A comprehensive review of genetic and epigenetic mechanisms that regulate BDNF expression and function with relevance to major depressive disorder.

Major depressive disorder (MDD) is a mood disorder that affects behavior and impairs cognition. A gene potentially important to this disorder is the brain derived neurotrophic factor (BDNF) as it is involved in processes controlling neuroplasticity. Various mechanisms exist to regulate BDNF's expression level, subcellular localization, and sorting to appropriate secretory pathways. Alterations to these processes by genetic factors and negative stressors can dysregulate its expression, with possible implications for MDD. Here, we review the mechanisms governing the regulation of BDNF expression, and discuss how disease-associated single nucleotide polymorphisms (SNPs) can alter these mechanisms, and influence MDD. As negative stressors increase the likelihood of MDD, we will also discuss the impact of these stressors on BDNF expression, the cellular effect of such a change, and its impact on behavior in animal models of stress. We will also describe epigenetic processes that mediate this change in BDNF expression. Similarities in BDNF expression between animal models of stress and those in MDD will be highlighted. We will also contrast epigenetic patterns at the BDNF locus between animal models of stress, and MDD patients, and address limitations to current clinical studies. Future work should focus on validating current genetic and epigenetic findings in tightly controlled clinical studies. Regions outside of BDNF promoters should also be explored, as should other epigenetic marks, to improve identification of biomarkers for MDD.

Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder.

BACKGROUND: Bipolar disorder (BD) is a chronic psychiatric mood disorder that is solely diagnosed based on clinical symptoms. These symptoms often overlap with other psychiatric disorders. Efforts to use machine learning (ML) to create predictive models for BD based on data from brain imaging are expanding but have often been limited using only a single modality and the exclusion of the cerebellum, which may be relevant in BD. METHODS: In this study, we sought to improve ML classification of BD by combining information from structural, functional, and diffusion-weighted imaging. Participants (108 BD I, 78 control) with BD type I and matched controls were recruited into an imaging study. This dataset was randomly divided into training and testing sets. For each of the three modalities, a separate ML model was selected, trained, and then used to generate a prediction of the class of each test subject. Majority voting was used to combine results from the three models to make a final prediction of whether a subject had BD. An independent replication sample was used to evaluate the ability of the ML classification to generalize to data collected at other sites. RESULTS: Combining the three machine learning models through majority voting resulted in an accuracy of 89.5 % for classification of the test subjects as being in the BD or control group. Bootstrapping resulted in a 95 % confidence interval of 78.9 %-97.4 % for test accuracy. Performance was reduced when only using 2 of the 3 modalities. Analysis of feature importance revealed that the cerebellum and nodes of the emotional control network were among the most important regions for classification. The machine learning model performed at chance on the independent replication sample. CONCLUSION: BD I could be identified with high accuracy in our relatively small sample by combining structural, functional, and diffusion-weighted imaging data within a single site but not generalize well to an independent replication sample. Future studies using harmonized imaging protocols may facilitate generalization of ML models.

Cerebellar morphological differences and associations with extrinsic factors in bipolar disorder type I.

BACKGROUND: The neural underpinnings of bipolar disorder (BD) remain poorly understood. The cerebellum is ideally positioned to modulate emotional regulation circuitry yet has been understudied in BD. Literature suggests differences in cerebellar activity and metabolism in BD, however findings on structural differences remain contradictory. Potential reasons include combining BD subtypes, small sample sizes, and potential moderators such as genetics, adverse childhood experiences (ACEs), and pharmacotherapy. METHODS: We collected 3 T MRI scans from participants with (N = 131) and without (N = 81) BD type I, as well as blood and questionnaires. We assessed differences in cerebellar volumes and explored potentially influential factors. RESULTS: The cerebellar cortex was smaller bilaterally in participants with BD. Polygenic propensity score did not predict any cerebellar volumes, suggesting that non-genetic factors may have greater influence on the cerebellar volume difference we observed in BD. Proportionate cerebellar white matter volumes appeared larger with more ACEs, but this may result from reduced ICV. Time from onset and symptom burden were not associated with cerebellar volumes. Finally, taking sedatives was associated with larger cerebellar white matter and non-significantly larger cortical volume. LIMITATIONS: This study was cross-sectional, limiting interpretation of possible mechanisms. Most of our participants were White, which could limit the generalizability. Additionally, we did not account for potential polypharmacy interactions. CONCLUSIONS: These findings suggest that external factors, such as sedatives and childhood experiences, may influence cerebellum structure in BD and may mask underlying differences. Accounting for such variables may be critical for consistent findings in future studies.

Functional connectivity of the cerebellar vermis in bipolar disorder and associations with mood.

Purpose: Studies of the neural underpinnings of bipolar type I disorder have focused on the emotional control network. However, there is also growing evidence for cerebellar involvement, including abnormal structure, function, and metabolism. Here, we sought to assess functional connectivity of the cerebellar vermis with the cerebrum in bipolar disorder and to assess whether connectivity might depend on mood. Methods: This cross-sectional study enrolled 128 participants with bipolar type I disorder and 83 control comparison participants who completed a 3 T magnetic resonance imaging (MRI) study, which included anatomical as well as resting state Blood Oxygenation Level Dependent (BOLD) imaging. Functional connectivity of the cerebellar vermis to all other brain regions was assessed. Based on quality control metrics of the fMRI data, 109 participants with bipolar disorder and 79 controls were included in the statistical analysis comparing connectivity of the vermis. In addition, the data was explored for the potential impacts of mood, symptom burden, and medication in those with bipolar disorder. Results: Functional connectivity between the cerebellar vermis and the cerebrum was found to be aberrant in bipolar disorder. The connectivity of the vermis was found to be greater in bipolar disorder to regions involved in motor control and emotion (trending), while reduced connectivity was observed to a region associated with language production. In the participants with bipolar disorder, past depression symptom burden affected connectivity; however, no effects of medication were observed. Functional connectivity between the cerebellar vermis and all other regions revealed an inverse association with current mood ratings. Conclusion: Together the findings may suggest that the cerebellum plays a compensatory role in bipolar disorder. The proximity of the cerebellar vermis to the skull may make this region a potential target for treatment with transcranial magnetic stimulation.

Functional Connectivity of the Cerebellar Vermis in Bipolar Disorder and Associations with Mood.

Purpose: Studies of the neural underpinnings of bipolar type I disorder have focused on the emotional control network. However, there is also growing evidence for cerebellar involvement, including abnormal structure, function, and metabolism. Here, we sought to assess functional connectivity of the cerebellum with the cerebrum in bipolar disorder and to assess whether any effects might depend on mood. Methods: This cross-sectional study enrolled 128 participants with bipolar type I disorder and 83 control comparison participants who completed a 3T MRI scan, which included anatomical imaging as well as resting state BOLD imaging. Functional connectivity of the cerebellar vermis to all other brain regions was assessed. Based on quality control metrics of the fMRI data, 109 participants with bipolar disorder and 79 controls were used to in the statistical analysis comparing connectivity of the vermis as well as associations with mood. Potential impacts of medications were also explored. Results: Functional connectivity of the cerebellar vermis in bipolar disorder was found to differ significantly between brain regions known to be involved in the control of emotion, motor function, and language. While connections with emotion and motor control areas were significantly stronger in bipolar disorder, connection to a region associated language production was significantly weaker. In the participants with bipolar disorder, ratings of depression and mania were inversely associated with vermis functional connectivity. No effect of medications on these connections were observed. Conclusion: Together the findings suggest cerebellum may play a compensatory role in bipolar disorder and when it can no longer fulfill this role, depression and mania develop. The proximity of the cerebellar vermis to the skull may make this region a potential target for treatment with transcranial magnetic stimulation.

Metabolic abnormalities in the basal ganglia and cerebellum in bipolar disorder: A multi-modal MR study.

AIMS: Bipolar type I disorder (BD) is characterized by severe mood swings and occurs in about 1% of the population. The mechanisms underlying the disorder remain unknown. Prior studies have suggested abnormalities in brain metabolism using 1H and 31P magnetic resonance spectroscopy (MRS). Supporting altered metabolism, in previous studies we found T1ρ relaxation times in the cerebellum were elevated in participants with BD. In addition, T1ρ relaxation times in the basal ganglia were lower in participants with BD experiencing depressed mood. Based on these findings, this study sought to probe brain metabolism with a focus of extending these assessments to the cerebellum. METHODS: This study collected data from 64 participants with Bipolar type I disorder (BD) and 42 controls. Subjects were scanned at both 3T (anatomical, functional, and T1ρ imaging data) and 7T (31P and 1H spectroscopy). Regions of interest defined by the 1H MRS data were used to explore metabolic and functional changes in the cerebellar vermis and putamen. RESULTS: Elevated concentrations of n-Acetyl-l-aspartate (NAA), glutamate, glutathione, taurine, and creatine were found in the cerebellar vermis along with decreased intra-cellular pH. Similar trends were observed in the right putamen for glutamate, creatine, and pH. We also observed a relationship between T1ρ relaxation times and mood in the putamen. We did not observe a significant effect of medications on these measures. LIMITATIONS: The study was cross sectional in design and employed a naturalistic approach for assessing the impact of medications on the results. CONCLUSION: This study supports prior findings of reduced pH in mitochondrial dysfunction in BD while also showing that these differences extend to the cerebellum.