Abstract

PURPOSE

Task-based functional MRI (tb-fMRI) effectiveness as a support tool in brain mapping may be limited by patients' poor cooperation. Resting-state fMRI (rs-fMRI) represents an alternative or complementary approach. In this work, we developed and validated an analysis pipeline for rs-fMRI acquisitions, primarily aimed at language mapping in drug-resistant epileptic patients. The workflow relies on open-source software and semi-automatized solutions, ensuring easy clinical adoption.

METHODS

Rs-fMRI data were acquired from 26 subjects (15 volunteers, 11 patients) using a 3 T-MRI scanner. The developed pipeline starts with preprocessing of raw data, subsequently analyzed through Independent Component Analysis (ICA), performed with MELODIC-FSL tool. Manual classification, semi-automated classifiers (FIX, ICA-AROMA) and a template matching procedure were employed to classify the ICA components and extract each patient rs-language network. Finally, verb-generation tb-fMRI and Diffusion Tensor Imaging were acquired to map language regions and reconstruct the arcuate fasciculus, respectively. The rs-language networks were validated evaluating the three acquisition modalities agreement.

RESULTS

Trained FIX showed AUC = 0.95 and ICA-AROMA 97 % of classification accuracy, considering manual classification as ground truth. Manual classification identified one (46 %), two (31 %), or three (19 %) language-related components per subject. The manually selected language components were among the top three ranked by the template matching in 88 % of cases, 100 % considering the top five. The Dice index between rs-fMRI and tb-fMRI language maps resulted 0.36 ± 0.13. Rs-language areas resulted qualitatively well-connected by the reconstructed arcuate fasciculus.

CONCLUSION

The developed pipeline confirmed strong potential for clinical applicability in a large general hospital, especially when tb-fMRI is infeasible.

MeSH

Language, Biophysics, Classification, Hospitals, General, Work, Software