Integrative bioinformatics and artificial intelligence analyses of transcriptomics data identified genes associated with major depressive disorders including NRG1.

Major depressive disorder (MDD) is a common mental disorder and is amongst the most prevalent psychiatric disorders. MDD remains challenging to diagnose and predict its onset due to its heterogeneous phenotype and complex etiology. Hence, early detection using diagnostic biomarkers is critical for rapid intervention. In this study, a mixture of AI and bioinformatics were used to mine transcriptomic data from publicly available datasets including 170 MDD patients and 121 healthy controls. Bioinformatics analysis using gene set enrichment analysis (GSEA) and machine learning (ML) algorithms were applied. The GSEA revealed that differentially expressed genes in MDD patients are mainly enriched in pathways related to immune response, inflammatory response, neurodegeneration pathways and cerebellar atrophy pathways. Feature selection methods and ML provided predicted models based on MDD-altered genes with ≥75% of accuracy. The integrative analysis between the bioinformatics and ML approaches identified ten key MDD-related biomarkers including NRG1, CEACAM8, CLEC12B, DEFA4, HP, LCN2, OLFM4, SERPING1, TCN1 and THBS1. Among them, NRG1, active in synaptic plasticity and neurotransmission, was the most robust and reliable to distinguish between MDD patients and healthy controls amongst independent external datasets consisting of a mixture of populations. Further evaluation using saliva samples from an independent cohort of MDD and healthy individuals confirmed the upregulation of NRG1 in patients with MDD compared to healthy controls. Functional mapping to the human brain regions showed NRG1 to have high expression in the main subcortical limbic brain regions implicated in depression. In conclusion, integrative bioinformatics and ML approaches identified putative non-invasive diagnostic MDD-related biomarkers panel for the onset of depression.

Temporal changes in ERP amplitudes during sustained performance of the Attention Network Test.

This study aimed to use Event-Related Potential (ERP) data to investigate how multiple attentional networks might contribute to vigilance decrement, based on Petersen and Posner's (2012) model of networks for executive control, alerting, and spatial orienting. The networks may differ in their sensitivity to effects of time on task. Based on the theory of attentional networks and previous findings, it was hypothesized that temporal decrements would be found in executive control and alerting networks. 102 participants (53 females) performed a version of the Attention Network Test (ANT) that was modified to require a prolonged period of continuous performance of approximately 70 min during electroencephalogram (EEG) recording. ERP amplitudes were measured in task conditions associated with executive control, alerting, and spatial orienting networks were measured across three consecutive task stages. N100 and P300 amplitudes were used to assess early selective attention and later target discrimination, respectively. Results largely replicated previous findings on ERP responses to the ANT. Amplitudes of the N100 wave and P300 declined over time in multiple conditions, consistent with increasing vulnerability to vigilance decrement. Evidence from cue × stage interactions suggested a temporal decrement in alerting processes, indicated by N100 amplitude, but behavioral data did not show any impairment specific to alerting. By contrast, there was a correspondence between a modest decline in frontal P300 amplitude seen in a no cue condition and slowing of response in this condition. A full explanation for vigilance decrement in terms of attention networks remains elusive. However, the current data suggest that parietal N100 and frontal P300 index two distinct processes that may contribute to loss of alertness and vigilance, depending on task demands.

The ANT Executive Control Index: No Evidence for Temporal Decrement.

OBJECTIVE: This study tested whether indices of executive control, alertness, and orienting measured with Attention Network Test (ANT) are vulnerable to temporal decrement in performance. BACKGROUND: Developing the resource theory of sustained attention requires identifying neurocognitive processes vulnerable to decrement. Executive control processes may be prone to impairment in fatigue states. Such processes are also highlighted in alternative theories. Determining the role of executive control in vigilance can both advance theory and contribute to practical countermeasures for decrement in human factors contexts. METHOD: In Study 1, 80 participants performed the standard ANT for an extended duration of about 55 to 60 min. Study 2 (160 participants) introduced manipulations of trial blocking and stimulus degradation intended to increase resource depletion. Reaction time and accuracy measures were analyzed. Subjective stress and workload were assessed in both studies. RESULTS: In both studies, the ANT induced levels of subjective workload and task disengagement consistent with previous sustained attention studies. No systematic decrement in any performance measure was observed. CONCLUSION: Executive control assessed by the ANT is not highly vulnerable to temporal decrement, even when task demands are elevated. Future work should differentiate executive control processes; proactive control may be more implicated in sustained attention decrement than in reactive control. APPLICATION: Designing systems and interfaces to reduce executive control demands may be generally beneficial but will not directly mitigate temporal performance decrement. Enhancing design guidelines and neuroergonomic methods for monitoring operator attention requires further work to identify key neurocognitive processes for decrement.