Josephs, Keith A.

Background/Objectives: The role of iron in Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD) has been debated. However, the effects of iron on protein burden in these diseases remain poorly elucidated. We hypothesized that there is an association between iron deposition and tau and beta-amyloid deposition in deep grey matter (GM) regions in FTLD. Methods: Neuropathological samples were collected from two control subjects and 10 disease subjects. This included 12 subjects, with several pathological subgroups represented by only one or two cases. Of the 10 disease subjects, one had low-level AD neuropathology (L-AD), and one had high-level AD neuropathology (H-AD), whereas the remaining eight subjects had FTLD-tau pathology, including progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), globular glial tauopathy (GGT), and Pick’s disease (PiD). Neuropathological samples were stained for iron with Prussian blue (PB) and ferritin, tau, and beta-amyloid in the basal ganglia (BG). All 12 subjects had also undergone diffusion MRI, amyloid-PET (PiB-PET), and Tau-PET (flortaucipir) before death. Diffusion tensor imaging mean diffusivity (MD) from BG was examined in each subject. Spearman’s rank correlations between imaging and histology were calculated. Results: Increased BG iron was observed in PSP subjects. Prussian blue and ferritin measures were associated with higher tau- and β-amyloid pathological burden. Tau-PET uptake correlated with iron in the BG. Conclusions: The results of this exploratory study suggest associations between iron burden and tau- and β-amyloid-related measures within basal ganglia regions that may help to explain increased PET signals in AD and FTLD-Tau. Our findings should be considered to be hypothesis-generating and require validation. These preliminary findings require validation in larger cohorts.

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.

Wearable inertial measurement units (IMUs) offer an accessible alternative to optical motion capture (MoCap) gait analysis, but their performance in Progressive Supranuclear Palsy (PSP) requires validation. We assessed the concurrent validity of IMU-derived versus MoCap-derived gait metrics and static postural sway in 30 patients with PSP using Bland–Altman analysis, Intraclass Correlation Coefficients (ICC), and Spearman rank correlations. Finally, we assessed equivalence using the Two one-sided tests (TOST) procedure. Multivariable linear regression was used to determine whether clinical severity, as measured by the PSP Rating Scale (PSPRS), independently predicted absolute IMU measurement error while controlling for patient age and gait velocity. IMUs demonstrated excellent between-system agreement for parameters such as cadence (100.76 ± 11.42 vs. 100.52 ± 11.59) and cycle time (1.21 ± 0.15 vs. 1.22 ± 0.15; ICC > 0.98), despite a systematic underestimation of gait velocity (p < 0.05). Agreement significantly diminished for micro-phases (e.g., single/double support times) and spatial asymmetry. Interestingly, the TOST procedure revealed that only sagittal and transverse trunk kinematics were equivalent between systems, with all other measures failing to find equivalency. For static sway, the IMU demonstrated strong rank-order correspondence for tracking relative postural instability (ρ = 0.82, p < 0.05). Multivariable analysis revealed that higher PSPRS scores are independently associated with greater between-system discrepancies in support phases and pelvic and trunk kinematics (p < 0.05), irrespective of reduced gait speed. These findings highlight the need to develop disease-specific algorithms, rather than relying on normative commercial models, to establish reliable digital biomarkers for monitoring progressive motor decline.

ABSTRACT TAR DNA‐binding protein 43 (TDP‐43) inclusions are defining pathological features of frontotemporal lobar degeneration (FTLD) but are also often observed in Alzheimer's disease ( AD ) and primary age‐related tauopathy (PART). TDP‐43 in AD is either associated with cognitive impairment or a protective‐life prolonging impact, and yet the localization, cellular and fragment characteristics of TDP‐43 need to be determined. We investigated the relationships between TDP‐43 volumetric inclusion burden in low likelihood AD (lAD) and definite PART by immunostaining against phosphorylated TDP‐43 (pTDP‐43), TDP‐43 C terminal (TDP‐C) and TDP‐43 N‐terminal (TDP‐N) fragments combined with 3D confocal imaging taken from eight regions: amygdala (basolateral [amygdala‐BL] and centromedial amygdala [amygdala‐CM]), the hippocampus (Cornu Ammonis [CA]‐1, CA2/3, CA4, dentate gyrus [DG] and subiculum [SUB]) and entorhinal cortex (ERC) and artificial intelligence (AI)–based segmentation via object recognition, reconstruction and quantification. We found amygdala‐CM in lAD and PART to have the overall greatest burden of pTDP‐43 whereas TDP‐N burden in amygdala‐BL of PART cases was greater than other TDP‐43 fragments. There was no difference in TDP‐43 burden in hippocampal subfields in PART. However, CA2/3 region showed greater pTDP‐43 burden while TDP‐N stood out in DG and SUB. Multiple comparisons among the groups revealed that TDP‐C was the only fragment showing differences among PART and lAD in CA2/3, DG and SUB regions. Overall, unbiased AI‐based volumetric burden analysis pipeline demonstrated unique fragment aggregation patterns in the neurodegenerative processes of PART and AD .

Abstract TAR DNA‐binding protein 43 (TDP‐43) inclusions are often associated with hyperphosphorylated tau, thus neurofibrillary tangles as the hallmark of Alzheimer's disease (AD) and primary age‐related tauopathy (PART). TDP‐43 in AD is associated with cognitive impairment, and while staging is known, the localization, cellular and inclusion characteristics of TDP‐43 are yet to be elucidated. We investigate relationships between TDP‐43 inclusions and the tangle maturation continuum in AD, PART, and co‐pathologies by multiplex immunostaining combined with artificial intelligence (AI)–based segmentation via object recognition, reconstruction, and quantification. We performed anti‐phosphorylated TDP‐43 immunofluorescence with phosphorylated tau labeling different stages and modifications of tangles (AT8, pS396, TauC3, MN423, GT38) in three controls, three cases with PART and TDP‐43 (PART‐TDP), five cases with high likelihood AD and TDP‐43 (AD‐TDP), and four cases of high likelihood AD with TDP‐43 and Lewy Body disease (AD‐TDP‐LBD). Confocal imaging was taken from eight regions: amygdala (amygdala‐BL and amygdala‐CM) and hippocampus (Cornu Ammonis (CA)‐1, CA2/3, CA4, dentate gyrus (DG), subiculum (SUB)), and entorhinal cortex (ERC) and quantified with AI segmentation to identify 3D spatial relations, thus the maturity of neurofibrillary tangle associated TDP‐43 (TAT) inclusions. TATs, which were either identified by pTDP‐43 and AT8 or pTDP‐43 and pS396 double positivity, were also investigated by Thioflavin S (ThioS) histochemistry. We found pS396 labeled mature TATs predominated in PART and AD in every region. Basolateral and centromedial amygdala displayed overall greatest number of pre‐TATs and mature TATs. Mature TATs were homogenously distributed among hippocampal subfields whereas CA4 and DG had the greatest mature TAT composition. ERC revealed closer numbers of pre‐TATs and mature TATs yet mature TATs predominated all groups. Unbiased AI‐based object identification, reconstruction, and TAT maturation analysis pipeline in conjunction with TDP‐43, tau, and ThioS multiplex immunostaining demonstrated unique aggregation and maturation patterns, highlighting region‐specific dynamics in the neurodegenerative processes of PART and AD.

Abstract Background Corticobasal syndrome (CBS) is a neurodegenerative syndrome that often results from a 4-repeat tauopathy. Abnormalities on tau PET, structural MRI, and diffusion MRI (dMRI) have been observed in CBS, although no prior work has systematically compared the relative sensitivity of these modalities. In addition, the relationships between tau uptake, volume loss, and white matter degeneration remain incompletely understood. Objective To compare the sensitivity of tau PET, structural volume, and dMRI metrics to differentiate CBS from controls, and to characterize relationships between regional tau uptake, volume loss, and white matter tract abnormalities. Methods Thirty-two participants meeting criteria for possible or probable CBS and 35 healthy controls underwent standardized 3T dMRI, 18 F-flortaucipir tau PET, and detailed neurologic assessment. Diffusion data were processed using complementary diffusion tensor, free-water, and Neurite Orientation Dispersion and Density Imaging (NODDI) pipelines, with tract-level metrics extracted using the Johns Hopkins University-Eve (JHU EVE) White Matter atlas. Regional gray matter volumes and flortaucipir standardized uptake value ratios (SUVRs) were calculated. Global tau PET uptake, represented by the first principal component (PC1), was removed in a secondary tau analysis. Modality-level discrimination was assessed using area under the curve (AUC) analyses. Cross-modality relationships were evaluated using regional correlations and covariate-adjusted models comparing volume- and tau-related predictors of white matter abnormalities. Results Relative to controls, CBS showed widespread higher mean diffusivity (MD) and lower intracellular volume fraction (ICVF), especially in sensorimotor and projection white matter pathways. Structural volume reductions were most prominent in precentral cortex and subcortical regions including putamen, thalamus, and pallidum, whereas tau PET abnormalities were weaker and less spatially extensive. MD showed the strongest overall discrimination between CBS and controls, followed by ICVF, structural volume, PC1-removed tau PET, and original tau PET. In targeted sensorimotor analyses, higher DTI-MD was most strongly associated with lower gray matter volume. PC1-removed sensorimotor tau uptake was also associated with higher sensorimotor DTI-MD, whereas original tau uptake and broader cortical or subcortical tau measures were not significant predictors. Conclusions White matter microstructural disruption is a dominant imaging signature of CBS and is more closely linked to gray matter volume loss than to measurable uptake on tau PET. dMRI, particularly MD and ICVF, may provide greatest sensitivity for detecting disease-related changes in CBS.

ABSTRACT Distinguishing atypical parkinsonian disorders (APS) from Parkinson's disease (PD) remains challenging due to overlapping clinical features, yet accurate differentiation is critical for prognosis and treatment. Here, we employed multimodel diffusion MRI (dMRI) analysis to characterize microstructural alterations across corticobasal syndrome (CBS), progressive supranuclear palsy‐Richardson syndrome (PSP‐RS), and PD, with the aim of identifying which dMRI model and regional metrics show the strongest group‐level separation. We analyzed 25 CBS, 42 PSP‐RS, and 21 PD participants compared to 35 age and sex‐matched controls. Using a clinically feasible 3‐shell high angular resolution diffusion imaging (HARDI) protocol, we applied 11 metrics from five complementary dMRI models—diffusion tensor imaging (DTI), free‐water‐eliminated model of DTI (FWE), neurite orientation dispersion and density imaging (NODDI), tissue‐weighted NODDI, and fixel density (FD) in fixel‐based analysis (FBA)—to comprehensively assess regional white and gray matter integrity. Group differentiation was quantified using covariate‐adjusted Cohen's d effect sizes and spearman correlations were assessed between dMRI metrics and clinical scales. Distinct microstructural signatures were observed across disorders, and the sensitivity of the dMRI models differed. In group contrasts, DTI and NODDI‐derived metrics consistently captured the strongest effects in midbrain and peduncular pathways for PSP‐RS, whereas precentral and corticospinal alterations in CBS were most prominent using NODDI and FBA measures. Free‐water‐corrected metrics showed attenuated group differences. Across clinical‐diffusion analyses, NODDI metrics exhibited the most robust associations with disease severity, while DTI and FWE measures detected more limited, regionally constrained effects. Together, these findings highlight complementary yet distinct sensitivities of tensor, free‐water, multicompartment, and fixel‐based models to APS‐related neurodegeneration.

Abstract Motor speech disorders (MSDs) are early markers of neurological disease, but expert perceptual analysis is rarely available outside specialized centers. Automated speech analysis offers a scalable alternative, yet prior studies have not systematically compared modeling approaches or assessed clinically relevant metrics in independent datasets. This study compared static acoustic features, articulatory informed Phonet features, and self-supervised pretrained models for binary and multi label MSD classification. We trained and evaluated models on 583 speech samples using speaker level splits. Baseline models included logistic regression and Gated Recurrent Units (GRUs) trained on eGeMAPS and MFCCs. We extracted three types of Phonet derived features and evaluated pretrained HuBERT and SSAST models in frozen, partially fine-tuned, and fully fine-tuned configurations. Binary classification distinguished MSDs from controls, while multi label classification identified six MSD subtypes. Models were assessed using validation AUC, and cut points were tested on two independent datasets. Pretrained and Phonet based models substantially outperformed static acoustic features. In binary classification, HuBERT achieved the highest AUC (0.95), while compact Phonet derived GRUs achieved comparable performance (up to 0.94). These models generalized well to independent datasets, maintaining high sensitivity (0.94) and specificity (0.97). In multi label classification, Phonet models achieved the highest macro average AUC (0.86), but threshold-based subtype performance declined on unseen data. Automated MSD detection is feasible and clinically promising. Binary classification generalized well, whereas multi label classification showed limited threshold stability across datasets.