Major depressive disorder (MDD) is characterized by both clinical symptoms and cognitive deficits. Prior studies have typically examined either symptoms or cognition correlated with brain measures, thus causing a notable paucity of stable brain markers that capture the full characteristics of MDD. Brain controllability derived from newly proposed brain model integrating both metabolism (energy cost) and dynamics from a control perspective has been considered as a sensitive biomarker for characterizing brain function. Thus, identifying such a biomarker of controllability related to both symptoms and cognition may provide a promising state monitor of MDD.

To assess the associations between two multi-dimensional clinical (symptoms and cognition) and brain controllability data of MDD in an integrative model.

Sparse canonical correlation analysis (sCCA) was used to investigate the association between brain controllability at a network level and both clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD. The potential mediation effect of cognition on relationship between controllability and symptoms was also tested.

Average controllability was significantly correlated with both symptoms and cognition (rmean=0.54, PBonferroni=0.03). Average controllability of dorsal attention network (DAN) (r=0.46) and visual network (r=0.29) had the highest correlation with both symptoms and cognition. Among clinical variables, depressed mood (r=-0.23) , suicide(r=-0.25), work and activities(r=-0.27), gastrointestinal symptoms (r=-0.25) were significantly negatively associated with average controllability, while cognitive flexibility (r=0.29) was most strongly positively correlated with average controllability. Additionally, cognitive flexibility fully mediated the association between average controllability of DAN and depressed mood (indirect effect=-0.11, 95% CI [-0.18, -0.04], P=0.001) in MDD.

Brain average controllability was correlated with both clinical symptoms and cognition in first-episode medication-naïve patients with MDD. The results suggest that average controllability of DAN and visual network reached high associations with clinical variates in MDD, thus these brain features may serve as stable biomarkers to control the brain functional states transitions to be relevant to cognitions deficits and clinical symptoms of MDD. Additionally, altered average controllability of DAN in patients could induce impairment of cognitive flexibility, and thus cause severe depressed mood, indicating that controllability of DAN may be a potential intervention target for alleviating depressed mood through improving cognitive flexibility in MDD.

None Declared

Coronary artery aneurysms have been considered the most serious complication of Kawasaki disease. However, some coronary artery aneurysms do regress. Therefore, the ability to predict the expected time of coronary artery aneurysm regression is critical. Herein, we have created a nomogram prediction system to determine the early regression (<1 month) among patients with small to medium coronary artery aneurysms.

Seventy-six Kawasaki disease patients identified with coronary artery aneurysms during the acute or subacute phase were included. All the patients who met inclusion criteria demonstrated regression of coronary artery aneurysms within the first-year post Kawasaki disease diagnosis. The clinical and laboratory parameters were compared between the groups of coronary artery aneurysms regression duration within and beyond 1 month. Multivariate logistic regression analysis was used to identify the independent parameters for early regression based on the results from the univariable analysis. Then nomogram prediction systems were established with associated receiver operating characteristic curves.

Among the 76 included patients, 40 cases recovered within 1 month. Haemoglobin, globulin, activated partial thromboplastin time, the number of lesions, location of the aneurysm, and coronary artery aneurysm size were identified as independent factors for early regression of coronary artery aneurysms in Kawasaki disease patients. The predictive nomogram models revealed a high efficacy in predicting early regression of coronary artery aneurysms.

The size of coronary artery aneurysms, the number of lesions, and the location of aneurysms presented better predictive value for predicting coronary artery aneurysms regression. The nomogram system created from the identified risk factors successfully predicted early coronary artery aneurysm regression.

In view of the problem that the psychological barriers of ski jumpers before competitions affect the athletes’ performance on the competition ground, this paper discusses the therapeutic effect of psychological training on the athletes’ psychological barriers before competitions, so as to find a reasonable and effective intervention method to help athletes avoid psychological barriers.

40 ski jumpers were selected as the research objects. With the help of computer, they were randomly divided into two groups. One group was the psychological training group, and 60 times of psychological training were carried out in the 6-month experiment. The other group was General group without any intervention. At the beginning and end of the experiment, the Trait Anxiety Inventory for Sport (TAIS) was used to compare the pre competition psychological disorders of the two groups of athletes.

The score of the TAIS scale of the two groups after intervention was recorded and compared, as shown in Table 1. As can be seen in Table 1, the average and total TAIS scores of members of the General group are relatively high. Taking dimensional movement tension tendency as an example, the average score of the General group was 12.5. The average score and total score of TAIS in the psychological training group were significantly lower than those in the General group. Taking dimensional movement tension tendency as an example, the average score of the psychological training group was 10.2. Table 1.

The score of the TAIS scale of the members of the psychological training group before and after the intervention

Psychological barriers will have a significant impact on the patient’s cognition, emotion, and behavior. In sports competitions, psychological barriers will greatly affect the performance of athletes. In view of this problem, the study took 40 ski jumpers as the research object and discussed the effect of psychological training on the treatment of athletes’ psychological disorders before competitions. The experimental results showed that the TAIS score of the psychological training group decreased significantly after the intervention, which indicated that psychological training could effectively help the athletes to avoid psychological obstacles before the competition, so as to give full play to the athletes’ due level.

Attention-deficit/hyperactivity disorder (ADHD) is a clinically heterogeneous neurodevelopmental disorder defined by characteristic behavioral and cognitive features. Abnormal brain dynamic functional connectivity (dFC) has been associated with the disorder. The full spectrum of ADHD-related variation of brain dynamics and its association with behavioral and cognitive features remain to be established.

We sought to identify patterns of brain dynamics linked to specific behavioral and cognitive dimensions using sparse canonical correlation analysis across a cohort of children with and without ADHD (122 children in total, 63 with ADHD). Then, using mediation analysis, we tested the hypothesis that cognitive deficits mediate the relationship between brain dynamics and ADHD-associated behaviors.

We identified four distinct patterns of dFC, each corresponding to a specific dimension of behavioral or cognitive function (r = 0.811–0.879). Specifically, the inattention/hyperactivity dimension was positively associated with dFC within the default mode network (DMN) and negatively associated with dFC between DMN and the sensorimotor network (SMN); the somatization dimension was positively associated with dFC within DMN and SMN; the inhibition and flexibility dimension and fluency and memory dimensions were both positively associated with dFC within DMN and between DMN and SMN, and negatively associated with dFC between DMN and the fronto-parietal network. Furthermore, we observed that cognitive functions of inhibition and flexibility mediated the relationship between brain dynamics and behavioral manifestations of inattention and hyperactivity.

These findings document the importance of distinct patterns of dynamic functional brain activity for different cardinal behavioral and cognitive features related to ADHD.

Numerous studies of resting-state functional imaging and voxel-based morphometry (VBM) have revealed differences in specific brain regions of patients with bipolar disorder (BD), but the results have been inconsistent.

A whole-brain voxel-wise meta-analysis was conducted on resting-state functional imaging and VBM studies that compared differences between patients with BD and healthy controls using Seed-based d Mapping with Permutation of Subject Images software.

A systematic literature search identified 51 functional imaging studies (1842 BD and 2190 controls) and 83 VBM studies (2790 BD and 3690 controls). Overall, patients with BD displayed increased resting-state functional activity in the left middle frontal gyrus, right inferior frontal gyrus (IFG) extending to the right insula, right superior frontal gyrus and bilateral striatum, as well as decreased resting-state functional activity in the left middle temporal gyrus extending to the left superior temporal gyrus and post-central gyrus, left cerebellum, and bilateral precuneus. The meta-analysis of VBM showed that patients with BD displayed decreased VBM in the right IFG extending to the right insula, temporal pole and superior temporal gyrus, left superior temporal gyrus extending to the left insula, temporal pole, and IFG, anterior cingulate cortex, left superior frontal gyrus (medial prefrontal cortex), left thalamus, and right fusiform gyrus.

The multimodal meta-analyses suggested that BD showed similar patterns of aberrant brain activity and structure in the insula extending to the temporal cortex, fronto-striatal-thalamic, and default-mode network regions, which provide useful insights for understanding the underlying pathophysiology of BD.

Apart from the psychiatric symptoms, cognitive deficits are also the core symptoms of schizophrenia. Brain network control theory provided information on the role of a specific brain region in the cognitive control process, helping understand the neural mechanism of cognitive impairment in schizophrenia.

To characterize the control properties of functional brain network in first-episode untreated patients with schizophrenia and the relationships between controllability and psychiatric symptoms, as well as exploring the predictive value of controllability in differentiating patients from healthy controls (HCs).

Average and modal controllability of brain networks were calculated and compared between 133 first-episode untreated patients with schizophrenia and 135 HCs. The associations between controllability and clinical symptoms were evaluated using sparse canonical correlation analysis. Support vector machine (SVM) and SVM-recursive feature elimination combined with the controllability were performed to establish the individual prediction model.

Compared to HCs, the patients with schizophrenia showed increased average controllability and decreased modal controllability in dorsal anterior cingulate cortex (dACC). Brain controllability predominantly in somatomotor, default mode, and visual networks was associated with the positive symptomatology of schizophrenia. The established model could identify patients with an accuracy of 0.68. Furthermore, the most discriminative features were located in dACC, medial prefrontal lobe, precuneus and superior temporal gyrus.

Altered controllability in dACC may play a critical role in the neuropathological mechanisms of cognitive deficit in schizophrenia, which could drive the brain function to different states to cope with varied cognitive tasks. As symptom-related biomarkers, controllability could be also beneficial to individual prediction in schizophrenia.

No significant relationships.

There is a growing consensus on brain networks that it is not immutable but rather a dynamic complex system for adapting environment. The neuroimaging research studying how brain regions work collaboratively with dynamic methods had demonstrated its effectiveness in revealing the neural mechanisms of schizophrenia.

To investigate altered dynamic brain functional topology in first-episode untreated schizophrenia patients (SZs) and establish classification models to find objective brain imaging biomarkers.

Resting-state-functional magnetic resonance data for SZs and matched healthy controls were obtained(Table1).

Power-264-template was used to extract nodes and sliding-window approach was carried out to establish functional connectivity matrices. Functional topology was assessed by eigenvector centrality(EC) and node efficiency and its time-fluctuating was evaluated with coefficient of variation(CV). Group differences of dynamic topology and correlation analysis between Positive and Negative Syndrome Scale(PANSS) scores and topology indices showing group differences, which also were used in establishing classification models, was examed.

The CV of node efficiency in angular and paracingulate gyrus was larger in SZs. There are 13 nodes assigned into several brain networks displaying altered CV of EC between groups(Figure1.A). Fluctuation of EC of the node in DMN, which was lower in SZs, showed negative correlation with PANSS total scores(Figure1.B). Dynamic functional topology of above nodes was used to train classification models and demonstrated 80% and 71% accuracy for support vector classification(SVC) and random forest(RF), respectively(Figure2).

Dynamic functional topology illustrated a capability in identifying SZs. Aberrated dynamics of DMN relevant to severity of patient’s symptoms could reveal the reason why it contributed to classification.

No significant relationships.

This study compares the detection rates of haemoglobin absorption spectral imaging and white light imaging in laryngeal papilloma surgery.

Seventeen patients with laryngeal papilloma who underwent surgery in our department from September 2019 to September 2021 were selected. All patients underwent carbon dioxide laser surgery under a microscope. The lesion sites were explored in white light mode and haemoglobin absorption spectral imaging mode. The pharynx and larynx anatomical sites were evaluated using Derkay's all-position scoring system. The numbers and scores for lesions observed in the two modes were compared.

In 17 cases, there were statistically significant differences in the numbers of laryngeal papillomas (Derkay score) detected by white light mode and haemoglobin absorption spectral imaging mode. In 9 of 17 patients (52.94 per cent), the haemoglobin absorption spectral imaging mode showed additional diseased tissues.

The haemoglobin absorption spectral imaging mode can dynamically identify diseased tissues in carbon dioxide laser surgery under a microscope and improve the laryngeal papilloma detection rate.

Identification of treatment-specific predictors of drug therapies for bipolar disorder (BD) is important because only about half of individuals respond to any specific medication. However, medication response in pediatric BD is variable and not well predicted by clinical characteristics.

A total of 121 youth with early course BD (acute manic/mixed episode) were prospectively recruited and randomized to 6 weeks of double-blind treatment with quetiapine (n = 71) or lithium (n = 50). Participants completed structural magnetic resonance imaging (MRI) at baseline before treatment and 1 week after treatment initiation, and brain morphometric features were extracted for each individual based on MRI scans. Positive antimanic treatment response at week 6 was defined as an over 50% reduction of Young Mania Rating Scale scores from baseline. Two-stage deep learning prediction model was established to distinguish responders and non-responders based on different feature sets.

Pre-treatment morphometry and morphometric changes occurring during the first week can both independently predict treatment outcome of quetiapine and lithium with balanced accuracy over 75% (all p < 0.05). Combining brain morphometry at baseline and week 1 allows prediction with the highest balanced accuracy (quetiapine: 83.2% and lithium: 83.5%). Predictions in the quetiapine and lithium group were found to be driven by different morphometric patterns.

These findings demonstrate that pre-treatment morphometric measures and acute brain morphometric changes can serve as medication response predictors in pediatric BD. Brain morphometric features may provide promising biomarkers for developing biologically-informed treatment outcome prediction and patient stratification tools for BD treatment development.