Biology subjects

Hu, D. K.

Publications and source records attributed to Hu, D. K..

3 recordsLinked to original sources

bioRxiv2025

EEG Functional Connectivity as a Marker of Evolution from Infantile Epileptic Spasms Syndrome to Lennox-Gastaut Syndrome

Background and ObjectivesTimely diagnosis and effective treatment of Lennox-Gastaut Syndrome (LGS) improve prognosis and lower healthcare costs, but the transition from infantile epileptic spasms syndrome (IESS) to LGS is highly variable and insidious. Objective biomarkers are needed to monitor this progression and guide clinical decision making. MethodsWe retrospectively collected longitudinal EEG data at the Childrens Hospital of Orange County from fifteen children who were diagnosed with IESS and later with LGS between 2012 and 2021. EEGs were from IESS and LGS diagnoses, between the two diagnoses, and following LGS diagnosis. Functional connectivity networks were calculated using a cross-correlation-based method and assessed relative to diagnostic timepoint, treatment response, presence of clinical markers of disease, age, and amplitude of interictal spikes. ResultsConnectivity strength was high at LGS diagnosis and decreased after favorable response to treatment, but it remained stable or increased when response was unfavorable. In all subjects, connectivity strength was higher at the time of LGS diagnosis than at the preceding timepoint. Presence of clinical markers of LGS were associated with high connectivity strength, but no single marker predicted connectivity strength. DiscussionComputational EEG analysis can be used to map the evolution from IESS to LGS. Changes in connectivity may enable prediction of impending LGS and treatment response monitoring, thus facilitating earlier LGS treatment and guiding medical management. Key pointsO_LIEEG functional connectivity analysis can track progression from infantile epileptic spasms syndrome (IESS) to Lennox-Gastaut Syndrome (LGS). C_LIO_LIHigh connectivity strength at LGS diagnosis decreases with favorable treatment response but remains high with poor response. C_LIO_LIClinical LGS markers correlate with high connectivity, but no single marker predicts connectivity strength. C_LIO_LIEEG functional connectivity analysis may help predict LGS onset, enabling early intervention and improving prognosis. C_LI

neuroscience↗

bioRxiv2020

Computational characteristics of interictal EEG as objective markers of epileptic spasms

ObjectiveFavorable neurodevelopmental outcomes in epileptic spasms (ES) are tied to early diagnosis and prompt treatment, but uncertainty in the identification of the disease can delay this process. Therefore, we investigated five computational electroencephalographic (EEG) measures as markers of ES. MethodsWe measured 1) amplitude, 2) power spectra, 3) entropy, 4) long-range temporal correlations, via detrended fluctuation analysis (DFA) and 5) functional connectivity of EEG data from ES patients (n=40 patients) and healthy controls (n=20 subjects), with multiple blinded measurements during wakefulness and sleep for each patient. ResultsIn ES patients, EEG amplitude was significantly higher in all electrodes. Shannon and permutation entropy were lower in ES patients than control subjects, while DFA intercept values in ES patients were significantly higher than control subjects. DFA exponent values were not significantly different between the groups. EEG functional connectivity networks in ES patients were significantly stronger than controls. Using logistic regression, a multi-attribute classifier was derived that accurately distinguished cases from controls (area under curve of 0.96). ConclusionsComputational EEG features successfully distinguish ES patients from controls in a large, blinded study. SignificanceThese objective EEG markers, in combination with other clinical factors, may speed the diagnosis and treatment of the disease, thereby improving long-term outcomes. HighlightsO_LIObjective computational EEG features may aid diagnosis of epileptic spasms (ES) C_LIO_LIES EEG has increased delta and theta power and decreased entropy relative to controls C_LIO_LIStronger functional connectivity networks differentiate ES patients from controls C_LI

neuroscience↗

bioRxiv2019

Effect of interictal epileptiform discharges on EEG-based functional connectivity networks

ObjectiveFunctional 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.\n\nMethodsWe 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 1,340 visually marked IEDs. Differences in network structure and strength were assessed.\n\nResultsIEDs 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.\n\nConclusionsIncreases 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.\n\nSignificanceDynamic changes in EEG-based FCNs during IEDs may be valuable for identification of pathological networks associated with epilepsy.\n\nHighlightsO_LIInfantile spasms subjects exhibit broadly increased connectivity strength during interictal spikes\nC_LIO_LIFunctional connectivity network structure is unaltered by interictal spikes in infantile spasms\nC_LIO_LISimulated spikes in healthy control EEG did not alter network strength or structure\nC_LI

neuroscience↗