Spike-timing-dependent plasticity leads to gamma band responses in a neural network

Personalized brain stimulation for effective neurointervention across participants

Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants-personalized Bayesian optimization (pBO)-that searches available parameter combinations to optimize an intervention as a function of an individual's ability. This novel technique was utilized to identify transcranial alternating current stimulation (tACS) frequency and current strength combinations most likely to improve arithmetic performance, based on a subject's baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, simulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.

Hippocampal network oscillations as mediators of behavioural metaplasticity: Insights from emotional learning

Behavioural metaplasticity is evident in experience-dependent changes of network activity patterns in neuronal circuits that connect the hippocampus, amygdala and medial prefrontal cortex. These limbic regions are key structures of a brain-wide neural network that translates emotionally salient events into persistent and vivid memories. Communication in this network by-and-large depends on behavioural state-dependent rhythmic network activity patterns that are typically generated and/or relayed via the hippocampus. In fact, specific hippocampal network oscillations have been implicated to the acquisition, consolidation and retrieval, as well as the reconsolidation and extinction of emotional memories. The hippocampal circuits that contribute to these network activities, at the same time, are subject to both Hebbian and non-Hebbian forms of plasticity during memory formation. Further, it has become evident that adaptive changes in the hippocampus-dependent network activity patterns provide an important means of adjusting synaptic plasticity. We here summarise our current knowledge on how these processes in the hippocampus in interaction with amygdala and medial prefrontal cortex mediate the formation and persistence of emotional memories.

Mapping entrained brain oscillations during transcranial alternating current stimulation (tACS)

Transcranial alternating current stimulation (tACS), a non-invasive and well-tolerated form of electric brain stimulation, can influence perception, memory, as well as motor and cognitive function. While the exact underlying neurophysiological mechanisms are unknown, the effects of tACS are mainly attributed to frequency-specific entrainment of endogenous brain oscillations in brain areas close to the stimulation electrodes, and modulation of spike timing dependent plasticity reflected in gamma band oscillatory responses. tACS-related electromagnetic stimulator artifacts, however, impede investigation of these neurophysiological mechanisms. Here we introduce a novel approach combining amplitude-modulated tACS during whole-head magnetoencephalography (MEG) allowing for artifact-free source reconstruction and precise mapping of entrained brain oscillations underneath the stimulator electrodes. Using this approach, we show that reliable reconstruction of neuromagnetic low- and high-frequency oscillations including high gamma band activity in stimulated cortical areas is feasible opening a new window to unveil the mechanisms underlying the effects of stimulation protocols that entrain brain oscillatory activity.

Stimulus statistics shape oscillations in nonlinear recurrent neural networks

Rhythmic activity plays a central role in neural computations and brain functions ranging from homeostasis to attention, as well as in neurological and neuropsychiatric disorders. Despite this pervasiveness, little is known about the mechanisms whereby the frequency and power of oscillatory activity are modulated, and how they reflect the inputs received by neurons. Numerous studies have reported input-dependent fluctuations in peak frequency and power (as well as couplings across these features). However, it remains unresolved what mediates these spectral shifts among neural populations. Extending previous findings regarding stochastic nonlinear systems and experimental observations, we provide analytical insights regarding oscillatory responses of neural populations to stimulation from either endogenous or exogenous origins. Using a deceptively simple yet sparse and randomly connected network of neurons, we show how spiking inputs can reliably modulate the peak frequency and power expressed by synchronous neural populations without any changes in circuitry. Our results reveal that a generic, non-nonlinear and input-induced mechanism can robustly mediate these spectral fluctuations, and thus provide a framework in which inputs to the neurons bidirectionally regulate both the frequency and power expressed by synchronous populations. Theoretical and computational analysis of the ensuing spectral fluctuations was found to reflect the underlying dynamics of the input stimuli driving the neurons. Our results provide insights regarding a generic mechanism supporting spectral transitions observed across cortical networks and spanning multiple frequency bands.

Neurophysiological studies of auditory verbal hallucinations

We discuss 3 neurophysiological approaches to study auditory verbal hallucinations (AVH). First, we describe "state" (or symptom capture) studies where periods with and without hallucinations are compared "within" a patient. These studies take 2 forms: passive studies, where brain activity during these states is compared, and probe studies, where brain responses to sounds during these states are compared. EEG (electroencephalography) and MEG (magnetoencephalography) data point to frontal and temporal lobe activity, the latter resulting in competition with external sounds for auditory resources. Second, we discuss "trait" studies where EEG and MEG responses to sounds are recorded from patients who hallucinate and those who do not. They suggest a tendency to hallucinate is associated with competition for auditory processing resources. Third, we discuss studies addressing possible mechanisms of AVH, including spontaneous neural activity, abnormal self-monitoring, and dysfunctional interregional communication. While most studies show differences in EEG and MEG responses between patients and controls, far fewer show symptom relationships. We conclude that efforts to understand the pathophysiology of AVH using EEG and MEG have been hindered by poor anatomical resolution of the EEG and MEG measures, poor assessment of symptoms, poor understanding of the phenomenon, poor models of the phenomenon, decoupling of the symptoms from the neurophysiology due to medications and comorbidites, and the possibility that the schizophrenia diagnosis breeds truer than the symptoms it comprises. These problems are common to studies of other psychiatric symptoms and should be considered when attempting to understand the basic neural mechanisms responsible for them.

Cortical γ responses: searching high and low

In this paper, a brief, preliminary attempt is made to frame a scientific debate about how functional responses at gamma frequencies in electrophysiological recordings (EEG, MEG, ECoG, and LFP) should be classified and interpreted. In general, are all gamma responses the same, or should they be divided into different classes according to criteria such as their spectral characteristics (frequency range and/or shape), their spatial-temporal patterns of occurrence, and/or their responsiveness under different task conditions? In particular, are the responses observed in intracranial EEG at a broad range of "high gamma" frequencies (~60-200Hz) different from gamma responses observed at lower frequencies (~30-80Hz), typically in narrower bands? And if they are different, how should they be interpreted? Does the broad spectral shape of high gamma responses arise from the summation of many different narrow-band oscillations, or does it reflect something completely different? If we are not sure, should we refer to high gamma activity as oscillations? A variety of theories have posited a mechanistic role for gamma activity in cortical function, often assuming narrow-band oscillations. These theories continue to influence the design of experiments and the interpretation of their results. Do these theories apply to all electrophysiological responses at gamma frequencies? Although no definitive answers to these questions are immediately anticipated, this paper will attempt to review the rationale for why they are worth asking and to point to some of the possible answers that have been proposed.

Gamma in motion: pattern reversal elicits stronger gamma-band responses than motion

Previous studies showed higher gamma-band responses (GBRs, ≈40 Hz) of the electroencephalogram (EEG) for moving compared to stationary stimuli. However, it is unclear whether this modulation by motion reflects a special responsiveness of the GBR to the stimulus feature "motion," or whether GBR enhancements of similar magnitude can be elicited also by a salient change within a static stimulus that does not include motion. Therefore, we measured the EEG of healthy subjects watching stationary square wave gratings of high contrast that either started to move or reversed their black and white pattern shortly after their onset. The strong contrast change of the pattern reversal represented a salient but motionless change within the grating that was compared to the onset of the stationary grating and the motion onset. Induced and evoked GBRs were analyzed for all three display conditions. In order to assess the influence of fixational eye movements on the induced GBRs, we also examined the time courses of microsaccade rates during the three display conditions. Amplitudes of both evoked and induced GBRs were stronger for pattern reversal than for motion onset. There was no significant amplitude difference between the onsets of the stationary and moving gratings. However, mean frequencies of the induced GBR were ~10 Hz higher in response to the onsets of moving compared to stationary gratings. Furthermore, the modulations of the induced GBR did not parallel the modulations of microsaccade rate, indicating that our induced GBRs reflect neuronal processes. These results suggest that, within the gamma-band range, the encoding of moving gratings in early visual cortex is primarily based on an upward frequency shift, whereas contrast changes within static gratings are reflected by amplitude enhancement.

Neurophysiological and computational principles of cortical rhythms in cognition

Synchronous rhythms represent a core mechanism for sculpting temporal coordination of neural activity in the brain-wide network. This review focuses on oscillations in the cerebral cortex that occur during cognition, in alert behaving conditions. Over the last two decades, experimental and modeling work has made great strides in elucidating the detailed cellular and circuit basis of these rhythms, particularly gamma and theta rhythms. The underlying physiological mechanisms are diverse (ranging from resonance and pacemaker properties of single cells to multiple scenarios for population synchronization and wave propagation), but also exhibit unifying principles. A major conceptual advance was the realization that synaptic inhibition plays a fundamental role in rhythmogenesis, either in an interneuronal network or in a reciprocal excitatory-inhibitory loop. Computational functions of synchronous oscillations in cognition are still a matter of debate among systems neuroscientists, in part because the notion of regular oscillation seems to contradict the common observation that spiking discharges of individual neurons in the cortex are highly stochastic and far from being clocklike. However, recent findings have led to a framework that goes beyond the conventional theory of coupled oscillators and reconciles the apparent dichotomy between irregular single neuron activity and field potential oscillations. From this perspective, a plethora of studies will be reviewed on the involvement of long-distance neuronal coherence in cognitive functions such as multisensory integration, working memory, and selective attention. Finally, implications of abnormal neural synchronization are discussed as they relate to mental disorders like schizophrenia and autism.