Exploring common changes after acute mental stress and acute tryptophan depletion: Resting-state fMRI studies

A deep learning approach for mental health quality prediction using functional network connectivity and assessment data

While one can characterize mental health using questionnaires, such tools do not provide direct insight into the underlying biology. By linking approaches that visualize brain activity to questionnaires in the context of individualized prediction, we can gain new insights into the biology and behavioral aspects of brain health. Resting-state fMRI (rs-fMRI) can be used to identify biomarkers of these conditions and study patterns of abnormal connectivity. In this work, we estimate mental health quality for individual participants using static functional network connectivity (sFNC) data from rs-fMRI. The deep learning model uses the sFNC data as input to predict four categories of mental health quality and visualize the neural patterns indicative of each group. We used guided gradient class activation maps (guided Grad-CAM) to identify the most discriminative sFNC patterns. The effectiveness of this model was validated using the UK Biobank dataset, in which we showed that our approach outperformed four alternative models by 4-18% accuracy. The proposed model's performance evaluation yielded a classification accuracy of 76%, 78%, 88%, and 98% for the excellent, good, fair, and poor mental health categories, with poor mental health accuracy being the highest. The findings show distinct sFNC patterns across each group. The patterns associated with excellent mental health consist of the cerebellar-subcortical regions, whereas the most prominent areas in the poor mental health category are in the sensorimotor and visual domains. Thus the combination of rs-fMRI and deep learning opens a promising path for developing a comprehensive framework to evaluate and measure mental health. Moreover, this approach had the potential to guide the development of personalized interventions and enable the monitoring of treatment response. Overall this highlights the crucial role of advanced imaging modalities and deep learning algorithms in advancing our understanding and management of mental health.

Preventing prefrontal dysfunction by tDCS modulates stress-induced creativity impairment in women: an fNIRS study

Stress is a major external factor threatening creative activity. The study explored whether left-lateralized activation in the dorsolateral prefrontal cortex manipulated through transcranial direct current stimulation could alleviate stress-induced impairment in creativity. Functional near-infrared spectroscopy was used to explore the underlying neural mechanisms. Ninety female participants were randomly assigned to three groups that received stress induction with sham stimulation, stress induction with true stimulation (anode over the left and cathode over the right dorsolateral prefrontal cortex), and control manipulation with sham stimulation, respectively. Participants underwent the stress or control task after the transcranial direct current stimulation manipulation, and then completed the Alternative Uses Task to measure creativity. Behavioral results showed that transcranial direct current stimulation reduced stress responses in heart rate and anxiety. The functional near-infrared spectroscopy results revealed that transcranial direct current stimulation alleviated dysfunction of the prefrontal cortex under stress, as evidenced by higher activation of the dorsolateral prefrontal cortex and frontopolar cortex, as well as stronger inter-hemispheric and intra-hemispheric functional connectivity within the prefrontal cortex. Further analysis demonstrated that the cortical regulatory effect prevented creativity impairment induced by stress. The findings validated the hemispheric asymmetry hypothesis regarding stress and highlighted the potential for brain stimulation to alleviate stress-related mental disorders and enhance creativity.

Capacity of Autonomous Sensory Meridian Response on the Reduction of Mental Stress

In a social environment, various types of stress can be overwhelming. Humans frequently encounter these stressful situations in social life. Stress is divided into physical stress and mental stress; the latter is induced by heavy mental workloads and has become a huge social problem, leading to mental disorders and possibly suicide in the worst scenario. Investigations into monitoring mental stress and reducing stressful conditions are, therefore, important for its prevention. In the present study, we focused on autonomous sensory meridian response (ASMR) sound, which is known to improve the human mental condition through its comforting and relaxing effects. We investigated the effect of ASMR on the mental workload induced by mental tasks by the evaluation of EEG activation patterns in normal subjects. Our results showed a significant decrease in alpha-band activity and a significant increase in gamma (high beta)-band activity under the induction of mental workload by mental tasks compared to the resting condition. When applying ASMR sound, alpha- and gamma-band activity under the induction of mental workload by mental tasks was restored to the level of the resting condition. In conclusion, these results indicate that ASMR sound reduces the mental stress induced by mental workload.

Acute tryptophan depletion balances altered resting-state functional connectivity of the salience network in female patients recovered from anorexia nervosa

BACKGROUND

It has been suggested that individuals predisposed to or recovered from anorexia nervosa experience a hyperserotonergic state associated with anxiety that might be mitigated by restricted food intake, because diminished levels of the tryptophan precursor lower the central availability of serotonin (5-HT). At the neural level, the salience network is a system of functionally connected brain regions; it has been closely associated with 5-HT functioning and mental disorders (including anorexia nervosa). The aim of the present study was to investigate the effect on the salience network of a temporary dietary manipulation of 5-HT synthesis in patients with anorexia nervosa.

METHODS

In this double-blind crossover study, we obtained data on resting-state functional connectivity from 22 weight-recovered female patients with a history of anorexia nervosa, and 22 age-matched female healthy controls. The study procedure included acute tryptophan depletion (a dietary intervention that lowers the central 5-HT synthesis rate) and a sham condition.

RESULTS

We identified an interaction of group and experimental condition in resting-state functional connectivity between the salience network and the orbitofrontal cortex extending to the frontal pole (F _1,42 = 12.52; p _FWE = 0.026). Further analysis revealed increased resting-state functional connectivity during acute tryptophan depletion in patients recovered from anorexia nervosa, resembling that of healthy controls during the sham condition (T ₄₂ = -0.66; p = 0.51).

LIMITATIONS

The effect of acute tryptophan depletion on the central availability of 5-HT can be judged only indirectly using plasma ratios of tryptophan to large neutral amino acids. Moreover, the definition of anorexia nervosa recovery varies widely across studies, limiting comparability.

CONCLUSION

Taken together, our findings support the notion of 5-HT dysregulation in anorexia nervosa and indicate that reduced 5-HT synthesis and availability during acute tryptophan depletion (and possibly with food restriction) may balance hyperserotonergic functioning and the associated resting-state functional connectivity of the salience network.

Depression mediates the association between insula-frontal functional connectivity and social interaction anxiety

High rates of comorbidity between depression and anxiety are frequently observed. However, few studies have investigated the relationship between depression and social interaction anxiety using a dimensional approach. The current study aimed to explore the associations between depression and social interaction anxiety with a multivariate approach in a comparably large dataset (n = 194, 95 males). All participants completed a structural and a resting-state functional magnetic resonance imaging (fMRI) scan and self-report measures of depression via Beck's Depression Inventory II and social interaction anxiety by social interaction anxiety scale. Voxel-based morphometry (VBM) results first identified grey matter volumes of insula were positively correlated with depression dimension scores. Next, whole brain seed-to-voxel analyses were conducted using a VBM-identified insula as a seed region to examine associations between depression/social anxiety and functional connectivity. The results suggested that a significant positive effect of depression/social anxiety was found on the connectivity between insula and dorsal lateral prefrontal cortex (dlPFC). Moreover, variations in depression meditated the association between insula-dlPFC connectivity and social interaction anxiety. Overall, the results indicate that individual differences in depression relate more to insula-dlPFC coupling compared to social interaction anxiety.

Neurodegenerative Diseases: Implications of Environmental and Climatic Influences on Neurotransmitters and Neuronal Hormones Activities

Neurodegenerative and neuronal-related diseases are major public health concerns. Human vulnerability to neurodegenerative diseases (NDDs) increases with age. Neuronal hormones and neurotransmitters are major determinant factors regulating brain structure and functions. The implications of environmental and climatic changes emerged recently as influence factors on numerous diseases. However, the complex interaction of neurotransmitters and neuronal hormones and their depletion under environmental and climatic influences on NDDs are not well established in the literature. In this review, we aim to explore the connection between the environmental and climatic factors to NDDs and to highlight the available and potential therapeutic interventions that could use to improve the quality of life and reduce susceptibility to NDDs.

Oxytocinergic Modulation of Stress-Associated Amygdala-Hippocampus Pathways in Humans Is Mediated by Serotonergic Mechanisms

BACKGROUND

The hypothalamic neuropeptide oxytocin (OXT) may exert anxiolytic and stress-reducing actions via modulatory effects on amygdala circuits. Animal models and initial findings in humans suggest that some of these effects are mediated by interactions with other neurotransmitter systems, in particular the serotonin (5-HT) system. Against this background, the present pharmacological resting-state functional magnetic resonance imaging study aimed to determine whether effects of OXT on stress-associated amygdala intrinsic networks are mediated by 5-HT.

METHODS

We employed a randomized, placebo-controlled, double-blind parallel-group, pharmacological functional magnetic resonance imaging resting-state experiment with 4 treatment groups in n = 112 healthy male participants. Participants underwent a transient decrease in 5-HT signaling via acute tryptophan depletion (ATD) or a corresponding placebo-control protocol before the administration of intranasal OXT (24 IU) or placebo intranasal spray.

RESULTS

OXT and 5-HT modulation exerted interactive effects on the coupling of the left amygdala with the ipsilateral hippocampus and adjacent midbrain. OXT increased intrinsic coupling in this pathway, whereas this effect of OXT was significantly attenuated during transiently decreased central serotonergic signaling induced via acute tryptophan depletion. In the absence of OXT or 5-HT modulation, this pathway showed a trend for an association with self-reported stress perception in everyday life. No interactive effects were observed for the right amygdala.

CONCLUSIONS

Together, the findings provide the first evidence, to our knowledge, that the effects of OXT on stress-associated amygdala-hippocampal-midbrain pathways are critically mediated by the 5-HT system in humans.

Inter-relationships between changes in stress, mindfulness, and dynamic functional connectivity in response to a social stressor

We conducted a study to understand how dynamic functional brain connectivity contributes to the moderating effect of trait mindfulness on the stress response. 40 male participants provided subjective reports of stress, cortisol assays, and functional MRI before and after undergoing a social stressor. Self-reported trait mindfulness was also collected. Experiencing stress led to significant decreases in the prevalence of a connectivity state previously associated with mindfulness, but no changes in two connectivity states with prior links to arousal. Connectivity did not return to baseline 30 min after stress. Higher trait mindfulness was associated with attenuated affective and neuroendocrine stress response, and smaller decreases in the mindfulness-related connectivity state. In contrast, we found no association between affective response and functional connectivity. Taken together, these data allow us to construct a preliminary brain-behaviour model of how mindfulness dampens stress reactivity and demonstrate the utility of time-varying functional connectivity in understanding psychological state changes.

Performance Evaluation of EEG Based Mental Stress Assessment Approaches for Wearable Devices

Mental stress has been identified as the root cause of various physical and psychological disorders. Therefore, it is crucial to conduct timely diagnosis and assessment considering the severe effects of mental stress. In contrast to other health-related wearable devices, wearable or portable devices for stress assessment have not been developed yet. A major requirement for the development of such a device is a time-efficient algorithm. This study investigates the performance of computer-aided approaches for mental stress assessment. Machine learning (ML) approaches are compared in terms of the time required for feature extraction and classification. After conducting tests on data for real-time experiments, it was observed that conventional ML approaches are time-consuming due to the computations required for feature extraction, whereas a deep learning (DL) approach results in a time-efficient classification due to automated unsupervised feature extraction. This study emphasizes that DL approaches can be used in wearable devices for real-time mental stress assessment.

A Review on Mental Stress Assessment Methods Using EEG Signals

Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

Dynamic changes in thalamic connectivity following stress and its association with future depression severity

INTRODUCTION

Tracking stress-induced brain activity and connectivity dynamically and examining activity/connectivity-associated recovery ability after stress might be an effective way of detecting stress vulnerability.

METHODS

Using two widely used stress paradigms, a speech task (social stress) and a mathematical calculation task (mental loading stress), we examined common changes in regional homogeneity (ReHo) and functional connectivity (FC) before, during, and after the two stressful tasks in thirty-nine college students. A counting breath relaxation task was employed as a contrast task. ReHo and FC were compared between subjects with higher versus lower depression symptoms (assessed by the Beck Depression Inventory, BDI). We developed a recovery index (RI) based on dynamic changes of ReHo/FC to evaluate individuals' ability to recover from a stressful state. To assess RI's usefulness in predicting future depression severity, BDI was also measured at one-year follow-up.

RESULTS

Our results revealed a ReHo decrease after both stressful tasks and a ReHo increase after the relaxation task in bilateral thalamus. The ReHo decrease after both stressful tasks was more significant in the higher BDI than the lower BDI group. Higher ReHo RI of the right thalamus in the higher BDI groups was significantly correlated with lower BDI severity at one-year follow-up. Bilateral thalamus also showed increased FC with the default mode network and decreased FC with the executive control network after the stressful tasks.

CONCLUSION

These findings highlight the importance of tracking resting activity and connectivity of thalamus dynamically for detecting stress vulnerability.