16
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Bo T, Li J, Hu G, Zhang G, Wang W, Lv Q, Zhao S, Ma J, Qin M, Yao X, Wang M, Wang GZ, Wang Z. Brain-wide and cell-specific transcriptomic insights into MRI-derived cortical morphology in macaque monkeys. Nat Commun 2023; 14:1499. [PMID: 36932104 PMCID: PMC10023667 DOI: 10.1038/s41467-023-37246-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 03/06/2023] [Indexed: 03/19/2023] Open
Abstract
Integrative analyses of transcriptomic and neuroimaging data have generated a wealth of information about biological pathways underlying regional variability in imaging-derived brain phenotypes in humans, but rarely in nonhuman primates due to the lack of a comprehensive anatomically-defined atlas of brain transcriptomics. Here we generate complementary bulk RNA-sequencing dataset of 819 samples from 110 brain regions and single-nucleus RNA-sequencing dataset, and neuroimaging data from 162 cynomolgus macaques, to examine the link between brain-wide gene expression and regional variation in morphometry. We not only observe global/regional expression profiles of macaque brain comparable to human but unravel a dorsolateral-ventromedial gradient of gene assemblies within the primate frontal lobe. Furthermore, we identify a set of 971 protein-coding and 34 non-coding genes consistently associated with cortical thickness, specially enriched for neurons and oligodendrocytes. These data provide a unique resource to investigate nonhuman primate models of human diseases and probe cross-species evolutionary mechanisms.
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Affiliation(s)
- Tingting Bo
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Neuroscience Center, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Ganlu Hu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China
| | - Ge Zhang
- Department of Medical Imaging, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan, China
| | - Wei Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Qian Lv
- School of Psychological and Cognitive Sciences; Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Shaoling Zhao
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Junjie Ma
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Meng Qin
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Xiaohui Yao
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao, Shandong, China
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, No. 7 Weiwu Road, Zhengzhou, Henan, China.
| | - Guang-Zhong Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
| | - Zheng Wang
- School of Psychological and Cognitive Sciences; Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
- School of Biomedical Engineering, Hainan University, Haikou, Hainan, China.
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17
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Pak K, Kantonen T, Pekkarinen L, Nuutila P, Nummenmaa L. Association of CNR1 gene and cannabinoid 1 receptor protein in the human brain. J Neurosci Res 2023; 101:327-337. [PMID: 36440544 PMCID: PMC10100072 DOI: 10.1002/jnr.25149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 11/14/2022] [Accepted: 11/16/2022] [Indexed: 11/29/2022]
Abstract
We aimed to integrate genomic mapping from brain mRNA atlas with the protein expression from positron emission tomography (PET) scans of type 1 cannabinoid (CB1) receptor and to compare the predictive power of CB1 receptor with those of other neuroreceptor/transporters using a meta-analysis. Volume of distribution (VT ) from F18-FMPEP-d2 PET scans, CNR1 gene (Cannabinoid receptor 1) expression, and H3-CP55940 binding were calculated and correlation analysis was performed. Between VT of F18-FMPEP-d2 PET scans and CNR1 mRNA expression, moderate strength of correlation was observed (rho = .5067, p = .0337). Strong positive correlation was also found between CNR1 mRNA expression and H3-CP55940 binding (r = .6336, p = .0364), validating the finding between F18-FMPEP-d2 PET scans and CNR1 mRNA. The correlation between VT of F18-FMPEP-d2 PET scans and H3-CP55940 binding was marginally significant (r = .5025, p = .0563). From the meta-analysis, the correlation coefficient between mRNA expression and protein expressions ranged from -.10 to .99, with a pooled effect of .76. In conclusion, we observed the moderate to strong associations between gene and protein expression for CB1 receptor in the human brain, which was validated by autoradiography. We combined the autoradiographic finding with PET of CB1 receptor, producing the density atlas map of CB1 receptor. From the meta-analysis, the moderate to strong correlation was observed between mRNA expression and protein expressions across multiple genes. Further study is needed to investigate the relationship between multiple genes and in vivo proteins to improve and accelerate drug development.
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Affiliation(s)
- Kyoungjune Pak
- Turku PET Centre, University of Turku, Turku, Finland.,Turku University Hospital, Turku, Finland.,Department of Nuclear Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Tatu Kantonen
- Turku PET Centre, University of Turku, Turku, Finland.,Turku University Hospital, Turku, Finland.,Clinical Neurosciences, University of Turku, Turku, Finland
| | - Laura Pekkarinen
- Turku PET Centre, University of Turku, Turku, Finland.,Turku University Hospital, Turku, Finland
| | - Pirjo Nuutila
- Turku PET Centre, University of Turku, Turku, Finland.,Turku University Hospital, Turku, Finland.,Department of Endocrinology, Turku University Hospital, Turku, Finland
| | - Lauri Nummenmaa
- Turku PET Centre, University of Turku, Turku, Finland.,Turku University Hospital, Turku, Finland.,Department of Psychology, University of Turku, Turku, Finland
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18
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Komorowski A, Murgaš M, Vidal R, Singh A, Gryglewski G, Kasper S, Wiltfang J, Lanzenberger R, Goya‐Maldonado R. Regional gene expression patterns are associated with task-specific brain activation during reward and emotion processing measured with functional MRI. Hum Brain Mapp 2022; 43:5266-5280. [PMID: 35796185 PMCID: PMC9812247 DOI: 10.1002/hbm.26001] [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: 03/17/2022] [Revised: 06/02/2022] [Accepted: 06/06/2022] [Indexed: 01/15/2023] Open
Abstract
The exploration of the spatial relationship between gene expression profiles and task-evoked response patterns known to be altered in neuropsychiatric disorders, for example depression, can guide the development of more targeted therapies. Here, we estimated the correlation between human transcriptome data and two different brain activation maps measured with functional magnetic resonance imaging (fMRI) in healthy subjects. Whole-brain activation patterns evoked during an emotional face recognition task were associated with topological mRNA expression of genes involved in cellular transport. In contrast, fMRI activation patterns related to the acceptance of monetary rewards were associated with genes implicated in cellular localization processes, metabolism, translation, and synapse regulation. An overlap of these genes with risk genes from major depressive disorder genome-wide association studies revealed the involvement of the master regulators TCF4 and PAX6 in emotion and reward processing. Overall, the identification of stable relationships between spatial gene expression profiles and fMRI data may reshape the prospects for imaging transcriptomics studies.
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Affiliation(s)
- Arkadiusz Komorowski
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH)Medical University of ViennaVienna
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH)Medical University of ViennaVienna
| | - Ramon Vidal
- Max Delbrück Center for Molecular MedicineBerlinGermany
| | - Aditya Singh
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP‐Lab), Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG)Georg‐August UniversityGoettingenGermany
| | - Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH)Medical University of ViennaVienna
- Child Study CenterYale UniversityNew HavenConnecticutUSA
| | - Siegfried Kasper
- Center for Brain ResearchMedical University of ViennaViennaAustria
| | - Jens Wiltfang
- Department of Psychiatry and PsychotherapyUniversity Medical Center Goettingen (UMG), Georg‐August UniversityGoettingenGermany
- German Center for Neurodegenerative Diseases (DZNE)GoettingenGermany
- Neurosciences and Signalling Group, Institute of Biomedicine (iBiMED), Department of Medical SciencesUniversity of AveiroAveiroPortugal
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH)Medical University of ViennaVienna
| | - Roberto Goya‐Maldonado
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP‐Lab), Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG)Georg‐August UniversityGoettingenGermany
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19
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Dincer A, Chen CD, McKay NS, Koenig LN, McCullough A, Flores S, Keefe SJ, Schultz SA, Feldman RL, Joseph-Mathurin N, Hornbeck RC, Cruchaga C, Schindler SE, Holtzman DM, Morris JC, Fagan AM, Benzinger TLS, Gordon BA. APOE ε4 genotype, amyloid-β, and sex interact to predict tau in regions of high APOE mRNA expression. Sci Transl Med 2022; 14:eabl7646. [PMID: 36383681 PMCID: PMC9912474 DOI: 10.1126/scitranslmed.abl7646] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The apolipoprotein E (APOE) ε4 allele is strongly linked with cerebral β-amyloidosis, but its relationship with tauopathy is less established. We investigated the relationship between APOE ε4 carrier status, regional amyloid-β (Aβ), magnetic resonance imaging (MRI) volumetrics, tau positron emission tomography (PET), APOE messenger RNA (mRNA) expression maps, and cerebrospinal fluid phosphorylated tau (CSF ptau181). Three hundred fifty participants underwent imaging, and 270 had ptau181. We used computational models to evaluate the main effect of APOE ε4 carrier status on regional neuroimaging values and then the interaction of ε4 status and global Aβ on regional tau PET and brain volumes as well as CSF ptau181. Separately, we also examined the additional interactive influence of sex. We found that, for the same degree of Aβ burden, APOE ε4 carriers showed greater tau PET signal relative to noncarriers in temporal regions, but no interaction was present for MRI volumes or CSF ptau181. This potentiation of tau aggregation irrespective of sex occurred in brain regions with high APOE mRNA expression, suggesting local vulnerabilities to tauopathy. There were greater effects of APOE genotype in females, although the interactive sex effects did not strongly mirror mRNA expression. Pathology is not homogeneously expressed throughout the brain but mirrors underlying biological patterns such as gene expression.
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Affiliation(s)
- Aylin Dincer
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Charles D Chen
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Nicole S McKay
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Lauren N Koenig
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Austin McCullough
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Shaney Flores
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Sarah J Keefe
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Stephanie A Schultz
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Rebecca L Feldman
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Nelly Joseph-Mathurin
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Russ C Hornbeck
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Carlos Cruchaga
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA.,Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Suzanne E Schindler
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA.,Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - David M Holtzman
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA.,Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA.,Hope Center for Neurological Disorders, Washington University School of Medicine, Saint Louis, MO, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA.,Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Anne M Fagan
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA.,Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Tammie LS Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | - Brian A Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.,Knight Alzheimer Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA.,Hope Center for Neurological Disorders, Washington University School of Medicine, Saint Louis, MO, USA.,Department of Psychological & Brain Sciences, Washington University, Saint Louis, MO, USA
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20
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Gunasekera B, Davies C, Blest-Hopley G, Veronese M, Ramsey NF, Bossong MG, Radua J, Bhattacharyya S. Task-independent acute effects of delta-9-tetrahydrocannabinol on human brain function and its relationship with cannabinoid receptor gene expression: A neuroimaging meta-regression analysis. Neurosci Biobehav Rev 2022; 140:104801. [PMID: 35914625 DOI: 10.1016/j.neubiorev.2022.104801] [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: 03/07/2022] [Revised: 06/07/2022] [Accepted: 07/26/2022] [Indexed: 11/20/2022]
Abstract
The neurobiological mechanisms underlying the effects of delta-9-tetrahydrocannabinol (THC) remain unclear. Here, we examined the spatial acute effect of THC on human regional brain activation or blood flow (hereafter called 'activation signal') in a 'core' network of brain regions from 372 participants, tested using a within-subject repeated measures design under experimental conditions. We also investigated whether the neuromodulatory effects of THC are related to the local expression of the cannabinoid-type-1 (CB1R) and type-2 (CB2R) receptors. Finally, we investigated the dose-response relationship between THC and key brain substrates. These meta-analytic findings shed new light on the localisation of the effects of THC in the human brain, suggesting that THC has neuromodulatory effects in regions central to many cognitive tasks and processes, related to dose, with greater effects in regions with higher levels of CB1R expression.
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Affiliation(s)
- Brandon Gunasekera
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Cathy Davies
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Grace Blest-Hopley
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Mattia Veronese
- Department of Neuroimaging, Centre for Neuroimaging Sciences, King's College London, UK; Department of Information Engineering, University of Padua, Italy
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - Matthijs G Bossong
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - Joaquim Radua
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Barcelona, Spain; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Sagnik Bhattacharyya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
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21
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Murgaš M, Michenthaler P, Reed MB, Gryglewski G, Lanzenberger R. Correlation of receptor density and mRNA expression patterns in the human cerebral cortex. Neuroimage 2022; 256:119214. [PMID: 35452805 DOI: 10.1016/j.neuroimage.2022.119214] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/13/2022] [Accepted: 04/13/2022] [Indexed: 01/06/2023] Open
Abstract
Changes in distribution of associated molecular targets have been reported across several neuropsychiatric disorders. However, the high-resolution topology of most proteins is unknown and simultaneous in vivo measurement in multi-receptor systems is complicated. To account for the missing proteomic information, messenger ribonucleic acid (mRNA) transcripts are typically used as a surrogate. Nonetheless, post-transcriptional and post-translational processes might cause the discrepancy between the final distribution of proteins and gene expression patterns. Therefore, this study aims to investigate ex vivo links between mRNA expression and corresponding receptor density in the human cerebral cortex. To this end, autoradiography data on the density of 15 different receptors in 38 brain regions were correlated with the expression patterns of 50 associated genes derived from microarray data (mA), RNA sequencing data (RNA-Seq) provided by the Allen Human Brain Atlas and predicted mRNA expression patterns (pred-mRNA). Spearman's rank correlation was used to evaluate the possible links between proteomic data and mRNA expression patterns. Correlations between mRNA and protein density varied greatly between targets: Positive associations were found for e.g. the serotonin 1A (pred-mRNA: rs = 0.708; mA: rs = 0.601) or kainate receptor (pred-mRNA: rs = 0.655; mA: rs = 0.601; RNA-Seq: rs = 0.575) as well as a few negative associations e.g. γ-Aminobutyric acid (GABA) A receptor subunit α3 (pred-mRNA: rs = -0.638; mA: rs = -0.619) or subunit α5 (pred-mRNA: rs = -0.565; mA: rs = -0.563), while most of the other investigated target receptors showed low correlations. The high variability in the correspondence of mRNA expression and receptor spatial distribution warrants caution when inferring the topology of molecular targets in the brain from transcriptome data. This not only highlights the longstanding value of molecular imaging but also indicates a need for comprehensive proteomic studies.
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Affiliation(s)
- Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Paul Michenthaler
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Murray Bruce Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Child Study Center, Yale University, New Haven, USA
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria.
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22
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Pascoal TA, Chamoun M, Lax E, Wey HY, Shin M, Ng KP, Kang MS, Mathotaarachchi S, Benedet AL, Therriault J, Lussier FZ, Schroeder FA, DuBois JM, Hightower BG, Gilbert TM, Zürcher NR, Wang C, Hopewell R, Chakravarty M, Savard M, Thomas E, Mohaddes S, Farzin S, Salaciak A, Tullo S, Cuello AC, Soucy JP, Massarweh G, Hwang H, Kobayashi E, Hyman BT, Dickerson BC, Guiot MC, Szyf M, Gauthier S, Hooker JM, Rosa-Neto P. [ 11C]Martinostat PET analysis reveals reduced HDAC I availability in Alzheimer's disease. Nat Commun 2022; 13:4171. [PMID: 35853847 PMCID: PMC9296476 DOI: 10.1038/s41467-022-30653-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 05/04/2022] [Indexed: 11/26/2022] Open
Abstract
Alzheimer’s disease (AD) is characterized by the brain accumulation of amyloid-β and tau proteins. A growing body of literature suggests that epigenetic dysregulations play a role in the interplay of hallmark proteinopathies with neurodegeneration and cognitive impairment. Here, we aim to characterize an epigenetic dysregulation associated with the brain deposition of amyloid-β and tau proteins. Using positron emission tomography (PET) tracers selective for amyloid-β, tau, and class I histone deacetylase (HDAC I isoforms 1–3), we find that HDAC I levels are reduced in patients with AD. HDAC I PET reduction is associated with elevated amyloid-β PET and tau PET concentrations. Notably, HDAC I reduction mediates the deleterious effects of amyloid-β and tau on brain atrophy and cognitive impairment. HDAC I PET reduction is associated with 2-year longitudinal neurodegeneration and cognitive decline. We also find HDAC I reduction in the postmortem brain tissue of patients with AD and in a transgenic rat model expressing human amyloid-β plus tau pathology in the same brain regions identified in vivo using PET. These observations highlight HDAC I reduction as an element associated with AD pathophysiology. The link between amyloid and tau proteins with Alzheimer’s disease progression remains unclear. Here, the authors propose HDACs I downregulation as an element linking the deleterious effects of brain proteinopathies with disease progression.
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Affiliation(s)
- Tharick A Pascoal
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada.,Departments of Psychiatry and Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.,Departments of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Mira Chamoun
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Elad Lax
- Department of Molecular Biology, Ariel University, Ariel, Israel.,Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Hsiao-Ying Wey
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Monica Shin
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Kok Pin Ng
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Min Su Kang
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sulantha Mathotaarachchi
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Andrea L Benedet
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Joseph Therriault
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Firoza Z Lussier
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Frederick A Schroeder
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Jonathan M DuBois
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Baileigh G Hightower
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Tonya M Gilbert
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Nicole R Zürcher
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Changning Wang
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Robert Hopewell
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Mallar Chakravarty
- Departments of Biological and Biomedical Engineering and Psychiatry, Douglas Mental Health University Institute, Brain Imaging Centre, Montreal, QC, Canada
| | - Melissa Savard
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Emilie Thomas
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Sara Mohaddes
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Sarah Farzin
- Departments of Biological and Biomedical Engineering and Psychiatry, Douglas Mental Health University Institute, Brain Imaging Centre, Montreal, QC, Canada
| | - Alyssa Salaciak
- Departments of Biological and Biomedical Engineering and Psychiatry, Douglas Mental Health University Institute, Brain Imaging Centre, Montreal, QC, Canada
| | - Stephanie Tullo
- Departments of Biological and Biomedical Engineering and Psychiatry, Douglas Mental Health University Institute, Brain Imaging Centre, Montreal, QC, Canada
| | - A Claudio Cuello
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Jean-Paul Soucy
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Gassan Massarweh
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Heungsun Hwang
- Department of Psychology, McGill University, Montreal, QC, Canada
| | - Eliane Kobayashi
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Bradley T Hyman
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bradford C Dickerson
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.,Department of Psychology, McGill University, Montreal, QC, Canada
| | | | - Moshe Szyf
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Jacob M Hooker
- Neurology Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, Department of Neurology and Neurosurgery, Faculty of Medicine, The McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC, Canada. .,Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
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23
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Miranda AM, Ashok A, Chan RB, Zhou B, Xu Y, McIntire LB, Area-Gomez E, Di Paolo G, Duff KE, Oliveira TG, Nuriel T. Effects of APOE4 allelic dosage on lipidomic signatures in the entorhinal cortex of aged mice. Transl Psychiatry 2022; 12:129. [PMID: 35351864 PMCID: PMC8964762 DOI: 10.1038/s41398-022-01881-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/10/2022] [Accepted: 02/25/2022] [Indexed: 12/22/2022] Open
Abstract
Apolipoprotein E ε4 (APOE4) is the primary genetic risk factor for the late-onset form of Alzheimer's disease (AD). Although the reason for this association is not completely understood, researchers have uncovered numerous effects of APOE4 expression on AD-relevant brain processes, including amyloid beta (Aβ) accumulation, lipid metabolism, endosomal-lysosomal trafficking, and bioenergetics. In this study, we aimed to determine the effect of APOE4 allelic dosage on regional brain lipid composition in aged mice, as well as in cultured neurons. We performed a targeted lipidomic analysis on an AD-vulnerable brain region (entorhinal cortex; EC) and an AD-resistant brain region (primary visual cortex; PVC) from 14-15 month-old APOE3/3, APOE3/4, and APOE4/4 targeted replacement mice, as well as on neurons cultured with conditioned media from APOE3/3 or APOE4/4 astrocytes. Our results reveal that the EC possesses increased susceptibility to APOE4-associated lipid alterations compared to the PVC. In the EC, APOE4 expression showed a dominant effect in decreasing diacylglycerol (DAG) levels, and a semi-dominant, additive effect in the upregulation of multiple ceramide, glycosylated sphingolipid, and bis(monoacylglycerol)phosphate (BMP) species, lipids known to accumulate as a result of endosomal-lysosomal dysfunction. Neurons treated with conditioned media from APOE4/4 vs. APOE3/3 astrocytes showed similar alterations of DAG and BMP species to those observed in the mouse EC. Our results suggest that APOE4 expression differentially modulates regional neuronal lipid signatures, which may underlie the increased susceptibility of EC-localized neurons to AD pathology.
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Affiliation(s)
- André Miguel Miranda
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, 4710-057, Braga, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Neuroradiology Unit, Department of Imagiology, Centro Hospitalar Vila Nova Gaia/Espinho, 4434-502, Vila Nova Gaia, Portugal
| | - Archana Ashok
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
- Department of Pathology and Cell Biology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
| | - Robin Barry Chan
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
- Department of Pathology and Cell Biology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
| | - Bowen Zhou
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
- Department of Pathology and Cell Biology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
| | - Yimeng Xu
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
- Department of Pathology and Cell Biology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
| | - Laura Beth McIntire
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
- Department of Pathology and Cell Biology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
| | - Estela Area-Gomez
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
- Department of Neurology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
| | - Gilbert Di Paolo
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
- Department of Pathology and Cell Biology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
- Denali Therapeutics Inc., South San Francisco, CA, 94080, USA
| | - Karen E Duff
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
- Department of Pathology and Cell Biology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
- UK Dementia Research Institute, University College London, Cruciform Building, Gower Street, London, WC1E 6BT, UK
| | - Tiago Gil Oliveira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, 4710-057, Braga, Portugal.
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal.
- Department of Neuroradiology, Hospital de Braga, 4710-243, Braga, Portugal.
| | - Tal Nuriel
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, 630 West 168th Street, New York, NY, 10032, USA.
- Department of Pathology and Cell Biology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA.
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24
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Matar E, Ehgoetz Martens KA, Phillips JR, Wainstein G, Halliday GM, Lewis SJG, Shine JM. Dynamic network impairments underlie cognitive fluctuations in Lewy body dementia. NPJ Parkinsons Dis 2022; 8:16. [PMID: 35177652 PMCID: PMC8854384 DOI: 10.1038/s41531-022-00279-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 01/12/2022] [Indexed: 11/10/2022] Open
Abstract
Cognitive fluctuations are a characteristic and distressing disturbance of attention and consciousness seen in patients with Dementia with Lewy bodies and Parkinson's disease dementia. It has been proposed that fluctuations result from disruption of key neuromodulatory systems supporting states of attention and wakefulness which are normally characterised by temporally variable and highly integrated functional network architectures. In this study, patients with DLB (n = 25) and age-matched controls (n = 49) were assessed using dynamic resting state fMRI. A dynamic network signature of reduced temporal variability and integration was identified in DLB patients compared to controls. Reduced temporal variability correlated significantly with fluctuation-related measures using a sustained attention task. A less integrated (more segregated) functional network architecture was seen in DLB patients compared to the control group, with regions of reduced integration observed across dorsal and ventral attention, sensorimotor, visual, cingulo-opercular and cingulo-parietal networks. Reduced network integration correlated positively with subjective and objective measures of fluctuations. Regions of reduced integration and unstable regional assignments significantly matched areas of expression of specific classes of noradrenergic and cholinergic receptors across the cerebral cortex. Correlating topological measures with maps of neurotransmitter/neuromodulator receptor gene expression, we found that regions of reduced integration and unstable modular assignments correlated significantly with the pattern of expression of subclasses of noradrenergic and cholinergic receptors across the cerebral cortex. Altogether, these findings demonstrate that cognitive fluctuations are associated with an imaging signature of dynamic network impairment linked to specific neurotransmitters/neuromodulators within the ascending arousal system, highlighting novel potential diagnostic and therapeutic approaches for this troubling symptom.
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Affiliation(s)
- Elie Matar
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia. .,Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia. .,Forefront Research Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.
| | - Kaylena A Ehgoetz Martens
- Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada
| | - Joseph R Phillips
- Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,School of Social Sciences and Psychology, Western Sydney University, Sydney, NSW, Australia
| | - Gabriel Wainstein
- Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Centro de Investigaciones Médicas, Pontifical Catholic University of Chile, Santiago, Chile
| | - Glenda M Halliday
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.,Forefront Research Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Simon J G Lewis
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.,Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - James M Shine
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.,Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Forefront Research Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
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25
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Larsen B, Cui Z, Adebimpe A, Pines A, Alexander-Bloch A, Bertolero M, Calkins ME, Gur RE, Gur RC, Mahadevan AS, Moore TM, Roalf DR, Seidlitz J, Sydnor VJ, Wolf DH, Satterthwaite TD. A developmental reduction of the excitation:inhibition ratio in association cortex during adolescence. SCIENCE ADVANCES 2022; 8:eabj8750. [PMID: 35119918 PMCID: PMC8816330 DOI: 10.1126/sciadv.abj8750] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 12/13/2021] [Indexed: 06/14/2023]
Abstract
Adolescence is hypothesized to be a critical period for the development of association cortex. A reduction of the excitation:inhibition (E:I) ratio is a hallmark of critical period development; however, it has been unclear how to assess the development of the E:I ratio using noninvasive neuroimaging techniques. Here, we used pharmacological fMRI with a GABAergic benzodiazepine challenge to empirically generate a model of E:I ratio based on multivariate patterns of functional connectivity. In an independent sample of 879 youth (ages 8 to 22 years), this model predicted reductions in the E:I ratio during adolescence, which were specific to association cortex and related to psychopathology. These findings support hypothesized shifts in E:I balance of association cortices during a neurodevelopmental critical period in adolescence.
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Affiliation(s)
- Bart Larsen
- Penn Lifespan Neuroinformatics Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zaixu Cui
- Penn Lifespan Neuroinformatics Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Azeez Adebimpe
- Penn Lifespan Neuroinformatics Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam Pines
- Penn Lifespan Neuroinformatics Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Max Bertolero
- Penn Lifespan Neuroinformatics Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Monica E. Calkins
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Arun S. Mahadevan
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tyler M. Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R. Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Valerie J. Sydnor
- Penn Lifespan Neuroinformatics Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel H. Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D. Satterthwaite
- Penn Lifespan Neuroinformatics Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
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26
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Vu T, Vallmitjana A, Gu J, La K, Xu Q, Flores J, Zimak J, Shiu J, Hosohama L, Wu J, Douglas C, Waterman ML, Ganesan A, Hedde PN, Gratton E, Zhao W. Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis. Nat Commun 2022; 13:169. [PMID: 35013281 PMCID: PMC8748653 DOI: 10.1038/s41467-021-27798-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 12/15/2021] [Indexed: 12/14/2022] Open
Abstract
Multiplexed mRNA profiling in the spatial context provides new information enabling basic research and clinical applications. Unfortunately, existing spatial transcriptomics methods are limited due to either low multiplexing or complexity. Here, we introduce a spatialomics technology, termed Multi Omic Single-scan Assay with Integrated Combinatorial Analysis (MOSAICA), that integrates in situ labeling of mRNA and protein markers in cells or tissues with combinatorial fluorescence spectral and lifetime encoded probes, spectral and time-resolved fluorescence imaging, and machine learning-based decoding. We demonstrate MOSAICA's multiplexing scalability in detecting 10-plex targets in fixed colorectal cancer cells using combinatorial labeling of five fluorophores with facile error-detection and removal of autofluorescence. MOSAICA's analysis is strongly correlated with sequencing data (Pearson's r = 0.96) and was further benchmarked using RNAscopeTM and LGC StellarisTM. We further apply MOSAICA for multiplexed analysis of clinical melanoma Formalin-Fixed Paraffin-Embedded (FFPE) tissues. We finally demonstrate simultaneous co-detection of protein and mRNA in cancer cells.
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Affiliation(s)
- Tam Vu
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, 92697, USA
| | - Alexander Vallmitjana
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA
- Laboratory for Fluorescence Dynamics, University of California, Irvine, Irvine, CA, 92697, USA
| | - Joshua Gu
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, 92697, USA
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
| | - Kieu La
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA
| | - Qi Xu
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA
| | - Jesus Flores
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, 92697, USA
- CIRM Stem Cell Research Biotechnology Training Program at California State University, Long Beach, Long Beach, CA, 90840, USA
| | - Jan Zimak
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA
| | - Jessica Shiu
- Department of Dermatology, University of California, Irvine, Irvine, CA, 92697, USA
| | - Linzi Hosohama
- Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA, 92697, USA
| | - Jie Wu
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, 92697, USA
| | - Christopher Douglas
- Department of Pathology & Laboratory Medicine, University of California, Irvine, Irvine, CA, 92617, USA
| | - Marian L Waterman
- Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA, 92697, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, 92697, USA
| | - Anand Ganesan
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
- Department of Dermatology, University of California, Irvine, Irvine, CA, 92697, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, 92697, USA
| | - Per Niklas Hedde
- Laboratory for Fluorescence Dynamics, University of California, Irvine, Irvine, CA, 92697, USA
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA
- Beckman Laser Institute & Medical Clinic, University of California, Irvine, Irvine, CA, 92697, USA
| | - Enrico Gratton
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA.
- Laboratory for Fluorescence Dynamics, University of California, Irvine, Irvine, CA, 92697, USA.
- Beckman Laser Institute & Medical Clinic, University of California, Irvine, Irvine, CA, 92697, USA.
| | - Weian Zhao
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA.
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, 92697, USA.
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92697, USA.
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA.
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, 92697, USA.
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27
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Parhizkar S, Holtzman DM. APOE mediated neuroinflammation and neurodegeneration in Alzheimer's disease. Semin Immunol 2022; 59:101594. [PMID: 35232622 PMCID: PMC9411266 DOI: 10.1016/j.smim.2022.101594] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/14/2022] [Indexed: 01/15/2023]
Abstract
Neuroinflammation is a central mechanism involved in neurodegeneration as observed in Alzheimer's disease (AD), the most prevalent form of neurodegenerative disease. Apolipoprotein E4 (APOE4), the strongest genetic risk factor for AD, directly influences disease onset and progression by interacting with the major pathological hallmarks of AD including amyloid-β plaques, neurofibrillary tau tangles, as well as neuroinflammation. Microglia and astrocytes, the two major immune cells in the brain, exist in an immune-vigilant state providing immunological defense as well as housekeeping functions that promote neuronal well-being. It is becoming increasingly evident that under disease conditions, these immune cells become progressively dysfunctional in regulating metabolic and immunoregulatory pathways, thereby promoting chronic inflammation-induced neurodegeneration. Here, we review and discuss how APOE and specifically APOE4 directly influences amyloid-β and tau pathology, and disrupts microglial as well as astroglial immunomodulating functions leading to chronic inflammation that contributes to neurodegeneration in AD.
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Affiliation(s)
- Samira Parhizkar
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer Disease, Research Center, Washington University, St. Louis, MO 63110, USA
| | - David M. Holtzman
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer Disease, Research Center, Washington University, St. Louis, MO 63110, USA
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28
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Martins D, Giacomel A, Williams SCR, Turkheimer F, Dipasquale O, Veronese M. Imaging transcriptomics: Convergent cellular, transcriptomic, and molecular neuroimaging signatures in the healthy adult human brain. Cell Rep 2021; 37:110173. [PMID: 34965413 DOI: 10.1016/j.celrep.2021.110173] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/30/2021] [Accepted: 12/03/2021] [Indexed: 12/12/2022] Open
Abstract
The integration of transcriptomic and neuroimaging data, "imaging transcriptomics," has recently emerged to generate hypotheses about potential biological pathways underlying regional variability in neuroimaging features. However, the validity of this approach is yet to be examined in depth. Here, we sought to bridge this gap by performing transcriptomic decoding of the regional distribution of well-known molecular markers spanning different elements of the biology of the healthy human brain. Imaging transcriptomics identifies biological and cell pathways that are consistent with the known biology of a wide range of molecular neuroimaging markers. The extent to which it can capture patterns of gene expression that align well with elements of the biology of the neuroinflammatory axis, at least in healthy controls without a proinflammatory challenge, is inconclusive. Imaging transcriptomics might constitute an interesting approach to improve our understanding of the biological pathways underlying regional variability in a wide range of neuroimaging phenotypes.
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Affiliation(s)
- Daniel Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK.
| | - Alessio Giacomel
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Steven C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Mattia Veronese
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK; Department of Information Engineering, University of Padua, Via Gradenigo, 6/b, 35131 Padova, Italy.
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29
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Godbersen GM, Murgaš M, Gryglewski G, Klöbl M, Unterholzner J, Rischka L, Spies M, Baldinger-Melich P, Winkler D, Lanzenberger R. Coexpression of Gene Transcripts with Monoamine Oxidase A Quantified by Human In Vivo Positron Emission Tomography. Cereb Cortex 2021; 32:3516-3524. [PMID: 34952543 DOI: 10.1093/cercor/bhab430] [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: 10/08/2021] [Revised: 11/03/2021] [Accepted: 11/03/2021] [Indexed: 12/12/2022] Open
Abstract
The monoamine oxidase A (MAO-A) is integral to monoamine metabolism and is thus relevant to the pathophysiology of various neuropsychiatric disorders; however, associated gene-enzyme relations are not well understood. This study aimed to unveil genes coexpressed with MAO-A. Therefore, 18 179 mRNA expression maps (based on the Allen Human Brain Atlas) were correlated with the cerebral distribution volume (VT) of MAO-A assessed in 36 healthy subjects (mean age ± standard deviation: 32.9 ± 8.8 years, 18 female) using [11C]harmine positron emission tomography scans. Coexpression analysis was based on Spearman's ρ, over-representation tests on Fisher's exact test with false discovery rate (FDR) correction. The analysis revealed 35 genes in cortex (including B-cell translocation gene family, member 3, implicated in neuroinflammation) and 247 genes in subcortex (including kallikrein-related peptidase 10, implicated in Alzheimer's disease). Significantly over-represented Gene Ontology terms included "neuron development", "neuron differentiation", and "cell-cell signaling" as well as "axon" and "neuron projection". In vivo MAO-A enzyme distribution and MAOA expression did not correlate in cortical areas (ρ = 0.08) while correlation was found in subcortical areas (ρ = 0.52), suggesting influences of region-specific post-transcriptional and -translational modifications. The herein reported information could contribute to guide future genetic studies, deepen the understanding of associated pathomechanisms and assist in the pursuit of novel therapeutic targets.
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Affiliation(s)
- G M Godbersen
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna 1090, Austria
| | - M Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna 1090, Austria
| | - G Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna 1090, Austria
| | - M Klöbl
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna 1090, Austria
| | - J Unterholzner
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna 1090, Austria
| | - L Rischka
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna 1090, Austria
| | - M Spies
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna 1090, Austria
| | - P Baldinger-Melich
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna 1090, Austria
| | - D Winkler
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna 1090, Austria
| | - R Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna 1090, Austria
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30
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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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31
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Wainstein G, Rojas-Líbano D, Medel V, Alnæs D, Kolskår KK, Endestad T, Laeng B, Ossandon T, Crossley N, Matar E, Shine JM. The ascending arousal system promotes optimal performance through mesoscale network integration in a visuospatial attentional task. Netw Neurosci 2021; 5:890-910. [PMID: 35024535 PMCID: PMC8746119 DOI: 10.1162/netn_a_00205] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 08/02/2021] [Indexed: 01/23/2023] Open
Abstract
Previous research has shown that the autonomic nervous system provides essential constraints over ongoing cognitive function. However, there is currently a relative lack of direct empirical evidence for how this interaction manifests in the brain at the macroscale level. Here, we examine the role of ascending arousal and attentional load on large-scale network dynamics by combining pupillometry, functional MRI, and graph theoretical analysis to analyze data from a visual motion-tracking task with a parametric load manipulation. We found that attentional load effects were observable in measures of pupil diameter and in a set of brain regions that parametrically modulated their BOLD activity and mesoscale network-level integration. In addition, the regional patterns of network reconfiguration were correlated with the spatial distribution of the α2a adrenergic receptor. Our results further solidify the relationship between ascending noradrenergic activity, large-scale network integration, and cognitive task performance.
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Affiliation(s)
| | - Daniel Rojas-Líbano
- Centro de Neurociencia Humana y Neuropsicología, Facultad de Psicología, Universidad Diego Portales, Santiago, Chile
| | - Vicente Medel
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, University of Oslo, and Oslo University Hospital, Oslo, Norway
- Bjørnnes College, Oslo, Norway
| | - Knut K. Kolskår
- NORMENT, Division of Mental Health and Addiction, University of Oslo, and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Tor Endestad
- Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Oslo, Norway
- Helgelandssykehuset Mosjøen, Helse Nord, Norway
| | - Bruno Laeng
- Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Oslo, Norway
| | - Tomas Ossandon
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Nicolás Crossley
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Elie Matar
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - James M. Shine
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
- Centre for Complexity, University of Sydney, Sydney, NSW, Australia
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32
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Quinn JP, Kandigian SE, Trombetta BA, Arnold SE, Carlyle BC. VGF as a biomarker and therapeutic target in neurodegenerative and psychiatric diseases. Brain Commun 2021; 3:fcab261. [PMID: 34778762 PMCID: PMC8578498 DOI: 10.1093/braincomms/fcab261] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/01/2021] [Accepted: 09/13/2021] [Indexed: 12/20/2022] Open
Abstract
Neurosecretory protein VGF (non-acronymic) belongs to the granin family of neuropeptides. VGF and VGF-derived peptides have been repeatedly identified in well-powered and well-designed multi-omic studies as dysregulated in neurodegenerative and psychiatric diseases. New therapeutics is urgently needed for these devastating and costly diseases, as are new biomarkers to improve disease diagnosis and mechanistic understanding. From a list of 537 genes involved in Alzheimer's disease pathogenesis, VGF was highlighted by the Accelerating Medicines Partnership in Alzheimer's disease as the potential therapeutic target of greatest interest. VGF levels are consistently decreased in brain tissue and CSF samples from patients with Alzheimer's disease compared to controls, and its levels correlate with disease severity and Alzheimer's disease pathology. In the brain, VGF exists as multiple functional VGF-derived peptides. Full-length human VGF1-615 undergoes proteolytic processing by prohormone convertases and other proteases in the regulated secretory pathway to produce at least 12 active VGF-derived peptides. In cell and animal models, these VGF-derived peptides have been linked to energy balance regulation, neurogenesis, synaptogenesis, learning and memory, and depression-related behaviours throughout development and adulthood. The C-terminal VGF-derived peptides, TLQP-62 (VGF554-615) and TLQP-21 (VGF554-574) have differential effects on Alzheimer's disease pathogenesis, neuronal and microglial activity, and learning and memory. TLQP-62 activates neuronal cell-surface receptors and regulates long-term hippocampal memory formation. TLQP-62 also prevents immune-mediated memory impairment, depression-like and anxiety-like behaviours in mice. TLQP-21 binds to microglial cell-surface receptors, triggering microglial chemotaxis and phagocytosis. These actions were reported to reduce amyloid-β plaques and decrease neuritic dystrophy in a transgenic mouse model of familial Alzheimer's disease. Expression differences of VGF-derived peptides have also been associated with frontotemporal lobar dementias, amyotrophic lateral sclerosis, Lewy body diseases, Huntington's disease, pain, schizophrenia, bipolar disorder, depression and antidepressant response. This review summarizes current knowledge and highlights questions for future investigation regarding the roles of VGF and its dysregulation in neurodegenerative and psychiatric disease. Finally, the potential of VGF and VGF-derived peptides as biomarkers and novel therapeutic targets for neurodegenerative and psychiatric diseases is highlighted.
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Affiliation(s)
- James P Quinn
- Department of Neurology, Alzheimer's Clinical & Translational Research Unit, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Savannah E Kandigian
- Department of Neurology, Alzheimer's Clinical & Translational Research Unit, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Bianca A Trombetta
- Department of Neurology, Alzheimer's Clinical & Translational Research Unit, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Steven E Arnold
- Department of Neurology, Alzheimer's Clinical & Translational Research Unit, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Becky C Carlyle
- Department of Neurology, Alzheimer's Clinical & Translational Research Unit, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
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33
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Hwang K, Shine JM, Bruss J, Tranel D, Boes A. Neuropsychological evidence of multi-domain network hubs in the human thalamus. eLife 2021; 10:69480. [PMID: 34622776 PMCID: PMC8526062 DOI: 10.7554/elife.69480] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 09/30/2021] [Indexed: 12/23/2022] Open
Abstract
Hubs in the human brain support behaviors that arise from brain network interactions. Previous studies have identified hub regions in the human thalamus that are connected with multiple functional networks. However, the behavioral significance of thalamic hubs has yet to be established. Our framework predicts that thalamic subregions with strong hub properties are broadly involved in functions across multiple cognitive domains. To test this prediction, we studied human patients with focal thalamic lesions in conjunction with network analyses of the human thalamocortical functional connectome. In support of our prediction, lesions to thalamic subregions with stronger hub properties were associated with widespread deficits in executive, language, and memory functions, whereas lesions to thalamic subregions with weaker hub properties were associated with more limited deficits. These results highlight how a large-scale network model can broaden our understanding of thalamic function for human cognition.
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Affiliation(s)
- Kai Hwang
- Department of Psychological and Brain Sciences, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Cognitive Control Collaborative, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Iowa Neuroscience Institute, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Psychiatry, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States
| | - James M Shine
- Brain and Mind Center, The University of Sydney, Sydney, Australia
| | - Joel Bruss
- Iowa Neuroscience Institute, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Neurology, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Pediatrics, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States
| | - Daniel Tranel
- Department of Psychological and Brain Sciences, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Iowa Neuroscience Institute, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Neurology, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States
| | - Aaron Boes
- Iowa Neuroscience Institute, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Psychiatry, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Neurology, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Pediatrics, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States
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34
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Pascoal TA, Benedet AL, Ashton NJ, Kang MS, Therriault J, Chamoun M, Savard M, Lussier FZ, Tissot C, Karikari TK, Ottoy J, Mathotaarachchi S, Stevenson J, Massarweh G, Schöll M, de Leon MJ, Soucy JP, Edison P, Blennow K, Zetterberg H, Gauthier S, Rosa-Neto P. Microglial activation and tau propagate jointly across Braak stages. Nat Med 2021; 27:1592-1599. [PMID: 34446931 DOI: 10.1038/s41591-021-01456-w] [Citation(s) in RCA: 224] [Impact Index Per Article: 74.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 06/28/2021] [Indexed: 11/09/2022]
Abstract
Compelling experimental evidence suggests that microglial activation is involved in the spread of tau tangles over the neocortex in Alzheimer's disease (AD). We tested the hypothesis that the spatial propagation of microglial activation and tau accumulation colocalize in a Braak-like pattern in the living human brain. We studied 130 individuals across the aging and AD clinical spectrum with positron emission tomography brain imaging for microglial activation ([11C]PBR28), amyloid-β (Aβ) ([18F]AZD4694) and tau ([18F]MK-6240) pathologies. We further assessed microglial triggering receptor expressed on myeloid cells 2 (TREM2) cerebrospinal fluid (CSF) concentrations and brain gene expression patterns. We found that [11C]PBR28 correlated with CSF soluble TREM2 and showed regional distribution resembling TREM2 gene expression. Network analysis revealed that microglial activation and tau correlated hierarchically with each other following Braak-like stages. Regression analysis revealed that the longitudinal tau propagation pathways depended on the baseline microglia network rather than the tau network circuits. The co-occurrence of Aβ, tau and microglia abnormalities was the strongest predictor of cognitive impairment in our study population. Our findings support a model where an interaction between Aβ and activated microglia sets the pace for tau spread across Braak stages.
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Affiliation(s)
- Tharick A Pascoal
- Departments of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. .,Departments of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. .,Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada. .,Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
| | - Andrea L Benedet
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Institute Clinical Neuroscience Institute, King's College London, London, UK.,NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley, NHS Foundation, London, UK
| | - Min Su Kang
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Joseph Therriault
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Mira Chamoun
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Melissa Savard
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Firoza Z Lussier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Cécile Tissot
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Julie Ottoy
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium.,LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Sulantha Mathotaarachchi
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Jenna Stevenson
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Gassan Massarweh
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Michael Schöll
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Mony J de Leon
- Department of Radiology Weill Medical Center Brain Health Imaging Institute, Cornell University, Ithaca, NY, USA
| | - Jean-Paul Soucy
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Paul Edison
- Department of Brain Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,UK Dementia Research Institute at UCL, London, UK
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada. .,Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
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35
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Abstract
PURPOSE OF REVIEW The prevalence of new public datasets of brain-wide and single-cell transcriptome data has created new opportunities to link neuroimaging findings with genetic data. The aim of this study is to present the different methodological approaches that have been used to combine this data. RECENT FINDINGS Drawing from various sources of open access data, several studies have been able to correlate neuroimaging maps with spatial distribution of brain expression. These efforts have enabled researchers to identify functional annotations of related genes, identify specific cell types related to brain phenotypes, study the expression of genes across life span and highlight the importance of selected brain genes in disease genetic networks. SUMMARY New transcriptome datasets and methodological approaches complement current neuroimaging work and will be crucial to improve our understanding of the biological mechanism that underlies many neurological conditions.
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Affiliation(s)
- Ibai Diez
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston MA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston MA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA
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36
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Shine JM, Müller EJ, Munn B, Cabral J, Moran RJ, Breakspear M. Computational models link cellular mechanisms of neuromodulation to large-scale neural dynamics. Nat Neurosci 2021; 24:765-776. [PMID: 33958801 DOI: 10.1038/s41593-021-00824-6] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 02/23/2021] [Indexed: 02/02/2023]
Abstract
Decades of neurobiological research have disclosed the diverse manners in which the response properties of neurons are dynamically modulated to support adaptive cognitive functions. This neuromodulation is achieved through alterations in the biophysical properties of the neuron. However, changes in cognitive function do not arise directly from the modulation of individual neurons, but are mediated by population dynamics in mesoscopic neural ensembles. Understanding this multiscale mapping is an important but nontrivial issue. Here, we bridge these different levels of description by showing how computational models parametrically map classic neuromodulatory processes onto systems-level models of neural activity. The ensuing critical balance of systems-level activity supports perception and action, although our knowledge of this mapping remains incomplete. In this way, quantitative models that link microscale neuronal neuromodulation to systems-level brain function highlight gaps in knowledge and suggest new directions for integrating theoretical and experimental work.
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Affiliation(s)
- James M Shine
- Brain and Mind Center, The University of Sydney, Camperdown, New South Wales, Australia.,Center for Complex Systems, The University of Sydney, Camperdown, New South Wales, Australia
| | - Eli J Müller
- Brain and Mind Center, The University of Sydney, Camperdown, New South Wales, Australia.,Center for Complex Systems, The University of Sydney, Camperdown, New South Wales, Australia
| | - Brandon Munn
- Brain and Mind Center, The University of Sydney, Camperdown, New South Wales, Australia.,Center for Complex Systems, The University of Sydney, Camperdown, New South Wales, Australia
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
| | | | - Michael Breakspear
- School of Psychology, College of Engineering, Science and the Environment, University of Newcastle, Callaghan, New South Wales, Australia. .,School of Medicine and Public Health, College of Health and Medicine, University of Newcastle, Callaghan, New South Wales, Australia.
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37
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Adewale Q, Khan AF, Carbonell F, Iturria-Medina Y. Integrated transcriptomic and neuroimaging brain model decodes biological mechanisms in aging and Alzheimer's disease. eLife 2021; 10:e62589. [PMID: 34002691 PMCID: PMC8131100 DOI: 10.7554/elife.62589] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 03/24/2021] [Indexed: 02/07/2023] Open
Abstract
Both healthy aging and Alzheimer's disease (AD) are characterized by concurrent alterations in several biological factors. However, generative brain models of aging and AD are limited in incorporating the measures of these biological factors at different spatial resolutions. Here, we propose a personalized bottom-up spatiotemporal brain model that accounts for the direct interplay between hundreds of RNA transcripts and multiple macroscopic neuroimaging modalities (PET, MRI). In normal elderly and AD participants, the model identifies top genes modulating tau and amyloid-β burdens, vascular flow, glucose metabolism, functional activity, and atrophy to drive cognitive decline. The results also revealed that AD and healthy aging share specific biological mechanisms, even though AD is a separate entity with considerably more altered pathways. Overall, this personalized model offers novel insights into the multiscale alterations in the elderly brain, with important implications for identifying effective genetic targets for extending healthy aging and treating AD progression.
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Affiliation(s)
- Quadri Adewale
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill UniversityMontrealCanada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
- Ludmer Centre for Neuroinformatics and Mental Health, McGill UniversityMontrealCanada
| | - Ahmed F Khan
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill UniversityMontrealCanada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
- Ludmer Centre for Neuroinformatics and Mental Health, McGill UniversityMontrealCanada
| | | | - Yasser Iturria-Medina
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill UniversityMontrealCanada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
- Ludmer Centre for Neuroinformatics and Mental Health, McGill UniversityMontrealCanada
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38
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Groot C, Grothe MJ, Mukherjee S, Jelistratova I, Jansen I, van Loenhoud AC, Risacher SL, Saykin AJ, Mac Donald CL, Mez J, Trittschuh EH, Gryglewski G, Lanzenberger R, Pijnenburg YAL, Barkhof F, Scheltens P, van der Flier WM, Crane PK, Ossenkoppele R. Differential patterns of gray matter volumes and associated gene expression profiles in cognitively-defined Alzheimer's disease subgroups. Neuroimage Clin 2021; 30:102660. [PMID: 33895633 PMCID: PMC8186562 DOI: 10.1016/j.nicl.2021.102660] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 02/25/2021] [Accepted: 03/30/2021] [Indexed: 01/04/2023]
Abstract
The clinical presentation of Alzheimer's disease (AD) varies widely across individuals but the neurobiological mechanisms underlying this heterogeneity are largely unknown. Here, we compared regional gray matter (GM) volumes and associated gene expression profiles between cognitively-defined subgroups of amyloid-β positive individuals clinically diagnosed with AD dementia (age: 66 ± 7, 47% male, MMSE: 21 ± 5). All participants underwent neuropsychological assessment with tests covering memory, executive-functioning, language and visuospatial-functioning domains. Subgroup classification was achieved using a psychometric framework that assesses which cognitive domain shows substantial relative impairment compared to the intra-individual average across domains, which yielded the following subgroups in our sample; AD-Memory (n = 41), AD-Executive (n = 117), AD-Language (n = 33), AD-Visuospatial (n = 171). We performed voxel-wise contrasts of GM volumes derived from 3Tesla structural MRI between subgroups and controls (n = 127, age 58 ± 9, 42% male, MMSE 29 ± 1), and observed that differences in regional GM volumes compared to controls closely matched the respective cognitive profiles. Specifically, we detected lower medial temporal lobe GM volumes in AD-Memory, lower fronto-parietal GM volumes in AD-Executive, asymmetric GM volumes in the temporal lobe (left < right) in AD-Language, and lower GM volumes in posterior areas in AD-Visuospatial. In order to examine possible biological drivers of these differences in regional GM volumes, we correlated subgroup-specific regional GM volumes to brain-wide gene expression profiles based on a stereotactic characterization of the transcriptional architecture of the human brain as provided by the Allen human brain atlas. Gene-set enrichment analyses revealed that variations in regional expression of genes involved in processes like mitochondrial respiration and metabolism of proteins were associated with patterns of regional GM volume across multiple subgroups. Other gene expression vs GM volume-associations were only detected in particular subgroups, e.g., genes involved in the cell cycle for AD-Memory, specific sets of genes related to protein metabolism in AD-Language, and genes associated with modification of gene expression in AD-Visuospatial. We conclude that cognitively-defined AD subgroups show neurobiological differences, and distinct biological pathways may be involved in the emergence of these differences.
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Affiliation(s)
- Colin Groot
- Department of Neurology & Alzheimer Center, Amsterdam University Medical Center - Location VU University Medical Center, Amsterdam, The Netherlands.
| | - Michel J Grothe
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain; German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.
| | | | | | - Iris Jansen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University, Amsterdam, The Netherlands.
| | - Anna Catharina van Loenhoud
- Department of Neurology & Alzheimer Center, Amsterdam University Medical Center - Location VU University Medical Center, Amsterdam, The Netherlands.
| | | | - Andrew J Saykin
- Indiana University School of Medicine, Indianapolis, IN, USA.
| | | | - Jesse Mez
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA; Alzheimer's Disease Center, Boston University School of Medicine, MA, USA.
| | - Emily H Trittschuh
- Psychiatry & Behavioral Science, University of Washington, Seattle, WA, USA; Veterans Affairs Puget Sound Health Care System, Geriatric Research, Education, & Clinical Center, Seattle, WA, USA.
| | - Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
| | - Yolande A L Pijnenburg
- Department of Neurology & Alzheimer Center, Amsterdam University Medical Center - Location VU University Medical Center, Amsterdam, The Netherlands.
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center - Location VU University Medical Center, Amsterdam, The Netherlands; University College London, Institutes of Neurology & Healthcare Engineering, London, United Kingdom.
| | - Philip Scheltens
- Department of Neurology & Alzheimer Center, Amsterdam University Medical Center - Location VU University Medical Center, Amsterdam, The Netherlands.
| | - Wiesje M van der Flier
- Department of Neurology & Alzheimer Center, Amsterdam University Medical Center - Location VU University Medical Center, Amsterdam, The Netherlands; Epidemiology and Biostatistics, Amsterdam University Medical Center - Location VU University Medical Center, Amsterdam, The Netherlands.
| | - Paul K Crane
- Department of Medicine, University of Washington, Seattle, WA, USA.
| | - Rik Ossenkoppele
- Department of Neurology & Alzheimer Center, Amsterdam University Medical Center - Location VU University Medical Center, Amsterdam, The Netherlands; Lund University, Clinical Memory Research Unit, Lund, Sweden.
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39
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Selvaggi P, Rizzo G, Mehta MA, Turkheimer FE, Veronese M. Integration of human whole-brain transcriptome and neuroimaging data: Practical considerations of current available methods. J Neurosci Methods 2021; 355:109128. [PMID: 33722642 DOI: 10.1016/j.jneumeth.2021.109128] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 02/12/2021] [Accepted: 03/01/2021] [Indexed: 12/20/2022]
Abstract
The Allen Human Brain Atlas (AHBA) is the first example of human brain transcriptomic mappings and detailed anatomical annotations which, for the first time, has allowed the integration of human brain transcriptomics with neuroimaging. This has been made possible because the AHBA offered an original dataset that contains mRNA expression measures for >20,000 genes covering the whole brain and, critically, in a standard stereotaxic space. In recent years many different methods have been used to integrate this data set with brain imaging data, although this endeavour has lacked harmony in terms of the workflow of data processing and subsequent analyses. In this work we discuss five main issues that experience has highlighted as in need of thorough consideration when integrating the AHBA with neuroimaging. These concerns are corroborated by comparing the performance of three different publicly available methods in correlating the same measures of serotonin receptors density with the correspondent AHBA mRNA maps. In this representative case, we were able to show how these methods can lead to discrepant results, suggesting that processing options are not neutral. We believe that the field should take into serious consideration these issues as they could undermine reproducibility and, in the end, the intrinsic value of the AHBA. We also advise on possible strategies to overcome these discrepancies. Finally, we encourage authors towards practices that will improve reproducibility such as transparency in reporting, algorithm and data sharing, collaboration.
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Affiliation(s)
- Pierluigi Selvaggi
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Gaia Rizzo
- Invicro, W12 0NN, London, UK; Division of Brain Sciences, Department of Medicine, Imperial College London, SW72AZ, London, UK
| | - Mitul A Mehta
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federico E Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Mattia Veronese
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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40
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Gryglewski G, Murgaš M, Klöbl M, Reed MB, Unterholzner J, Michenthaler P, Lanzenberger R. Enrichment of Disease-Associated Genes in Cortical Areas Defined by Transcriptome-Based Parcellation. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 7:10-23. [PMID: 33711548 DOI: 10.1016/j.bpsc.2021.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 02/05/2021] [Accepted: 02/23/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Parcellation of the cerebral cortex serves the investigation of the emergence of uniquely human brain functions and disorders. Transcriptome data enable the characterization of the molecular properties of cortical areas in unprecedented detail. Previously, we predicted the expression of 18,686 genes in the entire human brain based on microarray data. Here, we employed these data to parcellate the cortex and study the regional enrichment of disease-associated genes. METHODS We performed agglomerative hierarchical clustering based on normalized transcriptome data to delineate areas with distinct gene expression profiles. Subsequently, we tested these profiles for the enrichment of gene sets associated with brain disorders by genome-wide association studies and expert-curated databases using gene set enrichment analysis. RESULTS Transcriptome-based parcellation identified borders in line with major anatomical landmarks and the functional differentiation of primary motor, somatosensory, visual, and auditory areas. Gene set enrichment analysis based on curated databases suggested new roles of specific areas in psychiatric and neurological disorders while reproducing well-established links for movement and neurodegenerative disorders, for example, amyotrophic lateral sclerosis (motor cortex) and Alzheimer's disease (entorhinal cortex). Meanwhile, gene sets derived from genome-wide association studies on psychiatric disorders exhibited similar enrichment patterns driven by pleiotropic genes expressed in the posterior fusiform gyrus and inferior parietal lobule. CONCLUSIONS The identified enrichment patterns suggest the vulnerability of specific cortical areas to various influences that might alter the risk of developing one or several brain disorders. For several diseases, specific genes were highlighted, which could lead to the discovery of novel disease mechanisms and urgently needed treatments.
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Affiliation(s)
- Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Manfred Klöbl
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Murray Bruce Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Jakob Unterholzner
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Paul Michenthaler
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
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41
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Komorowski A, Weidenauer A, Murgaš M, Sauerzopf U, Wadsak W, Mitterhauser M, Bauer M, Hacker M, Praschak-Rieder N, Kasper S, Lanzenberger R, Willeit M. Association of dopamine D 2/3 receptor binding potential measured using PET and [ 11C]-(+)-PHNO with post-mortem DRD 2/3 gene expression in the human brain. Neuroimage 2020; 223:117270. [PMID: 32818617 PMCID: PMC7610745 DOI: 10.1016/j.neuroimage.2020.117270] [Citation(s) in RCA: 9] [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] [Received: 05/05/2020] [Revised: 08/12/2020] [Accepted: 08/12/2020] [Indexed: 01/11/2023] Open
Abstract
Open access post-mortem transcriptome atlases such as the Allen Human Brain Atlas (AHBA) can inform us about mRNA expression of numerous proteins of interest across the whole brain, while in vivo protein binding in the human brain can be quantified by means of neuroreceptor positron emission tomography (PET). By combining both modalities, the association between regional gene expression and receptor distribution in the living brain can be approximated. Here, we compare the characteristics of D2 and D3 dopamine receptor distribution by applying the dopamine D2/3 receptor agonist radioligand [11C]-(+)-PHNO and human gene expression data. Since [11C]-(+)-PHNO has a higher affinity for D3 compared to D2 receptors, we hypothesized that there is a stronger relationship between D2/3 non-displaceable binding potentials (BPND) and D3 mRNA expression. To investigate the relationship between D2/3 BPND and mRNA expression of DRD2 and DRD3 we performed [11C]-(+)-PHNO PET scans in 27 healthy subjects (12 females) and extracted gene expression data from the AHBA. We also calculated D2/D3 mRNA expression ratios to imitate the mixed D2/3 signal of [11C]-(+)-PHNO. In accordance with our a priori hypothesis, a strong correlation between [11C]-(+)-PHNO and DRD3 expression was found. However, there was no significant correlation with DRD2 expression. Calculated D2/D3 mRNA expression ratios also showed a positive correlation with [11C]-(+)-PHNO binding, reflecting the mixed D2/3 signal of the radioligand. Our study supports the usefulness of combining gene expression data from open access brain atlases with in vivo imaging data in order to gain more detailed knowledge on neurotransmitter signaling.
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Affiliation(s)
- Arkadiusz Komorowski
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Ana Weidenauer
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Ulrich Sauerzopf
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Wolfgang Wadsak
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria; Center for Biomarker Research in Medicine (CBmed), Graz, Austria
| | - Markus Mitterhauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria; Ludwig Boltzmann Institute for Applied Diagnostics, Vienna, Austria
| | - Martin Bauer
- Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Nicole Praschak-Rieder
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Siegfried Kasper
- Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria.
| | - Matthäus Willeit
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
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42
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Gao R, van den Brink RL, Pfeffer T, Voytek B. Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture. eLife 2020; 9:e61277. [PMID: 33226336 PMCID: PMC7755395 DOI: 10.7554/elife.61277] [Citation(s) in RCA: 104] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 11/22/2020] [Indexed: 12/21/2022] Open
Abstract
Complex cognitive functions such as working memory and decision-making require information maintenance over seconds to years, from transient sensory stimuli to long-term contextual cues. While theoretical accounts predict the emergence of a corresponding hierarchy of neuronal timescales, direct electrophysiological evidence across the human cortex is lacking. Here, we infer neuronal timescales from invasive intracranial recordings. Timescales increase along the principal sensorimotor-to-association axis across the entire human cortex, and scale with single-unit timescales within macaques. Cortex-wide transcriptomic analysis shows direct alignment between timescales and expression of excitation- and inhibition-related genes, as well as genes specific to voltage-gated transmembrane ion transporters. Finally, neuronal timescales are functionally dynamic: prefrontal cortex timescales expand during working memory maintenance and predict individual performance, while cortex-wide timescales compress with aging. Thus, neuronal timescales follow cytoarchitectonic gradients across the human cortex and are relevant for cognition in both short and long terms, bridging microcircuit physiology with macroscale dynamics and behavior.
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Affiliation(s)
- Richard Gao
- Department of Cognitive Science, University of California, San DiegoLa JollaUnited States
| | - Ruud L van den Brink
- Section Computational Cognitive Neuroscience, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Thomas Pfeffer
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu FabraBarcelonaSpain
| | - Bradley Voytek
- Department of Cognitive Science, University of California, San DiegoLa JollaUnited States
- Halıcıoğlu Data Science Institute, University of California, San DiegoLa JollaUnited States
- Neurosciences Graduate Program, University of California, San DiegoLa JollaUnited States
- Kavli Institute for Brain and Mind, University of California, San DiegoLa JollaUnited States
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43
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Emrani S, Arain HA, DeMarshall C, Nuriel T. APOE4 is associated with cognitive and pathological heterogeneity in patients with Alzheimer's disease: a systematic review. ALZHEIMERS RESEARCH & THERAPY 2020; 12:141. [PMID: 33148345 PMCID: PMC7643479 DOI: 10.1186/s13195-020-00712-4] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 10/22/2020] [Indexed: 02/06/2023]
Abstract
Possession of the ε4 allele of apolipoprotein E (APOE) is the primary genetic risk factor for the sporadic form of Alzheimer’s disease (AD). While researchers have extensively characterized the impact that APOE ε4 (APOE4) has on the susceptibility of AD, far fewer studies have investigated the phenotypic differences of patients with AD who are APOE4 carriers vs. those who are non-carriers. In order to understand these differences, we performed a qualitative systematic literature review of the reported cognitive and pathological differences between APOE4-positive (APOE4+) vs. APOE4-negative (APOE4−) AD patients. The studies performed on this topic to date suggest that APOE4 is not only an important mediator of AD susceptibility, but that it likely confers specific phenotypic heterogeneity in AD presentation, as well. Specifically, APOE4+ AD patients appear to possess more tau accumulation and brain atrophy in the medial temporal lobe, resulting in greater memory impairment, compared to APOE4− AD patients. On the other hand, APOE4− AD patients appear to possess more tau accumulation and brain atrophy in the frontal and parietal lobes, resulting in greater impairment in executive function, visuospatial abilities, and language, compared to APOE4+ AD patients. Although more work is necessary to validate and interrogate these findings, these initial observations of pathological and cognitive heterogeneity between APOE4+ vs. APOE4− AD patients suggest that there is a fundamental divergence in AD manifestation related to APOE genotype, which may have important implications in regard to the therapeutic treatment of these two patient populations.
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Affiliation(s)
- Sheina Emrani
- Department of Psychology, Rowan University, 201 Mullica Hill Road, Glassboro, NJ, 08028, USA
| | - Hirra A Arain
- Department of Pathology and Cell Biology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA.,Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, 630 West 168th Street, New York, NY, 10032, USA
| | - Cassandra DeMarshall
- Department of Geriatrics and Gerontology, Rowan University School of Osteopathic Medicine, One Medical Center Drive, Stratford, NJ, 08084, USA
| | - Tal Nuriel
- Department of Pathology and Cell Biology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA. .,Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, 630 West 168th Street, New York, NY, 10032, USA.
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44
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Core and matrix thalamic sub-populations relate to spatio-temporal cortical connectivity gradients. Neuroimage 2020; 222:117224. [DOI: 10.1016/j.neuroimage.2020.117224] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 07/28/2020] [Accepted: 07/30/2020] [Indexed: 01/24/2023] Open
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Burt JB, Helmer M, Shinn M, Anticevic A, Murray JD. Generative modeling of brain maps with spatial autocorrelation. Neuroimage 2020; 220:117038. [PMID: 32585343 DOI: 10.1016/j.neuroimage.2020.117038] [Citation(s) in RCA: 186] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 06/02/2020] [Accepted: 06/05/2020] [Indexed: 01/02/2023] Open
Abstract
Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in brain maps across multiple modalities. Evaluating the significance of these findings requires establishing statistical expectations under a null hypothesis of interest. Through generative modeling of synthetic data that instantiate a specific null hypothesis, quantitative benchmarks can be derived for arbitrarily complex statistical measures. Here, we present a generative null model, provided as an open-access software platform, that generates surrogate maps with spatial autocorrelation (SA) matched to SA of a target brain map. SA is a prominent and ubiquitous property of brain maps that violates assumptions of independence in conventional statistical tests. Our method can simulate surrogate brain maps, constrained by empirical data, that preserve the SA of cortical, subcortical, parcellated, and dense brain maps. We characterize how SA impacts p-values in pairwise brain map comparisons. Furthermore, we demonstrate how SA-preserving surrogate maps can be used in gene set enrichment analyses to test hypotheses of interest related to brain map topography. Our findings demonstrate the utility of SA-preserving surrogate maps for hypothesis testing in complex statistical analyses, and underscore the need to disambiguate meaningful relationships from chance associations in studies of large-scale brain organization.
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Affiliation(s)
| | | | - Maxwell Shinn
- Yale University, Interdepartmental Neuroscience Program, USA
| | - Alan Anticevic
- Yale University, Department of Psychiatry, USA; Yale University, Interdepartmental Neuroscience Program, USA
| | - John D Murray
- Yale University, Department of Physics, USA; Yale University, Department of Psychiatry, USA; Yale University, Interdepartmental Neuroscience Program, USA.
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46
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Noe CR, Noe-Letschnig M, Handschuh P, Noe CA, Lanzenberger R. Dysfunction of the Blood-Brain Barrier-A Key Step in Neurodegeneration and Dementia. Front Aging Neurosci 2020; 12:185. [PMID: 32848697 PMCID: PMC7396716 DOI: 10.3389/fnagi.2020.00185] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 05/27/2020] [Indexed: 12/18/2022] Open
Abstract
The vascular endothelium in the brain is an essential part of the blood-brain-barrier (BBB) because of its very tight structure to secure a functional and molecular separation of the brain from the rest of the body and to protect neurons from pathogens and toxins. Impaired transport of metabolites across the BBB due to its increasing dysfunction affects brain health and cognitive functioning, thus providing a starting point of neurodegenerative diseases. The term “cerebral metabolic syndrome” is proposed to highlight the importance of lifestyle factors in neurodegeneration and to describe the impact of increasing BBB dysfunction on neurodegeneration and dementia, especially in elderly patients. If untreated, the cerebral metabolic syndrome may evolve into dementia. Due to the high energy demand of the brain, impaired glucose transport across the BBB via glucose transporters as GLUT1 renders the brain increasingly susceptible to neurodegeneration. Apoptotic processes are further supported by the lack of essential metabolites of the phosphocholine synthesis. In Alzheimer’s disease (AD), inflammatory and infectious processes at the BBB increase the dysfunction and might be pace-making events. At this point, the potentially highly relevant role of the thrombocytic amyloid precursor protein (APP) in endothelial inflammation of the BBB is discussed. Chronic inflammatory processes of the BBB transmitted to an increasing number of brain areas might cause a lasting build-up of spreading, pore-forming β-amyloid fragments explaining the dramatic progression of the disease. In the view of the essential requirement of an early diagnosis to investigate and implement causal therapeutic strategies against dementia, brain imaging methods are of great importance. Therefore, status and opportunities in the field of diagnostic imaging of the living human brain will be portrayed, comprising diverse techniques such as positron emissions tomography (PET) and functional magnetic resonance imaging (fMRI) to uncover the patterns of atrophy, protein deposits, hypometabolism, and molecular as well as functional alterations in AD.
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Affiliation(s)
- Christian R Noe
- Department of Medicinal Chemistry, University of Vienna, Vienna, Austria
| | | | - Patricia Handschuh
- Neuroimaging Lab (NIL), Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Chiara Anna Noe
- Department of Otorhinolaryngology, University Clinic St. Poelten, St. Poelten, Austria
| | - Rupert Lanzenberger
- Neuroimaging Lab (NIL), Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
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47
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Therriault J, Benedet AL, Pascoal TA, Mathotaarachchi S, Chamoun M, Savard M, Thomas E, Kang MS, Lussier F, Tissot C, Parsons M, Qureshi MNI, Vitali P, Massarweh G, Soucy JP, Rej S, Saha-Chaudhuri P, Gauthier S, Rosa-Neto P. Association of Apolipoprotein E ε4 With Medial Temporal Tau Independent of Amyloid-β. JAMA Neurol 2020; 77:470-479. [PMID: 31860000 DOI: 10.1001/jamaneurol.2019.4421] [Citation(s) in RCA: 139] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Importance Apolipoprotein E ε4 (APOEε4) is the single most important genetic risk factor for Alzheimer disease. While APOEε4 is associated with increased amyloid-β burden, its association with cerebral tau pathology has been controversial. Objective To determine whether APOEε4 is associated with medial temporal tau pathology independently of amyloid-β, sex, clinical status, and age. Design, Setting, and Participants This is a study of 2 cross-sectional cohorts of volunteers who were cognitively normal, had mild cognitive impairment (MCI), or had Alzheimer disease dementia: the Translational Biomarkers in Aging and Dementia (TRIAD) study (data collected between October 2017 and July 2019) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) (collected between November 2015 and June 2019). The first cohort (TRIAD) comprised cognitively normal elderly participants (n = 124), participants with MCI (n = 50), and participants with Alzheimer disease (n = 50) who underwent tau positron emission tomography (PET) with fluorine 18-labeled MK6240 and amyloid-β PET with [18F]AZD4694. The second sample (ADNI) was composed of cognitively normal elderly participants (n = 157), participants with MCI (n = 83), and participants with Alzheimer disease (n = 25) who underwent tau PET with [18F]flortaucipir and amyloid-β PET with [18F]florbetapir. Exclusion criteria were a history of other neurological disorders, stroke, or head trauma. There were 489 eligible participants, selected based on availability of amyloid-PET, tau-PET, magnetic resonance imaging, and genotyping for APOEε4. Forty-five young adults (<30 years) from the TRIAD cohort were not selected for this study. Main Outcomes and Measures A main association between APOEε4 and tau-PET standardized uptake value ratio, correcting for age, sex, clinical status, and neocortical amyloid-PET standardized uptake value ratio. Results The mean (SD) age of the 489 participants was 70.5 (7.1) years; 171 were APOEε4 carriers (34.9%), and 230 of 489 were men. In both cohorts, APOEε4 was associated in increased tau-PET uptake in the entorhinal cortex and hippocampus independently of amyloid-β, sex, age, and clinical status after multiple comparisons correction (TRIAD: β = 0.33; 95% CI, 0.19-0.49; ADNI: β = 0.13; 95% CI, 0.08-0.19; P < .001). Conclusions and Relevance Our results indicate that the elevated risk of developing dementia conferred by APOEε4 genotype involves mechanisms associated with both amyloid-β and tau aggregation. These results contribute to an evolving framework in which APOEε4 has deleterious consequences in Alzheimer disease beyond its link with amyloid-β and suggest APOEε4 as a potential target for future disease-modifying therapeutic trials targeting tau pathology.
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Affiliation(s)
- Joseph Therriault
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada.,Montreal Neurological Institute, Montreal, Québec, Canada
| | - Andrea L Benedet
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada.,Montreal Neurological Institute, Montreal, Québec, Canada
| | - Tharick A Pascoal
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada.,Montreal Neurological Institute, Montreal, Québec, Canada
| | - Sulantha Mathotaarachchi
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada
| | - Mira Chamoun
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada
| | - Melissa Savard
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada
| | - Emilie Thomas
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada
| | - Min Su Kang
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada.,Montreal Neurological Institute, Montreal, Québec, Canada
| | - Firoza Lussier
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada.,Montreal Neurological Institute, Montreal, Québec, Canada
| | - Cecile Tissot
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada.,Montreal Neurological Institute, Montreal, Québec, Canada
| | - Marlee Parsons
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada.,Montreal Neurological Institute, Montreal, Québec, Canada
| | - Muhammad Naveed Iqbal Qureshi
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada
| | - Paolo Vitali
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada
| | - Gassan Massarweh
- Montreal Neurological Institute, Montreal, Québec, Canada.,Department of Radiochemistry, McGill University, Montreal, Québec, Canada
| | - Jean-Paul Soucy
- Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada.,Montreal Neurological Institute, Montreal, Québec, Canada
| | - Soham Rej
- Department of Psychiatry, McGill University, Montreal, Québec, Canada
| | | | - Serge Gauthier
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada.,Department of Psychiatry, McGill University, Montreal, Québec, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Québec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada.,Montreal Neurological Institute, Montreal, Québec, Canada.,Department of Psychiatry, McGill University, Montreal, Québec, Canada
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48
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Martins DA, Mazibuko N, Zelaya F, Vasilakopoulou S, Loveridge J, Oates A, Maltezos S, Mehta M, Wastling S, Howard M, McAlonan G, Murphy D, Williams SCR, Fotopoulou A, Schuschnig U, Paloyelis Y. Effects of route of administration on oxytocin-induced changes in regional cerebral blood flow in humans. Nat Commun 2020; 11:1160. [PMID: 32127545 PMCID: PMC7054359 DOI: 10.1038/s41467-020-14845-5] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 02/04/2020] [Indexed: 11/17/2022] Open
Abstract
Could nose-to-brain pathways mediate the effects of peptides such as oxytocin (OT) on brain physiology when delivered intranasally? We address this question by contrasting two methods of intranasal administration (a standard nasal spray, and a nebulizer expected to improve OT deposition in nasal areas putatively involved in direct nose-to-brain transport) to intravenous administration in terms of effects on regional cerebral blood flow during two hours post-dosing. We demonstrate that OT-induced decreases in amygdala perfusion, a key hub of the OT central circuitry, are explained entirely by OT increases in systemic circulation following both intranasal and intravenous OT administration. Yet we also provide robust evidence confirming the validity of the intranasal route to target specific brain regions. Our work has important translational implications and demonstrates the need to carefully consider the method of administration in our efforts to engage specific central oxytocinergic targets for the treatment of neuropsychiatric disorders.
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Affiliation(s)
- D A Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - N Mazibuko
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - F Zelaya
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - S Vasilakopoulou
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - J Loveridge
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - A Oates
- South London and Maudsley NHS Foundation Trust, London, UK
| | - S Maltezos
- Adult Autism and ADHD Service, South London and Maudsley NHS Foundation Trust, London, UK
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - M Mehta
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - S Wastling
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK
| | - M Howard
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - G McAlonan
- Department of Forensic and Neurodevelopmental Science (SM), Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - D Murphy
- Department of Forensic and Neurodevelopmental Science (SM), Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - S C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - A Fotopoulou
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | | | - Y Paloyelis
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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49
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Unterholzner J, Gryglewski G, Philippe C, Seiger R, Pichler V, Godbersen GM, Berroterán-Infante N, Murgaš M, Hahn A, Wadsak W, Mitterhauser M, Kasper S, Lanzenberger R. Topologically Guided Prioritization of Candidate Gene Transcripts Coexpressed with the 5-HT1A Receptor by Combining In Vivo PET and Allen Human Brain Atlas Data. Cereb Cortex 2020; 30:3771-3780. [PMID: 31989157 PMCID: PMC7232988 DOI: 10.1093/cercor/bhz341] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 11/25/2019] [Indexed: 12/20/2022] Open
Abstract
The serotonin-1A receptor (5-HT1AR) represents a viable target in the treatment of disorders of the brain. However, development of psychiatric drugs continues to be hindered by the relative inaccessibility of brain tissue. Although the efficacy of drugs selective for the 5-HT1AR has not been proven, research continues to focus on drugs that influence this receptor subtype. To further knowledge on this topic, we investigated the topological coexpression patterns of the 5-HT1AR. We calculated Spearman’s rho for the correlation of positron emission tomography-binding potentials (BPND) of the 5-HT1AR assessed in 30 healthy subjects using the tracer [carbonyl-11C]WAY-100635 and predicted whole-brain mRNA expression of 18 686 genes. After applying a threshold of r > 0.3 in a leave-one-out cross-validation of the prediction of mRNA expression, genes with ρ ≥ 0.7 were considered to be relevant. In cortical regions, 199 genes showed high correlation with the BPND of the 5-HT1AR, in subcortical regions 194 genes. Using our approach, we could consolidate the role of BDNF and implicate new genes (AnxA8, NeuroD2) in serotonergic functioning. Despite its explorative nature, the analysis can be seen as a gene prioritization approach to reduce the number of genes potentially connected to 5-HT1AR functioning and guide future in vitro studies.
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Affiliation(s)
- Jakob Unterholzner
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Cecile Philippe
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Rene Seiger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Verena Pichler
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Godber M Godbersen
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Neydher Berroterán-Infante
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Wolfgang Wadsak
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.,Centre for Biomarker Research in Medicine (CBmed), Stiftingtalstrasse 5, 8010, Graz, Austria
| | - Markus Mitterhauser
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.,Ludwig Boltzmann Institute Applied Diagnostics, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
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50
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van den Brink RL, Pfeffer T, Donner TH. Brainstem Modulation of Large-Scale Intrinsic Cortical Activity Correlations. Front Hum Neurosci 2019; 13:340. [PMID: 31649516 PMCID: PMC6794422 DOI: 10.3389/fnhum.2019.00340] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 09/17/2019] [Indexed: 12/22/2022] Open
Abstract
Brain activity fluctuates continuously, even in the absence of changes in sensory input or motor output. These intrinsic activity fluctuations are correlated across brain regions and are spatially organized in macroscale networks. Variations in the strength, topography, and topology of correlated activity occur over time, and unfold upon a backbone of long-range anatomical connections. Subcortical neuromodulatory systems send widespread ascending projections to the cortex, and are thus ideally situated to shape the temporal and spatial structure of intrinsic correlations. These systems are also the targets of the pharmacological treatment of major neurological and psychiatric disorders, such as Parkinson's disease, depression, and schizophrenia. Here, we review recent work that has investigated how neuromodulatory systems shape correlations of intrinsic fluctuations of large-scale cortical activity. We discuss studies in the human, monkey, and rodent brain, with a focus on non-invasive recordings of human brain activity. We provide a structured but selective overview of this work and distil a number of emerging principles. Future efforts to chart the effect of specific neuromodulators and, in particular, specific receptors, on intrinsic correlations may help identify shared or antagonistic principles between different neuromodulatory systems. Such principles can inform models of healthy brain function and may provide an important reference for understanding altered cortical dynamics that are evident in neurological and psychiatric disorders, potentially paving the way for mechanistically inspired biomarkers and individualized treatments of these disorders.
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Affiliation(s)
- R. L. van den Brink
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - T. Pfeffer
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - T. H. Donner
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Center for Brain and Cognition, Institute for Interdisciplinary Studies, Amsterdam, Netherlands
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