Uncovering spatiotemporal patterns of atrophy in progressive supranuclear palsy using unsupervised machine learning

A radiomics approach to distinguish Progressive Supranuclear Palsy Richardson's syndrome from other phenotypes starting from MR images

BACKGROUND AND OBJECTIVE

Progressive Supranuclear Palsy (PSP) is an uncommon neurodegenerative disorder with different clinical onset, including Richardson's syndrome (PSP-RS) and other variant phenotypes (vPSP). Recognising the clinical progression of different phenotypes would enhance the accuracy of detection and treatment of PSP. The study goal was to identify radiomic biomarkers for distinguishing PSP phenotypes extracted from T1-weighted magnetic resonance images (MRI).

METHODS

Forty PSP patients (20 PSP-RS and 20 vPSP) took part in the present work. Radiomic features were collected from 21 regions of interest (ROIs) mainly from frontal cortex, supratentorial white matter, basal nuclei, brainstem, cerebellum, 3rd and 4th ventricles. After features selection, three tree-based machine learning (ML) classifiers were implemented to classify PSP phenotypes.

RESULTS

10 out of 21 ROIs performed best about sensitivity, specificity, accuracy and area under the receiver operating characteristic curve (AUCROC). Particularly, features extracted from the pons region obtained the best accuracy (0.92) and AUCROC (0.83) values while by using the other 10 ROIs, evaluation metrics range from 0.67 to 0.83. Eight features of the Gray Level Dependence Matrix were recurrently extracted for the 10 ROIs. Furthermore, by combining these ROIs, the results exceeded 0.83 in phenotypes classification and the selected areas were brain stem, pons, occipital white matter, precentral gyrus and thalamus regions.

CONCLUSIONS

Based on the achieved results, our proposed approach could represent a promising tool for distinguishing PSP-RS from vPSP.

Data-driven neuroanatomical subtypes of primary progressive aphasia

The primary progressive aphasias are rare, language-led dementias, with three main variants: semantic, non-fluent/agrammatic and logopenic. Although the semantic variant has a clear neuroanatomical profile, the non-fluent/agrammatic and logopenic variants are difficult to discriminate from neuroimaging. Previous phenotype-driven studies have characterized neuroanatomical profiles of each variant on MRI. In this work, we used a machine learning algorithm known as SuStaIn to discover data-driven neuroanatomical 'subtype' progression profiles and performed an in-depth subtype-phenotype analysis to characterize the heterogeneity of primary progressive aphasia. Our study included 270 participants with primary progressive aphasia seen for research in the UCL Queen Square Institute of Neurology Dementia Research Centre, with follow-up scans available for 137 participants. This dataset included individuals diagnosed with all three main variants (semantic, n = 94; non-fluent/agrammatic, n = 109; logopenic, n = 51) and individuals with unspecified primary progressive aphasia (n = 16). A dataset of 66 patients (semantic, n = 37; non-fluent/agrammatic, n = 29) from the ARTFL LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Research Study was used to validate our results. MRI scans were segmented, and SuStaIn was used on 19 regions of interest to identify neuroanatomical profiles independent of the diagnosis. We assessed the assignment of subtypes and stages, in addition to their longitudinal consistency. We discovered four neuroanatomical subtypes of primary progressive aphasia, labelled S1 (left temporal), S2 (insula), S3 (temporoparietal) and S4 (frontoparietal), exhibiting robustness to statistical scrutiny. S1 was correlated strongly with the semantic variant, whereas S2, S3 and S4 showed mixed associations with the logopenic and non-fluent/agrammatic variants. Notably, S3 displayed a neuroanatomical signature akin to a logopenic-only signature, yet a significant proportion of logopenic cases were allocated to S2. The non-fluent/agrammatic variant demonstrated diverse associations with S2, S3 and S4. No clear relationship emerged between any of the neuroanatomical subtypes and the unspecified cases. At first follow-up, subtype assignment was stable for 84% of patients, and stage assignment was stable for 91.9% of patients. We partially validated our findings in the ALLFTD dataset, finding comparable qualitative patterns. Our study, leveraging machine learning on a large primary progressive aphasia dataset, delineated four distinct neuroanatomical patterns. Our findings suggest that separable spatiotemporal neuroanatomical phenotypes do exist within the primary progressive aphasia spectrum, but that these are noisy, particularly for the non-fluent/agrammatic non-fluent/agrammatic and logopenic variants. Furthermore, these phenotypes do not always conform to standard formulations of clinico-anatomical correlation. Understanding the multifaceted profiles of the disease, encompassing neuroanatomical, molecular, clinical and cognitive dimensions, has potential implications for clinical decision support.

Annual percentage change of MR Parkinsonism index in progressive supranuclear palsy: a feasibility study

OBJECTIVES

Considerable evidence suggests that midbrain-based magnetic resonance Parkinsonism index (MRPI) measurements are reliable biomarkers for the diagnosis of progressive supranuclear palsy (PSP). However, the longitudinal atrophy pattern of PSP and potential differences in change rates among PSP phenotypic spectrum remain unclear. This study aims to investigate the longitudinal changes of MRPI measurements and explore their potential role in PSP phenotype progression monitoring.

MATERIALS AND METHODS

Thirty-six patients with PSP-Richardson's syndrome (PSP-RS), 21 patients with variant PSP (vPSP), and 21 healthy controls (HCs) with longitudinal MRI and clinical follow-up were enrolled. Midbrain-based morphometric measurements and the corresponding annual percentage changes (APCs) were measured and further used to evaluate the associations with disease progression.

RESULTS

Significant differences in midbrain-based morphometric biomarkers were observed both at baseline and longitudinal trajectories between PSP and HC groups, but no significant differences were found between PSP-RS and vPSP subgroups. Baseline comprehensive measurements were significantly associated with the baseline PSP rating scale (PSPrs) in all PSP and PSP phenotypes. The APC of MRPI was significantly associated with the APC of PSPrs in all PSP (r = 0.267, p = 0.046) and the PSP-RS subgroup (r = 0.386, p = 0.020).

CONCLUSIONS

This study characterizes the longitudinal atrophy trajectory of PSP phenotypes and the significant associations between morphometric measurements and disease severity. Dissecting the causal associations among core 4R-tau, dopamine, and subsequent atrophy trajectories may enhance the application of these biomarkers for phenotype attribution.

KEY POINTS

Questions The use of midbrain-based MRPI measurements to assess the longitudinal prognosis of the PSP phenotype remains uncertain. Findings Significant differences in midbrain-based morphometric biomarkers were observed both at baseline and longitudinal trajectories between PSP and health control groups. Clinical relevance Midbrain-based morphometric measurements hold promise as potential radiological biomarkers for monitoring PSP disease progression and assessment.

Distinct spatiotemporal atrophy patterns in corticobasal syndrome are associated with different underlying pathologies

Although the corticobasal syndrome was originally most closely linked with the pathology of corticobasal degeneration, the 2013 Armstrong clinical diagnostic criteria, without the addition of aetiology-specific biomarkers, have limited positive predictive value for identifying corticobasal degeneration pathology in life. Autopsy studies demonstrate considerable pathological heterogeneity in corticobasal syndrome, with corticobasal degeneration pathology accounting for only ∼50% of clinically diagnosed individuals. Individualized disease stage and progression modelling of brain changes in corticobasal syndrome may have utility in predicting this underlying pathological heterogeneity, and in turn improve the design of clinical trials for emerging disease-modifying therapies. The aim of this study was to jointly model the phenotypic and temporal heterogeneity of corticobasal syndrome, to identify unique imaging subtypes based solely on a data-driven assessment of MRI atrophy patterns and then investigate whether these subtypes provide information on the underlying pathology. We applied Subtype and Stage Inference, a machine learning algorithm that identifies groups of individuals with distinct biomarker progression patterns, to a large cohort of 135 individuals with corticobasal syndrome (52 had a pathological or biomarker defined diagnosis) and 252 controls. The model was fit using volumetric features extracted from baseline T1-weighted MRI scans and then used to subtype and stage follow-up scans. The subtypes and stages at follow-up were used to validate the longitudinal consistency of the baseline subtype and stage assignments. We then investigated whether there were differences in associated pathology and clinical phenotype between the subtypes. Subtype and Stage Inference identified at least two distinct and longitudinally stable spatiotemporal subtypes of atrophy progression in corticobasal syndrome; four-repeat-tauopathy confirmed cases were most commonly assigned to the Subcortical subtype (83% of individuals with progressive supranuclear palsy pathology and 75% of individuals with corticobasal-degeneration pathology), whilst those with Alzheimer's pathology were most commonly assigned to the Fronto-parieto-occipital subtype (81% of individuals). Subtype assignment was stable at follow-up (98% of cases), and individuals consistently progressed to higher stages (100% stayed at the same stage or progressed), supporting the model's ability to stage progression. By jointly modelling disease stage and subtype, we provide data-driven evidence for at least two distinct and longitudinally stable spatiotemporal subtypes of atrophy in corticobasal syndrome that are associated with different underlying pathologies. In the absence of sensitive and specific biomarkers, accurately subtyping and staging individuals with corticobasal syndrome at baseline has important implications for screening on entry into clinical trials, as well as for tracking disease progression.

Identification of metabolic progression and subtypes in progressive supranuclear palsy by PET molecular imaging

INTRODUCTION

Progressive supranuclear palsy (PSP) is a neurodegenerative disorder with diverse clinical presentations that are linked to tau pathology. Recently, Subtype and Stage Inference (SuStaIn) algorithm, an innovative data-driven method, has been developed to model both the spatial-temporal progression and subtypes of disease. This study explores PSP progression using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging and the SuStaIn algorithm to identify PSP metabolic progression subtypes and understand disease mechanisms.

METHODS

The study included 72 PSP patients and 70 controls, with an additional 24 PSP patients enrolled as a test set, undergoing FDG-PET, dopamine transporter (DAT) PET, and neuropsychological assessments. The SuStaIn algorithm was employed to analyze the FDG-PET data, identifying progression subtypes and sequences.

RESULTS

Two PSP subtypes were identified: the cortical subtype with early prefrontal hypometabolism and the brainstem subtype with initial midbrain alterations. The cortical subtype displayed greater cognitive impairment and DAT reduction than the brainstem subtype. The test set demonstrates the robustness and reproducibility of the findings. Pathway analysis indicated that disruptions in dopaminergic cortico-basal ganglia pathways are crucial for elucidating the mechanisms of cognitive and behavioral impairment in PSP, leading to the two metabolic progression subtypes.

CONCLUSION

This study identified two spatiotemporal progression subtypes of PSP based on FDG-PET imaging, revealing significant differences in metabolic patterns, striatal dopaminergic uptake, and clinical profiles, particularly cognitive impairments. The findings highlight the crucial role of dopaminergic cortico-basal ganglia pathways in PSP pathophysiology, especially in the cortical subtype, providing insights into PSP heterogeneity and potential avenues for personalized treatments.

A data-driven model of disability progression in progressive multiple sclerosis

This study applies the Gaussian process progression model, a Bayesian data-driven disease progression model, to analyse the evolution of primary progressive multiple sclerosis. Utilizing data from 1521 primary progressive multiple sclerosis participants collected within the International Progressive Multiple Sclerosis Alliance Project, the analysis includes 18 581 longitudinal time-points (average follow-up time: 28.2 months) of disability assessments including the expanded disability status scale, symbol digit modalities, timed 25-foot-walk, 9-hole-peg test and of MRI metrics such as T1 and T2 lesion volume and normalized brain volume. From these data, Gaussian process progression model infers a data-driven description of the progression common to all individuals, alongside scores measuring the individual progression rates relative to the population, spanning ∼50 years of disease duration. Along this timeline, Gaussian process progression model identifies an initial steep worsening of the expanded disability status scale that stabilizes after ∼30 years of disease duration, suggesting its diminished utility in monitoring disease progression beyond this time. Conversely, it underscores the slower evolution of normalized brain volume across the disease duration. The individual progression rates estimated by Gaussian process progression model can be used to identify three distinct sub-groups within the primary progressive multiple sclerosis population: a normative group (76% of the population) and two 'outlier' sub-groups displaying either accelerated (13% of the population) or decelerated (11%) progression compared to the normative one. Notably, fast progressors exhibit older age at symptom onset (38.5 versus 35.0, P < 0.0001), a higher prevalence of males (61.1% versus 48.5%, P = 0.013) and a higher lesion volumes both in T1 (4.1 versus 0.6, P < 0.0001) and T2 (16.5 versus 7.9, P < 0.0001) compared to slow progressors. Prognostically, fast progressors demonstrate a significantly worse prognosis, with double the risk of experiencing a 3-month confirmed disease progression on expanded disability status scale compared to the normative population according to Cox proportional hazard modelling (HR = 2.09, 95% CI: 1.66-2.62, P < 0.0001) and a shorter median time from the onset of disease symptoms to reaching a confirmed expanded disability status scale 6 (95% CI: 5.83-7.68 years, P < 0.0001). External validation on a test set comprising 227 primary progressive multiple sclerosis participants from the SPI2 trial produced consistent results, with slow progressors exhibiting a reduced risk of experiencing 3-month confirmed disease progression determined through expanded disability status scale (HR = 0.21), while fast progressors facing an increased risk (HR = 1.45). This study contributes to our understanding of disability accrual in primary progressive multiple sclerosis, integrating diverse disability assessments and MRI measurements. Moreover, the identification of distinct sub-groups underscores the heterogeneity in progression rates among patients, offering invaluable insights for patient stratification and monitoring in clinical trials, potentially facilitating more targeted and personalized interventions.

Delineating three distinct spatiotemporal patterns of brain atrophy in Parkinson's disease

The clinical manifestation of Parkinson's disease exhibits significant heterogeneity in the prevalence of non-motor symptoms and the rate of progression of motor symptoms, suggesting that Parkinson's disease can be classified into distinct subtypes. In this study, we aimed to explore this heterogeneity by identifying a set of subtypes with distinct patterns of spatiotemporal trajectories of neurodegeneration. We applied Subtype and Stage Inference (SuStaIn), an unsupervised machine learning algorithm that combined disease progression modelling with clustering methods, to cortical and subcortical neurodegeneration visible on 3 T structural MRI of a large cross-sectional sample of 504 patients and 279 healthy controls. Serial longitudinal data were available for a subset of 178 patients at the 2-year follow-up and for 140 patients at the 4-year follow-up. In a subset of 210 patients, concomitant Alzheimer's disease pathology was assessed by evaluating amyloid-β concentrations in the CSF or via the amyloid-specific radiotracer 18F-flutemetamol with PET. The SuStaIn analysis revealed three distinct subtypes, each characterized by unique patterns of spatiotemporal evolution of brain atrophy: neocortical, limbic and brainstem. In the neocortical subtype, a reduction in brain volume occurred in the frontal and parietal cortices in the earliest disease stage and progressed across the entire neocortex during the early stage, although with relative sparing of the striatum, pallidum, accumbens area and brainstem. The limbic subtype represented comparative regional vulnerability, which was characterized by early volume loss in the amygdala, accumbens area, striatum and temporal cortex, subsequently spreading to the parietal and frontal cortices across disease stage. The brainstem subtype showed gradual rostral progression from the brainstem extending to the amygdala and hippocampus, followed by the temporal and other cortices. Longitudinal MRI data confirmed that 77.8% of participants at the 2-year follow-up and 84.0% at the 4-year follow-up were assigned to subtypes consistent with estimates from the cross-sectional data. This three-subtype model aligned with empirically proposed subtypes based on age at onset, because the neocortical subtype demonstrated characteristics similar to those found in the old-onset phenotype, including older onset and cognitive decline symptoms (P < 0.05). Moreover, the subtypes correspond to the three categories of the neuropathological consensus criteria for symptomatic patients with Lewy pathology, proposing neocortex-, limbic- and brainstem-predominant patterns as different subgroups of α-synuclein distributions. Among the subtypes, the prevalence of biomarker evidence of amyloid-β pathology was comparable. Upon validation, the subtype model might be applied to individual cases, potentially serving as a biomarker to track disease progression and predict temporal evolution.

Magnetic Resonance Imaging Measures to Track Atrophy Progression in Progressive Supranuclear Palsy in Clinical Trials

BACKGROUND

Several magnetic resonance imaging (MRI) measures have been suggested as progression biomarkers in progressive supranuclear palsy (PSP), and some PSP staging systems have been recently proposed.

OBJECTIVE

Comparing structural MRI measures and staging systems in tracking atrophy progression in PSP and estimating the sample size to use them as endpoints in clinical trials.

METHODS

Progressive supranuclear palsy-Richardson's syndrome (PSP-RS) patients with one-year-follow-up longitudinal brain MRI were selected from the placebo arms of international trials (NCT03068468, NCT01110720, NCT01049399) and the DescribePSP cohort. The discovery cohort included patients from the NCT03068468 trial; the validation cohort included patients from other sources. Multisite age-matched healthy controls (HC) were included for comparison. Several MRI measures were compared: automated atlas-based volumetry (44 regions), automated planimetric measures of brainstem regions, and four previously described staging systems, applied to volumetric data.

RESULTS

Of 508 participants, 226 PSP patients including discovery (n = 121) and validation (n = 105) cohorts, and 251 HC were included. In PSP patients, the annualized percentage change of brainstem and midbrain volume, and a combined index including midbrain, frontal lobe, and third ventricle volume change, were the progression biomarkers with the highest effect size in both cohorts (discovery: >1.6; validation cohort: >1.3). These measures required the lowest sample sizes (n < 100) to detect 30% atrophy progression, compared with other volumetric/planimetric measures and staging systems.

CONCLUSIONS

This evidence may inform the selection of imaging endpoints to assess the treatment efficacy in reducing brain atrophy rate in PSP clinical trials, with automated atlas-based volumetry requiring smaller sample size than staging systems and planimetry to observe significant treatment effects. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Patterns of brain volume and metabolism predict clinical features in the progressive supranuclear palsy spectrum

Progressive supranuclear palsy (PSP) is a neurodegenerative tauopathy that presents with highly heterogenous clinical syndromes. We perform cross-sectional data-driven discovery of independent patterns of brain atrophy and hypometabolism across the entire PSP spectrum. We then use these patterns to predict specific clinical features and to assess their relationship to phenotypic heterogeneity. We included 111 patients with PSP (60 with Richardson syndrome and 51 with cortical and subcortical variant subtypes). Ninety-one were used as the training set and 20 as a test set. The presence and severity of granular clinical variables such as postural instability, parkinsonism, apraxia and supranuclear gaze palsy were noted. Domains of akinesia, ocular motor impairment, postural instability and cognitive dysfunction as defined by the Movement Disorders Society criteria for PSP were also recorded. Non-negative matrix factorization was used on cross-sectional MRI and fluorodeoxyglucose-positron emission tomography (FDG-PET) scans. Independent models for each as well as a combined model for MRI and FDG-PET were developed and used to predict the granular clinical variables. Both MRI and FDG-PET were better at predicting presence of a symptom than severity, suggesting identification of disease state may be more robust than disease stage. FDG-PET predicted predominantly cortical abnormalities better than MRI such as ideomotor apraxia, apraxia of speech and frontal dysexecutive syndrome. MRI demonstrated prediction of cortical and more so sub-cortical abnormalities, such as parkinsonism. Distinct neuroanatomical foci were predictive in MRI- and FDG-PET-based models. For example, vertical gaze palsy was predicted by midbrain atrophy on MRI, but frontal eye field hypometabolism on FDG-PET. Findings also differed by scale or instrument used. For example, prediction of ocular motor abnormalities using the PSP Saccadic Impairment Scale was stronger than with the Movement Disorders Society Diagnostic criteria for PSP oculomotor impairment designation. Combination of MRI and FDG-PET demonstrated enhanced detection of parkinsonism and frontal syndrome presence and apraxia, cognitive impairment and bradykinesia severity. Both MRI and FDG-PET patterns were able to predict some measures in the test set; however, prediction of global cognition measured by Montreal Cognitive Assessment was the strongest. MRI predictions generalized more robustly to the test set. PSP leads to neurodegeneration in motor, cognitive and ocular motor networks at cortical and subcortical foci, leading to diverse yet overlapping clinical syndromes. To advance understanding of phenotypic heterogeneity in PSP, it is essential to consider data-driven approaches to clinical neuroimaging analyses.

Frontal hypometabolism in the diagnosis of progressive supranuclear palsy clinical variants

OBJECTIVE

Frontal hypometabolism on FDG-PET is observed in progressive supranuclear palsy (PSP), although it is unclear whether it is a feature of all PSP clinical variants and hence whether it is a useful diagnostic feature. We aimed to compare the frequency, severity, and pattern of frontal hypometabolism across PSP variants and determine whether frontal hypometabolism is related to clinical dysfunction.

METHODS

Frontal hypometabolism in prefrontal, premotor, and sensorimotor cortices was visually graded on a 0-3 scale using CortexID Z-score images in 137 PSP patients. Frontal asymmetry was recorded. Severity scores were used to categorize patients as premotor-predominant, prefrontal-predominant, sensorimotor-predominant, mixed-predominance, or no regional predominance. Frontal ratings were compared across PSP clinical variants, and Spearman correlations were used to assess relationships with the Frontal Assessment Battery (FAB).

RESULTS

97% showed evidence of frontal hypometabolism which was most common (100%) in the speech-language (PSP-SL), corticobasal (PSP-CBS), and frontal (PSP-F) variants and least common in the progressive gait freezing (PSP-PGF) variant (73%). PSP-SL and PSP-CBS showed more severe hypometabolism than Richardson's syndrome (PSP-RS), Parkinsonism (PSP-P), and PSP-PGF. A premotor-predominant pattern was most common in PSP-SL and PSP-CBS, with more mixed patterns in the other variants. Hypometabolism was most commonly asymmetric in PSP-SL, PSP-P, PSP-F and PSP-CBS. Worse hypometabolism in nearly all frontal regions correlated with worse scores on the FAB.

CONCLUSIONS

Frontal hypometabolism is a common finding in PSP, although it varies in severity and pattern across PSP variants and will likely be the most diagnostically useful in PSP-SL and PSP-CBS.

Dominance of Tau Burden in Cortical Over Subcortical Regions Mediates Glymphatic Activity and Clinical Severity in PSP

BACKGROUND

Progressive supranuclear palsy (PSP) is a tauopathy that involves subcortical regions but also extends to cortical areas. The clinical impact of different tau protein sites and their influence on glymphatic dysfunction have not been investigated.

PATIENTS AND METHODS

Participants (n = 55; 65.6 ± 7.1 years; 29 women) with PSP (n = 32) and age-matched normal controls (NCs; n = 23) underwent 18 F-Florzolotau tau PET, MRI, PSP Rating Scale (PSPRS), and Mini-Mental State Examination. Cerebellar gray matter (GM) and parametric estimation of reference signal intensity were used as references for tau burden measured by SUV ratios. Glymphatic activity was measured by diffusion tensor image analysis along the perivascular space (DTI-ALPS).

RESULTS

Parametric estimation of reference signal intensity is a better reference than cerebellar GM to distinguish tau burden between PSP and NCs. PSP patients showed higher cortical and subcortical tau SUV ratios than NCs ( P < 0.001 and <0.001). Cortical and subcortical tau deposition correlated with PSPRS, UPDRS, and Mini-Mental State Examination scores (all P 's < 0.05). Cortical tau deposition was further associated with the DTI-ALPS index and frontal-temporal-parietal GM atrophy. The DTI-ALPS indexes showed a significantly negative correlation with the PSPRS total scores ( P < 0.01). Finally, parietal and occipital lobe tau depositions showed mediating effects between the DTI-ALPS index and PSPRS score.

CONCLUSIONS

Cortical tau deposition is associated with glymphatic dysfunction and plays a role in mediating glymphatic dysfunction and clinical severity. Our results provide a possible explanation for the worsening of clinical severity in patients with PSP.

Histologic tau lesions and magnetic resonance imaging biomarkers differ across two progressive supranuclear palsy variants

Progressive supranuclear palsy is a neurodegenerative disease characterized by the deposition of four-repeat tau in neuronal and glial lesions in the brainstem, cerebellar, subcortical and cortical brain regions. There are varying clinical presentations of progressive supranuclear palsy with different neuroimaging signatures, presumed to be due to different topographical distributions and burden of tau. The classic Richardson syndrome presentation is considered a subcortical variant, whilst progressive supranuclear palsy with predominant speech and language impairment is considered a cortical variant, although the pathological underpinnings of these variants are unclear. In this case-control study, we aimed to determine whether patterns of regional tau pathology differed between these variants and whether tau burden correlated with neuroimaging. Thirty-three neuropathologically confirmed progressive supranuclear palsy patients with either the Richardson syndrome (n = 17) or speech/language (n = 16) variant and ante-mortem magnetic resonance imaging were included. Tau lesion burden was semi-quantitatively graded in cerebellar, brainstem, subcortical and cortical regions and combined to form neuronal and glial tau scores. Regional magnetic resonance imaging volumes were converted to Z-scores using 33 age- and sex-matched controls. Diffusion tensor imaging metrics, including fractional anisotropy and mean diffusivity, were calculated. Tau burden and neuroimaging metrics were compared between groups and correlated using linear regression models. Neuronal and glial tau burden were higher in motor and superior frontal cortices in the speech/language variant. In the subcortical and brainstem regions, only the glial tau burden differed, with a higher burden in globus pallidus, subthalamic nucleus, substantia nigra and red nucleus in Richardson's syndrome. No differences were observed in the cerebellar dentate and striatum. Greater volume loss was observed in the motor cortex in the speech/language variant and in the subthalamic nucleus, red nucleus and midbrain in Richardson's syndrome. Fractional anisotropy was lower in the midbrain and superior cerebellar peduncle in Richardson's syndrome. Mean diffusivity was greater in the superior frontal cortex in the speech/language variant and midbrain in Richardson's syndrome. Neuronal tau burden showed associations with volume loss, lower fractional anisotropy and higher mean diffusivity in the superior frontal cortex, although these findings did not survive correction for multiple comparisons. Results suggest that a shift in the distribution of tau, particularly neuronal tau, within the progressive supranuclear palsy network of regions is driving different clinical presentations in progressive supranuclear palsy. The possibility of different disease epicentres in these clinical variants has potential implications for the use of imaging biomarkers in progressive supranuclear palsy.

Distinct spatiotemporal atrophy patterns in corticobasal syndrome are associated with different underlying pathologies

Objective

To identify imaging subtypes of the cortico-basal syndrome (CBS) based solely on a data-driven assessment of MRI atrophy patterns, and investigate whether these subtypes provide information on the underlying pathology.

Methods

We applied Subtype and Stage Inference (SuStaIn), a machine learning algorithm that identifies groups of individuals with distinct biomarker progression patterns, to a large cohort of 135 CBS cases (52 had a pathological or biomarker defined diagnosis) and 252 controls. The model was fit using volumetric features extracted from baseline T1-weighted MRI scans and validated using follow-up MRI. We compared the clinical phenotypes of each subtype and investigated whether there were differences in associated pathology between the subtypes.

Results

SuStaIn identified two subtypes with distinct sequences of atrophy progression; four-repeat-tauopathy confirmed cases were most commonly assigned to the Subcortical subtype (83% of CBS-PSP and 75% of CBS-CBD), while CBS-AD was most commonly assigned to the Fronto-parieto-occipital subtype (81% of CBS-AD). Subtype assignment was stable at follow-up (98% of cases), and individuals consistently progressed to higher stages (100% stayed at the same stage or progressed), supporting the model's ability to stage progression.

Interpretation

By jointly modelling disease stage and subtype, we provide data-driven evidence for at least two distinct and longitudinally stable spatiotemporal subtypes of atrophy in CBS that are associated with different underlying pathologies. In the absence of sensitive and specific biomarkers, accurately subtyping and staging individuals with CBS at baseline has important implications for screening on entry into clinical trials, as well as for tracking disease progression.

Staging of progressive supranuclear palsy-Richardson syndrome using MRI brain charts for the human lifespan

Brain charts for the human lifespan have been recently proposed to build dynamic models of brain anatomy in normal aging and various neurological conditions. They offer new possibilities to quantify neuroanatomical changes from preclinical stages to death, where longitudinal MRI data are not available. In this study, we used brain charts to model the progression of brain atrophy in progressive supranuclear palsy-Richardson syndrome. We combined multiple datasets (n = 8170 quality controlled MRI of healthy subjects from 22 cohorts covering the entire lifespan, and n = 62 MRI of progressive supranuclear palsy-Richardson syndrome patients from the Four Repeat Tauopathy Neuroimaging Initiative (4RTNI)) to extrapolate lifetime volumetric models of healthy and progressive supranuclear palsy-Richardson syndrome brain structures. We then mapped in time and space the sequential divergence between healthy and progressive supranuclear palsy-Richardson syndrome charts. We found six major consecutive stages of atrophy progression: (i) ventral diencephalon (including subthalamic nuclei, substantia nigra, and red nuclei), (ii) pallidum, (iii) brainstem, striatum and amygdala, (iv) thalamus, (v) frontal lobe, and (vi) occipital lobe. The three structures with the most severe atrophy over time were the thalamus, followed by the pallidum and the brainstem. These results match the neuropathological staging of tauopathy progression in progressive supranuclear palsy-Richardson syndrome, where the pathology is supposed to start in the pallido-nigro-luysian system and spreads rostrally via the striatum and the amygdala to the cerebral cortex, and caudally to the brainstem. This study supports the use of brain charts for the human lifespan to study the progression of neurodegenerative diseases, especially in the absence of specific biomarkers as in PSP.

Clinical and neuroimaging features of the progressive supranuclear palsy- corticobasal degeneration continuum

PURPOSE OF REVIEW

The aim of this study was to discuss how recent work has increased our understanding of progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). The investigation of large and autopsy-confirmed cohorts, imaging modalities to assess different aspects of pathophysiology, clinical phenotypes and the application of advanced machine learning techniques, have led to recent advances in the field that will be discussed.

RECENT FINDINGS

Literature over the past 18 months will be discussed under the following themes: studies assessing how different neuroimaging modalities can improve the diagnosis of PSP and CBD from other neurodegenerative and parkinsonian disorders, including the investigation of pathological targets such as tau, iron, neuromelanin and dopamine and cholinergic systems; work improving our understanding of clinical, neuroanatomical and pathological heterogeneity in PSP and CBD; and work using advanced neuroimaging tools to investigate patterns of disease spread, as well as biological mechanisms potentially driving spread through the brain in PSP and CBD.

SUMMARY

The findings help improve the imaging-based diagnosis of PSP and CBD, allow more targeted prognostic estimates for patients accounting for phenotype or disease, and will aid in the development of appropriate and better-targeted disease biomarkers for clinical treatment trials.