Traditional side-by-side visual interpretation of ictal and interictal single-photon emission computed tomography (SPECT) scans can be difficult in identifying the surgical focus, particularly in patients with extratemporal or otherwise unlocalized intractable epilepsy. Computer-aided subtraction ictal SPECT co-registered to MRI (SISCOM) may improve the clinical usefulness of SPECT in localizing the surgical seizure focus. We studied 51 consecutive intractable partial epilepsy patients who had interictal and ictal scans. The SPECT studies were blindly reviewed and classified as either localizing to 1 of 16 sites in the brain or as nonlocalizing. SISCOM images were localizing in 45 of 51 (88.2%) compared with 20 of 51 (39.2%) for traditional side-by-side inspection of ictal and interictal SPECT images (p < 0.0001). Inter-rater agreement for two independent reviewers was better for SISCOM (84.3% versus 41.2%, K = 0.83 versus 0.26; p < 0.0001). Concordance of seizure localization with the more established tests was also higher for SISCOM. Late injection of the radiotracer (>45 seconds), but not secondary generalization of the seizure, was associated with a falsely localizing or nonlocalizing SISCOM. Epilepsy surgery patients whose SISCOM localization was concordant with the surgical site were more likely to have excellent outcome than patients with nonconcordant or nonlocalizing findings (62.5% [10/16] versus 20% [2/10]; p < 0.05). On the other hand, seizure localization by the traditional method of SPECT inspection had no significant association with postsurgical outcome. We conclude that SISCOM improves the sensitivity and the specificity of SPECT in localizing the seizure focus for epilepsy surgery. Concordance between SISCOM localization and site of surgery is predictive of postsurgical improvement in seizure outcome.

Focal seizures appear to start abruptly and unpredictably when recorded from volumes of brain probed by clinical intracranial electroencephalograms. To investigate the spatiotemporal scale of focal epilepsy, wide-bandwidth electrophysiological recordings were obtained using clinical macro- and research microelectrodes in patients with epilepsy and control subjects with intractable facial pain. Seizure-like events not detectable on clinical macroelectrodes were observed on isolated microelectrodes. These ‘microseizures’ were sparsely distributed, more frequent in brain regions that generated seizures, and sporadically evolved into large-scale clinical seizures. Rare microseizures observed in control patients suggest that this phenomenon is ubiquitous, but their density distinguishes normal from epileptic brain. Epileptogenesis may involve the creation of these topographically fractured microdomains and ictogenesis (seizure generation), the dynamics of their interaction and spread.

See Mormann and Andrzejak (doi:10.1093/brain/aww091) for a scientific commentary on this article.   Accurate forecasting of epileptic seizures has the potential to transform clinical epilepsy care. However, progress toward reliable seizure forecasting has been hampered by lack of open access to long duration recordings with an adequate number of seizures for investigators to rigorously compare algorithms and results. A seizure forecasting competition was conducted on kaggle.com using open access chronic ambulatory intracranial electroencephalography from five canines with naturally occurring epilepsy and two humans undergoing prolonged wide bandwidth intracranial electroencephalographic monitoring. Data were provided to participants as 10-min interictal and preictal clips, with approximately half of the 60 GB data bundle labelled (interictal/preictal) for algorithm training and half unlabelled for evaluation. The contestants developed custom algorithms and uploaded their classifications (interictal/preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted on a public leader board. The contest ran from August to November 2014, and 654 participants submitted 17 856 classifications of the unlabelled test data. The top performing entry scored 0.84 area under the classification curve. Following the contest, additional held-out unlabelled data clips were provided to the top 10 participants and they submitted classifications for the new unseen data. The resulting area under the classification curves were well above chance forecasting, but did show a mean 6.54 ± 2.45% (min, max: 0.30, 20.2) decline in performance. The kaggle.com model using open access data and algorithms generated reproducible research that advanced seizure forecasting. The overall performance from multiple contestants on unseen data was better than a random predictor, and demonstrates the feasibility of seizure forecasting in canine and human epilepsy.

High-frequency oscillations (HFO; gamma: 40–100 Hz, ripples: 100–200 Hz, and fast ripples: 250–500 Hz) have been widely studied in health and disease. These phenomena may serve as biomarkers for epileptic brain; however, a means of differentiating between pathological and normal physiological HFO is essential. We categorized task-induced physiological HFO during periods of HFO induced by a visual or motor task by measuring frequency, duration, and spectral amplitude of each event in single trial time-frequency spectra and compared them to pathological HFO similarly measured. Pathological HFO had higher mean spectral amplitude, longer mean duration, and lower mean frequency than physiological-induced HFO. In individual patients, support vector machine analysis correctly classified pathological HFO with sensitivities ranging from 70–98% and specificities >90% in all but one patient. In this patient, infrequent high-amplitude HFO were observed in the motor cortex just before movement onset in the motor task. This finding raises the possibility that in epileptic brain physiological-induced gamma can assume higher spectral amplitudes similar to those seen in pathologic HFO. This method if automated and validated could provide a step towards differentiating physiological HFO from pathological HFO and improving localization of epileptogenic brain.

High frequency oscillations are associated with normal brain function, but also increasingly recognized as potential biomarkers of the epileptogenic brain. Their role in human cognition has been predominantly studied in classical gamma frequencies (30–100 Hz), which reflect neuronal network coordination involved in attention, learning and memory. Invasive brain recordings in animals and humans demonstrate that physiological oscillations extend beyond the gamma frequency range, but their function in human cognitive processing has not been fully elucidated. Here we investigate high frequency oscillations spanning the high gamma (50–125 Hz), ripple (125–250 Hz) and fast ripple (250–500 Hz) frequency bands using intracranial recordings from 12 patients (five males and seven females, age 21–63 years) during memory encoding and recall of a series of affectively charged images. Presentation of the images induced high frequency oscillations in all three studied bands within the primary visual, limbic and higher order cortical regions in a sequence consistent with the visual processing stream. These induced oscillations were detected on individual electrodes localized in the amygdala, hippocampus and specific neocortical areas, revealing discrete oscillations of characteristic frequency, duration and latency from image presentation. Memory encoding and recall significantly modulated the number of induced high gamma, ripple and fast ripple detections in the studied structures, which was greater in the primary sensory areas during the encoding (Wilcoxon rank sum test, P = 0.002) and in the higher-order cortical association areas during the recall (Wilcoxon rank sum test, P = 0.001) of memorized images. Furthermore, the induced high gamma, ripple and fast ripple responses discriminated the encoded and the affectively charged images. In summary, our results show that high frequency oscillations, spanning a wide range of frequencies, are associated with memory processing and generated along distributed cortical and limbic brain regions. These findings support an important role for fast network synchronization in human cognition and extend our understanding of normal physiological brain activity during memory processing.