Computer-automated focus lateralization of temporal lobe epilepsy using fMRI - PubMed (original) (raw)

Computer-automated focus lateralization of temporal lobe epilepsy using fMRI

Sharon Chiang et al. J Magn Reson Imaging. 2015 Jun.

Abstract

Purpose: To compare the performance of computer-automated diagnosis using functional magnetic resonance imaging (fMRI) interictal graph theory (CADFIG) to that achieved in standard clinical practice with MRI, for lateralizing the affected hemisphere in temporal lobe epilepsy (TLE).

Materials and methods: Interictal resting state fMRI and high-resolution MRI were performed on 14 left and 10 right TLE patients. Functional topology measures were calculated from fMRI using graph theory, and used to lateralize the epileptogenic hemisphere using quadratic discriminant analysis. Leave-one-out cross-validation prediction accuracy of CADFIG was compared to performance based on expert manual analysis (MA) of MRI, using video EEG as the "gold standard" for focus lateralization.

Results: CADFIG correctly lateralized 95.8% (23/24) of cases, compared to 66.7% (16/24) with expert MA of MRI. Combining MA with CADFIG allowed all cases (24/24) to be correctly lateralized. CADFIG correctly identified the affected hemisphere for all patients (8/8) where MRI failed to lateralize.

Conclusion: CADFIG based on fMRI lateralized the affected hemisphere in TLE with superior performance compared to expert MA of MRI. These results demonstrate that functional patterns in fMRI can be used with automated machine learning for diagnostic lateralization in TLE. Addition of fMRI-based tests to existing protocols for identifying the affected hemisphere in presurgical assessment can improve diagnostic accuracy and surgical outcome in TLE.

Keywords: automated pattern recognition; functional connectivity; functional magnetic resonance imaging; graph theory; lateralization; temporal lobe epilepsy.

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Figures

Figure 1

Sensitivity of manual analysis of high-resolution structural MRI (MA), CADFIG, and combined approach of MA and CADFIG in lateralizing left and right TLE. Proportion of correctly lateralized patients are shown above the corresponding bars. TLE, temporal lobe epilepsy.

Figure 2

Receiver operating characteristic (ROC) curve for classification performance of CADFIG. AUC, area under the ROC curve.

Figure 3

FLAIR images from (a) a left TLE patient with left hippocampal hyperintensity and atrophy (white arrow) in keeping with mesial temporal sclerosis (Subject 1), and (b) a left TLE patient with non-lateralizing MRI (Subject 13). For the subject shown in (a), both MA and CADFIG correctly identified the affected hemisphere. For the subject shown in (b), MRI sequences were non-lateralizing but CADFIG was able to accurately identify focus laterality. Images are displayed in radiologic convention.

Figure 4

Scatterplot matrix visualizing separation of left (L) and right (R) TLE subjects based on variables identified from optimal feature subset (connectivity diversity, global efficiency, and betweenness centrality of the left and right hippocampi). Purple and turquoise areas indicate whether future out-of-sample observations would be classified as right (purple) or left (turquoise) TLE, based on a quadratic discriminant function trained on the sample examined in this study. _x_- and _y-_axes are given by the corresponding graph theory metrics listed on the main diagonal. CD, connectivity diversity; GE, global efficiency; BC (L), left hippocampal betweenness centrality; BC (R), right hippocampal betweenness centrality.

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Computer-automated focus lateralization of temporal lobe epilepsy using fMRI - PubMed (original) (raw)