The impact of sample size on the reproducibility of voxel-based lesion-deficit mappings

Research output: Contribution to journalArticlepeer-review

Authors

  • Diego L. Lorca-Puls
  • Andrea Gajardo-Vidal
  • Jitrachote White
  • Mohamed L. Seghier
  • Alexander P. Leff
  • David W. Green
  • Jenny T. Crinion
  • Philipp Ludersdorfer
  • Thomas M. H. Hope
  • Cathy J. Price

Colleges, School and Institutes

External organisations

  • University College London
  • Department of Speech, Language and Hearing Sciences, Faculty of Medicine, Universidad de Concepcion
  • Department of Speech, Language and Hearing Sciences, Faculty of Health Sciences, Universidad del Desarrollo
  • Cognitive Neuroimaging Unit, Emirates College for Advanced Education
  • Institute of Cognitive Neuroscience, Division of Psychology and Language Sciences, University College London
  • Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London
  • Department of Experimental Psychology, Division of Psychology and Language Sciences, University College London

Abstract

This study investigated how sample size affects the reproducibility of findings from univariate voxel-based lesion-deficit analyses (e.g., voxel-based lesion-symptom mapping and voxel-based morphometry). Our effect of interest was the strength of the mapping between brain damage and speech articulation difficulties, as measured in terms of the proportion of variance explained. First, we identified a region of interest by searching on a voxel-by-voxel basis for brain areas where greater lesion load was associated with poorer speech articulation using a large sample of 360 right-handed English-speaking stroke survivors. We then randomly drew thousands of bootstrap samples from this data set that included either 30, 60, 90, 120, 180, or 360 patients. For each resample, we recorded effect size estimates and p values after conducting exactly the same lesion-deficit analysis within the previously identified region of interest and holding all procedures constant. The results show (1) how often small effect sizes in a heterogeneous population fail to be detected; (2) how effect size and its statistical significance varies with sample size; (3) how low-powered studies (due to small sample sizes) can greatly over-estimate as well as under-estimate effect sizes; and (4) how large sample sizes (N ≥ 90) can yield highly significant p values even when effect sizes are so small that they become trivial in practical terms. The implications of these findings for interpreting the results from univariate voxel-based lesion-deficit analyses are discussed.

Details

Original languageEnglish
JournalNeuropsychologia
Early online date15 Mar 2018
Publication statusE-pub ahead of print - 15 Mar 2018

Keywords

  • Voxel-based, Lesion-symptom, Lesion, Deficit, Reproducibility, Stroke, Speech production