Four concurrent feedforward and feedback networks with different roles in the visual cortical hierarchy

Evolving alterations of structural organization and functional connectivity in feedforward neural networks after induced P301L tau mutation

Reciprocal structure-function relationships underlie both healthy and pathological behaviours in complex neural networks. Thus, understanding neuropathology and network dysfunction requires a thorough investigation of the complex interactions between structural and functional network reconfigurations in response to perturbation. Such adaptations are often difficult to study in vivo. For example, subtle, evolving changes in synaptic connectivity, transmission and the electrophysiological shift from healthy to pathological states, for example alterations that may be associated with evolving neurodegenerative disease, such as Alzheimer's, are difficult to study in the brain. Engineered in vitro neural networks are powerful models that enable selective targeting, manipulation and monitoring of dynamic neural network behaviour at the micro- and mesoscale in physiological and pathological conditions. In this study, we engineered feedforward cortical neural networks using two-nodal microfluidic devices with controllable connectivity interfaced with microelectrode arrays (mMEAs). We induced P301L mutated tau protein to the presynaptic node of these networks and monitored network dynamics over three weeks. Induced perturbation resulted in altered structural organization and extensive axonal retraction starting in the perturbed node. Perturbed networks also exhibited functional changes in intranodal activity, which manifested as an overall decline in both firing rate and bursting activity, with a progressive increase in synchrony over time and a decrease in internodal signal propagation between pre- and post-synaptic nodes. These results provide insights into dynamic structural and functional reconfigurations at the micro- and mesoscale as a result of evolving pathology and illustrate the utility of engineered networks as models of network function and dysfunction.

Visual experience reduces the spatial redundancy between cortical feedback inputs and primary visual cortex neurons

The role of experience in the organization of cortical feedback (FB) remains unknown. We measured the effects of manipulating visual experience on the retinotopic specificity of supragranular and infragranular projections from the lateromedial (LM) visual area to layer (L)1 of the mouse primary visual cortex (V1). LM inputs were, on average, retinotopically matched with V1 neurons in normally and dark-reared mice, but visual exposure reduced the fraction of spatially overlapping inputs to V1. FB inputs from L5 conveyed more surround information to V1 than those from L2/3. The organization of LM inputs from L5 depended on their orientation preference and was disrupted by dark rearing. These observations were recapitulated by a model where visual experience minimizes receptive field overlap between LM inputs and V1 neurons. Our results provide a mechanism for the dependency of surround modulations on visual experience and suggest how expected interarea coactivation patterns are learned in cortical circuits.

Laminar dissociation of feedforward and feedback in high-level ventral visual cortex during imagery and perception

Visual imagery and perception share neural machinery but rely on different information flow. While perception is driven by the integration of sensory feedforward and internally generated feedback information, imagery relies on feedback only. This suggests that although imagery and perception may activate overlapping brain regions, they do so in informationally distinctive ways. Using lamina-resolved MRI at 7 T, we measured the neural activity during imagery and perception of faces and scenes in high-level ventral visual cortex at the mesoscale of laminar organization that distinguishes feedforward from feedback signals. We found distinctive laminar profiles for imagery and perception of scenes and faces in the parahippocampal place area and the fusiform face area, respectively. Our findings provide insight into the neural basis of the phenomenology of visual imagery versus perception and shed new light into the mesoscale organization of feedforward and feedback information flow in high-level ventral visual cortex.

The direction of theta and alpha travelling waves modulates human memory processing

To support a range of behaviours, the brain must flexibly coordinate neural activity across widespread brain regions. One potential mechanism for this coordination is a travelling wave, in which a neural oscillation propagates across the brain while organizing the order and timing of activity across regions. Although travelling waves are present across the brain in various species, their potential functional relevance has remained unknown. Here, using rare direct human brain recordings, we demonstrate a distinct functional role for travelling waves of theta- and alpha-band (2-13 Hz) oscillations in the cortex. Travelling waves propagate in different directions during separate cognitive processes. In episodic memory, travelling waves tended to propagate in a posterior-to-anterior direction during successful memory encoding and in an anterior-to-posterior direction during recall. Because travelling waves of oscillations correspond to local neuronal spiking, these patterns indicate that rhythmic pulses of activity move across the brain in different directions for separate behaviours. More broadly, our results suggest a fundamental role for travelling waves and oscillations in dynamically coordinating neural connectivity, by flexibly organizing the timing and directionality of network interactions across the cortex to support cognition and behaviour.

A ubiquitous spectrolaminar motif of local field potential power across the primate cortex

The mammalian cerebral cortex is anatomically organized into a six-layer motif. It is currently unknown whether a corresponding laminar motif of neuronal activity patterns exists across the cortex. Here we report such a motif in the power of local field potentials (LFPs). Using laminar probes, we recorded LFPs from 14 cortical areas across the cortical hierarchy in five macaque monkeys. The laminar locations of recordings were histologically identified by electrolytic lesions. Across all areas, we found a ubiquitous spectrolaminar pattern characterized by an increasing deep-to-superficial layer gradient of high-frequency power peaking in layers 2/3 and an increasing superficial-to-deep gradient of alpha-beta power peaking in layers 5/6. Laminar recordings from additional species showed that the spectrolaminar pattern is highly preserved among primates-macaque, marmoset and human-but more dissimilar in mouse. Our results suggest the existence of a canonical layer-based and frequency-based mechanism for cortical computation.

Cellular and laminar architecture: A short history and commentary

The feedforward/feedback classification, as originally stated in relation to early visual areas in the macaque monkey, has had a significant influence on ideas of laminar interactions, area reciprocity, and cortical hierarchical organization. In some contrast with this macroscale "laminar connectomics," a more cellular approach to cortical connections, as briefly surveyed here, points to a still underappreciated heterogeneity of neuronal subtypes and complex microcircuitries. From the perspective of heterogeneities, the question of how brain regions interact and influence each other quickly leads to discussions about concurrent hierarchical and nonhierarchical cortical features, brain organization as a multiscale system forming nested groups and hierarchies, connectomes annotated by multiple biological attributes, and interleaved and overlapping scales of organization.

Functional geometry of auditory cortical resting state networks derived from intracranial electrophysiology

Understanding central auditory processing critically depends on defining underlying auditory cortical networks and their relationship to the rest of the brain. We addressed these questions using resting state functional connectivity derived from human intracranial electroencephalography. Mapping recording sites into a low-dimensional space where proximity represents functional similarity revealed a hierarchical organization. At a fine scale, a group of auditory cortical regions excluded several higher-order auditory areas and segregated maximally from the prefrontal cortex. On mesoscale, the proximity of limbic structures to the auditory cortex suggested a limbic stream that parallels the classically described ventral and dorsal auditory processing streams. Identities of global hubs in anterior temporal and cingulate cortex depended on frequency band, consistent with diverse roles in semantic and cognitive processing. On a macroscale, observed hemispheric asymmetries were not specific for speech and language networks. This approach can be applied to multivariate brain data with respect to development, behavior, and disorders.

Integrating Statistical and Machine Learning Approaches for Neural Classification

Neurons can code for multiple variables simultaneously and neuroscientists are often interested in classifying neurons based on their receptive field properties. Statistical models provide powerful tools for determining the factors influencing neural spiking activity and classifying individual neurons. However, as neural recording technologies have advanced to produce simultaneous spiking data from massive populations, classical statistical methods often lack the computational efficiency required to handle such data. Machine learning (ML) approaches are known for enabling efficient large scale data analyses; however, they typically require massive training sets with balanced data, along with accurate labels to fit well. Additionally, model assessment and interpretation are often more challenging for ML than for classical statistical methods. To address these challenges, we develop an integrated framework, combining statistical modeling and machine learning approaches to identify the coding properties of neurons from large populations. In order to demonstrate this framework, we apply these methods to data from a population of neurons recorded from rat hippocampus to characterize the distribution of spatial receptive fields in this region.