NeuroImage
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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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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 core concept within the field of brain mapping is the use of a standardized, or "stereotaxic", 3D coordinate frame for data analysis and reporting of findings from neuroimaging experiments. This simple construct allows brain researchers to combine data from many subjects such that group-averaged signals, be they structural or functional, can be detected above the background noise that would swamp subtle signals from any single subject. Where the signal is robust enough to be detected in individuals, it allows for the exploration of inter-individual variance in the location of that signal. ⋯ Accounting, or not, for these various factors in defining stereotaxic space has created the specter of an ever-expanding set of atlases, customized for a particular experiment, that are mutually incompatible. These difficulties continue to plague the brain mapping field. This review article summarizes the evolution of stereotaxic space in term of the basic principles and associated conceptual challenges, the creation of population atlases and the future trends that can be expected in atlas evolution.
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Review Historical Article
Future trends in Neuroimaging: Neural processes as expressed within real-life contexts.
Human neuroscience research has changed dramatically with the proliferation and refinement of functional magnetic resonance imaging (fMRI) technologies. The early years of the technique were largely devoted to methods development and validation, and to the coarse-grained mapping of functional topographies. This paper will cover three emerging trends that we believe will be central to fMRI research in the coming decade. ⋯ In the last section, we highlight the trend towards more ecologically valid fMRI experiments, which engage neural circuits in real life conditions. We note that many of our cognitive faculties emerge from interpersonal interactions, and that a complete understanding of the cognitive processes within a single individual's brain cannot be achieved without understanding the interactions among individuals. Looking forward to the future of human fMRI, we conclude that the major constraint on new discoveries will not be related to the spatiotemporal resolution of the BOLD signal, which is constantly improving, but rather to the precision of our hypotheses and the creativity of our methods for testing them.