Improving recorded volume in mesial temporal lobe by optimizing stereotactic intracranial electrode implantation planning.

PURPOSE: Intracranial electrodes are sometimes implanted in patients with refractory epilepsy to identify epileptic foci and propagation. Maximal recording of EEG activity from regions suspected of seizure generation is paramount. However, the location of individual contacts cannot be considered with current manual planning approaches. We propose and validate a procedure for optimizing intracranial electrode implantation planning that maximizes the recording volume, while constraining trajectories to safe paths. METHODS: Retrospective data from 20 patients with epilepsy that had electrodes implanted in the mesial temporal lobes were studied. Clinical imaging data (CT/A and T1w MRI) were automatically segmented to obtain targets and structures to avoid. These data were used as input to the optimization procedure. Each electrode was modeled to assess risk, while individual contacts were modeled to estimate their recording capability. Ordered lists of trajectories per target were obtained. Global optimization generated the best set of electrodes. The procedure was integrated into a neuronavigation system. RESULTS: Trajectories planned automatically covered statistically significant larger target volumes than manual plans [Formula: see text]. Median volume coverage was [Formula: see text] for automatic plans versus [Formula: see text] for manual plans. Furthermore, automatic plans remained at statistically significant safer distance to vessels [Formula: see text] and sulci [Formula: see text]. Surgeon's scores of the optimized electrode sets indicated that 95% of the automatic trajectories would be likely considered for use in a clinical setting. CONCLUSIONS: This study suggests that automatic electrode planning for epilepsy provides safe trajectories and increases the amount of information obtained from the intracranial investigation.

A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood.

Cross-sectional analysis of longitudinal anatomical magnetic resonance imaging (MRI) data may be suboptimal as each dataset is analyzed independently. In this study, we evaluate how much variability can be reduced by analyzing structural volume changes in longitudinal data using longitudinal analysis. We propose a two-part pipeline that consists of longitudinal registration and longitudinal classification. The longitudinal registration step includes the creation of subject-specific linear and nonlinear templates that are then registered to a population template. The longitudinal classification step comprises a four-dimensional expectation-maximization algorithm, using a priori classes computed by averaging the tissue classes of all time points obtained cross-sectionally. To study the impact of these two steps, we apply the framework completely ("LL method": Longitudinal registration and Longitudinal classification) and partially ("LC method": Longitudinal registration and Cross-sectional classification) and compare these with a standard cross-sectional framework ("CC method": Cross-sectional registration and Cross-sectional classification). The three methods are applied to (1) a scan-rescan database to analyze reliability and (2) the NIH pediatric population to compare gray matter growth trajectories evaluated with a linear mixed model. The LL method, and the LC method to a lesser extent, significantly reduced the variability in the measurements in the scan-rescan study and gave the best-fitted gray matter growth model with the NIH pediatric MRI database. The results confirm that both steps of the longitudinal framework reduce variability and improve accuracy in comparison with the cross-sectional framework, with longitudinal classification yielding the greatest impact. Using the improved method to analyze longitudinal data, we study the growth trajectories of anatomical brain structures in childhood using the NIH pediatric MRI database. We report age- and gender-related growth trajectories of specific regions of the brain during childhood that could be used as a reference in studying the impact of neurological disorders on brain development.