Task-positive and task-negative networks in major depressive disorder: A combined fMRI and EEG study

Knyazev, Gennady G.; Savostyanov, Alexander N.; Bocharov, Andrey V.; Brak, Ivan V.; Osipov, Evgeny; Filimonova, Elena; Saprigyn, Alexander E.; Aftanas, Lyubomir I.

doi:10.1016/j.jad.2018.04.003

Cited by 40 publications

(25 citation statements)

References 79 publications

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“…Previous studies focused on the connectivity stability in MDD have also demonstrated the great importance of DMN [35,36]. Patients with MDD show a stronger functional correlation between the anterior parts of DMN and the amygdala during self-referential processing and elevated functional connectivity (FC) between the dorsal and rostral parts of ACC during cognitive and affective challenges [37]. A meta-analysis of restingstate FC in MDD has confirmed functional hyper-connectivity within DMN [38].…”

Section: Discussionmentioning

confidence: 97%

Meta-analysis of cortical thickness abnormalities in medication-free patients with major depressive disorder

Zhao

Chen

et al. 2019

Neuropsychopharmacol.

114

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Alterations in cortical thickness have been identified in major depressive disorder (MDD), but findings have been variable and inconsistent. To date, no reliable tools have been available for the meta-analysis of surface-based morphometric (SBM) studies to effectively characterize what has been learned in previous studies, and drug treatments may have differentially impacted findings. We conducted a comprehensive meta-analysis of magnetic resonance imaging (MRI) studies that explored cortical thickness in medication-free patients with MDD, using a newly developed meta-analytic mask compatible with seed-based d mapping (SDM) meta-analytic software. We performed the meta-regression to explore the effects of demographics and clinical characteristics on variation in cortical thickness in MDD. Fifteen studies describing 529 patients and 586 healthy controls (HCs) were included. Medication-free patients with MDD, relative to HCs, showed a complex pattern of increased cortical thickness in some areas (posterior cingulate cortex, ventromedial prefrontal cortex, and anterior cingulate cortex) and decreased cortical thickness in others (gyrus rectus, orbital segment of the superior frontal gyrus, and middle temporal gyrus). Most findings in the whole sample analysis were confirmed in a meta-analysis of studies recruiting medication-naive patients. Using the new mask specifically developed for SBM studies, this SDM meta-analysis provides evidence for regional cortical thickness alterations in MDD, mainly involving increased cortical thickness in the default mode network and decreased cortical thickness in the orbitofrontal and temporal cortex.

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Section: Discussionmentioning

confidence: 97%

Meta-analysis of cortical thickness abnormalities in medication-free patients with major depressive disorder

Zhao

Chen

et al. 2019

Neuropsychopharmacol.

114

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“…One star (*) stands for significant statistical difference with p < 0.05 and two stars (**) for p < 0.001. Power in [4][5][6][7][8][9][10][11][12] Hz significantly increases in patients compared to controls in all examined anatomical brain structures.…”

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confidence: 77%

“…www.nature.com/scientificreports www.nature.com/scientificreports/ we restricted the network analysis to this [4][5][6][7][8][9][10][11][12] Hz frequency range. To evaluate how much the system is fault tolerant and how much the communication is efficient, the global efficiency for the whole brain and the local efficiency, clustering coefficient, strength and outflow for each of these twelve investigated ROIs were computed.…”

Section: Eeg Source Estimationmentioning

confidence: 99%

“…S2) but the significant differences corresponded only to the right precentral, amygdala and caudate regions (p < 0.05). We observed significant correlations between the local efficiency and power in the [4][5][6][7][8][9][10][11][12] Hz frequency range in subcortical ROIs but it was not generalized among all twelve subcortical ROIs (see Supplementary Fig. S3).…”

mentioning

confidence: 90%

“…3). At the single-subject level, the global efficiency was significantly higher (p < 0.05) in patients (mean ± standard Boxplots to graphical illustrate the distribution of power of controls (green boxes) and patients (red boxes) in (a) [1][2][3][4] Hz, (b) [4][5][6][7][8][9][10][11][12] Hz and (c) [12][13][14][15][16][17][18] Hz. One star (*) stands for significant statistical difference with p < 0.05 and two stars (**) for p < 0.001.…”

mentioning

confidence: 99%

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Altered directed functional connectivity of the right amygdala in depression: high-density EEG study

Damborská

Honzírková

Barteček

et al. 2020

Sci Rep

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the cortico-striatal-pallidal-thalamic and limbic circuits are suggested to play a crucial role in the pathophysiology of depression. Stimulation of deep brain targets might improve symptoms in treatment-resistant depression. However, a better understanding of connectivity properties of deep brain structures potentially implicated in deep brain stimulation (DBS) treatment is needed. Using highdensity EEG, we explored the directed functional connectivity at rest in 25 healthy subjects and 26 patients with moderate to severe depression within the bipolar affective disorder, depressive episode, and recurrent depressive disorder. We computed the Partial Directed Coherence on the source EEG signals focusing on the amygdala, anterior cingulate, putamen, pallidum, caudate, and thalamus. The global efficiency for the whole brain and the local efficiency, clustering coefficient, outflow, and strength for the selected structures were calculated. In the right amygdala, all the network metrics were significantly higher (p < 0.001) in patients than in controls. The global efficiency was significantly higher (p < 0.05) in patients than in controls, showed no correlation with status of depression, but decreased with increasing medication intake (-=. =. R 0 59 and p 1 52e 05 2). the amygdala seems to play an important role in neurobiology of depression. Practical treatment studies would be necessary to assess the amygdala as a potential future DBS target for treating depression. Affective disorders belong to the most common and most serious psychiatric disorders 1. A crucial role of the cortico-striatal-pallidal-thalamic and limbic circuits in the neurobiology of depression was repeatedly reported 2-4. Magnetic resonance imaging, functional magnetic resonance imaging (fMRI), magnetoencephalographic, and electroencephalographic (EEG) studies have confirmed that depressive patients show structural impairments and functional dysbalances of brain networks that involve structures engaged in (a) emotions, i.e. amygdala, subgenual anterior cingulate, caudate, putamen and pallidum 3,5-12 ; (b) self-referential processes, i.e. medial prefrontal cortex, precuneus, and posterior cingulate cortex 13,14 ; (c) memory, i.e. hippocampus, parahippocampal cortex 15 ; (d) visual processing, i.e. fusiform gyrus, lingual gyrus, and lateral temporal cortex 16 ; and (e) attention, i.e. dorsolateral prefrontal cortex, anterior cingulate cortex (ACC), thalamus, and insula 10-12,17. Moreover, post-mortem morphometric measurements revealed smaller volumes of the hypothalamus, pallidum, putamen and thalamus in patients with affective disorders 18. Many depressive patients fail to respond to pharmacological therapy resulting in 1-3% prevalence of treatment-resistant depression (TRD) 19. One of the newest therapeutic approaches for TRD is an invasive direct electrical stimulation of relevant deep brain structures 20. Both unipolar and bipolar depression patients might benefit from deep brain stimulation (DBS) treatment 21 , although an optimal approach, incl...

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Decoding task specific and task general functional architectures of the brain

Gupta

Lim

Rajapakse

2022

Human Brain Mapping

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Functional magnetic resonance imaging (fMRI) is used to capture complex and dynamic interactions between brain regions while performing tasks. Task related alterations in the brain have been classified as task specific and task general, depending on whether they are particular to a task or common across multiple tasks.Using recent attempts in interpreting deep learning models, we propose an approach to determine both task specific and task general architectures of the functional brain.We demonstrate our methods with a reference-based decoder on deep learning classifiers trained on 12,500 rest and task fMRI samples from the Human Connectome Project (HCP). The decoded task general and task specific motor and language architectures were validated with findings from previous studies. We found that unlike intersubject variability that is characteristic of functional pathology of neurological diseases, a small set of connections are sufficient to delineate the rest and task states. The nodes and connections in the task general architecture could serve as potential disease biomarkers as alterations in task general brain modulations are known to be implicated in several neuropsychiatric disorders.

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Task-positive and task-negative networks in major depressive disorder: A combined fMRI and EEG study

Cited by 40 publications

References 79 publications

Meta-analysis of cortical thickness abnormalities in medication-free patients with major depressive disorder

Meta-analysis of cortical thickness abnormalities in medication-free patients with major depressive disorder

Altered directed functional connectivity of the right amygdala in depression: high-density EEG study

Decoding task specific and task general functional architectures of the brain

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