The laminar integration of sensory inputs with feedback signals in human cortex

The cortex constitutes the largest area of the human brain. Yet we have only a basic understanding of how the cortex performs one vital function: the integration of sensory signals (carried by feedforward pathways) with internal representations (carried by feedback pathways). A multi-scale, multi-species approach is essential for understanding the site of integration, computational mechanism and functional role of this processing. To improve our knowledge we must rely on brain imaging with improved spatial and temporal resolution and paradigms which can measure internal processes in the human brain, and on the bridging of disciplines in order to characterize this processing at cellular and circuit levels. We highlight apical amplification as one potential mechanism for integrating feedforward and feedback inputs within pyramidal neurons in the rodent brain. We reflect on the challenges and progress in applying this model neuronal process to the study of human cognition. We conclude that cortical-layer specific measures in humans will be an essential contribution for better understanding the landscape of information in cortical feedback, helping to bridge the explanatory gap

Brain rhythms of pain

Pain is an integrative phenomenon that results from dynamic interactions between sensory and contextual (i.e., cognitive, emotional, and motivational) processes. In the brain the experience of pain is associated with neuronal oscillations and synchrony at different frequencies. However, an overarching framework for the significance of oscillations for pain remains lacking. Recent concepts relate oscillations at different frequencies to the routing of information flow in the brain and the signaling of predictions and prediction errors. The application of these concepts to pain promises insights into how flexible routing of information flow coordinates diverse processes that merge into the experience of pain. Such insights might have implications for the understanding and treatment of chronic pain

Loss of brain inter-frequency hubs in Alzheimer's disease

Alzheimer's disease (AD) causes alterations of brain network structure and function. The latter consists of connectivity changes between oscillatory processes at different frequency channels. We proposed a multi-layer network approach to analyze multiple-frequency brain networks inferred from magnetoencephalographic recordings during resting-states in AD subjects and age-matched controls. Main results showed that brain networks tend to facilitate information propagation across different frequencies, as measured by the multi-participation coefficient (MPC). However, regional connectivity in AD subjects was abnormally distributed across frequency bands as compared to controls, causing significant decreases of MPC. This effect was mainly localized in association areas and in the cingulate cortex, which acted, in the healthy group, as a true inter-frequency hub. MPC values significantly correlated with memory impairment of AD subjects, as measured by the total recall score. Most predictive regions belonged to components of the default-mode network that are typically affected by atrophy, metabolism disruption and amyloid-beta deposition. We evaluated the diagnostic power of the MPC and we showed that it led to increased classification accuracy (78.39%) and sensitivity (91.11%). These findings shed new light on the brain functional alterations underlying AD and provide analytical tools for identifying multi-frequency neural mechanisms of brain diseases.Comment: 27 pages, 6 figures, 3 tables, 3 supplementary figure

Electrophysiological Low-Frequency Coherence and Cross-Frequency Coupling Contribute to BOLD Connectivity

SummaryBrain networks are commonly defined using correlations between blood oxygen level-dependent (BOLD) signals in different brain areas. Although evidence suggests that gamma-band (30–100 Hz) neural activity contributes to local BOLD signals, the neural basis of interareal BOLD correlations is unclear. We first defined a visual network in monkeys based on converging evidence from interareal BOLD correlations during a fixation task, task-free state, and anesthesia, and then simultaneously recorded local field potentials (LFPs) from the same four network areas in the task-free state. Low-frequency oscillations (<20 Hz), and not gamma activity, predominantly contributed to interareal BOLD correlations. The low-frequency oscillations also influenced local processing by modulating gamma activity within individual areas. We suggest that such cross-frequency coupling links local BOLD signals to BOLD correlations across distributed networks

A multimodal perspective on the composition of cortical oscillations.

An expanding corpus of research details the relationship between functional magnetic resonance imaging (fMRI) measures and neuronal network oscillations. Typically, integrated electroencephalography and fMRI, or parallel magnetoencephalography (MEG) and fMRI are used to draw inference about the consanguinity of BOLD and electrical measurements. However, there is a relative dearth of information about the relationship between E/MEG and the focal networks from which these signals emanate. Consequently, the genesis and composition of E/MEG oscillations requires further clarification. Here we aim to contribute to understanding through a series of parallel measurements of primary motor cortex (M1) oscillations, using human MEG and in vitro rodent local field potentials. We compare spontaneous activity in the ∼10 Hz mu and 15-30 Hz beta frequency ranges and compare MEG signals with independent and integrated layers III and V (LIII/LV) from in vitro recordings. We explore the mechanisms of oscillatory generation, using specific pharmacological modulation with the GABA-A alpha-1 subunit modulator zolpidem. Finally, to determine the contribution of cortico-cortical connectivity, we recorded in vitro M1, during an incision to sever lateral connections between M1 and S1 cortices. We demonstrate that frequency distribution of MEG signals appear have closer statistically similarity with signals from integrated rather than independent LIII/LV laminae. GABAergic modulation in both modalities elicited comparable changes in the power of the beta band. Finally, cortico-cortical connectivity in sensorimotor cortex (SMC) appears to directly influence the power of the mu rhythm in LIII. These findings suggest that the MEG signal is an amalgam of outputs from LIII and LV, that multiple frequencies can arise from the same cortical area and that in vitro and MEG M1 oscillations are driven by comparable mechanisms. Finally, cortico-cortical connectivity is reflected in the power of the SMC mu rhythm

A multimodal perspective on the composition of cortical oscillations:frontiers in human neuroscience

An expanding corpus of research details the relationship between functional magnetic resonance imaging (fMRI) measures and neuronal network oscillations. Typically, integratedelectroencephalography(EEG) and fMRI,orparallel magnetoencephalography (MEG) and fMRI are used to draw inference about the consanguinity of BOLD and electrical measurements. However, there is a relative dearth of information about the relationship between E/MEG and the focal networks from which these signals emanate. Consequently, the genesis and composition of E/MEG oscillations requires further clarification. Here we aim to contribute to understanding through a series of parallel measurements of primary motor cortex (M1) oscillations, using human MEG and in-vitro rodent local field potentials. We compare spontaneous activity in the ~10Hz mu and 15-30Hz beta frequency ranges and compare MEG signals with independent and integrated layers III and V(LIII/LV) from in vitro recordings. We explore the mechanisms of oscillatory generation, using specific pharmacological modulation with the GABA-A alpha-1 subunit modulator zolpidem. Finally, to determine the contribution of cortico-cortical connectivity, we recorded in-vitro M1, during an incision to sever lateral connections between M1 and S1 cortices. We demonstrate that frequency distribution of MEG signals appear have closer statistically similarity with signals from integrated rather than independent LIII/LV laminae. GABAergic modulation in both modalities elicited comparable changes in the power of the beta band. Finally, cortico-cortical connectivity in sensorimotor cortex (SMC) appears to directly influence the power of the mu rhythm in LIII. These findings suggest that the MEG signal is an amalgam of outputs from LIII and LV, that multiple frequencies can arise from the same cortical area and that in vitro and MEG M1 oscillations are driven by comparable mechanisms. Finally, corticocortical connectivity is reflected in the power of the SMC mu rhythm. © 2013 Ronnqvist, Mcallister, Woodhall, Stanford and Hall

Emergent complex neural dynamics

A large repertoire of spatiotemporal activity patterns in the brain is the basis for adaptive behaviour. Understanding the mechanism by which the brain's hundred billion neurons and hundred trillion synapses manage to produce such a range of cortical configurations in a flexible manner remains a fundamental problem in neuroscience. One plausible solution is the involvement of universal mechanisms of emergent complex phenomena evident in dynamical systems poised near a critical point of a second-order phase transition. We review recent theoretical and empirical results supporting the notion that the brain is naturally poised near criticality, as well as its implications for better understanding of the brain

Coupling between gamma-band power and cerebral blood volume during recurrent acute neocortical seizures

Characterization of neural and hemodynamic biomarkers of epileptic activity that can be measured using non-invasive techniques is fundamental to the accurate identification of the epileptogenic zone (EZ) in the clinical setting. Recently, oscillations at gamma-band frequencies and above (>30 Hz) have been suggested to provide valuable localizing information of the EZ and track cortical activation associated with epileptogenic processes. Although a tight coupling between gamma-band activity and hemodynamic-based signals has been consistently demonstrated in non-pathological conditions, very little is known about whether such a relationship is maintained in epilepsy and the laminar etiology of these signals. Confirmation of this relationship may elucidate the underpinnings of perfusion-based signals in epilepsy and the potential value of localizing the EZ using hemodynamic correlates of pathological rhythms. Here, we use concurrent multi-depth electrophysiology and 2-dimensional optical imaging spectroscopy to examine the coupling between multi-band neural activity and cerebral blood volume (CBV) during recurrent acute focal neocortical seizures in the urethane-anesthetized rat. We show a powerful correlation between gamma-band power (25-90 Hz) and CBV across cortical laminae, in particular layer 5, and a close association between gamma measures and multi-unit activity (MUA). Our findings provide insights into the laminar electrophysiological basis of perfusion-based imaging signals in the epileptic state and may have implications for further research using non-invasive multi-modal techniques to localize epileptogenic tissue

The mechanisms of tinnitus: perspectives from human functional neuroimaging

In this review, we highlight the contribution of advances in human neuroimaging to the current understanding of central mechanisms underpinning tinnitus and explain how interpretations of neuroimaging data have been guided by animal models. The primary motivation for studying the neural substrates of tinnitus in humans has been to demonstrate objectively its representation in the central auditory system and to develop a better understanding of its diverse pathophysiology and of the functional interplay between sensory, cognitive and affective systems. The ultimate goal of neuroimaging is to identify subtypes of tinnitus in order to better inform treatment strategies. The three neural mechanisms considered in this review may provide a basis for TI classification. While human neuroimaging evidence strongly implicates the central auditory system and emotional centres in TI, evidence for the precise contribution from the three mechanisms is unclear because the data are somewhat inconsistent. We consider a number of methodological issues limiting the field of human neuroimaging and recommend approaches to overcome potential inconsistency in results arising from poorly matched participants, lack of appropriate controls and low statistical power

Maturation trajectories of cortical resting-state networks depend on the mediating frequency band

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13–30 Hz) and gamma (31–80 Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development.This work was supported by grants from the Nancy Lurie Marks Family Foundation (TK, SK, MGK), Autism Speaks (TK), The Simons Foundation (SFARI 239395, TK), The National Institute of Child Health and Development (R01HD073254, TK), National Institute for Biomedical Imaging and Bioengineering (P41EB015896, 5R01EB009048, MSH), and the Cognitive Rhythms Collaborative: A Discovery Network (NFS 1042134, MSH). (Nancy Lurie Marks Family Foundation; Autism Speaks; SFARI 239395 - Simons Foundation; R01HD073254 - National Institute of Child Health and Development; P41EB015896 - National Institute for Biomedical Imaging and Bioengineering; 5R01EB009048 - National Institute for Biomedical Imaging and Bioengineering; NFS 1042134 - Cognitive Rhythms Collaborative: A Discovery Network