2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Multilayer MEG functional connectivity as a potential marker for suicidal thoughts in major depressive disorder

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Highlights

          • Generalized slowing of connectivity was seen in those with MDD vs healthy subjects.

          • Slowing from alpha/beta-mediated to delta/theta-mediated connectivity was observed.

          • Connectivity strength and suicidal thoughts were linked in MDD subjects.

          • “Hub” regions in the brain potentially underlying suicidal thoughts were identified.

          • Connectivity differed between those with high suicidal thought scores and controls.

          Abstract

          Major depressive disorder (MDD) is highly heterogeneous in its clinical presentation. The present exploratory study used magnetoencephalography (MEG) to investigate electrophysiological intrinsic connectivity differences between healthy volunteers and unmedicated participants with treatment-resistant MDD. The study examined canonical frequency bands from delta through gamma. In addition to group comparisons, correlational studies were conducted to determine whether connectivity was related to five symptom factors: depressed mood, tension, negative cognition, suicidal thoughts, and amotivation. The MDD and healthy volunteer groups did not differ significantly at baseline when corrected across all frequencies and clusters, although evidence of generalized slowing in MDD was observed. Notably, however, electrophysiological connectivity was strongly related to suicidal thoughts, particularly as coupling of low frequency power fluctuations (delta and theta) with alpha and beta power. This analysis revealed hub areas underlying this symptom cluster, including left hippocampus, left anterior insula, and bilateral dorsolateral prefrontal cortex. No other symptom cluster demonstrated a relationship with neurophysiological connectivity, suggesting a specificity to these results as markers of suicidal ideation.

          Related collections

          Most cited references55

          • Record: found
          • Abstract: found
          • Article: not found

          Investigating the electrophysiological basis of resting state networks using magnetoencephalography.

          In recent years the study of resting state brain networks (RSNs) has become an important area of neuroimaging. The majority of studies have used functional magnetic resonance imaging (fMRI) to measure temporal correlation between blood-oxygenation-level-dependent (BOLD) signals from different brain areas. However, BOLD is an indirect measure related to hemodynamics, and the electrophysiological basis of connectivity between spatially separate network nodes cannot be comprehensively assessed using this technique. In this paper we describe a means to characterize resting state brain networks independently using magnetoencephalography (MEG), a neuroimaging modality that bypasses the hemodynamic response and measures the magnetic fields associated with electrophysiological brain activity. The MEG data are analyzed using a unique combination of beamformer spatial filtering and independent component analysis (ICA) and require no prior assumptions about the spatial locations or patterns of the networks. This method results in RSNs with significant similarity in their spatial structure compared with RSNs derived independently using fMRI. This outcome confirms the neural basis of hemodynamic networks and demonstrates the potential of MEG as a tool for understanding the mechanisms that underlie RSNs and the nature of connectivity that binds network nodes.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Functional neuroimaging of major depressive disorder: a meta-analysis and new integration of base line activation and neural response data.

            Functional neuroimaging investigations of major depressive disorder can advance both the neural theory and treatment of this debilitating illness. Inconsistency of neuroimaging findings and the use of region-of-interest approaches have hindered the development of a comprehensive, empirically informed neural model of major depression. In this context, the authors sought to identify reliable anomalies in baseline neural activity and neural response to affective stimuli in major depressive disorder. The authors applied voxel-wise, whole-brain meta-analysis to neuroimaging investigations comparing depressed to healthy comparison groups with respect to baseline neural activity or neural response to positively and/or negatively valenced stimuli. Relative to healthy subjects, those with major depression had reliably higher baseline activity, bilaterally, in the pulvinar nucleus. The analysis of neural response studies using negative stimuli showed greater response in the amygdala, insula, and dorsal anterior cingulate cortex and lower response in the dorsal striatum and dorsolateral prefrontal cortex in individuals with major depressive disorder than in healthy subjects. The meta-analytic results support an elegant and neuroanatomically viable model of the salience of negative information in major depressive disorder. In this proposed model, high baseline pulvinar activity in depression first potentiates responding of the brain's salience network to negative information; next, and owing potentially to low striatal dopamine levels in depression, this viscerally charged information fails to propagate up the cortical-striatal-pallidalthalamic circuit to the dorsolateral prefrontal cortex for contextual processing and reappraisal.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Multi-scale brain networks

              The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales—of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain’s multiscale network structure—irrespective of species, imaging modality, or spatial resolution.
                Bookmark

                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                08 August 2020
                2020
                08 August 2020
                : 28
                : 102378
                Affiliations
                [a ]MEG Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
                [b ]Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
                [c ]Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK
                Author notes
                [* ]Corresponding author at: 10 Center Drive, MSC 1059, Bldg, 10, Rm. 4N242, Bethesda, MD 20892-1059, USA. nugenta@ 123456nih.gov
                Article
                S2213-1582(20)30215-1 102378
                10.1016/j.nicl.2020.102378
                7451429
                32836087
                dbd21938-a3d8-4998-803c-4d655aab8738
                © 2020 Published by Elsevier Inc.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 2 December 2019
                : 18 June 2020
                : 6 August 2020
                Categories
                Regular Article

                magnetoencephalography,connectivity,suicide,major depressive disorder,frequency,oscillation

                Comments

                Comment on this article