Role of optically pumped magnetometers in presurgical epilepsy evaluation: Commentary of the American Clinical Magnetoencephalography Society.

One of the major challenges of modern epileptology is the underutilization of epilepsy surgery for treatment of patients with focal, medication resistant epilepsy (MRE). Aggravating this distressing failure to deliver optimum care to these patients is the underuse of proven localizing tools, such as magnetoencephalography (MEG), a clinically validated, non-invasive, neurophysiological method used to directly measure and localize brain activity. A sizable mass of published evidence indicates that MEG can improve identification of surgical candidates and guide pre-surgical planning, increasing the yield of SEEG and improving operative outcomes. However, despite at least 10 common, evidence supported, clinical scenarios in MRE patients where MEG can offer non-redundant information and improve the pre-surgical evaluation, it is regularly used by only a minority of USA epilepsy centers. The current state of the art in MEG sensors employs SQUIDs, which require cooling with liquid helium to achieve superconductivity. This sensor technology has undergone significant generational improvement since whole head MEG scanners were introduced around in 1990s, but still has limitations. Further advances in sensor technology which may make ME G more easily accessible and affordable have been eagerly awaited, and development of new techniques should be encouraged. Of late, optically pumped magnetometers (OPMs) have received considerable attention, even prompting some potential acquisitions of new MEG systems to be put on hold, based on a hope that OPMs will usher in a new generation of MEG equipment and procedures. The development of any new clinical test used to guide intracranial EEG monitoring and/or surgical planning must address several specific issues. The goal of this commentary is to recognize the current state of OPM technology and to suggest a framework for it to advance in the clinical realm where it can eventually be deemed clinically valuable to physicians and patients. The American Clinical MEG Society (ACMEGS) strongly supports more advanced and less expensive technology and looks forward to continuing work with researchers to develop new sensors and clinical devices which will improve the experience and outcome for patients, and perhaps extend the role of MEG. However, currently, there are no OPM devices ready for practical clinical use. Based on the engineering obstacles and the clinical tradeoffs to be resolved, the assessment of experts suggests that there will most likely be another decade relying solely on "frozen SQUIDs" in the clinical MEG field.

Containment measures during the COVID pandemic: The role of non-pharmaceutical health policies.

Many countries have imposed a set of non-pharmaceutical health policy interventions in an effort to slow the spread of the COVID-19 pandemic. The objective of this paper is to examine the effects of the interventions, drawing on evidence from the OECD countries. A special feature here is the mechanism that underlies the impact of the containment policies. To this end, a causal mediation analysis decomposing the total effect into a direct and an indirect effect is conducted. The key finding is a dual cause-effect channel. On the one hand, there is a direct effect of the non-pharmaceutical interventions on the various health variables. Beyond this, a quantitatively dominant indirect impact of non-pharmaceutical interventions operating via voluntary changes in social distancing is shown.

Has Germany's temporary VAT rates cut as part of the COVID-19 fiscal stimulus boosted growth?

On 3 June 2020, the German government announced a EUR 130 billion fiscal stimulus package to stimulate market demand and jumpstart the economy in the wake of the COVID-19 pandemic lockdown in the spring of 2020. The most prominent measure of this package is an unconventional fiscal policy in the form of a temporary VAT rates cut for six months, from 1 July to 31 December 2020. Employing a dynamic stochastic general equilibrium (DSGE) framework, we study the efficiency of the VAT tax rates cut for ameliorating the consequences of the pandemic recession. The simulation of the calibrated DSGE model yields a tax policy-induced real GDP increase of about 0.3% points for 2020.

The People's bank of China's response to the coronavirus pandemic: A quantitative assessment.

The People's Bank of China (PBoC) has implemented numerous measures to cushion the impacts of the COVID-19 health crisis on the Chinese economy. Since the current monetary policy framework features a multi-instrument mix of liquidity tools and pricing signals, we employ a dynamic-factor modelling approach to derive a composite indicator of China's monetary policy stance. Our quantitative assessment shows that the PBoC's policy response to the outbreak of the COVID-19 pandemic has been swift and decisive. Specifically, our estimates reveal that the PBoC has implemented novel policy measures to ensure that commercial banks maintain liquidity access and credit provision during the COVID-19 crisis.

Quality of Maternal Height and Weight Data from the Revised Birth Certificate and Pregnancy Risk Assessment Monitoring System.

BACKGROUND: The 2003 revision of the US Standard Certificate of Live Birth (birth certificate) and Pregnancy Risk Assessment Monitoring System (PRAMS) are important for maternal weight research and surveillance. We examined quality of prepregnancy body mass index (BMI), gestational weight gain, and component variables from these sources. METHODS: Data are from a PRAMS data quality improvement study among a subset of New York City and Vermont respondents in 2009. We calculated mean differences comparing prepregnancy BMI data from the birth certificate and PRAMS (n = 734), and gestational weight gain data from the birth certificate (n = 678) to the medical record, considered the gold standard. We compared BMI categories (underweight, normal weight, overweight, obese) and gestational weight gain categories (below, within, above recommendations), classified by different sources, using percent agreement and the simple κ statistic. RESULTS: For most maternal weight variables, mean differences between the birth certificate and PRAMS compared with the medical record were less than 1 kg. Compared with the medical record, the birth certificate classified similar proportions into prepregnancy BMI categories (agreement = 89%, κ = 0.83); PRAMS slightly underestimated overweight and obesity (agreement = 84%, κ = 0.73). Compared with the medical record, the birth certificate overestimated gestational weight gain below recommendations and underestimated weight gain within recommendations (agreement = 81%, κ = 0.69). Agreement varied by maternal and pregnancy-related characteristics. CONCLUSIONS: Classification of prepregnancy BMI and gestational weight gain from the birth certificate or PRAMS was mostly similar to the medical record but varied by maternal and pregnancy-related characteristics. Efforts to understand how misclassification influences epidemiologic associations are needed.

Reliability of Reported Maternal Smoking: Comparing the Birth Certificate to Maternal Worksheets and Prenatal and Hospital Medical Records, New York City and Vermont, 2009.

Maternal smoking is captured on the 2003 US Standard Birth Certificate based on self-reported tobacco use before and during pregnancy collected on post-delivery maternal worksheets. Study objectives were to compare smoking reported on the birth certificate to maternal worksheets and prenatal and hospital medical records. The authors analyzed a sample of New York City (NYC) and Vermont women (n = 1,037) with a live birth from January to August 2009 whose responses to the Pregnancy Risk Assessment Monitoring System survey were linked with birth certificates and abstracted medical records and maternal worksheets. We calculated smoking prevalence and agreement (kappa) between sources overall and by maternal and hospital characteristics. Smoking before and during pregnancy was 13.7 and 10.4% using birth certificates, 15.2 and 10.7% using maternal worksheets, 18.1 and 14.1% using medical records, and 20.5 and 15.0% using either maternal worksheets or medical records. Birth certificates had "almost perfect" agreement with maternal worksheets for smoking before and during pregnancy (κ = 0.92 and 0.89) and "substantial" agreement with medical records (κ = 0.70 and 0.74), with variation by education, insurance, and parity. Smoking information on NYC and Vermont birth certificates closely agreed with maternal worksheets but was underestimated compared with medical records, with variation by select maternal characteristics. Opportunities exist to improve birth certificate smoking data, such as reducing the stigma of smoking, and improving the collection, transcription, and source of information.

Validation of self-reported maternal and infant health indicators in the Pregnancy Risk Assessment Monitoring System.

To assess the validity of self-reported maternal and infant health indicators reported by mothers an average of 4 months after delivery. Three validity measures-sensitivity, specificity and positive predictive value (PPV)-were calculated for pregnancy history, pregnancy complications, health care utilization, and infant health indicators self-reported on the Pregnancy Risk Assessment Monitoring System (PRAMS) questionnaire by a representative sample of mothers delivering live births in New York City (NYC) (n = 603) and Vermont (n = 664) in 2009. Data abstracted from hospital records served as gold standards. All data were weighted to be representative of women delivering live births in NYC or Vermont during the study period. Most PRAMS indicators had >90 % specificity. Indicators with >90 % sensitivity and PPV for both sites included prior live birth, any diabetes, and Medicaid insurance at delivery, and for Vermont only, infant admission to the NICU and breastfeeding in the hospital. Indicators with poor sensitivity and PPV (<70 %) for both sites (i.e., NYC and Vermont) included placenta previa and/or placental abruption, urinary tract infection or kidney infection, and for NYC only, preterm labor, prior low-birth-weight birth, and prior preterm birth. For Vermont only, receipt of an HIV test during pregnancy had poor sensitivity and PPV. Mothers accurately reported information on prior live births and Medicaid insurance at delivery; however, mothers' recall of certain pregnancy complications and pregnancy history was poor. These findings could be used to prioritize data collection of indicators with high validity.

Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline.

An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimer's disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls. We demonstrated that these embeddings preserve both local and global geometric information, presenting reduced distortion compared to rival models, even when brain networks are mapped into low-dimensional spaces. In addition, our findings showed that the hyperbolic embeddings encompass unique SCD-related information that improves the discriminatory power above and beyond that of connectivity features alone. Notably, we introduced a unique metric-the radius of the node embeddings-which effectively proxies the hierarchical organization of the brain. Using this metric, we identified subtle hierarchy organizational differences between the two participant groups, suggesting increased hierarchy in the dorsal attention, frontoparietal, and ventral attention subnetworks among the SCD group. Last, we assessed the correlation between these hierarchical variations and cognitive assessment scores, revealing associations with diminished performance across multiple cognitive evaluations in the SCD group. Overall, this study presents the first evaluation of hyperbolic embeddings of MEG brain networks, offering novel insights into brain organization, cognitive decline, and potential diagnostic avenues of Alzheimer's disease.

Comparison of 2 Robotic Systems for Pediatric Stereoelectroencephalography Implantation.

BACKGROUND: Stereoelectroencephalography (SEEG) remains critical in guiding epilepsy surgery. Robot-assisted techniques have shown promise in improving SEEG implantation outcomes but have not been directly compared. In this single-institution series, we compared ROSA and Stealth AutoGuide robots in pediatric SEEG implantation. METHODS: We retrospectively reviewed 21 sequential pediatric SEEG implantations consisting of 6 ROSA and 15 AutoGuide procedures. We determined mean operative time, time per electrode, root mean square (RMS) registration error, and surgical complications. Three-dimensional radial distances were calculated between each electrode's measured entry and target points with respective errors from the planned trajectory line. RESULTS: Mean overall/per electrode operating time was 73.5/7.5 minutes for ROSA and 126.1/10.9 minutes for AutoGuide (P = 0.030 overall, P = 0.082 per electrode). Mean RMS registration error was 0.77 mm (0.55-0.93 mm) for ROSA and 0.6 mm (0.2-1.0 mm) for AutoGuide (P = 0.26). No procedures experienced complications. The mean radial (entry point error was 1.23 ± 0.11 mm for ROSA and 2.65 ± 0.12 mm for AutoGuide (P < 0.001), while the mean radial target point error was 1.86 ± 0.15 mm for ROSA and 3.25 ± 0.16 mm for AutoGuide (P < 0.001). CONCLUSIONS: Overall operative time was greater for AutoGuide procedures, although there was no statistically significant difference in time per electrode. Both systems are highly accurate with no significant RMS error difference. While the ROSA robot yielded significantly lower entry and target point errors, both robots are safe and reliable for deep electrode insertion in pediatric epilepsy.

Noise characteristics in spaceflight multichannel EEG.

The cognitive performance of the crew has a major impact on mission safety and success in space flight. Monitoring of cognitive performance during long-duration space flight therefore is of paramount importance and can be performed using compact state-of-the-art mobile EEG. However, signal quality of EEG may be compromised due to the vicinity to various electronic devices and constant movements. We compare noise characteristics between in-flight extraterrestrial microgravity and ground-level terrestrial electroencephalography (EEG) recordings. EEG data recordings from either aboard International Space Station (ISS) or on earth's surface, utilizing three EEG amplifiers and two electrode types, were compared. In-flight recordings showed noise level of an order of magnitude lower when compared to pre- and post-flight ground-level recordings with the same EEG system. Noise levels between ground-level recordings with actively shielded cables, and in-flight recordings without shielded cables, were similar. Furthermore, noise level characteristics of shielded ground-level EEG recordings, using wet and dry electrodes, and in-flight EEG recordings were similar. Actively shielded mobile dry EEG systems will support neuroscientific research and neurocognitive monitoring during spaceflight, especially during long-duration space missions.

Effects of spaceflight on the EEG alpha power and functional connectivity.

Electroencephalography (EEG) can detect changes in cerebral activity during spaceflight. This study evaluates the effect of spaceflight on brain networks through analysis of the Default Mode Network (DMN)'s alpha frequency band power and functional connectivity (FC), and the persistence of these changes. Five astronauts' resting state EEGs under three conditions were analyzed (pre-flight, in-flight, and post-flight). DMN's alpha band power and FC were computed using eLORETA and phase-locking value. Eyes-opened (EO) and eyes-closed (EC) conditions were differentiated. We found a DMN alpha band power reduction during in-flight (EC: p < 0.001; EO: p < 0.05) and post-flight (EC: p < 0.001; EO: p < 0.01) when compared to pre-flight condition. FC strength decreased during in-flight (EC: p < 0.01; EO: p < 0.01) and post-flight (EC: ns; EO: p < 0.01) compared to pre-flight condition. The DMN alpha band power and FC strength reduction persisted until 20 days after landing. Spaceflight caused electrocerebral alterations that persisted after return to earth. Periodic assessment by EEG-derived DMN analysis has the potential to become a neurophysiologic marker of cerebral functional integrity during exploration missions to space.

Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline.

An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimer's disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls. We demonstrated that these embeddings preserve both local and global geometric information, presenting reduced distortion compared to rival models, even when brain networks are mapped into low-dimensional spaces. In addition, our findings showed that the hyperbolic embeddings encompass unique SCD-related information that improves the discriminatory power above and beyond that of connectivity features alone. Notably, we introduced a unique metric-the radius of the node embeddings-which effectively proxies the hierarchical organization of the brain. Using this metric, we identified subtle hierarchy organizational differences between the two participant groups, suggesting increased hierarchy in the dorsal attention, frontoparietal, and ventral attention subnetworks among the SCD group. Last, we assessed the correlation between these hierarchical variations and cognitive assessment scores, revealing associations with diminished performance across multiple cognitive evaluations in the SCD group. Overall, this study presents the first evaluation of hyperbolic embeddings of MEG brain networks, offering novel insights into brain organization, cognitive decline, and potential diagnostic avenues of Alzheimer's disease.

An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application.

Epilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30-70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone. This retrospective study aims to improve surgical outcome through in silico testing of surgical hypotheses through a personalized computational neurosurgery model created from individualized patient's magnetoencephalography recording and MRI. The framework assesses the extent of the epileptic network and evaluates underlying spike dynamics, resulting in identification of one single brain volume as a candidate for resection. Dynamic-locked networks were utilized for virtual cortical resection. This in silico protocol was tested in a cohort of 24 paediatric patients with focal drug-resistant epilepsy who underwent epilepsy surgery. Of 24 patients who were included in the analysis, 79% (19 of 24) of the models agreed with the patient's clinical surgery outcome and 21% (5 of 24) were considered as model failures (accuracy 0.79, sensitivity 0.77, specificity 0.82). Patients with unsuccessful surgery outcome typically showed a model cluster outside of the resected cavity, while those with successful surgery showed the cluster model within the cavity. Two of the model failures showed the cluster in the vicinity of the resected tissue and either a functional disconnection or lack of precision of the magnetoencephalography-MRI overlapping could explain the results. Two other cases were seizure free for 1 year but developed late recurrence. This is the first study that provides in silico personalized protocol for epilepsy surgery planning using magnetoencephalography spike network analysis. This model could provide complementary information to the traditional pre-surgical assessment methods and increase the proportion of patients achieving seizure-free outcome from surgery.

Neurophysiological investigation of idiopathic acquired auditory-visual synesthesia.

We present a case of acquired auditory-visual synesthesia and its neurophysiological investigation in a healthy 42-year-old woman. She started experiencing persistent positive and intermittent negative visual phenomena at age 37 followed by auditory-visual synesthesia. Her neurophysiological investigation included video-EEG, fMRI, and MEG. Auditory stimuli (700 Hz, 50 ms duration, 0.5 s ISI) were presented binaurally at 60 db above the hearing threshold in a dark room. The patient had bilateral symmetrical auditory-evoked neuromagnetic responses followed by an occipital-evoked field 16.3 ms later. The activation of occipital cortex following auditory stimuli may represent recruitment of existing cross-modal sensory pathways.

Complete corpus callosotomy using a frameless navigation probe through a minicraniotomy in children with medically refractory epilepsy: A case series and technical note.

Background: Medically refractory epilepsy constitutes up to one-third of the epilepsy pediatric patients. Corpus callosotomy (CC) has been used for the treatment of medically refractory epilepsy in children with atonic seizures and generalized tonic-clonic (GTC) seizures. In this case series study, we are describing a novel technique for CC using the frameless navigation probe through a minicraniotomy. Methods: Thirteen pediatric patients with the diagnosis of medically refractory epilepsy predominantly GTC with drop attack who underwent extensive Phase I. An L-shape was done, then through a 4 × 3 cm craniotomy, we were able to open the interhemispheric fissure until the corpus callosum is visualized. The Stealth probe is then used to go down to the midline raphe which is followed anteriorly then traced posteriorly to the anterior border of the vein of Galen. Finally, the Stealth probe is used to confirm the completeness of the callosotomy. Results: The procedure was accomplished successfully with no intraoperative complications; mean surgical time is 3 h:07 m. The mean follow-up was 31.5 months. All patients achieved significant seizure control. No patients experienced worsening of their atonic seizures after surgery compared with their preoperative state; however, six patients achieved Engel Class I, four patients achieved Engel Class II, and three patients achieved Engel Class III. Conclusion: Complete CC using a frameless navigation probe is a novel and effective technique for the treatment of medically refractory epilepsy with a very good surgical and seizure outcomes, minimal neurological morbidity, minimal blood loss, and short OR time.

Gamma band functional connectivity reduction in patients with amnestic mild cognitive impairment and epileptiform activity.

There is growing evidence for neuronal hyperexcitability in Alzheimer's disease. Hyperexcitability is associated with an increase in epileptiform activity and the disruption of inhibitory activity of interneurons. Interneurons fire at a high rate and are frequently associated with high-frequency oscillations in the gamma frequency band (30-150 Hz). It is unclear how hyperexcitability affects the organization of functional brain networks. A sample of 63 amnestic mild cognitive impairment patients underwent a magnetoencephalography resting-state recording with eyes closed. Twenty (31.75%) mild cognitive impairment patients had epileptiform activity. A cluster-based analysis of the magnetoencephalography functional connectivity revealed a region within the right temporal cortex whose global connectivity in the gamma frequency band was significantly reduced in patients with epileptiform activity relative to those without epileptiform activity. A subsequent seed-based analysis showed that this was largely due to weaker gamma band connectivity of this region with ipsilateral frontal and medial regions, and the upper precuneus area. In addition, this reduced functional connectivity was associated with higher grey matter atrophy across several cortical regions in the patients with epileptiform activity. These functional network disruptions and changes in brain physiology and morphology have important clinical implications as they may contribute to cognitive decline in mild cognitive impairment and Alzheimer's disease.

The role of magnetoencephalography in "nonlesional" epilepsy.

The surgical management of neocortical epilepsy is challenging because many patients are without obvious structural lesions, or lesions are small and easily overlooked during routine clinical interpretation of magnetic resonance imaging (MRI) data. Even when functional imaging data suggest focal epileptiform pathology, in the absence of a concordant structural lesion, invasive monitoring is often required to confirm that an appropriate surgical target has been identified. This study sought to determine the extent to which knowledge of magnetoencephalography (MEG) data can augment the MRI-based detection of structural brain lesions. MRI and whole-head MEG data were obtained from 40 patients with neocortical epilepsy. As a result of MEG data, 29 cases were sent for MRI reevaluation. In seven of these cases, MEG-guided review led to specification of now clear, but previously unidentified, lesions. There were two additional cases for which follow-up high-resolution imaging did not confirm structural abnormalities. In patients with neocortical epilepsy, MEG is a useful adjunct to MRI for the identification of structural lesions.

Practical Fundamentals of Clinical MEG Interpretation in Epilepsy.

Magnetoencephalography (MEG) is a neurophysiologic test that offers a functional localization of epileptic sources in patients considered for epilepsy surgery. The understanding of clinical MEG concepts, and the interpretation of these clinical studies, are very involving processes that demand both clinical and procedural expertise. One of the major obstacles in acquiring necessary proficiency is the scarcity of fundamental clinical literature. To fill this knowledge gap, this review aims to explain the basic practical concepts of clinical MEG relevant to epilepsy with an emphasis on single equivalent dipole (sECD), which is one the most clinically validated and ubiquitously used source localization method, and illustrate and explain the regional topology and source dynamics relevant for clinical interpretation of MEG-EEG.

Indications for Inpatient Magnetoencephalography in Children - An Institution's Experience.

Magnetoencephalography (MEG) is recognized as a valuable non-invasive clinical method for localization of the epileptogenic zone and critical functional areas, as part of a pre-surgical evaluation for patients with pharmaco-resistant epilepsy. MEG is also useful in localizing functional areas as part of pre-surgical planning for tumor resection. MEG is usually performed in an outpatient setting, as one part of an evaluation that can include a variety of other testing modalities including 3-Tesla MRI and inpatient video-electroencephalography monitoring. In some clinical circumstances, however, completion of the MEG as an inpatient can provide crucial ictal or interictal localization data during an ongoing inpatient evaluation, in order to expedite medical or surgical planning. Despite well-established clinical indications for performing MEG in general, there are no current reports that discuss indications or considerations for completion of MEG on an inpatient basis. We conducted a retrospective institutional review of all pediatric MEGs performed between January 2012 and December 2020, and identified 34 cases where MEG was completed as an inpatient. We then reviewed all relevant medical records to determine clinical history, all associated diagnostic procedures, and subsequent treatment plans including epilepsy surgery and post-surgical outcomes. In doing so, we were able to identify five indications for completing the MEG on an inpatient basis: (1) super-refractory status epilepticus (SRSE), (2) intractable epilepsy with frequent electroclinical seizures, and/or frequent or repeated episodes of status epilepticus, (3) intractable epilepsy with infrequent epileptiform discharges on EEG or outpatient MEG, or other special circumstances necessitating inpatient monitoring for successful and safe MEG data acquisition, (4) MEG mapping of eloquent cortex or interictal spike localization in the setting of tumor resection or other urgent neurosurgical intervention, and (5) international or long-distance patients, where outpatient MEG is not possible or practical. MEG contributed to surgical decision-making in the majority of our cases (32 of 34). Our clinical experience suggests that MEG should be considered on an inpatient basis in certain clinical circumstances, where MEG data can provide essential information regarding the localization of epileptogenic activity or eloquent cortex, and be used to develop a treatment plan for surgical management of children with complicated or intractable epilepsy.

A comparison of machine learning classifiers for pediatric epilepsy using resting-state functional MRI latency data.

Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls. The normal distribution of latency z-scores from the epilepsy and healthy control cohorts were analyzed for overlap in 36 seed regions. In these seed regions, overlap between the study cohorts ranged from 0.44-0.58. Machine learning features were extracted from latency z-score maps using principal component analysis. Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest algorithms were trained with these features. Area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity and F1-scores were used to evaluate model performance. The XGBoost model outperformed all other models with a test AUC of 0.79, accuracy of 74%, specificity of 73%, and a sensitivity of 77%. The Random Forest model performed comparably to XGBoost across multiple metrics, but it had a test sensitivity of 31%. The SVM model did not perform >70% in any of the test metrics. The XGBoost model had the highest sensitivity and accuracy for the detection of epilepsy. Development of machine learning algorithms trained with rfMRI latency data could provide an adjunctive method for the diagnosis and evaluation of epilepsy with the goal of enabling timely and appropriate care for patients.