Article

Variability in fMRI: a re-examination of inter-session differences.

Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Department of Clinical Neurology, Oxford University, John Radcliffe Hospital, Headington, Oxford, United Kingdom.
Human Brain Mapping (Impact Factor: 6.92). 04/2005; 24(3):248-57. DOI: 10.1002/hbm.20080
Source: PubMed

ABSTRACT We revisit a previous study on inter-session variability (McGonigle et al. [2000]: Neuroimage 11:708-734), showing that contrary to one popular interpretation of the original article, inter-session variability is not necessarily high. We also highlight how evaluating variability based on thresholded single-session images alone can be misleading. Finally, we show that the use of different first-level preprocessing, time-series statistics, and registration analysis methodologies can give significantly different inter-session analysis results.

0 Bookmarks
 · 
77 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: The volume, diversity and velocity of biomedical data are exponentially increasing providing petabytes of new neuroimaging and genetics data every year. At the same time, tens-of-thousands of computational algorithms are developed and reported in the literature along with thousands of software tools and services. Users demand intuitive, quick and platform-agnostic access to data, software tools, and infrastructure from millions of hardware devices. This explosion of information, scientific techniques, computational models, and technological advances leads to enormous challenges in data analysis, evidence-based biomedical inference and reproducibility of findings. The Pipeline workflow environment provides a crowd-based distributed solution for consistent management of these heterogeneous resources. The Pipeline allows multiple (local) clients and (remote) servers to connect, exchange protocols, control the execution, monitor the states of different tools or hardware, and share complete protocols as portable XML workflows. In this paper, we demonstrate several advanced computational neuroimaging and genetics case-studies, and end-to-end pipeline solutions. These are implemented as graphical workflow protocols in the context of analyzing imaging (sMRI, fMRI, DTI), phenotypic (demographic, clinical), and genetic (SNP) data.
    Brain Imaging and Behavior 08/2013; · 2.67 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: INTRODUCTION: People with epilepsy often need to deal with the stigma, usually worse than the epilepsy itself. In general, epilepsy is a condition that affects the behavior and the quality of life, not only for the person with epilepsy, but also for the entire family, especially because of stigma. For this reason, we can say that epilepsy has an bio-psycho-social impact on the people's life. Paradoxically, this facet of epilepsy is not often studied, especially in resource-poor countries, as Brazil, where superstitions, negative attitudes and lack of knowledge impair the relation between the community and people with epilepsy. PURPOSE: This article has the objective to introduce important aspects of stigma in epilepsy: conceptualization and models of stigma in the medical and social area; stigma and quality of life; neurobiological aspects and strategies to deal with it. CONCLUSIONS: This paper provides an overview of the stigma, including its different aspects. Stigma is a multifactorial concept and for this reason, to combat stigma it requires a broad intervention, involving medical, psychological and social areas. The understanding of the process of stigma contributes to a change of the social interpretation of the epilepsy, in a direction of a construction of a society more tolerant, where the differences are respected.
    Journal of Epilepsy and Clinical Neurophysiology 12/2006; 12(4):207-218.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Functional Magnetic Resonance Imagine (fMRI) is an important assessment tool in longitudinal studies of mental illness and its treatment. Understanding the psychometric properties of fMRI-based metrics, and the factors that influence them, will be critical for properly interpreting the results of these efforts. The current study examined whether the choice among alternative model specifications affects estimates of test-retest reliability in key emotion processing regions across a 6-month interval. Subjects (N = 46) performed an emotional-faces paradigm during fMRI in which neutral faces dynamically morphed into one of four emotional faces. Median voxelwise intraclass correlation coefficients (mvICCs) were calculated to examine stability over time in regions showing task-related activity as well as in bilateral amygdala. Four modeling choices were evaluated: a default model that used the canonical hemodynamic response function (HRF), a flexible HRF model that included additional basis functions, a modified CompCor (mCompCor) model that added corrections for physiological noise in the global signal, and a final model that combined the flexible HRF and mCompCor models. Model residuals were examined to determine the degree to which each pipeline met modeling assumptions. Results indicated that the choice of modeling approaches impacts both the degree to which model assumptions are met and estimates of test-retest reliability. ICC estimates in the visual cortex increased from poor (mvICC = 0.31) in the default pipeline to fair (mvICC = 0.45) in the full alternative pipeline - an increase of 45%. In nearly all tests, the models with the fewest assumption violations generated the highest ICC estimates. Implications for longitudinal treatment studies that utilize fMRI are discussed.
    PLoS ONE 08/2014; 9(8):e105169. · 3.53 Impact Factor

Full-text (2 Sources)

Download
35 Downloads
Available from
May 29, 2014

Narender Ramnani