A framework for a brain-derived nosology of psychiatric disorders.

Current psychiatric diagnoses are not defined by neurobiological measures which hinders the development of therapies targeting mechanisms underlying mental illness 1,2 . Research confined to diagnostic boundaries yields heterogeneous biological results, whereas transdiagnostic studies often investigate individual symptoms in isolation. There is currently no paradigm available to comprehensively investigate the relationship between different clinical symptoms, individual disorders, and the underlying neurobiological mechanisms. Here, we propose a framework that groups clinical symptoms derived from ICD-10/DSM-V according to shared brain mechanisms defined by brain structure, function, and connectivity. The reassembly of existing ICD-10/DSM-5 symptoms reveal six cross-diagnostic psychopathology scores related to mania symptoms, depressive symptoms, anxiety symptoms, stress symptoms, eating pathology, and fear symptoms. They were consistently associated with multimodal neuroimaging components in the training sample of young adults aged 23, the independent test sample aged 23, participants aged 14 and 19 years, and in psychiatric patients. The identification of symptom groups of mental illness robustly defined by precisely characterized brain mechanisms enables the development of a psychiatric nosology based upon quantifiable neurobiological measures. As the identified symptom groups align well with existing diagnostic categories, our framework is directly applicable to clinical research and patient care.

Evaluation of behavioral variance/covariance explained by the neuroimaging data through a pattern-based regression.

Neuroimaging data have been widely used to understand the neural bases of human behaviors. However, most studies were either based on a few predefined regions of interest or only able to reveal limited vital regions, hence not providing an overarching description of the relationship between neuroimaging and behaviors. Here, we proposed a voxel-based pattern regression that not only could investigate the overall brain-associated variance (BAV) for a given behavioral measure but could also evaluate the shared neural bases between different behaviors across multiple neuroimaging data. The proposed method demonstrated consistently high reliability and accuracy through comprehensive simulations. We further implemented this approach on real data of adolescents (IMAGEN project, n = 2089) and adults (HCP project, n = 808) to investigate brain-based variances of multiple behavioral measures, for instance, cognitive behaviors, substance use, and psychiatric disorders. Notably, intelligence-related scores showed similar high BAVs with the gray matter volume across both datasets. Further, our approach allows us to reveal the latent brain-based correlation across multiple behavioral measures, which are challenging to obtain otherwise. For instance, we observed a shared brain architecture underlying depression and externalizing problems in adolescents, while the symptom comorbidity may only emerge later in adults. Overall, our approach will provide an important statistical tool for understanding human behaviors using neuroimaging data.

A neural signature for the subjective experience of threat anticipation under uncertainty.

Uncertainty about potential future threats and the associated anxious anticipation represents a key feature of anxiety. However, the neural systems that underlie the subjective experience of threat anticipation under uncertainty remain unclear. Combining an uncertainty-variation threat anticipation paradigm that allows precise modulation of the level of momentary anxious arousal during functional magnetic resonance imaging (fMRI) with multivariate predictive modeling, we train a brain model that accurately predicts subjective anxious arousal intensity during anticipation and test it across 9 samples (total n = 572, both gender). Using publicly available datasets, we demonstrate that the whole-brain signature specifically predicts anxious anticipation and is not sensitive in predicting pain, general anticipation or unspecific emotional and autonomic arousal. The signature is also functionally and spatially distinguishable from representations of subjective fear or negative affect. We develop a sensitive, generalizable, and specific neuroimaging marker for the subjective experience of uncertain threat anticipation that can facilitate model development.

Dopaminergic-related Anatomical Pattern of Dorsal Striatum in Schizophrenia.

Striatal dopaminergic overactivity was hypothesized as the core pathophysiology of schizophrenia. However, morphological alterations of striatum in schizophrenia remains exclusive, largely because brain regional heterogeneity limited traditional group-mean based approach. Leveraging third-party brain maps of neurotransmitter and cognition behaviours, we developed a pattern-based representation feature score (ReFS) to investigate structural spatial pattern variation in schizophrenia. Structural ReFS of subcortical regions, particularly the striatum, were linked to schizophrenia diagnosis, symptom severity, and genetic susceptibility. Dopaminergic-ReFS of striatum was increased in schizophrenia patients and reliably reproduced across 13 datasets. The pattern-based ReFS effectively captured the shared genetic pathways underlying both schizophrenia and striatum. The results provide convergent, multimodal suggest the central role of striatal spatial patterns in schizophrenia psychopathologies and and open new avenues to develop individualized treatments for psychotic disorders.

Fractionation of neural reward processing into independent components by novel decoding principle.

How to retrieve latent neurobehavioural processes from complex neurobiological signals is an important yet unresolved challenge. Here, we develop a novel approach, orthogonal-Decoding multi-Cognitive Processes (DeCoP), to reveal underlying latent neurobehavioural processing and show that its performance is superior to traditional non-orthogonal decoding in terms of both false inference and robustness. Processing value and salience information are two fundamental but mutually confounded pathways of reward reinforcement essential for decision making. During reward/punishment anticipation, we applied DeCoP to decode brain-wide responses into spatially overlapping, yet functionally independent, evaluation and readiness processes, which are modulated differentially by meso­limbic vs nigro-striatal dopamine systems. Using DeCoP, we further demonstrated that most brain regions only encoded abstract information but not the exact input, except for dorsal anterior cingulate cortex and insula. Furthermore, we anticipate our novel analytical principle to be applied generally in decoding multiple latent neurobehavioral processes and thus advance both the design and hypothesis testing for cognitive tasks.

Structural neurodevelopment at the individual level - a life-course investigation using ABCD, IMAGEN and UK Biobank data.

Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages and the neurobiological basis underlying individual heterogeneity remains poorly understood. Using structural magnetic resonance imaging from the IMAGEN cohort (n=1,543), we show that adolescents can be clustered into three groups defined by distinct developmental patterns of whole-brain gray matter volume (GMV). Genetic and epigenetic determinants of group clustering and long-term impacts of neurodevelopment in mid-to-late adulthood were investigated using data from the ABCD, IMAGEN and UK Biobank cohorts. Group 1, characterized by continuously decreasing GMV, showed generally the best neurocognitive performances during adolescence. Compared to Group 1, Group 2 exhibited a slower rate of GMV decrease and worsened neurocognitive development, which was associated with epigenetic changes and greater environmental burden. Further, Group 3 showed increasing GMV and delayed neurocognitive development during adolescence due to a genetic variation, while these disadvantages were attenuated in mid-to-late adulthood. In summary, our study revealed novel clusters of adolescent structural neurodevelopment and suggested that genetically-predicted delayed neurodevelopment has limited long-term effects on mental well-being and socio-economic outcomes later in life. Our results could inform future research on policy interventions aimed at reducing the financial and emotional burden of mental illness.

Decoding anxiety-impulsivity subtypes in preadolescent internalising disorders: findings from the Adolescent Brain Cognitive Development study.

BACKGROUND: Internalising disorders are highly prevalent emotional dysregulations during preadolescence but clinical decision-making is hampered by high heterogeneity. During this period impulsivity represents a major risk factor for psychopathological trajectories and may act on this heterogeneity given the controversial anxiety-impulsivity relationships. However, how impulsivity contributes to the heterogeneous symptomatology, neurobiology, neurocognition and clinical trajectories in preadolescent internalising disorders remains unclear. AIMS: The aim was to determine impulsivity-dependent subtypes in preadolescent internalising disorders that demonstrate distinct anxiety-impulsivity relationships, neurobiological, genetic, cognitive and clinical trajectory signatures. METHOD: We applied a data-driven strategy to determine impulsivity-related subtypes in 2430 preadolescents with internalising disorders from the Adolescent Brain Cognitive Development study. Cross-sectional and longitudinal analyses were employed to examine subtype-specific signatures of the anxiety-impulsivity relationship, brain morphology, cognition and clinical trajectory from age 10 to 12 years. RESULTS: We identified two distinct subtypes of patients who internalise with comparably high anxiety yet distinguishable levels of impulsivity, i.e. enhanced (subtype 1) or decreased (subtype 2) compared with control participants. The two subtypes exhibited opposing anxiety-impulsivity relationships: higher anxiety at baseline was associated with higher lack of perseverance in subtype 1 but lower sensation seeking in subtype 2 at baseline/follow-up. Subtype 1 demonstrated thicker prefrontal and temporal cortices, and genes enriched in immune-related diseases and glutamatergic and GABAergic neurons. Subtype 1 exhibited cognitive deficits and a detrimental trajectory characterised by increasing emotional/behavioural dysregulations and suicide risks during follow-up. CONCLUSIONS: Our results indicate impulsivity-dependent subtypes in preadolescent internalising disorders and unify past controversies about the anxiety-impulsivity interaction. Clinically, individuals with a high-impulsivity subtype exhibit a detrimental trajectory, thus early interventions are warranted.

Hepatobiliary surgery based on intelligent image segmentation technology.

Liver disease is an important disease that seriously threatens human health. It accounts for the highest proportion in various malignant tumors, and its incidence rate and mortality are on the rise, seriously affecting human health. Modern imaging has developed rapidly, but the application of image segmentation in liver tumor surgery is still rare. The application of image processing technology represented by artificial intelligence (AI) in surgery can greatly improve the efficiency of surgery, reduce surgical complications, and reduce the cost of surgery. Hepatocellular carcinoma is the most common malignant tumor in the world, and its mortality is second only to lung cancer. The resection rate of liver cancer surgery is high, and it is a multidisciplinary surgery, so it is necessary to explore the possibility of effective switching between different disciplines. Resection of hepatobiliary and pancreatic tumors is one of the most challenging and lethal surgical procedures. The operation requires a high level of doctors' experience and understanding of anatomical structures. The surgical segmentation is slow and there may be obvious complications. Therefore, the surgical system needs to make full use of the relevant functions of AI technology and computer vision analysis software, and combine the processing strategy based on image processing algorithm and computer vision analysis model. Intelligent optimization algorithm, also known as modern heuristic algorithm, is an algorithm with global optimization performance, strong universality, and suitable for parallel processing. This algorithm generally has a strict theoretical basis, rather than relying solely on expert experience. In theory, the optimal solution or approximate optimal solution can be found in a certain time. This work studies the hepatobiliary surgery through intelligent image segmentation technology, and analyzes them through intelligent optimization algorithm. The research results showed that when other conditions were the same, there were three patients who had adverse reactions in hepatobiliary surgery through intelligent image segmentation technology, accounting for 10%. The number of patients with adverse reactions in hepatobiliary surgery by conventional methods was nine, accounting for 30%, which was significantly higher than the former, indicating a positive relationship between intelligent image segmentation technology and hepatobiliary surgery.

Addressing Global Environmental Challenges to Mental Health Using Population Neuroscience: A Review.

Importance: Climate change, pollution, urbanization, socioeconomic inequality, and psychosocial effects of the COVID-19 pandemic have caused massive changes in environmental conditions that affect brain health during the life span, both on a population level as well as on the level of the individual. How these environmental factors influence the brain, behavior, and mental illness is not well known. Observations: A research strategy enabling population neuroscience to contribute to identify brain mechanisms underlying environment-related mental illness by leveraging innovative enrichment tools for data federation, geospatial observation, climate and pollution measures, digital health, and novel data integration techniques is described. This strategy can inform innovative treatments that target causal cognitive and molecular mechanisms of mental illness related to the environment. An example is presented of the environMENTAL Project that is leveraging federated cohort data of over 1.5 million European citizens and patients enriched with deep phenotyping data from large-scale behavioral neuroimaging cohorts to identify brain mechanisms related to environmental adversity underlying symptoms of depression, anxiety, stress, and substance misuse. Conclusions and Relevance: This research will lead to the development of objective biomarkers and evidence-based interventions that will significantly improve outcomes of environment-related mental illness.

Association between vmPFC gray matter volume and smoking initiation in adolescents.

Smoking of cigarettes among young adolescents is a pressing public health issue. However, the neural mechanisms underlying smoking initiation and sustenance during adolescence, especially the potential causal interactions between altered brain development and smoking behaviour, remain elusive. Here, using large longitudinal adolescence imaging genetic cohorts, we identify associations between left ventromedial prefrontal cortex (vmPFC) gray matter volume (GMV) and subsequent self-reported smoking initiation, and between right vmPFC GMV and the maintenance of smoking behaviour. Rule-breaking behaviour mediates the association between smaller left vmPFC GMV and smoking behaviour based on longitudinal cross-lagged analysis and Mendelian randomisation. In contrast, smoking behaviour associated longitudinal covariation of right vmPFC GMV and sensation seeking (especially hedonic experience) highlights a potential reward-based mechanism for sustaining addictive behaviour. Taken together, our findings reveal vmPFC GMV as a possible biomarker for the early stages of nicotine addiction, with implications for its prevention and treatment.

A shared neural basis underlying psychiatric comorbidity.

Recent studies proposed a general psychopathology factor underlying common comorbidities among psychiatric disorders. However, its neurobiological mechanisms and generalizability remain elusive. In this study, we used a large longitudinal neuroimaging cohort from adolescence to young adulthood (IMAGEN) to define a neuropsychopathological (NP) factor across externalizing and internalizing symptoms using multitask connectomes. We demonstrate that this NP factor might represent a unified, genetically determined, delayed development of the prefrontal cortex that further leads to poor executive function. We also show this NP factor to be reproducible in multiple developmental periods, from preadolescence to early adulthood, and generalizable to the resting-state connectome and clinical samples (the ADHD-200 Sample and the Stratify Project). In conclusion, we identify a reproducible and general neural basis underlying symptoms of multiple mental health disorders, bridging multidimensional evidence from behavioral, neuroimaging and genetic substrates. These findings may help to develop new therapeutic interventions for psychiatric comorbidities.

Global diversity in individualized cortical network topography.

Individualized cortical network topography (ICNT) varies between people and exhibits great variability in the association networks in the human brain. However, these findings were mainly discovered in Western populations. It remains unclear whether and how ICNT is shaped by the non-Western populations. Here, we leveraged a multisession hierarchical Bayesian model to define individualized functional networks in White American and Han Chinese populations with data from both US and Chinese Human Connectome Projects. We found that both the size and spatial topography of individualized functional networks differed between White American and Han Chinese groups, especially in the heteromodal association cortex (including the ventral attention, control, language, dorsal attention, and default mode networks). Employing a support vector machine, we then demonstrated that ethnicity-related ICNT diversity can be used to identify an individual's ethnicity with high accuracy (74%, pperm < 0.0001), with heteromodal networks contributing most to the classification. This finding was further validated through mass-univariate analyses with generalized additive models. Moreover, we reveal that the spatial heterogeneity of ethnic diversity in ICNT correlated with fundamental properties of cortical organization, including evolutionary cortical expansion, brain myelination, and cerebral blood flow. Altogether, this case study highlights a need for more globally diverse and publicly available neuroimaging datasets.

Intersecting distributed networks support convergent linguistic functioning across different languages in bilinguals.

How bilingual brains accomplish the processing of more than one language has been widely investigated by neuroimaging studies. The assimilation-accommodation hypothesis holds that both the same brain neural networks supporting the native language and additional new neural networks are utilized to implement second language processing. However, whether and how this hypothesis applies at the finer-grained levels of both brain anatomical organization and linguistic functions remains unknown. To address this issue, we scanned Chinese-English bilinguals during an implicit reading task involving Chinese words, English words and Chinese pinyin. We observed broad brain cortical regions wherein interdigitated distributed neural populations supported the same cognitive components of different languages. Although spatially separate, regions including the opercular and triangular parts of the inferior frontal gyrus, temporal pole, superior and middle temporal gyrus, precentral gyrus and supplementary motor areas were found to perform the same linguistic functions across languages, indicating regional-level functional assimilation supported by voxel-wise anatomical accommodation. Taken together, the findings not only verify the functional independence of neural representations of different languages, but show co-representation organization of both languages in most language regions, revealing linguistic-feature specific accommodation and assimilation between first and second languages.

Associations of DNA Methylation With Behavioral Problems, Gray Matter Volumes, and Negative Life Events Across Adolescence: Evidence From the Longitudinal IMAGEN Study.

BACKGROUND: Negative life events (NLEs) increase the risk for externalizing behaviors (EBs) and internalizing behaviors (IBs) in adolescence and adult psychopathology. DNA methylation associated with behavioral problems may reflect this risk and long-lasting effects of NLEs. METHODS: To identify consistent associations between blood DNA methylation and EBs or IBs across adolescence, we conducted longitudinal epigenome-wide association studies (EWASs) using data from the IMAGEN cohort, collected at ages 14 and 19 years (n = 506). Significant findings were validated in a separate subsample (n = 823). Methylation risk scores were generated by 10-fold cross-validation and further tested for their associations with gray matter volumes and NLEs. RESULTS: No significant findings were obtained for the IB-EWAS. The EB-EWAS identified a genome-wide significant locus in a gene linked to attention-deficit/hyperactivity disorder (ADHD) (IQSEC1, cg01460382; p = 1.26 × 10-8). Other most significant CpG sites were near ADHD-related genes and enriched for genes regulating tumor necrosis factor and interferon-γ signaling, highlighting the relevance of EB-EWAS findings for ADHD. Analyses with the EB methylation risk scores suggested that it partly reflected comorbidity with IBs in late adolescence. Specific to EBs, EB methylation risk scores correlated with smaller gray matter volumes in medial orbitofrontal and anterior/middle cingulate cortices, brain regions known to associate with ADHD and conduct problems. Longitudinal mediation analyses indicated that EB-related DNA methylation were more likely the outcomes of problematic behaviors accentuated by NLEs, and less likely the epigenetic bases of such behaviors. CONCLUSIONS: Our findings suggest that novel epigenetic mechanisms through which NLEs exert short and longer-term effects on behavior may contribute to ADHD.

Adult alcohol drinking and emotional tone are mediated by neutral sphingomyelinase during development in males.

Alcohol use, abuse, and addiction, and resulting health hazards are highly sex-dependent with unknown mechanisms. Previously, strong links between the SMPD3 gene and its coded protein neutral sphingomyelinase 2 (NSM) and alcohol abuse, emotional behavior, and bone defects were discovered and multiple mechanisms were identified for females. Here we report strong sex-dimorphisms for central, but not for peripheral mechanisms of NSM action in mouse models. Reduced NSM activity resulted in enhanced alcohol consumption in males, but delayed conditioned rewarding effects. It enhanced the acute dopamine response to alcohol, but decreased monoaminergic systems adaptations to chronic alcohol. Reduced NSM activity increased depression- and anxiety-like behavior, but was not involved in alcohol use for the self-management of the emotional state. Constitutively reduced NSM activity impaired structural development in the brain and enhanced lipidomic sensitivity to chronic alcohol. While the central effects were mostly opposite to NSM function in females, similar roles in bone-mediated osteocalcin release and its effects on alcohol drinking and emotional behavior were observed. These findings support the view that the NSM and multiple downstream mechanism may be a source of the sex-differences in alcohol use and emotional behavior.

Big Data Analysis and Application of Liver Cancer Gene Sequence Based on Second-Generation Sequencing Technology.

In big data analysis with the rapid improvement of computer storage capacity and the rapid development of complex algorithms, the exponential growth of massive data has also made science and technology progress with each passing day. Based on omics data such as mRNA data, microRNA data, or DNA methylation data, this study uses traditional clustering methods such as kmeans, K-nearest neighbors, hierarchical clustering, affinity propagation, and nonnegative matrix decomposition to classify samples into categories, obtained: (1) The assumption that the attributes are independent of each other reduces the classification effect of the algorithm to a certain extent. According to the idea of multilevel grid, there is a one-to-one mapping from high-dimensional space to one-dimensional. The complexity is greatly simplified by encoding the one-dimensional grid of the hierarchical grid. The logic of the algorithm is relatively simple, and it also has a very stable classification efficiency. (2) Convert the two-dimensional representation of the data into the one-dimensional representation of the binary, realize the dimensionality reduction processing of the data, and improve the organization and storage efficiency of the data. The grid coding expresses the spatial position of the data, maintains the original organization method of the data, and does not make the abstract expression of the data object. (3) The data processing of nondiscrete and missing values provides a new opportunity for the identification of protein targets of small molecule therapy and obtains a better classification effect. (4) The comparison of the three models shows that Naive Bayes is the optimal model. Each iteration is composed of alternately expected steps and maximal steps and then identified and quantified by MS.

Clinical and Molecular Analysis of ST11-K47 Carbapenem-Resistant Hypervirulent Klebsiella pneumoniae: A Strain Causing Liver Abscess.

Klebsiella pneumoniae has been the predominant pathogen of liver abscess, but ST11-K47 carbapenem-resistant hypervirulent Klebsiella pneumoniae (CR-hvKP) has rarely been studied as the causative organism. We identified an ST11-K47 CR-hvKP (HvKp-su1) from the drainage fluid of a liver abscess in a Chinese man who was diagnosed with liver abscess combined with diabetes, pneumonia, pleural infection, abdominal abscess, and splenic abscess. HvKp-su1 was non-hypermucoviscous and lacked the magA and rmpA genes and pLVPK plasmid but exhibited high virulence, with a high mortality rate (90%) to wax moth larvae (G. mellonella), similar to the hypervirulent Klebsiella pneumoniae ATCC43816 (91.67%). Whole-genome sequencing and bioinformatics analysis indicated that HvKp-su1 possesses a plasmid similar to a type of pLVPK-like plasmid (JX-CR-hvKP-2-P2), which is an uncommon plasmid in CR-hvKP. HvKp-su1 carried multiple resistance genes, including blaKPC-2. blaTEM-1, blaSHV-55, and blaCTX-M-65; hypervirulence genes such as aerobactin (iutA), salmochelin (iroEN), and yersiniabactin (ybtAEPQSTUX); and the type 3 fimbriae-encoding system (mrkACDF). Moreover, v_5377 and v_5429 (cofT, CFA/III (CS8)) located on plasmid 1 were simultaneously predicted to be virulence genes. After the long-term combination use of antibiotics, the patient successfully recovered. In summary, our study clarified the clinical and molecular characteristics of a rare ST11-K47 CR-hvKP (HvKp-su1), raising great concerns about the emergence of ST11-K47 CR-hvKP with multidrug resistance and hypervirulence, and providing insights into the control and treatment of liver abscess caused by ST11-K47 CR-hvKP.

A model-based approach to assess reproducibility for large-scale high-throughput MRI-based studies.

Magnetic Resonance Imaging (MRI) technology has been increasingly used in neuroscience studies. Reproducibility of statistically significant findings generated by MRI-based studies, especially association studies (phenotype vs. MRI metric) and task-induced brain activation, has been recently heavily debated. However, most currently available reproducibility measures depend on thresholds for the test statistics and cannot be use to evaluate overall study reproducibility. It is also crucial to elucidate the relationship between overall study reproducibility and sample size in an experimental design. In this study, we proposed a model-based reproducibility index to quantify reproducibility which could be used in large-scale high-throughput MRI-based studies including both association studies and task-induced brain activation. We performed the model-based reproducibility assessments for a few association studies and task-induced brain activation by using several recent large sMRI/fMRI databases. For large sample size association studies between brain structure/function features and some basic physiological phenotypes (i.e. Sex, BMI), we demonstrated that the model-based reproducibility of these studies is more than 0.99. For MID task activation, similar results could be observed. Furthermore, we proposed a model-based analytical tool to evaluate minimal sample size for the purpose of achieving a desirable model-based reproducibility. Additionally, we evaluated the model-based reproducibility of gray matter volume (GMV) changes for UK Biobank (UKB) vs. Parkinson Progression Marker Initiative (PPMI) and UK Biobank (UKB) vs. Human Connectome Project (HCP). We demonstrated that both sample size and study-specific experimental factors play important roles in the model-based reproducibility assessments for different experiments. In summary, a systematic assessment of reproducibility is fundamental and important in the current large-scale high-throughput MRI-based studies.