Depression and Cognitive Impairment: Current Understanding of Its Neurobiology and Diagnosis

Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine

Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.

Neurocognitive Changes in Patients with Post-COVID Depression

Background: Depression and cognitive impairment are recognized complications of COVID-19. This study aimed to assess cognitive performance in clinically diagnosed post-COVID depression (PCD, n = 25) patients using neuropsychological testing. Methods: The study involved 71 post-COVID patients with matched control groups: recovered COVID-19 individuals without complications (n = 18) and individuals without prior COVID-19 history (n = 19). A post-COVID depression group (PCD, n = 25) was identified based on psychiatric diagnosis, and a comparison group (noPCD, n = 46) included participants with neurological COVID-19 complications, excluding clinical depression. Results: The PCD patients showed gender-dependent significant cognitive impairment in the MoCA, Word Memory Test (WMT), Stroop task (SCWT), and Trail Making Test (TMT) compared to the controls and noPCD patients. Men with PCD showed worse performances on the SCWT, in MoCA attention score, and on the WMT (immediate and delayed word recall), while women with PCD showed a decline in MoCA total score, an increased processing time with less errors on the TMT, and worse immediate recall. No differences between groups in Sniffin's stick test were found. Conclusions: COVID-related direct (post-COVID symptoms) and depression-mediated (depression itself, male sex, and severity of COVID-19) predictors of decline in memory and information processing speed were identified. Our findings may help to personalize the treatment of depression, taking a patient's gender and severity of previous COVID-19 disease into account.

Diagnosing and tracking depression based on eye movement in response to virtual reality

Introduction

Depression is a prevalent mental illness that is primarily diagnosed using psychological and behavioral assessments. However, these assessments lack objective and quantitative indices, making rapid and objective detection challenging. In this study, we propose a novel method for depression detection based on eye movement data captured in response to virtual reality (VR).

Methods

Eye movement data was collected and used to establish high-performance classification and prediction models. Four machine learning algorithms, namely eXtreme Gradient Boosting (XGBoost), multilayer perceptron (MLP), Support Vector Machine (SVM), and Random Forest, were employed. The models were evaluated using five-fold cross-validation, and performance metrics including accuracy, precision, recall, area under the curve (AUC), and F1-score were assessed. The predicted error for the Patient Health Questionnaire-9 (PHQ-9) score was also determined.

Results

The XGBoost model achieved a mean accuracy of 76%, precision of 94%, recall of 73%, and AUC of 82%, with an F1-score of 78%. The MLP model achieved a classification accuracy of 86%, precision of 96%, recall of 91%, and AUC of 86%, with an F1-score of 92%. The predicted error for the PHQ-9 score ranged from -0.6 to 0.6.To investigate the role of computerized cognitive behavioral therapy (CCBT) in treating depression, participants were divided into intervention and control groups. The intervention group received CCBT, while the control group received no treatment. After five CCBT sessions, significant changes were observed in the eye movement indices of fixation and saccade, as well as in the PHQ-9 scores. These two indices played significant roles in the predictive model, indicating their potential as biomarkers for detecting depression symptoms.

Discussion

The results suggest that eye movement indices obtained using a VR eye tracker can serve as useful biomarkers for detecting depression symptoms. Specifically, the fixation and saccade indices showed promise in predicting depression. Furthermore, CCBT demonstrated effectiveness in treating depression, as evidenced by the observed changes in eye movement indices and PHQ-9 scores. In conclusion, this study presents a novel approach for depression detection using eye movement data captured in VR. The findings highlight the potential of eye movement indices as biomarkers and underscore the effectiveness of CCBT in treating depression.

PAK3 downregulation induces cognitive impairment following cranial irradiation

Cranial irradiation is used for prophylactic brain radiotherapy as well as the treatment of primary brain tumors. Despite its high efficiency, it often induces unexpected side effects, including cognitive dysfunction. Herein, we observed that mice exposed to cranial irradiation exhibited cognitive dysfunction, including altered spontaneous behavior, decreased spatial memory, and reduced novel object recognition. Analysis of the actin cytoskeleton revealed that ionizing radiation (IR) disrupted the filamentous/globular actin (F/G-actin) ratio and downregulated the actin turnover signaling pathway p21-activated kinase 3 (PAK3)-LIM kinase 1 (LIMK1)-cofilin. Furthermore, we found that IR could upregulate microRNA-206-3 p (miR-206-3 p) targeting PAK3. As the inhibition of miR-206-3 p through antagonist (antagomiR), IR-induced disruption of PAK3 signaling is restored. In addition, intranasal administration of antagomiR-206-3 p recovered IR-induced cognitive impairment in mice. Our results suggest that cranial irradiation-induced cognitive impairment could be ameliorated by regulating PAK3 through antagomiR-206-3 p, thereby affording a promising strategy for protecting cognitive function during cranial irradiation, and promoting quality of life in patients with radiation therapy.

The Cerebellum's Role in Affective Disorders: The Onset of Its Social Dimension

Major Depressive Disorder (MDD) and Bipolar Disorder (BD) are the most frequent mental disorders whose indeterminate etiopathogenesis spurs to explore new aetiologic scenarios. In light of the neuropsychiatric symptoms characterizing Cerebellar Cognitive Affective Syndrome (CCAS), the objective of this narrative review is to analyze the involvement of the cerebellum (Cbm) in the onset of these conditions. It aims at detecting the repercussions of the Cbm activities on mood disorders based on its functional subdivision in vestibulocerebellum (vCbm), pontocerebellum (pCbm) and spinocerebellum (sCbm). Despite the Cbm having been, for decades, associated with somato-motor functions, the described intercellular pathways, without forgiving the molecular impairment and the alteration in the volumetric relationships, make the Cbm a new important therapeutic target for MDD and BD. Given that numerous studies have showed its activation during mnestic activities and socio-emotional events, this review highlights in the Cbm, in which the altered external space perception (vCbm) is strictly linked to the cognitive-limbic Cbm (pCbm and sCbm), a crucial role in the MDD and BD pathogenesis. Finally, by the analysis of the cerebellar activity, this study aims at underlying not only the Cbm involvement in affective disorders, but also its role in social relationship building.