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BackgroundWith expanding neurosurgical options in epilepsy, it is important to characterise each options’ risk for postoperative cognitive decline. Here, we characterise how patients’ preoperative white matter (WM) networks relates to postoperative memory changes following different epilepsy surgeries.MethodsEighty-nine patients with temporal lobe epilepsy with T1-weighted and diffusion-weighted imaging as well as preoperative and postoperative verbal memory scores (prose recall) underwent either anterior temporal lobectomy (ATL: n=38) or stereotactic laser amygdalohippocampotomy (SLAH; n=51). We computed laterality indices (ie, asymmetry) for volume of the hippocampus and fractional anisotropy (FA) of two deep WM tracts (uncinate fasciculus (UF) and inferior longitudinal fasciculus (ILF)).ResultsPreoperatively, left-lateralised FA of the ILF was associated with higher prose recall (p<0.01). This pattern was not observed for the UF or hippocampus (ps>0.05). Postoperatively, right-lateralised FA of the UF was associated with less decline following left ATL (p<0.05) but not left SLAH (p>0.05), while right-lateralised hippocampal asymmetry was associated with less decline following both left ATL and SLAH (ps<0.05). After accounting for preoperative memory score, age of onset and hippocampal asymmetry, the association between UF and memory decline in left ATL remained significant (p<0.01).ConclusionsAsymmetry of the hippocampus is an important predictor of risk for memory decline following both surgeries. However, asymmetry of UF integrity, which is only severed during ATL, is an important predictor of memory decline after ATL only. As surgical procedures and pre-surgical mapping evolve, understanding the role of frontal-temporal WM in memory networks could help to guide more targeted surgical approaches to mitigate cognitive decline.

We report a case study of a surgical candidate, a 51‐year‐old woman with left temporal lobe epilepsy, who failed a left injection intracarotid amobarbital procedure (e.g., Wada test), scoring 0 of 8 items. This raised concerns for postoperative memory decline. However, the patient was uninterested in a neuromodulatory approach and wished to be reconsidered for surgery. A stereotactic laser amygdalohippocampotomy (SLAH) was considered, encouraging the need for an alternative test to evaluate risk of memory decline. We developed a novel approach to testing memory during stimulation of a depth electrode implanted in the hippocampus, i.e., an electric Wada. During multiple stimulation trials across a range of amplitudes, the patient scored up to 8 of 8 items, which suggested strong contralateral memory support. The surgical team proceeded with a radiofrequency ablation and a subsequent SLAH. The patient remains seizure‐free at 12 months post SLAH with no evidence of verbal or visuospatial memory decline based on a post‐surgical neuropsychological battery. We believe that this case study provides a proof of concept for the feasibility and possible utility of an electric version of the Wada procedure. Future studies are needed to develop an optimal paradigm and to validate this approach.

The deployment of machine learning to augment clinical judgment is an exciting avenue in the effort to improve patient care. In basic terms, machine learning can be thought of as an algorithm that learns how to distinguish patterns within data, and it can learn better with larger amounts of data. Distinguishing patterns is at the heart of many aspects of clinical care. EEG interpretation is a prime example: a trained, laborious task that might benefit from algorithms. The challenge, however, is to build an algorithm that is reliable enough to be useful and trustworthy in standard clinical care.