Browsing by Author "Johnson, Keith A."
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Item Longitudinal Accumulation of Cerebral Microhemorrhages in Dominantly Inherited Alzheimer Disease(American Academy of Neurology, 2021-03-23) Joseph-Mathurin, Nelly; Wang, Guoqiao; Kantarci, Kejal; Jack, Clifford R., Jr.; McDade, Eric; Hassenstab, Jason; Blazey, Tyler M.; Gordon, Brian A.; Su, Yi; Chen, Gengsheng; Massoumzadeh, Parinaz; Hornbeck, Russ C.; Allegri, Ricardo F.; Ances, Beau M.; Berman, Sarah B.; Brickman, Adam M.; Brooks, William S.; Cash, David M.; Chhatwal, Jasmeer P.; Chui, Helena C.; Correia, Stephen; Cruchaga, Carlos; Farlow, Martin R.; Fox, Nick C.; Fulham, Michael; Ghetti, Bernardino; Graff-Radford, Neill R.; Johnson, Keith A.; Karch, Celeste M.; Laske, Christoph; Lee, Athene K.W.; Levin, Johannes; Masters, Colin L.; Noble, James M.; O’Connor, Antoinette; Perrin, Richard J.; Preboske, Gregory M.; Ringman, John M.; Rowe, Christopher C.; Salloway, Stephen; Saykin, Andrew J.; Schofield, Peter R.; Shimada, Hiroyuki; Shoji, Mikio; Suzuki, Kazushi; Villemagne, Victor L.; Xiong, Chengjie; Yakushev, Igor; Morris, John C.; Bateman, Randall J.; Benzinger, Tammie L.S.; Pathology and Laboratory Medicine, School of MedicineObjective: To investigate the inherent clinical risks associated with the presence of cerebral microhemorrhages (CMHs) or cerebral microbleeds and characterize individuals at high risk for developing hemorrhagic amyloid-related imaging abnormality (ARIA-H), we longitudinally evaluated families with dominantly inherited Alzheimer disease (DIAD). Methods: Mutation carriers (n = 310) and noncarriers (n = 201) underwent neuroimaging, including gradient echo MRI sequences to detect CMHs, and neuropsychological and clinical assessments. Cross-sectional and longitudinal analyses evaluated relationships between CMHs and neuroimaging and clinical markers of disease. Results: Three percent of noncarriers and 8% of carriers developed CMHs primarily located in lobar areas. Carriers with CMHs were older, had higher diastolic blood pressure and Hachinski ischemic scores, and more clinical, cognitive, and motor impairments than those without CMHs. APOE ε4 status was not associated with the prevalence or incidence of CMHs. Prevalent or incident CMHs predicted faster change in Clinical Dementia Rating although not composite cognitive measure, cortical thickness, hippocampal volume, or white matter lesions. Critically, the presence of 2 or more CMHs was associated with a significant risk for development of additional CMHs over time (8.95 ± 10.04 per year). Conclusion: Our study highlights factors associated with the development of CMHs in individuals with DIAD. CMHs are a part of the underlying disease process in DIAD and are significantly associated with dementia. This highlights that in participants in treatment trials exposed to drugs, which carry the risk of ARIA-H as a complication, it may be challenging to separate natural incidence of CMHs from drug-related CMHs.Item Partial Volume Correction in Quantitative Amyloid Imaging.(Elsevier, 2015-02-15) Su, Yi; Blazey, Tyler M.; Snyder, Abraham Z.; Raichle, Marcus E.; Marcus, Daniel S.; Ances, Beau M.; Bateman, Randall J.; Cairns, Nigel J.; Aldea, Patricia; Cash, Lisa; Christensen, Jon J.; Friedrichsen, Karl; Hornbeck, Russ C.; Farrar, Angela M.; Owen, Christopher J.; Mayeux, Richard; Brickman, Adam M.; Klunk, William; Price, Julie C.; Thompson, Paul M.; Ghetti, Bernardino; Saykin, Andrew J.; Sperling, Reisa A.; Johnson, Keith A.; Schofield, Peter R.; Buckles, Virginia; Morris, John C.; Benzinger, Tammie LS; Department of Pathology & Laboratory Medicine, IU School of MedicineAmyloid imaging is a valuable tool for research and diagnosis in dementing disorders. As positron emission tomography (PET) scanners have limited spatial resolution, measured signals are distorted by partial volume effects. Various techniques have been proposed for correcting partial volume effects, but there is no consensus as to whether these techniques are necessary in amyloid imaging, and, if so, how they should be implemented. We evaluated a two-component partial volume correction technique and a regional spread function technique using both simulated and human Pittsburgh compound B (PiB) PET imaging data. Both correction techniques compensated for partial volume effects and yielded improved detection of subtle changes in PiB retention. However, the regional spread function technique was more accurate in application to simulated data. Because PiB retention estimates depend on the correction technique, standardization is necessary to compare results across groups. Partial volume correction has sometimes been avoided because it increases the sensitivity to inaccuracy in image registration and segmentation. However, our results indicate that appropriate PVC may enhance our ability to detect changes in amyloid deposition.Item Preferential degradation of cognitive networks differentiates Alzheimer's disease from ageing(Oxford University Press, 2018-05-01) Chhatwal, Jasmeer P.; Schultz, Aaron P.; Johnson, Keith A.; Hedden, Trey; Jaimes, Sehily; Benzinger, Tammie L S.; Jack, Clifford; Ances, Beau M.; Ringman, John M.; Marcus, Daniel S.; Ghetti, Bernardino; Farlow, Martin R.; Danek, Adrian; Levin, Johannes; Yakushev, Igor; Laske, Christoph; Koeppe, Robert A.; Galasko, Douglas R.; Xiong, Chengjie; Masters, Colin L.; Schofield, Peter R.; Kinnunen, Kirsi M.; Salloway, Stephen; Martins, Ralph N.; McDade, Eric; Cairns, Nigel J.; Buckles, Virginia D.; Morris, John C.; Bateman, Randall; Sperling, Reisa A.; Pathology and Laboratory Medicine, School of MedicineConverging evidence from structural, metabolic and functional connectivity MRI suggests that neurodegenerative diseases, such as Alzheimer's disease, target specific neural networks. However, age-related network changes commonly co-occur with neuropathological cascades, limiting efforts to disentangle disease-specific alterations in network function from those associated with normal ageing. Here we elucidate the differential effects of ageing and Alzheimer's disease pathology through simultaneous analyses of two functional connectivity MRI datasets: (i) young participants harbouring highly-penetrant mutations leading to autosomal-dominant Alzheimer's disease from the Dominantly Inherited Alzheimer's Network (DIAN), an Alzheimer's disease cohort in which age-related comorbidities are minimal and likelihood of progression along an Alzheimer's disease trajectory is extremely high; and (ii) young and elderly participants from the Harvard Aging Brain Study, a cohort in which imaging biomarkers of amyloid burden and neurodegeneration can be used to disambiguate ageing alone from preclinical Alzheimer's disease. Consonant with prior reports, we observed the preferential degradation of cognitive (especially the default and dorsal attention networks) over motor and sensory networks in early autosomal-dominant Alzheimer's disease, and found that this distinctive degradation pattern was magnified in more advanced stages of disease. Importantly, a nascent form of the pattern observed across the autosomal-dominant Alzheimer's disease spectrum was also detectable in clinically normal elderly with clear biomarker evidence of Alzheimer's disease pathology (preclinical Alzheimer's disease). At the more granular level of individual connections between node pairs, we observed that connections within cognitive networks were preferentially targeted in Alzheimer's disease (with between network connections relatively spared), and that connections between positively coupled nodes (correlations) were preferentially degraded as compared to connections between negatively coupled nodes (anti-correlations). In contrast, ageing in the absence of Alzheimer's disease biomarkers was characterized by a far less network-specific degradation across cognitive and sensory networks, of between- and within-network connections, and of connections between positively and negatively coupled nodes. We go on to demonstrate that formalizing the differential patterns of network degradation in ageing and Alzheimer's disease may have the practical benefit of yielding connectivity measurements that highlight early Alzheimer's disease-related connectivity changes over those due to age-related processes. Together, the contrasting patterns of connectivity in Alzheimer's disease and ageing add to prior work arguing against Alzheimer's disease as a form of accelerated ageing, and suggest multi-network composite functional connectivity MRI metrics may be useful in the detection of early Alzheimer's disease-specific alterations co-occurring with age-related connectivity changes. More broadly, our findings are consistent with a specific pattern of network degradation associated with the spreading of Alzheimer's disease pathology within targeted neural networks.Item Presenilin-1 mutation position influences amyloidosis, small vessel disease, and dementia with disease stage(Wiley, 2024) Joseph-Mathurin, Nelly; Feldman, Rebecca L.; Lu, Ruijin; Shirzadi, Zahra; Toomer, Carmen; Saint Clair, Junie R.; Ma, Yinjiao; McKay, Nicole S.; Strain, Jeremy F.; Kilgore, Collin; Friedrichsen, Karl A.; Chen, Charles D.; Gordon, Brian A.; Chen, Gengsheng; Hornbeck, Russ C.; Massoumzadeh, Parinaz; McCullough, Austin A.; Wang, Qing; Li, Yan; Wang, Guoqiao; Keefe, Sarah J.; Schultz, Stephanie A.; Cruchaga, Carlos; Preboske, Gregory M.; Jack, Clifford R., Jr.; Llibre-Guerra, Jorge J.; Allegri, Ricardo F.; Ances, Beau M.; Berman, Sarah B.; Brooks, William S.; Cash, David M.; Day, Gregory S.; Fox, Nick C.; Fulham, Michael; Ghetti, Bernardino; Johnson, Keith A.; Jucker, Mathias; Klunk, William E.; la Fougère, Christian; Levin, Johannes; Niimi, Yoshiki; Oh, Hwamee; Perrin, Richard J.; Reischl, Gerald; Ringman, John M.; Saykin, Andrew J.; Schofield, Peter R.; Su, Yi; Supnet-Bell, Charlene; Vöglein, Jonathan; Yakushev, Igor; Brickman, Adam M.; Morris, John C.; McDade, Eric; Xiong, Chengjie; Bateman, Randall J.; Chhatwal, Jasmeer P.; Benzinger, Tammie L. S.; Dominantly Inherited Alzheimer Network; Pathology and Laboratory Medicine, School of MedicineIntroduction: Amyloidosis, including cerebral amyloid angiopathy, and markers of small vessel disease (SVD) vary across dominantly inherited Alzheimer's disease (DIAD) presenilin-1 (PSEN1) mutation carriers. We investigated how mutation position relative to codon 200 (pre-/postcodon 200) influences these pathologic features and dementia at different stages. Methods: Individuals from families with known PSEN1 mutations (n = 393) underwent neuroimaging and clinical assessments. We cross-sectionally evaluated regional Pittsburgh compound B-positron emission tomography uptake, magnetic resonance imaging markers of SVD (diffusion tensor imaging-based white matter injury, white matter hyperintensity volumes, and microhemorrhages), and cognition. Results: Postcodon 200 carriers had lower amyloid burden in all regions but worse markers of SVD and worse Clinical Dementia Rating® scores compared to precodon 200 carriers as a function of estimated years to symptom onset. Markers of SVD partially mediated the mutation position effects on clinical measures. Discussion: We demonstrated the genotypic variability behind spatiotemporal amyloidosis, SVD, and clinical presentation in DIAD, which may inform patient prognosis and clinical trials. Highlights: Mutation position influences Aβ burden, SVD, and dementia. PSEN1 pre-200 group had stronger associations between Aβ burden and disease stage. PSEN1 post-200 group had stronger associations between SVD markers and disease stage. PSEN1 post-200 group had worse dementia score than pre-200 in late disease stage. Diffusion tensor imaging-based SVD markers mediated mutation position effects on dementia in the late stage.Item The prevalence of tau‐PET positivity in aging and dementia(Wiley, 2025-01-09) Coomans, Emma M.; Groot, Colin; Rowe, Christopher C.; Dore, Vincent; Villemagne, Victor L.; van de Giessen, Elsmarieke; van der Flier, Wiesje M.; Pijnenburg, Yolande A. L.; Visser, Pieter Jelle; den Braber, Anouk; Pontecorvo, Michael; Shcherbinin, Sergey; Kennedy, Ian A.; Jagust, William J.; Baker, Suzanne L.; Harrison, Theresa M.; Gispert, Juan Domingo; Shekari, Mahnaz; Minguillon, Carolina; Smith, Ruben; Mattsson-Carlgren, Niklas; Palmqvist, Sebastian; Strandberg, Olof; Stomrud, Erik; Malpetti, Maura; O'Brien, John T.; Rowe, James B.; Jäger, Elena; Bischof, Gérard N.; Drzezga, Alexander; Garibotto, Valentina; Frisoni, Giovanni; Peretti, Débora Elisa; Schöll, Michael; Skoog, Ingmar; Kern, Silke; Sperling, Reisa A.; Johnson, Keith A.; Risacher, Shannon L.; Saykin, Andrew J.; Carrillo, Maria C.; Dickerson, Brad C.; Apostolova, Liana G.; Barthel, Henryk; Rullmann, Michael; Messerschmidt, Konstantin; Vandenberghe, Rik; Van Laere, Koen; Spruyt, Laure; Franzmeier, Nicolai; Brendel, Matthias; Gnörich, Johannes; Benzinger, Tammie L. S.; Lagarde, Julien; Sarazin, Marie; Bottlaender, Michel; Villeneuve, Sylvia; Poirier, Judes; Seo, Sang Won; Gu, Yuna; Kim, Jun Pyo; Mormino, Elizabeth; Young, Christina B.; Vossler, Hillary; Rosa-Neto, Pedro; Therriault, Joseph; Rahmouni, Nesrine; Coath, William; Cash, David M.; Schott, Jonathan M.; Rabinovici, Gil D.; La Joie, Renaud; Rosen, Howard J.; Johnson, Sterling C.; Christian, Bradley T.; Betthauser, Tobey J.; Hansson, Oskar; Ossenkoppele, Rik; Radiology and Imaging Sciences, School of MedicineBackground Tau‐PET imaging allows in‐vivo detection of neurofibrillary tangles. One tau‐PET tracer (i.e., [18F]flortaucipir) has received FDA‐approval for clinical use, and multiple other tau‐PET tracers have been implemented into clinical trials for participant selection and/or as a primary or secondary outcome measure. To optimize future use of tau‐PET, it is essential to understand how demographic, clinical and genetic factors affect tau‐PET‐positivity rates. Method This large‐scale multi‐center study includes 9713 participants from 35 cohorts worldwide who underwent tau‐PET with [18F]flortaucipir (n = 6420), [18F]RO948 (n = 1999), [18F]MK6240 (n = 878) or [18F]PI2620 (n = 416) (Table‐1). We analyzed individual‐level tau‐PET SUVR data using a cerebellar reference region that were processed either centrally (n = 3855) or by each cohort (n = 5858). We computed cohort‐specific SUVR thresholds based on the mean + 2 standard deviations in a temporal meta‐region of amyloid‐negative cognitively normal (CN) individuals aged >50. Logistic generalized estimating equations were used to estimate tau‐PET‐positivity probabilities, using an exchangeable correlation structure to account for within‐cohort correlations. Analyses were performed with (interactions between) age, amyloid‐status, and APOE‐e4 carriership as independent variables, stratified for syndrome diagnosis. Result The study included 5962 CN participants (7.5% tau‐PET‐positive), 1683 participants with mild cognitive impairment (MCI, 33.8% tau‐PET‐positive) and 2068 participants with a clinical diagnosis of dementia (62.1% tau‐PET‐positive) (Figure‐1). From age 60 to 80 years, the estimated prevalence of tau‐PET‐positivity increased from 1.2% [95% CI: 0.9%‐1.5%] to 3.7% [2.3%‐5.1%] among CN amyloid‐negative participants; and from 16.4% [10.8%‐22.1%] to 20.5% [18.8%‐22.2%] among CN amyloid‐positive participants. Among amyloid‐negative participants with MCI and dementia, from age 60 to 80 years, the estimated prevalence of tau‐PET‐positivity increased from 3.5% [1.6%‐5.3%] to 11.8% [7.1%‐16.5%] and from 12.6% [4.5%‐20.7%] to 15.9% [6.7%‐25.1%] respectively. In contrast, among amyloid‐positive participants with MCI and dementia, from age 60 to 80 years, the estimated prevalence of tau‐PET‐positivity decreased from 66.5% [57.0%‐76.0%] to 48.3% [42.9%‐53.8%] and from 92.3% [88.7%‐95.9%] to 73.4% [67.5%‐79.3%] respectively. APOE‐e4 status primarily modulated the association of age with tau‐PET‐positivity estimates among CN and MCI amyloid‐positive participants (Figure‐2). Conclusion This large‐scale multi‐cohort study provides robust prevalence estimates of tau‐PET‐positivity, which can aid the interpretation of tau‐PET in the clinic and inform clinical trial designs.