A Study of Upper-Limb Motion using Kinematic Measures for Clinical Assessment of Cerebellar Ataxia.

Cerebellar Ataxia (CA) is a group of diseases affecting the cerebellum, which is responsible for movement coordination. It causes uncoordinated movements and can also impact balance, speech, and eye movements. There are no approved disease-modifying medications for CA, so clinical studies to assess potential treatments are crucial. These studies require robust, objective measurements of CA severity to reflect changes in the progression of the disease due to medication. In recent years, studies have used kinematic measures to evaluate CA severity, but the current method relies on subjective clinical observations and is insufficient for telehealth. There is a need for a non-intrusive system that can monitor people with CA regularly to better understand the disease and develop an automated assessment system. In this study, we analyzed kinematic measures of upper-limb movements during a ballistic tracking test, which primarily involves movements at the shoulder joint. We aimed to understand the challenges of identifying CA and evaluating its severity when measuring such movements. Statistical features of the kinematic signals were used to develop machine learning models for classification and regression. The Gradient Boosting Classifier model had a maximum accuracy of 74%, but the models had low specificity and performed poorly in regression, suggesting that kinematic measures from shoulder-dominated movements during ballistic tracking are not as viable for CA assessment as other measures.

Modified Recurrence Quantification Analysis for Objective Assessment of Cerebellar Ataxia.

Cerebellar Ataxia (CA) is a neurological condition that affects coordination, balance and speech. Assessing its severity is important for developing effective treatment and rehabilitation plans. Traditional assessment methods involve a clinician instructing a person with ataxia to perform tests and assigning a severity score based on their performance. However, this approach is subjective as it relies on the clinician's experience, and can vary between clinicians. To address this subjectivity, some researchers have developed automated assessment methods using signal processing and data-driven approaches, such as supervised machine learning. These methods still rely on subjective ground truth and can perform poorly in real-world scenarios. This research proposed an alternative approach that uses signal processing to modify recurrence plots and compare the severity of ataxia in a person with CA to a control cohort. The highest correlation score obtained was 0.782 on the back sensor with the feet-apart and eyes-open test. The contributions of the research include modifying the recurrence plot as a measurement tool for assessing CA severity, proposing a new approach to assess severity by comparing kinematic data between people with CA and a control reference group, and identifying the best subtest and sensor position for practical use in CA assessments.

A Bayesian Network Approach for Friedreich Ataxia Severity Classification using Probability Modelling.

Friedreich ataxia (FRDA) requires an objective measure of severity to overcome the shortcoming of clinical scales when applied to trials for treatments. This is hindered due to the rarity of the disease resulting in small datasets. Further, the published quantitative measures for ataxia do not incorporate or underutilise expert knowledge. Bayesian Networks (BNs) provide a structure to adopt both subjective and objective measures to give a severity value while addressing these issues. The BN presented in this paper uses a hybrid learning approach, which utilises both subjective clinical assessments as well as instrumented measurements of disordered upper body movement of individuals with FRDA. The final model's estimates gave a 0.93 Pearson correlation with low error, 9.42 root mean square error and 7.17 mean absolute error. Predicting the clinical scales gave 94% accuracy for Upright Stability and Lower Limb Coordination and 67% accuracy for Functional Staging, Upper Limb Coordination and Activities of Daily Living.Clinical relevance- Due to the nature of rare diseases conventional machine learning is difficult. Most clinical trials only generate small datasets. This approach allows the combination of expert knowledge with instrumented measures to develop a clinical decision support system for the prediction of severity.

A Novel Feature from Instrumented Utensils for Clinical Assessment of Friedreich Ataxia.

Friedreich Ataxia (FRDA) is an inherited disorder that affects the cerebellum and other regions of the human nervous system. It causes impaired movement that affects quality and reduces lifespan. Clinical assessment of movement is a key part of diagnosis and assessment of severity. Recent studies have examined instrumented measurement of movement to support clinical assessments. This paper presents a frequency domain approach based on Average Band Power (ABP) estimation for clinical assessment using Inertial Measurement Unit (IMU) signals. The IMUs were attached to a 3D printed spoon and a cup. Participants used them to mimic eating and drinking activities during data collection. For both activities, the ABP of frequency components from individuals with FRDA clustered in 0 to 0.2Hz band. This suggests that the ABP of this frequency is affected by FRDA irrespective of the device or activity. The ABP in this frequency band was used to distinguish between FRDA and non-ataxic participants using the Area Under the Receiver-Operating-Characteristic Curve (AUC) which produced peak values greater than 0.8. The machine learning models (logistic regression and neural networks) produced accuracy greater than 80% with these features common to both devices.

Machine Learning-Based Scoring System to Predict the Risk and Severity of Ataxic Speech Using Different Speech Tasks.

The assessment of speech in Cerebellar Ataxia (CA) is time-consuming and requires clinical interpretation. In this study, we introduce a fully automated objective algorithm that uses significant acoustic features from time, spectral, cepstral, and non-linear dynamics present in microphone data obtained from different repeated Consonant-Vowel (C-V) syllable paradigms. The algorithm builds machine-learning models to support a 3-tier diagnostic categorisation for distinguishing Ataxic Speech from healthy speech, rating the severity of Ataxic Speech, and nomogram-based supporting scoring charts for Ataxic Speech diagnosis and severity prediction. The selection of features was accomplished using a combination of mass univariate analysis and elastic net regularization for the binary outcome, while for the ordinal outcome, Spearman's rank-order correlation criterion was employed. The algorithm was developed and evaluated using recordings from 126 participants: 65 individuals with CA and 61 controls (i.e., individuals without ataxia or neurotypical). For Ataxic Speech diagnosis, the reduced feature set yielded an area under the curve (AUC) of 0.97 (95% CI 0.90-1), the sensitivity of 97.43%, specificity of 85.29%, and balanced accuracy of 91.2% in the test dataset. The mean AUC for severity estimation was 0.74 for the test set. The high C-indexes of the prediction nomograms for identifying the presence of Ataxic Speech (0.96) and estimating its severity (0.81) in the test set indicates the efficacy of this algorithm. Decision curve analysis demonstrated the value of incorporating acoustic features from two repeated C-V syllable paradigms. The strong classification ability of the specified speech features supports the framework's usefulness for identifying and monitoring Ataxic Speech.

At-Home Blood Pressure Measurements Provide Better Assessments of Orthostatic Hypotension in Parkinson's Disease.

Orthostatic hypotension (OH) is common in Parkinson's Disease (PD). It is intermittent, exacerbated by stressors including meals, medications, and dehydration, and frequently is unrecognized. Although intermittent, assessment is usually by a single "in clinic" BP measurement. This study examines whether 10 home measurements are more sensitive in detecting OH than a single "in clinic" measurement. Participants (44 people with PD and 16 controls) were instructed to measure lying and standing BP at home. BP was measured on five consecutive days upon waking and before bedtime. Symptoms were also assessed using the Movement Disorder Society United Parkinson's Disease Rating Scale and the Non-Motor Questionnaire. While a postural drop in systolic BP (≥20 mmHg) was recorded "in clinic" in thirteen of the forty-four PD participants, a postural drop was found in at least one of the ten home measurements in twenty-eight of the forty-four participants. Morning hypertension and variability in lying systolic BP was more common in these subjects than in those without a postural drop or the controls. A greater number of measurements of lying and standing BP are more likely to reveal orthostatic hypotension, variation in systolic BP, and hypertension than a single office measurement in people with PD.

Measurement of bradykinesia and chorea in Huntington's Disease using ambulatory monitoring.

Objectives: The feasibility of measuring bradykinesia and chorea in Huntington's Disease using a wearable sensor system (Parkinson's Kinetigraph: PKG) developed for measuring bradykinesia and dyskinesia in Parkinson's Disease was assessed. Methods: Unified Huntington's Disease Rating Scales (UHDRS) and a PKG were obtained for 25 people with Huntington's Disease. Bradykinesia and Chorea Score were derived from relevant sub-scores of the UHDRS and compared with the PKG's bradykinesia and dyskinesia scores. The PKG's daytime sleepiness score was also used. Results: There was good correlation between Chorea Scores and the PKG's dyskinesia score (Pearson's ρ = 0.66). Correlation between the Bradykinesia Scores and the PKG's bradykinesia score was also good (Pearson's ρ = 0.51) in cases whose PKG scores were in the normal or bradykinetic range. The PKG's bradykinesia score of 23, which is in the higher range of control subjects, separated participants into those with Independence Score ≥ 80 or < 80 and a Functional Assessment (FAS) score ≥ 18 or < 18. The PKG's daytime sleep score was high in 44 % of participants, whose average time asleep was 21 % compared to 1.6 % in participants with a normal sleep index. Participants with high sleep scores were significantly more likely to have low Independence and TFC scores. Conclusions: Measures of bradykinesia and dyskinesia from clinical scales have acceptable correlations with those from the PKG. Continuous monitoring provides information about daytime sleep, which was associated with lower functional status. Further studies and larger sample sizes are required to confirm these findings and the utility of this measure in Huntington's Disease.

Blockchained Federated Learning for Privacy and Security Preservation: Practical Example of Diagnosing Cerebellar Ataxia.

Cerebellar ataxia (CA) refers to the incoordination of movements of the eyes, speech, trunk, and limbs caused by cerebellar dysfunction. Conventional machine learning (ML) utilizes centralised databases to train a model of diagnosing CA. Despite the high accuracy, these approaches raise privacy concern as participants' data revealed in the data centre. Federated learning is an effective distributed solution to exchange only the ML model weight rather than the raw data. However, FL is also vulnerable to network attacks from malicious devices. In this study, we depict the concept of blockchained FL with individual's validators. We simulate the proposed approach with real-world dataset collected from kinematic sensors of CA individuals with four geographically separated clinics. Experimental results show the blockchained FL maintains competitive accuracy of 89.30%, while preserving both privacy and security.

Accuracy of Step Count Estimations in Parkinson's Disease Can Be Predicted Using Ambulatory Monitoring.

Objectives: There are concerns regarding the accuracy of step count in Parkinson's disease (PD) when wearable sensors are used. In this study, it was predicted that providing the normal rhythmicity of walking was maintained, the autocorrelation function used to measure step count would provide relatively low errors in step count. Materials and Methods: A total of 21 normal walkers (10 without PD) and 27 abnormal walkers were videoed while wearing a sensor [Parkinson's KinetiGraph (PKG)]. Median step count error rates were observed to be <3% in normal walkers but ≥3% in abnormal walkers. The simultaneous accelerometry data and data from a 6-day PKG were examined and revealed that the 5th percentile of the spectral entropy distribution, among 10-s walking epochs (obtained separately), predicted whether subjects had low error rate on step count with reference to the manual step count from the video recording. Subjects with low error rates had lower Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS III) scores and UPDRS III Q10-14 scores than the high error rate counterparts who also had high freezing of gait scores (i.e., freezing of gait questionnaire). Results: Periods when walking occurred were identified in a 6-day PKG from 190 non-PD subjects aged over 60, and 155 people with PD were examined and the 5th percentile of the spectral entropy distribution, among 10-s walking epochs, was extracted. A total of 84% of controls and 72% of people with PD had low predicted error rates. People with PD with low bradykinesia scores (measured by the PKG) had step counts similar to controls, whereas those with high bradykinesia scores had step counts similar to those with high error rates. On subsequent PKGs, step counts increased when bradykinesia was reduced by treatment and decreased when bradykinesia increased. Among both control and people with PD, low error rates were associated with those who spent considerable time making walks of more than 1-min duration. Conclusion: Using a measure of the loss of rhythmicity in walking appears to be a useful method for detecting the likelihood of error in step count. Bradykinesia in subjects with low predicted error in their step count is related to overall step count but when the predicted error is high, the step count should be assessed with caution.

Sensor Measurements Can Characterize Fluctuations and Wearing Off in Parkinson's Disease and Guide Therapy to Improve Motor, Non-motor and Quality of Life Scores.

Objectives: The aim was to examine the role of sensor measurement in identifying and managing fluctuations in bradykinesia of Parkinson's Disease. Method: Clinical scales and data from wearable sensors obtained before and after optimization of treatment from 107 participants who participated in a previous study was used. Fluctuators were identified by a levodopa response or wearing off in their sensor data and were subdivided according to whether the sensor's bradykinesia scores were in target range, representing acceptable bradykinesia for part of the dose (Controlled Fluctuator: n = 22) or above target for the whole dose period (Uncontrolled Fluctuator; n = 28). Uncontrolled Non-fluctuators (n = 24) were cases without a levodopa response or wearing-off and sensor bradykinesia scores above target throughout the day (un-controlled). Controlled Non-fluctuators (n = 33) were below target throughout the day (controlled) and used as a reference for good control (MDS-UPDRS III = 33 ± 8.6 and PDQ39 = 28 ± 18). Results: Treating Fluctuators significantly improved motor and quality of life scores. Converting fluctuators into Controlled Non-fluctuators significantly improved motor, non-motor and quality of life scores and a similar but less significant improvement was obtained by conversion to a Controlled Fluctuator. There was a significantly greater likelihood of achieving these changes when objective measurement was used to guide management. Conclusions: The sensor's classification of fluctuators bore a relation to severity of clinical scores and treatment of fluctuation improved clinical scores. The sensor measurement aided in recognizing and removing fluctuations with treatment and resulted in better clinical scores, presumably by assisting therapeutic decisions.

Federated Deep Learning for the Diagnosis of Cerebellar Ataxia: Privacy Preservation and Auto-Crafted Feature Extractor.

Cerebellar ataxia (CA) is concerned with the incoordination of movement caused by cerebellar dysfunction. Movements of the eyes, speech, trunk, and limbs are affected. Conventional machine learning approaches utilizing centralised databases have been used to objectively diagnose and quantify the severity of CA. Although these approaches achieved high accuracy, large scale deployment will require large clinics and raises privacy concerns. In this study, we propose an image transformation-based approach to leverage the advantages of state-of-the-art deep learning with federated learning in diagnosing CA. We use motion capture sensors during the performance of a standard neurological balance test obtained from four geographically separated clinics. The recurrence plot, melspectrogram, and poincaré plot are three transformation techniques explored. Experimental results indicate that the recurrence plot yields the highest validation accuracy (86.69%) with MobileNetV2 model in diagnosing CA. The proposed scheme provides a practical solution with high diagnosis accuracy, removing the need for feature engineering and preserving data privacy for a large-scale deployment.

Instrumented Objective Clinical Examination of Cerebellar Ataxia: the Upper and Lower Limb-a Review.

Cerebellar dysfunction results in impairments in co-ordination or 'ataxia'. Bedside examination of cerebellar function has changed little since the early nineteenth century with the exception being the oculomotor examination which has become instrumented. Otherwise, competence and confidence in performing the clinical assessment relies heavily on the skill and experience of the clinician. Potentially, instrumented objective measurement will more accurately assess the severity of ataxia and the changes brought about by advancing therapies in pharmaceutical trials and in rehabilitation intervention. This study describes instrumented versions of several bedside tests of cerebellar function, including rhythmic tapping of the hand (RTH), finger-nose test (FNT), dysdiadochokinesia (DDK), ramp tracking (RMT), ballistic tracking (BT), rhythmic tapping of the foot (RTF) and the heel shin (HST) examination which were validated against scores from Ataxia Rating Scales (ARS) such as the Scale of Assessment and Rating of Ataxia (SARA). While all of the instrumented tests accurately distinguished between ataxic subjects and controls, there was a difference in performance, with the best four performing upper limb tests being RTH, FNT, DDK and BT. A combination of BT plus RTH provided the best correlation with the SARA and outperformed a combination of all the bedside tests (Spearman 0.8; p < 0.001 compared to 0.68; p < 0.001 for the combined set) in identifying the presence and severity of ataxia. This indicates that there is redundancy in the information provided by the bedside tests and that adding other tests to BT plus RTH does not add accuracy to the assessment of ataxia. This analysis highlighted the need for metrics that could be generalised to each of the assessments of ataxia, so, in turn, domains of stability, timing, accuracy and rhythmicity (STAR domains) were developed and compared to the SARA. The STAR criteria could potentially influence the future of instrumented assessment in CA and pave the way for further research into the objective measurement of the cerebellar examination.

Diagnosis Cerebellar Ataxia using Deep Learning with Time Series Transformed Image.

Cerebellar ataxia (CA) is defined by disrupted coordination of movement suffering from disease of the cerebellum. It reflects fragmented movements of the eyes, vocal, upper limbs, balance, gait, and lower limbs. This study aims to use a motion sensor to form a simple yet effective CA quantitative assessment framework. We suggest a pendant device to use a single kinematic sensor attached to the wearer's chest to investigate the balance capability. Via a standard neurological test (Romberg's standing), the device may reveal an early symptom of Cerebellar Ataxia tailoring toward rehabilitation or therapeutic program. We adopt a transformed-image based approach to leverage the advantage of state-of-the-art deep learning models into diagnosis CA. Three transform techniques are employed including recurrence plot, melspectrogram, and Poincaré plot. Experiment results show that melspectrogram transform technique performs best in implementation with MobileNetV2 to diagnose CA with an average validation accuracy of 89.99%.

Objective Assessment of Progression and Disease Characterization of Friedreich Ataxia via an Instrumented Drinking Cup: Preliminary Results.

The monitoring of disease progression in certain neurodegenerative conditions can significantly be quantified with the help of objective assessments. The severity assessment of diseases like Friedreich ataxia (FRDA) are usually based on different subjective measures. The ability of a participant with FRDA to perform standard neurological tests is the most common way of assessing disease progression. In this feasibility study, an Ataxia Instrumented Measurement-Cup (AIM-C) is proposed to quantify the disease progression of 10 participants (mean age 39 years, onset of disease 16.3 years) in longitudinal timepoints. The device consists of a sensing system with the provision of extracting both kinetic and kinematic information while engaging in an activity closely associated with activities of daily living (ADL). A common functional task of simulated drinking was used to capture features that possesses disease progression information as well as certain other features which intrinsically correlate with commonly used clinical scales such as the modified Friedreich Ataxia Rating Scale (mFARS), the Functional Staging of Ataxia score and the ADL scale. Frequency and time-frequency domain features allowed the longitudinal assessment of participants with FRDA. Furthermore, both kinetic and kinematic measures captured clinically relevant features and correlated 85% with clinical assessments.

Changes in the Gut Microbiome and Predicted Functional Metabolic Effects in an Australian Parkinson's Disease Cohort.

Background: There has been increasing recognition of the importance of the gut microbiome in Parkinson's disease (PD), but the influence of geographic location has received little attention. The present study characterized the gut microbiota and associated changes in host metabolic pathways in an Australian cohort of people with PD (PwP). Methods: The study involved recruitment and assessment of 87 PwP from multiple Movement Disorders Clinics in Australia and 47 healthy controls. Illumina sequencing of the V3 and V4 regions of the 16S rRNA gene was used to distinguish inter-cohort differences in gut microbiota; KEGG analysis was subsequently performed to predict functional changes in host metabolic pathways. Results: The current findings identified significant differences in relative abundance and diversity of microbial operational taxonomic units (OTUs), and specific bacterial taxa between PwP and control groups. Alpha diversity was significantly reduced in PwP when compared to controls. Differences were found in two phyla (Synergistetes and Proteobacteria; both increased in PwP), and five genera (Colidextribacter, Intestinibacter, Kineothrix, Agathobaculum, and Roseburia; all decreased in PwP). Within the PD cohort, there was no association identified between microbial composition and gender, constipation or use of gastrointestinal medication. Furthermore, KEGG analysis identified 15 upregulated and 11 downregulated metabolic pathways which were predicted to be significantly altered in PwP. Conclusion: This study provides the first comprehensive characterization of the gut microbiome and predicted functional metabolic effects in a southern hemisphere PD population, further exploring the possible mechanisms whereby the gut microbiota may exert their influence on this disease, and providing evidence for the incorporation of such data in future individualized therapeutic strategies.

A six-metabolite panel as potential blood-based biomarkers for Parkinson's disease.

Characterisation and diagnosis of idiopathic Parkinson's disease (iPD) is a current challenge that hampers both clinical assessment and clinical trial development with the potential inclusion of non-PD cases. Here, we used a targeted mass spectrometry approach to quantify 38 metabolites extracted from the serum of 231 individuals. This cohort is currently one of the largest metabolomic studies including iPD patients, drug-naïve iPD, healthy controls and patients with Alzheimer's disease as a disease-specific control group. We identified six metabolites (3-hydroxykynurenine, aspartate, beta-alanine, homoserine, ornithine (Orn) and tyrosine) that are significantly altered between iPD patients and control participants. A multivariate model to predict iPD from controls had an area under the curve (AUC) of 0.905, with an accuracy of 86.2%. This panel of metabolites may serve as a potential prognostic or diagnostic assay for clinical trial prescreening, or for aiding in diagnosing pathological disease in the clinic.

A method for measuring time spent in bradykinesia and dyskinesia in people with Parkinson's disease using an ambulatory monitor.

BACKGROUND: Fluctuations in motor function in Parkinson's Disease (PD) are frequent and cause significant disability. Frequently device assisted therapies are required to treat them. Currently, fluctuations are self-reported through diaries and history yet frequently people with PD do not accurately identify and report fluctuations. As the management of fluctuations and the outcomes of many clinical trials depend on accurately measuring fluctuations a means of objectively measuring time spent with bradykinesia or dyskinesia would be important. The aim of this study was to present a system that uses wearable sensors to measure the percentage of time that bradykinesia or dyskinesia scores are above a target as a means for assessing levels of treatment and fluctuations in PD. METHODS: Data in a database of 228 people with Parkinson's Disease and 157 control subjects, who had worn the Parkinson's Kinetigraph ((PKG, Global Kinetics Corporation™, Australia) and scores from the Unified Parkinson's Disease Rating Scale (UPDRS) and other clinic scales were used. The PKG's provided score for bradykinesia and dyskinesia every two minutes and these were compared to a previously established target range representing a UPDRS III score of 35. The proportion of these scores above target over the 6 days that the PKG was worn were used to derive the percent time in bradykinesia (PTB) and percent time in dyskinesia (PTD). As well, a previously describe algorithm for estimating the amplitude of the levodopa response was used to determine whether a subject was a fluctuator or non-fluctuator. RESULTS: Using this approach, a normal range of PTB and PTD based on Control subject was developed. The level of PTB and PTD experienced by people with PD was compared with their levels of fluctuation. There was a correlation (Pearson's ρ = 0.4) between UPDRS II scores and PTB: the correlation between Parkinson Disease Questionnaire scores and UPDRS Total scores and PTB and slightly lower. PTB and PTD fell in response to treatment for bradykinesia or dyskinesia (respectively) with greater sensitivity than clinical scales. CONCLUSIONS: This approach provides an objective assessment of the severity of fluctuations in Parkinson's Disease that could be used in in clinical trials and routine care.

Elevated HDL Levels Linked to Poorer Cognitive Ability in Females With Parkinson's Disease.

INTRODUCTION: Cholesterol levels have been associated with age-related cognitive decline, however, such an association has not been comprehensively explored in people with Parkinson's disease (PD). To address this uncertainty, the current cross-sectional study examined the cholesterol profile and cognitive performance in a cohort of PD patients. METHODS: Cognitive function was evaluated using two validated assessments (ACE-R and SCOPA-COG) in 182 people with PD from the Australian Parkinson's Disease Registry. Total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and Triglyceride (TRG) levels were examined within this cohort. The influence of individual lipid subfractions on domain-specific cognitive performance was investigated using covariate-adjusted generalised linear models. RESULTS: Females with PD exhibited significantly higher lipid subfraction levels (TC, HDL, and LDL) when compared to male counterparts. While accounting for covariates, HDL levels were strongly associated with poorer performance across multiple cognitive domains in females but not males. Conversely, TC and LDL levels were not associated with cognitive status in people with PD. CONCLUSION: Higher serum HDL associates with poorer cognitive function in females with PD and presents a sex-specific biomarker for cognitive impairment in PD.

Quantitative Assessment of Friedreich Ataxia via Self-Drinking Activity.

Effective monitoring of the progression of neurodegenerative conditions can be significantly improved by objective assessments. Clinical assessments of conditions such as Friedreich's Ataxia (FA), currently rely on subjective measures commonly practiced in clinics as well as the ability of the affected individual to perform conventional tests of the neurological examination. In this study, we propose an ataxia measuring device, in the form of a pressure canister capable of sensing certain kinetic and kinematic parameters of interest to quantify the impairment levels of participants particularly when engaged in an activity that is closely associated with daily living. In particular, the functional task of simulated drinking was utilised to capture characteristic features of disability manifestation in terms of diagnosis (separation of individuals with FA and controls) and severity assessment of individuals diagnosed with the debilitating condition of FA. Time and frequency domain analysis of these biomarkers enabled the classification of individuals with FA and control subjects to reach an accuracy of 98% and a correlation level reaching 96% with the clinical scores.

Differential effects of sex on longitudinal patterns of cognitive decline in Parkinson's disease.

BACKGROUND: Cognitive impairment is an important and diverse symptom of Parkinson's disease (PD). Sex is a purported risk variable for cognitive decline in PD, but has not been comprehensively investigated. OBJECTIVES: This cross-sectional and longitudinal study examined sex differences in global and domain-specific cognitive performance in a large PD cohort. METHODS: Cognitive function was evaluated using the Addenbrooke's Cognitive Examination in 392 people with PD (PwP) from the Australian Parkinson's Disease Registry. The influence of sex on domain-specific cognitive performance was investigated using covariate-corrected generalised linear models. In a repeated measures longitudinal subset of 127 PwP, linear mixed models were used to assess the impact of sex on cognition over time, while accounting for covariates. RESULTS: Cross-sectional-corrected modelling revealed that sex was significantly predictive of cognitive performance, with males performing worse than females on global cognition, and memory and fluency domains. Longitudinally, sex was significantly predictive of cognitive decline, with males exhibiting a greater reduction in global cognition and language, whereas females showed a greater decline in attention/orientation, memory and visuospatial domains, despite starting with higher baseline scores. At follow-up, a significantly higher proportion of males than females fulfilled criteria for mild cognitive impairment or PD dementia. CONCLUSIONS: Sex was revealed as a significant determinant of overall cognitive performance as well as specific cognitive domains, with a differential pattern of decline in male and female participants. Such sex-specific findings appear to explain some of the heterogeneity observed in PD, warranting further investigation of mechanisms underlying this sexual dimorphism.