The latest medical research on Psychiatry

The research magnet gathers the latest research from around the web, based on your specialty area. Below you will find a sample of some of the most recent articles from reputable medical journals about psychiatry gathered by our medical AI research bot.

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Long-term deep brain stimulation of the ventral anterior limb of the internal capsule for treatment-resistant depression.

Journal Neurol Neurosurg Psychiatry

Deep brain stimulation (DBS) reduces depressive symptoms in approximately 40%-60% of patients with treatment-resistant depression (TRD), but data on long-term efficacy and safety are scarce. Our objective was to assess the efficacy and safety of DBS targeted at the ventral anterior limb of the internal capsule (vALIC) in 25 patients with TRD during a 1-year, open-label, maintenance period, which followed a 1-year optimisation period.

Depression severity was measured using the 17-item Hamilton Depression Rating Scale (HAM-D-17), Montgomery-Asberg Depression Rating Scale (MADRS) and self-reported Inventory of Depressive Symptomatology (IDS-SR). Primary outcomes were response rate (≥50% HAM-D-17 score reduction) after the maintenance phase, approximately 2 years after DBS surgery, and changes in depression scores and occurrence of adverse events during the maintenance phase.

Of 25 operated patients, 21 entered and 18 completed the maintenance phase. After the maintenance phase, eight patients were classified as responder (observed response rate: 44.4%; intention-to-treat: 32.0%). During the maintenance phase, HAM-D-17 and MADRS scores did not change, but the mean IDS-SR score decreased from 38.8 (95% CI 31.2 to 46.5) to 35.0 (95% CI 26.1 to 43.8) (p=0.008). Non-responders after optimisation did not improve during the maintenance phase. Four non-DBS-related serious adverse events occurred, including one suicide attempt.

vALIC DBS for TRD showed continued efficacy 2 years after surgery, with symptoms remaining stable after optimisation as rated by clinicians and with patient ratings improving. This supports DBS as a viable treatment option for patients with TRD.

NTR2118.

Neuroplasticity in cognitive and psychological mechanisms of depression: an integrative model.

Molecular Psychiatry

Chronic stress and depressive-like behaviors in basic neuroscience research have been associated with impairments of neuroplasticity, such as neuro...

Replicability of structural brain alterations associated with general psychopathology: evidence from a population-representative birth cohort.

Molecular Psychiatry

Transdiagnostic research has identified a general psychopathology factor-often called the 'p' factor-that accounts for shared variation across inte...

Differential biomarker signatures in unipolar and bipolar depression: A machine learning approach.

Aust N Z

This study used machine learning techniques combined with peripheral biomarker measurements to build signatures to help differentiating (1) patients with bipolar depression from patients with unipolar depression, and (2) patients with bipolar depression or unipolar depression from healthy controls.

We assessed serum levels of interleukin-2, interleukin-4, interleukin-6, interleukin-10, tumor necrosis factor-α, interferon-γ, interleukin-17A, brain-derived neurotrophic factor, lipid peroxidation and oxidative protein damage in 54 outpatients with bipolar depression, 54 outpatients with unipolar depression and 54 healthy controls, matched by sex and age. Depressive symptoms were assessed using the Hamilton Depression Rating Scale. Variable selection was performed with recursive feature elimination with a linear support vector machine kernel, and the leave-one-out cross-validation method was used to test and validate our model.

Bipolar vs unipolar depression classification achieved an area under the receiver operating characteristics (ROC) curve (AUC) of 0.69, with 0.62 sensitivity and 0.66 specificity using three selected biomarkers (interleukin-4, thiobarbituric acid reactive substances and interleukin-10). For the comparison of bipolar depression vs healthy controls, the model retained five variables (interleukin-6, interleukin-4, thiobarbituric acid reactive substances, carbonyl and interleukin-17A), with an AUC of 0.70, 0.62 sensitivity and 0.7 specificity. Finally, unipolar depression vs healthy controls comparison retained seven variables (interleukin-6, Carbonyl, brain-derived neurotrophic factor, interleukin-10, interleukin-17A, interleukin-4 and tumor necrosis factor-α), with an AUC of 0.74, a sensitivity of 0.68 and 0.70 specificity.

Our findings show the potential of machine learning models to aid in clinical practice, leading to more objective assessment. Future studies will examine the possibility of combining peripheral blood biomarker data with other biological data to develop more accurate signatures.

A clinical staging approach to improving diagnostics in anxiety disorders: Is it the way to go?

Aust N Z

Clinical staging is a paradigm in which stages of disease progression are identified; these, in turn, have prognostic value. A staging model that enables the prediction of long-term course in anxiety disorders is currently unavailable but much needed as course trajectories are highly heterogenic. This study therefore tailored a heuristic staging model to anxiety disorders and assessed its validity.

A clinical staging model was tailored to anxiety disorders, distinguishing nine stages of disease progression varying from subclinical stages (0, 1A, 1B) to clinical stages (2A-4B). At-risk subjects and subjects with anxiety disorders (n = 2352) from the longitudinal Netherlands Study of Depression and Anxiety were assigned to these nine stages. The model's validity was assessed by comparing baseline (construct validity) and 2-year, 4-year and 6-year follow-up (predictive validity) differences in anxiety severity measures across stages. Differences in depression severity and disability were assessed as secondary outcome measures.

Results showed that the anxiety disorder staging model has construct and predictive validity. At baseline, differences in anxiety severity, social avoidance behaviors, agoraphobic avoidance behaviors, worrying, depressive symptoms and levels of disability existed across all stages (all p-values < 0.001). Over time, these differences between stages remained present until the 6-year follow-up. Differences across stages followed a linear trend in all analyses: higher stages were characterized by the worst outcomes. Regarding the stages, subjects with psychiatric comorbidity (stages 2B, 3B, 4B) showed a deteriorated course compared with those without comorbidity (stages 2A, 3A, 4A).

A clinical staging tool would be useful in clinical practice to predict disease course in anxiety disorders.

Imaging suicidal thoughts and behaviors: a comprehensive review of 2 decades of neuroimaging studies.

Molecular Psychiatry

Identifying brain alterations that contribute to suicidal thoughts and behaviors (STBs) are important to develop more targeted and effective strate...

Identification of genetic overlap and novel risk loci for attention-deficit/hyperactivity disorder and bipolar disorder.

Molecular Psychiatry

Differential diagnosis between childhood onset attention-deficit/hyperactivity disorder (ADHD) and bipolar disorder (BD) remains a challenge, mainl...

Blood biomarkers for memory: toward early detection of risk for Alzheimer disease, pharmacogenomics, and repurposed drugs.

Molecular Psychiatry

Short-term memory dysfunction is a key early feature of Alzheimer's disease (AD). Psychiatric patients may be at higher risk for memory dysfunction...

Kinetics of oxytocin effects on amygdala and striatal reactivity vary between women and men.

Neuropsychopharmacology

Accumulating evidence suggests that intranasal oxytocin (OXT; 24 IU) reduces amygdala responses to fear-related stimuli in men, while exerting inve...

FGFR3 autoantibodies in sensory neuronopathy.

Journal Neurol Neurosurg Psychiatry

FGFR3 antibodies have been associated with sensory neuropathy, but many questions remain regarding their use in clinical practice.

Genetic variants in incident SUDEP cases from a community-based prospective cohort with epilepsy.

Journal Neurol Neurosurg Psychiatry

Sudden unexpected death in epilepsy (SUDEP) is a leading cause of epilepsy-related mortality in young adults. It has been suggested that SUDEP may kill over 20 000 people with epilepsy in China yearly. The aetiology of SUDEP is unclear. Little is known about candidate genes for SUDEP in people of Chinese origin as most studies have ascertained this in Caucasians. No candidate genes for SUDEP in Chinese people have been identified.

We performed whole exome sequencing (WES) in DNA samples collected from five incident cases of SUDEP identified in a large epilepsy cohort in rural China. We filtered rare variants identified from these cases as well as screened for SUDEP, epilepsy, heart disease or respiratory disease-related genes from previous published reports and compared them with publicly available data, living epilepsy controls and ethnicity-match non-epilepsy controls, to identify potential candidate genes for SUDEP.

After the filtering process, the five cases carried 168 qualified mutations in 167 genes. Among these genetic anomalies, we identified rare variants in SCN5A (1/5:20% in our cases), KIF6 (1/5:20% in our cases) and TBX18 (1/5:20% in our cases) which were absent in 330 living epilepsy control alleles from the same original cohort and 320 ethnicity-match non-epilepsy control alleles.

These three genes were previously related to heart disease, providing support to the hypothesis that underlying heart disorder may be a driver of SUDEP risk.