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.
Artificial intelligence–based
3D
segmentation of tangle‐associated
TDP
‐43 in neurodegeneration
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.
Automated Detection of Motor Speech Disorders and Subtype Classification
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.
Biochemical and Immunohistochemical Associations of
TDP
‐43 and Cryptic
RNA
With Hippocampal and Amygdala Volumetrics in Alzheimer's Disease
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
Longitudinal Videofluorographic Dysphagia Measures in Progressive Supranuclear Palsy
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.
Multi-model Diffusion MRI Signatures in Atypical Parkinsonian Disorders
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.
Localized Iron Deposition and Shape Changes of Cerebellar Dentate in Progressive Supranuclear Palsy Clinical Variants
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.
Patterns of Longitudinal Regional Brain Atrophy in Patients With Different Progressive Supranuclear Palsy Clinical Variants
Objective: Progressive apraxia of speech (PAOS) is a neurodegenerative syndrome characterized by impaired motor speech planning and programming, whereas behavioral variant frontotemporal dementia (bvFTD) is characterized by deviant behavioral (e.g., personality and social) features. Clinical and anatomic characteristics of bvFTD in the context of PAOS are understudied. Methods: We identified 12 participants with PAOS and features that were consistent with bvFTD at baseline or follow-up. Eleven completed a head MRI scan. We compared clinical features and anatomical patterns of atrophy in these 11 PAOS-bvFTD participants to 11 matched PAOS participants without bvFTD and 22 age- and sex-matched healthy controls. Statistical Parametric Mapping (SPM) was applied to visualize gray matter volume across both groups compared to controls and each other. Medians and 25th and 75th percentiles were assessed in patients and across groups; Fisher’s Exact Test and Mann–Whitney U tests were applied using BlueSky software, version 10.3.1-Pro. Results: As expected, PAOS-bvFTD participants performed worse on the Frontal Behavioral Inventory (median: 33/72 vs. 10/72), 20-item behavioral assessment scale (4.5/20 vs. 1.0/20), and the Neuropsychiatry inventory (4/36 vs. 1.5/36) compared to the PAOS group (p < 0.01 for all), with no differences in other demographic, neurological, or language tests. Seven of the eleven PAOS-bvFTD participants had bvFTD features develop within three years of symptom onset. The PAOS-bvFTD and PAOS groups showed volume loss in frontal lobe regions compared to controls, with PAOS-bvFTD participants having more prefrontal volume loss than PAOS participants. Conclusions: Behavioral features consistent with bvFTD can co-occur in patients with PAOS and are related to greater atrophy of the prefrontal cortex.