Occlusion of LTP-Like Plasticity in Human Primary Motor Cortex by Action Observation

Passive observation of motor actions induces cortical activity in the primary motor cortex (M1) of the onlooker, which could potentially contribute to motor learning. While recent studies report modulation of motor performance following action observation, the neurophysiological mechanism supporting these behavioral changes remains to be specifically defined. Here, we assessed whether the observation of a repetitive thumb movement – similarly to active motor practice – would inhibit subsequent long-term potentiation-like (LTP) plasticity induced by paired-associative stimulation (PAS). Before undergoing PAS, participants were asked to either 1) perform abductions of the right thumb as fast as possible; 2) passively observe someone else perform thumb abductions; or 3) passively observe a moving dot mimicking thumb movements. Motor evoked potentials (MEP) were used to assess cortical excitability before and after motor practice (or observation) and at two time points following PAS. Results show that, similarly to participants in the motor practice group, individuals observing repeated motor actions showed marked inhibition of PAS-induced LTP, while the “moving dot” group displayed the expected increase in MEP amplitude, despite differences in baseline excitability. Interestingly, LTP occlusion in the action-observation group was present even if no increase in cortical excitability or movement speed was observed following observation. These results suggest that mere observation of repeated hand actions is sufficient to induce LTP, despite the absence of motor learning

Clustering-based approaches to SAGE data mining

Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical applications, such as the prediction of new gene functions and the identification of biomarkers in human cancer research. Clustering techniques have become fundamental approaches in these applications. This paper reviews relevant clustering techniques specifically designed for this type of data. It places an emphasis on current limitations and opportunities in this area for supporting biologically-meaningful data mining and visualisation

Reorganizing the Intrinsic Functional Architecture of the Human Primary Motor Cortex during Rest with Non-Invasive Cortical Stimulation

The primary motor cortex (M1) is the main effector structure implicated in the generation of voluntary movements and is directly involved in motor learning. The intrinsic horizontal neuronal connections of M1 exhibit short-term and long-term plasticity, which is a strong substrate for learning-related map reorganization. Transcranial direct current stimulation (tDCS) applied for few minutes over M1 has been shown to induce relatively long-lasting plastic alterations and to modulate motor performance. Here we test the hypothesis that the relatively long-lasting synaptic modification induced by tDCS over M1 results in the alteration of associations among populations of M1 neurons which may be reflected in changes of its functional architecture. fMRI resting-state datasets were acquired immediately before and after 10 minutes of tDCS during rest, with the anode/cathode placed over the left M1. For each functional dataset, grey-matter voxels belonging to Brodmann area 4 (BA4) were labelled and afterwards BA4 voxel-based synchronization matrices were calculated and thresholded to construct undirected graphs. Nodal network parameters which characterize the architecture of functional networks (connectivity degree, clustering coefficient and characteristic path-length) were computed, transformed to volume maps and compared before and after stimulation. At the dorsolateral-BA4 region cathodal tDCS boosted local connectedness, while anodal-tDCS enhanced long distance functional communication within M1. Additionally, the more efficient the functional architecture of M1 was at baseline, the more efficient the tDCS-induced functional modulations were. In summary, we show here that it is possible to non-invasively reorganize the intrinsic functional architecture of M1, and to image such alterations

Cortical Plasticity Induced by Transcranial Magnetic Stimulation during Wakefulness Affects Electroencephalogram Activity during Sleep

BACKGROUND:Sleep electroencephalogram (EEG) brain oscillations in the low-frequency range show local signs of homeostatic regulation after learning. Such increases and decreases of slow wave activity are limited to the cortical regions involved in specific task performance during wakefulness. Here, we test the hypothesis that reorganization of motor cortex produced by long-term potentiation (LTP) affects EEG activity of this brain area during subsequent sleep. METHODOLOGY/PRINCIPAL FINDINGS:By pairing median nerve stimulation with transcranial magnetic stimulation over the contralateral motor cortex, one can potentiate the motor output, which is presumed to reflect plasticity of the neural circuitry. This paired associative stimulation increases M1 cortical excitability at interstimulus intervals of 25 ms. We compared the scalp distribution of sleep EEG power following paired associative stimulation at 25 ms to that following a control paradigm with 50 ms intervals. It is shown that the experimental manipulation by paired associative stimulation at 25 ms induces a 48% increase in amplitude of motor evoked potentials. This LTP-like potentiation, induced during waking, affects delta and theta EEG power in both REM and non-REM sleep, measured during the following night. Slow-wave activity increases in some frontal and prefrontal derivations and decreases at sites neighboring and contralateral to the stimulated motor cortex. The magnitude of increased amplitudes of motor evoked potentials by the paired associative stimulation at 25 ms predicts enhancements of slow-wave activity in prefrontal regions. CONCLUSIONS/SIGNIFICANCE:An LTP-like paradigm, presumably inducing increased synaptic strength, leads to changes in local sleep regulation, as indexed by EEG slow-wave activity. Enhancement and depression of slow-wave activity are interpreted in terms of a simultaneous activation of both excitatory and inhibitory circuits consequent to the paired associative stimulation at 25 ms

Gene Expression Changes in the Motor Cortex Mediating Motor Skill Learning

The primary motor cortex (M1) supports motor skill learning, yet little is known about the genes that contribute to motor cortical plasticity. Such knowledge could identify candidate molecules whose targeting might enable a new understanding of motor cortical functions, and provide new drug targets for the treatment of diseases which impair motor function, such as ischemic stroke. Here, we assess changes in the motor-cortical transcriptome across different stages of motor skill acquisition. Adult rats were trained on a gradually acquired appetitive reach and grasp task that required different strategies for successful pellet retrieval, or a sham version of the task in which the rats received pellet reward without needing to develop the reach and grasp skill. Tissue was harvested from the forelimb motor-cortical area either before training commenced, prior to the initial rise in task performance, or at peak performance. Differential classes of gene expression were observed at the time point immediately preceding motor task improvement. Functional clustering revealed that gene expression changes were related to the synapse, development, intracellular signaling, and the fibroblast growth factor (FGF) family, with many modulated genes known to regulate synaptic plasticity, synaptogenesis, and cytoskeletal dynamics. The modulated expression of synaptic genes likely reflects ongoing network reorganization from commencement of training till the point of task improvement, suggesting that motor performance improves only after sufficient modifications in the cortical circuitry have accumulated. The regulated FGF-related genes may together contribute to M1 remodeling through their roles in synaptic growth and maturation.McGovern Institute for Brain Research at MITNational Institutes of Health (U.S.) ((NIH grant 1-RC1-NS068103-01)National Institutes of Health (U.S.) (NIH grant R01-MH084966)Roberto Rocca Education Program (Fellowship)Massachusetts Institute of Technology. Undergraduate Research Opportunities Program (Fellowship)Italy. Ministero dell'istruzione, dell'università e della ricerca (MIUR grant RBIN04H5AS)Italy. Ministero dell'istruzione, dell'università e della ricerca (MIUR grant RBLA03FLJC)Italy. Ministero dell'istruzione, dell'università e della ricerca (FIRB n. RBAP10L8TY

Quantifying kinematics of purposeful movements to real, imagined, or absent functional objects: Implications for modelling trajectories for robot-assisted ADL tasks**

BACKGROUND: Robotic therapy is at the forefront of stroke rehabilitation. The Activities of Daily Living Exercise Robot (ADLER) was developed to improve carryover of gains after training by combining the benefits of Activities of Daily Living (ADL) training (motivation and functional task practice with real objects), with the benefits of robot mediated therapy (repeatability and reliability). In combining these two therapy techniques, we seek to develop a new model for trajectory generation that will support functional movements to real objects during robot training. We studied natural movements to real objects and report on how initial reaching movements are affected by real objects and how these movements deviate from the straight line paths predicted by the minimum jerk model, typically used to generate trajectories in robot training environments. We highlight key issues that to be considered in modelling natural trajectories. METHODS: Movement data was collected as eight normal subjects completed ADLs such as drinking and eating. Three conditions were considered: object absent, imagined, and present. This data was compared to predicted trajectories generated from implementing the minimum jerk model. The deviations in both the plane of the table (XY) and the saggital plane of torso (XZ) were examined for both reaches to a cup and to a spoon. Velocity profiles and curvature were also quantified for all trajectories. RESULTS: We hypothesized that movements performed with functional task constraints and objects would deviate from the minimum jerk trajectory model more than those performed under imaginary or object absent conditions. Trajectory deviations from the predicted minimum jerk model for these reaches were shown to depend on three variables: object presence, object orientation, and plane of movement. When subjects completed the cup reach their movements were more curved than for the spoon reach. The object present condition for the cup reach showed more curvature than in the object imagined and absent conditions. Curvature in the XZ plane of movement was greater than curvature in the XY plane for all movements. CONCLUSION: The implemented minimum jerk trajectory model was not adequate for generating functional trajectories for these ADLs. The deviations caused by object affordance and functional task constraints must be accounted for in order to allow subjects to perform functional task training in robotic therapy environments. The major differences that we have highlighted include trajectory dependence on: object presence, object orientation, and the plane of movement. With the ability to practice ADLs on the ADLER environment we hope to provide patients with a therapy paradigm that will produce optimal results and recovery

Enriched environment and physical activity reduce microglia and influence the fate of NG2 cells in the amygdala of adult mice

Proliferative cells expressing proteoglycan neuron-glia 2 (NG2) are considered to represent parenchymal precursor cells in the adult brain and are thought to differentiate primarily into oligodendrocytes. We have studied cell genesis in the adult amygdala and found that, up to 1 year after the labeling of proliferating cells with bromodeoxyuridine, most proliferating NG2 cells remain NG2 cells, and only a few slowly differentiate into mature oligodendrocytes, as assessed by the expression of 2',3'-cyclic nucleotide 3'-phosphodiesterase. We have detected no signs of neurogenesis but have confirmed the expression of “neuronal” markers such as Doublecortin in NG2 cells. Nestin-expressing NG2 cells in the amygdala show electrophysiological properties known for oligodendrocyte precursor cells in the corpus callosum. Application of the glutamate agonist kainate elicits a “complex” response consisting of a rapid and long-lasting blockade of the resting K+ conductance, a transient cationic current, and a transient increase of an outwardly directed K+ conductance, suggesting the responsiveness of NG2 cells to excitation. Proliferation of NG2 cells increases in response to behavioral stimuli of activity, voluntary wheel running, and environmental enrichment. In addition to reducing the number of newborn microglia, behavioral activity results in a decrease in S100β-expressing newborn NG2 cells in the amygdala. Because S100β expression in NG2 cells ceases with oligodendrocyte maturation, this finding suggests that NG2 cells in the amygdala undergo activity-dependent functional alterations, without resulting in a measurable increase in new mature oligodendrocytes over the time period covered by the present study. The adult amygdala thus shows signs of mixed activity-dependent plasticity: reduced numbers of microglia and, presumably, an altered fate of NG2 cells

Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network

Current cognitive theories postulate either localist representations of knowledge or fully overlapping, distributed ones. We use a connectionist model that closely replicates known anatomical properties of the cerebral cortex and neurophysiological principles to show that Hebbian learning in a multi-layer neural network leads to memory traces (cell assemblies) that are both distributed and anatomically distinct. Taking the example of word learning based on action-perception correlation, we document mechanisms underlying the emergence of these assemblies, especially (i) the recruitment of neurons and consolidation of connections defining the kernel of the assembly along with (ii) the pruning of the cell assembly’s halo (consisting of very weakly connected cells). We found that, whereas a learning rule mapping covariance led to significant overlap and merging of assemblies, a neurobiologically grounded synaptic plasticity rule with fixed LTP/LTD thresholds produced minimal overlap and prevented merging, exhibiting competitive learning behaviour. Our results are discussed in light of current theories of language and memory. As simulations with neurobiologically realistic neural networks demonstrate here spontaneous emergence of lexical representations that are both cortically dispersed and anatomically distinct, both localist and distributed cognitive accounts receive partial support

A reappraisal of Lignier's Mesozoic fossil wood collection: Ages, nomenclature and taxonomy

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