Dysfunction of proprioceptive sensory synapses is a pathogenic event and therapeutic target in mice and humans with spinal muscular atrophy.

Spinal muscular atrophy (SMA) is a neurodegenerative disease characterized by a varying degree of severity that correlates with the reduction of SMN protein levels. Motor neuron degeneration and skeletal muscle atrophy are hallmarks of SMA, but it is unknown whether other mechanisms contribute to the spectrum of clinical phenotypes. Here, through a combination of physiological and morphological studies in mouse models and SMA patients, we identify dysfunction and loss of proprioceptive sensory synapses as key signatures of SMA pathology. We demonstrate that SMA patients exhibit impaired proprioception, and their proprioceptive sensory synapses are dysfunctional as measured by the neurophysiological test of the Hoffmann reflex (H-reflex). We further show that loss of excitatory afferent synapses and altered potassium channel expression in SMA motor neurons are conserved pathogenic events found in both severely affected patients and mouse models. Lastly, we report that improved motor function and fatigability in ambulatory SMA patients and mouse models treated with SMN-inducing drugs correlate with increased function of sensory-motor circuits that can be accurately captured by the H-reflex assay. Thus, sensory synaptic dysfunction is a clinically relevant event in SMA, and the H-reflex is a suitable assay to monitor disease progression and treatment efficacy of motor circuit pathology.

Effect of handedness on muscle synergies during upper limb planar movements.

Handedness is a prominent but poorly understood aspect of human motor performances. Despite it is generally accepted that it results from differences in the neural control of the arm, the mechanisms at the origin of the side-difference in motor performances are still unknown. In this work, we propose to deepen this aspect by investigating muscle synergies organization. We obtained muscle synergies through the factorization of the superficial electromyographical (EMG) activity related to fifteen upper limb muscles in the dominant and non-dominant side of 5 healthy young right and left dominant subjects, while executing planar wide and tight circular trajectories. Our preliminary results showed that right and left handed subjects performed the circular trajectories with a different muscle organization. Moreover, a task-related side-difference in muscle synergies was observed. Further investigations in a larger cohort of individuals are necessary to determine the neural mechanisms generating the differences in number and organization of muscle synergies between left and right handed individuals.

Modular organization of reaching and grasping movements investigated using EEG microstates.

How movements are generated and controlled by the central nervous system (CNS) is still not well understood. In this work, we tested the hypothesis of a modular organization of the brain activity during the execution of voluntary movements. In particular, we extracted meta-stable topographies as a measure for global brain state, so-called microstates, from electroencephalography (EEG) data during pure planar reaching movements as well as reaching and grasping of different objects, and we compared them with those extracted during resting-state. The results showed the emergence of specific EEG microstates related to movement execution. Our results provide evidence about the benefits of EEG microstate analysis for motor control studies and their importance to better understand brain reorganization in neurological pathologies.

A spatially-regularized dynamic source localization algorithm for EEG.

Cortical activity can be estimated from electroencephalogram (EEG) or magnetoencephalogram (MEG) data by solving an ill-conditioned inverse problem that is regularized using neuroanatomical, computational, and dynamic constraints. Recent methods have incorporated spatio-temporal dynamics into the inverse problem framework. In this approach, spatio-temporal interactions between neighboring sources enforce a form of spatial smoothing that enhances source localization quality. However, spatial smoothing could also occur by way of correlations within the state noise process that drives the underlying dynamic model. Estimating the spatial covariance structure of this state noise is challenging, particularly in EEG and MEG data where the number of underlying sources is far greater than the number of sensors. However, the EEG/MEG data are sparse compared to the large number of sources, and thus sparse constraints could be used to simplify the form of the state noise spatial covariance. In this work, we introduce an empirically tailored basis to represent the spatial covariance structure within the state noise processes of a cortical dynamic model for EEG source localization. We augment the method presented in Lamus, et al. (2011) to allow for sparsity enforcing priors on the covariance parameters. Simulation studies as well as analysis of real data reveal significant gains in the source localization performance over existing algorithms.