baseline") were extracted to obtain a coordinates-based database that was used to run a meta-analysis using both CluB and the popular Activation Likelihood Estimation method implemented in the software GingerALE. The results of the two meta-analyses were compared against the "Gold Standard" to compute performance measures, i.e., sensitivity, specificity, and accuracy. The GingerALE method obtained a high level of accuracy (0.967) associated with a high sensitivity (0.728) and specificity (0.971). The CluB method obtained a similar level of accuracy (0.956) and specificity (0.969), notwithstanding a lower level of sensitivity (0.14) due to the lack of prior Gaussian transformation of the data. Finally, the two methods obtained a good-level of concordance (AC" />
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Berlingeri M, Devoto F, Gasparini F, et al. Clustering the Brain With "CluB": A New Toolbox for Quantitative Meta-Analysis of Neuroimaging Data. Front Neurosci. 2019;13:1037doi: 10.3389/fnins.2019.01037.
Berlingeri, M., Devoto, F., Gasparini, F., Saibene, A., Corchs, S. E., Clemente, L., Danelli, L., Gallucci, M., Borgoni, R., Borghese, N. A., & Paulesu, E. (2019). Clustering the Brain With "CluB": A New Toolbox for Quantitative Meta-Analysis of Neuroimaging Data. Frontiers in neuroscience, 131037. https://doi.org/10.3389/fnins.2019.01037
Berlingeri, Manuela, et al. "Clustering the Brain With "CluB": A New Toolbox for Quantitative Meta-Analysis of Neuroimaging Data." Frontiers in neuroscience vol. 13 (2019): 1037. doi: https://doi.org/10.3389/fnins.2019.01037
Berlingeri M, Devoto F, Gasparini F, Saibene A, Corchs SE, Clemente L, Danelli L, Gallucci M, Borgoni R, Borghese NA, Paulesu E. Clustering the Brain With "CluB": A New Toolbox for Quantitative Meta-Analysis of Neuroimaging Data. Front Neurosci. 2019 Oct 22;13:1037. doi: 10.3389/fnins.2019.01037. eCollection 2019. PMID: 31695593; PMCID: PMC6817507.
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Front Neurosci. 2019 Oct 22;13:1037. doi: 10.3389/fnins.2019.01037. eCollection 2019.

Clustering the Brain With "CluB": A New Toolbox for Quantitative Meta-Analysis of Neuroimaging Data.

Frontiers in neuroscience

Manuela Berlingeri, Francantonio Devoto, Francesca Gasparini, Aurora Saibene, Silvia E Corchs, Lucia Clemente, Laura Danelli, Marcello Gallucci, Riccardo Borgoni, Nunzio Alberto Borghese, Eraldo Paulesu

Affiliations

  1. DISTUM, Department of Humanistic Studies, University of Urbino Carlo Bo, Urbino, Italy.
  2. NeuroMI, Milan Centre for Neuroscience, Milan, Italy.
  3. Center of Developmental Neuropsychology, ASUR Marche, Pesaro, Italy.
  4. Psychology Department and PhD Program in Neuroscience of the School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy.
  5. fMRI Unit, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
  6. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.
  7. Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy.
  8. Psychology Department, University of Milano-Bicocca, Milan, Italy.
  9. Department of Computer Science, Università degli Studi di Milano, Milan, Italy.

PMID: 31695593 PMCID: PMC6817507 DOI: 10.3389/fnins.2019.01037

Abstract

In this paper we describe and validate a new coordinate-based method for meta-analysis of neuroimaging data based on an optimized hierarchical clustering algorithm: CluB (Clustering the Brain). The CluB toolbox permits both to extract a set of spatially coherent clusters of activations from a database of stereotactic coordinates, and to explore each single cluster of activation for its composition according to the cognitive dimensions of interest. This last step, called "cluster composition analysis," permits to explore neurocognitive effects by adopting a factorial-design logic and by testing the working hypotheses using either asymptotic tests, or exact tests either in a classic inference, or in a Bayesian-like context. To perform our validation study, we selected the fMRI data from 24 normal controls involved in a reading task. We run a standard random-effects second level group analysis to obtain a "Gold Standard" of reference. In a second step, the subject-specific reading effects (i.e., the linear t-contrast "reading > baseline") were extracted to obtain a coordinates-based database that was used to run a meta-analysis using both CluB and the popular Activation Likelihood Estimation method implemented in the software GingerALE. The results of the two meta-analyses were compared against the "Gold Standard" to compute performance measures, i.e., sensitivity, specificity, and accuracy. The GingerALE method obtained a high level of accuracy (0.967) associated with a high sensitivity (0.728) and specificity (0.971). The CluB method obtained a similar level of accuracy (0.956) and specificity (0.969), notwithstanding a lower level of sensitivity (0.14) due to the lack of prior Gaussian transformation of the data. Finally, the two methods obtained a good-level of concordance (AC

Copyright © 2019 Berlingeri, Devoto, Gasparini, Saibene, Corchs, Clemente, Danelli, Gallucci, Borgoni, Borghese and Paulesu.

Keywords: anatomical segregation; clustering; clusters composition; cognitive dimensions; coordinate-based meta-analysis; non-parametric statistics

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