Browsing by Author "Schonhaut, Daniel R."

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    A harmonized memory composite score for cross‐cohort Alzheimer’s disease and related dementia research: development and validation
    (Wiley, 2025-01-03) Sanderson-Cimino, Mark E.; Gross, Alden L.; Gaynor, Leslie S.; Paolillo, Emily W.; Casaletto, Kaitlin B.; Chatterjee, Ankita; Albert, Marilyn S.; Apostolova, Liana G.; Boersema, Brooke; Boxer, Adam L.; Boeve, Brad F.; Clark, Lindsay R.; La Joie, Renaud; Eloyan, Ani; Tomaszewski Farias, Sarah; Gonzales, Mitzi M.; Hammers, Dustin B.; Wise, Amy B.; Cobigo, Yann; Yballa, Claire; Schonhaut, Daniel R.; Hampstead, Benjamin M.; Mechanic-Hamilton, Dawn; Miller, Bruce L.; Rabinovici, Gil D.; Rascovsky, Katya; Ringman, John M.; Rosen, Howard J.; Ryman, Sephira; Salmon, David P.; Smith, Glenn E.; Decarli, Charles; Kramer, Joel H.; Staffaroni, Adam M.; Neurology, School of Medicine
    Background: The Uniform Data Set (UDS) neuropsychological battery, administered across Alzheimer’s Disease Centers (ADC), includes memory tests but lacks a list‐learning paradigm. ADCs often supplement the UDS with their own preferred list‐learning task. Given the importance of list‐learning for characterizing memory, we aimed to develop a harmonized memory score that incorporates UDS memory tests while allowing centers to contribute differing list‐learning tasks. Method: We applied item‐banking confirmatory factor analysis to develop a composite memory score in 5,287 participants (mean age 67.1; SD = 12.2) recruited through 18 ADCs and four consortia (DiverseVCID, MarkVCID, ALLFTD, LEADS) who completed UDS memory tasks (used as linking‐items) and one of five list‐learning tasks. All analyses used linear regression. We tested whether memory scores were affected by which list‐learning task was administered. To assess construct validity, we tested associations of memory scores with demographics, disease severity (CDR Box Score), an independent memory task (TabCAT Favorites, n = 675), and hippocampal volume (n = 811). We compared performances between cognitively unimpaired (n = 279), AD‐biomarker+ MCI (n = 26), and AD‐biomarker+ dementia (n = 98). In a subsample with amyloid‐ and tau‐PET (n = 49), we compared memory scores from participants with positive vs negative scans determined using established quantitative cutoffs. Result: Model fit indices were excellent (e.g., CFI = 0.998) and factor loadings were strong (0.43‐0.93). Differences in list‐learning task had a negligible effect on scores (average Cohen’s d = 0.11). Higher memory scores were significantly (p’s<.001) correlated with younger age (β = ‐0.18), lower CDR Box Scores (β = ‐0.63), female sex (β = 0.12), higher education (β = 0.19), larger hippocampal volume (β = 0.42), and an independent memory task (β = 0.71, p<0.001). The memory composite declined in a stepwise fashion by diagnosis (cognitively unimpaired>MCI>AD dementia, p<0.001). On average, amyloid‐PET positivity was associated with lower composite scores, but was not statistically significant (β = ‐0.34; p = 0.25; d = 0.40). Tau‐PET positivity was associated with worse performance, demonstrating a large effect size (β = ‐0.75; p<0.002; d = 0.91). Conclusion: The harmonized memory score developed in a large national sample was stable regardless of contributing list‐learning task and its validity for cross‐cohort ADRD research is supported by expected associations with demographics, clinical measures, and Alzheimer’s biomarkers. A processing script will be made available to enhance cross‐cohort ADRD research.
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    Amyloid‐PET in patients with a clinical diagnosis of sporadic early‐ versus late‐onset AD: comparison of the LEADS and ADNI cohorts
    (Wiley, 2025-01-09) Lagarde, Julien; Maiti, Piyush; Schonhaut, Daniel R.; Zhang, Jiaxiuxiu; Soleimani-meigooni, David N.; Zeltzer, Ehud; Windon, Charles; Raya, Maison Abu; Vrillon, Agathe; Hammers, Dustin B.; Dage, Jeffrey L.; Nudelman, Kelly N.; Eloyan, Ani; Koeppe, Robert A.; Landau, Susan M.; Carrillo, Maria C.; Touroutoglou, Alexandra; Vemuri, Prashanthi; Dickerson, Bradford C.; Apostolova, Liana G.; Rabinovici, Gil D.; La Joie, Renaud; LEADS Consortium, Alzheimer’s Disease Neuroimaging Initiative; Neurology, School of Medicine
    Background: Large‐scale studies comparing sporadic early‐onset AD (EOAD, age<65) and late‐onset AD (LOAD, age≥65) are lacking. We compared amyloid‐PET outcomes (positivity rate and amyloid burden) between patients clinically diagnosed with sporadic EOAD vs LOAD, leveraging data from the Longitudinal Early‐Onset AD Study (LEADS) and the Alzheimer’s Disease Neuroimaging Initiative 3 (ADNI3). Method: 731 patients meeting the 2011 NIA‐AA criteria for AD dementia or MCI were included (505 early‐onset from LEADS, 226 late‐onset from ADNI3, Table 1). All participants underwent amyloid‐PET with [18F]Florbetaben or [18F]Florbetapir. Amyloid positivity was centrally determined by a process involving a visual read by a trained expert and PET‐only quantification; in case of a discrepancy, a read from an independent physician acted as a tiebreaker. Logistic regressions in each cohort examined relations between amyloid positivity and age, sex, MMSE and APOE4 genotype. Amyloid burden was independently quantified in Centiloids using an MRI‐based pipeline. Mean Centiloids in LEADS and ADNI were compared with two‐way ANOVA, for visually positive and visually negative scans. Result: Amyloid positivity rate was higher in LEADS (76%) than ADNI (64%, p<0.001, Figure 1A). Lower MMSE and APOE4 genotype increased odds of amyloid positivity in both cohorts, although the APOE4 effect was stronger in ADNI than LEADS (OR=10.1 versus 2.4, p=0.007, Table 2). Amyloid positivity was more common in females across cohorts, but this effect was only statistically significant in LEADS (Table 2). Centiloids were bimodally distributed in both cohorts, although the separation between positive and negative scans was more prominent in LEADS (Figure 1B). Visually positive scans had significantly higher Centiloids in LEADS than in ADNI, whereas no cohort difference was observed for visually negative scans (Figure 1C). Sensitivity analyses showed that this effect was driven by patients with MCI (CDR≤0.5; Figure 1D‐E). Conclusion: The lower amyloid positivity rate in ADNI might be due to AD‐mimicking pathologies being more common at an older age. The higher amyloid burden in early‐onset, amyloid‐positive patients could reflect younger patients being diagnosed later in the disease course compared to typical, late‐onset patients. Alternatively, younger patients might tolerate higher neuropathology burden due to higher brain reserve or fewer co‐pathologies.
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    Characterization of the heterogeneity of amyloid‐PET‐negative patients with a clinical diagnosis of sporadic early‐onset AD: an FDG‐PET study in the LEADS cohort
    (Wiley, 2025-01-09) Lagarde, Julien; Schonhaut, Daniel R.; Maiti, Piyush; Zhang, Jiaxiuxiu; Soleimani-Meigooni, David N.; Zeltzer, Ehud; Windon, Charles; Hammers, Dustin B.; Dage, Jeffrey L.; Nudelman, Kelly N.; Eloyan, Ani; Koeppe, Robert A.; Carrillo, Maria C.; Touroutoglou, Alexandra; Vemuri, Prashanthi; Dickerson, Bradford C.; Apostolova, Liana G.; Rabinovici, Gil D.; La Joie, Renaud; Neurology, School of Medicine
    Background Diagnosing sporadic early‐onset AD (EOAD, age‐at‐onset<65) is challenging: in the multi‐center Longitudinal Early‐onset Alzheimer’s Disease Study, ∼25% of patients with clinically diagnosed EOAD are amyloid‐PET‐negative. Here we used FDG‐PET to characterize the heterogeneity of hypometabolic profiles in these patients and better identify underlying etiologies. Method Seventy‐four amyloid‐PET‐negative patients with clinical diagnosis of sporadic EOAD (MCI or mild dementia stage) underwent FDG‐PET. Patients were classified as having normal or hypometabolic FDG‐PET based on a data‐driven approach that compared each patient to a group of 61 age‐matched amyloid‐PET‐negative controls using 12 methodological combinations (3 reference regions, 2 voxel‐level thresholds, 2 outlier detection methods). We then assessed clinical and demographic differences between patients with normal versus hypometabolic FDG‐PET, and further compared groups using independent biomarkers of neurodegeneration (structural MRI and fluid biomarkers). Finally, we applied hierarchical clustering to hypometabolic FDG‐PET scans to identify patterns of hypometabolism. Result Thirty‐six amyloid‐negative patients (49%) had hypometabolic FDG‐PET scans. They were older and more severely impaired across most cognitive domains than patients with normal FDG‐PET (Table 1). They also had reduced hippocampal volumes and cortical thickness (Figure 1A), higher plasma and CSF neurofilament light chain (NfL) levels, and elevated plasma GFAP compared to patients with normal FDG‐PET (Figure 1B). In contrast, the latter, who had intermediate cognitive scores between hypometabolic patients and controls, had MRI and fluid biomarker levels in the range of controls (Figure 1). In hypometabolic patients, hierarchical clustering identified four profiles: i) anterior temporal extending to temporo‐parietal and frontal regions (n = 5), ii) anterior temporal and orbitofrontal (n = 11), iii) occipito‐parietal (n = 6), and iv) lateral frontal and parietal (n = 14) (Figure 2). Genetic testing identified two patients with Frontotemporal Lobar Degeneration (FTLD)‐associated pathogenic variants, both considered hypometabolic and assigned to the first (MAPT) and second (c9orf72) metabolic profiles. Conclusion Fifty‐one percent of amyloid‐negative patients had normal FDG‐PET: they had milder clinical impairment, normal MRI measures, and normal NfL values, suggesting non‐neurodegenerative etiologies. Patients with abnormal FDG showed heterogeneous hypometabolic patterns suggestive of multiple etiologies including Lewy body disease, FTLD or corticobasal degeneration. Longitudinal follow‐up to autopsy will ultimately clarify the amyloid‐negative clinical mimics of sporadic EOAD.