Sekiya, Hiroaki

Abstract Parkinson's disease (PD) and multiple system atrophy are defined by α‐synuclein (αSYN)‐positive inclusions – Lewy bodies (LBs) and glial cytoplasmic inclusions – yet mounting evidence indicates that these inclusions represent only a fraction of disease‐relevant pathology. αSYN exists in dynamic conformational states, and soluble oligomeric assemblies, often undetectable by conventional immunohistochemistry, are increasingly implicated as key neurotoxic species. The αSYN proximity ligation assay (αSYN‐PLA) enables in situ detection of widespread non‐inclusion oligomeric pathology across synucleinopathies. Notably, PLA studies in LRRK2 ‐associated PD demonstrate abundant oligomeric αSYN even in cases lacking LBs, challenging the centrality of inclusions in defining disease. In this Perspective, we outline the principles of αSYN‐PLA, discuss antibody strategies and structural implications of detected species, review current evidence – including LB‐negative LRRK2 ‐PD – and consider temporal dynamics and clinical applications of PLA‐positive aggregates as biomarkers and therapeutic targets. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Objective Pre‐mortem diagnosis of parkinsonism is often challenging due to atypical presentations, overlapping syndromes, and co‐pathologies. This study aimed to develop a machine learning‐based algorithm predicting neuropathology in parkinsonism using chronological clinical presentations, which has previously been underexplored. Methods Clinical information was automatically abstracted from medical records of the Mayo Clinic Brain Bank using fine‐tuned Generative Pre‐trained Transformer 4 models. Patients who developed parkinsonism within 3 years of disease onset were included. Six machine learning models were trained with age, sex, family history, and 197 clinical presentations paired with onset information to predict neuropathologic diagnoses, including co‐pathologies. Results Among 7,825 donors, 949 met inclusion criteria, representing 9 neuropathologic categories: Lewy body disease (LBD; n = 128), LBD with Alzheimer's disease (AD; n = 136), progressive supranuclear palsy (PSP; n = 303), PSP with AD (n = 56), PSP with LBD (n = 27), multiple system atrophy (MSA; n = 120), corticobasal degeneration (CBD; n = 99), AD (n = 43), and frontotemporal lobar degeneration (FTLD; n = 37). The CatBoost algorithm achieved an area under the receiver operating characteristic curve of 0.83 across the 9 diagnostic categories at 3 years after onset. Important predictors included age at onset, restricted eye movement, and tremor. The model remained robust to incomplete data, requiring only 23 of 200 parameters for reliable predictions with an area under the curve of 0.80. The algorithm was implemented into a user‐friendly program providing diagnostic probabilities with visualizations of parameter contributions. Interpretation This neuropathology‐confirmed diagnostic algorithm provides a cost‐effective and interpretable screening tool for parkinsonism, bridging biomarker testing and molecular‐targeted therapies. ANN NEUROL 2026;99:1405–1414

Objectives Multiple system atrophy (MSA) is classified into parkinsonian (MSA‐P) and cerebellar (MSA‐C) phenotypes based on predominant motor features, with corresponding pathological classifications of striatonigral (SN) degeneration and olivopontocerebellar (OPC) atrophy; however, disease progression patterns remain poorly understood. We aimed to identify novel MSA subtypes based on neuronal loss patterns using unsupervised machine learning. Methods We applied the Subtype and Stage Inference (SuStaIn) algorithm to analyze neuronal loss patterns in 167 autopsy‐confirmed MSA. Neuronal loss was semi‐quantitatively assessed in 5 brain regions: putamen, substantia nigra, pontine nucleus, inferior olivary nucleus, and cerebellar Purkinje cells. Obtained subtypes were validated by clinicopathological information. Results Three distinct subtypes were identified: SN‐early subtype (54% [86/160]) with initial SN neuronal loss, OPC‐early subtype (28% [44/160]) with early OPC involvement, and SN‐OPC‐synchronous subtype (19% [30/160]) with concurrent early neuronal loss in both systems. These subtypes corresponded well with clinical phenotypes. The SN‐OPC‐synchronous subtype exhibited significantly shorter survival (median 6.2 years vs 6.9 and 7.4 years in SN‐early and OPC‐early subtypes; p = 0.0049), more frequent rapid progression (57% vs 24% and 41% in SN‐early and OPC‐early subtypes; p = 0.0048), and more frequent early falls (70% vs 36% and 50% in SN‐early and OPC‐early subtypes; p = 0.0039). Immunohistochemical validation confirmed extensive α‐synuclein pathology in both SN and OPC systems in the SN‐OPC‐synchronous subtype. Interpretation The SN‐OPC‐synchronous subtype provides insights into α‐synuclein propagation mechanisms, suggesting multiple initial seeding sites rather than unidirectional spread. This computational approach uncovered disease heterogeneity undetectable by conventional methods, potentially benefiting clinical trials through patient stratification. ANN NEUROL 2026;99:989–1000

Abstract INTRODUCTION Visual hallucinations (VHs) represent one of the core clinical features of dementia with Lewy bodies (DLB); however, their underlying pathology remains unclear. METHODS We employed proximity ligation assay (PLA) and phosphorylated α‐synuclein (αSYN) immunohistochemistry to compare αSYN oligomers and Lewy‐related pathology across brain regions along the ventral visual pathway in patients with and without VHs (five patients each). RESULTS Greater αSYN oligomer burden in the parahippocampal cortex was observed in patients with VHs compared to those without ( p = 0.041), whereas the burden of Lewy‐related pathology was similar between groups. DISCUSSION Our findings suggest that αSYN oligomers, rather than conventional Lewy‐related pathology, may be more closely associated with VHs in DLB. This provides novel evidence linking αSYN oligomers to a core clinical feature in DLB and suggests potential therapeutic targets for managing VH in these patients. Highlights Visualization of αSYN oligomers by PLA and quantitative neuropathologic analysis. Abundant αSYN oligomers in parahippocampal cortex of DLB with VHs. First human brain study linking αSYN oligomers to VHs. Potential oligomer‐targeted therapy for managing VH in DLB.

Background The purpose of this study was to clarify the usefulness of the ‘hot cross bun’ sign (HCBS) as a diagnostic imaging marker in a large cohort of patients with multiple system atrophy (MSA) and spinocerebellar ataxia (SCA). Methods This multicentre study included 97 patients with neuropathologically confirmed MSA, and 105 patients with genetically confirmed SCA. Neuroimaging features, including HCBS and middle cerebellar peduncle (MCP) hyperintensities, were assessed. HCBS was graded from 0 to 2: 0, none; 1, only a vertical hyperintense line; and 2, a cruciform hyperintense line. The neuropathological correlates of HCBS were evaluated in 15 patients with MSA with ≤3 months between MRI and autopsy. Results In patients with a disease duration <3 years, grade 1 or 2 HCBS was detected in 100% patients with MSA with predominant cerebellar ataxia (MSA-C) and 39.0% with SCA; whereas grade 2 HCBS was observed in 50% with MSA-C and 2.4% with SCA. Moreover, the coexistence of grade 2 HCBS and MCP hyperintensities exhibited a specificity of 100%. A neuropathological assessment revealed myelin loss, alpha-synuclein aggregates and astrocytic reaction in the MCP, transverse fibres, central zone between longitudinal fasciculi and raphe nucleus, with relative preservation in the longitudinal fasciculi and medial lemniscus in patients with MSA and grade 2 HCBS. Conclusions Grade 1 or 2 HCBS is a highly sensitive finding in patients with MSA-C, and the observation of grade 2 HCBS within 3 years of motor symptom onset has excellent specificity for discriminating MSA-C from SCA, especially when accompanied by MCP hyperintensities.

Abstract Background Quantitative susceptibility mapping ( QSM ) is a promising neuroimaging biomarker for detecting abnormal iron deposition in progressive supranuclear palsy ( PSP ); however, the QSM profile and its accuracy in autopsy‐confirmed PSP remain unexplored. Objective The aim of the study was to elucidate and validate the QSM profiles in autopsy‐confirmed PSP. Methods Eight patients with a clinically diagnosed PSP syndrome ( PSPS ) underwent antemortem 3 T magnetic resonance imaging ( MRI) scanning and subsequent pathological diagnosis, confirming 6 PSP pathology and 2 multiple system atrophy ( MSA ) pathology cases. QSM analysis and Prussian blue staining were performed across subcortical regions. Results PSP cases showed higher susceptibility than age‐matched controls in the pallidum, subthalamic nucleus, substantia nigra, and cerebellar dentate (false discovery rate [ FDR] ‐corrected P < 0.05). PSP had higher susceptibility in the subthalamic nucleus and lower susceptibility in the putamen than the clinically atypical MSA ( P = 0.08). Subcortical susceptibilities correlated with Prussian blue–stained areas ( r = 0.63, P < 0.001). Conclusions QSM could be a reliable measure to evaluate abnormal iron accumulation patterns in PSP.

AbstractINTRODUCTIONIncreasing evidence indicates that α‐synuclein (αSYN) oligomers are toxic. We sought to determine whether αSYN oligomers were associated with faster cognitive decline in prospectively‐followed patients with dementia with Lewy bodies (DLB).METHODSEight autopsy‐confirmed patients with DLB were selected based on rapid or slow cognitive decline determined by the rate of change of Mini‐Mental State Examination (MMSE) scores. Quantitative neuropathologic analysis of αSYN oligomers, Lewy‐related pathology, phosphorylated tau, and amyloid‐β was conducted in hippocampal subfields (CA1‐4 and subiculum) and the entorhinal cortex.RESULTSDLB with rapid cognitive decline showed greater CA1 αSYN oligomer burden (p = 0.029) and tau burden (p = 0.029) than DLB with slow decline. The groups showed comparable burden of Lewy‐related pathology and amyloid‐β pathology in the hippocampal formation and entorhinal cortex.DISCUSSIONHippocampal accumulation of αSYN oligomers and phosphorylated tau is associated with rapid cognitive decline in DLB. Therapeutic strategies targeting αSYN oligomers warrant further investigation.Highlights Proximity ligation assay (PLA) and digital pathology for oligomer quantification. Abundant α‐synuclein (αSYN) oligomers in CA1 of patients with dementia with Lewy bodies (DLB) with rapid decline. First human brain study linking αSYN oligomers to cognitive trajectory. Potential therapeutic implications of targeting αSYN oligomers in DLB.

ObjectivesThe four‐repeat (4R) tauopathies are a group of neurodegenerative diseases, including progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and globular glial tauopathy (GGT). This study aimed to characterize spatiotemporal atrophy progression using structural magnetic resonance imaging (MRI) and to examine its relationship with clinical course and neuropathology in a cohort of autopsy‐confirmed 4R tauopathies.MethodsThe study included 85 autopsied patients (54 with PSP, 28 with CBD, and 3 with GGT) who underwent multiple 3T MRI scans, as well as neuropsychological, neurological, and speech/language examinations, and standardized postmortem neuropathological evaluations. An unsupervised machine‐learning algorithm, Subtype and Stage Inference (SuStaIn), was applied to the cross‐sectional brain volumes to estimate spatiotemporal atrophy patterns and data‐driven subtypes and stages in each patient. The relationships among estimated subtypes, pathological diagnoses, and longitudinal changes in clinical testing were examined.ResultsThe SuStaIn algorithm identified 2 distinct subtypes: (1) the subcortical subtype, in which atrophy progresses from the midbrain to the cortex, and (2) the cortical subtype, in which atrophy progresses from the frontal cortex to the subcortical regions. The subcortical subtype was more associated with typical PSP, whereas the cortical subtype was more associated with atypical PSP with a cortical distribution of pathology and CBD (p < 0.001). The cortical subtype had a faster rate of change on the PSP Rating Scale than the subcortical subtype (p < 0.05).InterpretationSuStaIn analysis revealed 2 MRI‐driven subtypes with distinct spatiotemporal atrophy patterns, clinical courses, and neuropathology. Our findings contribute to a comprehensive and improved understanding of disease progression and its relationship to tau pathology in 4R tauopathies. ANN NEUROL 2025;98:492–507

We report a patient who presented clinically with progressive supranuclear palsy (PSP) but was pathologically diagnosed as having primary lateral sclerosis (PLS) with magnetic resonance imaging (MRI) with a quantitative susceptibility mapping (QSM) protocol. A 70‐year‐old man was clinically diagnosed with PSP due to early falls and unresponsiveness to levodopa therapy. Postmortem pathological examination revealed mild loss of Betz cells, gliosis, and transactive response DNA binding protein of 43 kDa (TDP‐43)‐positive inclusions in the motor cortex, leading to the pathological diagnosis of PLS. To explore methods for differentiating PLS from PSP, ante‐mortem QSM images were visually and quantitatively assessed for abnormal increases in magnetic susceptibility in the motor cortex. Prussian blue and Luxol fast blue combined with periodic acid‐Schiff staining were also performed to understand the source of the susceptibility increases. QSM showed clear hyperintense signals in the motor cortex. Magnetic susceptibility in the motor cortex was higher in the PLS patient (Z = 4.7, p < 0.001) compared to normal controls and pathologically diagnosed PSP patients. Pathological examination of the region showed intracortical myelin loss, as well as iron deposition. Underlying pathological processes for the increased magnetic susceptibility include not only iron deposition but also intracortical myelin. Our case suggests that QSM is a potential tool to differentiate PLS from PSP, providing insights for accurate diagnosis and enhancing clinical decision‐making.