brainlife.io: a decentralized and open-source cloud platform to support neuroscience research.

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.

brainlife.io: A decentralized and open source cloud platform to support neuroscience research.

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation.

Virtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bundles from diffusion Magnetic Resonance Imaging (dMRI) data indirectly, by exploiting either the idea of connectivity between regions or the geometry of fiber paths obtained with tractography techniques, or, directly, through the information in volumetric data. Despite the remarkable improvement in automatic segmentation methods over the years, their segmentation quality is not yet satisfactory, especially when dealing with datasets with very diverse characteristics, such as different tracking methods, bundle sizes or data quality. In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model. With a wide range of experiments on multiple datasets that span from research to clinical domains, we show that Classifyber substantially improves the quality of segmentation as compared to other state-of-the-art methods and, more importantly, that it is robust across very diverse settings. We provide an implementation of the proposed method as open source code, as well as web service.

Assessing and treating pain associated with stroke, multiple sclerosis, cerebral palsy, spinal cord injury and spasticity. Evidence and recommendations from the Italian Consensus Conference on Pain in Neurorehabilitation.

Pain is a common and disabling symptom in patients with stroke, multiple sclerosis (MS), cerebral palsy (CP), spinal cord injury (SCI) and other conditions associated with spasticity, but data on its prevalence, and natural history, as well as guidelines on its assessment and treatment in the field of neurorehabilitation, are largely lacking. The Italian Consensus Conference on Pain in Neurorehabilitation (ICCPN) searched and evaluated current evidence on the frequency, evolution, predictors, assessment, and pharmacological and non-pharmacological treatment of pain in patients with stroke, MS, CP, SCI and other conditions associated with spasticity. Patients with stroke, MS, CP, and SCI may suffer from pain related to spasticity, as well as nociceptive and neuropathic pain (NP), whose prevalence, natural history, impact on functional outcome, and predictors are only partially known. Diagnosis and assessment of the different types of pain in these patients is important, because their treatment may differ. Botulinum neurotoxin is the first choice treatment for spasticity, while some antidepressant and antiepileptic drugs may be effective on NP, but pharmacological treatment varies according to the underlying disease. In most cases, a single therapy is not sufficient to treat pain, and a multidisciplinary approach, which include pharmacological and non-pharmacological treatments is needed. Further studies, and in particular randomized controlled trials, are needed on these topics.

Accuracy of botulinum toxin type A injection into the forearm muscles of chronic stroke patients with spastic flexed wrist and clenched fist: manual needle placement evaluated using ultrasonography.

OBJECTIVE: To investigate the accuracy of manual needle placement for injection of botulinum toxin type A into the forearm muscles of adults with spastic flexed wrist and clenched fist as a consequence of stroke. DESIGN: Prospective clinical study. PATIENTS: A total of 41 adults with chronic stroke who were scheduled to receive botulinum toxin type A injection into the following forearm muscles: flexor carpi radialis, flexor carpi ulnaris, flexor digitorum superficialis and flexor digitorum profundus. METHODS: According to Huber & Heck's atlas suggestions on treatment of spasticity with botulinum toxin, surface identification of muscles to inject was performed by means of palpation and anatomical landmarks. Accuracy of needle placement and muscle thickness at the site of needle insertion were assessed using ultrasonography. RESULTS: Overall accuracy of manual needle placement evaluated using ultrasonography was 51.2%. Accuracy was significantly higher for the finger flexors than for the wrist flexors (63.4% vs 39.0%). The finger flexors were significantly thicker than the wrist flexors (mean 1.58 vs 0.49 cm). CONCLUSION: Instrumental guidance should be used in order to achieve an acceptable accuracy of needle placement when performing botulinum toxin type A injections into the forearm muscles of chronic stroke patients with spastic flexed wrist and clenched fist.

Relationship between ultrasonographic, electromyographic, and clinical parameters in adult stroke patients with spastic equinus: an observational study.

OBJECTIVE: To find more accurate indices that could affect decisions in spasticity treatment by investigating the relation between ultrasonographic, electromyographic, and clinical parameters of the gastrocnemius muscle in adults with spastic equinus after stroke. DESIGN: Observational study. SETTING: University hospitals. PARTICIPANTS: Chronic patients with stroke with spastic equinus (N=43). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Ultrasonographic features were spastic gastrocnemius muscle echo intensity, muscle thickness, and posterior pennation angle of the gastrocnemius medialis (GM) and gastrocnemius lateralis (GL) in both legs. Electromyographic evaluation included compound muscle action potentials (CMAPs) recorded from the GM and GL of both legs. Clinical assessment of the spastic gastrocnemius muscle was performed with the Modified Ashworth Scale (MAS) and by measuring ankle dorsiflexion passive range of motion (PROM). RESULTS: Spastic muscle echo intensity was inversely associated with proximal (GM and GL: P=.002) and distal (GM and GL: P=.001) muscle thickness, pennation angle (GM: P< .001; GL: P=.01), CMAP (GM: P=.014; GL: P=.026), and ankle PROM (GM: P=.038; GL: P=.024). The pennation angle was directly associated with the proximal (GM and GL: P< .001) and distal (GM: P=.001; GL: P< .001) muscle thickness of the spastic gastrocnemius muscle. The MAS score was directly associated with muscle echo intensity (GM: P=.039; GL: P=.027) and inversely related to the pennation angle (GM and GL: P=.001) and proximal (GM: P=.016; GL: P=.009) and distal (GL: P=.006) muscle thickness of the spastic gastrocnemius. CONCLUSIONS: Increased spastic muscle echo intensity was associated with reduced muscle thickness, posterior pennation angle, and CMAP amplitude in the gastrocnemius muscle. Building on previous evidence that these instrumental features are related to botulinum toxin response, these new findings may usefully inform spasticity treatment decisions.