How does human motor cortex regulate vocal pitch in singers?

Vocal pitch is used as an important communicative device by humans, as found in the melodic dimension of both speech and song. Vocal pitch is determined by the degree of tension in the vocal folds of the larynx, which itself is influenced by complex and nonlinear interactions among the laryngeal muscles. The relationship between these muscles and vocal pitch has been described by a mathematical model in the form of a set of ‘control rules’. We searched for the biological implementation of these control rules in the larynx motor cortex of the human brain. We scanned choral singers with functional magnetic resonance imaging as they produced discrete pitches at four different levels across their vocal range. While the locations of the larynx motor activations varied across singers, the activation peaks for the four pitch levels were highly consistent within each individual singer. This result was corroborated using multi-voxel pattern analysis, which demonstrated an absence of patterned activations differentiating any pairing of pitch levels. The complex and nonlinear relationships between the multiple laryngeal muscles that control vocal pitch may obscure the neural encoding of vocal pitch in the brain

Activated Microglia Inhibit Axonal Growth through RGMa

By causing damage to neural networks, spinal cord injuries (SCI) often result in severe motor and sensory dysfunction. Functional recovery requires axonal regrowth and regeneration of neural network, processes that are quite limited in the adult central nervous system (CNS). Previous work has shown that SCI lesions contain an accumulation of activated microglia, which can have multiple pathophysiological influences. Here, we show that activated microglia inhibit axonal growth via repulsive guidance molecule a (RGMa). We found that microglia activated by lipopolysaccharide (LPS) inhibited neurite outgrowth and induced growth cone collapse of cortical neurons in vitro—a pattern that was only observed when there was direct contact between microglia and neurons. After microglia were activated by LPS, they increased expression of RGMa; however, treatment with RGMa-neutralizing antibodies or transfection of RGMa siRNA attenuated the inhibitory effects of microglia on axonal outgrowth. Furthermore, minocycline, an inhibitor of microglial activation, attenuated the effects of microglia and RGMa expression. Finally, we examined whether these in vitro patterns could also be observed in vivo. Indeed, in a mouse SCI model, minocycline treatment reduced the accumulation of microglia and decreased RGMa expression after SCI, leading to reduced dieback in injured corticospinal tracts. These results suggest that activated microglia play a major role in inhibiting axon regeneration via RGMa in the injured CNS

Inflammogenesis of Secondary Spinal Cord Injury

Spinal cord injury (SCI) and spinal infarction lead to neurological complications and eventually to paraplegia or quadriplegia. These extremely debilitating conditions are major contributors to morbidity. Our understanding of SCI has certainly increased during the last decade, but remains far from clear. SCI consists of two defined phases: the initial impact causes primary injury, which is followed by a prolonged secondary injury consisting of evolving sub-phases that may last for years. The underlying pathophysiological mechanisms driving this condition are complex. Derangement of the vasculature is a notable feature of the pathology of SCI. In particular, an important component of SCI is the ischemia-reperfusion injury (IRI) that leads to endothelial dysfunction and changes in vascular permeability. Indeed, together with endothelial cell damage and failure in homeostasis, ischemia reperfusion injury triggers full-blown inflammatory cascades arising from activation of residential innate immune cells (microglia and astrocytes) and infiltrating leukocytes (neutrophils and macrophages). These inflammatory cells release neurotoxins (proinflammatory cytokines and chemokines, free radicals, excitotoxic amino acids, nitric oxide (NO)), all of which partake in axonal and neuronal deficit. Therefore, our review considers the recent advances in SCI mechanisms, whereby it becomes clear that SCI is a heterogeneous condition. Hence, this leads towards evidence of a restorative approach based on monotherapy with multiple targets or combinatorial treatment. Moreover, from evaluation of the existing literature, it appears that there is an urgent requirement for multi-centered, randomized trials for a large patient population. These clinical studies would offer an opportunity in stratifying SCI patients at high risk and selecting appropriate, optimal therapeutic regimens for personalized medicine.Grant #NPRP 4-571-3-171 from the Qatar National Research Fund(a member of Qatar Foundation)

Basic illustration of how a GNB classifier can perform well in a categorization task, even when there is task-relevant covariance between the input dimensions.

<p>The task, showing in panel (<b>a</b>) is to distinguish between sumo wrestlers and basketball players, based on the input dimensions of height and weight. Only considering one dimension at a time is insufficient to perform the categorization. However, as panel (<b>b</b>) illustrates, the classification boundary drawn by a GNB (shown in green) is almost identical to that drawn by linear discriminant analysis (LDA, shown in purple). The two different classifiers give different class predictions only in a very small part of the input space, marked with black crosses. The LDA classifier is Fisher's Linear Discriminant, which is similar to a GNB in that it models the mean and variance of the data's input dimensions, but different in that it also models the covariance of the dimensions.</p

Illustration of how a Gaussian Naive Bayes (GNB) classifier works.

<p>For each data point, the z-score distance between that point and each class-mean is calculated, namely the distance from the class mean divided by the standard deviation of that class. Note that this schematic just shows one dimension, whereas a crucial distinction between GNBs and other classifiers arises only when there is more than one input dimension: the GNB does not model the covariance between dimensions, but other types of classifier do.</p

Comparison of the smoothness of searchlight maps generated by different classifiers.

<p>(<b>a</b>) Illustrative slices drawn from one individual. It can be seen from a simple visual comparison that the smoothest information maps arise from using GNB classifiers, which do not model covariance. (<b>b</b>) <b>A quantitative comparison, showing</b> Fourier power of the different images at a range of spatial frequencies, averaged across all 13 subjects. Images that are less smooth have more “salt and pepper” noise, and therefore have more power in the higher spatial frequencies. Error bars show the standard error of the mean, across the 13 subjects. The curves are statistically significantly different from each other (two-sample t-test, p<0.05) for spatial frequencies of 21 cycles per image and over.</p

ANALYSIS OF DEBRIS FLOW BEHAVIOR USING AIRBORNE LIDAR AND IMAGE DATA

The frequency of debris flow events caused by severe rainstorms has increased in Korea. LiDAR provides high-resolution topographical data that can represent the land surface more effectively than other methods. This study describes the analysis of geomorphologic changes using digital surface models derived from airborne LiDAR and aerial image data acquired before and after a debris flow event in the southern part of Seoul, South Korea in July 2011. During this event, 30 houses were buried, 116 houses were damaged, and 22 human casualties were reported. Longitudinal and cross-sectional profiles of the debris flow path reconstructed from digital surface models were used to analyze debris flow behaviors such as landslide initiation, transport, erosion, and deposition. LiDAR technology integrated with GIS is a very useful tool for understanding debris flow behavior