The Functional Role of Large-scale Brain Network Coordination in Placebo-induced Anxiolysis

2018 ◽  
Vol 29 (8) ◽  
pp. 3201-3210 ◽  
Author(s):  
Benjamin Meyer ◽  
Kenneth S L Yuen ◽  
Victor Saase ◽  
Raffael Kalisch
Keyword(s):  
Functional Role ◽  
Large Scale ◽  
Activity Patterns ◽  
Clinical Importance ◽  
Mental States ◽  
Brain Network ◽  
Anxiety Reduction ◽  
Anterior Cingulate ◽  
Network Activity

Abstract Anxiety reduction through mere expectation of anxiolytic treatment effects (placebo anxiolysis) has enormous clinical importance. Recent behavioral and electrophysiological data suggest that placebo anxiolysis involves reduced vigilance and enhanced internalization of attention; however, the underlying neurobiological mechanisms are not yet clear. Given the fundamental function of intrinsic connectivity networks (ICNs) in basic cognitive processes, we investigated ICN activity patterns associated with externally and internally directed mental states under the influence of an anxiolytic placebo medication. Based on recent findings, we specifically analyzed the functional role of the rostral anterior cingulate cortex (rACC) in coordinating placebo-dependent cue-related (phasic) and cue-unrelated (sustained) network activity. Under placebo, we observed a down-regulation of the entire salience network (SN), particularly in response to threatening cues. The rACC exhibited enhanced cue-unrelated functional connectivity (FC) with the SN, which correlated with reductions in tonic arousal and anxiety. Hence, apart from the frequently reported modulation of aversive cue responses, the rACC appears to be crucially involved in exerting a tonically dampening control over salience-responsive structures. In line with a more internally directed mental state, we also found enhanced FC within the default mode network (DMN), again predicting reductions in anxiety under placebo.

scholarly journals Selective entrainment of gamma subbands by different slow network oscillations

2017 ◽  
Vol 114 (17) ◽  
pp. 4519-4524 ◽  
Author(s):  
Weiwei Zhong ◽  
Mareva Ciatipis ◽  
Thérèse Wolfenstetter ◽  
Jakob Jessberger ◽  
Carola Müller ◽  
...  
Keyword(s):  
Large Scale ◽  
Dorsal Hippocampus ◽  
Activity Patterns ◽  
Specific Interaction ◽  
Brain Regions ◽  
Network Activity ◽  
Gamma Frequency ◽  
Cornu Ammonis ◽  
Oscillatory Coupling ◽  
Fast Oscillations

Theta oscillations (4–12 Hz) are thought to provide a common temporal reference for the exchange of information among distant brain networks. On the other hand, faster gamma-frequency oscillations (30–160 Hz) nested within theta cycles are believed to underlie local information processing. Whether oscillatory coupling between global and local oscillations, as showcased by theta-gamma coupling, is a general coding mechanism remains unknown. Here, we investigated two different patterns of oscillatory network activity, theta and respiration-induced network rhythms, in four brain regions of freely moving mice: olfactory bulb (OB), prelimbic cortex (PLC), parietal cortex (PAC), and dorsal hippocampus [cornu ammonis 1 (CA1)]. We report differential state- and region-specific coupling between the slow large-scale rhythms and superimposed fast oscillations. During awake immobility, all four regions displayed a respiration-entrained rhythm (RR) with decreasing power from OB to CA1, which coupled exclusively to the 80- to 120-Hz gamma subband (γ2). During exploration, when theta activity was prevailing, OB and PLC still showed exclusive coupling of RR with γ2 and no theta-gamma coupling, whereas PAC and CA1 switched to selective coupling of theta with 40- to 80-Hz (γ1) and 120- to 160-Hz (γ3) gamma subbands. Our data illustrate a strong, specific interaction between neuronal activity patterns and respiration. Moreover, our results suggest that the coupling between slow and fast oscillations is a general brain mechanism not limited to the theta rhythm.

Identifying neuroanatomical signatures in insomnia and migraine comorbidity

SLEEP ◽  
2020 ◽  
Author(s):  
Kun-Hsien Chou ◽  
Pei-Lin Lee ◽  
Chih-Sung Liang ◽  
Jiunn-Tay Lee ◽  
Hung-Wen Kao ◽  
...  
Keyword(s):  
Large Scale ◽  
Gray Matter Volume ◽  
Brain Structures ◽  
Brain Network ◽  
Structural Covariance ◽  
Matter Volume ◽  
The Right ◽  
Global Brain ◽  
The Brain

Abstract Study Objectives While insomnia and migraine are often comorbid, the shared and distinct neuroanatomical substrates underlying these disorders and the brain structures associated with the comorbidity are unknown. We aimed to identify patterns of neuroanatomical substrate alterations associated with migraine and insomnia comorbidity. Methods High-resolution T1-weighted images were acquired from subjects with insomnia, migraine, and comorbid migraine and insomnia, respectively, and healthy controls (HC). Direct group comparisons with HC followed by conjunction analyses identified shared regional gray matter volume (GMV) alterations between the disorders. To further examine large-scale anatomical network changes, a seed-based structural covariance network (SCN) analysis was applied. Conjunction analyses also identified common SCN alterations in two disease groups, and we further evaluated these shared regional and global neuroanatomical signatures in the comorbid group. Results Compared with controls, patients with migraine and insomnia showed GMV changes in the cerebellum and the lingual, precentral, and postcentral gyri (PCG). The bilateral PCG were common GMV alteration sites in both groups, with decreased structural covariance integrity observed in the cerebellum. In patients with comorbid migraine and insomnia, shared regional GMV and global SCN changes were consistently observed. The GMV of the right PCG also correlated with sleep quality in these patients. Conclusion These findings highlight the specific role of the PCG in the shared pathophysiology of insomnia and migraine from a regional and global brain network perspective. These multilevel neuroanatomical changes could be used as potential image markers to decipher the comorbidity of the two disorders.

Probing the “Default Network Interference Hypothesis” With EEG: An RDoC Approach Focused on Attention

2019 ◽  
Vol 50 (6) ◽  
pp. 404-412 ◽  
Author(s):  
Berrie Gerrits ◽  
Madelon A. Vollebregt ◽  
Sebastian Olbrich ◽  
Hanneke van Dijk ◽  
Donna Palmer ◽  
...  
Keyword(s):  
Sustained Attention ◽  
Resting State ◽  
Mental Health Research ◽  
Brain Network ◽  
Anterior Cingulate ◽  
Network Activity ◽  
Gamma Activity ◽  
Default Mode ◽  
Component Loading ◽  
Interference Hypothesis

Studies have shown that specific networks (default mode network [DMN] and task positive network [TPN]) activate in an anticorrelated manner when sustaining attention. Related EEG studies are scarce and often lack behavioral validation. We performed independent component analysis (ICA) across different frequencies (source-level), using eLORETA-ICA, to extract brain-network activity during resting-state and sustained attention. We applied ICA to the voxel domain, similar to functional magnetic resonance imaging methods of analyses. The obtained components were contrasted and correlated to attentional performance (omission errors) in a large sample of healthy subjects (N = 1397). We identified one component that robustly correlated with inattention and reflected an anticorrelation of delta activity in the anterior cingulate and precuneus, and delta and theta activity in the medial prefrontal cortex and with alpha and gamma activity in medial frontal regions. We then compared this component between optimal and suboptimal attentional performers. For the latter group, we observed a greater change in component loading between resting-state and sustained attention than for the optimal performers. Following the National Institute of Mental Health Research Domain Criteria (RDoC) approach, we prospectively replicated and validated these findings in subjects with attention deficit/hyperactivity disorder. Our results provide further support for the “default mode interference hypothesis.”

scholarly journals Brain network constraints and recurrent neural networks reproduce unique trajectories and state transitions seen over the span of minutes in resting-state fMRI

2020 ◽  
Vol 4 (2) ◽  
pp. 448-466
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Resting State ◽  
Large Scale ◽  
Brain Network ◽  
Generative Models ◽  
Network Activity ◽  
Fmri Data ◽  
State Transitions ◽  
Whole Brain

Large-scale patterns of spontaneous whole-brain activity seen in resting-state functional magnetic resonance imaging (rs-fMRI) are in part believed to arise from neural populations interacting through the structural network (Honey, Kötter, Breakspear, & Sporns, 2007 ). Generative models that simulate this network activity, called brain network models (BNM), are able to reproduce global averaged properties of empirical rs-fMRI activity such as functional connectivity (FC) but perform poorly in reproducing unique trajectories and state transitions that are observed over the span of minutes in whole-brain data (Cabral, Kringelbach, & Deco, 2017 ; Kashyap & Keilholz, 2019 ). The manuscript demonstrates that by using recurrent neural networks, it can fit the BNM in a novel way to the rs-fMRI data and predict large amounts of variance between subsequent measures of rs-fMRI data. Simulated data also contain unique repeating trajectories observed in rs-fMRI, called quasiperiodic patterns (QPP), that span 20 s and complex state transitions observed using k-means analysis on windowed FC matrices (Allen et al., 2012 ; Majeed et al., 2011 ). Our approach is able to estimate the manifold of rs-fMRI dynamics by training on generating subsequent time points, and it can simulate complex resting-state trajectories better than the traditional generative approaches.

How Do You Feel?

2014 ◽  
Author(s):  
A. D. (Bud) Craig
Keyword(s):  
Scientific Inquiry ◽  
Fluid Intelligence ◽  
William James ◽  
Scientific Discovery ◽  
Activity Patterns ◽  
Insular Cortex ◽  
Mental States ◽  
Mammalian Brain ◽  
Energy Needs

This book brings together startling evidence from neuroscience, psychology, and psychiatry to present revolutionary new insights into how our brains enable us to experience the range of sensations and mental states known as feelings. Drawing on own cutting-edge research, the author has identified an area deep inside the mammalian brain—the insular cortex—as the place where interoception, or the processing of bodily stimuli, generates feelings. The book shows how this crucial pathway for interoceptive awareness gives rise in humans to the feeling of being alive, vivid perceptual feelings, and a subjective image of the sentient self across time. The book explains how feelings represent activity patterns in our brains that signify emotions, intentions, and thoughts, and how integration of these patterns is driven by the unique energy needs of the hominid brain. It describes the essential role of feelings and the insular cortex in such diverse realms as music, fluid intelligence, and bivalent emotions, and relates these ideas to the philosophy of William James and even to feelings in dogs. The book is also a compelling insider's account of scientific discovery, one that takes readers behind the scenes as the astonishing answer to this neurological puzzle is pursued and pieced together from seemingly unrelated fields of scientific inquiry. This book will fundamentally alter the way that neuroscientists and psychologists categorize sensations and understand the origins and significance of human feelings.

scholarly journals Revealing neural correlates of behavior without behavioral measurements

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Alon Rubin ◽  
Liron Sheintuch ◽  
Noa Brande-Eilat ◽  
Or Pinchasof ◽  
Yoav Rechavi ◽  
...  
Keyword(s):  
Internal Structure ◽  
Large Scale ◽  
Tuning Curve ◽  
Activity Patterns ◽  
A Priori ◽  
Network Activity ◽  
Head Direction ◽  
External Variable ◽  
Unknown Variable ◽  
Tuning Curves

Abstract Measuring neuronal tuning curves has been instrumental for many discoveries in neuroscience but requires a priori assumptions regarding the identity of the encoded variables. We applied unsupervised learning to large-scale neuronal recordings in behaving mice from circuits involved in spatial cognition and uncovered a highly-organized internal structure of ensemble activity patterns. This emergent structure allowed defining for each neuron an ‘internal tuning-curve’ that characterizes its activity relative to the network activity, rather than relative to any predefined external variable, revealing place-tuning and head-direction tuning without relying on measurements of place or head-direction. Similar investigation in prefrontal cortex revealed schematic representations of distances and actions, and exposed a previously unknown variable, the ‘trajectory-phase’. The internal structure was conserved across mice, allowing using one animal’s data to decode another animal’s behavior. Thus, the internal structure of neuronal activity itself enables reconstructing internal representations and discovering new behavioral variables hidden within a neural code.

scholarly journals Memory Replay in Balanced Recurrent Networks

2016 ◽  
Author(s):  
Nikolay Chenkov ◽  
Henning Sprekeler ◽  
Richard Kempter
Keyword(s):  
Large Scale ◽  
Neuronal Excitability ◽  
Activity Patterns ◽  
Recurrent Network ◽  
Network Activity ◽  
Sharp Waves ◽  
Sequence Of Events ◽  
Up States ◽  
Plausible Model ◽  
Network States

AbstractComplex patterns of neural activity appear during up-states in the neocortex and sharp waves in the hippocampus, including sequences that resemble those during prior behavioral experience. The mechanisms underlying this replay are not well understood. How can small synaptic footprints engraved by experience control large-scale network activity during memory retrieval and consolidation? We hypothesize that sparse and weak synaptic connectivity between Hebbian assemblies are boosted by pre-existing recurrent connectivity within them. To investigate this idea, we connect sequences of assemblies in randomly connected spiking neuronal networks with a balance of excitation and inhibition. Simulations and analytical calculations show that recurrent connections within assemblies allow for a fast amplification of signals that indeed reduces the required number of inter-assembly connections. Replay can be evoked by small sensory-like cues or emerge spontaneously by activity fluctuations. Global—potentially neuromodulatory—alterations of neuronal excitability can switch between network states that favor retrieval and consolidation.Author SummarySynaptic plasticity is the basis for learning and memory, and many experiments indicate that memories are imprinted in synaptic connections. However, basic mechanisms of how such memories are retrieved and consolidated remain unclear. In particular, how can one-shot learning of a sequence of events achieve a sufficiently strong synaptic footprint to retrieve or replay this sequence? Using both numerical simulations of spiking neural networks and an analytic approach, we provide a biologically plausible model for understanding how minute synaptic changes in a recurrent network can nevertheless be retrieved by small cues or even manifest themselves as activity patterns that emerge spontaneously. We show how the retrieval of exceedingly small changes in the connections across assemblies is robustly facilitated by recurrent connectivity within assemblies. This interaction between recurrent amplification within an assembly and the feed-forward propagation of activity across the network establishes a basis for the retrieval of memories.

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