Browsing by Subject "Alzheimer’s disease (AD) pathology"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Association between brain tau deposition and default mode network connectivity in cognitively normal older adults
    (Wiley, 2025-01-09) Cha, Woo-Jin; Yi, Dahyun; Chumin, Evgeny J.; Byun, Min Soo; Jung, Joon Hyung; Ahn, Hyejin; Kim, Yu Kyeong; Lee, Yun-Sang; Kang, Koung Mi; Sohn, Chul-Ho; Risacher, Shannon L.; Sporns, Olaf; Nho, Kwangsik; Saykin, Andrew J.; Lee, Dong Young; KBASE Research Group; Radiology and Imaging Sciences, School of Medicine
    Background: Alzheimer’s disease (AD) pathology occurs in the brain before manifestation of significant cognitive decline. Growing evidence suggests that brain networks such as default mode network (DMN) or salience network, identified through resting‐state functional magnetic resonance imaging (MRI), are affected by AD pathology. In this study, we investigated the relationship between network segregation and the key in vivo AD pathologies including beta‐amyloid (Aβ) and tau deposition in old adults with no cognitive impairment. Method: A total 283 older adults with normal cognition aging from 55 to 87 were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE) cohort. The participants underwent comprehensive clinical and neuropsychological assessment, [11C] Pittsburgh Compound B PET for measuring Aβ deposition, [18F] AV‐1451 PET for measuring tau deposition, structural MRI, and resting‐state functional MRI for measuring functional connectivity (FC). For PET scans, standard uptake value ratio (SUVR) was used for the analyses; combined regions of inferior cerebellum and pons were used as the reference region when obtaining SUVRs. For FC, segregation values (ratios between median z‐transformed Pearson correlation of within‐ and between‐network connectivity) for overall and the seven individual resting state networks were computed (Table). The relationships between Aβ or tau deposition and network connectivity segregation were examined through cross‐sectional approach using multiple regression analyses. In the analyses, Aβ or tau deposition was used as an independent variable and segregation values of the networks were used as dependent variables. Result: Tau deposition had a significant negative association with the DMN segregation (β = ‐0.249, p = 0.007); but, tau had no relationships with any other networks (Table). Aβ deposition was not associated with any segregation values for the seven brain networks (Table). Conclusion: Our finding suggests that impaired functional connectivity of DMN is closely linked to tau deposition even in cognitively unimpaired older individuals.
  • Thumbnail Image
    Item
    Characterization of Language Profiles in Cognitively‐Defined Subgroups of Alzheimer’s Disease
    (Wiley, 2025-01-03) Gallée, Jeanne; Gibbons, Laura E.; Mukherjee, Shubhabrata; Scollard, Phoebe; Choi, Seo-Eun; James, Bryan D.; Klinedinst, Brandon S.; Lee, Michael L.; Mez, Jesse; Trittschuh, Emily H.; Saykin, Andrew J.; Crane, Paul K.; Medical and Molecular Genetics, School of Medicine
    Background: The relationship between Alzheimer’s disease (AD) pathology and the associated clinical syndrome a patient presents with remains indeterminate. Cognitively‐defined subgroups of AD have revealed distinctions based on relative cognitive impairments, including AD‐Language, where challenges in language are substantial, and AD‐No Domain, where no relative asymmetries across cognitive domains occur. Pathological features of AD have been associated as the primary neuropathology of the logopenic variant of primary progressive aphasia (lvPPA). Hallmark clinical features of lvPPA include relatively spared comprehension in the face of decline in naming and repetition abilities. This work aimed to test the hypothesis that the lvPPA language profile was overrepresented in AD‐Language when compared to AD‐No Domain. Method: Measures of verbal comprehension, confrontation naming, and phrase‐level repetition were obtained from all participants from the Religious Orders Study (ROS), the RUSH Memory and Aging Project (MAP) and the Minority Aging Research Study (MARS) using confirmatory factor analyses. We subsetted the data to include participants belonging to the AD‐Language and AD‐No Domain groups at their initial AD diagnosis visit. We compared patterns of language profiles based on strengths and weaknesses in comprehension, naming, and repetition. Pearson’s Chi‐squared tests with Yates continuity correction was used to test if the language patterns were statistically different between the two AD subgroups. Results: We analyzed language performance in 642 participants across AD‐Language (31.8%) and AD‐No Domain (68.2%) groups (Table 1). Thresholds were based on AD‐No Domain and set as the median for each subdomain (comprehension = ‐.101, naming = ‐.957, repetition = .233) to establish whether a score represented a relative strength or weakness in the language profile. Eight patterns of language profiles based on strengths and weaknesses in comprehension, naming, and repetition were formed (Figure 1). The distribution of language patterns differed significantly between AD‐Language and AD‐No Domain (χ2 = 97.6, p <.001). Furthermore, the lvPPA pattern was found more frequently in AD‐Language (χ2 = 28.1, p <.001). Conclusion: Heterogeneity within the AD‐Language spectrum includes a significant proportion that is consistent with the language profile of lvPPA. Relative performance in domains of verbal comprehension, confrontation naming, and phrase‐level repetition varied by AD subgroup.