IntroductionFibromyalgia (FM) is a common debilitating condition with limited therapeutic options. Medications have low efficacy and are often associated with adverse effects. Given that FM is associated with a defective endogenous pain control system and central sensitisation, combining interventions such as transcranial direct current stimulation (tDCS) and aerobic exercise (AE) to modulate pain-processing circuits may enhance pain control.Methods and analysisA prospective, randomised (1:1:1:1), placebo-controlled, double-blind, factorial clinical trial will test the hypothesis that optimised tDCS (16 anodal tDCS sessions combined with AE) can restore of the pain endogenous control system. Participants with FM (n=148) will undergo a conditioning exercise period and be randomly allocated to one of four groups: (1) active tDCS and AE, (2) sham tDCS and AE, (3) active tDCS and non-aerobic exercise (nAE) or (4) sham tDCS and nAE. Pain inhibitory activity will be assessed using conditioned pain modulation (CPM) and temporal slow pain summation (TSPS)—primary outcomes. Secondary outcomes will include the following assessments: Transcranial magnetic stimulation and electroencephalography as cortical markers of pain inhibitory control and thalamocortical circuits; secondary clinical outcomes on pain, FM, quality of life, sleep and depression. Finally, the relationship between the two main mechanistic targets in this study—CPM and TSPS—and changes in secondary clinical outcomes will be tested. The change in the primary efficacy endpoint, CPM and TSPS, from baseline to week 4 of stimulation will be tested with a mixed linear model and adjusted for important demographic variables.Ethics and disseminationThis study obeys the Declaration of Helsinki and was approved by the Institutional Review Board (IRB) of Partners Healthcare under the protocol number 2017P002524. Informed consent will be obtained from participants. Study findings will be reported in conferences and peer-reviewed journal publications.Trial registration number NCT03371225.

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.

Conditioned pain modulation (CPM) can discriminate between healthy and chronic pain patients. However, its relationship with neurophysiological pain mechanisms is poorly understood. Brain oscillations measured by electroencephalography (EEG) might help gain insight into this complex relationship. Objective To investigate the relationship between CPM response and self-reported pain intensity in non-specific chronic low back pain (NSCLBP) and explore respective EEG signatures associated to these mechanisms. Design Cross-sectional analysis. Participants: Thirty NSCLBP patients participated. Methods Self-reported low back pain, questionnaires, mood scales, CPM (static and dynamic quantitative sensory tests), and resting surface EEG data were collected and analyzed. Linear regression models were used for statistical analysis. Results CPM was not significantly correlated with self-reported pain intensity scores. Relative power of EEG in the beta and high beta bands as recorded from the frontal, central, and parietal cortical areas were significantly associated with CPM. EEG relative power at delta and theta bands as recorded from the central area were significantly correlated with self-reported pain intensity scores while controlling for self-reported depression. Conclusions Faster EEG frequencies recorded from pain perception areas may provide a signature of a potential cortical compensation caused by chronic pain states. Slower EEG frequencies may have a critical role in abnormal pain processing.