Cluster success: fMRI inferences for spatial extent have acceptable false-positive rates

Abstract

In an editorial (this issue), I argued that Eklund, Nichols, and Knutsson's 'null data' reflected resting-state/default network activity that inflated their false-positive rates. Commentaries on that paper were received by Nichols, Eklund, and Knutsson (this issue), Hopfinger (this issue), and Cunningham and Koscik (this issue). In this author response, I consider these commentaries. Many issues stemming from Nichols et al. are identified including: (1) Nichols et al. did not provide convincing arguments that resting-state fMRI data reflect null data. (2) Eklund et al. presented one-sample t-test results in the main body of their paper showing that their permutation method was acceptable, while their supplementary results showed that this method produced false-positive rates that were similar to other methods. (3) Eklund et al. used the same event protocol for all the participants, which artifactually inflated the one-sample t-test false-positive rates. (4) At p < .001, using two-sample t-tests (which corrected for the flawed analysis), all the methods employed to correct for multiple comparisons had acceptable false-positive rates. (5) Eklund et al. contrasted resting-state periods, which produced many significant clusters of activity, while null data should arguably be associated with few, if any, significant activations. Eklund et al.'s entire set of results show that commonly employed methods to correct for multiple comparisons have acceptable false-positive rates. Following Hopfinger along with Cunningham and Koscik, it is also highlighted that rather than focusing on only type I error, type I error and type II error should be balanced in fMRI analysis.

Interictal Hyperperfusion in the Higher Visual Cortex in Patients With Episodic Migraine

“…51 Based on the ASL recording parameters, a cluster size of k = 44 was required to cluster correct the results at a threshold of P ≤ .05 (cluster-corrected). 51 Based on the ASL recording parameters, a cluster size of k = 44 was required to cluster correct the results at a threshold of P ≤ .05 (cluster-corrected).…”

“…51,52 In brief, the automated surface-based reconstruction processing stream consists of skull stripping, Talairach space transformation, gray matter (GM), white matter (WM) and cerebral spinal fluid (CSF) boundaries optimization, segmentation of subcortical structures, tessellation, and surface deformation. 51,52 In brief, the automated surface-based reconstruction processing stream consists of skull stripping, Talairach space transformation, gray matter (GM), white matter (WM) and cerebral spinal fluid (CSF) boundaries optimization, segmentation of subcortical structures, tessellation, and surface deformation.…”

“…Recently, the validity of the applied method has been demonstrated, ie, fMRI inferences for spatial extent have acceptable false-positive rates. 51 Based on the ASL recording parameters, a cluster size of k = 44 was required to cluster correct the results at a threshold of P ≤ .05 (cluster-corrected). Age, sex, and global CBF were included as covariates in all analyses.…”

Electrophysiological evidence of sustained spatial attention effects over anterior cortex: Possible contribution of the anterior insula

“…ERP studies, usually focusing on poststimulus brain activity, showed that visuospatial attention enhances activities originating in dorsal and ventral extrastriate pathways, indexed by the P1 and the N1 components. Furthermore, visuospatial attention does not affect the afferent activities originating in the primary visual area (V1), indexed by the C1 component (Baumgartner, Graulty, Hillyard, & Pitts, ; Di Russo, ; Gómez‐Gonzalez, Clark, Fan, Luck, & Hillyard, ; Pitts & Hillyard, ; however, see Slotnick, for opposite results). Regarding attention effects on frontal areas, ERP evidence is scarce.…”

“…However, none of them identified this component as the later and multimodal decisional process of the anterior insula. Summarizing, studies in this field associated each of the three prefrontal ERPs with specific and known stages of processing of the anterior insula: the pN1 with perceptual awareness, the pP1 with the conscious experience of the sensory-motor coupling, and the pP2 with the decisional process of categorization and evidence accumulation Bianco, Berchicci, Perri, Quinzi, & Di Russo, 2017a;Di Russo et al, 2016,2017Gonçalves et al, 2018;Perri, Berchicci, Bianco, Quinzi, et al, 2018;Perri, Berchicci, Lucci, Spinelli, & Di Russo, 2015a,2015bRagazzoni et al, 2019;Sulpizio et al, 2017). So far, these prefrontal components were measured using centrally displayed stimuli, without manipulating spatial attention; thus, the question is open on whether spatial attention may affect them.…”