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      Adaptive thresholding for reliable topological inference in single subject fMRI analysis

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          Abstract

          Single subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumor resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However, because current thresholding methods were developed mainly in the context of cognitive neuroscience group studies, most single subject fMRI maps are thresholded manually to satisfy specific criteria related to single subject analyzes. Here, we propose a new adaptive thresholding method which combines Gamma-Gaussian mixture modeling with topological thresholding to improve cluster delineation. In a series of simulations we show that by adapting to the signal and noise properties, the new method performs well in terms of total number of errors but also in terms of the trade-off between false negative and positive cluster error rates. Similarly, simulations show that adaptive thresholding performs better than fixed thresholding in terms of over and underestimation of the true activation border (i.e., higher spatial accuracy). Finally, through simulations and a motor test–retest study on 10 volunteer subjects, we show that adaptive thresholding improves reliability, mainly by accounting for the global signal variance. This in turn increases the likelihood that the true activation pattern can be determined offering an automatic yet flexible way to threshold single subject fMRI maps.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

            Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.
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              Assignment of functional activations to probabilistic cytoarchitectonic areas revisited.

              Probabilistic cytoarchitectonic maps in standard reference space provide a powerful tool for the analysis of structure-function relationships in the human brain. While these microstructurally defined maps have already been successfully used in the analysis of somatosensory, motor or language functions, several conceptual issues in the analysis of structure-function relationships still demand further clarification. In this paper, we demonstrate the principle approaches for anatomical localisation of functional activations based on probabilistic cytoarchitectonic maps by exemplary analysis of an anterior parietal activation evoked by visual presentation of hand gestures. After consideration of the conceptual basis and implementation of volume or local maxima labelling, we comment on some potential interpretational difficulties, limitations and caveats that could be encountered. Extending and supplementing these methods, we then propose a supplementary approach for quantification of structure-function correspondences based on distribution analysis. This approach relates the cytoarchitectonic probabilities observed at a particular functionally defined location to the areal specific null distribution of probabilities across the whole brain (i.e., the full probability map). Importantly, this method avoids the need for a unique classification of voxels to a single cortical area and may increase the comparability between results obtained for different areas. Moreover, as distribution-based labelling quantifies the "central tendency" of an activation with respect to anatomical areas, it will, in combination with the established methods, allow an advanced characterisation of the anatomical substrates of functional activations. Finally, the advantages and disadvantages of the various methods are discussed, focussing on the question of which approach is most appropriate for a particular situation.
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                Author and article information

                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                25 August 2012
                2012
                : 6
                : 245
                Affiliations
                [1] 1simpleSchool of Informatics, Nauroinformatics and Computational Neuroscience Doctoral Training Centre, University of Edinburgh Edinburgh, UK
                [2] 2simpleBrain Research Imaging Centre, a SINAPSE Collaboration Centre, University of Edinburgh Edinburgh, UK
                [3] 3simpleSchool of Informatics, Institute for Adaptive and Neural Computation, University of Edinburgh Edinburgh, UK
                [4] 4simpleHealth Sciences (Medical Physics), University of Edinburgh Edinburgh, UK
                Author notes

                Edited by: John J. Foxe, Albert Einstein College of Medicine, USA

                Reviewed by: Seppo P. Ahlfors, Massachusetts General Hospital/Harvard Medical School, USA; Manuel Gomez-ramirez, The Johns Hopkins University, USA; Jim Voyvodic, Duke University, USA

                *Correspondence: Krzysztof J. Gorgolewski, Neuroinformatics and Computational Neuroscience, Doctoral Training Centre, School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh, EH8 9AB, UK. e-mail: k.j.gorgolewski@ 123456sms.ed.ac.uk
                Article
                10.3389/fnhum.2012.00245
                3427544
                22936908
                9bc6f60e-a5cf-460d-96ba-51b0e51fe9f7
                Copyright © 2012 Gorgolewski, Storkey, Bastin and Pernet.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

                History
                : 12 March 2012
                : 06 August 2012
                Page count
                Figures: 7, Tables: 4, Equations: 1, References: 29, Pages: 14, Words: 8609
                Categories
                Neuroscience
                Methods Article

                Neurosciences
                reliability,spatial accuracy,random field theory,false negative errors,mixture models

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