Josephs, Keith A

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 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 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.

Objective Immunohistochemically (IHC) measured transactive response DNA‐binding protein 43 (TDP‐43) inclusions are observed in Alzheimer's disease (AD) and are associated with medial temporal lobe atrophy. Accumulation of cryptic exons occurs in AD in response to TDP‐43 pathology. We aimed to assess relationships between IHC and biochemically measured insoluble TDP‐43 and cryptic exons and assess associations with hippocampal and amygdala volume loss and atrophy rates on magnetic resonance imaging (MRI). Methods Eighty‐one neuropathologically diagnosed AD cases were analyzed. For biochemistry, insoluble TDP‐43 was quantified using a Meso‐scale discovery (MSD) immunoassay. IHC‐TDP burden was quantified with digital histopathology. Cryptic RNAs were assessed via quantitative real‐time polymerase chain reaction (qRT‐PCR). Thirty‐eight cases had serial brain MRI. Hippocampal and amygdala volumes were calculated using FreeSurfer. Regression models were used to investigate associations among IHC‐TDP‐43 status/burden, MSD‐TDP status/levels, cryptic RNAs, and hippocampal and amygdala volumes and atrophy rates. Results IHC‐TDP(+) cases exhibited elevated levels of MSD‐TDP and cryptic RNAs ( KCNQ2 , STMN2 , and UNC13A ) and increased MSD‐TDP levels were associated with increased cryptic RNA levels, in the hippocampus and amygdala. IHC‐TDP(+) cases had smaller hippocampal and amygdala volumes compared to IHC‐TDP(−) cases. MSD‐TDP(+) cases had smaller hippocampal volumes and faster amygdala rates of atrophy compared with MSD‐TDP(−) cases. Higher KCNQ2 and UNC13A levels were associated with smaller amygdala volumes. Interpretation MSD‐TDP level is a reliable surrogate for IHC‐based TDP‐43 status. Both TDP‐43 and cryptic RNA levels are associated with reduced medial temporal volumes, suggesting cryptic exons may be playing a role in brain volume loss in AD. ANN NEUROL 2026;100:193–205

Transactive response DNA-binding protein of 43 kDa (TDP-43) type-A is associated with frontotemporal lobar degeneration (FTLD). In primary age-related tauopathy (PART), TDP-type-α displays similar features to FTLD-TDP type-A. We compared antemortem MRI volumes of amygdala nuclei and hippocampal subfields between 16 PART-TDP-α and 12 FTLD-TDP-A autopsy-confirmed cases. Hippocampal tail and CA1 body volumes were smaller in PART-TDP-α group, which also had smaller lateral and central amygdala nuclei compared to FTLD-TDP-A group, but differences were non-significant after FDR correction. There is no evidence suggesting that TDP-43 type-A in FTLD affects hippocampal and amygdala volume loss differently than TDP-43 type-α in PART.

Background TAR DNA-binding protein 43 (TDP-43) proteinopathy contributes to Alzheimer's disease progression, but diagnosis remains autopsy-dependent. Objective This study aimed to develop a predictive model using FDG-PET imaging to identify brain regions associated with TDP-43 pathology in vivo. Methods Penalized logistic regression analyzed data from 294 participants and a subset of 159 with Braak and Thal ≥4. Features included FDG standard uptake value ratios (SUVRs) from 123 brain regions and age. Participants were split into training (90%) and testing (10%) datasets, with significant predictors identified by non-zero coefficients at optimal lambda. Results The model achieved 68% accuracy (AUC = 0.70) in 294 participants, highlighting the middle temporal gyrus, parahippocampal gyrus, and hippocampus. In the subset (n = 159), accuracy was 65% (AUC = 0.71), with the medial amygdala and precentral gyrus as key predictors of TDP-43 pathology. Conclusions This study suggests a moderate predictive accuracy of FDG-PET to identify brain regions associated with Alzheimer's disease-related TDP-43 proteinopathy.

Abstract Background Dysphagia can lead to fatal aspiration pneumonia in progressive supranuclear palsy (PSP). Little is known about the longitudinal progression of dysphagia or whether it differs across PSP clinical variants. Objectives To characterize longitudinal changes in dysphagia across PSP variants and determine relationships with disease severity and rate of atrophy in PSP‐related regions. Methods Sixty‐one PSP patients were recruited and underwent at least two serial evaluations, comprising neurological and videofluorographic exams, and MRI. Variables‐of‐interest were the oral and pharyngeal total sum scores (OTSS/PTSS) from the Modified Barium Swallow Impairment Profile, the Penetration Aspiration Scale (PAS), Functional Oral Intake Scale (FOIS), and lingual pressure assessments. Rates of change were compared across PSP variants and related to baseline scores, PSP Rating Scale and regional brain atrophy rates. Results Dysphagia progressed longitudinally, particularly with worsening in OTSS and anterior and posterior lingual pressures. Rate of change in OTSS was fastest in PSP with predominant speech‐language disorder. Negative correlations were observed between rates of change and baseline scores for OTSS, PTSS and PAS. Rates of change in lingual pressure measures correlated with rates of change in the PSP Rating Scale. Rate of change in the FOIS correlated with rates of change in OTSS and lingual pressure. Rates of brain atrophy were associated with baseline OTSS, PTSS, and PAS. Conclusions Dysphagia and lingual pressure worsen over time in PSP, with variability in rates of change in the OTSS across PSP variants and evidence that dysphagia is related to rates of atrophy.

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 multi-model 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 provides optimum differentiation. 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 assessed using 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, multi-compartment, and fixel-based models to APS-related neurodegeneration.