Visual feature integration and the temporal correlation hypothesis

Distinct roles of PV and Sst interneurons in visually induced gamma oscillations

Gamma-frequency oscillations are a hallmark of active information processing and are generated by interactions between excitatory and inhibitory neurons. To examine the contribution of distinct inhibitory interneurons to visually induced gamma oscillations, we recorded from optogenetically identified PV+ (parvalbumin) and Sst+ (somatostatin) interneurons in mouse primary visual cortex (V1). PV and Sst inhibitory interneurons exhibited distinct correlations to gamma oscillations. PV cells were strongly phase locked, while Sst cells were weakly phase locked, except for narrow-waveform Sst cells. PV cells fired at a substantially earlier phase in the gamma cycle (≈6 ms) than Sst cells. PV cells fired shortly after the onset of tightly synchronized burst events in excitatory cells, while Sst interneurons fired after subsequent burst spikes or single spikes. These findings indicate a main role of PV interneurons in synchronizing network activity and suggest that PV and Sst interneurons control the excitability of somatic and dendritic neural compartments with precise time delays coordinated by gamma oscillations.

Musical neurodynamics

A great deal of research in the neuroscience of music suggests that neural oscillations synchronize with musical stimuli. Although neural synchronization is a well-studied mechanism underpinning expectation, it has even more far-reaching implications for music. In this Perspective, we survey the literature on the neuroscience of music, including pitch, harmony, melody, tonality, rhythm, metre, groove and affect. We describe how fundamental dynamical principles based on known neural mechanisms can explain basic aspects of music perception and performance, as summarized in neural resonance theory. Building on principles such as resonance, stability, attunement and strong anticipation, we propose that people anticipate musical events not through predictive neural models, but because brain-body dynamics physically embody musical structure. The interaction of certain kinds of sounds with ongoing pattern-forming dynamics results in patterns of perception, action and coordination that we collectively experience as music. Statistically universal structures may have arisen in music because they correspond to stable states of complex, pattern-forming dynamical systems. This analysis of empirical findings from the perspective of neurodynamic principles sheds new light on the neuroscience of music and what makes music powerful.

The Supplementary Motor Area as a Flexible Hub Mediating Behavioral and Neuroplastic Changes in Motor Sequence Learning: A TMS and TMS-EEG Study

Attempts have been made to modulate motor sequence learning (MSL) through repetitive transcranial magnetic stimulation, targeting different sites within the sensorimotor network. However, the target with the optimum modulatory effect on neural plasticity associated with MSL remains unclarified. This study was therefore designed to compare the role of the left primary motor cortex and the left supplementary motor area proper (SMAp) in modulating MSL across different complexity levels and for both hands, as well as the associated neuroplasticity by applying intermittent theta burst stimulation together with the electroencephalogram and concurrent transcranial magnetic stimulation. Our data demonstrated the role of SMAp stimulation in modulating neural communication to support MSL, which is achieved by facilitating regional activation and orchestrating neural coupling across distributed brain regions, particularly in interhemispheric connections. These findings may have important clinical implications, particularly for motor rehabilitation in populations such as post-stroke patients.

Precise Spatial Tuning of Visually Driven Alpha Oscillations in Human Visual Cortex

Neuronal oscillations at about 10 Hz, called alpha oscillations, are often thought to arise from synchronous activity across occipital cortex, reflecting general cognitive states such as arousal and alertness. However, there is also evidence that modulation of alpha oscillations in visual cortex can be spatially specific. Here, we used intracranial electrodes in human patients to measure alpha oscillations in response to visual stimuli whose location varied systematically across the visual field. We separated the alpha oscillatory power from broadband power changes. The variation in alpha oscillatory power with stimulus position was then fit by a population receptive field (pRF) model. We find that the alpha pRFs have similar center locations to pRFs estimated from broadband power (70-180 Hz) but are several times larger. The results demonstrate that alpha suppression in human visual cortex can be precisely tuned. Finally, we show how the pattern of alpha responses can explain several features of exogenous visual attention.

Significance Statement

The alpha oscillation is the largest electrical signal generated by the human brain. An important question in systems neuroscience is the degree to which this oscillation reflects system-wide states and behaviors such as arousal, alertness, and attention, versus much more specific functions in the routing and processing of information. We examined alpha oscillations at high spatial precision in human patients with intracranial electrodes implanted over visual cortex. We discovered a surprisingly high spatial specificity of visually driven alpha oscillations, which we quantified with receptive field models. We further use our discoveries about properties of the alpha response to show a link between these oscillations and the spread of visual attention.Grant support: NIH R01 MH111417 (Petridou, Winawer, Ramsey, Devinsky); JSPS Overseas Research Fellowship (Yuasa)The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Dynamic changes in large-scale functional connectivity prior to stimulation determine performance in a multisensory task

Complex behavior and task execution require fast changes of local activity and functional connectivity in cortical networks at multiple scales. The roles that changes of power and connectivity play during these processes are still not well understood. Here, we study how fluctuations of functional cortical coupling across different brain areas determine performance in an audiovisual, lateralized detection task in the ferret. We hypothesized that dynamic variations in the network's state determine the animals' performance. We evaluated these by quantifying changes of local power and of phase coupling across visual, auditory and parietal regions. While power for hit and miss trials showed significant differences only during stimulus and response onset, phase coupling already differed before stimulus onset. An analysis of principal components in coupling at the single-trial level during this period allowed us to reveal the subnetworks that most strongly determined performance. Whereas higher global phase coupling of visual and auditory regions to parietal cortex was predictive of task performance, a second component revealed a reduction in coupling between subnetworks of different sensory modalities, probably to allow a better detection of the unimodal signals. Furthermore, we observed that long-range coupling became more predominant during the task period compared to the pre-stimulus baseline. Taken together, our results show that fluctuations in the network state, as reflected in large-scale coupling, are key determinants of the animals' behavior.

Visual activity enhances neuronal excitability in thalamic relay neurons

Amblyopia, a highly prevalent loss of visual acuity, is classically thought to result from cortical plasticity. The dorsal lateral geniculate nucleus (dLGN) has long been held to act as a passive relay for visual information, but recent findings suggest a largely underestimated functional plasticity in the dLGN. However, the cellular mechanisms supporting this plasticity have not yet been explored. We show here that monocular deprivation (MD), an experimental model of amblyopia, reduces the intrinsic excitability of dLGN cells. Furthermore, dLGN neurons exhibit long-term potentiation of their intrinsic excitability (LTP-IE) when suprathreshold afferent retinal inputs are stimulated at 40 hertz or when spikes are induced with current injection. LTP-IE is observed after eye opening, requires calcium influx, is expressed through the down-regulation of Kv1 channels, and is altered following MD. In conclusion, our study provides the first evidence for intrinsic plasticity in dLGN neurons induced by natural stimuli.

Evolutionary origins of synchronization for integrating information in neurons

The evolution of brain-expressed genes is notably slower than that of genes expressed in other tissues, a phenomenon likely due to high-level functional constraints. One such constraint might be the integration of information by neuron assemblies, enhancing environmental adaptability. This study explores the physiological mechanisms of information integration in neurons through three types of synchronization: chemical, electromagnetic, and quantum. Chemical synchronization involves the diffuse release of neurotransmitters like dopamine and acetylcholine, causing transmission delays of several milliseconds. Electromagnetic synchronization encompasses action potentials, electrical gap junctions, and ephaptic coupling. Electrical gap junctions enable rapid synchronization within cortical GABAergic networks, while ephaptic coupling allows structures like axon bundles to synchronize through extracellular electromagnetic fields, surpassing the speed of chemical processes. Quantum synchronization is hypothesized to involve ion coherence during ion channel passage and the entanglement of photons within the myelin sheath. Unlike the finite-time synchronization seen in chemical and electromagnetic processes, quantum entanglement provides instantaneous non-local coherence states. Neurons might have evolved from slower chemical diffusion to rapid temporal synchronization, with ion passage through gap junctions within cortical GABAergic networks potentially facilitating both fast gamma band synchronization and quantum coherence. This mini-review compiles literature on these three synchronization types, offering new insights into the physiological mechanisms that address the binding problem in neuron assemblies.

Neural Correlates of Visual Feature Binding

We perceive visual objects as unified although different brain areas process different features. An attentional mechanism has been proposed to be involved with feature binding, as evidenced by observations of binding errors (i.e., illusory conjunctions) when attention is diverted. However, the neural underpinnings of this feature binding are not well understood. We examined the neural mechanisms of feature binding by recording EEG during an attentionally demanding discrimination task. Unlike prestimulus alpha oscillatory activity and early ERPs (i.e., the N1 and P1 components), the N1pc, reflecting stimulus-evoked spatial attention, was reduced for errors relative to correct responses and illusory conjunctions. However, the later sustained posterior contralateral negativity, reflecting visual short-term memory, was reduced for illusory conjunctions and errors compared with correct responses. Furthermore, binding errors were associated with distinct posterior lateralized activity during a 200- to 300-msec window. These results implicate a temporal binding window that integrates visual features after stimulus-evoked attention but before encoding into visual short-term memory.

Continuum limit of the adaptive Kuramoto model

We investigate the dynamics of the adaptive Kuramoto model with slow adaptation in the continuum limit, N→∞. This model is distinguished by dense multistability, where multiple states coexist for the same system parameters. The underlying cause of this multistability is that some oscillators can lock at different phases or switch between locking and drifting depending on their initial conditions. We identify new states, such as two-cluster states. To simplify the analysis, we introduce an approximate reduction of the model via row-averaging of the coupling matrix. We derive a self-consistency equation for the reduced model and present a stability diagram illustrating the effects of positive and negative adaptation. Our theoretical findings are validated through numerical simulations of a large finite system. Comparisons of previous work highlight the significant influence of adaptation on synchronization behavior.

Interactions of transcranial magnetic stimulation with brain oscillations: a narrative review

Brain responses to transcranial magnetic stimulation (TMS) can be recorded with electroencephalography (EEG) and comprise TMS-evoked potentials and TMS-induced oscillations. Repetitive TMS may entrain endogenous brain oscillations. In turn, ongoing brain oscillations prior to the TMS pulse can influence the effects of the TMS pulse. These intricate TMS-EEG and EEG-TMS interactions are increasingly attracting the interest of researchers and clinicians. This review surveys the literature of TMS and its interactions with brain oscillations as measured by EEG in health and disease.

Three mechanisms of language comprehension are revealed through cluster analysis of individuals with language deficits

Analysis of linguistic abilities that are concurrently impaired in individuals with language deficits allows identification of a shared underlying mechanism. If any two linguistic abilities are mediated by the same underlying mechanism, then both abilities will be absent if this mechanism is broken. Clustering techniques automatically arrange these abilities according to their co-occurrence and therefore group together abilities mediated by the same mechanism. This study builds upon the discovery of three distinct mechanisms of language comprehension in 31,845 autistic individuals1. The current clustering analysis of a more diverse group of individuals with language impairments resulted in the three mechanisms identical to those found previously: (1) the most-basic command-language-comprehension-mechanism; (2) the intermediate modifier-language-comprehension-mechanism mediating comprehension of color, size, and number modifiers; and (3) the most-advanced syntactic-language-comprehension-mechanism. This discovery calls for mapping of the three empirically-defined language-comprehension-mechanisms in the context of cognitive neuroscience, which is the main goal of this study.

The Influence of Music Reading on Spatial Working Memory and Self-Assessment Accuracy

BACKGROUND/OBJECTIVES

Previous research has suggested that Western musicians, who generally demonstrate proficiency in reading musical scores, exhibit superior performance in visuospatial working memory tasks compared to non-musicians. Evidence indicates brain activation in regions such as the left inferior parietal lobe and the right posterior fusiform gyrus during music reading, which are associated with visuospatial processing. This study aimed to explore how musical training influences spatial working memory and to examine the relationship between self-assessment accuracy and cognitive performance.

METHODS

A visuospatial working memory test, the Corsi block-tapping test (CBT), was administered to 70 participants, including 35 musicians with experience in music reading and 35 non-musicians. CBT performances were compared between groups, controlling for sex and age differences using analysis of covariance. Participants were also asked to self-assess their visuospatial capabilities.

RESULTS

Musicians performed significantly better than non-musicians in the CBT and demonstrated greater metacognitive accuracy in evaluating their visuospatial memory capacities. A total of 46.34% of musicians who claimed good performance on the CBT did in fact perform well, in comparison with 14.63% of non-musicians. Sex influenced the outcomes of spatial working memory, while age did not significantly affect performance.

CONCLUSIONS

This self-awareness of visuospatial capabilities reflects a form of metacompetence, encompassing reflective thinking and the ability to assess one's cognitive skills. Furthermore, while differences in spatial working memory between musicians and non-musicians appear to be related to executive functions associated with general music practice, further investigation is needed to explore other potential influences beyond musical experience.

Fate of vortex-synchronized state in oscillator networks with node defects

We investigate synchronization behaviors of a Kuramoto oscillator network with a two-dimensional square-lattice configuration. We show that the oscillator network can reach a phase-locking vortex synchronized state in the long time limit starting from random initial oscillator phases sampled according to the von Mises distribution characterized by a zero mean and a finite concentration parameter. We further reveal that the stability of the vortex synchronized state is sensitive to the presence of local node defects, in contrast to the usual knowledge that oscillator networks should exhibit robustness against local perturbations. Moreover, we explore the behaviors of the vortex synchronized state in networks with an additional temporal white noise on the oscillator phases or a spatial noise due to randomly distributed oscillator frequencies. Interestingly, we find that the vortex synchronized state can become immune to local node defects when the variance of spatial noise is above a certain threshold, suggesting a beneficial role of usually unwanted spatial noise in protecting vortex-synchronized networks.

KETAMINE: Neural- and network-level changes

Ketamine is a widely used clinical drug that has several functional and clinical applications, including its use as an anaesthetic, analgesic, anti-depressive, anti-suicidal agent, among others. Among its diverse behavioral effects, it influences short-term memory and induces psychedelic effects. At the neural level across different brain areas, it modulates neural firing rates, neural tuning, brain oscillations, and modularity, while promoting hypersynchrony and random connectivity between neurons. In our recent studies we demonstrated that topical application of ketamine on the visual cortex alters neural tuning and promotes vigorous connectivity between neurons by decreasing their firing variability. Here, we begin with a brief review of the literature, followed by results from our lab, where we synthesize a dendritic model of neural tuning and network changes following ketamine application. This model has potential implications for focused modulation of cortical networks in clinical settings. Finally, we identify current gaps in research and suggest directions for future studies, particularly emphasizing the need for more animal experiments to establish a platform for effective translation and synergistic therapies combining ketamine with other protocols such as training and adaptation. In summary, investigating ketamine's broader systemic effects, not only provides deeper insight into cognitive functions and consciousness but also paves the way to advance therapies for neuropsychiatric disorders.

Understanding the neurobiology of social behavior through exploring brain-wide dynamics of neural activity

Social behavior is highly complex and adaptable. It can be divided into multiple temporal stages: detection, approach, and consummatory behavior. Each stage can be further divided into several cognitive and behavioral processes, such as perceiving social cues, evaluating the social and non-social contexts, and recognizing the internal/emotional state of others. Recent studies have identified numerous brain-wide circuits implicated in social behavior and suggested the existence of partially overlapping functional brain networks underlying various types of social and non-social behavior. However, understanding the brain-wide dynamics underlying social behavior remains challenging, and several brain-scale dynamics (macro-, meso-, and micro-scale levels) need to be integrated. Here, we suggest leveraging new tools and concepts to explore social brain networks and integrate those different levels. These include studying the expression of immediate-early genes throughout the entire brain to impartially define the structure of the neuronal networks involved in a given social behavior. Then, network dynamics could be investigated using electrode arrays or multi-channel fiber photometry. Finally, tools like high-density silicon probes and miniscopes can probe neural activity in specific areas and across neuronal populations at the single-cell level.

Task difficulty of visually guided gait modifications involves differences in central drive to spinal motor neurons

Walking in natural environments requires visually guided modifications, which can be more challenging when involving sideways steps rather than longer steps. This exploratory study investigated whether these two types of modifications involve different changes in the central drive to spinal motor neurons of leg muscles. Fifteen adults [age: 36 ± 6 (SD) years] walked on a treadmill (4 km/h) while observing a screen displaying the real-time position of their toes. At the beginning of the swing phase, a visual target appeared in front (forward) or medial-lateral (sideways) of the ground contact in random step cycles (approximately every third step). We measured three-dimensional kinematics and electromyographic activity from leg muscles bilaterally. Intermuscular coherence was calculated in the alpha (5-15 Hz), beta (15-30 Hz), and gamma bands (30-45 Hz) approximately 230 ms before and after ground contact in control and target steps. Results showed that adjustments toward sideways targets were associated with significantly higher error, lower foot lift, and higher cocontraction between antagonist ankle muscles. Movements toward sideways targets were associated with larger beta-band soleus (SOL): medial gastrocnemius (MG) coherence and a more narrow and larger peak of synchronization in the cumulant density before ground contact. In contrast, movements toward forward targets showed no significant differences in coherence or synchronization compared with control steps. Larger SOL:MG beta-band coherence and short-term synchronization were observed during sideways, but not forward, gait modifications. This suggests that visually guided gait modifications may involve differences in the central drive to spinal ankle motor neurons dependent on the level of task difficulty.NEW & NOTEWORTHY This exploratory study suggests a specific and temporally restricted increase of central (likely corticospinal) drive to ankle muscles in relation to visually guided gait modifications. The findings indicate that a high level of visual attention to control the position of the ankle joint precisely before ground contact may involve increased central drive to ankle muscles. These findings are important for understanding the neural mechanisms underlying visually guided gait and may help develop rehabilitation interventions.

Neuronal oscillations in cognition: Down syndrome as a model of mouse to human translation

Down syndrome (DS), a prevalent cognitive disorder resulting from trisomy of human chromosome 21 (Hsa21), poses a significant global health concern. Affecting approximately 1 in 800 live births worldwide, DS is the leading genetic cause of intellectual disability and a major predisposing factor for early-onset Alzheimer's dementia. The estimated global population of individuals with DS is 6 million, with increasing prevalence due to advances in DS health care. Global efforts are dedicated to unraveling the mechanisms behind the varied clinical outcomes in DS. Recent studies on DS mouse models reveal disrupted neuronal circuits, providing insights into DS pathologies. Yet, translating these findings to humans faces challenges due to limited systematic electrophysiological analyses directly comparing human and mouse. Additionally, disparities in experimental procedures between the two species pose hurdles to successful translation. This review provides a concise overview of neuronal oscillations in human and rodent cognition. Focusing on recent DS mouse model studies, we highlight disruptions in associated brain function. We discuss various electrophysiological paradigms and suggest avenues for exploring molecular dysfunctions contributing to DS-related cognitive impairments. Deciphering neuronal oscillation intricacies holds promise for targeted therapies to alleviate cognitive disabilities in DS individuals.

High frequency oscillations in human memory and cognition: a neurophysiological substrate of engrams?

Despite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected in the human mesial temporal lobe and neocortical intracranial recordings spanning gamma/epsilon (60-150 Hz), ripple (80-250 Hz) and higher frequency ranges. Separate from other non-oscillatory activities, these brief electrophysiological oscillations of distinct duration, frequency and amplitude are thought to be generated by coordinated spiking of neuronal ensembles within volumes as small as a single cortical column. Although the exact origins, mechanisms and physiological roles in health and disease remain elusive, they have been associated with human memory consolidation and cognitive processing. Recent studies suggest their involvement in encoding and recall of episodic memory with a possible role in the formation and reactivation of memory traces. High frequency oscillations are detected during encoding, throughout maintenance, and right before recall of remembered items, meeting a basic definition for an engram activity. The temporal coordination of high frequency oscillations reactivated across cortical and subcortical neural networks is ideally suited for integrating multimodal memory representations, which can be replayed and consolidated during states of wakefulness and sleep. High frequency oscillations have been shown to reflect coordinated bursts of neuronal assembly firing and offer a promising substrate for tracking and modulation of the hypothetical electrophysiological engram.

Multi-timescale neural dynamics for multisensory integration

Carrying out any everyday task, be it driving in traffic, conversing with friends or playing basketball, requires rapid selection, integration and segregation of stimuli from different sensory modalities. At present, even the most advanced artificial intelligence-based systems are unable to replicate the multisensory processes that the human brain routinely performs, but how neural circuits in the brain carry out these processes is still not well understood. In this Perspective, we discuss recent findings that shed fresh light on the oscillatory neural mechanisms that mediate multisensory integration (MI), including power modulations, phase resetting, phase-amplitude coupling and dynamic functional connectivity. We then consider studies that also suggest multi-timescale dynamics in intrinsic ongoing neural activity and during stimulus-driven bottom-up and cognitive top-down neural network processing in the context of MI. We propose a new concept of MI that emphasizes the critical role of neural dynamics at multiple timescales within and across brain networks, enabling the simultaneous integration, segregation, hierarchical structuring and selection of information in different time windows. To highlight predictions from our multi-timescale concept of MI, real-world scenarios in which multi-timescale processes may coordinate MI in a flexible and adaptive manner are considered.

Resting-state frontal electroencephalography (EEG) biomarkers for detecting the severity of chronic neuropathic pain

Increasing evidence is present to enable pain measurement by using frontal channel EEG-based signals with spectral analysis and phase-amplitude coupling. To identify frontal channel EEG-based biomarkers for quantifying pain severity, we investigated band-power features to more complex features and employed various machine learning algorithms to assess the viability of these features. We utilized a public EEG dataset obtained from 36 patients with chronic pain during an eyes-open resting state and performed correlation analysis between clinically labelled pain scores and EEG features from Fp1 and Fp2 channels (EEG band-powers, phase-amplitude couplings (PAC), and its asymmetry features). We also conducted regression analysis with various machine learning models to predict patients' pain intensity. All the possible feature sets combined with five machine learning models (Linear Regression, random forest and support vector regression with linear, non-linear and polynomial kernels) were intensively checked, and regression performances were measured by adjusted R-squared value. We found significant correlations between beta power asymmetry (r = -0.375), gamma power asymmetry (r = -0.433) and low beta to low gamma coupling (r = -0.397) with pain scores while band power features did not show meaningful results. In the regression analysis, Support Vector Regression with a polynomial kernel showed the best performance (R squared value = 0.655), enabling the regression of pain intensity within a clinically usable error range. We identified the four most selected features (gamma power asymmetry, PAC asymmetry of theta to low gamma, low beta to low/high gamma). This study addressed the importance of complex features such as asymmetry and phase-amplitude coupling in pain research and demonstrated the feasibility of objectively observing pain intensity using the frontal channel-based EEG, that are clinically crucial for early intervention.

Cortical dynamics of perception as trains of coherent gamma oscillations, with the pulvinar as central coordinator

Synchronization of spikes carried by the visual streams is strategic for the proper binding of cortical assemblies, hence for the perception of visual objects as coherent units. Perception of a complex visual scene involves multiple trains of gamma oscillations, coexisting at each stage in visual and associative cortex. Here, we analyze how this synchrony is managed, so that the perception of each visual object can emerge despite this complex interweaving of cortical activations. After a brief review of structural and temporal facts, we analyze the interactions which make the oscillations coherent for the visual elements related to the same object. We continue with the propagation of these gamma oscillations within the sensory chain. The dominant role of the pulvinar and associated reticular thalamic nucleus as cortical coordinator is the common thread running through this step-by-step description. Synchronization mechanisms are analyzed in the context of visual perception, although the present considerations are not limited to this sense. A simple experiment is described, with the aim of assessing the validity of the elements developed here. A first set of results is provided, together with a proposed method to go further in this investigation.

Human perception of flicker-fused letters that are luminance balanced

The Talbot-Plateau law specifies what combinations of flash frequency, duration, and intensity will yield a flicker-fused stimulus that matches the brightness of a steady stimulus. It has proven to be remarkably robust in its predictions, and here we provide additional support though the use of a contrast discrimination task. However, we also find that the visual system can register flicker-fused letters when the combination of frequency and duration is relatively low. The letters are recognized even though they have the same physical luminance as background. We hypothesize that the letters elicit synchronous oscillations that encode for stimulus attributes, which prevents the letter from blending into the background.

Establishing the relationship between subjective perception and neural responses: Insights from correlation analysis and representational similarity analysis

Exploring the relationship between sensory perception and brain responses holds important theoretical and clinical implications. However, commonly used methodologies like correlation analysis performed either intra- or inter- individually often yield inconsistent results across studies, limiting their generalizability. Representational similarity analysis (RSA), a method that assesses the perception-response relationship by calculating the correlation between behavioral and neural patterns, may offer a fresh perspective to reveal novel findings. Here, we delivered a series of graded sensory stimuli of four modalities (i.e., nociceptive somatosensory, non-nociceptive somatosensory, visual, and auditory) to/near the left or right hand of 107 healthy subjects and collected their single-trial perceptual ratings and electroencephalographic (EEG) responses. We examined the relationship between sensory perception and brain responses using within- and between-subject correlation analysis and RSA, and assessed their stability across different numbers of subjects and trials. We found that within-subject and between-subject correlations yielded distinct results: within-subject correlation revealed strong and reliable correlations between perceptual ratings and most brain responses, while between-subject correlation showed weak correlations that were vulnerable to the change of subject number. In addition to verifying the correlation results, RSA revealed some novel findings, i.e., correlations between behavioral and neural patterns were observed in some additional neural responses, such as "γ-ERS" in the visual modality. RSA results were sensitive to the trial number, but not to the subject number, suggesting that consistent results could be obtained for studies with relatively small sample sizes. In conclusion, our study provides a novel perspective on establishing the relationship between behavior and brain activity, emphasizing that RSA holds promise as a method for exploring this pattern relationship in future research.

Biological Basis and Computer Vision Applications of Image Phase Congruency: A Comprehensive Survey

The concept of Image Phase Congruency (IPC) is deeply rooted in the way the human visual system interprets and processes spatial frequency information. It plays an important role in visual perception, influencing our capacity to identify objects, recognize textures, and decipher spatial relationships in our environments. IPC is robust to changes in lighting, contrast, and other variables that might modify the amplitude of light waves yet leave their relative phase unchanged. This characteristic is vital for perceptual tasks as it ensures the consistent detection of features regardless of fluctuations in illumination or other environmental factors. It can also impact cognitive and emotional responses; cohesive phase information across elements fosters a perception of unity or harmony, while inconsistencies can engender a sense of discord or tension. In this survey, we begin by examining the evidence from biological vision studies suggesting that IPC is employed by the human perceptual system. We proceed to outline the typical mathematical representation and different computational approaches to IPC. We then summarize the extensive applications of IPC in computer vision, including denoise, image quality assessment, feature detection and description, image segmentation, image registration, image fusion, and object detection, among other uses, and illustrate its advantages with a number of examples. Finally, we discuss the current challenges associated with the practical applications of IPC and potential avenues for enhancement.

Signal switching may enhance processing power of the brain

Our ability to perceive multiple objects is mysterious. Sensory neurons are broadly tuned, producing potential overlap in the populations of neurons activated by each object in a scene. This overlap raises questions about how distinct information is retained about each item. We present a novel signal switching theory of neural representation, which posits that neural signals may interleave representations of individual items across time. Evidence for this theory comes from new statistical tools that overcome the limitations inherent to standard time-and-trial-pooled assessments of neural signals. Our theory has implications for diverse domains of neuroscience, including attention, figure binding/scene segregation, oscillations, and divisive normalization. The general concept of switching between functions could also lend explanatory power to theories of grounded cognition.

Application of EEG in the Diagnosis and Classification of Migraine: A Scoping Review

Migraine is a chronic debilitating disease affecting a significant number of people, more often women than men. The gold standard for diagnosis is the International Classification of Headache Disorders-3 (ICHD-3). Authors have identified multiple tight spots in the present method of diagnosis. An alternative method of diagnosis has always been coveted. Electroencephalogram (EEG) is one of the most researched of such alternatives. The visually evoked potential is the most studied; auditory evoked potentials and transcranial direct current stimulation are also being studied. Cortical hyperexcitability and habituation deficit to sensory stimuli are some of the consistent findings. Alpha oscillations are among the most frequently studied bands; spectral analysis of EEG waves has often shown more reliable and consistent results than features read off the EEG directly. EEG microstate is a novel and promising method showing characteristic identifiable features that may help diagnose Migraine patients. An alternative to the ICHD-3 criterion for diagnosing Migraines would be instrumental in promptly diagnosing the disease. EEG is one of the most explored alternatives within which enumerable features can be used to identify Migraines, of which the most promising are EEG microstates.

Region-Related Differences in Short-Term Synaptic Plasticity and Synaptotagmin-7 in the Male and Female Hippocampus of a Rat Model of Fragile X Syndrome

Fragile X syndrome (FXS) is an intellectual developmental disorder characterized, inter alia, by deficits in the short-term processing of neural information, such as sensory processing and working memory. The primary cause of FXS is the loss of fragile X messenger ribonucleoprotein (FMRP), which is profoundly involved in synaptic function and plasticity. Short-term synaptic plasticity (STSP) may play important roles in functions that are affected by FXS. Recent evidence points to the crucial involvement of the presynaptic calcium sensor synaptotagmin-7 (Syt-7) in STSP. However, how the loss of FMRP affects STSP and Syt-7 have been insufficiently studied. Furthermore, males and females are affected differently by FXS, but the underlying mechanisms remain elusive. The aim of the present study was to investigate possible changes in STSP and the expression of Syt-7 in the dorsal (DH) and ventral (VH) hippocampus of adult males and females in a Fmr1-knockout (KO) rat model of FXS. We found that the paired-pulse ratio (PPR) and frequency facilitation/depression (FF/D), two forms of STSP, as well as the expression of Syt-7, are normal in adult KO males, but the PPR is increased in the ventral hippocampus of KO females (6.4 ± 3.7 vs. 18.3 ± 4.2 at 25 ms in wild type (WT) and KO, respectively). Furthermore, we found no gender-related differences, but did find robust region-dependent difference in the STSP (e.g., the PPR at 50 ms: 50.0 ± 5.5 vs. 17.6 ± 2.9 in DH and VH of WT male rats; 53.1 ± 3.6 vs. 19.3 ± 4.6 in DH and VH of WT female rats; 48.1 ± 2.3 vs. 19.1 ± 3.3 in DH and VH of KO male rats; and 51.2 ± 3.3 vs. 24.7 ± 4.3 in DH and VH of KO female rats). AMPA receptors are similarly expressed in the two hippocampal segments of the two genotypes and in both genders. Also, basal excitatory synaptic transmission is higher in males compared to females. Interestingly, we found more than a twofold higher level of Syt-7, not synaptotagmin-1, in the dorsal compared to the ventral hippocampus in the males of both genotypes (0.43 ± 0.1 vs. 0.16 ± 0.02 in DH and VH of WT male rats, and 0.6 ± 0.13 vs. 0.23 ± 0.04 in DH and VH of KO male rats) and in the WT females (0.97 ± 0.23 vs. 0.31 ± 0.09 in DH and VH). These results point to the susceptibility of the female ventral hippocampus to FMRP loss. Importantly, the different levels of Syt-7, which parallel the higher score of the dorsal vs. ventral hippocampus on synaptic facilitation, suggest that Syt-7 may play a pivotal role in defining the striking differences in STSP along the long axis of the hippocampus.

Nuclei-level prior knowledge constrained multiple instance learning for breast histopathology whole slide image classification

New breast cancer cases have surpassed lung cancer, becoming the world's most prevalent cancer. Despite advancing medical image analysis, deep learning's lack of interpretability limits its adoption among pathologists. Hence, a nuclei-level prior knowledge constrained multiple instance learning (MIL) (NPKC-MIL) for breast whole slide image (WSI) classification is proposed. NPKC-MIL primarily involves the following steps: Initially, employing the transfer learning to extract patch-level features and aggregate them into slide-level features through attention pooling. Subsequently, abstract the extracted nuclei as nodes, establish nucleus topology using the K-NN (K-Nearest Neighbors, K-NN) algorithm, and create handcrafted features for nodes. Finally, combine patch-level deep learning features with nuclei-level handcrafted features to fine-tune classification results generated by slide-level deep learning features. The experimental results demonstrate that NPKC-MIL outperforms current comparable deep learning models. NPKC-MIL expands the analytical dimension of WSI classification tasks and integrates prior knowledge into deep learning models to improve interpretability.

Evaluating integration of letter fragments through contrast and spatially targeted masking

Four experiments were conducted to gain a better understanding of the visual mechanisms related to how integration of partial shape cues provides for recognition of the full shape. In each experiment, letters formed as outline contours were displayed as a sequence of adjacent segments (fragments), each visible during a 17-ms time frame. The first experiment varied the contrast of the fragments. There were substantial individual differences in contrast sensitivity, so stimulus displays in the masking experiments that followed were calibrated to the sensitivity of each participant. Masks were displayed either as patterns that filled the entire screen (full field) or as successive strips that were sliced from the pattern, each strip lying across the location of the letter fragment that had been shown a moment before. Contrast of masks were varied to be lighter or darker than the letter fragments. Full-field masks, whether light or dark, provided relatively little impairment of recognition, as was the case for mask strips that were lighter than the letter fragments. However, dark strip masks proved to be very effective, with the degree of recognition impairment becoming larger as mask contrast was increased. A final experiment found the strip masks to be most effective when they overlapped the location where the letter fragments had been shown a moment before. They became progressively less effective with increased spatial separation from that location. Results are discussed with extensive reference to potential brain mechanisms for integrating shape cues.

Dichotomous frequency-dependent phase synchrony in the sensorimotor network characterizes simplistic movement

It is hypothesized that disparate brain regions interact via synchronous activity to control behavior. The nature of these interconnected ensembles remains an area of active investigation, and particularly the role of high frequency synchronous activity in simplistic behavior is not well known. Using intracranial electroencephalography, we explored the spectral dynamics and network connectivity of sensorimotor cortical activity during a simple motor task in seven epilepsy patients. Confirming prior work, we see a "spectral tilt" (increased high-frequency (HF, 70-100 Hz) and decreased low-frequency (LF, 3-33 Hz) broadband oscillatory activity) in motor regions during movement compared to rest, as well as an increase in LF synchrony between these regions using time-resolved phase-locking. We then explored this phenomenon in high frequency and found a robust but opposite effect, where time-resolved HF broadband phase-locking significantly decreased during movement. This "connectivity tilt" (increased LF synchrony and decreased HF synchrony) displayed a graded anatomical dependency, with the most robust pattern occurring in primary sensorimotor cortical interactions and less robust pattern occurring in associative cortical interactions. Connectivity in theta (3-7 Hz) and high beta (23-27 Hz) range had the most prominent low frequency contribution during movement, with theta synchrony building gradually while high beta having the most prominent effect immediately following the cue. There was a relatively sharp, opposite transition point in both the spectral and connectivity tilt at approximately 35 Hz. These findings support the hypothesis that task-relevant high-frequency spectral activity is stochastic and that the decrease in high-frequency synchrony may facilitate enhanced low frequency phase coupling and interregional communication. Thus, the "connectivity tilt" may characterize behaviorally meaningful cortical interactions.

New clues for the role of cerebellum in schizophrenia and the associated cognitive impairment

Schizophrenia (SZ) is a complex neuropsychiatric disorder associated with severe cognitive dysfunction. Although research has mainly focused on forebrain abnormalities, emerging results support the involvement of the cerebellum in SZ physiopathology, particularly in Cognitive Impairment Associated with SZ (CIAS). Besides its role in motor learning and control, the cerebellum is implicated in cognition and emotion. Recent research suggests that structural and functional changes in the cerebellum are linked to deficits in various cognitive domains including attention, working memory, and decision-making. Moreover, cerebellar dysfunction is related to altered cerebellar circuit activities and connectivity with brain regions associated with cognitive processing. This review delves into the role of the cerebellum in CIAS. We initially consider the major forebrain alterations in CIAS, addressing impairments in neurotransmitter systems, synaptic plasticity, and connectivity. We then focus on recent findings showing that several mechanisms are also altered in the cerebellum and that cerebellar communication with the forebrain is impaired. This evidence implicates the cerebellum as a key component of circuits underpinning CIAS physiopathology. Further studies addressing cerebellar involvement in SZ and CIAS are warranted and might open new perspectives toward understanding the physiopathology and effective treatment of these disorders.

Intensity-dependent modulation of the early auditory gamma-band response in first-episode schizophrenia and its association with disease symptoms

BACKGROUND

Gamma-band activity has been the focus of considerable research in schizophrenia. Discrepancies exist regarding the integrity of the early auditory gamma-band response (EAGBR), a stimulus-evoked oscillation, and its relationship to symptoms in early disease. Variability in task design may play a role. This study examined sensitivity of the EAGBR to stimulus intensity and its relation to symptoms and functional impairments in the first-episode schizophrenia spectrum (FESz).

METHOD

Magnetoencephalography was recorded from 35 FESz and 40 matched healthy controls (HC) during presentation of 3 tone intensities (75 dB, 80 dB, 85 dB). MRIs were collected to localize auditory cortex activity. Wavelet-transformed single trial epochs and trial averages were used to assess EAGBR intertrial phase coherence (ITPC) and evoked power, respectively. Symptoms were assessed using the Positive and Negative Syndrome Scale.

RESULTS

Groups did not differ in overall EAGBR power or ITPC. While HC exhibited EAGBR enhancement to increasing intensity, FESz exhibited reduced power to the 80 dB tone and, relative to HC, increased power to the 75 dB tone. Larger power and ITPC were correlated with more severe negative, thought disorganization, and resistance symptoms. Stronger ITPC was associated with impaired social functioning.

DISCUSSION

EAGBR showed no overall deficit at disease onset. Rather, FESz exhibited a differential response across tone intensity relative to HC, emphasizing the importance of stimulus characteristics in EAGBR studies. Associations between larger EAGBR and more severe symptoms suggest aberrant synchronization driving overinclusive perceptual binding that may relate to deficits in executive inhibition of initial sensory activity.

Delineating mechanisms underlying parvalbumin neuron impairment in different neurological and neurodegenerative disorders: the emerging role of mitochondrial dysfunction

Given the current paucity of effective treatments in many neurological disorders, delineating pathophysiological mechanisms among the major psychiatric and neurodegenerative diseases may fuel the development of novel, potent treatments that target shared pathways. Recent evidence suggests that various pathological processes, including bioenergetic failure in mitochondria, can perturb the function of fast-spiking, parvalbumin-positive neurons (PV+). These inhibitory neurons critically influence local circuit regulation, the generation of neuronal network oscillations and complex brain functioning. Here, we survey PV+ cell vulnerability in the major neuropsychiatric, and neurodegenerative diseases and review associated cellular and molecular pathophysiological alterations purported to underlie disease aetiology.

Contribution of chemical and electrical transmission to the low delta-like intrinsic retinal oscillation in mice: A role for daylight-activated neuromodulators

Basal electroretinogram (ERG) oscillations have shown predictive value for modifiable risk factors for type 2 diabetes. However, their origin remains unknown. Here, we seek to establish the pharmacological profile of the low delta-like (δ1) wave in the mouse because it shows light sensitivity in the form of a decreased peak frequency upon photopic exposure. Applying neuropharmacological drugs by intravitreal injection, we eliminated the δ1 wave using lidocaine or by blocking all chemical and electrical synapses. The δ1 wave was insensitive to the blockade of photoreceptor input, but was accelerated when all inhibitory or ionotropic inhibitory receptors in the retina were antagonized. The sole blockade of GABAA, GABAB, GABAC, and glycine receptors also accelerated the δ1 wave. In contrast, the gap junction blockade slowed the δ1 wave. Both GABAA receptors and gap junctions contribute to the light sensitivity of the δ1 wave. We further found that the day light-activated neuromodulators dopamine and nitric oxide donors mimicked the effect of photopic exposure on the δ1 wave. All drug effects were validated through light flash-evoked ERG responses. Our data indicate that the low δ-like intrinsic wave detected by the non-photic ERG arises from an inner retinal circuit regulated by inhibitory neurotransmission and nitric oxide/dopamine-sensitive gap junction-mediated communication.

Blooming and pruning: learning from mistakes with memristive synapses

Blooming and pruning is one of the most important developmental mechanisms of the biological brain in the first years of life, enabling it to adapt its network structure to the demands of the environment. The mechanism is thought to be fundamental for the development of cognitive skills. Inspired by this, Chialvo and Bak proposed in 1999 a learning scheme that learns from mistakes by eliminating from the initial surplus of synaptic connections those that lead to an undesirable outcome. Here, this idea is implemented in a neuromorphic circuit scheme using CMOS integrated HfO2-based memristive devices. The implemented two-layer neural network learns in a self-organized manner without positive reinforcement and exploits the inherent variability of the memristive devices. This approach provides hardware, local, and energy-efficient learning. A combined experimental and simulation-based parameter study is presented to find the relevant system and device parameters leading to a compact and robust memristive neuromorphic circuit that can handle association tasks.

Recurrent neural networks that learn multi-step visual routines with reinforcement learning

Many cognitive problems can be decomposed into series of subproblems that are solved sequentially by the brain. When subproblems are solved, relevant intermediate results need to be stored by neurons and propagated to the next subproblem, until the overarching goal has been completed. We will here consider visual tasks, which can be decomposed into sequences of elemental visual operations. Experimental evidence suggests that intermediate results of the elemental operations are stored in working memory as an enhancement of neural activity in the visual cortex. The focus of enhanced activity is then available for subsequent operations to act upon. The main question at stake is how the elemental operations and their sequencing can emerge in neural networks that are trained with only rewards, in a reinforcement learning setting. We here propose a new recurrent neural network architecture that can learn composite visual tasks that require the application of successive elemental operations. Specifically, we selected three tasks for which electrophysiological recordings of monkeys' visual cortex are available. To train the networks, we used RELEARNN, a biologically plausible four-factor Hebbian learning rule, which is local both in time and space. We report that networks learn elemental operations, such as contour grouping and visual search, and execute sequences of operations, solely based on the characteristics of the visual stimuli and the reward structure of a task. After training was completed, the activity of the units of the neural network elicited by behaviorally relevant image items was stronger than that elicited by irrelevant ones, just as has been observed in the visual cortex of monkeys solving the same tasks. Relevant information that needed to be exchanged between subroutines was maintained as a focus of enhanced activity and passed on to the subsequent subroutines. Our results demonstrate how a biologically plausible learning rule can train a recurrent neural network on multistep visual tasks.

Hierarchical consciousness: the Nested Observer Windows model

Foremost in our experience is the intuition that we possess a unified conscious experience. However, many observations run counter to this intuition: we experience paralyzing indecision when faced with two appealing behavioral choices, we simultaneously hold contradictory beliefs, and the content of our thought is often characterized by an internal debate. Here, we propose the Nested Observer Windows (NOW) Model, a framework for hierarchical consciousness wherein information processed across many spatiotemporal scales of the brain feeds into subjective experience. The model likens the mind to a hierarchy of nested mosaic tiles-where an image is composed of mosaic tiles, and each of these tiles is itself an image composed of mosaic tiles. Unitary consciousness exists at the apex of this nested hierarchy where perceptual constructs become fully integrated and complex behaviors are initiated via abstract commands. We define an observer window as a spatially and temporally constrained system within which information is integrated, e.g. in functional brain regions and neurons. Three principles from the signal analysis of electrical activity describe the nested hierarchy and generate testable predictions. First, nested observer windows disseminate information across spatiotemporal scales with cross-frequency coupling. Second, observer windows are characterized by a high degree of internal synchrony (with zero phase lag). Third, observer windows at the same spatiotemporal level share information with each other through coherence (with non-zero phase lag). The theoretical framework of the NOW Model accounts for a wide range of subjective experiences and a novel approach for integrating prominent theories of consciousness.

The Primate Cortical LFP Exhibits Multiple Spectral and Temporal Gradients and Widespread Task-Dependence During Visual Short-Term Memory

Although cognitive functions are hypothesized to be mediated by synchronous neuronal interactions in multiple frequency bands among widely distributed cortical areas, we still lack a basic understanding of the distribution and task dependence of oscillatory activity across the cortical map. Here, we ask how the spectral and temporal properties of the local field potential (LFP) vary across the primate cerebral cortex, and how they are modulated during visual short-term memory. We measured the LFP from 55 cortical areas in two macaque monkeys while they performed a visual delayed match to sample task. Analysis of peak frequencies in the LFP power spectra reveals multiple discrete frequency bands between 3-80 Hz that differ between the two monkeys. The LFP power in each band, as well as the Sample Entropy, a measure of signal complexity, display distinct spatial gradients across the cortex, some of which correlate with reported spine counts in layer 3 pyramidal neurons. Cortical areas can be robustly decoded using a small number of spectral and temporal parameters, and significant task dependent increases and decreases in spectral power occur in all cortical areas. These findings reveal pronounced, widespread and spatially organized gradients in the spectral and temporal activity of cortical areas. Task-dependent changes in cortical activity are globally distributed, even for a simple cognitive task.

Electome network factors: Capturing emotional brain networks related to health and disease

Therapeutic development for mental disorders has been slow despite the high worldwide prevalence of illness. Unfortunately, cellular and circuit insights into disease etiology have largely failed to generalize across individuals that carry the same diagnosis, reflecting an unmet need to identify convergent mechanisms that would facilitate optimal treatment. Here, we discuss how mesoscale networks can encode affect and other cognitive processes. These networks can be discovered through electrical functional connectome (electome) analysis, a method built upon explainable machine learning models for analyzing and interpreting mesoscale brain-wide signals in a behavioral context. We also outline best practices for identifying these generalizable, interpretable, and biologically relevant networks. Looking forward, translational electome analysis can span species and various moods, cognitive processes, or other brain states, supporting translational medicine. Thus, we argue that electome analysis provides potential translational biomarkers for developing next-generation therapeutics that exhibit high efficacy across heterogeneous disorders.

Information content and temporal structure of face selective local field potentials frequency bands in IT cortex

Sensory stimulation triggers synchronized bioelectrical activity in the brain across various frequencies. This study delves into network-level activities, specifically focusing on local field potentials as a neural signature of visual category representation. Specifically, we studied the role of different local field potential frequency oscillation bands in visual stimulus category representation by presenting images of faces and objects to three monkeys while recording local field potential from inferior temporal cortex. We found category selective local field potential responses mainly for animate, but not inanimate, objects. Notably, face-selective local field potential responses were evident across all tested frequency bands, manifesting in both enhanced (above mean baseline activity) and suppressed (below mean baseline activity) local field potential powers. We observed four different local field potential response profiles based on frequency bands and face selective excitatory and suppressive responses. Low-frequency local field potential bands (1-30 Hz) were more prodominstaly suppressed by face stimulation than the high-frequency (30-170 Hz) local field potential bands. Furthermore, the low-frequency local field potentials conveyed less face category informtion than the high-frequency local field potential in both enhansive and suppressive conditions. Furthermore, we observed a negative correlation between face/object d-prime values in all the tested local field potential frequency bands and the anterior-posterior position of the recording sites. In addition, the power of low-frequency local field potential systematically declined across inferior temporal anterior-posterior positions, whereas high-frequency local field potential did not exhibit such a pattern. In general, for most of the above-mentioned findings somewhat similar results were observed for body, but not, other stimulus categories. The observed findings suggest that a balance of face selective excitation and inhibition across time and cortical space shape face category selectivity in inferior temporal cortex.

Crossmodal Associations and Working Memory in the Brain

Crossmodal associations between stimuli from different sensory modalities could emerge in non-synesthetic people and be stored in working memory to guide goal-directed behaviors. This chapter reviews a plethora of studies in this field to summarize where, when, and how crossmodal associations and working memory are processed. It has been found that in those brain regions that are traditionally considered as unimodal primary sensory areas, neural activity could be influenced by crossmodal sensory signals at temporally very early stage of information processing. This phenomenon could not be due to feedback projections from higher level associative areas. Sequentially, neural processes would then occur in associative cortical areas including the posterior parietal cortex and prefrontal cortex. Neural oscillations in multiple frequency bands may reflect brain activity in crossmodal associations, and it is likely that neural synchrony is related to potential neural mechanisms underlying these processes. Primary sensory areas and associative areas coordinate together through neural synchrony to fulfil crossmodal associations and to guide working memory performance.

Gamma Oscillations as a Biomarker of Neural Circuit Function in Psychosis: Where Are We, and Where Do We Go from Here?

This chapter is a selective and critical review of the literature on gamma oscillations in schizophrenia and related studies in other relevant fields that pertain to the hypothesis that abnormal gamma oscillations underlie symptoms of psychosis in individuals with schizophrenia. These gamma abnormalities result from deficient recurrent inhibition, in which parvalbumin-expressing, fast-spiking inhibitory interneurons do not receive sufficient excitation from N-methyl-D-aspartate receptors, resulting in a loss of phasic control over pyramidal cell spiking and impairment of gamma generation. The evidence for this hypothesis is critically reviewed, focusing on studies in the areas of visual feature binding, auditory steady-state response, and spontaneous gamma activity. The current state of the field is discussed, and recommendations for future directions are presented.

Morphological disruption and visual tuning alterations in the primary visual cortex in glaucoma (DBA/2J) mice

Glaucoma is a leading cause of irreversible blindness worldwide, and previous studies have shown that, in addition to affecting the eyes, it also causes abnormalities in the brain. However, it is not yet clear how the primary visual cortex (V1) is altered in glaucoma. This study used DBA/2J mice as a model for spontaneous secondary glaucoma. The aim of the study was to compare the electrophysiological and histomorphological characteristics of neurons in the V1 between 9-month-old DBA/2J mice and age-matched C57BL/6J mice. We conducted single-unit recordings in the V1 of light-anesthetized mice to measure the visually induced responses, including single-unit spiking and gamma band oscillations. The morphology of layer II/III neurons was determined by neuronal nuclear antigen staining and Nissl staining of brain tissue sections. Eighty-seven neurons from eight DBA/2J mice and eighty-one neurons from eight C57BL/6J mice were examined. Compared with the C57BL/6J group, V1 neurons in the DBA/2J group exhibited weaker visual tuning and impaired spatial summation. Moreover, fewer neurons were observed in the V1 of DBA/2J mice compared with C57BL/6J mice. These findings suggest that DBA/2J mice have fewer neurons in the V1 compared with C57BL/6J mice, and that these neurons have impaired visual tuning. Our findings provide a better understanding of the pathological changes that occur in V1 neuron function and morphology in the DBA/2J mouse model. This study might offer some innovative perspectives regarding the treatment of glaucoma.

Beta- and gamma-band cortico-cortical interactions support naturalistic reading of continuous text

Large-scale integration of information across cortical structures, building on neural connectivity, has been proposed to be a key element in supporting human cognitive processing. In electrophysiological neuroimaging studies of reading, quantification of neural interactions has been limited to the level of isolated words or sentences due to artefacts induced by eye movements. Here, we combined magnetoencephalography recording with advanced artefact rejection tools to investigate both cortico-cortical coherence and directed neural interactions during naturalistic reading of full-page texts. Our results show that reading versus visual scanning of text was associated with wide-spread increases of cortico-cortical coherence in the beta and gamma bands. We further show that the reading task was linked to increased directed neural interactions compared to the scanning task across a sparse set of connections within a wide range of frequencies. Together, the results demonstrate that neural connectivity flexibly builds on different frequency bands to support continuous natural reading.

Neural Oscillations and Multisensory Processing

Neural oscillations play a role in sensory processing by coordinating synchronized neuronal activity. Synchronization of gamma oscillations is engaged in local computation of feedforward signals and synchronization of alpha-beta oscillations is engaged in feedback processing over long-range areas. These spatially and spectrally segregated bi-directional signals may be integrated by a mechanism of cross-frequency coupling. Synchronization of neural oscillations has also been proposed as a mechanism for information integration across multiple sensory modalities. A transient stimulus or rhythmic stimulus from one modality may lead to phase alignment of ongoing neural oscillations in multiple sensory cortices, through a mechanism of cross-modal phase reset or cross-modal neural entrainment. Synchronized activities in multiple sensory cortices are more likely to boost stronger activities in downstream areas. Compared to synchronized oscillations, asynchronized oscillations may impede signal processing, and may contribute to sensory selection by setting the oscillations in the target-related cortex and the oscillations in the distractor-related cortex to opposite phases.

Phase-amplitude coupling of Go/Nogo task-related neuronal oscillation decreases for humans with insufficient sleep

Phase-amplitude coupling (PAC) across frequency might be associated with the long-range synchronization of brain networks, facilitating the spatiotemporal integration of multiple cell assemblies for information transmission during inhibitory control. However, sleep problems may affect these cortical information transmissions based on cross-frequency PAC, especially when humans work in environments of social isolation. This study aimed to evaluate changes in the theta-beta/gamma PAC of task-related electroencephalography (EEG) for humans with insufficient sleep. Here, we monitored the EEG signals of 60 healthy volunteers and 18 soldiers in the normal environment, performing a Go/Nogo task. Soldiers also participated in the same test in isolated cabins. These measures demonstrated theta-beta PACs between the frontal and central-parietal, and robust theta-gamma PACs between the frontal and occipital cortex. Unfortunately, these PACs significantly decreased when humans experienced insufficient sleep, which was positively correlated with the behavioral performance of inhibitory control. The evaluation of theta-beta/gamma PAC of Go/Nogo task-related EEG is necessary to help understand the different influences of sleep problems in humans.

A Network Model of the Modulation of γ Oscillations by NMDA Receptors in Cerebral Cortex

Psychotic drugs such as ketamine induce symptoms close to schizophrenia and stimulate the production of γ oscillations, as also seen in patients, but the underlying mechanisms are still unclear. Here, we have used computational models of cortical networks generating γ oscillations, and have integrated the action of drugs such as ketamine to partially block NMDA receptors (NMDARs). The model can reproduce the paradoxical increase of γ oscillations by NMDA receptor antagonists, assuming that antagonists affect NMDA receptors with higher affinity on inhibitory interneurons. We next used the model to compare the responsiveness of the network to external stimuli, and found that when NMDA channels are blocked, an increase of γ power is observed altogether with an increase of network responsiveness. However, this responsiveness increase applies not only to γ states, but also to asynchronous states with no apparent γ. We conclude that NMDA antagonists induce an increased excitability state, which may or may not produce γ oscillations, but the response to external inputs is exacerbated, which may explain phenomena such as altered perception or hallucinations.

Atypical development of causal inference in autism inferred through a neurocomputational model

In everyday life, the brain processes a multitude of stimuli from the surrounding environment, requiring the integration of information from different sensory modalities to form a coherent perception. This process, known as multisensory integration, enhances the brain's response to redundant congruent sensory cues. However, it is equally important for the brain to segregate sensory inputs from distinct events, to interact with and correctly perceive the multisensory environment. This problem the brain must face, known as the causal inference problem, is strictly related to multisensory integration. It is widely recognized that the ability to integrate information from different senses emerges during the developmental period, as a function of our experience with multisensory stimuli. Consequently, multisensory integrative abilities are altered in individuals who have atypical experiences with cross-modal cues, such as those on the autistic spectrum. However, no research has been conducted on the developmental trajectories of causal inference and its relationship with experience thus far. Here, we used a neuro-computational model to simulate and investigate the development of causal inference in both typically developing children and those in the autistic spectrum. Our results indicate that higher exposure to cross-modal cues accelerates the acquisition of causal inference abilities, and a minimum level of experience with multisensory stimuli is required to develop fully mature behavior. We then simulated the altered developmental trajectory of causal inference in individuals with autism by assuming reduced multisensory experience during training. The results suggest that causal inference reaches complete maturity much later in these individuals compared to neurotypical individuals. Furthermore, we discuss the underlying neural mechanisms and network architecture involved in these processes, highlighting that the development of causal inference follows the evolution of the mechanisms subserving multisensory integration. Overall, this study provides a computational framework, unifying causal inference and multisensory integration, which allows us to suggest neural mechanisms and provide testable predictions about the development of such abilities in typically developed and autistic children.

40-Hz Auditory Steady-State Responses in Schizophrenia: Toward a Mechanistic Biomarker for Circuit Dysfunctions and Early Detection and Diagnosis

There is converging evidence that 40-Hz auditory steady-state responses (ASSRs) are robustly impaired in schizophrenia and could constitute a potential biomarker for characterizing circuit dysfunctions as well as enable early detection and diagnosis. Here, we provide an overview of the mechanisms involved in 40-Hz ASSRs, drawing on computational, physiological, and pharmacological data with a focus on parameters modulating the balance between excitation and inhibition. We will then summarize findings from electro- and magnetoencephalographic studies in participants at clinical high risk for psychosis, patients with first-episode psychosis, and patients with schizophrenia to identify the pattern of deficits across illness stages, the relationship with clinical variables, and the prognostic potential. Finally, data on genetics and developmental modifications will be reviewed, highlighting the importance of late modifications of 40-Hz ASSRs during adolescence, which are closely related to the underlying changes in GABA (gamma-aminobutyric acid) interneurons. Together, our review suggests that 40-Hz ASSRs may constitute an informative electrophysiological approach to characterize circuit dysfunctions in psychosis that could be relevant for the development of mechanistic biomarkers.

Transient Oscillations of Neural Firing Rate Associated With Routing of Evidence in a Perceptual Decision

To form a perceptual decision, the brain must acquire samples of evidence from the environment and incorporate them in computations that mediate choice behavior. While much is known about the neural circuits that process sensory information and those that form decisions, less is known about the mechanisms that establish the functional linkage between them. We trained monkeys of both sexes to make difficult decisions about the net direction of visual motion under conditions that required trial-by-trial control of functional connectivity. In one condition, the motion appeared at different locations on different trials. In the other, two motion patches appeared, only one of which was informative. Neurons in the parietal cortex produced brief oscillations in their firing rate at the time routing was established: upon onset of the motion display when its location was unpredictable across trials, and upon onset of an attention cue that indicated in which of two locations an informative patch of dots would appear. The oscillation was absent when the stimulus location was fixed across trials. We interpret the oscillation as a manifestation of the mechanism that establishes the source and destination of flexibly routed information, but not the transmission of the information per se Significance Statement It has often been suggested that oscillations in neural activity might serve a role in routing information appropriately. We observe an oscillation in neural firing rate in the lateral intraparietal area consistent with such a role. The oscillations are transient. They coincide with the establishment of routing, but they do not appear to play a role in the transmission (or conveyance) of the routed information itself.