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      A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

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          Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

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          Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

          An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
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            The Psychophysics Toolbox.

            The Psychophysics Toolbox is a software package that supports visual psychophysics. Its routines provide an interface between a high-level interpreted language (MATLAB on the Macintosh) and the video display hardware. A set of example programs is included with the Toolbox distribution.
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              Complex brain networks: graph theoretical analysis of structural and functional systems.

              Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.

                Author and article information

                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                10 November 2016
                : 10
                1Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho Braga, Portugal
                2ICVS/3B's - PT Government Associate Laboratory Braga, Portugal
                3Department of Informatics, University of Minho Braga, Portugal
                4Neuropsychophysiology Lab, CIPsi, School of Psychology, University of Minho Braga, Portugal
                5Clinical Academic Center – Braga Braga, Portugal
                Author notes

                Edited by: Amir Shmuel, McGill University, Canada

                Reviewed by: Xin Di, New Jersey Institute of Technology, USA; Gerard R. Ridgway, University of Oxford, UK; Boris Bernhardt, Montreal Neurological Institute and Hospital, Canada

                *Correspondence: José M. Soares josesoares@

                This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience

                Copyright © 2016 Soares, Magalhães, Moreira, Sousa, Ganz, Sampaio, Alves, Marques and Sousa.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Figures: 2, Tables: 4, Equations: 1, References: 549, Pages: 35, Words: 32995
                Funded by: Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa 10.13039/501100005856
                Award ID: FCT-ANR/NEU-OSD/0258/2012
                Award ID: PDE/BDE/113604/2015
                Award ID: PDE/BDE/113601/2015
                Funded by: Fundação Calouste Gulbenkian 10.13039/501100005635
                Award ID: P-139977
                Funded by: European Regional Development Fund 10.13039/501100008530


                fmri, hitchhiker's guide, acquisition, preprocessing, analysis


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