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Russo ML, Ayala G, Neal D, Rogalsky AE, Ahmad S, Musial TF, Pearlman M, Bean LA, Farooqi AK, Ahmed A, Castaneda A, Patel A, Parduhn Z, Haddad LG, Gabriel A, Disterhoft JF, Nicholson DA. Alzheimer's-linked axonal changes accompany elevated antidromic action potential failure rate in aged mice. Brain Res 2024; 1841:149083. [PMID: 38866308 PMCID: PMC11323114 DOI: 10.1016/j.brainres.2024.149083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 04/22/2024] [Accepted: 06/09/2024] [Indexed: 06/14/2024]
Abstract
Alzheimer's disease (AD) affects both grey and white matter (WM), but considerably more is known about the former. Interestingly, WM disruption has been consistently observed and thoroughly described using imaging modalities, particularly MRI which has shown WM functional disconnections between the hippocampus and other brain regions during AD pathogenesis when early neurodegeneration and synapse loss are also evident. Nonetheless, high-resolution structural and functional analyses of WM during AD pathogenesis remain scarce. Given the importance of the myelinated axons in the WM for conveying information across brain regions, such studies will provide valuable information on the cellular drivers and consequences of WM disruption that contribute to the characteristic cognitive decline of AD. Here, we employed a multi-scale approach to investigate hippocampal WM disruption during AD pathogenesis and determine whether hippocampal WM changes accompany the well-documented grey matter losses. Our data indicate that ultrastructural myelin disruption is elevated in the alveus in human AD cases and increases with age in 5xFAD mice. Unreliable action potential propagation and changes to sodium channel expression at the node of Ranvier co-emerged with this deterioration. These findings provide important insight to the neurobiological substrates and functional consequences of decreased WM integrity and are consistent with the notion that hippocampal disconnection contributes to cognitive changes in AD.
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Affiliation(s)
- Matthew L Russo
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA.
| | - Gelique Ayala
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Demetria Neal
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Annalise E Rogalsky
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Suzan Ahmad
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Timothy F Musial
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Morgan Pearlman
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Linda A Bean
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Anise K Farooqi
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Aysha Ahmed
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Adrian Castaneda
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Aneri Patel
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Zachary Parduhn
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Loreece G Haddad
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Ashley Gabriel
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - John F Disterhoft
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Daniel A Nicholson
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
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Hojjati SH, Butler TA, Luchsinger JA, Benitez R, de Leon M, Nayak S, Razlighi QR, Chiang GC. Increased between-network connectivity: A risk factor for tau elevation and disease progression. Neurosci Lett 2024; 840:137943. [PMID: 39153526 DOI: 10.1016/j.neulet.2024.137943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 06/26/2024] [Accepted: 08/14/2024] [Indexed: 08/19/2024]
Abstract
One of the pathologic hallmarks of Alzheimer's disease (AD) is neurofibrillary tau tangles. Despite our knowledge that tau typically initiates in the medial temporal lobe (MTL), the mechanisms driving tau to spread beyond MTL remain unclear. Emerging evidence reveals distinct patterns of functional connectivity change during aging and preclinical AD: while connectivity within-network decreases, connectivity between-network increases. Building upon increased between-network connectivity, our study hypothesizes that this increase may play a critical role in facilitating tau spread in early stages. We conducted a longitudinal study over two to three years intervals on a cohort of 46 healthy elderly participants (mean age 64.23 ± 3.15 years, 26 females). Subjects were examined clinically and utilizing advanced imaging techniques that included resting-state functional MRI (rs-fMRI), structural magnetic resonance imaging (MRI), and a second-generation positron emission tomography (PET) tau tracer, 18F-MK6240. Through unsupervised agglomerative clustering and increase in between-network connectivity, we successfully identified individuals at increased risk of future tau elevation and AD progression. Our analysis revealed that individuals with increased between-network connectivity are more likely to experience more future tau deposition, entorhinal cortex thinning, and lower selective reminding test (SRT) delayed scores. Additionally, in the limbic network, we found a strong association between tau progression and increased between-network connectivity, which was mainly driven by beta-amyloid (Aβ) positive participants. These findings provide evidence for the hypothesis that an increase in between-network connectivity predicts future tau deposition and AD progression, also enhancing our understanding of AD pathogenesis in the preclinical stages.
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Affiliation(s)
- Seyed Hani Hojjati
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United States.
| | - Tracy A Butler
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United States
| | - José A Luchsinger
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States; Department of Epidemiology, Columbia University Irving Medical Center, New York, NY, United States
| | - Richard Benitez
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States
| | - Mony de Leon
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United States
| | - Siddharth Nayak
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United States
| | - Qolamreza R Razlighi
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United States
| | - Gloria C Chiang
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United States
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Catterson JH, Mouofo EN, López De Toledo Soler I, Lean G, Dlamini S, Liddell P, Voong G, Katsinelos T, Wang YC, Schoovaerts N, Verstreken P, Spires-Jones TL, Durrant CS. Drosophila appear resistant to trans-synaptic tau propagation. Brain Commun 2024; 6:fcae256. [PMID: 39130515 PMCID: PMC11316205 DOI: 10.1093/braincomms/fcae256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 05/22/2024] [Accepted: 08/07/2024] [Indexed: 08/13/2024] Open
Abstract
Alzheimer's disease is the most common cause of dementia in the elderly, prompting extensive efforts to pinpoint novel therapeutic targets for effective intervention. Among the hallmark features of Alzheimer's disease is the development of neurofibrillary tangles comprised of hyperphosphorylated tau protein, whose progressive spread throughout the brain is associated with neuronal death. Trans-synaptic propagation of tau has been observed in mouse models, and indirect evidence for tau spread via synapses has been observed in human Alzheimer's disease. Halting tau propagation is a promising therapeutic target for Alzheimer's disease; thus, a scalable model system to screen for modifiers of tau spread would be very useful for the field. To this end, we sought to emulate the trans-synaptic spread of human tau in Drosophila melanogaster. Employing the trans-Tango circuit mapping technique, we investigated whether tau spreads between synaptically connected neurons. Immunohistochemistry and confocal imaging were used to look for tau propagation. Examination of hundreds of flies expressing four different human tau constructs in two distinct neuronal populations reveals a robust resistance in Drosophila to the trans-synaptic spread of human tau. This resistance persisted in lines with concurrent expression of amyloid-β, in lines with global human tau knock-in to provide a template for human tau in downstream neurons, and with manipulations of temperature. These negative data are important for the field as we establish that Drosophila expressing human tau in subsets of neurons are unlikely to be useful to perform screens to find mechanisms to reduce the trans-synaptic spread of tau. The inherent resistance observed in Drosophila may serve as a valuable clue, offering insights into strategies for impeding tau spread in future studies.
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Affiliation(s)
- James H Catterson
- Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK
- UK Dementia Research Institute, The University of Edinburgh, Edinburgh EH8 9XD, UK
| | - Edmond N Mouofo
- Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK
- UK Dementia Research Institute, The University of Edinburgh, Edinburgh EH8 9XD, UK
| | | | - Gillian Lean
- Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK
| | - Stella Dlamini
- Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK
| | - Phoebe Liddell
- Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK
| | - Graham Voong
- Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK
| | - Taxiarchis Katsinelos
- Schaller Research Group at the University of Heidelberg and the DKFZ, German Cancer Research Center, Proteostasis in Neurodegenerative Disease (B180), INF 581, 69120 Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, INF 234, 69120 Heidelberg, Germany
| | - Yu-Chun Wang
- VIB-KU Leuven Center for Brain & Disease Research, Department of Neurosciences, 3000 Leuven, Belgium
- KU Leuven, Department of Neurosciences, Leuven Brain Institute, 3000 Leuven, Belgium
| | - Nils Schoovaerts
- VIB-KU Leuven Center for Brain & Disease Research, Department of Neurosciences, 3000 Leuven, Belgium
- KU Leuven, Department of Neurosciences, Leuven Brain Institute, 3000 Leuven, Belgium
| | - Patrik Verstreken
- VIB-KU Leuven Center for Brain & Disease Research, Department of Neurosciences, 3000 Leuven, Belgium
- KU Leuven, Department of Neurosciences, Leuven Brain Institute, 3000 Leuven, Belgium
| | - Tara L Spires-Jones
- Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK
- UK Dementia Research Institute, The University of Edinburgh, Edinburgh EH8 9XD, UK
| | - Claire S Durrant
- Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh EH8 9XD, UK
- UK Dementia Research Institute, The University of Edinburgh, Edinburgh EH8 9XD, UK
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Raj A, Torok J, Ranasinghe K. Understanding the complex interplay between tau, amyloid and the network in the spatiotemporal progression of Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583407. [PMID: 38559176 PMCID: PMC10979926 DOI: 10.1101/2024.03.05.583407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
INTRODUCTION The interaction of amyloid and tau in neurodegenerative diseases is a central feature of AD pathophysiology. While experimental studies point to various interaction mechanisms, their causal direction and mode (local, remote or network-mediated) remain unknown in human subjects. The aim of this study was to compare mathematical reaction-diffusion models encoding distinct cross-species couplings to identify which interactions were key to model success. METHODS We tested competing mathematical models of network spread, aggregation, and amyloid-tau interactions on publicly available data from ADNI. RESULTS Although network spread models captured the spatiotemporal evolution of tau and amyloid in human subjects, the model including a one-way amyloid-to-tau aggregation interaction performed best. DISCUSSION This mathematical exposition of the "pas de deux" of co-evolving proteins provides quantitative, whole-brain support to the concept of amyloid-facilitated-tauopathy rather than the classic amyloid-cascade or pure-tau hypotheses, and helps explain certain known but poorly understood aspects of AD.
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Roemer SN, Brendel M, Gnörich J, Malpetti M, Zaganjori M, Quattrone A, Gross M, Steward A, Dewenter A, Wagner F, Dehsarvi A, Ferschmann C, Wall S, Palleis C, Rauchmann BS, Katzdobler S, Jäck A, Stockbauer A, Fietzek UM, Bernhardt AM, Weidinger E, Zwergal A, Stöcklein S, Perneczky R, Barthel H, Sabri O, Levin J, Höglinger GU, Franzmeier N. Subcortical tau is linked to hypoperfusion in connected cortical regions in 4-repeat tauopathies. Brain 2024; 147:2428-2439. [PMID: 38842726 DOI: 10.1093/brain/awae174] [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: 10/17/2023] [Revised: 02/07/2024] [Accepted: 04/28/2024] [Indexed: 06/07/2024] Open
Abstract
Four-repeat (4R) tauopathies are neurodegenerative diseases characterized by cerebral accumulation of 4R tau pathology. The most prominent 4R tauopathies are progressive supranuclear palsy (PSP) and corticobasal degeneration characterized by subcortical tau accumulation and cortical neuronal dysfunction, as shown by PET-assessed hypoperfusion and glucose hypometabolism. Yet, there is a spatial mismatch between subcortical tau deposition patterns and cortical neuronal dysfunction, and it is unclear how these two pathological brain changes are interrelated. Here, we hypothesized that subcortical tau pathology induces remote neuronal dysfunction in functionally connected cortical regions to test a pathophysiological model that mechanistically links subcortical tau accumulation to cortical neuronal dysfunction in 4R tauopathies. We included 51 Aβ-negative patients with clinically diagnosed PSP variants (n = 26) or corticobasal syndrome (n = 25) who underwent structural MRI and 18F-PI-2620 tau-PET. 18F-PI-2620 tau-PET was recorded using a dynamic one-stop-shop acquisition protocol to determine an early 0.5-2.5 min post tracer-injection perfusion window for assessing cortical neuronal dysfunction, as well as a 20-40 min post tracer-injection window to determine 4R-tau load. Perfusion-PET (i.e. early window) was assessed in 200 cortical regions, and tau-PET was assessed in 32 subcortical regions of established functional brain atlases. We determined tau epicentres as subcortical regions with the highest 18F-PI-2620 tau-PET signal and assessed the connectivity of tau epicentres to cortical regions of interest using a resting-state functional MRI-based functional connectivity template derived from 69 healthy elderly controls from the ADNI cohort. Using linear regression, we assessed whether: (i) higher subcortical tau-PET was associated with reduced cortical perfusion; and (ii) cortical perfusion reductions were observed preferentially in regions closely connected to subcortical tau epicentres. As hypothesized, higher subcortical tau-PET was associated with overall lower cortical perfusion, which remained consistent when controlling for cortical tau-PET. Using group-average and subject-level PET data, we found that the seed-based connectivity pattern of subcortical tau epicentres aligned with cortical perfusion patterns, where cortical regions that were more closely connected to the tau epicentre showed lower perfusion. Together, subcortical tau-accumulation is associated with remote perfusion reductions indicative of neuronal dysfunction in functionally connected cortical regions in 4R-tauopathies. This suggests that subcortical tau pathology may induce cortical dysfunction, which may contribute to clinical disease manifestation and clinical heterogeneity.
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Affiliation(s)
- Sebastian N Roemer
- Department of Neurology, University Hospital, LMU Munich, 81377 Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany
| | - Matthias Brendel
- Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany
| | - Johannes Gnörich
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Maura Malpetti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 1TN, UK
| | - Mirlind Zaganjori
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Andrea Quattrone
- Institute of Neurology, Magna Graecia University, 88100 Catanzaro, Italy
| | - Mattes Gross
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Anna Steward
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany
| | - Fabian Wagner
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany
| | - Amir Dehsarvi
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany
| | - Christian Ferschmann
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Stephan Wall
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Carla Palleis
- Department of Neurology, University Hospital, LMU Munich, 81377 Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany
| | - Boris S Rauchmann
- Department of Neuroradiology, University Hospital, LMU Munich, 81377 Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, 80336 Munich, Germany
| | - Sabrina Katzdobler
- Department of Neurology, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Alexander Jäck
- Department of Neurology, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Anna Stockbauer
- Department of Neurology, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Urban M Fietzek
- Department of Neurology, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Alexander M Bernhardt
- Department of Neurology, University Hospital, LMU Munich, 81377 Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany
| | - Endy Weidinger
- Department of Neurology, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Andreas Zwergal
- Department of Neurology, University Hospital, LMU Munich, 81377 Munich, Germany
- German Center for Vertigo and Balance Disorders (DSGZ), University Hospital, LMU Munich, 81377 Munich, Germany
| | - Sophia Stöcklein
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Robert Perneczky
- Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, 80336 Munich, Germany
- Ageing Epidemiology (AGE) Research Unit, School of Public Health, Imperial College London, London SW7 2BX, UK
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield S10 2HQ, UK
| | - Henryk Barthel
- Department of Nuclear Medicine, University Hospital of Leipzig, 04103 Leipzig, Germany
| | - Osama Sabri
- Department of Nuclear Medicine, University Hospital of Leipzig, 04103 Leipzig, Germany
| | - Johannes Levin
- Department of Neurology, University Hospital, LMU Munich, 81377 Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany
| | - Günter U Höglinger
- Department of Neurology, University Hospital, LMU Munich, 81377 Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany
- Department of Psychiatry and Neurochemistry, University of Gothenburg, The Sahlgrenska Academy, Institute of Neuroscience and Physiology, SE 413 90 Mölndal and Gothenburg, Sweden
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Nabizadeh F. Disruption in functional networks mediated tau spreading in Alzheimer's disease. Brain Commun 2024; 6:fcae198. [PMID: 38978728 PMCID: PMC11227975 DOI: 10.1093/braincomms/fcae198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/27/2024] [Accepted: 06/07/2024] [Indexed: 07/10/2024] Open
Abstract
Alzheimer's disease may be conceptualized as a 'disconnection syndrome', characterized by the breakdown of neural connectivity within the brain as a result of amyloid-beta plaques, tau neurofibrillary tangles and other factors leading to progressive degeneration and shrinkage of neurons, along with synaptic dysfunction. It has been suggested that misfolded tau proteins spread through functional connections (known as 'prion-like' properties of tau). However, the local effect of tau spreading on the synaptic function and communication between regions is not well understood. I aimed to investigate how the spreading of tau aggregates through connections can locally influence functional connectivity. In total, the imaging data of 211 participants including 117 amyloid-beta-negative non-demented and 94 amyloid-beta-positive non-demented participants were recruited from the Alzheimer's Disease Neuroimaging Initiative. Furthermore, normative resting-state functional MRI connectomes were used to model tau spreading through functional connections, and functional MRI of the included participants was used to determine the effect of tau spreading on functional connectivity. I found that lower functional connectivity to tau epicentres is associated with tau spreading through functional connections in both amyloid-beta-negative and amyloid-beta-positive participants. Also, amyloid-beta-PET in tau epicentres mediated the association of tau spreading and functional connectivity to epicentres suggesting a partial mediating effect of amyloid-beta deposition in tau epicentres on the local effect of tau spreading on functional connectivity. My findings provide strong support for the notion that tau spreading through connection is locally associated with disrupted functional connectivity between tau epicentre and non-epicentre regions independent of amyloid-beta pathology. Also, I defined several groups based on the relationship between tau spreading and functional disconnection, which provides quantitative assessment to investigate susceptibility or resilience to functional disconnection related to tau spreading. I showed that amyloid-beta, other copathologies and the apolipoprotein E epsilon 4 allele can be a leading factor towards vulnerability to tau relative functional disconnection.
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Affiliation(s)
- Fardin Nabizadeh
- School of Medicine, Iran University of Medical Sciences, Tehran 441265421414, Iran
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7
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Ottoy J, Kang MS, Tan JXM, Boone L, Vos de Wael R, Park BY, Bezgin G, Lussier FZ, Pascoal TA, Rahmouni N, Stevenson J, Fernandez Arias J, Therriault J, Hong SJ, Stefanovic B, McLaurin J, Soucy JP, Gauthier S, Bernhardt BC, Black SE, Rosa-Neto P, Goubran M. Tau follows principal axes of functional and structural brain organization in Alzheimer's disease. Nat Commun 2024; 15:5031. [PMID: 38866759 PMCID: PMC11169286 DOI: 10.1038/s41467-024-49300-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 05/24/2024] [Indexed: 06/14/2024] Open
Abstract
Alzheimer's disease (AD) is a brain network disorder where pathological proteins accumulate through networks and drive cognitive decline. Yet, the role of network connectivity in facilitating this accumulation remains unclear. Using in-vivo multimodal imaging, we show that the distribution of tau and reactive microglia in humans follows spatial patterns of connectivity variation, the so-called gradients of brain organization. Notably, less distinct connectivity patterns ("gradient contraction") are associated with cognitive decline in regions with greater tau, suggesting an interaction between reduced network differentiation and tau on cognition. Furthermore, by modeling tau in subject-specific gradient space, we demonstrate that tau accumulation in the frontoparietal and temporo-occipital cortices is associated with greater baseline tau within their functionally and structurally connected hubs, respectively. Our work unveils a role for both functional and structural brain organization in pathology accumulation in AD, and supports subject-specific gradient space as a promising tool to map disease progression.
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Affiliation(s)
- Julie Ottoy
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Min Su Kang
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | | | - Lyndon Boone
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Bo-Yong Park
- Department of Data Science, Inha University, Incheon, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Gleb Bezgin
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
- Neuroinformatics for Personalized Medicine lab, Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Firoza Z Lussier
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Tharick A Pascoal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nesrine Rahmouni
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Jenna Stevenson
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Jaime Fernandez Arias
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Joseph Therriault
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Seok-Jun Hong
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Bojana Stefanovic
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Physical Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - JoAnne McLaurin
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Biological Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Jean-Paul Soucy
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Serge Gauthier
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Sandra E Black
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Medicine (Division of Neurology), University of Toronto, Toronto, ON, Canada
| | - Pedro Rosa-Neto
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Maged Goubran
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
- Physical Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.
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8
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Rapaka D, Tebogo MO, Mathew EM, Adiukwu PC, Bitra VR. Targeting papez circuit for cognitive dysfunction- insights into deep brain stimulation for Alzheimer's disease. Heliyon 2024; 10:e30574. [PMID: 38726200 PMCID: PMC11079300 DOI: 10.1016/j.heliyon.2024.e30574] [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: 06/30/2023] [Revised: 04/28/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024] Open
Abstract
Hippocampus is the most widely studied brain area coupled with impairment of memory in a variety of neurological diseases and Alzheimer's disease (AD). The limbic structures within the Papez circuit have been linked to various aspects of cognition. Unfortunately, the brain regions that include this memory circuit are often ignored in terms of understanding cognitive decline in these diseases. To properly comprehend where cognition problems originate, it is crucial to clarify any aberrant contributions from all components of a specific circuit -on both a local and a global level. The pharmacological treatments currently available are not long lasting. Deep Brain Stimulation (DBS) emerged as a new powerful therapeutic approach for alleviation of the cognitive dysfunctions. Metabolic, functional, electrophysiological, and imaging studies helped to find out the crucial nodes that can be accessible for DBS. Targeting these nodes within the memory circuit produced significant improvement in learning and memory by disrupting abnormal circuit activity and restoring the physiological network. Here, we provide an overview of the neuroanatomy of the circuit of Papez along with the mechanisms and various deep brain stimulation targets of the circuit structures which could be significant for improving cognitive dysfunctions in AD.
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Affiliation(s)
| | - Motshegwana O. Tebogo
- School of Pharmacy, Faculty of Health Sciences, University of Botswana, Gaborone, Botswana, P/Bag-0022
| | - Elizabeth M. Mathew
- School of Pharmacy, Faculty of Health Sciences, University of Botswana, Gaborone, Botswana, P/Bag-0022
| | | | - Veera Raghavulu Bitra
- School of Pharmacy, Faculty of Health Sciences, University of Botswana, Gaborone, Botswana, P/Bag-0022
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9
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Tao X, Zhu Z, Wang L, Li C, Sun L, Wang W, Gong W. Biomarkers of Aging and Relevant Evaluation Techniques: A Comprehensive Review. Aging Dis 2024; 15:977-1005. [PMID: 37611906 PMCID: PMC11081160 DOI: 10.14336/ad.2023.00808-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 08/08/2023] [Indexed: 08/25/2023] Open
Abstract
The risk of developing chronic illnesses and disabilities is increasing with age. To predict and prevent aging, biomarkers relevant to the aging process must be identified. This paper reviews the known molecular, cellular, and physiological biomarkers of aging. Moreover, we discuss the currently available technologies for identifying these biomarkers, and their applications and potential in aging research. We hope that this review will stimulate further research and innovation in this emerging and fast-growing field.
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Affiliation(s)
- Xue Tao
- Department of Research, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
| | - Ziman Zhu
- Beijing Rehabilitation Medicine Academy, Capital Medical University, Beijing, China.
| | - Liguo Wang
- Key Laboratory of Protein Sciences, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China.
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.
| | - Liwei Sun
- School of Biomedical Engineering, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.
| | - Wei Wang
- Department of Rehabilitation Radiology, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
| | - Weijun Gong
- Department of Neurological Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
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10
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Doering S, McCullough A, Gordon BA, Chen CD, McKay N, Hobbs D, Keefe S, Flores S, Scott J, Smith H, Jarman S, Jackson K, Hornbeck RC, Ances BM, Xiong C, Aschenbrenner AJ, Hassenstab J, Cruchaga C, Daniels A, Bateman RJ, Morris JC, Benzinger TLS. Deconstructing pathological tau by biological process in early stages of Alzheimer disease: a method for quantifying tau spatial spread in neuroimaging. EBioMedicine 2024; 103:105080. [PMID: 38552342 PMCID: PMC10995809 DOI: 10.1016/j.ebiom.2024.105080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Neuroimaging studies often quantify tau burden in standardized brain regions to assess Alzheimer disease (AD) progression. However, this method ignores another key biological process in which tau spreads to additional brain regions. We have developed a metric for calculating the extent tau pathology has spread throughout the brain and evaluate the relationship between this metric and tau burden across early stages of AD. METHODS 445 cross-sectional participants (aged ≥ 50) who had MRI, amyloid PET, tau PET, and clinical testing were separated into disease-stage groups based on amyloid positivity and cognitive status (older cognitively normal control, preclinical AD, and symptomatic AD). Tau burden and tau spatial spread were calculated for all participants. FINDINGS We found both tau metrics significantly elevated across increasing disease stages (p < 0.0001) and as a function of increasing amyloid burden for participants with preclinical (p < 0.0001, p = 0.0056) and symptomatic (p = 0.010, p = 0.0021) AD. An interaction was found between tau burden and tau spatial spread when predicting amyloid burden (p = 0.00013). Analyses of slope between tau metrics demonstrated more spread than burden in preclinical AD (β = 0.59), but then tau burden elevated relative to spread (β = 0.42) once participants had symptomatic AD, when the tau metrics became highly correlated (R = 0.83). INTERPRETATION Tau burden and tau spatial spread are both strong biomarkers for early AD but provide unique information, particularly at the preclinical stage. Tau spatial spread may demonstrate earlier changes than tau burden which could have broad impact in clinical trial design. FUNDING This research was supported by the Knight Alzheimer Disease Research Center (Knight ADRC, NIH grants P30AG066444, P01AG026276, P01AG003991), Dominantly Inherited Alzheimer Network (DIAN, NIH grants U01AG042791, U19AG03243808, R01AG052550-01A1, R01AG05255003), and the Barnes-Jewish Hospital Foundation Willman Scholar Fund.
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Affiliation(s)
- Stephanie Doering
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Austin McCullough
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Brian A Gordon
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Charles D Chen
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Nicole McKay
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Diana Hobbs
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Sarah Keefe
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Shaney Flores
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Jalen Scott
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Hunter Smith
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Stephen Jarman
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Kelley Jackson
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Russ C Hornbeck
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Beau M Ances
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Chengjie Xiong
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | | | - Jason Hassenstab
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Carlos Cruchaga
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Alisha Daniels
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Randall J Bateman
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - John C Morris
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
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11
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Wang M, Lu J, Zhang Y, Zhang Q, Wang L, Wu P, Brendel M, Rominger A, Shi K, Zhao Q, Jiang J, Zuo C. Characterization of tau propagation pattern and cascading hypometabolism from functional connectivity in Alzheimer's disease. Hum Brain Mapp 2024; 45:e26689. [PMID: 38703095 PMCID: PMC11069321 DOI: 10.1002/hbm.26689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/16/2024] [Accepted: 04/08/2024] [Indexed: 05/06/2024] Open
Abstract
Tau pathology and its spatial propagation in Alzheimer's disease (AD) play crucial roles in the neurodegenerative cascade leading to dementia. However, the underlying mechanisms linking tau spreading to glucose metabolism remain elusive. To address this, we aimed to examine the association between pathologic tau aggregation, functional connectivity, and cascading glucose metabolism and further explore the underlying interplay mechanisms. In this prospective cohort study, we enrolled 79 participants with 18F-Florzolotau positron emission tomography (PET), 18F-fluorodeoxyglucose PET, resting-state functional, and anatomical magnetic resonance imaging (MRI) images in the hospital-based Shanghai Memory Study. We employed generalized linear regression and correlation analyses to assess the associations between Florzolotau accumulation, functional connectivity, and glucose metabolism in whole-brain and network-specific manners. Causal mediation analysis was used to evaluate whether functional connectivity mediates the association between pathologic tau and cascading glucose metabolism. We examined 22 normal controls and 57 patients with AD. In the AD group, functional connectivity was associated with Florzolotau covariance (β = .837, r = 0.472, p < .001) and glucose covariance (β = 1.01, r = 0.499, p < .001). Brain regions with higher tau accumulation tend to be connected to other regions with high tau accumulation through functional connectivity or metabolic connectivity. Mediation analyses further suggest that functional connectivity partially modulates the influence of tau accumulation on downstream glucose metabolism (mediation proportion: 49.9%). Pathologic tau may affect functionally connected neurons directly, triggering downstream glucose metabolism changes. This study sheds light on the intricate relationship between tau pathology, functional connectivity, and downstream glucose metabolism, providing critical insights into AD pathophysiology and potential therapeutic targets.
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Affiliation(s)
- Min Wang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Jiaying Lu
- Department of Nuclear Medicine & PET Center, Huashan HospitalFudan UniversityShanghaiChina
- National Clinical Research Center for Aging and Medicine, Huashan HospitalFudan UniversityShanghaiChina
- National Center for Neurological Disorders, Huashan HospitalFudan UniversityShanghaiChina
| | - Ying Zhang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Qi Zhang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Luyao Wang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Ping Wu
- Department of Nuclear Medicine & PET Center, Huashan HospitalFudan UniversityShanghaiChina
- National Clinical Research Center for Aging and Medicine, Huashan HospitalFudan UniversityShanghaiChina
- National Center for Neurological Disorders, Huashan HospitalFudan UniversityShanghaiChina
| | | | - Axel Rominger
- Department of Nuclear Medicine, InselspitalBern University Hospital, University of BernBernSwitzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, InselspitalBern University Hospital, University of BernBernSwitzerland
- Computer Aided Medical Procedures, School of Computation, Information and TechnologyTechnical University of MunichMunichGermany
| | - Qianhua Zhao
- National Clinical Research Center for Aging and Medicine, Huashan HospitalFudan UniversityShanghaiChina
- National Center for Neurological Disorders, Huashan HospitalFudan UniversityShanghaiChina
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Jiehui Jiang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Chuantao Zuo
- Department of Nuclear Medicine & PET Center, Huashan HospitalFudan UniversityShanghaiChina
- National Clinical Research Center for Aging and Medicine, Huashan HospitalFudan UniversityShanghaiChina
- National Center for Neurological Disorders, Huashan HospitalFudan UniversityShanghaiChina
- Human Phenome InstituteFudan UniversityShanghaiChina
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12
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Basheer N, Buee L, Brion JP, Smolek T, Muhammadi MK, Hritz J, Hromadka T, Dewachter I, Wegmann S, Landrieu I, Novak P, Mudher A, Zilka N. Shaping the future of preclinical development of successful disease-modifying drugs against Alzheimer's disease: a systematic review of tau propagation models. Acta Neuropathol Commun 2024; 12:52. [PMID: 38576010 PMCID: PMC10993623 DOI: 10.1186/s40478-024-01748-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 02/21/2024] [Indexed: 04/06/2024] Open
Abstract
The transcellular propagation of the aberrantly modified protein tau along the functional brain network is a key hallmark of Alzheimer's disease and related tauopathies. Inoculation-based tau propagation models can recapitulate the stereotypical spread of tau and reproduce various types of tau inclusions linked to specific tauopathy, albeit with varying degrees of fidelity. With this systematic review, we underscore the significance of judicious selection and meticulous functional, biochemical, and biophysical characterization of various tau inocula. Furthermore, we highlight the necessity of choosing suitable animal models and inoculation sites, along with the critical need for validation of fibrillary pathology using confirmatory staining, to accurately recapitulate disease-specific inclusions. As a practical guide, we put forth a framework for establishing a benchmark of inoculation-based tau propagation models that holds promise for use in preclinical testing of disease-modifying drugs.
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Affiliation(s)
- Neha Basheer
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska Cesta 9, 845 10, Bratislava, Slovakia
| | - Luc Buee
- Inserm, CHU Lille, CNRS, LilNCog - Lille Neuroscience & Cognition, University of Lille, 59000, Lille, France.
| | - Jean-Pierre Brion
- Faculty of Medicine, Laboratory of Histology, Alzheimer and Other Tauopathies Research Group (CP 620), ULB Neuroscience Institute (UNI), Université Libre de Bruxelles, 808, Route de Lennik, 1070, Brussels, Belgium
| | - Tomas Smolek
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska Cesta 9, 845 10, Bratislava, Slovakia
| | - Muhammad Khalid Muhammadi
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska Cesta 9, 845 10, Bratislava, Slovakia
| | - Jozef Hritz
- CEITEC Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic
- Department of Chemistry, Faculty of Science, Masaryk University, Kamenice 5, 62500, Brno, Czech Republic
| | - Tomas Hromadka
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska Cesta 9, 845 10, Bratislava, Slovakia
| | - Ilse Dewachter
- Biomedical Research Institute, BIOMED, Hasselt University, 3500, Hasselt, Belgium
| | - Susanne Wegmann
- German Center for Neurodegenerative Diseases, Charitéplatz 1, 10117, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Isabelle Landrieu
- CNRS EMR9002 - BSI - Integrative Structural Biology, 59000, Lille, France
- Inserm, CHU Lille, Institut Pasteur de Lille, U1167 - RID-AGE - Risk Factors and Molecular Determinants of Aging-Related Diseases, University of Lille, 59000, Lille, France
| | - Petr Novak
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska Cesta 9, 845 10, Bratislava, Slovakia
| | - Amritpal Mudher
- School of Biological Sciences, Faculty of Environment and Life Sciences, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, UK
| | - Norbert Zilka
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska Cesta 9, 845 10, Bratislava, Slovakia.
- AXON Neuroscience R&D Services SE, Dubravska Cesta 9, 845 10, Bratislava, Slovakia.
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13
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Gordón Pidal JM, Moreno-Guzmán M, Montero-Calle A, Valverde A, Pingarrón JM, Campuzano S, Calero M, Barderas R, López MÁ, Escarpa A. Micromotor-based electrochemical immunoassays for reliable determination of amyloid-β (1-42) in Alzheimer's diagnosed clinical samples. Biosens Bioelectron 2024; 249:115988. [PMID: 38194814 DOI: 10.1016/j.bios.2023.115988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/22/2023] [Accepted: 12/27/2023] [Indexed: 01/11/2024]
Abstract
Alzheimer's disease (AD), in addition to being the most common cause of dementia, is very difficult to diagnose, with the 42-amino acid form of Aβ (Aβ-42) being one of the main biomarkers used for this purpose. Despite the enormous efforts made in recent years, the technologies available to determine Aβ-42 in human samples require sophisticated instrumentation, present high complexity, are sample and time-consuming, and are costly, highlighting the urgent need not only to develop new tools to overcome these limitations but to provide an early detection and treatment window for AD, which is a top-challenge. In recent years, micromotor (MM) technology has proven to add a new dimension to clinical biosensing, enabling ultrasensitive detections in short times and microscale environments. To this end, here an electrochemical immunoassay based on polypyrrole (PPy)/nickel (Ni)/platinum nanoparticles (PtNPs) MM is proposed in a pioneering manner for the determination of Aβ-42 in left prefrontal cortex brain tissue, cerebrospinal fluid, and plasma samples from patients with AD. MM combines the high binding capacity of their immunorecognition external layer with self-propulsion through the catalytic generation of oxygen bubbles in the internal layer due to decomposition of hydrogen peroxide as fuel, allowing rapid bio-detection (15 min) of Aβ-42 with excellent selectivity and sensitivity (LOD = 0.06 ng/mL). The application of this disruptive technology to the analysis of just 25 μL of the three types of clinical samples provides values concordant with the clinical values reported, thus confirming the potential of the MM approach to assist in the reliable, simple, fast, and affordable diagnosis of AD by determining Aβ-42.
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Affiliation(s)
- José M Gordón Pidal
- Department of Analytical Chemistry, Physical Chemistry and Chemical Engineering, Faculty of Sciences, University of Alcalá, Ctra. Madrid-Barcelona, Km. 33.600, Alcalá de Henares, 28802, Madrid, Spain
| | - María Moreno-Guzmán
- Department of Chemistry in Pharmaceutical Sciences, Analytical Chemistry, Faculty of Pharmacy, Complutense University of Madrid, Plaza Ramón y Cajal, s/n, 28040, Madrid, Spain
| | - Ana Montero-Calle
- Chronic Disease Programme, UFIEC, Carlos III Health Institute, Majadahonda, Madrid, 28220, Spain
| | - Alejandro Valverde
- Department of Analytical Chemistry, Faculty of Chemistry Science, Complutense University of Madrid, Pza. de las Ciencias 2, Madrid, 28040, Spain
| | - José M Pingarrón
- Department of Analytical Chemistry, Faculty of Chemistry Science, Complutense University of Madrid, Pza. de las Ciencias 2, Madrid, 28040, Spain
| | - Susana Campuzano
- Department of Analytical Chemistry, Faculty of Chemistry Science, Complutense University of Madrid, Pza. de las Ciencias 2, Madrid, 28040, Spain.
| | - Miguel Calero
- Chronic Disease Programme, UFIEC, Carlos III Health Institute, Majadahonda, Madrid, 28220, Spain
| | - Rodrigo Barderas
- Chronic Disease Programme, UFIEC, Carlos III Health Institute, Majadahonda, Madrid, 28220, Spain.
| | - Miguel Ángel López
- Department of Analytical Chemistry, Physical Chemistry and Chemical Engineering, Faculty of Sciences, University of Alcalá, Ctra. Madrid-Barcelona, Km. 33.600, Alcalá de Henares, 28802, Madrid, Spain; Chemical Research Institute "Andrés M. Del Rio", University of Alcalá, Madrid, Spain
| | - Alberto Escarpa
- Department of Analytical Chemistry, Physical Chemistry and Chemical Engineering, Faculty of Sciences, University of Alcalá, Ctra. Madrid-Barcelona, Km. 33.600, Alcalá de Henares, 28802, Madrid, Spain; Chemical Research Institute "Andrés M. Del Rio", University of Alcalá, Madrid, Spain.
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14
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Décarie-Labbé L, Dialahy IZ, Corriveau-Lecavalier N, Mellah S, Belleville S. Examining the relationship between brain activation and proxies of disease severity using quantile regression in individuals at risk of Alzheimer's disease. Cortex 2024; 173:234-247. [PMID: 38432175 DOI: 10.1016/j.cortex.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 10/27/2023] [Accepted: 01/25/2024] [Indexed: 03/05/2024]
Abstract
Previous studies have reported a pattern of hyperactivation in the pre-dementia phase of Alzheimer's disease (AD), followed by hypoactivation in later stages of the disease. This pattern was modeled as an inverse U-shape function between activation and markers of disease severity. In this study, we used quantile regression to model the association between task-related brain activation in AD signature regions and three markers of disease severity (hippocampal volume, cortical thickness, and associative memory). This approach offers distinct advantages over standard regression models as it analyzes the relationship between brain activation and disease severity across various levels of brain activation. Participants were 54 older adults with subjective cognitive decline+ (SCD+) or mild cognitive impairment (MCI) from the CIMA-Q cohort. The analysis revealed an inverse U-shape quadratic function depicting the relationship between disease severity markers and the activation of the left superior parietal region, while a linear relationship was observed for activation of the hippocampal and temporal regions. Quantile differences were observed for temporal and parietal activation, with more pronounced effects observed in the higher quantiles of activation. When comparing quantiles, we found that higher quantile of activation featured a greater number of individuals with SCD+ compared to mild cognitive impairment (MCI). Results are globally consistent with the presence of an inverse-U shape function of activation in relation to disease severity. They study also underscores the utility of employing quantile regression modeling as the modeling approach revealed the presence of non-homogeneous effects across various quantiles.
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Affiliation(s)
- Laurie Décarie-Labbé
- Research Center, Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada; Department of Psychology, Université de Montréal, Montreal, Quebec, Canada
| | - Isaora Zefania Dialahy
- Research Center, Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
| | | | - Samira Mellah
- Research Center, Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
| | - Sylvie Belleville
- Research Center, Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada; Department of Psychology, Université de Montréal, Montreal, Quebec, Canada.
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15
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Lee J, Burkett BJ, Min HK, Senjem ML, Dicks E, Corriveau-Lecavalier N, Mester CT, Wiste HJ, Lundt ES, Murray ME, Nguyen AT, Reichard RR, Botha H, Graff-Radford J, Barnard LR, Gunter JL, Schwarz CG, Kantarci K, Knopman DS, Boeve BF, Lowe VJ, Petersen RC, Jack CR, Jones DT. Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning. Brain 2024; 147:980-995. [PMID: 37804318 PMCID: PMC10907092 DOI: 10.1093/brain/awad346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 08/30/2023] [Accepted: 09/24/2023] [Indexed: 10/09/2023] Open
Abstract
Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging's use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.
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Affiliation(s)
- Jeyeon Lee
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea
| | - Brian J Burkett
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hoon-Ki Min
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ellen Dicks
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Carly T Mester
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Heather J Wiste
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Emily S Lundt
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Aivi T Nguyen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ross R Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | | | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
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16
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Kim J, Kim S, Um YH, Wang SM, Kim REY, Choe YS, Lee J, Kim D, Lim HK, Lee CU, Kang DW. Associations between Education Years and Resting-state Functional Connectivity Modulated by APOE ε4 Carrier Status in Cognitively Normal Older Adults. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2024; 22:169-181. [PMID: 38247423 PMCID: PMC10811405 DOI: 10.9758/cpn.23.1113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/13/2023] [Accepted: 09/26/2023] [Indexed: 01/23/2024]
Abstract
Objective : Cognitive reserve has emerged as a concept to explain the variable expression of clinical symptoms in the pathology of Alzheimer's disease (AD). The association between years of education, a proxy of cognitive reserve, and resting-state functional connectivity (rFC), a representative intermediate phenotype, has not been explored in the preclinical phase, considering risk factors for AD. We aimed to evaluate whether the relationship between years of education and rFC in cognitively preserved older adults differs depending on amyloid-beta deposition and APOE ε4 carrier status as effect modifiers. Methods : A total of 121 participants underwent functional magnetic resonance imaging, [18F] flutemetamol positron emission tomography-computed tomography, APOE genotyping, and a neuropsychological battery. Potential interactions between years of education and AD risk factors for rFC of AD-vulnerable neural networks were assessed with whole-brain voxel-wise analysis. Results : We found a significant education years-by-APOE ε4 carrier status interaction for the rFC from the seed region of the central executive (CEN) and dorsal attention networks. Moreover, there was a significant interaction of rFC between right superior occipital gyrus and the CEN seed region by APOE ε4 carrier status for memory performances and overall cognitive function. Conclusion : In preclinical APOE ε4 carriers, higher years of education were associated with higher rFC of the AD vulnerable network, but this contributed to lower cognitive function. These results contribute to a deeper understanding of the impact of cognitive reserve on sensitive functional intermediate phenotypic markers in the preclinical phase of AD.
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Affiliation(s)
- Jiwon Kim
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sunghwan Kim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Suwon, Korea
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | | | | | - Jiyeon Lee
- Research Institute, NEUROPHET Inc., Seoul, Korea
| | | | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Research Institute, NEUROPHET Inc., Seoul, Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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17
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Zheng L, Rubinski A, Denecke J, Luan Y, Smith R, Strandberg O, Stomrud E, Ossenkoppele R, Svaldi DO, Higgins IA, Shcherbinin S, Pontecorvo MJ, Hansson O, Franzmeier N, Ewers M. Combined Connectomics, MAPT Gene Expression, and Amyloid Deposition to Explain Regional Tau Deposition in Alzheimer Disease. Ann Neurol 2024; 95:274-287. [PMID: 37837382 DOI: 10.1002/ana.26818] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/07/2023] [Accepted: 10/03/2023] [Indexed: 10/16/2023]
Abstract
OBJECTIVE We aimed to test whether region-specific factors, including spatial expression patterns of the tau-encoding gene MAPT and regional levels of amyloid positron emission tomography (PET), enhance connectivity-based modeling of the spatial variability in tau-PET deposition in the Alzheimer disease (AD) spectrum. METHODS We included 685 participants (395 amyloid-positive participants within AD spectrum and 290 amyloid-negative controls) with tau-PET and amyloid-PET from 3 studies (Alzheimer's Disease Neuroimaging Initiative, 18 F-AV-1451-A05, and BioFINDER-1). Resting-state functional magnetic resonance imaging was obtained in healthy controls (n = 1,000) from the Human Connectome Project, and MAPT gene expression from the Allen Human Brain Atlas. Based on a brain-parcellation atlas superimposed onto all modalities, we obtained region of interest (ROI)-to-ROI functional connectivity, ROI-level PET values, and MAPT gene expression. In stepwise regression analyses, we tested connectivity, MAPT gene expression, and amyloid-PET as predictors of group-averaged and individual tau-PET ROI values in amyloid-positive participants. RESULTS Connectivity alone explained 21.8 to 39.2% (range across 3 studies) of the variance in tau-PET ROI values averaged across amyloid-positive participants. Stepwise addition of MAPT gene expression and amyloid-PET increased the proportion of explained variance to 30.2 to 46.0% and 45.0 to 49.9%, respectively. Similarly, for the prediction of patient-level tau-PET ROI values, combining all 3 predictors significantly improved the variability explained (mean adjusted R2 range across studies = 0.118-0.148, 0.156-0.196, and 0.251-0.333 for connectivity alone, connectivity plus MAPT expression, and all 3 modalities combined, respectively). INTERPRETATION Across 3 study samples, combining the functional connectome and molecular properties substantially enhanced the explanatory power compared to single modalities, providing a valuable tool to explain regional susceptibility to tau deposition in AD. ANN NEUROL 2024;95:274-287.
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Affiliation(s)
- Lukai Zheng
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Anna Rubinski
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Jannis Denecke
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
| | - Ying Luan
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands
| | | | | | | | - Michael J Pontecorvo
- Eli Lilly and Company, Indianapolis, IN, USA
- Avid Radiopharmaceuticals, Philadelphia, PA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- Munich Cluster for Systems Neurology, Munich, Germany
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Michael Ewers
- Institute for Stroke and Dementia Research, University Hospital, LMU, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
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18
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Chumin EJ, Cutts SA, Risacher SL, Apostolova LG, Farlow MR, McDonald BC, Wu YC, Betzel R, Saykin AJ, Sporns O. Edge time series components of functional connectivity and cognitive function in Alzheimer's disease. Brain Imaging Behav 2024; 18:243-255. [PMID: 38008852 PMCID: PMC10844434 DOI: 10.1007/s11682-023-00822-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2023] [Indexed: 11/28/2023]
Abstract
Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer's disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer's Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer's disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.
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Affiliation(s)
- Evgeny J Chumin
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA.
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA.
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA.
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA.
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA.
| | - Sarah A Cutts
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA
- Program in Neuroscience, IU, Bloomington, IN, USA
| | - Shannon L Risacher
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
| | - Liana G Apostolova
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Martin R Farlow
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Brenna C McDonald
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Program in Neuroscience, IU, Bloomington, IN, USA
| | - Andrew J Saykin
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Program in Neuroscience, IU, Bloomington, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
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19
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Hahn L, Eickhoff SB, Mueller K, Schilbach L, Barthel H, Fassbender K, Fliessbach K, Kornhuber J, Prudlo J, Synofzik M, Wiltfang J, Diehl-Schmid J, Otto M, Dukart J, Schroeter ML. Resting-state alterations in behavioral variant frontotemporal dementia are related to the distribution of monoamine and GABA neurotransmitter systems. eLife 2024; 13:e86085. [PMID: 38224473 PMCID: PMC10789488 DOI: 10.7554/elife.86085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 12/14/2023] [Indexed: 01/16/2024] Open
Abstract
Background Aside to clinical changes, behavioral variant frontotemporal dementia (bvFTD) is characterized by progressive structural and functional alterations in frontal and temporal regions. We examined if there is a selective vulnerability of specific neurotransmitter systems in bvFTD by evaluating the link between disease-related functional alterations and the spatial distribution of specific neurotransmitter systems and their underlying gene expression levels. Methods Maps of fractional amplitude of low-frequency fluctuations (fALFF) were derived as a measure of local activity from resting-state functional magnetic resonance imaging for 52 bvFTD patients (mean age = 61.5 ± 10.0 years; 14 females) and 22 healthy controls (HC) (mean age = 63.6 ± 11.9 years; 13 females). We tested if alterations of fALFF in patients co-localize with the non-pathological distribution of specific neurotransmitter systems and their coding mRNA gene expression. Furthermore, we evaluated if the strength of co-localization is associated with the observed clinical symptoms. Results Patients displayed significantly reduced fALFF in frontotemporal and frontoparietal regions. These alterations co-localized with the distribution of serotonin (5-HT1b and 5-HT2a) and γ-aminobutyric acid type A (GABAa) receptors, the norepinephrine transporter (NET), and their encoding mRNA gene expression. The strength of co-localization with NET was associated with cognitive symptoms and disease severity of bvFTD. Conclusions Local brain functional activity reductions in bvFTD followed the distribution of specific neurotransmitter systems indicating a selective vulnerability. These findings provide novel insight into the disease mechanisms underlying functional alterations. Our data-driven method opens the road to generate new hypotheses for pharmacological interventions in neurodegenerative diseases even beyond bvFTD. Funding This study has been supported by the German Consortium for Frontotemporal Lobar Degeneration, funded by the German Federal Ministry of Education and Research (BMBF; grant no. FKZ01GI1007A).
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Affiliation(s)
- Lisa Hahn
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre JülichJülichGermany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University DüsseldorfDüsseldorfGermany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre JülichJülichGermany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University DüsseldorfDüsseldorfGermany
| | - Karsten Mueller
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Leonhard Schilbach
- LVR-Klinikum DüsseldorfDüsseldorfGermany
- Medical Faculty, Ludwig-Maximilians-UniversitätMünchenGermany
| | - Henryk Barthel
- Department for Nuclear Medicine, University Hospital LeipzigLeipzigGermany
| | - Klaus Fassbender
- Department of Neurology, Saarland University HospitalHomburgGermany
| | - Klaus Fliessbach
- Department of Psychiatry and Psychotherapy, University Hospital BonnBonnGermany
- German Center for Neurodegenerative Diseases (DZNE)BonnGermany
| | - Johannes Kornhuber
- Department of Psychiatry and Psychotherapy, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-NurembergErlangenGermany
| | - Johannes Prudlo
- German Center for Neurodegenerative Diseases (DZNE)BonnGermany
- Department of Neurology, University Medicine RostockRostockGermany
| | - Matthis Synofzik
- German Center for Neurodegenerative Diseases (DZNE)BonnGermany
- Department of Neurodegenerative Diseases, Center of Neurology, Hertie Institute for Clinical Brain ResearchTübingenGermany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE)BonnGermany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Medical University GöttingenGöttingenGermany
- Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of AveiroAveiroPortugal
| | - Janine Diehl-Schmid
- Department of Psychiatry and Psychotherapy, Technical University of MunichMunichGermany
- kbo-Inn-Salzach-Klinikum, Clinical Center for Psychiatry, Psychotherapy, Psychosomatic Medicine, Geriatrics and NeurologyWasserburg/InnGermany
| | | | - Markus Otto
- Department of Neurology, Ulm UniversityUlmGermany
- Department of Neurology, Martin-Luther-University Halle-WittenbergHalleGermany
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre JülichJülichGermany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University DüsseldorfDüsseldorfGermany
| | - Matthias L Schroeter
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Clinic for Cognitive Neurology, University Hospital LeipzigLeipzigGermany
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20
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Franzmeier N, Dehsarvi A, Steward A, Biel D, Dewenter A, Roemer SN, Wagner F, Groß M, Brendel M, Moscoso A, Arunachalam P, Blennow K, Zetterberg H, Ewers M, Schöll M. Elevated CSF GAP-43 is associated with accelerated tau accumulation and spread in Alzheimer's disease. Nat Commun 2024; 15:202. [PMID: 38172114 PMCID: PMC10764818 DOI: 10.1038/s41467-023-44374-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
In Alzheimer's disease, amyloid-beta (Aβ) triggers the trans-synaptic spread of tau pathology, and aberrant synaptic activity has been shown to promote tau spreading. Aβ induces aberrant synaptic activity, manifesting in increases in the presynaptic growth-associated protein 43 (GAP-43), which is closely involved in synaptic activity and plasticity. We therefore tested whether Aβ-related GAP-43 increases, as a marker of synaptic changes, drive tau spreading in 93 patients across the aging and Alzheimer's spectrum with available CSF GAP-43, amyloid-PET and longitudinal tau-PET assessments. We found that (1) higher GAP-43 was associated with faster Aβ-related tau accumulation, specifically in brain regions connected closest to subject-specific tau epicenters and (2) that higher GAP-43 strengthened the association between Aβ and connectivity-associated tau spread. This suggests that GAP-43-related synaptic changes are linked to faster Aβ-related tau spread across connected regions and that synapses could be key targets for preventing tau spreading in Alzheimer's disease.
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Affiliation(s)
- Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
- University of Gothenburg, The Sahlgrenska Academy, Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Mölndal and Gothenburg, Sweden.
| | - Amir Dehsarvi
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Anna Steward
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Davina Biel
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Sebastian Niclas Roemer
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Fabian Wagner
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Mattes Groß
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Matthias Brendel
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Alexis Moscoso
- University of Gothenburg, The Sahlgrenska Academy, Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Mölndal and Gothenburg, Sweden
| | - Prithvi Arunachalam
- University of Gothenburg, The Sahlgrenska Academy, Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Mölndal and Gothenburg, Sweden
| | - Kaj Blennow
- University of Gothenburg, The Sahlgrenska Academy, Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Mölndal and Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- University of Gothenburg, The Sahlgrenska Academy, Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Mölndal and Gothenburg, 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, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Michael Schöll
- University of Gothenburg, The Sahlgrenska Academy, Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Mölndal and Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
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21
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Hojjati SH, Chiang GC, Butler TA, de Leon M, Gupta A, Li Y, Sabuncu MR, Feiz F, Nayak S, Shteingart J, Ozoria S, Gholipour Picha S, Stern Y, Luchsinger JA, Devanand DP, Razlighi QR. Remote Associations Between Tau and Cortical Amyloid-β Are Stage-Dependent. J Alzheimers Dis 2024; 98:1467-1482. [PMID: 38552116 DOI: 10.3233/jad-231362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
Background Histopathologic studies of Alzheimer's disease (AD) suggest that extracellular amyloid-β (Aβ) plaques promote the spread of neurofibrillary tau tangles. However, these two proteinopathies initiate in spatially distinct brain regions, so how they interact during AD progression is unclear. Objective In this study, we utilized Aβ and tau positron emission tomography (PET) scans from 572 older subjects (476 healthy controls (HC), 14 with mild cognitive impairment (MCI), 82 with mild AD), at varying stages of the disease, to investigate to what degree tau is associated with cortical Aβ deposition. Methods Using multiple linear regression models and a pseudo-longitudinal ordering technique, we investigated remote tau-Aβ associations in four pathologic phases of AD progression based on tau spread: 1) no-tau, 2) pre-acceleration, 3) acceleration, and 4) post-acceleration. Results No significant tau-Aβ association was detected in the no-tau phase. In the pre-acceleration phase, the earliest stage of tau deposition, associations emerged between regional tau in medial temporal lobe (MTL) (i.e., entorhinal cortex, parahippocampal gyrus) and cortical Aβ in lateral temporal lobe regions. The strongest tau-Aβ associations were found in the acceleration phase, in which tau in MTL regions was strongly associated with cortical Aβ (i.e., temporal and frontal lobes regions). Strikingly, in the post-acceleration phase, including 96% of symptomatic subjects, tau-Aβ associations were no longer significant. Conclusions The results indicate that associations between tau and Aβ are stage-dependent, which could have important implications for understanding the interplay between these two proteinopathies during the progressive stages of AD.
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Affiliation(s)
- Seyed Hani Hojjati
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Gloria C Chiang
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Tracy A Butler
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Mony de Leon
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Yi Li
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Mert R Sabuncu
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Farnia Feiz
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Siddharth Nayak
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Jacob Shteingart
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Sindy Ozoria
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
| | - Saman Gholipour Picha
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Yaakov Stern
- Departments of Neurology, Psychiatry, GH Sergievsky Center, The Taub Institute for the Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - José A Luchsinger
- Departments of Medicine and Epidemiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Davangere P Devanand
- Division of Geriatric Psychiatry, New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, NY, USA
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
- Department of Psychiatry, New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Qolamreza R Razlighi
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, USA
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22
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Chumin EJ, Cutts SA, Risacher SL, Apostolova LG, Farlow MR, McDonald BC, Wu YC, Betzel R, Saykin AJ, Sporns O. Edge Time Series Components of Functional Connectivity and Cognitive Function in Alzheimer's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.13.23289936. [PMID: 38014005 PMCID: PMC10680898 DOI: 10.1101/2023.05.13.23289936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer's disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer's Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer's disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.
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Affiliation(s)
- Evgeny J. Chumin
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
| | - Sarah A. Cutts
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Program in Neuroscience, IU, Bloomington, IN, United States
| | - Shannon L. Risacher
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
| | - Liana G. Apostolova
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Martin R. Farlow
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Brenna C. McDonald
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Yu-Chien Wu
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Program in Neuroscience, IU, Bloomington, IN, United States
| | - Andrew J. Saykin
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Program in Neuroscience, IU, Bloomington, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
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23
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Burnham SC, Iaccarino L, Pontecorvo MJ, Fleisher AS, Lu M, Collins EC, Devous MD. A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles. Brain Commun 2023; 6:fcad305. [PMID: 38187878 PMCID: PMC10768888 DOI: 10.1093/braincomms/fcad305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/13/2023] [Accepted: 11/14/2023] [Indexed: 01/09/2024] Open
Abstract
Alzheimer's disease is defined by the presence of β-amyloid plaques and neurofibrillary tau tangles potentially preceding clinical symptoms by many years. Previously only detectable post-mortem, these pathological hallmarks are now identifiable using biomarkers, permitting an in vivo definitive diagnosis of Alzheimer's disease. 18F-flortaucipir (previously known as 18F-T807; 18F-AV-1451) was the first tau positron emission tomography tracer to be introduced and is the only Food and Drug Administration-approved tau positron emission tomography tracer (Tauvid™). It has been widely adopted and validated in a number of independent research and clinical settings. In this review, we present an overview of the published literature on flortaucipir for positron emission tomography imaging of neurofibrillary tau tangles. We considered all accessible peer-reviewed literature pertaining to flortaucipir through 30 April 2022. We found 474 relevant peer-reviewed publications, which were organized into the following categories based on their primary focus: typical Alzheimer's disease, mild cognitive impairment and pre-symptomatic populations; atypical Alzheimer's disease; non-Alzheimer's disease neurodegenerative conditions; head-to-head comparisons with other Tau positron emission tomography tracers; and technical considerations. The available flortaucipir literature provides substantial evidence for the use of this positron emission tomography tracer in assessing neurofibrillary tau tangles in Alzheimer's disease and limited support for its use in other neurodegenerative disorders. Visual interpretation and quantitation approaches, although heterogeneous, mostly converge and demonstrate the high diagnostic and prognostic value of flortaucipir in Alzheimer's disease.
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Affiliation(s)
| | | | | | | | - Ming Lu
- Avid, Eli Lilly and Company, Philadelphia, PA 19104, USA
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24
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Bitra VR, Challa SR, Adiukwu PC, Rapaka D. Tau trajectory in Alzheimer's disease: Evidence from the connectome-based computational models. Brain Res Bull 2023; 203:110777. [PMID: 37813312 DOI: 10.1016/j.brainresbull.2023.110777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/08/2023] [Accepted: 10/06/2023] [Indexed: 10/11/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an impairment of cognition and memory. Current research on connectomics have now related changes in the network organization in AD to the patterns of accumulation and spread of amyloid and tau, providing insights into the neurobiological mechanisms of the disease. In addition, network analysis and modeling focus on particular use of graphs to provide intuition into key organizational principles of brain structure, that stipulate how neural activity propagates along structural connections. The utility of connectome-based computational models aids in early predicting, tracking the progression of biomarker-directed AD neuropathology. In this article, we present a short review of tau trajectory, the connectome changes in tau pathology, and the dependent recent connectome-based computational modelling approaches for tau spreading, reproducing pragmatic findings, and developing significant novel tau targeted therapies.
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Affiliation(s)
- Veera Raghavulu Bitra
- School of Pharmacy, Faculty of Health Sciences, University of Botswana, P/Bag-0022, Gaborone, Botswana.
| | - Siva Reddy Challa
- Department of Cancer Biology and Pharmacology, University of Illinois College of Medicine, Peoria, IL 61614, USA; KVSR Siddartha College of Pharmaceutical Sciences, Vijayawada, Andhra Pradesh, India
| | - Paul C Adiukwu
- School of Pharmacy, Faculty of Health Sciences, University of Botswana, P/Bag-0022, Gaborone, Botswana
| | - Deepthi Rapaka
- Pharmacology Division, D.D.T. College of Medicine, Gaborone, Botswana.
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25
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Schoonhoven DN, Coomans EM, Millán AP, van Nifterick AM, Visser D, Ossenkoppele R, Tuncel H, van der Flier WM, Golla SSV, Scheltens P, Hillebrand A, van Berckel BNM, Stam CJ, Gouw AA. Tau protein spreads through functionally connected neurons in Alzheimer's disease: a combined MEG/PET study. Brain 2023; 146:4040-4054. [PMID: 37279597 PMCID: PMC10545627 DOI: 10.1093/brain/awad189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 03/03/2023] [Accepted: 04/10/2023] [Indexed: 06/08/2023] Open
Abstract
Recent studies on Alzheimer's disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be involved in this process: spreading between brain regions that interact strongly (functional connectivity); through the pattern of anatomical connections (structural connectivity); or simple diffusion. Using magnetoencephalography (MEG), we investigated which spreading pathways influence tau protein spreading by modelling the tau propagation process using an epidemic spreading model. We compared the modelled tau depositions with 18F-flortaucipir PET binding potentials at several stages of the AD continuum. In this cross-sectional study, we analysed source-reconstructed MEG data and dynamic 100-min 18F-flortaucipir PET from 57 subjects positive for amyloid-β pathology [preclinical AD (n = 16), mild cognitive impairment (MCI) due to AD (n = 16) and AD dementia (n = 25)]. Cognitively healthy subjects without amyloid-β pathology were included as controls (n = 25). Tau propagation was modelled as an epidemic process (susceptible-infected model) on MEG-based functional networks [in alpha (8-13 Hz) and beta (13-30 Hz) bands], a structural or diffusion network, starting from the middle and inferior temporal lobe. The group-level network of the control group was used as input for the model to predict tau deposition in three stages of the AD continuum. To assess performance, model output was compared to the group-specific tau deposition patterns as measured with 18F-flortaucipir PET. We repeated the analysis by using networks of the preceding disease stage and/or using regions with most observed tau deposition during the preceding stage as seeds. In the preclinical AD stage, the functional networks predicted most of the modelled tau-PET binding potential, with best correlations between model and tau-PET [corrected amplitude envelope correlation (AEC-c) alpha C = 0.584; AEC-c beta C = 0.569], followed by the structural network (C = 0.451) and simple diffusion (C = 0.451). Prediction accuracy declined for the MCI and AD dementia stages, although the correlation between modelled tau and tau-PET binding remained highest for the functional networks (C = 0.384; C = 0.376). Replacing the control-network with the network from the preceding disease stage and/or alternative seeds improved prediction accuracy in MCI but not in the dementia stage. These results suggest that in addition to structural connections, functional connections play an important role in tau spread, and highlight that neuronal dynamics play a key role in promoting this pathological process. Aberrant neuronal communication patterns should be taken into account when identifying targets for future therapy. Our results also suggest that this process is more important in earlier disease stages (preclinical AD/MCI); possibly, in later stages, other processes may be influential.
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Affiliation(s)
- Deborah N Schoonhoven
- Department of Clinical Neurophysiology and MEG Center, Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
| | - Emma M Coomans
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Ana P Millán
- Department of Clinical Neurophysiology and MEG Center, Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Anne M van Nifterick
- Department of Clinical Neurophysiology and MEG Center, Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
| | - Denise Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, 221 00 Lund, Sweden
| | - Hayel Tuncel
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
| | - Sandeep S V Golla
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neuroscience, 1081 HV Amsterdam, The Netherlands
| | - Bart N M van Berckel
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
| | - Alida A Gouw
- Department of Clinical Neurophysiology and MEG Center, Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
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26
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Vogel JW, Corriveau-Lecavalier N, Franzmeier N, Pereira JB, Brown JA, Maass A, Botha H, Seeley WW, Bassett DS, Jones DT, Ewers M. Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight. Nat Rev Neurosci 2023; 24:620-639. [PMID: 37620599 DOI: 10.1038/s41583-023-00731-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.
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Affiliation(s)
- Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden.
| | - Nick Corriveau-Lecavalier
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Acadamy, University of Gothenburg, Mölndal and Gothenburg, Sweden
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Neuro Division, Department of Clinical Neurosciences, Karolinska Institute, Stockholm, Sweden
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, CA, USA
| | - Dani S Bassett
- Departments of Bioengineering, Electrical and Systems Engineering, Physics and Astronomy, Neurology and Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.
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27
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Cassady KE, Chen X, Adams JN, Harrison TM, Zhuang K, Maass A, Baker S, Jagust W. Effect of Alzheimer's Pathology on Task-Related Brain Network Reconfiguration in Aging. J Neurosci 2023; 43:6553-6563. [PMID: 37604690 PMCID: PMC10513069 DOI: 10.1523/jneurosci.0023-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 08/02/2023] [Accepted: 08/06/2023] [Indexed: 08/23/2023] Open
Abstract
Large-scale brain networks undergo widespread changes with older age and in neurodegenerative diseases such as Alzheimer's disease (AD). Research in young adults (YA) suggest that the underlying functional architecture of brain networks remains relatively consistent between rest and task states. However, it remains unclear whether the same is true in aging and to what extent any changes may be related to accumulation of AD pathology such as β-amyloid (Aβ) and tau. Here, we examined age-related differences in functional connectivity (FC) between rest and an object-scene mnemonic discrimination task using fMRI in young and older adults (OA; both females and males). We used an a priori episodic memory network (EMN) parcellation scheme associated with object and scene processing, that included anterior-temporal regions and posterior-medial regions. We also used positron emission topography to measure Aβ and tau in older adults. The correlation between rest and task FC (i.e., FC similarity) was reduced in older compared with younger adults. Older adults with lower FC similarity in EMN had higher levels of tau in the same EMN regions and performed worse during object, but not scene, trials during the fMRI task. These findings link AD pathology, particularly tau, to a less stable functional architecture in memory networks. They also suggest that smaller changes in FC organization between rest and task states may facilitate better performance in older age. Interpretations are limited by methodological factors related to different acquisition directions and durations between rest and task scans.SIGNIFICANCE STATEMENT The brain's large-scale network organization is relatively consistent between rest and task states in young adults (YA). We found that memory networks in older adults (OA) were less correlated between rest and (memory) task states compared with young adults. Older adults with less correlated brain networks also had higher levels of Alzheimer's disease (AD) pathology in the same regions, suggesting that a less stable network architecture may reflect the early evolution of AD. Older adults with less correlated brain networks also performed worse during the memory task suggesting that more similar network organization between rest and task states may facilitate better performance in older age.
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Affiliation(s)
- Kaitlin E Cassady
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California 94720
| | - Xi Chen
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California 94720
| | - Jenna N Adams
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California 94720
| | - Theresa M Harrison
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California 94720
| | - Kailin Zhuang
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California 94720
| | - Anne Maass
- German Center for Neurodegenerative Disease, 39120 Magdeburg, Germany
| | - Suzanne Baker
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - William Jagust
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California 94720
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28
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Han F, Lee J, Chen X, Ziontz J, Ward T, Landau SM, Baker SL, Harrison TM, Jagust WJ. Global brain activity and its coupling with cerebrospinal fluid flow is related to tau pathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.12.557492. [PMID: 37745434 PMCID: PMC10515801 DOI: 10.1101/2023.09.12.557492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Amyloid-β (Aβ) and tau deposition constitute Alzheimer's disease (AD) neuropathology. Cortical tau deposits first in the entorhinal cortex and hippocampus and then propagates to neocortex in an Aβ-dependent manner. Tau also tends to accumulate earlier in higher-order association cortex than in lower-order primary sensory-motor cortex. While previous research has examined the production and spread of tau, little attention has been paid to its clearance. Low-frequency (<0.1 Hz) global brain activity during the resting state is coupled with cerebrospinal fluid (CSF) flow and potentially reflects glymphatic clearance. Here we report that tau deposition in subjects with evaluated Aβ, accompanied by cortical thinning and cognitive decline, is strongly associated with decreased coupling between CSF flow and global brain activity. Substantial modulation of global brain activity is also manifested as propagating waves of brain activation between higher- and lower-order regions, resembling tau spreading. Together, the findings suggest an important role of resting-state global brain activity in AD tau pathology.
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Affiliation(s)
- Feng Han
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - JiaQie Lee
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Xi Chen
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jacob Ziontz
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Tyler Ward
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Susan M Landau
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | | | - Theresa M Harrison
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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29
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Adams JN, Chappel-Farley MG, Yaros JL, Taylor L, Harris AL, Mikhail A, McMillan L, Keator DB, Yassa MA. Functional network structure supports resilience to memory deficits in cognitively normal older adults with amyloid-β pathology. Sci Rep 2023; 13:13953. [PMID: 37626094 PMCID: PMC10457346 DOI: 10.1038/s41598-023-40092-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 08/04/2023] [Indexed: 08/27/2023] Open
Abstract
Older adults may harbor large amounts of amyloid-β (Aβ) pathology, yet still perform at age-normal levels on memory assessments. We tested whether functional brain networks confer resilience or compensatory mechanisms to support memory in the face of Aβ pathology. Sixty-five cognitively normal older adults received high-resolution resting state fMRI to assess functional networks, 18F-florbetapir-PET to measure Aβ, and a memory assessment. We characterized functional networks with graph metrics of local efficiency (information transfer), modularity (specialization of functional modules), and small worldness (balance of integration and segregation). There was no difference in functional network measures between older adults with high Aβ (Aβ+) compared to those with no/low Aβ (Aβ-). However, in Aβ+ older adults, increased local efficiency, modularity, and small worldness were associated with better memory performance, while this relationship did not occur Aβ- older adults. Further, the association between increased local efficiency and better memory performance in Aβ+ older adults was localized to local efficiency of the default mode network and hippocampus, regions vulnerable to Aβ and involved in memory processing. Our results suggest functional networks with modular and efficient structures are associated with resilience to Aβ pathology, providing a functional target for intervention.
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Affiliation(s)
- Jenna N Adams
- Department of Neurobiology and Behavior, University of California, Irvine, 1400 Biological Sciences 3, Irvine, CA, 92697-3800, USA.
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, 92697, USA.
| | - Miranda G Chappel-Farley
- Department of Neurobiology and Behavior, University of California, Irvine, 1400 Biological Sciences 3, Irvine, CA, 92697-3800, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, 92697, USA
| | - Jessica L Yaros
- Department of Neurobiology and Behavior, University of California, Irvine, 1400 Biological Sciences 3, Irvine, CA, 92697-3800, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, 92697, USA
| | - Lisa Taylor
- Department of Psychiatry and Human Behavior, University of California, Irvine, 1418 Biological Sciences 3, Irvine, CA, 92697-3800, USA
| | - Alyssa L Harris
- Department of Neurobiology and Behavior, University of California, Irvine, 1400 Biological Sciences 3, Irvine, CA, 92697-3800, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, 92697, USA
| | - Abanoub Mikhail
- Department of Neurobiology and Behavior, University of California, Irvine, 1400 Biological Sciences 3, Irvine, CA, 92697-3800, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, 92697, USA
| | - Liv McMillan
- Department of Neurobiology and Behavior, University of California, Irvine, 1400 Biological Sciences 3, Irvine, CA, 92697-3800, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, 92697, USA
| | - David B Keator
- Department of Psychiatry and Human Behavior, University of California, Irvine, 1418 Biological Sciences 3, Irvine, CA, 92697-3800, USA
| | - Michael A Yassa
- Department of Neurobiology and Behavior, University of California, Irvine, 1400 Biological Sciences 3, Irvine, CA, 92697-3800, USA.
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, 92697, USA.
- Department of Psychiatry and Human Behavior, University of California, Irvine, 1418 Biological Sciences 3, Irvine, CA, 92697-3800, USA.
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30
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Devrome M, Van Laere K, Koole M. Multiplex core of the human brain using structural, functional and metabolic connectivity derived from hybrid PET-MR imaging. FRONTIERS IN NEUROIMAGING 2023; 2:1115965. [PMID: 37645694 PMCID: PMC10461102 DOI: 10.3389/fnimg.2023.1115965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 07/06/2023] [Indexed: 08/31/2023]
Abstract
With the increasing success of mapping brain networks and availability of multiple MR- and PET-based connectivity measures, the need for novel methodologies to unravel the structure and function of the brain at multiple spatial and temporal scales is emerging. Therefore, in this work, we used hybrid PET-MR data of healthy volunteers (n = 67) to identify multiplex core nodes in the human brain. First, monoplex networks of structural, functional and metabolic connectivity were constructed, and consequently combined into a multiplex SC-FC-MC network by linking the same nodes categorically across layers. Taking into account the multiplex nature using a tensorial approach, we identified a set of core nodes in this multiplex network based on a combination of eigentensor centrality and overlapping degree. We introduced a coreness coefficient, which mitigates the effect of modeling parameters to obtain robust results. The proposed methodology was applied onto young and elderly healthy volunteers, where differences observed in the monoplex networks persisted in the multiplex as well. The multiplex core showed a decreased contribution to the default mode and salience network, while an increased contribution to the dorsal attention and somatosensory network was observed in the elderly population. Moreover, a clear distinction in eigentensor centrality was found between young and elderly healthy volunteers.
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Affiliation(s)
- Martijn Devrome
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
| | - Koen Van Laere
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
- Division of Nuclear Medicine, Universitair Ziekenhuis (UZ) Leuven, Leuven, Belgium
| | - Michel Koole
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
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31
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Lamontagne-Kam D, Ulfat AK, Hervé V, Vu TM, Brouillette J. Implication of tau propagation on neurodegeneration in Alzheimer's disease. Front Neurosci 2023; 17:1219299. [PMID: 37483337 PMCID: PMC10360202 DOI: 10.3389/fnins.2023.1219299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/07/2023] [Indexed: 07/25/2023] Open
Abstract
Propagation of tau fibrils correlate closely with neurodegeneration and memory deficits seen during the progression of Alzheimer's disease (AD). Although it is not well-established what drives or attenuates tau spreading, new studies on human brain using positron emission tomography (PET) have shed light on how tau phosphorylation, genetic factors, and the initial epicenter of tau accumulation influence tau accumulation and propagation throughout the brain. Here, we review the latest PET studies performed across the entire AD continuum looking at the impact of amyloid load on tau pathology. We also explore the effects of structural, functional, and proximity connectivity on tau spreading in a stereotypical manner in the brain of AD patients. Since tau propagation can be quite heterogenous between individuals, we then consider how the speed and pattern of propagation are influenced by the starting localization of tau accumulation in connected brain regions. We provide an overview of some genetic variants that were shown to accelerate or slow down tau spreading. Finally, we discuss how phosphorylation of certain tau epitopes affect the spreading of tau fibrils. Since tau pathology is an early event in AD pathogenesis and is one of the best predictors of neurodegeneration and memory impairments, understanding the process by which tau spread from one brain region to another could pave the way to novel therapeutic avenues that are efficient during the early stages of the disease, before neurodegeneration induces permanent brain damage and severe memory loss.
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32
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Fountain-Zaragoza S, Liu H, Benitez A. Functional Network Alterations Associated with Cognition in Pre-Clinical Alzheimer's Disease. Brain Connect 2023; 13:275-286. [PMID: 36606679 PMCID: PMC10280291 DOI: 10.1089/brain.2022.0032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Objective: Accumulation of cerebral amyloid-β (Aβ) is a risk factor for cognitive decline and defining feature of Alzheimer's disease (AD). Aβ is implicated in brain network disruption, but the extent to which these changes correspond with observable cognitive deficits in pre-clinical AD has not been tested. This study utilized individual-specific functional parcellations to sensitively evaluate the relationship between network connectivity and cognition in adults with and without Aβ deposition. Participants and Methods: Cognitively unimpaired adults ages 45-85 completed amyloid positron emission tomography, resting-state-functional magnetic resonance imaging (fMRI), and neuropsychological tests of episodic memory and executive function (EF). Participants in the upper tertile of mean standard uptake value ratio were considered Aβ+ (n = 50) while others were Aβ- (n = 99). Individualized functional network parcellations were generated from resting-state fMRI data. We examined the effects of group, network, and group-by-network interactions on memory and EF. Results: We observed several interactions such that within the Aβ+ group, preserved network integrity (i.e., greater connectivity within specific networks) was associated with better cognition, whereas network desegregation (i.e., greater connectivity between relative to within networks) was associated with worse cognition. This dissociation was most apparent for cognitive networks (frontoparietal, dorsal and ventral attention, limbic, and default mode), with connectivity relating to EF in the Aβ+ group specifically. Conclusions: Using an innovative approach to constructing individual-specified resting-state functional connectomes, we were able to detect differences in brain-cognition associations in pre-clinical AD. Our findings provide novel insight into specific functional network alterations occurring in the presence of Aβ that relate to cognitive function in asymptomatic individuals. Impact statement Elevated cerebral amyloid-β is a biomarker of pre-clinical Alzheimer's disease (AD). Associations between amyloidosis, functional network disruption, and cognitive impairment are evident in the later stages of AD, but these effects have not been substantiated in pre-clinical AD. Using individual-specific parcellations that maximally localize functional networks, we identify network alterations that relate to cognition in pre-clinical AD that have not been previously reported. We demonstrate that these effects localize to networks implicated in cognition. Our findings suggest that there may be subtle, amyloid-related alterations in the functional connectome that are detectable in pre-clinical AD, with potential implications for cognition in asymptomatic individuals.
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Affiliation(s)
- Stephanie Fountain-Zaragoza
- Department of Health Sciences and Research, Medical University of South Carolina, Charleston, South Carolina, USA
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Hesheng Liu
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Andreana Benitez
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Neurology, and Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Radiology and Radiological Sciences, Medical University of South Carolina, Charleston, South Carolina, USA
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33
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Zhang J, Wang J, Xu X, You Z, Huang Q, Huang Y, Guo Q, Guan Y, Zhao J, Liu J, Xu W, Deng Y, Xie F, Li B. In vivo synaptic density loss correlates with impaired functional and related structural connectivity in Alzheimer's disease. J Cereb Blood Flow Metab 2023; 43:977-988. [PMID: 36718002 PMCID: PMC10196742 DOI: 10.1177/0271678x231153730] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 12/29/2022] [Accepted: 01/02/2023] [Indexed: 02/01/2023]
Abstract
Synapse loss has been considered as a major pathological change in Alzheimer's disease (AD). It remains unclear about whether and how synapse loss relates to functional and structural connectivity dysfunction in AD. We measured synaptic vesicle glycoprotein 2 A (SV2A) binding using 18F-SynVesT-1 PET to evaluate synaptic alterations in 33 participants with AD, 31 with mild cognitive impairment (MCI), and 30 controls. We examined the correlation between synaptic density and cognitive function. Functional MRI was performed to analyze functional connectivity in lower synaptic density regions. We tracked the white matter tracts between impaired functional connectivity regions using Diffusion MRI. In AD group, lower synaptic density in bilateral cortex and hippocampus was found when compared with controls. The synaptic density changes in right insular cortex and bilateral caudal middle frontal gyrus (MFG) were correlated with cognitive decline. Among them, right MFG synaptic density was positively associated with right MFG - bilateral superior frontal gyrus (SFG) functional connectivity. AD had lower probability of tract (POT) between right MFG and SFG than controls, which was significantly associated with global cognition. These findings provide evidence supporting synapse loss contributes to functional and related structural connectivity alterations underlying cognitive impairment of AD.
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Affiliation(s)
- Junfang Zhang
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Jie Wang
- Department of Nuclear Medicine
& PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaomeng Xu
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Zhiwen You
- Department of Nuclear Medicine,
Shanghai East Hospital, Tongji University School of Medicine, Shanghai,
China
| | - Qi Huang
- Department of Nuclear Medicine
& PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yiyun Huang
- PET Center, Department of Radiology
and Biomedical Imaging, Yale University School of Medicine, New Haven,
Connecticut, USA
| | - Qihao Guo
- Department of Gerontology, Shanghai
Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Yihui Guan
- Department of Nuclear Medicine
& PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jun Zhao
- Department of Nuclear Medicine,
Shanghai East Hospital, Tongji University School of Medicine, Shanghai,
China
| | - Jun Liu
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
- Clinical Neuroscience Center,
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai,
China
| | - Wei Xu
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Yulei Deng
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
- Clinical Neuroscience Center,
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai,
China
- Department of Neurology, Ruijin
Hospital LuWan Branch, Shanghai Jiao Tong University School of Medicine,
Shanghai, China
| | - Fang Xie
- Department of Nuclear Medicine
& PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Binyin Li
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
- Clinical Neuroscience Center,
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai,
China
- Department of Neurology, Ruijin
Hospital LuWan Branch, Shanghai Jiao Tong University School of Medicine,
Shanghai, China
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34
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Ahmadi K, Pereira JB, Berron D, Vogel J, Ingala S, Strandberg OT, Janelidze S, Barkhof F, Pfeuffer J, Knutsson L, van Westen D, Palmqvist S, Mutsaerts HJ, Hansson O. Gray matter hypoperfusion is a late pathological event in the course of Alzheimer's disease. J Cereb Blood Flow Metab 2023; 43:565-580. [PMID: 36412244 PMCID: PMC10063832 DOI: 10.1177/0271678x221141139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Several studies have shown decreased cerebral blood flow (CBF) in Alzheimer's disease (AD). However, the role of hypoperfusion in the disease pathogenesis remains unclear. Combining arterial spin labeling MRI, PET, and CSF biomarkers, we investigated the associations between gray matter (GM)-CBF and the key mechanisms in AD including amyloid-β (Aβ) and tau pathology, synaptic and axonal degeneration. Further, we applied a disease progression modeling to characterize the temporal sequence of different AD biomarkers. Lower perfusion was observed in temporo-occipito-parietal cortex in the Aβ-positive cognitively impaired compared to both Aβ-negative and Aβ-positive cognitively unimpaired individuals. In participants along the AD spectrum, GM-CBF was associated with tau, synaptic and axonal dysfunction, but not Aβ in similar cortical regions. Axonal degeneration was further associated with hypoperfusion in cognitively unimpaired individuals. Disease progression modeling revealed that GM-CBF disruption Followed the abnormality of biomarkers of Aβ, tau and brain atrophy. These findings indicate that tau tangles and neurodegeneration are more closely connected with GM-CBF changes than Aβ pathology. Although subjected to the sensitivity of the employed neuroimaging techniques and the modeling approach, these findings suggest that hypoperfusion might not be an early event associated with the build-up of Aβ in preclinical phase of AD.
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Affiliation(s)
- Khazar Ahmadi
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Jacob Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location VUmc, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Olof T Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location VUmc, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Queen's Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Josef Pfeuffer
- Application Development, Siemens Healthcare, Erlangen, Germany
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden.,The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Danielle van Westen
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Diagnostic Radiology, Lund University, Lund, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Henk Jmm Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location VUmc, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Queen's Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
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35
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Helwegen K, Libedinsky I, van den Heuvel MP. Statistical power in network neuroscience. Trends Cogn Sci 2023; 27:282-301. [PMID: 36725422 DOI: 10.1016/j.tics.2022.12.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 01/31/2023]
Abstract
Network neuroscience has emerged as a leading method to study brain connectivity. The success of these investigations is dependent not only on approaches to accurately map connectivity but also on the ability to detect real effects in the data - that is, statistical power. We review the state of statistical power in the field and discuss sample size, effect size, measurement error, and network topology as key factors that influence the power of brain connectivity investigations. We use the term 'differential power' to describe how power can vary between nodes, edges, and graph metrics, leaving traces in both positive and negative connectome findings. We conclude with strategies for working with, rather than around, power in connectivity studies.
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Affiliation(s)
- Koen Helwegen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ilan Libedinsky
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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36
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Hoenig MC, Drzezga A. Clear-headed into old age: Resilience and resistance against brain aging-A PET imaging perspective. J Neurochem 2023; 164:325-345. [PMID: 35226362 DOI: 10.1111/jnc.15598] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/18/2022] [Accepted: 02/22/2022] [Indexed: 11/28/2022]
Abstract
With the advances in modern medicine and the adaptation towards healthier lifestyles, the average life expectancy has doubled since the 1930s, with individuals born in the millennium years now carrying an estimated life expectancy of around 100 years. And even though many individuals around the globe manage to age successfully, the prevalence of aging-associated neurodegenerative diseases such as sporadic Alzheimer's disease has never been as high as nowadays. The prevalence of Alzheimer's disease is anticipated to triple by 2050, increasing the societal and economic burden tremendously. Despite all efforts, there is still no available treatment defeating the accelerated aging process as seen in this disease. Yet, given the advances in neuroimaging techniques that are discussed in the current Review article, such as in positron emission tomography (PET) or magnetic resonance imaging (MRI), pivotal insights into the heterogenous effects of aging-associated processes and the contribution of distinct lifestyle and risk factors already have and are still being gathered. In particular, the concepts of resilience (i.e. coping with brain pathology) and resistance (i.e. avoiding brain pathology) have more recently been discussed as they relate to mechanisms that are associated with the prolongation and/or even stop of the progressive brain aging process. Better understanding of the underlying mechanisms of resilience and resistance may one day, hopefully, support the identification of defeating mechanism against accelerating aging.
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Affiliation(s)
- Merle C Hoenig
- Research Center Juelich, Institute for Neuroscience and Medicine II, Molecular Organization of the Brain, Juelich, Germany.,Department of Nuclear Medicine, Faculty of Medicine, University Hospital Cologne, Cologne, Germany
| | - Alexander Drzezga
- Research Center Juelich, Institute for Neuroscience and Medicine II, Molecular Organization of the Brain, Juelich, Germany.,Department of Nuclear Medicine, Faculty of Medicine, University Hospital Cologne, Cologne, Germany.,German Center for Neurodegenerative Diseases, Bonn/Cologne, Germany
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37
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Katsumi Y, Putcha D, Eckbo R, Wong B, Quimby M, McGinnis S, Touroutoglou A, Dickerson BC. Anterior dorsal attention network tau drives visual attention deficits in posterior cortical atrophy. Brain 2023; 146:295-306. [PMID: 36237170 DOI: 10.1093/brain/awac245] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 05/16/2022] [Accepted: 06/21/2022] [Indexed: 01/11/2023] Open
Abstract
Posterior cortical atrophy (PCA), usually an atypical clinical syndrome of Alzheimer's disease, has well-characterized patterns of cortical atrophy and tau deposition that are distinct from typical amnestic presentations of Alzheimer's disease. However, the mechanisms underlying the cortical spread of tau in PCA remain unclear. Here, in a sample of 17 biomarker-confirmed (A+/T+/N+) individuals with PCA, we sought to identify functional networks with heightened vulnerability to tau pathology by examining the cortical distribution of elevated tau as measured by 18F-flortaucipir (FTP) PET. We then assessed the relationship between network-specific FTP uptake and visuospatial cognitive task performance. As predicted, we found consistent and prominent localization of tau pathology in the dorsal attention network and visual network of the cerebral cortex. Elevated FTP uptake within the dorsal attention network (particularly the ratio of FTP uptake between the anterior and posterior nodes) was associated with poorer visuospatial attention in PCA; associations were also identified in other functional networks, although to a weaker degree. Furthermore, using functional MRI data collected from each patient at wakeful rest, we found that a greater anterior-to-posterior ratio in FTP uptake was associated with stronger intrinsic functional connectivity between anterior and posterior nodes of the dorsal attention network. Taken together, we conclude that our cross-sectional marker of anterior-to-posterior FTP ratio could indicate tau propagation from posterior to anterior dorsal attention network nodes, and that this anterior progression occurs in relation to intrinsic functional connectivity within this network critical for visuospatial attention. Our findings help to clarify the spatiotemporal pattern of tau propagation in relation to visuospatial cognitive decline and highlight the key role of the dorsal attention network in the disease progression of PCA.
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Affiliation(s)
- Yuta Katsumi
- Frontotemporal Disorders Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.,Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Deepti Putcha
- Frontotemporal Disorders Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.,Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Ryan Eckbo
- Frontotemporal Disorders Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Bonnie Wong
- Frontotemporal Disorders Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.,Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Megan Quimby
- Frontotemporal Disorders Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Scott McGinnis
- Frontotemporal Disorders Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.,Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Alexandra Touroutoglou
- Frontotemporal Disorders Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.,Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Bradford C Dickerson
- Frontotemporal Disorders Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.,Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.,Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.,Alzheimer's Disease Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
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38
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Zhang J, Liu Q, Zhang H, Dai M, Song Q, Yang D, Wu G, Chen M. Uncovering the System Vulnerability and Criticality of Human Brain Under Dynamical Neuropathological Events in Alzheimer's Disease. J Alzheimers Dis 2023; 95:1201-1219. [PMID: 37661878 PMCID: PMC11177206 DOI: 10.3233/jad-230027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
BACKGROUND Despite the striking efforts in investigating neurobiological factors behind the acquisition of amyloid-β (A), protein tau (T), and neurodegeneration ([N]) biomarkers, the mechanistic pathways of how AT[N] biomarkers spreading throughout the brain remain elusive. OBJECTIVE To disentangle the massive heterogeneities in Alzheimer's disease (AD) progressions and identify vulnerable/critical brain regions to AD pathology. METHODS In this work, we characterized the interaction of AT[N] biomarkers and their propagation across brain networks using a novel bistable reaction-diffusion model, which allows us to establish a new systems biology underpinning of AD progression. We applied our model to large-scale longitudinal neuroimages from the ADNI database and studied the systematic vulnerability and criticality of brains. RESULTS Our model yields long term prediction that is statistically significant linear correlated with temporal imaging data, produces clinically consistent risk prediction, and captures the Braak-like spreading pattern of AT[N] biomarkers in AD development. CONCLUSIONS Our major findings include (i) tau is a stronger indicator of regional risk compared to amyloid, (ii) temporal lobe exhibits higher vulnerability to AD-related pathologies, (iii) proposed critical brain regions outperform hub nodes in transmitting disease factors across the brain, and (iv) comparing the spread of neuropathological burdens caused by amyloid-β and tau diffusions, disruption of metabolic balance is the most determinant factor contributing to the initiation and progression of AD.
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Affiliation(s)
- Jingwen Zhang
- Department of Computer Science, Wake Forest University, Winston-Salem, NC, USA
| | - Qing Liu
- Department of Mathematics, University of North Georgia, Oakwood, GA, USA
| | - Haorui Zhang
- Department of Mathematics, University of North Georgia, Oakwood, GA, USA
| | - Michelle Dai
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Qianqian Song
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Defu Yang
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Minghan Chen
- Department of Computer Science, Wake Forest University, Winston-Salem, NC, USA
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39
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Li H, Jiang S, Dong D, Hu J, He C, Hou C, He H, Huang H, Shen D, Pei H, Zhao G, Dong L, Yao D, Luo C. Vascular feature as a modulator of the aging brain. Cereb Cortex 2022; 32:5609-5621. [PMID: 35174854 DOI: 10.1093/cercor/bhac039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 01/25/2023] Open
Abstract
The cerebral functional reorganization and declined cognitive function of aging might associate with altered vascular features. Here, we explored the altered cerebral hierarchical functional network of 2 conditions (task-free and naturalistic stimuli) in older adults and its relationship with vascular features (systemic microvascular and perfusion features, measured by magnetic resonance imaging) and behavior. Using cerebral gradient analysis, we found that compressive gradient of resting-state mainly located on the primary sensory-motor system and transmodal regions in aging, and further compress in these regions under the continuous naturalistic stimuli. Combining cerebral functional gradient, vascular features, and cognitive performance, the more compressive gradient in the resting-state, the worse vascular state, the lower cognitive function in older adults. Further modulation analysis demonstrated that both vascular features can regulate the relationship between gradient scores in the insula and behavior. Interestingly, systemic microvascular oxygenation also can modulate the relationship between cerebral gradient and cerebral perfusion. Furthermore, the less alteration of the compressive gradient with naturalistic stimuli came with lower cognitive function. Our findings demonstrated that the altered cerebral hierarchical functional structure in aging was linked with changed vascular features and behavior, offering a new framework for studying the physiological mechanism of functional connectivity in aging.
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Affiliation(s)
- Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- Radiology Department, Chengdu Mental Health Center, Chengdu 610036, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
| | - Debo Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Jian Hu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Chuan He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Changyue Hou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Radiology Department, Chengdu Mental Health Center, Chengdu 610036, P. R. China
| | - Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Dai Shen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
| | - Guocheng Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Radiology Department, Chengdu Mental Health Center, Chengdu 610036, P. R. China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Cancer Hospital affiliate to School of Medicine, University of Electronic Science and Technology of China, Chengdu 610042, P. R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, P. R. China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Cancer Hospital affiliate to School of Medicine, University of Electronic Science and Technology of China, Chengdu 610042, P. R. China
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Steward A, Biel D, Brendel M, Dewenter A, Roemer S, Rubinski A, Luan Y, Dichgans M, Ewers M, Franzmeier N. Functional network segregation is associated with attenuated tau spreading in Alzheimer's disease. Alzheimers Dement 2022; 19:2034-2046. [PMID: 36433865 DOI: 10.1002/alz.12867] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 09/01/2022] [Accepted: 10/05/2022] [Indexed: 11/27/2022]
Abstract
INTRODUCTION Lower network segregation is associated with accelerated cognitive decline in Alzheimer's disease (AD), yet it is unclear whether less segregated brain networks facilitate connectivity-mediated tau spreading. METHODS We combined resting state functional magnetic resonance imaging (fMRI) with longitudinal tau positron emission tomography (PET) in 42 betamyloid-negative controls and 81 amyloid beta positive individuals across the AD spectrum. Network segregation was determined using resting-state fMRI-assessed connectivity among 400 cortical regions belonging to seven networks. RESULTS AD subjects with higher network segregation exhibited slower brain-wide tau accumulation relative to their baseline entorhinal tau PET burden (typical onset site of tau pathology). Second, by identifying patient-specific tau epicenters with highest baseline tau PET we found that stronger epicenter segregation was associated with a slower rate of tau accumulation in the rest of the brain in relation to baseline epicenter tau burden. DISCUSSION Our results indicate that tau spreading is facilitated by a more diffusely organized connectome, suggesting that brain network topology modulates tau spreading in AD. HIGHLIGHTS Higher brain network segregation is associated with attenuated tau pathology accumulation in Alzheimer's disease (AD). A patient-tailored approach allows for the more precise localization of tau epicenters. The functional segregation of subject-specific tau epicenters predicts the rate of future tau accumulation.
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Affiliation(s)
- Anna Steward
- Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Germany
| | - Davina Biel
- Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Germany
| | - Matthias Brendel
- Department of Nuclear Medicine University Hospital LMU Munich Munich Germany
- German Center for Neurodegenerative Diseases (DZNE) Munich Germany
- Munich Cluster for Systems Neurology (SyNergy) Munich Germany
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Germany
| | - Sebastian Roemer
- Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Germany
- Department of Neurology University Hospital LMU Munich Munich Germany
| | - Anna Rubinski
- Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Germany
| | - Ying Luan
- Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Germany
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Germany
- German Center for Neurodegenerative Diseases (DZNE) Munich Germany
- Munich Cluster for Systems Neurology (SyNergy) Munich Germany
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Germany
- German Center for Neurodegenerative Diseases (DZNE) Munich Germany
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Germany
- Munich Cluster for Systems Neurology (SyNergy) Munich Germany
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41
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Pichet Binette A, Franzmeier N, Spotorno N, Ewers M, Brendel M, Biel D, Strandberg O, Janelidze S, Palmqvist S, Mattsson-Carlgren N, Smith R, Stomrud E, Ossenkoppele R, Hansson O. Amyloid-associated increases in soluble tau relate to tau aggregation rates and cognitive decline in early Alzheimer's disease. Nat Commun 2022; 13:6635. [PMID: 36333294 PMCID: PMC9636262 DOI: 10.1038/s41467-022-34129-4] [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: 02/26/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022] Open
Abstract
For optimal design of anti-amyloid-β (Aβ) and anti-tau clinical trials, we need to better understand the pathophysiological cascade of Aβ- and tau-related processes. Therefore, we set out to investigate how Aβ and soluble phosphorylated tau (p-tau) relate to the accumulation of tau aggregates assessed with PET and subsequent cognitive decline across the Alzheimer's disease (AD) continuum. Using human cross-sectional and longitudinal neuroimaging and cognitive assessment data, we show that in early stages of AD, increased concentration of soluble CSF p-tau is strongly associated with accumulation of insoluble tau aggregates across the brain, and CSF p-tau levels mediate the effect of Aβ on tau aggregation. Further, higher soluble p-tau concentrations are mainly related to faster accumulation of tau aggregates in the regions with strong functional connectivity to individual tau epicenters. In this early stage, higher soluble p-tau concentrations is associated with cognitive decline, which is mediated by faster increase of tau aggregates. In contrast, in AD dementia, when Aβ fibrils and soluble p-tau levels have plateaued, cognitive decline is related to the accumulation rate of insoluble tau aggregates. Our data suggest that therapeutic approaches reducing soluble p-tau levels might be most favorable in early AD, before widespread insoluble tau aggregates.
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Affiliation(s)
- Alexa Pichet Binette
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, 205 02, Sweden.
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Nicola Spotorno
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, 205 02, Sweden
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Davina Biel
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Olof Strandberg
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, 205 02, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, 205 02, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, 205 02, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, 205 02, Sweden
- Department of Neurology, Skåne University Hospital, Lund, 205 02, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - Ruben Smith
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, 205 02, Sweden
- Department of Neurology, Skåne University Hospital, Lund, 205 02, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, 205 02, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, 205 02, Sweden
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Oskar Hansson
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, 205 02, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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Hyperconnectivity matters in early-onset Alzheimer's disease: a resting-state EEG connectivity study. Neurophysiol Clin 2022; 52:459-471. [DOI: 10.1016/j.neucli.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 10/17/2022] [Accepted: 10/21/2022] [Indexed: 11/11/2022] Open
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Cachide M, Carvalho L, Rosa IM, Wiltfang J, Henriques AG, da Cruz e Silva OAB. BIN1 rs744373 SNP and APOE alleles specifically associate to common diseases. FRONTIERS IN DEMENTIA 2022; 1:1001113. [PMID: 39081475 PMCID: PMC11285651 DOI: 10.3389/frdem.2022.1001113] [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: 07/22/2022] [Accepted: 10/12/2022] [Indexed: 08/02/2024]
Abstract
APOE ε4 and BIN1 are the two main genetic risk factors for sporadic Alzheimer's Disease (AD). Among several BIN1 variants, the rs744373 is frequently associated with AD risk by contributing to tau pathology and poor cognitive performance. This study addressed the association of APOE and BIN1 rs744373 to specific characteristics in a Portuguese primary care-based study group, denoted pcb-Cohort. The study included 590 participants from five primary care health centers in the Aveiro district of Portugal. Individuals were evaluated and scored for cognitive and clinical characteristics, and blood samples were collected from the volunteers meeting the inclusion and exclusion criteria (N = 505). APOE and BIN1 genotypes were determined, and their association with cognitive characteristics and other diseases that might contribute to cognitive deficits, namely depression, hypertension, type 2 diabetes, dyslipidemia, osteoarticular diseases, gastrointestinal diseases, cardiovascular and respiratory diseases, was assessed. The diseases attributed to the study group were those previously diagnosed and confirmed by specialists. The results generated through multivariate analysis show that APOE ε4 carriers significantly associated with poorer cognitive performance (OR = 2.527; p = 0.031). Additionally, there was a significant risk of dyslipidemia for APOE ε4 carriers (OR = 1.804; p = 0.036), whereas BIN1 rs744373 risk-allele carriers were at a significantly lower risk of having dyslipidemia (OR = 0.558; p = 0.006). Correlations were evident for respiratory diseases in which APOE ε4 showed a protective tendency (OR = 0.515; p = 0.088), and BIN1 had a significative protective profile (OR = 0.556; p = 0.026). Not of statistical significance, APOE ε2 showed a trend to protect against type 2 diabetes (OR = 0.342; p = 0.093), in contrast BIN1 rs744373 risk-allele carriers were more likely to exhibit the disease (OR = 1.491; p = 0.099). The data here presented clearly show, for the first time, that the two top genetic risk factors for sporadic AD impact a similar group of common diseases, namely dyslipidemia, respiratory diseases, and type 2 diabetes.
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Affiliation(s)
- Maria Cachide
- Neurosciences and Signalling Group, Medical Sciences Department, Institute of Biomedicine - iBiMED, University of Aveiro, Aveiro, Portugal
| | - Liliana Carvalho
- Neurosciences and Signalling Group, Medical Sciences Department, Institute of Biomedicine - iBiMED, University of Aveiro, Aveiro, Portugal
| | - Ilka Martins Rosa
- Neurosciences and Signalling Group, Medical Sciences Department, Institute of Biomedicine - iBiMED, University of Aveiro, Aveiro, Portugal
| | - Jens Wiltfang
- Neurosciences and Signalling Group, Medical Sciences Department, Institute of Biomedicine - iBiMED, University of Aveiro, Aveiro, Portugal
- Department of Psychiatry and Psychotherapy, University Medical Centre Goettingen (UMG), Georg-August University, Goettingen, Germany
- German Centre for Neurodegenerative Diseases (DZNE), Goettingen, Germany
| | - Ana Gabriela Henriques
- Neurosciences and Signalling Group, Medical Sciences Department, Institute of Biomedicine - iBiMED, University of Aveiro, Aveiro, Portugal
| | - Odete A. B. da Cruz e Silva
- Neurosciences and Signalling Group, Medical Sciences Department, Institute of Biomedicine - iBiMED, University of Aveiro, Aveiro, Portugal
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Selvarasu K, Singh AK, Iyaswamy A, Gopalkrishnashetty Sreenivasmurthy S, Krishnamoorthi S, Bera AK, Huang JD, Durairajan SSK. Reduction of kinesin I heavy chain decreases tau hyperphosphorylation, aggregation, and memory impairment in Alzheimer's disease and tauopathy models. Front Mol Biosci 2022; 9:1050768. [PMID: 36387285 PMCID: PMC9641281 DOI: 10.3389/fmolb.2022.1050768] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 10/10/2022] [Indexed: 08/29/2023] Open
Abstract
Many neurodegenerative diseases, such as Alzheimer's disease (AD) and frontotemporal dementia with Parkinsonism linked to chromosome 17, are characterized by tau pathology. Numerous motor proteins, many of which are involved in synaptic transmission, mediate transport in neurons. Dysfunction in motor protein-mediated neuronal transport mechanisms occurs in several neurodegenerative disorders but remains understudied in AD. Kinesins are the most important molecular motor proteins required for microtubule-dependent transport in neurons, and kinesin-1 is crucial for neuronal transport among all kinesins. Although kinesin-1 is required for normal neuronal functions, the dysfunction of these motor domains leading to neurodegenerative diseases is not fully understood. Here, we reported that the kinesin-I heavy chain (KIF5B), a key molecular motor protein, is involved in tau homeostasis in AD cells and animal models. We found that the levels of KIF5B in P301S tau mice are high. We also found that the knockdown and knockout (KO) of KIFf5B significantly decreased the tau stability, and overexpression of KIF5B in KIF5B-KO cells significantly increased the expression of phosphorylated and total tau levels. This suggested that KIF5B might prevent tau accumulation. By conducting experiments on P301S tau mice, we showed that partially reducing KIF5B levels can reduce hyperphosphorylation of the human tau protein, formation of insoluble aggregates, and memory impairment. Collectively, our results suggested that decreasing KIF5B levels is sufficient to prevent and/or slow down abnormal tau behavior of AD and other tauopathies.
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Affiliation(s)
- Karthikeyan Selvarasu
- Molecular Mycology and Neurodegenerative Disease Research Laboratory, Department of Microbiology, Central University of Tamil Nadu, Thiruvarur, India
| | - Abhay Kumar Singh
- Molecular Mycology and Neurodegenerative Disease Research Laboratory, Department of Microbiology, Central University of Tamil Nadu, Thiruvarur, India
| | - Ashok Iyaswamy
- Mr. and Mrs. Ko Chi-Ming Centre for Parkinson’s Disease Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | | | - Senthilkumar Krishnamoorthi
- Centre for Trans-Disciplinary Research, Department of Pharmacology, Saveetha Dental College and Hospitals, Chennai, India
| | - Amal Kanti Bera
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Jian-Dong Huang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Siva Sundara Kumar Durairajan
- Molecular Mycology and Neurodegenerative Disease Research Laboratory, Department of Microbiology, Central University of Tamil Nadu, Thiruvarur, India
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45
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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] [Key Words] [MESH Headings] [Grants] [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.
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Affiliation(s)
- Anna Rubinski
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Anna Dewenter
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Ying Luan
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Ruben Smith
- Department of Neurology, Skåne University Hospital, Lund, Sweden
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Michael Ewers
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
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Otero-Garcia M, Mahajani SU, Wakhloo D, Tang W, Xue YQ, Morabito S, Pan J, Oberhauser J, Madira AE, Shakouri T, Deng Y, Allison T, He Z, Lowry WE, Kawaguchi R, Swarup V, Cobos I. Molecular signatures underlying neurofibrillary tangle susceptibility in Alzheimer's disease. Neuron 2022; 110:2929-2948.e8. [PMID: 35882228 PMCID: PMC9509477 DOI: 10.1016/j.neuron.2022.06.021] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 03/08/2022] [Accepted: 06/27/2022] [Indexed: 01/01/2023]
Abstract
Tau aggregation in neurofibrillary tangles (NFTs) is closely associated with neurodegeneration and cognitive decline in Alzheimer's disease (AD). However, the molecular signatures that distinguish between aggregation-prone and aggregation-resistant cell states are unknown. We developed methods for the high-throughput isolation and transcriptome profiling of single somas with NFTs from the human AD brain, quantified the susceptibility of 20 neocortical subtypes for NFT formation and death, and identified both shared and cell-type-specific signatures. NFT-bearing neurons shared a marked upregulation of synaptic transmission-related genes, including a core set of 63 genes enriched for synaptic vesicle cycling. Oxidative phosphorylation and mitochondrial dysfunction were highly cell-type dependent. Apoptosis was only modestly enriched, and the susceptibilities of NFT-bearing and NFT-free neurons for death were highly similar. Our analysis suggests that NFTs represent cell-type-specific responses to stress and synaptic dysfunction. We provide a resource for biomarker discovery and the investigation of tau-dependent and tau-independent mechanisms of neurodegeneration.
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Affiliation(s)
- Marcos Otero-Garcia
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sameehan U Mahajani
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Debia Wakhloo
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Weijing Tang
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yue-Qiang Xue
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Samuel Morabito
- Mathematical, Computational and Systems Biology Program, University of California, Irvine, CA 92697, USA; Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA 92697, USA
| | - Jie Pan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jane Oberhauser
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Angela E Madira
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Tamara Shakouri
- Department of Pathology, University of California, Los Angeles, CA 90095, USA
| | - Yongning Deng
- Department of Pathology, University of California, Los Angeles, CA 90095, USA; Department of Neurology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Thomas Allison
- Department of Biological Chemistry, University of California, Los Angeles, CA 90095, USA
| | - Zihuai He
- Department Neurology and Neurological Sciences and Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - William E Lowry
- Department of Molecular Cell and Developmental Biology, Broad Center for Regenerative Medicine and Molecular Biology Institute, University of California, Los Angeles, CA 90095, USA
| | - Riki Kawaguchi
- Department of Psychiatry and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Vivek Swarup
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA 92697, USA; Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA
| | - Inma Cobos
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
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Lee WJ, Cho H, Baek MS, Kim HK, Lee JH, Ryu YH, Lyoo CH, Seong JK. Dynamic network model reveals distinct tau spreading patterns in early- and late-onset Alzheimer disease. Alzheimers Res Ther 2022; 14:121. [PMID: 36056405 PMCID: PMC9438183 DOI: 10.1186/s13195-022-01061-0] [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: 12/15/2021] [Accepted: 08/09/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND The clinical features of Alzheimer's disease (AD) vary substantially depending on whether the onset of cognitive deficits is early or late. The amount and distribution patterns of tau pathology are thought to play a key role in the clinical characteristics of AD, which spreads throughout the large-scale brain network. Here, we describe the differences between tau-spreading processes in early- and late-onset symptomatic individuals on the AD spectrum. METHODS We divided 74 cognitively unimpaired (CU) and 68 cognitively impaired (CI) patients receiving 18F-flortaucipir positron emission tomography scans into two groups by age and age at onset. Members of each group were arranged in a pseudo-longitudinal order based on baseline tau pathology severity, and potential interregional tau-spreading pathways were defined following the order using longitudinal tau uptake. We detected a multilayer community structure through consecutive tau-spreading networks to identify spatio-temporal changes in the propagation hubs. RESULTS In each group, ordered tau-spreading networks revealed the stage-dependent dynamics of tau propagation, supporting distinct tau accumulation patterns. In the young CU/early-onset CI group, tau appears to spread through a combination of three independent communities with partially overlapped territories, whose specific driving regions were the basal temporal regions, left medial and lateral temporal regions, and left parietal regions. For the old CU/late-onset CI group, however, continuation of major communities occurs in line with the appearance of hub regions in the order of bilateral entorhinal cortices, parahippocampal and fusiform gyri, and lateral temporal regions. CONCLUSION Longitudinal tau propagation depicts distinct spreading pathways of the early- and late-onset AD spectrum characterized by the specific location and appearance period of several hub regions that dominantly provide tau.
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Affiliation(s)
- Wha Jin Lee
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, South Korea
| | - Hanna Cho
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, 20 Eonjuro 63-gil, Gangnam-gu, Seoul, South Korea
| | - Min Seok Baek
- Department of Neurology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Gangwon-do, South Korea
| | - Han-Kyeol Kim
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, 20 Eonjuro 63-gil, Gangnam-gu, Seoul, South Korea
| | - Jae Hoon Lee
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Young Hoon Ryu
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Chul Hyoung Lyoo
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, 20 Eonjuro 63-gil, Gangnam-gu, Seoul, South Korea.
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, South Korea.
- Department of Artificial Intelligence, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, South Korea.
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea.
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48
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Mandino F, Yeow LY, Bi R, Sejin L, Bae HG, Baek SH, Lee CY, Mohammad H, Horien C, Teoh CL, Lee JH, Lai MK, Jung S, Fu Y, Olivo M, Gigg J, Grandjean J. The lateral entorhinal cortex is a hub for local and global dysfunction in early Alzheimer's disease states. J Cereb Blood Flow Metab 2022; 42:1616-1631. [PMID: 35466772 PMCID: PMC9441719 DOI: 10.1177/0271678x221082016] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Functional network activity alterations are one of the earliest hallmarks of Alzheimer's disease (AD), detected prior to amyloidosis and tauopathy. Better understanding the neuronal underpinnings of such network alterations could offer mechanistic insight into AD progression. Here, we examined a mouse model (3xTgAD mice) recapitulating this early AD stage. We found resting functional connectivity loss within ventral networks, including the entorhinal cortex, aligning with the spatial distribution of tauopathy reported in humans. Unexpectedly, in contrast to decreased connectivity at rest, 3xTgAD mice show enhanced fMRI signal within several projection areas following optogenetic activation of the entorhinal cortex. We corroborate this finding by demonstrating neuronal facilitation within ventral networks and synaptic hyperexcitability in projection targets. 3xTgAD mice, thus, reveal a dichotomic hypo-connected:resting versus hyper-responsive:active phenotype. This strong homotopy between the areas affected supports the translatability of this pathophysiological model to tau-related, early-AD deficits in humans.
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Affiliation(s)
- Francesca Mandino
- Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore.,Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.,Department of Radiology and Bioimaging Sciences, Yale School of Medicine, New Haven, CT, USA
| | - Ling Yun Yeow
- Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore
| | - Renzhe Bi
- Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore
| | - Lee Sejin
- Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore
| | - Han Gyu Bae
- Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore.,Department of Life Sciences, Yeungnam University, Gyeongsan, South Korea
| | - Seung Hyun Baek
- Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore
| | - Chun-Yao Lee
- Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore
| | - Hasan Mohammad
- Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore
| | - Corey Horien
- Department of Radiology and Bioimaging Sciences, Yale School of Medicine, New Haven, CT, USA
| | - Chai Lean Teoh
- Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore
| | - Jasinda H Lee
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Mitchell Kp Lai
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Sangyong Jung
- Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore
| | - Yu Fu
- Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore
| | - Malini Olivo
- Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore
| | - John Gigg
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Joanes Grandjean
- Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore.,Department of Radiology and Nuclear Medicine & Donders Institute for Brain, Cognition, and Behaviour, Donders Institute, Radboud University Medical Centre, The Netherlands
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49
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Therriault J, Pascoal TA, Savard M, Mathotaarachchi S, Benedet AL, Chamoun M, Tissot C, Lussier FZ, Rahmouni N, Stevenson J, Qureshi MNI, Kang MS, Thomas É, Vitali P, Soucy JP, Massarweh G, Saha-Chaudhuri P, Gauthier S, Rosa-Neto P. Intrinsic connectivity of the human brain provides scaffold for tau aggregation in clinical variants of Alzheimer's disease. Sci Transl Med 2022; 14:eabc8693. [PMID: 36001678 DOI: 10.1126/scitranslmed.abc8693] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease (AD) phenotypes might result from differences in selective vulnerability. Evidence from preclinical models suggests that tau pathology has cell-to-cell propagation properties. Therefore, here, we tested the cell-to-cell propagation framework in the amnestic, visuospatial, language, and behavioral/dysexecutive phenotypes of AD. We report that each AD phenotype is associated with a distinct network-specific pattern of tau aggregation, where tau aggregation is concentrated in brain network hubs. In all AD phenotypes, regional tau load could be predicted by connectivity patterns of the human brain. Furthermore, regions with greater connectivity displayed similar rates of longitudinal tau accumulation in an independent cohort. Connectivity-based tau deposition was not restricted to a specific vulnerable network but was rather a general property of brain organization, linking selective vulnerability and transneuronal spreading models of neurodegeneration. Together, this study indicates that intrinsic brain connectivity provides a framework for tau aggregation across diverse phenotypic manifestations of AD.
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Affiliation(s)
- Joseph Therriault
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Tharick A Pascoal
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Mélissa Savard
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada
| | - Sulantha Mathotaarachchi
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada
| | - Andréa L Benedet
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Mira Chamoun
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Cécile Tissot
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Firoza Z Lussier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Nesrine Rahmouni
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Jenna Stevenson
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Muhammad Naveed Iqbal Qureshi
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Min Su Kang
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Émilie Thomas
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Paolo Vitali
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Jean-Paul Soucy
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Gassan Massarweh
- Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada.,Department of Radiochemistry, McGill University, Montreal, Quebec H3A 2B4, Canada
| | | | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec H3A 1G1, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada.,Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec H3A 1G1, Canada
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50
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Methamphetamine induced neurotoxic diseases, molecular mechanism, and current treatment strategies. Biomed Pharmacother 2022; 154:113591. [PMID: 36007276 DOI: 10.1016/j.biopha.2022.113591] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 08/18/2022] [Accepted: 08/18/2022] [Indexed: 11/21/2022] Open
Abstract
Methamphetamine (MA) is a extremely addictive psychostimulant drug with a significant abuse potential. Long-term MA exposure can induce neurotoxic effects through oxidative stress, mitochondrial functional impairment, endoplasmic reticulum stress, the activation of astrocytes and microglial cells, axonal transport barriers, autophagy, and apoptosis. However, the molecular and cellular mechanisms underlying MA-induced neurotoxicity remain unclear. MA abuse increases the chances of developing neurotoxic conditions such as Parkinson's disease (PD), Alzheimer's disease (AD) and other neurotoxic diseases. MA increases the risk of PD by increasing the expression of alpha-synuclein (ASYN). Furthermore, MA abuse is linked to high chances of developing AD and subsequent neurodegeneration due to biological variations in the brain region or genetic and epigenetic variations. To date, there is no Food and Drug Administration (FDA)-approved therapy for MA-induced neurotoxicity, although many studies are being conducted to develop effective therapeutic strategies. Most current studies are now focused on developing therapies to diminish the neurotoxic effects of MA, based on the underlying mechanism of neurotoxicity. This review article highlights current research on several therapeutic techniques targeting multiple pathways to reduce the neurotoxic effects of MA in the brain, as well as the putative mechanism of MA-induced neurotoxicity.
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