Qiu Jiang

Influence of resting-state functional brain network’s time duration on recognizing major depressive disorder

Machine learning has recently been applied into automatically recognizing major depressive disorder by taking functional connectivities as classification features. It opened the good clinical application of computer assisted major depressive disorder diagnosis. However, it is still unclear how much the neuropathology information of major depressive disorder can be captured by resting-state functional brain networks with different time durations, and it is unknown what the influence of resting-state functional brain network’s time duration on recognizing major depressive disorder is. The present research established the nonlinear models that describes the influence of functional brain network’s time duration on recognizing major depressive disorder by using the method of nonlinear regression, and illustrated the neuropathology information of major depressive disorder that is represented by functional brain networks with typical time durations. In the experiment, the resting-state functional magnetic resonance imaging (rs-fMRI) and Hamilton depression ratings were acquired, and the data of 64 clinical first-episode major depressive disorder patients and 53 control subjects were analyzed. The study constructed the resting-state large scale functional brain networks and the functional connectivity matrices under the anatomical automatic labeling (AAL) atlas and rs-fMRI data for each subject, and found the significant functional connectivities used as classification features by two sample t-test between the patient and control groups. Then, the sensitivity, specificity and accuracy of the support vector machine classifier were obtained by the leave one subject out cross validation. By modeling the relationship between the functional brain network’s time durations and classification performances, we obtained the nonlinear curve models of classification performances. The functional brain networks with about 46 TRs length have credible classification performance for the first time, and it show that the patient have enhanced functional connectivities between the posterior cingulate gyrus and the orbital frontal cortex, between the right orbital frontal middle gyrus and the left angular gyrus, and between the left gyrus rectus and the left hippocampus. The functional brain networks with about 114 TRs length have the best classification performance, and capture some more weakened functional connectivities between the right angular gyrus and the bilateral inferior parietal but supramarginal and angular gyri, middle frontal gyrus, between left amygdala and left supramarginal gyrus, right rolandic operculum. These abnormal functional connectivities support the hypothesis that major depressive disorder is the neurogenic disorder of distributed brain network with abnormal interactions. With the increase of time duration, functional brain networks capture certain functional connectivities that may not be directly related to the neuropathology mechanisms of major depressive disorder, and hence the classification performance decrease. Thus, the law of the influence of resting-state functional brain network’s time duration on recognizing major depressive disorder appears to be the inverted U-shape tendency. This may provide certain new references for further effectively investigating the neuropathology mechanisms and improving the intelligent recognition effects of major depressive disorder.

Chinese Color Nest Project: Growing up in China

To face the challenges of keeping healthy in increasing population sizes of both ageing and developing people in China, a fundamental request from the public health is the development of lifespan normative trajectories of brain and behavior. This paper introduces the Chinese Color Nest Project (CCNP 2013–2022), a large-scale ten-year program of modeling brain and behavioral trajectories for human lifespan (6–85 years old). We plan to gradually collect the behavioral and brain imaging data at ages across the lifespan on nationwide and depict the normal trajectory of Chinese brain development across the lifespan, based on the accelerated longitudinal design in the coming next 10 years starting at 2013. Various psychiatric disorders have been demonstrated highly relevant to abnormal events during the neurodevelopment regarding their onset ages of first episodes. Therefore, delineation of normative growth curves of brain and cognition in typically developing children is extremely useful for monitoring, early detecting and intervention of various neurodevelopmental disorders. In this paper, we detailed the developing part of CCNP, devCCNP. It tracked 192 healthy children and adolescents (6–18 years old) in Beibei district of Chongqing for the first 5 years of the full CCNP cohort (2013–2017). To demonstrate the feasibility of implementing the long-term follow-up of CCNP, we here comprehensively document devCCNP in terms of its experimental design, sample strategies, data acquisition and storage as well as some preliminary results and data sharing roadmap for future. Specifically, we first describe the accelerated longitudinal sampling design as well as its exact ratioof sample dropping off during the data collection. Second, we present several initial findings such as canonical growth curves of cortical surface areas of a set of well-established large-scale functional networks of the human brain. Finally, together with records generated by many psychological and behavioral tests, we will provide an individual growing-up report for each family participating the program, initiating the potential guidance on the individual academic and social development. The resources introduced in the current work can provide first-hand data for a series of coming Chinese brain development studies, such as Chinese Standard MRI Brain Templates, Normative Growth Curves of Chinese Brain and Cognition as well as Mapping of Language Areas in Chinese Developing Brain. These would not only offer normative references of the atypical brain and cognition development for Chinese population but also serve as a strong force on accelerating the pace of integrating Chinese brain development into the national brain program or Chinese Brain Project.

Neural mechanisms underlying susceptibilityfactors in depression

Major depressive disorder (MDD) is one of the most common mental disorders. The World Health Organization currently estimates that there are approximately 350 million depressive patients worldwide. MDD not only affects the life quality of individuals and their families, but also brings about a heavy financial burden to the society. The factors that contribute to the onset of MDD are complex and its underlying neural mechanisms have remained unclear. The modern medical science proposes “early detection, early treatment” of diseases. Therefore, early prediction and diagnosis of the MDD onsets are becoming a trend in depressive studies.

Gene expression analysis of multiple brain regions in major depressive disorder

Major depressive disorder (MDD), a polygenic disease resulting from complicated interactions between environmental and genetic factors, is becoming an increasingly serious threat to global public health. Genome-wide gene expression analysis is widely used for psychiatric disorders because of its ability to detect spatio-temporal patterns of gene expression. However, it is difficult to elucidate the molecular mechanism of MDD by methods that analyze brain regions and/or genes individually because activation patterns may differ between brain regions and MDD may be regulated by multiple genes with moderate or weak effects. In this study, we used a strategy that leverages information across multiple brain regions and genes. We performed gene set enrichment analysis of transcriptomics data from different brain regions of MDD patients and health controls in 13 datasets from the Gene Expression Omnibus. Our results show that the neuron markers gene set was significantly down-regulated in the associative striatum, anterior cingulate cortex (ACC), and amygdala, which is consistent with current MDD knowledge. The gene expression of glial cell markers was also altered in MDD patients, but there were no consistent patterns across brain regions or datasets from the same region. Moreover, gender-specific activation patterns of the astrocyte markers and oligodendrocyte markers gene sets were observed in the ACC and amygdala. In a comparative analysis of psychiatric disorders, the neuron markers gene set were also significantly down-regulated in multiple brain regions of patients with bipolar disorder and schizophrenia, although the activation patterns of two glial cell markers gene sets and the choroid plexus markers gene set in these two disorders differed from that of MDD. Our results reveal potential candidate brain regions, gene sets, and genes that are altered in MDD.

Self-concept clarity is predicted by amygdala: Evidence from a VBM study

Self-concept clarity (SCC) refers to the degree to which individual has clearly defined, internally consistent, and temporally stable self-concept. Given that individuals attempt to define themselves positively, SCC is also regarded as the degree of individuals seeing themselves in positive and consistent ways. According to previous studies, SCC is mainly influenced by negative daily events, such as setbacks in daily personally valued goals, social rejection and inappropriate social comparison. Because the favorable views about ourselves are questioned, contradicted, impugned, mocked, or challenged by this daily events, SCC is influenced by self-threat, which is a very important and common threat in our daily life (such as mental threat and physical threat). Thus, the brain regions which are involved in self-threat processing, such as amygdala, might predict the level of SCC. Therefore, in the present study, the predictability of amygdala to SCC was investigated by using voxel-based morphology (VBM). According to the perspective of uncertainty and anticipation in anxiety, anxious individuals have defection in threat processing, which was related to abnormal structure in amygdala. Thus, we might learn about the relationship the brain regions related to threat processing in amygdala by analyzing the relationship between anxiety and amygdala among anxious individuals, which might be helpful for us to investigate the predictability of amygdala to SCC furtherly. Trait anxiety is a stable disposition related to frequent and/or intense state anxiety. Considering that trait anxiety is very common and easy-operation, the predictability of amygdala to SCC was investigated based on analyzing the relationship between trait anxiety and amygdala. In addition, although amygdala is very important in threat processing, such as lateral prefrontal cortex (LPFC), ventromedial prefrontal cortex (VMPFC), anterior insula (AI), hippocampi, para hippocampal gyrus and anterior cingulated cortex (ACC) had also been found to be involved in threat processing. Except amygdala, the relationship between trait anxiety and the brain regions motioned above were also investigated using whole-brain analysis. The result of the present research showed that trait anxiety was negatively correlated with self-concept clarity; trait anxiety was positively associated with right basolateral amygdala (BLA), which can negatively predict SCC. According to these results, we believed that the enlarged BLA might make individuals be prone to detect threats around them and form, consolidate and retrieve threatened memory. Therefore, the negative predictability of BLA to SCC might reflect that with BLA increasing, individuals might be easily influenced by self-threat which make them be more easy to develop lower SCC. In addition, according to our whole-brain analysis, trait anxiety was also positively associated with right AI, which can also negatively predict SCC. Based on the role of AI playing in processing aware visceral responses into subjective feeling states, we thought that with BLA increasing individuals might easily generate more intense subjective feeling, which also make them be prone to be influenced by self-threat and develop lower SCC.

From groupwise to individual brain functionalnetworks parcellation and application

Numerous brain imaging studies revealed that human behavior and cognition, which included perception, attention, memory and personality, are complex processes. The neural correlates underlying these fundamental cognitive functions are not associated with a particular brain region, but closely related with our human brain functional networks. In recent years, the development of new brain connectivity technologies for mapping the whole brain functional networks advanced the knowledge of comprehensive neuronal circuits and systems. For instance, we can examine the architecture of these functional networks by a wide variety of graph theory tools. Previous studies suggested that brain networks were modular and hierarchically organized, and they consisted of blocks or subnetworks that were particularly densely connected, but not spatially depended.

The brain anatomical basis of emotional face advantage

Human beings are quite efficient in emotional face processing, which is of great importance for individuals in their social interactions. Previous studies showed that processing of emotional face was significantly faster than that of neutral face, and individuals showed variability in emotional face processing efficiency. Evidences from functional magnetic resonance imaging researches indicated that brain regions such as amygdala were significantly activated at the present of emotional faces. However, the brain structural basis of this superiority of emotional face processing had not been revealed. The present study combined visual search paradigm and structural magnetic resonance imaging to explore the brain structural basis of individual difference of emotional processing efficiency. In the visual search experiment, we asked the subjects to search for an emotional (a happy or fearful face) face or a target gender (a neutral face). Two-way (set size×target type) repeated measure analysis of RTs were done respective to happy and fearful face and their corresponding face gender search. We found significant interaction of set size and target type for both of them, and the main effects of set size and target type were significant as well. For further analysis, we compare the search slopes of emotional face and face gender search, and found that every subject showed a shallower search function for emotional face search than face gender search, indicating a stable emotional face advantage. Happy and fearful face advantage indexes were calculated for each subject. We then collected structural MRI images of each subject. Then we used multiple regression analysis to explore the correlation between the emotional face advantage indexes and the brain gray matter volume. We found that both the processing advantages of happy and fearful face were significantly correlated with gray matter volume of brain regions in the right posterior cingulate cortex and the left superior parietal lobe. The GMV in the left superior parietal lobe was positively correlated with the emotional face advantage indexes while the GMV in the right posterior cingulate cortex was negatively correlated with them. Previous studies found that superior parietal lobe was involved in attentional shift for different targets, larger GMV of this region might indicate better ability to search a certain target. Posterior cingulate cortex was widely found to participate in the interaction of emotion regulation and memory arousal. It was also an important part in the target-negative network and showed decreased activation in attention-related task, which was consistent with the results in the present study. In addition to these common areas, there were brain areas that only showed correlation with one of the emotional face advantage index. In the fearful face advantage analysis, left temporal gyrus and left posterior cingulate cortex were also significantly correlated with fearful face advantage index. In the happy face advantage analysis, right superior parietal lobe was found to be significantly correlated with happy face advantage index. These results indicated that different emotional face processing included different composition. These results showed that regardless of different composition of different emotional face processing, individual difference for different emotional face processing might have a common brain structural basis.

The Brain Mechanism of Bistable Perception

当对视觉输入的信息有多种解释时, 人们的知觉状态会在这些解释之间随机切换. 目前, 这种多稳态或双稳态知觉的神经机制仍处于争论之中. 本研究分别以鲁宾花瓶和内克尔立方体两种双稳态图形为对象来研究双稳态知觉在大脑结构上的机制. 首先, 通过计算两种双稳态知觉的切换频率, 发现两者切换频率之间有正相关关系. 在此基础上, 通过计算两种知觉切换频率与大脑灰质体积的相关性, 发现鲁宾花瓶和内克尔立方体图形的切换频率均和右侧额下回的灰质体积存在显著的负相关. 本研究表明, 不同双稳态知觉之间具有共同的神经基础, 这一共同的基础位于右侧额下回, 支持了自上而下的加工在双稳态知觉中具有重要的作用.