Citations

The adolescent mental health landscape is rapidly evolving as AI reshapes how young people connect and navigate developmental challenges. From the known harms of machine learning algorithms reinforcing pathology on social media to the emerging risks of generative AI fostering emotional attachments in vulnerable youth and future risks of agentic AI pursuing its users proactively, AI’s evolution will have important implications for clinical practice and public policy.

Noninvasive electrophysiological source imaging (ESI) is a valuable tool for localizing and imaging brain activity, with significant potential to aid presurgical planning in focal drug-resistant epilepsy (fDRE) patients. Scalp electroencephalography (EEG) biomarkers, including interictal spikes, high-frequency oscillations (HFOs), and seizures, each offer unique capabilities in estimating the epileptogenic zone (EZ). However, there is a limited quantitative understanding of how these biomarkers differ in source-imaging precision, requiring distinct processing pipelines. Here, we developed a spatial–temporal–spectral imaging (STSI) framework for precision source imaging, and quantitatively evaluated various epilepsy biomarkers for source imaging in 2,081 individual events (spikes, HFOs, and seizures) from a cohort of 42 fDRE patients, comparing results to clinical ground truth such as surgical resection outcomes and intracranial EEG-defined seizure onset zones. The STSI enabled quantitative comparisons across key EEG epilepsy-related biomarkers, with averaged localization errors of 6.67 mm for seizures, 8.73 mm for HFOs overlapping with spikes (pHFO), 10.28 mm for HFO-riding spikes (pSpike), 19.59 mm for general spikes (aSpike), and 36.53 mm for general HFOs (aHFO), respectively, for seizure-free patients. These findings indicate that HFOs overlapping with spikes is the most spatially accurate interictal biomarker for mapping the EZ. The proposed STSI framework not only establishes a unified analysis approach for epileptic biomarkers to enhance presurgical planning in focal drug-resistant epilepsy, but could also generalize as a versatile tool for mapping event-related potentials, neural oscillations, and dynamic brain states, within a single framework to advance cognitive neuroscience research and clinical management of neurological and psychiatric disorders.

Abstract Immune checkpoint inhibitor–associated immune thrombocytopenia (ICI-ITP) has been described in case reports and small case series, but comprehensive data on its incidence, risk factors, clinical features, treatment, and outcomes are lacking. We reviewed medical records of all adults initiating ICI therapy between 2016 and 2023 at 29 US hospitals across 7 major cancer centers to identify patients with ICI-ITP. Multivariable logistic regression was used to identify risk factors, and Cox modeling was performed to assess the association between ICI-ITP, its severity, and mortality. Among 86 467 patients, ICI-ITP occurred in 214 (0.25%). Independent risk factors included lower baseline platelet count, combination ICI therapy, stage IV cancer, and additional immune-related adverse events. ICI-ITP occurred at a median of 8 weeks (interquartile range [IQR], 4-18) after ICI initiation, with a median nadir platelet count of 41 × 109/L (IQR, 17 × 109/L to 64 × 109/L). Patients were treated with glucocorticoids (n = 106 [49.5%]), immune globulin (n = 39 [18.2%]), and thrombopoietin receptor agonists (n = 29 [13.6%]). Recovery occurred in 161 patients (75.2%) at a median of 2.3 weeks (IQR, 1.0-5.3). Of 76 patients rechallenged with ICIs, 23 (30.3%) developed recurrent ICI-ITP. ICI-ITP and its severity were associated with higher all-cause mortality, with a nearly threefold increase in risk among patients with severe ICI-ITP than those without ICI-ITP (adjusted hazard ratio, 2.96 [95% confidence interval, 2.14-4.08]). These findings establish ICI-ITP as a rare but clinically significant complication of ICI therapy, provide, to our knowledge, the first large-scale description of its risk factors and clinical course, and underscore the importance of timely recognition and management.

OBJECTIVE Glucagon-like peptide-1 (GLP-1) agonists are established therapeutics for weight loss that are increasingly used for BMI optimization prior to spine surgery. However, emerging evidence has suggested that GLP-1 agonists might reduce bone mineral density (BMD), increasing the risk of postoperative biomechanical complications. CT-based Hounsfield units (HUs) have gained popularity as a method for measuring spinal BMD. The aim of this study was to evaluate the effects of GLP-1 agonist–induced weight loss on spinal BMD as measured by opportunistic CT-based HUs. METHODS Patients who were treated with any GLP-1 agonist (2017–2022) for > 3 months were retrospectively identified. Patient body weight (BW), BMI, and HU measurements in the L1 vertebral body were measured before and after GLP-1 therapy. Patients were grouped according to the percentage of weight loss during GLP-1 therapy. One-way ANOVA was used to compare the mean changes in spinal HUs across the groups. RESULTS Among the 102 included patients, the mean ± standard error of the mean BW reduction was 14.5 ± 2.5 kg over 15.9 ± 1.2 months of GLP-1 agonist therapy. Of these, 7 patients (6.9%) lost > 20% BW (mean decrease of 31.9 ± 7.0 HU, p = 0.011), 14 (13.7%) lost 15%–20% BW (mean decrease of 19.1 ± 8.3 HU, p = 0.038), 14 (13.7%) lost 10%–15% BW (mean decrease of 12.2 ± 5.1 HU, p = 0.036), 24 (23.5%) lost 5%–10% BW (mean decrease of 15.7 ± 3.3 HU, p < 0.0001), 26 (25.5%) lost < 5% BW (mean decrease of 14.7 ± 6.0 HU, p = 0.022), and 17 (16.7%) gained BW during GLP-1 therapy (mean decrease of 6.3 ± 5.0 HU, p = 0.229). One-way ANOVA testing did not show a significant difference in HU reduction across the weight loss groups (F = 1.12, p = 0.355). In addition, there was a significant correlation between the decrease in L1 HUs and the duration of GLP-1 therapy (r = −0.38, p = 0.0001) but not the amount of weight loss (r = −0.18, p = 0.070). In the multivariate analysis, the duration of GLP-1 therapy (p = 0.032) was a significant independent predictor of vertebral HU reduction, but the amount of weight loss (p = 0.664) was not. CONCLUSIONS Despite similar pretreatment BW and HU measurements, all weight loss groups had significant decreases in vertebral HUs following GLP-1 agonist therapy, with a significant correlation observed between HU reduction and treatment duration. This suggests that long-term GLP-1 agonist therapy can significantly diminish spinal BMD regardless of the amount of weight loss achieved.

OBJECTIVE Achieving submillimetric accuracy in stereotactic neurosurgery remains critical for safely targeting deep brain structures. Current workflows rely on intraoperative CT to register stereotactic frames to preoperative imaging, but this introduces additional radiation exposure, cost, and workflow complexity. Three-dimensional surface scanning (3DSS) potentially offers a fast radiation-free alternative capable of capturing detailed craniofacial anatomy and frame geometry. Therefore, the aim of this cadaveric study was to evaluate the feasibility, accuracy, and navigational compatibility of integrating 3DSS into stereotactic workflows as a potential replacement for intraoperative CT. METHODS A human cadaveric head was imaged with both thin-slice CT and structured-light 3DSS after mounting stereotactic frame components. CT, 3DSS, and computer-aided design (CAD) models were registered using a multistep point cloud alignment pipeline involving the fast point feature histogram, the random sample consensus method, and the iterative closest point algorithm. Accuracy was assessed using surface deviation error, root mean square error (RMSE), and fiducial registration error within a commercial neuronavigation platform. RESULTS Fusion of the 3D facial scan with CT provided a mean surface deviation error of approximately 0.4 mm. Registration of the 3DSS-derived N-bar localizer to its CAD reference model produced a frame alignment RMSE of 1.3 mm. Within the navigation system, the 3DSS-based workflow achieved a mean fiducial registration error of 0.14 mm (range 0.02–0.20 mm), outperforming the conventional CT-based method (mean error 0.20 mm, range 0.09–0.40 mm). CONCLUSIONS The 3DSS-based method enabled precise submillimetric stereotactic registration without the need for intraoperative CT, reducing radiation exposure and operative complexity. This workflow is fully compatible with existing navigation systems and could serve as a practical radiation-free alternative in stereotactic neurosurgery. Future work will focus on automating frame detection, incorporating artificial intelligence–driven fusion methods, and validating this approach in live surgical settings.

OBJECTIVE The aim of this study was to compare 3 electrode designs—a novel 22-contact circular grid, standard 6-contact strip, and high-density (HD) grid between 32 and 64 contacts—for the detection of epileptiform activity during awake craniotomy. METHODS This study included patients who underwent functional brain mapping with and without direct electrical stimulation during awake craniotomy. Demographic, clinical, and electrocorticography (ECoG) data were collected. RESULTS A total of 194 patients were included, with 264 instances of electrode use: 113 circular grid, 96 strip electrode, and 55 HD grid. The HD grid (15%) was used less to record ECoG during direct electrical stimulation than the circular grid (80%, p < 0.001) and strip electrodes (69%, p < 0.001). Sporadic interictal epileptiform activity was better detected with circular (45%, p < 0.001) and HD (40%, p = 0.006) grids compared with strip electrodes (16%). Spontaneous seizures were recorded more frequently with the circular grid (16%, p = 0.018) compared with the strip electrode (9.4%) and HD grid (1.8%). Afterdischarges were more frequently detected by the circular grid (69%, p = 0.031) and with circular and HD grids (combined) compared with strip electrodes (67% vs 48%, p = 0.024), at lower median intensity (3 mA vs 5 mA, p < 0.001). Multivariable analyses demonstrated circular grids (p < 0.001) to be more sensitive in detecting spontaneous epileptiform activity than strip and HD grid electrodes, and grids (circular and HD) to be better in recording stimulus-induced epileptiform activity (p = 0.01). CONCLUSIONS This study demonstrates a higher rate of detection of epileptiform activity with circular and standard grids when compared with strip electrodes. The standard HD grid was used less during electrical stimulation, demonstrating the importance of both the number of contacts and design array when considering optimal conditions for ECoG and functional brain mapping.