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There is accumulating evidence from behavioral, neurophysiological, and neuroimaging studies that the acquisition of motor skills involves both perceptual and motor learning. Perceptual learning alters movements, motor learning, and motor networks of the brain. Motor learning changes perceptual function and the sensory circuits of the brain. Here, we review studies of both human limb movement and speech that indicate that plasticity in sensory and motor systems is reciprocally linked. Taken together, this points to an approach to motor learning in which perceptual learning and sensory plasticity have a fundamental role.

In recent years, rapid technological developments in the field of neuroimaging have provided new methods for assessing residual cognition, detecting consciousness, and even communicating with patients who clinically appear to be in a vegetative state. Here, I highlight some of the major implications of these developments, discuss their scientific, clinical, legal, and ethical relevance, and make my own recommendations for future directions in this field. In this NeuroView, Owen highlights some of the major implications of technological developments in neuroimaging, discusses their scientific, clinical, legal, and ethical relevance, and makes recommendations for future directions in this field.

International audience; Hypovitaminosis D is associated with cognitive decline in the elderly, but the issue of causality remains unresolved. Definitive evidence would include the visualization of brain lesions resulting from hypovitaminosis D. The aim of the present article is to determine, through a literature review, the location and nature of possible brain disorders in hypovitaminosis D. We found limited brain-imaging data, which reported ischemic infarcts and white matter hyperintensities in hypovitaminosis D, though did not provide their specific location or report any focal atrophy. Based on the finding of executive dysfunctions (i.e., mental shifting and information updating impairments) in the presence of hypovitaminosis D, we suggest that hypovitaminosis D is associated with a dysfunction of the frontal-subcortical neuronal circuits, particularly the dorsolateral circuit. Further imaging studies are required to corroborate this assumption and to determine whether hypovitaminosis D results in degenerative and / or vascular lesions.

AbstractIntroductionIndependent component analysis (ICA) has been extensively used for reducing task‐free BOLD fMRI recordings into spatial maps and their associated time‐courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non‐contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data.ObjectiveHere, we detail a graph building technique that allows these ICNs to be analyzed with graph theory.MethodsFirst, ICA was performed at the single‐subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple‐template matching procedure and a subsequent component classification based on the network “neuronal” properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between‐node functional connectivity was established by building edge weights for each networks. Group‐level graph analysis was finally performed for each network and compared to the classical network.ResultsNetwork graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small‐worldness.ConclusionsThis novel approach permits us to take advantage of the well‐recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well‐established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.

In this project, we will uncover any existing interaction between violent media and the capacity for empathic reactions (known as trait empathy) at a neural level, as the foundational milestone for the kind of large-scale study discussed above. The level of trait empathy varies by individual - we will explore whether exposure to violent media exacerbates the difficulties that those with low trait empathy already show in relating to the emotions of others https://ir.lib.uwo.ca/brainscanprojectsummaries/1021/thumbnail.jpg

Neuroimaging research has demonstrated that the posterior cingulate cortex (PCC) is functionally compromised in individuals diagnosed with amnestic Mild Cognitive Impairment (MCI), a major risk factor for the development of Alzheimer’s disease (AD). In functional magnetic resonance imaging (fMRI) studies with healthy participants, this same region is active during self-appraisal (requiring retrieval of semantic knowledge about the self) as well as episodic recognition of recently-learned information. Administering both types of tasks to people with MCI may reveal important information regarding the role of the PCC in recollection. This study investigated fMRI activation in the PCC in individuals with MCI and age, gender, and education-matched controls across two tasks. The first task was a visual episodic recognition task in which participants indicated whether pictures had or had not been presented during a study session. The second task was an autobiographical self-appraisal task in which subjects rated themselves on a set of trait adjectives. Results of a conjunction analysis revealed the PCC as the sole region commonly active during both tasks in the healthy older adults. Furthermore, additional analysis revealed an interaction in the PCC indicating a task-dependent response in the MCI group. MCI participants showed PCC activation during self-appraisal, but not during episodic retrieval. These results suggest in MCI that the PCC shows functional degradation during episodic retrieval of visual information learned in the laboratory. In contrast, the PCC’s role in retrieval and evaluation of highly-elaborated information regarding the self is more well-preserved.

Abstract Humans can mentally represent auditory information without an external stimulus, but the specificity of these internal representations remains unclear. Here, we asked how similar the temporally unfolding neural representations of imagined music are compared to those during the original perceived experience. We also tested whether rhythmic motion can influence the neural representation of music during imagery as during perception. Participants first memorized six 1-min-long instrumental musical pieces with high accuracy. Functional MRI data were collected during: 1) silent imagery of melodies to the beat of a visual metronome; 2) same but while tapping to the beat; and 3) passive listening. During imagery, inter-subject correlation analysis showed that melody-specific temporal response patterns were reinstated in right associative auditory cortices. When tapping accompanied imagery, the melody-specific neural patterns were reinstated in more extensive temporal-lobe regions bilaterally. These results indicate that the specific contents of conscious experience are encoded similarly during imagery and perception in the dynamic activity of auditory cortices. Furthermore, rhythmic motion can enhance the reinstatement of neural patterns associated with the experience of complex sounds, in keeping with models of motor to sensory influences in auditory processing.

How do populations of neurons represent a variable of interest? The notion of feature spaces is a useful concept to approach this question: According to this model, the activation patterns across a neuronal population are composed of different pattern components. The strength of each of these components varies with one latent feature, which together are the dimensions along which the population represents the variable. Here we propose a new method to determine the number of feature dimensions that best describes the activation patterns. The method is based on Gaussian linear classifiers that use only the first d most important pattern dimensions. Using cross-validation, we can identify the classifier that best matches the dimensionality of the neuronal representation. We test this method on two datasets of motor cortical activation patterns measured with functional magnetic resonance imaging (fMRI), during (i) simultaneous presses of all fingers of a hand at different force levels and (ii) presses of different individual fingers at a single force level. As expected, the new method shows that the representation of force is low-dimensional; the neural activation for different force levels is scaled versions of each other. In comparison, individual finger presses are represented in a full, four-dimensional feature space. The approach can be used to determine an important characteristic of neuronal population codes without knowing the form of the underlying features. It therefore provides a novel tool in the building of quantitative models of neuronal population activity as measured with fMRI or other approaches. Highlights • Neural population activity represents external variables using multiple feature dimensions. • We present a general method to determine the dimensionality of this feature space. • Primary motor cortex represents force through a low-dimensional feature space. • Individual finger movements are represented using a four-dimensional feature space.