Johnson, Graham W.

Abstract Objective Resective surgery achieves seizure freedom in approximately 60%–80% of patients with drug‐resistant temporal lobe epilepsy (TLE), yet the extent to which seizure cessation permits recovery of disrupted functional brain networks remains unclear. Increasing evidence suggests that recurrent seizures alter subcortical–cortical communication involved in arousal and cognition, contributing to chronic interictal deficits, such as in executive function. Prior work demonstrates that the nucleus basalis of Meynert (NBM), the primary source of cortical cholinergic input to the cortex, exhibits aberrant functional connectivity in patients with TLE. We examined NBM connectivity in patients with TLE before and after surgery using patient‐specific NBM segmentations. We sought to investigate whether seizure freedom is associated with recovery of NBM functional connectivity, consistent with previous observations of interictal network dysfunction reversal in other subcortical arousal structures. Methods Resting‐state functional magnetic resonance imaging was analyzed in 75 patients with TLE, including 34 patients postoperatively, and 106 controls. Patient‐specific NBM segmentations were used to compute functional connectivity between the NBM and frontoparietal association cortex (FPAC) and across the whole brain. Subcortical arousal network community structure was assessed using community detection. Postoperative network changes were evaluated relative to seizure outcome. Results Prior to surgery, patients exhibited reduced bilateral NBM connectivity to the FPAC and whole brain compared to controls, indicating widespread cholinergic network disruption beyond the ipsilateral hemisphere. Following surgery, NBM connectivity increased and no longer differed from controls. Seizure‐free patients demonstrated greater postoperative increases in ipsilateral NBM‐FPAC connectivity than non‐seizure‐free patients, which was not explained by time to postoperative scan or volumetric change. Subcortical arousal community structure remained altered postoperatively. Significance Successful TLE surgery is associated with partial restoration of cholinergic arousal network connectivity, supporting the idea that seizure cessation enables recovery of brain networks disrupted by recurrent seizures. These findings position the NBM as a potential biomarker of network recovery following epilepsy surgery.

BACKGROUND In patients with drug-resistant epilepsy (DRE) who are not candidates for resective surgery, thalamic neuromodulation targeting structures such as the anterior nucleus of the thalamus (ANT), centromedian nucleus (CM), and pulvinar (PUL) has emerged as a promising therapy. The clinical investigation of these targets’ efficacy and side effect profiles is ongoing, complicating discussions between neurosurgeons, neurologists, and patients. The implementation of single-pulse and continuous stimulation protocols to conduct patient-specific side effect screens and generate prognostic biomarkers could offer guidance for target selection. OBSERVATIONS A 32-year-old male with extensive polymicrogyria and DRE underwent stereo-electroencephalography (SEEG) with multisite thalamic sampling to inform neuromodulation target selection. Single-pulse stimulation was conducted to screen for acute sensorimotor effects, followed by continuous (12- to 24-hour) stimulation using clinically relevant parameters. Intolerable paresthesia occurred during CM single-pulse stimulation, leading to its exclusion as a target. Single-pulse and continuous stimulation of the ANT and PUL were well tolerated. PUL stimulation offered superior suppression of epileptiform discharges during sleep. LESSONS These findings meaningfully informed the selection of the PUL as a neuromodulation target. This case illustrates the safety, feasibility, and utility of patient-specific single-pulse and continuous stimulation assessments during SEEG monitoring to evaluate efficacy and tolerability prior to chronic neuromodulation, particularly for novel thalamic targets. https://thejns.org/doi/10.3171/CASE25947

Neuromodulation is becoming a common treatment strategy for patients with drug-resistant epilepsy who are not candidates for resective surgery. Responsive neurostimulation can effectively reduce seizure burden, however it remains unclear which neural signals are most important for triggered stimulation. Furthermore, the high-dimensional parameter space remains systematically underexamined. As more patients become candidates for neurostimulation, clinical teams will need to decide on the optimal site and stimulation program for therapeutic neuromodulation. In this review, we highlight how recent insight into the biological rhythms of epilepsy may pair with personalization protocols in the emerging field of neuropsychiatric neuromodulation. Finally, we discuss future directions for epilepsy neuromodulation, specifically how machine learning can offer clinical teams actionable feedback on neuromodulation efficacy.

OBJECTIVE Mechanical complications are a vexing occurrence after adult spinal deformity (ASD) surgery. While achieving ideal spinal alignment in ASD surgery is critical, alignment alone may not fully explain all mechanical complications. The authors sought to determine which combination of inputs produced the most sensitive and specific machine learning model to predict mechanical complications using postoperative alignment, bone quality, and soft tissue data. METHODS A retrospective cohort study was performed in patients undergoing ASD surgery from 2009 to 2021. Inclusion criteria were a fusion ≥ 5 levels, sagittal/coronal deformity, and at least 2 years of follow-up. The primary exposure variables were 1) alignment, evaluated in both the sagittal and coronal planes using the L1–pelvic angle ± 3°, L4–S1 lordosis, sagittal vertical axis, pelvic tilt, and coronal vertical axis; 2) bone quality, evaluated by the T-score from a dual-energy x-ray absorptiometry scan; and 3) soft tissue, evaluated by the paraspinal muscle–to–vertebral body ratio and fatty infiltration. The primary outcome was mechanical complications. Alongside demographic data in each model, 7 machine learning models with all combinations of domains (alignment, bone quality, and soft tissue) were trained. The positive predictive value (PPV) was calculated for each model. RESULTS Of 231 patients (24% male) undergoing ASD surgery with a mean age of 64 ± 17 years, 147 (64%) developed at least one mechanical complication. The model with alignment alone performed poorly, with a PPV of 0.85. However, the model with alignment, bone quality, and soft tissue achieved a high PPV of 0.90, sensitivity of 0.67, and specificity of 0.84. Moreover, the model with alignment alone failed to predict 15 complications of 100, whereas the model with all three domains only failed to predict 10 of 100. CONCLUSIONS These results support the notion that not every mechanical failure is explained by alignment alone. The authors found that a combination of alignment, bone quality, and soft tissue provided the most accurate prediction of mechanical complications after ASD surgery. While achieving optimal alignment is essential, additional data including bone and soft tissue are necessary to minimize mechanical complications.

Abstract How do brain networks limit seizure activity? In the interictal suppression hypothesis, we recently postulated that high inward connectivity to seizure onset zones (SOZs) from non-involved zones (NIZs) is a sign of broader network suppression. If broad networks appear to be responsible for interictal SOZ suppression, what changes during seizure initiation, spread and termination? For patients with drug-resistant epilepsy, intracranial monitoring offers a view into the electrographic networks that organize around and in response to the SOZ. In this manuscript, we investigate network dynamics in the peri-ictal periods to assess possible mechanisms of seizure suppression and the consequences of this suppression being overwhelmed. Peri-ictal network dynamics were derived from stereo electroencephalography recordings from 75 patients with drug-resistant epilepsy undergoing presurgical evaluation at Vanderbilt University Medical Center. We computed directed connectivity from 5 s windows in the periods between, immediately before, during and after 668 seizures. We aligned connectivity matrices across seizures and patients, then calculated net connectivity changes from the SOZ, propagative zone and NIZ. Across all seizure types, we observed two phases: a rapid increase in directed communication towards the SOZ, followed by a collapse in network connectivity. During the first phase, SOZs could be distinguished from all other regions (one-way ANOVA, P = 8.32 × 10−19–2.22 × 10−7). In the second phase and post-ictal period, SOZ inward connectivity decreased yet remained distinct (one-way ANOVA, P = 2.58 × 10−10–1.66 × 10−2). NIZs appeared to drive increased SOZ connectivity while intra-NIZ connectivity decreased concordantly. Stratifying by seizure subtype, we found that consciousness-impairing seizures decrease inward connectivity from the NIZ earlier than consciousness-sparing seizures (one-way ANOVA, P < 0.01 after false discovery correction). Tracking network reorganization against a surrogate for seizure involvement highlighted a possible antagonism between seizure propagation and the ability of the NIZ to maintain high connectivity to the SOZ. Finally, we found that inclusion of peri-ictal connectivity improved the accuracy of SOZ classification from previous models to a combined area under the curve of 93%. Overall, NIZs appear actively to increase inhibitory signaling towards the SOZ during seizure onset, possibly to thwart seizure activity. Although inhibition appears insufficient to prevent seizure onset, the inability to restrict seizure propagation might contribute to loss of consciousness during larger seizures. Dynamic connectivity patterns uncovered in this work might: (i) facilitate accurate delineation of surgical targets in focal epilepsy; (ii) reveal why interictal suppression of SOZs might be insufficient to prevent all seizures; and (iii) provide insight into mechanisms of loss of consciousness during certain seizures.

AbstractObjectiveEpilepsy is a debilitating disorder affecting more than 50 million people worldwide, and one third of patients continue to have seizures despite maximal medical management. If patients' seizures localize to a discrete brain region, termed a seizure onset zone, resection may be curative. Localization is often confirmed with stereotactic electroencephalography; however, this may require patients to stay in the hospital for weeks to capture spontaneous seizures. Automated localization of seizure onset zones could therefore improve presurgical evaluation and decrease morbidity.MethodsUsing more than 1 000 000 interictal stereotactic electroencephalography segments collected from 78 patients, we performed five‐fold cross‐validation and testing on a multichannel, multiscale, one‐dimensional convolutional neural network to classify seizure onset zones.ResultsAcross held‐out test sets, our models achieved a seizure onset zone classification sensitivity of .702 (95% confidence interval [CI] = .549–.805), specificity of .741 (95% CI = .652–.835), and accuracy of .738 (95% CI = .687–.795), which was significantly better than models trained on random labels. The models performed well across the entire brain, with top five region performance demonstrating accuracies between 70.0% and 88.4%. When split by outcomes, the models performed significantly better on patients with favorable Engel outcomes after resection or who were responsive neurostimulation responders. Finally, SHAP (Shapley Additive Explanation) value analysis on median‐normalized input data assigned consistently high feature importance to interictal spikes and large deflections, whereas similar analyses on histogram‐equalized data revealed differences in feature importance assignments to low‐amplitude segments.SignificanceThis work serves as evidence that deep learning on brief interictal intracranial data can classify seizure onset zones across the brain. Furthermore, our findings corroborate current understandings of interictal epileptiform discharges and may help uncover novel interictal morphologies. Clinical application of our models may reduce dependence on recorded seizures for localization and shorten presurgical evaluation time for drug‐resistant epilepsy patients, reducing patient morbidity and hospital costs.

OBJECTIVE This study aimed to develop an artificial intelligence (AI) model for automatically detecting Hounsfield unit (HU) values at the L1 vertebra in preoperative thoracolumbar CT scans. This model serves as a screening tool for osteoporosis in patients undergoing spine surgery, offering an alternative to traditional bone mineral density measurement methods like dual-energy x-ray absorptiometry. METHODS The authors utilized two CT scan datasets, comprising 501 images, which were split into training, validation, and test subsets. The nnU-Net framework was used for segmentation, followed by an algorithm to calculate HU values from the L1 vertebra. The model’s performance was validated against manual HU calculations by expert raters on 56 CT scans. Statistical measures included the Dice coefficient, Pearson correlation coefficient, intraclass correlation coefficient (ICC), and Bland-Altman plots to assess the agreement between AI and human-derived HU measurements. RESULTS The AI model achieved a high Dice coefficient of 0.91 for vertebral segmentation. The Pearson correlation coefficient between AI-derived HU and human-derived HU values was 0.96, indicating strong agreement. ICC values for interrater reliability were 0.95 and 0.94 for raters 1 and 2, respectively. The mean difference between AI and human HU values was 7.0 HU, with limits of agreement ranging from −21.1 to 35.2 HU. A paired t-test showed no significant difference between AI and human measurements (p = 0.21). CONCLUSIONS The AI model demonstrated strong agreement with human experts in measuring HU values, validating its potential as a reliable tool for automated osteoporosis screening in spine surgery patients. This approach can enhance preoperative risk assessment and perioperative bone health optimization. Future research should focus on external validation and inclusion of diverse patient demographics to ensure broader applicability.