Browsing by Subject "Cerebrospinal fluid (CSF)"

Now showing 1 - 7 of 7
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    APOE effect on Alzheimer's disease biomarkers in older adults with significant memory concern
    (Elsevier, 2015-12) Risacher, Shannon L.; Kim, Sungeun; Nho, Kwangsik; Foroud, Tatiana; Shen, Li; Peterson, Ronald C.; Jack Jr, Clifford R.; Beckett, Laurel A.; Aisen, Paul S.; Koeppe, Robert A.; Jagust, William J.; Shaw, Leslie M.; Trojanowski, John Q.; Department of Radiology and Imaging Sciences, IU School of Medicine
    INTRODUCTION: This study assessed apolipoprotein E (APOE) ε4 carrier status effects on Alzheimer's disease imaging and cerebrospinal fluid (CSF) biomarkers in cognitively normal older adults with significant memory concerns (SMC). METHODS: Cognitively normal, SMC, and early mild cognitive impairment participants from Alzheimer's Disease Neuroimaging Initiative were divided by APOE ε4 carrier status. Diagnostic and APOE effects were evaluated with emphasis on SMC. Additional analyses in SMC evaluated the effect of the interaction between APOE and [(18)F]Florbetapir amyloid positivity on CSF biomarkers. RESULTS: SMC ε4+ showed greater amyloid deposition than SMC ε4-, but no hypometabolism or medial temporal lobe (MTL) atrophy. SMC ε4+ showed lower amyloid beta 1-42 and higher tau/p-tau than ε4-, which was most abnormal in APOE ε4+ and cerebral amyloid positive SMC. DISCUSSION: SMC APOE ε4+ show abnormal changes in amyloid and tau biomarkers, but no hypometabolism or MTL neurodegeneration, reflecting the at-risk nature of the SMC group and the importance of APOE in mediating this risk.
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    Author Correction: Predicting Alzheimer’s disease progression using multi-modal deep learning approach
    (Springer Nature, 2023-08-01) Lee, Garam; Nho, Kwangsik; Kang, Byungkon; Sohn, Kyung‑Ah; Kim, Dokyoon; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Correction to: Scientific Reports 10.1038/s41598-018-37769-z, published online 13 February 2019 This Article contains errors. A Supplementary Information file was omitted from the original version of this Article. The Supplementary Information file is now linked to this correction notice.
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    Blood-based biomarkers for Alzheimer's disease and related dementias: Keys to success and things to consider
    (Elsevier, 2019-11-14) Zetterberg, Henrik; Apostolova, Liana G.; Snyder, Peter J.; Radiology and Imaging Sciences, School of Medicine
    During the last two decades, considerable progress has been made in the field of fluid and imaging biomarkers for neurodegenerative dementias. As a result, the most recent research and clinical guidelines (the National Institute on Aging and Alzheimer's Association, International Working Group 2, National Institute for Health and Care Excellence) incorporate cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers in the diagnostic criteria of dementia and mild cognitive impairment due to Alzheimer's disease (AD) [[1], [2], [3]]. However, as both CSF and amyloid PET examinations require expert knowledge and are of limited availability outside specialized memory clinics, there is no doubt that blood tests would be much easier to implement in clinical medicine and as screening tools when recruiting patients for clinical trials.
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    Parenchymal border macrophages regulate the flow dynamics of the cerebrospinal fluid
    (Springer, 2022-11-09) Drieu, Antoine; Du, Siling; Storck, Steffen E.; Rustenhoven, Justin; Papadopoulos, Zachary; Dykstra, Taitea; Zhong, Fenghe; Kim, Kyungdeok; Blackburn, Susan; Mamuladze, Tornike; Harari, Oscar; Karch, Celeste M.; Bateman, Randall J.; Perrin, Richard; Farlow, Martin; Chhatwal, Jasmeer; Dominantly Inherited Alzheimer Network; Hu, Song; Randolph, Gwendalyn J.; Smirnov, Igor; Kipnis, Jonathan; Neurology, School of Medicine
    Macrophages are important players for the maintenance of tissue homeostasis1. Perivascular and leptomeningeal macrophages reside in close proximity to the central nervous system (CNS) parenchyma2, and their role in CNS physiology has not been well enough studied to date. Given their continuous interaction with the cerebrospinal fluid (CSF) and strategic positioning, we refer to these cells collectively as parenchymal border macrophages (PBMs). Here, we demonstrate that PBMs regulate CSF flow dynamics. We identify a subpopulation of PBMs expressing high levels of CD163 and Lyve1 (scavenger receptor proteins), located in close proximity to the brain arterial tree, and show that Lyve1+ PBMs regulate arterial motion that drives CSF flow. Pharmacological or genetic depletion of PBMs led to accumulation of extracellular matrix proteins, obstructing CSF access to perivascular spaces hence impairing CNS perfusion and clearance. Aging-associated alterations in PBMs and impairment of CSF dynamics were restored upon intracisternal injection of macrophage colony-stimulating growth factor (M-CSF). Human single-nuclei RNA sequencing data obtained from Alzheimer’s disease (AD) patients and healthy controls point to changes in phagocytosis/endocytosis and interferon-gamma (IFNγ) signaling on PBMs, pathways that are corroborated in a mouse AD model. Collectively, our results identify PBMs as novel cellular regulators of CSF flow dynamics, which could potentially be targeted pharmacologically to alleviate brain clearance deficits associated with aging and AD.
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    Predicting Alzheimer's disease progression using multi-modal deep learning approach
    (Springer Nature, 2019-02-13) Lee, Garam; Nho, Kwangsik; Kang, Byungkon; Sohn, Kyung-Ah; Kim, Dokyoon; Radiology and Imaging Sciences, School of Medicine
    Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.
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    Resting-State Functional Connectivity Disruption as a Pathological Biomarker in Autosomal Dominant Alzheimer Disease
    (Mary Ann Liebert, 2021) Smith, Robert X.; Strain, Jeremy F.; Tanenbaum, Aaron; Fagan, Anne M.; Hassenstab, Jason; McDade, Eric; Schindler, Suzanne E.; Gordon, Brian A.; Xiong, Chengjie; Chhatwal, Jasmeer; Jack, Clifford, Jr.; Karch, Celeste; Berman, Sarah; Brosch, Jared R.; Lah, James J.; Brickman, Adam M.; Cash, David M.; Fox, Nick C.; Graff-Radford, Neill R.; Levin, Johannes; Noble, James; Holtzman, David M.; Masters, Colin L.; Farlow, Martin R.; Laske, Christoph; Schofield, Peter R.; Marcus, Daniel S.; Morris, John C.; Benzinger, Tammie L. S.; Bateman, Randall J.; Ances, Beau M.; Neurology, School of Medicine
    Aim: Identify a global resting-state functional connectivity (gFC) signature in mutation carriers (MC) from the Dominantly Inherited Alzheimer Network (DIAN). Assess the gFC with regard to amyloid (A), tau (T), and neurodegeneration (N) biomarkers, and estimated years to symptom onset (EYO). Introduction: Cross-sectional measures were assessed in MC (n = 171) and mutation noncarrier (NC) (n = 70) participants. A functional connectivity (FC) matrix that encompassed multiple resting-state networks was computed for each participant. Methods: A global FC was compiled as a single index indicating FC strength. The gFC signature was modeled as a nonlinear function of EYO. The gFC was linearly associated with other biomarkers used for assessing the AT(N) framework, including cerebrospinal fluid (CSF), positron emission tomography (PET) molecular biomarkers, and structural magnetic resonance imaging. Results: The gFC was reduced in MC compared with NC participants. When MC participants were differentiated by clinical dementia rating (CDR), the gFC was significantly decreased in MC CDR >0 (demented) compared with either MC CDR 0 (cognitively normal) or NC participants. The gFC varied nonlinearly with EYO and initially decreased at EYO = −24 years, followed by a stable period followed by a further decline near EYO = 0 years. Irrespective of EYO, a lower gFC associated with values of amyloid PET, CSF Aβ1–42, CSF p-tau, CSF t-tau, 18F-fluorodeoxyglucose, and hippocampal volume. Conclusions: The gFC correlated with biomarkers used for defining the AT(N) framework. A biphasic change in the gFC suggested early changes associated with CSF amyloid and later changes associated with hippocampal volume.
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    Type 2 diabetes mellitus and cerebrospinal fluid Alzheimer's disease biomarker amyloid β1-42 in Alzheimer's Disease Neuroimaging Initiative participants
    (2017-11-23) Li, Wei; Risacher, Shannon L.; Gao, Sujuan; Boehm, Stephen L.; Elmendorf, Jeffrey S.; Saykin, Andrew J.; Radiology and Imaging Sciences, School of Medicine
    Introduction Type 2 diabetes mellitus (T2DM) is a risk factor for Alzheimer's disease. Cerebrospinal fluid (CSF) amyloid β (Aβ) 1-42 is an important Alzheimer's disease biomarker. However, it is inconclusive on how T2DM is related to CSF Aβ1-42. Methods Participants with T2DM were selected from the Alzheimer's Disease Neuroimaging Initiative by searching keywords from the medical history database. A two-way analysis of covariance model was used to analyze how T2DM associates with CSF Aβ1-42 or cerebral cortical Aβ. Results CSF Aβ1-42 was higher in the T2DM group than the nondiabetic group. The inverse relation between CSF Aβ1-42 and cerebral cortical Aβ was independent of T2DM status. Participants with T2DM had a lower cerebral cortical Aβ in anterior cingulate, precuneus, and temporal lobe than controls. Discussion T2DM is positively associated with CSF Aβ1-42 but negatively with cerebral cortical Aβ. The decreased cerebral cortical Aβ associated with T2DM is preferentially located in certain brain regions.