NeuroImage
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This article provides a personal perspective of the adoption of path analysis (structural equation modeling) to neuroimaging. The paper covers the motivation stemming from the need to merge functional measures with neuroanatomy and early innovations in its application. The use of path analysis as a means to test directional hypotheses about networks is presented along with the development of the complementary method, partial least squares. A method is useful when it provides insights that were previously inaccessible, and reflecting this, the paper concludes with a synopsis of the theoretical developments that arose for the routine use of methods like path analysis.
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Connectivity is fundamental for understanding the nature of brain function. The intricate web of synaptic connections among neurons is critically important for shaping neural responses, representing statistical features of the sensory environment, coordinating distributed resources for brain-wide processing, and retaining a structural record of the past in order to anticipate future events and infer their relations. ⋯ Network science approaches have been productively deployed in other domains of science and technology and are now beginning to make contributions across many areas of neuroscience. This article offers a personal perspective on the confluence of networks and neuroimaging, charting the origins of some of its major intellectual themes.
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The opportunity to explore the human connectome using cutting-edge neuroimaging methods has elicited widespread interest. How far will the field be able to progress in deciphering long-distance connectivity patterns and in relating differences in connectivity to phenotypic characteristics in health and disease? We discuss the daunting nature of this challenge in relation to specific complexities of brain circuitry and known limitations of in vivo imaging methods. We also discuss the excellent prospects for continuing improvements in data acquisition and analysis. Accordingly, we are optimistic that major insights will emerge from human connectomics in the coming decade.
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In this short review article I will summarize the path we took over the years towards increasing the spatial resolution of fMRI. To fully capitalize on the fMRI technique, a better understanding of the origin of the hemodynamic signals, and what factors are governing their spatial control is necessary. Here, I will briefly describe the studies and developments that ultimately led to our successful effort in mapping orientation columns in humans that is considered by many as the current state-of-the-art for fMRI studies.
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Blood inflow from the upstream has contribution or contamination to the blood oxygen level-dependent (BOLD) functional signal both in its magnitude and time courses. During neuronal activations, regional blood flow velocity increases which results in increased fMRI signals near the macrovasculatures. The inflow effects are dependent on RF pulse history, slice geometry, flow velocity, blood relaxation times and imaging parameters. ⋯ This article reviews the basic principle of the inflow effects, its appearances in conventional GRE, fast spin-echo (FSE) and echo-planar imaging (EPI) acquisitions, methods for separating the inflow from the BOLD effect as well as the interplay between imaging parameters and other physiological factors with the inflow effects in fMRI. Based on theoretical derivation and human experiments, the inflow effects have been shown to contribute significantly in conventional GRE but negligible in FSE acquisitions. For gradient-echo EPI experiments, the blood inflow could modulate both amplitude and the temporal information of the fMRI signal, depending on the imaging parameters and settings.