Opioidergic System and Functional Architecture of Intrinsic Brain Activity: Implications for Psychiatric Disorders

The opioidergic system and intrinsic brain activity, as organized in large-scale networks such as the salience network (SN), sensorimotor network (SMN), and default-mode network (DMN), play core roles in healthy behavior and psychiatric disorders. This work aimed to investigate how opioidergic signaling affects intrinsic brain activity in healthy individuals by reviewing relevant neuroanatomical, molecular, functional, and pharmacological magnetic resonance imaging studies in order to clarify their physiological links and changes in psychiatric disorders. The SN shows dense opioidergic innervations of subcortical structures and high expression levels of opioid receptors in subcortical-cortical areas, with enhanced or reduced activity with low or very high doses of opioids, respectively. The SMN shows high levels of opioid receptors in subcortical areas and functional disconnection caused by opioids. The DMN shows low levels of opioid receptors in cortical areas and inhibited or enhanced activity with low or high doses of opioids, respectively. Finally, we proposed a working model. Opioidergic signaling enhances SN and suppresses SMN (and DMN) activity, resulting in affective excitation with psychomotor inhibition; stronger increases in opioidergic signaling attenuate the SN and SMN while disinhibiting the DMN, dissociating affective and psychomotor functions from the internal states; the opposite occurs with a deficit of opioidergic signaling.

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Opposing Changes in the Functional Architecture of Large-Scale Networks in Bipolar Mania and Depression

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa004 ◽

2020 ◽

Vol 46 (4) ◽

pp. 971-980

Author(s):

Daniel Russo ◽

Matteo Martino ◽

Paola Magioncalda ◽

Matilde Inglese ◽

Mario Amore ◽

...

Keyword(s):

Resting State ◽

Large Scale ◽

Depressive Symptomatology ◽

Brain Activity ◽

Intrinsic Activity ◽

Functional Architecture ◽

Sensorimotor Network ◽

Large Scale Networks ◽

Intrinsic Brain Activity ◽

Affective Dimensions

Abstract Objective Manic and depressive phases of bipolar disorder (BD) show opposite symptoms in psychomotor, thought, and affective dimensions. Neuronally, these may depend on distinct patterns of alterations in the functional architecture of brain intrinsic activity. Therefore, the study aimed to characterize the spatial and temporal changes of resting-state activity in mania and depression, by investigating the regional homogeneity (ReHo) and degree of centrality (DC), in different frequency bands. Methods Using resting-state functional magnetic resonance imaging (fMRI), voxel-wise ReHo and DC were calculated—in the standard frequency band (SFB: 0.01–0.10 Hz), as well as in Slow5 (0.01–0.027 Hz) and Slow4 (0.027–0.073 Hz)—and compared between manic (n = 36), depressed (n = 43), euthymic (n = 29) patients, and healthy controls (n = 112). Finally, clinical correlations were investigated. Results Mania was mainly characterized by decreased ReHo and DC in Slow4 in the medial prefrontal cortex (as part of the default-mode network [DMN]), which in turn correlated with manic symptomatology. Conversely, depression was mainly characterized by decreased ReHo in SFB in the primary sensory-motor cortex (as part of the sensorimotor network [SMN]), which in turn correlated with depressive symptomatology. Conclusions Our data show a functional reconfiguration of the spatiotemporal structure of intrinsic brain activity to occur in BD. Mania might be characterized by a predominance of sensorimotor over associative networks, possibly driven by a deficit of the DMN (reflecting in internal thought deficit). Conversely, depression might be characterized by a predominance of associative over sensorimotor networks, possibly driven by a deficit of the SMN (reflecting in psychomotor inhibition).

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Brainstem modulation of large-scale intrinsic cortical activity correlations

10.31219/osf.io/tczwn ◽

2019 ◽

Author(s):

Ruud L. van den Brink ◽

Thomas Pfeffer ◽

Tobias Donner

Keyword(s):

Psychiatric Disorders ◽

Large Scale ◽

Brain Activity ◽

Cortical Activity ◽

Brain Regions ◽

Intrinsic Activity ◽

Non Invasive ◽

Correlated Activity ◽

Intrinsic Fluctuations ◽

Specific Receptors

Brain activity fluctuates continuously, even in the absence of changes in sensory input or motor output. These intrinsic activity fluctuations are correlated across brain regions and are spatially organized in macroscale networks. Variations in the strength, topography, and topology of correlated activity occur over time, and unfold upon a backbone of long-range anatomical connections. Subcortical neuromodulatory systems send widespread ascending projections to the cortex, and are thus ideally situated to shape the temporal and spatial structure of intrinsic correlations. These systems are also the targets of the pharmacological treatment of major neurological and psychiatric disorders, such as Parkinson’s disease, depression, and schizophrenia. Here, we review recent work that has investigated how neuromodulatory systems shape correlations of intrinsic fluctuations of large-scale cortical activity. We discuss studies in the human, monkey, and rodent brain, with a focus on non-invasive recordings of human brain activity. We provide a structured but selective overview of this work and distill a number of emerging principles. Future efforts to chart the effect of specific neuromodulators and, in particular, specific receptors, on intrinsic correlations may help identify shared or antagonistic principles between different neuromodulatory systems. Such principles can inform models of healthy brain function and may provide an important reference for understanding altered cortical dynamics that are evident in neurological and psychiatric disorders, potentially paving the way for mechanistically-inspired biomarkers and individualized treatments of these disorders.

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fMRI Dependent Components Analysis Reveals Dynamic Relations Between Functional Large Scale Cortical Networks

10.1101/066282 ◽

2016 ◽

Author(s):

Uri Hertz ◽

Daniel Zoran ◽

Yair Weiss ◽

Amir Amedi

Keyword(s):

Large Scale ◽

Brain Activity ◽

Group Analysis ◽

Mental States ◽

Cortical Networks ◽

Experimental Conditions ◽

Dependency Structure ◽

Cortical Areas ◽

Novel Approach ◽

Components Analysis

AbstractOne of the major advantages of whole brain fMRI is the detection of large scale cortical networks. Dependent Components Analysis (DCA) is a novel approach designed to extract both cortical networks and their dependency structure. DCA is fundamentally different from prevalent data driven approaches, i.e. spatial ICA, in that instead of maximizing the independence of components it optimizes their dependency (in a tree graph structure, tDCA) depicting cortical areas as part of multiple cortical networks. Here tDCA was shown to reliably detect large scale functional networks in single subjects and in group analysis, by clustering non-noisy components on one branch of the tree structure. We used tDCA in three fMRI experiments in which identical auditory and visual stimuli were presented, but novelty information and task relevance were modified. tDCA components tended to include two anticorrelated networks, which were detected in two separate ICA components, or belonged in one component in seed functional connectivity. Although sensory components remained the same across experiments, other components changed as a function of the experimental conditions. These changes were either within component, where it encompassed other cortical areas, or between components, where the pattern of anticorrelated networks and their statistical dependency changed. Thus tDCA may prove to be a useful, robust tool that provides a rich description of the statistical structure underlying brain activity and its relationships to changes in experimental conditions. This tool may prove effective in detection and description of mental states, neural disorders and their dynamics.

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Linking electrophysiological brain activity to neurological and psychiatric liability genes: Large-scale collaborative studies by the ENIGMA-EEG group.

10.31234/osf.io/gyjb4 ◽

2020 ◽

Cited By ~ 1

Author(s):

Dirk Smit

Keyword(s):

Psychiatric Disorders ◽

Brain Function ◽

Large Scale ◽

Genome Wide Association Study ◽

Temporal Dynamics ◽

Brain Activity ◽

Meta Analysis ◽

Genome Wide ◽

Oscillatory Brain Activity ◽

Eeg Features

The ENIGMA-EEG working group was established to enable large scale international collaborations among cohorts who investigate the genetics of brain function measured with electroencephalography (EEG). The collaboration resulted in the currently largest genome-wide association study of oscillatory brain activity in EEG recordings by meta-analyzing the results across five participating cohorts’ results. Our endeavor has resulted in the first genome-wide significant hits for oscillatory brain function, and significant genes that were previously associated with psychiatric disorders. Our results have provided insight into the influence that psychitaric liability genes have on the functioning brain. In this overview, we also highlight how we have tackled methodological issues surrounding genetic meta-analysis of EEG features, and identify possible sources of heterogeneity across cohorts, which could affect the results of our meta-analysis. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features to be investigated in our future studies, e.g. temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations. We argue that these represent some of the most important characteristics of the functioning brain. We conclude that disentangling the genetics of EEG will elucidate effects that genes have on brain function, as well as pathways from genes to neurological and psychiatric disorders.

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Three major psychiatric disorders share specific dynamic alterations of intrinsic brain activity

Schizophrenia Research ◽

10.1016/j.schres.2021.06.014 ◽

2021 ◽

Author(s):

Luheng Zhang ◽

Ran Zhang ◽

Shaoqiang Han ◽

Fay Y. Womer ◽

Yange Wei ◽

...

Keyword(s):

Psychiatric Disorders ◽

Brain Activity ◽

Intrinsic Brain Activity

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Longitudinal Spatial Patterns of Intrinsic Brain Activity and Functional Connectivity in Upper Limb Amputees Patients

10.21203/rs.2.21498/v2 ◽

2020 ◽

Author(s):

Bingbo Bao ◽

Lei Duan ◽

Haifeng Wei ◽

Pengbo Luo ◽

Hongyi Zhu ◽

...

Keyword(s):

Functional Connectivity ◽

Spatial Patterns ◽

Upper Limb ◽

Large Scale ◽

Brain Plasticity ◽

Brain Activity ◽

Low Frequency ◽

Resting State Fmri ◽

Brain Regions ◽

Intrinsic Brain Activity

Abstract Background: Amputation in adults is a serious condition and previous studies suggested a remapping of representations in motor and sensory brain networks. However, little is known about the longitudinal reorganizing pattern in upper limb amputees’ patients.Methods: The present study included 8 healthy volunteers and 16 patients with amputation. We use resting-state fMRI to investigate the local and large-scale brain plasticity in patients suffering from amputation. Both the amplitude of low-frequency fluctuations (ALFF) and degree centrality (DC) were used for the assessment of neuroplasticity.Results: We described changes in spatial patterns of intrinsic brain activity and functional connectivity in amputees; and we found that not only the sensory and motor cortex, but also the cognitive-related brain regions involved in the functional plasticity after upper extremity deafferentation.Conclusion: Our findings showed local and extensive cortical changes in the sensorimotor and cognitive-related brain regions, which may imply the dysfunction in not only sensory and motor function, but also sensorimotor integration and motor plan. The changes in activation and intrinsic connectivity in the brain showed correlation with the deafferentation status.

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Propagated infra-slow intrinsic brain activity reorganizes across wake and slow wave sleep

eLife ◽

10.7554/elife.10781 ◽

2015 ◽

Vol 4 ◽

Cited By ~ 61

Author(s):

Anish Mitra ◽

Abraham Z Snyder ◽

Enzo Tagliazucchi ◽

Helmut Laufs ◽

Marcus E Raichle

Keyword(s):

Slow Wave ◽

Resting State ◽

Slow Wave Sleep ◽

Brain Activity ◽

Resting State Fmri ◽

Blood Oxygen Level Dependent ◽

Intrinsic Activity ◽

Subcortical Structures ◽

Blood Oxygen Level ◽

Intrinsic Brain Activity

Propagation of slow intrinsic brain activity has been widely observed in electrophysiogical studies of slow wave sleep (SWS). However, in human resting state fMRI (rs-fMRI), intrinsic activity has been understood predominantly in terms of zero-lag temporal synchrony (functional connectivity) within systems known as resting state networks (RSNs). Prior rs-fMRI studies have found that RSNs are generally preserved across wake and sleep. Here, we use a recently developed analysis technique to study propagation of infra-slow intrinsic blood oxygen level dependent (BOLD) signals in normal adults during wake and SWS. This analysis reveals marked changes in propagation patterns in SWS vs. wake. Broadly, ordered propagation is preserved within traditionally defined RSNs but lost between RSNs. Additionally, propagation between cerebral cortex and subcortical structures reverses directions, and intra-cortical propagation becomes reorganized, especially in visual and sensorimotor cortices. These findings show that propagated rs-fMRI activity informs theoretical accounts of the neural functions of sleep.

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