Phase-encoded fMRI tracks down brainstorms of natural language processing with subsecond precision.

Natural language processing unfolds information overtime as spatially separated, multimodal, and interconnected neural processes. Existing noninvasive subtraction-based neuroimaging techniques cannot simultaneously achieve the spatial and temporal resolutions required to visualize ongoing information flows across the whole brain. Here we have developed rapid phase-encoded designs to fully exploit the temporal information latent in functional magnetic resonance imaging data, as well as overcoming scanner noise and head-motion challenges during overt language tasks. We captured real-time information flows as coherent hemodynamic waves traveling over the cortical surface during listening, reading aloud, reciting, and oral cross-language interpreting tasks. We were able to observe the timing, location, direction, and surge of traveling waves in all language tasks, which were visualized as "brainstorms" on brain "weather" maps. The paths of hemodynamic traveling waves provide direct evidence for dual-stream models of the visual and auditory systems as well as logistics models for crossmodal and cross-language processing. Specifically, we have tracked down the step-by-step processing of written or spoken sentences first being received and processed by the visual or auditory streams, carried across language and domain-general cognitive regions, and finally delivered as overt speeches monitored through the auditory cortex, which gives a complete picture of information flows across the brain during natural language functioning. PRACTITIONER POINTS: Phase-encoded fMRI enables simultaneous imaging of high spatial and temporal resolution, capturing continuous spatiotemporal dynamics of the entire brain during real-time overt natural language tasks. Spatiotemporal traveling wave patterns provide direct evidence for constructing comprehensive and explicit models of human information processing. This study unlocks the potential of applying rapid phase-encoded fMRI to indirectly track the underlying neural information flows of sequential sensory, motor, and high-order cognitive processes.

Phase-encoded fMRI tracks down brainstorms of natural language processing with sub-second precision.

The human language system interacts with cognitive and sensorimotor regions during natural language processing. However, where, when, and how these processes occur remain unclear. Existing noninvasive subtraction-based neuroimaging techniques cannot simultaneously achieve the spatial and temporal resolutions required to visualize ongoing information flows across the whole brain. Here we have developed phase-encoded designs to fully exploit the temporal information latent in functional magnetic resonance imaging (fMRI) data, as well as overcoming scanner noise and head-motion challenges during overt language tasks. We captured neural information flows as coherent waves traveling over the cortical surface during listening, reciting, and oral cross-language interpreting. The timing, location, direction, and surge of traveling waves, visualized as 'brainstorms' on brain 'weather' maps, reveal the functional and effective connectivity of the brain in action. These maps uncover the functional neuroanatomy of language perception and production and motivate the construction of finer-grained models of human information processing.

Effectiveness of Exercise Interventions for Pain Reduction in People With Multiple Sclerosis: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

OBJECTIVE: To systematically review the evidence of the effect of exercise compared with passive control on pain in people with multiple sclerosis. DATA SOURCE AND STUDY SELECTION: Five electronic databases were searched for randomized controlled trials published up to March 2017 that recruited people with multiple sclerosis where exercise was the intervention and pain was an outcome (PROSPERO registration number CRD42017060489). STATISTICAL ANALYSIS: A random-effects meta-analysis was conducted to estimate the standardized mean difference of the effect of exercise on pain between treatment and control groups. We assessed risk of bias, fitted meta-regression models to explore heterogeneity between studies, and assessed small study effects. DATA SYNTHESIS: Ten studies met the inclusion criteria (total sample size=389), and all studies were at high risk of bias. We found that exercise interventions were associated with less pain compared with passive control groups (standardized mean difference=-.46; 95% CI, -.92 to .00). There was high between-study heterogeneity (I2=77.0%), which was not explained by the prespecified study characteristics. There was also some evidence of small study effects. CONCLUSION: This is the first systematic review of the effect of exercise interventions on pain in people with multiple sclerosis, a chronic neurological disorder that affects 2.5 million people. We found some evidence that exercise compared with passive control alleviates pain in this population, but there were limitations in reporting and study quality with high risk of bias of individual studies and heterogeneity between studies.