Neural oscillations are a start toward understanding brain activity rather than the end

A simple MATLAB toolbox for analyzing calcium imaging data in vitro and in vivo

BACKGROUND

Fluorescence imaging of calcium dynamics in neuronal populations is powerful because it offers a way of relating the activity of individual cells to the broader population of nearby cells. The method's growth across neuroscience has particularly been driven by the introduction of sophisticated mathematical techniques related to motion correction, image registration, cell detection, spike estimation, and population characterization. However, for many researchers, making good use of these techniques has been difficult because they have been devised by different workers and impose differing - and sometimes stringent - technical requirements on those who seek to use them.

NEW METHOD

We have built a simple toolbox of analysis routines that encompass the complete workflow for analyzing calcium imaging data. The workflow begins with preprocessing of data, includes motion correction and longitudinal image registration, detects active cells using constrained non-negative matrix factorization, and offers multiple options for estimating spike times and characterizing population activity. The routines can be navigated through a simple graphical user interface. Although written in MATLAB, a standalone version for researchers who do not have access to MATLAB is included.

RESULTS

We have used the toolbox on two very different preparations: spontaneously active brain slices and microendoscopic imaging from deep structures in awake behaving mice. In both cases, the toolbox offered a seamless flow from raw data all the way through to prepared graphs.

CONCLUSION

The field of calcium imaging has benefited from the development of numerous innovative mathematical techniques. Here we offer a simple toolbox that allows ordinary researchers to fully exploit these techniques.

Exploring the Interplay Between Language Comprehension and Cortical Tracking: The Bilingual Test Case

Cortical tracking, the synchronization of brain activity to linguistic rhythms is a well-established phenomenon. However, its nature has been heavily contested: Is it purely epiphenomenal or does it play a fundamental role in speech comprehension? Previous research has used intelligibility manipulations to examine this topic. Here, we instead varied listeners' language comprehension skills while keeping the auditory stimulus constant. To do so, we tested 22 native English speakers and 22 Spanish/Catalan bilinguals learning English as a second language (SL) in an EEG cortical entrainment experiment and correlated the responses with the magnitude of the N400 component of a semantic comprehension task. As expected, native listeners effectively tracked sentential, phrasal, and syllabic linguistic structures. In contrast, SL listeners exhibited limitations in tracking sentential structures but successfully tracked phrasal and syllabic rhythms. Importantly, the amplitude of the neural entrainment correlated with the amplitude of the detection of semantic incongruities in SLs, showing a direct connection between tracking and the ability to understand speech. Together, these findings shed light on the interplay between language comprehension and cortical tracking, to identify neural entrainment as a fundamental principle for speech comprehension.

Repertoire of timescales in uni - and transmodal regions mediate working memory capacity

Working memory (WM) describes the dynamic process of maintenance and manipulation of information over a certain time delay. Neuronally, WM recruits a distributed network of cortical regions like the visual and dorsolateral prefrontal cortex as well as the subcortical hippocampus. How the input dynamics and subsequent neural dynamics impact WM remains unclear though. To answer this question, we combined the analysis of behavioral WM capacity with measuring neural dynamics through task-related power spectrum changes, e.g., median frequency (MF) in functional magnetic resonance imaging (fMRI). We show that the processing of the input dynamics, e.g., the task structure's specific timescale, leads to changes in the unimodal visual cortex's corresponding timescale which also relates to working memory capacity. While the more transmodal hippocampus relates to working memory capacity through its balance across multiple timescales or frequencies. In conclusion, we here show the relevance of both input dynamics and different neural timescales for WM capacity in uni - and transmodal regions like visual cortex and hippocampus for the subject's WM performance.

Dissecting deep brain stimulation evoked neural activity in the basal ganglia

Deep brain stimulation (DBS) is an established therapeutic tool for the treatment of Parkinson's disease (PD). The mechanisms of DBS for PD are likely rooted in modulation of the subthalamo-pallidal network. However, it can be difficult to electrophysiologically interrogate that network in human patients. The recent identification of large amplitude evoked potential (EP) oscillations from DBS in the subthalamic nucleus (STN) or globus pallidus internus (GPi) are providing new scientific opportunities to expand understanding of human basal ganglia network activity. In turn, the goal of this review is to provide a summary of DBS-induced EPs in the basal ganglia and attempt to explain various components of the EP waveforms from their likely network origins. Our analyses suggest that DBS-induced antidromic activation of globus pallidus externus (GPe) is a key driver of these oscillatory EPs, independent of stimulation location (i.e. STN or GPi). This suggests a potentially more important role for GPe in the mechanisms of DBS for PD than typically assumed. And from a practical perspective, DBS EPs are poised to become clinically useful electrophysiological biomarker signals for verification of DBS target engagement.

Neural tracking of continuous acoustics: properties, speech-specificity and open questions

Human speech is a particularly relevant acoustic stimulus for our species, due to its role of information transmission during communication. Speech is inherently a dynamic signal, and a recent line of research focused on neural activity following the temporal structure of speech. We review findings that characterise neural dynamics in the processing of continuous acoustics and that allow us to compare these dynamics with temporal aspects in human speech. We highlight properties and constraints that both neural and speech dynamics have, suggesting that auditory neural systems are optimised to process human speech. We then discuss the speech-specificity of neural dynamics and their potential mechanistic origins and summarise open questions in the field.

Rhythms in Speech

Speech can be defined as the human ability to communicate through a sequence of vocal sounds. Consequently, speech requires an emitter (the speaker) capable of generating the acoustic signal and a receiver (the listener) able to successfully decode the sounds produced by the emitter (i.e., the acoustic signal). Time plays a central role at both ends of this interaction. On the one hand, speech production requires precise and rapid coordination, typically within the order of milliseconds, of the upper vocal tract articulators (i.e., tongue, jaw, lips, and velum), their composite movements, and the activation of the vocal folds. On the other hand, the generated acoustic signal unfolds in time, carrying information at different timescales. This information must be parsed and integrated by the receiver for the correct transmission of meaning. This chapter describes the temporal patterns that characterize the speech signal and reviews research that explores the neural mechanisms underlying the generation of these patterns and the role they play in speech comprehension.

40 Hz Electroacupuncture relieves the memory dysfunction of 5xFAD mice by regulating neuronal electrical activity

In this investigation, we probed the impacts of 40 Hz Electroacupuncture (EA) on the cognitive function and brain activity in 5xFAD mice. Three groups of mice were constituted: the Model group of 5xFAD mice, the Wild Type (WT) group of littermate controls, and the EA group of 5xFAD mice subjected to EA treatment. Behavioral tests were conducted to evaluate memory function and anxiety levels, while the presence of Aβ plaques were detected via immunostaining, and neuronal activity was measured using multichannel recordings. Our results indicated that EA therapy enhanced memory function and anxiety-like behavior in 5xFAD mice, as well as diminishing the abundance and dimensions of Aβ plaques in the hippocampus and mPFC regions. Notably, the suppression of astrocyte activation was observed, which was potentially associated with alterations in gamma oscillation. Furthermore, the synaptic transmission of neurons was amplified, suggesting a possible modulation in neural activity. These findings indicate that 40 Hz EA could influence cognitive performance and potentially affect neuronal activity in 5xFAD mice, while the direct connection between EA and neuronal electrical activity regulation requires further exploration. The potential frequency-specific effects of EA on protective mechanisms in the brain was not addressed in this study and thus presents a direction for future research.

Rhythmic visual stimulation as a window into early brain development: A systematic review

Rhythmic visual stimulation (RVS), the periodic presentation of visual stimuli to elicit a rhythmic brain response, is increasingly applied to reveal insights into early neurocognitive development. Our systematic review identified 69 studies applying RVS in 0- to 6-year-olds. RVS has long been used to study the development of the visual system and applications have more recently been expanded to uncover higher cognitive functions in the developing brain, including overt and covert attention, face and object perception, numeral cognition, and predictive processing. These insights are owed to the unique benefits of RVS, such as the targeted frequency and stimulus-specific neural responses, as well as a remarkable signal-to-noise ratio. Yet, neural mechanisms underlying the RVS response are still poorly understood. We discuss critical challenges and avenues for future research, and the unique potentials the method holds. With this review, we provide a resource for researchers interested in the breadth of developmental RVS research and hope to inspire the future use of this cutting-edge method in developmental cognitive neuroscience.

Adaptive oscillators support Bayesian prediction in temporal processing

Humans excel at predictively synchronizing their behavior with external rhythms, as in dance or music performance. The neural processes underlying rhythmic inferences are debated: whether predictive perception relies on high-level generative models or whether it can readily be implemented locally by hard-coded intrinsic oscillators synchronizing to rhythmic input remains unclear and different underlying computational mechanisms have been proposed. Here we explore human perception for tone sequences with some temporal regularity at varying rates, but with considerable variability. Next, using a dynamical systems perspective, we successfully model the participants behavior using an adaptive frequency oscillator which adjusts its spontaneous frequency based on the rate of stimuli. This model better reflects human behavior than a canonical nonlinear oscillator and a predictive ramping model-both widely used for temporal estimation and prediction-and demonstrate that the classical distinction between absolute and relative computational mechanisms can be unified under this framework. In addition, we show that neural oscillators may constitute hard-coded physiological priors-in a Bayesian sense-that reduce temporal uncertainty and facilitate the predictive processing of noisy rhythms. Together, the results show that adaptive oscillators provide an elegant and biologically plausible means to subserve rhythmic inference, reconciling previously incompatible frameworks for temporal inferential processes.

"What" and "when" predictions modulate auditory processing in a mutually congruent manner

Introduction

Extracting regularities from ongoing stimulus streams to form predictions is crucial for adaptive behavior. Such regularities exist in terms of the content of the stimuli and their timing, both of which are known to interactively modulate sensory processing. In real-world stimulus streams such as music, regularities can occur at multiple levels, both in terms of contents (e.g., predictions relating to individual notes vs. their more complex groups) and timing (e.g., pertaining to timing between intervals vs. the overall beat of a musical phrase). However, it is unknown whether the brain integrates predictions in a manner that is mutually congruent (e.g., if "beat" timing predictions selectively interact with "what" predictions falling on pulses which define the beat), and whether integrating predictions in different timing conditions relies on dissociable neural correlates.

Methods

To address these questions, our study manipulated "what" and "when" predictions at different levels - (local) interval-defining and (global) beat-defining - within the same stimulus stream, while neural activity was recorded using electroencephalogram (EEG) in participants (N = 20) performing a repetition detection task.

Results

Our results reveal that temporal predictions based on beat or interval timing modulated mismatch responses to violations of "what" predictions happening at the predicted time points, and that these modulations were shared between types of temporal predictions in terms of the spatiotemporal distribution of EEG signals. Effective connectivity analysis using dynamic causal modeling showed that the integration of "what" and "when" predictions selectively increased connectivity at relatively late cortical processing stages, between the superior temporal gyrus and the fronto-parietal network.

Discussion

Taken together, these results suggest that the brain integrates different predictions with a high degree of mutual congruence, but in a shared and distributed cortical network. This finding contrasts with recent studies indicating separable mechanisms for beat-based and memory-based predictive processing.

Living-Neuron-Based Autogenerator

We present a novel closed-loop system designed to integrate biological and artificial neurons of the oscillatory type into a unified circuit. The system comprises an electronic circuit based on the FitzHugh-Nagumo model, which provides stimulation to living neurons in acute hippocampal mouse brain slices. The local field potentials generated by the living neurons trigger a transition in the FitzHugh-Nagumo circuit from an excitable state to an oscillatory mode, and in turn, the spikes produced by the electronic circuit synchronize with the living-neuron spikes. The key advantage of this hybrid electrobiological autogenerator lies in its capability to control biological neuron signals, which holds significant promise for diverse neuromorphic applications.

Characterizing endogenous delta oscillations in human MEG

Rhythmic activity in the delta frequency range (0.5-3 Hz) is a prominent feature of brain dynamics. Here, we examined whether spontaneous delta oscillations, as found in invasive recordings in awake animals, can be observed in non-invasive recordings performed in humans with magnetoencephalography (MEG). In humans, delta activity is commonly reported when processing rhythmic sensory inputs, with direct relationships to behaviour. However, rhythmic brain dynamics observed during rhythmic sensory stimulation cannot be interpreted as an endogenous oscillation. To test for endogenous delta oscillations we analysed human MEG data during rest. For comparison, we additionally analysed two conditions in which participants engaged in spontaneous finger tapping and silent counting, arguing that internally rhythmic behaviours could incite an otherwise silent neural oscillator. A novel set of analysis steps allowed us to show narrow spectral peaks in the delta frequency range in rest, and during overt and covert rhythmic activity. Additional analyses in the time domain revealed that only the resting state condition warranted an interpretation of these peaks as endogenously periodic neural dynamics. In sum, this work shows that using advanced signal processing techniques, it is possible to observe endogenous delta oscillations in non-invasive recordings of human brain dynamics.

Attentional modulation of inter-areal coherence explained by frequency shifts

Inter-areal coherence has been hypothesized as a mechanism for inter-areal communication. Indeed, empirical studies have observed an increase in inter-areal coherence with attention. Yet, the mechanisms underlying changes in coherence remain largely unknown. Both attention and stimulus salience are associated with shifts in the peak frequency of gamma oscillations in V1, which suggests that the frequency of oscillations may play a role in facilitating changes in inter-areal communication and coherence. In this study, we used computational modeling to investigate how the peak frequency of a sender influences inter-areal coherence. We show that changes in the magnitude of coherence are largely determined by the peak frequency of the sender. However, the pattern of coherence depends on the intrinsic properties of the receiver, specifically whether the receiver integrates or resonates with its synaptic inputs. Because resonant receivers are frequency-selective, resonance has been proposed as a mechanism for selective communication. However, the pattern of coherence changes produced by a resonant receiver is inconsistent with empirical studies. By contrast, an integrator receiver does produce the pattern of coherence with frequency shifts in the sender observed in empirical studies. These results indicate that coherence can be a misleading measure of inter-areal interactions. This led us to develop a new measure of inter-areal interactions, which we refer to as Explained Power. We show that Explained Power maps directly to the signal transmitted by the sender filtered by the receiver, and thus provides a method to quantify the true signals transmitted between the sender and receiver. Together, these findings provide a model of changes in inter-areal coherence and Granger-causality as a result of frequency shifts.

Dynamic models for musical rhythm perception and coordination

Rhythmicity permeates large parts of human experience. Humans generate various motor and brain rhythms spanning a range of frequencies. We also experience and synchronize to externally imposed rhythmicity, for example from music and song or from the 24-h light-dark cycles of the sun. In the context of music, humans have the ability to perceive, generate, and anticipate rhythmic structures, for example, "the beat." Experimental and behavioral studies offer clues about the biophysical and neural mechanisms that underlie our rhythmic abilities, and about different brain areas that are involved but many open questions remain. In this paper, we review several theoretical and computational approaches, each centered at different levels of description, that address specific aspects of musical rhythmic generation, perception, attention, perception-action coordination, and learning. We survey methods and results from applications of dynamical systems theory, neuro-mechanistic modeling, and Bayesian inference. Some frameworks rely on synchronization of intrinsic brain rhythms that span the relevant frequency range; some formulations involve real-time adaptation schemes for error-correction to align the phase and frequency of a dedicated circuit; others involve learning and dynamically adjusting expectations to make rhythm tracking predictions. Each of the approaches, while initially designed to answer specific questions, offers the possibility of being integrated into a larger framework that provides insights into our ability to perceive and generate rhythmic patterns.

Scale-free dynamics in the core-periphery topography and task alignment decline from conscious to unconscious states

Scale-free physiological processes are ubiquitous in the human organism. Resting-state functional MRI studies observed the loss of scale-free dynamics under anesthesia. In contrast, the modulation of scale-free dynamics during task-related activity remains an open question. We investigate scale-free dynamics in the cerebral cortex's unimodal periphery and transmodal core topography in rest and task states during three conscious levels (awake, sedation, and anesthesia) complemented by computational modelling (Stuart-Landau model). The empirical findings demonstrate that the loss of the brain's intrinsic scale-free dynamics in the core-periphery topography during anesthesia, where pink noise transforms into white noise, disrupts the brain's neuronal alignment with the task's temporal structure. The computational model shows that the stimuli's scale-free dynamics, namely pink noise distinguishes from brown and white noise, also modulate task-related activity. Together, we provide evidence for two mechanisms of consciousness, temporo-spatial nestedness and alignment, suggested by the Temporo-Spatial Theory of Consciousness (TTC).

Neural tracking of speech envelope does not unequivocally reflect intelligibility

During listening, brain activity tracks the rhythmic structures of speech signals. Here, we directly dissociated the contribution of neural envelope tracking in the processing of speech acoustic cues from that related to linguistic processing. We examined the neural changes associated with the comprehension of Noise-Vocoded (NV) speech using magnetoencephalography (MEG). Participants listened to NV sentences in a 3-phase training paradigm: (1) pre-training, where NV stimuli were barely comprehended, (2) training with exposure of the original clear version of speech stimulus, and (3) post-training, where the same stimuli gained intelligibility from the training phase. Using this paradigm, we tested if the neural responses of a speech signal was modulated by its intelligibility without any change in its acoustic structure. To test the influence of spectral degradation on neural envelope tracking independently of training, participants listened to two types of NV sentences (4-band and 2-band NV speech), but were only trained to understand 4-band NV speech. Significant changes in neural tracking were observed in the delta range in relation to the acoustic degradation of speech. However, we failed to find a direct effect of intelligibility on the neural tracking of speech envelope in both theta and delta ranges, in both auditory regions-of-interest and whole-brain sensor-space analyses. This suggests that acoustics greatly influence the neural tracking response to speech envelope, and that caution needs to be taken when choosing the control signals for speech-brain tracking analyses, considering that a slight change in acoustic parameters can have strong effects on the neural tracking response.

ROSE: A Neurocomputational Architecture for Syntax

A comprehensive model of natural language processing in the brain must accommodate four components: representations, operations, structures and encoding. It further requires a principled account of how these different components mechanistically, and causally, relate to each another. While previous models have isolated regions of interest for structure-building and lexical access, and have utilized specific neural recording measures to expose possible signatures of syntax, many gaps remain with respect to bridging distinct scales of analysis that map onto these four components. By expanding existing accounts of how neural oscillations can index various linguistic processes, this article proposes a neurocomputational architecture for syntax, termed the ROSE model (Representation, Operation, Structure, Encoding). Under ROSE, the basic data structures of syntax are atomic features, types of mental representations (R), and are coded at the single-unit and ensemble level. Elementary computations (O) that transform these units into manipulable objects accessible to subsequent structure-building levels are coded via high frequency broadband γ activity. Low frequency synchronization and cross-frequency coupling code for recursive categorial inferences (S). Distinct forms of low frequency coupling and phase-amplitude coupling (δ-θ coupling via pSTS-IFG; θ-γ coupling via IFG to conceptual hubs in lateral and ventral temporal cortex) then encode these structures onto distinct workspaces (E). Causally connecting R to O is spike-phase/LFP coupling; connecting O to S is phase-amplitude coupling; connecting S to E is a system of frontotemporal traveling oscillations; connecting E back to lower levels is low-frequency phase resetting of spike-LFP coupling. This compositional neural code has important implications for algorithmic accounts, since it makes concrete predictions for the appropriate level of study for psycholinguistic parsing models. ROSE is reliant on neurophysiologically plausible mechanisms, is supported at all four levels by a range of recent empirical research, and provides an anatomically precise and falsifiable grounding for the basic property of natural language syntax: hierarchical, recursive structure-building.

Cortical encoding of rhythmic kinematic structures in biological motion

Biological motion (BM) perception is of great survival value to human beings. The critical characteristics of BM information lie in kinematic cues containing rhythmic structures. However, how rhythmic kinematic structures of BM are dynamically represented in the brain and contribute to visual BM processing remains largely unknown. Here, we probed this issue in three experiments using electroencephalogram (EEG). We found that neural oscillations of observers entrained to the hierarchical kinematic structures of the BM sequences (i.e., step-cycle and gait-cycle for point-light walkers). Notably, only the cortical tracking of the higher-level rhythmic structure (i.e., gait-cycle) exhibited a BM processing specificity, manifested by enhanced neural responses to upright over inverted BM stimuli. This effect could be extended to different motion types and tasks, with its strength positively correlated with the perceptual sensitivity to BM stimuli at the right temporal brain region dedicated to visual BM processing. Modeling results further suggest that the neural encoding of spatiotemporally integrative kinematic cues, in particular the opponent motions of bilateral limbs, drives the selective cortical tracking of BM information. These findings underscore the existence of a cortical mechanism that encodes periodic kinematic features of body movements, which underlies the dynamic construction of visual BM perception.

What neural oscillations can and cannot do for syntactic structure building

Understanding what someone says requires relating words in a sentence to one another as instructed by the grammatical rules of a language. In recent years, the neurophysiological basis for this process has become a prominent topic of discussion in cognitive neuroscience. Current proposals about the neural mechanisms of syntactic structure building converge on a key role for neural oscillations in this process, but they differ in terms of the exact function that is assigned to them. In this Perspective, we discuss two proposed functions for neural oscillations - chunking and multiscale information integration - and evaluate their merits and limitations taking into account a fundamentally hierarchical nature of syntactic representations in natural languages. We highlight insights that provide a tangible starting point for a neurocognitive model of syntactic structure building.

Transcranial electrical stimulation: How can a simple conductor orchestrate complex brain activity?

Transcranial electrical stimulation (tES) is one of the oldest and yet least understood forms of brain stimulation. The idea that a weak electrical stimulus, applied outside the head, can meaningfully affect neural activity is often regarded as mysterious. Here, we argue that the direct effects of tES are not so mysterious: Extensive data from a wide range of model systems shows it has appreciable effects on the activity of individual neurons. Instead, the real mysteries are how tES interacts with the brain's own activity and how these dynamics can be controlled to produce desirable therapeutic effects. These are challenging problems, akin to repairing a complex machine while it is running, but they are not unique to tES or even neuroscience. We suggest that models of coupled oscillators, a common tool for studying interactions in other fields, may provide valuable insights. By combining these tools with our growing, interdisciplinary knowledge of brain dynamics, we are now in a good position to make progress in this area and meet the high demand for effective neuromodulation in neuroscience and psychiatry.

Neural synchronization is strongest to the spectral flux of slow music and depends on familiarity and beat salience

Neural activity in the auditory system synchronizes to sound rhythms, and brain–environment synchronization is thought to be fundamental to successful auditory perception. Sound rhythms are often operationalized in terms of the sound’s amplitude envelope. We hypothesized that – especially for music – the envelope might not best capture the complex spectro-temporal fluctuations that give rise to beat perception and synchronized neural activity. This study investigated (1) neural synchronization to different musical features, (2) tempo-dependence of neural synchronization, and (3) dependence of synchronization on familiarity, enjoyment, and ease of beat perception. In this electroencephalography study, 37 human participants listened to tempo-modulated music (1–4 Hz). Independent of whether the analysis approach was based on temporal response functions (TRFs) or reliable components analysis (RCA), the spectral flux of music – as opposed to the amplitude envelope – evoked strongest neural synchronization. Moreover, music with slower beat rates, high familiarity, and easy-to-perceive beats elicited the strongest neural response. Our results demonstrate the importance of spectro-temporal fluctuations in music for driving neural synchronization, and highlight its sensitivity to musical tempo, familiarity, and beat salience.

When we listen to a melody, the activity of our neurons synchronizes to the music: in fact, it is likely that the closer the match, the better we can perceive the piece. However, it remains unclear exactly which musical features our brain cells synchronize to. Previous studies, which have often used ‘simplified’ music, have highlighted that the amplitude envelope (how the intensity of the sounds changes over time) could be involved in this phenomenon, alongside factors such as musical training, attention, familiarity with the piece or even enjoyment. Whether differences in neural synchronization could explain why musical tastes vary between people is also still a matter of debate.

In their study, Weineck et al. aim to better understand what drives neuronal synchronization to music. A technique known as electroencephalography was used to record brain activity in 37 volunteers listening to instrumental music whose tempo ranged from 60 to 240 beats per minute. The tunes varied across an array of features such as familiarity, enjoyment and how easy the beat was to perceive. Two different approaches were then used to calculate neural synchronization, which yielded converging results.

The analyses revealed that three types of factors were associated with a strong neural synchronization. First, amongst the various cadences, a tempo of 60-120 beats per minute elicited the strongest match with neuronal activity. Interestingly, this beat is commonly found in Western pop music, is usually preferred by listeners, and often matches spontaneous body rhythms such as walking pace. Second, synchronization was linked to variations in pitch and sound quality (known as ‘spectral flux’) rather than in the amplitude envelope. And finally, familiarity and perceived beat saliency – but not enjoyment or musical expertise – were connected to stronger synchronization.

These findings help to better understand how our brains allow us to perceive and connect with music. The work conducted by Weineck et al. should help other researchers to investigate this field; in particular, it shows how important it is to consider spectral flux rather than amplitude envelope in experiments that use actual music.

Language specificity in cortical tracking of speech rhythm at the mora, syllable, and foot levels

Recent research shows that adults’ neural oscillations track the rhythm of the speech signal. However, the extent to which this tracking is driven by the acoustics of the signal, or by language-specific processing remains unknown. Here adult native listeners of three rhythmically different languages (English, French, Japanese) were compared on their cortical tracking of speech envelopes synthesized in their three native languages, which allowed for coding at each of the three language’s dominant rhythmic unit, respectively the foot (2.5 Hz), syllable (5 Hz), or mora (10 Hz) level. The three language groups were also tested with a sequence in a non-native language, Polish, and a non-speech vocoded equivalent, to investigate possible differential speech/nonspeech processing. The results first showed that cortical tracking was most prominent at 5 Hz (syllable rate) for all three groups, but the French listeners showed enhanced tracking at 5 Hz compared to the English and the Japanese groups. Second, across groups, there were no differences in responses for speech versus non-speech at 5 Hz (syllable rate), but there was better tracking for speech than for non-speech at 10 Hz (not the syllable rate). Together these results provide evidence for both language-general and language-specific influences on cortical tracking.

Language acquisition and speech rhythm patterns: an auditory neuroscience perspective

All human infants acquire language, but their brains do not know which language/s to prepare for. This observation suggests that there are fundamental components of the speech signal that contribute to building a language system, and fundamental neural processing mechanisms that use these components, which are shared across languages. Equally, disorders of language acquisition are found across all languages, with the most prevalent being developmental language disorder (approx. 7% prevalence), where oral language comprehension and production is atypical, and developmental dyslexia (approx. 7% prevalence), where written language acquisition is atypical. Recent advances in auditory neuroscience, along with advances in modelling the speech signal from an amplitude modulation (AM, intensity or energy change) perspective, have increased our understanding of both language acquisition and these developmental disorders. Speech rhythm patterns turn out to be fundamental to both sensory and neural linguistic processing. The rhythmic routines typical of childcare in many cultures, the parental practice of singing lullabies to infants, and the ubiquitous presence of BabyTalk (infant-directed speech) all enhance the fundamental AM components that contribute to building a linguistic brain.

Spike-Gamma Phase Relationship in the Visual Cortex

Gamma oscillations (30-70 Hz) have been hypothesized to play a role in cortical function. Most of the proposed mechanisms involve rhythmic modulation of neuronal excitability at gamma frequencies, leading to modulation of spike timing relative to the rhythm. I first show that the gamma band could be more privileged than other frequencies in observing spike-field interactions even in the absence of genuine gamma rhythmicity and discuss several biases in spike-gamma phase estimation. I then discuss the expected spike-gamma phase according to several hypotheses. Inconsistent with the phase-coding hypothesis (but not with others), the spike-gamma phase does not change with changes in stimulus intensity or attentional state, with spikes preferentially occurring 2-4 ms before the trough, but with substantial variability. However, this phase relationship is expected even when gamma is a byproduct of excitatory-inhibitory interactions. Given that gamma occurs in short bursts, I argue that the debate over the role of gamma is a matter of semantics. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Brain stimulation competes with ongoing oscillations for control of spike timing in the primate brain

Transcranial alternating current stimulation (tACS) is a popular method for modulating brain activity noninvasively. In particular, tACS is often used as a targeted intervention that enhances a neural oscillation at a specific frequency to affect a particular behavior. However, these interventions often yield highly variable results. Here, we provide a potential explanation for this variability: tACS competes with the brain's ongoing oscillations. Using neural recordings from alert nonhuman primates, we find that when neural firing is independent of ongoing brain oscillations, tACS readily entrains spiking activity, but when neurons are strongly entrained to ongoing oscillations, tACS often causes a decrease in entrainment instead. Consequently, tACS can yield categorically different results on neural activity, even when the stimulation protocol is fixed. Mathematical analysis suggests that this competition is likely to occur under many experimental conditions. Attempting to impose an external rhythm on the brain may therefore often yield precisely the opposite effect.

Memory-Paced Tapping to Auditory Rhythms: Effects of Rate, Speech, and Motor Engagement

PURPOSE

Humans have a near-automatic tendency to entrain their motor actions to rhythms in the environment. Entrainment has been hypothesized to play an important role in processing naturalistic stimuli, such as speech and music, which have intrinsically rhythmic properties. Here, we studied two facets of entraining one's rhythmic motor actions to an external stimulus: (a) synchronized finger tapping to auditory rhythmic stimuli and (b) memory-paced reproduction of a previously heard rhythm.

METHOD

Using modifications of the Synchronization-Continuation tapping paradigm, we studied how these two rhythmic behaviors were affected by different stimulus and task features. We tested synchronization and memory-paced tapping for a broad range of rates, from stimulus onset asynchrony of subsecond to suprasecond, both for strictly isochronous tone sequences and for rhythmic speech stimuli (counting from 1 to 10), which are more ecological yet less isochronous. We also asked what role motor engagement plays in forming a stable internal representation for rhythms and guiding memory-paced tapping.

RESULTS AND CONCLUSIONS

Our results show that individuals can flexibly synchronize their motor actions to a very broad range of rhythms. However, this flexibility does not extend to memory-paced tapping, which is accurate only in a narrower range of rates, around ~1.5 Hz. This pattern suggests that intrinsic rhythmic defaults in the auditory and/or motor system influence the internal representation of rhythms, in the absence of an external pacemaker. Interestingly, memory-paced tapping for speech rhythms and simple tone sequences shared similar "optimal rates," although with reduced accuracy, suggesting that internal constraints on rhythmic entrainment generalize to more ecological stimuli. Last, we found that actively synchronizing to tones versus passively listening to them led to more accurate memory-paced tapping performance, which emphasizes the importance of action-perception interactions in forming stable entrainment to external rhythms.

Pushing the envelope: Evaluating speech rhythm with different envelope extraction techniques

The amplitude of the speech signal varies over time, and the speech envelope is an attempt to characterise this variation in the form of an acoustic feature. Although tacitly assumed, the similarity between the speech envelope-derived time series and that of phonetic objects (e.g., vowels) remains empirically unestablished. The current paper, therefore, evaluates several speech envelope extraction techniques, such as the Hilbert transform, by comparing different acoustic landmarks (e.g., peaks in the speech envelope) with manual phonetic annotation in a naturalistic and diverse dataset. Joint speech tasks are also introduced to determine which acoustic landmarks are most closely coordinated when voices are aligned. Finally, the acoustic landmarks are evaluated as predictors for the temporal characterisation of speaking style using classification tasks. The landmark that performed most closely to annotated vowel onsets was peaks in the first derivative of a human audition-informed envelope, consistent with converging evidence from neural and behavioural data. However, differences also emerged based on language and speaking style. Overall, the results show that both the choice of speech envelope extraction technique and the form of speech under study affect how sensitive an engineered feature is at capturing aspects of speech rhythm, such as the timing of vowels.

On the Role of Neural Oscillations Across Timescales in Speech and Music Processing

This mini review is aimed at a clinician-scientist seeking to understand the role of oscillations in neural processing and their functional relevance in speech and music perception. We present an overview of neural oscillations, methods used to study them, and their functional relevance with respect to music processing, aging, hearing loss, and disorders affecting speech and language. We first review the oscillatory frequency bands and their associations with speech and music processing. Next we describe commonly used metrics for quantifying neural oscillations, briefly touching upon the still-debated mechanisms underpinning oscillatory alignment. Following this, we highlight key findings from research on neural oscillations in speech and music perception, as well as contributions of this work to our understanding of disordered perception in clinical populations. Finally, we conclude with a look toward the future of oscillatory research in speech and music perception, including promising methods and potential avenues for future work. We note that the intention of this mini review is not to systematically review all literature on cortical tracking of speech and music. Rather, we seek to provide the clinician-scientist with foundational information that can be used to evaluate and design research studies targeting the functional role of oscillations in speech and music processing in typical and clinical populations.

Breaking Down a Rhythm: Dissecting the Mechanisms Underlying Task-Related Neural Oscillations

A century worth of research has linked multiple cognitive, perceptual and behavioral states to various brain oscillations. However, the mechanistic roles and circuit underpinnings of these oscillations remain an area of active study. In this review, we argue that the advent of optogenetic and related systems neuroscience techniques has shifted the field from correlational to causal observations regarding the role of oscillations in brain function. As a result, studying brain rhythms associated with behavior can provide insight at different levels, such as decoding task-relevant information, mapping relevant circuits or determining key proteins involved in rhythmicity. We summarize recent advances in this field, highlighting the methods that are being used for this purpose, and discussing their relative strengths and limitations. We conclude with promising future approaches that will help unravel the functional role of brain rhythms in orchestrating the repertoire of complex behavior.

Oscillatory visual mechanisms revealed by random temporal sampling

It is increasingly apparent that functionally significant neural activity is oscillatory in nature. Demonstrating the implications of this mode of operation for perceptual/cognitive function remains somewhat elusive. This report describes the technique of random temporal sampling for the investigation of visual oscillatory mechanisms. The technique is applied in visual recognition experiments using different stimulus classes (words, familiar objects, novel objects, and faces). Classification images reveal variations of perceptual effectiveness according to the temporal features of stimulus visibility. These classification images are also decomposed into their power and phase spectra. Stimulus classes lead to distinct outcomes and the power spectra of classification images are highly generalizable across individuals. Moreover, stimulus class can be reliably decoded from the power spectrum of individual classification images. These findings and other aspects of the results validate random temporal sampling as a promising new method to study oscillatory visual mechanisms.