Confounding effect of EEG implantation surgery: Inadequacy of surgical control in a two hit model of temporal lobe epilepsy

AbstractIn rodent models of epilepsy, EEG implantation surgery is an essential modality to evaluate electrographic seizures. The inflammatory consequences of EEG electrode-implantation and their resultant effects on seizure susceptibility are unclear. We evaluated electrode-implantation in a two-hit model of epileptogenesis in C57BL/6 mice that included brief, recurrent febrile seizures (FS) at P14 and kainic acid induced seizures (KA-SZ) at P28. During KA-SZ, latencies to first electrographic and behavioral seizures, seizure severity, and KA dose sensitivity were measured. Mice that received subdural screw electrode implants at P25 for EEG monitoring at P28 had significantly shorter latencies to seizures than sham mice, regardless of early life seizure experience. Electrode-implanted mice were sensitive to low dose KA as shown by high mortality rate at KA doses above 10mg/kg. We then directly compared electrode-implantation and KA-SZ in seizure naive CX3CR1GFP/+ transgenic C57BL/6 mice, wherein microglia express green fluorescent protein (GFP), to determine if microglia activation related to surgery was associated with the increased seizure susceptibility in electrode-implanted mice from the two-hit model. Hippocampal microglia activation, as demonstrated by percent area GFP signal and GFP positive cell counts, prior to seizures was indistinguishable between electrode-implanted mice and controls, but was significantly greater in electrode-implanted mice following seizures. Electrode-implantation had a confounding priming effect on the inflammatory response to subsequent seizures

Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans

Objective. Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy, and DBS may actually further disturb normal sleep patterns and sleep quality. Novel implantable devices capable of DBS and streaming of continuous intracranial electroencephalography (iEEG) signals enable detailed assessments of therapy efficacy and tracking of sleep related comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez's circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT). Approach. The iEEG recorded from HPC is used to classify sleep during concurrent DBS targeting ANT. Simultaneous polysomnography (PSG) and sensing from HPC were used to train, validate and test an automated classifier for a range of ANT DBS frequencies: no stimulation, 2 Hz, 7 Hz, and high frequency (>100 Hz). Main results. We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC iEEG sensing channel. The patient specific classifiers performed well under all thalamic DBS frequencies with an average F1-score 0.894, and provided viable classification into awake and major sleep categories, rapid eye movement (REM) and non-REM. We retrospectively analyzed classification performance with gold-standard PSG annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment. Significance. The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing DBS for epilepsy and associated sleep, cognitive and mood comorbidities

Relationship Between Seizure Frequency and Functional Abnormalities in Limbic Network of Medial Temporal Lobe Epilepsy

Background: We compared resting-state functional connectivity (RSFC) among limbic and temporal lobe regions between patients with medial temporal lobe epilepsy (mTLE) and healthy control subjects to identify imaging evidence of functional networks related to seizure frequency, age of seizure onset, and duration of epilepsy.Methods: Twelve patients with drug-resistant, unilateral medial temporal lobe epilepsy and 12 healthy control subjects matched for age, sex, and handedness participated in the imaging experiments. We used network-based statistics to compare functional connectivity graphs in patients with mTLE and healthy controls to investigate the relationship between functional connectivity abnormalities and seizure frequency.Results: Among mTLE patients, we found functional network abnormalities throughout the limbic system, but primarily in the hemisphere ipsilateral to the seizure focus. The RSFCs between ipsilateral hypothalamus and ventral anterior cingulate cortex and between ipsilateral subiculum and contralateral posterior cingulate cortex were highly correlated with seizure frequency.Discussion: These findings suggest that in mTLE, changes in limbic networks ipsilateral to the epileptic focus are common. The pathological changes in connectivity between cingulate cortex, hypothalamus and subiculum ipsilateral to the seizure focus were correlated with increased seizure frequency

Thalamic deep brain stimulation modulates cycles of seizure risk in epilepsy

Chronic brain recordings suggest that seizure risk is not uniform, but rather varies systematically relative to daily (circadian) and multiday (multidien) cycles. Here, one human and seven dogs with naturally occurring epilepsy had continuous intracranial EEG (median 298 days) using novel implantable sensing and stimulation devices. Two pet dogs and the human subject received concurrent thalamic deep brain stimulation (DBS) over multiple months. All subjects had circadian and multiday cycles in the rate of interictal epileptiform spikes (IES). There was seizure phase locking to circadian and multiday IES cycles in five and seven out of eight subjects, respectively. Thalamic DBS modified circadian (all 3 subjects) and multiday (analysis limited to the human participant) IES cycles. DBS modified seizure clustering and circadian phase locking in the human subject. Multiscale cycles in brain excitability and seizure risk are features of human and canine epilepsy and are modifiable by thalamic DBS

Nuclei-specific thalamic connectivity predicts seizure frequency in drug-resistant medial temporal lobe epilepsy

Background and objectives: We assessed correlations between the resting state functional connectivity (RSFC) of different thalamic nuclei and seizure frequency in patients with drug-resistant medial temporal lobe epilepsy (mTLE). Methods: Seventeen patients with mTLE and 17 sex-/age-/handedness-matched controls participated. A seed-based correlation method for the resting-state FMRI data was implemented to get RSFC maps of 70 thalamic nuclei seed masks. Group statistics for individual RSFC for subjects and seed masks were performed to obtain within-group characteristics and between-group differences with age covariates. A linear regression was applied to test whether seizure frequency correlated with thalamic nuclear RSFC with the whole brain in mTLE patients. Results: RSFC of thalamic nuclei showed spatially distinguishable connectivity patterns that reflected principal inputs and outputs that were derived from priori anatomical knowledge. We found group differences between normal control and mTLE groups in RSFC for nuclei seeds located in various subdivisions of thalamus. The RSFCs in some of those nuclei were strongly correlated with seizure frequency. Conclusions: Mediodorsal thalamic nuclei may play important roles in seizure activity or in the regulation of neuronal activity in the limbic system. The RSFC of motor- and sensory-relay nuclei may help elucidate sensory-motor deficits associated with chronic seizure activity. RSFC of the pulvinar nuclei of the thalamus could also be a key reflection of symptom-related functional deficits in mTLE

Description of method objectives and signal assumptions.

(A) The core assumption of the model is that the underlying signal (ii) is the linear sum of (i) oscillations, a polynomial trend, and noise. Part (B) describes the overall workflow including (i) data input to the model, (ii) outputs, and (iii) estimated signal reconstructions. (Ci) Equation representing the core signal assumption that the observations come from a combination of oscillations, a polynomial trend, and noise or error. Notation includes y (m x 1 vector of observed data), Ψ (n x n discrete cosine transform (DCT) basis), Φ (m x n binary row subsampling matrix), x (n x 1 DCT coefficients), Τ (n x p Vandermonde matrix), z (p x 1 polynomial coefficients), (m x 1 error terms). (Cii) Expression for basis pursuit denoising containing x and z as unknowns, yielding a 2D minimization problem that is reduced to a 1D minimization problem (Ciii) by variable projection. (D) Schematic representation of the equations and sampling approach in (C).</p

Impact of data drops on BPWP outputs, participant 2.

(A) Raw data showing hourly rate of IEDs detected from the left hippocampus, updated every 20 minutes. Timeseries consists of over 20,000 samples. (Bi) Raw data are in gray and random sampling excluding 12-day data drops are in orange. (Bii) Raw data are in gray and the reconstructed signal using model output based on input data with 12-day data drops is in orange. C) Average complex wavelet transform (CWT) spectrum from the raw data in (A) is in gray. The BPWP spectral output based on the sampling in (Bi) input is shown in orange. Black stars denote significant peaks; peaks whose amplitude was above the 99th percentile of the distribution created by shuffling the input data and re-calculating BPWP 100 times. Data drops of thirty- and sixty- days duration, signal reconstructions, and method spectra are shown in (D) and (E) and in (F) and (G) respectively. The total number of samples for BPWP is fixed across the conditions at n = 1307 which is approximately 4 samples per day assuming no drops. (TIF)</p

BPWP method performance, participant 2.

(A) Diagram depicting the approach to comparing the original IED rate timeseries (black) with the BPWP model output (orange). Each timeseries was bandpass filtered and the two filtered timeseries were used to calculate the Pearson correlation coefficient. (B) The Y axis shows the Pearson correlation coefficient for each bandpass filter with central periods listed on the X axis. P value for each point depicted was (TIF)</p