NeuroImage
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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.
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Functional imaging or diffusion-weighted imaging techniques are widely used to understand brain connectivity at the systems level and its relation to normal neurodevelopment, cognition or brain disorders. It is also possible to extract information about brain connectivity from the covariance of morphological metrics derived from anatomical MRI. These covariance patterns may arise from genetic influences on normal development and aging, from mutual trophic reinforcement as well as from experience-related plasticity. This review describes the basic methodological strategies, the biological basis of the observed covariance as well as applications in normal brain and brain disease before a final review of future prospects for the technique.
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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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Review Meta Analysis
A meta-analysis of fMRI studies on Chinese orthographic, phonological, and semantic processing.
A growing body of neuroimaging evidence has shown that Chinese character processing recruits differential activation from alphabetic languages due to its unique linguistic features. As more investigations on Chinese character processing have recently become available, we applied a meta-analytic approach to summarize previous findings and examined the neural networks for orthographic, phonological, and semantic processing of Chinese characters independently. The activation likelihood estimation (ALE) method was used to analyze eight studies in the orthographic task category, eleven in the phonological and fifteen in the semantic task categories. ⋯ Functional dissociation was identified in the left inferior frontal gyrus, with the posterior dorsal part for phonological processing and the anterior ventral part for semantic processing. Moreover, bilateral involvement of the ventral occipito-temporal regions was found for both phonological and semantic processing. The results provide better understanding of the neural networks underlying Chinese orthographic, phonological, and semantic processing, and consolidate the findings of additional recruitment of the left middle frontal gyrus and the right fusiform gyrus for Chinese character processing as compared with the universal language network that has been based on alphabetic languages.