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Hossain I, Blennow K, Posti JP, Zetterberg H. Tau as a fluid biomarker of concussion and neurodegeneration. CONCUSSION (LONDON, ENGLAND) 2022; 7:CNC98. [PMID: 36687115 PMCID: PMC9841393 DOI: 10.2217/cnc-2022-0004] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 11/16/2022] [Indexed: 12/12/2022]
Abstract
Concussion is predominant among the vast number of traumatic brain injuries that occur worldwide. Difficulties in timely identification, whether concussion led to neuronal injury or not, diagnosis and the lack of prognostic tools for adequate management could lead this type of brain injury to progressive neurodegenerative diseases. Tau has been extensively studied in recent years, particularly in repetitive mild traumatic brain injuries and sports-related concussions. Tauopathies, the group of neurodegenerative diseases, have also been studied with advanced functional imaging. Nevertheless, neurodegenerative diseases, such as chronic traumatic encephalopathy, are still conclusively diagnosed at autopsy. Here, we discuss the diagnostic dilemma and the relationship between concussion and neurodegenerative diseases and review the literature on tau as a promising biomarker for concussion.
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Affiliation(s)
- Iftakher Hossain
- Department of Neurosurgery, Neurocenter, Turku University Hospital, Finland,Turku Brain Injury Center, Turku University Hospital, Finland,Department of Clinical Neurosciences, University of Turku, Finland,Department of Clinical Neurosciences, Neurosurgery Unit, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK,Author for correspondence: Tel.: +358 2 313 0282;
| | - Kaj Blennow
- Institute of Neuroscience & Physiology, Department of Psychiatry & Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Jussi P Posti
- Department of Neurosurgery, Neurocenter, Turku University Hospital, Finland,Turku Brain Injury Center, Turku University Hospital, Finland,Department of Clinical Neurosciences, University of Turku, Finland
| | - Henrik Zetterberg
- Institute of Neuroscience & Physiology, Department of Psychiatry & Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden,Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK,UK Dementia Research Institute at UCL, University College London, London, UK,Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
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52
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Reaction-diffusion models in weighted and directed connectomes. PLoS Comput Biol 2022; 18:e1010507. [DOI: 10.1371/journal.pcbi.1010507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 11/23/2022] [Accepted: 08/22/2022] [Indexed: 11/07/2022] Open
Abstract
Connectomes represent comprehensive descriptions of neural connections in a nervous system to better understand and model central brain function and peripheral processing of afferent and efferent neural signals. Connectomes can be considered as a distinctive and necessary structural component alongside glial, vascular, neurochemical, and metabolic networks of the nervous systems of higher organisms that are required for the control of body functions and interaction with the environment. They are carriers of functional epiphenomena such as planning behavior and cognition, which are based on the processing of highly dynamic neural signaling patterns. In this study, we examine more detailed connectomes with edge weighting and orientation properties, in which reciprocal neuronal connections are also considered. Diffusion processes are a further necessary condition for generating dynamic bioelectric patterns in connectomes. Based on our high-precision connectome data, we investigate different diffusion-reaction models to study the propagation of dynamic concentration patterns in control and lesioned connectomes. Therefore, differential equations for modeling diffusion were combined with well-known reaction terms to allow the use of connection weights, connectivity orientation and spatial distances.
Three reaction-diffusion systems Gray-Scott, Gierer-Meinhardt and Mimura-Murray were investigated. For this purpose, implicit solvers were implemented in a numerically stable reaction-diffusion system within the framework of neuroVIISAS. The implemented reaction-diffusion systems were applied to a subconnectome which shapes the mechanosensitive pathway that is strongly affected in the multiple sclerosis demyelination disease. It was found that demyelination modeling by connectivity weight modulation changes the oscillations of the target region, i.e. the primary somatosensory cortex, of the mechanosensitive pathway.
In conclusion, a new application of reaction-diffusion systems to weighted and directed connectomes has been realized. Because the implementation were performed in the neuroVIISAS framework many possibilities for the study of dynamic reaction-diffusion processes in empirical connectomes as well as specific randomized network models are available now.
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Sokolowski HM, Levine B. Common neural substrates of diverse neurodevelopmental disorders. Brain 2022; 146:438-447. [PMID: 36299249 PMCID: PMC9924912 DOI: 10.1093/brain/awac387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 09/02/2022] [Accepted: 09/19/2022] [Indexed: 11/14/2022] Open
Abstract
Neurodevelopmental disorders are categorized and studied according to their manifestations as distinct syndromes. For instance, congenital prosopagnosia and dyslexia have largely non-overlapping research literatures and clinical pathways for diagnosis and intervention. On the other hand, the high incidence of neurodevelopmental comorbidities or co-existing extreme strengths and weaknesses suggest that transdiagnostic commonalities may be greater than currently appreciated. The core-periphery model holds that brain regions within the stable core perceptual and motor regions are more densely connected to one another compared to regions in the flexible periphery comprising multimodal association regions. This model provides a framework for the interpretation of neural data in normal development and clinical disorders. Considering network-level commonalities reported in studies of neurodevelopmental disorders, variability in multimodal association cortex connectivity may reflect a shared origin of seemingly distinct neurodevelopmental disorders. This framework helps to explain both comorbidities in neurodevelopmental disorders and profiles of strengths and weaknesses attributable to competitive processing between cognitive systems within an individual.
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Affiliation(s)
- H Moriah Sokolowski
- Correspondence may also be addressed to: H. Moriah Sokolowski E-mail: Twitter: https://twitter.com/hm_sokolowski
| | - Brian Levine
- Correspondence to: Brian Levine 3560 Bathurst St, North York, ON M6A 2E1, Canada E-mail: Website: www.LevineLab.ca Twitter: https://twitter.com/briantlevine
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54
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The impact of aging on human brain network target controllability. Brain Struct Funct 2022; 227:3001-3015. [DOI: 10.1007/s00429-022-02584-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 10/09/2022] [Indexed: 11/27/2022]
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Rubinski A, Franzmeier N, Dewenter A, Luan Y, Smith R, Strandberg O, Ossenkoppele R, Dichgans M, Hansson O, Ewers M. Higher levels of myelin are associated with higher resistance against tau pathology in Alzheimer’s disease. Alzheimers Res Ther 2022; 14:139. [PMID: 36153607 PMCID: PMC9508747 DOI: 10.1186/s13195-022-01074-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/28/2022] [Indexed: 11/10/2022]
Abstract
Background In Alzheimer’s disease (AD), fibrillar tau initially occurs locally and progresses preferentially between closely connected regions. However, the underlying sources of regional vulnerability to tau pathology remain unclear. Previous brain-autopsy findings suggest that the myelin levels—which differ substantially between white matter tracts in the brain—are a key modulating factor of region-specific susceptibility to tau deposition. Here, we investigated whether myelination differences between fiber tracts of the human connectome are predictive of the interregional spreading of tau pathology in AD. Methods We included two independently recruited samples consisting of amyloid-PET-positive asymptomatic and symptomatic elderly individuals, in whom tau-PET was obtained at baseline (ADNI: n = 275; BioFINDER-1: n = 102) and longitudinally in a subset (ADNI: n = 123, mean FU = 1.53 [0.69–3.95] years; BioFINDER-1: n = 39, mean FU = 1.87 [1.21–2.78] years). We constructed MRI templates of the myelin water fraction (MWF) in 200 gray matter ROIs and connecting fiber tracts obtained from adult cognitively normal participants. Using the same 200 ROI brain-parcellation atlas, we obtained tau-PET ROI values from each individual in ADNI and BioFINDER-1. In a spatial regression analysis, we first tested the association between cortical myelin and group-average tau-PET signal in the amyloid-positive and control groups. Secondly, employing a previously established approach of modeling tau-PET spreading based on functional connectivity between ROIs, we estimated in a linear regression analysis, whether the level of fiber-tract myelin modulates the association between functional connectivity and longitudinal tau-PET spreading (i.e., covariance) between ROIs. Results We found that higher myelinated cortical regions show lower tau-PET uptake (ADNI: rho = − 0.267, p < 0.001; BioFINDER-1: rho = − 0.175, p = 0.013). Fiber-tract myelin levels modulated the association between functional connectivity and tau-PET spreading, such that at higher levels of fiber-tract myelin, the association between stronger connectivity and higher covariance of tau-PET between the connected ROIs was attenuated (interaction fiber-tract myelin × functional connectivity: ADNI: β = − 0.185, p < 0.001; BioFINDER-1: β = − 0.166, p < 0.001). Conclusion Higher levels of myelin are associated with lower susceptibility of the connected regions to accumulate fibrillar tau. These results enhance our understanding of brain substrates that explain regional variation in tau accumulation and encourage future studies to investigate potential underlying mechanisms. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01074-9.
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56
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Paoletti M, Caverzasi E, Mandelli ML, Brown JA, Henry RG, Miller BL, Rosen HJ, DeArmond SJ, Bastianello S, Seeley WW, Geschwind MD. Default Mode Network quantitative diffusion and resting-state functional magnetic resonance imaging correlates in sporadic Creutzfeldt-Jakob disease. Hum Brain Mapp 2022; 43:4158-4173. [PMID: 35662331 PMCID: PMC9374887 DOI: 10.1002/hbm.25945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 04/14/2022] [Accepted: 05/01/2022] [Indexed: 11/25/2022] Open
Abstract
Grey matter involvement is a well-known feature in sporadic Creutzfeldt-Jakob disease (sCJD), yet precise anatomy-based quantification of reduced diffusivity is still not fully understood. Default Mode Network (DMN) areas have been recently demonstrated as selectively involved in sCJD, and functional connectivity has never been investigated in prion diseases. We analyzed the grey matter involvement using a quantitatively multi-parametric MRI approach. Specifically, grey matter mean diffusivity of 37 subjects with sCJD was compared with that of 30 age-matched healthy controls with a group-wise approach. Differences in mean diffusivity were also examined between the cortical (MM(V)1, MM(V)2C, and VV1) and subcortical (VV2 and MV2K) subgroups of sCJD for those with autopsy data available (n = 27, 73%). We also assessed resting-state functional connectivity of both ventral and dorsal components of DMN in a subset of subject with a rs-fMRI dataset available (n = 17). Decreased diffusivity was predominantly present in posterior cortical regions of the DMN, but also outside of the DMN in temporal areas and in a few limbic and frontal areas, in addition to extensive deep nuclei involvement. Both subcortical and cortical sCJD subgroups showed decreased diffusivity subcortically, whereas only the cortical type expressed significantly decreased diffusivity cortically, mainly in parietal, occipital, and medial-inferior temporal cortices bilaterally. Interestingly, we found abnormally increased connectivity in both dorsal and ventral components of the DMN in sCJD subjects compared with healthy controls. The significance and possible utility of functional imaging as a biomarker for tracking disease progression in prion disease needs to be explored further.
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Affiliation(s)
- Matteo Paoletti
- Memory and Aging Center, Department of Neurology, Weill Institute for NeuroscienceUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of NeuroradiologyIRCCS Mondino FoundationPaviaItaly
| | - Eduardo Caverzasi
- Weill Institute for Neurosciences, Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of Brain and Behavioral SciencesUniversity of PaviaPaviaItaly
| | - Maria Luisa Mandelli
- Memory and Aging Center, Department of Neurology, Weill Institute for NeuroscienceUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Jesse A. Brown
- Memory and Aging Center, Department of Neurology, Weill Institute for NeuroscienceUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Roland G. Henry
- Weill Institute for Neurosciences, Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Graduate Group in BioengineeringUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Bruce L. Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for NeuroscienceUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Howard J. Rosen
- Memory and Aging Center, Department of Neurology, Weill Institute for NeuroscienceUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | | | - Stefano Bastianello
- Department of NeuroradiologyIRCCS Mondino FoundationPaviaItaly
- Department of Brain and Behavioral SciencesUniversity of PaviaPaviaItaly
| | - William W. Seeley
- Memory and Aging Center, Department of Neurology, Weill Institute for NeuroscienceUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of PathologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Michael D. Geschwind
- Memory and Aging Center, Department of Neurology, Weill Institute for NeuroscienceUniversity of California San FranciscoSan FranciscoCaliforniaUSA
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Zhang Y, Zhang Y, Ai H, Van Dam NT, Qian L, Hou G, Xu P. Microstructural deficits of the thalamus in major depressive disorder. Brain Commun 2022; 4:fcac236. [PMID: 36196087 PMCID: PMC9525011 DOI: 10.1093/braincomms/fcac236] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/14/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022] Open
Abstract
Macroscopic structural abnormalities in the thalamus and thalamic circuits have been implicated in the neuropathology of major depressive disorder. However, cytoarchitectonic properties underlying these macroscopic abnormalities remain unknown. Here, we examined systematic deficits of brain architecture in depression, from structural brain network organization to microstructural properties. A multi-modal neuroimaging approach including diffusion, anatomical and quantitative MRI was used to examine structural-related alternations in 56 patients with depression compared with 35 age- and sex-matched controls. The seed-based probabilistic tractography showed multiple alterations of structural connectivity within a set of subcortical areas and their connections to cortical regions in patients with depression. These subcortical regions included the putamen, thalamus and caudate, which are predominantly involved in the limbic-cortical-striatal-pallidal-thalamic network. Structural connectivity was disrupted within and between large-scale networks, including the subcortical network, default-mode network and salience network. Consistently, morphometric measurements, including cortical thickness and voxel-based morphometry, showed widespread volume reductions of these key regions in patients with depression. A conjunction analysis identified common structural alternations of the left orbitofrontal cortex, left putamen, bilateral thalamus and right amygdala across macro-modalities. Importantly, the microstructural properties, longitudinal relaxation time of the left thalamus was increased and inversely correlated with its grey matter volume in patients with depression. Together, this work to date provides the first macro–micro neuroimaging evidence for the structural abnormalities of the thalamus in patients with depression, shedding light on the neuropathological disruptions of the limbic-cortical-striatal-pallidal-thalamic circuit in major depressive disorder. These findings have implications in understanding the abnormal changes of brain structures across the development of depression.
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Affiliation(s)
- Yuxuan Zhang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University , Beijing 100875 , China
| | - Yingli Zhang
- Department of Depressive Disorders, Shenzhen Kangning Hospital, Shenzhen Mental Health Center , Shenzhen 518020 , China
| | - Hui Ai
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen University , Shenzhen 518052 , China
| | - Nicholas T Van Dam
- Melbourne School of Psychological Sciences, The University of Melbourne , Melbourne 3010 , Australia
| | - Long Qian
- MR Research, GE Healthcare , Beijing 100176 , China
| | - Gangqiang Hou
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen Mental Health Center , Shenzhen 518020 , China
| | - Pengfei Xu
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University , Beijing 100875 , China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience , Shenzhen 518107 , China
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Kim S, Park S, Chang I. Development of quantitative and continuous measure for severity degree of Alzheimer's disease evaluated from MRI images of 761 human brains. BMC Bioinformatics 2022; 23:357. [PMID: 36038842 PMCID: PMC9422149 DOI: 10.1186/s12859-022-04903-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 08/24/2022] [Indexed: 12/02/2022] Open
Abstract
Background Alzheimer’s disease affects profoundly the quality of human behavior and cognition. The very broad distribution of its severity across various human subjects requires the quantitative diagnose of Alzheimer’s disease beyond the conventional tripartite classification of cohorts such as cognitively normal (CN), mild cognitive impairment (MCI), Alzheimer’s disease (AD). The unfolding of such broad distributions by the quantitative and continuous degree of AD severity is necessary for the precise diagnose in the cross-sectional study of different stages in AD. Results We conducted the massive reanalysis on MRI images of 761 human brains based on the accumulated bigdata of Alzheimer’s Disease Neuroimaging Initiative. The score matrix of cortical thickness profile at cortex points of subjects was constructed by statistically learning the cortical thickness data of 761 human brains. We also developed a new and simple algebraic predictor which provides the quantitative and continuous degree of AD severity of subjects along the scale from 0 for fully CN to 1 for fully AD state. The mathematical measure of a new predictor for the degree of AD severity is presented based on a covariance correlation matrix of cortical thickness profile between human subjects. One can remove the uncertainty in the determination of different stages in AD by the quantitative degree of AD severity and thus go far beyond the tripartite classification of cohorts. Conclusions We unfold the nature of broad distribution of AD severity of subjects even within a given cohort by the scale from 0 for fully CN to 1 for fully AD state. The quantitative and continuous degree of AD severity developed in this study would be a good practical measure for diagnosing the different stages in AD severity. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04903-8.
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Affiliation(s)
- Sangyeol Kim
- Department of Brain Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea. .,Brain Sciences Research Institute, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea.
| | - Seongjun Park
- Department of Emerging Materials Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea
| | - Iksoo Chang
- Department of Brain Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea. .,Brain Sciences Research Institute, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea. .,Supercomputing Bigdata Center, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea.
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Bhattarai A, Chen Z, Chua P, Talman P, Mathers S, Chapman C, Howe J, Lee CMS, Lie Y, Poudel GR, Egan GF. Network diffusion model predicts neurodegeneration in limb-onset Amyotrophic Lateral Sclerosis. PLoS One 2022; 17:e0272736. [PMID: 35951510 PMCID: PMC9371353 DOI: 10.1371/journal.pone.0272736] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 07/25/2022] [Indexed: 12/03/2022] Open
Abstract
Objective Emerging evidences suggest that the trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS. Methods We measured degeneration in limb-onset ALS patients (n = 14 at baseline, 12 at 6-months, and 9 at 12 months) and controls (n = 12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. In order to investigate whether network spread on the brain connectome derived from healthy individuals were significant findings, we compared our findings against network spread simulated on random networks. Results The cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus (pars triangularis and pars orbitalis) significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Network spread simulated on the random networks showed that the findings using healthy brain connectomes are significantly different from null models. Interpretation Our findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS.
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Affiliation(s)
- Anjan Bhattarai
- Department of Psychiatry, Monash University, Clayton, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
- * E-mail:
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Phyllis Chua
- Department of Psychiatry, Monash University, Clayton, Victoria, Australia
- Statewide Progressive Neurological Disease Service, Calvary Health Care Bethlehem, South Caulfield, Victoria, Australia
| | - Paul Talman
- School of Medicine, Faculty of Health, Deakin University, Geelong, Victoria, Australia
| | - Susan Mathers
- Statewide Progressive Neurological Disease Service, Calvary Health Care Bethlehem, South Caulfield, Victoria, Australia
| | - Caron Chapman
- Neurosciences Department, University Hospital, Geelong, Victoria, Australia
| | - James Howe
- Statewide Progressive Neurological Disease Service, Calvary Health Care Bethlehem, South Caulfield, Victoria, Australia
| | - C. M. Sarah Lee
- Statewide Progressive Neurological Disease Service, Calvary Health Care Bethlehem, South Caulfield, Victoria, Australia
| | - Yenni Lie
- Statewide Progressive Neurological Disease Service, Calvary Health Care Bethlehem, South Caulfield, Victoria, Australia
| | - Govinda R. Poudel
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
| | - Gary F. Egan
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
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60
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Hansen JY, Shafiei G, Vogel JW, Smart K, Bearden CE, Hoogman M, Franke B, van Rooij D, Buitelaar J, McDonald CR, Sisodiya SM, Schmaal L, Veltman DJ, van den Heuvel OA, Stein DJ, van Erp TGM, Ching CRK, Andreassen OA, Hajek T, Opel N, Modinos G, Aleman A, van der Werf Y, Jahanshad N, Thomopoulos SI, Thompson PM, Carson RE, Dagher A, Misic B. Local molecular and global connectomic contributions to cross-disorder cortical abnormalities. Nat Commun 2022; 13:4682. [PMID: 35948562 PMCID: PMC9365855 DOI: 10.1038/s41467-022-32420-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 07/28/2022] [Indexed: 12/21/2022] Open
Abstract
Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.
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Affiliation(s)
- Justine Y Hansen
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Jacob W Vogel
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kelly Smart
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Carrie E Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Martine Hoogman
- Departments of Psychiatry and Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Barbara Franke
- Departments of Psychiatry and Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Daan van Rooij
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Jan Buitelaar
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Carrie R McDonald
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Odile A van den Heuvel
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Anatomy & Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Dan J Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Dept of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, & Center for the Neurobiology of Leaning and Memory, University of California Irvine, 309 Qureshey Research Lab, Irvine, CA, USA
| | - Christopher R K Ching
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Nils Opel
- Institute of Translational Psychiatry, University of Münster, Münster, Germany & Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany
| | - Gemma Modinos
- Department of Psychosis Studies & MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - André Aleman
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, Groningen, The Netherlands
| | - Ysbrand van der Werf
- Department of Anatomy & Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Neda Jahanshad
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Sophia I Thomopoulos
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Paul M Thompson
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada.
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Cobigo Y, Goh MS, Wolf A, Staffaroni AM, Kornak J, Miller BL, Rabinovici GD, Seeley WW, Spina S, Boxer AL, Boeve BF, Wang L, Allegri R, Farlow M, Mori H, Perrin RJ, Kramer J, Rosen HJ. Detection of emerging neurodegeneration using Bayesian linear mixed-effect modeling. Neuroimage Clin 2022; 36:103144. [PMID: 36030718 PMCID: PMC9428846 DOI: 10.1016/j.nicl.2022.103144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 07/20/2022] [Accepted: 08/02/2022] [Indexed: 01/18/2023]
Abstract
Early detection of neurodegeneration, and prediction of when neurodegenerative diseases will lead to symptoms, are critical for developing and initiating disease modifying treatments for these disorders. While each neurodegenerative disease has a typical pattern of early changes in the brain, these disorders are heterogeneous, and early manifestations can vary greatly across people. Methods for detecting emerging neurodegeneration in any part of the brain are therefore needed. Prior publications have described the use of Bayesian linear mixed-effects (BLME) modeling for characterizing the trajectory of change across the brain in healthy controls and patients with neurodegenerative disease. Here, we use an extension of such a model to detect emerging neurodegeneration in cognitively healthy individuals at risk for dementia. We use BLME to quantify individualized rates of volume loss across the cerebral cortex from the first two MRIs in each person and then extend the BLME model to predict future values for each voxel. We then compare observed values at subsequent time points with the values that were expected from the initial rates of change and identify voxels that are lower than the expected values, indicating accelerated volume loss and neurodegeneration. We apply the model to longitudinal imaging data from cognitively normal participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI), some of whom subsequently developed dementia, and two cognitively normal cases who developed pathology-proven frontotemporal lobar degeneration (FTLD). These analyses identified regions of accelerated volume loss prior to or accompanying the earliest symptoms, and expanding across the brain over time, in all cases. The changes were detected in regions that are typical for the likely diseases affecting each patient, including medial temporal regions in patients at risk for Alzheimer's disease, and insular, frontal, and/or anterior/inferior temporal regions in patients with likely or proven FTLD. In the cases where detailed histories were available, the first regions identified were consistent with early symptoms. Furthermore, survival analysis in the ADNI cases demonstrated that the rate of spread of accelerated volume loss across the brain was a statistically significant predictor of time to conversion to dementia. This method for detection of neurodegeneration is a potentially promising approach for identifying early changes due to a variety of diseases, without prior assumptions about what regions are most likely to be affected first in an individual.
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Affiliation(s)
- Yann Cobigo
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States.
| | - Matthew S. Goh
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Amy Wolf
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Adam M. Staffaroni
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - John Kornak
- University of California, San Francisco, Department of Epidemiology and Biostatistics, United States
| | - Bruce L. Miller
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Gil D. Rabinovici
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - William W. Seeley
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Salvatore Spina
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Adam L. Boxer
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | | | - Lei Wang
- Northwestern University Feinberg School of Medicine, Department of Psychiatry and Behavioral Sciences and Department Radiology, United States
| | - Ricardo Allegri
- FLENI Institute of Neurological Research (Fundacion para la Lucha contra las Enfermedades Neurologicas de la Infancia), Argentina
| | | | - Hiroshi Mori
- Osaka City University Medical School, Department of Neurosciences, Japan
| | | | - Joel Kramer
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
| | - Howard J. Rosen
- University of California, San Francisco, Department of Neurology, Memory and Aging Center, United States
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Huang J, Cao Y, Zhang D, Lei X, Chang J. Research trends of the neuroimaging in aphasia: A bibliometric analysis and visualization analysis from 2004 to 2021. Front Hum Neurosci 2022; 16:945160. [PMID: 35911602 PMCID: PMC9334888 DOI: 10.3389/fnhum.2022.945160] [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: 05/16/2022] [Accepted: 06/27/2022] [Indexed: 11/25/2022] Open
Abstract
Objectives To review the current research status of the neuroimaging of aphasia, and reveal the hotspots and frontiers of research in this field. Methods We searched articles related to the neuroimaging research on aphasia since Web of Science (WOS) database construction and extracted the data. CiteSpace and VOSviewer were used for the country/institution analysis, journal analysis, discipline analysis, burst keyword analysis and cited-reference cluster analysis. Results Of the studies retrieved from WOS, 2922 studies that related to the neuroimaging of aphasia were screened and finally included 2799 articles for research. The United States of America and University of California San Francisco were the main countries and institutions in this field. Brain had the highest impact factor in both published and cited journals. Through the discipline and topic analysis of this field, the most common category was Neurosciences and Neurology. The keyword with the strongest citation strength was “functional connectivity,” and the recent burst keywords were “functional connectivity” and “network.” The co-citation network showed seven clusters greater than 100. Among the top 5 clusters, the most recently formed cluster, Cluster #2 (progressive supranuclear palsy), had an average year of 2017. The literature in the top 5 clusters mainly focused on 3 aspects, specifically, the discovery of language processing models, injury and recovery mechanisms of post-stroke aphasia (PSA), and diagnosis of primary progressive aphasia (PPA) variants. Conclusion The results of this bibliometric study revealed the following three research hotspots in the neuroimaging of aphasia: clarifying the connotation of the most recognized language processing model, the dual-stream model, exploring the injury mechanism based on the dual-stream model and the recovery mechanism involving the left and right hemispheres of PSA, and determining the diagnostic criteria for PPA variants. A major research trend is to combine new neuroimaging technology, such as PET tracer technology, to realize the visual presentation of disease-specific proteins to improve the pathological diagnostic criteria of PPA variants. Accordingly, a visualized analysis of literature that uses CiteSpace provides a more rapid, repeatable and flexible method, which is more conducive to capturing research hotspots and emerging trends.
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Vlegels N, Ossenkoppele R, van der Flier WM, Koek HL, Reijmer YD, Wisse LEM, Biessels GJ. Does Loss of Integrity of the Cingulum Bundle Link Amyloid-β Accumulation and Neurodegeneration in Alzheimer’s Disease? J Alzheimers Dis 2022; 89:39-49. [DOI: 10.3233/jad-220024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Alzheimer’s disease is characterized by the accumulation of amyloid-β (Aβ) into plaques, aggregation of tau into neurofibrillary tangles, and neurodegenerative processes including atrophy. However, there is a poorly understood spatial discordance between initial Aβ deposition and local neurodegeneration. Objective: Here, we test the hypothesis that the cingulum bundle links Aβ deposition in the cingulate cortex to medial temporal lobe (MTL) atrophy. Methods: 21 participants with mild cognitive impairment (MCI) from the UMC Utrecht memory clinic (UMCU, discovery sample) and 37 participants with MCI from Alzheimer’s Disease Neuroimaging Initiative (ADNI, replication sample) with available Aβ-PET scan, T1-weighted and diffusion-weighted MRI were included. Aβ load of the cingulate cortex was measured by the standardized uptake value ratio (SUVR), white matter integrity of the cingulum bundle was assessed by mean diffusivity and atrophy of the MTL by normalized MTL volume. Relationships were tested with linear mixed models, to accommodate multiple measures for each participant. Results: We found at most a weak association between cingulate Aβ and MTL volume (added R2 <0.06), primarily for the posterior hippocampus. In neither sample, white matter integrity of the cingulum bundle was associated with cingulate Aβ or MTL volume (added R2 <0.01). Various sensitivity analyses (Aβ-positive individuals only, posterior cingulate SUVR, MTL sub region volume) provided similar results. Conclusion: These findings, consistent in two independent cohorts, do not support our hypothesis that loss of white matter integrity of the cingulum is a connecting factor between cingulate gyrus Aβ deposition and MTL atrophy.
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Affiliation(s)
- Naomi Vlegels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Epidemiology and Data Science, VU University Medical Center, Amsterdam, The Netherlands
| | - Huiberdina L. Koek
- Department of Geriatrics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D. Reijmer
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Deslauriers-Gauthier S, Zucchelli M, Laghrissi H, Deriche R. A Riemannian Revisiting of Structure-Function Mapping Based on Eigenmodes. FRONTIERS IN NEUROIMAGING 2022; 1:850266. [PMID: 37555180 PMCID: PMC10406294 DOI: 10.3389/fnimg.2022.850266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 03/23/2022] [Indexed: 08/10/2023]
Abstract
Understanding the link between brain structure and function may not only improve our knowledge of brain organization, but also lead to better quantification of pathology. To quantify this link, recent studies have attempted to predict the brain's functional connectivity from its structural connectivity. However, functional connectivity matrices live in the Riemannian manifold of the symmetric positive definite space and a specific attention must be paid to operate on this appropriate space. In this work we investigated the implications of using a distance based on an affine invariant Riemannian metric in the context of structure-function mapping. Specifically, we revisit previously proposed structure-function mappings based on eigendecomposition and test them on 100 healthy subjects from the Human Connectome Project using this adapted notion of distance. First, we show that using this Riemannian distance significantly alters the notion of similarity between subjects from a functional point of view. We also show that using this distance improves the correlation between the structural and functional similarity of different subjects. Finally, by using a distance appropriate to this manifold, we demonstrate the importance of mapping function from structure under the Riemannian manifold and show in particular that it is possible to outperform the group average and the so-called glass ceiling on the performance of mappings based on eigenmodes.
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65
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García Vicente A, Tello Galán M, Pena Pardo F, Amo-Salas M, Mondejar Marín B, Navarro Muñoz S, Rueda Medina I, Poblete García V, Marsal Alonso C, Soriano Castrejón Á. Aumento de la confianza en la interpretación del PET con 18F-Florbetaben: “machine learning” basado en la aproximación cuantitativa. Rev Esp Med Nucl Imagen Mol 2022. [DOI: 10.1016/j.remn.2021.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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66
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Pandya S, Maia PD, Freeze B, Menke RAL, Talbot K, Turner MR, Raj A. Modeling seeding and neuroanatomic spread of pathology in amyotrophic lateral sclerosis. Neuroimage 2022; 251:118968. [PMID: 35143975 PMCID: PMC10729776 DOI: 10.1016/j.neuroimage.2022.118968] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 01/26/2022] [Accepted: 02/02/2022] [Indexed: 12/12/2022] Open
Abstract
The neurodegenerative disorder amyotrophic lateral sclerosis (ALS) is characterized by the progressive loss of upper and lower motor neurons, with pathological involvement of cerebral motor and extra-motor areas in a clinicopathological spectrum with frontotemporal dementia (FTD). A key unresolved issue is how the non-random distribution of pathology in ALS reflects differential network vulnerability, including molecular factors such as regional gene expression, or preferential spread of pathology via anatomical connections. A system of histopathological staging of ALS based on the regional burden of TDP-43 pathology observed in postmortem brains has been supported to some extent by analysis of distribution of in vivo structural MRI changes. In this paper, computational modeling using a Network Diffusion Model (NDM) was used to investigate whether a process of focal pathological 'seeding' followed by structural network-based spread recapitulated postmortem histopathological staging and, secondly, whether this had any correlation to the pattern of expression of a panel of genes implicated in ALS across the healthy brain. Regionally parcellated T1-weighted MRI data from ALS patients (baseline n=79) was studied in relation to a healthy control structural connectome and a database of associated regional cerebral gene expression. The NDM provided strong support for a structural network-based basis for regional pathological spread in ALS, but no simple relationship to the spatial distribution of ALS-related genes in the healthy brain. Interestingly, OPTN gene was identified as a significant but a weaker non-NDM contributor within the network-gene interaction model (LASSO). Intriguingly, the critical seed regions for spread within the model were not within the primary motor cortex but basal ganglia, thalamus and insula, where NDM recapitulated aspects of the postmortem histopathological staging system. Within the ALS-FTD clinicopathological spectrum, non-primary motor structures may be among the earliest sites of cerebral pathology.
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Affiliation(s)
- Sneha Pandya
- Department of Radiology, Weill Cornell Medicine, 1300 York Avenue, New York, NY, United States.
| | - Pedro D Maia
- Department of Mathematics, University of Texas at Arlington, TX, United States
| | - Benjamin Freeze
- Scripps Health/MD Anderson Cancer Center, Department of Radiology, CA, United States
| | - Ricarda A L Menke
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, West Wing Level 6, Oxford OX2 7PZ, United Kingdom
| | - Kevin Talbot
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Martin R Turner
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, West Wing Level 6, Oxford OX2 7PZ, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Ashish Raj
- Department of Radiology, Weill Cornell Medicine, 1300 York Avenue, New York, NY, United States; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94121, United States.
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Thomas PJ, Leow A, Klumpp H, Phan KL, Ajilore O. Network Diffusion Embedding Reveals Transdiagnostic Subnetwork Disruption and Potential Treatment Targets in Internalizing Psychopathologies. Cereb Cortex 2022; 32:1823-1839. [PMID: 34521109 PMCID: PMC9070362 DOI: 10.1093/cercor/bhab314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 11/14/2022] Open
Abstract
Network diffusion models are a common and powerful way to study the propagation of information through a complex system and they offer straightforward approaches for studying multimodal brain network data. We developed an analytic framework to identify brain subnetworks with perturbed information diffusion capacity using the structural basis that best maps to resting state functional connectivity and applied it towards a heterogeneous dataset of internalizing psychopathologies (IPs), a set of psychiatric conditions in which similar brain network deficits are found across the swath of the disorders, but a unifying neuropathological substrate for transdiagnostic symptom expression is currently unknown. This research provides preliminary evidence of a transdiagnostic brain subnetwork deficit characterized by information diffusion impairment of the right area 8BM, a key brain region involved in organizing a broad spectrum of cognitive tasks, which may underlie previously reported dysfunction of multiple brain circuits in the IPs. We also demonstrate that models of neuromodulation involving targeting this brain region normalize IP diffusion dynamics towards those of healthy controls. These analyses provide a framework for multimodal methods that identify both brain subnetworks with disrupted information diffusion and potential targets of these subnetworks for therapeutic neuromodulatory intervention based on previously well-characterized methodology.
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Affiliation(s)
- Paul J Thomas
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Alex Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Heide Klumpp
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - K Luan Phan
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH 43210, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
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Lee WJ, Brown JA, Kim HR, La Joie R, Cho H, Lyoo CH, Rabinovici GD, Seong JK, Seeley WW. Regional Aβ-tau interactions promote onset and acceleration of Alzheimer's disease tau spreading. Neuron 2022; 110:1932-1943.e5. [PMID: 35443153 DOI: 10.1016/j.neuron.2022.03.034] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 02/19/2022] [Accepted: 03/28/2022] [Indexed: 12/22/2022]
Abstract
Amyloid-beta and tau are key molecules in the pathogenesis of Alzheimer's disease, but it remains unclear how these proteins interact to promote disease. Here, by combining cross-sectional and longitudinal molecular imaging and network connectivity analyses in living humans, we identified two amyloid-beta/tau interactions associated with the onset and propagation of tau spreading. First, we show that the lateral entorhinal cortex, an early site of tau neurofibrillary tangle formation, is subject to remote, connectivity-mediated amyloid-beta/tau interactions linked to initial tau spreading. Second, we identify the inferior temporal gyrus as the region featuring the greatest local amyloid-beta/tau interactions and a connectivity profile well suited to accelerate tau propagation. Taken together, our data address long-standing questions regarding the topographical dissimilarity between early amyloid-beta and tau deposition.
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Affiliation(s)
- Wha Jin Lee
- School of Biomedical Engineering, Korea University, Seoul 02841, South Korea
| | - Jesse A Brown
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Hye Ryun Kim
- School of Biomedical Engineering, Korea University, Seoul 02841, South Korea; Global Health Technology Research Center, College of Health Science, Korea University, Seoul 02841, South Korea
| | - Renaud La Joie
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Hanna Cho
- Department of Neurology, Gangnam Severance Hospital, Seoul 06273, South Korea
| | - Chul Hyoung Lyoo
- Department of Neurology, Gangnam Severance Hospital, Seoul 06273, South Korea
| | - Gil D Rabinovici
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, Seoul 02841, South Korea; Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea.
| | - William W Seeley
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA.
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69
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Chételat G. How to use neuroimaging biomarkers in the diagnosis framework of neurodegenerative diseases? Rev Neurol (Paris) 2022; 178:490-497. [DOI: 10.1016/j.neurol.2022.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 11/29/2022]
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Characterizing the Propagation Pathway of Neuropathological Events of Alzheimer's Disease Using Harmonic Wavelet Analysis. Med Image Anal 2022; 79:102446. [DOI: 10.1016/j.media.2022.102446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 03/07/2022] [Accepted: 04/01/2022] [Indexed: 11/19/2022]
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71
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Disrupted default mode network and executive control network are associated with depression severity on structural network. Neuroreport 2022; 33:227-235. [PMID: 35287146 DOI: 10.1097/wnr.0000000000001773] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Major depressive disorder (MDD) is a psychiatric disorder with a relatively limited response to treatment. It is necessary to better understand the neuroanatomical mechanisms of structural networks. METHODS The current study recruited 181 first-onset, untreated adult MDD patients: slight MDD (SD, N = 23), moderate MDD (MD, N = 77), Heavy MDD (HD, N = 81) groups; along with a healthy control group (HC, N = 81) with matched general clinical data. FreeSurfer was used to preprocess T1 images for gray matter volume (GMV), and the default mode network (DMN) and the execution control network (ECN) were analyzed by structural covariance network (SCN). RESULTS Present study found that the GMV of brain regions reduced with the severity of the disease. Specifically, the GMV of the left anterior cingulate gyrus (ACC.L) is negatively correlated with MDD severity. In addition, the SCN connectivity of the whole-brain network increases with the increase of severity in MDD. ACC.L is a key brain region with increased connectivity between the left orbitofrontal in DMN and between the right orbitofrontal in ECN, which leads to damage to the balance of neural circuits. CONCLUSIONS Patients with smaller GMV of ACC.L are more likely to develop severe MDD, and as a key region in both networks which have distinct structural network models in DMN and ECN. MDD patients with different severity have different neuroimaging changes in DMN and ECN.
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72
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He M, Ping L, Chu Z, Zeng C, Shen Z, Xu X. Identifying Changes of Brain Regional Homogeneity and Cingulo-Opercular Network Connectivity in First-Episode, Drug-Naïve Depressive Patients With Suicidal Ideation. Front Neurosci 2022; 16:856366. [PMID: 35310111 PMCID: PMC8924659 DOI: 10.3389/fnins.2022.856366] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 01/31/2022] [Indexed: 11/15/2022] Open
Abstract
Objective Adult patients with major depressive disorder (MDD) may not actively reveal their suicidal ideation (SI). Therefore, this study is committed to finding the alterations in the cingulo-opercular network (CON) that are closely related to SI with multi-imaging methods, thus providing neuroimaging basis for SI. Method A total of 198 participants (129 MDD patients and 69 healthy controls) were recruited and evaluated with the Montgomery–Asberg Depression Rating Scale (MADRS). The healthy individuals formed the HC group, while the MDD patients were subdivided into no SI MDD (NSI, n = 32), mild SI MDD (MSI, n = 64), and severe SI MDD (SSI, n = 33) according to their MADRS item 10. We obtained MRI data of all participants and applied regional homogeneity (ReHo) analysis to verify a previous finding that links CON abnormality to SI. In addition, we employed the structural covariance network (SCN) analysis to investigate the correlation between abnormal structural connectivity of CON and SI severity. Results Compared to those of the HC group, MDD ReHo values and gray matter volume (GMV) were consistently found abnormal in CON. ReHo values and GMV of the right orbital inferior frontal gyrus (ORBinf.R) in the MDD group decreased with the increase of SI. Compared to the HC group, the MDD patients showed enhanced structural connectivity of three pairs of brain regions in CON [ACC.L–left superior frontal gyrus (SFG.L), SFG.L–left middle temporal gyrus (MTG.L), and the SFG.L–left post-central gyrus (PoCG.L)]. Compared with that of the NSI and MSI groups, the structural connectivity of three pairs of brain regions in CON is enhanced in the SSI groups [ORBinf.L–right ventral posterior cingulate gyrus (VPCC.R), VPCC.R–SFG.R, and SFG.R–PoCG.R]. Conclusion Our findings showed the distinctive ReHo, GMV, and SCN pattern of CON in MDD patients with SI; and with the severity of suicide, abnormal brain regions increased. Our finding suggested that MDD patients with different severity of SI have different neuroimaging changes.
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Affiliation(s)
- Mengxin He
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Mental Health Institute of Yunnan, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Liangliang Ping
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Zhaosong Chu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chunqiang Zeng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zonglin Shen
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Mental Health Institute of Yunnan, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Yunnan Clinical Research Center for Mental Disorders, First Affiliated Hospital of Kunming Medical University, Kunming, China
- *Correspondence: Zonglin Shen,
| | - Xiufeng Xu
- Mental Health Institute of Yunnan, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Yunnan Clinical Research Center for Mental Disorders, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Xiufeng Xu,
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Signature laminar distributions of pathology in frontotemporal lobar degeneration. Acta Neuropathol 2022; 143:363-382. [PMID: 34997851 PMCID: PMC8858288 DOI: 10.1007/s00401-021-02402-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 12/11/2021] [Accepted: 12/28/2021] [Indexed: 12/24/2022]
Abstract
Frontotemporal lobar degeneration (FTLD) with either tau (FTLD-tau) or TDP-43 (FTLD-TDP) inclusions are distinct proteinopathies that frequently cause similar frontotemporal dementia (FTD) clinical syndromes. FTD syndromes often display macroscopic signatures of neurodegeneration at the level of regions and networks, but it is unclear if subregional laminar pathology display patterns unique to proteinopathy or clinical syndrome. We hypothesized that FTLD-tau and FTLD-TDP accumulate pathology in relatively distinct cortical layers independent of clinical syndrome, with greater involvement of lower layers in FTLD-tau. The current study examined 170 patients with either FTLD-tau (n = 73) or FTLD-TDP (n = 97) spanning dementia and motor phenotypes in the FTD spectrum. We digitally measured the percent area occupied by tau and TDP-43 pathology in upper layers (I-III), lower layers (IV-VI), and juxtacortical white matter (WM) from isocortical regions in both hemispheres where available. Linear mixed-effects models compared ratios of upper to lower layer pathology between FTLD groups and investigated relationships with regions, WM pathology, and global cognitive impairment while adjusting for demographics. We found lower ratios of layer pathology in FTLD-tau and higher ratios of layer pathology in FTLD-TDP, reflecting lower layer-predominant tau pathology and upper layer-predominant TDP-43 pathology, respectively (p < 0.001). FTLD-tau displayed lower ratios of layer pathology related to greater WM tau pathology (p = 0.002) and to earlier involved/severe pathology regions (p = 0.007). In contrast, FTLD-TDP displayed higher ratios of layer pathology not related to either WM pathology or regional severity. Greater cognitive impairment was associated with higher ratios of layer pathology in FTLD-tau (p = 0.018), but was not related to ratios of layer pathology in FTLD-TDP. Lower layer-predominant tau pathology and upper layer-predominant TDP-43 pathology are proteinopathy-specific, regardless of clinical syndromes or regional networks that define these syndromes. Thus, patterns of laminar change may provide a useful anatomical framework for investigating how degeneration of select cells and corresponding laminar circuits influence large-scale networks and clinical symptomology in FTLD.
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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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75
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Wang MB, Boring MJ, Ward MJ, Richardson RM, Ghuman AS. Deep brain stimulation for parkinson's disease induces spontaneous cortical hypersynchrony in extended motor and cognitive networks. Cereb Cortex 2022; 32:4480-4491. [PMID: 35136991 PMCID: PMC9574237 DOI: 10.1093/cercor/bhab496] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 12/04/2021] [Accepted: 12/05/2021] [Indexed: 11/14/2022] Open
Abstract
The mechanism of action of deep brain stimulation (DBS) to the basal ganglia for Parkinson's disease remains unclear. Studies have shown that DBS decreases pathological beta hypersynchrony between the basal ganglia and motor cortex. However, little is known about DBS's effects on long range corticocortical synchronization. Here, we use machine learning combined with graph theory to compare resting-state cortical connectivity between the off and on-stimulation states and to healthy controls. We found that turning DBS on increased high beta and gamma band synchrony (26 to 50 Hz) in a cortical circuit spanning the motor, occipitoparietal, middle temporal, and prefrontal cortices. The synchrony in this network was greater in DBS on relative to both DBS off and controls, with no significant difference between DBS off and controls. Turning DBS on also increased network efficiency and strength and subnetwork modularity relative to both DBS off and controls in the beta and gamma band. Thus, unlike DBS's subcortical normalization of pathological basal ganglia activity, it introduces greater synchrony relative to healthy controls in cortical circuitry that includes both motor and non-motor systems. This increased high beta/gamma synchronization may reflect compensatory mechanisms related to DBS's clinical benefits, as well as undesirable non-motor side effects.
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Affiliation(s)
- Maxwell B Wang
- Address correspondence to Maxwell B Wang, BS, Medical Scientist Training Program, University of Pittsburgh School of Medicine, Program of Neural Computation, Carnegie Mellon University, Pittsburgh, PA 15213. Tel: 815-200-9533;
| | - Matthew J Boring
- Center for Neuroscience at the University of Pittsburgh, Pittsburgh, PA 15213, USA,Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213, USA,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Michael J Ward
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - R Mark Richardson
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA,Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA,Harvard Medical School, Boston, MA 02115, USA
| | - Avniel Singh Ghuman
- Program of Neural Computation, Carnegie Mellon University, Pittsburgh, PA 15213, USA,Center for Neuroscience at the University of Pittsburgh, Pittsburgh, PA 15213, USA,Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213, USA,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
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76
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Modelling Female Physiology from Head to Toe: Impact of Sex Hormones, Menstrual Cycle, and Pregnancy. J Theor Biol 2022; 540:111074. [DOI: 10.1016/j.jtbi.2022.111074] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 12/14/2022]
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77
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Basaia S, Agosta F, Diez I, Bueichekú E, d'Oleire Uquillas F, Delgado-Alvarado M, Caballero-Gaudes C, Rodriguez-Oroz M, Stojkovic T, Kostic VS, Filippi M, Sepulcre J. Neurogenetic traits outline vulnerability to cortical disruption in Parkinson's disease. Neuroimage Clin 2022; 33:102941. [PMID: 35091253 PMCID: PMC8800137 DOI: 10.1016/j.nicl.2022.102941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/03/2021] [Accepted: 01/10/2022] [Indexed: 01/18/2023]
Abstract
The genetic traits that underlie vulnerability to neuronal damage across specific brain circuits in Parkinson's disease (PD) remain to be elucidated. In this study, we characterized the brain topological intersection between propagating connectivity networks in controls and PD participants and gene expression patterns across the human cortex - such as the SNCA gene. We observed that brain connectivity originated from PD-related pathology epicenters in the brainstem recapitulated the anatomical distribution of alpha-synuclein histopathology in postmortem data. We also discovered that the gene set most related to cortical propagation patterns of PD-related pathology was primarily involved in microtubule cellular components. Thus, this study sheds light on new avenues for enhancing detection of PD neuronal vulnerability via an evaluation of in vivo connectivity trajectories across the human brain and successful integration of neuroimaging-genetic strategies.
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Affiliation(s)
- Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Federica Agosta
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy
| | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Elisenda Bueichekú
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Federico d'Oleire Uquillas
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Manuel Delgado-Alvarado
- Neurology Department, Sierrallana Hospital, Torrelavega, Spain; IDIVAL, Valdecilla Biomedical Research Institute, Santander, Spain; Biomedical Research Networking Center for Mental Health (CIBERSAM), Madrid, Spain
| | | | - MariCruz Rodriguez-Oroz
- Neurology Department, Clínica Universidad de Navarra, Neuroscience Unit, CIMA Universidad de Navarra, Spain
| | - Tanja Stojkovic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Vladimir S Kostic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
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78
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Putra P, Thompson TB, Chaggar P, Goriely A. Braiding Braak and Braak: Staging patterns and model selection in network neurodegeneration. Netw Neurosci 2022; 5:929-956. [PMID: 35024537 PMCID: PMC8746141 DOI: 10.1162/netn_a_00208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 08/25/2021] [Indexed: 01/12/2023] Open
Abstract
A hallmark of Alzheimer’s disease is the aggregation of insoluble amyloid-beta plaques and tau protein neurofibrillary tangles. A key histopathological observation is that tau protein aggregates follow a structured progression pattern through the brain. Mathematical network models of prion-like propagation have the ability to capture such patterns, but a number of factors impact the observed staging result, thus introducing questions regarding model selection. Here, we introduce a novel approach, based on braid diagrams, for studying the structured progression of a marker evolving on a network. We apply this approach to a six-stage ‘Braak pattern’ of tau proteins, in Alzheimer’s disease, motivated by a recent observation that seed-competent tau precedes tau aggregation. We show that the different modeling choices, from the model parameters to the connectome resolution, play a significant role in the landscape of observable staging patterns. Our approach provides a systematic way to approach model selection for network propagation of neurodegenerative diseases that ensures both reproducibility and optimal parameter fitting. Network diffusion models of neurodegenerative diseases are a class of dynamical systems that simulate the evolution of toxic proteins on the connectome. These models predict, from an initial seed, a pattern of invasion called staging. The generalized staging problem seeks to systematically study the effect of various model choices on staging. We introduce methods based on braid diagrams to test the possible staging landscape of a model and how it depends on the choice of connectome, as well as the model parameters. Our primary finding is that connectome construction, the choice of the graph Laplacian, and transport models all have an impact on staging that should be taken into account in any study.
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Affiliation(s)
- Prama Putra
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Travis B Thompson
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Pavanjit Chaggar
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Alain Goriely
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
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79
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Lei H, Hu R, Luo G, Yang T, Shen H, Deng H, Chen C, Zhao H, Liu J. Altered Structural and Functional MRI Connectivity in Type 2 Diabetes Mellitus Related Cognitive Impairment: A Review. Front Hum Neurosci 2022; 15:755017. [PMID: 35069149 PMCID: PMC8770326 DOI: 10.3389/fnhum.2021.755017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 12/13/2021] [Indexed: 12/16/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment in many domains. There are several pieces of evidence that changes in neuronal neuropathies and metabolism have been observed in T2DM. Structural and functional MRI shows that abnormal connections and synchronization occur in T2DM brain circuits and related networks. Neuroplasticity and energy metabolism appear to be principal effector systems, which may be related to amyloid beta (Aβ) deposition, although there is no unified explanation that includes the complex etiology of T2DM with cognitive impairment. Herein, we assume that cognitive impairment in diabetes may lead to abnormalities in neuroplasticity and energy metabolism in the brain, and those reflected to MRI structural connectivity and functional connectivity, respectively.
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80
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Borghesani V, DeLeon J, Gorno-Tempini ML. Frontotemporal dementia: A unique window on the functional role of the temporal lobes. HANDBOOK OF CLINICAL NEUROLOGY 2022; 187:429-448. [PMID: 35964986 PMCID: PMC9793689 DOI: 10.1016/b978-0-12-823493-8.00011-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Frontotemporal dementia (FTD) is an umbrella term covering a plethora of progressive changes in executive functions, motor abilities, behavior, and/or language. Different clinical syndromes have been described in relation to localized atrophy, informing on the functional networks that underlie these specific cognitive, emotional, and behavioral processes. These functional declines are linked with the underlying neurodegeneration of frontal and/or temporal lobes due to diverse molecular pathologies. Initially, the accumulation of misfolded proteins targets specifically susceptible cell assemblies, leading to relatively focal neurodegeneration that later spreads throughout large-scale cortical networks. Here, we discuss the most recent clinical, neuropathological, imaging, and genetics findings in FTD-spectrum syndromes affecting the temporal lobe. We focus on the semantic variant of primary progressive aphasia and its mirror image, the right temporal variant of FTD. Incipient focal atrophy of the left anterior temporal lobe (ATL) manifests with predominant naming, word comprehension, reading, and object semantic deficits, while cases of predominantly right ATL atrophy present with impairments of socioemotional, nonverbal semantic, and person-specific knowledge. Overall, the observations in FTD allow for crucial clinical-anatomic inferences, shedding light on the role of the temporal lobes in both cognition and complex behaviors. The concerted activity of both ATLs is critical to ensure that percepts are translated into concepts, yet important hemispheric differences should be acknowledged. On one hand, the left ATL attributes meaning to linguistic, external stimuli, thus supporting goal-oriented, action-related behaviors (e.g., integrating sounds and letters into words). On the other hand, the right ATL assigns meaning to emotional, visceral stimuli, thus guiding socially relevant behaviors (e.g., integrating body sensations into feelings of familiarity).
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Affiliation(s)
- Valentina Borghesani
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montréal, QC, Canada; Department of Psychology, Université de Montréal, Montréal, QC, Canada.
| | - Jessica DeLeon
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, United States; Department of Neurology, Dyslexia Center, University of California, San Francisco, CA, United States
| | - Maria Luisa Gorno-Tempini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, United States; Department of Neurology, Dyslexia Center, University of California, San Francisco, CA, United States
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81
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Tuladhar A, Moore JA, Ismail Z, Forkert ND. Modeling Neurodegeneration in silico With Deep Learning. Front Neuroinform 2021; 15:748370. [PMID: 34867256 PMCID: PMC8640525 DOI: 10.3389/fninf.2021.748370] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 10/21/2021] [Indexed: 11/13/2022] Open
Abstract
Deep neural networks, inspired by information processing in the brain, can achieve human-like performance for various tasks. However, research efforts to use these networks as models of the brain have primarily focused on modeling healthy brain function so far. In this work, we propose a paradigm for modeling neural diseases in silico with deep learning and demonstrate its use in modeling posterior cortical atrophy (PCA), an atypical form of Alzheimer’s disease affecting the visual cortex. We simulated PCA in deep convolutional neural networks (DCNNs) trained for visual object recognition by randomly injuring connections between artificial neurons. Results showed that injured networks progressively lost their object recognition capability. Simulated PCA impacted learned representations hierarchically, as networks lost object-level representations before category-level representations. Incorporating this paradigm in computational neuroscience will be essential for developing in silico models of the brain and neurological diseases. The paradigm can be expanded to incorporate elements of neural plasticity and to other cognitive domains such as motor control, auditory cognition, language processing, and decision making.
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Affiliation(s)
- Anup Tuladhar
- Department of Radiology, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Jasmine A Moore
- Department of Radiology, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Biomedical Engineering Program, University of Calgary, Calgary, AB, Canada
| | - Zahinoor Ismail
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada.,Department of Psychiatry, University of Calgary, Calgary, AB, Canada.,O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada
| | - Nils D Forkert
- Department of Radiology, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
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82
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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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83
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Blinkouskaya Y, Caçoilo A, Gollamudi T, Jalalian S, Weickenmeier J. Brain aging mechanisms with mechanical manifestations. Mech Ageing Dev 2021; 200:111575. [PMID: 34600936 PMCID: PMC8627478 DOI: 10.1016/j.mad.2021.111575] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 09/09/2021] [Accepted: 09/22/2021] [Indexed: 12/14/2022]
Abstract
Brain aging is a complex process that affects everything from the subcellular to the organ level, begins early in life, and accelerates with age. Morphologically, brain aging is primarily characterized by brain volume loss, cortical thinning, white matter degradation, loss of gyrification, and ventricular enlargement. Pathophysiologically, brain aging is associated with neuron cell shrinking, dendritic degeneration, demyelination, small vessel disease, metabolic slowing, microglial activation, and the formation of white matter lesions. In recent years, the mechanics community has demonstrated increasing interest in modeling the brain's (bio)mechanical behavior and uses constitutive modeling to predict shape changes of anatomically accurate finite element brain models in health and disease. Here, we pursue two objectives. First, we review existing imaging-based data on white and gray matter atrophy rates and organ-level aging patterns. This data is required to calibrate and validate constitutive brain models. Second, we review the most critical cell- and tissue-level aging mechanisms that drive white and gray matter changes. We focuse on aging mechanisms that ultimately manifest as organ-level shape changes based on the idea that the integration of imaging and mechanical modeling may help identify the tipping point when normal aging ends and pathological neurodegeneration begins.
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Affiliation(s)
- Yana Blinkouskaya
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Andreia Caçoilo
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Trisha Gollamudi
- Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Shima Jalalian
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Johannes Weickenmeier
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States.
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84
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Powell F, Tosun D, Raj A. Network-constrained technique to characterize pathology progression rate in Alzheimer's disease. Brain Commun 2021; 3:fcab144. [PMID: 34704025 PMCID: PMC8376686 DOI: 10.1093/braincomms/fcab144] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 02/12/2021] [Accepted: 03/19/2021] [Indexed: 11/30/2022] Open
Abstract
Current methods for measuring the chronic rates of cognitive decline and degeneration in Alzheimer’s disease rely on the sensitivity of longitudinal neuropsychological batteries and clinical neuroimaging, particularly structural magnetic resonance imaging of brain atrophy, either at a global or regional scale. There is particular interest in approaches predictive of future disease progression and clinical outcomes using a single time point. If successful, such approaches could have great impact on differential diagnosis, therapeutic treatment and clinical trial inclusion. Unfortunately, it has proven quite challenging to accurately predict clinical and degeneration progression rates from baseline data. Specifically, a key limitation of the previously proposed approaches for disease progression based on the brain atrophy measures has been the limited incorporation of the knowledge from disease pathology progression models, which suggest a prion-like spread of disease pathology and hence the neurodegeneration. Here, we present a new metric for disease progression rate in Alzheimer that uses only MRI-derived atrophy data yet is able to infer the underlying rate of pathology transmission. This is enabled by imposing a spread process driven by the brain networks using a Network Diffusion Model. We first fit this model to each patient’s longitudinal brain atrophy data defined on a brain network structure to estimate a patient-specific rate of pathology diffusion, called the pathology progression rate. Using machine learning algorithms, we then build a baseline data model and tested this rate metric on data from longitudinal Alzheimer’s Disease Neuroimaging Initiative study including 810 subjects. Our measure of disease progression differed significantly across diagnostic groups as well as between groups with different genetic risk factors. Remarkably, hierarchical clustering revealed 3 distinct clusters based on CSF profiles with >90% accuracy. These pathological clusters exhibit progressive atrophy and clinical impairments that correspond to the proposed rate measure. We demonstrate that a subject’s degeneration speed can be best predicted from baseline neuroimaging volumetrics and fluid biomarkers for subjects in the middle of their degenerative course, which may be a practical, inexpensive screening tool for future prognostic applications.
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Affiliation(s)
- Fon Powell
- Department of Radiology, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, AC-116, Parnassus, Box 0628, San Francisco, CA 94122, USA.,San Francisco Veterans Affairs Medical Center, San Francisco, CA 94121, USA
| | - Ashish Raj
- Department of Radiology, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA.,Department of Radiology and Biomedical Imaging, University of California San Francisco, AC-116, Parnassus, Box 0628, San Francisco, CA 94122, USA
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85
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Arnatkeviciute A, Fulcher BD, Bellgrove MA, Fornito A. Imaging Transcriptomics of Brain Disorders. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 2:319-331. [PMID: 36324650 PMCID: PMC9616271 DOI: 10.1016/j.bpsgos.2021.10.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/06/2021] [Accepted: 10/11/2021] [Indexed: 01/05/2023] Open
Abstract
Noninvasive neuroimaging is a powerful tool for quantifying diverse aspects of brain structure and function in vivo, and it has been used extensively to map the neural changes associated with various brain disorders. However, most neuroimaging techniques offer only indirect measures of underlying pathological mechanisms. The recent development of anatomically comprehensive gene expression atlases has opened new opportunities for studying the transcriptional correlates of noninvasively measured neural phenotypes, offering a rich framework for evaluating pathophysiological hypotheses and putative mechanisms. Here, we provide an overview of some fundamental methods in imaging transcriptomics and outline their application to understanding brain disorders of neurodevelopment, adulthood, and neurodegeneration. Converging evidence indicates that spatial variations in gene expression are linked to normative changes in brain structure during age-related maturation and neurodegeneration that are in part associated with cell-specific gene expression markers of gene expression. Transcriptional correlates of disorder-related neuroimaging phenotypes are also linked to transcriptionally dysregulated genes identified in ex vivo analyses of patient brains. Modeling studies demonstrate that spatial patterns of gene expression are involved in regional vulnerability to neurodegeneration and the spread of disease across the brain. This growing body of work supports the utility of transcriptional atlases in testing hypotheses about the molecular mechanism driving disease-related changes in macroscopic neuroimaging phenotypes.
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Affiliation(s)
- Aurina Arnatkeviciute
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
- Address correspondence to Aurina Arnatkeviciute, Ph.D
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, Camperdown, New South Wales, Australia
| | - Mark A. Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
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86
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Sandini C, Zöller D, Schneider M, Tarun A, Armondo M, Nelson B, Amminger PG, Yuen HP, Markulev C, Schäffer MR, Mossaheb N, Schlögelhofer M, Smesny S, Hickie IB, Berger GE, Chen EY, de Haan L, Nieman DH, Nordentoft M, Riecher-Rössler A, Verma S, Thompson A, Yung AR, McGorry PD, Van De Ville D, Eliez S. Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing. eLife 2021; 10:59811. [PMID: 34569937 PMCID: PMC8476129 DOI: 10.7554/elife.59811] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/09/2021] [Indexed: 11/21/2022] Open
Abstract
Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clinical trajectories observed in early psychopathology. Current diagnostic approaches merge clinical manifestations that co-occur across subjects and could significantly hinder our understanding of clinical pathways connecting individual symptoms. Network analysis techniques have emerged as alternative approaches that could help shed light on the complex dynamics of early psychopathology. The present study attempts to address the two main limitations that have in our opinion hindered the application of network approaches in the clinical setting. Firstly, we show that a multi-layer network analysis approach, can move beyond a static view of psychopathology, by providing an intuitive characterization of the role of specific symptoms in contributing to clinical trajectories over time. Secondly, we show that a Graph-Signal-Processing approach, can exploit knowledge of longitudinal interactions between symptoms, to predict clinical trajectories at the level of the individual. We test our approaches in two independent samples of individuals with genetic and clinical vulnerability for developing psychosis. Novel network approaches can allow to embrace the dynamic complexity of early psychopathology and help pave the way towards a more a personalized approach to clinical care.
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Affiliation(s)
- Corrado Sandini
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Daniela Zöller
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland.,Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Maude Schneider
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland.,Center for Contextual Psychiatry, Research Group Psychiatry, Department of Neuroscience, KU Leuven, Leuven, Belgium
| | - Anjali Tarun
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Marco Armondo
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Barnaby Nelson
- Orygen, Parkville, Australia.,The Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Paul G Amminger
- Orygen, Parkville, Australia.,The Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia.,Department of Psychiatry and Psychotherapy, Clinical Division of Social Psychiatry, Medical University Vienna, Vienna, Austria
| | - Hok Pan Yuen
- Orygen, Parkville, Australia.,The Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Connie Markulev
- Orygen, Parkville, Australia.,The Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Monica R Schäffer
- The Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia.,Department of Psychiatry and Psychotherapy, Clinical Division of Social Psychiatry, Medical University Vienna, Vienna, Austria
| | - Nilufar Mossaheb
- Department of Psychiatry and Psychotherapy, Clinical Division of Social Psychiatry, Medical University Vienna, Vienna, Austria
| | - Monika Schlögelhofer
- Department of Psychiatry and Psychotherapy, Clinical Division of Social Psychiatry, Medical University Vienna, Vienna, Austria
| | - Stefan Smesny
- Department of Psychiatry and Psychotherapy, Clinical Division of Social Psychiatry, Medical University Vienna, Vienna, Austria
| | - Ian B Hickie
- Department of Psychiatry, University Hospital Jena, Jena, Germany
| | | | - Eric Yh Chen
- Child and Adolescent Psychiatric Service of the Canton of Zurich, Zurich, Switzerland
| | - Lieuwe de Haan
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Dorien H Nieman
- Department of Psychiatry, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | | | | | - Swapna Verma
- Institute of Mental Health, Singapore, Singapore
| | - Andrew Thompson
- Orygen, Parkville, Australia.,The Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia.,Division of Mental Health and Wellbeing, Warwick Medical School, University of Warwick, Coventry, United Kingdom.,North Warwickshire Early Intervention in Psychosis Service, Conventry and Warwickshire National Health Service Partnership Trust, Coventry, United Kingdom
| | - Alison Ruth Yung
- Orygen, Parkville, Australia.,The Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia.,Division of Psychology and Mental Health, University of Manchester, Manchester, United Kingdom.,Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
| | - Patrick D McGorry
- Orygen, Parkville, Australia.,The Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Dimitri Van De Ville
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Stephan Eliez
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland.,Department of Genetic Medicine and Development, University of Geneva School of Medicine, Geneva, Switzerland
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87
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Moody JF, Adluru N, Alexander AL, Field AS. The Connectomes: Methods of White Matter Tractography and Contributions of Resting State fMRI. Semin Ultrasound CT MR 2021; 42:507-522. [PMID: 34537118 DOI: 10.1053/j.sult.2021.07.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
A comprehensive mapping of the structural and functional circuitry of the brain is a major unresolved problem in contemporary neuroimaging research. Diffusion-weighted and functional MRI have provided investigators with the capability to assess structural and functional connectivity in-vivo, driven primarily by methods of white matter tractography and resting-state fMRI, respectively. These techniques have paved the way for the construction of the functional and structural connectomes, which are quantitative representations of brain architecture as neural networks, comprised of nodes and edges. The connectomes, typically depicted as matrices or graphs, possess topological properties that inherently characterize the strength, efficiency, and organization of the connections between distinct brain regions. Graph theory, a general mathematical framework for analyzing networks, can be implemented to derive metrics from the connectomes that are sensitive to changes in brain connectivity associated with age, sex, cognitive function, and disease. These quantities can be assessed at either the global (whole brain) or local levels, allowing for the identification of distinct regional connectivity hubs and associated localized brain networks, which together serve crucial roles in establishing the structural and functional architecture of the brain. As a result, structural and functional connectomes have each been employed to study the brain circuitry underlying early brain development, neuroplasticity, developmental disorders, psychopathology, epilepsy, aging, neurodegenerative disorders, and traumatic brain injury. While these studies have yielded important insights into brain structure, function, and pathology, a precise description of the innate relationship between functional and structural networks across the brain remains unachieved. To date, connectome research has merely scratched the surface of potential clinical applications and related characterizations of brain-wide connectivity. Continued advances in diffusion and functional MRI acquisition, the delineation of functional and structural networks, and the quantification of neural network properties in specific brain regions, will be invaluable to future progress in neuroimaging science.
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Affiliation(s)
- Jason F Moody
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI; Waisman Center, University of Wisconsin-Madison, Madison, WI
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI; Department of Radiology, University of Wisconsin-Madison, Madison, WI
| | - Andrew L Alexander
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI; Waisman Center, University of Wisconsin-Madison, Madison, WI
| | - Aaron S Field
- Department of Radiology, University of Wisconsin-Madison, Madison, WI.
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88
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Frau-Pascual A, Augustinack J, Varadarajan D, Yendiki A, Salat DH, Fischl B, Aganj I. Conductance-Based Structural Brain Connectivity in Aging and Dementia. Brain Connect 2021; 11:566-583. [PMID: 34042511 PMCID: PMC8558081 DOI: 10.1089/brain.2020.0903] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Background: Structural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer's disease (AD) progression. Methods: In this work, we used our recently proposed structural connectivity quantification measure derived from diffusion magnetic resonance imaging, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the second phase of Alzheimer's Disease Neuroimaging Initiative and third release in the Open Access Series of Imaging Studies data sets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these data sets to the Human Connectome Project data set, as a reference, and eventually validated externally on two cohorts of the European DTI Study in Dementia database. Results: Our analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among subcortical regions, and cingulate and temporal cortices. Discussion: Results indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anticorrelation in structural connections. Impact statement This work presents a methodology and a set of analyses that open new possibilities in the study of healthy and pathological aging. The methodology used here is sensitive to direct and indirect pathways in deriving brain connectivity measures from diffusion-weighted magnetic resonance imaging, and therefore provides information that many state-of-the-art methods do not account for. As a result, this technique may provide the research community with ways to detect subtle effects of healthy aging and Alzheimer's disease.
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Affiliation(s)
- Aina Frau-Pascual
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Divya Varadarajan
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - David H. Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Iman Aganj
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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89
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Chen N, Liu G, Guo M, Li Y, Yao Z, Hu B. Calcarine as a bridge between brain function and structure in irritable bowel syndrome: A multiplex network analysis. J Gastroenterol Hepatol 2021; 36:2408-2415. [PMID: 33354807 DOI: 10.1111/jgh.15382] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 11/09/2020] [Accepted: 12/16/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIM Jointly analyzing structural and functional brain networks enables a better understanding of pathological underpinnings of irritable bowel syndrome (IBS). Multiplex network analysis provides a novel framework to study complex networks consisting of different types of connectivity patterns in multimodal data. METHODS In the present work, we integrated functional and structural networks to a multiplex network. Then, the multiplex metrics and the inner-layer/inter-layer hub nodes were investigated through 34 patients with IBS and 33 healthy controls. RESULTS Significantly differential multiplex degree in both left and right parts of calcarine was found, and meanwhile, IBS patients lost inner-layer hub properties in these regions. In addition, the left fusiform was no longer practicing as an inner-layer hub node, while the right median cingulate acted as a new inner-layer hub node in the IBS patients. Besides, the right calcarine, which lost its inner-layer hub identity, became a new inter-layer hub node, and the multiplex degree of the left hippocampus, which lost its inter-layer hub identity in IBS patients, was significantly positively correlated with the IBS Symptom Severity Score scores. CONCLUSIONS Inner-layer hub nodes of multiplex networks were preferentially vulnerable, and some inner-layer hub nodes would convert into inter-layer hub nodes in IBS patients. Besides, the inter-layer hub nodes might be influenced by IBS severity and therefore converted to general nodes.
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Affiliation(s)
- Nan Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Man Guo
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yongchao Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.,Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University and Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,Ministry of Education, Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Lanzhou, China
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90
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Yu M, Sporns O, Saykin AJ. The human connectome in Alzheimer disease - relationship to biomarkers and genetics. Nat Rev Neurol 2021; 17:545-563. [PMID: 34285392 PMCID: PMC8403643 DOI: 10.1038/s41582-021-00529-1] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/10/2021] [Indexed: 02/06/2023]
Abstract
The pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the patterns of amyloid-β and tau accumulation and spread, providing insights into the neurobiological mechanisms of the disease. In addition, the detection of gene-related connectome changes might aid in the early diagnosis of AD and facilitate the development of personalized therapeutic strategies that are effective at earlier stages of the disease spectrum. In this article, we review studies of the associations between connectome changes and amyloid-β and tau pathologies as well as molecular genetics in different subtypes and stages of AD. We also highlight the utility of connectome-derived computational models for replicating empirical findings and for tracking and predicting the progression of biomarker-indicated AD pathophysiology.
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Affiliation(s)
- Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Network Science Institute, Bloomington, IN, USA
| | - Olaf Sporns
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Network Science Institute, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Andrew J Saykin
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
- Indiana University Network Science Institute, Bloomington, IN, USA.
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91
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Small Neuron-Derived Extracellular Vesicles from Individuals with Down Syndrome Propagate Tau Pathology in the Wildtype Mouse Brain. J Clin Med 2021; 10:jcm10173931. [PMID: 34501378 PMCID: PMC8432237 DOI: 10.3390/jcm10173931] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 08/24/2021] [Accepted: 08/28/2021] [Indexed: 12/11/2022] Open
Abstract
Individuals with Down syndrome (DS) exhibit Alzheimer's disease (AD) pathology at a young age, including amyloid plaques and neurofibrillary tangles (NFTs). Tau pathology can spread via extracellular vesicles, such as exosomes. The cargo of neuron-derived small extracellular vesicles (NDEVs) from individuals with DS contains p-Tau at an early age. The goal of the study was to investigate whether NDEVs isolated from the blood of individuals with DS can spread Tau pathology in the brain of wildtype mice. We purified NDEVs from the plasma of patients with DS-AD and controls and injected small quantities using stereotaxic surgery into the dorsal hippocampus of adult wildtype mice. Seeding competent Tau conformers were amplified in vitro from DS-AD NDEVs but not NDEVs from controls. One month or 4 months post-injection, we examined Tau pathology in mouse brains. We found abundant p-Tau immunostaining in the hippocampus of the mice injected with DS-AD NDEVs compared to injections of age-matched control NDEVs. Double labeling with neuronal and glial markers showed that p-Tau staining was largely found in neurons and, to a lesser extent, in glial cells and that p-Tau immunostaining was spreading along the corpus callosum and the medio-lateral axis of the hippocampus. These studies demonstrate that NDEVs from DS-AD patients exhibit Tau seeding capacity and give rise to tangle-like intracellular inclusions.
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92
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Amico E, Abbas K, Duong-Tran DA, Tipnis U, Rajapandian M, Chumin E, Ventresca M, Harezlak J, Goñi J. Toward an information theoretical description of communication in brain networks. Netw Neurosci 2021; 5:646-665. [PMID: 34746621 PMCID: PMC8567835 DOI: 10.1162/netn_a_00185] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 01/18/2021] [Indexed: 11/21/2022] Open
Abstract
Modeling communication dynamics in the brain is a key challenge in network neuroscience. We present here a framework that combines two measurements for any system where different communication processes are taking place on top of a fixed structural topology: path processing score (PPS) estimates how much the brain signal has changed or has been transformed between any two brain regions (source and target); path broadcasting strength (PBS) estimates the propagation of the signal through edges adjacent to the path being assessed. We use PPS and PBS to explore communication dynamics in large-scale brain networks. We show that brain communication dynamics can be divided into three main "communication regimes" of information transfer: absent communication (no communication happening); relay communication (information is being transferred almost intact); and transducted communication (the information is being transformed). We use PBS to categorize brain regions based on the way they broadcast information. Subcortical regions are mainly direct broadcasters to multiple receivers; Temporal and frontal nodes mainly operate as broadcast relay brain stations; visual and somatomotor cortices act as multichannel transducted broadcasters. This work paves the way toward the field of brain network information theory by providing a principled methodology to explore communication dynamics in large-scale brain networks.
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Affiliation(s)
- Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Duy Anh Duong-Tran
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Uttara Tipnis
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | | | - Evgeny Chumin
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Mario Ventresca
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, Bloomington, IN, USA
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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93
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Functional harmonics reveal multi-dimensional basis functions underlying cortical organization. Cell Rep 2021; 36:109554. [PMID: 34433059 PMCID: PMC8411120 DOI: 10.1016/j.celrep.2021.109554] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 04/06/2021] [Accepted: 07/27/2021] [Indexed: 11/27/2022] Open
Abstract
The human brain consists of specialized areas that flexibly interact to form a multitude of functional networks. Complementary to this notion of modular organization, brain function has been shown to vary along a smooth continuum across the whole cortex. We demonstrate a mathematical framework that accounts for both of these perspectives: harmonic modes. We calculate the harmonic modes of the brain's functional connectivity graph, called "functional harmonics," revealing a multi-dimensional, frequency-ordered set of basis functions. Functional harmonics link characteristics of cortical organization across several spatial scales, capturing aspects of intra-areal organizational features (retinotopy, somatotopy), delineating brain areas, and explaining macroscopic functional networks as well as global cortical gradients. Furthermore, we show how the activity patterns elicited by seven different tasks are reconstructed from a very small subset of functional harmonics. Our results suggest that the principle of harmonicity, ubiquitous in nature, also underlies functional cortical organization in the human brain.
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94
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Raj A, Tora V, Gao X, Cho H, Choi JY, Ryu YH, Lyoo CH, Franchi B. Combined Model of Aggregation and Network Diffusion Recapitulates Alzheimer's Regional Tau-Positron Emission Tomography. Brain Connect 2021; 11:624-638. [PMID: 33947253 DOI: 10.1089/brain.2020.0841] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Background: Alzheimer's disease involves widespread and progressive deposition of misfolded protein tau (τ), first appearing in the entorhinal cortex, coagulating in longer polymers and insoluble fibrils. There is mounting evidence for "prion-like" trans-neuronal transmission, whereby misfolded proteins cascade along neuronal pathways, giving rise to networked spread. However, the cause-effect mechanisms by which various oligomeric τ species are produced, aggregate, and disseminate are unknown. The question of how protein aggregation and subsequent spread lead to stereotyped progression in the Alzheimer brain remains unresolved. Materials and Methods: We address these questions by using mathematically precise parsimonious modeling of these pathophysiological processes, extrapolated to the whole brain. We model three key processes: τ monomer production; aggregation into oligomers and then into tangles; and the spatiotemporal progression of misfolded τ as it ramifies into neural circuits via the brain connectome. We model monomer seeding and production at the entorhinal cortex, aggregation using Smoluchowski equations; and networked spread using our prior Network-Diffusion model. Results: This combined aggregation-network-diffusion model exhibits all hallmarks of τ progression seen in human patients. Unlike previous theoretical studies of protein aggregation, we present here an empirical validation on in vivo imaging and fluid τ measurements from large datasets. The model accurately captures not just the spatial distribution of empirical regional τ and atrophy but also patients' cerebrospinal fluid phosphorylated τ profiles as a function of disease progression. Conclusion: This unified quantitative and testable model has the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing in silico.
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California, USA
| | - Veronica Tora
- Dipartimento di Matematica, Universita' di Bologna, Bologna, Italy
| | - Xiao Gao
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California, USA
| | - Hanna Cho
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seodaemun-gu, Republic of Korea
| | - Jae Yong Choi
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seodaemun-gu, Republic of Korea
- Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul, Republic of Korea
| | - Young Hoon Ryu
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seodaemun-gu, Republic of Korea
| | - Chul Hyoung Lyoo
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seodaemun-gu, Republic of Korea
| | - Bruno Franchi
- Dipartimento di Matematica, Universita' di Bologna, Bologna, Italy
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95
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Schäfer A, Peirlinck M, Linka K, Kuhl E. Bayesian Physics-Based Modeling of Tau Propagation in Alzheimer's Disease. Front Physiol 2021; 12:702975. [PMID: 34335308 PMCID: PMC8322942 DOI: 10.3389/fphys.2021.702975] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 06/22/2021] [Indexed: 11/24/2022] Open
Abstract
Amyloid-β and hyperphosphorylated tau protein are known drivers of neuropathology in Alzheimer's disease. Tau in particular spreads in the brains of patients following a spatiotemporal pattern that is highly sterotypical and correlated with subsequent neurodegeneration. Novel medical imaging techniques can now visualize the distribution of tau in the brain in vivo, allowing for new insights to the dynamics of this biomarker. Here we personalize a network diffusion model with global spreading and local production terms to longitudinal tau positron emission tomography data of 76 subjects from the Alzheimer's Disease Neuroimaging Initiative. We use Bayesian inference with a hierarchical prior structure to infer means and credible intervals for our model parameters on group and subject levels. Our results show that the group average protein production rate for amyloid positive subjects is significantly higher with 0.019±0.27/yr, than that for amyloid negative subjects with -0.143±0.21/yr (p = 0.0075). These results support the hypothesis that amyloid pathology drives tau pathology. The calibrated model could serve as a valuable clinical tool to identify optimal time points for follow-up scans and predict the timeline of disease progression.
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Affiliation(s)
- Amelie Schäfer
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| | - Mathias Peirlinck
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| | - Kevin Linka
- Institute of Continuum and Materials Mechanics, Hamburg University of Technology, Hamburg, Germany
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
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96
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Torok J, Maia PD, Verma P, Mezias C, Raj A. Emergence of directional bias in tau deposition from axonal transport dynamics. PLoS Comput Biol 2021; 17:e1009258. [PMID: 34314441 PMCID: PMC8345857 DOI: 10.1371/journal.pcbi.1009258] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 08/06/2021] [Accepted: 07/07/2021] [Indexed: 12/11/2022] Open
Abstract
Defects in axonal transport may partly underpin the differences between the observed pathophysiology of Alzheimer's disease (AD) and that of other non-amyloidogenic tauopathies. Particularly, pathological tau variants may have molecular properties that dysregulate motor proteins responsible for the anterograde-directed transport of tau in a disease-specific fashion. Here we develop the first computational model of tau-modified axonal transport that produces directional biases in the spread of tau pathology. We simulated the spatiotemporal profiles of soluble and insoluble tau species in a multicompartment, two-neuron system using biologically plausible parameters and time scales. Changes in the balance of tau transport feedback parameters can elicit anterograde and retrograde biases in the distributions of soluble and insoluble tau between compartments in the system. Aggregation and fragmentation parameters can also perturb this balance, suggesting a complex interplay between these distinct molecular processes. Critically, we show that the model faithfully recreates the characteristic network spread biases in both AD-like and non-AD-like mouse tauopathy models. Tau transport feedback may therefore help link microscopic differences in tau conformational states and the resulting variety in clinical presentations.
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Affiliation(s)
- Justin Torok
- Department of Computational Biology and Medicine, Weill Cornell Medical School, New York, New York, United States of America
| | - Pedro D. Maia
- Department of Mathematics, University of Texas at Arlington, Arlington, Texas, United States of America
| | - Parul Verma
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California, United States of America
| | - Christopher Mezias
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California, United States of America
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California, United States of America
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97
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Interhemispheric co-alteration of brain homotopic regions. Brain Struct Funct 2021; 226:2181-2204. [PMID: 34170391 PMCID: PMC8354999 DOI: 10.1007/s00429-021-02318-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 06/07/2021] [Indexed: 11/11/2022]
Abstract
Asymmetries in gray matter alterations raise important issues regarding the pathological co-alteration between hemispheres. Since homotopic areas are the most functionally connected sites between hemispheres and gray matter co-alterations depend on connectivity patterns, it is likely that this relationship might be mirrored in homologous interhemispheric co-altered areas. To explore this issue, we analyzed data of patients with Alzheimer’s disease, schizophrenia, bipolar disorder and depressive disorder from the BrainMap voxel-based morphometry database. We calculated a map showing the pathological homotopic anatomical co-alteration between homologous brain areas. This map was compared with the meta-analytic homotopic connectivity map obtained from the BrainMap functional database, so as to have a meta-analytic connectivity modeling map between homologous areas. We applied an empirical Bayesian technique so as to determine a directional pathological co-alteration on the basis of the possible tendencies in the conditional probability of being co-altered of homologous brain areas. Our analysis provides evidence that: the hemispheric homologous areas appear to be anatomically co-altered; this pathological co-alteration is similar to the pattern of connectivity exhibited by the couples of homologues; the probability to find alterations in the areas of the left hemisphere seems to be greater when their right homologues are also altered than vice versa, an intriguing asymmetry that deserves to be further investigated and explained.
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98
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Cornblath EJ, Li HL, Changolkar L, Zhang B, Brown HJ, Gathagan RJ, Olufemi MF, Trojanowski JQ, Bassett DS, Lee VMY, Henderson MX. Computational modeling of tau pathology spread reveals patterns of regional vulnerability and the impact of a genetic risk factor. SCIENCE ADVANCES 2021; 7:eabg6677. [PMID: 34108219 PMCID: PMC8189700 DOI: 10.1126/sciadv.abg6677] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 04/21/2021] [Indexed: 05/09/2023]
Abstract
Neuropathological staging studies have suggested that tau pathology spreads through the brain in Alzheimer's disease (AD) and other tauopathies, but it is unclear how neuroanatomical connections, spatial proximity, and regional vulnerability contribute. In this study, we seed tau pathology in the brains of nontransgenic mice with AD tau and quantify pathology development over 9 months in 134 brain regions. Network modeling of pathology progression shows that diffusion through the connectome is the best predictor of tau pathology patterns. Further, deviations from pure neuroanatomical spread are used to estimate regional vulnerability to tau pathology and identify related gene expression patterns. Last, we show that pathology spread is altered in mice harboring a mutation in leucine-rich repeat kinase 2. While tau pathology spread is still constrained by anatomical connectivity in these mice, it spreads preferentially in a retrograde direction. This study provides a framework for understanding neuropathological progression in tauopathies.
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Affiliation(s)
- Eli J Cornblath
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Howard L Li
- Institute on Aging and Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lakshmi Changolkar
- Institute on Aging and Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bin Zhang
- Institute on Aging and Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hannah J Brown
- Institute on Aging and Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ronald J Gathagan
- Institute on Aging and Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Modupe F Olufemi
- Institute on Aging and Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John Q Trojanowski
- Institute on Aging and Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Virginia M Y Lee
- Institute on Aging and Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael X Henderson
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI 49503, USA.
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99
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Raj A. Graph Models of Pathology Spread in Alzheimer's Disease: An Alternative to Conventional Graph Theoretic Analysis. Brain Connect 2021; 11:799-814. [PMID: 33858198 DOI: 10.1089/brain.2020.0905] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Background: Graph theory and connectomics are new techniques for uncovering disease-induced changes in the brain's structural network. Most prior studied have focused on network statistics as biomarkers of disease. However, an emerging body of work involves exploring how the network serves as a conduit for the propagation of disease factors in the brain and has successfully mapped the functional and pathological consequences of disease propagation. In Alzheimer's disease (AD), progressive deposition of misfolded proteins amyloid and tau is well-known to follow fiber projections, under a "prion-like" trans-neuronal transmission mechanism, through which misfolded proteins cascade along neuronal pathways, giving rise to network spread. Methods: In this review, we survey the state of the art in mathematical modeling of connectome-mediated pathology spread in AD. Then we address several open questions that are amenable to mathematically precise parsimonious modeling of pathophysiological processes, extrapolated to the whole brain. We specifically identify current formal models of how misfolded proteins are produced, aggregate, and disseminate in brain circuits, and attempt to understand how this process leads to stereotyped progression in Alzheimer's and other related diseases. Conclusion: This review serves to unify current efforts in modeling of AD progression that together have the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing in silico. Impact statement Graph theory is a powerful new approach that is transforming the study of brain processes. There do not exist many focused reviews of the subfield of graph modeling of how Alzheimer's and other dementias propagate within the brain network, and how these processes can be mapped mathematically. By providing timely and topical review of this subfield, we fill a critical gap in the community and present a unified view that can serve as an in silico test-bed for future hypothesis generation and testing. We also point to several open and unaddressed questions and controversies that future practitioners can tackle.
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California, USA
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100
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Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box. Commun Biol 2021; 4:614. [PMID: 34021244 PMCID: PMC8140107 DOI: 10.1038/s42003-021-02133-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/22/2021] [Indexed: 02/04/2023] Open
Abstract
Understanding and treating heterogeneous brain disorders requires specialized techniques spanning genetics, proteomics, and neuroimaging. Designed to meet this need, NeuroPM-box is a user-friendly, open-access, multi-tool cross-platform software capable of characterizing multiscale and multifactorial neuropathological mechanisms. Using advanced analytical modeling for molecular, histopathological, brain-imaging and/or clinical evaluations, this framework has multiple applications, validated here with synthetic (N > 2900), in-vivo (N = 911) and post-mortem (N = 736) neurodegenerative data, and including the ability to characterize: (i) the series of sequential states (genetic, histopathological, imaging or clinical alterations) covering decades of disease progression, (ii) concurrent intra-brain spreading of pathological factors (e.g., amyloid, tau and alpha-synuclein proteins), (iii) synergistic interactions between multiple biological factors (e.g., toxic tau effects on brain atrophy), and (iv) biologically-defined patient stratification based on disease heterogeneity and/or therapeutic needs. This freely available toolbox ( neuropm-lab.com/neuropm-box.html ) could contribute significantly to a better understanding of complex brain processes and accelerating the implementation of Precision Medicine in Neurology.
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