The outbreak of major epidemics, such as COVID-19, has had a significant impact on supply chains. This study aimed to explore knowledge innovation in the field of emergency supply chain during pandemics with a systematic quantitative analysis.

Based on the Web of Science (WOS) Core Collection, proposing a 3-stage systematic analysis framework, and utilizing bibliometrics, Dynamic Topic Models (DTM), and regression analysis to comprehensively examine supply chain innovations triggered by pandemics.

A total of 888 literature were obtained from the WOS database. There was a surge in the number of publications in recent years, indicating a new field of research on Pandemic Triggered Emergency Supply Chain (PTESC) is gradually forming. Through a 3-stage analysis, this study identifies the literature knowledge base and distribution of research hotspots in this field and predicts future research hotspots and trends mainly boil down to 3 aspects: pandemic-triggered emergency supply chain innovations in key industries, management, and technologies.

COVID-19 strengthened academic exchange and cooperation and promoted knowledge output in this field. This study provides an in-depth perspective on emergency supply chain research and helps researchers understand the overall landscape of the field, identifying future research directions.

Numerous studies have explored the relationship between brain aging and major depressive disorder (MDD) and attempted to explain the phenomenon of faster brain aging in patients with MDD from multiple perspectives. However, a major challenge in this field is elucidating the ontological basis of these changes. Here, we aimed to explore the relationship between brain structural changes in MDD-related brain aging and neurotransmitter expression levels and transcriptomics.

Imaging data from 670 Japanese participants (MDD: health controls = 233:437) and the support vector regression model were utilized to predict and compare brain age between MDD patients and healthy controls. A map of differences in cortical thickness was generated, furthermore, spatial correlation analysis with neurotransmitters and correlation analysis with gene expression were performed.

The degree of brain aging was found to be significantly higher in patients with MDD. Moreover, significant cortical thinning was observed in the left ventral area, and premotor eye field in patients with MDD. A significant correlation was observed between MDD-related cortical thinning and neurotransmitter receptors/transporters, including dopaminergic, serotonergic, and glutamatergic systems. Enriched Gene Ontology terms, including protein binding, plasma membrane, and protein processing, contribute to MDD-related cortical thinning.

The findings of this study provide further evidence that patients with MDD experience more severe brain aging, deepening our understanding of the underlying neural mechanisms and genetic basis of the brain changes involved. Additionally, these findings hold promise for the development of interventions aimed at preventing further deterioration in MDD-related brain aging, thus offering potential therapeutic avenues.

Working memory deficit, a key feature of schizophrenia, is a heritable trait shared with unaffected siblings. It can be attributed to dysregulation in transitions from one brain state to another.

Using network control theory, we evaluate if defective brain state transitions underlie working memory deficits in schizophrenia.

We examined average and modal controllability of the brain's functional connectome in 161 patients with schizophrenia, 37 unaffected siblings and 96 healthy controls during a two-back task. We use one-way analysis of variance to detect the regions with group differences, and correlated aberrant controllability to task performance and clinical characteristics. Regions affected in both unaffected siblings and patients were selected for gene and functional annotation analysis.

Both average and modal controllability during the two-back task are reduced in patients compared to healthy controls and siblings, indicating a disruption in both proximal and distal state transitions. Among patients, reduced average controllability was prominent in auditory, visual and sensorimotor networks. Reduced modal controllability was prominent in default mode, frontoparietal and salience networks. Lower modal controllability in the affected networks correlated with worse task performance and higher antipsychotic dose in schizophrenia (uncorrected). Both siblings and patients had reduced average controllability in the paracentral lobule and Rolandic operculum. Subsequent out-of-sample gene analysis revealed that these two regions had preferential expression of genes relevant to bioenergetic pathways (calmodulin binding and insulin secretion).

Aberrant control of brain state transitions during task execution marks working memory deficits in patients and their siblings.

In contemporary neuroimaging studies, it has been observed that patients with major depressive disorder (MDD) exhibit aberrant spontaneous neural activity, commonly quantified through the amplitude of low-frequency fluctuations (ALFF). However, the substantial individual heterogeneity among patients poses a challenge to reaching a unified conclusion.

To address this variability, our study adopts a novel framework to parse individualized ALFF abnormalities. We hypothesize that individualized ALFF abnormalities can be portrayed as a unique linear combination of shared differential factors. Our study involved two large multi-center datasets, comprising 2424 patients with MDD and 2183 healthy controls. In patients, individualized ALFF abnormalities were derived through normative modeling and further deconstructed into differential factors using non-negative matrix factorization.

Two positive and two negative factors were identified. These factors were closely linked to clinical characteristics and explained group-level ALFF abnormalities in the two datasets. Moreover, these factors exhibited distinct associations with the distribution of neurotransmitter receptors/transporters, transcriptional profiles of inflammation-related genes, and connectome-informed epicenters, underscoring their neurobiological relevance. Additionally, factor compositions facilitated the identification of four distinct depressive subtypes, each characterized by unique abnormal ALFF patterns and clinical features. Importantly, these findings were successfully replicated in another dataset with different acquisition equipment, protocols, preprocessing strategies, and medication statuses, validating their robustness and generalizability.

This research identifies shared differential factors underlying individual spontaneous neural activity abnormalities in MDD and contributes novel insights into the heterogeneity of spontaneous neural activity abnormalities in MDD.

Psychiatric diagnosis is based on categorical diagnostic classification, yet similarities in genetics and clinical features across disorders suggest that these classifications share commonalities in neurobiology, particularly regarding neurotransmitters. Glutamate (Glu) and gamma-aminobutyric acid (GABA), the brain's primary excitatory and inhibitory neurotransmitters, play critical roles in brain function and physiological processes.

We examined the levels of Glu, combined glutamate and glutamine (Glx), and GABA across psychiatric disorders by pooling data from 121 1H-MRS studies and further divided the sample based on Axis I disorders.

Statistically significant differences in GABA levels were found in the combined psychiatric group compared with healthy controls (Hedge's g = −0.112, p = 0.008). Further analyses based on brain regions showed that brain GABA levels significantly differed across Axis I disorders and controls in the parieto-occipital cortex (Hedge's g = 0.277, p = 0.019). Furthermore, GABA levels were reduced in affective disorders in the occipital cortex (Hedge's g = −0.468, p = 0.043). Reductions in Glx levels were found in neurodevelopmental disorders (Hedge's g = −0.287, p = 0.022). Analysis focusing on brain regions suggested that Glx levels decreased in the frontal cortex (Hedge's g = −0.226, p = 0.025), and the reduction of Glu levels in patients with affective disorders in the frontal cortex is marginally significant (Hedge's g = −0.172, p = 0.052). When analyzing the anterior cingulate cortex and prefrontal cortex separately, reductions were only found in GABA levels in the former (Hedge's g = − 0.191, p = 0.009) across all disorders.

Altered glutamatergic and GABAergic metabolites were found across psychiatric disorders, indicating shared dysfunction. We found reduced GABA levels across psychiatric disorders and lower Glu levels in affective disorders. These results highlight the significance of GABA and Glu in psychiatric etiology and partially support rethinking current diagnostic categories.

Harsh parenting in early childhood is related to offspring's adverse behavioral outcomes. Due to the scarcity of longitudinal neuroimaging data, few studies have explored the neurobiological underpinnings of this association, focusing on within-person variability. This study examined the temporal associations among harsh parenting, later behavioral problems, and the developmental trajectories of amygdala volume and amygdala resting-state functional connectivity (RSFC) profiles, using longitudinal neuroimaging data.

The study was embedded in the Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort. T1-weighted (296 children, 642 scans) and resting-state functional scans (256 children, 509 scans) were collected at ages 4.5, 6, 7.5, and 10.5 years. Amygdala volume and RSFC between the amygdala and six brain regions that have leading roles in emotional regulation were extracted. Harsh parenting at 4.5 years and child behavioral problems at 10.5 years were assessed via parent-report questionnaires. Linear regression and linear mixed models were applied.

Harsh parenting was associated with more severe externalizing problems in girls (β = 0.24, 95% CI 0.08–0.40) but not boys (pint = 0.07). In the overall sample, harsh parenting was associated with the developmental trajectories of amygdala-ACC, amygdala-OFC, and amygdala-DLPFC RSFC. In addition, the developmental trajectory of amygdala-ACC RSFC mediated the harsh parenting–externalizing problems association in girls (indirect effect = 0.06, 95% CI 0.01–0.14).

Harsh parenting in early childhood was associated with amygdala neurocircuitry development and behavioral problems. The developmental trajectory of amygdala-ACC RSFC is a potential neural mechanism linking harsh parenting and externalizing problems in girls.

Screen time in infancy is linked to changes in social-emotional development but the pathway underlying this association remains unknown. We aim to provide mechanistic insights into this association using brain network topology and to examine the potential role of parent–child reading in mitigating the effects of screen time.

We examined the association of screen time on brain network topology using linear regression analysis and tested if the network topology mediated the association between screen time and later socio-emotional competence. Lastly, we tested if parent–child reading time was a moderator of the link between screen time and brain network topology.

Infant screen time was significantly associated with the emotion processing-cognitive control network integration (p = 0.005). This network integration also significantly mediated the association between screen time and both measures of socio-emotional competence (BRIEF-2 Emotion Regulation Index, p = 0.04; SEARS total score, p = 0.04). Parent–child reading time significantly moderated the association between screen time and emotion processing-cognitive control network integration (β = −0.640, p = 0.005).

Our study identified emotion processing-cognitive control network integration as a plausible biological pathway linking screen time in infancy and later socio-emotional competence. We also provided novel evidence for the role of parent–child reading in moderating the association between screen time and topological brain restructuring in early childhood.

Preterm children with very low birthweight (VLBW) / extremely low birthweight (ELBW) with normal early development had been found poorer executive functions (EFs) at preschool-age (Ni, Huang & Guo, 2011). The previous study found that the risks of deficits in EFs at preschool-age of preterm children can be attenuated by more supportive home environment (Taylor & Clark, 2016). However, former studies didn't investigate the effect of birthweight and home environment on cognitive EFs of preterm children simultaneously, especially those with normal early development. The present study aims to investigate the predictive effect of birthweight and home environment on the cognitive EFs of VLBW / ELBW preterm children.

The preterm children were recruited from the Premature Baby Foundation of Taiwan. Inclusion criteria were their scores of Bayley Scales of Infant and Toddler Development, second or third edition at 12 and 24 months, and Wechsler Preschool and Primary Scale of Intelligence, Revised Edition at 5 years old were higher than 70. Exclusion criteria were visual impairment, hearing impairment, and cerebral palsy. There was a total of 287 preterm children with age 6 recruited in the present study. Preterm children were then divided into VLBW group (n=202, birthweight between 1001-1500g) and ELBW group (n=85, birthweight less than 1000g). The typical children included 89 term-born healthy and typically developing children with age 6, who were recruited from comparable social status families in the community. Four types of cognitive EFs including 22 indicators were assessed. Inhibition ability including 8 indicators was assessed through Comprehensive Nonverbal Attention Test Battery (CNAT), cognitive flexibility including 6 indicators was assessed through Wisconsin Card Sorting Test (WCST), working memory including 2 indicators was assessed through Digit Span Subtest of Wechsler Intelligence Scale for Children-IV (WISC-IV) and Knox's Cube Test (KCT), planning ability including 6 indicators was assessed through Tower of London (ToL). The home environment was assessed through Home Observation for Measurement of the Environment (HOME), Revised edition. Data were analyzed with Stepwise Regression.

Results showed that the regression model with birthweight significantly predicted 83.3% of planning ability indicators, 83.3% of cognitive flexibility indicators, and 50% of working memory indicators. Among indicators mentioned above, birthweight has been found the greatest predictive effect on summation-of score of ToL (R2=.04, p<.001). The regression model with HOME significantly predicted 66.7% of planning ability indicators, 16.7% of cognitive flexibility indicators, and 12.5% of inhibition ability indicators. Among the indicators mentioned above, HOME has been found the greatest predictive effect on rule-1 of ToL (R2=.027, p=.001). The regression model with birthweight and HOME significantly predicted 50% of planning ability indicators. Among indicators mentioned above, the regression model has been found the greatest predictive effect on summation-of-score of ToL (R2=.061, p<.001).

Both birthweight and home environment have been found significantly predicted different types of cognitive EFs at preschool-age of VLBW / ELBW preterm children with normal early development. Though the home environment doesn't have such a great predictive effect as birthweight is, both birthweight and home environment are significant predictors of planning ability.