NeuroImage
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Gray's Reinforcement Sensitivity Theory (RST) has developed into one of the most prominent personality theories of the last decades. The RST postulates a Behavioral Inhibition System (BIS) modulating the reaction to stimuli indicating aversive events. A number of psychiatric disorders including depression, anxiety disorders, and psychosomatic illnesses have been associated with extreme BIS responsiveness. ⋯ Thus, we show converging evidence of different neural implementation of the BIS depending on genotype-dependent levels of serotonin. We provide evidence suggesting that genotype-dependent serotonin levels and thus putative changes in the efficiency of serotonergic neurotransmission might not only alter brain activation levels directly, but also more fundamentally impact the neural implementation of personality traits. We outline the direct clinical implications arising from this finding and discuss the complex interplay of neural responses, genes and personality traits in this context.
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Perhaps more than any other "-omics" endeavor, the accuracy and level of detail obtained from mapping the major connection pathways in the living human brain with diffusion MRI depend on the capabilities of the imaging technology used. The current tools are remarkable; allowing the formation of an "image" of the water diffusion probability distribution in regions of complex crossing fibers at each of half a million voxels in the brain. Nonetheless our ability to map the connection pathways is limited by the image sensitivity and resolution, and also the contrast and resolution in encoding of the diffusion probability distribution. ⋯ To augment this accelerated imaging approach we developed a 64-channel, tight-fitting brain array coil and show its performance benefit compared to a commercial 32-channel coil at all locations in the brain for these accelerated acquisitions. The technical challenges of developing the over-all system are discussed as well as results from SNR comparisons, ODF metrics and fiber tracking comparisons. The ultra-high gradients yielded substantial and immediate gains in the sensitivity through reduction of TE and improved signal detection and increased efficiency of the DSI or HARDI acquisition, accuracy and resolution of diffusion tractography, as defined by identification of known structure and fiber crossing.
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Imaging the connectome in vivo has become feasible through the integration of several rapidly developing fields of science and engineering, namely magnetic resonance imaging and in particular diffusion MRI on one side, image processing and network theory on the other side. This framework brings in vivo brain imaging closer to the real topology of the brain, contributing to narrow the existing gap between our understanding of brain structural organization on one side and of human behavior and cognition on the other side. Given the seminal technical progresses achieved in the last few years, it may be ready to tackle even greater challenges, namely exploring disease mechanisms. ⋯ We analyze for each step (i.e. MRI acquisition, network building and network statistical analysis) the advantages and potential limitations. In the second part we review the current literature available on a selected subset of diseases, namely, dementia, schizophrenia, multiple sclerosis and others, and try to extract for each disease the common findings and main differences between reports.
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With the increasing availability of advanced imaging technologies, we are entering a new era of neuroscience. Detailed descriptions of the complex brain network enable us to map out a structural connectome, characterize it with graph theoretical methods, and compare it to the functional networks with increasing detail. ⋯ Recently, resting-state models with varying local dynamics have reproduced empirical functional connectivity patterns, and given support to the view that the brain works at a critical point at the edge of a bifurcation of the system. Here, we present an overview of a modeling approach of the resting brain network and give an application of a neural mass model in the study of complexity changes in aging.
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The human connectome refers to a map of the brain's structural connections, rendered as a connection matrix or network. This article attempts to trace some of the historical origins of the connectome, in the process clarifying its definition and scope, as well as its putative role in illuminating brain function. Current efforts to map the connectome face a number of significant challenges, including the issue of capturing network connectivity across multiple spatial scales, accounting for individual variability and structural plasticity, as well as clarifying the role of the connectome in shaping brain dynamics. Throughout, the article argues that these challenges require the development of new approaches for the statistical analysis and computational modeling of brain network data, and greater collaboration across disciplinary boundaries, especially with researchers in complex systems and network science.