What affects detectability of lesion-deficit relationships in lesion studies?
Elucidating the brain basis for psychological processes and behavior is a fundamental aim of cognitive neuroscience. The lesion method, using voxel-based statistical analysis, is an important approach to this goal, identifying neural structures that are necessary for the support of specific mental operations, and complementing the strengths of functional imaging techniques. Lesion coverage in a population is by nature spatially heterogeneous and biased, systematically affecting the ability of lesion-deficit correlation methods to detect and localize functional associations. We have developed a simulator that allows investigators to model parameters in a lesion-deficit study and characterize the statistical bias in lesion deficit detection coverage that will result from specific assumptions. We used the simulator to assess the signal detection properties and localization accuracy of standard lesion-deficit correlation methods, under a simple truth model - that a critical region of interest (CR), when damaged, gives rise to a deficit. We considered voxel-based lesion-symptom mapping (VLSM) and proportional MAP-3 (PM3). Using regression analysis, we examined if the pattern of outcome statistics can be explained by simulation parameters, factors that are inherent to anatomic parcels, and lesion coverage of the population, which consisted of a representative sample of 351 subjects drawn from the Iowa Patient Registry. We examined the effect of using nonparametric versus parametric statistics to obtain thresholded maps and the effect of correcting for multiple comparisons using false discovery rate or cluster-based correction. Our results, which are derived from samples of realistic lesions, indicate that even a simple truth model yields localization errors that are systematic and pervasive, averaging 2 cm in the standard anatomic space, and tending to be directed towards areas of greater anatomic coverage. This displacement positions the center of mass of the detected region in a different anatomical region 87% of the time. This basic result is not affected by the choice of PM3 vs VLSM as the fundamental approach, nor is localization error ameliorated by incorporation of lesion size as a covariate in the VLSM approach, or by data distribution-driven approaches to controlling multiple spatial comparisons (false discovery rate or cluster-based correction approaches). Our simulations offer a quantitative basis for interpreting lesion studies in cognitive neuroscience. We suggest ways in which lesion simulation and analysis frameworks could be productively extended.
Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA ; Integrated Brain Imaging Center, University of Washington Medical Center, Seattle, WA, USA.
Kayo
K
Inoue
Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA ; Integrated Brain Imaging Center, University of Washington Medical Center, Seattle, WA, USA.
Tara
T
Madhyastha
Grenoble Institut des Neurosciences Inserm U 836 - UJF - CEA - CHU, Grenoble, France.
David
D
Rudrauf
Department of Psychology, University of Washington, Seattle, WA, USA.
Sonya
S
Mehta
Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA ; Integrated Brain Imaging Center, University of Washington Medical Center, Seattle, WA, USA ; Department of Neurology, University of Washington Medical Center, Seattle, WA, USA.
Thomas
T
Grabowski
D001921:Brain / Q000473:pathology*; D001930:Brain Injuries / Q000175:diagnosis* / Q000523:psychology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted; D006801:Humans
Lesion studies; Lesion–deficit relationship; PM3; VLSM