Otis Smart

Multimodal Approaches to Define Network Oscillations in Depression

The renaissance in the use of encephalography-based research methods to probe the pathophysiology of neuropsychiatric disorders is well afoot and continues to advance. Building on the platform of neuroimaging evidence on brain circuit models, magnetoencephalography, scalp electroencephalography, and even invasive electroencephalography are now being used to characterize brain network dysfunctions that underlie major depressive disorder using brain oscillation measurements and associated treatment responses. Such multiple encephalography modalities provide avenues to study pathologic network dynamics with high temporal resolution and over long time courses, opportunities to complement neuroimaging methods and findings, and new approaches to identify quantitative biomarkers that indicate critical targets for brain therapy. Such goals have been facilitated by the ongoing testing of novel invasive neuromodulation therapies, notably, deep brain stimulation, where clinically relevant treatment effects can be monitored at multiple brain sites in a time-locked causal manner. We review key brain rhythms identified in major depressive disorder as foundation for development of putative biomarkers for objectively evaluating neuromodulation success and for guiding deep brain stimulation or other target-based neuromodulation strategies for treatment-resistant depression patients.

Automatic detection of high frequency epileptiform oscillations from intracranial EEG recordings of patients with neocortical epilepsy

High frequency epileptiform oscillations (HFEOs) have been observed before neocortical seizures on intracranial EEG recordings. There is suggestion that HFEOs may localize epileptic brain regions important to seizure generation in humans, a finding that would be valuable for understanding, diagnosing, and treating epilepsy. In this paper, an automated approach for detecting HFEOs is described. Fuzzy clustering and histograms are used to characterize HFEO events. Compared to neurologist markings, the algorithm detected 87% of the HFEOs while achieving 68% precision and 90% specificity, without training. Applied to thirty-five minute seizure records obtained from six patients, spatial and temporal localization of HFEOs were observed in 77% and 61% of the segments respectively. Results highlight the potential of the method to identify brain regions vital to seizure generation by tracking the spatio-temporal evolution of high frequency seizure precursors in the epileptic network.

Genetic programming of conventional features to detect seizure precursors

This paper presents an application of genetic programming (GP) to optimally select and fuse conventional features (C-features) for the detection of epileptic waveforms within intracranial electroencephalogram (IEEG) recordings that precede seizures, known as seizure-precursors. Evidence suggests that seizure-precursors may localize regions important to seizure generation on the IEEG and epilepsy treatment. However, current methods to detect epileptic precursors lack a sound approach to automatically select and combine C-features that best distinguish epileptic events from background, relying on visual review predominantly. This work suggests GP as an optimal alternative to create a single feature after evaluating the performance of a binary detector that uses: 1) genetically programmed features; 2) features selected via GP; 3) forward sequentially selected features; and 4) visually selected features. Results demonstrate that a detector with a genetically programmed feature outperforms the other three approaches, achieving over 78.5% positive predictive value, 83.5% sensitivity, and 93% specificity at the 95% level of confidence.

Grammatical evolution for features of epileptic oscillations in clinical intracranial electroencephalograms

This paper presents grammatical evolution (GE) as an approach to select and combine features for detecting epileptic oscillations within clinical intracranial electroencephalogram (iEEG) recordings of patients with epilepsy. Clinical iEEG is used in preoperative evaluations of a patient who may have surgery to treat epileptic seizures. Literature suggests that pathological oscillations may indicate the region(s) of brain that cause epileptic seizures, which could be surgically removed for therapy. If this presumption is true, then the effectiveness of surgical treatment could depend on the effectiveness in pinpointing critically diseased brain, which in turn depends on the most accurate detection of pathological oscillations. Moreover, the accuracy of detecting pathological oscillations depends greatly on the selected feature(s) that must objectively distinguish epileptic events from average activity, a task that visual review is inevitably too subjective and insufficient to resolve. Consequently, this work suggests an automated algorithm that incorporates grammatical evolution (GE) to construct the most sufficient feature(s) to detect epileptic oscillations within the iEEG of a patient. We estimate the performance of GE relative to three alternative methods of selecting or combining features that distinguish an epileptic gamma (~65-95 Hz) oscillation from normal activity: forward sequential feature-selection, backward sequential feature-selection, and genetic programming. We demonstrate that a detector with a grammatically evolved feature exhibits a sensitivity and selectivity that is comparable to a previous detector with a genetically programmed feature, making GE a useful alternative to designing detectors.

Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data

Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient.

Hippocampal seizure-onset laterality can change over long timescales: A same-patient observation over 500 days.

This study describes seizure laterality and localization changes over 500 consecutive days in a patient with bilateral temporal lobe epilepsy (BTLE) and implanted NeuroPace RNS™ System. During a continuous two-year time period, the RNS™ device stored 54 hippocampal electrocorticography (ECoG) seizures, which we analyzed to determine their distribution and time variance across hippocampi. We report nonrandom long-term seizure laterality and localization variations, especially in the first 200 days postimplant, despite equivalent total seizure counts in both hippocampi. This case suggests that hippocampal seizures dynamically progress over extensive timescales.

Mapping and mining interictal pathological gamma (30-100Hz) oscillations with clinical intracranial EEG in patients with epilepsy

Localizing an epileptic network is essential for guiding neurosurgery and antiepileptic medical devices as well as elucidating mechanisms that may explain seizure-generation and epilepsy. There is increasing evidence that pathological oscillations may be specific to diseased networks in patients with epilepsy and that these oscillations may be a key biomarker for generating and indentifying epileptic networks. We present a semi-automated method that detects, maps, and mines pathological gamma (30-100 Hz) oscillations (PGOs) in human epileptic brain to possibly localize epileptic networks. We apply the method to standard clinical iEEG (<100 Hz) with interictal PGOs and seizures from six patients with medically refractory epilepsy. We demonstrate that electrodes with consistent PGO discharges do not always coincide with clinically determined seizure onset zone (SOZ) electrodes but at times PGO-dense electrodes include secondary seizure-areas (SS) or even areas without seizures (NS). In 4/5 patients with epilepsy surgery, we observe poor (Engel Class 4) post-surgical outcomes and identify more PGO-activity in SS or NS than in SOZ. Additional studies are needed to further clarify the role of PGOs in epileptic brain.

A non-invasive technique to monitor wakefulness during electrophysiologic recording experiments in primates

Although the state of wakefulness has an impact on many physiological parameters, this variable is seldom controlled for in in vivo experiments, because the existing techniques to identify periods of wakefulness are laborious and difficult to implement. We here report on a simple non-invasive technique to achieve this goal, using the analysis of video material, collected along with the electrophysiologic data, to analyze eyelid movements. The technique was applied to recordings in non-human primates, and allowed us to automatically identify periods during which the subject has its eyes open. A comparison with frontal electroencephalographic records confirmed that such periods corresponded to wakefulness.

Unsupervised seizure detection using modulation spectra measures: A preliminary study

Epileptic seizures affect millions worldwide, impairing their quality of life in incapacitating ways. Many epilepsy patients undergo electroencephalography (EEG) continuously for days to weeks in a hospital to have seizures that might help doctors identify the anatomical focus of those seizures. But screening days to weeks of iEEG for seizures that can happen anytime or not at all is a time-consuming clinical burden for clinicians and staff. Thus, a computerized method to perform this duty objectively in lieu of subjective manual labor is highly beneficial toward robust timely clinical care of patients. I present such a method using two unsupervised machine learning techniques applied to cross-frequency coupling measures, comparing the classification performance of the methods. The methods perform similarly in accuracy, sensitivity, specificity, and selectivity (positive predictive value). Overall a proof-of-concept for a new approach is made. With more development, either approach could be used in practical clinical settings.

Application of modulation spectrum for iEEG seizure analysis

Millions of people worldwide suffer with recurrent epileptic seizures that often require hospitalized intracranial electroencephalography (iEEG) for diagnosing their seizure onset zone (SOZ) for surgical therapy. The standard diagnostic procedures for the brain signals rely on time-consuming human review of data via visual analysis. This study investigates whether modulation spectrum measures, with further study, could provide a quantitative algorithmic approach to reliably identify the SOZ for a patient and reduce or eliminate the burden of manual iEEG review. In a pilot dataset of four patients (one seizure per person), we analyzed human iEEG before, during, and after their seizure from signals inside and outside their clinically annotated SOZ to observe changes in 49 modulation spectrum measures. Regarding electrode location effects (i.e., inside vs. outside SOZ), we observed statistically significant differences (p <; 0.001) in modulation measures at certain cross-frequency bins (e.g., δ:γ bandwidths) with high effect size (g ≥ 0.80) for some patients. For seizure state effects, we observed significant differences with high effect size in many measures across almost all cross-frequency bins between various pairings of seizure states (e.g., during vs. before) for all patients. We concluded that the modulation spectrum had potential in quantifying SOZ and seizure states for diagnostic purposes and in understanding the effect of seizures on brain networks.