OBJECTIVE:Epileptic spasms (ES) are associated with pathological neuronal networks, which may underlie characteristic EEG patterns such as hypsarrhythmia. Here we evaluate EEG functional connectivity as a quantitative marker of treatment response, in comparison to classic visual EEG features. METHODS:We retrospectively identified 21 ES patients and 21 healthy controls. EEG data recorded before treatment and after ≥10 days of treatment underwent blinded visual assessment, and functional connectivity was measured using cross-correlation techniques. Short-term treatment response and long-term outcome data were collected. RESULTS:Subjects with ES had stronger, more stable functional networks than controls. After treatment initiation, all responders (defined by cessation of spasms) exhibited decreases in functional connectivity strength, while an increase in connectivity strength occurred only in non-responders. There were six subjects with unusually strong pre-treatment functional connectivity, and all were responders. Visually assessed EEG features were not predictive of treatment response. CONCLUSIONS:Changes in network connectivity and stability correlate to treatment response for ES, and high pre-treatment connectivity may predict favorable short-term treatment response. Quantitative measures outperform visual analysis of the EEG. SIGNIFICANCE:Functional networks may have value as objective markers of treatment response in ES, with potential to facilitate rapid identification of personalized, effective treatments.

OBJECTIVE:Epileptic spasms (ES) are associated with pathological neuronal networks, which may underlie characteristic EEG patterns such as hypsarrhythmia. Here we evaluate EEG functional connectivity as a quantitative marker of treatment response, in comparison to classic visual EEG features. METHODS:We retrospectively identified 21 ES patients and 21 healthy controls. EEG data recorded before treatment and after ≥10 days of treatment underwent blinded visual assessment, and functional connectivity was measured using cross-correlation techniques. Short-term treatment response and long-term outcome data were collected. RESULTS:Subjects with ES had stronger, more stable functional networks than controls. After treatment initiation, all responders (defined by cessation of spasms) exhibited decreases in functional connectivity strength, while an increase in connectivity strength occurred only in non-responders. There were six subjects with unusually strong pre-treatment functional connectivity, and all were responders. Visually assessed EEG features were not predictive of treatment response. CONCLUSIONS:Changes in network connectivity and stability correlate to treatment response for ES, and high pre-treatment connectivity may predict favorable short-term treatment response. Quantitative measures outperform visual analysis of the EEG. SIGNIFICANCE:Functional networks may have value as objective markers of treatment response in ES, with potential to facilitate rapid identification of personalized, effective treatments.

Hippocampal-dependent memory consolidation during sleep is hypothesized to depend on the synchronization of distributed neuronal ensembles, organized by the hippocampal sharp-wave ripples (SWRs, 80 to 150 Hz), subcortical/cortical slow-wave activity (SWA, 0.5 to 4 Hz), and sleep spindles (SP, 7 to 15 Hz). However, the precise role of these interactions in synchronizing subcortical/cortical neuronal activity is unclear. Here, we leverage intracranial electrophysiological recordings from the human hippocampus, amygdala, and temporal and frontal cortices to examine activity modulation and cross-regional coordination during SWRs. Hippocampal SWRs are associated with widespread modulation of high-frequency activity (HFA, 70 to 200 Hz), a measure of local neuronal activation. This peri-SWR HFA modulation is predicted by the coupling between hippocampal SWRs and local subcortical/cortical SWA or SP. Finally, local cortical SWA phase offsets and SWR amplitudes predicted functional connectivity between the frontal and temporal cortex during individual SWRs. These findings suggest a selection mechanism wherein hippocampal SWR and cortical slow-wave synchronization governs the transient engagement of distributed neuronal populations supporting hippocampal-dependent memory consolidation.

Individualized genetic therapies-medicines that precisely target a genetic variant that may only be found in a small number of individuals, as few as only one-offer promise for addressing unmet needs in genetic disease, but present unique challenges for trial design. By nature these new individualized medicines require testing in individualized N-of-1 trials. Here, we provide a framework for maintaining scientific rigor in N-of-1 trials. Building upon best practices from traditional clinical trial design, recent guidance from the United States Food and Drug Administration, and our own clinical research experience, we suggest key considerations including comprehensive baseline natural history, selection of appropriate clinical outcome assessments (COAs) individualized to the patient genotype-phenotype for safety and efficacy assessment over time, and specific statistical considerations. Standardization of N-of-1 trial designs in this fashion will maximize efficient learning from this next generation of targeted individualized therapeutics.