Adolescence is a period marked by highest vulnerability to the onset of depression, with profound implications for adult health. Neuroimaging studies have revealed considerable atrophy in brain structure in these patients with depression. Of particular importance are regions responsible for cognitive control, reward, and self-referential processing. However, the causal structural networks underpinning brain region atrophies in adolescents with depression remain unclear.

This study aimed to investigate the temporal course and causal relationships of gray matter atrophy within the brains of adolescents with depression.

We analyzed T1-weighted structural images using voxel-based morphometry in first-episode adolescent patients with depression (n=80, 22 males; age = 15.57±1.78) and age, gender matched healthy controls (n=82, 25 males; age = 16.11±2.76) to identify the disease stage-specific gray matter abnormalities. Then, with granger causality analysis, we arranged the patients’ illness duration chronologically to construct the causal structural covariance networks that investigated the causal relationships of those atypical structures.

Compared to controls, smaller volumes in ventral medial prefrontal cortex (vmPFC), dorsal anterior cingulate cortex (dACC), middle cingulate cortex (MCC) and insula areas were identified in patients with less than 1 year illness duration, and further progressed to the subgenual ACC, regions of default, frontoparietal networks in longer duration. Causal network results revealed that dACC, vmPFC, MCC and insula were prominent nodes projecting exerted positive causal effects to regions of the default mode and frontoparietal networks. The dACC, vmPFC and insula also had positive projections to the reward network, which included mainly the thalamus, caudate and putamen, while MCC also exerted a positive causal effect on the insula and thalamus.

These findings revealed the progression of structural atrophy in adolescent patients with depression and demonstrated the causal relationships between regions involving cognitive control, reward and self-referential processes.

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The occurrence of depression in adolescence, a critical period of brain development, linked with neuroanatomical and cognitive abnormalities. Neuroimaging studies have identified hippocampal abnormalities in those of adolescent patients. However, few studies have investigated the atypically developmental trends in hippocampal subfields in adolescents with depression and their relationships with cognitive dysfunctions.

To explore the structural abnormalities of hippocampal subfields in patients with youth depression and examine how these abnormalities associated with cognitive deficits.

We included a sample of 79 first-episode depressive patients (17 males, age = 15.54±1.83) and 71 healthy controls (23 males, age = 16.18±2.85). The severity of these adolescent patients was assessed by depression scale, suicidal risk and self-harm behavior. Nine cognitive tasks were used to evaluate memory, cognitive control and attention abilities for all participants. Bilateral hippocampus were segmented into 12 subfields with T1 and T2 weighted images using Freesurfer v6.0. A mixed analysis of variance was performed to assess the differences in subfields volumes between all patients and controls, and between patients with mild and severe depression. Finally, LASSO regression was conducted to explore the associations between hippocampal subfields and cognitive abnormalities in patients.

We found significant subfields atrophy in the CA1, CA2/3, CA4, dentate gyrus, hippocampal fissure, hippocampal tail and molecular layer subfields in patients. For those patients with severe depression, hippocampal subfields showed greater extensive atrophy than those in mild, particularly in CA1-4 subfields extending towards the subiculum. These results were similar across various severity assessments. Regression indicated that hippocampal subfields abnormalities had the strongest associations with memory dysfunction, and relatively week associations with cognitive control and attention. Notably, CA4 and dentate gyrus had the highest weights in the regression model.

As depressive severity increases, hippocampal subfield atrophy tends to spread from CA regions to surrounding areas, and primarily affects memory function in patients with youth depression. These results suggest hippocampus might be markers in progression of adolescent depression, offering new directions for early clinical intervention.

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Daylength and the rates of changes in daylength have been associated with seasonal fluctuations in psychiatric symptoms and in cognition and mood in healthy adults. However, variations in human brain glucose metabolism in concordance with seasonal changes remain under explored.

In this cross-sectional study, we examined seasonal effects on brain glucose metabolism, which we measured using 18F-fluorodeoxyglucose-PET in 97 healthy participants. To maximize the sensitivity of regional effects, we computed relative metabolic measures by normalizing the regional measures to white matter metabolism. Additionally, we explored the role of rest–activity rhythms/sleep–wake activity measured with actigraphy in the seasonal variations of regional brain metabolic activity.

We found that seasonal variations of cerebral glucose metabolism differed across brain regions. Glucose metabolism in prefrontal regions increased with longer daylength and with greater day-to-day increases in daylength. The cuneus and olfactory bulb had the maximum and minimum metabolic values around the summer and winter solstice respectively (positively associated with daylength), whereas the temporal lobe, brainstem, and postcentral cortex showed maximum and minimum metabolic values around the spring and autumn equinoxes, respectively (positively associated with faster daylength gain). Longer daylength was associated with greater amplitude and robustness of diurnal activity rhythms suggesting circadian involvement.

The current findings advance our knowledge of seasonal patterns in a key indicator of brain function relevant for mood and cognition. These data could inform treatment interventions for psychiatric symptoms that peak at specific times of the year.

Disparities in the recruitment of minority populations in research are well-documented. However, the degree of participation and retention of minorities following enrollment is less known, particularly in decentralized studies. Although decentralized clinical research methods may allow researchers to engage broader study populations with less participation burden, they may present different retention challenges. To evaluate racial and ethnic differences in the degree of participation after enrollment in a decentralized study, we analyzed data from a cohort of patients with inflammatory bowel diseases following COVID-19 immunization.

We compared by race and ethnicity the following post-enrollment participation metrics: response to > 50% of follow-up surveys, donation of a blood sample for antibody testing, consent to use of bio samples for future research, and withdrawal prior to study completion.

Overall, we observed higher levels of post-enrollment study participation among non-Hispanic White (NHW) participants as compared to Black or Hispanic participants: 95% of NHW participants completed follow-up versus 87% of Black participants and 91% of Hispanic participants, 73% of NHW participants provided bio samples versus 64% Black participants and 67% Hispanic participants, and 65% of NHW participants provided consent for future research versus 62% of Black participants and 52% of Hispanic participants.

Our findings demonstrate that the degree of study participation after enrollment in this decentralized study differed by race and ethnicity, indicating that attention to diversity, equity, and inclusion is needed not only in clinical research recruitment but also throughout study administration.

While there exist numerous validated neuropsychological tests and batteries to measure cognitive and behavioral capacities, the vast majority of these are time intensive and difficult to administer and score outside of the clinic. Moreover, many existing assessments may have limited ecological validity in some contexts (e.g., military operations). Therefore, we have been developing a novel approach to administering neuropsychological assessment using a virtual reality (VR) “game” that will collect simultaneously acquired multidimensional data that is synthesized by machine learning algorithms to identify neurocognitive strengths and weaknesses in a fraction of the time of typical assessment approaches. For our initial pilot project, we developed a preliminary VR task that involved a brief game-like military “shoot/no-shoot” task that collected data on hits, false alarms, discriminability, and response times under a context-dependent rule set. This prototype task will eventually be expanded to include a significantly more complex set of tasks with greater cognitive demands, sensor feeds, and response variables that could be modified to fit many other contexts. The objective of this project was to construct a rudimentary pilot version and demonstrate whether it could predict outcomes on standard neuropsychological assessments.

To demonstrate proof-of-concept, we collected data from 20 healthy participants from the general population (11 male; age=24.8, SD=7.8) with high average intelligence (IQ = 112, SD=10.7). All participants completed the Wechsler Abbreviated Scale of Intelligence-II (WASI-II), and several neuropsychological tests including the ImPACT, the Attention and Executive Function modules of the Neuropsychological Assessment Battery (NAB), and the VR task. Initially, we used a prior dataset from 359 participants (n=191 mild traumatic brain injury; n=120healthy control; n=48 sleep deprived) to serve as a training sample for machine learning models. Based on these outcomes, we applied machine learning, as well as standard multiple regression approaches to predict neuropsychological outcomes in the 20 test participants.

In this limited study, the machine learning approach did not converge on a meaningful prediction due to the instability of the small sample. However, standard multiple linear regression using stepwise entry/deletion of the VR task variables significantly predicted neuropsychological performance. The VR task predicted WASI-II vocabulary (R=.457, p=.043), NAB Attention Index (R=.787, p=.001), and NAB Executive Function Index (R=.715, p=.002). Interestingly, these performances were generally as good or better than the predictions resulting from the ImPACT, a commercially available neuropsychological test battery, which correlated with WASI-II vocabulary (R=.557, p=.011), NAB Attention Index (R=.574, p=.008), and NAB Executive Function Index (R=.619, p=.004).

Our pilot VR task was able to predict performances on standard neuropsychological assessment measures at a level comparable to that of a commercially available computerized assessment battery, providing preliminary evidence of concurrent validity. Ongoing work is expanding this rudimentary task into one involving greater complexity and nuance. As multivariate data integration models are incorporated into the tasks and extraction features, future work will collect data on much larger samples of individuals to develop and refine the machine learning models. With additional work this approach may provide an important advance in neuropsychological assessment methods.

Coronavirus infection has shown a significant impact not only on physical health but also on mental health. Among the long-term consequences in the post-COVID period, depressive and anxiety disorders are well observed. A potential mechanism for developing mental disorders after undergoing SARS-CoV-2 is a neuroinflammatory process in the central nervous system.

This study aimed to discover the features of anxiety and depressive disorders in people who suffered from COVID-19.

The study was conducted from October 2021 to September 2022 in outpatient and inpatient conditions of the S. S. Korsakov Psychiatric Clinic of Sechenov University. 58 patients (17 (29.3%) men and 41 (70.7%) women) with a diagnosis of F32, F34.1, F41-F48.0, or F06.3-06.4 according to ICD-10 who underwent COVID-19, mainly of mild and moderate severity (46 (79,3%) and (8 (13,8%) consequently), were examined clinically.

The median age among the respondents was 34 years. Mental illness in the family history had 38 (65.5%) people. Among the examined patients, 21 ((36.2%) people had psychasthenic traits premorbid, 13 (22.4%) – hyperthymic, 9 (15.5%) – hysterical. 15 patients (25.9%) had a history of maternal disorders during pregnancy and childbirth – 16 (27,6%) had neurotic disorders in childhood, 27 (46.6%) had a traumatic brain injury or general anesthesia. 2 patients (3.4%) reported substance abuse, 7 (12.1%) abused alcohol. The median period from somatic well-being after infection to the onset of mental illness was 3 months. The duration from the onset of symptoms of a mental disorder to treatment with a psychiatrist was 3 months. The main symptoms in the clinical picture were impaired concentration (100.0%), decreased productivity (98.3%), sleep disorders (96.6%), decreased mood (98.3%), anxiety (89.7%), anhedonia (75.9%), decreased appetite (65.5%), asthenia (65.5%), emotional lability (44.8%), self-blame ideas (43.1%), obsessive thoughts (39.7%), irritability (37.9%), suicidal thoughts (19.0%).

About two-thirds of respondents were female. The main reasons for consulting a psychiatrist were decreased concentration, sleep disorders, and reduced productivity. In addition, symptoms of anxiety, asthenia, and low mood were revealed, and more than half of the patients complained of apathy. Although the severity of depression was moderate, and the level of anxiety was mild, all these disorders were accompanied by clinically significant fatigue.

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Insomnia is a prevalent global health problem that affects 11.7% - 36% of the population (Grewal et al. Clinical Handbook of Insomnia Int 2017; 13 - 25). It is a risk factor for depression, poor quality of life, and accidents. Increasingly, insomnia has been identified as a suicide risk factor (Lin et al. BMC Psychiatry 2018: 18; 117) We present a case report of a 43-year-old male patient with insomnia-induced suicidal ideation (SI).

1. 1. Learn the mechanism of insomnia-induced SI

2. 2. Understand the current insomnia treatments

3. 3. Discuss the possible mechanism of ECT treating insomnia-induced SI

A 43-year-old single male with past psychiatric diagnoses of social anxiety, borderline personality disorder, chronic SI, and severe recurrent depression was admitted to inpatient due to intractable SI from insomnia. He failed trials on SSRIs/SNRIs, bupropion, trazodone, lithium, vortioxetine, quetiapine, zolpidem, and ketamine. The patient was initiated on electroconvulsive therapy (ECT) three times a week with 20mg vortioxetine and 100mg quetiapine for sleep initiation.

After 6-sessions, the patient’s mood, affect and sleep had improved considerably, and his suicidal ideations resolved. The patient was discharged with outpatient follow-up and ECT as rescue therapy. His sleep gradually improved to 4-6hrs/night and his mood was back at baseline.

ECT is an effective treatment for refractory insomnia-induced SI, likely due to persistent REM suppression and reduced dendritic arborization and excitatory synapses in the amygdala (Doghramji et al. Sleep 2000; 23 - S16 - S20; Lahmeyer et al. Sleep Respiratory 1989; 18 346). Possible mechanisms for insomnia-induced SI include impaired decision-making, abnormalities in 5-HT function, or HPA dysfunction leading to a hyperarousal state and cortisol release (Chatzittofis et al. Euro Neuropsychopharmacology 2013; Elmenhorst et al. Sleep 2012; 35 1615 - 1623; Keilp et al. Psychol Medicine 2013; 43 539-551; Novati et al. 2008 Sleep; 31 1579 - 1585). Current insomnia treatments address underlying medical/psychological problems and non-pharmacologic and pharmacologic strategies. The predominant non-pharmacologic approach is Cognitive Behavioral Therapy Insomnia (CBT-I), such as relaxation techniques, sleep hygiene education, cognitive structuring, and sleep restriction (Rossman et al. American Journal of Lifestyle Medicine 2019; 13 544 - 547). Pharmacologic options include benzodiazepines, non-benzodiazepine hypnotics, tricyclic antidepressants, trazodone, and antihistamines (Saddichha et al. Annals of Indian Academy of Neurology 2010; 13 94-102). ECT should be strongly considered for the treatment of refractory insomnia-induced SI, and its early application may avoid rapid deterioration improving the quality of life.

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