Article

A critique of functional localisers.

The Wellcome Department of Imaging Neuroscience, Institute of Neurology, UCL, 12 Queen Square, London WC1N 3BG, UK.
NeuroImage (Impact Factor: 6.13). 06/2006; 30(4):1077-87. DOI: 10.1016/j.neuroimage.2005.08.012
Source: PubMed

ABSTRACT In this critique, we review the usefulness of functional localising scans in functional MRI studies. We consider their conceptual motivations and the implications for experimental design and inference. Functional localisers can often be viewed as acquiring data from cells that have been removed from an implicit factorial design. This perspective reveals their potentially restrictive nature. We deconstruct two examples from the recent literature to highlight the key issues. We conclude that localiser scans can be unnecessary and, in some instances, lead to a biased and inappropriately constrained characterisation of functional anatomy.

Full-text

Available from: Pia Rotshtein, May 30, 2015
0 Followers
 · 
131 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: At present, functional magnetic resonance imaging (fMRI) is one of the most useful methods of studying cognitive processes in the human brain in vivo, both for basic science and clinical goals. Although neuroscience studies often rely on group analysis, clinical applications must investigate single subjects (patients) only. Particularly for the latter, issues regarding the reliability of fMRI readings remain to be resolved. To determine the ability of intra-run variability (IRV) weighting to consistently detect active voxels, we first acquired fMRI data from a sample of healthy subjects, each of whom performed 4 runs (4 blocks each) of self-paced finger-tapping. Each subject's data was analyzed using single-run general linear model (GLM), and each block was then analyzed separately to calculate the IRV weighting. Results show that integrating IRV information into standard single-subject GLM activation maps significantly improved the reliability (p=0.007) of the single-subject fMRI data. This suggests that taking IRV into account can help identify the most constant and relevant neuronal activity at the single-subject level. Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 04/2015; 8. DOI:10.1016/j.neuroimage.2015.03.076 · 6.13 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The conventional fMRI image analysis approach to associating stimuli to brain activation is performed by carrying out a massive number of parallel univariate regression analyses. fMRI blood-oxygen-level dependent (BOLD) signal, the basis of these analyses, is known for its low signal-noise-ratio and high spatial and temporal signal correlation. In order to ensure accurate localization of brain activity, stimulus administration in an fMRI session is often lengthy and repetitive. Real-time fMRI BOLD signal analysis is carried out as the signal is observed. This method allows for dynamic, real-time adjustment of stimuli through sequential experimental designs. We have developed a voxel-wise sequential probability ratio test (SPRT) approach for dynamically determining localization, as well as decision rules for stopping stimulus administration. SPRT methods and general linear model (GLM) approaches are combined to identify brain regions that are activated by specific elements of stimuli. Stimulus administration is dynamically stopped when sufficient statistical evidence is collected to determine activation status across regions of interest, following predetermined statistical error thresholds. Simulation experiments and an example based on real fMRI data show that scan volumes can be substantially reduced when compared with pre-determined, fixed designs while achieving similar or better accuracy in detecting activated voxels. Moreover, the proposed approach is also able to accurately detect differentially activated areas, and other comparisons between task-related GLM parameters that can be formulated in a hypothesis-testing framework. Finally, we give a demonstration of SPRT being employed in conjunction with a halving algorithm to dynamically adjust stimuli.
    PLoS ONE 03/2015; 10(3):e0117942. DOI:10.1371/journal.pone.0117942 · 3.53 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Purpose: In the quantification of functional neuroimaging data, region-of-interest (ROI) analysis can be used to assess a variety of properties of the activation signal, but taken alone these properties are susceptible to noise and may fail to accurately describe overall regional involvement. Here, the authors present and evaluate an automated method for quantification and localization of functional neuroimaging data that combines multiple properties of the activation signal to generate rank-order lists of regional activation results.
    Medical Physics 02/2015; 42(2):892. DOI:10.1118/1.4906130 · 3.01 Impact Factor