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Alcohol use disorder (AUD) is a worldwide problem and the most common substance use disorder. Chronic alcohol consumption may have negative effects on the body, the mind, the family, and even society. With the progress of current neuroimaging methods, an increasing number of imaging techniques are being used to objectively detect brain impairment induced by alcoholism and serve a vital role in the diagnosis, prognosis, and treatment assessment of AUD. This article organizes and analyzes the research on alcohol dependence concerning the main noninvasive neuroimaging methods, structural magnetic resonance imaging, functional magnetic resonance imaging, and electroencephalography, as well as the most common noninvasive brain stimulation – transcranial magnetic stimulation, and intersperses the article with joint intra- and intergroup studies, providing an outlook on future research directions.

Recently, functional homotopy (FH) architecture, defined as robust functional connectivity (FC) between homotopic regions, has been frequently reported to be altered in MDD patients (MDDs) but with divergent locations.In this study, we obtained resting-state functional magnetic resonance imaging (R-fMRI) data from 1004 MDDs (mean age, 33.88 years; age range, 18-60 years) and 898 matched healthy controls (HCs) from an aggregated dataset from 20 centers in China. We focused on interhemispheric function integration in MDDs and its correlation with clinical characteristics using voxel-mirrored homotopic connectivity (VMHC) devised to inquire about FH patterns.As compared with HCs, MDDs showed decreased VMHC in visual, motor, somatosensory, limbic, angular gyrus, and cerebellum, particularly in posterior cingulate gyrus/precuneus (PCC/PCu) (false discovery rate [FDR] q 0.002, z = -7.07). Further analysis observed that the reduction in SMG and insula was more prominent with age, of which SMG reflected such age-related change in males instead of females. Besides, the reduction in MTG was found to be a male-special abnormal pattern in MDDs. VMHC alterations were markedly related to episode type and illness severity. The higher Hamilton Depression Rating Scale score, the more apparent VMHC reduction in the primary visual cortex. First-episode MDDs revealed stronger VMHC reduction in PCu relative to recurrent MDDs.We confirmed a significant VMHC reduction in MDDs in broad areas, especially in PCC/PCu. This reduction was affected by gender, age, episode type, and illness severity. These findings suggest that the depressive brain tends to disconnect information exchange across hemispheres.

Background The subgenual anterior cingulate cortex (sgACC) appears to play a central role in the pathophysiology of major depressive disorder (MDD). To wit, its functional interactive profile with the left dorsal lateral prefrontal cortex (DLPFC) has been shown to be related to treatment outcomes with transcranial magnetic stimulation (TMS) treatment outcomes. Nevertheless, previous research on sgACC functional connectivity (FC) in MDD has yielded inconsistent results, partly due to small sample sizes and limited statistical power of prior work.Methods Here, leveraging a large multi-site sample (1660 MDD patients vs. 1341 healthy controls) from Phase II of the Depression Imaging REsearch ConsorTium (DIRECT), we systematically delineated case-control difference maps of sgACC FC and examined their clinical relevance to previously identified TMS targets. We also investigated case-control FC difference maps of left DLPFC sub-fields.Results In MDD patients we found significantly increased FC between sgACC and thalamus and reduced FC to a broad array of brain regions, including somatosensory area, occipital lobe, medial and lateral temporal lobe, and insular cortex, when global signal regression (GSR) was not implemented. Intriguingly, we found enhanced left DLPFC-sgACC FC in MDD patients when GSR was performed. We leveraged an prior independent sample to explore the possible relationship between the case-control differences regarding sgACC’s FC profiles and the treatment out comes of TMS. In sites in which open TMS treatment was administered, case-control differences in sgACC FC, with GSR, were related to clinical improvement. Next we tested whether the position of peak of the FC maps (previously identified TMS target) could be altered in MDD patients as compred with healthy controls (HC)s. We found the optimized TMS target differed in MDD patients. Several DLPFC sub-fields yielded case-control differences in whole-brain FC maps.Conclusion In summary, we reliably delineated MDD-related abnormalities of sgACC FC profiles in a large sample. GSR was essential in applying case-control difference maps to identify optimized TMS targets. Our results highlight the functional heterogeneity of the left DLPFC and of precise TMS targets therein.

Atretium yunnanensis was originally recognized as a variant of Atretium schistosum. It was subsequently elevated to full species status and has long been assigned to the natricine genus Atretium. However, the taxonomic status of A. yunnanensis still remained unevaluated by a polyphasic taxonomic approach. In the present study, the generic assignment of A. yunnanensis was re-evaluated based on both morphological and genetic evidences from newly collected specimens (including topotypes). The genus Atretium was recovered as polyphyletic with reference to A. yunnanensis, whereas A. yunnanensis was nested within the genus Fowlea Theobald, 1868. Moreover, Atretium yunnanensis is morphologically closer to the members of Fowlea rather than the genus Atretium. Since the continuous recognition of A. yunnanensis in the genus Atretium will preclude the monophyly of genus Atretium, we transfer A. yunnanensis to the genus Fowlea. In addition, we provided detailed descriptions of Fowlea yunnanensis, including hemipenial description, colored photos, distribution map, and natural history.

To explore the relationship between cognitive function and blood–brain barrier leakage in non-brain metastasis lung cancer and healthy controls. 75 lung cancers without brain metastasis and 29 healthy controls matched with age, sex, and education were evaluated by cognitive assessment, and the Patlak pharmacokinetic model was used to calculate the average leakage in each brain region according to the automated anatomical labeling atlas. After that, the relationships between cognitive and blood–brain barrier leakage were evaluated. Compared with healthy controls, the leakage of bilateral temporal gyrus and whole brain gyrus were higher in patients with lung cancers (P P P > 0.05). The cognitive impairment of advanced lung cancers was mainly reflected in the damage of visuospatial/executive, and delayed recall. The left temporal gyrus with increased blood–brain barrier leakage showed negative correlations with delayed recall (r = -0.201, P = 0.042). An increase in blood–brain barrier leakage was found in non-brain metastases advanced lung cancers that corresponded to decreased delayed recall. With progression in lung cancer staging, blood–brain barrier shows higher leakage and may lead to brain metastases and lower cognitive development.

Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.

Background : Functional specialization is a feature of human brain for understanding the pathophysiology of major depressive disorder (MDD). The degree of human specialization refers to within and cross hemispheric interactions. However, most previous studies only focused on interhemispheric connectivity in MDD, and the results varied across studies. Hence, brain functional connectivity asymmetry in MDD should be further studied. Methods : Resting-state fMRI data of 753 patients with MDD and 451 healthy controls were provided by REST-meta-MDD Project. Twenty-five project contributors preprocessed their data locally with the Data Processing Assistant State fMRI software and shared final indices. The parameter of asymmetry (PAS), a novel voxel-based whole-brain quantitative measure that reflects inter- and intrahemispheric asymmetry, was reported. We also examined the effects of age, sex and clinical variables (including symptom severity, illness duration and three depressive phenotypes). Results : Compared with healthy controls, patients with MDD showed increased PAS scores (decreased hemispheric specialization) in most of the areas of default mode network, control network, attention network and some regions in the cerebellum and visual cortex. Demographic characteristics and clinical variables have significant effects on these abnormalities. Limitations : Although a large sample size could improve statistical power, future independent efforts are needed to confirm our results. Conclusions : Our results highlight the idea that many brain networks contribute to broad clinical pathophysiology of MDD, and indicate that a lateralized, efficient and economical brain information processing system is disrupted in MDD. These findings may help comprehensively clarify the pathophysiology of MDD in a new hemispheric specialization perspective.

Significance Functional connectivity within the default mode network in patients with major depressive disorder has been frequently reported to be abnormal but with contradicting directions in previous studies with small sample sizes. In creating the REST-meta-MDD consortium containing neuroimaging data of 1,300 depressed patients and 1,128 normal controls from 25 research groups in China, we found decreased default mode network functional connectivity in depressed patients, driven by patients with recurrent depression, and associated with current medication treatment but not with disease duration. These findings suggest that default mode network functional connectivity remains a prime target for understanding the pathophysiology of depression, with particular relevance to revealing mechanisms of effective treatments., Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

In three-dimensional measurement of large-scale parts, such as car bodies and aircraft wings, massive points (up to hundreds of millions) are collected. If all point cloud data is processed, a large amount of computing resources and storage space will be consumed. It is an important task to reduce the burden of processing while maintaining the feature of the point cloud as much as possible. This paper proposes a novel point cloud simplification method using local conditional information that is utilized to evaluate the features of each point in point clouds.The proposed method can reduce the size of point clouds while reserving significant features. Firstly, the original information of each point is evaluated by calculating the curvature of the original point cloud. Secondly, the point with the maximal information is selected and reserved in the target point cloud that will be updated as the alternative point cloud after simplification. The conditional information of each rest point is evaluated by the neighborhood and updated to the information of the current point, which attenuates with the distance from the other reserved points. Finally, the point with the maximal information is reserved iteratively until the number of points reserved satisfies the requirements of simplification according to enterprise needs. Experiments verify the effectiveness and performance of the proposed method. The experimental results indicate that the proposed method performs much better on feature retention than Voxel simplification and Curvature simplification.

Highlights • Two subtypes with distinct default mode network profiles exist in major depression. • Subtypes of major depression are robust in validation datasets across brain atlases. • Hyper- & hypo-connectivity DMN subgroups have comparable clinical symptom variables. • Future studies should examine whether two subtypes have differing treatment response., Background Major depressive disorder (MDD) is heterogeneous disorder associated with aberrant functional connectivity within the default mode network (DMN). This study focused on data-driven identification and validation of potential DMN-pattern-based MDD subtypes to parse heterogeneity of the disorder. Methods The sample comprised 1397 participants including 690 patients with MDD and 707 healthy controls (HC) registered from multiple sites based on the REST-meta-MDD Project in China. Baseline resting-state functional magnetic resonance imaging (rs-fMRI) data was recorded for each participant. Discriminative features were selected from DMN between patients and HC. Patient subgroups were defined by K-means and principle component analysis in the multi-site datasets and validated in an independent single-site dataset. Statistical significance of resultant clustering were confirmed. Demographic and clinical variables were compared between identified patient subgroups. Results Two MDD subgroups with differing functional connectivity profiles of DMN were identified in the multi-site datasets, and relatively stable in different validation samples. The predominant dysfunctional connectivity profiles were detected among superior frontal cortex, ventral medial prefrontal cortex, posterior cingulate cortex and precuneus, whereas one subgroup exhibited increases of connectivity (hyperDMN MDD) and another subgroup showed decreases of connectivity (hypoDMN MDD). The hyperDMN subgroup in the discovery dataset had age-related severity of depressive symptoms. Patient subgroups had comparable demographic and clinical symptom variables. Conclusions Findings suggest the existence of two neural subtypes of MDD associated with different dysfunctional DMN connectivity patterns, which may provide useful evidence for parsing heterogeneity of depression and be valuable to inform the search for personalized treatment strategies.

While gastrointestinal (GI) symptoms are very common in patients with major depressive disorder (MDD), few studies have investigated the neural basis behind these symptoms. In this study, we sought to elucidate the neural basis of GI symptoms in MDD patients by analyzing the changes in regional gray matter volume (GMV) and gray matter density (GMD) in brain structure.Subjects were recruited from 13 clinical centers and categorized into three groups, each of which is based on the presence or absence of GI symptoms: the GI symptoms group (MDD patients with at least one GI symptom), the non-GI symptoms group (MDD patients without any GI symptoms), and the healthy control group (HCs). Structural magnetic resonance images (MRI) were collected of 335 patients in the GI symptoms group, 149 patients in the non-GI symptoms group, and 446 patients in the healthy control group. The 17-item Hamilton Depression Rating Scale (HAMD-17) was administered to all patients. Correlation analysis and logistic regression analysis were used to determine if there was a correlation between the altered brain regions and the clinical symptoms.There were significantly higher HAMD-17 scores in the GI symptoms group than that of the non-GI symptoms group (P 0.001). Both GMV and GMD were significant different among the three groups for the bilateral superior temporal gyrus, bilateral middle temporal gyrus, left lingual gyrus, bilateral caudate nucleus, right Fusiform gyrus and bilateral Thalamus (GRF correction, cluster-P 0.01, voxel-P 0.001). Compared to the HC group, the GI symptoms group demonstrated increased GMV and GMD in the bilateral superior temporal gyrus, and the non-GI symptoms group demonstrated an increased GMV and GMD in the right superior temporal gyrus, right fusiform gyrus and decreased GMV in the right Caudate nucleus (GRF correction, cluster-P 0.01, voxel-P 0.001). Compared to the non-GI symptoms group, the GI symptoms group demonstrated significantly increased GMV and GMD in the bilateral thalamus, as well as decreased GMV in the bilateral superior temporal gyrus and bilateral insula lobe (GRF correction, cluster-P 0.01, voxel-P 0.001). While these changed brain areas had significantly association with GI symptoms (P 0.001), they were not correlated with depressive symptoms (P 0.05). Risk factors for gastrointestinal symptoms in MDD patients (p 0.05) included age, increased GMD in the right thalamus, and decreased GMV in the bilateral superior temporal gyrus and left Insula lobe.MDD patients with GI symptoms have more severe depressive symptoms. MDD patients with GI symptoms exhibited larger GMV and GMD in the bilateral thalamus, and smaller GMV in the bilateral superior temporal gyrus and bilateral insula lobe that were correlated with GI symptoms, and some of them and age may contribute to the presence of GI symptoms in MDD patients.

The pattern of decreased prefronto-thalamic connectivity and increased sensorimotor-thalamic connectivity has been consistently documented in schizophrenia. However, whether this thalamo-cortical abnormality pattern is of genetic predisposition remains unknown. The present study for the first time aimed to investigate the common and distinct characteristics of this circuit in schizophrenia patients and their unaffected siblings who share half of the patient's genotype. Totally 293 participants were recruited into this study including 94 patients with schizophrenia, 96 their healthy siblings, and 103 healthy controls scanned using gradient-echo echo-planar imaging at rest. By using a fine-grained atlas of thalamus with 16 sub-regions, we mapped the thalamocortical network in three groups. Decreased thalamo-prefronto-cerebellar connectivity was shared between schizophrenia and their healthy siblings, but increased sensorimotor-thalamic connectivity was only found in schizophrenia. The shared thalamo-prefronto-cerebellar dysconnectivity showed an impressively gradient reduction pattern in patients and siblings comparing to controls: higher in the controls, lower in the patients and intermediate in the siblings. Anatomically, the decreased thalamic connectivity mostly centered on the pre-frontal thalamic subregions locating at the mediodorsal nucleus, while the increased functional connectivity with sensorimotor cortices was only observed in the caudal temporal thalamic subregion anchoring at the dorsal and ventral lateral nuclei. Moreover, both decreased thalamo-prefronto-cerebellar connectivity and increased sensorimotor-thalamic connectivity were related to clinical symptoms in patients. Our findings extend the evidence that the decreased thalamo-prefronto-cerebellar connectivity may be related to the high genetic risk in schizophrenia, while increased sensorimotor-thalamic connectivity potentially represents a neural biomarker for this severe mental disorder.

Background: Major depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach. Methods: Resting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels. Results: The group of MDD patients showed significantly higher temporal variability, lower temporal correlation coefficient (indicating decreased temporal clustering) and shorter characteristic temporal path length (indicating increased temporal efficiency) compared with healthy controls (corrected p < 3.14×10−3). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naïve (FEDN) and non-FEDN patients. Conclusions: Our findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD.

The etiology of autism is so complicated because it involves the effects of variants of several hundred risk genes along with the contribution of environmental factors. Therefore, it has been challenging to identify the causal paths that lead to the core autistic symptoms such as social deficit, repetitive behaviors, and behavioral inflexibility. As an alternative approach, extensive efforts have been devoted to identifying the convergence of the targets and functions of the autism-risk genes to facilitate mapping out causal paths. In this study, we used a reversal-learning task to measure behavioral flexibility in Drosophila and determined the effects of loss-of-function mutations in multiple autism-risk gene homologs in flies. Mutations of five autism-risk genes with diversified molecular functions all led to a similar phenotype of behavioral inflexibility indicated by impaired reversal-learning. These reversal-learning defects resulted from the inability to forget or rather, specifically, to activate Rac1 (Ras-related C3 botulinum toxin substrate 1)-dependent forgetting. Thus, behavior-evoked activation of Rac1-dependent forgetting has a converging function for autism-risk genes.