Dickson, Dennis W

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

Abstract Recent advances in long-read transcriptome sequencing enable high-throughput profiling of full-length RNA isoforms in bulk, single-cell, and single-nucleus samples. However, long-read datasets typically contain a mixture of complete and partial transcripts, leading to pervasive ambiguity in read-to-isoform assignment and complicating accurate isoform identification and quantification, particularly in the absence of reliable reference annotations. These challenges are further amplified in single-cell and single-nucleus samples, where coverage is sparse and transcriptional heterogeneity is high. Here, we present the Long Read Alignment Assembler (LRAA), a unified and versatile computational framework for isoform identification and quantification from long-read RNA sequencing data across bulk, single-cell, and single-nucleus transcriptomic samples. LRAA combines splice-graph based structural modeling with expectation maximization based optimization to probabilistically resolve ambiguous read assignments and improve isoform abundance estimation. The framework supports quantification-only, reference-guided, and fully reference-free (de novo) modes of analysis within a single methodological paradigm. We benchmarked LRAA using both simulated and genuine long-read datasets spanning sequencing standards and whole transcriptomes. Central to this evaluation is a novel benchmarking strategy based on Multiplexed Overexpression of Regulatory Factors (MORFs), which provides biologically expressed, barcoded isoforms with unambiguous read-level ground truth. Across all benchmarks, including MORFs, synthetic spike-ins, and whole-transcriptome datasets, LRAA consistently outperformed state-of-the-art methods in isoform identification accuracy, sensitivity, and expression quantification. Finally, we demonstrate the biological utility of LRAA by resolving cell-type-specific isoform usage across peripheral blood immune cell populations and by detecting a pathogenic cryptic isoform of STMN2 with associated transcriptional changes in single-nucleus RNA-seq data from frontal cortex tissue of an individual with frontotemporal dementia (FTD). Together, these results establish LRAA as a robust and general solution for resolving transcript diversity in complex biological systems, from development to disease.

Abstract The human X chromosome accounts for ∼5% of the genome. Despite its size, the role of X-chromosomal variants in human diseases is not well understood, mainly due to its distinct inheritance pattern and the frequent omission of sex chromosomes from genome-wide association studies. This study used whole-genome sequencing data from 888 multiple system atrophy (MSA) cases and 7128 controls to perform an X-chromosome-wide common variant association study. Our analyses revealed two sex-differential risk loci: one located at Xq28, associated with increased risk for MSA in females [index variant: rs4898389, odds ratio (OR) = 1.69, 95% confidence interval (CI) = 1.40–2.03, P-value = 2.43 × 10⁻⁸], and another at Xp22.2 within the TBL1X gene, identified in males (index variant: rs6638956, OR = 1.48, 95%CI = 1.26–1.73, P-value = 9.92 × 10⁻⁷). In the Xq28 locus, colocalization analyses pointed to FAM3A and PLXNA3 as genes of interest. These findings emphasize the vital role of sex-differential genetic factors in the pathogenesis of MSA.

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 Background Colony stimulating factor 1 receptor–related disorder (CSF1R‐RD) is a rare, rapidly progressive neurodegenerative disease with significant clinical heterogeneity. Apolipoprotein E (ApoE) polymorphism, known to modulate microglial function and influence neurodegeneration, may act as a genetic modifier in CSF1R‐RD. Objectives The objectives were to evaluate the distribution of ApoE alleles in CSF1R‐RD and assess their association with clinical and neuropathological features. Methods ApoE genotyping was performed in 55 individuals with CSF1R‐RD. Clinical data from 25 deceased patients were analyzed based on ApoE genotype. Neuropathological evaluation was conducted on brain tissue from 14 patients, with semiquantitative scoring of white matter pathology and microglial burden. Statistical comparisons were made between carriers and noncarriers of the ApoE4 allele. Results ApoE allele frequencies in CSF1R‐RD mirrored those of the general population (ε2: 7.3%, ε3: 79.1%, ε4: 13.6%). However, ApoE4 carriers exhibited significantly earlier symptom onset (median: 37.9 vs. 50.3 years, p = 0.0123) and death (median: 41.1 vs. 55.2 years, p = 0.0072). Neuropathological analysis revealed more severe white matter involvement and reduced microglial preservation in ApoE4 carriers ( p = 0.034). Conclusions Although ApoE allele distribution does not differ from the general population, the presence of the ApoE4 allele may influence the clinical trajectory and white matter pathology in CSF1R‐RD. These findings suggest that ApoE polymorphism is a potential modifier of disease course and should be considered in therapeutic planning and future research. © 2026 International Parkinson and Movement Disorder Society.

Abstract Pick’s disease (PiD) is a rare cause of sporadic frontotemporal dementia, neuropathologically defined by the presence of Pick bodies consisting of aggregates of 3-repeat tau. Given the genetic aetiology of PiD remains unresolved, we assembled the Pick’s disease International Consortium (PIC) to identify susceptibility loci through a genome-wide association study (GWAS). A GWAS was conducted in 294 autopsy confirmed PiD cases and 1,055 controls. Lead variants were annotated using the Functional Mapping and Annotation of GWAS (FUMA) platform, followed by co-localisation analyses using the METABRAIN dataset and statistical finemapping using FINEMAP and SuSiE. After exclusion of 3 cases of MAPT mutations, no variants were associated with risk of PiD at genome-wide significance ( p < 5 × 10 -8 ). The strongest association was on chromosome 4 (4p13, lead SNP rs112161979, OR = 7.53, 95% Confidence Interval (CI) = 3.62-15.65, p = 6.37 × 10 -8 ) followed by chromosome 11 (11p15.4, lead SNP rs66481907, OR = 2.10, 95% CI = 1.54-2.84, p = 1.83 × 10 -6 ). rs112161979 is an intronic SNP in the KCDT8 gene, encoding a potassium channel tetramerization domain that acts as an auxiliary subunit for GABA B receptors, whilst rs66481907 is an intronic SNP in TRIM22 , encoding an E3 ubiquitin-protein ligase. Our GWAS provides the first evidence of possible genetic risk for PiD that implicate the modulation of GABA B receptor signalling and inflammation, in disease pathogenesis. Replication of these findings will be important, but our results suggest that, if present, the genetic risk of PiD beyond MAPT mutations is low.

Objective Colony-stimulating factor-1 receptor (<i>CSF1R</i>)-related disorder (CSF1R-RD) is a rapidly progressive neurodegenerative disease with a median onset of 43 years. More than 200 <i>CSF1R</i> pathogenic variants have been identified. Patients develop rapidly progressive dementia and motor symptoms, with a median disease duration of 6.8 years. The p.R777Q variant causes CSF1R-RD; it is deleterious to protein function. We describe the clinical and pathological features of CSF1R-RD associated with p.R777Q and report a new family carrying this variant. We compare the neuropathological findings of an index patient (with short-duration disease) with those of a patient with long-duration CSF1R-RD.Methods We present clinical and imaging data from a new family with p.R777Q. We also describe the neuropathology of an index patient and contrast these findings with features of a patient with CSF1R-RD with an 11-year disease course caused by the c.2656_2657insC variant.Results We reviewed the literature on 13 individuals from 8 families with the p.R777Q variant from Asia, Europe, and North America. The mean age at onset was 41±14 years, ranging from 22 to 63 years, and the mean survival was 3.3±2.5 years. Neuropathologic studies of our index patient (10-month disease duration) revealed features consistent with CSF1R-RD. Axonal and myelin pathology was severe in the periventricular white matter. Compared with the patient with an 11-year disease duration, the index patient had less severe white matter lesions.Conclusion Compared with CSF1R-RD associated with other variants, the p.R777Q variant has an aggressive course. Compared with the patient with a long disease duration, the index patient had milder neuropathological findings. These differences may be specific to p.R777Q or associated with rapid clinical progression.

Objective Lewy body disease (LBD) is a complex neurodegenerative disorder characterized by the accumulation of misfolded α‐synuclein in the brain. Neuroinflammation has long been implicated in LBD pathogenesis, and recent genetic studies in Parkinson's disease (a clinical manifestation of LBD) have shown consistent association with the human leukocyte antigen (HLA) gene complex. Here, we assessed whether variation in HLA alleles influences neuropathological burden in a neuropathologically‐defined series of LBD cases. Methods We conducted a comprehensive analysis of HLA allelic variants in a cohort of 539 LBD cases of European descent from the Mayo Clinic brain bank. High‐resolution whole‐genome sequencing was used, and the HLA alleles of each sample were called using the HLA*LA tool and assessed for association with neuropathological outcomes. Results Our analysis identified 1 significant ( P < 3.43 × 10 −5 ) and 4 suggestive ( P < 0.001) associations between certain HLA alleles and specific neuropathological outcomes in LBD, suggesting a potential role for HLA‐mediated immune mechanisms in disease progression and subtype differentiation. Specifically, HLA‐DPB1*06:01 was significantly associated with lower Lewy body counts in the parahippocampal gyrus ( P = 3.30 × 10 −5 ), with weaker and suggestive associations observed in the middle frontal ( P = 1.80 × 10 −4 ) and inferior parietal gyrus ( P = 6.33 × 10 −4 ). Additionally, although only suggestive, HLA‐DRB1*11:01 correlated with a lower Thal amyloid phase ( P = 1.56 x 10 −4 ), and HLA‐B*15:01 correlated with an increased risk of diffuse LBD ( P = 7.58 x 10 −4 ). Interpretation This study provides a detailed evaluation of the relationship between HLA alleles and LBD pathology, highlighting the importance of immune‐related genetic factors in the etiology of LBD. ANN NEUROL 2026;99:492–501