Clinical evaluation and resting state fMRI analysis of virtual reality based training in Parkinson’s disease through a randomized controlled trial

Assisted technology in Parkinson's disease gait: what's up?

BACKGROUND

 Gait disturbances are prevalent and debilitating symptoms, diminishing mobility and quality of life for Parkinson's disease (PD) individuals. While traditional treatments offer partial relief, there is a growing interest in alternative interventions to address this challenge. Recently, a remarkable surge in assisted technology (AT) development was witnessed to aid individuals with PD.

OBJECTIVE

 To explore the burgeoning landscape of AT interventions tailored to alleviate PD-related gait impairments and describe current research related to such aim.

METHODS

 In this review, we searched on PubMed for papers published in English (2018-2023). Additionally, the abstract of each study was read to ensure inclusion. Four researchers searched independently, including studies according to our inclusion and exclusion criteria.

RESULTS

 We included studies that met all inclusion criteria. We identified key trends in assistive technology of gait parameters analysis in PD. These encompass wearable sensors, gait analysis, real-time feedback and cueing techniques, virtual reality, and robotics.

CONCLUSION

 This review provides a resource for guiding future research, informing clinical decisions, and fostering collaboration among researchers, clinicians, and policymakers. By delineating this rapidly evolving field's contours, it aims to inspire further innovation, ultimately improving the lives of PD patients through more effective and personalized interventions.

Physical Exercise for Treating the Anxiety and Depression Symptoms of Parkinson's Disease: Systematic Review and Meta-Analysis

BACKGROUND

Depression and anxiety are non-motor symptoms of Parkinson's disease (PD). Physical exercise is a promising approach to reducing neuropsychological burden. We aimed to comprehensively synthesize evidence regarding the use of exercise for treating depression and anxiety symptoms in PD.

METHODS

Systematic review and meta-analysis following PRISMA recommendations. Searches on PubMed, Cochrane Library, Scopus, Web of Science, Embase, and Physiotherapy Evidence Database (PEDro) was conducted. The random-effects model was employed for all analyses with the standardized mean difference as the effect estimate.

RESULTS

Fifty records were retrieved, but only 17 studies met the criteria for the meta-analyses. A moderate to large effect was observed for depression (-.71 [95% CI = -.96 to -.46], 11 studies, 728 individuals), and a small to moderate effect for anxiety (-.39 [95% CI = -.65 to -.14], 6 studies, 241 individuals), when comparing exercise to non-exercise controls. Subgroup analysis revealed significant effects from aerobic (-.95 [95% CI = -1.60, -.31]), mind-body (-1.85 [95% CI = -2.63, -1.07]), and resistance modalities (-1.61 [95% CI = -2.40, -.83]) for depression, and from mind-body (-.67 [95% CI = -1.19 to -.15]) and resistance exercises (-1.00 [95% CI = -1.70 to -.30]) for anxiety.

CONCLUSION

Physical exercise has a relevant clinical impact on depression and anxiety in PD. We discuss the level of the evidence, the methodological limitations of the studies, and give recommendations.

Efficacy of virtual reality technology interventions for cognitive and mental outcomes in older people with cognitive disorders: An umbrella review comprising meta-analyses of randomized controlled trials

We conducted an umbrella review of virtual reality (VR) technology interventions and cognitive improvement in older adults with cognitive disorders to establish a hierarchy of evidence. We systematically searched PubMed, Web of Science, Scopus, and PsycINFO databases from database creation to February 2023. We included meta-analyses relevant to our study objectives for the overall review. We assessed the methodological quality according to AMSTAR2, and we used the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) method to assess the credibility of the evidence. This overall review was registered with the International Prospective Register of Systematic Reviews (CRD42023423063). We identified six meta-analyses that included 12 cognitive outcomes, but only memory (Standardized Mean Difference(SMD) = 0.27, 95% confidence interval (CI): 0.04 to 0.49), depression (SMD = -1.26, 95% CI: -1.8 to -0.72), and global cognition (SMD = 0.42, 95% CI: 0.18 to 0.66) improved through the VR technology intervention. Using the 95% prediction interval (PI) results, we found that VR technology did not significantly affect the cognitive abilities of people with cognitive decline despite increasing the subject size. We conclude that the VR technology intervention improved only specific cognitive abilities.

EEG-Based Parkinson's Disease Recognition via Attention-Based Sparse Graph Convolutional Neural Network

Parkinson's disease (PD) is a complicated neurological ailment that affects both the physical and mental wellness of elderly individuals which makes it problematic to diagnose in its initial stages. Electroencephalogram (EEG) promises to be an efficient and cost-effective method for promptly detecting cognitive impairment in PD. Nevertheless, prevailing diagnostic practices utilizing EEG features have failed to examine the functional connectivity among EEG channels and the response of associated brain areas causing an unsatisfactory level of precision. Here, we construct an attention-based sparse graph convolutional neural network (ASGCNN) for diagnosing PD. Our ASGCNN model uses a graph structure to represent channel relationships, the attention mechanism for selecting channels, and the L1 norm to capture channel sparsity. We conduct extensive experiments on the publicly available PD auditory oddball dataset, which consists of 24 PD patients (under ON/OFF drug status) and 24 matched controls, to validate the effectiveness of our method. Our results show that the proposed method provides better results compared to the publicly available baselines. The achieved scores for Recall, Precision, F1-score, Accuracy and Kappa measures are 90.36%, 88.43%, 88.41%, 87.67%, and 75.24%, respectively. Our study reveals that the frontal and temporal lobes show significant differences between PD patients and healthy individuals. In addition, EEG features extracted by ASGCNN demonstrate significant asymmetry in the frontal lobe among PD patients. These findings can offer a basis for the establishment of a clinical system for intelligent diagnosis of PD by using auditory cognitive impairment features.

Habits and Persistent Food Restriction in Patients with Anorexia Nervosa: A Scoping Review

The aetiology of anorexia nervosa (AN) presents a puzzle for researchers. Recent research has sought to understand the behavioural and neural mechanisms of these patients' persistent choice of calorie restriction. This scoping review aims to map the literature on the contribution of habit-based learning to food restriction in AN. PRISMA-ScR guidelines were adopted. The search strategy was applied to seven databases and to grey literature. A total of 35 studies were included in this review. The results indicate that the habit-based learning model has gained substantial attention in current research, employing neuroimaging methods, scales, and behavioural techniques. Food choices were strongly associated with dorsal striatum activity, and habitual food restriction based on the self-report restriction index was associated with clinical impairment in people chronically ill with restricting AN. High-frequency repetitive transcranial magnetic stimulation (HF-rTMS) and Regulating Emotions and Changing Habits (REaCH) have emerged as potential treatments. Future research should employ longitudinal studies to investigate the time required for habit-based learning and analyse how developmental status, such as adolescence, influences the role of habits in the progression and severity of diet-related illnesses. Ultimately, seeking effective strategies to modify persistent dietary restrictions controlled by habits remains essential.

Clinical trials for cognition in Parkinson's disease: Where are we and how can we do better?

BACKGROUND

Cognitive impairment is common in Parkinson's disease (PD) and has a substantial impact on quality of life. Despite numerous trials targeting various PD features, we still lack effective treatments for cognition beyond cholinesterase inhibitors.

OBJECTIVE

To identify the gaps in recent clinical trials with cognitive outcomes in PD and consider areas for improvement.

METHODS

We examined recent clinical trials with cognitive outcomes in PD registered on ClinicalTrials.gov, excluding trials without cognitive outcomes, non-interventional studies, and in atypical Parkinsonian disorders. Included trials were categorized by treatment approach (investigational medicinal product, behavioral, physical activity, device-based). Details of trial design and outcomes were collected.

RESULTS

178 trials at different stages of trial completion were considered. 46 trials were completed, 25 had available results. Mean follow-up duration was 29.9 weeks. Most common cognitive measure was Montreal Cognitive Assessment. Most were performed in North America or Europe. Majority of the participants identified as non-Hispanic and White. Only eight trials showed improvement in cognition, none showed improvement beyond four months. These included trials of international medicinal products, cognitive and physical interventions and devices. GRADE certainty levels ranged from Moderate to Very Low. Only mevidalen had a Moderate certainty for potential clinical effectiveness.

CONCLUSIONS

Amongst a large number of trials for cognition in PD, only a small proportion were completed. Few showed significant improvement, with no proven long-lasting effects. Trial design, lack of enrichment for at-risk groups, short follow-up duration, insensitive outcome measures likely contribute to lack of detectable benefit and should be considered in future trials.

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning

Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets.