Faculty Bibliography

Noninvasive transcranial brain stimulation has been widely used in experimental and clinical applications to perturb the brain activity, aiming at promoting synaptic plasticity or enhancing functional connectivity within targeted brain regions. However, there are different types of neurostimulations and various choices of stimulation parameters; how these choices influence the intermediate neurophysiological effects and brain connectivity remain incompletely understood. We propose several quantitative methods to investigate the brain connectivity of an epileptic patient before and after transcranial alternating/direct current stimulation (tACS/tDCS). The neuro-feedback derived from our analyses may provide useful cues for the effectiveness of neurostimulation.

Magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE) may be a distinct syndrome from TLE with mesial temporal sclerosis (TLE-MTS). Imaging and neuropsychological features of TLE-MTS are well-known; yet, these features are only beginning to be described in MRI-negative TLE. This study examined whether a quantitative measure of cortical gray and white matter blurring (GWB) was elevated in the temporal lobes ipsilateral to the seizure onset zone of individuals with MRI-negative TLE relative to TLE-MTS and healthy controls (HCs) and whether GWB elevations were associated with neuropsychological comorbidity. Gray-white matter blurring from 34 cortical regions and hippocampal volumes were quantified and compared across 28 people with MRI-negative TLE, 15 people with TLE-MTS, and 51 HCs. Declarative memory was assessed with standard neuropsychological tests and the intracarotid amobarbital procedure (IAP). In the group with MRI-negative TLE (left and right onsets combined), hippocampal volumes were within normal range but GWB was elevated, relative to HCs, across several mesial and lateral temporal lobe regions ipsilateral to the seizure onset zone. Gray-white matter blurring did not differ between the groups with TLE-MTS and HC or between the groups with TLE-MTS and MRI-negative TLE. The group with MRI-negative TLE could not be distinguished from the group with TLE-MTS on any of the standard neuropsychological tests; however, ipsilateral hippocampal volumes and IAP memory scores were lower in the group with TLE-MTS than in the group with MRI-negative TLE. The group with left MRI-negative TLE had lower general cognitive abilities and verbal fluency relative to the HC group, which adds to the characterization of neuropsychological comorbidities in left MRI-negative TLE. In addition, ipsilateral IAP memory performance was reduced relative to contralateral memory performance in MRI-negative TLE, indicating some degree of ipsilateral memory dysfunction. There was no relationship between hippocampal volume and IAP memory scores in MRI-negative TLE; however, decreased ipsilateral IAP memory scores were correlated with elevated GWB in the ipsilateral superior temporal sulcus of people with left MRI-negative TLE. In sum, GWB elevations in the ipsilateral temporal lobe of people with MRI-negative TLE suggest that GWB may serve as a marker for reduced structural integrity in regions in or near the seizure onset zone. Although mesial temporal abnormalities might be the major driver of memory dysfunction in TLE-MTS, a loss of structural integrity in lateral temporal lobe regions may contribute to IAP memory dysfunction in MRI-negative TLE.

OBJECTIVE:Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications. APPROACH/METHODS:Here we propose a novel deep learning strategy (SpindleNet) to detect sleep spindles based on a single EEG channel. While the majority of spindle detection methods are used for off-line applications, our method is well suited for online applications. MAIN RESULTS/RESULTS:Compared with other spindle detection methods, SpindleNet achieves superior detection accuracy and speed, as demonstrated in two publicly available expert-validated EEG sleep spindle datasets. Our real-time detection of spindle onset achieves detection latencies of 150-350 ms (~2-3 spindle cycles) and retains excellent performance under low EEG sampling frequencies and low signal-to-noise ratios. SpindleNet has good generalization across different sleep datasets from various subject groups of different ages and species. SIGNIFICANCE/CONCLUSIONS:SpindleNet is ultra-fast and scalable to multichannel EEG recordings, with an accuracy level comparable to human experts, making it appealing for long-term sleep monitoring and closed-loop neuroscience experiments. &#13.

Direct recordings from the human brain have historically involved epilepsy patients undergoing invasive electroencephalography (iEEG) for surgery. However, these measurements are temporally limited and affected by clinical variables. The RNS System (NeuroPace, Inc.) is a chronic, closed-loop electrographic seizure detection and stimulation system. When adapted by investigators for research, it facilitates cognitive testing in a controlled ambulatory setting, with measurements collected over months to years. We utilized an associative learning paradigm in 5 patients with traditional iEEG and 3 patients with chronic iEEG, and found increased hippocampal gamma (60-100 Hz) sustained at 1.3-1.5 seconds during encoding in successful versus failed trials in surgical patients, with similar results in our RNS System patients (1.4-1.6 seconds). Our findings replicate other studies demonstrating that sustained hippocampal gamma supports encoding. Importantly, we have validated the RNS System to make sensitive measurements of hippocampal dynamics during cognitive tasks in a chronic ambulatory research setting.

Slow-oscillations and spindle activity during non-REM sleep have been implicated in memory consolidation. Closed-loop acoustic stimulation has previously been shown to enhance slow oscillations and spindle activity during sleep and improve verbal associative memory. We assessed the effect of closed-loop acoustic stimulation during a daytime nap on a virtual reality spatial navigation task in 12 healthy human subjects in a randomized within-subject crossover design. We show robust enhancement of slow-spindle activity during sleep. However, no effects on behavioral performance were observed when comparing real versus sham stimulation. To explore whether memory enhancement effects were task-specific and dependent on nocturnal sleep, in a second experiment with 19 healthy subjects, we aimed to replicate a previous study which used closed-loop acoustic stimulation to enhance memory for word pairs. Methods were as close as possible to the original study, except we used a double-blind protocol, in which both subject and experimenter were unaware of the test condition. Again, we successfully enhanced slow-spindle power, but again did not strengthen associative memory performance with stimulation. We conclude that enhancement of slow-spindle oscillations may be insufficient to enhance memory performance in spatial navigation or verbal association tasks, and provide possible explanations for lack of behavioral replication.SIGNIFICANCE STATEMENT Prior studies have demonstrated that a closed-loop acoustic pulse paradigm during sleep can enhance verbal memory performance. This technique has widespread scientific and clinical appeal due to its non-invasive nature and ease of application. We tested with a rigorous double-blind design whether this technique could enhance key sleep rhythms associated sleep-dependent memory performance. We discovered that we could reliably enhance slow and spindle rhythms, but did not improve memory performance in the stimulation condition compared to sham condition. Our findings suggest that enhancing slow-spindle rhythms is insufficient to enhance sleep-dependent learning.

Noninvasive brain stimulation techniques are used in experimental and clinical fields for their potential effects on brain network dynamics and behavior. Transcranial electrical stimulation (TES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), has gained popularity because of its convenience and potential as a chronic therapy. However, a mechanistic understanding of TES has lagged behind its widespread adoption. Here, we review data and modelling on the immediate neurophysiological effects of TES in vitro as well as in vivo in both humans and other animals. While it remains unclear how typical TES protocols affect neural activity, we propose that validated models of current flow should inform study design and artifacts should be carefully excluded during signal recording and analysis. Potential indirect effects of TES (e.g., peripheral stimulation) should be investigated in more detail and further explored in experimental designs. We also consider how novel technologies may stimulate the next generation of TES experiments and devices, thus enhancing validity, specificity, and reproducibility.

Some patients with medically refractory focal epilepsy are chronically implanted with a brain-responsive neurostimulation device (the RNS System), permitting neurophysiological mea-surements at millisecond resolution. This clinical device can be adapted to measure hippocampal dynamics time-locked to cognitive tasks. We illustrate the technique with a proof of concept in three patients previously implanted with the RNS System as they engage in an associative memory task, measured months apart. Hippocampal activity measured in successful encoding in RNS System patients mirrors that in surgical patients during intracranial electroencephalography (iEEG), suggesting that chronic iEEG allows sensitive measurements of hippocampal physiology over prolonged timescales

It has come to our attention that we did not specify whether the stimulation magnitudes we report in this Article are peak amplitudes or peak-to-peak. All references to intensity given in mA in the manuscript refer to peak-to-peak amplitudes, except in Fig. 2, where the model is calibrated to 1 mA peak amplitude, as stated. In the original version of the paper we incorrectly calibrated the computational models to 1 mA peak-to-peak, rather than 1 mA peak amplitude. This means that we divided by a value twice as large as we should have. The correct estimated fields are therefore twice as large as shown in the original Fig. 2 and Supplementary Figure 11. The corrected figures are now properly calibrated to 1 mA peak amplitude. Furthermore, the sentence in the first paragraph of the Results section 'Intensity ranged from 0.5 to 2.5 mA (current density 0.125-0.625 mA mA/cm²), which is stronger than in previous reports', should have read 'Intensity ranged from 0.5 to 2.5 mA peak to peak (peak current density 0.0625-0.3125 mA/cm²), which is stronger than in previous reports.' These errors do not affect any of the Article's conclusions.