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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.

PurposeTo develop and evaluate a local frequency shift (LFS) mapping method specifically designed for multi‐echo acquisitions and multi‐channel receive coils.MethodsThe proposed method uses the pixel‐by‐pixel inter‐echo variance (IEV) as a weighting factor during channel‐combination. Five healthy volunteers were scanned at 7 T. The IEV‐weighted method was quantitatively compared to established (adaptive and Hermitian product) channel‐combination methods with respect to IEV of LFS over the entire brain.ResultsIn all experiments, the IEV‐weighted method generated LFS maps free of artifacts caused by unwrapping errors. Based on measurements of the inter‐echo frequency variance throughout the whole brain, the IEV‐weighted method produced the lowest variation and the best contrast at the edge of the brain.ConclusionThe primary finding of the present study is that accurate LFS maps are achievable if the data from each channel is processed independently prior to combination followed by a weighted combination using IEV as the weighting term. The software is freely available to the scientific community. Magn Reson Med 73:1654–1661, 2015. © 2014 Wiley Periodicals, Inc.

AbstractModels of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.

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

We examined ERP responses during the generation of word associates or mental images in response to concrete and abstract concepts. Of interest were the predictions of dual coding theory (DCT), which proposes that processing lexical concepts depends on functionally independent but interconnected verbal and nonverbal systems. ERP responses were time-locked to either stimulus onset or response to compensate for potential latency differences across conditions. During word associate generation, but not mental imagery, concrete items elicited a greater N400 than abstract items. A concreteness effect emerged at a later time point during the mental imagery task. Data were also analyzed using time-frequency analysis that investigated synchronization of neuronal populations over time during processing. Concrete words elicited an enhanced late going desynchronization of theta-band power (723-938 ms post stimulus onset) during associate generation. During mental imagery, abstract items elicited greater delta-band power from 800 to 1,000 ms following stimulus onset, theta-band power from 350 to 205 ms before response, and alpha-band power from 900 to 800 ms before response. Overall, the findings support DCT in suggesting that lexical concepts are not amodal and that concreteness effects are modulated by tasks that focus participants on verbal versus nonverbal, imagery-based knowledge.

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.

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.