Biomarkers For Bipolar Disorder Research Articles

Cerebrospinal fluid synaptic biomarker changes in bipolar disorder – A longitudinal case-control study

BackgroundThis exploratory study investigated cerebrospinal fluid (CSF) synaptic protein biomarkers in bipolar disorder (BD), aiming to highlight the neurobiological basis of the disorder. With shared cognitive impairment features between BD and Alzheimer's disease, and considering increased dementia risk in BD patients, the study explores potential connections. MethodsFifty-nine well-characterized patients with BD and thirty-seven healthy control individuals were examined and followed for one year. Synaptic proteins encompassing neuronal pentraxins (NPTX)1, NPTX2, and NPTX-receptor, 14–3-3 protein family epsilon, and zeta/delta, activating protein-2 complex subunit beta, synucleins beta-synuclein and gamma-synuclein, complexin-2, phosphatidylethanolamine-binding protein 1, rab GDP dissociation inhibitor alpha, and syntaxins 1B and 7 were measured in CSF using a microflow liquid chromatography-mass spectrometric multiple reaction monitoring set-up. Biomarker levels were compared between BD and HC and in BD before, during, and after mood episodes. ResultsThe synaptic proteins revealed no statistically significant differences between BD and HC, neither at baseline, one-year follow-up, or in terms of changes from baseline to follow-up. Moreover, the CSF synaptic protein levels in patients with BD were unaltered compared to baseline when they stabilized in euthymia following an affective episode and at one-year follow-up. LimitationIt is uncertain what the CSF biomarker concentrations reflect since we yet do not know the mechanisms of release of these proteins, and we are uncertain of what increased or decreased levels reflect. ConclusionThis first-ever investigation of a panel of CSF protein biomarkers of synaptic dysfunction in patients with BD and HC individuals found no statistically significant differences cross-sectionally or longitudinally.

Plasma IGFBP-3 and IGFBP-5 levels are decreased during acute manic episodes in bipolar disorder patients.

Introduction: Bipolar disorder (BD) is a recurrent and disabling psychiatric disorder related to low-grade peripheral inflammation and altered levels of the members of the insulin-like growth factor (IGF) family. The aim of this study was to evaluate the plasma levels of IGF-2, insulin-like growth factor-binding protein 1 (IGFBP-1), IGFBP-3, IGFBP-5, IGFBP-7, and inflammatory markers such as tumor necrosis factor α (TNF-α), monocyte chemoattractant protein 1 (MCP-1), and macrophage inflammatory protein 1β (MIP-1β). Methods: We used the Young Mania Rating Scale (YMRS) to determine the severity of the symptomatology, while proteins were measured by enzyme-linked immunosorbent assay (ELISA). We included 20 patients with BD who suffered a manic episode and 20 controls. Some BD patients (n = 10) were evaluated after a period (17 ± 8days) of pharmacological treatment. Results: No statistical difference was found in IGF-2, IGFBP-1, IGFBP-7, TNF-α, and MIP-1β levels. However, IGFBP-3 and IGFBP-5 levels were found to be statistically decreased in BD patients. Conversely, the MCP-1 level was significantly increased in BD patients, but their levels were normalized after treatment. Intriguingly, only IGFBP-1 levels were significantly decreased after treatment. No significant correlation was found between the YMRS and any of the proteins studied either before or after treatment or between IGF proteins and inflammatory markers. Discussion: To some extent, IGFBP-3 and IGFBP-5 might be further explored as potential indicators of treatment responsiveness or diagnosis biomarkers in BD.

Oculomotor Abnormalities and Aberrant Neuro-Developmental Markers: Composite Endophenotype for Bipolar I Disorder: Anomalies Oculomotrices et Marqueurs Neuro-Développementaux Aberrants : Endophénotype Composite du Trouble Bipolaire I.

Neurological soft signs (NSSs), minor physical anomalies (MPAs), and oculomotor abnormalities were plausible biomarkers in bipolar disorder (BD). However, specific impairments in these markers in patients after the first episode mania (FEM), in comparison with first-degree relatives (high risk [HR]) of BD and healthy subjects (health control [HC]) are sparse. This study aimed at examining NSSs, MPAs, and oculomotor abnormalities in remitted adult subjects following FEM and HR subjects in comparison with matched healthy controls. Investigated when taken together, could serve as composite endophenotype for BD. NSSs, MPAs, and oculomotor abnormalities were evaluated in FEM (n = 31), HR (n = 31), and HC (n = 30) subjects, matched for age (years) (p = 0.44) and sex (p = 0.70) using neurological evaluation scale, Waldrop's physical anomaly scale and eye tracking (SPEM) and antisaccades (AS) paradigms, respectively. Significant differences were found between groups on NSSs, MPAs, and oculomotor parameters. Abnormalities are higher in FEM subjects compared to HR and HC subjects. Using linear discriminant analysis, all 3 markers combined accurately classified 72% of the original 82 subjects (79·2% BD, 56·70% HR, and 82·1% HC subjects). AS and SPEM could enhance the utility of NSSs, and MPAs as markers for BD. The presence of these abnormalities in FEM suggests their role in understanding the etiopathogenesis of BD in patients who are in the early course of illness. These have the potential to be composite endophenotypes and have further utility in early identification in BD.

Exploring hub genes and crucial pathways linked to oxidative stress in bipolar disorder depressive episodes through bioinformatics analysis.

Bipolar disorder (BD) is a complex and serious psychiatric condition primarily characterized by bipolar depression, with the underlying genetic determinants yet to be elucidated. There is a substantial body of literature linking psychiatric disorders, including BD, to oxidative stress (OS). Consequently, this study aims to assess the relationship between BD and OS by identifying key hub genes implicated in OS pathways. We acquired gene microarray data from GSE5392 through the Gene Expression Omnibus (GEO). Our approach encompassed differential expression analysis, weighted gene co-expression network analysis (WGCNA), and Protein-Protein Interaction (PPI) Network analysis to pinpoint hub genes associated with BD. Subsequently, we utilized Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to identify hub genes relevant to OS. To evaluate the diagnostic accuracy of these hub genes, we performed receiver operating characteristic curve (ROC) analysis on both GSE5388 and GSE5389 datasets. Furthermore, we conducted a study involving ten BD patients and ten healthy controls (HCs) who met the special criteria, assessing the expression levels of these hub genes in their peripheral blood mononuclear cells (PBMCs). We identified 411 down-regulated genes and 69 up-regulated genes for further scrutiny. Through WGCNA, we obtained 22 co-expression modules, with the sienna3 module displaying the strongest association with BD. By integrating differential analysis with genes linked to OS, we identified 44 common genes. Subsequent PPI Network and WGCNA analyses confirmed three hub genes as potential biomarkers for BD. Functional enrichment pathway analysis revealed their involvement in neuronal signal transduction, oxidative phosphorylation, and metabolic obstacle pathways. Using the Cytoscape plugin "ClueGo assay," we determined that a majority of these targets regulate neuronal synaptic plasticity. ROC curve analysis underscored the excellent diagnostic value of these three hub genes. Quantitative reverse transcription-PCR (RT-qPCR) results indicated significant changes in the expression of these hub genes in the PBMCs of BD patients compared to HCs. We identified three hub genes (TAC1, MAP2K1, and MAP2K4) in BD associated with OS, potentially influencing the diagnosis and treatment of BD. Based on the GEO database, our study provides novel insights into the relationship between BD and OS, offering promising therapeutic targets.

MicroRNAs as potential diagnostic biomarkers for bipolar disorder

Abstract Abnormal expression of microRNAs is connected to brain development and disease and could provide novel biomarkers for the diagnosis and prognosis of bipolar disorder. We performed a PubMed search for microRNA biomarkers in bipolar disorder and found 18 original research articles on studies performed with human patients and published from January 2011 to June 2023. These studies included microRNA profiling in blood-and brain-based materials. From the studies that had validated the preliminary findings, potential candidate biomarkers for bipolar disorder in adults could be miR-140-3p, -30d- 5p, -330-5p, -378a-5p, -21-3p, -330-3p, -345-5p in whole blood, miR-19b-3p, -1180-3p, -125a-5p, let-7e-5p in blood plasma, and miR-7-5p, -23b-5p, -142-3p, -221-5p, -370-3p in the blood serum. Two of the studies had investigated the changes in microRNA expression of patients with bipolar disorder receiving treatment. One showed a significant increase in plasma miR-134 compared to baseline after 4 weeks of treatment which included typical antipsychotics, atypical antipsychotics, and benzodiazepines. The other study had assessed the effects of prescribed medications which included neurotransmitter receptor-site binders (drug class B) and sedatives, hypnotics, anticonvulsants, and analgesics (drug class C) on microRNA results. The combined effects of the two drug classes increased the significance of the results for miR-219 and -29c with miR-30e-3p and -526b* acquiring significance. MicroRNAs were tested to see if they could serve as biomarkers of bipolar disorder at different clinical states of mania, depression, and euthymia. One study showed that upregulation in whole blood of miR-9-5p, -29a-3p, -106a-5p, -106b-5p, -107, -125a-3p, -125b-5p and of miR-107, -125a-3p occurred in manic and euthymic patients compared to controls, respectively, and that upregulation of miR-106a-5p, -107 was found for manic compared to euthymic patients. In two other studies using blood plasma, downregulation of miR-134 was observed in manic patients compared to controls, and dysregulation of miR-134, -152, -607, -633, -652. -155 occurred in euthymic patients compared to controls. Finally, microRNAs such as miR-34a, -34b, -34c, -137 and -140- 3p, -21-3p, -30d-5p, -330-5p, -378a-5p, -134, -19b-3p were shown to have diagnostic potential in distinguishing bipolar disorder patients from schizophrenia or major depressive disorder patients, respectively. Further studies are warranted with adolescents and young adults having bipolar disorder and consideration should be given to using animal models of the disorder to investigate the effects of suppressing or overexpressing specific microRNAs.

Response inhibition related neural oscillatory patterns show reliable early identification of bipolar from unipolar depression in a Go/No-Go task

BackgroundResponse inhibition is a key neurocognitive factor contributing to impulsivity in mood disorders. Here, we explored the common and differential alterations of neural circuits associated with response inhibition in bipolar disorder (BD) and unipolar disorder (UD) and whether the oscillatory signatures can be used as early biomarkers in BD. Methods39 patients with BD, 36 patients with UD, 29 patients initially diagnosed with UD who later underwent diagnostic conversion to BD, and 36 healthy controls performed a Go/No-Go task during MEG scanning. We carried out time-frequency and connectivity analysis on MEG data. Further, we performed machine learning using oscillatory features as input to identify bipolar from unipolar depression at the early clinical stage. ResultsCompared to healthy controls, patients had reduced rIFG-to-pre-SMA connectivity and delayed activity of rIFG. Among patients, lower beta power and higher peak frequency were observed in BD patients than in UD patients. These changes enabled accurate classification between BD and UD with an accuracy of approximately 80 %. ConclusionsThe inefficiency of the prefrontal control network is a shared mechanism in mood disorders, while the abnormal activity of rIFG is more specific to BD. Neuronal responses during response inhibition could serve as a diagnostic biomarker for BD in early stage.

State- and trait-related dysfunctions in bipolar disorder across different mood states: a graph theory study.

The interplay between state- and trait-related disruptions in structural networks remains unclear in bipolar disorder (BD), but graph theory can offer insights into global and local network changes. We sought to use diffusion-tensor imaging (DTI) and graph theory approaches to analyze structural topological properties across distinct mood states and identify high-risk individuals by examining state- and trait-related impairments in BD. We studied changes in white matter network among patients with BD and healthy controls, exploring relationships with clinical variables. Secondary analysis involved comparing patients with BD with unaffected people at high genetic risk for BD. We included 152 patients with BD, including 52 with depressive BD (DBD), 64 with euthymic BD (EBD) and 36 with manic BD (MBD); we also included 75 healthy controls. Secondary analyses involved 27 unaffected people at high genetic risk for BD. Patients with DBD and MBD exhibited significantly lower global efficiencies than those with EBD and healthy controls, with patients with DBD showing the lowest global efficiencies. In addition, patients with DBD displayed impaired local efficiency and normalized clustering coefficient (γ). At a global level, γ correlated negatively with depression and anxiety. Compared with healthy controls, and across mood states, patients with BD showed abnormal shortest path lengths in the frontolimbic circuit, a trend mirrored among those at high genetic risk for BD. Considerations include medication effects, absence of recorded BD episode counts and the cross-sectional nature of the study. Mood-specific whole-brain network metrics could serve as potential biomarkers in BD for transitions between mood states. Moreover, these findings contribute to evidence of trait-related frontolimbic circuit irregularities, shedding light on underlying pathophysiological mechanisms in BD.

Attention TurkerNeXt: Investigations into Bipolar Disorder Detection Using OCT Images.

Background and Aim: In the era of deep learning, numerous models have emerged in the literature and various application domains. Transformer architectures, particularly, have gained popularity in deep learning, with diverse transformer-based computer vision algorithms. Attention convolutional neural networks (CNNs) have been introduced to enhance image classification capabilities. In this context, we propose a novel attention convolutional model with the primary objective of detecting bipolar disorder using optical coherence tomography (OCT) images. Materials and Methods: To facilitate our study, we curated a unique OCT image dataset, initially comprising two distinct cases. For the development of an automated OCT image detection system, we introduce a new attention convolutional neural network named "TurkerNeXt". This proposed Attention TurkerNeXt encompasses four key modules: (i) the patchify stem block, (ii) the Attention TurkerNeXt block, (iii) the patchify downsampling block, and (iv) the output block. In line with the swin transformer, we employed a patchify operation in this study. The design of the attention block, Attention TurkerNeXt, draws inspiration from ConvNeXt, with an added shortcut operation to mitigate the vanishing gradient problem. The overall architecture is influenced by ResNet18. Results: The dataset comprises two distinctive cases: (i) top to bottom and (ii) left to right. Each case contains 987 training and 328 test images. Our newly proposed Attention TurkerNeXt achieved 100% test and validation accuracies for both cases. Conclusions: We curated a novel OCT dataset and introduced a new CNN, named TurkerNeXt in this research. Based on the research findings and classification results, our proposed TurkerNeXt model demonstrated excellent classification performance. This investigation distinctly underscores the potential of OCT images as a biomarker for bipolar disorder.

The effect of subthreshold depressive symptoms on cognitive functions and peripheral biomarkers in bipolar disorder

Objective A global approach to factors responsible for functional impairment in patients with BD is necessary. Method Ninety-three euthymic patients with BD [49 patients with SD (subthreshold depression) and 44 patients without SD] and 48 healthy controls were invited for evaluation of demographic, clinical, and cognitive characteristics. To define SD, the lower limit was HDRS ≥4 points and the upper limit was HDRS <9 points. Stroop test, California verbal learning test, digit-span test, controlled word association test, and clock drawing test were performed. Serum BDNF levels were measured. Additionally in the BD group; blood drug (lithium, valproic acid), leukocyte, C-reactive protein (CRP), TSH, and vitamin B12 levels were measured. Results We found no difference between serum BDNF levels of BD (n = 93) and controls. The cognitive performances of the BD group were worse than the control group (p < 0.001). Attention, working memory, and stroop performance of patients with SD were worse than patients without SD (p < 0.05). Verbal fluency, stroop test, and planning performance decreased as serum CRP level increased in patients with BD (p < 0.05). Conclusions Although the patient group with SD was in euthymia, their cognitive performance was worse than the group without SD. Poor cognitive performance in BD was associated with serum CRP levels.

Search for serum biomarkers in patients with bipolar disorder and major depressive disorder using metabolome analysis.

Bipolar disorder (BD) and major depressive disorder (MDD) are two common psychiatric disorders. Due to the overlapping clinical symptoms and the lack of objective diagnostic biomarkers, bipolar disorder (BD) is easily misdiagnosed as major depressive disorder (MDD), which in turn affects treatment decisions and prognosis. This study aimed to investigate biomarkers that could be used to differentiate BD from MDD. Nuclear magnetic resonance (NMR) spectroscopy was performed to assess serum metabolic profiles in depressed patients with BD (n = 59), patients with MDD (n = 14), and healthy controls (n = 10). Data was analyzed using partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis and t-tests. Different metabolites (VIP > 1 and p < 0.05) were identified and further analyzed using Metabo Analyst 5.0 to identify relevant metabolic pathways. The metabolic phenotypes of the BD and MDD groups were significantly different from those of the healthy controls, and there were different metabolite differences between them. In the BD group, the levels of 3-hydroxybutyric acid, n-acetyl glycoprotein, β-glucose, pantothenic acid, mannose, glycerol, and lipids were significantly higher than those in the healthy control group, and the levels of lactate and acetoacetate were significantly lower than those in the healthy control group. In the MDD group, the levels of 3-hydroxybutyric acid, n-acetyl glycoprotein, pyruvate, choline, acetoacetic acid, and lipids were significantly higher than those of healthy controls, and the levels of acetic acid and glycerol were significantly lower than those of healthy controls. Glycerolipid metabolism is significantly involved in BD and MDD. Pyruvate metabolism is significantly involved in MDD. Pyruvate, choline, and acetate may be potential biomarkers for MDD to distinguish from BD, and pantothenic acid may be a potential biomarker for BD to distinguish from MDD.

Can Brain-Derived Neurotrophic Factor Be Considered a Biomarker for Bipolar Disorder? An Analysis of the Current Evidence.

Brain-derived neurotrophic factor (BDNF) plays a key role in brain development, contributing to neuronal survival and neuroplasticity. Previous works have found that BDNF is involved in several neurological or psychiatric diseases. In this review, we aimed to collect all available data on BDNF and bipolar disorder (BD) and assess if BDNF could be considered a biomarker for BD. We searched the most relevant medical databases and included studies reporting original data on BDNF circulating levels or Val66Met polymorphism. Only articles including a direct comparison with healthy controls (HC) and patients diagnosed with BD according to international classification systems were included. Of the 2430 identified articles, 29 were included in the present review. Results of the present review show a reduction in BDNF circulating levels during acute phases of BD compared to HC, which increase after effective therapy of the disorders. The Val66Met polymorphism was related to features usually associated with worse outcomes. High heterogeneity has been observed regarding sample size, clinical differences of included patients, and data analysis approaches, reducing comparisons among studies. Although more studies are needed, BDNF seems to be a promising biomarker for BD.

RNA Biomarkers in Bipolar Disorder and Response to Mood Stabilizers.

Bipolar disorder (BD) is a severe chronic disorder that represents one of the main causes of disability among young people. To date, no reliable biomarkers are available to inform the diagnosis of BD or clinical response to pharmacological treatment. Studies focused on coding and noncoding transcripts may provide information complementary to genome-wide association studies, allowing to correlate the dynamic evolution of different types of RNAs based on specific cell types and developmental stage with disease development or clinical course. In this narrative review, we summarize findings from human studies that evaluated the potential utility of messenger RNAs and noncoding transcripts, such as microRNAs, circular RNAs and long noncoding RNAs, as peripheral markers of BD and/or response to lithium and other mood stabilizers. The majority of available studies investigated specific targets or pathways, with large heterogeneity in the included type of cells or biofluids. However, a growing number of studies are using hypothesis-free designs, with some studies also integrating data on coding and noncoding RNAs measured in the same participants. Finally, studies conducted in neurons derived from induced-pluripotent stem cells or in brain organoids provide promising preliminary findings supporting the power and utility of these cellular models to investigate the molecular determinants of BD and clinical response.

Metabolomic Profiling of Bipolar Disorder by 1H-NMR in Serbian Patients.

Bipolar disorder (BD) is a brain disorder that causes changes in a person's mood, energy, and ability to function. It has a prevalence of 60 million people worldwide, and it is among the top 20 diseases with the highest global burden. The complexity of this disease, including diverse genetic, environmental, and biochemical factors, and diagnoses based on the subjective recognition of symptoms without any clinical test of biomarker identification create significant difficulties in understanding and diagnosing BD. A 1H-NMR-based metabolomic study applying chemometrics of serum samples of Serbian patients with BD (33) and healthy controls (39) was explored, providing the identification of 22 metabolites for this disease. A biomarker set including threonine, aspartate, gamma-aminobutyric acid, 2-hydroxybutyric acid, serine, and mannose was established for the first time in BD serum samples by an NMR-based metabolomics study. Six identified metabolites (3-hydroxybutyric acid, arginine, lysine, tyrosine, phenylalanine, and glycerol) are in agreement with the previously determined NMR-based sets of serum biomarkers in Brazilian and/or Chinese patient samples. The same established metabolites (lactate, alanine, valine, leucine, isoleucine, glutamine, glutamate, glucose, and choline) in three different ethnic and geographic origins (Serbia, Brazil, and China) might have a crucial role in the realization of a universal set of NMR biomarkers for BD.

Comparison of Clinical Features, Serum Lipid, and Uric Acid Levels in Patients with Unipolar Depression and Bipolar Depression.

The level of uric acid and serum lipids has been suggested as a possible biomarker of bipolar disorder. We aimed to investigate the differences in clinical features and serum levels of lipids and uric acid in patients with bipolar depression or unipolar depression in order to distinguish them. The clinical data of 53 patients with unipolar depression (unipolar group) and 61 patients with bipolar depression (bipolar group), who all met the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders-IV of the American Psychiatric Association, were compared with each other retrospectively. The serum levels of uric acid and lipids (including total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides) were measured after hospital admission. The Statistical Package for the Social Science version 22.0 software was used for statistical analysis, and logistic regression was employed to identify the susceptible factors of bipolar depression. Taking into account confounding factors, logistic regression analysis revealed that the high levels of uric acid (odds ratio = 1.016, P = .001) and low levels of triglycerides (odds ratio = 0.457, P = .025) were significantly correlated with bipolar depression. It has been demonstrated from this study that individuals with bipolar depression have higher serum uric acid levels and lower triglyceride levels than unipolar depression ones. Therefore, serum levels of uric acid and triglycerides might have the potential to be the biomarkers for differential diagnosis between bipolar and unipolar depression.

Bipolar disorder traits: An electroencephalogram systematic review

Bipolar disorder (BD) is a serious mental disorder that globally affected 40 million people in 2019. According to the National Alliance on Mental Illness (NAMI), the present state of scientific knowledge only permits psychiatrists to diagnose BD using subjective and imprecise questionnaires. Therefore, developing a diagnostic tool with objective and precise biomarkers should be a major focus of research in this field. Among the potential biomarkers for BD, electroencephalogram (EEG)-based signatures of BD are considered to be the most optimal marker due to their strong links with behavioural symptoms and also their non-invasiveness. The goal of this review is to give a detailed summary of current techniques for investigating the traces of BD through EEG abnormalities. In this review, 13 studies from databases such as ScienceDirect and PubMed seeking to utilize EEG characteristics to diagnose BD were selected. The search keywords were “EEG in BD diagnosis”, “EEG microstates in BD”, and “EEG features for BD patients”. The publication date was set from 2007 to 2021. From these studies, we synthesize the effects of BD on each EEG feature, as well as detail the pros and cons when using each feature as a biomarker for BD. Results showed that EEG microstates demonstrate their potential among the seven EEG properties discussed in this article, as shown by several studies. By definition, EEG microstates are a dynamic representation of the spatial distribution of the scalp's electric potential as it varies over time. Specifically, four microstate classes recorded in different brain regions are classified into A (right-frontal left-posterior), B (left-frontal right-posterior), C (midline frontal-occipital), and D (midline frontal topographies). Greater presence of microstate class B in BD patients during task-free resting states are a distinctive characteristic of BD patients from which BD can be differentiated from other psychiatric illnesses. Besides microstates, EEG resting states are also considered to have a bright future in BD diagnosis. Specifically, by investigating brain frequency bands, researchers have discovered that BD patients exhibit abnormal delta and alpha signals as compared to healthy controls (HCs). The abnormalities of microstate B in EEG microstate characteristics would be the most promising biomarker for detecting BD. In addition, anomalies in delta and alpha signals during resting EEG states are possible BD diagnostic indicators.

Disrupted network switching in euthymic bipolar disorder: Working memory and self-referential paradigms

BackgroundPatients with bipolar disorder (BD) frequently suffer from neurocognitive deficits that can persist during periods of clinical stability. Specifically, impairments in executive functioning such as working memory and in self-processing have been identified as the main components of the neurocognitive profile observed in euthymic BD patients. The study of the neurobiological correlates of these state-independent alterations may be a prerequisite to develop reliable biomarkers in BD. MethodsA sample of 27 euthymic BD patients and 25 healthy participants (HC) completed working memory and self-referential functional Magnetic Resonance Imaging (fMRI) tasks. Activation maps obtained for each group and contrast images (i.e., 2-back > 1-back/self > control) were used for comparisons between patients and HC. ResultsEuthymic BD patients, in comparison to HC, showed a higher ventromedial prefrontal cortex activation during working memory, a result driven by the lack of deactivation in BD patients. In addition, euthymic BD patients displayed a greater dorsomedial and dorsolateral prefrontal cortex activation during self-reference processing. LimitationsPharmacotherapy was described but not included as a confounder in our models. Sample size was modest. ConclusionOur findings revealed a lack of deactivation in the anterior default mode network (aDMN) during a working memory task, a finding consistent with prior research in BD patients, but also a higher activation in frontal regions within the central executive network (CEN) during self-processing. These results suggest that an imbalance of neural network dynamics underlying external/internal oriented cognition (the CEN and the aDMN, respectively) may be one of the first reliable biomarkers in euthymic bipolar patients.

25.4 41032: Neural Predictors of Mood Dysregulation in Youth at Familial and Symptomatic Risk of Bipolar Disorder

Biomarkers of bipolar disorder are needed to aid in early diagnosis, and identifying neural markers of known prodromal behaviors is a step toward that goal. Mood dysregulation factors within affective lability scales are recognized as prodromal to the development of bipolar disorder. We identified neural and behavioral markers of future affective lability in at-risk youth from the Pittsburgh Bipolar Offspring Study (BIOS) and validated these in an independent sample of youth from the Longitudinal Assessment of Manic Symptoms (LAMS) study.

Data-driven learning to identify biomarkers in bipolar disorder

Background and Objective: Bipolar disorder (BD) is one of the primary causes of disability globally and can be easily misdiagnosed as schizophrenia or major depression due to their similar symptoms. Hence, it is of great significance to explore the pathogenesis of BD. Statistical analysis is currently the most common method for exploring the neuropathological mechanisms of psychiatric disorders. However, this method only considers the relationship between groups and does not reflect the individual-level diagnosis. Therefore, we developed machine learning algorithms to measure pathological brain changes in psychiatric disorders. Methods: An autoencoder and a feature selection method are proposed to identify the abnormal structural patterns of BD in this study. The autoencoder was constructed using structural imaging data from 1113 healthy controls, which aims to define the normal range of anatomical deviations to distinguish healthy individuals from BD patients. The biomarkers of BD were identified by the reconstruction errors in each brain region. The proposed feature selection (FS)-select framework aimed to determine the optimal FS method and identify the most reproducible feature associated with BD. Results: We found that the left orbital region of the middle frontal gyrus had the greatest difference between healthy controls and BD patients using a trained autoencoder. The most reproducible feature was the left orbital region of the middle frontal gyrus by FS-select framework when using the different cross-validation strategies. Conclusions: A consistent result was obtained from the above two proposed methods wherein a significant difference between healthy controls and BD patients was identified in the left orbital region of the middle frontal gyrus.

Decreased miR-15b-5p/miR-155-5p levels and increased miR-134-5p/miR-652-3p levels among BD patients under lithium treatment

BackgroundThere is an increasing interest about the role of miRNAs in the pathogenesis of bipolar disorder (BD). In this study, we aimed to examine the role of miRNAs as potential diagnostic and clinical biomarkers in BD. MethodsFifteen miRNAs in plasmas obtained from BD patients (n = 66) and from the healthy control group (n = 66) were analyzed by a qPCR test. Clinical variables including lithium treatment response were assessed with various test batteries. The correlation of the miRNA levels with the clinical variables and scale scores was examined. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the DIANA-miRPath v.3.0 software to identify the possible target genes. ResultsThe miR-132, miR-134, miR-152, miR-607, miR-633, and miR-652 levels were significantly increased, whereas the miR-15b and miR-155 levels were found to be significantly decreased in the patient group compared to the controls. The miR-15b-5p and miR-155-5p levels and increases in the miR-134-5p and miR-652-3p levels were calculated to have 83.3 % sensitivity and 78.8 % specificity in determining the risk of BD. miR-155-5p was associated with the disease burden and severity. Fatty acid biosynthesis and metabolism, viral carcinogenesis, the EBV infection, and extracellular matrix and adhesion pathways were highlighted as target pathways. ConclusionWe can conclude that miRNAs may play a role in the pathophysiology of BD through various biological pathways and that miRNAs may be used as a screening test to distinguish bipolar patients from healthy controls. Our findings will provide a basis for long-term follow-up studies with larger samples.

Precision neuroimaging biomarkers for bipolar disorder

Bipolar disorder (BD) is a severe mental illness associated with alterations in brain organization. Neuroimaging studies have generated a large body of knowledge regarding brain morphological and functional abnormalities in BD. Current advances in the field have focussed on the need for more precise neuroimaging biomarkers. Here we present a selective overview of precision neuroimaging biomarkers for BD, focussing on personalized metrics and novel neuroimaging methods aiming to provide mechanistic insights into the brain alterations associated with BD. The evidence presented covers (a) machine learning techniques applied to neuroimaging data to differentiate patients with BD from healthy individuals or other clinical groups; (b) the ‘brain-age-gap-estimation (brainAGE), which is an individualized measure of brain health; (c) diffusional kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI) and Positron Emission Tomography (PET) techniques that open new opportunities to measure microstructural changes in neurite/synaptic integrity and function.