Indices of callosal axonal density and radius from diffusion MRI relate to upper and lower limb motor performance.

Understanding the relationship between human brain structure and functional outcome is of critical importance in systems neuroscience. Diffusion MRI (dMRI) studies show that fractional anisotropy (FA) is predictive of motor control, underscoring the importance of white matter (WM). However, as FA is a surrogate marker of WM, we aim to shed new light on the structural underpinnings of this relationship by applying a multi-compartment microstructure model providing axonal density/radius indices. Sixteen young adults (7 males / 9 females), performed a hand/foot tapping task and a Multi Limb Reaction Time task. Furthermore, diffusion (STEAM &HARDI) and fMRI (localizer hand/foot activations) data were obtained. Sphere ROIs were placed on activation clusters with highest t value to guide interhemispheric WM tractography. Axonal radius/density indices of callosal parts intersecting with tractography were calculated from STEAM, using the diffusion-time dependent AxCaliber model, and correlated with behavior. Results indicated a possible association between larger apparent axonal radii of callosal motor fibers of the hand and higher tapping scores of both hands, and faster selection-related processing (normalized reaction) times (RTs) on diagonal limb combinations. Additionally, a trend was present for faster selection-related processing (normalized reaction) times for lower limbs being related with higher axonal density of callosal foot motor fibers, and for higher FA values of callosal motor fibers in general being related with better tapping and faster selection-related processing (normalized reaction) times. Whereas FA is sensitive in demonstrating associations with motor behavior, axon radius/density (i.e., fiber geometry) measures are promising to explain the physiological source behind the observed FA changes, contributing to deeper insights into brain-behavior interactions.

Age-related differences in network flexibility and segregation at rest and during motor performance.

Brain networks undergo widespread changes in older age. A large body of knowledge gathered about those changes evidenced an increase of functional connectivity between brain networks. Previous work focused mainly on cortical networks during the resting state. Subcortical structures, however, are of critical importance during the performance of motor tasks. In this study, we investigated age-related changes in cortical, striatal and cerebellar functional connectivity at rest and its modulation by motor task execution. To that end, functional MRI from twenty-five young (mean age 21.5 years) and eighteen older adults (mean age 68.6 years) were analysed during rest and while performing a bimanual tracking task practiced over a two-week period. We found that inter-network connectivity among cortical structures was more positive in older adults both during rest and task performance. Functional connectivity within striatal structures decreased with age during rest and task execution. Network flexibility, the changes in network composition from rest to task, was also reduced in older adults, but only in networks with an age-related increase in connectivity. Finally, flexibility of areas in the prefrontal cortex were associated with lower error scores during task execution, especially in older adults. In conclusion, our findings indicate an age-related reduction in the ability to suppress irrelevant network communication, leading to less segregated and less flexible cortical networks. At the same time, striatal connectivity is impaired in older adults, while cerebellar connectivity shows heterogeneous age-related effects during rest and task execution. Future research is needed to clarify how cortical and subcortical connectivity changes relate to one another.

Simulation and analysis of needle electromyogram in Emery-Dreifuss muscular dystrophy by using line source model.

Electromyography (EMG) is a valuable clinical test in detection of muscle and nerve pathology and distinguishing between myogenic and neurogenic conditions from normal condition. By using EMG, one assesses the pathophysiology on the basis of the waveform characteristics of the recorded signal. This requires detailed knowledge of the relationship between the waveform generators and the waveform measurements. In this study, we manipulated parameters of improved line source model for normal EMG generation to simulate Emery-Dreifuss Muscular Dystrophy (EDMD) disease. Common features of simulated signals in normal and EDMD conditions were extracted and quantitative analyses were performed. Finally, the simulation results and clinical results were compared and discussed. The results indicate the ability and validity of line source model in simulation EDMD disease and also confirm that EMG recordings in EDMD generally fulfill the criteria for myopathy.