Predictive modeling of antidepressant efficacy based on cognitive neuropsychological theory.

BACKGROUND: We aimed to develop a clinical predictive model based on the cognitive neuropsychological (CNP) theory and machine-learning to examine SSRI efficacy in the treatment of MDD. METHODS: Baseline assessments including clinical symptoms (HAMD, HAMA, BDI, and TEPS scores), negative biases (NEO-PI-R-N and NCPBQ scores), sociodemographic characteristics (social support and SES), and a 5-min eye-opening resting-state EEG were completed by 69 participants with first-episode major depressive disorder (MDD) and 36 healthy controls. The clinical symptoms and negative bias were again assessed after an 8-week treatment of depression with selective serotonin reuptake inhibitors (SSRIs). A multi-modality machine-learning model was developed to predict the effectiveness of SSRI antidepressants. RESULTS: At baseline, we observed significant differences between MDD patients and healthy controls in terms of social support, clinical symptoms, and negative bias characteristics (p < 0.001). A negative association was found (p < 0.05) between neuroticism and alpha asymmetry in both the central and central-parietal areas, as well as between negative cognitive processing bias and alpha asymmetry in the parietal region. Compared to responders, non-responders exhibited less negative cognitive processing bias and greater alpha asymmetry in both central and central-parietal regions. Importantly, we developed a multi-modality machine-learning model with 83 % specificity using the above salient features. CONCLUSIONS: Research results support the CNP theory of depression treatment. To some extent, the multimodal clinical model constructed based on the CNP theory effectively predicted the efficacy of this treatment in this population. LIMITATIONS: Small sample and only focus on the mechanisms of delayed-onset SSRI treatment.

Potential neuropsychological mechanism involved in the transition from suicide ideation to action - a resting-state fMRI study implicating the insula.

BACKGROUND: Understanding the neural mechanism underlying the transition from suicidal ideation to action is crucial but remains unclear. To explore this mechanism, we combined resting-state functional connectivity (rsFC) and computational modeling to investigate differences between those who attempted suicide(SA) and those who hold only high levels of suicidal ideation(HSI). METHODS: A total of 120 MDD patients were categorized into SA group (n=47) and HSI group (n=73). All participants completed a resting-state functional MRI scan, with three subregions of the insula and the dorsal anterior cingulate cortex (dACC) being chosen as the region of interest (ROI) in seed-to-voxel analyses. Additionally, 86 participants completed the balloon analogue risk task (BART), and a five-parameter Bayesian modeling of BART was estimated. RESULTS: In the SA group, the FC between the ventral anterior insula (vAI) and the superior/middle frontal gyrus (vAI-SFG, vAI-MFG), as well as the FC between posterior insula (pI) and MFG (pI-MFG), were lower than those in HSI group. The correlation analysis showed a negative correlation between the FC of vAI-SFG and psychological pain avoidance in SA group, whereas a positive correlation in HSI group. Furthermore, the FC of vAI-MFG displayed a negative correlation with loss aversion in SA group, while a positive correlation was found with psychological pain avoidance in HSI group. CONCLUSION: In current study, two distinct neural mechanisms were identified in the insula which involving in the progression from suicidal ideation to action. Dysfunction in vAI FCs may gradually stabilize as individuals experience heightened psychological pain, and a shift from positive to negative correlation patterns of vAI-MFC may indicate a transition from state to trait impairment. Additionally, the dysfunction in PI FC may lead to a lowered threshold for suicide by blunting the perception of physical harm.

Decision-making biases in suicide attempters with major depressive disorder: A computational modeling study using the balloon analog risk task (BART).

BACKGROUND: In the last decade, suicidality has been increasingly theorized as a distinct phenomenon from major depressive disorder (MDD), with unique psychological and neural mechanisms, rather than being mostly a severe symptom of MDD. Although decision-making biases have been widely reported in suicide attempters with MDD, little is known regarding what components of these biases can be distinguished from depressiveness itself. METHODS: Ninety-three patients with current MDD (40 with suicide attempts [SA group] and 53 without suicide attempts [NS group]) and 65 healthy controls (HCs) completed psychometric assessments and the balloon analog risk task (BART). To analyze and compare decision-making components among the three groups, we applied a five-parameter Bayesian computational modeling. RESULTS: Psychological assessments showed that the SA group had greater suicidal ideation and psychological pain avoidance than the NS group. Computational modeling showed that both MDD groups had higher risk preference and lower ability to learn and adapt from within-task observations than HCs, without differences between the SA and NS patient groups. The SA group also had higher loss aversion than the NS and HC groups, which had similar loss aversion. CONCLUSIONS: Our BART and computational modeling findings suggest that psychological pain avoidance and loss aversion may be important suicide risk factor that are distinguishable from depression illness itself.