Network Redundancy Analysis of Effective Brain Networks; a Comparison of Healthy Controls and Patients with Major Depression (original) (raw)
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Depressive symptoms moderate functional connectivity within the emotional brain in chronic pain
BJPsych Open
Background Depressive symptoms are often comorbid with chronic pain. These conditions share aberrant emotion processing and regulation, as well as having common brain networks. However, the relationship between depressive symptoms and chronic pain and the effects on emotional brain function are unclear. Aims The present study aimed to disentangle the effects of chronic pain and depressive symptoms on functional connectivity between regions implicated in both these conditions. Method Twenty-six individuals with chronic pain (referred to as the pain group) and 32 healthy controls underwent resting-state functional magnetic resonance imaging and completed the Beck Depression Inventory. Main effects of group, depressive symptoms (total severity score) and their interaction on the functional connectivity of three seed regions (the left and right amygdalae and the medial prefrontal cortex; mPFC) with the rest of the brain were evaluated. In cases of significant interaction, moderation ana...
2022
Background. Depressive symptoms are often comorbid to chronic pain. These conditions share aberrant emotion processing and regulation, as well as common brain networks. However, the relationship between depressive symptoms and chronic pain on emotional brain function is unclear. Methods. Participants were 26 individuals with chronic pain (referred to as the Pain group) and 32 healthy controls (HC), who underwent resting-state functional magnetic resonance imaging and completed the Beck Depressive Inventory. Main effects of group, depressive symptom severity (total score), and their interaction were evaluated on functional connectivity from three seed regions (separately, the left and right amygdalae, the medial prefrontal cortex, mPFC) and the rest of the brain. In case of significant interaction, moderation analyses were conducted. Results. The group-by-depressive symptoms interaction was significantly associated with changes in connectivity between the right amygdala and the mPFC ...
2011
Considerable evidences have shown a decrease of neuronal activity in the left frontal lobe of depressed patients, but the underlying cortical network is still unclear. The present study intends to investigate the conscious-state brain network patterns in depressed patients compared with control individuals. Cortical functional connectivity is quantified by the partial directed coherence (PDC) analysis of multichannel EEG signals from 12 depressed patients and 12 healthy volunteers. The corresponding PDC matrices are first converted into unweighted graphs by applying a threshold to obtain the topographic property in-degree (Kin). A significantly larger Kin in the left hemisphere is identified in depressed patients, while a symmetric pattern is found in the control group. Another two topographic measures, i.e., clustering coefficients (C) and characteristic path length (L), are obtained from the original weighted PDC digraphs. Compared with control individuals, significantly smaller C and L are revealed in the depression group, indicating a random network-like architecture due to affective disorder. This study thereby provides further support for the presence of a hemispheric asymmetry syndrome in the depressed patients. More importantly, we present evidence that depression is characterized by a loss of optimal small-world network characteristics in conscious state.
Intrinsic connectivity networks in major depressive disorder: A combined fMRI and EEG study
Major depressive disorder (MDD) is one of the most serious psychiatric diseases, but its pathophysiology remains poorly understood. The application of neuroimaging techniques has enhanced our means to understand this illness. The study of intrinsic connectivity networks, i.e., sets of brain regions that show a high degree of interconnectedness even in the absence of a task, showed that MDD patients demonstrate an increased connectivity within the default mode network (DMN), which is active in a resting state and is implicated in self-referential processing, and a decreased connectivity in task-positive networks (TPNs), which increase their activity in attention tasks. This suggests a 'dominance' of the DMN over the TPN in MDD patients, but the cortical localization of this 'dominance' is not fully understood. Besides, this effect has been investigated using fMRI and its electrophysiological underpinning is not known. In this study,
Neural Plasticity
Network mechanisms of depression development and especially of improvement from nonpharmacological treatment remain understudied. The current study is aimed at examining brain networks functional connectivity in depressed patients and its dynamics in nonpharmacological treatment. Resting state fMRI data of 21 healthy adults and 51 patients with mild or moderate depression were analyzed with spatial independent component analysis; then, correlations between time series of the components were calculated and compared between-group (study 1). Baseline and repeated-measure data of 14 treated (psychotherapy or fMRI neurofeedback) and 15 untreated depressed participants were similarly analyzed and correlated with changes in depression scores (study 2). Aside from diverse findings, studies 1 and 2 both revealed changes in within-default mode network (DMN) and DMN to executive control network (ECN) connections. Connectivity in one pair, initially lower in depression, decreased in no treatmen...
Functional connectivity of emotional processing in depression
Journal of Affective Disorders, 2011
Objectives: The aim of the study is to map a neural network of emotion processing and to identify differences in major depression compared to healthy controls. It is hypothesized that intentional perception of emotional faces activates connections between amygdala (Demir et al.), orbitofrontal cortex (OFC), anterior cingulate cortex (ACC) and prefrontal cortex (PFC) and that frontal-amygdala connections are altered in major depressive disorder (MDD). Methods: Fifteen medication-free patients with MDD and fifteen healthy controls were enrolled. All subjects were assessed using the same face-matching functional Magnetic Resonance Imaging (fMRI) task, known to involve those areas. Brain activations were obtained using Statistical Parametric Mapping version 5 (SPM5) for data analysis and MARSBAR for extracting of fMRI time series. Then data was analyzed using structural equation modeling (SEM).
Effective Connectivity in Depression
Biological psychiatry. Cognitive neuroscience and neuroimaging, 2018
Resting-state functional connectivity reflects correlations in the activity between brain areas, whereas effective connectivity between different brain areas measures directed influences of brain regions on each other. Using the latter approach, we compare effective connectivity results in patients with major depressive disorder (MDD) and control subjects. We used a new approach to the measurement of effective connectivity, in which each brain area has a simple dynamical model, and known anatomical connectivity is used to provide constraints. This helps the approach to measure the effective connectivity between the 94 brain areas parceled in the automated anatomical labeling (AAL2) atlas, using resting-state functional magnetic resonance imaging. Moreover, we show how the approach can be used to measure the differences in effective connectivity between different groups of individuals, using as an example effective connectivity in the healthy brain and in individuals with depression....
A brain network model for depression: From symptom understanding to disease intervention
CNS neuroscience & therapeutics, 2018
Understanding the neural substrates of depression is crucial for diagnosis and treatment. Here, we review recent studies of functional and effective connectivity in depression, in terms of functional integration in the brain. Findings from these studies, including our own, point to the involvement of at least four networks in patients with depression. Elevated connectivity of a ventral limbic affective network appears to be associated with excessive negative mood (dysphoria) in the patients; decreased connectivity of a frontal-striatal reward network has been suggested to account for loss of interest, motivation, and pleasure (anhedonia); enhanced default mode network connectivity seems to be associated with depressive rumination; and diminished connectivity of a dorsal cognitive control network is thought to underlie cognitive deficits especially ineffective top-down control of negative thoughts and emotions in depressed patients. Moreover, the restoration of connectivity of these ...
Changes in Community Structure of Resting State Functional Connectivity in Unipolar Depression
PLoS ONE, 2012
Major depression is a prevalent disorder that imposes a significant burden on society, yet objective laboratory-style tests to assist in diagnosis are lacking. We employed network-based analyses of ''resting state'' functional neuroimaging data to ascertain group differences in the endogenous cortical activity between healthy and depressed subjects. We additionally sought to use machine learning techniques to explore the ability of these network-based measures of resting state activity to provide diagnostic information for depression. Resting state fMRI data were acquired from twenty two depressed outpatients and twenty two healthy subjects matched for age and gender. These data were anatomically parcellated and functional connectivity matrices were then derived using the linear correlations between the BOLD signal fluctuations of all pairs of cortical and subcortical regions. We characterised the hierarchical organization of these matrices using networkbased matrics, with an emphasis on their mid-scale ''modularity'' arrangement. Whilst whole brain measures of organization did not differ between groups, a significant rearrangement of their community structure was observed. Furthermore we were able to classify individuals with a high level of accuracy using a support vector machine, primarily through the use of a modularity-based metric known as the participation index. In conclusion, the application of machine learning techniques to features of resting state fMRI network activity shows promising potential to assist in the diagnosis of major depression, now suggesting the need for validation in independent data sets. Citation: Lord A, Horn D, Breakspear M, Walter M (2012) Changes in Community Structure of Resting State Functional Connectivity in Unipolar Depression. PLoS ONE 7(8): e41282.
Whole brain resting-state analysis reveals decreased functional connectivity in major depression
2010
Recently, both increases and decreases in resting-state functional connectivity have been found in major depression. However, these studies only assessed functional connectivity within a specific network or between a few regions of interest, while comorbidity and use of medication was not always controlled for. Therefore, the aim of the current study was to investigate wholebrain functional connectivity, unbiased by a priori definition of regions or networks of interest, in medication-free depressive patients without comorbidity. We analyzed resting-state fMRI data of 19 medication-free patients with a recent diagnosis of major depression (within 6 months before inclusion) and no comorbidity, and 19 age-and gender-matched controls. Independent component analysis was employed on the concatenated data sets of all participants. Thirteen functionally relevant networks were identified, describing the entire study sample. Next, individual representations of the networks were created using a dual regression method. Statistical inference was subsequently done on these spatial maps using voxel-wise permutation tests. Abnormal functional connectivity was found within three resting-state networks in depression: