Abstracts

Rationale: This research presents a novel approach for Lateralization Index (LI) calculation in support of a decision making process for the classification of subjects based on their brain activation patterns using functional magnetic resonance imaging (fMRI) datasets. The decision process considers the subject grouping based on a novel LI computational method using sub-sampling of the analyzed brain area, and the respective behavior for each individual when masking for specific Broca-Wernicke language areas. Methods: 114 de-identified fMRI datasets, obtained during the execution of the language oriented paradigm referred as “auditory description decision task” (ADDT), were analyzed using the FSL (FMRIB software library). The data was obtained from 5 different hospitals using the online web-based repository (mri-cate.fiu.edu). All the images were Z (Gaussianised T/F) statistic images thresholded by Z>2.3 and a (corrected) cluster significance of P=0.05. Masks were used for Broca’s and Wernicke’s language areas using a normal brain, and masks were used for each of the 48 Brodmann areas (BA). The algorithm, as structured in Figure 1, was implemented in MATLAB. The decision for subject classification is made based on the range of the LI obtained: strong lateralized (|LI|≥0.5), lateralized (0.2≥|AI|<0.5) and bilateral (|LI|<0.2). Results: Activation maps were obtained on 103 (90%) and no activation on 11 (10%) of the population; from the 103 subjects, 64 were control subjects and 39 were location- related epilepsy (LRE) subjects. Analyzing the LI, computed from control and LRE (c%, e%) datasets, five groups were identified: 1) strong right lateralization: (0%, 18%), 2) right lateralization: (0%, 10%), 3) bilateral: (14%, 10%), 4) left lateralization: (19%, 5%), and 5) strong left lateralization: (67%, 56%). Conclusions: None of the control data displayed right activation as opposed to 28% on the epilepsy data, since this area is not a typical language area this finding may lead us to believe on a potential language network re-localization. It is also noted that subjects with epilepsy exhibit a lower LI as shown in Figure 2.