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Anand C, Torok J, Abdelnour F, Maia PD, Raj A. Selective vulnerability and resilience to Alzheimer's disease tauopathy as a function of genes and the connectome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583403. [PMID: 38496606 PMCID: PMC10942335 DOI: 10.1101/2024.03.04.583403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Brain regions in Alzheimer's (AD) exhibit distinct vulnerability to the disease's hallmark pathology, with the entorhinal cortex and hippocampus succumbing early to tau tangles while others like primary sensory cortices remain resilient. The quest to understand how local/regional genetic factors, pathogenesis, and network-mediated spread of pathology together govern this selective vulnerability (SV) or resilience (SR) is ongoing. Although many risk genes in AD are known from gene association and transgenic studies, it is still not known whether and how their baseline expression signatures confer SV or SR to brain structures. Prior analyses have yielded conflicting results, pointing to a disconnect between the location of genetic risk factors and downstream tau pathology. We hypothesize that a full accounting of genes' role in mediating SV/SR would require the modeling of network-based vulnerability, whereby tau misfolds, aggregates, and propagates along fiber projections. We therefore employed an extended network diffusion model (eNDM) and tested it on tau pathology PET data from 196 AD patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Thus the fitted eNDM model becomes a reference process from which to assess the role of innate genetic factors. Using the residual (observed - model-predicted) tau as a novel target outcome, we obtained its association with 100 top AD risk-genes, whose baseline spatial transcriptional profiles were obtained from the Allen Human Brain Atlas (AHBA). We found that while many risk genes at baseline showed a strong association with regional tau, many more showed a stronger association with residual tau. This suggests that both direct vulnerability, related to the network, as well as network-independent vulnerability, are conferred by risk genes. We then classified risk genes into four classes: network-related SV (SV-NR), network-independent SV (SV-NI), network-related SR (SR-NR), and network-independent SR (SR-NI). Each class has a distinct spatial signature and associated vulnerability to tau. Remarkably, we found from gene-ontology analyses, that genes in these classes were enriched in distinct functional processes and encompassed different functional networks. These findings offer new insights into the factors governing innate vulnerability or resilience in AD pathophysiology and may prove helpful in identifying potential intervention targets.
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2
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Xu E, Zhang J, Li J, Song Q, Yang D, Wu G, Chen M. Pathology steered stratification network for subtype identification in Alzheimer's disease. Med Phys 2024; 51:1190-1202. [PMID: 37522278 PMCID: PMC10828102 DOI: 10.1002/mp.16655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 06/25/2023] [Accepted: 07/19/2023] [Indexed: 08/01/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by three neurobiological factors beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for AD at a late stage, urging for early detection and prevention. However, existing statistical inference approaches in neuroimaging studies of AD subtype identification do not take into account the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles. PURPOSE Integrating systems biology modeling with machine learning, the study aims to assist clinical AD prognosis by providing a subpopulation classification in accordance with essential biological principles, neurological patterns, and cognitive symptoms. METHODS We propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along the brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term evolution trajectories that capture individual characteristic progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. RESULTS Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. CONCLUSIONS The proposed PSSN (i) reduces neuroimage data to low-dimensional feature vectors, (ii) combines AT[N]-Net based on real pathological pathways, (iii) predicts long-term biomarker trajectories, and (iv) stratifies subjects into fine-grained subtypes with distinct neurological underpinnings. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases.
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Affiliation(s)
- Enze Xu
- Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109, U.S
| | - Jingwen Zhang
- Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109, U.S
| | - Jiadi Li
- Department of Psychology, Wake Forest University, Winston-Salem, NC 27109, U.S
| | - Qianqian Song
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, U.S
| | - Defu Yang
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, U.S
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, U.S
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, U.S
| | - Minghan Chen
- Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109, U.S
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Torok J, Maia PD, Anand C, Raj A. Cellular underpinnings of the selective vulnerability to tauopathic insults in Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.06.548027. [PMID: 38076913 PMCID: PMC10705232 DOI: 10.1101/2023.07.06.548027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2023]
Abstract
Neurodegenerative diseases such as Alzheimer's disease (AD) exhibit pathological changes in the brain that proceed in a stereotyped and regionally specific fashion, but the cellular and molecular underpinnings of regional vulnerability are currently poorly understood. Recent work has identified certain subpopulations of neurons in a few focal regions of interest, such as the entorhinal cortex, that are selectively vulnerable to tau pathology in AD. However, the cellular underpinnings of regional susceptibility to tau pathology are currently unknown, primarily because whole-brain maps of a comprehensive collection of cell types have been inaccessible. Here, we deployed a recent cell-type mapping pipeline, Matrix Inversion and Subset Selection (MISS), to determine the brain-wide distributions of pan-hippocampal and neocortical neuronal and non-neuronal cells in the mouse using recently available single-cell RNA sequencing (scRNAseq) data. We then performed a robust set of analyses to identify general principles of cell-type-based selective vulnerability using these cell-type distributions, utilizing 5 transgenic mouse studies that quantified regional tau in 12 distinct PS19 mouse models. Using our approach, which constitutes the broadest exploration of whole-brain selective vulnerability to date, we were able to discover cell types and cell-type classes that conferred vulnerability and resilience to tau pathology. Hippocampal glutamatergic neurons as a whole were strongly positively associated with regional tau deposition, suggesting vulnerability, while cortical glutamatergic and GABAergic neurons were negatively associated. Among glia, we identified oligodendrocytes as the single-most strongly negatively associated cell type, whereas microglia were consistently positively correlated. Strikingly, we found that there was no association between the gene expression relationships between cell types and their vulnerability or resilience to tau pathology. When we looked at the explanatory power of cell types versus GWAS-identified AD risk genes, cell type distributions were consistently more predictive of end-timepoint tau pathology than regional gene expression. To understand the functional enrichment patterns of the genes that were markers of the identified vulnerable or resilient cell types, we performed gene ontology analysis. We found that the genes that are directly correlated to tau pathology are functionally distinct from those that constitutively embody the vulnerable cells. In short, we have demonstrated that regional cell-type composition is a compelling explanation for the selective vulnerability observed in tauopathic diseases at a whole-brain level and is distinct from that conferred by risk genes. These findings may have implications in identifying cell-type-based therapeutic targets.
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Affiliation(s)
- Justin Torok
- University of California, San Francisco, Department of Radiology, San Francisco, CA, 94143, United States
| | - Pedro D. Maia
- University of Texas at Arlington, Department of Mathematics, Arlington, TX, 76019, United States
| | - Chaitali Anand
- University of California, San Francisco, Institute for Neurodegenerative Diseases, San Francisco, CA, 94143, United States
| | - Ashish Raj
- University of California, San Francisco, Department of Radiology, San Francisco, CA, 94143, United States
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4
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Bitra VR, Challa SR, Adiukwu PC, Rapaka D. Tau trajectory in Alzheimer's disease: Evidence from the connectome-based computational models. Brain Res Bull 2023; 203:110777. [PMID: 37813312 DOI: 10.1016/j.brainresbull.2023.110777] [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: 05/23/2023] [Revised: 07/08/2023] [Accepted: 10/06/2023] [Indexed: 10/11/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an impairment of cognition and memory. Current research on connectomics have now related changes in the network organization in AD to the patterns of accumulation and spread of amyloid and tau, providing insights into the neurobiological mechanisms of the disease. In addition, network analysis and modeling focus on particular use of graphs to provide intuition into key organizational principles of brain structure, that stipulate how neural activity propagates along structural connections. The utility of connectome-based computational models aids in early predicting, tracking the progression of biomarker-directed AD neuropathology. In this article, we present a short review of tau trajectory, the connectome changes in tau pathology, and the dependent recent connectome-based computational modelling approaches for tau spreading, reproducing pragmatic findings, and developing significant novel tau targeted therapies.
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Affiliation(s)
- Veera Raghavulu Bitra
- School of Pharmacy, Faculty of Health Sciences, University of Botswana, P/Bag-0022, Gaborone, Botswana.
| | - Siva Reddy Challa
- Department of Cancer Biology and Pharmacology, University of Illinois College of Medicine, Peoria, IL 61614, USA; KVSR Siddartha College of Pharmaceutical Sciences, Vijayawada, Andhra Pradesh, India
| | - Paul C Adiukwu
- School of Pharmacy, Faculty of Health Sciences, University of Botswana, P/Bag-0022, Gaborone, Botswana
| | - Deepthi Rapaka
- Pharmacology Division, D.D.T. College of Medicine, Gaborone, Botswana.
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5
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Bertsch M, Franchi B, Tesi MC, Tora V. The role of A[Formula: see text] and Tau proteins in Alzheimer's disease: a mathematical model on graphs. J Math Biol 2023; 87:49. [PMID: 37646953 PMCID: PMC10468937 DOI: 10.1007/s00285-023-01985-7] [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: 02/17/2023] [Revised: 06/25/2023] [Accepted: 08/13/2023] [Indexed: 09/01/2023]
Abstract
In this Note we study a mathematical model for the progression of Alzheimer's Disease in the human brain. The novelty of our approach consists in the representation of the brain as two superposed graphs where toxic proteins diffuse, the connectivity graph which represents the neural network, and the proximity graph which takes into account the extracellular space. Toxic proteins such as [Formula: see text] amyloid and Tau play in fact a crucial role in the development of Alzheimer's disease and, separately, have been targets of medical treatments. Recent biomedical literature stresses the potential impact of the synergetic action of these proteins. We numerically test various modelling hypotheses which confirm the relevance of this synergy.
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Affiliation(s)
- Michiel Bertsch
- Department of Mathematics, University of Roma “Tor Vergata”, Rome, Italy
- Istituto per le Applicazioni del Calcolo “M. Picone”, Consiglio Nazionale delle Ricerche, Rome, Italy
| | - Bruno Franchi
- Department of Mathematics, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Maria Carla Tesi
- Department of Mathematics, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Veronica Tora
- Department of Mathematics, University of Roma “Tor Vergata”, Rome, Italy
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6
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Torok J, Anand C, Verma P, Raj A. Connectome-based biophysics models of Alzheimer's disease diagnosis and prognosis. Transl Res 2023; 254:13-23. [PMID: 36031051 PMCID: PMC11019890 DOI: 10.1016/j.trsl.2022.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/08/2022] [Indexed: 11/22/2022]
Abstract
With the increasing prevalence of Alzheimer's disease (AD) among aging populations and the limited therapeutic options available to slow or reverse its progression, the need has never been greater for improved diagnostic tools for identifying patients in the preclinical and prodomal phases of AD. Biophysics models of the connectome-based spread of amyloid-beta (Aβ) and microtubule-associated protein tau (τ) have enjoyed recent success as tools for predicting the time course of AD-related pathological changes. However, given the complex etiology of AD, which involves not only connectome-based spread of protein pathology but also the interactions of many molecular and cellular players over multiple spatiotemporal scales, more robust, complete biophysics models are needed to better understand AD pathophysiology and ultimately provide accurate patient-specific diagnoses and prognoses. Here we discuss several areas of active research in AD whose insights can be used to enhance the mathematical modeling of AD pathology as well as recent attempts at developing improved connectome-based biophysics models. These efforts toward a comprehensive yet parsimonious mathematical description of AD hold great promise for improving both the diagnosis of patients at risk for AD and our mechanistic understanding of how AD progresses.
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Affiliation(s)
- Justin Torok
- Department of Radiology, University of California, San Francisco, San Francisco, California.
| | - Chaitali Anand
- Department of Radiology, University of California, San Francisco, San Francisco, California
| | - Parul Verma
- Department of Radiology, University of California, San Francisco, San Francisco, California
| | - Ashish Raj
- Department of Radiology, University of California, San Francisco, San Francisco, California; Department of Bioengineering, University of California, Berkeley and University of California, San Francisco, Berkeley, California; Department of Radiology, Weill Cornell Medicine, New York, New York.
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7
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Montal V, Diez I, Kim CM, Orwig W, Bueichekú E, Gutiérrez-Zúñiga R, Bejanin A, Pegueroles J, Dols-Icardo O, Vannini P, El-Fakhri G, Johnson KA, Sperling RA, Fortea J, Sepulcre J. Network Tau spreading is vulnerable to the expression gradients of APOE and glutamatergic-related genes. Sci Transl Med 2022; 14:eabn7273. [PMID: 35895837 PMCID: PMC9942690 DOI: 10.1126/scitranslmed.abn7273] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
A key hallmark of Alzheimer's disease (AD) pathology is the intracellular accumulation of tau protein in the form of neurofibrillary tangles across large-scale networks of the human brain cortex. Currently, it is still unclear how tau accumulates within specific cortical systems and whether in situ genetic traits play a role in this circuit-based propagation progression. In this study, using two independent cohorts of cognitively normal older participants, we reveal the brain network foundation of tau spreading and its association with using high-resolution transcriptomic genetic data. We observed that specific connectomic and genetic gradients exist along the tau spreading network. In particular, we identified 577 genes whose expression is associated with the spatial spreading of tau. Within this set of genes, APOE and glutamatergic synaptic genes, such as SLC1A2, play a central role. Thus, our study characterizes neurogenetic topological vulnerabilities in distinctive brain circuits of tau spreading and suggests that drug development strategies targeting the gradient expression of this set of genes should be explored to help reduce or prevent pathological tau accumulation.
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Affiliation(s)
- Victor Montal
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA.,Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autonoma de Barcelona; Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); Madrid, Spain
| | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School; Charlestown, Massachusetts, USA
| | - Chan-Mi Kim
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA
| | - William Orwig
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA
| | - Elisenda Bueichekú
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA
| | - Raquel Gutiérrez-Zúñiga
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA
| | - Alexandre Bejanin
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autonoma de Barcelona; Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); Madrid, Spain
| | - Jordi Pegueroles
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autonoma de Barcelona; Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); Madrid, Spain
| | - Oriol Dols-Icardo
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autonoma de Barcelona; Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); Madrid, Spain
| | - Patrizia Vannini
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School; Charlestown, Massachusetts, USA.,Center for Alzheimer research and treatment, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School; Boston, MA
| | - Georges El-Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA
| | - Keith A. Johnson
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA
| | - Reisa A. Sperling
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School; Charlestown, Massachusetts, USA
| | - Juan Fortea
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autonoma de Barcelona; Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); Madrid, Spain
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School; Charlestown, Massachusetts, USA.,Correspondence should be addressed to Jorge Sepulcre, 149 13th St, Office 5.209, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; ; +1 617 726 2899
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8
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Li W, Zhao J, Shen C, Zhang J, Hu J, Xiao M, Zhang J, Chen M. Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis. Front Neuroinform 2022; 16:886365. [PMID: 35571869 PMCID: PMC9100702 DOI: 10.3389/fninf.2022.886365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/30/2022] [Indexed: 11/24/2022] Open
Abstract
Alzheimer's disease (AD) has raised extensive concern in healthcare and academia as one of the most prevalent health threats to the elderly. Due to the irreversible nature of AD, early and accurate diagnoses are significant for effective prevention and treatment. However, diverse clinical symptoms and limited neuroimaging accuracy make diagnoses challenging. In this article, we built a brain network for each subject, which assembles several commonly used neuroimaging data simply and reasonably, including structural magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and amyloid positron emission tomography (PET). Based on some existing research results, we applied statistical methods to analyze (i) the distinct affinity of AD burden on each brain region, (ii) the topological lateralization between left and right hemispheric sub-networks, and (iii) the asymmetry of the AD attacks on the left and right hemispheres. In the light of advances in graph convolutional networks for graph classifications and summarized characteristics of brain networks and AD pathologies, we proposed a regional brain fusion-graph convolutional network (RBF-GCN), which is constructed with an RBF framework mainly, including three sub-modules, namely, hemispheric network generation module, multichannel GCN module, and feature fusion module. In the multichannel GCN module, the improved GCN by our proposed adaptive native node attribute (ANNA) unit embeds within each channel independently. We not only fully verified the effectiveness of the RBF framework and ANNA unit but also achieved competitive results in multiple sets of AD stages' classification tasks using hundreds of experiments over the ADNI clinical dataset.
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Affiliation(s)
- Wenchao Li
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Jiaqi Zhao
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Chenyu Shen
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Jingwen Zhang
- Department of Computer Science, Wake Forest University, Winston-Salem, NC, United States
| | - Ji Hu
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Mang Xiao
- Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jiyong Zhang
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
- *Correspondence: Jiyong Zhang
| | - Minghan Chen
- Department of Computer Science, Wake Forest University, Winston-Salem, NC, United States
- Minghan Chen
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9
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Shafiei G, Bazinet V, Dadar M, Manera AL, Collins DL, Dagher A, Borroni B, Sanchez-Valle R, Moreno F, Laforce R, Graff C, Synofzik M, Galimberti D, Rowe JB, Masellis M, Tartaglia MC, Finger E, Vandenberghe R, de Mendonça A, Tagliavini F, Santana I, Butler C, Gerhard A, Danek A, Levin J, Otto M, Sorbi S, Jiskoot LC, Seelaar H, van Swieten JC, Rohrer JD, Misic B, Ducharme S. Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia. Brain 2022; 146:321-336. [PMID: 35188955 PMCID: PMC9825569 DOI: 10.1093/brain/awac069] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/14/2021] [Accepted: 01/30/2022] [Indexed: 01/13/2023] Open
Abstract
Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome.
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Affiliation(s)
| | | | - Mahsa Dadar
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada,Radiology and Nuclear Medicine, Laval University, Quebec City, QC, Canada
| | - Ana L Manera
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Barbara Borroni
- Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Raquel Sanchez-Valle
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d’Investigacións Biomèdiques August Pi I Sunyer, University of Barcelona, Barcelona, Spain
| | - Fermin Moreno
- Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain,Neuroscience Area, Biodonostia Health Research Institute, San Sebastian, Gipuzkoa, Spain
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Faculté de Médecine, Université Laval, Quebec, QC, Canada
| | - Caroline Graff
- Department of Geriatric Medicine, Karolinska University Hospital-Huddinge, Stockholm, Sweden,Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden
| | - Matthis Synofzik
- Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany,Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Daniela Galimberti
- Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy,Department of Biomedical, Surgical and Dental Sciences, University of Milan, Dino Ferrari Center, Milan, Italy
| | - James B Rowe
- University of Cambridge, Department of Clinical Neurosciences, Cambridge University Hospitals NHS Trust, and MRC Cognition and Brain Sciences Unit, Cambridge, UK
| | - Mario Masellis
- Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Maria Carmela Tartaglia
- Toronto Western Hospital, Tanz Centre for Research in Neurodegenerative Disease, Toronto, ON, Canada
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium,Neurology Service, University Hospitals Leuven, Leuven, Belgium,Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | | | - Fabrizio Tagliavini
- Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milan, Italy
| | - Isabel Santana
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal,Center for Neuroscience and Cell Biology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Chris Butler
- Department of Clinical Neurology, University of Oxford, Oxford, UK,Department of Brain Sciences, Imperial College London, London, UK
| | - Alex Gerhard
- Division of Neuroscience and Experimental Psychology, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK,Department of Geriatric Medicine and Nuclear Medicine, University of Duisburg-Essen, Duisburg and Essen, Germany
| | - Adrian Danek
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany,Clinical Research Unit, German Center for Neurodegenerative Diseases (DZNE), Munich, Germany,Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Markus Otto
- Department of Neurology, University Hospital Ulm, Ulm, Germany
| | - Sandro Sorbi
- Department of Neurofarba, University of Florence, Florence, Italy,IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Lize C Jiskoot
- Department of Neurology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Harro Seelaar
- Department of Neurology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Jonathan D Rohrer
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, UK
| | - Bratislav Misic
- Correspondence to: Bratislav Misic 3801 Rue University Webster 211, Montreal QC H3A 2B4, Canada E-mail:
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Bertsch M, Franchi B, Raj A, Tesi MC. Macroscopic modelling of Alzheimer’s disease: difficulties and challenges. BRAIN MULTIPHYSICS 2021. [DOI: 10.1016/j.brain.2021.100040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
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