A Large-Scale Circuit Mechanism for Hierarchical Dynamical Processing in the Primate Cortex

The brain's internal echo: Longer timescales, stronger recurrent connections and higher neural excitation in self regions

BACKGROUND

Understanding the brain's intrinsic architecture has long been a central focus of neuroscience, with recent advances shedding light on its topographic organization along uni and transmodal regions. How the brain's global uni-transmodal topography relates to psychological features like our sense of self remains yet unclear, though.

METHOD

We here combine fMRI brain imaging with computational modeling (Wilson Cowan model) to better understand the temporal, spatial and physiological features underlying the distinction of self and non-self regions within the brain's global topography.

RESULTS

fMRI resting state shows lower myelin content, longer timescales (measured by the autocorrelation window/ACW), and lower global functional connectivity/synchronization (measured by global signal correlation/GSCORR) in self regions (based on the three-layer self topography; Qin et al. 2020) compared to non-self regions. Next, we fit the fMRI data with a neural mass model, the Wilson-Cowan model, which is enriched by structural and functional connectivity data from human MRI/fMRI. We first replicate the empirical data with longer ACW and lower GSCORR in self regions. Next, we demonstrate that self and non-self regions can, based on the same measures in the model, not only be distinguished within the brain's global topography but also within the unimodal and transmodal regions themselves, respectively. Finally, the neural mass model shows that such topographic differentiation relates to two physiological features: self regions exhibit higher intra-regional excitatory recurrent connection and higher levels in their basal neural excitation than non-self regions.

CONCLUSION

Our findings demonstrate the intrinsic nature of the distinction of self and non-self regions within the brain's global uni-transmodal topography as well as their underlying physiological differences with higher levels in both recurrent connections and neural excitation in self regions. The increased recurrent connections in self regions, together with their higher levels of neural excitation and the longer autocorrelation window, may be ideally suited to mediate their self-referential processing: this can thus be seen as a form of 'psychological recurrence' where one and the same input/stimulus is processed in a prolonged echo-chamber like way, that is, an internal echo within the self regions themselves.

Selective control of prefrontal neural timescales by parietal cortex

Intrinsic neural timescales quantify how long spontaneous neuronal activity patterns persist, reflecting dynamics of endogenous fluctuations. We measured intrinsic timescales of frontal eye field (FEF) neurons and examined their changes during posterior parietal cortex (PPC) inactivation. We observed two distinct classes of FEF neurons based on their intrinsic timescales: short-timescale neurons (∼25 ms) or long-timescale neurons (∼100 ms). Short-timescale neurons showed stronger transient visual responses, suggesting their role in rapid visual processing, whereas long-timescale neurons exhibited pronounced sustained attentional modulation, suggesting their role in maintaining stimulus-driven attention. During PPC inactivation, intrinsic timescales increased in both neuron types, with a significantly larger effect in short-timescale neurons. In addition, PPC inactivation reduced attentional modulation, particularly in long-timescale neurons. Our findings provide the first causal evidence linking intrinsic local neural timescales to long-range inter-area communications. These findings also suggest the presence of at least two distinct network motifs that support different neuronal dynamics and functional computations within the FEF.

Consistent Hierarchies of Single-Neuron Timescales in Mice, Macaques, and Humans

The intrinsic timescales of single neurons are thought to be hierarchically organized across the cortex, but whether hierarchical variation in timescales is a general brain organizing principle across mammalian species remains unclear. Here, we took a cross-species approach and estimated neuronal timescales of thousands of single neurons recorded across frontal cortex, amygdala, and hippocampus in mice, monkeys, and humans of both sexes using a task-agnostic method. We identify largely consistent hierarchies of timescales in frontal and limbic regions across species: hippocampus had the shortest timescale whereas anterior cingulate cortex had the longest. Within this scheme, variability across species was found, most notably in amygdala and orbitofrontal cortex. We show that variation in timescales is not simply related to differences in spiking statistics nor the result of cytoarchitectonic features such as cortical granularity. Thus, hierarchically organized timescales are a consistent organizing principle across species and appear to be related to a combination of intrinsic and extrinsic factors.

Disentangling sources of variability in decision-making

Even the most highly-trained observers presented with identical choice-relevant stimuli will reliably exhibit substantial trial-to-trial variability in the timing and accuracy of their choices. Despite being a pervasive feature of choice behaviour and a prominent phenotype for numerous clinical disorders, the capability to disentangle the sources of such intra-individual variability (IIV) remains limited. In principle, computational models of decision-making offer a means of parsing and estimating these sources, but methodological limitations have prevented this potential from being fully realized in practice. In this Review, we first discuss current limitations of algorithmic models for understanding variability in decision-making behaviour. We then highlight recent advances in behavioural paradigm design, novel analyses of cross-trial behavioural and neural dynamics, and the development of neurally grounded computational models that are now making it possible to link distinct components of IIV to well-defined neural processes. Taken together, we demonstrate how these methods are opening up new avenues for systematically analysing the neural origins of IIV, paving the way for a more refined, holistic understanding of decision-making in health and disease.

Disentangling the Switching Behavior in Functional Connectivity Dynamics in Autism Spectrum Disorder: Insights from Developmental Cohort Analysis and Molecular-Cellular Associations

Characterizing the transition or switching behavior between multistable brain states in functional connectivity dynamics (FCD) holds promise for uncovering the underlying neuropathology of Autism Spectrum Disorder (ASD). However, whether and how switching behaviors in FCD change in patients with developmental ASD, as well as their cellular and molecular basis, remains unexplored. This study develops a region-wise FCD switching index (RFSI) to investigate the drivers of FCD. This work finds that brain regions within the salience, default mode, and frontoparietal networks serve as abnormal drivers of FCD in ASD across different developmental stages. Additionally, changes in RFSI at different developmental stages of ASD correlated with transcriptomic profiles and neurotransmitter density maps. Importantly, the abnormal RFSI identifies in humans has also been observed in genetically edited ASD monkeys. Finally, single-nucleus RNA sequencing data from patients with developmental ASD are analyzed and aberrant switching behaviors in FCD may be mediated by somatostatin-expressing interneurons and altered differentiation patterns in astrocyte State2. In conclusion, this study provides the first evidence of abnormal drivers of FCD across different stages of ASD and their associated cellular and molecular mechanisms. These findings deepen the understanding of ASD neuropathology and offer valuable insights into treatment strategies.

A general framework for characterizing optimal communication in brain networks

Efficient communication in brain networks is foundational for cognitive function and behavior. However, how communication efficiency is defined depends on the assumed model of signaling dynamics, e.g., shortest path signaling, random walker navigation, broadcasting, and diffusive processes. Thus, a general and model-agnostic framework for characterizing optimal neural communication is needed. We address this challenge by assigning communication efficiency through a virtual multi-site lesioning regime combined with game theory, applied to large-scale models of human brain dynamics. Our framework quantifies the exact influence each node exerts over every other, generating optimal influence maps given the underlying model of neural dynamics. These descriptions reveal how communication patterns unfold if regions are set to maximize their influence over one another. Comparing these maps with a variety of brain communication models showed that optimal communication closely resembles a broadcasting regime in which regions leverage multiple parallel channels for information dissemination. Moreover, we found that the brain's most influential regions are its rich-club, exploiting their topological vantage point by broadcasting across numerous pathways that enhance their reach even if the underlying connections are weak. Altogether, our work provides a rigorous and versatile framework for characterizing optimal brain communication, and uncovers the most influential brain regions, and the topological features underlying their influence.

ECoGScope: An integrated platform for real-time Electrophysiology and fluorescence imaging

In this study, we present ECoGScope, a versatile neural interface platform designed to integrate multiple functions for advancing neural network research. ECoGScope combines an electrocorticography (ECoG) electrode array with a commercial microendoscope, enabling simultaneous recording of ECoG signals and fluorescence imaging. The electrode array, constructed from highly flexible and transparent polymers, ensures conformal contact with the brain surface, allowing unobstructed optical monitoring of neural activity alongside electrophysiological recordings. A key innovation is the compact connection module, which securely integrates the ECoG array and microendoscope while minimizing interference with animal behavior. The device was successfully tested in the visual, somatosensory, and frontal cortex, demonstrating its capability for simultaneous electrophysiological and fluorescent measurements. These results highlight the potential of the ECoGScope platform to advance the development of multifunctional tools for studying brain function and addressing neurological disorders.

Sleep Modulates Neural Timescales and Spatiotemporal Integration in the Human Cortex

Spontaneous neural dynamics manifest across multiple temporal and spatial scales, which are thought to be intrinsic to brain areas and exhibit hierarchical organization across the cortex. In wake, a hierarchy of timescales is thought to naturally emerge from microstructural properties, gene expression, and recurrent connections. A fundamental question is timescales' organization and changes in sleep, where physiological needs are different. Here, we describe two measures of neural timescales, obtained from broadband activity and gamma power, which display complementary properties. We leveraged intracranial electroencephalography in 106 human epilepsy patients (48 females) to characterize timescale changes from wake to sleep across the cortical hierarchy. We show that both broadband and gamma timescales are globally longer in sleep than in wake. While broadband timescales increase along the sensorimotor-association axis, gamma ones decrease. During sleep, slow waves can explain the increase of broadband and gamma timescales, but only broadband ones show a positive association with slow-wave density across the cortex. Finally, we characterize spatial correlations and their relationship with timescales as a proxy for spatiotemporal integration, finding high integration at long distances in wake for broadband and at short distances in sleep for gamma timescales. Our results suggest that mesoscopic neural populations possess different timescales that are shaped by anatomy and are modulated by the sleep/wake cycle.

Critical dynamics predicts cognitive performance and provides a common framework for heterogeneous mechanisms impacting cognition

The brain criticality hypothesis postulates that brain dynamics are set at a phase transition where information processing is optimized. Long-range temporal correlations (TCs) characterizing the dissipation of information within a signal have been shown to be a hallmark of brain criticality. However, the experimental link between cognitive performance, criticality, and thus TCs has remained elusive due to limitations in recording length and spatial and temporal resolution. In this study, we investigate multiday invasive EEG recordings of 104 persons with epilepsy (PwE) together with an extensive cognitive test battery. We show that short TCs predict cognitive impairment. Further, we show that heterogeneous factors, including interictal epileptiform discharges (IEDs), antiseizure medications (ASMs), and intermittent periods with slow-wave activity (SWSs), all act directly to perturb critical dynamics and thus cognition. Our work suggests critical dynamics to be the setpoint to measure optimal network function, thereby providing a unifying framework for the heterogeneous mechanisms impacting cognition in conditions like epilepsy.

Adaptive Whole-Brain Dynamics Predictive Method: Relevancy to Mental Disorders

The Hopf whole-brain model, based on structural connectivity, overcomes limitations of traditional structural or functional connectivity-focused methods by incorporating heterogeneity parameters, quantifying dynamic brain characteristics in healthy and diseased states. Traditional parameter fitting techniques lack precision, restricting broader use. To address this, we validated parameter fitting methods using simulated networks and synthetic models, introducing improvements such as individual-specific initialization and optimized gradient descent, which reduced individual data loss. We also developed an approximate loss function and gradient adjustment mechanism, enhancing parameter fitting accuracy and stability. Applying this refined method to datasets for major depressive disorder (MDD) and autism spectrum disorder (ASD), we identified differences in brain regions between patients and healthy controls, explaining related anomalies. This rigorous validation is crucial for clinical application, paving the way for precise neuropathological identification and novel treatments in neuropsychiatric research, demonstrating substantial potential in clinical neurology.

Modeling the interplay between regional heterogeneity and critical dynamics underlying brain functional networks

The human brain exhibits heterogeneity across regions and network connectivity patterns; However, how these heterogeneities contribute to whole-brain network functions and cognitive capacities remains unclear. In this study, we focus on the regional heterogeneity reflected in local dynamics and study how it contributes to the emergence of functional connectivity patterns, network ignition dynamics of the empirical brains. We find that the level of synchrony among voxelwise neural activities measured from the fMRI data is significantly correlated with the transcriptional variations in excitatory and inhibitory receptor gene expression. Consequently, we construct heterogeneous whole-brain network models with nodal excitability calibrated by the synchronization measure of regional dynamics. We demonstrate that as the extent of heterogeneity increases, the models operating around the critical point between order and disorder generate simulated functional connectivity networks increasingly similar to empirical resting-state or working memory task-evoked function connectivity networks. Furthermore, the heterogeneous models can predict individual differences in resting-state and task-evoked reconfiguration of the functional connectivity, as well as the comparative causal effect of empirical brain networks-that is, how the dynamics of one brain region affect whole-brain synchronization. Overall, this work demonstrates the viability of using regional heterogeneous functional signals to improve the performance of the whole-brain models, and illustrates how regional heterogeneity in human brains interplays with structural connections and critical dynamics to contribute to the emergence of functional connectivity networks.

Asymmetric Sampling in Time: Evidence and perspectives

Auditory and speech signals are undisputedly processed in both left and right hemispheres, but this bilateral allocation is likely unequal. The Asymmetric Sampling in Time (AST) hypothesis proposed a division of labor that has its neuroanatomical basis in the distribution of neuronal ensembles with differing temporal integration constants: left auditory areas house a larger proportion of ensembles with shorter temporal integration windows (tens of milliseconds), suited to process rapidly changing signals; right auditory areas host a larger proportion with longer time constants (∼150-300 ms), ideal for slowly changing signals. Here we evaluate the large body of findings that clarifies this relationship between auditory temporal structure and functional lateralization. In this reappraisal, we unpack whether this relationship is influenced by stimulus type (speech/nonspeech), stimulus temporal extent (long/short), task engagement (high/low), or (imaging) modality (hemodynamic/electrophysiology/behavior). We find that the right hemisphere displays a clear preference for slowly changing signals whereas the left-hemispheric preference for rapidly changing signals is highly dependent on the experimental design. We consider neuroanatomical properties potentially linked to functional lateralization, contextualize the results in an evolutionary perspective, and highlight future directions.

A dynamic bifurcation mechanism explains cortex-wide neural correlates of conscious access

Conscious access is suggested to involve "ignition," an all-or-none activation across cortical areas. To elucidate this phenomenon, we carry out computer simulations of a detection task using a mesoscale connectome-based model for the multiregional macaque cortex. The model uncovers a dynamic bifurcation mechanism that gives rise to ignition in a network of associative regions. A hierarchical N-methyl-D-aspartate (NMDA)/α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor gradient plays a critical role: fast AMPA receptors drive feedforward signal propagation, while slow NMDA receptors in feedback pathways shape and sustain the ignited network. Intriguingly, the model suggests higher NMDA-to-AMPA receptor ratios in sensory areas compared to association areas, a prediction supported by in vitro autoradiography data. Furthermore, the model accounts for diverse behavioral and physiological phenomena linked to consciousness. This work sheds light on how receptor gradients along the cortical hierarchy enable distributed cognitive functions and provides a biologically constrained computational framework for investigating the neurophysiological basis of conscious access.

Integrative gray matter volume and molecular analyses of altered intrinsic neural timescale in internet gaming disorder

BACKGROUND

Internet gaming disorder (IGD) frequently features abnormalities in emotional and cognitive processing, for which the specific neurobiological mechanisms are not known. The intrinsic neural timescale (INT) gradient reflects how long neural information is stored in a specialized brain region and represents its function. Therefore, we investigated whether IGD exhibited altered INT and accompanying gray matter volume (GMV) and underlying molecular architectural abnormalities.

METHODS

Resting-state functional magnetic resonance data from 57 patients with IGD (IGDs) and 50 demographically matched healthy controls (HCs) were collected, and INT was calculated by assessing the autocorrelation of intrinsic neural signals. Voxel-based morphometric analysis was conducted to calculate whole-brain GMV. Then, comparing INT between groups and correlation analysis with clinical characteristics was performed. Furthermore, correlations between INT and PET- and SPECT-driven maps were used to examine specific neurotransmitter system alternations.

RESULT

Compared to HCs, IGDs exhibited shorter timescales in the bilateral insula, bilateral parahippocampal gyrus, left amygdala, and left superior temporal pole. The decreased INT in the right insula was positively correlated with the severity of internet addiction. Interestingly, the shorter timescales are spatially associated with the serotonergic system.

CONCLUSION

This study suggests atypical emotional and cognitive processing deficits in localized brain regions of IGDs. And these findings establish a link between abnormal local neurodynamics and structures and neurotransmitters, which facilitates synthesized comprehension of IGDs and provides new perspectives for treatment.

Uncovering a Timescale Hierarchy by Studying the Brain in a Natural Context

As we train multiple generations of students to narrowly design clever, carefully controlled experiments in our confined lab spaces, we may fail to notice, as a field, that we have overlooked fundamental aspects of human cognition. This is a first-person account of how our research and understanding of the neural code were forever transformed when we decided to open the lab's door to the natural world. This journey started with the decision to shift from controlled stimuli to natural dynamic and "messy" stimuli. This transition enabled us to focus on how information is accumulated and processed over time. As a result, we have discovered a new topographic mapping of the hierarchy of cortical processing timescales. I will conclude with a general observation of the paradigm shift occurring in the field as it increasingly emphasizes the study of the neural processes that underlie human behavior in natural, everyday contexts. I am excited to share this journey with you.

Synapses mediate the effects of different types of stress on working memory: a brain-inspired spiking neural network study

Acute stress results from sudden short-term events, and individuals need to quickly adjust their physiological and psychological to re-establish balance. Chronic stress, on the other hand, results in long-term physiological and psychological burdens due to the continued existence of stressors, making it difficult for individuals to recover and prone to pathological symptoms. Both types of stress can affect working memory and change cognitive function. In this study, we explored the impact of acute and chronic stress on synaptic modulation using a biologically inspired, data-driven rodent prefrontal neural network model. The model consists of a specific number of excitatory and inhibitory neurons that are connected through AMPA, NMDA, and GABA synapses. The study used a short-term recall to simulate working memory tasks and assess the ability of neuronal populations to maintain information over time. The results showed that acute stress can enhance working memory information retention by enhancing AMPA and NMDA synaptic currents. In contrast, chronic stress reduces dendritic spine density and weakens the regulatory effect of GABA currents on working memory tasks. In addition, this structural damage can be complemented by strong connections between excitatory neurons with the same selectivity. These findings provide a reference scheme for understanding the neural basis of working memory under different stress conditions.

From comparative connectomics to large-scale working memory modeling in macaque and marmoset

Although macaques and marmosets are both primates of choice for studying the brain mechanisms of cognition, they differ in key aspects of anatomy and behavior. Interestingly, recent connectomic analysis revealed that strong top-down projections from the prefrontal cortex to the posterior parietal cortex, present in macaques and important for executive function, are absent in marmosets. Here, we propose a consensus mapping that bridges the two species' cortical atlases and allows for direct area-to-area comparison of their connectomes, which are then used to build comparative computational large-scale modeling of the frontoparietal circuit for working memory. We found that the macaque model exhibits resilience against distractors, a prerequisite for normal working memory function. By contrast, the marmoset model is sensitive to distractibility commonly observed behaviorally in this species. Surprisingly, this contrasting trend can be swapped by scaling intrafrontal and frontoparietal connections. Finally, the relevance to primate ethology and evolution is discussed.

Local Differences in Network Organization in the Auditory and Parietal Cortex, Revealed with Single Neuron Activation

The structure of local circuits is highly conserved across the cortex, yet the spatial and temporal properties of population activity differ fundamentally in sensory-level and association-level areas. In the sensory cortex, population activity has a shorter timescale and decays sharply over distance, supporting a population code for the fine-scale features of sensory stimuli. In the association cortex, population activity has a longer timescale and spreads over wider distances, a code that is suited to holding information in memory and driving behavior. We tested whether these differences in activity dynamics could be explained by differences in network structure. We targeted photostimulations to single excitatory neurons of layer 2/3, while monitoring surrounding population activity using two-photon calcium imaging. Experiments were performed in the auditory (AC) and posterior parietal cortex (PPC) within the same mice of both sexes, which also expressed a red fluorophore in somatostatin-expressing interneurons (SOM). In both cortical regions, photostimulations resulted in a spatially restricted zone of positive influence on neurons closely neighboring the targeted neuron and a more spatially diffuse zone of negative influence affecting more distant neurons (akin to a network-level "suppressive surround"). However, the relative spatial extents of positive and negative influence were different in AC and PPC. In PPC, the central zone of positive influence was wider, but the negative suppressive surround was more narrow than in AC, which could account for the larger-scale network dynamics in PPC. The more narrow central positive influence zone and wider suppressive surround in AC could serve to sharpen sensory representations.

Facial gestures are enacted via a cortical hierarchy of dynamic and stable codes

Successful communication requires the generation and perception of a shared set of signals. Facial gestures are one fundamental set of communicative behaviors in primates, generated through the dynamic arrangement of dozens of fine muscles. While much progress has been made uncovering the neural mechanisms of face perception, little is known about those controlling facial gesture production. Commensurate with the importance of facial gestures in daily social life, anatomical work has shown that facial muscles are under direct control from multiple cortical regions, including primary and premotor in lateral frontal cortex, and cingulate in medial frontal cortex. Furthermore, neuropsychological evidence from focal lesion patients has suggested that lateral cortex controls voluntary movements, and medial emotional expressions. Here we show that lateral and medial cortical face motor regions encode both types of gestures. They do so through unique temporal activity patterns, distinguishable well-prior to movement onset. During gesture production, cortical regions encoded facial kinematics in a context-dependent manner. Our results show how cortical regions projecting in parallel downstream, but each situated at a different level of a posterior-anterior hierarchy form a continuum of gesture coding from dynamic to temporally stable, in order to produce context-related, coherent motor outputs during social communication.

Hierarchy between forelimb premotor and primary motor cortices and its manifestation in their firing patterns

Though hierarchy is commonly invoked in descriptions of motor cortical function, its presence and manifestation in firing patterns remain poorly resolved. Here we use optogenetic inactivation to demonstrate that short-latency influence between forelimb premotor and primary motor cortices is asymmetric during reaching in mice, demonstrating a partial hierarchy between the endogenous activity in each region. Multi-region recordings revealed that some activity is captured by similar but delayed patterns where the activity of either region leads, with premotor activity leading more. Yet firing in each region is dominated by patterns shared between regions and is equally predictive of firing in the other region at the single-neuron level. In dual-region network models fit to data, regions differed in their dependence on across-region input, rather than the amount of such input they received. Our results indicate that motor cortical hierarchy, while present, may not be exposed when inferring interactions between populations from firing patterns alone.

Network Mechanisms Underlying the Regional Diversity of Variance and Time Scales of the Brain's Spontaneous Activity Fluctuations

The brain's activity fluctuations have different temporal scales across the brain regions, with associative regions displaying slower timescales than sensory areas. This hierarchy of timescales has been shown to correlate with both structural brain connectivity and intrinsic regional properties. Here, using publicly available human resting-state fMRI and dMRI data, it was found that, while more structurally connected brain regions presented activity fluctuations with longer timescales, their activity fluctuations presented lower variance. The opposite relationships between the structural connectivity and the variance and temporal scales of resting-state fluctuations, respectively, were not trivially explained by simple network propagation principles. To understand these structure-function relationships, two commonly used whole-brain models were studied, namely, the Hopf and Wilson-Cowan models. These models use the brain's connectome to couple local nodes (representing brain regions) displaying noise-driven oscillations. The models show that the variance and temporal scales of activity fluctuations can oppositely relate to connectivity within specific parameter regions, even when all nodes have the same intrinsic dynamics-but also when intrinsic dynamics are constrained by the myelinization-related macroscopic gradient. These results show that, setting aside intrinsic regional differences, connectivity and network state are sufficient to explain the regional differences in fluctuations' scales. State dependence supports the vision that structure-function relationships can serve as biomarkers of altered brain states. Finally, the results indicate that the hierarchies of timescales and variances reflect a balance between stability and responsivity, with greater and faster responsiveness at the network periphery, while the network core ensures overall system robustness.

Analyzing the brain's dynamic response to targeted stimulation using generative modeling

Generative models of brain activity have been instrumental in testing hypothesized mechanisms underlying brain dynamics against experimental datasets. Beyond capturing the key mechanisms underlying spontaneous brain dynamics, these models hold an exciting potential for understanding the mechanisms underlying the dynamics evoked by targeted brain stimulation techniques. This paper delves into this emerging application, using concepts from dynamical systems theory to argue that the stimulus-evoked dynamics in such experiments may be shaped by new types of mechanisms distinct from those that dominate spontaneous dynamics. We review and discuss (a) the targeted experimental techniques across spatial scales that can both perturb the brain to novel states and resolve its relaxation trajectory back to spontaneous dynamics and (b) how we can understand these dynamics in terms of mechanisms using physiological, phenomenological, and data-driven models. A tight integration of targeted stimulation experiments with generative quantitative modeling provides an important opportunity to uncover novel mechanisms of brain dynamics that are difficult to detect in spontaneous settings.

The architecture of the human default mode network explored through cytoarchitecture, wiring and signal flow

The default mode network (DMN) is implicated in many aspects of complex thought and behavior. Here, we leverage postmortem histology and in vivo neuroimaging to characterize the anatomy of the DMN to better understand its role in information processing and cortical communication. Our results show that the DMN is cytoarchitecturally heterogenous, containing cytoarchitectural types that are variably specialized for unimodal, heteromodal and memory-related processing. Studying diffusion-based structural connectivity in combination with cytoarchitecture, we found the DMN contains regions receptive to input from sensory cortex and a core that is relatively insulated from environmental input. Finally, analysis of signal flow with effective connectivity models showed that the DMN is unique amongst cortical networks in balancing its output across the levels of sensory hierarchies. Together, our study establishes an anatomical foundation from which accounts of the broad role the DMN plays in human brain function and cognition can be developed.

Deciphering neuronal variability across states reveals dynamic sensory encoding

Influenced by non-stationary factors such as brain states and behavior, neurons exhibit substantial response variability even to identical stimuli. However, it remains unclear how their relative impact on neuronal variability evolves over time. To address this question, we designed an encoding model conditioned on latent states to partition variability in the mouse visual cortex across internal brain dynamics, behavior, and external visual stimulus. Applying a hidden Markov model to local field potentials, we consistently identified three distinct oscillation states, each with a unique variability profile. Regression models within each state revealed a dynamic composition of factors influencing spiking variability, with the dominant factor switching within seconds. The state-conditioned regression model uncovered extensive diversity in source contributions across units, varying in accordance with anatomical hierarchy and internal state. This heterogeneity in encoding underscores the importance of partitioning variability over time, particularly when considering the influence of non-stationary factors on sensory processing.

Contextual neural dynamics during time perception in the primate ventral premotor cortex

Understanding how time perception adapts to cognitive demands remains a significant challenge. In some contexts, the brain encodes time categorically (as "long" or "short"), while in others, it encodes precise time intervals on a continuous scale. Although the ventral premotor cortex (VPC) is known for its role in complex temporal processes, such as speech, its specific involvement in time estimation remains underexplored. In this study, we investigated how the VPC processes temporal information during a time interval comparison task (TICT) and a time interval categorization task (TCT) in primates. We found a notable heterogeneity in neuronal responses associated with time perception across both tasks. While most neurons responded during time interval presentation, a smaller subset retained this information during the working memory periods. Population-level analysis revealed distinct dynamics between tasks: In the TICT, population activity exhibited a linear and parametric relationship with interval duration, whereas in the TCT, neuronal activity diverged into two distinct dynamics corresponding to the interval categories. During delay periods, these categorical or parametric representations remained consistent within each task context. This contextual shift underscores the VPC's adaptive role in interval estimation and highlights how temporal representations are modulated by cognitive demands.

Simulating the impact of white matter connectivity on processing time scales using brain network models

The capacity of the brain to process input across temporal scales is exemplified in human narrative, which requires integration of information ranging from words, over sentences to long paragraphs. It has been shown that this processing is distributed in a hierarchy across multiple areas in the brain with areas close to the sensory cortex, processing on a faster time scale than areas in associative cortex. In this study we used reservoir computing with human derived connectivity to investigate the effect of the structural connectivity on time scales across brain regions during a narrative task paradigm. We systematically tested the effect of removal of selected fibre bundles (IFO, ILF, MLF, SLF I/II/III, UF, AF) on the processing time scales across brain regions. We show that long distance pathways such as the IFO provide a form of shortcut whereby input driven activation in the visual cortex can directly impact distant frontal areas. To validate our model we demonstrated significant correlation of our predicted time scale ordering with empirical results from the intact/scrambled narrative fMRI task paradigm. This study emphasizes structural connectivity's role in brain temporal processing hierarchies, providing a framework for future research on structure and neural dynamics across cognitive tasks.

Test-retest reliability of resting-state EEG intrinsic neural timescales

Intrinsic neural timescales, which reflect the duration of neural information storage within local brain regions and capacity for information integration, are typically measured using autocorrelation windows (ACWs). Extraction of intrinsic neural timescales from resting-state brain activity has been extensively applied in psychiatric disease research. Given the potential of intrinsic neural timescales as a neural marker for psychiatric disorders, investigating their reliability is crucial. This study, using an open-source database, aimed to evaluate the test-retest reliability of ACW-0 and ACW-50 under both eyes-open and eyes-closed conditions across three sessions. The intraclass correlation coefficients (ICCs) were employed to quantify the reliability of the intrinsic neural timescales. Our results showed that intrinsic neural timescales exhibited good reliability (ICC > 0.6) at the whole-brain level across different index types and eye states. Spatially, except for the right temporal region in the eyes-open condition, all other regions showed moderate-to-high ICCs. Over 60% of the electrodes demonstrated moderate-to-high intrinsic neural timescale ICCs under both eyes-open and eyes-closed conditions, with ACW-0 being more stable than ACW-50. Moreover, in the new dataset, the above results were consistently reproduced. The present study comprehensively assessed the reliability of intrinsic neural timescale under various conditions, providing robust evidence for their stability in neuroscience and psychiatry.

Mapping and modeling age-related changes in intrinsic neural timescales

Intrinsic timescales of brain regions exhibit heterogeneity, escalating with hierarchical levels, and are crucial for the temporal integration of external stimuli. Aging, often associated with cognitive decline, involves progressive neuronal and synaptic loss, reshaping brain structure and dynamics. However, the impact of these structural changes on temporal coding in the aging brain remains unclear. We mapped intrinsic timescales and gray matter volume (GMV) using magnetic resonance imaging (MRI) in young and elderly adults. We found shorter intrinsic timescales across multiple large-scale functional networks in the elderly cohort, and a significant positive association between intrinsic timescales and GMV. Additionally, age-related decline in performance on visual discrimination tasks was linked to a reduction in intrinsic timescales in the cuneus. To explain these age-related shifts, we developed an age-dependent spiking neuron network model. In younger subjects, brain regions were near a critical branching regime, while regions in elderly subjects had fewer neurons and synapses, pushing the dynamics toward a subcritical regime. The model accurately reproduced the empirical results, showing longer intrinsic timescales in young adults due to critical slowing down. Our findings reveal how age-related structural brain changes may drive alterations in brain dynamics, offering testable predictions and informing possible interventions targeting cognitive decline.

Ramping dynamics in the frontal cortex unfold over multiple timescales during motor planning

Plans are formulated and refined throughout the period leading up to their execution, ensuring that the appropriate behaviors are enacted at the appropriate times. Although existing evidence suggests that memory circuits convey the passage of time through diverse neuronal responses, it remains unclear whether the neural circuits involved in planning exhibit analogous temporal dynamics. Using publicly available data, we analyzed how activity in the mouse frontal motor cortex evolves during motor planning. Individual neurons exhibited diverse ramping activity throughout a delay interval that preceded a planned movement. The collective activity of these neurons was useful for making temporal predictions that became increasingly precise as the movement time approached. This temporal diversity gave rise to a spectrum of encoding patterns, ranging from stable to dynamic representations of the upcoming movement. Our results indicate that ramping activity unfolds over multiple timescales during motor planning, suggesting a shared mechanism in the brain for processing temporal information related to both memories from the past and plans for the future. NEW & NOTEWORTHY Neuronal responses in the cortex are diverse, but the nature and functional consequences of this diversity remain ambiguous. We identified a specific pattern of temporal heterogeneity in the mouse frontal motor cortex, whereby the firing of different neurons ramps up at varying speeds before the execution of a movement. Our decoding analyses reveal that this heterogeneity in ramping dynamics enables precise and reliable encoding of movement plans and time across various timescales.

The cell-type underpinnings of the human functional cortical connectome

The functional properties of the human brain arise, in part, from the vast assortment of cell types that pattern the cerebral cortex. The cortical sheet can be broadly divided into distinct networks, which are embedded into processing streams, or gradients, that extend from unimodal systems through higher-order association territories. Here using microarray data from the Allen Human Brain Atlas and single-nucleus RNA-sequencing data from multiple cortical territories, we demonstrate that cell-type distributions are spatially coupled to the functional organization of cortex, as estimated through functional magnetic resonance imaging. Differentially enriched cells follow the spatial topography of both functional gradients and associated large-scale networks. Distinct cellular fingerprints were evident across networks, and a classifier trained on postmortem cell-type distributions was able to predict the functional network allegiance of cortical tissue samples. These data indicate that the in vivo organization of the cortical sheet is reflected in the spatial variability of its cellular composition.

Symmetry breaking organizes the brain's resting state manifold

Spontaneously fluctuating brain activity patterns that emerge at rest have been linked to the brain's health and cognition. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a mechanistic description of the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major data features across scales and imaging modalities. These include spontaneous high-amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability, and characteristic functional connectivity dynamics. When aggregated across cortical hierarchies, these match the profiles from empirical data. The understanding of the brain's resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function. In addition, it shifts the focus from the single recordings towards the brain's capacity to generate certain dynamics characteristic of health and pathology.

Bifurcation in space: Emergence of functional modularity in the neocortex

How does functional modularity emerge in a cortex composed of repeats of a canonical local circuit? Focusing on distributed working memory, we show that a rigorous description of bifurcation in space describes the emergence of modularity. A connectome-based model of monkey cortex displays bifurcation in space during decision-making and working memory, demonstrating this new concept's generality. In a generative model and multi-regional cortex models of both macaque monkey and mouse, we found an inverted-V-shaped profile of neuronal timescales across the cortical hierarchy during working memory, providing an experimentally testable prediction of modularity. The cortex displays simultaneously many bifurcations in space, so that the corresponding modules could potentially subserve distinct internal mental processes. Therefore, a distributed process subserves the brain's functional specificity. We propose that bifurcation in space, resulting from connectivity and macroscopic gradients of neurobiological properties across the cortex, represents a fundamental principle for understanding the brain's modular organization.

Flexibility of intrinsic neural timescales during distinct behavioral states

Recent neuroimaging studies demonstrate a heterogeneity of timescales prevalent in the brain's ongoing spontaneous activity, labeled intrinsic neural timescales (INT). At the same time, neural timescales also reflect stimulus- or task-related activity. The relationship of the INT during the brain's spontaneous activity with their involvement in task states including behavior remains unclear. To address this question, we combined calcium imaging data of spontaneously behaving mice and human electroencephalography (EEG) during rest and task states with computational modeling. We obtained four primary findings: (i) the distinct behavioral states can be accurately predicted from INT, (ii) INT become longer during behavioral states compared to rest, (iii) INT change from rest to task is correlated negatively with the variability of INT during rest, (iv) neural mass modeling shows a key role of recurrent connections in mediating the rest-task change of INT. Extending current findings, our results show the dynamic nature of the brain's INT in reflecting continuous behavior through their flexible rest-task modulation possibly mediated by recurrent connections.

Hierarchical gradients of multiple timescales in the mammalian forebrain

Many anatomical and physiological features of cortical circuits, ranging from the biophysical properties of synapses to the connectivity patterns among different neuron types, exhibit consistent variation along the hierarchical axis from sensory to association areas. Notably, the temporal correlation of neural activity at rest, known as the intrinsic timescale, increases systematically along this hierarchy in both primates and rodents, analogous to the increasing scale and complexity of spatial receptive fields. However, how the timescales for task-related activity vary across brain regions and whether their hierarchical organization appears consistently across different mammalian species remain unexplored. Here, we show that both the intrinsic timescale and those of task-related activity follow a similar hierarchical gradient in the cortices of monkeys, rats, and mice. We also found that these timescales covary similarly in both the cortex and basal ganglia, whereas the timescales of thalamic activity are shorter than cortical timescales and do not conform to the hierarchical order predicted by their cortical projections. These results suggest that the hierarchical gradient of cortical timescales might represent a universal feature of intracortical circuits in the mammalian brain.

Cognitive network interactions through communication subspaces in large-scale models of the neocortex

Neocortex-wide neural activity is organized into distinct networks of areas engaged in different cognitive processes. To elucidate the underlying mechanism of flexible network reconfiguration, we developed connectivity-constrained macaque and human whole-cortex models. In our model, within-area connectivity consists of a mixture of symmetric, asymmetric, and random motifs that give rise to stable (attractor) or transient (sequential) heterogeneous dynamics. Assuming sparse low-rank plus random inter-areal connectivity constrained by cognitive networks' activation maps, we show that our model captures key aspects of the cognitive networks' dynamics and interactions observed experimentally. In particular, the anti-correlation between the default mode network and the dorsal attention network. Communication between networks is shaped by the alignment of long-range communication subspaces with local connectivity motifs and is switchable in a bottom-up salience-dependent routing mechanism. Furthermore, the frontoparietal multiple-demand network displays a coexistence of stable and dynamic coding, suitable for top-down cognitive control. Our work provides a theoretical framework for understanding the dynamic routing in the cortical networks during cognition.

In Search of Transcriptomic Correlates of Neuronal Firing-Rate Adaptation across Subtypes, Regions and Species: A Patch-seq Analysis

Can the transcriptomic profile of a neuron predict its physiological properties? Using a Patch-seq dataset of the primary visual cortex, we addressed this question by focusing on spike rate adaptation (SRA), a well-known phenomenon that depends on small conductance calcium (Ca)-dependent potassium (SK) channels. We first show that in parvalbumin-expressing (PV) and somatostatin-expressing (SST) interneurons (INs), expression levels of genes encoding the ion channels underlying action potential generation are correlated with the half-width (HW) of spikes. Surprisingly, the SK encoding gene is not correlated with the degree of SRA (dAdap). Instead, genes that encode proteins upstream from the SK current are correlated with dAdap, a finding validated by a different dataset from the mouse's primary motor cortex that includes pyramidal cells and interneurons, as well as physiological datasets from multiple regions of macaque monkeys. Finally, we construct a minimal model to reproduce observed heterogeneity across cells, with testable predictions.

Decoding cortical chronotopy-Comparing the influence of different cortical organizational schemes

The brain's diverse intrinsic timescales enable us to perceive stimuli with varying temporal persistency. This study aimed to uncover the cortical organizational schemes underlying these variations, revealing the neural architecture for processing a wide range of sensory experiences. We collected resting-state fMRI, task-fMRI, and diffusion-weighted imaging data from 47 individuals. Based on this data, we extracted six organizational schemes: (1) the structural Rich Club (RC) architecture, shown to synchronize the connectome; (2) the structural Diverse Club architecture, as an alternative to the RC based on the network's module structure; (3) the functional uni-to-multimodal gradient, reflected in a wide range of structural and functional features; and (4) the spatial posterior/lateral-to-anterior/medial gradient, established for hierarchical levels of cognitive control. Also, we explored the effects of (5) structural graph theoretical measures of centrality and (6) cytoarchitectural differences. Using Bayesian model comparison, we contrasted the impact of these organizational schemes on (1) intrinsic resting-state timescales and (2) inter-subject correlation (ISC) from a task involving hierarchically nested digit sequences. As expected, resting-state timescales were slower in structural network hubs, hierarchically higher areas defined by the functional and spatial gradients, and thicker cortical regions. ISC analysis demonstrated hints for the engagement of higher cortical areas with more temporally persistent stimuli. Finally, the model comparison identified the uni-to-multimodal gradient as the best organizational scheme for explaining the chronotopy in both task and rest. Future research should explore the microarchitectural features that shape this gradient, elucidating how our brain adapts and evolves across different modes of processing.

Identification of movie encoding neurons enables movie recognition AI

Natural visual scenes are dominated by spatiotemporal image dynamics, but how the visual system integrates "movie" information over time is unclear. We characterized optic tectal neuronal receptive fields using sparse noise stimuli and reverse correlation analysis. Neurons recognized movies of ~200-600 ms durations with defined start and stop stimuli. Movie durations from start to stop responses were tuned by sensory experience though a hierarchical algorithm. Neurons encoded families of image sequences following trigonometric functions. Spike sequence and information flow suggest that repetitive circuit motifs underlie movie detection. Principles of frog topographic retinotectal plasticity and cortical simple cells are employed in machine learning networks for static image recognition, suggesting that discoveries of principles of movie encoding in the brain, such as how image sequences and duration are encoded, may benefit movie recognition technology. We built and trained a machine learning network that mimicked neural principles of visual system movie encoders. The network, named MovieNet, outperformed current machine learning image recognition networks in classifying natural movie scenes, while reducing data size and steps to complete the classification task. This study reveals how movie sequences and time are encoded in the brain and demonstrates that brain-based movie processing principles enable efficient machine learning.

Firing rate models for gamma oscillations in I-I and E-I networks

Firing rate models for describing the mean-field activities of neuronal ensembles can be used effectively to study network function and dynamics, including synchronization and rhythmicity of excitatory-inhibitory populations. However, traditional Wilson-Cowan-like models, even when extended to include an explicit dynamic synaptic activation variable, are found unable to capture some dynamics such as Interneuronal Network Gamma oscillations (ING). Use of an explicit delay is helpful in simulations at the expense of complicating mathematical analysis. We resolve this issue by introducing a dynamic variable, u, that acts as an effective delay in the negative feedback loop between firing rate (r) and synaptic gating of inhibition (s). In effect, u endows synaptic activation with second order dynamics. With linear stability analysis, numerical branch-tracking and simulations, we show that our r-u-s rate model captures some key qualitative features of spiking network models for ING. We also propose an alternative formulation, a v-u-s model, in which mean membrane potential v satisfies an averaged current-balance equation. Furthermore, we extend the framework to E-I networks. With our six-variable v-u-s model, we demonstrate in firing rate models the transition from Pyramidal-Interneuronal Network Gamma (PING) to ING by increasing the external drive to the inhibitory population without adjusting synaptic weights. Having PING and ING available in a single network, without invoking synaptic blockers, is plausible and natural for explaining the emergence and transition of two different types of gamma oscillations.

Strategies for motion- and respiration-robust estimation of fMRI intrinsic neural timescales

Intrinsic neural timescales (INT) reflect the time window of neural integration within a brain region and can be measured via resting-state functional magnetic resonance imaging (rs-fMRI). Despite the potential relevance of INT to cognition, brain organization, and neuropsychiatric illness, the influences of physiological artifacts on rs-fMRI INT have not been systematically considered. Two artifacts, head motion and respiration, pose serious issues in rs-fMRI studies. Here, we described their impact on INT estimation and tested the ability of two denoising strategies for mitigating these artifacts, high-motion frame censoring and global signal regression (GSR). We used a subset of the Human Connectome Project Young Adult (HCP-YA) dataset with runs annotated for breathing patterns (Lynch et al., 2020) and at least one "clean" (reference) run that had minimal head motion and no respiration artifacts; other runs from the same participants ( n = 46 ) were labeled as "non-clean." We found that non-clean runs exhibited brain-wide increases in INT compared with their respective clean runs and that the magnitude of error in INT between non-clean and clean runs correlated with the amount of head motion. Importantly, effect sizes were comparable with INT effects reported in the clinical literature. GSR and high-motion frame censoring improved the similarity between INT maps from non-clean runs and their respective clean run. Using a pseudo-random frame-censoring approach, we uncovered a relationship between the number of censored frames and both the mean INT and mean error, suggesting that frame censoring itself biases INT estimation. A group-level correction procedure reduced this bias and improved similarity between non-clean runs and their respective clean run. Based on our findings, we offer recommendations for rs-fMRI INT studies, which include implementing GSR and high-motion frame censoring with Lomb-Scargle interpolation of censored frames, and performing group-level correction of the bias introduced by frame censoring.

Between-area communication through the lens of within-area neuronal dynamics

A core problem in systems and circuits neuroscience is deciphering the origin of shared dynamics in neuronal activity: Do they emerge through local network interactions, or are they inherited from external sources? We explore this question with large-scale networks of spatially ordered spiking neuron models where a downstream network receives input from an upstream sender network. We show that linear measures of the communication between the sender and receiver networks can discriminate between emergent or inherited population dynamics. A match in the dimensionality of the sender and receiver population activities promotes faithful communication. In contrast, a nonlinear mapping between the sender to receiver activity, for example, through downstream emergent population-wide fluctuations, can impair linear communication. Our work exposes the benefits and limitations of linear measures when analyzing between-area communication in circuits with rich population-wide neuronal dynamics.

Temporal neural dynamics of understanding communicative intentions from speech prosody

Understanding the correct intention of a speaker is critical for social interaction. Speech prosody is an important source for understanding speakers' intentions during verbal communication. However, the neural dynamics by which the human brain translates the prosodic cues into a mental representation of communicative intentions in real time remains unclear. Here, we recorded EEG (electroencephalograph) while participants listened to dialogues. The prosodic features of the critical words at the end of sentences were manipulated to signal either suggestion, warning, or neutral intentions. The results showed that suggestion and warning intentions evoked enhanced late positive event-related potentials (ERPs) compared to the neutral condition. Linear mixed-effects model (LMEM) regression and representational similarity analysis (RSA) analyses revealed that these ERP effects were distinctively correlated with prosodic acoustic analysis, emotional valence evaluation, and intention interpretation in different time windows; The onset latency significantly increased as the processing level of abstractness and communicative intentionality increased. Neural representations of intention and emotional information emerged and parallelly persisted over a long time window, guiding the correct identification of communicative intention. These results provide new insights into understanding the structural components of intention processing and their temporal neural dynamics underlying communicative intention comprehension from speech prosody in online social interactions.

Temporal integration in human auditory cortex is predominantly yoked to absolute time, not structure duration

Sound structures such as phonemes and words have highly variable durations. Thus, there is a fundamental difference between integrating across absolute time (e.g., 100 ms) vs. sound structure (e.g., phonemes). Auditory and cognitive models have traditionally cast neural integration in terms of time and structure, respectively, but the extent to which cortical computations reflect time or structure remains unknown. To answer this question, we rescaled the duration of all speech structures using time stretching/compression and measured integration windows in the human auditory cortex using a new experimental/computational method applied to spatiotemporally precise intracranial recordings. We observed significantly longer integration windows for stretched speech, but this lengthening was very small (~5%) relative to the change in structure durations, even in non-primary regions strongly implicated in speech-specific processing. These findings demonstrate that time-yoked computations dominate throughout the human auditory cortex, placing important constraints on neurocomputational models of structure processing.

Lateral prefrontal cortex controls interplay between working memory and actions

Humans must often keep multiple task goals in mind, at different levels of priority and immediacy, while also interacting with the environment. We might need to remember information for an upcoming task while engaged in more immediate actions. Consequently, actively maintained working memory (WM) content may bleed into ongoing but unrelated motor behavior. Here, we experimentally test the impact of WM maintenance on action execution, and we transcranially stimulate lateral prefrontal cortex (PFC) to parse its functional contributions to WM-motor interactions. We first created a task scenario wherein human participants (both sexes) executed cued hand movements during WM maintenance. We manipulated the compatibility between WM and movement goals at the trial level and the statistical likelihood that the two would be compatible at the block level. We found that remembering directional words (e.g., 'left', 'down') biased the trajectory and speed of hand movements that occurred during the WM delay, but the bias was dampened in blocks when WM content predictably conflicted with movement goals. Then we targeted left lateral PFC with two different transcranial magnetic stimulation (TMS) protocols before participants completed the task. We found that an intermittent theta-burst protocol, which is thought to be excitatory, dampened sensitivity to block-level control demands (i.e., proactive control), while a continuous theta-burst protocol, which is thought to be inhibitory, dampened adaptation to trial-by-trial conflict (i.e., reactive control). Therefore, lateral PFC is involved in controlling the interplay between WM content and manual action, but different PFC mechanisms may support different time-scales of adaptive control.

Dendrites contribute to the gradient of intrinsic timescales encompassing cortical and subcortical brain networks

Introduction

Cytoarchitectonic studies have uncovered a correlation between higher levels of cortical hierarchy and reduced dendritic size. This hierarchical organization extends to the brain's timescales, revealing longer intrinsic timescales at higher hierarchical levels. However, estimating the contribution of single-neuron dendritic morphology to the hierarchy of timescales, which is typically characterized at a macroscopic level, remains challenging.

Method

Here we mapped the intrinsic timescales of six functional networks using functional magnetic resonance imaging (fMRI) data, and characterized the influence of neuronal dendritic size on intrinsic timescales of brain regions, utilizing a multicompartmental neuronal modeling approach based on digitally reconstructed neurons.

Results

The fMRI results revealed a hierarchy of intrinsic timescales encompassing both cortical and subcortical brain regions. The neuronal modeling indicated that neurons with larger dendritic structures exhibit shorter intrinsic timescales. Together these findings highlight the contribution of dendrites at the neuronal level to the hierarchy of intrinsic timescales at the whole-brain level.

Discussion

This study sheds light on the intricate relationship between neuronal structure, cytoarchitectonic maps, and the hierarchy of timescales in the brain.

The Neural and Computational Architecture of Feedback Dynamics in Mouse Cortex during Stimulus Report

Conscious reportability of visual input is associated with a bimodal neural response in the primary visual cortex (V1): an early-latency response coupled to stimulus features and a late-latency response coupled to stimulus report or detection. This late wave of activity, central to major theories of consciousness, is thought to be driven by the prefrontal cortex (PFC), responsible for "igniting" it. Here we analyzed two electrophysiological studies in mice performing different stimulus detection tasks and characterized neural activity profiles in three key cortical regions: V1, posterior parietal cortex (PPC), and PFC. We then developed a minimal network model, constrained by known connectivity between these regions, reproducing the spatiotemporal propagation of visual- and report-related activity. Remarkably, while PFC was indeed necessary to generate report-related activity in V1, this occurred only through the mediation of PPC. PPC, and not PFC, had the final veto in enabling the report-related late wave of V1 activity.

Timescales of learning in prefrontal cortex

The lateral prefrontal cortex (PFC) in humans and other primates is critical for immediate, goal-directed behaviour and working memory, which are classically considered distinct from the cognitive and neural circuits that support long-term learning and memory. Over the past few years, a reconsideration of this textbook perspective has emerged, in that different timescales of memory-guided behaviour are in constant interaction during the pursuit of immediate goals. Here, we will first detail how neural activity related to the shortest timescales of goal-directed behaviour (which requires maintenance of current states and goals in working memory) is sculpted by long-term knowledge and learning - that is, how the past informs present behaviour. Then, we will outline how learning across different timescales (from seconds to years) drives plasticity in the primate lateral PFC, from single neuron firing rates to mesoscale neuroimaging activity patterns. Finally, we will review how, over days and months of learning, dense local and long-range connectivity patterns in PFC facilitate longer-lasting changes in population activity by changing synaptic weights and recruiting additional neural resources to inform future behaviour. Our Review sheds light on how the machinery of plasticity in PFC circuits facilitates the integration of learned experiences across time to best guide adaptive behaviour.

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.

Searching for long timescales without fine tuning

Animal behavior occurs on timescales much longer than the response times of individual neurons. In many cases, it is plausible that these long timescales emerge from the recurrent dynamics of electrical activity in networks of neurons. In linear models, timescales are set by the eigenvalues of a dynamical matrix whose elements measure the strengths of synaptic connections between neurons. It is not clear to what extent these matrix elements need to be tuned to generate long timescales; in some cases, one needs not just a single long timescale but a whole range. Starting from the simplest case of random symmetric connections, we combine maximum entropy and random matrix theory methods to construct ensembles of networks, exploring the constraints required for long timescales to become generic. We argue that a single long timescale can emerge generically from realistic constraints, but a full spectrum of slow modes requires more tuning. Langevin dynamics that generates patterns of synaptic connections drawn from these ensembles involves a combination of Hebbian learning and activity-dependent synaptic scaling.

Maintenance of delay-period activity in working memory task is modulated by local network structure

Revealing the relationship between neural network structure and function is one central theme of neuroscience. In the context of working memory (WM), anatomical data suggested that the topological structure of microcircuits within WM gradient network may differ, and the impact of such structural heterogeneity on WM activity remains unknown. Here, we proposed a spiking neural network model that can replicate the fundamental characteristics of WM: delay-period neural activity involves association cortex but not sensory cortex. First, experimentally observed receptor expression gradient along the WM gradient network is reproduced by our network model. Second, by analyzing the correlation between different local structures and duration of WM activity, we demonstrated that small-worldness, excitation-inhibition balance, and cycle structures play crucial roles in sustaining WM-related activity. To elucidate the relationship between the structure and functionality of neural networks, structural circuit gradients in brain should also be subject to further measurement. Finally, combining anatomical data, we simulated the duration of WM activity across different brain regions, its maintenance relies on the interaction between local and distributed networks. Overall, network structural gradient and interaction between local and distributed networks are of great significance for WM.