Implementation of clinically relevant and robust fMRI-based language lateralization: Choosing the laterality index calculation method

Abstract

The assessment of language lateralization has become widely used when planning neurosurgery close to language areas, due to individual specificities and potential influence of brain pathology. Functional magnetic resonance imaging (fMRI) allows non-invasive and quantitative assessment of language lateralization for presurgical planning using a laterality index (LI). However, the conventional method is limited by the dependence of the LI on the chosen activation threshold. To overcome this limitation, different threshold-independent LI calculations have been reported. The purpose of this study was to propose a simplified approach to threshold-independent LI calculation and compare it with three previously reported methods on the same cohort of subjects. Fifteen healthy subjects, who performed picture naming, verb generation, and word fluency tasks, were scanned. LI values were calculated for all subjects using four methods, and considering either the whole hemisphere or an atlas-defined language area. For each method, the subjects were ranked according to the calculated LI values, and the obtained rankings were compared. All LI calculation methods agreed in differentiating strong from weak lateralization on both hemispheric and regional scales (Spearman's correlation coefficients 0.59-1.00). In general, a more lateralized activation was found in the language area than in the whole hemisphere. The new method is well suited for application in the clinical practice as it is simple to implement, fast, and robust. The good agreement between LI calculation methods suggests that the choice of method is not key. Nevertheless, it should be consistent to allow a relative comparison of language lateralization between subjects.

Cerebral Polymorphisms for Lateralisation: Modelling the Genetic and Phenotypic Architectures of Multiple Functional Modules

“…Lateralisation can also be inferred from rates of aphasia after cerebral lesions [6]. fMRI laterality indices have used various approaches, reflecting differences in threshold and the number and particular voxels included [208], with LI-tool being a popular method [209]. In one study using whole hemispheres, the majority of participants showed left-hemisphere dominance (67/82; 81.7%), with a higher rate of left-hemisphere dominance amongst right-handers than lefthanders [210].…”

Power spectral analysis can determine language laterality from resting‐state functional MRI data in healthy controls

“…The PLI was compared between BA and V1 and between BA and HM using the one‐tailed Mann‐Whitney U test. In defining the PLI, we followed the logic of the standard method of defining laterality index (LI) used for analyzing tbfMRI data 36,47,48 …”

“…46 The samples were assumed to be independent; hence, the nonparametric one- followed the logic of the standard method of defining laterality index (LI) used for analyzing tbfMRI data. 36,47,48 In order to compare the distribution of power across frequencies, the number of peaks within 25% of the maximum peak were identified for the BA, HM, and V1 power spectra by adding the corresponding number of peaks from the left and right hemispheres. 46 The one-tailed Mann-Whitney U test was applied to determine the statistical significance of the difference between the number of peaks for BA and V1…”

Organizing Variables Affecting fMRI Estimates of Language Dominance in Patients with Brain Tumors

“…Not surprisingly, a survey showed that 79% of 63 epilepsy surgical programs used statistical thresholding that varied by the patient [63]. Several approaches have been proposed to calculate threshold-independent LI in the clinical setting, including a recent simplified method of LI calculation [22]. (b) Number and type of language tasks-lateralization patterns of language may change depending on the type of task used [11,13,64].…”

“…As will be presented below, different approaches to an fMRI analysis can significantly alter the LI values in a single subject. The approaches include using threshold-dependent versus threshold-independent calculations [22,23], applying a full-hemisphere versus an region of interest (ROI) approach [24,25], and administering a single task versus a panel of language tasks [11].…”