title: Coordinate based random effect size meta-analysis of neuroimaging studies.
abstract: Low power in neuroimaging studies can make them difficult to interpret, and Coordinate based meta-analysis (CBMA) may go some way to mitigating this issue. CBMA has been used in many analyses to detect where published functional MRI or voxel-based morphometry studies testing similar hypotheses report significant summary results (coordinates) consistently. Only the reported coordinates and possibly t statistics are analysed, and statistical significance of clusters is determined by coordinate density. Here a method of performing coordinate based random effect size meta-analysis and meta-regression is introduced. The algorithm (ClusterZ) analyses both coordinates and reported t statistic or Z score, standardised by the number of subjects. Statistical significance is determined not by coordinate density, but by a random effects meta-analyses of reported effects performed cluster-wise using standard statistical methods and taking account of censoring inherent in the published summary results. Type 1 error control is achieved using the false cluster discovery rate (FCDR), which is based on the false discovery rate. This controls both the family wise error rate under the null hypothesis that coordinates are randomly drawn from a standard stereotaxic space, and the proportion of significant clusters that are expected under the null. Such control is necessary to avoid propagating and even amplifying the very issues motivating the meta-analysis in the first place. ClusterZ is demonstrated on both numerically simulated data and on real data from reports of grey matter loss in multiple sclerosis (MS) and syndromes suggestive of MS, and of painful stimulus in healthy controls. The software implementation is available to download and use freely.
authors.affiliation: Division of Clinical Neurosciences, Clinical Neurology, University of Nottingham, Queen's Medical Centre, Nottingham, UK. Electronic address: Christopher.Tench@Nottingham.ac.uk.
authors.forename: C R
authors.initials: CR
authors.lastname: Tench
authors.affiliation: Division of Clinical Neurosciences, Clinical Neurology, University of Nottingham, Queen's Medical Centre, Nottingham, UK; Department of Neurology, University of Medicine and Pharmacy Carol Davila Bucharest, Colentina Hospital, Bucharest, Romania. Electronic address: Radu.Tanasescu@nottingham.ac.uk.
authors.forename: Radu
authors.initials: R
authors.lastname: Tanasescu
authors.affiliation: Division of Clinical Neurosciences, Clinical Neurology, University of Nottingham, Queen's Medical Centre, Nottingham, UK. Electronic address: Cris.Constantinescu@Nottingham.ac.uk.
authors.forename: C S
authors.initials: CS
authors.lastname: Constantinescu
authors.affiliation: Division of Clinical Neuroscience, Radiological Sciences, University of Nottingham, Queen's Medical Centre, Nottingham, UK. Electronic address: dorothee.auer@nottingham.ac.uk.
authors.forename: D P
authors.initials: DP
authors.lastname: Auer
authors.affiliation: Division of Clinical Neuroscience, Radiological Sciences, University of Nottingham, Queen's Medical Centre, Nottingham, UK. Electronic address: William.cottam@nottingham.ac.uk.
authors.forename: W J
authors.initials: WJ
authors.lastname: Cottam
mesh_terms: D006801:Humans; D000465:Algorithms; D001921:Brain / Q000503:physiopathology; D001931:Brain Mapping; D016000:Cluster Analysis; D003198:Computer Simulation; D015201:Meta-Analysis as Topic; D009103:Multiple Sclerosis / Q000503:physiopathology; D059906:Neuroimaging; D010146:Pain / Q000503:physiopathology; D012044:Regression Analysis; D015203:Reproducibility of Results
keywords: Functional MRI; Meta-analysis; Neuroimaging; Voxel based morphometry