Diagnosis of Major Depressive Disorder Based on Individualized Brain Functional and Structural Connectivity.

BACKGROUND: Traditional neuroimaging studies have primarily emphasized analysis at the group level, often neglecting the specificity at the individual level. Recently, there has been a growing interest in individual differences in brain connectivity. Investigating individual-specific connectivity is important for understanding the mechanisms of major depressive disorder (MDD) and the variations among individuals. PURPOSE: To integrate individualized functional connectivity and structural connectivity with machine learning techniques to distinguish people with MDD and healthy controls (HCs). STUDY TYPE: Prospective. SUBJECTS: A total of 182 patients with MDD and 157 HCs and a verification cohort including 54 patients and 46 HCs. FIELD STRENGTH/SEQUENCE: 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and diffusion tensor imaging with single-shot spin echo. ASSESSMENT: Functional and structural brain networks from rs-fMRI and DTI data were constructed, respectively. Based on these networks, individualized functional connectivity (IFC) and individualized structural connectivity (ISC) were extracted using common orthogonal basis extraction (COBE). Subsequently, multimodal canonical correlation analysis combined with joint independent component analysis (mCCA + jICA) was conducted to fusion analysis to identify the joint and unique independent components (ICs) across multiple modes. These ICs were utilized to generate features, and a support vector machine (SVM) model was implemented for the classification of MDD. STATISTICAL TESTS: The differences in individualized connectivity between patients and controls were compared using two-sample t test, with a significance threshold set at P < 0.05. The established model was tested and evaluated using the receiver operating characteristic (ROC) curve. RESULTS: The classification performance of the constructed individualized connectivity feature model after multisequence fusion increased from 72.2% to 90.3%. Furthermore, the prediction model showed significant predictive power for assessing the severity of depression in patients with MDD (r = 0.544). DATA CONCLUSION: The integration of IFC and ISC through multisequence fusion enhances our capacity to identify MDD, highlighting the advantages of the individualized approach and underscoring its significance in MDD research. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Regional structural-functional connectivity coupling in major depressive disorder is associated with neurotransmitter and genetic profiles.

BACKGROUND: Abnormalities in structural-functional connectivity (SC-FC) coupling have been identified globally in patients with major depressive disorder (MDD). However, investigations have neglected the variability and hierarchical distribution of these abnormalities across different brain regions. Furthermore, the biological mechanisms underlying regional SC-FC coupling patterns are not well understood. METHODS: We enrolled 182 patients with MDD and 157 healthy control (HC) subjects, quantifying the intergroup differences in regional SC-FC coupling. The extreme gradient boosting (XGBoost), support vector machines (SVM) and random forest (RF) models were constructed to assess the potential of SC-FC coupling as biomarkers for MDD diagnosis and symptom prediction. Then, we examined the link between changes in regional SC-FC coupling in patients with MDD, neurotransmitter distributions, and gene expression. RESULTS: We observed increased regional SC-FC coupling in default mode network (T = 3.233) and decreased coupling in frontoparietal network (T = -3.471) in MDD relative to HC. XGBoost (AUC = 0.853), SVM (AUC = 0.832) and RF (p < 0.05) models exhibited good prediction performance. The alterations in regional SC-FC coupling in patients with MDD were correlated with the distributions of four neurotransmitters (p < 0.05) and expression maps of specific genes. These genes were strongly enriched in genes implicated in excitatory neurons, inhibitory neurons, cellular metabolism, synapse function, and immune signaling. These findings were replicated on two brain atlases. CONCLUSIONS: This work enhances our understanding of MDD and pave the way for the development of additional targeted therapeutic interventions.

Altered intersubject functional variability of brain white-matter in major depressive disorder and its association with gene expression profiles.

Major depressive disorder (MDD) is a clinically heterogeneous disorder. Its mechanism is still unknown. Although the altered intersubject variability in functional connectivity (IVFC) within gray-matter has been reported in MDD, the alterations to IVFC within white-matter (WM-IVFC) remain unknown. Based on the resting-state functional MRI data of discovery (145 MDD patients and 119 healthy controls [HCs]) and validation cohorts (54 MDD patients, and 78 HCs), we compared the WM-IVFC between the two groups. We further assessed the meta-analytic cognitive functions related to the alterations. The discriminant WM-IVFC values were used to classify MDD patients and predict clinical symptoms in patients. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging association analyses were further conducted to investigate gene expression profiles associated with WM-IVFC alterations in MDD, followed by a set of gene functional characteristic analyses. We found extensive WM-IVFC alterations in MDD compared to HCs, which were associated with multiple behavioral domains, including sensorimotor processes and higher-order functions. The discriminant WM-IVFC could not only effectively distinguish MDD patients from HCs with an area under curve ranging from 0.889 to 0.901 across three classifiers, but significantly predict depression severity (r = 0.575, p = 0.002) and suicide risk (r = 0.384, p = 0.040) in patients. Furthermore, the variability-related genes were enriched for synapse, neuronal system, and ion channel, and predominantly expressed in excitatory and inhibitory neurons. Our results obtained good reproducibility in the validation cohort. These findings revealed intersubject functional variability changes of brain WM in MDD and its linkage with gene expression profiles, providing potential implications for understanding the high clinical heterogeneity of MDD.

Hippocampal Subregions Volume and Texture for the Diagnosis of Mild Cognitive Impairment.

The aim was to examine the diagnostic efficacy of hippocampal subregions volume and texture in differentiating amnestic mild cognitive impairment (MCI) from normal aging changes. Ninety MCI subjects and eighty-eight well-matched healthy controls (HCs) were selected. Twelve hippocampal subregions volume and texture features were extracted using Freesurfer and MaZda based on T1 weighted MRI. Then, two-sample t-test and Least Absolute Shrinkage and Selection Operator (LASSO) regression were developed to select a subset of the original features. Support vector machine (SVM) was used to perform the classification task and the area under the curve (AUC), sensitivity, specificity and accuracy were calculated to evaluate the diagnostic efficacy of the model. The volume features with high discriminative power were mainly located in the bilateral CA1 and CA4, while texture feature were gray-level non-uniformity, run length non-uniformity and fraction. Our model based on hippocampal subregions volume and texture features achieved better classification performance with an AUC of 0.90. The volume and texture of hippocampal subregions can be utilized for the diagnosis of MCI. Moreover, we found that the features that contributed most to the model were mainly textural features, followed by volume. These results may guide future studies using structural scans to classify patients with MCI.

Diagnosis of Major Depressive Disorder Using Machine Learning Based on Multisequence MRI Neuroimaging Features.

BACKGROUND: Previous studies have found qualitative structural and functional brain changes in major depressive disorder (MDD) patients. However, most studies ignored the complementarity of multisequence MRI neuroimaging features and cannot determine accurate biomarkers. PURPOSE: To evaluate machine-learning models combined with multisequence MRI neuroimaging features to diagnose patients with MDD. STUDY TYPE: Prospective. SUBJECTS: A training cohort including 111 patients and 90 healthy controls (HCs) and a test cohort including 28 patients and 22 HCs. FIELD STRENGTH/SEQUENCE: A 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and single-shot echo-planar diffusion tensor imaging. ASSESSMENT: Recruitment and integration were used to reflect the dynamic changes of functional networks, while gray matter volume and fractional anisotropy were used to reflect the changes in the morphological and anatomical network. We then fused features with significant differences in functional, morphological, and anatomical networks to evaluate a random forest (RF) classifier to diagnose patients with MDD. Furthermore, a support vector machine (SVM) classifier was used to verify the stability of neuroimaging features. Linear regression analyses were conducted to investigate the relationships among multisequence neuroimaging features and the suicide risk of patients. STATISTICAL TESTS: The comparison of functional network attributes between patients and controls by two-sample t-test. Network-based statistical analysis was used to identify structural and anatomical connectivity changes between MDD and HCs. The performance of the model was evaluated by receiver operating characteristic (ROC) curves. RESULTS: The performance of the RF model integrating multisequence neuroimaging features in the diagnosis of depression was significantly improved, with an AUC of 93.6%. In addition, we found that multisequence neuroimaging features could accurately predict suicide risk in patients with MDD (r = 0.691). DATA CONCLUSION: The RF model fusing functional, morphological, and anatomical network features performed well in diagnosing patients with MDD and provided important insights into the pathological mechanisms of MDD. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 2.

Classification of Major Depressive Disorder Based on Integrated Temporal and Spatial Functional MRI Variability Features of Dynamic Brain Network.

BACKGROUND: Characterization of the dynamics of functional brain network has gained increased attention in the study of depression. However, most studies have focused on single temporal dimension, while ignoring spatial dimensional information, hampering the discovery of validated biomarkers for depression. PURPOSE: To integrate temporal and spatial functional MRI variability features of dynamic brain network in machine-learning techniques to distinguish patients with major depressive disorder (MDD) from healthy controls (HCs). STUDY TYPE: Prospective. POPULATION: A discovery cohort including 119 patients and 106 HCs and an external validation cohort including 126 patients and 124 HCs from Rest-meta-MDD consortium. FIELD STRENGTH/SEQUENCE: A 3.0 T/resting-state functional MRI using the gradient echo sequence. ASSESSMENT: A random forest (RF) model integrating temporal and spatial variability features of dynamic brain networks with separate feature selection method (MSFS ) was implemented for MDD classification. Its performance was compared with three RF models that used: temporal variability features (MTVF ), spatial variability features (MSVF ), and integrated temporal and spatial variability features with hybrid feature selection method (MHFS ). A linear regression model based on MSFS was further established to assess MDD symptom severity, with prediction performance evaluated by the correlations between true and predicted scores. STATISTICAL TESTS: Receiver operating characteristic analyses with the area under the curve (AUC) were used to evaluate models' performance. Pearson's correlation was used to assess relationship of predicted scores and true scores. P < 0.05 was considered statistically significant. RESULTS: The model with MSFS achieved the best performance, with AUCs of 0.946 and 0.834 in the discovery and validation cohort, respectively. Additionally, altered temporal and spatial variability could significantly predict the severity of depression (r = 0.640) and anxiety (r = 0.616) in MDD. DATA CONCLUSION: Integration of temporal and spatial variability features provides potential assistance for clinical diagnosis and symptom prediction of MDD. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 2.

Classification of major depression disorder via using minimum spanning tree of individual high-order morphological brain network.

BACKGROUND: Major depressive disorder (MDD) is an overbroad and heterogeneous diagnosis with no reliable or quantifiable markers. We aim to combine machine-learning techniques with the individual minimum spanning tree of the morphological brain network (MST-MBN) to determine whether the network properties can provide neuroimaging biomarkers to identify patients with MDD. METHOD: Eight morphometric features of each region of interest (ROI) were extracted from 3D T1 structural images of 106 patients with MDD and 97 healthy controls. Six feature distances of the eight morphometric features were calculated to generate a feature distance matrix, which was defined as low-order MBN. Further linear correlations of feature distances between ROIs were calculated on the basis of low-order MBN to generate individual high-order MBN. The Kruskal's algorithm was used to generate the MST to obtain the core framework of individual low-order and high-order MBN. The regional and global properties of the individual MSTs were defined as the feature. The support vector machine and back-propagation neural network was used to diagnose MDD and assess its severity, respectively. RESULT: The low-order and high-order MST-MBN constructed by cityblock distance had the excellent classification performance. The high-order MST-MBN significantly improved almost 20 % diagnostic accuracy compared with the low-order MST-MBN, and had a maximum R2 value of 0.939 between the predictive and true Hamilton Depression Scale score. The different group-level connectivity strength mainly involves the central executive network and default mode network (no statistical significance after FDR correction). CONCLUSION: We proposed an innovative individual high-order MST-MBN to capture the cortical high-order morphological correlation and make an excellent performance for individualized diagnosis and assessment of MDD.

Altered dynamic amplitude of low-frequency fluctuations in patients with postpartum depression.

BACKGROUND: Postpartum depression (PPD) is a common mood disorder with increasing incidence year by year. However, the dynamic changes in local neural activity of patients with PPD remain unclear. In this study, we utilized the dynamic amplitude of low-frequency fluctuation (dALFF) method to investigate the abnormal temporal variability of local neural activity and its potential correlation with clinical severity in PPD. METHODS: Twenty-four patients with PPD and nineteen healthy primiparous mothers controls (HCs) matched for age, education level and body mass index were examined by resting-state functional magnetic resonance imaging (rs-fMRI). A sliding-window method was used to assess the dALFF, and a k-means clustering method was used to identify dALFF states. Two-sample t-test was used to compare the differences of dALFF variability and state metrics between PPD and HCs. Pearson correlation analysis was used to analyze the relationship between dALFF variability, states metrics and clinical severity. RESULTS: (1) Patients with PPD had lower variance of dALFF than HCs in the cognitive control network, cerebellar network and sensorimotor network. (2) Four dALFF states were identified, and patients with PPD spent more time on state 2 than the other three states. The number of transitions between the four dALFF states increased in the patients compared with that in HCs. (3) Multiple dALFF states were found to be correlated with the severity of depression. The variance of dALFF in the right middle frontal gyrus was negatively correlated with the Edinburgh postnatal depression scale score. CONCLUSION: This study provides new insights into the brain dysfunction of PPD from the perspective of dynamic local brain activity, highlighting the important role of dALFF variability in understanding the neurophysiological mechanisms of PPD.

Pregnancy leads to changes in the brain functional network: a connectome analysis.

Pregnancy leads to long-lasting changes in human brain structure; however, little is known regarding alterations in the topological organization of functional networks. In this study, we investigated the effect of pregnancy on human brain function networks. Resting-state fMRI data was collected from eighteen primiparous mothers and twenty-four nulliparous control women of similar age, education level and body mass index (BMI). The functional brain network and topological properties were calculated by using GRETNA toolbox. The demographic data differences between two groups were computed by the independent two sample t-test. We tested group differences in network metrics' area under curve (AUC) using non-parametric permutation test of 1,000 permutations and corrected for false discovery rate (FDR). Differences in regional networks between groups were evaluated using non-parametric permutation tests by network-based statistical analysis (NBS). Compared with the nulliparous control women, a hub node changed from left inferior temporal gyrus to right precentral gyrus in primiparous mothers, while primiparous mothers showed enhanced network global efficiency (p = 0.247), enhanced local efficiency (p = 0.410), larger clustering coefficient (p = 0.410), but shorter characteristic path length (p = 0.247), smaller normalized clustering coefficient (p = 0.111), and shorter normalized characteristic path length (p = 0.705). Although both groups of functional networks have small-world property (σ > 1), the σ values of primiparous mothers were decreased significantly. NBS evaluation showed the majority of altered connected sub-network in the primiparous mothers occurred in the bilateral frontal lobe gyrus (p < 0.05). Altered functional network metrics and an abnormal sub-network were found in primiparous mothers, suggested that pregnancy may lead to changes in the brain functional network.

Predictors of posttraumatic growth of the family members of neurosurgical intensive care unit patients: A cross-sectional study.

OBJECTIVES: Confronted with the potentially traumatic experience of a patients intensive care unit hospitalisation, family members may show positive changes associated with growth in addition to negative impact. This study aimed to identify the level of posttraumatic growth of the family members of neurosurgical intensive care unit patients and to explore its relation to positive personality characteristics, such as gratitude, resilience and hope. DESIGN AND SETTING: A cross-sectional study involving 340 family members of patients admitted to the neurosurgical intensive care unit at a general tertiary hospital in Shanghai, China. METHODS: Before the patients' hospital discharge, the participants completed questionnaires, assessing posttraumatic growth (PTG Inventory), social support (Social Support Rating Scale), resilience (Chinese version of the Connor-Davidson Resilience Scale), hope (Herth Hope Index) and gratitude (Gratitude Questionnaire Six-Item Form). RESULTS: The mean total posttraumatic growth score was 73.38 (14.02). Hope, gratitude, resilience and social support showed a positive correlation with the posttraumatic growth Inventory scores. There were significant differences in the posttraumatic growth scores of the family members of neurosurgical intensive care patients with respect to their different religious beliefs, payment methods, family relationship quality and presence of chronic diseases among family members. Multiple linear regression analysis showed that gratitude, resilience and social support were independent predictors of the posttraumatic growth Inventory score. CONCLUSION: Family members may experience some degree of posttraumatic growth during hospitalisation of patients in the neurosurgical intensive care units. Gratitude, social support and resilience are predictive factors for posttraumatic growth.

Effect of ERAS Combined with Comfortable Nursing on Quality of Life and Complications in Femoral Neck Fractures of the Aged People.

OBJECTIVE: To explore the effect of enhanced recovery after surgery (ERAS) combined with comfortable nursing on the quality of life and complications of elderly patients with femoral neck fracture (FNF). METHODS: From May 2019 to May 2020, 80 senile FNF patients who admitted to our hospital were treated by total hip arthroplasty (THA). All patients were randomly divided upon admission into a control group (CG) with usual care and a study group (RG) with ERAS combined with comfort care of 40 patients each. The postoperative efficacy was assessed by Harris score of hip joint function, and the psychology was evaluated by self-rating anxiety scale (SAS). The SF-36 score of quality of life, the time of catheter removal, the time of getting out of bed, the hospital stays, the satisfaction of nursing, and the Barthel score of self-care ability were compared between the two groups before and after nursing, and the incidence of postoperative complications was also evaluated. RESULTS: Compared with the CG, the SF-36 score of quality of life and Barthel score of self-care ability in the RG were dramatically higher, while the SAS score of anxiety was dramatically lower. Besides, the time of catheter removal, the time of getting out of bed, and the hospital stays in the RG were dramatically lower (P < 0.05). Furthermore, the nursing satisfaction and postoperative efficacy of patients in the RG were obviously higher (both P < 0.05), while the incidence of complications in the RG was obviously lower (P < 0.05). CONCLUSION: ERAS combined with comfortable nursing can improve the hip joint function, quality of life, and self-care ability scores of senile FNF patients; relieve the anxiety in patients; and reduce the incidence of postoperative complications, which is valuable to be applied extensively.

Function of a Psychological Nursing Intervention on Depression, Anxiety, and Quality of Life in Older Adult Patients With Osteoporotic Fracture.

BACKGROUND: Osteoporotic fracture is a serious complication of osteoporosis. The long-term therapy process and the heavy restriction to physical activities give rise to a psychological burden on osteoporotic fracture patients, especially older adult patients. Psychological nursing interventions significantly alleviate negative emotional reactions in cancer patients. This research aimed to investigate the function of psychological nursing interventions in the reduction of depression and anxiety and the improvement of quality of life in older adult patients with osteoporotic fracture. METHODS: Osteoporotic fracture patients (n = 106) were divided into control group (n = 53) or intervention group (n = 53). In the control group, the participants were given conventional nursing care. In the intervention group, the participants were given psychological nursing interventions. Anxiety, depression, and quality of life were evaluated and compared between the two groups. RESULTS: After 5 weeks of psychological nursing intervention, the anxiety and depression scores significantly decreased in the intervention group. The Mental Function in Quality of Life Questionnaire of the European Foundation for Osteoporosis score also decreased in the intervention group. LINKING EVIDENCE TO ACTION: Psychological nursing interventions alleviate anxiety and depression in older adult osteoporotic fracture patients and enhance their mental function.

Altered gray matter structural covariance networks in postpartum depression: a graph theoretical analysis.

BACKGROUND: Postpartum depression (PPD) is a serious postpartum mental health problem worldwide. To date, minimal is known about the alteration of topographical organization in the brain structural covariance network of patients with PPD. This study investigates the brain structural covariance networks of patients with PPD by using graph theoretical analysis. METHODS: High-resolution 3D T1 structural images were acquired from 21 drug-naive patients with PPD and 18 healthy postpartum women. Cortical thickness was extracted from 64 brain regions to construct the whole-brain structural covariance networks by calculating the Pearson correlation coefficients, and their topological properties (e.g., small-worldness, efficiency, and nodal centrality) were analyzed by using graph theory. Nonparametric permutation tests were further used for group comparisons of topological metrics. A node was set as a hub if its betweenness centrality (BC) was at least two standard deviations higher than the mean nodal centrality. Network-based statistic (NBS) was used to determine the connected subnetwork. RESULTS: The PPD and control groups showed small-worldness of group networks, but the small-world network was more evidently in the PPD group. Moreover, the PPD group showed increased network local efficiency and almost similar network global efficiency. However, the difference of the network metrics was not significant across the range of network densities. The hub nodes of the patients with PPD were right inferior parietal lobule (BC = 13.69) and right supramarginal gyrus (BC = 13.15), whereas those for the HCs were left cuneus (BC = 14.96), right caudal anterior-cingulate cortex (BC = 15.51), and right precuneus gyrus (BC = 15.74). NBS demonstrated two disrupted subnetworks that are present in PPD: the first subnetwork with decreased internodal connections is mainly involved in the cognitive-control network and visual network, and the second subnetwork with increased internodal connections is mainly involved in the default mode network, cognitive-control network and visual network. CONCLUSIONS: This study demonstrates the alteration of topographical organization in the brain structural covariance network of patients with PPD, providing in sight on the notion that PPD could be characterized as a systems-level disorder.

Abnormalities of cortical structures in patients with postpartum depression: A surface-based morphometry study.

BACKGROUND: Postpartum depression (PPD) is a serious postpartum mental health problem worldwide. However, the cortical structural alterations in patients with PPD remain unclear. This study investigated the cortical structural alterations of PPD patients through multidimensional structural patterns and their potential correlations with clinical severity. METHODS: High-resolution 3D T1 structural images were acquired from 21 drug-naive patients with PPD and 18 healthy postpartum women matched for age, educational level, and body mass index. The severity of PPD was assessed by using the Hamilton Depression Scale (HAMD) and Edinburgh Postnatal Depression Scale (EPDS) scores. Cortical morphological parameters including cortical thickness, surface area, and mean curvature were calculated using the surface-based morphometric (SBM) method. General linear model (GLM) analyses were performed to evaluate the relationship of cortical morphological parameters with clinical scales. RESULTS: In the present study, PPD patients showed a thinner cortical thickness in the right inferior parietal lobule compared with the healthy controls. Increased surface area was observed in the left superior frontal gyrus, caudal middle frontal gyrus, middle temporal gyrus, insula, and right supramarginal cortex in PPD patients. Likewise, PPD patients exhibited a higher mean curvature in the left superior and right inferior parietal lobule. Furthermore, increased cortical surface area in the left insula had a positive correlation with EPDS scores, and higher mean curvature in the left superior parietal lobule was negatively correlated with EPDS scores. LIMITATIONS: First, SBM cannot reflect the changes of subcortical structures that are considered to play a role in the development of PPD. Second, the sample size of this study is small. These positive results should be interpreted with caution. Third, this cross-sectional study does not involve a comparison of structural MRI before and after pregnancy. CONCLUSIONS: The complex cortical structural alterations of patients with PPD mainly involved the prefrontal and parietal regions. The morphometric alterations in these specific regions may provide promising markers for assessing the severity of PPD.

Halomonas bachuensis sp. nov., Isolated from Gobi Soil.

A novel aerobic bacterium designated DX6T was isolated from a Gobi soil sample collected in Bachu County, China. Cells are Gram-stain-negative and rod-shaped and colonies are creamy, circular and smooth. The growth range of NaCl concentration was 1-15% (optimum 2-10%, w/v). Growth occurs at 10-45 °C (optimum 37 °C) and pH 5.0-10.0 (optimum pH 7.0-9.0). Phylogenetic analysis of the 16S rRNA gene sequences indicated that strain DX6T formed a distinct lineage in the clade of genus Halomonas and is related to Halomonas desiderata DSM 9502T (98.3%), Halomonas kenyensis AIR-2T (97.7%), Halomonas daqingensis DQD2-30T (97.6%), Halomonas saliphila LCB169T (97.4%) and Halomonas endophytica MC28T (96.2%). Analysis of the housekeeping genes gryB and rpoD and calculation of the average nucleotide identities and the digital DNA-DNA hybridization values between strain DX6T and the related type Halomonas strains further revealed that strain DX6T represented a distinct species. The main respiratory quinones of strain DX6T were ubiquinone 9 (Q-9) and ubiquinone 8 (Q-8). The predominant cellular fatty acids were summed feature 8 (C18:1ω7c and/or C18:1ω6c), summed feature 3 (C16:1ω7c and/or C16:1ω6c) and C16:0. The major polar lipids consisted of diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, two unidentified phospholipids, an unidentified phosphatidylglycolipid, and four unidentified lipids. Based on the phenotypic, phylogenetic, chemotaxonomic and genomic features, strain DX6T represents a novel species of the genus Halomonas. The name Halomonas bachuensis sp. nov. is proposed with strain DX6T (= CCTCC AB 2020094T = KCTC 82196T) designated as the type strain.