Early warning for human mental sub-health based on fMRI data analysis: an example from a seafarers' resting-data study

Characteristics of symptoms and development of psychological status in late Chinese adolescence

BACKGROUND

Many late adolescents experience a state of psychological sub-health, requiring early recognition and intervention. This study aims to assess the psychological state of late Chinese adolescents and uncover developmental trend of mental health through network analysis.

METHOD

We analyzed data from 9072 Chinese high school adolescents in Shandong Province surveyed in 2020-2021, and divided them into the normal, the suspected, and the abnormal groups based on Symptom Checklist 90 (SCL-90) scores. Network analysis was employed to identify the core symptoms and bridge symptoms across different states.

RESULTS

Anxiety and depression were the most central symptoms, without gender differences. Core symptoms, network structure, and network invulnerability varied across different psychological states. The abnormal group exhibited the highest value of natural connectivity, followed by the suspected and normal groups. This pattern extended to bridge networks. While not meeting diagnostic criteria, the suspected group demonstrated abnormalities in network edge invariance and global strength invariance.

LIMITATIONS

The cross-sectional design cannot establish causality, and biases in self-report measurements cannot be ignored.

CONCLUSION

Compared to traditional scale indicators, network structural characteristics may be a more sensitive assessment indicator.

The integrative functional connectivity analysis between seafarer’s brain networks using functional magnetic resonance imaging data of different states

The particularity of seafarers’ occupation makes their brain functional activities vulnerable to the influence of working environments, which leads to abnormal functional connectivities (FCs) between brain networks. To further investigate the influences of maritime environments on the seafarers’ functional brain networks, the functional magnetic resonance imaging (fMRI) datasets of 33 seafarers before and after sailing were used to study FCs among the functional brain networks in this paper. On the basis of making full use of the intrinsic prior information from fMRI data, six resting-state brain functional networks of seafarers before and after sailing were obtained by using group independent component analysis with intrinsic reference, and then the differences between the static and dynamic FCs among these six brain networks of seafarers before and after sailing were, respectively, analyzed from both group and individual levels. Subsequently, the potential dynamic functional connectivity states of seafarers before and after sailing were extracted by using the affine propagation clustering algorithm and the probabilities of state transition between them were obtained simultaneously. The results show that the dynamic FCs among large-scale brain networks have significant difference seafarers before and after sailing both at the group level and individual level, while the static FCs between them varies only at the individual level. This suggests that the maritime environments can indeed affect the brain functional activity of seafarers in real time, and the degree of influence is different for different subjects, which is of a great significance to explore the neural changes of seafarer’s brain functional network.

Empirical Study on the Relationship Between Vacation Schedule and Seafarers' Fatigue in Chinese Seafarer Population

Background

Fatigue is an important factor for the safety of ships. In order to alleviate fatigue of the seafarers, the STCW Convention (International Convention on Standards of Training, Certification, and Watchkeeping for Seafarers) has made many regulations on the working time of seafarers. At present, if a crew member takes only one day off at home before returning to work on the ship, the working time on the ship must be re-calculated again. If the time spent at home is not sufficient to allow the crew to recover, the regulations of only stipulating the working time, not stipulating the home vacation time, cannot guarantee the crew's fatigue been well controlled. The aim of present study is to explore the relationship between vacation schedule and fatigue of the seafarers.

Methods

In present study, a simplified stress scale developed by the Ministry of Labor of Japan has been used as a measurement tool. The method of stratified sampling was adopted. Data collection mainly came from domestic ocean-going seafarers (n = 165). Analysis was conducted using the Cross (chi-square) analysis and hierarchical multiple regression analysis methods.

Results

We found that there was no difference between crew members of different positions in terms of average vacation time and on-board service time (p > 0.05). The length of last vacation time and this service time for seafarers of different positions showed obvious differences (p < 0.01). The rank has a significant effect on the length of the last vacation (χ2 = 101.560, p = 0.000 < 0.01) and the length of this service time (χ2 = 75.624, p = 0.000 < 0.01). Also, the results showed that there was a significant negative correlation between the duration of vacation and overall fatigue (t = -7.160, p = 0.000 < 0.01), while there was a significant positive correlation between the length of service time on board and overall fatigue (t = 3.474, p = 0.001 < 0.01).

Conclusion

The results indicated that a reasonable vacation schedule was crucial for the relief of the seafarers' fatigue, and also played a positive role in the state of working on the ship again.

Global Functional Connectivity Analysis Indicating Dysconnectivity of the Hate Circuit in Major Depressive Disorder

Background

Abnormalities of functional connectivity (FC) in certain brain regions are closely related to the pathophysiology of major depressive disorder (MDD). Findings are inconsistent with different presuppositions in regions of interest. Our research focused on voxel-wise brain-wide FC changes in patients with MDD in an unbiased manner.

Method

We examined resting-state functional MRI in 23 patients with MDD and 26 healthy controls. Imaging data were analyzed by using global-brain FC (GFC) and used to explore the correlation of abnormal GFC values with clinical variables.

Results

Increased GFC values in the left medial superior frontal gyrus (SFGmed) and decreased GFC values in the right supplementary motor area (SMA) were observed in the patients with MDD compared with the controls. The decreased GFC values in the right SMA had a positive correlation with vitamin D and Hamilton Anxiety Scale (HAM-A) scores.

Conclusion

Abnormal GFC in the hate circuit, particularly increased GFC in the left SFGmed and decreased GFC in the right SMA, appears to be a new sight for comprehending the pathological alterations in MDD.

The Brain Alteration of Seafarer Revealed by Activated Functional Connectivity Mode in fMRI Data Analysis

As a special occupational group, the working and living environments faced by seafarers are greatly different from those of land. It is easy to affect the psychological and physiological activities of seafarers, which inevitably lead to changes in the brain functional activities of seafarers. Therefore, it is of great significance to study the neural activity rules of seafarers' brain. In view of this, this paper studied the seafarers' brain alteration at the activated voxel level based on functional magnetic resonance imaging technology by comparing the differences in functional connectivities (FCs) between seafarers and non-seafarers. Firstly, the activated voxels of each group were obtained by independence component analysis, and then the distribution of these voxels in the brain and the common activated voxels between the two groups were statistically analyzed. Next, the FCs between the common activated voxels of the two groups were calculated and obtained the FCs that had significant differences between them through two-sample T-test. Finally, all FCs and FCs with significant differences (DFCs) between the common activated voxels were used as the features for the support vector machine to classify seafarers and non-seafarers. The results showed that DFCs between the activated voxels had better recognition ability for seafarers, especially for Precuneus_L and Precuneus_R, which may play an important role in the classification prediction of seafarers and non-seafarers, so that provided a new perspective for studying the specificity of neurological activities of seafarers.

The Alternate Consumption of Quercetin and Alliin in the Traditional Asian Diet Reshaped Microbiota and Altered Gene Expression of Colonic Epithelial Cells in Rats

The diet of traditional Asian is similar to the Mediterranean that was considered as a healthy dietary pattern. The report was scarce on whether different plant-derived components with similar anti-oxidative and anti-inflammatory function such as quercetin and alliin in traditional Asian diet consumed in an alternate style cooperatively affect health including the growth of host and the status of the gut microbiota and colonic epithelial immunity. In the present study, the effects of alternate consumption of quercetin and alliin on host health judging by the profile of gut microbiota and gene expression of colonic epithelial cells were investigated with the Illumina MiSeq sequencing (16S rRNA genes) and Illumina HiSeq (RNA-seq) technique, respectively. The results showed that the alternate consumption significantly increased the rat body weight and reshaped the gut microbiota composition. At the phylum level, it significantly increased the relative abundance of fecal Firmicutes and Cyanobacteria but decreased that of Bacteroidetes (P < 0.05) and increased the relative abundance of Candidatus Arthromitus, Lactococcus, Geobacillus, and Ruminococcus at the genus level that benefits the host's health. The alternate consumption of quercetin and alliin also altered 13 genes expression involved in the KEGG pathways of complement and coagulation cascades and hematopoietic cell lineage to improve the gut immunity. Therefore, the alternate consumption of quercetin and alliin in traditional Asian diet can contribute beneficial metabolic effects by optimizing gut microbiota and altering the immunologic function of colonic epithelial cells, resulting in its potential to improve the sub-health status.

SCTICA: Sub-packet constrained temporal ICA method for fMRI data analysis

Independent component analysis (ICA) has become a widely used method for functional magnetic resonance imaging (fMRI) data analysis. However, spatial ICA usually performs better than temporal ICA with regard to the stability and accuracy of functional connectivity detection, and temporal ICA is often not feasible when it is applied to the analysis of real fMRI data of the whole brain because of the excessive spatial dimensions. In this paper, to overcome these problems, we propose a sub-packet constrained temporal ICA (SCTICA) method to take advantage of the a priori information using a multi-objective optimization framework with the Newton iterative algorithm. Moreover, a splitting strategy is presented to improve the feasibility of the temporal ICA for whole brain fMRI data analysis. The experimental results of real data show that the splitting strategy improved the ability of the temporal ICA to analyze whole brain fMRI data. Furthermore, the experimental results also demonstrated that the proposed SCTICA method can not only improve the stability of the temporal ICA, but can also improve the functional connectivity detection ability compared with the classical ICA and ICA with a priori information methods. In brief, the proposed SCTICA method overcomes the problem that prevents temporal ICA from being applied to fMRI data of the whole brain, and the functional connectivity detection performance is greatly improved compared with that of traditional methods.

Brain Functional Plasticity Driven by Career Experience: A Resting-State fMRI Study of the Seafarer

The functional connectome derived from BOLD resting-state functional magnetic resonance imaging data represents meaningful functional organizations and a shift between distinct cognitive states. However, the body of knowledge on how the long-term career experience affects the brain's functional plasticity is still very limited. In this study, we used a dynamic functional connectome characterization (DBFCC) model with the automatic target generation process K-Means clustering to explore the functional reorganization property of resting brain states, driven by long-term career experience. Taking sailors as an example, DBFCC generated seventeen reproducibly common atomic connectome patterns (ACP) and one reproducibly distinct ACP, i.e., ACP14. The common ACPs indicating the same functional topology of the resting brain state transitions were shared by two control groups, while the distinct ACP, which mainly represented functional plasticity and only existed in the sailors, showed close relationships with the long-term career experience of sailors. More specifically, the distinct ACP14 of the sailors was made up of four specific sub-networks, such as the auditory network, visual network, executive control network, and vestibular function-related network, which were most likely linked to sailing experience, i.e., continuously suffering auditory noise, maintaining balance, locating one's position in three-dimensional space at sea, obeying orders, etc. Our results demonstrated DBFCC's effectiveness in revealing the specifically functional alterations modulated by sailing experience and particularly provided the evidence that functional plasticity was beneficial in reorganizing brain's functional topology, which could be driven by career experience.

A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets

Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data analysis to evaluate functional connectivity of the brain; however, there are still some limitations on ICA simultaneously handling neuroimaging datasets with diverse acquisition parameters, e.g., different repetition time, different scanner, etc. Therefore, it is difficult for the traditional ICA framework to effectively handle ever-increasingly big neuroimaging datasets. In this research, a novel feature-map based ICA framework (FMICA) was proposed to address the aforementioned deficiencies, which aimed at exploring brain functional networks (BFNs) at different scales, e.g., the first level (individual subject level), second level (intragroup level of subjects within a certain dataset) and third level (intergroup level of subjects across different datasets), based only on the feature maps extracted from the fMRI datasets. The FMICA was presented as a hierarchical framework, which effectively made ICA and constrained ICA as a whole to identify the BFNs from the feature maps. The simulated and real experimental results demonstrated that FMICA had the excellent ability to identify the intergroup BFNs and to characterize subject-specific and group-specific difference of BFNs from the independent component feature maps, which sharply reduced the size of fMRI datasets. Compared with traditional ICAs, FMICA as a more generalized framework could efficiently and simultaneously identify the variant BFNs at the subject-specific, intragroup, intragroup-specific and intergroup levels, implying that FMICA was able to handle big neuroimaging datasets in neuroscience research.