Dysconnectivity of a brain functional network was associated with blood inflammatory markers in depression

Objective: There is increasing evidence for a subgroup of major depressive disorder (MDD) associated with heightened peripheral blood inflammatory markers. In this study, the authors sought to understand the mechanistic brain-immune axis in inflammation-linked depression by investigating associations between functional connectivity (FC) of brain networks and peripheral inflammation in depression. Methods: Resting-state functional magnetic resonance imaging (fMRI) and peripheral blood immune marker data (C-reactive protein; CRP, interleukin-6; IL-6 and immune cells) were collected on N=46 healthy controls (HC; CRP ≤ 3mg/L) and N=83 cases of MDD, stratified further into low CRP (loCRP MDD; ≤ 3 mg/L; N=50) and high CRP (hiCRP MDD; > 3 mg/L; N=33). In a two-part analysis, network-based statistics (NBS) was firstly performed to ascertain FC differences via HC vs hiCRP MDD comparison. Association between this network of interconnected brain regions and peripheral CRP (N=83), IL-6 (N=72), neutrophils and CD4+ T-cells (N=36) were then examined in MDD cases only. Results: Case-control NBS testing revealed a single network of abnormally attenuated FC in hiCRP MDD, chiefly comprising default mode network (DMN) and ventral attentional network (VA) coupled regions, anatomically connecting the insula/frontal-operculum and posterior cingulate cortex (PCC). Across all MDD cases, FC within the identified network scaled negatively with CRP, IL-6 and neutrophils. Conclusions: The findings suggest that inflammation is associated with attenuation of functional connectivity within a brain network deemed critical for interoceptive signalling, e.g. accurate communication of peripheral bodily signals such as immune states to the brain, with implications for the etiology of inflammation-linked depression. keywords: functional connectivity, network-based statistics, peripheral inflammation, immune cells, depression.

Download Full-text

Peripheral inflammation is associated with micro-structural and functional connectivity changes in depression-related brain networks

Molecular Psychiatry ◽

10.1038/s41380-021-01272-1 ◽

2021 ◽

Author(s):

Manfred G. Kitzbichler ◽

Athina R. Aruldass ◽

Gareth J. Barker ◽

Tobias C. Wood ◽

Nicholas G. Dowell ◽

...

Keyword(s):

Functional Connectivity ◽

Venous Blood ◽

Regional Analysis ◽

Brain Magnetic Resonance Imaging ◽

Structural Mri ◽

Posterior Cingulate Cortex ◽

Proton Density ◽

Peripheral Inflammation ◽

Tissue Water ◽

Weighted Degree

AbstractInflammation is associated with depressive symptoms and innate immune mechanisms are likely causal in some cases of major depression. Systemic inflammation also perturbs brain function and microstructure, though how these are related remains unclear. We recruited N = 46 healthy controls, and N = 83 depressed cases stratified by CRP (> 3 mg/L: N = 33; < 3 mg/L: N = 50). All completed clinical assessment, venous blood sampling for C-reactive protein (CRP) assay, and brain magnetic resonance imaging (MRI). Micro-structural MRI parameters including proton density (PD), a measure of tissue water content, were measured at 360 cortical and 16 subcortical regions. Resting-state fMRI time series were correlated to estimate functional connectivity between individual regions, as well as the sum of connectivity (weighted degree) of each region. Multiple tests for regional analysis were controlled by the false discovery rate (FDR = 5%). We found that CRP was significantly associated with PD in precuneus, posterior cingulate cortex (pC/pCC) and medial prefrontal cortex (mPFC); and with functional connectivity between pC/pCC, mPFC and hippocampus. Depression was associated with reduced weighted degree of pC/pCC, mPFC, and other nodes of the default mode network (DMN). Thus CRP-related increases in proton density—a plausible marker of extracellular oedema—and changes in functional connectivity were anatomically co-localised with DMN nodes that also demonstrated significantly reduced hubness in depression. We suggest that effects of peripheral inflammation on DMN node micro-structure and connectivity may mediate inflammatory effects on depression.

Download Full-text

Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽

10.7554/elife.32696 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 4

Author(s):

Ruedeerat Keerativittayayut ◽

Ryuta Aoki ◽

Mitra Taghizadeh Sarabi ◽

Koji Jimura ◽

Kiyoshi Nakahara

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Large Scale ◽

Brain Regions ◽

Brain Network ◽

Time Varying ◽

Memory Encoding ◽

Graph Metrics ◽

Connectivity Patterns ◽

Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

Download Full-text

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

10.21203/rs.2.21498/v1 ◽

2020 ◽

Author(s):

bingbo bao ◽

xuyun hua ◽

haifeng wei ◽

pengbo luo ◽

hongyi zhu ◽

...

Keyword(s):

Functional Connectivity ◽

Spatial Patterns ◽

Brain Plasticity ◽

Brain Activity ◽

Low Frequency ◽

Resting State Fmri ◽

Brain Regions ◽

Brain Network ◽

Intrinsic Connectivity ◽

Intrinsic Brain Activity

Abstract Background: Amputation in adults is a serious condition and most patients were associated with the remapping of representations in motor and sensory brain network. Methods: The present study includes 8 healthy volunteers and 16 patients with amputation. We use resting-state fMRI to investigate the local and extent brain plasticity in patients suffering from amputation simultaneously. Both the amplitude of low-frequency fluctuations (ALFF) and degree centrality (DC) were used for the assessment of neuroplasticity in central level. Results: We described changes in spatial patterns of intrinsic brain activity and functional connectivity in amputees in the present study and we found that not only the sensory and motor cortex, but also the 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 activation and intrinsic connectivity in the brain changed a lot showed correlation with the deafferentation status.

Download Full-text

Brain Resting State Functional Networks in Infants with Prenatal Opioid Exposure

Proceedings of IMPRS ◽

10.18060/25736 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Nicole Steinhardt ◽

Ramana Vishnubhotla ◽

Yi Zhao ◽

David M. Haas ◽

Gregory M. Sokol ◽

...

Keyword(s):

Functional Connectivity ◽

Functional Mri ◽

Gestational Age ◽

Resting State ◽

Brain Regions ◽

Brain Network ◽

Anterior Cingulate ◽

Global Network ◽

Neurodevelopmental Outcomes ◽

Distinct Component

Purpose: Infants of mothers with opioid and substance use can present with postnatal withdrawal symptoms and are at risk of poor neurodevelopmental outcomes in later childhood. Identifying methods to evaluate the consequences of substance exposure on the developing brain can help initiate proactive therapies to improve outcomes for opioid-exposed neonates. Additionally, early brain imaging in infancy has the potential to identify early brain developmental alterations that could prognosticate neurodevelopmental outcomes in these children. In this study, we aim to identify differences in global brain network connectivity in infants with prenatal opioid exposure compared to healthy control infants, using resting-state functional MRI performed at less than 2 months completed gestational age. Materials and Methods: In this prospective, IRB-approved study, we recruited 20 infants with prenatal opioid exposure and 20 healthy, opioid naïve infants. Anatomic imaging and resting-state functional MRI were performed at less than 48 weeks corrected gestational age, and rs-fMRI images were co-registered to the UNC neonate brain template and 90 anatomic atlas-labelled regions. Covariate Assisted Principal (CAP) regression was performed to identify brain network functional connectivity that was significantly different among infants with prenatal opioid exposure compared to healthy neonates. Results: Of the 5 significantly different CAP components identified, the most distinct component (CAP5, p= 3.86 x 10-6) spanned several brain regions, including the right inferior temporal gyrus, bilateral Hesch’s gyrus, left thalamus, left supramarginal gyrus, left inferior parietal lobule, left superior parietal gyrus, right anterior cingulate gyrus, right gyrus rectus, left supplementary motor area, and left pars triangularis. Functional connectivity in this network was lower in the infants with prenatal opioid exposure compared to non-opioid exposed infants. Conclusion: This study demonstrates global network alterations in infants with prenatal opioid exposure compared to non-opioid exposed infants. Future studies should be aimed at identifying clinical significance of this altered connectivity.

Download Full-text

Causally informed activity flow models provide mechanistic insight into the emergence of cognitive processes from brain network interactions

10.1101/2021.04.16.440226 ◽

2021 ◽

Author(s):

Ruben Sanchez-Romero ◽

Takuya Ito ◽

Ravi D. Mill ◽

Stephen José Hanson ◽

Michael W. Cole

Keyword(s):

Functional Connectivity ◽

Effective Connectivity ◽

Pearson Correlation ◽

Explanatory Power ◽

Memory Task ◽

Brain Regions ◽

Brain Network ◽

List Type ◽

Flow Models ◽

Insight Into

AbstractBrain activity flow models estimate the movement of task-evoked activity over brain connections to help explain the emergence of task-related functionality. Activity flow estimates have been shown to accurately predict task-evoked brain activations across a wide variety of brain regions and task conditions. However, these predictions have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. Starting from Pearson correlation (the current field standard), we progress from FC measures with poor to excellent causal grounding, demonstrating a continuum of causal validity using simulations and empirical fMRI data. Finally, we apply a causal FC method to a dorsolateral prefrontal cortex region, demonstrating causal network mechanisms contributing to its strong activation during a 2-back (relative to a 0-back) working memory task. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.Highlights-Activity flow models provide insight into how cognitive neural effects emerge from brain network interactions.-Functional connectivity methods grounded in causal principles facilitate mechanistic interpretations of task activity flow models.-Mechanistic activity flow models accurately predict task-evoked neural effects across a wide variety of brain regions and cognitive tasks.

Download Full-text

Heterogeneity of EEG resting-state brain networks in absolute pitch

10.1101/2020.05.03.063206 ◽

2020 ◽

Author(s):

Marielle Greber ◽

Carina Klein ◽

Simon Leipold ◽

Silvano Sele ◽

Lutz Jäncke

Keyword(s):

Functional Connectivity ◽

Auditory Cortex ◽

Resting State ◽

Large Scale ◽

Absolute Pitch ◽

Brain Regions ◽

Brain Network ◽

Higher Order ◽

Neural Basis ◽

Whole Brain

AbstractThe neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks.Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability.In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

Download Full-text

Abnormal Functional Brain Network in Parkinson's Disease and the Effect of Acute Deep Brain Stimulation

Frontiers in Neurology ◽

10.3389/fneur.2021.715455 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhibao Li ◽

Chong Liu ◽

Qiao Wang ◽

Kun Liang ◽

Chunlei Han ◽

...

Keyword(s):

Deep Brain Stimulation ◽

Functional Connectivity ◽

Frequency Band ◽

Brain Regions ◽

Brain Network ◽

Clustering Coefficient ◽

Functional Brain ◽

Frequency Bands ◽

The Brain ◽

Deep Brain

Objective: The objective of this study was to use functional connectivity and graphic indicators to investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) and the effect of acute deep brain stimulation (DBS) on those characteristics in patients with PD.Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) and 20 patients with PD who were in the DBS-OFF state and DBS-ON state during the resting state with eyes closed. A high-density EEG source connectivity method was used to identify functional brain networks. Power spectral density (PSD) analysis was compared between the groups. Functional connectivity was calculated for 68 brain regions in the theta (4–8 Hz), alpha (8–13 Hz), beta1 (13–20 Hz), and beta2 (20–30 Hz) frequency bands. Network estimates were measured at both the global (network topology) and local (inter-regional connection) levels.Results: Compared with HC, PSD was significantly increased in the theta (p = 0.003) frequency band and was decreased in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD. However, there were no differences in any frequency bands between patients with PD with DBS-OFF and DBS-ON. The clustering coefficient and local efficiency of patients with PD showed a significant decrease in the alpha, beta1, and beta2 frequency bands (p < 0.001). In addition, edgewise statistics showed a significant difference between the HC and patients with PD in all analyzed frequency bands (p < 0.005). However, there were no significant differences between the DBS-OFF state and DBS-ON state in the brain network, except for the functional connectivity in the beta2 frequency band (p < 0.05).Conclusion: Compared with HC, patients with PD showed the following characteristics: slowed EEG background activity, decreased clustering coefficient and local efficiency of the brain network, as well as both increased and decreased functional connectivity between different brain areas. Acute DBS induces a local response of the brain network in patients with PD, mainly showing decreased functional connectivity in a few brain regions in the beta2 frequency band.

Download Full-text

Pain Enhances Functional Connectivity of a Brain Network Evoked by Performance of a Cognitive Task

Journal of Neurophysiology ◽

10.1152/jn.01210.2006 ◽

2007 ◽

Vol 97 (5) ◽

pp. 3651-3659 ◽

Cited By ~ 109

Author(s):

David A. Seminowicz ◽

Karen D. Davis

Keyword(s):

Functional Connectivity ◽

Cognitive Ability ◽

Cognitive Task ◽

Low Frequency ◽

Posterior Cingulate Cortex ◽

Brain Network ◽

Focused Attention ◽

Negative Part ◽

Components Analysis ◽

Task Positive Network

Experimental and clinical evidence indicates that pain can affect cognitive processes, but the cortical networks involved in pain-cognition interactions are unclear. In this study, we determined the effect of pain on the activity of cortical areas involved in cognition acting as a whole (i.e., a network). Subjects underwent functional magnetic resonance imaging (fMRI) while engaged in an attention-demanding cognitive task (multisource interference task) of varying difficulty and simultaneously receiving painful stimuli at varying intensities. The control (baseline) condition was simple finger tapping that had minimal cognitive demands and without pain. Functional connectivity analysis revealed a cortical network consisting of two anti-correlated parts: a task-negative part (precuneus/posterior cingulate cortex, medial frontal and inferior parietal/temporal) the activity of which correlated negatively with the cognitive task and positively with the control baseline, and a task-positive part (inferior frontal, superior parietal, premotor, and anterior insula cortices) the activity of which correlated positively with the cognitive task and negatively with the baseline. Independent components analysis revealed these opposing networks were operating at a low frequency (0.03–0.08 Hz). The functional connectivity of the task-positive network was increased by cognitive demand and by pain. We suggest this attention-specific network balances the needs of general self-referential and environmental awareness versus focused attention to salient information. We postulate that pain affects cognitive ability by its reliance on this common attention-specific network. These data provide evidence that pain can modulate a network presumed to be involved in focused attention, suggesting a mechanism for the interference of pain on cognitive ability by the consumption of attentional resources.

Download Full-text

Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach

Brain Sciences ◽

10.3390/brainsci10110777 ◽

2020 ◽

Vol 10 (11) ◽

pp. 777

Author(s):

Nicholas John Simos ◽

Stavros I. Dimitriadis ◽

Eleftherios Kavroulakis ◽

Georgios C. Manikis ◽

George Bertsias ◽

...

Keyword(s):

Machine Learning ◽

Functional Connectivity ◽

Lupus Erythematosus ◽

Statistical Power ◽

Functional Neuroimaging ◽

Brain Regions ◽

Brain Network ◽

Functional Brain ◽

Graph Metrics ◽

Functional Brain Network

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.

Download Full-text

Sexually dimorphic development of depression-related brain networks during healthy human adolescence

10.1101/2020.07.06.184473 ◽

2020 ◽

Author(s):

L. Dorfschmidt ◽

R.A.I. Bethlehem ◽

J. Seidlitz ◽

F. Váša ◽

S.R. White ◽

...

Keyword(s):

Sex Differences ◽

Functional Connectivity ◽

Emotional Processing ◽

Brain Regions ◽

Brain Network ◽

Sexually Dimorphic ◽

Least Squares Regression ◽

Network Development ◽

Healthy Human ◽

Increased Risk

AbstractAdolescence is a period of critical development of the brain, that coincides with a sexually dimorphic increase in risk of depression for females. We hypothesized that there might be sexual dimorphisms in human brain network development underlying the dimorphism in depression. First, we tested for sex differences in parameters of brain network development (baseline connectivity at age 14, FC14, adolescent change in connectivity FC14−26, and maturational index, MI), measured repeatedly in resting state functional MRI scans from N=298 healthy young people aged 14-26 years, scanned a total of 520 times. We measured the maturational index (−1 < MI < 1) at each of 346 regions for each sex separately. Regions with negative MI were located in the cortical default mode network (DMN), the limbic system and subcortical nuclei. This cortico-subcortical system shared a disruptive pattern of development, e.g., weak functional connectivity with these regions at age 14 became stronger over the course of adolescence. This developmentally disruptive system was sexually dimorphic, i.e., the sex difference in MI was significantly less than zero at 83 regions. We then investigated the biological plausibility, and relevance to depression, of this fMRI-derived map of dimorphic brain development. It was significantly co-located with the cortical expression map of a weighted function of whole genome transcription, by partial least squares regression on prior adult post mortem data. Genes that were most strongly expressed in disruptively developing brain regions were enriched for X chromosome genes; genes specialized for perinatal and adolescent phases of cortical and subcortical development, respectively; and risk genes for major depressive disorder (MDD), defined by genome-wide significant association. The dimorphic development map was also significantly co-located with (i) brain regions activated by prior task-activated fMRI studies of reward and emotional processing and (ii) a map of adult MDD-related differences in functional connectivity from an independent case-control fMRI study (N=96). We conclude that sex differences in adolescent development of cortico-subcortical functional network connectivity were biologically validated by anatomical co-location with brain tissue expression of sex-, development- and MDD-related genes. Dimorphically disruptive development of DMN, limbic and subcortical connectivity could be relevant to the increased risk of depressive symptoms in adolescent females.

Download Full-text