Influence of Multiple Sclerosis on Spatiotemporal Gait Parameters: A Systematic Review and Meta-Regression

Characterizing gait in people with multiple sclerosis using digital data from smartphone sensors: A proposed framework

BACKGROUND

Mobility assessment is essential for monitoring disease progression in people with multiple sclerosis (PwMS). Technologies such as wearable sensors show potential for this purpose, but consensus is needed to optimize collection and interpretation of digital measures in PwMS.

OBJECTIVE

To propose a framework for measuring and interpreting key aspects of impaired gait in PwMS using a smartphone worn at the waist level.

METHODS

The framework was developed on the basis of clinical understanding and knowledge of sensor signal processing, supported by a systematic literature review (SLR). The SLR targeted articles published after 2011 that measured gait characteristics in PwMS. Findings were used to propose standardized definitions for complementary gait domains and define digital measures that should be captured for each domain.

RESULTS

The resulting framework for PwMS recommends definitions for pace, rhythm, stability, symmetry, variability, smoothness, complexity and fatigability gait domains. For each domain, a set of digital measures is described with respect to their interpretability and associated caveats.

CONCLUSION

This framework provides recommendations for measuring complex gait patterns in PwMS using widely available technology. This work promotes the use of standardized gait domain definitions and harmonized descriptions of associated digital measures, paving the way for future validation efforts.

Decreased functional mobility in individuals with mild to moderate expanded disability status from relapsing multiple sclerosis: Analysis of the Glittre-ADL test

INTRODUCTION

Multiple sclerosis (MS) is a chronic inflammatory and autoimmune disease that significantly limits an individual's activities of daily living (ADLs) and negatively affects their social participation as it progresses. The impact of activities and participation must be continuously assessed, and the Glittre-ADL is a validated test for MS to assess functional capacity in tasks similar to ADLs.

OBJECTIVE

To evaluate whether the Glittre-ADL test is a valid method for assessing functional mobility in individuals with MS and moderate disability or those who use assistive devices.

METHODS

This cross-sectional study enrolled 30 individuals in two groups: 1) MS group (n = 15); and 2) healthy control group (n = 15). The MS group underwent three functional mobility tests: 1) Glittre-ADL; 2) Timed 25-Foot Walk (T25FWT); and 3) Timed Up and Go (TUG) while the healthy group underwent only the Glittre-ADL test.

RESULTS

An association was found between the Glittre-ADL time and T25FWT (r = 0.78, p < .001) and TUG (r = 0.56, p = .030) times. In the MS group, statistically significant differences were found in time (F = 2.88, p = .038) and speed (F = 5.17, p = .024) between laps. A statistically significant difference was observed between the total time in the MS and control groups (Area Under Curve - AUC: 0.982, p < .0001). A total time > 46.0s represents the reduction of functional performance during ADLs in individuals with MS (sensitivity: 93.3%; specificity: 92.2%).

CONCLUSION

The Glittre-ADL test is a valid tool for assessing functional mobility in individuals with MS and mild to moderate disability (EDSS score ≤ 6.5).

Are clinical tests and biomechanical measures of gait stability able to differentiate fallers from non-fallers in hereditary spastic paraplegia?

INTRODUCTION

Balance and gait impairments are common in people with hereditary spastic paraplegia (HSP) and often result in falls. Measures that identify patients at risk of falling are clinically relevant, but relatively unexplored in HSP. Here, we evaluated the potential of different balance and gait constructs to (1) identify differences between healthy controls and people with HSP and (2) discriminate between fallers and non-fallers with HSP.

METHODS

We included 33 people with pure-HSP and 15 healthy controls. We assessed balance confidence (six-item Activities-specific Balance Confidence scale), clinical balance capacity (Mini-Balance Evaluation Systems Test) and gait capacity (ten-meter Walk Test). Biomechanical measures included spatiotemporal gait variability, mediolateral Margin of Stability (MoS), Foot Placement Deviation (FPD), and Local Divergence Exponents (LDEs) of trunk and pelvis, derived from treadmill-walking at comfortable and fixed gait speeds. People with HSP logged their falls during a fifteen-week period and were categorized as 'faller' (≥1 fall) or 'non-faller'.

RESULTS

People with HSP had significantly lower balance confidence, balance capacity, and gait capacity compared to age-matched controls. People with HSP also showed reduced gait stability, reflected by increased spatiotemporal gait variability, FPD, and LDEs of trunk and pelvis. Overall, 44 % of people with HSP were categorized as 'faller'. Balance confidence (AUC:0.84) and balance capacity (AUC:0.75) discriminated fallers from non-fallers, whereas none of the biomechanical measures significantly differed.

CONCLUSION

Balance confidence, clinical balance and gait capacity, and biomechanical measures are affected in HSP, but clinical measures showed potential to differentiate fallers from non-fallers in people with HSP.

Actual and Imagined Music-Cued Gait Training in People with Multiple Sclerosis: A Double-Blind Randomized Parallel Multicenter Trial

BACKGROUND

Actual and imagined cued gait trainings have not been compared in people with multiple sclerosis (MS).

OBJECTIVE

To analyze the effects of cued motor imagery (CMI), cued gait training (CGT), and combined CMI and cued gait training (CMI-CGT) on motor, cognitive, and emotional functioning, and health-related quality of life in people with MS.

METHODS

In this double-blind randomized parallel-group multicenter trial, people with MS were randomized (1:1:1) to CMI, CMI-CGT, or CGT for 30 minutes, 4×/week for 4 weeks. Patients practiced at home, using recorded instructions, and supported by ≥6 phone calls. Data were collected at weeks 0, 4, and 13. Co-primary outcomes were walking speed and distance, analyzed by intention-to-treat. Secondary outcomes were global cognitive impairment, anxiety, depression, suicidality, fatigue, HRQoL, motor imagery ability, music-induced motivation, pleasure and arousal, self-efficacy, and cognitive function. Adverse events and falls were continuously monitored.

RESULTS

Of 1559 screened patients, 132 were randomized: 44 to CMI, 44 to CMI-CGT, and 44 to CGT. None of the interventions demonstrated superiority in influencing walking speed or distance, with negligible effects on walking speed (η2 = 0.019) and distance (η2 = 0.005) observed in the between-group comparison. Improvements in walking speed and walking distance over time corresponded to large effects for CMI, CMI-CGT, and CGT (η2 = 0.348 and η2 = 0.454 respectively). No severe study-related adverse events were reported.

CONCLUSIONS

CMI-GT did not lead to improved walking speed and distance compared with CMI and CGT alone in people with MS. Lack of a true control group represents a study limitation.

TRIAL REGISTRATION

German Clinical Trials Register, DRKS00023978.

Vision-based motion capture for the gait analysis of neurodegenerative diseases: A review

BACKGROUND

Developments in vision-based systems and human pose estimation algorithms have the potential to detect, monitor and intervene early on neurodegenerative diseases through gait analysis. However, the gap between the technology available and actual clinical practice is evident as most clinicians still rely on subjective observational gait analysis or objective marker-based analysis that is time-consuming.

RESEARCH QUESTION

This paper aims to examine the main developments of vision-based motion capture and how such advances may be integrated into clinical practice.

METHODS

The literature review was conducted in six online databases using Boolean search terms. A commercial system search was also included. A predetermined methodological criterion was then used to assess the quality of the selected articles.

RESULTS

A total of seventeen studies were evaluated, with thirteen studies focusing on gait classification systems and four studies on gait measurement systems. Of the gait classification systems, nine studies utilized artificial intelligence-assisted techniques, while four studies employed statistical techniques. The results revealed high correlations of gait features identified by classifier models with existing clinical rating scales. These systems demonstrated generally high classification accuracies and were effective in diagnosing disease severity levels. Gait measurement systems that extract spatiotemporal and kinematic joint information from video data generally found accurate measurements of gait parameters with low mean absolute errors, high intra- and inter-rater reliability.

SIGNIFICANCE

Low cost, portable vision-based systems can provide proof of concept for the quantification of gait, expansion of gait assessment tools, remote gait analysis of neurodegenerative diseases and a point of care system for orthotic evaluation. However, certain challenges, including small sample sizes, occlusion risks, and selection bias in training models, need to be addressed. Nevertheless, these systems can serve as complementary tools, equipping clinicians with essential gait information to objectively assess disease severity and tailor personalized treatment for enhanced patient care.

Complementary use of statistical parametric mapping and gait profile score to describe walking alterations in multiple sclerosis: a cross-sectional study

Gait analysis is often used to study locomotor alterations in people with multiple sclerosis (PwMS), but the large number of extracted variables challenges the interpretability. In this paper, we analysed gait alterations by combining the Gait Profile Score (GPS), which summarizes kinematic locomotor deviations, and Statistical Parametric Mapping (SPM), which compares kinematics and kinetics over the whole gait cycle. Eleven PwMS and 11 speed-matched Healthy Controls (HC) underwent overground gait analysis. GPS were compared through independent-samples t-tests; sagittal-plane kinematics and power at hip, knee, and ankle were compared through SPM Hotelling's-T2 and SPM t-tests. Spearman's correlation coefficients (r) between GPS and clinical outcomes were also calculated. PwMS had higher GPS than HC (PwMS = 8.74 ± 2.13°; HC = 5.01 ± 1.41°;p < 0.001). Multivariate SPM found statistically significant differences at 0-49%, 70-80%, and 93-99% of stride (p < 0.05) and univariate analysis showed reduced ankle dorsiflexion, and lower knee flexion during pre-swing and swing. GPS correlated with Expanded Disability Status Scale (r = 0.65; 95%C.I.[0.04,0.91]; p = 0.04) and 2-Minute Walking Test (r = -0.65; 95%C.I.[-0.91,-0.04]; p = 0.04). GPS in conjunction with SPM revealed multi-joint kinematic alterations on sagittal plane involving distal joint angles, ankle and knee, during the stance phase with no changes at the proximal level. Gait deviations were more pronounced in PwMS with higher disability and walking limitations.

Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors

Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading to difficulties in daily life and increased fall risk. Frequent assessment of dynamic balance and gait adaptability is therefore essential for monitoring the evolution of these impairments and/or the long-term effects of rehabilitation. The modified dynamic gait index (mDGI) is a validated clinical test specifically devoted to evaluating gait facets in clinical settings under a physiotherapist's supervision. The need of a clinical environment, consequently, limits the number of assessments. Wearable sensors are increasingly used to measure balance and locomotion in real-world contexts and may permit an increase in monitoring frequency. This study aims to provide a preliminary test of this opportunity by using nested cross-validated machine learning regressors to predict the mDGI scores of 95 PwND via inertial signals collected from short steady-state walking bouts derived from the 6-minute walk test. Four different models were compared, one for each pathology (multiple sclerosis, Parkinson's disease, and stroke) and one for the pooled multipathological cohort. Model explanations were computed on the best-performing solution; the model trained on the multipathological cohort yielded a median (interquartile range) absolute test error of 3.58 (5.38) points. In total, 76% of the predictions were within the mDGI's minimal detectable change of 5 points. These results confirm that steady-state walking measurements provide information about dynamic balance and gait adaptability and can help clinicians identify important features to improve upon during rehabilitation. Future developments will include training of the method using short steady-state walking bouts in real-world settings, analysing the feasibility of this solution to intensify performance monitoring, providing prompt detection of worsening/improvements, and complementing clinical assessments.

High intensity exercise training on functional outcomes in persons with multiple sclerosis: A systematic review

BACKGROUND

There is growing interest and evidence for high intensity training (HIT) in clinical populations, including persons with multiple sclerosis (MS). While HIT has been shown to be a safe modality in this group, it is still unclear what collective knowledge exists for HIT on functional outcomes. This study examined HIT modalities (e.g., aerobic, resistance, functional training) on functional outcomes such as walking, balance, postural control, and mobility in persons with MS.

METHODS

High intensity training studies, including RCTs and non-RCTs, that targeted functional outcomes in persons with MS were included in the review. A literature search was conducted in MEDLINE, EMBASE, PsycINFO, SPORTSDiscus, and CINAHL in April 2022. Other literature search methods were performed via website and citation searching. The methodological quality of included studies was assessed by TESTEX for RCTs and ROBINS-I for non-RCTs. This review synthesized the following data: study design and characteristics, participant characteristics, intervention characteristics, outcome measures, and effect sizes.

RESULTS

Thirteen studies (6 RCTs and 7 non-RCTs) were included in the systematic review. The included participants (N = 375) had varying functional levels (EDSS range: 0-6.5) and phenotypes (relapsing remitting, secondary progressive, primary progressive). HIT modalities involving high intensity aerobic training (n = 4), high intensity resistance training (n = 7), and high intensity functional training (n = 2), revealed a significant and consistent benefit on walking speed and walking endurance in response to HIT, while the evidence regarding balance and mobility improvement was less clear.

CONCLUSION

Persons with MS can successfully tolerate and adhere to HIT. While HIT appears to be an effective modality for improving some functional outcomes, the heterogeneous testing protocols, HIT modalities, and exercise doses among the studies preclude any conclusive evidence for its effectiveness thus necessitating future inquiry.

Virtual reality-based therapy improves balance and reduces fear of falling in patients with multiple sclerosis. a systematic review and meta-analysis of randomized controlled trials

OBJECTIVE

This study aims to conduct a meta-analysis to assess the effect of virtual reality-based therapy (VRBT) on balance dimensions and fear of falling in patients with multiple sclerosis (PwMS). Secondarily, to determine the most recommendable dose of VRBT to improve balance.

METHODS

PubMed Medline, Web of Science, Scopus, CINAHL and PEDro were screened, without publication date restrictions, until September 30th, 2021. Randomized controlled trials (RCTs) comparing the effectiveness of VRBT against other interventions in PwMS were included. Functional and dynamic balance, confidence of balance, postural control in posturography, fear of falling and gait speed were the variables assessed. A meta-analysis was performed by pooling the Cohen's standardized mean difference (SMD) with 95% confidence interval (95% CI) using Comprehensive Meta-Analysis 3.0.

RESULTS

Nineteen RCTs, reporting 858 PwMS, were included. Our findings reported that VRBT is effective in improving functional balance (SMD = 0.8; 95%CI 0.47 to 1.14; p < 0.001); dynamic balance (SMD = - 0.3; 95%CI - 0.48 to - 0.11; p = 0.002); postural control with posturography (SMD = - 0.54; 95%CI - 0.99 to - 0.1; p = 0.017); confidence of balance (SMD = 0.43; 95%CI 0.15 to 0.71; p = 0.003); and in reducing fear of falling (SMD = - 1.04; 95%CI - 2 to - 0.07; p = 0.035); but not on gait speed (SMD = - 0.11; 95%CI: - 0.35 to 0.14; p = 0.4). Besides, the most adequate dose of VRBT to achieve the greatest improvement in functional balance was at least 40 sessions, five sessions per week and 40-45 min per sessions; and for dynamic balance, it would be between 8 and 19 weeks, twice a week and 20-30 min per session.

CONCLUSION

VRBT may have a short-term beneficial role in improving balance and reducing fear of falling in PwMS.

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

Objective

Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases.

Methods

A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection.

Results

The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance.

Conclusions

This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

Spatiotemporal gait changes in people with multiple sclerosis with different disease progression subtypes

BACKGROUND

Gait impairment is common in people with multiple sclerosis (MS), but less is known about gait differences between MS disease progression subtypes. The objective here was to examine differences in spatiotemporal gait in MS and between relapsing-remitting and progressive subtypes during the timed-25-ft-walk test. Our specific aims were to investigate (1) spatiotemporal, (2) spatiotemporal variability, and (3) gait modulation differences between healthy controls and MS subtypes at preferred and fast walking speed.

METHODS

This study included 27 controls, 18 relapsing-remitting MS, and 13 progressive MS participants. Participants wore six inertial sensors and walked overground without walking aids at preferred and fast-as-possible speeds.

FINDINGS

Both MS groups had significantly lower walking speed than controls, with a trend towards lower preferred gait speed in progressive compared to relapsing-remitting MS (ES = 0.502). Although most spatiotemporal gait parameters differed between controls and MS groups, differences were not significant between MS subtypes in these parameters and their variability, with low to moderate effect sizes during preferred and fast walking. Both MS groups showed reduced modulation in gait compared to controls and no significant differences between MS subtypes.

INTERPRETATION

Gait in MS is altered compared to controls. Although gait may change with progressive MS, the overall small differences in the gait parameters between the MS subtypes observed in this sample suggests that those with the progressive form of MS who are independently ambulatory and without further clinically meaningful changes in gait speed may not show gait decrements greater than the relapsing-remitting form of the disease.

Multimodal Agility-Based Exercise Training for Persons With Multiple Sclerosis: A New Framework

INTRODUCTION

Multimodal agility-based exercise training (MAT) has been described as a framework for fall prevention in the elderly but might also be a valuable concept for exercise training in persons with Multiple Sclerosis (pwMS).

THE PROBLEM

Current recommendations advise pwMS to perform a multitude of different exercise training activities, as each of these has its separate evidence. However, pwMS struggle even more than the general population to be physically active. Additionally, Multiple Sclerosis often leads to co-occurring mobility and cognitive dysfunctions, for which simultaneous, time-efficient, and engaging training approaches are still limited in clinical practice and healthcare.

THE SOLUTION

The MAT framework has been developed to integratively improve cardiovascular, neuromuscular, and cognitive function by combining aspects of perception and orientation, change of direction, as well as stop-and-go patterns (ie, agility), in a group-training format. For pwMS, the MAT framework is conceptualized to include 3 Components: standing balance, dynamic balance (including functional leg strength), and agility-based exercises. Within these Components sensory, cognitive, and cardiovascular challenges can be adapted to individual needs.

RECOMMENDATIONS

We recommend investigating multimodal exercise interventions that go beyond easily standardized, unimodal types of exercise (eg, aerobic or resistance exercise), which could allow for time-efficient training, targeting multiple frequent symptoms of persons with mild disability at once. MAT should be compared to unimodal approaches, regarding sensor-based gait outcomes, fatigue-related outcomes, cognition, as well as neuroprotective, and (supportive) disease-modifying effects.

Association of Cerebral Small Vessel Disease With Gait and Balance Disorders

Cerebral small vessel disease (CSVD) is a common cerebrovascular disease and an important cause of gait and balance disorders. Gait and balance disorders can further lead to an increased risk of falls and a decreased quality of life. CSVD can damage gait and balance function by affecting cognitive function or directly disrupting motor pathways, and different CSVD imaging features have different characteristics of gait and balance impairment. In this article, the correlation between different imaging features of sporadic CSVD and gait and balance disorders has been reviewed as follows, which can provide beneficial help for standardized management of CSVD.

Longitudinal relationships between disability and gait characteristics in people with MS

Longitudinal data are vital in order to understand intra individual gait changes with the progression of multiple sclerosis (MS). Therefore, the primary aim of this study was to explore the relationship between changes in disability with changes in major spatio-temporal parameters of gait in people with MS (PwMS). PwMS (n = 83) completed two gait assessments performed at separate time points (M1, M2). For each individual, the absolute difference between the Expanded Disability Status Scale (EDSS) score, key spatio-temporal parameters of gait, Falls Efficacy Scale International (FES-I), and the 12-item Multiple Sclerosis Walking Scale (MSWS-12), were calculated. The mean time difference between M1 and M2 was 2.5 (SD = 1.7) years. At M2, PwMS presented with shorter strides, a wider base of support, increased perceived mobility difficulties and fear of falling compared with M1. According to the odds ratio (OR) analysis, the odds of experiencing an increase in the EDSS score was significantly higher once the MSWS-12 score increased at M2 compared with M1 (OR = 7.930, p = 0.004). This observation was highlighted specifically in people with mild-moderate MS (OR = 12.427, p < 0.001). The increase in the EDSS score was not associated with changes in key spatio-temporal parameters of gait. The present study provides a better understanding of gait and disease progression in PwMS, highlighting the significant role of the MSWS-12.

What does evidence tell us about the use of gait robotic devices in patients with multiple sclerosis? A comprehensive systematic review on functional outcomes and clinical recommendations

INTRODUCTION

There is growing evidence on the efficacy of gait robotic rehabilitation in patients with multiple sclerosis (MS), but most of the studies have focused on gait parameters. Moreover, clear indications on the clinical use of robotics still lack. As part of the CICERONE Italian Consensus on Robotic Rehabilitation, the aim of this systematic review was to investigate the existing evidence concerning the role of lower limb robotic rehabilitation in improving functional recovery in patients with MS.

EVIDENCE ACQUISITION

We searched for and systematically reviewed evidence-based studies on gait robotic rehabilitation in MS, between January 1^st, 2010 and December 31^st, 2020, in the following databases: Cochrane Library, PEDro, PubMed and Google Scholar. The study quality was assessed by the 16-item assessment of multiple systematic reviews 2 (AMSTAR 2) and the 10-item PEDro scale for the other research studies.

EVIDENCE SYNTHESIS

After an accurate screening, only 17 papers were included in the review, and most of them (13 RCT) had a level II evidence. Most of the studies used the Lokomat as a grounded robotic device, two investigated the efficacy of end-effectors and two powered exoskeletons. Generally speaking, robotic treatment has beneficial effects on gait speed, endurance and balance with comparable outcomes to those of conventional treatments. However, in more severe patients (EDSS >6), robotics leads to better functional outcomes. Notably, after gait training with robotics (especially when coupled to virtual reality) MS patients also reach better non-motor outcomes, including spasticity, fatigue, pain, psychological well-being and quality of life. Unfortunately, no clinical indications emerge on the treatment protocols.

CONCLUSIONS

The present comprehensive systematic review highlights the potential beneficial role on functional outcomes of the lower limb robotic devices in people with MS. Future studies are warranted to evaluate the role of robotics not only for walking and balance outcomes, but also for other gait-training-related benefits, to identify appropriate outcome measures related to a specific subgroup of MS subjects' disease severity.

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

In multiple sclerosis (MS), gait impairment is one of the most prominent symptoms. For a sensitive assessment of pathological gait patterns, a comprehensive analysis and processing of several gait analysis systems is necessary. The objective of this work was to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) using six machine learning algorithms for the differentiation between people with multiple sclerosis (pwMS) and healthy controls, between pwMS with and without fatigue and between pwMS with mild and moderate impairment. The data of the three gait systems were assessed on 54 pwMS and 38 healthy controls. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, radial basis function (rbf) and polynomial kernel were applied for the detection of subtle walking changes. The best performance for a healthy-sick classification was achieved on the DIERS data with a SVM rbf kernel (κ = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (κ = 0.61 ± 0.06) showed the best performance. This study demonstrates that machine learning methods are suitable for identifying pathologic gait patterns in early MS.

Human Gait Analysis in Neurodegenerative Diseases: a Review

This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegnerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. The work continues with a survey on the publicly available datasets principally used for comparing results. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined.