Perception and propagation of activity through the cortical hierarchy is determined by neural variability

The brains of higher organisms are composed of anatomically and functionally distinct regions performing specialised tasks; but regions do not operate in isolation. Orchestration of complex behaviours requires communication between brain regions, but how neural activity dynamics are organised to facilitate reliable transmission is not well understood. We studied this process directly by generating neural activity that propagates between brain regions and drives behaviour, allowing us to assess how populations of neurons in sensory cortex cooperate to transmit information. We achieved this by imaging two hierarchically organised and densely interconnected regions, the primary and secondary somatosensory cortex (S1 and S2) in mice while performing two-photon photostimulation of S1 neurons and assigning behavioural salience to the photostimulation. We found that the probability of perception is determined not only by the strength of the photostimulation signal, but also by the variability of S1 neural activity. Therefore, maximising the signal-to-noise ratio of the stimulus representation in cortex is critical to its continued propagation downstream. Further, we show that propagated, behaviourally salient activity elicits balanced, persistent, and generalised activation of the downstream region. Hence, our work adds to existing understanding of cortical function by identifying how population activity is formatted to ensure robust transmission of information, allowing specialised brain regions to communicate and coordinate behaviour.

Categorical working memory codes in human visual cortex

Working memory contents are represented in neural activity patterns across multiple regions of the cortical hierarchy. It has remained unclear to which degree this reflects a specialization for different levels of abstraction. Here, we demonstrate that for color stimuli categorical codes are already present at the level of extrastriate visual cortex (V4 and VO1). Importantly, this categorical coding was observed during working memory, but not during perception.

Information flow across the cortical timescales hierarchy during narrative comprehension

When listening to spoken narratives, we must integrate information over multiple, concurrent timescales, building up from words to phrases to sentences to a coherent narrative. Recent evidence suggests that the brain relies on a chain of hierarchically organized areas with increasing temporal receptive windows to process naturalistic narratives. In this study, we use inter-subject functional connectivity to reveal a stimulus-driven information flow along the cortical hierarchy. Using cross-correlation analysis to estimate the time lags between six functional networks, we found a fixed temporal sequence of information flow, starting in early auditory areas, followed language areas, the attention network, and lastly the default mode network. This gradient is consistent across eight distinct stories but absent in resting-state and scrambled story data, indicating that the lag gradient reflects the construction of narrative features. Finally, we simulate a variety of narrative integration models and demonstrate that nested narrative structure along with the gradual accumulation of information within the boundaries of linguistic events at each level of the processing hierarchy is sufficient to reproduce the lag gradient. Taken together, this study provides a computational framework for how information flows along the cortical hierarchy during narrative comprehension.

Attention controls multisensory perception via 2 distinct mechanisms at different levels of the cortical hierarchy

To form a percept of the multisensory world, the brain needs to integrate signals from common sources weighted by their reliabilities and segregate those from independent sources. Previously, we have shown that anterior parietal cortices combine sensory signals into representations that take into account the signals’ causal structure (i.e., common versus independent sources) and their sensory reliabilities as predicted by Bayesian causal inference. The current study asks to what extent and how attentional mechanisms can actively control how sensory signals are combined for perceptual inference. In a pre- and postcueing paradigm, we presented observers with audiovisual signals at variable spatial disparities. Observers were precued to attend to auditory or visual modalities prior to stimulus presentation and postcued to report their perceived auditory or visual location. Combining psychophysics, functional magnetic resonance imaging (fMRI), and Bayesian modelling, we demonstrate that the brain moulds multisensory inference via 2 distinct mechanisms. Prestimulus attention to vision enhances the reliability and influence of visual inputs on spatial representations in visual and posterior parietal cortices. Poststimulus report determines how parietal cortices flexibly combine sensory estimates into spatial representations consistent with Bayesian causal inference. Our results show that distinct neural mechanisms control how signals are combined for perceptual inference at different levels of the cortical hierarchy.

Supercritical dynamics at the edge-of-chaos underlies optimal decision-making

Critical dynamics, characterized by scale-free neuronal avalanches, is thought to underlie optimal function in the sensory cortices by maximizing information transmission, capacity, and dynamic range. In contrast, deviations from criticality have not yet been considered to support any cognitive processes. Nonetheless, neocortical areas related to working memory and decision-making seem to rely on long-lasting periods of ignition-like persistent firing. Such firing patterns are reminiscent of supercritical states where runaway excitation dominates the circuit dynamics. In addition, a macroscopic gradient of the relative density of Somatostatin (SST+) and Parvalbumin (PV+) inhibitory interneurons throughout the cortical hierarchy has been suggested to determine the functional specialization of low- versus high-order cortex. These observations thus raise the question of whether persistent activity in high-order areas results from the intrinsic features of the neocortical circuitry. We used an attractor model of the canonical cortical circuit performing a perceptual decision-making task to address this question. Our model reproduces the known saddle-node bifurcation where persistent activity emerges, merely by increasing the SST+/PV+ ratio while keeping the input and recurrent excitation constant. The regime beyond such a phase transition renders the circuit increasingly sensitive to random fluctuations of the inputs -i.e., chaotic-, defining an optimal SST+/PV+ ratio around the edge-of-chaos. Further, we show that both the optimal SST+/PV+ ratio and the region of the phase transition decrease monotonically with increasing input noise. This suggests that cortical circuits regulate their intrinsic dynamics via inhibitory interneurons to attain optimal sensitivity in the face of varying uncertainty. Hence, on the one hand, we link the emergence of supercritical dynamics at the edge-of-chaos to the gradient of the SST+/PV+ ratio along the cortical hierarchy, and, on the other hand, explain the behavioral effects of the differential regulation of SST+ and PV+ interneurons by neuromodulators like acetylcholine in the presence of input uncertainty.

Intrinsic timescales across the basal ganglia

Recent studies have shown that temporal stability of the neuronal activity over time can be estimated by the structure of the spike-count autocorrelation of neuronal populations. This estimation, called the intrinsic timescale, has been computed for several cortical areas and can be used to propose a cortical hierarchy reflecting a scale of temporal receptive windows between areas. In this study, we performed an autocorrelation analysis on neuronal populations of three basal ganglia (BG) nuclei, including the striatum and the subthalamic nucleus (STN), the input structures of the BG, and the external globus pallidus (GPe). The analysis was performed during the baseline period of a motivational visuomotor task in which monkeys had to apply different amounts of force to receive different amounts of reward. We found that the striatum and the STN have longer intrinsic timescales than the GPe. Moreover, our results allow for the placement of these subcortical structures within the already-defined scale of cortical temporal receptive windows. Estimates of intrinsic timescales are important in adding further constraints in the development of computational models of the complex dynamics among these nuclei and throughout cortico-BG-thalamo-cortical loops.

The Directionality of Fronto-Posterior Brain Connectivity Is Associated with the Degree of Individual Autistic Traits

Altered patterns of brain connectivity have been found in autism spectrum disorder (ASD) and associated with specific symptoms and behavioral features. Growing evidence suggests that the autistic peculiarities are not confined to the clinical population but extend along a continuum between healthy and maladaptive conditions. The aim of this study was to investigate whether a differentiated connectivity pattern could also be tracked along the continuum of autistic traits in a non-clinical population. A Granger causality analysis conducted on a resting-state EEG recording showed that connectivity along the posterior-frontal gradient is sensitive to the magnitude of individual autistic traits and mostly conveyed through fast oscillatory activity. Specifically, participants with higher autistic traits were characterized by a prevalence of ascending connections starting from posterior regions ramping the cortical hierarchy. These findings point to the presence of a tendency within the neural mapping of individuals with higher autistic features in conveying proportionally more bottom-up information. This pattern of findings mimics those found in clinical forms of autism, supporting the idea of a neurobiological continuum between autistic traits and ASD.

Neural Mechanisms and Psychology of Psychedelic Ego Dissolution

Neuroimaging studies of psychedelics have advanced our understanding of hierarchical brain organisation and the mechanisms underlying their subjective and therapeutic effects. The primary mechanism of action of classic psychedelics is binding to serotonergic 5HT2A receptors. Agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy that can have a profound effect on hierarchical message passing in the brain. Here, we review the cognitive and neuroimaging evidence for the effects of psychedelics; in particular, their influence on selfhood and subject-object boundaries—known as ego dissolution—surmised to underwrite their subjective and therapeutic effects. Agonist of 5HT2A receptors, located at the apex of the cortical hierarchy may have a particularly powerful effect on sentience and consciousness. These effects can endure well after the pharmacological half life, suggesting that psychedelics may have long-term effects on neural plasticity – that may play a role in their therapeutic efficacy. Psychologically, this may be accompanied by a surrender of ego resistance that increases the repertoire of perceptual hypotheses, including those that undergird selfhood. We consider the interaction between serotonergic neuromodulation and sentience through the lens of hierarchical predictive coding, which speaks to the value of psychedelics in understanding how we make sense of the world—and specific predictions about effective connectivity in cortical hierarchies that can be tested using functional neuroimaging.

Mapping electromagnetic networks to haemodynamic networks in the human brain

Whole-brain neural communication is typically estimated from statistical associations among electromagnetic or haemodynamic time-series. The relationship between functional network architectures recovered from these two types of neural activity remains unknown. Here we map electromagnetic networks (measured using magnetoencephalography; MEG) to haemodynamic networks (measured using functional magnetic resonance imaging; fMRI). We find that the relationship between the two modalities is regionally heterogeneous and systematically follows the cortical hierarchy, with close correspondence in unimodal cortex and poor correspondence in transmodal cortex, potentially reflecting patterns of laminar differentiation, recurrent subcortical input and neurovascular coupling. Correspondence between the two is largely driven by slower rhythms, particularly the delta (2-4 Hz) and beta (15-29 Hz) frequency band. Moreover, haemodynamic connectivity cannot be explained by electromagnetic activity in a single frequency band, but rather arises from the mixing of multiple neurophysiological rhythms. Collectively, these findings demonstrate highly organized but only partly overlapping patterns of connectivity in MEG and fMRI functional networks, opening fundamentally new avenues for studying the relationship between cortical micro-architecture and multi-modal connectivity patterns.