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Journal of Affective Disorders Jun 2024Bipolar disorder (BD) is a mental disorder associated with increased morbidity/mortality. Adverse outcome prediction helps with the management of patients with BD. (Review)
Review
BACKGROUND
Bipolar disorder (BD) is a mental disorder associated with increased morbidity/mortality. Adverse outcome prediction helps with the management of patients with BD.
METHODS
We systematically reviewed the performance of machine learning (ML) studies in predicting adverse outcomes (relapse or recurrence, hospital admission, and suicide-related events) in patients with BD. Demographic, clinical, and neuroimaging-related poor outcome predictors were also reviewed. Three databases (PubMed, Scopus, and Web of Science) were explored from inception to July 2023.
RESULTS
Eighteen studies, accounting for >30,000 patients, were included. Support vector machine, decision trees, random forest, and logistic regression were the most frequently used ML algorithms. ML models' area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity ranged from 0.71 to 0.98, 72.7-92.8 %, and 59.0-95.2 % for relapse/recurrence prediction (5 studies (3 on relapses and 1 on recurrences). The corresponding values were 0.78-0.88, 21.4-100 %, and 77.0-99.7 % for hospital admissions (3 studies, 21,266 patients), and 0.71-0.99, 44.4-97.9 %, and 38.9-95.0 % for suicide-related events (10 studies, 5558 patients). Also, one study addressed a combination of the interest outcomes. Adverse outcome predictors included early onset BD, type I BD, comorbid psychiatric or substance use disorder, circadian rhythm disruption, hospitalization characteristics, and neuroimaging parameters, including increased dynamic amplitude of low-frequency fluctuation, decreased frontolimbic functional connectivity and aberrant dynamic FC in corticostriatal circuitry.
CONCLUSIONS
ML models can predict adverse outcomes of BD with relatively acceptable performance measures. Future studies with larger samples and nested cross-validation validation should be conducted to reach more reliable results.
PubMed: 38908556
DOI: 10.1016/j.jad.2024.06.061 -
Ultrasonics Sonochemistry Jun 2024Bipolar disorder is commonly treated with lithium carbonate. The concentration of lithium in the blood serum should be closely monitored in patients who require...
Bipolar disorder is commonly treated with lithium carbonate. The concentration of lithium in the blood serum should be closely monitored in patients who require long-term lithium therapy. To date, no colorimetric method of detecting lithium ions has been reported using nanosensors. We have developed a novel chemosensor based on nanozyme (NZ) to address this clinical need. The GO-AgO NZs were synthesized by a sonochemical method and used as a colorimetric nanosensor to detect lithium ions in human blood serum (Li (I)). To characterize NZs, various techniques were employed, including XRD, FTIR, TEM, FESEM, EDX, Raman spectroscopy, BET, DLS, Zeta potential, and ICP-OES. According to TEM and FESEM images of GO-AgO, the nanoparticles (NPs) of AgO are uniformly distributed on the surface of 2D graphene oxide sheets. In addition, silver oxide nanoparticles exhibited a cubic morphology with an average size of 3.5 nm. We have examined the performance of the NZs in an aqueous medium and in human blood serum that contains Li (I). A colorimetric test revealed that NZs synthesized in the presence of ultrasound were more sensitive to Li (I). According to the linearity of the calibration curves' ranges, Li (I) has a limit of detection (LOD) of 0.01 µg/mL. Furthermore, it displayed a linear range between 0 and 12 µg/mL. GO-AgO NZs showed noticeable color changes from green to orange after exposure to Li (I). An incubation time of two minutes was found to be the most effective for sensing. This innovative approach provides a reliable method for monitoring lithium levels and ensuring patient safety during long-term lithium therapy for bipolar disorder.
PubMed: 38908076
DOI: 10.1016/j.ultsonch.2024.106960 -
Der Nervenarzt Jun 2024The aim of this article is to summarize the current state of research on the effectiveness of psychotherapeutic treatment of posttraumatic stress disorder (PTSD). (Review)
Review
BACKGROUND
The aim of this article is to summarize the current state of research on the effectiveness of psychotherapeutic treatment of posttraumatic stress disorder (PTSD).
METHODS
The results of current meta-analyses and trend-setting individual studies are summarized and the most important forms of intervention are explained.
RESULTS
The psychotherapeutic treatment methods for PTSD are very effective, the effect sizes are large and superior to those of pharmacotherapy. Trauma exposure and cognitive restructuring are most effective. Trauma-focused procedures are generally superior to other forms of psychotherapy. A range of different cognitive behavioral procedures as well as eye movement desensitization and reprocessing are recommended. The most recent initial findings confirm a very good effectiveness for imagery rescripting methods as protective interventions without a formal confrontation with trauma. Individual therapy works better than group psychotherapy. In the group setting cognitive processing therapy has proven to be the best intervention. Trauma-focused treatment should also be used when comorbid conditions such as schizophrenia, bipolar disorder or addiction are present.
DISCUSSION
Trauma-focused psychotherapy in an individual setting is the treatment of choice for PTSD. A large selection of effective methods and well-reviewed manuals are available. The German language S3 guidelines are currently being updated.
PubMed: 38906997
DOI: 10.1007/s00115-024-01694-6 -
Evaluation of the neuroprotective effect of antipsychotics by serum quantification of protein S100B.Farmacia Hospitalaria : Organo Oficial... Jun 2024This research delves into the intricate interplay between antipsychotic medications and neuroprotection focusing on the S100B protein-a central player in the regulation...
OBJECTIVE
This research delves into the intricate interplay between antipsychotic medications and neuroprotection focusing on the S100B protein-a central player in the regulation of neuroapoptotic activity.
METHOD
Blood samples were collected to assess serum S100B protein levels using an immunoassay of immunoelectrochemiluminescence. The first two samples were collected with a 3-month interval between each, and the third sample was obtained 6 months after the previous one. Changes in S100B protein levels throughout the study were assessed using Friedman's ANOVA test. This was followed by the Wilcoxon signed-rank test with Bonferroni correction to account for multiple comparisons.
RESULTS
This study involved 40 patients diagnosed with severe mental disorders (34 schizophrenia, 4 schizoaffective disorder, 1 bipolar disorder, and 1 borderline personality disorder). These patients had been receiving antipsychotic treatment for an average duration of 17 years. The results revealed that the S100B protein remained within physiological levels (median values 39.0 ng/L for the first sample, median values 41.0 ng/L for the second sample, and median values 40.5 ng/L for the third sample) with no significant changes (p = 0.287), with all anti-psychotic medicaments values consistently below 50 ng/L, a lower value compared to maximum range of 105 ng/L. Importantly, there were no significant differences in S100B protein levels between patients on monotherapy and those on combination antipsychotic therapy (p = 0.873), suggesting that combination therapy did not increase neuroapoptotic activity.
CONCLUSIONS
These findings provide compelling evidence for the potential neuroprotective effects of long-term antipsychotic treatment in individuals with severe mental disorders. By maintaining physiological levels of the S100B protein, antipsychotic medications may help protect against neuronal damage and dysfunction. This research contributes valuable insights into the neuroprotective mechanisms of antipsychotic drugs, enhancing our understanding of their potential benefits in the treatment of severe mental disorders.
PubMed: 38906717
DOI: 10.1016/j.farma.2024.05.013 -
American Family Physician Jun 2024
Topics: Humans; Bipolar Disorder; United States; Practice Guidelines as Topic; United States Department of Veterans Affairs; Antipsychotic Agents; United States Department of Defense; Antimanic Agents
PubMed: 38905567
DOI: No ID Found -
PloS One 2024This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping...
This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, incorporating both electroencephalography (EEG) patterns and cognitive abnormalities for enhanced classification. Data were collected from 45 participants, including 20 with BD II and 25 with BPD. Analysis involved utilizing EEG signals and cognitive tests, specifically the Wisconsin Card Sorting Test and Integrated Cognitive Assessment. The k-nearest neighbors (KNN) algorithm achieved a balanced accuracy of 93%, with EEG features proving to be crucial, while cognitive features had a lesser impact. Despite the strengths, such as diverse model usage, it's important to note limitations, including a small sample size and reliance on DSM diagnoses. The study suggests that future research should explore multimodal data integration and employ advanced techniques to improve classification accuracy and gain a better understanding of the neurobiological distinctions between BD II and BPD.
Topics: Humans; Borderline Personality Disorder; Bipolar Disorder; Machine Learning; Electroencephalography; Adult; Female; Male; Diagnosis, Differential; Young Adult; Cognition; Algorithms
PubMed: 38905185
DOI: 10.1371/journal.pone.0303699 -
Frontiers in Endocrinology 2024Epidemiologic studies have suggested co-morbidity between hypothyroidism and psychiatric disorders. However, the shared genetic etiology and causal relationship between...
BACKGROUND
Epidemiologic studies have suggested co-morbidity between hypothyroidism and psychiatric disorders. However, the shared genetic etiology and causal relationship between them remain currently unclear.
METHODS
We assessed the genetic correlations between hypothyroidism and psychiatric disorders [anxiety disorders (ANX), schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP)] using summary association statistics from genome-wide association studies (GWAS). Two disease-associated pleiotropic risk loci and genes were identified, and pathway enrichment, tissue enrichment, and other analyses were performed to determine their specific functions. Furthermore, we explored the causal relationship between them through Mendelian randomization (MR) analysis.
RESULTS
We found significant genetic correlations between hypothyroidism with ANX, SCZ, and MDD, both in the Linkage disequilibrium score regression (LDSC) approach and the high-definition likelihood (HDL) approach. Meanwhile, the strongest correlation was observed between hypothyroidism and MDD (LDSC: rg=0.264, =7.35×10; HDL: rg=0.304, =4.14×10). We also determined a significant genetic correlation between MDD with free thyroxine (FT4) and thyroid-stimulating hormone (TSH) levels. A total of 30 pleiotropic risk loci were identified between hypothyroidism and psychiatric disorders, of which the 15q14 locus was identified in both ANX and SCZ ( values are 6.59×10 and 2.10×10, respectively) and the 6p22.1 locus was identified in both MDD and SCZ ( values are 1.05×10 and 5.75×10, respectively). Sixteen pleiotropic risk loci were identified between MDD and indicators of thyroid function, of which, four loci associated with MDD (1p32.3, 6p22.1, 10q21.1, 11q13.4) were identified in both FT4 normal level and Hypothyroidism. Further, 79 pleiotropic genes were identified using Magma gene analysis (<0.05/18776 = 2.66×10). Tissue-specific enrichment analysis revealed that these genes were highly enriched into six brain-related tissues. The pathway analysis mainly involved nucleosome assembly and lipoprotein particles. Finally, our two-sample MR analysis showed a significant causal effect of MDD on the increased risk of hypothyroidism, and BIP may reduce TSH normal levels.
CONCLUSIONS
Our findings not only provided evidence of a shared genetic etiology between hypothyroidism and psychiatric disorders, but also provided insights into the causal relationships and biological mechanisms that underlie their relationship. These findings contribute to a better understanding of the pleiotropy between hypothyroidism and psychiatric disorders, while having important implications for intervention and treatment goals for these disorders.
Topics: Humans; Hypothyroidism; Genome-Wide Association Study; Mendelian Randomization Analysis; Genetic Predisposition to Disease; Mental Disorders; Polymorphism, Single Nucleotide; Schizophrenia; Bipolar Disorder; Depressive Disorder, Major; Linkage Disequilibrium; Anxiety Disorders
PubMed: 38904036
DOI: 10.3389/fendo.2024.1370019 -
NeuroImage Jun 2024Numerous studies show that electroconvulsive therapy (ECT) induces hippocampal neuroplasticity, but findings are inconsistent regarding its clinical relevance. This...
BACKGROUND
Numerous studies show that electroconvulsive therapy (ECT) induces hippocampal neuroplasticity, but findings are inconsistent regarding its clinical relevance. This study aims to investigate ECT-induced plasticity of anterior and posterior hippocampi using mathematical complexity measures in neuroimaging, namely Higuchi's fractal dimension (HFD) for fMRI time series and the fractal dimension of cortical morphology (FD-CM). Furthermore, we explore the potential of these complexity measures to predict ECT treatment response.
METHODS
Twenty patients with a current depressive episode (16 with major depressive disorder and 4 with bipolar disorder) underwent MRI-scans before and after an ECT-series. Twenty healthy controls matched for age and sex were also scanned twice for comparison purposes. Resting-state fMRI data were processed, and HFD was computed for anterior and posterior hippocampi. Group-by-time effects for HFD in anterior and posterior hippocampi were calculated and correlations between HFD changes and improvement in depression severity were examined. For FD-CM analyses, we preprocessed structural MRI with CAT12's surface-based methods. We explored group-by-time effects for FD-CM and the predictive value of baseline HFD and FD-CM for treatment outcome.
RESULTS
Patients exhibited a significant increase in bilateral hippocampal HFD from baseline to follow-up scans. Right anterior hippocampal HFD increase was associated with reductions in depression severity. We found no group differences and group-by-time effects in FD-CM. After applying a whole-brain regression analysis, we found that baseline FD-CM in the left temporal pole predicted reduction of overall depression severity after ECT. Baseline hippocampal HFD did not predict treatment outcome.
CONCLUSION
This study suggests that HFD and FD-CM are promising imaging markers to investigate ECT-induced neuroplasticity associated with treatment response.
PubMed: 38901774
DOI: 10.1016/j.neuroimage.2024.120671 -
Journal of Affective Disorders Jun 2024Bipolar disorder (BD) has a high disease burden and the highest mortality risk in BD comes from suicide. Bipolar disorder type II (BD-II) has been described as a milder... (Review)
Review
BACKGROUND
Bipolar disorder (BD) has a high disease burden and the highest mortality risk in BD comes from suicide. Bipolar disorder type II (BD-II) has been described as a milder form of bipolar disorder; however, extant literature is inconsistent with this description and instead describe illness burden and notably suicidality comparable to persons with bipolar I disorder (BD-I). Towards quantifying the hazard of BD-II, herein we aim via systematic review and meta-analysis to evaluate the rates of completed suicide in BD-I and BD-II.
METHOD
We conducted a literature search on PubMed, OVID (Embase, Medline) and PsychINFO databases from inception to June 30th, 2023, according to PRISMA guidelines. Articles were selected based on the predetermined eligibility criteria. A meta-analysis was performed, comparing the risk of completed suicide between individuals diagnosed with BD-I to BD-II.
RESULTS
Four out of eight studies reported higher suicide completion rates in persons living with BD-II when compared to persons living with BD-I; however, two of the studies reported non-significance. Two studies reported significantly higher suicide completion rates for BD-I than BD-II. The pooled odds ratio of BD-II suicide rates to BD-I was 1.00 [95 % CI = 0.75, 1.34].
LIMITATIONS
The overarching limitation is the small number of studies and heterogeneity of studies that report on suicide completion in BD-I and BD-II.
CONCLUSION
Our study underscores the severity of BD-II, with a risk for suicide not dissimilar from BD-I. The greater propensity to depression, comorbidity and rapid-cycling course reported in BD-II are contributing factors to the significant mortality hazard in BD-II.
PubMed: 38901691
DOI: 10.1016/j.jad.2024.06.045 -
Psychiatry Research. Neuroimaging Jun 2024This study investigates computational models of electric field strength for transcranial magnetic stimulation (TMS) of the left dorsolateral prefrontal cortex (DLPFC)...
This study investigates computational models of electric field strength for transcranial magnetic stimulation (TMS) of the left dorsolateral prefrontal cortex (DLPFC) based on individual MRI data of patients with schizophrenia (SZ), major depressive disorder (MDD), bipolar disorder (BP), and healthy controls (HC). In addition, it explores the association of electric field intensities with age, gender and intracranial volume. The subjects were 23 SZ (12 male, mean age = 45.30), 24 MDD (16 male, mean age = 43.57), 23 BP (16 male, mean age = 39.29), 23 HC (13 male, mean age = 40.91). Based on individual MRI sequences, electric fields were computationally modeled by two independent investigators using SimNIBS ver. 2.1.1. There was no significant difference in electric field strength between the groups (HC vs SZ, HC vs MDD, HC vs BP, SCZ vs MDD, SCZ vs BP, MDD vs BP). Female subjects showed higher electric field intensities in widespread areas than males, and age was positively significantly associated with electric field strength in the left parahippocampal area as observed. Our results suggest differences in electric field strength of left DLPFC TMS for gender and age. It may open future avenues for individually modeling TMS based on structural MRI data.
PubMed: 38901089
DOI: 10.1016/j.pscychresns.2024.111844