Dataset Information

Novel Deep Learning Network Analysis of Electrical Stimulation Mapping-Driven Diffusion MRI Tractography to Improve Preoperative Evaluation of Pediatric Epilepsy.

ABSTRACT:

Objective

To investigate the clinical utility of deep convolutional neural network (DCNN) tract classification as a new imaging tool in the preoperative evaluation of children with focal epilepsy (FE).

Methods

A DCNN tract classification deeply learned spatial trajectories of DWI white matter pathways linking electrical stimulation mapping (ESM) findings from 89 children with FE, and then automatically identified white matter pathways associated with eloquent functions (i.e., primary motor, language, and vision). Clinical utility was examined by 1) measuring the nearest distance between DCNN-determined pathways and ESM, 2) evaluating the effectiveness of DCNN-determined pathways to optimize surgical margins via Kalman filter analysis, and 3) evaluating how accurately changes in DCNN-determined language pathway volume can predict changes in language ability via canonical correlation analysis.

Results

DCNN tract classification outperformed other existing methods, achieving an excellent accuracy of 98 % while non-invasively detecting eloquent areas within the spatial resolution of ESM (i.e., 1 cm). The Kalman filter analysis found that the preservation of brain areas within a surgical margin determined by DCNN tract classification predicted lack of postoperative deficit with a high accuracy of 92 %. Postoperative change of DCNN-determined language pathway volume showed a significant correlation with postoperative changes in language ability (R = 0.7, p 0.001).

Conclusion

Our findings demonstrate that postoperative functional deficits substantially differ according to the extent of resected white matter, and that DCNN tract classification may offer key translational information by identifying these pathways in pediatric epilepsy surgery.

Significance

DCNN tract classification may be an effective tool to improve surgical outcome of children with FE.

SUBMITTER: Lee MH

PROVIDER: S-EPMC7598774 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectiveIn this study the authors investigated the clinical reliability of diffusion weighted imaging maximum a posteriori probability (DWI-MAP) analysis with Kalman filter prediction in pediatric epilepsy surgery. This approach can yield a suggested resection margin as a dynamic variable based on preoperative DWI-MAP pathways. The authors sought to determine how well the suggested margin would have maximized occurrence of postoperative seizure freedom (benefit) and minimized occurrence of postoperative neurological deficits (risk).MethodsThe study included 77 pediatric patients with drug-resistant focal epilepsy (age 10.0 ± 4.9 years) who underwent resection of their presumed epileptogenic zone. In preoperative DWI tractography from the resected hemisphere, 9 axonal pathways, Ci=1-9, were identified using DWI-MAP as follows: C1-3 supporting face, hand, and leg motor areas; C4 connecting Broca's and Wernicke's areas; C5-8 connecting Broca's, Wernicke's, parietal, and premotor areas; and C9 connecting the occipital lobe and lateral geniculate nucleus. For each Ci, the resection margin, di, was measured by the minimal Euclidean distance between the voxels of Ci and the resection boundary determined by spatially coregistered postoperative MRI. If Ci was resected, di was assumed to be negative (calculated as -1 × average Euclidean distance between every voxel inside the resected Ci volume, ri). Kalman filter prediction was then used to estimate an optimal resection margin, d*i, to balance benefit and risk by approximating the relationship between di and ri. Finally, the authors defined the preservation zone of Ci that can balance the probability of benefit and risk by expanding the cortical area of Ci up to d*i on the 3D cortical surface.ResultsIn the whole group (n = 77), nonresection of the preoperative preservation zone (i.e., actual resection margin d*i greater than the Kalman filter-defined d*i) accurately predicted the absence of postoperative motor (d*1-3: 0.93 at seizure-free probability of 0.80), language (d*4-8: 0.91 at seizure-free probability of 0.81), and visual deficits (d*9: 0.90 at seizure-free probability of 0.75), suggesting that the preservation of preoperative Ci within d*i supports a balance between postoperative functional deficit and seizure freedom. The subsequent subgroup analyses found that preservation of preoperative Ci =1-4,9 within d*i =1-4,9 may provide accurate deficit predictions independent of age and seizure frequency, suggesting that the DWI-based surgical margin can be effective for surgical planning even in young children and across a range of epilepsy severity.ConclusionsIntegrating DWI-MAP analysis with Kalman filter prediction may help guide epilepsy surgery by visualizing the margins of the eloquent white matter pathways to be preserved.