A Causal Network Analysis of Neuromodulation in the Mood Processing Network.
Ontology highlight
ABSTRACT:
SUBMITTER: Qiao S
PROVIDER: S-EPMC7486259 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Chronic subjective tinnitus is characterized by abnormal neuronal synchronization in the central auditory system. As shown in a controlled clinical trial, acoustic coordinated reset (CR) neuromodulation causes a significant relief of tinnitus symptoms along with a significant decrease of pathological oscillatory activity in a network comprising auditory and non-auditory brain areas, which is often accompanied with a significant tinnitus pitch change. Here we studied if the tinnitus pitch change correlates with a reduction of tinnitus loudness and/or annoyance as assessed by visual analog scale (VAS) scores. Furthermore, we studied if the changes of the pattern of brain synchrony in tinnitus patients induced by 12 weeks of CR therapy depend on whether or not the patients undergo a pronounced tinnitus pitch change. Therefore, we applied standardized low-resolution brain electromagnetic tomography (sLORETA) to EEG recordings from two groups of patients with a sustained CR-induced relief of tinnitus symptoms with and without tinnitus pitch change. We found that absolute changes of VAS loudness and VAS annoyance scores significantly correlate with the modulus, i.e., the absolute value, of the tinnitus pitch change. Moreover, as opposed to patients with small or no pitch change we found a significantly stronger decrease in gamma power in patients with pronounced tinnitus pitch change in right parietal cortex (Brodmann area, BA 40), right frontal cortex (BA 9, 46), left temporal cortex (BA 22, 42), and left frontal cortex (BA 4, 6), combined with a significantly stronger increase of alpha (10-12 Hz) activity in the right and left anterior cingulate cortex (ACC; BA 32, 24). In addition, we revealed a significantly lower functional connectivity in the gamma band between the right dorsolateral prefrontal cortex (BA 46) and the right ACC (BA 32) after 12 weeks of CR therapy in patients with pronounced pitch change. Our results indicate a substantial, CR-induced reduction of tinnitus-related auditory binding in a pitch processing network.
| S-EPMC3319974 | biostudies-literature
Project description:The advent of neuroimaging has provided foundational insights into the neural basis of psychiatric conditions, such as major depression. Across countless studies, dysfunction has been localized to distinct parts of the limbic system. Specific knowledge about affected locations has led to the development of circuit modulation therapies to correct dysfunction, notably deep brain stimulation (DBS). This and other emerging neuromodulation approaches have shown great promise, but their refinement has been slow and fundamental questions about their mechanisms of action remain. Here, we argue that their continued development requires reverse translation to animal models with close homology to humans, namely, nonhuman primates. With a particular focus on DBS approaches for depression, we highlight the parts of the brain that have been targeted by neuromodulation in humans, their efficacy, and why nonhuman primates are the most suitable model in which to conduct their refinement. We finish by highlighting key gaps in our knowledge that need to be filled to allow more rapid progress toward effective therapies in patients for whom all other treatment attempts have failed.
| S-EPMC6936427 | biostudies-literature
Project description:Accumulating evidence has suggested functional interactions between prefrontal cortex (PFC) and dissociable large-scale networks. However, how these networks interact in the human brain to enable complex behaviors is not well-understood. Here, using a combination of behavioral, brain stimulation and neuroimaging paradigms, we tested the hypothesis that human PFC is required for successful reinforced skill formation. We additionally tested the extent to which PFC-dependent skill formation is related to intrinsic functional communication with this region. We report that inhibitory noninvasive transcranial magnetic stimulation over lateral PFC, a hub region with a diverse connectivity profile, causally modulated effective reinforcement-based motor skill acquisition. Furthermore, PFC-dependent skill formation was strongly related to the strength of functional connectivity between the PFC and regions in the sensorimotor network. These results point to the involvement of lateral PFC in the neural architecture that underlies the acquisition of complex skills, and suggest that, in relation to skill acquisition, this region may be involved in functional interactions with sensorimotor networks.
| S-EPMC6866473 | biostudies-literature
Project description:Learned and adaptive behaviors rely on neural circuits that flexibly couple the same sensory input to alternative output pathways. Here, we show that the Drosophila mushroom body functions like a switchboard in which neuromodulation reroutes the same odor signal to different behavioral circuits, depending on the state and experience of the fly. Using functional synaptic imaging and electrophysiology, we reveal that dopaminergic inputs to the mushroom body modulate synaptic transmission with exquisite spatial specificity, allowing individual neurons to differentially convey olfactory signals to each of their postsynaptic targets. Moreover, we show that the dopaminergic neurons function as an interconnected network, encoding information about both an animal's external context and internal state to coordinate synaptic plasticity throughout the mushroom body. Our data suggest a general circuit mechanism for behavioral flexibility in which neuromodulatory networks act with synaptic precision to transform a single sensory input into different patterns of output activity. PAPERCLIP.
| S-EPMC4732734 | biostudies-literature
Project description:We developed the transcription factor (TF)-target gene database and the Systems Genetics Network Analysis (SYGNAL) pipeline to decipher transcriptional regulatory networks from multi-omic and clinical patient data, and we applied these tools to 422 patients with glioblastoma multiforme (GBM). The resulting gbmSYGNAL network predicted 112 somatically mutated genes or pathways that act through 74 TFs and 37 microRNAs (miRNAs) (67 not previously associated with GBM) to dysregulate 237 distinct co-regulated gene modules associated with patient survival or oncogenic processes. The regulatory predictions were associated to cancer phenotypes using CRISPR-Cas9 and small RNA perturbation studies and also demonstrated GBM specificity. Two pairwise combinations (ETV6-NFKB1 and romidepsin-miR-486-3p) predicted by the gbmSYGNAL network had synergistic anti-proliferative effects. Finally, the network revealed that mutations in NF1 and PIK3CA modulate IRF1-mediated regulation of MHC class I antigen processing and presentation genes to increase tumor lymphocyte infiltration and worsen prognosis. Importantly, SYGNAL is widely applicable for integrating genomic and transcriptomic measurements from other human cohorts.
| S-EPMC5001912 | biostudies-literature
Project description:Objective/backgroundPrior research has emphasized the bidirectional relationships between sleep, stress, and affective states, such as depression. Given the inherent variability and fluctuations associated with sleep, assessing how sleep and affective variables function within a dynamic system may help further uncover possible causes and consequences of sleep disturbances, as well as find candidate targets for intervention. To this end, we examined dynamic relationships between self-reported stress, depressed mood, and clinically-relevant sleep parameters via temporal network analysis.MethodsParticipants were 401 nurses (92% female, 78% White, Mage = 39.47 years) who completed 14 days of sleep diaries incorporating self-reported stress and depression, as well as total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset.Results and conclusionsOverall, total sleep time emerged as a highly influential variable in the context of "outstrength centrality," meaning total sleep time had numerous outward connections with other variables (e.g., stress and sleep efficiency). The high outstrength centrality of total sleep time suggests this variable is a source of activation within this dynamic system. Conversely, stress showed high "instrength centrality," suggesting this variable was highly impacted by other variables in the system, such as depressed mood and sleep efficiency. These findings emphasize the importance of assessing unfolding sleep processes within a naturalistic setting, and implicate the role of total sleep time in fueling depressed mood and stress. Discussion emphasizes implications of these results for understanding the connections between sleep, stress, and depression as well as clinical relevance of these findings.
| S-EPMC10006381 | biostudies-literature
Project description:Networks are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models. However, the dynamics of biochemical systems are generally non-linear, suggesting that suitable non-linear formulations may offer gains with respect to causal network inference and aid in associated prediction problems.We present a general framework for network inference and dynamical prediction using time course data that is rooted in non-linear biochemical kinetics. This is achieved by considering a dynamical system based on a chemical reaction graph with associated kinetic parameters. Both the graph and kinetic parameters are treated as unknown; inference is carried out within a Bayesian framework. This allows prediction of dynamical behavior even when the underlying reaction graph itself is unknown or uncertain. Results, based on (i) data simulated from a mechanistic model of mitogen-activated protein kinase signaling and (ii) phosphoproteomic data from cancer cell lines, demonstrate that non-linear formulations can yield gains in causal network inference and permit dynamical prediction and uncertainty quantification in the challenging setting where the reaction graph is unknown.MATLAB R2014a software is available to download from warwick.ac.uk/chrisoates.Supplementary data are available at Bioinformatics online.
| S-EPMC4147905 | biostudies-other
Project description:(1) Does experienced mood affect emotion perception in faces and is this perception mood-congruent or mood-incongruent?(2) Are there age-group differences in the interplay between experienced mood and emotion perception? (3) Does emotion perception in faces change as a function of the temporal sequence of study sessions and stimuli presentation, and (4) does emotion perception in faces serve a mood-regulatory function? One hundred fifty-four adults of three different age groups (younger: 20-31 years; middle-aged: 44-55 years; older adults: 70-81 years) were asked to provide multidimensional emotion ratings of a total of 1026 face pictures of younger, middle-aged, and older men and women, each displaying six different prototypical (primary) emotional expressions. By analyzing the likelihood of ascribing an additional emotional expression to a face whose primary emotion had been correctly recognized, the multidimensional rating approach permits the study of emotion perception while controlling for emotion recognition. Following up on previous research on mood responses to recurring unpleasant situations using the same dataset (Voelkle et al., 2013), crossed random effects analyses supported a mood-congruent relationship between experienced mood and perceived emotions in faces. In particular older adults were more likely to perceive happiness in faces when being in a positive mood and less likely to do so when being in a negative mood. This did not apply to younger adults. Temporal sequence of study sessions and stimuli presentation had a strong effect on the likelihood of ascribing an additional emotional expression. In contrast to previous findings, however, there was neither evidence for a change from mood-congruent to mood-incongruent responses over time nor evidence for a mood-regulatory effect.
| S-EPMC4071858 | biostudies-other
Project description:The present study employed dynamic causal modeling to investigate the effective functional connectivity between regions of the neural network involved in top-down letter processing. We used an illusory letter detection paradigm in which participants detected letters while viewing pure noise images. When participants detected letters, the response of the right middle occipital gyrus (MOG) in the visual cortex was enhanced by increased feed-backward connectivity from the left inferior frontal gyrus (IFG). In addition, illusory letter detection increased feed-forward connectivity from the right MOG to the left inferior parietal lobules. Originating in the left IFG, this top-down letter processing network may facilitate the detection of letters by activating letter processing areas within the visual cortex. This activation in turns may highlight the visual features of letters and send letter information to activate the associated phonological representations in the identified parietal region.
| S-EPMC3817006 | biostudies-literature
Project description:Over the past few years, online learning has exploded in popularity due to the potentially unlimited enrollment, lack of geographical limitations, and free accessibility of many courses. However, learners are prone to have poor performance due to the unconstrained learning environment, lack of academic pressure, and low interactivity. Personalized intervention design with the learners’ background and learning behavior factors in mind may improve the learners’ performance. Causality strictly distinguishes cause from outcome factors and plays an irreplaceable role in designing guiding interventions. The goal of this paper is to construct a Bayesian network to make causal analysis and then provide personalized interventions for different learners to improve learning. This paper first constructs a Bayesian network based on background and learning behavior factors, combining expert knowledge and a structure learning algorithm. Then the important factors in the constructed network are selected using mutual information based on entropy. At last, we identify learners with poor performance using inference and propose personalized interventions, which may help with successful applications in education. Experimental results verify the effectiveness of the proposed method and demonstrate the impact of factors on learning performance.
| S-EPMC7514446 | biostudies-literature