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Open Access Publications from the University of California

Scholarly Works (5 results)

  • Thesis
  • Peer Reviewed
UC Irvine Electronic Theses and Dissertations (2023)

ABSTRACT OF THE DISSERTATIONIdentifying Quantitative Biomarkers in the Electroencephalogram for the Evaluation of Developmental and Epileptic Encephalopathies By Derek Kuan-Yu Hu Doctor of Philosophy in Biomedical Engineering University of California, Irvine, 2023 Associate Professor Beth A. Lopour, Irvine, Chair

Developmental epileptic encephalopathies, such as infantile epileptic spasms syndrome (IESS) and Lennox-Gastaut Syndrome (LGS), are severe epilepsy syndromes that begin early in life. These syndromes are characterized by drug-resistant seizures, which can cause developmental delays and lifelong impairments. A favorable clinical outcome is reliant on a prompt diagnosis, but this is complicated by the variability in individual cases and the evolution of the epileptic encephalopathy into other refractory epilepsies. The electroencephalogram (EEG) is an indispensable tool to help evaluate these syndromes, as it provides a noninvasive measure of time-varying neural activity and can contain waveforms that are indicators of epilepsy. Furthermore, the application of computational techniques, such as functional connectivity and time-frequency analysis, can help identify and quantify the features of these waveforms, providing an objective measurement to guide clinical diagnosis and decision making. Therefore, we aimed to develop quantitative EEG biomarkers for IESS and LGS for three purposes: (1) to improve diagnosis by providing clinicians a normalized baseline for healthy development, (2) to evaluate the functional network changes associated with pathological EEG waveforms, and (3) to discover EEG biomarkers that cannot be reliably detected using visual review. First, we measured the changes in EEG functional connectivity networks during normal development in the first two years of life. We showed that an increase in network strength was typical during normal infant development, while the network structure remained stable. This work can serve as a baseline for future investigations in IESS, which is associated with altered functional connectivity networks. Second, we assessed the functional connectivity changes in patients with IESS during interictal epileptiform discharges (IEDs), a common epilepsy-associated waveform seen in EEG recordings. We found an increase in connectivity strength during IEDs that could be attributed to the underlying pathophysiology of the disease, rather than an increase in connectivity due to the broadly distributed waveform. Lastly, we developed a novel method for the discovery of EEG biomarkers using unsupervised analysis of the time-frequency decomposition. We applied this method to patients with LGS and confirmed that it could detect established EEG biomarkers of the disease. It also highlighted novel candidate biomarkers that occurred more frequently in patients with LGS than healthy controls but were not recognized as salient waveforms during visual review by epileptologists. A separate study confirmed the low interrater reliability for classifying EEG waveforms associated with LGS, which emphasizes the need for this type of objective, automated algorithm. In all, this work advanced computational EEG techniques, such as functional connectivity and time-frequency analysis, to complement standard visual review. In the long-term, incorporating such methods into clinical practice can lead to improvements in diagnosis and patient care which were previously not possible.

    Cover page: Identifying Quantitative Biomarkers in Electroencephalogram for the Evaluation of Developmental and Epileptic Encephalopathies
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2020)
    OBJECTIVE: Functional connectivity networks (FCNs) based on interictal electroencephalography (EEG) can identify pathological brain networks associated with epilepsy. FCNs are altered by interictal epileptiform discharges (IEDs), but it is unknown whether this is due to the morphology of the IED or the underlying pathological activity. Therefore, we characterized the impact of IEDs on the FCN through simulations and EEG analysis. METHODS: We introduced simulated IEDs to sleep EEG recordings of eight healthy controls and analyzed the effect of IED amplitude and rate on the FCN. We then generated FCNs based on epochs with and without IEDs and compared them to the analogous FCNs from eight subjects with infantile spasms (IS), based on 1340 visually marked IEDs. Differences in network structure and strength were assessed. RESULTS: IEDs in IS subjects caused increased connectivity strength but no change in network structure. In controls, simulated IEDs with physiological amplitudes and rates did not alter network strength or structure. CONCLUSIONS: Increases in connectivity strength in IS subjects are not artifacts caused by the interictal spike waveform and may be related to the underlying pathophysiology of IS. SIGNIFICANCE: Dynamic changes in EEG-based FCNs during IEDs may be valuable for identification of pathological networks associated with epilepsy.
      Cover page: Effect of interictal epileptiform discharges on EEG-based functional connectivity networks.
      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2024)
      OBJECTIVE: Identification of EEG waveforms is critical for diagnosing Lennox-Gastaut Syndrome (LGS) but is complicated by the progressive nature of the disease. Here, we assess the interrater reliability (IRR) among pediatric epileptologists for classifying EEG waveforms associated with LGS. METHODS: A novel automated algorithm was used to objectively identify epochs of EEG with transient high power, which were termed events of interest (EOIs). The algorithm was applied to EEG from 20 LGS subjects and 20 healthy controls during NREM sleep, and 1350 EOIs were identified. Three raters independently reviewed the EOIs within isolated 15-second EEG segments in a randomized, blinded fashion. For each EOI, the raters assigned a waveform label (spike and slow wave, generalized paroxysmal fast activity, seizure, spindle, vertex, muscle, artifact, nothing, or other) and indicated the perceived subject type (LGS or control). RESULTS: Labeling of subject type had 85% accuracy across all EOIs and an IRR of κ =0.790, suggesting that brief segments of EEG containing high-power waveforms can be reliably classified as pathological or normal. Waveform labels were less consistent, with κ =0.558, and the results were highly variable for different categories of waveforms. Label mismatches typically occurred when one reviewer selected nothing, suggesting that reviewers had different thresholds for applying named labels. SIGNIFICANCE: Classification of EEG waveforms associated with LGS has weak IRR, due in part to varying thresholds applied during visual review. Computational methods to objectively define EEG biomarkers of LGS may improve IRR and aid clinical decision-making.
        Cover page: Interrater reliability of interictal EEG waveforms in Lennox-Gastaut Syndrome.
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2021)
        People with schizophrenia often experience a profound lack of motivation for social affiliation-a facet of negative symptoms that detrimentally impairs functioning. However, the mechanisms underlying social affiliative deficits remain poorly understood, particularly under realistic social contexts. Here, we investigated subjective reports and electroencephalography (EEG) functional connectivity in schizophrenia during a live social interaction. Individuals with schizophrenia (n = 16) and healthy controls (n = 29) completed a face-to-face interaction with a confederate while having EEG recorded. Participants were randomly assigned to either a Closeness condition designed to elicit feelings of closeness through self-disclosure or a Small-Talk condition with minimal disclosure. Compared to controls, patients reported lower positive emotional experiences and feelings of closeness across conditions, but they showed comparably greater subjective affiliative responses for the Closeness (vs. Small-Talk) condition. Additionally, patients in the Closeness (vs. Small-Talk) condition displayed a global increase in connectivity in theta and alpha frequency bands that was not observed for controls. Importantly, greater theta and alpha connectivity was associated with greater subjective affiliative responding, greater negative symptoms, and lower disorganized symptoms in patients. Collectively, findings indicate that patients, because of pronounced negative symptoms, utilized a less efficient, top-down mediated strategy to process social affiliation.
          Cover page: An Effortful Approach to Social Affiliation in Schizophrenia: Preliminary Evidence of Increased Theta and Alpha Connectivity during a Live Social Interaction
          • Article
          • Peer Reviewed
          UC Irvine Previously Published Works (2022)
          During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0-2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21-24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.
            Cover page: Evolution of Cortical Functional Networks in Healthy Infants.
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