Pitfalls in scalp high-frequency oscillation detection from long-term EEG monitoring

Aims: Intracranially recorded high-frequency oscillations (>80 Hz) are considered a candidate epilepsy biomarker. Recent studies claimed their detectability on the scalp surface. We aimed to investigate the applicability of high-frequency oscillation analysis to routine surface EEG obtained at an epilepsy monitoring unit. Methods: We retrospectively analyzed surface EEGs of 18 patients with focal epilepsy and six controls, recorded during sleep under maximal medication withdrawal. As a proof of principle, the occurrence of motor task-related events during wakefulness was analyzed in a subsample of six patients with seizure- or syncope-related motor symptoms. Ripples (80–250 Hz) and fast ripples (>250 Hz) were identified by semi-automatic detection. Using semi-parametric statistics, differences in spontaneous and task-related occurrence rates were examined within subjects and between diagnostic groups considering the factors diagnosis, brain region, ripple type, and task condition. Results: We detected high-frequency oscillations in 17 out of 18 patients and in four out of six controls. Results did not show statistically significant differences in the mean rates of event occurrences, neither regarding the laterality of the epileptic focus, nor with respect to active and inactive task conditions, or the moving hand laterality. Significant differences in general spontaneous incidence [WTS(1) = 9.594; p = 0.005] that indicated higher rates of fast ripples compared to ripples, notably in patients with epilepsy compared to the control group, may be explained by variations in data quality. Conclusion: The current analysis methods are prone to biases. A common agreement on a standard operating procedure is needed to ensure reliable and economic detection of high-frequency oscillations.The presented research was supported by the Austrian Science Fund (FWF): T 798-B27 and KLI657-B31 and by the Research Fund of the Paracelsus Medical University (PMU-FFF): A16/02/021-HÖL and A-18/01/029-HÖL.Peer Reviewe

Accelerated Forgetting in People with Epilepsy: Pathologic Memory Loss, Its Neural Basis, and Potential Therapies

While forgetting is vital to human functioning, delineating between normative and disordered forgetting can become incredibly complex. This thesis characterizes a pathologic form of forgetting in epilepsy, identifies a neural basis, and investigates the potential of stimulation as a therapeutic tool. Chapter 2 presents a behavioral characterization of the time course of Accelerated Long-Term Forgetting (ALF) in people with epilepsy (PWE). This chapter shows evidence of ALF on a shorter time scale than previous studies, with a differential impact on recall and recognition. Chapter 3 builds upon the work in Chapter 2 by extending ALF time points and investigating the role of interictal epileptiform activity (IEA) in ALF. These findings lend support for distinct forgetting patterns between recall and recognition memory. We also demonstrate the contribution of hippocampal IEA during slow-wave sleep to this aberrant forgetting. Chapter 4 investigates the potential of intracranial stimulation to ameliorate IEA burden. Our findings suggest that stimulation does not appear to have a direct effect on IEA rate. Further studies are necessary to explore the potential of stimulation as a therapeutic tool outside of seizure cessation. Overall, this thesis provides further evidence and classification of long-term memory impairment in epilepsy and identifies a neural correlate that can be targeted for future clinical intervention

Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

BACKGROUND: Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis. METHODS: Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD). RESULTS: Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample. CONCLUSIONS: These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.K23 DC017983 - NIDCD NIH HHS; P50 HD105351 - NICHD NIH HHS; R01 DC010290 - NIDCD NIH HHS; R21 DC008637 - NIDCD NIH HHSPublished versio

Informatics for EEG biomarker discovery in clinical neuroscience

Neurological and developmental disorders (NDDs) impose an enormous burden of disease on children throughout the world. Two of the most common are autism spectrum disorder (ASD) and epilepsy. ASD has recently been estimated to affect 1 in 68 children, making it the most common neurodevelopmental disorder in children. Epilepsy is also a spectrum disorder that follows a developmental trajectory, with an estimated prevalence of 1%, nearly as common as autism. ASD and epilepsy co-occur in approximately 30% of individuals with a primary diagnosis of either disorder. Although considered to be different disorders, the relatively high comorbidity suggests the possibility of common neuropathological mechanisms. Early interventions for NDDs lead to better long-term outcomes. But early intervention is predicated on early detection. Behavioral measures have thus far proven ineffective in detecting autism before about 18 months of age, in part because the behavioral repertoire of infants is so limited. Similarly, no methods for detecting emerging epilepsy before seizures begin are currently known. Because atypical brain development is likely to precede overt behavioral manifestations by months or even years, a critical developmental window for early intervention may be opened by the discovery of brain based biomarkers. Analysis of brain activity with EEG may be under-utilized for clinical applications, especially for neurodevelopment. The hypothesis investigated in this dissertation is that new methods of nonlinear signal analysis, together with methods from biomedical informatics, can extract information from EEG data that enables detection of atypical neurodevelopment. This is tested using data collected at Boston Children’s Hospital. Several results are presented. First, infants with a family history of ASD were found to have EEG features that may enable autism to be detected as early as 9 months. Second, significant EEG-based differences were found between children with absence epilepsy, ASD and control groups using short 30-second EEG segments. Comparison of control groups using different EEG equipment supported the claim that EEG features could be computed that were independent of equipment and lab conditions. Finally, the potential for this technology to help meet the clinical need for neurodevelopmental screening and monitoring in low-income regions of the world is discussed

Phenotyping paroxysmal conditions to empower genetic research

I describe the process of preparing cohorts of individuals with two paediatric onset paroxysmal disorders – hyperekplexia and juvenile myoclonic epilepsy – for second generation sequencing. This involves: i) listening to the individual; ii) identifying subgroups; iii) using non-­‐core features to create subgroups; iv) and assessing the importance of copy number variation. Using focus groups and an interpretative phenomenological approach clinicians and people with epilepsy produced 398 questions focused on epilepsy treatment. The most important themes for the professionals were – teatment pogrammes or non-­‐epileptic attack disorder and concerns about side effectsinutero.For patients cognitive drug side effects and managing the consequences of drug side effects were most important. Studying ninety-­‐seven individuals with hyperekplexia confirmed that all gene-­‐positive cases present in the neonatal period and that clonazepam is the treatment of choice (95% found it efficacious). Patients with SLC6A5 and GLRB mutations were more likely to have developmental delay (RR1.5 p<0.01; RR1.9 p<0.03) than those with GLRA1 mutations; 92% of GLRB cases reported a mild to severe delay in speech acquisition. Juvenile myoclonic epilepsy is challenging to subdivide based on seizure and EEG features. The neuropsychological profile of limited number of patients 39) as examined in great detail including tests Q WAIS), emory TYM,WMS),executive function (BADS, DKEFS), affect (HADS). TYM was as sensitive as a full WMS for identifying cognitive errors and the zoo map and key search tests were performed particularly poorly. Personality profiling (EPQ-­‐BV) identifies the cohort as having high levels of neurotic and introvert traits. Three atypical ‘hyperekplexia’ cases had alternative diagnoses suggested by copy number analysis. The juvenile myoclonic epilepsy patients had an 8% frequency of recognised pathogenic CNVs– but no recurrent variants were identified.A number of non-­‐epilepsy related findings were identified including a potentially preventable cause of SUDEP