Singh, Neha Atulkumar

Abstract Clinical syndromes of frontotemporal dementia present both distinct and overlapping neuroimaging abnormalities and, consequently, domains of impairment. The aim of this study was to determine how well covariance patterns from multiple neuroimaging modalities discriminate across clinical frontotemporal dementia syndromes. Four-hundred participants with a clinical frontotemporal dementia diagnosis, including behavioral variant of frontotemporal dementia, semantic variant of primary progressive aphasia, right temporal variant of frontotemporal dementia, primary progressive aphasia, apraxia of speech with agrammatic aphasia, primary progressive apraxia of speech, progressive supranuclear palsy and corticobasal syndrome, and 109 cognitively unimpaired participants underwent extensive neurological and neuropsychological assessments, structural MRI, flortaucipir-PET for tau and [18F] fluorodeoxyglucose-PET. Multimodal imaging covariance was investigated at the voxel-level using principal component analysis after adjusting for age and sex effects. Linear regression models were fit to investigate the relationship between principal components and clinical scores. Multinomial logistic regression models investigated the ability of imaging principal components to discriminate across syndromes. The first principal component, which describes the largest source of variability in the data, was a pattern of widespread cortical atrophy (21% variability explained) and of frontoparietal and temporal hypometabolism (21%): scores were lowest in behavioral variant of frontotemporal dementia and correlated with general cognitive impairment. On tau-PET, the first principal component (43% variability) was not significantly different among syndromes and captured a widespread cortical tau uptake, with a focus on the temporal lobe. In all three modalities, the second principal component captured temporal lobe imaging abnormalities, contrasting the motor and semantic clinical syndromes. Subsequent components described smaller variability. On cross-validation, the classifiers based on principal components from MRI (multiclass area under the receiver operating characteristic: 0.76) and [18F]fluorodeoxyglucose-PET (0.79) outperformed tau-PET (0.71), while combining all three modalities did not significantly improve the classifier performance. On cross-validation, the sensitivity of the image-based classifiers was heterogeneous across syndromes and modalities, with highest values for progressive supranuclear palsy and semantic variant of primary progressive aphasia and lowest for primary progressive aphasia and corticobasal syndrome. Our study found that the covariance components of structural and molecular neuroimaging underlying frontotemporal dementia clinical syndromes exist both as a continuum and with syndrome-specific patterns. More than one imaging model was able to correctly classify several frontotemporal dementia clinical syndromes with good specificity and sensitivity, highlighting the value of imaging in aiding patient diagnosis.

Background: Posterior cortical atrophy (PCA) patients typically show progressive visual decline, although language impairment can be a prominent feature. Previous studies have explored language connectivity impairment in PCA; not much is known about the exact nature of language processing disruptions in PCA. Methods: Thirty-seven PCA patients were compared with 39 cognitively unimpaired (CU) individuals. Functional connectivity was evaluated for the language processing system, which consisted of language, perceptual, motor, and cognitive networks. Linear regression was performed to compare within- and between-network connectivity across networks and within the language network in PCA compared to CU individuals. Relationships between language network connectivity, clinical performance, and gray matter volumes were also evaluated. Results: PCA showed reduced connectivity within the language and perceptual networks, along with reduced between-network connectivity compared to CU individuals. Regional connectivity breakdown within the language network was also noted. Language processing system connectivity was not associated with clinical performance on language processing tests. However, a breakdown in Broca-Wernicke, Wernicke-middle frontal gyrus, and Wernicke-frontal orbital gyrus connectivity was related to worse performance on sentence repetition. Lastly, gray matter volume was not associated with language network connectivity. Discussion: Together, these results reiterate the breakdown of language network connectivity in PCA and highlight the disruption in connectivity at the regional level within the language network. Reduced connectivity within the perceptual network and associations between reduced language network connectivity and worse performance on sentence repetition imply a breakdown within the language processing systems in PCA.

ObjectiveTo determine whether a machine learning model of voxel level [18f]fluorodeoxyglucose positron emission tomography (PET) data could predict progressive supranuclear palsy (PSP) pathology, as well as outperform currently available biomarkers.MethodsOne hundred and thirty‐seven autopsied patients with PSP (n = 42) and other neurodegenerative diseases (n = 95) who underwent antemortem [18f]fluorodeoxyglucose PET and 3.0 Tesla magnetic resonance imaging (MRI) scans were analyzed. A linear support vector machine was applied to differentiate pathological groups with sensitivity analyses performed to assess the influence of voxel size and region removal. A radial basis function was also prepared to create a secondary model using the most important voxels. The models were optimized on the main dataset (n = 104), and their performance was compared with the magnetic resonance parkinsonism index measured on MRI in the independent test dataset (n = 33).ResultsThe model had the highest accuracy (0.91) and F‐score (0.86) when voxel size was 6mm. In this optimized model, important voxels for differentiating the groups were observed in the thalamus, midbrain, and cerebellar dentate. The secondary models found the combination of thalamus and dentate to have the highest accuracy (0.89) and F‐score (0.81). The optimized secondary model showed the highest accuracy (0.91) and F‐scores (0.86) in the test dataset and outperformed the magnetic resonance parkinsonism index (0.81 and 0.70, respectively).InterpretationThe results suggest that glucose hypometabolism in the thalamus and cerebellar dentate have the highest potential for predicting PSP pathology. Our optimized machine learning model outperformed the best currently available biomarker to predict PSP pathology. ANN NEUROL 2025;98:410–420

Background Studies suggest that plasma Alzheimer's disease (AD) biomarkers may aid in the overall diagnosis of AD, but their utility among patients with atypical clinical presentations of AD are unknown. Objective The main objective of this study was to determine the relationship between amyloid-β (Aβ) and tau plasma biomarkers and PET measures of both Aβ and tau in atypical AD. The secondary objective was to determine if plasma biomarkers could differentiate patients with different atypical AD phenotypes and whether they were related to measures of disease severity. Methods We assessed whether plasma p-tau 181 and Aβ 42/40 were associated with Aβ and tau PET uptake, clinical phenotype and severity in 77 patients with PET biomarker-confirmed atypical AD. Results Plasma Aβ 42/40 ratio showed positive associations with tau PET uptake, with higher (more normal) Aβ 42/40 ratio associated with higher tau uptake; the ratio was not associated with Aβ PET. No associations were noted with plasma p-tau 181. Plasma Aβ 42/40 ratio and p-tau 181 concentrations were not associated with AD phenotype or cognitive severity. Conclusion Plasma Aβ 42/40 ratio and p-tau 181 concentrations are not associated with amyloid or tau PET or with clinical severity among individuals presenting with atypical AD.

AbstractINTRODUCTIONCorticobasal syndrome (CBS) can result from underlying Alzheimer's disease (AD) pathologies. Little is known about the utility of blood plasma metrics to predict positron emission tomography (PET) biomarker‐confirmed AD in CBS.METHODSA cohort of eighteen CBS patients (8 amyloid beta [Aβ]+; 10 Aβ−) and 8 cognitively unimpaired (CU) individuals underwent PET imaging and plasma analysis. Plasma concentrations were compared using a Kruskal–Wallis test. Spearman correlations assessed relationships between plasma concentrations and PET uptake.RESULTSCBS Aβ+ group showed a reduced Aβ42/40 ratio, with elevated phosphorylated tau (p‐tau)181, glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) concentrations, while CBS Aβ− group only showed elevated NfL concentration compared to CU. Both p‐tau181 and GFAP were able to differentiate CBS Aβ− from CBS Aβ+ and showed positive associations with Aβ and tau PET uptake.DISCUSSIONThis study supports use of plasma p‐tau181 and GFAP to detect AD in CBS. NfL shows potential as a non‐specific disease biomarker of CBS regardless of underlying pathology.Highlights Plasma phosphorylated tau (p‐tau)181 and glial fibrillary acidic protein (GFAP) concentrations differentiate corticobasal syndrome (CBS) amyloid beta (Aβ)− from CBS Aβ+. Plasma neurofilament light concentrations are elevated in CBS Aβ− and Aβ+ compared to controls. Plasma p‐tau181 and GFAP concentrations were associated with Aβ and tau positron emission tomography (PET) uptake. Aβ42/40 ratio showed a negative correlation with Aβ PET uptake.

AbstractBackgroundMidbrain atrophy is a characteristic feature of progressive supranuclear palsy (PSP), observed in PSP‐Richardson's syndrome (PSP‐RS) and to a lesser extent PSP‐parkinsonism (PSP‐P).ObjectiveOur aim was to critically evaluate the utility of manual magnetic resonance imaging measurements of the midbrain tectal plate as a diagnostic biomarker in PSP.MethodsLength of the tectal plate and width of the superior and inferior colliculi were measured in 40 PSP (20 PSP‐RS and 20 PSP‐P) patients and compared with 20 Parkinson's disease and 20 healthy control subjects.ResultsTectal plate length was reduced in both PSP groups compared with Parkinson's disease and control subjects and was most abnormal in PSP‐RS followed by PSP‐P. Reduced tectal plate length was associated with worse PSP Rating Scale scores.ConclusionsSimple manual measurements of tectal plate length show utility as a diagnostic biomarker in PSP, particularly for PSP‐RS.

AbstractINTRODUCTIONGlial fibrillary acidic protein (GFAP) in plasma is a proxy for astrocytic activity and is elevated in amyloid‐β (Aβ)‐positive individuals, making GFAP a potential blood‐based biomarker for Alzheimer's disease (AD).METHODSWe assessed plasma GFAP in 72 Aβ‐positive participants diagnosed with the visual or language variant of AD who underwent Aβ‐ and tau‐PET. Fifty‐nine participants had follow‐up imaging. Linear regression was applied on GFAP and imaging quantities.RESULTSGFAP did not correlate with Aβ‐ or tau‐PET cross‐sectionally. There was a limited positive correlation between GFAP and rates of tau accumulation, particularly in the language variant of AD, although associations were weaker after removing one outlier patient with the highest GFAP level.DISCUSSIONAmong Aβ‐positive AD participants with atypical presentations, plasma GFAP did not correlate with levels of AD pathology on PET, suggesting that the associations between GFAP and AD pathology might plateau during the advanced phase of the disease.

Abstract Disruption of the default mode network is a hallmark of Alzheimer’s disease, which has not been extensively examined in atypical phenotypes. We investigated cross-sectional and 1-year longitudinal changes in default mode network sub-systems in the visual and language variants of Alzheimer’s disease, in relation to age and tau. Sixty-one amyloid-positive Alzheimer’s disease participants diagnosed with posterior cortical atrophy (n = 33) or logopenic progressive aphasia (n = 28) underwent structural MRI, resting-state functional MRI and [18F]flortaucipir PET. One-hundred and twenty-two amyloid-negative cognitively unimpaired individuals and 60 amyloid-positive individuals diagnosed with amnestic Alzheimer’s disease were included as controls and as a comparison group, respectively, and had structural and resting-state functional MRI. Forty-one atypical Alzheimer’s disease participants, 26 amnestic Alzheimer’s disease participants and 40 cognitively unimpaired individuals had one follow-up functional MRI ∼1–2 years after the baseline scan. Default mode network connectivity was calculated using the dual regression method for posterior, ventral, anterior ventral and anterior dorsal sub-systems derived from independent component analysis. A global measure of default mode network connectivity, the network failure quotient, was also calculated. Linear mixed-effects models and voxel-based analyses were computed for each connectivity measure. Both atypical and amnestic Alzheimer’s disease participants had lower cross-sectional posterior and ventral and higher anterior dorsal connectivity and network failure quotient relative to cognitively unimpaired individuals. Age had opposite effects on connectivity in Alzheimer’s disease participants and cognitively unimpaired individuals. While connectivity declined with age in cognitively unimpaired individuals, younger Alzheimer’s disease participants had lower connectivity than the older ones, particularly in the ventral default mode network. Greater baseline tau-PET uptake was associated with lower ventral and anterior ventral default mode network connectivity in atypical Alzheimer’s disease. Connectivity in the ventral default mode network declined over time in atypical Alzheimer’s disease, particularly in older participants, with lower tau burden. Voxel-based analyses validated the findings of higher anterior dorsal default mode network connectivity, lower posterior and ventral default mode network connectivity and decline in ventral default mode network connectivity over time in atypical Alzheimer’s disease. Visuospatial symptoms were associated with default mode network connectivity disruption. In summary, default mode connectivity disruption was similar between atypical and amnestic Alzheimer’s disease variants, and discriminated Alzheimer’s disease from cognitively unimpaired individuals, with decreased posterior and increased anterior connectivity and with disruption more pronounced in younger participants. The ventral default mode network declined over time in atypical Alzheimer’s disease, suggesting a shift in default mode network connectivity likely related to tau pathology.

Background: Posterior cortical atrophy (PCA) and logopenic progressive aphasia (LPA) are two common atypical Alzheimer’s disease (AD) variants. Little is known about behavioral and neuropsychiatric symptoms or activities of daily living (ADLs) in PCA and LPA, and whether they differ across syndromes. Objective: To characterize the behavioral and neuropsychiatric profiles and ADLs of PCA and LPA and compare presence/absence and severity of symptoms between syndromes. Methods: Seventy-eight atypical AD patients, 46 with PCA and 32 with LPA, completed the Neuropsychiatric Inventory Questionnaire (NPI-Q) and Cambridge Behavioral Inventory-Revised (CBI-R) at baseline and longitudinally over-time. Mann-Whitney U and Fisher’s Exact Tests assessed for differences in symptoms between the two syndromes with significance set at p ≤0.01. To eliminate demographic differences as confounders the groups were matched, and differences reanalyzed. Results: PCA were younger at onset ( p = 0.006), at time of baseline assessment ( p = 0.02) and had longer disease duration ( p = 0.01). Neuropsychiatric symptoms were common in PCA and LPA, although more common and severe in PCA. At baseline, PCA had a higher NPI-Q total score ( p = 0.01) and depression subscore ( p = 0.01) than LPA. Baseline total CBI-R scores were also higher in PCA than LPA ( p = 0.001) with PCA having worse scores in all 10 CBI-R categories. Longitudinally, there was no difference between groups on the NPI-Q. However, on the CBI-R, PCA had faster rates of worsening on self-grooming ( p = 0.01) and self-dressing ( p = 0.01) compared to LPA. Conclusions: Behavioral and neuropsychiatric symptoms are common in PCA and LPA although these symptoms are more common and severe in PCA.

AbstractINTRODUCTIONAtypical variants of Alzheimer's disease (AD) include the visual variant, known as posterior cortical atrophy (PCA), and the language variant, known as logopenic progressive aphasia (LPA). Clinically, rates of disease progression differ between them.METHODSWe evaluated 34 PCA and 29 LPA participants. Structural magnetic resonance imaging and 18F‐flortaucipir positron emission tomography were performed at baseline and at 1‐year follow‐up. Rates of change in tau uptake and grey matter volumes were compared between PCA and LPA with linear mixed‐effects models and voxel‐based analyses.RESULTSPCA had faster rates of occipital atrophy. LPA had faster rates of left temporal atrophy and faster rates of tau accumulation in the parietal, right temporal, and occipital lobes. Age was negatively associated with rates of atrophy and tau accumulation.DISCUSSIONLongitudinal patterns of neuroimaging abnormalities differed between PCA and LPA, although with divergent results for tau accumulation and atrophy.HIGHLIGHTS The language variant of Alzheimer's disease accumulates tau faster than the visual variant. Each variant shows faster rates of atrophy than the other in its signature regions. Age negatively influences rates of atrophy and tau accumulation in both variants.