Oscillatory Brain States Research Articles

A transcriptomic axis predicts state modulation of cortical interneurons

Transcriptomics has revealed that cortical inhibitory neurons exhibit a great diversity of fine molecular subtypes1–6, but it is not known whether these subtypes have correspondingly diverse patterns of activity in the living brain. Here we show that inhibitory subtypes in primary visual cortex (V1) have diverse correlates with brain state, which are organized by a single factor: position along the main axis of transcriptomic variation. We combined in vivo two-photon calcium imaging of mouse V1 with a transcriptomic method to identify mRNA for 72 selected genes in ex vivo slices. We classified inhibitory neurons imaged in layers 1–3 into a three-level hierarchy of 5 subclasses, 11 types and 35 subtypes using previously defined transcriptomic clusters3. Responses to visual stimuli differed significantly only between subclasses, with cells in the Sncg subclass uniformly suppressed, and cells in the other subclasses predominantly excited. Modulation by brain state differed at all hierarchical levels but could be largely predicted from the first transcriptomic principal component, which also predicted correlations with simultaneously recorded cells. Inhibitory subtypes that fired more in resting, oscillatory brain states had a smaller fraction of their axonal projections in layer 1, narrower spikes, lower input resistance and weaker adaptation as determined in vitro7, and expressed more inhibitory cholinergic receptors. Subtypes that fired more during arousal had the opposite properties. Thus, a simple principle may largely explain how diverse inhibitory V1 subtypes shape state-dependent cortical processing.

Manipulating Single-Trial Motor Performance in Chronic Stroke Patients by Closed-Loop Brain State Interaction

Motor impaired patients performing repetitive motor tasks often reveal large single-trial performance variations. Based on a data-driven framework, we extracted robust oscillatory brain states from pre-trial intervals, which are predictive for the upcoming motor performance on the level of single trials. Based on the brain state estimate, i.e. whether the brain state predicts a good or bad upcoming performance, we implemented a novel gating strategy for the start of trials by selecting specifically suitable or unsuitable trial starting time points. In a pilot study with four chronic stroke patients with hand motor impairments, we conducted a total of 41 sessions. After few initial calibration sessions, patients completed approximately 15 hours of effective hand motor training during eight online sessions using the gating strategy. Patients' reaction times were significantly reduced for suitable trials compared to unsuitable trials and shorter overall trial durations under suitable states were found in two patients. Overall, this successful proof-of-concept pilot study motivates to transfer this closed-loop training framework to a clinical study and to other application fields, such as cognitive rehabilitation, sport sciences or systems neuroscience.

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography.

Pre-stimulus oscillatory brain activity influences upcoming perception. The characteristics of this pre-stimulus activity can predict whether a near-threshold stimulus will be perceived or not perceived, but can they also predict which one of two competing stimuli with different perceptual contents is perceived? Ambiguous visual stimuli, which can be seen in one of two possible ways at a time, are ideally suited to investigate this question. Magnetoencephalography (MEG) is a neurophysiological measurement technique that records magnetic signals emitted as a result of brain activity. The millisecond temporal resolution of MEG allows for a characterization of oscillatory brain states from as little as 1 second of recorded data. Presenting an empty screen around 1 second prior to the ambiguous stimulus onset therefore provides a time window in which one can investigate whether pre-stimulus oscillatory activity biases the content of upcoming perception, as indicated by participants'reports. The spatial resolution of MEG is not excellent, but sufficient to localise sources of brain activity at the centimetre scale. Source reconstruction of MEG activity then allows for testing hypotheses about the oscillatory activity of specific regions of interest, as well as the time- and frequency-resolved connectivity between regions of interest. The described protocol enables a better understanding of the influence of spontaneous, ongoing brain activity on visual perception.

Targeting cortical oscillations with EEG-informed TMS: Potential and challenges

Background: EEG-triggered transcranial magnetic stimulation (TMS) allows to target specific oscillatory brain states. This method can be used in real time to test whether or how the functional brain response to TMS is modulated by the phase and power of pericentral alpha oscillations (mu-activity).

Local field potentials get funny.

The local field potential (LFP) is widely used experimentally to index cortical activity and information processing. Classic studies using recordings of LFPs include the first demonstration of long‐term potentiation (Bliss & Lomo, 1973) and descriptions of oscillatory brain states (Buzsaki & Draguhn, 2004), while more recent applications extend as far as control of brain–machine interfaces by indirect monitoring of single neurone activity (Hall et al. 2014). The power of LFP recordings comes from both their high temporal and spatial resolution, and the relative ease with which they can be obtained.

Alpha phase determines successful lexical decision in noise.

Psychophysical target detection has been shown to be modulated by slow oscillatory brain phase. However, thus far, only low-level sensory stimuli have been used as targets. The current human electroencephalography (EEG) study examined the influence of neural oscillatory phase on a lexical-decision task performed for stimuli embedded in noise. Neural phase angles were compared for correct versus incorrect lexical decisions using a phase bifurcation index (BI), which quantifies differences in mean phase angles and phase concentrations between correct and incorrect trials. Neural phase angles in the alpha frequency range (8-12 Hz) over right anterior sensors were approximately antiphase in a prestimulus time window, and thus successfully distinguished between correct and incorrect lexical decisions. Moreover, alpha-band oscillations were again approximately antiphase across participants for correct versus incorrect trials during a later peristimulus time window (∼500 ms) at left-central electrodes. Strikingly, lexical decision accuracy was not predicted by either event-related potentials (ERPs) or oscillatory power measures. We suggest that correct lexical decisions depend both on successful sensory processing, which is made possible by the alignment of stimulus onset with an optimal alpha phase, as well as integration and weighting of decisional information, which is coupled to alpha phase immediately following the critical manipulation that differentiated words from pseudowords. The current study constitutes a first step toward characterizing the role of dynamic oscillatory brain states for higher cognitive functions, such as spoken word recognition.

Oscillatory Brain States and Variability in Visual Short-Term Memory

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Information-geometric measures estimate neural interactions during oscillatory brain states

The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG), a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.

Functional Dissociation of Ongoing Oscillatory Brain States

The state of a neural assembly preceding an incoming stimulus is assumed to modulate the processing of subsequently presented stimuli. The nature of this state can differ with respect to the frequency of ongoing oscillatory activity. Oscillatory brain activity of specific frequency range such as alpha (8–12 Hz) and gamma (above 30 Hz) band oscillations are hypothesized to play a functional role in cognitive processing. Therefore, a selective modulation of this prestimulus activity could clarify the functional role of these prestimulus fluctuations. For this purpose, we adopted a novel non-invasive brain-computer-interface (BCI) strategy to selectively increase alpha or gamma band activity in the occipital cortex combined with an adaptive presentation of visual stimuli within specific brain states. During training, oscillatory brain activity was estimated online and fed back to the participants to enable a deliberate modulation of alpha or gamma band oscillations. Results revealed that volunteers selectively increased alpha and gamma frequency oscillations with a high level of specificity regarding frequency range and localization. At testing, alpha or gamma band activity was classified online and at defined levels of activity, visual objects embedded in noise were presented instantly and had to be detected by the volunteer. In experiment I, the effect of two levels of prestimulus gamma band activity on visual processing was examined. During phases of increased gamma band activity significantly more visual objects were detected. In experiment II, the effect was compared against increased levels of alpha band activity. An improvement of visual processing was only observed for enhanced gamma band activity. Both experiments demonstrate the specific functional role of prestimulus gamma band oscillations for perceptual processing. We propose that the BCI method permits the selective modulation of oscillatory activity and the direct assessment of behavioral consequences to test for functional dissociations of different oscillatory brain states.

Oscillatory brain states interact with late cognitive components of the somatosensory evoked potential

The question of interaction between ongoing neuronal activity and evoked responses has been addressed for different species, sensory systems and measurement modalities. Among other findings, there is converging evidence for an interaction of occipital alpha-rhythm amplitude with the visual evoked potential. Here, we test the hypothesis that the modulatory role of an ongoing rhythm might not be confined to the visual system and the occipital alpha rhythm, but instead may be generalized to other sensory systems. Using an online EEG analysis approach, we investigated the influence of the Rolandic alpha-rhythm on the somatosensory evoked potential (SEP). We triggered vibrotactile stimulation during periods of high Rolandic alpha-rhythm amplitude. Analysis revealed significant effects of pre-stimulus Rolandic alpha amplitude on the amplitude of the N140 and P260 components of the SEP, known to be linked to cognitive processing, but not on early sensory components. The N140–P260 complex shows a different focus in topography than the early sensory components and the pre-stimulus Rolandic alpha rhythm. These results indicate an involvement of Rolandic alpha-rhythm in higher cognitive processing. In more general terms – and in the context of similar studies in the visual system – our findings suggest that modulation of late EP components by ongoing rhythms might be a characteristic and possibly universal feature of sensory systems.

A Top-down drive Modulates Oscillatory Brain States Predictive of Motor Performance

A Top-down drive Modulates Oscillatory Brain States Predictive of Motor Performance

Oscillatory brain states and learning: Impact of hippocampal theta-contingent training.

Eyeblink classical conditioning is a relatively simple form of associative learning that has become an invaluable tool in our understanding of the neural mechanisms of learning. When studying rabbits in this paradigm, we observed a dramatic modification of learning rate by conducting training during episodes of either hippocampal theta or hippocampal non-theta activity as determined by on-line slow-wave spectral analysis. Specifically, if animals were given trials only when a computer analysis verified a predominance of slow-wave oscillations at theta frequencies (3-8 Hz), they learned in half as many trials as animals trained during non-theta hippocampal activity (58 vs. 115). This finding provides important evidence from awake, behaving animals that supports recent advances in our knowledge of (i) brain sites and neurobiological mechanisms of learning and memory, specifically hippocampus and theta oscillations, (ii) the biological plausibility of current models of hippocampal function that posit important roles for oscillatory potentials, and (iii) the design of interfaces between biological and cybernetic (electronic) systems that can optimize cognitive processes and performance.