Integrating Artificial Intelligence with Real-time Intracranial EEG Monitoring to Automate Interictal Identification of Seizure Onset Zones in Focal Epilepsy

An ability to map seizure-generating brain tissue, i.e., the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy. A widely-adopted practice in the literature is to compare the incidence (events/time) of putative pathological electrophysiological biomarkers associated with epileptic brain tissue with the SOZ determined from spontaneous seizures recorded with intracranial EEG, primarily using a single biomarker. Clinical translation of the previous efforts suffers from their inability to generalize across multiple patients because of (a) the inter-patient variability and (b) the temporal variability in the epileptogenic activity. Here, we report an artificial intelligence-based approach for combining multiple interictal electrophysiological biomarkers and their temporal characteristics as a way of accounting for the above barriers and show that it can reliably identify seizure onset zones in a study cohort of 82 patients who underwent evaluation for drug-resistant epilepsy. Our investigation provides evidence that utilizing the complementary information provided by multiple electrophysiological biomarkers and their temporal characteristics can significantly improve the localization potential compared to previously published single-biomarker incidence-based approaches, resulting in an average area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our results also suggest that recording durations between ninety minutes and two hours are sufficient to localize SOZs with accuracies that may prove clinically relevant. The successful validation of our approach on a large cohort of 82 patients warrants future investigation on the feasibility of utilizing intra-operative EEG monitoring and artificial intelligence to localize epileptogenic brain tissue.Comment: 25 pages, Journal of neural engineering (2018

Electrical Stimulation Modulates High γ Activity and Human Memory Performance.

Direct electrical stimulation of the brain has emerged as a powerful treatment for multiple neurological diseases, and as a potential technique to enhance human cognition. Despite its application in a range of brain disorders, it remains unclear how stimulation of discrete brain areas affects memory performance and the underlying electrophysiological activities. Here, we investigated the effect of direct electrical stimulation in four brain regions known to support declarative memory: hippocampus (HP), parahippocampal region (PH) neocortex, prefrontal cortex (PF), and lateral temporal cortex (TC). Intracranial EEG recordings with stimulation were collected from 22 patients during performance of verbal memory tasks. We found that high γ (62-118 Hz) activity induced by word presentation was modulated by electrical stimulation. This modulatory effect was greatest for trials with poor memory encoding. The high γ modulation correlated with the behavioral effect of stimulation in a given brain region: it was negative, i.e., the induced high γ activity was decreased, in the regions where stimulation decreased memory performance, and positive in the lateral TC where memory enhancement was observed. Our results suggest that the effect of electrical stimulation on high γ activity induced by word presentation may be a useful biomarker for mapping memory networks and guiding therapeutic brain stimulation

Raytheon -- Design of Boat Hull Segments Using Additive Manufacturing

The purpose of this project was to assess the effectiveness of designing and producing a boat hull in segments, using additive manufacturing. The group accomplished this, by completing an in-depth research into additive manufacturing processes and 3 Dimensional (3D) printing techniques. The design was to be dimensionally stable, and have a process that strives for easy repeatability and reproducibility. Evaluated was the V-Bottom, Round Bottom and Flat Bottom style. Through the use of modeling the different hull styles in SolidWorks it was determined the Flat bottom was more stable and reproducible. The hull was broken into four segments and used finger joints to align and join the segments. The 3D printer used was capable of printing Acrylonitrile styrene acrylate (ASA), Polyethylene Terephthalate (PETG), Acrylonitrile Butadiene Styrene (ABS), Nylon, Carbon Fiber and Polycarbonate. After printing with all these materials it was determined ASA would be the best fit for additive manufacturing of a boat hull. After the segments were printed and joined together with adhesives a waterproof coating was applied. The assembled hull was subjected to a series of strength tests to determine its effectiveness in this application. The finished product rode smoothly in water, was weather resistant, safe, buoyant, and reliable to manufacture

Utility of Independent Component Analysis for Interpretation of Intracranial EEG

Electrode arrays are sometimes implanted in the brains of patients with intractable epilepsy to better localize seizure foci before epilepsy surgery. Analysis of intracranial EEG (iEEG) recordings is typically performed in the electrode channel domain without explicit separation of the sources that generate the signals. However, intracranial EEG signals, like scalp EEG signals, could be linear mixtures of local activity and volume-conducted activity arising in multiple source areas. Independent component analysis (ICA) has recently been applied to scalp EEG data, and shown to separate the signal mixtures into independently generated brain and non-brain source signals. Here, we applied ICA to unmix source signals from intracranial EEG recordings from four epilepsy patients during a visually cued finger movement task in the presence of background pathological brain activity. This ICA decomposition demonstrated that the iEEG recordings were not maximally independent, but rather are linear mixtures of activity from multiple sources. Many of the independent component (IC) projections to the iEEG recording grid were consistent with sources from single brain regions, including components exhibiting classic movement-related dynamics. Notably, the largest IC projection to each channel accounted for no more than 20–80% of the channel signal variance, implying that in general intracranial recordings cannot be accurately interpreted as recordings of independent brain sources. These results suggest that ICA can be used to identify and monitor major field sources of local and distributed functional networks generating iEEG data. ICA decomposition methods are useful for improving the fidelity of source signals of interest, likely including distinguishing the sources of pathological brain activity

High hospital research participation and improved colorectal cancer survival outcomes: a population-based study

Objective: In 2001, the National Institute for Health Research (NIHR) Cancer Research Network (NCRN) was established, leading to a rapid increase in clinical research activity across the English NHS. Using colorectal cancer (CRC) as an example, we test the hypothesis that high, sustained hospital-level participation in interventional clinical trials improves outcomes for all CRC patients managed in those research-intensive hospitals. Design: Data for patients diagnosed with CRC in England in 2001-2008 (n=209,968) were linked with data on accrual to NCRN CRC studies (n=30,998). Hospital Trusts were categorised by the proportion of patients accrued to interventional studies annually. Multivariable models investigated the relationship between 30-day post-operative mortality and five-year survival and the level and duration of study participation. Results: Most of the Trusts achieving high participation were district general hospitals and the effects were not limited to cancer “centres of excellence”, although such centres do make substantial contributions. Patients treated in Trusts with high research participation (≥16%) in their year of diagnosis had lower post-operative mortality (p<0.001) and improved survival (p<0.001) after adjustment for casemix and hospital-level variables. The effects increased with sustained research participation, with a reduction in post-operative mortality of 1.5% (6.5% to 5%, p<2.2*10-6) and an improvement in survival (p<10 19; 5-year difference: 3.8% (41.0% to 44.8%)) comparing high participation for ≥4 years with 0 years. Conclusion: There is a strong independent association between survival and participation in interventional clinical studies for all CRC patients treated in the hospital, not only study participants. Improvement precedes and increases with the level and years of sustained participation

Muscle of obese insulin-resistant humans exhibits losses in proteostasis and attenuated proteome dynamics that are improved by exercise training

We examined muscle proteostasis in obese insulin-resistant (OIR) individuals to determine whether endurance exercise could positively influence proteome dynamics in this population. Male OIR (n = 3) and lean, healthy controls (LHC; n = 4) were recruited and underwent a 14-d measurement protocol of daily deuterium oxide (D2O) consumption and serial biopsies of vastus lateralis muscle. The OIR group then completed 10-weeks of high-intensity interval training (HIIT), encompassing 3 sessions per week of cycle ergometer exercise with 1 min intervals at 100 % maximum aerobic power (Wmax) interspersed by 1 min recovery periods. The number of intervals per session progressed from 4 to 8, and during weeks 8-10 the 14-d measurement protocol was repeated. The abundance and turnover rates of 880 and 301 proteins, respectively, were measured. OIR and LHC muscle exhibited 352 differences (p &lt; 0.05, false discovery rate (p &lt; 0.05) differences in protein turnover. OIR muscle was enriched with markers of metabolic stress, protein misfolding and components of the ubiquitin-proteasome system, and the turnover rate of many of these proteins was less compared to LHC muscle. HIIT altered the abundance of 53 proteins and increased the turnover rate of 22 proteins (p &lt; 0.05) in OIR muscle and tended to restore proteostasis, evidenced by increasing muscle protein turnover rates and normalizing proteasome composition in OIR participants. In conclusion, obesity and insulin resistance are associated with compromised muscle proteostasis, which can be partially restored by endurance exercise

Fixing the Future Annual Report 2024

Annual Report for the Fixing the Future Project. Full Citation: Urquhart, L. Stead, M. Sailaja, N. Darzentas, D. Terras, M. Lechelt, S. Luger, E. Coulton, P.Lindley, J. Boniface, C. D McAuley. Castle-Green, T. Pilling, M. Primlani, N. and D Kilic “Fixing the Future Annual Report 2024

Forecasting Seizures in Dogs with Naturally Occurring Epilepsy

Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low-gamma (30-70 Hz), and high-gamma (70-180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring

Epilepsy Personal Assistant Device-A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation

Epilepsy is one of the most common neurological disorders, and it affects almost 1% of the population worldwide. Many people living with epilepsy continue to have seizures despite anti-epileptic medication therapy, surgical treatments, and neuromodulation therapy. The unpredictability of seizures is one of the most disabling aspects of epilepsy. Furthermore, epilepsy is associated with sleep, cognitive, and psychiatric comorbidities, which significantly impact the quality of life. Seizure predictions could potentially be used to adjust neuromodulation therapy to prevent the onset of a seizure and empower patients to avoid sensitive activities during high-risk periods. Long-term objective data is needed to provide a clearer view of brain electrical activity and an objective measure of the efficacy of therapeutic measures for optimal epilepsy care. While neuromodulation devices offer the potential for acquiring long-term data, available devices provide very little information regarding brain activity and therapy effectiveness. Also, seizure diaries kept by patients or caregivers are subjective and have been shown to be unreliable, in particular for patients with memory-impairing seizures. This paper describes the design, architecture, and development of the Mayo Epilepsy Personal Assistant Device (EPAD). The EPAD has bi-directional connectivity to the implanted investigational Medtronic Summit RC+S-TM device to implement intracranial EEG and physiological monitoring, processing, and control of the overall system and wearable devices streaming physiological time-series signals. In order to mitigate risk and comply with regulatory requirements, we developed a Quality Management System (QMS) to define the development process of the EPAD system, including Risk Analysis, Verification, Validation, and protocol mitigations. Extensive verification and validation testing were performed on thirteen canines and benchtop systems. The system is now under a first-in-human trial as part of the US FDA Investigational Device Exemption given in 2018 to study modulated responsive and predictive stimulation using the Mayo EPAD system and investigational Medtronic Summit RC+S-TM in ten patients with non-resectable dominant or bilateral mesial temporal lobe epilepsy. The EPAD system coupled with an implanted device capable of EEG telemetry represents a next-generation solution to optimizing neuromodulation therapy