Temporally Targeted Interactions With Pathologic Oscillations as Therapeutical Targets in Epilepsy and Beyond

Self-organized neuronal oscillations rely on precisely orchestrated ensemble activity in reverberating neuronal networks. Chronic, non-malignant disorders of the brain are often coupled to pathological neuronal activity patterns. In addition to the characteristic behavioral symptoms, these disturbances are giving rise to both transient and persistent changes of various brain rhythms. Increasing evidence support the causal role of these “oscillopathies” in the phenotypic emergence of the disease symptoms, identifying neuronal network oscillations as potential therapeutic targets. While the kinetics of pharmacological therapy is not suitable to compensate the disease related fine-scale disturbances of network oscillations, external biophysical modalities (e.g., electrical stimulation) can alter spike timing in a temporally precise manner. These perturbations can warp rhythmic oscillatory patterns via resonance or entrainment. Properly timed phasic stimuli can even switch between the stable states of networks acting as multistable oscillators, substantially changing the emergent oscillatory patterns. Novel transcranial electric stimulation (TES) approaches offer more reliable neuronal control by allowing higher intensities with tolerable side-effect profiles. This precise temporal steerability combined with the non- or minimally invasive nature of these novel TES interventions make them promising therapeutic candidates for functional disorders of the brain. Here we review the key experimental findings and theoretical background concerning various pathological aspects of neuronal network activity leading to the generation of epileptic seizures. The conceptual and practical state of the art of temporally targeted brain stimulation is discussed focusing on the prevention and early termination of epileptic seizures.

Cell and circuit origins of fast network oscillations in the mammalian main olfactory bulb

Neural synchrony generates fast network oscillations throughout the brain, including the main olfactory bulb (MOB), the first processing station of the olfactory system. Identifying the mechanisms synchronizing neurons in the MOB will be key to understanding how network oscillations support the coding of a high-dimensional sensory space. Here, using paired recordings and optogenetic activation of glomerular sensory inputs in MOB slices, we uncovered profound differences in principal mitral cell (MC) vs. tufted cell (TC) spike-time synchrony: TCs robustly synchronized across fast- and slow-gamma frequencies, while MC synchrony was weaker and concentrated in slow-gamma frequencies. Synchrony among both cell types was enhanced by shared glomerular input but was independent of intraglomerular lateral excitation. Cell-type differences in synchrony could also not be traced to any difference in the synchronization of synaptic inhibition. Instead, greater TC than MC synchrony paralleled the more periodic firing among resonant TCs than MCs and emerged in patterns consistent with densely synchronous network oscillations. Collectively, our results thus reveal a mechanism for parallel processing of sensory information in the MOB via differential TC vs. MC synchrony, and further contrast mechanisms driving fast network oscillations in the MOB from those driving the sparse synchronization of irregularly-firing principal cells throughout cortex.

Cell and circuit origins of fast network oscillations in the mammalian main olfactory bulb

Neural synchrony generates fast network oscillations throughout the brain, including the main olfactory bulb (MOB), the first processing station of the olfactory system. Identifying the mechanisms synchronizing neurons in the MOB will be key to understanding how network oscillations support the coding of a high-dimensional sensory space. Here, using paired recordings and optogenetic activation of glomerular sensory inputs in MOB slices, we uncovered profound differences in principal mitral cell (MC) vs. tufted cell (TC) spike-time synchrony: TCs robustly synchronized across fast- and slow-gamma frequencies, while MC synchrony was weaker and concentrated in slow-gamma frequencies. Synchrony among both cell types was enhanced by shared glomerular input but was independent of intraglomerular lateral excitation. Cell-type differences in synchrony could also not be traced to any difference in the synchronization of synaptic inhibition. Instead, greater TC than MC synchrony paralleled the more periodic firing among resonant TCs than MCs and emerged in patterns consistent with densely synchronous network oscillations. Collectively, our results thus reveal a mechanism for parallel processing of sensory information in the MOB via differential TC vs. MC synchrony, and further contrast mechanisms driving fast network oscillations in the MOB from those driving the sparse synchronization of irregularly-firing principal cells throughout cortex.

Parvalbumin interneuron dendrites enhance gamma oscillations

Dendrites are important determinants of the input-output relationship of single neurons, but their role in network computations is not well understood. Here, we used a combination of dendritic patch-clamp recordings and in silico modeling to determine how dendrites of parvalbumin (PV)- expressing basket cells contribute to network oscillations in the gamma frequency band. Simultaneous soma-dendrite recordings from PV basket cells in the dentate gyrus revealed that the slope, or gain, of the dendritic input-output relationship is exceptionally low, thereby reducing the cell's sensitivity to changes in its input. By simulating gamma oscillations in detailed network models, we demonstrate that the low gain is key to increase spike synchrony in PV neuron assemblies when cells are driven by spatially and temporally heterogeneous synaptic input. These results highlight the role of dendritic computations in synchronized network oscillations.

TREM2 Deficiency Disrupts Network Oscillations Leading to Epileptic Activity and Aggravates Amyloid-β-Related Hippocampal Pathophysiology in Mice

Background: Genetic mutations in triggering receptor expressed on myeloid cells-2 (TREM2) have been strongly associated with increased risk of developing Alzheimer’s disease (AD) and other progressive dementias. In the brain, TREM2 protein is specifically expressed on microglia suggesting their active involvement in driving disease pathology. Using various transgenic AD models to interfere with microglial function through TREM2, several recent studies provided important data indicating a causal link between TREM2 and underlying amyloid-β (Aβ) and tau pathology. However, mechanisms by which TREM2 contributes to increased predisposition to clinical AD and influences its progression still remain largely unknown. Objective: Our aim was to elucidate the potential contribution of TREM2 on specific oscillatory dynamic changes associated with AD pathophysiology. Methods: Spontaneous and brainstem nucleus pontis oralis stimulation-induced hippocampal oscillation paradigm was used to investigate the impact of TREM2 haploinsufficiency TREM2(Het) or total deficiency TREM2(Hom) on hippocampal network function in wild-type and Aβ overproducing Tg2576 mice under urethane anesthesia. Results: Partial (TREM2(Het)) or total (TREM2(Hom)) deletion of TREM2 led to increased incidence of spontaneous epileptiform seizures in both wild-type and Tg2576 mice. Importantly, deficiency of TREM2 in Tg2576 mice significantly diminished power of theta oscillation in the hippocampus elicited by brainstem-stimulation compared to wild-type mice. However, it did not affect hippocampal theta-phase gamma-amplitude coupling significantly, since over a 60%reduction was found in coupling in Tg2576 mice regardless of TREM2 function. Conclusion: Our findings indicate a role for TREM2-dependent microglial function in the hippocampal neuronal excitability in both wild type and Aβ overproducing mice, whereas deficiency in TREM2 function exacerbates disruptive effects of Aβ on hippocampal network oscillations.