ABSTRACT
Objective: Current neuronal imaging methods mostly use bulky lenses that either impede animal behavior or prohibit multi-depth imaging. To overcome these limitations, we developed a lightweight lensless biophotonic system for neuronal imaging, enabling compact and simultaneous visualization of multiple brain layers.Approach: Our developed 'CIS-NAIST' device integrates a micro-CMOS image sensor, thin-film fluorescence filter, micro-LEDs, and a needle-shaped flexible printed circuit. With this device, we monitored neuronal calcium dynamics during seizures across the different layers of the hippocampus and employed machine learning techniques for seizure classification and prediction.Main results: The CIS-NAIST device revealed distinct calcium activity patterns across the CA1, molecular interlayer, and dentate gyrus. Our findings indicated an elevated calcium amplitude activity specifically in the dentate gyrus compared to other layers. Then, leveraging the multi-layer data obtained from the device, we successfully classified seizure calcium activity and predicted seizure behavior using Long Short-Term Memory and Hidden Markov models.Significance: Taken together, our 'CIS-NAIST' device offers an effective and minimally invasive method of seizure monitoring that can help elucidate the mechanisms of temporal lobe epilepsy.
Subject(s)
Calcium , Hippocampus , Seizures , Animals , Hippocampus/metabolism , Seizures/metabolism , Seizures/physiopathology , Calcium/metabolism , Male , Needles , Rats , SemiconductorsABSTRACT
Protein phosphorylation, a key regulator of cellular processes, plays a central role in brain function and is implicated in neurological disorders. Information on protein phosphorylation is expected to be a clue for understanding various neuropsychiatric disorders and developing therapeutic strategies. Nonetheless, existing databases lack a specific focus on phosphorylation events in the brain, which are crucial for investigating the downstream pathway regulated by neurotransmitters. To overcome the gap, we have developed a web-based database named "Kinase-Associated Neural PHOspho-Signaling (KANPHOS)." This paper presents the design concept, detailed features, and a series of improvements for KANPHOS. KANPHOS is designed to support data-driven research by fulfilling three key objectives: (1) enabling the search for protein kinases and their substrates related to extracellular signals or diseases; (2) facilitating a consolidated search for information encompassing phosphorylated substrate genes, proteins, mutant mice, diseases, and more; and (3) offering integrated functionalities to support pathway and network analysis. KANPHOS is also equipped with API functionality to interact with external databases and analysis tools, enhancing its utility in data-driven investigations. Those key features represent a critical step toward unraveling the complex landscape of protein phosphorylation in the brain, with implications for elucidating the molecular mechanisms underlying neurological disorders. KANPHOS is freely accessible to all researchers at https://kanphos.jp.
ABSTRACT
Acute myocarditis (AM) is an inflammatory disease of the heart muscle that can progress to fulminant myocarditis (FM), a severe and life-threatening condition. The cytokine profile of myocarditis in children, especially in relation to fulminant myocarditis, is not well understood. This study aims to evaluate the cytokine profiles of acute and fulminant myocarditis in children. Pediatric patients diagnosed with myocarditis were included in the study. Cytokine levels were measured using a multiplexed fluorescent bead-based immunoassay. Statistical analysis was performed to compare patient characteristics and cytokine levels between FM, AM, and healthy control (HC) groups. Principal component analysis (PCA) was applied to cytokine groups that were independent among the FM, AM, and HC groups. The study included 22 patients with FM and 14 with AM patients. We identified four cytokines that were significantly higher in the FM group compared to the AM group: IL1-RA (p = 0.002), IL-8 (p = 0.005), IL-10 (p = 0.011), and IL-15 (p = 0.005). IL-4 was significantly higher in the AM group compared to FM and HC groups (p = 0.006 and 0.0015). PDGF-AA, and VEGF-A were significantly lower in the FM group than in the AM group (p = 0.013 and <0.001). Similar results were obtained in PCA. Cytokine profiles might be used to differentiate pediatric FM from AM, stratify severity, and predict prognosis. The targeted therapy that works individual cytokines might provide a potential treatment for reducing the onset of the FM and calming the condition, and further studies are needed.
ABSTRACT
The unraveling of the regulatory mechanisms that govern neuronal excitability is a major challenge for neuroscientists worldwide. Neurotransmitters play a critical role in maintaining the balance between excitatory and inhibitory activity in the brain. The balance controls cognitive functions and emotional responses. Glutamate and γ-aminobutyric acid (GABA) are the primary excitatory and inhibitory neurotransmitters of the brain, respectively. Disruptions in the balance between excitatory and inhibitory transmission are implicated in several psychiatric disorders, including anxiety disorders, depression, and schizophrenia. Neuromodulators such as dopamine and acetylcholine control cognition and emotion by regulating the excitatory/inhibitory balance initiated by glutamate and GABA. Dopamine is closely associated with reward-related behaviors, while acetylcholine plays a role in aversive and attentional behaviors. Although the physiological roles of neuromodulators have been extensively studied neuroanatomically and electrophysiologically, few researchers have explored the interplay between neuronal excitability and cell signaling and the resulting impact on emotion regulation. This review provides an in-depth understanding of "cell signaling crosstalk" in the context of neuronal excitability and emotion regulation. It also anticipates that the next generation of neurochemical analyses, facilitated by integrated phosphorylation studies, will shed more light on this topic.
ABSTRACT
BACKGROUND AND PURPOSE: Multiple system atrophy (MSA) is a neurodegenerative disease with characteristic motor and autonomic symptoms. Impaired brain serotonergic innervation can be associated with various clinical indices of MSA; however, the relationship between clinical symptoms and cerebrospinal fluid (CSF) levels of 5-hydroxyindole acetic acid (5-HIAA), a main serotonin metabolite, has not been fully elucidated. METHODS: To compare CSF 5-HIAA levels between patients with MSA and healthy controls, we included 33 controls and 69 MSA patients with either predominant parkinsonian or cerebellar ataxia subtypes. CSF 5-HIAA levels were measured using high-performance liquid chromatography. Additionally, we investigated correlations between CSF 5-HIAA and various clinical indices in 34 MSA patients. RESULTS: CSF 5-HIAA levels were significantly lower in MSA patients than in controls (p < 0.0001). Probable MSA patients had lower CSF 5-HIAA levels than possible MSA patients (p < 0.001). In MSA patients, CSF 5-HIAA levels were inversely correlated with scores in Parts 1, 2, and 4 of the Unified Multiple System Atrophy Rating Scale, and with systolic and diastolic blood pressure in Part 3. Structural equation modeling revealed significant paths between serotonin and clinical symptoms, and significance was highest for activities of daily living, walking, and body sway. CONCLUSIONS: Serotonin dysfunction, as assessed by CSF 5-HIAA levels, may implicate greater MSA severity.
Subject(s)
Cerebellar Ataxia , Multiple System Atrophy , Humans , Serotonin , Hydroxyindoleacetic Acid/cerebrospinal fluid , Activities of Daily LivingABSTRACT
During reward-based learning tasks, animals make orofacial movements that globally influence brain activity at the timings of reward expectation and acquisition. These orofacial movements are not explicitly instructed and typically appear along with goal-directed behaviors. Here, we show that reinforcing optogenetic stimulation of dopamine neurons in the ventral tegmental area (oDAS) in mice is sufficient to induce orofacial movements in the whiskers and nose without accompanying goal-directed behaviors. Pavlovian conditioning with a sensory cue and oDAS elicited cue-locked and oDAS-aligned orofacial movements, which were distinguishable by a machine-learning model. Inhibition or knockout of dopamine D1 receptors in the nucleus accumbens inhibited oDAS-induced motion but spared cue-locked motion, suggesting differential regulation of these two types of orofacial motions. In contrast, inactivation of the whisker primary motor cortex (wM1) abolished both types of orofacial movements. We found specific neuronal populations in wM1 representing either oDAS-aligned or cue-locked whisker movements. Notably, optogenetic stimulation of wM1 neurons successfully replicated these two types of movements. Our results thus suggest that accumbal D1-receptor-dependent and -independent neuronal signals converge in the wM1 for facilitating distinct uninstructed orofacial movements during a reward-based learning task.
Subject(s)
Nucleus Accumbens , Ventral Tegmental Area , Mice , Animals , Nucleus Accumbens/physiology , Ventral Tegmental Area/physiology , Movement , Dopaminergic Neurons/physiology , Receptors, Dopamine D1 , RewardABSTRACT
Background: Phenotyping analysis that includes time course is useful for understanding the mechanisms and clinical management of postoperative delirium. However, postoperative delirium has not been fully phenotyped. Hypothesis-free categorization of heterogeneous symptoms may be useful for understanding the mechanisms underlying delirium, although evidence is currently lacking. Therefore, we aimed to explore the phenotypes of postoperative delirium following invasive cancer surgery using a data-driven approach with minimal prior knowledge. Methods: We recruited patients who underwent elective invasive cancer resection. After surgery, participants completed 5 consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale. We categorized 65 (13 questionnaire items/day × 5 days) dimensional DRS-R-98 scores using unsupervised machine learning (K-means clustering) to derive a small set of grouped features representing distinct symptoms across all participants. We then reapplied K-means clustering to this set of grouped features to delineate multiple clusters of delirium symptoms. Results: Participants were 286 patients, of whom 91 developed delirium defined according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Following the first K-means clustering, we derived four grouped symptom features: (1) mixed motor, (2) cognitive and higher-order thinking domain with perceptual disturbance and thought content abnormalities, (3) acute and temporal response, and (4) sleep-wake cycle disturbance. Subsequent K-means clustering permitted classification of participants into seven subgroups: (i) cognitive and higher-order thinking domain dominant delirium, (ii) prolonged delirium, (iii) acute and brief delirium, (iv) subsyndromal delirium-enriched, (v) subsyndromal delirium-enriched with insomnia, (vi) insomnia, and (vii) fit. Conclusion: We found that patients who have undergone invasive cancer resection can be delineated using unsupervised machine learning into three delirium clusters, two subsyndromal delirium clusters, and an insomnia cluster. Validation of clusters and research into the pathophysiology underlying each cluster will help to elucidate the mechanisms of postoperative delirium after invasive cancer surgery.
ABSTRACT
OBJECTIVE: Recent studies have revealed an association between Parkinson's disease (PD) and Fabry disease, a lysosomal storage disorder; however, the underlying mechanisms remain to be elucidated. This study aimed to investigate the enzymatic properties of serum alpha-galactosidase A (GLA) and compared them with the clinical parameters of PD. METHODS: The study participants consisted of 66 sporadic PD patients and 52 controls. We measured serum GLA activity and calculated the apparent Michaelis constant (Km ) and maximal velocity (Vmax ) by Lineweaver-Burk plot analysis. Serum GLA protein concentration was measured by enzyme-linked immunosorbent assay. We examined the potential correlations between serum GLA activity and GLA protein concentration and clinical features and the plasma neurofilament light chain (NfL) level. RESULTS: Compared to controls, PD patients showed significantly lower serum GLA activity (P < 0.0001) and apparent Vmax (P = 0.0131), but no change in the apparent Km value. Serum GLA protein concentration was lower in the PD group (P = 0.0168) and was positively associated with GLA activity. Serum GLA activity and GLA protein concentration in the PD group showed a negative correlation with age. Additionally, serum GLA activity was negatively correlated with the motor severity score and the level of plasma NfL, and was positively correlated with the score of frontal assessment battery. INTERPRETATION: This study highlights that the lower serum GLA activity in PD is the result of a quantitative decrement of GLA protein in the serum and that it may serve as a biomarker of disease severity.
Subject(s)
Fabry Disease , Parkinson Disease , Humans , alpha-Galactosidase/metabolism , Biomarkers , Patient AcuityABSTRACT
The recent success of sequential learning models, such as deep recurrent neural networks, is largely due to their superior representation-learning capability for learning the informative representation of a targeted time series. The learning of these representations is generally goal-directed, resulting in their task-specific nature, giving rise to excellent performance in completing a single downstream task but hindering between-task generalisation. Meanwhile, with increasingly intricate sequential learning models, learned representation becomes abstract to human knowledge and comprehension. Hence, we propose a unified local predictive model based on the multi-task learning paradigm to learn the task-agnostic and interpretable subsequence-based time series representation, allowing versatile use of learned representations in temporal prediction, smoothing, and classification tasks. The targeted interpretable representation could convey the spectral information of the modelled time series to the level of human comprehension. Through a proof-of-concept evaluation study, we demonstrate the empirical superiority of learned task-agnostic and interpretable representation over task-specific and conventional subsequence-based representation, such as symbolic and recurrent learning-based representation, in solving temporal prediction, smoothing, and classification tasks. These learned task-agnostic representations can also reveal the ground-truth periodicity of the modelled time series. We further propose two applications of our unified local predictive model in functional magnetic resonance imaging (fMRI) analysis to reveal the spectral characterisation of cortical areas at rest and reconstruct more smoothed temporal dynamics of cortical activations in both resting-state and task-evoked fMRI data, giving rise to robust decoding.
Subject(s)
Brain Mapping , Brain , Humans , Brain/diagnostic imaging , Brain/physiology , Brain Mapping/methods , Magnetic Resonance Imaging/methods , Time Factors , LearningABSTRACT
AIM: Recently, a machine-learning (ML) technique has been used to create generalizable classifiers for psychiatric disorders based on information of functional connections (FCs) between brain regions at resting state. These classifiers predict diagnostic labels by a weighted linear sum (WLS) of the correlation values of a small number of selected FCs. We aimed to develop a generalizable classifier for gambling disorder (GD) from the information of FCs using the ML technique and examine relationships between WLS and clinical data. METHODS: As a training dataset for ML, data from 71 GD patients and 90 healthy controls (HCs) were obtained from two magnetic resonance imaging sites. We used an ML algorithm consisting of a cascade of an L1-regularized sparse canonical correlation analysis and a sparse logistic regression to create the classifier. The generalizability of the classifier was verified using an external dataset. This external dataset consisted of six GD patients and 14 HCs, and was collected at a different site from the sites of the training dataset. Correlations between WLS and South Oaks Gambling Screen (SOGS) and duration of illness were examined. RESULTS: The classifier distinguished between the GD patients and HCs with high accuracy in leave-one-out cross-validation (area under curve (AUC = 0.89)). This performance was confirmed in the external dataset (AUC = 0.81). There was no correlation between WLS, and SOGS and duration of illness in the GD patients. CONCLUSION: We developed a generalizable classifier for GD based on information of functional connections between brain regions at resting state.
Subject(s)
Gambling , Algorithms , Brain/diagnostic imaging , Gambling/diagnostic imaging , Humans , Machine Learning , Magnetic Resonance Imaging/methodsABSTRACT
Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated-whether as a subtype of depression, or as a distinct disorder altogethe-interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics.
ABSTRACT
Managing depression relapse is a challenge given factors such as inconsistent follow-up and cumbersome psychological distress evaluation methods which leaves patients with a high risk of relapse to leave their symptoms untreated. In an attempt to bridge this gap, we proposed an approach on the use of personal longitudinal lifelog activity data gathered from individual smartphones of patients in remission and maintenance therapy (N=87) to predict their risk of depression relapse. Through the use of survival models, we modeled the activity data as covariates to predict survival curves to determine if patients are at risk of relapse. We compared three models: CoxPH, Random Survival Forests, and DeepSurv, and found that DeepSurv performed the best in terms of Concordance Index and Brier Score. Our results show the possibility of utilizing lifelog data as a means of predicting the onset of relapse and towards building eventual tools for a more coherent patient evaluation and intervention system.
Subject(s)
Depression , Chronic Disease , Depression/diagnosis , Humans , RecurrenceABSTRACT
Neurofeedback (NF) aptitude, which refers to an individual's ability to change brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical applications to screen patients suitable for NF treatment. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude, independent of NF-targeting brain regions. We combined the data from fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the multiple regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Subsequently, the reproducibility of the prediction model was validated using independent test data from another site. The identified FC model revealed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting that NF aptitude may be involved in the attentional mode-orientation modulation system's characteristics in task-free resting-state brain activity.
Subject(s)
Depressive Disorder, Major/therapy , Gyrus Cinguli/diagnostic imaging , Gyrus Cinguli/physiology , Magnetic Resonance Imaging , Neurofeedback , Parietal Lobe/diagnostic imaging , Parietal Lobe/physiology , Adult , Connectome , Datasets as Topic , Female , Healthy Volunteers , Humans , Male , Middle Aged , Predictive Value of Tests , RestABSTRACT
Recently, the dimensional approach has attracted much attention, bringing a paradigm shift to a continuum of understanding of different psychiatric disorders. In line with this new paradigm, we examined whether there was common functional connectivity related to various psychiatric disorders in an unsupervised manner without explicitly using diagnostic label information. To this end, we uniquely applied a newly developed network-based multiple clustering method to resting-state functional connectivity data, which allowed us to identify pairs of relevant brain subnetworks and subject cluster solutions accordingly. Thus, we identified four subject clusters, which were characterized as major depressive disorder (MDD), young healthy control (young HC), schizophrenia (SCZ)/bipolar disorder (BD), and autism spectrum disorder (ASD), respectively, with the relevant brain subnetwork represented by the cerebellum-thalamus-pallidum-temporal circuit. The clustering results were validated using independent datasets. This study is the first cross-disorder analysis in the framework of unsupervised learning of functional connectivity based on a data-driven brain subnetwork.
ABSTRACT
Scintillators emit visible luminescence when irradiated with X-rays. Given the unlimited tissue penetration of X-rays, the employment of scintillators could enable remote optogenetic control of neural functions at any depth of the brain. Here we show that a yellow-emitting inorganic scintillator, Ce-doped Gd3(Al,Ga)5O12 (Ce:GAGG), can effectively activate red-shifted excitatory and inhibitory opsins, ChRmine and GtACR1, respectively. Using injectable Ce:GAGG microparticles, we successfully activated and inhibited midbrain dopamine neurons in freely moving mice by X-ray irradiation, producing bidirectional modulation of place preference behavior. Ce:GAGG microparticles are non-cytotoxic and biocompatible, allowing for chronic implantation. Pulsed X-ray irradiation at a clinical dose level is sufficient to elicit behavioral changes without reducing the number of radiosensitive cells in the brain and bone marrow. Thus, scintillator-mediated optogenetics enables minimally invasive, wireless control of cellular functions at any tissue depth in living animals, expanding X-ray applications to functional studies of biology and medicine.
Subject(s)
Brain/physiology , Animals , Behavior, Animal/physiology , Behavior, Animal/radiation effects , Brain/radiation effects , Cerium , Female , HEK293 Cells , Humans , Luminescence , Male , Mice , Mice, Inbred C57BL , Opsins/metabolism , Opsins/radiation effects , Optogenetics/instrumentation , Scintillation Counting , Wireless Technology/instrumentation , X-RaysABSTRACT
In neuroscience, the functional magnetic resonance imaging (fMRI) is a vital tool to non-invasively access brain activity. Using fMRI, the functional connectivity (FC) between brain regions can be inferred, which has contributed to a number of findings of the fundamental properties of the brain. As an important clinical application of FC, clustering of subjects based on FC recently draws much attention, which can potentially reveal important heterogeneity in subjects such as subtypes of psychiatric disorders. In particular, a multiple clustering method is a powerful analytical tool, which identifies clustering patterns of subjects depending on their FC in specific brain areas. However, when one applies an existing multiple clustering method to fMRI data, there is a need to simplify the data structure, independently dealing with elements in a FC matrix, i.e., vectorizing a correlation matrix. Such a simplification may distort the clustering results. To overcome this problem, we propose a novel multiple clustering method based on Wishart mixture models, which preserves the correlation matrix structure without vectorization. The uniqueness of this method is that the multiple clustering of subjects is based on particular networks of nodes (or regions of interest, ROIs), optimized in a data-driven manner. Hence, it can identify multiple underlying pairs of associations between a subject cluster solution and a ROI sub-network. The key assumption of the method is independence among sub-networks, which is effectively addressed by whitening correlation matrices. We applied the proposed method to synthetic and fMRI data, demonstrating the usefulness and power of the proposed method.
Subject(s)
Brain Mapping , Brain , Magnetic Resonance Imaging , Attention , Brain/diagnostic imaging , Cluster Analysis , HumansABSTRACT
Psychiatric and neurological disorders are afflictions of the brain that can affect individuals throughout their lifespan. Many brain magnetic resonance imaging (MRI) studies have been conducted; however, imaging-based biomarkers are not yet well established for diagnostic and therapeutic use. This article describes an outline of the planned study, the Brain/MINDS Beyond human brain MRI project (BMB-HBM, FY2018 ~ FY2023), which aims to establish clinically-relevant imaging biomarkers with multi-site harmonization by collecting data from healthy traveling subjects (TS) at 13 research sites. Collection of data in psychiatric and neurological disorders across the lifespan is also scheduled at 13 sites, whereas designing measurement procedures, developing and analyzing neuroimaging protocols, and databasing are done at three research sites. A high-quality scanning protocol, Harmonization Protocol (HARP), was established for five high-quality 3 T scanners to obtain multimodal brain images including T1 and T2-weighted, resting-state and task functional and diffusion-weighted MRI. Data are preprocessed and analyzed using approaches developed by the Human Connectome Project. Preliminary results in 30 TS demonstrated cortical thickness, myelin, functional connectivity measures are comparable across 5 scanners, suggesting sensitivity to subject-specific connectome. A total of 75 TS and more than two thousand patients with various psychiatric and neurological disorders are scheduled to participate in the project, allowing a mixed model statistical harmonization. The HARP protocols are publicly available online, and all the imaging, demographic and clinical information, harmonizing database will also be made available by 2024. To the best of our knowledge, this is the first project to implement a prospective, multi-level harmonization protocol with multi-site TS data. It explores intractable brain disorders across the lifespan and may help to identify the disease-specific pathophysiology and imaging biomarkers for clinical practice.
Subject(s)
Brain Diseases , Connectome , Brain/diagnostic imaging , Humans , Longevity , Magnetic Resonance Imaging , Prospective StudiesABSTRACT
Protein phosphorylation plays critical roles in a variety of intracellular signaling pathways and physiological functions that are controlled by neurotransmitters and neuromodulators in the brain. Dysregulation of these signaling pathways has been implicated in neurodevelopmental disorders, including autism spectrum disorder, attention deficit hyperactivity disorder and schizophrenia. While recent advances in mass spectrometry-based proteomics have allowed us to identify approximately 280,000 phosphorylation sites, it remains largely unknown which sites are phosphorylated by which kinases. To overcome this issue, previously, we developed methods for comprehensive screening of the target substrates of given kinases, such as PKA and Rho-kinase, upon stimulation by extracellular signals and identified many candidate substrates for specific kinases and their phosphorylation sites. Here, we developed a novel online database to provide information about the phosphorylation signals identified by our methods, as well as those previously reported in the literature. The "KANPHOS" (Kinase-Associated Neural Phospho-Signaling) database and its web portal were built based on a next-generation XooNIps neuroinformatics tool. To explore the functionality of the KANPHOS database, we obtained phosphoproteomics data for adenosine-A2A-receptor signaling and its downstream MAPK-mediated signaling in the striatum/nucleus accumbens, registered them in KANPHOS, and analyzed the related pathways.
Subject(s)
Brain/metabolism , Databases, Protein , Neurons/metabolism , Protein Kinases/metabolism , Animals , Calcium Channels/metabolism , MAP Kinase Signaling System , Male , Mice, Inbred C57BL , Phosphoproteins/metabolism , Phosphorylation , Receptor, Adenosine A2A/metabolism , Substrate SpecificityABSTRACT
BACKGROUND: The relationship between depression and personality has long been suggested, however, biomarker investigations for depression have mostly overlooked this connection. METHODS: We collected personality traits from 100 drug-free patients with major depressive disorders (MDD) and 100 healthy controls based on the Five-Factor Model (FFM) such as Neuroticism (N) and Extraversion (E), and also obtained 63 plasma metabolites profiles by LCMS-based metabolome analysis. RESULTS: Partitional clustering analysis using the NEO-FFI data classified all subjects into three major clusters. Eighty-six subjects belonging to Cluster 1 (C1: less personality-biased group) constituted half of MDD patients and half of healthy controls. C2 constituted 50 subjects mainly MDD patients (N high + E low), and C3 constituted 64 subjects mainly healthy subjects (N low + E high). Using metabolome information, the machine learning model was optimized to discriminate MDD patients from healthy controls among all subjects and C1, respectively. The performance of the model for all subjects was moderate (AUC = 0. 715), while the performance was extremely improved when limited to C1 (AUC = 0. 907). Tryptophan-pathway plasma metabolites including tryptophan, serotonin and kynurenine were significantly lower in MDD patients especially among C1. We also validated metabolomic findings using a social-defeat mice model of stress-induced depression. LIMITATIONS: A case-control study design and sample size is not large. CONCLUSIONS: Our results suggest that personality classification enhances blood biomarker analysis for MDD patients and further translational investigations should be conducted to clarify the biological relationship between personality traits, stress and depression.