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

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).

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

2020 ◽  
Vol 26 (4) ◽  
pp. 343-358
Author(s):  
Giulio Rocchi ◽  
Bruno Sterlini ◽  
Samuele Tardito ◽  
Matilde Inglese ◽  
Anna Corradi ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Opioid Receptors ◽  
Large Scale ◽  
Brain Activity ◽  
Subcortical Structures ◽  
Opioidergic System ◽  
Sensorimotor Network ◽  
Cortical Areas ◽  
High Doses ◽  
Intrinsic Brain Activity

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.

scholarly journals Quasi-Periodic Patterns of Intrinsic Brain Activity in Individuals and their Relationship to Global Signal

2017 ◽  
Author(s):  
Behnaz Yousefi ◽  
Jaemin Shin ◽  
Eric H. Schumacher ◽  
Shella D. Keilholz
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Primary Source ◽  
Intrinsic Activity ◽  
Periodic Patterns ◽  
Physiological Noise ◽  
Global Signal ◽  
Human Connectome Project ◽  
Large Scale Networks ◽  
Induced Signal

AbstractQuasiperiodic patterns (QPPs) as reported by Majeed et al., 2011 are prominent features of the brain’s intrinsic activity that involve important large-scale networks (default mode, DMN; task positive, TPN) and are likely to be major contributors to widely used measures of functional connectivity. We examined the variability of these patterns in 470 individuals from the Human Connectome Project resting state functional MRI dataset. The QPPs from individuals can be coarsely categorized into two types: one where strong anti-correlation between the DMN and TPN is present, and another where most areas are strongly correlated. QPP type could be predicted by an individual’s global signal, with lower global signal corresponding to QPPs with strong anti-correlation. After regression of global signal, all QPPs showed strong anti-correlation between DMN and TPN. QPP occurrence and type was similar between a subgroup of individuals with extremely low motion (or even high motion) and the rest of the sample, which shows that motion is not a major contributor to the QPPs. After regression of estimates of slow respiratory and cardiac induced signal fluctuations, more QPPs showed strong anti-correlation between DMN and TPN, an indication that while physiological noise influences the QPP type, it is not the primary source of the QPP itself. QPPs were more similar for the same subjects scanned on different days than for different subjects. These results provide the first assessment of the variability in individual QPPs and their relationship to physiological parameters.

scholarly journals Lag threads organize the brain’s intrinsic activity

2015 ◽  
Vol 112 (17) ◽  
pp. E2235-E2244 ◽  
Author(s):  
Anish Mitra ◽  
Abraham Z. Snyder ◽  
Tyler Blazey ◽  
Marcus E. Raichle
Keyword(s):  
Spontaneous Activity ◽  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Intrinsic Activity ◽  
Resting State Networks ◽  
Slow Activity ◽  
Intrinsic Brain Activity ◽  
Temporal Sequences ◽  
The Brain

It has been widely reported that intrinsic brain activity, in a variety of animals including humans, is spatiotemporally structured. Specifically, propagated slow activity has been repeatedly demonstrated in animals. In human resting-state fMRI, spontaneous activity has been understood predominantly in terms of zero-lag temporal synchrony within widely distributed functional systems (resting-state networks). Here, we use resting-state fMRI from 1,376 normal, young adults to demonstrate that multiple, highly reproducible, temporal sequences of propagated activity, which we term “lag threads,” are present in the brain. Moreover, this propagated activity is largely unidirectional within conventionally understood resting-state networks. Modeling experiments show that resting-state networks naturally emerge as a consequence of shared patterns of propagation. An implication of these results is that common physiologic mechanisms may underlie spontaneous activity as imaged with fMRI in humans and slowly propagated activity as studied in animals.

scholarly journals Contrasting variability patterns in the default mode and sensorimotor networks balance in bipolar depression and mania

2016 ◽  
Vol 113 (17) ◽  
pp. 4824-4829 ◽  
Author(s):  
Matteo Martino ◽  
Paola Magioncalda ◽  
Zirui Huang ◽  
Benedetta Conio ◽  
Niccolò Piaggio ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Resting State ◽  
Large Scale ◽  
Bipolar Depression ◽  
Default Mode ◽  
Sensorimotor Network ◽  
Global Signal ◽  
Clinical Correlations ◽  
Large Scale Networks ◽  
Psychomotor Symptoms

Depressive and manic phases in bipolar disorder show opposite constellations of affective, cognitive, and psychomotor symptoms. At a neural level, these may be related to topographical disbalance between large-scale networks, such as the default mode network (DMN) and sensorimotor network (SMN). We investigated topographical patterns of variability in the resting-state signal—measured by fractional SD (fSD) of the BOLD signal—of the DMN and SMN (and other networks) in two frequency bands (Slow5 and Slow4) with their ratio and clinical correlations in depressed (n = 20), manic (n = 20), euthymic (n = 20) patients, and healthy controls (n = 40). After controlling for global signal changes, the topographical balance between the DMN and SMN, specifically in the lowest frequency band, as calculated by the Slow5 fSD DMN/SMN ratio, was significantly increased in depression, whereas the same ratio was significantly decreased in mania. Additionally, Slow5 variability was increased in the DMN and decreased in the SMN in depressed patients, whereas the opposite topographical pattern was observed in mania. Finally, the Slow5 fSD DMN/SMN ratio correlated positively with clinical scores of depressive symptoms and negatively with those of mania. Results were replicated in a smaller independent bipolar disorder sample. We demonstrated topographical abnormalities in frequency-specific resting-state variability in the balance between DMN and SMN with opposing patterns in depression and mania. The Slow5 DMN/SMN ratio was tilted toward the DMN in depression but was shifted toward the SMN in mania. The Slow5 fSD DMN/SMN pattern could constitute a state-biomarker in diagnosis and therapy.

scholarly journals Characterisation of Haemodynamic Activity in Resting State Networks by Fractal Analysis

2020 ◽  
Vol 30 (12) ◽  
pp. 2050061 ◽  
Author(s):  
Camillo Porcaro ◽  
Stephen D. Mayhew ◽  
Marco Marino ◽  
Dante Mantini ◽  
Andrew P. Bagshaw
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Blood Oxygen Level Dependent ◽  
Resting State Networks ◽  
Blood Oxygen Level ◽  
Spatial And Temporal Scales ◽  
Functional Roles ◽  
Large Scale Networks ◽  
Haemodynamic Activity

Intrinsic brain activity is organized into large-scale networks displaying specific structural–functional architecture, known as resting-state networks (RSNs). RSNs reflect complex neurophysiological processes and interactions, and have a central role in distinct sensory and cognitive functions, making it crucial to understand and quantify their anatomical and functional properties. Fractal dimension (FD) provides a parsimonious way of summarizing self-similarity over different spatial and temporal scales but despite its suitability for functional magnetic resonance imaging (fMRI) signal analysis its ability to characterize and investigate RSNs is poorly understood. We used FD in a large sample of healthy participants to differentiate fMRI RSNs and examine how the FD property of RSNs is linked with their functional roles. We identified two clusters of RSNs, one mainly consisting of sensory networks (C1, including auditory, sensorimotor and visual networks) and the other more related to higher cognitive (HCN) functions (C2, including dorsal default mode network and fronto-parietal networks). These clusters were defined in a completely data-driven manner using hierarchical clustering, suggesting that quantification of Blood Oxygen Level Dependent (BOLD) signal complexity with FD is able to characterize meaningful physiological and functional variability. Understanding the mechanisms underlying functional RSNs, and developing tools to study their signal properties, is essential for assessing specific brain alterations and FD could potentially be used for the early detection and treatment of neurological disorders.

scholarly journals Networks Are Associated With Depression in Patients With Parkinson’s Disease: A Resting-State Imaging Study

2021 ◽  
Vol 14 ◽  
Author(s):  
Haiyan Liao ◽  
Sainan Cai ◽  
Qin Shen ◽  
Jie Fan ◽  
Tianyu Wang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Resting State ◽  
Large Scale ◽  
Brain Connectivity ◽  
Imaging Study ◽  
Sensorimotor Network ◽  
Depression Level ◽  
Large Scale Networks ◽  
The Right

BackgroundDisturbance of networks was recently proposed to be associated with the occurrence of depression in Parkinson’s disease (PD). However, the neurobiological mechanism of depression underlying PD remains unclear.ObjectiveThis study was conducted to investigate whether intra-network and inter-network brain connectivity is differently changed in PD patients with and without depression (PDD and PDND patients, respectively).MethodsForty-one PDD patients, 64 PDND patients, and 55 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (fMRI). The default mode network (DMN), executive control network (ECN), salience network (SN), precuneus network (PCUN), and sensorimotor network (SMN) were extracted using independent component analysis (ICA), and then the functional connectivity (FC) values within and between these networks were measured.ResultsPDD patients exhibited abnormal FC values within the DMN, ECN, SN, PCUN, and SMN. In addition, PDD patients demonstrated decreased connectivity between anterior SN (aSN) and bilateral ECN, between posterior SN (pSN) and dorsal DMN (dDMN), and between PCUN and dDMN/SMN/bilateral ECN. Connectivity within the left hippocampus of dDMN and the right medial superior frontal gyrus of aSN was a significant predictor of depression level in PD patients.ConclusionsAberrant intra- and inter-network FC is involved in several important hubs in the large-scale networks, which can be a biomarker for distinguishing PDD from PDND.

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

eLife ◽  
2015 ◽  
Vol 4 ◽  
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.

scholarly journals Unilateral thalamic glioma disrupts large-scale functional architecture of human brain during resting state

2019 ◽  
Vol Volume 15 ◽  
pp. 947-956
Author(s):  
Sirui Li ◽  
Lei Gao ◽  
Ying Liu ◽  
Yawen Ao ◽  
Haibo Xu
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Large Scale ◽  
Functional Architecture ◽  
Thalamic Glioma

scholarly journals Organization of Propagated Intrinsic Brain Activity in Individual Humans

2019 ◽  
Vol 30 (3) ◽  
pp. 1716-1734 ◽  
Author(s):  
Ryan V Raut ◽  
Anish Mitra ◽  
Scott Marek ◽  
Mario Ortega ◽  
Abraham Z Snyder ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Cortical Excitability ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Organization ◽  
Individual Level ◽  
Slow Activity ◽  
Lag Structure

Abstract Spontaneous infra-slow (<0.1 Hz) fluctuations in functional magnetic resonance imaging (fMRI) signals are temporally correlated within large-scale functional brain networks, motivating their use for mapping systems-level brain organization. However, recent electrophysiological and hemodynamic evidence suggest state-dependent propagation of infra-slow fluctuations, implying a functional role for ongoing infra-slow activity. Crucially, the study of infra-slow temporal lag structure has thus far been limited to large groups, as analyzing propagation delays requires extensive data averaging to overcome sampling variability. Here, we use resting-state fMRI data from 11 extensively-sampled individuals to characterize lag structure at the individual level. In addition to stable individual-specific features, we find spatiotemporal topographies in each subject similar to the group average. Notably, we find a set of early regions that are common to all individuals, are preferentially positioned proximal to multiple functional networks, and overlap with brain regions known to respond to diverse behavioral tasks—altogether consistent with a hypothesized ability to broadly influence cortical excitability. Our findings suggest that, like correlation structure, temporal lag structure is a fundamental organizational property of resting-state infra-slow activity.

scholarly journals Increased Resting State Triple Network Functional Connectivity in Undergraduate Problematic Cannabis Users: A Preliminary EEG Coherence Study

2020 ◽  
Vol 10 (3) ◽  
pp. 136 ◽  
Author(s):  
Claudio Imperatori ◽  
Chiara Massullo ◽  
Giuseppe Alessio Carbone ◽  
Angelo Panno ◽  
Marta Giacchini ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Posterior Parietal Cortex ◽  
Cannabis Use ◽  
Anterior Cingulate ◽  
Dorsal Anterior Cingulate Cortex ◽  
Addictive Disorders ◽  
Eeg Recordings ◽  
Large Scale Networks

An increasing body of experimental data have suggested that aberrant functional interactions between large-scale networks may be the most plausible explanation of psychopathology across multiple mental disorders, including substance-related and addictive disorders. In the current research, we have investigated the association between problematic cannabis use (PCU) and triple-network electroencephalographic (EEG) functional connectivity. Twelve participants with PCU and 24 non-PCU participants were included in the study. EEG recordings were performed during resting state (RS). The exact Low-Resolution Electromagnetic Tomography software (eLORETA) was used for all EEG analyses. Compared to non-PCU, PCU participants showed an increased delta connectivity between the salience network (SN) and central executive network (CEN), specifically, between the dorsal anterior cingulate cortex and right posterior parietal cortex. The strength of delta connectivity between the SN and CEN was positively and significantly correlated with higher problematic patterns of cannabis use after controlling for age, sex, educational level, tobacco use, problematic alcohol use, and general psychopathology (rp = 0.40, p = 0.030). Taken together, our results show that individuals with PCU could be characterized by a specific dysfunctional interaction between the SN and CEN during RS, which might reflect the neurophysiological underpinnings of attentional and emotional processes of cannabis-related thoughts, memories, and craving.

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