No abstract

Objective: To develop language functional MRI (fMRI) methods that accurately predict postsurgical naming decline in temporal lobe epilepsy (TLE). Methods: Forty-six patients with TLE (25 left) and 19 controls underwent two overt fMRI paradigms (auditory naming and picture naming, both with active baseline conditions) and one covert task (verbal fluency). Clinical naming performance was assessed preoperatively and 4 months following anterior temporal lobe resection. Preoperative fMRI activations were correlated with postoperative naming decline. Individual laterality indices (LI) were calculated for temporal (auditory and picture naming) and frontal regions (verbal fluency) and were considered as predictors of naming decline in multiple regression models, along with other clinical variables (age at onset of seizures, preoperative naming scores, hippocampal volume, age). Results: In left TLE patients, activation of the left posterior inferior temporal gyrus during auditory naming and activation of left fusiform gyrus during picture naming were related to greater postoperative naming decline. Activation LI were the best individual predictors of naming decline in a multivariate regression model. For picture naming, an LI of higher than 0.34 gave 100% sensitivity and 92% specificity (positive predictive value (PPV) 91.6%). For auditory naming, a temporal lobe LI higher than 0.18 identified all patients with a clinically significant naming decline with 100% sensitivity and 58% specificity (PPV: 58.3%). No effect was seen for verbal fluency. Interpretation: Auditory and picture naming fMRI are clinically applicable to predict postoperative naming decline after left temporal lobe resection in individual patients, with picture naming being more specific. 2186

Generally, the interpretation of functional MRI (fMRI) activation maps continues to rely on assessing their relationship to anatomical structures, mostly in a qualitative and often subjective way. Recently, the existence of persistent and stable brain networks of functional nature has been revealed; in particular these so-called intrinsic connectivity networks (ICNs) appear to link patterns of resting state and task-related state connectivity. These networks provide an opportunity of functionally-derived description and interpretation of fMRI maps, that may be especially important in cases where the maps are predominantly task-unrelated, such as studies of spontaneous brain activity e.g. in the case of seizure-related fMRI maps in epilepsy patients or sleep states. Here we present a new toolbox (ICN_Atlas) aimed at facilitating the interpretation of fMRI data in the context of ICN. More specifically, the new methodology was designed to describe fMRI maps in function-oriented, objective and quantitative way using a set of 15 metrics conceived to quantify the degree of ‘engagement’ of ICNs for any given fMRI-derived statistical map of interest. We demonstrate that the proposed framework provides a highly reliable quantification of fMRI activation maps using a publicly available longitudinal (test-retest) resting-state fMRI dataset. The utility of the ICN_Atlas is also illustrated on a parametric task-modulation fMRI dataset, and on a dataset of a patient who had repeated seizures during resting-state fMRI, confirmed on simultaneously recorded EEG. The proposed ICN_Atlas toolbox is freely available for download at http://icnatlas.com and at http://www.nitrc.org for researchers to use in their fMRI investigations.