Ali, Farwa

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

Abstract Normal pressure hydrocephalus is typically defined by the triad of gait disturbance, cognitive impairment, and urinary incontinence, and is characterized on MRI by disproportionately enlarged subarachnoid-space. Gait disturbance is also a commonly reported symptom in Parkinsonian disorders, especially progressive supranuclear palsy, although the frequency, clinical significance and mechanisms of hydrocephalus in these disorders are unclear. We aimed to assess the prevalence of hydrocephalic MRI parameters in a large cohort of Parkinsonian disorders and evaluate associations with clinical features and abnormalities on MRI and PET. Two hundred and thirty-eight participants with a Parkinsonian disorder, including 181 progressive supranuclear palsy, 36 corticobasal syndrome and 21 Parkinson’s disease, were enrolled from Mayo Clinic by the Neurodegenerative Research Group between September 2009 to October 2023. Automated detection of disproportionately enlarged subarachnoid-space hydrocephalus (D) was applied and using Evans’ index (E) cut-off point &gt;0.3, participants were classified based on both measures as imaging-suggestive of hydrocephalus (D+E+), enlarged subarachnoid-space only (D+E−), large Evans’ index only (D−E+) and no imaging evidence of hydrocephalus (D−E−). Demographic, clinical and imaging features, including magnetic resonance parkinsonism index, cortical and subcortical volumes, white matter hyperintensities, diffusion tractography metrics, and metabolism on PET, were compared across groups. Among the 238 participants, 24 had borderline subarachnoid space scores and were excluded. The remaining 214 participants were classified as: D+E+ (n = 20, 9%); D+E− (n = 8, 4%); D−E+ (n = 71, 33%) and D−E− (n = 115, 54%). Among the progressive supranuclear palsy participants, 11% were D+E+, 4% D+E−, 34% D−E+ and 51% D−E−. Most cases (n = 18) in the imaging-suggestive of hydrocephalus D+E+ group had progressive supranuclear palsy. The D+E+ participants were older, had more disorientation, more downgaze palsy, greater midbrain and cortical atrophy, lower striatal metabolism, greater degeneration of long-range white matter tracts, larger cistern areas and more periventricular and deep white matter hyperintensities compared to the D−E− participants without imaging evidence of hydrocephalus. The D+E− participants had the highest metabolism in the paracentral lobule and superior parietal gyrus. The D−E+ participants showed worse disease severity and greater midbrain and cortical atrophy compared to the D−E− participants. These findings demonstrate that disproportionately enlarged subarachnoid-space hydrocephalus occurs in ∼15% of progressive supranuclear palsy participants, and is associated with worse clinical and imaging outcomes, as well as white matter hyperintensities. We hypothesize that disproportionately enlarged subarachnoid-space may be a mechanistic byproduct of degeneration and subsequent cerebrospinal fluid flow re-distribution in progressive supranuclear palsy.

ObjectiveTo determine whether a machine learning model of voxel level [18f]fluorodeoxyglucose positron emission tomography (PET) data could predict progressive supranuclear palsy (PSP) pathology, as well as outperform currently available biomarkers.MethodsOne hundred and thirty‐seven autopsied patients with PSP (n = 42) and other neurodegenerative diseases (n = 95) who underwent antemortem [18f]fluorodeoxyglucose PET and 3.0 Tesla magnetic resonance imaging (MRI) scans were analyzed. A linear support vector machine was applied to differentiate pathological groups with sensitivity analyses performed to assess the influence of voxel size and region removal. A radial basis function was also prepared to create a secondary model using the most important voxels. The models were optimized on the main dataset (n = 104), and their performance was compared with the magnetic resonance parkinsonism index measured on MRI in the independent test dataset (n = 33).ResultsThe model had the highest accuracy (0.91) and F‐score (0.86) when voxel size was 6mm. In this optimized model, important voxels for differentiating the groups were observed in the thalamus, midbrain, and cerebellar dentate. The secondary models found the combination of thalamus and dentate to have the highest accuracy (0.89) and F‐score (0.81). The optimized secondary model showed the highest accuracy (0.91) and F‐scores (0.86) in the test dataset and outperformed the magnetic resonance parkinsonism index (0.81 and 0.70, respectively).InterpretationThe results suggest that glucose hypometabolism in the thalamus and cerebellar dentate have the highest potential for predicting PSP pathology. Our optimized machine learning model outperformed the best currently available biomarker to predict PSP pathology. ANN NEUROL 2025;98:410–420

Abstract Background Classic progressive supranuclear palsy RS syndrome (PSP‐RS) is characterized by early postural instability/falls and vertical supranuclear gaze palsy, although other signs/symptoms can occur, for example, axial rigidity, bradykinesia, and cognitive dysfunction. The Movement Disorders Society‐PSP (MDS‐PSP) criteria consider probable PSP‐RS a homogenous syndrome, but this has not been tested. Objectives We aim to investigate relationships between signs/symptoms of PSP‐RS beyond its core features and determine whether PSP‐RS is a homogeneous syndrome. Methods 118 participants who met MDS‐PSP criteria for probable PSP‐RS were analyzed. All participants received an independent clinical diagnosis based on clinical judgment. We first performed a Network Analysis using qgraph with Spearman's correlation to quantify relationships between 16 signs/symptoms followed by a k‐means clustering analysis using the same signs/symptoms to identify possible heterogeneity of the PSP‐RS designation. Results Five network clusters were identified, reflecting: (1) tremor, (2) sensitivity to bright light, (3) oculomotor function, (4) cognition, and (5) akinetic‐rigidity‐praxis. Worse akinetic‐rigidity was associated with better oculomotor function, but worse cognition. Cluster analysis revealed two clusters. Cluster I ( n = 89) had more severe clinical features than Cluster II ( n = 29). Cluster II had a higher frequency of participants (37.9%) clinically judged as not PSP‐RS: these participants were less likely to have PSP pathology on autopsy ( P = 0.03). Conclusions Some signs/symptoms of PSP‐RS strongly associate and are more likely to co‐occur. PSP‐RS, as defined by the MDS‐PSP criteria includes a subset of participants not clinically judged as PSP‐RS who are less likely to have PSP pathology.

Background Falls in people with multiple sclerosis (pwMS) lead to morbidity and expense. Objective Identify clinical metrics associated with falls. Methods Eighty-six pwMS completed fall surveys, timed 25-foot walk (T25FW), and motion analysis with Clario Opal devices. Logistic regression models were created. Results Median age was 54.5 years (range 21–73), 62% (53) were female. The cohort included 58% with relapsing (50) and 42% with progressive MS (36). Those who reported falling in the last year were older (median age 58 vs 52.5, p = .03) and had a higher Patient Determined Disease Step (PDDS) score (median 3 vs 1, p < .0001). Falls were associated with worse balance metrics including sway area (median 2.3 degrees 2 vs 1.2, p = .01), jerk (median 3.3 m 2 /s 5 vs 1.6, p = .005), and slower T25FW (median 11.5 s vs 8; p < .0001). A multivariable regression model based on gait aid use and T25FW time >10.8 s (c = 0.80) was derived. Having both features portended a probability of falling of 0.97, while having neither, a probability of 0.26. Conclusions Falls in pwMS are more frequent in patients who are older, have higher PDDS, slower walking, and worse balance. Gait aid use and T25FW >10.8 s were strongly associated with falls in the past year.