Pre-stimulus influences on auditory perception arising from sensory representations and decision processes

The qualities of perception depend not only on the sensory inputs but also on the brain state before stimulus presentation. Although the collective evidence from neuroimaging studies for a relation between prestimulus state and perception is strong, the interpretation in the context of sensory computations or decision processes has remained difficult. In the auditory system, for example, previous studies have reported a wide range of effects in terms of the perceptually relevant frequency bands and state parameters (phase/power). To dissociate influences of state on earlier sensory representations and higher-level decision processes, we collected behavioral and EEG data in human participants performing two auditory discrimination tasks relying on distinct acoustic features. Using single-trial decoding, we quantified the relation between prestimulus activity, relevant sensory evidence, and choice in different task-relevant EEG components. Within auditory networks, we found that phase had no direct influence on choice, whereas power in task-specific frequency bands affected the encoding of sensory evidence. Within later-activated frontoparietal regions, theta and alpha phase had a direct influence on choice, without involving sensory evidence. These results delineate two consistent mechanisms by which prestimulus activity shapes perception. However, the timescales of the relevant neural activity depend on the specific brain regions engaged by the respective task

Prefrontal rhythms for cognitive control

Goal-directed behavior requires flexible selection among action plans and updating behavioral strategies when they fail to achieve desired goals. Lateral prefrontal cortex (LPFC) is implicated in the execution of behavior-guiding rule-based cognitive control while anterior cingulate cortex (ACC) is implicated in monitoring processes and updating rules. Rule-based cognitive control requires selective processing while process monitoring benefits from combinatorial processing. I used a combination of computational and experimental methods to investigate how network oscillations and neuronal heterogeneity contribute to cognitive control through their effects on selective versus combinatorial processing modes in LPFC and ACC. First, I adapted an existing LPFC model to explore input frequency- and coherence-based output selection mechanisms for flexible routing of rate-coded signals. I show that the oscillatory states of input encoding populations can exhibit a stronger influence over downstream competition than their activity levels. This enables an output driven by a weaker resonant input signal to suppress lower-frequency competing responses to stronger, less resonant (though possibly higher-frequency) input signals. While signals are encoded in population firing rates, output selection and signal routing can be governed independently by the frequency and coherence of oscillatory inputs and their correspondence with output resonant properties. Flexible response selection and gating can be achieved by oscillatory state control mechanisms operating on input encoding populations. These dynamic mechanisms enable experimentally-observed LPFC beta and gamma oscillations to flexibly govern the selection and gating of rate-coded signals for downstream read-out. Furthermore, I demonstrate how differential drives to distinct interneuron populations can switch working memory representations between asynchronous and oscillatory states that support rule-based selection. Next, I analyzed physiological data from the LeBeau laboratory and built a de novo model constrained by the biological data. Experimental data demonstrated that fast network oscillations at both the beta- and gamma frequency bands could be elicited in vitro in ACC and neurons exhibited a wide range of intrinsic properties. Computational modeling of the ACC network revealed that the frequency of network oscillation generated was dependent upon the time course of inhibition. Principal cell heterogeneity broadened the range of frequencies generated by the model network. In addition, with different frequency inputs to two neuronal assemblies, heterogeneity decreased competition and increased spike coherence between the networks thus conferring a combinatorial advantage to the network. These findings suggest that oscillating neuronal populations can support either response selection (routing), or combination, depending on the interplay between the kinetics of synaptic inhibition and the degree of heterogeneity of principal cell intrinsic conductances. Such differences may support functional differences between the roles of LPFC and ACC in cognitive control

Prefrontal oscillations modulate the propagation of neuronal activity required for working memory

[EN] Cognition involves using attended information, maintained in working memory (WM), to guide action. During a cognitive task, a correct response requires flexible, selective gating so that only the appropriate information flows from WM to downstream effectors that carry out the response. In this work, we used biophysically-detailed modeling to explore the hypothesis that network oscillations in prefrontal cortex (PFC), leveraging local inhibition, can independently gate responses to items in WM. The key role of local inhibition was to control the period between spike bursts in the outputs, and to produce an oscillatory response no matter whether the WM item was maintained in an asynchronous or oscillatory state. We found that the WM item that induced an oscillatory population response in the PFC output layer with the shortest period between spike bursts was most reliably propagated. The network resonant frequency (i.e., the input frequency that produces the largest response) of the output layer can be flexibly tuned by varying the excitability of deep layer principal cells. Our model suggests that experimentally-observed modulation of PFC beta-frequency (15-30 Hz) and gamma -frequency (30-80 Hz) oscillations could leverage network resonance and local inhibition to govern the flexible routing of signals in service to cognitive processes like gating outputs from working memory and the selection of rule-based actions. Importantly, we show for the first time that nonspecific changes in deep layer excitability can tune the output gate's resonant frequency, enabling the specific selection of signals encoded by populations in asynchronous or fast oscillatory states. More generally, this represents a dynamic mechanism by which adjusting network excitability can govern the propagation of asynchronous and oscillatory signals throughout neocortex.This work was supported by the U.S. Army Research Office under award number ARO W911NF-12-R-0012-02 to N. K., the U.S. Office of Naval Research under award number ONR MURI N00014-16-1-2832 to M. H. and E. M., the National Institute of Mental Health under award number NIMH R37MH087027 to E. M., and The MIT Picower Institute Faculty Innovation Fund to E. M. We would like to acknowledge Joachim Hass and Michelle McCarthy for early discussions of our modeling results, as well as Andre Bastos and Mikael Lundqvist for discussions relating our modeling work to their experiments.Sherfey, J.; Ardid-Ramírez, JS.; Miller, EK.; Hasselmo, ME.; Kopell, NJ. (2020). Prefrontal oscillations modulate the propagation of neuronal activity required for working memory. 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Dynamic Oscillatory Interactions Between Neural Attention and Sensorimotor Systems

The adaptive and flexible ability of the human brain to preference the processing of salient environmental features in the visual space is essential to normative cognitive function, and various neurologically afflicted patient groups report negative impacts on visual attention. While the brain-bases of human attentional processing have begun to be unraveled, very little is known regarding the interactions between attention systems and systems supporting sensory and motor processing. This is essential, as these interactions are dynamic; evolving rapidly in time and across a wide range of functionally defined rhythmic frequencies. Using magnetoencephalography (MEG) and a range of novel cognitive paradigms and analytical techniques, this work attempts to fill critical gaps in this knowledge. Specifically, we unravel the role of dynamic oscillatory interactions between attention and three sensorimotor systems. First, we establish the importance of sub-second occipital alpha (8 – 14 Hz) oscillatory responses in visual distractor suppression during selective attention (Chapter 1) and their essential role in fronto-parietal attention networks during visual orienting (Chapter 2). Next, we examine the divergent effects of directed attention on multi-frequency primary somatosensory neural oscillations in the theta (4 – 8 Hz), alpha, and beta (18 – 26 Hz) bands (Chapter 3). Finally, we extend these findings to the motor system (Chapter 4), and find that the frontal and parietal beta-frequency oscillations known to support motor planning and execution are modulated equivalently by differing subtypes of attentional interference, whereas frontal gamma (64 – 84 Hz) oscillations specifically index the superadditive effect of this interference. These findings provide new insight into the dynamic nature of attention-sensorimotor interactions in the human brain, and will be the foundation for groundbreaking new studies of attentional deficits in patients with common neurological disorders (e.g., Alzheimer’s disease, HIV-associated neurocognitive disorders, Parkinson’s disease). With an enhanced knowledge of the temporal and spectral definitions of these impairments, new therapeutic interventions utilizing frequency-targeted neural stimulation can be developed

An embedded process perspective

It remains a dogma in cognitive neuroscience to separate human attention and memory into distinct modules and processes. Here we propose that brain rhythms reflect the embedded nature of these processes in the human brain, as evident from their shared neural signatures: gamma oscillations (30–90 Hz) reflect sensory information processing and activated neural representations (memory items). The theta rhythm (3–8 Hz) is a pacemaker of explicit control processes (central executive), structuring neural information processing, bit by bit, as reflected in the theta-gamma code. By representing memory items in a sequential and time-compressed manner the theta-gamma code is hypothesized to solve key problems of neural computation: (1) attentional sampling (integrating and segregating information processing), (2) mnemonic updating (implementing Hebbian learning), and (3) predictive coding (advancing information processing ahead of the real time to guide behavior). In this framework, reduced alpha oscillations (8–14 Hz) reflect activated semantic networks, involved in both explicit and implicit mnemonic processes. Linking recent theoretical accounts and empirical insights on neural rhythms to the embedded-process model advances our understanding of the integrated nature of attention and memory – as the bedrock of human cognition

The thalamic reticular nucleus: a functional hub for thalamocortical network dysfunction in schizophrenia and a target for drug discovery

The thalamus (comprising many distinct nuclei) plays a key role in facilitating sensory discrimination and cognitive processes through connections with the cortex. Impaired thalamocortical processing has long been considered to be involved in schizophrenia. In this review we focus on the thalamic reticular nucleus (TRN) providing evidence for it being an important communication hub between the thalamus and cortex and how it may play a key role in the pathophysiology of schizophrenia. We first highlight the functional neuroanatomy, neurotransmitter localisation and physiology of the TRN. We then present evidence of the physiological roles of the TRN in relation to oscillatory activity, cognition and behaviour. Next we discuss the role of the TRN in rodent models of risk factors for schizophrenia (genetic and pharmacological) and provide evidence for TRN deficits in schizophrenia. Finally we discuss new drug targets for schizophrenia in relation to restoring TRN circuitry dysfunction

Speech rhythms and multiplexed oscillatory sensory coding in the human brain

Cortical oscillations are likely candidates for segmentation and coding of continuous speech. Here, we monitored continuous speech processing with magnetoencephalography (MEG) to unravel the principles of speech segmentation and coding. We demonstrate that speech entrains the phase of low-frequency (delta, theta) and the amplitude of high-frequency (gamma) oscillations in the auditory cortex. Phase entrainment is stronger in the right and amplitude entrainment is stronger in the left auditory cortex. Furthermore, edges in the speech envelope phase reset auditory cortex oscillations thereby enhancing their entrainment to speech. This mechanism adapts to the changing physical features of the speech envelope and enables efficient, stimulus-specific speech sampling. Finally, we show that within the auditory cortex, coupling between delta, theta, and gamma oscillations increases following speech edges. Importantly, all couplings (i.e., brain-speech and also within the cortex) attenuate for backward-presented speech, suggesting top-down control. We conclude that segmentation and coding of speech relies on a nested hierarchy of entrained cortical oscillations

Communication between Brain Areas Based on Nested Oscillations

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