Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data’s impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer’s Disease, Stroke, Depression, Parkinson’s Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

One of the key challenges in Big Data for clinical research and healthcare is how to integrate new sources of data, whose relation to disease processes are often not well understood, with multiple classical clinical measurements that have been used by clinicians for years to describe disease processes and interpret therapeutic outcomes. Without such integration, even the most promising data from emerging technologies may have limited, if any, clinical utility. This paper presents an approach to address this challenge, illustrated through an example in Parkinson’s Disease (PD) management. We show how data from various sensing sources can be integrated with traditional clinical measurements used in PD; furthermore, we show how leveraging Big Data frameworks, augmented by Artificial Intelligence (AI) algorithms, can distinctively enrich the data resources available to clinicians. We showcase the potential of this approach in a cohort of 50 PD patients who underwent both evaluations with an Integrated Motion Analysis Suite (IMAS) composed of a battery of multimodal, portable, and wearable sensors and traditional Unified Parkinson's Disease Rating Scale (UPDRS)-III evaluations. Through techniques including Principal Component Analysis (PCA), elastic net regression, and clustering analysis we demonstrate how this combined approach can be used to improve clinical motor assessments and to develop personalized treatments. The scalability of our approach enables systematic data generation and analysis on increasingly larger datasets, confirming the integration potential of IMAS, whose use in PD assessments is validated herein, within Big Data paradigms. Compared to existing approaches, our solution offers a more comprehensive, multi-dimensional view of patient data, enabling deeper clinical insights and greater potential for personalized treatment strategies. Additionally, we show how IMAS can be integrated into established clinical practices, facilitating its adoption in routine care and complementing emerging methods, for instance, non-invasive brain stimulation. Future work will aim to augment our data repositories with additional clinical data, such as imaging and biospecimen data, to further broaden and enhance these foundational methodologies, leveraging the full potential of Big Data and AI.

Background: This study aimed to explore the potential associations between voice metrics of patients with PD and their motor symptoms. Methods: Motor and vocal data including the Unified Parkinson’s Disease Rating Scale part III (UPDRS-III), Harmonic–Noise Ratio (HNR), jitter, shimmer, and smoothed cepstral peak prominence (CPPS) were analyzed through exploratory correlations followed by univariate linear regression analyses. We employed these four voice metrics as independent variables and the total and sub-scores of the UPDRS-III as dependent variables. Results: Thirteen subjects were included, 76% males and 24% females, with a mean age of 62.9 ± 10.1 years, and a median Hoehn and Yahr stage of 2.3 ± 0.7. The regression analysis showed that CPPS is associated with posture (UPDRS-III posture scores: β = −0.196; F = 10.0; p = 0.01; R² = 0.50) and UPDRS-III postural stability scores (β = −0.130; F = 5.57; p = 0.04; R² = 0.35). Additionally, the associations between CPPS and rapid alternating movement (β = −0.297; p = 0.07), rigidity (β= −0.36; p = 0.11), and body bradykinesia (β = −0.16; p = 0.13) showed a trend towards significance. Conclusion: These findings highlight the potential role of CPPS as a predictor of postural impairments secondary to PD, emphasizing the need for further investigation.

Stroke is a leading cause of long-term disability, with highly variable recovery trajectories and challenges in prediction and monitoring. Frequently used measures (e.g., National Institute of Health Stroke Scale (NIHSS) and Fugl-Meyer (FM) assessment of motor impairment) have significant limitations. As the societal burden of stroke increases, developing robust methodologies for assessing and predicting recovery is essential to optimize treatment plans and improve outcomes. This paper presents our Integrated Motion Analysis Suite (IMAS), which leverages multimodal data (clinical, sensor, and neuroimaging inputs) and multimodal machine learning (MML) to predict FM scores and motor recovery in stroke. Its potential is demonstrated via analysis of 28 stroke patients in acute and subacute phases of recovery, where features extracted from a set of motor tasks were used to predict FM scores and motor recovery, achieving a coefficient of determination (R²) of 0.75 and Mean Absolute Error (MAE) of 2.83 and R² = 0.83 and MAE = 2.6 %, respectively. IMAS is designed to continuously improve through its integration with a Big Data database, allowing for ongoing refinement of predictive algorithms as new data is collected in real-world clinical environments. Its ability to complement inherently limited clinical scales, handle incomplete data, and adapt to diverse applications highlights its potential for broader use in recovery after stroke, including long-term monitoring and precision rehabilitation.

Electromagnetic neuromodulation is increasingly used as an adjunct or alternative to pharmacologic therapy for pain, with several approaches cleared for select indications and many others under active investigation. We review implanted neuromodulation approaches (intracranial and spinal), vagal neuromodulation, noninvasive central/cranial neuromodulation, peripheral neuromodulation, and hybrid/multimodal approaches, with a primary focus on chronic pain management and brief coverage of selected acute-use indications (e.g., primary headache disorders) where relevant. Beyond summarizing individual modalities, we highlight the recurring patterns that have shaped the evolution of pain neuromodulation. For each modality, we summarize putative mechanism of action, typical stimulation paradigms, and key strengths and limitations of the clinical evidence, emphasizing sources of heterogeneity that complicate cross-study comparison and translation. We conclude by outlining priorities for the field, including more rigorous trial design, standardized reporting of stimulation parameters and outcomes, and clearer indication-specific evidence to support clinical translation.

Objective: To synthesize randomized clinical trial (RCT) evidence comparing the efficacy of medical device-based neuromodulation (MDBN) techniques for improving motor symptoms in Parkinson’s disease (PD). Background: Device-based neuromodulation therapies, including deep brain stimulation (DBS), transcranial magnetic stimulation (TMS), and transcranial direct current stimulation (tDCS), are used to target PD motor symptoms, but reported effect sizes vary substantially across trials. Methods: A systematic review was conducted in accordance with PRISMA guidelines. RCTs evaluating MDBN for PD motor function, measured by the Unified Parkinson’s Disease Rating Scale Part III (UPDRS3), were identified through MEDLINE, Embase, and Web of Science. A random-effects meta-analysis was performed for the primary outcome (UPDRS3). Risk of bias was assessed using the Cochrane RoB tool, and certainty of evidence with GRADE. Meta-regression examined methodological, population, and intervention variables associated with effect size variation. Results: Forty-seven RCTs (56 intervention comparisons; 2,638 participants) were included. Thirty trials investigated non-invasive neuromodulation (mainly rTMS and tDCS) and 17 invasive approaches (DBS). Meta-analysis of 39 effect sizes (from studies during L-Dopa ON, near-term follow-up) showed a small but significant improvement with active MDBN (SMD = −0.18, p = 0.008; I ² = 61%). Meta-regression showed larger effects in studies using intention-to-treat analysis and smaller effects when sham stimulation was used as a comparator. Conclusion: MDBN is associated with modest yet significant motor improvements in PD. Effect size estimates are influenced by methodological characteristics, particularly analysis strategy and comparator selection. This is the first review in the field to calculate and incorporate separate effect sizes for each active intervention group in multi-arm trials, enabling more precise data inclusion.