Multicenter Validation of Metabolic Abnormalities Related to PSP According to the MDS-PSP Criteria

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.

Radiomics-Guided Deep Learning Networks Classify Differential Diagnosis of Parkinsonism

The differential diagnosis between atypical Parkinsonian syndromes may be challenging and critical. We aimed to proposed a radiomics-guided deep learning (DL) model to discover interpretable DL features and further verify the proposed model through the differential diagnosis of Parkinsonian syndromes. We recruited 1495 subjects for 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scanning, including 220 healthy controls and 1275 patients diagnosed with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA), or progressive supranuclear palsy (PSP). Baseline radiomics and two DL models were developed and tested for the Parkinsonian diagnosis. The DL latent features were extracted from the last layer and subsequently guided by radiomics. The radiomics-guided DL model outperformed the baseline radiomics approach, suggesting the effectiveness of the DL approach. DenseNet showed the best diagnosis ability (sensitivity: 95.7%, 90.1%, and 91.2% for IPD, MSA, and PSP, respectively) using retained DL features in the test dataset. The retained DL latent features were significantly associated with radiomics features and could be interpreted through biological explanations of handcrafted radiomics features. The radiomics-guided DL model offers interpretable high-level abstract information for differential diagnosis of Parkinsonian disorders and holds considerable promise for personalized disease monitoring.

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.

Levodopa modulates semantic fluency and uniqueness in non-demented patients with progressive supranuclear palsy

INTRODUCTION

Semantic fluency is the ability to name items from a given category within a limited time, which relies on semantic knowledge, working memory, and executive function. Similar to patients with Parkinson's disease (PD), patients with progressive supranuclear palsy (PSP) scored lower than healthy adults in the well-established semantic fluency test. However, it is unclear how unique are the produced words. This study examined the relationship between semantic fluency and words' uniqueness in patients with PSP.

METHODS

Twenty-seven patients with PSP Richardson's syndrome (PSP-RS), 37 patients with PD, and 41 healthy controls (HC) performed a standard semantic fluency test (animals), and their verbal responses were audio-recorded. We used the uniqueness to reflect the ability to produce both original and effective work, that is, creativity.

RESULTS

The PSP-RS group produced fewer correct words and fewer unique words than the PD and HC groups. Moreover, the correlation between fluency and uniqueness was positive in the HC and PD groups but negative in the PSP-RS group. Importantly, the actual levodopa dose was positively correlated with the fluency but negatively correlated with the uniqueness in PSP-RS. The PSP-RS patients who took a greater dose of levodopa tended to produce more correct words but fewer unique words.

CONCLUSIONS

These results suggested that levodopa may modulate semantic fluency and uniqueness in the early stages of PSP-RS.

Subspace corrected relevance learning with application in neuroimaging

In machine learning, data often comes from different sources, but combining them can introduce extraneous variation that affects both generalization and interpretability. For example, we investigate the classification of neurodegenerative diseases using FDG-PET data collected from multiple neuroimaging centers. However, data collected at different centers introduces unwanted variation due to differences in scanners, scanning protocols, and processing methods. To address this issue, we propose a two-step approach to limit the influence of center-dependent variation on the classification of healthy controls and early vs. late-stage Parkinson's disease patients. First, we train a Generalized Matrix Learning Vector Quantization (GMLVQ) model on healthy control data to identify a "relevance space" that distinguishes between centers. Second, we use this space to construct a correction matrix that restricts a second GMLVQ system's training on the diagnostic problem. We evaluate the effectiveness of this approach on the real-world multi-center datasets and simulated artificial dataset. Our results demonstrate that the approach produces machine learning systems with reduced bias - being more specific due to eliminating information related to center differences during the training process - and more informative relevance profiles that can be interpreted by medical experts. This method can be adapted to similar problems outside the neuroimaging domain, as long as an appropriate "relevance space" can be identified to construct the correction matrix.

Differentiating between common PSP phenotypes using structural MRI: a machine learning study

BACKGROUND

Differentiating Progressive supranuclear palsy-Richardson's syndrome (PSP-RS) from PSP-Parkinsonism (PSP-P) may be extremely challenging. In this study, we aimed to distinguish these two PSP phenotypes using MRI structural data.

METHODS

Sixty-two PSP-RS, 40 PSP-P patients and 33 control subjects were enrolled. All patients underwent brain 3 T-MRI; cortical thickness and cortical/subcortical volumes were extracted using Freesurfer on T1-weighted images. We calculated the automated MR Parkinsonism Index (MRPI) and its second version including also the third ventricle width (MRPI 2.0) and tested their classification performance. We also employed a Machine learning (ML) classification approach using two decision tree-based algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) with different combinations of structural MRI data in differentiating between PSP phenotypes.

RESULTS

MRPI and MRPI 2.0 had AUC of 0.88 and 0.81, respectively, in differentiating PSP-RS from PSP-P. ML models demonstrated that the combination of MRPI and volumetric/thickness data was more powerful than each feature alone. The two ML algorithms showed comparable results, and the best ML model in differentiating between PSP phenotypes used XGBoost with a combination of MRPI, cortical thickness and subcortical volumes (AUC 0.93 ± 0.04). Similar performance (AUC 0.93 ± 0.06) was also obtained in a sub-cohort of 59 early PSP patients.

CONCLUSION

The combined use of MRPI and volumetric/thickness data was more accurate than each MRI feature alone in differentiating between PSP-RS and PSP-P. Our study supports the use of structural MRI to improve the early differential diagnosis between common PSP phenotypes, which may be relevant for prognostic implications and patient inclusion in clinical trials.

Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease

Network analysis of functional brain scans acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET, to map cerebral glucose metabolism), or resting-state functional magnetic resonance imaging (rs-fMRI, to map blood oxygen level-dependent brain activity) has increasingly been used to identify and validate reproducible circuit abnormalities associated with neurodegenerative disorders such as Parkinson's disease (PD). In addition to serving as imaging markers of the underlying disease process, these networks can be used singly or in combination as an adjunct to clinical diagnosis and as a screening tool for therapeutics trials. Disease networks can also be used to measure rates of progression in natural history studies and to assess treatment responses in individual subjects. Recent imaging studies in PD subjects scanned before and after treatment have revealed therapeutic effects beyond the modulation of established disease networks. Rather, other mechanisms of action may be at play, such as the induction of novel functional brain networks directly by treatment. To date, specific treatment-induced networks have been described in association with novel interventions for PD such as subthalamic adeno-associated virus glutamic acid decarboxylase (AAV2-GAD) gene therapy, as well as sham surgery or oral placebo under blinded conditions. Indeed, changes in the expression of these networks with treatment have been found to correlate consistently with clinical outcome. In aggregate, these attributes suggest a role for functional brain networks as biomarkers in future clinical trials.

Automatic covariance pattern analysis outperforms visual reading of 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) in variant progressive supranuclear palsy

BACKGROUND

To date, studies on positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) in progressive supranuclear palsy (PSP) usually included PSP cohorts overrepresenting patients with Richardson's syndrome (PSP-RS).

OBJECTIVES

To evaluate FDG-PET in a patient sample representing the broad phenotypic PSP spectrum typically encountered in routine clinical practice.

METHODS

This retrospective, multicenter study included 41 PSP patients, 21 (51%) with RS and 20 (49%) with non-RS variants of PSP (vPSP), and 46 age-matched healthy controls. Two state-of-the art methods for the interpretation of FDG-PET were compared: visual analysis supported by voxel-based statistical testing (five readers) and automatic covariance pattern analysis using a predefined PSP-related pattern.

RESULTS

Sensitivity and specificity of the majority visual read for the detection of PSP in the whole cohort were 74% and 72%, respectively. The percentage of false-negative cases was 10% in the PSP-RS subsample and 43% in the vPSP subsample. Automatic covariance pattern analysis provided sensitivity and specificity of 93% and 83% in the whole cohort. The percentage of false-negative cases was 0% in the PSP-RS subsample and 15% in the vPSP subsample.

CONCLUSIONS

Visual interpretation of FDG-PET supported by voxel-based testing provides good accuracy for the detection of PSP-RS, but only fair sensitivity for vPSP. Automatic covariance pattern analysis outperforms visual interpretation in the detection of PSP-RS, provides clinically useful sensitivity for vPSP, and reduces the rate of false-positive findings. Thus, pattern expression analysis is clinically useful to complement visual reading and voxel-based testing of FDG-PET in suspected PSP. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Sporadic Creutzfeldt-Jakob disease is associated with reorganization of metabolic connectivity in a pathological brain network

BACKGROUND AND PURPOSE

Although sporadic Creutzfeldt-Jakob disease (sCJD) is a rare cause of dementia, it is critical to understand its functional networks as the prion protein spread throughout the brain may share similar mechanisms with other more common neurodegenerative disorders. In this study, the metabolic brain network associated with sCJD was investigated and its internal network organization was explored.

METHODS

We explored 2-[¹⁸ F]fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) brain scans of 29 sCJD patients, 56 normal controls (NCs) and 46 other dementia patients from two independent centers. sCJD-related pattern (CJDRP) was identified in a cohort of 16 pathologically proven sCJD patients and 16 age-matched NCs using scaled subprofile modeling/principal component analysis and was prospectively validated in an independent cohort of 13 sCJD patients and 20 NCs. The pattern's specificity was tested on other dementia patients and its clinical relevance by clinical correlations. The pattern's internal organization was further studied using graph theory methods.

RESULTS

The CJDRP was characterized by relative hypometabolism in the bilateral caudate, thalami, middle and superior frontal gyri, parietal lobe and posterior cingulum in association with relative hypermetabolism in the hippocampi, parahippocampal gyri and cerebellum. The pattern's expression significantly discriminated sCJD from NCs and other dementia patients (p < 0.005; receiver operating characteristic analysis CJD vs. NCs area under the curve [AUC] 0.90-0.96, sCJD vs. Alzheimer's disease AUC 0.78, sCJD vs. behavioral variant of frontotemporal dementia AUC 0.84). The pattern's expression significantly correlated with cognitive, functional decline and disease duration. The metabolic connectivity analysis revealed inefficient information transfer with specific network reorganization.

CONCLUSIONS

The CJDRP is a robust metabolic biomarker of sCJD. Due to its excellent clinical correlations it has the potential to monitor disease in emerging disease-modifying trials.

Functional brain networks in the evaluation of patients with neurodegenerative disorders

Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.

Stereotyped Relationship Between Motor and Cognitive Metabolic Networks in Parkinson's Disease

BACKGROUND

Idiopathic Parkinson's disease (iPD) is associated with two distinct brain networks, PD-related pattern (PDRP) and PD-related cognitive pattern (PDCP), which correlate respectively with motor and cognitive symptoms. The relationship between the two networks in individual patients is unclear.

OBJECTIVE

To determine whether a consistent relationship exists between these networks, we measured the difference between PDRP and PDCP expression, termed delta, on an individual basis in independent populations of patients with iPD (n = 356), patients with idiopathic REM sleep behavioral disorder (iRBD) (n = 21), patients with genotypic PD (gPD) carrying GBA1 variants (n = 12) or the LRRK2-G2019S mutation (n = 14), patients with atypical parkinsonian syndromes (n = 238), and healthy control subjects (n = 95) from the United States, Slovenia, India, and South Korea.

METHODS

We used [18 F]-fluorodeoxyglucose positron emission tomography and resting-state fMRI to quantify delta and to compare the measure across samples; changes in delta over time were likewise assessed in longitudinal patient samples. Lastly, we evaluated delta in prodromal individuals with iRBD and subjects with gPD.

RESULTS

Delta was abnormally elevated in each of the four iPD samples (P < 0.05), as well as in the at-risk iRBD group (P < 0.05), with increasing values over time (P < 0.001). PDRP predominance was also present in gPD, with higher values in patients with GBA1 variants compared with the less aggressive LRRK2-G2019S mutation (P = 0.005). This trend was not observed in patients with atypical parkinsonian syndromes, who were accurately discriminated from iPD based on PDRP expression and delta (area under the curve = 0.85; P < 0.0001).

CONCLUSIONS

PDRP predominance, quantified by delta, assays the spread of dysfunction from motor to cognitive networks in patients with PD. Delta may therefore aid in differential diagnosis and in tracking disease progression in individual patients. © 2022 International Parkinson and Movement Disorder Society.

Molecular Imaging in Parkinsonian Disorders—What’s New and Hot?

Highlights

Abstract

Neurodegenerative parkinsonian disorders are characterized by a great diversity of clinical symptoms and underlying neuropathology, yet differential diagnosis during lifetime remains probabilistic. Molecular imaging is a powerful method to detect pathological changes in vivo on a cellular and molecular level with high specificity. Thereby, molecular imaging enables to investigate functional changes and pathological hallmarks in neurodegenerative disorders, thus allowing to better differentiate between different forms of degenerative parkinsonism, improve the accuracy of the clinical diagnosis and disentangle the pathophysiology of disease-related symptoms. The past decade led to significant progress in the field of molecular imaging, including the development of multiple new and promising radioactive tracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) as well as novel analytical methods. Here, we review the most recent advances in molecular imaging for the diagnosis, prognosis, and mechanistic understanding of parkinsonian disorders. First, advances in imaging of neurotransmission abnormalities, metabolism, synaptic density, inflammation, and pathological protein aggregation are reviewed, highlighting our renewed understanding regarding the multiplicity of neurodegenerative processes involved in parkinsonian disorders. Consequently, we review the role of molecular imaging in the context of disease-modifying interventions to follow neurodegeneration, ensure stratification, and target engagement in clinical trials.

Progressive supranuclear palsy and corticobasal degeneration: novel clinical concepts and advances in biomarkers

BACKGROUND

Progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) are sporadic adult-onset primary tauopathies clinically classified among the atypical parkinsonian syndromes. They are intrinsically related with regard to their clinical features, pathology, biochemistry, and genetic risk factors.

OBJECTIVES

This review highlights the current knowledge on PSP and CBD, focusing on evolving clinical concepts, new diagnostic criteria, and advances in biomarkers.

METHODS

We performed a non-systematic literature review through the PubMed database. The search was restricted to articles written in English, published from 1964 to date.

RESULTS

Clinicopathologic and in vivo biomarkers studies have broadened PSP and CBD clinical phenotypes. They are now recognized as a range of motor and behavioral syndromes associated with underlying 4R-tauopathy neuropathology. The Movement Disorders Society PSP diagnostic criteria included clinical variants apart from the classical description, increasing diagnostic sensitivity. Meanwhile, imaging biomarkers have explored the complexity of symptoms and pathological processes related to corticobasal syndrome and CBD.

CONCLUSIONS

In recent years, several prospective or clinicopathologic studies have assessed clinical, radiological, and fluid biomarkers that have helped us gain a better understanding of the complexity of the 4R-tauopathies, mainly PSP and CBD.

A replication study, systematic review and meta-analysis of automated image-based diagnosis in parkinsonism

Differential diagnosis of parkinsonism early upon symptom onset is often challenging for clinicians and stressful for patients. Several neuroimaging methods have been previously evaluated; however specific routines remain to be established. The aim of this study was to systematically assess the diagnostic accuracy of a previously developed ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET) based automated algorithm in the diagnosis of parkinsonian syndromes, including unpublished data from a prospective cohort. A series of 35 patients prospectively recruited in a movement disorder clinic in Stockholm were assessed, followed by systematic literature review and meta-analysis. In our cohort, automated image-based classification method showed excellent sensitivity and specificity for Parkinson Disease (PD) vs. atypical parkinsonian syndromes (APS), in line with the results of the meta-analysis (pooled sensitivity and specificity 0.84; 95% CI 0.79-0.88 and 0.96; 95% CI 0.91 -0.98, respectively). In conclusion, FDG-PET automated analysis has an excellent potential to distinguish between PD and APS early in the disease course and may be a valuable tool in clinical routine as well as in research applications.

EANM procedure guidelines for brain PET imaging using [18F]FDG, version 3

The present procedural guidelines summarize the current views of the EANM Neuro-Imaging Committee (NIC). The purpose of these guidelines is to assist nuclear medicine practitioners in making recommendations, performing, interpreting, and reporting results of [¹⁸F]FDG-PET imaging of the brain. The aim is to help achieve a high-quality standard of [¹⁸F]FDG brain imaging and to further increase the diagnostic impact of this technique in neurological, neurosurgical, and psychiatric practice. The present document replaces a former version of the guidelines that have been published in 2009. These new guidelines include an update in the light of advances in PET technology such as the introduction of digital PET and hybrid PET/MR systems, advances in individual PET semiquantitative analysis, and current broadening clinical indications (e.g., for encephalitis and brain lymphoma). Further insight has also become available about hyperglycemia effects in patients who undergo brain [¹⁸F]FDG-PET. Accordingly, the patient preparation procedure has been updated. Finally, most typical brain patterns of metabolic changes are summarized for neurodegenerative diseases. The present guidelines are specifically intended to present information related to the European practice. The information provided should be taken in the context of local conditions and regulations.

Autoimmune cerebellar hypermetabolism: Report of three cases and literature overview

We report three cases of vermian cerebellar hypermetabolism in patients with autoimmune encephalitis. One of our patients was positive for anti-Ma2 antibodies and one for anti-Zic4 antibodies while the remaining patient did not present any known antibodies. The seronegative patient deteriorated after immune checkpoint inhibitor treatment for a pulmonary adenocarcinoma and improved with immunosuppressive drugs, which is in favour of an underlying autoimmune mechanism. They all presented with subacute neurological symptoms. Brain magnetic resonance imaging was normal except in one patient, where hyperintensities were present on FLAIR sequence around the third ventricle and the cerebral aqueduct. ¹⁸F-FDG brain positron emission tomography with computed tomography (¹⁸F-FDG PET-CT) demonstrated an unusual vermian cerebellar hypermetabolism in the three cases. While cerebellar hypermetabolism on ¹⁸F-FDG PET-CT has been described in various neurological diseases, such vermian - and more broadly cerebellar - hypermetabolism was seldom described in previous studies on autoimmune encephalitis. When differential diagnoses have been ruled out, this pattern may be of interest for the positive diagnosis of autoimmune encephalitis in difficult diagnostic cases.

Evolving concepts in progressive supranuclear palsy and other 4-repeat tauopathies

Tauopathies are classified according to whether tau deposits predominantly contain tau isoforms with three or four repeats of the microtubule-binding domain. Those in which four-repeat (4R) tau predominates are known as 4R-tauopathies, and include progressive supranuclear palsy, corticobasal degeneration, argyrophilic grain disease, globular glial tauopathies and conditions associated with specific MAPT mutations. In these diseases, 4R-tau deposits are found in various cell types and anatomical regions of the brain and the conditions share pathological, pathophysiological and clinical characteristics. Despite being considered 'prototype' tauopathies and, therefore, ideal for studying neuroprotective agents, 4R-tauopathies are still severe and untreatable diseases for which no validated biomarkers exist. However, advances in research have addressed the issues of phenotypic overlap, early clinical diagnosis, pathophysiology and identification of biomarkers, setting a road map towards development of treatments. New clinical criteria have been developed and large cohorts with early disease are being followed up in prospective studies. New clinical trial readouts are emerging and biomarker research is focused on molecular pathways that have been identified. Lessons learned from failed trials of neuroprotective drugs are being used to design new trials. In this Review, we present an overview of the latest research in 4R-tauopathies, with a focus on progressive supranuclear palsy, and discuss how current evidence dictates ongoing and future research goals.

"Parkinson's disease" on the way to progressive supranuclear palsy: a review on PSP-parkinsonism

Progressive supranuclear palsy (PSP) is a progressive atypical parkinsonian syndrome characterised by postural instability, supranuclear ophthalmoplegia, dysarthria, dysphagia, executive dysfunction and other features. This clinical presentation represents the classic PSP-Richardson syndrome (PSP-RS). However, several other clinical subtypes have been recognised, including PSP-parkinsonism (PSP-P), probably the second most common PSP variant. Unlike PSP-RS, PSP-P often presents with an asymmetric onset, tremor and a moderate initial response to levodopa, especially during the first years of the disease, thus resembling Parkinson's disease (PD). It runs a more favourable course, but over time, PSP-P may evolve clinically into PSP-RS. Therefore, it may seem that PSP-P stands clinically between PD and PSP. There are several peculiarities that can distinguish PSP-P from these entities. As there is lack of systematic reviews on PSP-P in the literature, we decided to summarise all the necessary data about the epidemiology, clinical picture, neuroimaging, genetics and other aspects of this PSP variant in order to provide complete information for the reader.

Molecular Imaging of Extrapyramidal Movement Disorders With Dementia: The 4R Tauopathies

Two pathologically distinct neurodegenerative conditions, progressive supranuclear palsy and corticobasal degeneration, share in common deposits of tau proteins that differ both molecularly and ultrastructurally from the common tau deposits diagnostic of Alzheimer disease. The proteinopathy in these disorders is characterized by fibrillary aggregates of 4R tau proteins. The clinical presentations of progressive supranuclear palsy and of corticobasal degeneration are often confused with more common disorders such as Parkinson disease or subtypes of frontotemporal lobar degeneration. Neither of these 4R tau disorders has effective therapy, and while there are emerging molecular imaging approaches to identify patients earlier in the course of disease, there are as yet no reliably sensitive and specific approaches to diagnoses in life. In this review, aspects of the clinical syndromes, neuropathology, and molecular biomarker imaging studies applicable to progressive supranuclear palsy and to corticobasal degeneration will be presented. Future development of more accurate molecular imaging approaches is proposed.

Cognitive and behavioral profile of progressive supranuclear palsy and its phenotypes

BACKGROUND

Although several progressive supranuclear palsy (PSP) phenotypes have recently been described, studies identifying cognitive and neuropsychiatric differences between them are lacking.

METHODS

An extensive battery of cognitive and behavioural assessments was administered to 63 PSP patients, 25 PD patients with similar sociodemographic characteristics, and 25 healthy controls. We analysed differences in phenomenology, frequency and severity of cognitive and neuropsychiatric symptoms between PSP, PD and HC, and between PSP subtypes.

RESULTS

Regarding phenotypes, 64.6% met criteria for Richardson's syndrome (PSP-RS), 10.7% PSP with predominant Parkinsonism (PSP-P), 10.7% with PSP progressive gait freezing (PSP-PGF), and 10.7% PSP with predominant speech/language disorder (PSP-SL). Impairment was more severe in the PSP group than in the PD and HC groups regarding motor scores, cognitive testing and neuropsychiatric scales. Cognitive testing did not clearly differentiate between PSP phenotypes, but PSP-RS and PSP-SL appeared to have more cognitive impairment than PSP-PGF and PSP-P, mainly due to an increased impairment in frontal executive domains. Regarding neuropsychiatric disturbances, no specific behavior was more common in any of the PSP subtypes.

CONCLUSION

Motor deficits delineate the phenotypes included in currently accepted MDS-PSP criteria. Cognition and behavioural disturbances are common in PSP and allow us to distinguish this disorder from other neurological diseases, but they do not differentiate between PSP phenotypes.