The comorbidity of obsessive–compulsive disorder (OCD) and major depressive disorder (MDD) is a prevalent clinical phenomenon, which is associated with greater symptom severity, suicide risk, and poorer treatment outcomes. However, the neural basis of this comorbidity remains unclear. The aim of this study was to investigate the common and unique neural basis of comorbidity compared with OCD and MDD alone.
A total of 67 patients with comorbid OCD and MDD, 89 patients with OCD alone, 94 patients with MDD alone, and 94 healthy controls (HCs) completed the acquisition of T1-weighted structural images and were included in the present study. The gray matter volume of each brain region in the AAL116 atlas was calculated, based on which the structural covariance between each pair of brain regions was measured. One-way ANCOVA were performed to explore the structural covariance differences among the four groups.
Compared with HC, patients with comorbidity and patients with OCD or MDD alone showed some common altered structural connections (p < 0.05, false discovery rate [FDR] correction). Patients with comorbidity showed unique altered correlation strength of gray matter volume between left cerebelum_crus1 and left orbital frontal cortex (OFC), left Rolandic operculum, right rectus, right parahippocampal gyrus, left and right fusiform gyrus (FG), left Heschl gyrus, left and right superior temporal gyrus (STG), and left cerebelum_crus2; these 10 connections were also significantly different when comparing comorbidity with OCD and MDD separately (p < 0.05, FDR correction).
Compared with OCD or MDD alone, comorbidity showed both common and unique altered structural covariant connections of gray matter structure. The unique structural connections observed in comorbidity were concentrated between the cerebellum and other brain regions. These findings highlight the crucial role of the cerebellum in the neural basis of comorbid OCD and MDD.
Poor sleep has been associated with mental health concerns such as anxiety and depression. Prior evidence suggests that smartphone use may be a factor in poor sleep and mental ill-health in young adults, though most studies have relied on self-reported measures of smartphone use and sleep, which can be unreliable. This study used objective and subjective measures to examine the relationship between time spent using smartphones, sleep duration, quality, regularity, and symptoms of depression and anxiety in a sample of self-selected poor sleepers.
Participants (N = 99; 70.7% female) wore an actigraph for 2 weeks to assess their habitual nightly sleep duration and regularity. Their average daily smartphone screen use was collected over 1 week with a smartphone application. Standardized questionnaires were used to assess sleep quality and mental health symptoms.
No statistically significant associations were found between objective smartphone screen use and any sleep or mental health variables. Sleep disturbance, sleep-related daytime impairment, anxiety, and depression were positively correlated. However, regression models identified that only sleep-related daytime impairment explained unique variance in anxiety and depression when adjusted for sleep disturbance and duration, gender, age, and screen time.
Specific timing of smartphone screen use (e.g., evening use) and/or application content were not collected as part of this study.
These results conflict with prior evidence demonstrating negative relationships between self-reported smartphone screen use, sleep, and mental health. Further research incorporating objective measurement of smartphone screen use, focusing on critical periods for sleep, may provide a more nuanced picture of this relationship. Results also demonstrate the differing roles of night-time sleep disturbance and daytime sleep-related impairment in mental health.
Trial Registration: Australian New Zealand Clinical Trials Registry number: ACTRN12621000132842
Anxiety’s prevalence is increasing, making it a widespread mental health concern. However, scale-based diagnostic methods have limitations. Music therapy helps regulate emotions and alleviate anxiety symptoms. Functional near-infrared spectroscopy (fNIRS) offers a novel approach to diagnosing mental disorders by measuring changes in the concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) in the superficial layers of the brain, thereby reflecting brain activation. This is the first study to use fNIRS to examine the impact of music therapy on anxiety. fNIRS was used to measure changes in HbO and HbR in the superficial brain regions of individuals with anxiety symptoms to evaluate music therapy effectiveness and identify brain regions associated with anxiety. This study recruited 83 participants: 17 comprised the healthy control group, and 66 comprised the anxiety group. The anxiety group was divided into an intervention group (34 participants) and a waiting-list group (32 participants). The intervention group underwent 12 music therapy sessions and exhibited significant changes compared with the waiting group. These changes included connectivity between Wernicke’s area and the dorsolateral prefrontal cortex (DLPFC) as well as the visual association cortex and Broca’s triangular area. These results suggested that the connectivity characteristics of these brain regions were associated with anxiety. Music therapy significantly improved brain network connectivity characteristics in individuals with anxiety symptoms. Furthermore, fNIRS indicators could serve as biomarkers for the auxiliary identification of anxiety symptoms, aiding early identification and intervention.
Trial Registration: ClinicalTrials.gov identifier: NCT05648539
Current assessment of depression primarily relies on psychological scales. Although the use of machine learning in depression has grown, limited reports are available on multiple neurophysiological measurements. We employed machine learning algorithms incorporating eye tracking, visual evoked potentials (VEPs), and auditory P300 to classify depression among Chinese medical students.
A total of 66 students with depression and 72 matched controls were recruited; eye tracking, VEPs, and auditory P300 data were collected. Descriptive analyses and group comparisons were performed between the depression and control groups. Then, multivariate logistic regression (LR) analysis was conducted to evaluate the relationship between eye tracking, VEPs, and auditory P300 features and Patient Health Questionnaire-9 (PHQ-9) scores. Furthermore, the study employed six classifiers to differentiate between depression and nondepression. Five-fold cross-validation was employed. Model performance was assessed using receiver operating characteristic (ROC) curves, area under the curve (AUC), precision, accuracy, recall, and F1 score. We applied SHapley Additive exPlanations (SHAP) values to explain the model.
Depression group was characterized by lower response search scores, higher D values, and prolonged P100 latencies in both eyes. No significant differences were observed in auditory P300 features. Random forest (RF) classifier demonstrated superior classification performance relative to the other five machine learning algorithms. Models utilizing combined features showed enhanced performance compared with those based solely on eye tracking or VEP features. Utilizing the SHAP method, we identified that P100 latency in the right eye was the most significant feature across all machine learning models.
Chinese medical students with depression exhibited reduced responsive search scores and extended P100 latencies, suggesting impairments in attention and visual information processing associated with depression. The combined eye tracking and VEPs proved to be more effective than single features for distinguishing depression and nondepression. P100 latency in the right eye may be the most significant predictor of depression.
Previous studies have shown that individuals with depression have an impaired ability in encoding single facial expressions. However, little is known about how depressive tendencies—subclinical emotional distress that may progress to clinical depression—affect the perception of the average emotion of multiple faces. To address this question, the current study investigated whether depressive tendencies affect explicit or implicit ensemble perception of emotion. In Study 1, participants viewed sets of four emotionally varying faces (ranging from angry to happy) for 2000 or 50 ms, then judged if a subsequent test face was angrier than the average emotion of the preceding set. Results showed that the high depressive symptom (HDS) group had a point of subjective equality (PSE) more biased toward anger compared to the low depressive symptom (LDS) group when exposure time was 2000 ms. However, this difference disappeared when the time was shortened to 50 ms. In Study 2, we assessed the automatic perception of ensemble emotion by requiring participants to judge whether a probe face was a member of the preceding set, a task that does not explicitly demand averaging. Results indicated that the HDS and LDS groups had a similar likelihood of misidentifying the set mean as a member under both 2000 and 50 ms conditions, indicating a comparable automatic coding of ensemble emotion. Together, the current research demonstrates that depressive tendencies can bias ensemble coding for emotional faces at explicit level but not at implicit level.
R. Pearson, C. Mendoza, J. D. Coppin, and S. K. Creech, “Associations between Predictors of PTSD and Psychosocial Functioning in Veterans: Results from a Longitudinal Assessment Study,” Depression and Anxiety 2024 (2024): 9719635, https://doi.org/10.1155/2024/9719635.
In the article titled “Associations between Predictors of PTSD and Psychosocial Functioning in Veterans: Results from a Longitudinal Assessment Study,” information was omitted in the Acknowledgments section, and a reference was omitted in error. The corrected Acknowledgments section appears below:
Acknowledgments
Financial support for this study was provided by VA Office of Rehabilitation Research and Development (RR&D) Merit Grant I01RX000304 to Dr. Creech. All of the authors of the manuscript are also supported by the Department of Veterans Affairs VISN 17 Center of Excellence for Research on Returning War Veterans and the Central Texas Veterans Affairs Healthcare System. The authors of this manuscript also acknowledge the contributions of the prior teams of investigators and research assistants who were involved with any phase of VA RR&D Merit Review Award # I01RX000304 (to Meyer, Morissette) and later (to Creech).
The reference is shown below:
Frankfurt, S. B., DeBeer, B. B., Morissette, S. B., Kimbrel, N. A., La Bash, H., & Meyer, E. C. (2018). Mechanisms of moral injury following military sexual trauma and combat in post-9/11 US war veterans. Frontiers in Psychiatry, 9, 520. doi: 10.3389/fpsyt.2018.00520
This should supplement the following sentences in text:
This study used data drawn from a longitudinal assessment study consisting of 4 timepoints which were scheduled approximately at 8-month intervals (month 0, month 8, month 16, and month 24) [1].
We apologize for these errors.
Past research showed that lower emotional clarity (EC) was associated with more maladaptive emotion regulation (ER) and psychopathology, such as obsessive-compulsive (OC) disorder (OCD). However, most of these studies used single time-point, retrospective self-reports. Next to high risk for recall biases and low ecological validity, this assessment method is only able to capture between-person differences (i.e., individuals generally high vs. low in EC). It therefore neglects temporal variations in EC and resulting within-person differences (i.e., moments with higher-than-usual vs. lower-than-usual EC within one individual). To address this gap, our study uses intensive longitudinal data based on a 6-day ecological momentary assessment (EMA) design with up to six measurements daily. In total, N = 72 individuals diagnosed with OCD and N = 54 mentally healthy controls (HCs) reported on EC, ER behavior, and OC symptoms. Our results confirm that EC was significantly lower in individuals with OCD, even when controlling for baseline depression. Furthermore, lower within-person EC was associated with a higher number of used avoidance-oriented ER strategies, a lower number of engagement-oriented ER strategies and lower ER effectiveness. Surprisingly, these associations were more pronounced in the control (vs. OCD) group. In individuals with OCD, results indicated a negative concurrent (but not subsequent) association between EC and OC symptoms. Explanations for nonsignificant findings and possible implications for the role of EC in OCD are discussed.
Previous studies have indicated an association between mood symptoms and cognitive decline in the Alzheimer’s disease (AD) spectrum. Amyloid-beta (Aβ) deposition in the brain, which is a core pathological characteristic of AD, along with the presence of plasma biomarkers, such as phosphorylated tau protein (p-tau), constitutes an early predictive indicator for AD. We attempted to explore the relationship between mood symptoms and the presence of AD-related plasma biomarkers in patients with brain Aβ deposition.
We included 2612 participants aged ≥50 years (707 males; average age 66.98 ± 7.75 years) in this study. We used the Hamilton depression rating scale (HAMD) and Hamilton anxiety rating scale (HAMA) to assess mood symptoms. Cognitive status was categorized into AD, mild cognitive impairment (MCI), subjective cognitive decline (SCD), and normal cognition (NC). We used analysis of covariance (ANCOVA) to compare mood symptoms assessment scores in different cognitive groups after making adjustments for age, gender, and education. Linear regression analysis was used to investigate the association between mood scores and plasma biomarker levels, adjusting for positivity in Aβ PET imaging.
Compared to NC patients, patients with AD exhibited higher levels of depression (mean of 4.72 versus 3.39, p < 0.05), whereas patients with SCD exhibited higher levels of anxiety (mean of 6.28 versus 4.26, p < 0.05). After accounting for brain Aβ deposition and presence of plasma biomarkers, the plasma neurofilament light chain (NFL) levels (B = 0.211, SE = 0.059, p = 0.001) were associated with HAMD scores. The plasma p-tau181 levels (B = 1.328, SE = 0.576, p = 0.025) were associated with HAMA scores.
Plasma biomarkers have significant potential in predicting anxiety and depressive symptoms in individuals with brain Aβ deposition. This can aid the early clinical diagnosis and intervention of AD.
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