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Zhi D, Jiang R, Pearlson G, Fu Z, Qi S, Yan W, Feng A, Xu M, Calhoun V, Sui J. Triple Interactions Between the Environment, Brain, and Behavior in Children: An ABCD Study. Biol Psychiatry 2024; 95:828-838. [PMID: 38151182 PMCID: PMC11006588 DOI: 10.1016/j.biopsych.2023.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND Environmental exposures play a crucial role in shaping children's behavioral development. However, the mechanisms by which these exposures interact with brain functional connectivity and influence behavior remain unexplored. METHODS We investigated the comprehensive environment-brain-behavior triple interactions through rigorous association, prediction, and mediation analyses, while adjusting for multiple confounders. Particularly, we examined the predictive power of brain functional network connectivity (FNC) and 41 environmental exposures for 23 behaviors related to cognitive ability and mental health in 7655 children selected from the Adolescent Brain Cognitive Development (ABCD) Study at both baseline and follow-up. RESULTS FNC demonstrated more predictability for cognitive abilities than for mental health, with cross-validation from the UK Biobank study (N = 20,852), highlighting the importance of thalamus and hippocampus in longitudinal prediction, while FNC+environment demonstrated more predictive power than FNC in both cross-sectional and longitudinal prediction of all behaviors, especially for mental health (r = 0.32-0.63). We found that family and neighborhood exposures were common critical environmental influencers on cognitive ability and mental health, which can be mediated by FNC significantly. Healthy perinatal development was a unique protective factor for higher cognitive ability, whereas sleep problems, family conflicts, and adverse school environments specifically increased risk of poor mental health. CONCLUSIONS This work revealed comprehensive environment-brain-behavior triple interactions based on the ABCD Study, identified cognitive control and default mode networks as the most predictive functional networks for a wide repertoire of behaviors, and underscored the long-lasting impact of critical environmental exposures on childhood development, in which sleep problems were the most prominent factors affecting mental health.
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Affiliation(s)
- Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Godfrey Pearlson
- Department of Psychiatry and Neurobiology, Yale School of Medicine, New Haven, Connecticut
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Emory University, and Georgia State University, Atlanta, Georgia
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Weizheng Yan
- National Institute on Alcohol Abuse and Alcoholism, Lab of Neuroimaging, National Institutes of Health, Bethesda, Maryland
| | - Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Emory University, and Georgia State University, Atlanta, Georgia.
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Emory University, and Georgia State University, Atlanta, Georgia.
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Yan W, Fu Z, Jiang R, Sui J, Calhoun VD. Maximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream Tasks. IEEE Trans Biomed Eng 2024; 71:1170-1178. [PMID: 38060365 PMCID: PMC11005005 DOI: 10.1109/tbme.2023.3330087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
OBJECTIVE Multi-site collaboration is essential for overcoming small-sample problems when exploring reproducible biomarkers in MRI studies. However, various scanner-specific factors dramatically reduce the cross-scanner replicability. Moreover, existing harmony methods mostly could not guarantee the improved performance of downstream tasks. METHODS we proposed a new multi-scanner harmony framework, called 'maximum classifier discrepancy generative adversarial network', or MCD-GAN, for removing scanner effects in the original feature space while preserving substantial biological information for downstream tasks. Specifically, the adversarial generative network was utilized for persisting the structural layout of each sample, and the maximum classifier discrepancy module was introduced for regulating GAN generators by incorporating the downstream tasks. RESULTS We compared the MCD-GAN with other state-of-the-art data harmony approaches (e.g., ComBat, CycleGAN) on simulated data and the Adolescent Brain Cognitive Development (ABCD) dataset. Results demonstrate that MCD-GAN outperformed other approaches in improving cross-scanner classification performance while preserving the anatomical layout of the original images. SIGNIFICANCE To the best of our knowledge, the proposed MCD-GAN is the first generative model which incorporates downstream tasks while harmonizing, and is a promising solution for facilitating cross-site reproducibility in various tasks such as classification and regression.
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Rosenblatt M, Tejavibulya L, Jiang R, Noble S, Scheinost D. Data leakage inflates prediction performance in connectome-based machine learning models. Nat Commun 2024; 15:1829. [PMID: 38418819 PMCID: PMC10901797 DOI: 10.1038/s41467-024-46150-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
Predictive modeling is a central technique in neuroimaging to identify brain-behavior relationships and test their generalizability to unseen data. However, data leakage undermines the validity of predictive models by breaching the separation between training and test data. Leakage is always an incorrect practice but still pervasive in machine learning. Understanding its effects on neuroimaging predictive models can inform how leakage affects existing literature. Here, we investigate the effects of five forms of leakage-involving feature selection, covariate correction, and dependence between subjects-on functional and structural connectome-based machine learning models across four datasets and three phenotypes. Leakage via feature selection and repeated subjects drastically inflates prediction performance, whereas other forms of leakage have minor effects. Furthermore, small datasets exacerbate the effects of leakage. Overall, our results illustrate the variable effects of leakage and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.
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Affiliation(s)
- Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, USA
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Liu T, Zhao Z, Liu M, Wang X, Jiang R. Response to: COVID-19 infection experience regarded as new traumatic stressors worsen mental health status of ICU patients' family members. QJM 2024; 117:89-90. [PMID: 37651592 DOI: 10.1093/qjmed/hcad202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Indexed: 09/02/2023] Open
Affiliation(s)
- T Liu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Key Laboratory of Post Neuro-injury Neuro-repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Z Zhao
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Key Laboratory of Post Neuro-injury Neuro-repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - M Liu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Key Laboratory of Post Neuro-injury Neuro-repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - X Wang
- Department of Neurology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - R Jiang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Key Laboratory of Post Neuro-injury Neuro-repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
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Ji Y, Pearlson G, Bustillo J, Kochunov P, Turner JA, Jiang R, Shao W, Zhang X, Fu Z, Li K, Liu Z, Xu X, Zhang D, Qi S, Calhoun VD. Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering. Schizophr Res 2024; 264:130-139. [PMID: 38128344 DOI: 10.1016/j.schres.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 07/19/2023] [Accepted: 12/10/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Similarities among schizophrenia (SZ), schizoaffective disorder (SAD) and bipolar disorder (BP) including clinical phenotypes, brain alterations and risk genes, make it challenging to perform reliable separation among them. However, previous subtype identification that transcend traditional diagnostic boundaries were based on group-level neuroimaging features, ignoring individual-level inferences. METHODS 455 psychoses (178 SZs, 134 SADs and 143 BPs), their first-degree relatives (N = 453) and healthy controls (HCs, N = 220) were collected from Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP I) consortium. Individualized covariance structural differential networks (ICSDNs) were constructed for each patient and multi-site clustering was used to identify psychosis subtypes. Group differences between subtypes in clinical phenotypes and voxel-wise fractional amplitude of low frequency fluctuation (fALFF) were calculated, as well as between the corresponding relatives. RESULTS Two psychosis subtypes were identified with increased whole brain structural covariance, with decreased connectivity between amygdala-hippocampus and temporal-occipital cortex in subtype I (S-I) compared to subtype II (S-II), which was replicated under different clustering methods, number of edges and across datasets (B-SNIP II) and different brain atlases. S-I had higher emotional-related symptoms than S-II and showed significant fALFF decrease in temporal and occipital cortex, while S-II was more similar to HC. This pattern was consistently validated on relatives of S-I and S-II in both fALFF and clinical symptoms. CONCLUSIONS These findings reconcile categorical and dimensional perspectives of psychosis neurobiological heterogeneity, indicating that relatives of S-I might have greater predisposition in developing psychosis, while relatives of S-II are more likely to be healthy. This study contributes to the development of neuroimaging informed diagnostic classifications within psychosis spectrum.
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Affiliation(s)
- Yixin Ji
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China
| | - Godfrey Pearlson
- Departments of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Juan Bustillo
- Departments of Neurosciences and Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Rongtao Jiang
- Departments of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Wei Shao
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China
| | - Xiao Zhang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhaowen Liu
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China.
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China.
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; Department of Electrical and Computer Engineering, Georgia Tech University, Atlanta, GA, USA
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Adkinson BD, Rosenblatt M, Dadashkarimi J, Tejavibulya L, Jiang R, Noble S, Scheinost D. Brain-phenotype predictions can survive across diverse real-world data. bioRxiv 2024:2024.01.23.576916. [PMID: 38328100 PMCID: PMC10849571 DOI: 10.1101/2024.01.23.576916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.
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Affiliation(s)
- Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Javid Dadashkarimi
- Department of Radiology, Athinoula. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02129, USA
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Bioengineering, Northeastern University, Boston, MA, 02120, USA
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06510, USA
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Sun C, Jiang R, Zhang XY, Zha L, Liu DY, Jin EZ. Role of low-density lipoprotein in mediating the effect of air pollution on coronary heart disease: a two-step multivariate Mendelian randomization study. Eur Rev Med Pharmacol Sci 2024; 28:525-533. [PMID: 38305598 DOI: 10.26355/eurrev_202401_35050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
OBJECTIVE Air pollution is affecting the health of millions of people all over the world. The causal correlations of PM2.5, PM10, and nitrogen dioxide (NOx), as the main fine particulate matter, and coronary heart disease (CHD) are yet to be explored. Low-density lipoprotein (LDL) has been a principal factor in the pathogenesis of CHD. It is an interesting issue to consider whether LDL mediates the effect of air pollutants in CHD pathogenesis. MATERIALS AND METHODS A genome-wide association study (GWAS) on the European population, followed up from 2010 to 2018, involving over 400,000 participants, was based on a land-use regression model. The annual mean concentrations of major air pollutant particles, PM2.5 (n=423,796), PM10 (n=423,796), and NOx (n=456,380), were recorded. The large GWAS database of CHD covered over ten million SNPs with independent single nucleotide polymorphisms (SNPs). LDL database collected major biochemical blood parameters from over 400,000 patients (n=440,546). Taken together, we conducted independent two-sample Mendelian randomization (MR) analyses for the causality between air pollutants (PM2.5, PM10, and NOx) and CHD. Multivariate MR analysis was conducted using causal relationships to determine the direct effects of exposure on outcome. The fixed-effect inverse variance weighted (IVW2) method was mainly employed to assess this relationship, with a confidence interval of 95% for the odds ratio (OR). Also, MR-Egger, weighted median, maximum likelihood ratio method, and random-effects inverse variance-weighted (IVW1) method were adopted as supplementary methods. RESULTS Two-sample MR results based on the IVW2 method suggested positive correlations between PM2.5 and CHD [OR 1.875 (1.279-2.748), p=0.001], PM10 and CHD [OR 2.586 (1.479-4.523), p=0.001], and NOx and CHD [OR 2.991 (2.021-4.427), p=4.37E-08]. The direct effect and mediating proportion were calculated using multivariable Mendelian randomization (MVMR). Lastly, the mediating proportions of LDL in the regulatory roles of PM2.5, PM10, and NOx in CHD were 2.82%, 4.73%, and 9.54%, respectively. CONCLUSIONS PM2.5, PM10, and NOx share direct causal associations with CHD, and LDL performs a mediating role in this pathogenic process. Early prevention against air pollution (such as increasing green areas and reducing large-scale industrial dust emissions) and early lipid-lowering treatment can effectively prevent the occurrence of CHD.
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Affiliation(s)
- C Sun
- Cardiovascular Medical Department, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
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Rosenblatt M, Tejavibulya L, Jiang R, Noble S, Scheinost D. The effects of data leakage on connectome-based machine learning models. bioRxiv 2023:2023.06.09.544383. [PMID: 38234740 PMCID: PMC10793416 DOI: 10.1101/2023.06.09.544383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Predictive modeling has now become a central technique in neuroimaging to identify complex brain-behavior relationships and test their generalizability to unseen data. However, data leakage, which unintentionally breaches the separation between data used to train and test the model, undermines the validity of predictive models. Previous literature suggests that leakage is generally pervasive in machine learning, but few studies have empirically evaluated the effects of leakage in neuroimaging data. Although leakage is always an incorrect practice, understanding the effects of leakage on neuroimaging predictive models provides insight into the extent to which leakage may affect the literature. Here, we investigated the effects of leakage on machine learning models in two common neuroimaging modalities, functional and structural connectomes. Using over 400 different pipelines spanning four large datasets and three phenotypes, we evaluated five forms of leakage fitting into three broad categories: feature selection, covariate correction, and lack of independence between subjects. As expected, leakage via feature selection and repeated subjects drastically inflated prediction performance. Notably, other forms of leakage had only minor effects (e.g., leaky site correction) or even decreased prediction performance (e.g., leaky covariate regression). In some cases, leakage affected not only prediction performance, but also model coefficients, and thus neurobiological interpretations. Finally, we found that predictive models using small datasets were more sensitive to leakage. Overall, our results illustrate the variable effects of leakage on prediction pipelines and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.
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Affiliation(s)
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
- Department of Bioengineering, Northeastern University, Boston, MA
- Department of Psychology, Northeastern University, Boston, MA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
- Child Study Center, Yale School of Medicine, New Haven, CT
- Department of Statistics & Data Science, Yale University, New Haven, CT
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Gao J, Jiang R, Tang X, Chen J, Yu M, Zhou C, Wang X, Zhang H, Huang C, Yang Y, Zhang X, Cui Z, Zhang X. A neuromarker for deficit syndrome in schizophrenia from a combination of structural and functional magnetic resonance imaging. CNS Neurosci Ther 2023; 29:3774-3785. [PMID: 37288482 PMCID: PMC10651988 DOI: 10.1111/cns.14297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 05/26/2023] [Accepted: 05/26/2023] [Indexed: 06/09/2023] Open
Abstract
AIM Deficit schizophrenia (DS), defined by primary and enduring negative symptoms, has been proposed as a promising homogeneous subtype of schizophrenia. It has been demonstrated that unimodal neuroimaging characteristics of DS were different from non-deficit schizophrenia (NDS), however, whether multimodal-based neuroimaging features could identify deficit syndrome remains to be determined. METHODS Functional and structural multimodal magnetic resonance imaging of DS, NDS and healthy controls were scanned. Voxel-based features of gray matter volume, fractional amplitude of low-frequency fluctuations, and regional homogeneity were extracted. The support vector machine classification models were constructed using these features separately and jointly. The most discriminative features were defined as the first 10% of features with the greatest weights. Moreover, relevance vector regression was applied to explore the predictive values of these top-weighted features in predicting negative symptoms. RESULTS The multimodal classifier achieved a higher accuracy (75.48%) compared with the single modal model in distinguishing DS from NDS. The most predictive brain regions were mainly located in the default mode and visual networks, exhibiting differences between functional and structural features. Further, the identified discriminative features significantly predicted scores of diminished expressivity factor in DS but not NDS. CONCLUSIONS The present study demonstrated that local properties of brain regions extracted from multimodal imaging data could distinguish DS from NDS with a machine learning-based approach and confirmed the relationship between distinctive features and the negative symptoms subdomain. These findings may improve the identification of potential neuroimaging signatures and improve the clinical assessment of the deficit syndrome.
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Affiliation(s)
- Ju Gao
- Institute of Mental HealthSuzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow UniversitySuzhouChina
- Department of Geriatric PsychiatryNanjing Brain Hospital Affiliated to Nanjing Medical UniversityNanjingChina
| | - Rongtao Jiang
- Department of Radiology & Biomedical ImagingYale School of MedicineNew HavenConnecticutUSA
| | - Xiaowei Tang
- Department of PsychiatryWutaishan Hospital of YangzhouYangzhouChina
| | - Jiu Chen
- Department of Geriatric PsychiatryNanjing Brain Hospital Affiliated to Nanjing Medical UniversityNanjingChina
| | - Miao Yu
- Department of Geriatric PsychiatryNanjing Brain Hospital Affiliated to Nanjing Medical UniversityNanjingChina
| | - Chao Zhou
- Department of Geriatric PsychiatryNanjing Brain Hospital Affiliated to Nanjing Medical UniversityNanjingChina
| | - Xiang Wang
- Medical Psychological Institute of the Second Xiangya HospitalChangshaChina
| | - Hongying Zhang
- Department of RadiologySubei People's Hospital of Jiangsu ProvinceYangzhouChina
| | - Chengbing Huang
- Department of Geriatric PsychiatryNanjing Brain Hospital Affiliated to Nanjing Medical UniversityNanjingChina
- Department of PsychiatryHuai'an No. 3 People's HospitalHuai'anChina
| | - Yong Yang
- Institute of Mental HealthSuzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow UniversitySuzhouChina
| | - Xiaobin Zhang
- Institute of Mental HealthSuzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow UniversitySuzhouChina
| | - Zaixu Cui
- Chinese Institute for Brain ResearchBeijingChina
| | - Xiangrong Zhang
- Department of Geriatric PsychiatryNanjing Brain Hospital Affiliated to Nanjing Medical UniversityNanjingChina
- Department of PsychiatryThe Affiliated Xuzhou Oriental Hospital of Xuzhou Medical UniversityXuzhouChina
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Liu T, Zhao Z, Wu C, Lu C, Liu M, An X, Sha Z, Wang X, Luo Z, Chen L, Liu C, Cao P, Zhang D, Jiang R. Impact of COVID-19 infection experience on mental health status of intensive care unit patients' family members: a real-world study. QJM 2023; 116:903-910. [PMID: 37498557 DOI: 10.1093/qjmed/hcad184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 07/05/2023] [Indexed: 07/28/2023] Open
Abstract
PURPOSE Family members of patients hospitalized in intensive care unit (ICU) are susceptible to adverse psychological outcomes. However, there is a paucity of studies specifically examining the mental health symptoms in ICU patients' family members with a prior history of coronavirus disease 2019 (COVID-19) infection. AIM This study aimed to investigate mental health status and its influencing factors of ICU patients' family members with previous COVID-19 infection experience in China. DESIGN Nationwide, cross-sectional cohort of consecutive participants of family members of ICU patients from 10 provinces randomly selected in mainland China conducted between October 2022 and May 2023. METHODS The basic information scale, Self-rating depression scale, Self-rating Anxiety Scale, Impact of Event Scale-Revised, Pittsburgh sleep quality index, Perceived Stress Scale, Connor-Davidson resilience scale, Simplified Coping Style Questionnaire were employed to explore mental health status among participants. RESULTS A total of 463 participants, comprising 156 individuals in Covid-19 family member cohort (infection group) and 307 individuals in control family member cohort (control group), met inclusion criteria. The infection group exhibited significantly higher incidence of composite mental health symptoms compared to control group (P = 0.017). Multivariable logistic regression analysis revealed that being female, engaging in physical/mental labor, residing in rural areas, and having children were identified as risk factors for the development of depression, anxiety, and post-traumatic stress disorder symptoms, while medical history of surgery was protective factor. A predictive model demonstrated a favorable discriminative ability and excellent calibration. CONCLUSION COVID-19 infection experience regarded as new traumatic stressors worsen mental health status of ICU patients' family members.
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Affiliation(s)
- T Liu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - Z Zhao
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - C Wu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - C Lu
- Department of Psychiatry, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China
| | - M Liu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - X An
- Department of Intensive Care Unit, Beijing Tiantan Hospital, Beijing, China
| | - Z Sha
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
| | - X Wang
- Department of Neurology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Z Luo
- Department of Neurosurgery, Shandong Provincial Third Hospital, Shandong, China
| | - L Chen
- Department of Intensive Care Unit, Shaoxing People's Hospital, Shaoxing, Zhejiang, China
| | - C Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Anhui, China
| | - P Cao
- Department of Intensive Care Unit, The First Affiliated Hospital of Bengbu Medical College, Anhui, China
| | - D Zhang
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Tianjin, 300052, China
| | - R Jiang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Ministry of Education, Tianjin, China
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11
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Rosenblatt M, Tejavibulya L, Camp CC, Jiang R, Westwater ML, Noble S, Scheinost D. Power and reproducibility in the external validation of brain-phenotype predictions. bioRxiv 2023:2023.10.25.563971. [PMID: 37961654 PMCID: PMC10634903 DOI: 10.1101/2023.10.25.563971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Identifying reproducible and generalizable brain-phenotype associations is a central goal of neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype models in unseen data. Most prediction studies train and evaluate a model in the same dataset. However, external validation, or the evaluation of a model in an external dataset, provides a better assessment of robustness and generalizability. Despite the promise of external validation and calls for its usage, the statistical power of such studies has yet to be investigated. In this work, we ran over 60 million simulations across several datasets, phenotypes, and sample sizes to better understand how the sizes of the training and external datasets affect statistical power. We found that prior external validation studies used sample sizes prone to low power, which may lead to false negatives and effect size inflation. Furthermore, increases in the external sample size led to increased simulated power directly following theoretical power curves, whereas changes in the training dataset size offset the simulated power curves. Finally, we compared the performance of a model within a dataset to the external performance. The within-dataset performance was typically within r=0.2 of the cross-dataset performance, which could help decide how to power future external validation studies. Overall, our results illustrate the importance of considering the sample sizes of both the training and external datasets when performing external validation.
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Affiliation(s)
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
| | - Chris C. Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Margaret L. Westwater
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
- Department of Bioengineering, Northeastern University, Boston, MA
- Department of Psychology, Northeastern University, Boston, MA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
- Child Study Center, Yale School of Medicine, New Haven, CT
- Department of Statistics & Data Science, Yale University, New Haven, CT
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12
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Ye J, Sun H, Gao S, Dadashkarimi J, Rosenblatt M, Rodriguez RX, Mehta S, Jiang R, Noble S, Westwater ML, Scheinost D. Altered Brain Dynamics Across Bipolar Disorder and Schizophrenia During Rest and Task Switching Revealed by Overlapping Brain States. Biol Psychiatry 2023; 94:580-590. [PMID: 37031780 PMCID: PMC10524212 DOI: 10.1016/j.biopsych.2023.03.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/11/2023]
Abstract
BACKGROUND Individuals with bipolar disorder (BD) and schizophrenia (SCZ) show aberrant brain dynamics (i.e., altered recruitment or traversal through different brain states over time). Existing investigations of brain dynamics typically assume that one dominant brain state characterizes each time point. However, as multiple brain states likely are engaged at any given moment, this approach can obscure alterations in less prominent but critical brain states. Here, we examined brain dynamics in BD and SCZ by implementing a novel framework that simultaneously assessed the engagement of multiple brain states. METHODS Four recurring brain states were identified by applying nonlinear manifold learning and k-means clustering to the Human Connectome Project task-based functional magnetic resonance imaging data. We then assessed moment-to-moment state engagement in 2 independent samples of healthy control participants and patients with BD or SCZ using resting-state (N = 336) or task-based (N = 217) functional magnetic resonance imaging data. Relative state engagement and state engagement variability were extracted and compared across groups using multivariate analysis of covariance, controlling for site, medication, age, and sex. RESULTS Our framework identified dynamic alterations in BD and SCZ, while a state discretization approach revealed no significant group differences. Participants with BD or SCZ showed reduced state engagement variability, but not relative state engagement, across multiple brain states during resting-state and task-based functional magnetic resonance imaging. We found decreased state engagement variability in older participants and preliminary evidence suggesting an association with avolition. CONCLUSIONS Assessing multiple brain states simultaneously can reflect the complexity of aberrant brain dynamics in BD and SCZ, providing a more comprehensive understanding of the neural mechanisms underpinning these conditions.
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Affiliation(s)
- Jean Ye
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut.
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Siyuan Gao
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | | | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | | | - Saloni Mehta
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Margaret L Westwater
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Child Study Center, Yale School of Medicine, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut
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13
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Sun H, Jiang R, Dai W, Dufford AJ, Noble S, Spann MN, Gu S, Scheinost D. Network controllability of structural connectomes in the neonatal brain. Nat Commun 2023; 14:5820. [PMID: 37726267 PMCID: PMC10509217 DOI: 10.1038/s41467-023-41499-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/06/2023] [Indexed: 09/21/2023] Open
Abstract
White matter connectivity supports diverse cognitive demands by efficiently constraining dynamic brain activity. This efficiency can be inferred from network controllability, which represents the ease with which the brain moves between distinct mental states based on white matter connectivity. However, it remains unclear how brain networks support diverse functions at birth, a time of rapid changes in connectivity. Here, we investigate the development of network controllability during the perinatal period and the effect of preterm birth in 521 neonates. We provide evidence that elements of controllability are exhibited in the infant's brain as early as the third trimester and develop rapidly across the perinatal period. Preterm birth disrupts the development of brain networks and altered the energy required to drive state transitions at different levels. In addition, controllability at birth is associated with cognitive ability at 18 months. Our results suggest network controllability develops rapidly during the perinatal period to support cognitive demands but could be altered by environmental impacts like preterm birth.
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Affiliation(s)
- Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA.
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06510, USA
| | - Alexander J Dufford
- Department of Psychiatry and Center for Mental Health Innovation, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Stephanie Noble
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
- Department of Bioengineering, Northeastern University, Boston, MA, 02115, USA
- Center for Cognitive and Brain Health, Northeastern University, Boston, USA
| | - Marisa N Spann
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA
- New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA.
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA.
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA.
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA.
- Wu Tsai Institute, Yale University, 100 College Street, New Haven, CT, 06510, USA.
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14
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Feng A, Feng Y, Zhi D, Jiang R, Fu Z, Xu M, Zhao M, Yu S, Stevens M, Sun L, Calhoun V, Sui J. Functional Imaging Derived ADHD Biotypes Based on Deep Clustering May Guide Personalized Medication Therapy. Res Sq 2023:rs.3.rs-3272441. [PMID: 37790426 PMCID: PMC10543279 DOI: 10.21203/rs.3.rs-3272441/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Attention deficit hyperactivity disorder (ADHD) is one prevalent neurodevelopmental disorder with childhood onset, however, there is no clear correspondence established between clinical ADHD subtypes and primary medications. Identifying objective and reliable neuroimaging markers for categorizing ADHD biotypes may lead to more individualized, biotype-guided treatment. Here we proposed graph convolutional network plus deep clustering for ADHD biotype detection using functional network connectivity (FNC), resulting in two biotypes based on 1069 ADHD patients selected from Adolescent Brain and Cognitive Development (ABCD) study, which were well replicated on independent ADHD adolescents undergoing longitudinal medication treatment (n=130). Interestingly, in addition to differences in cognitive performance and hyperactivity/impulsivity symptoms, biotype 1 treated with methylphenidate demonstrated significantly better recovery than biotype 2 treated with atomoxetine (p<0.05, FDR corrected). This imaging-driven, biotype-guided approach holds promise for facilitating personalized treatment of ADHD, exploring possible boundaries through innovative deep learning algorithms aimed at improving medication treatment effectiveness.
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Affiliation(s)
- Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 100049
| | - Yuan Feng
- Peking University Sixth Hospital/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China, 100191
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 100875
| | - Rongtao Jiang
- Department of Radiology and Biomedical imaging, Yale University, New Haven, Connecticut, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, Georgia, United States, 30303
| | - Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 100049
| | - Min Zhao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 100049
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 100049
| | - Michael Stevens
- Department of Psychiatry, Olin Neuropsychiatry Research Center, Institute of Living, Hartford Healthcare Corporation, Hartford, CT, United States
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Li Sun
- Peking University Sixth Hospital/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China, 100191
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, Georgia, United States, 30303
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 100875
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, Georgia, United States, 30303
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15
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Yang M, Xin L, Li H, Lu X, Pan X, Lei S, Li Y, Zhu L, Zhu Q, Jiang R, Jia Z, Cheng G, Zeng L, Zhang L. Risk factors for bloodstream infection in paediatric haematopoietic stem cell transplantation: a systematic review and meta-analysis. J Hosp Infect 2023; 139:11-22. [PMID: 37308062 DOI: 10.1016/j.jhin.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 06/14/2023]
Abstract
BACKGROUND Haematopoietic stem cell transplantation (HSCT), a standard treatment for paediatric haematological diseases, is highly associated with bloodstream infection (BSI), which may increase mortality. AIM To explore the risk factors for BSI in paediatric HSCT recipients. METHODS Three English databases and four Chinese databases were searched from inception to March 17th, 2022. Eligible studies included randomized controlled trials, cohort studies, and case-control studies that enrolled HSCT recipients aged ≤18 years and reported BSI risk factors. Two reviewers independently screened studies, extracted data, and assessed the risk of bias. Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE), certainty of body of evidence was assessed. FINDINGS Fourteen studies involving 4602 persons were included. The incidences of BSI and associated mortality in paediatric HSCT recipients were approximately 10-50% and 5-15%, respectively. Meta-analysis of all studies revealed that previous BSI before HSCT (relative effect (RE): 2.28; 95% confidence interval (CI) 1.19-4.34, moderate certainty) and receiving an umbilical cord blood transplant (RE: 1.55; 95% CI: 1.22-1.97, moderate certainty) were probably associated with an increased risk of BSI. Meta-analysis of studies with low risk of bias reassured that previous BSI before HSCT probably increased the risk of BSI (RE: 2.28; 95% CI: 1.19-4.34, moderate certainty), and revealed that steroid use (RE: 2.72; 95% CI: 1.31-5.64, moderate certainty) was likely a risk factor whereas autologous HSCT was probably a protective factor of BSI (RE: 0.65; 95% CI: 0.45-0.94, moderate certainty). CONCLUSION These findings could inform the management of paediatric HSCT recipients, helping identify who may benefit from prophylactic antibiotics.
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Affiliation(s)
- M Yang
- Department of Pharmacy, West China Second University Hospital, Sichuan University, Chengdu 610000, China; Evidence-Based Pharmacy Centre, West China Second University Hospital, Sichuan University, Chengdu 610000, China; NMPA Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu 610000, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu 610000, China; West China School of Medicine, Sichuan University, Chengdu 610000, China
| | - L Xin
- Department of Clinical Pharmacy, Affiliated Hospital of Yunnan University, Kunming 650000, China
| | - H Li
- Department of Pharmacy, West China Second University Hospital, Sichuan University, Chengdu 610000, China; Evidence-Based Pharmacy Centre, West China Second University Hospital, Sichuan University, Chengdu 610000, China; NMPA Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu 610000, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu 610000, China
| | - X Lu
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu 610000, China; Department of Paediatric Haematology and Oncology, West China Second Hospital, Sichuan University, Chengdu 610000, China
| | - X Pan
- Department of Pharmacy, West China Second University Hospital, Sichuan University, Chengdu 610000, China; Evidence-Based Pharmacy Centre, West China Second University Hospital, Sichuan University, Chengdu 610000, China; NMPA Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu 610000, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu 610000, China
| | - S Lei
- West China School of Pharmacy, Sichuan University, Chengdu 610000, China
| | - Y Li
- West China School of Pharmacy, Sichuan University, Chengdu 610000, China
| | - L Zhu
- West China School of Pharmacy, Sichuan University, Chengdu 610000, China
| | - Q Zhu
- West China School of Pharmacy, Sichuan University, Chengdu 610000, China
| | - R Jiang
- West China School of Pharmacy, Sichuan University, Chengdu 610000, China
| | - Z Jia
- Department of Pharmacy, West China Second University Hospital, Sichuan University, Chengdu 610000, China; Evidence-Based Pharmacy Centre, West China Second University Hospital, Sichuan University, Chengdu 610000, China; NMPA Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu 610000, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu 610000, China; West China School of Pharmacy, Sichuan University, Chengdu 610000, China
| | - G Cheng
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu 610000, China; Department of Paediatrics, West China Second University Hospital, Sichuan University, Chengdu 610000, China; Laboratory of Molecular Translational Medicine, Centre for Translational Medicine, Sichuan University, Chengdu 610000, China
| | - L Zeng
- Department of Pharmacy, West China Second University Hospital, Sichuan University, Chengdu 610000, China; Evidence-Based Pharmacy Centre, West China Second University Hospital, Sichuan University, Chengdu 610000, China; NMPA Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu 610000, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu 610000, China.
| | - L Zhang
- Department of Pharmacy, West China Second University Hospital, Sichuan University, Chengdu 610000, China; Evidence-Based Pharmacy Centre, West China Second University Hospital, Sichuan University, Chengdu 610000, China; NMPA Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu 610000, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu 610000, China; Chinese Evidence-based Medicine Centre, West China Hospital, Sichuan University, Chengdu 610000, China.
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16
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Dadashkarimi J, Karbasi A, Liang Q, Rosenblatt M, Noble S, Foster M, Rodriguez R, Adkinson B, Ye J, Sun H, Camp C, Farruggia M, Tejavibulya L, Dai W, Jiang R, Pollatou A, Scheinost D. Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available. Med Image Anal 2023; 88:102864. [PMID: 37352650 PMCID: PMC10526726 DOI: 10.1016/j.media.2023.102864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 02/10/2023] [Accepted: 05/31/2023] [Indexed: 06/25/2023]
Abstract
Open-source, publicly available neuroimaging datasets - whether from large-scale data collection efforts or pooled from multiple smaller studies - offer unprecedented sample sizes and promote generalization efforts. Releasing data can democratize science, increase the replicability of findings, and lead to discoveries. Partly due to patient privacy, computational, and data storage concerns, researchers typically release preprocessed data with the voxelwise time series parcellated into a map of predefined regions, known as an atlas. However, releasing preprocessed data also limits the choices available to the end-user. This is especially true for connectomics, as connectomes created from different atlases are not directly comparable. Since there exist several atlases with no gold standards, it is unrealistic to have processed, open-source data available from all atlases. Together, these limitations directly inhibit the potential benefits of open-source neuroimaging data. To address these limitations, we introduce Cross Atlas Remapping via Optimal Transport (CAROT) to find a mapping between two atlases. This approach allows data processed from one atlas to be directly transformed into a connectome based on another atlas without the need for raw data access. To validate CAROT, we compare reconstructed connectomes against their original counterparts (i.e., connectomes generated directly from an atlas), demonstrate the utility of transformed connectomes in downstream analyses, and show how a connectome-based predictive model can generalize to publicly available data that was processed with different atlases. Overall, CAROT can reconstruct connectomes from an extensive set of atlases - without needing the raw data - allowing already processed connectomes to be easily reused in a wide range of analyses while eliminating redundant processing efforts. We share this tool as both source code and as a stand-alone web application (http://carotproject.com/).
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Affiliation(s)
| | - Amin Karbasi
- Computer Science Department, Yale University, New Haven, CT, USA; Department of Electrical Engineering, Yale University, New Haven, CT, USA; Department of Statistics & Data Science, Yale University, New Haven, CT, USA
| | - Qinghao Liang
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Maya Foster
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Raimundo Rodriguez
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Brendan Adkinson
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Jean Ye
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Chris Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Michael Farruggia
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Wei Dai
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Angeliki Pollatou
- Developing Brain Institute, Children's National Hospital, Washington DC, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Statistics & Data Science, Yale University, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Radiology and Biomedical Engineering, Yale University, New Haven, CT, USA
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17
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Jiang R, Wu J, Rosenblatt M, Dai W, Rodriguez RX, Sui J, Qi S, Liang Q, Xu B, Meng Q, Calhoun VD, Scheinost D. Elevated C-reactive protein mediates the liver-brain axis: a preliminary study. EBioMedicine 2023; 93:104679. [PMID: 37356206 PMCID: PMC10320521 DOI: 10.1016/j.ebiom.2023.104679] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 06/10/2023] [Accepted: 06/11/2023] [Indexed: 06/27/2023] Open
Abstract
BACKGROUND Chronic liver diseases of all etiologies exist along a spectrum with varying degrees of hepatic fibrosis. Despite accumulating evidence implying associations between liver fibrosis and cognitive functioning, there is limited research exploring the underlying neurobiological factors and the possible mediating role of inflammation on the liver-brain axis. METHODS Using data from the UK Biobank, we examined the cross-sectional association of liver fibrosis (as measured by Fibrosis-4 score) with cognitive functioning and regional grey matter volumes (GMVs) while adjusting for numerous covariates and multiple comparisons. We further performed post-hoc preliminary analysis to investigate the mediating effect of C-reactive protein (CRP) on the association between liver fibrosis and both cognitive functioning and GMVs. FINDINGS We analysed behaviour from up to 447,626 participants (N ranged from 45,055 to 447,533 per specific cognitive metric) 37 years and older. 38,244 participants (age range 44-82 years) had GMV data collected at a median 9-year follow-up. Liver fibrosis showed significant associations with cognitive performance in reasoning, working memory, visual memory, prospective memory, executive function, and processing speed. Subgroup analysis indicated larger effects sizes for symbol digital substitution but smaller effect sizes for trail making in middle-aged people than their old counterparts. Neuroimaging analyses revealed significant associations between liver fibrosis and reduced regional GMVs, primarily in the hippocampus, thalamus, ventral striatum, parahippocampal gyrus, brain stem, and cerebellum. CRP levels were significantly higher in adults with advanced liver fibrosis than those without, indicating an elevated systemic inflammation. Moreover, the serum CRP significantly mediated the effect of liver fibrosis on most cognitive measures and regional GMVs in the hippocampus and brain stem. INTERPRETATION This study provides a well-powered characterization of associations between liver fibrosis, cognitive impairment, and grey matter atrophy. It also highlights the possibly mediating role of systemic inflammation on the liver-brain axis. Early surveillance and prevention of liver diseases may reduce cognitive decline and brain GMV loss. FUNDING National Science Foundation, and National Institutes of Health.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA.
| | - Jing Wu
- Second Department of Liver Disease Center, Youan Hospital, Capital Medical University, Beijing, 100069, China
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Wei Dai
- Department of Biostatistics, Yale University, New Haven, CT 06520, USA
| | - Raimundo X Rodriguez
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100088, China
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Qinghao Liang
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Bin Xu
- Second Department of Liver Disease Center, Youan Hospital, Capital Medical University, Beijing, 100069, China.
| | - Qinghua Meng
- Department of Medical Oncology, Beijing You-An Hospital, Capital Medical University, Beijing, 100069, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA; Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA; Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA; Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA; Wu Tsai Institute, Yale University, 100 College Street, New Haven, CT 06510, USA.
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Li X, Xu M, Jiang R, Li X, Calhoun VD, Zhou X, Sui J. ICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive Disorder. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-5. [PMID: 38082692 DOI: 10.1109/embc40787.2023.10340456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified large scale structural brain alterations in MDD, yet most are group analyses with atlas-parcellated anatomical regions. Here we proposed a method to measure individual difference by independent component analysis (ICA)-based individual difference structural similarity network (IDSSN). This approach provided a data-adaptive, atlas-free solution that can be applied to new individual subjects. Specifically, we constructed individualized whole-brain structural covariance networks based on network perturbation approach using spatially constrained ICA. First, a set of benchmark independent components (ICs) were generated using gray matter volume (GMV) from all healthy controls. Then individual heterogeneity was obtained by calculating differences and other similarity metrics between ICs derived from "each one patient + all controls" and the benchmark ICs, resulting in 32 imaging features and structural similarity networks for each patient, which can be used for predicting multiple clinical symptoms. We estimated IDSSN for 189 adolescent MDD patients aged 10-20 years and compared them to 112 healthy adolescents. We tested their predictability of the Hamilton Anxiety Scale , the 17-item Hamilton Depression Scale and six clinical syndromes using connectome-based predictive modeling. The prediction results showed that ICA-based IDSSN features reveal more disease-specific information than those using other brain templates. We also found that depression-associated networks mainly involved the default-mode network and visual network. In conclusion, our study proposed an adaptive method that improves the ability to detect GMV deviations and specificity between one individual patient and healthy groups, providing a new perspectives and insights for evaluating individual-level clinical heterogeneity based on brain structures.
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Jiang R, Calhoun VD, Noble S, Sui J, Liang Q, Qi S, Scheinost D. A functional connectome signature of blood pressure in >30 000 participants from the UK biobank. Cardiovasc Res 2023; 119:1427-1440. [PMID: 35875865 PMCID: PMC10262183 DOI: 10.1093/cvr/cvac116] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/07/2022] [Accepted: 07/01/2022] [Indexed: 11/12/2022] Open
Abstract
AIMS Elevated blood pressure (BP) is a prevalent modifiable risk factor for cardiovascular diseases and contributes to cognitive decline in late life. Despite the fact that functional changes may precede irreversible structural damage and emerge in an ongoing manner, studies have been predominantly informed by brain structure and group-level inferences. Here, we aim to delineate neurobiological correlates of BP at an individual level using machine learning and functional connectivity. METHODS AND RESULTS Based on whole-brain functional connectivity from the UK Biobank, we built a machine learning model to identify neural representations for individuals' past (∼8.9 years before scanning, N = 35 882), current (N = 31 367), and future (∼2.4 years follow-up, N = 3 138) BP levels within a repeated cross-validation framework. We examined the impact of multiple potential covariates, as well as assessed these models' generalizability across various contexts.The predictive models achieved significant correlations between predicted and actual systolic/diastolic BP and pulse pressure while controlling for multiple confounders. Predictions for participants not on antihypertensive medication were more accurate than for currently medicated patients. Moreover, the models demonstrated robust generalizability across contexts in terms of ethnicities, imaging centres, medication status, participant visits, gender, age, and body mass index. The identified connectivity patterns primarily involved the cerebellum, prefrontal, anterior insula, anterior cingulate cortex, supramarginal gyrus, and precuneus, which are key regions of the central autonomic network, and involved in cognition processing and susceptible to neurodegeneration in Alzheimer's disease. Results also showed more involvement of default mode and frontoparietal networks in predicting future BP levels and in medicated participants. CONCLUSION This study, based on the largest neuroimaging sample currently available and using machine learning, identifies brain signatures underlying BP, providing evidence for meaningful BP-associated neural representations in connectivity profiles.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, GA 30303, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
| | - Jing Sui
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, GA 30303, USA
| | - Qinghao Liang
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, GA 30303, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA
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20
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Jiang R, Noble S, Sui J, Yoo K, Rosenblatt M, Horien C, Qi S, Liang Q, Sun H, Calhoun VD, Scheinost D. Associations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank. Lancet Digit Health 2023; 5:e350-e359. [PMID: 37061351 PMCID: PMC10257912 DOI: 10.1016/s2589-7500(23)00043-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 02/12/2023] [Accepted: 02/16/2023] [Indexed: 04/17/2023]
Abstract
BACKGROUND Physical frailty is a state of increased vulnerability to stressors and is associated with serious health issues. However, how frailty affects and is affected by numerous other factors, including mental health and brain structure, remains underexplored. We aimed to investigate the mutual effects of frailty and health using large, multidimensional data. METHODS For this population-based study, we used data from the UK Biobank to examine the pattern and direction of association between physical frailty and 325 health-related measures across multiple domains, using linear mixed-effect models and adjusting for numerous confounders. Participants were included if complete data were available for all five indicators of frailty, all covariates, and at least one health measure. We further examined the association between frailty and brain structure and the role of this association in mediating the relationship between frailty and health outcomes. FINDINGS 483 033 participants aged 38-73 years were included in the study at baseline (between Dec 19, 2006, and Oct 1, 2010); at a median follow-up of 9 years (IQR 8-10), behavioural data were available for 46 501 participants and neuroimaging data for 40 210 participants. The severity of physical frailty was significantly associated with decreased cognitive performance (Cohen's d=0·025-0·162), increased early-life risks (d=0·026-0·111), unhealthy lifestyle (d=0·013-0·394), poor physical fitness (d=0·007-0·668), increased symptoms of poor mental health (d=0·032-0·607), severe environmental pollution (d=0·013-0·064), and adverse biochemical markers (d=0·025-0·198). Some associations were bidirectional, with the strongest effects on mental health measures. The severity of frailty correlated with increased total white matter hyperintensity and lower grey matter volume, particularly in subcortical regions (d=0·027-0·082), which significantly mediated the association between frailty and health-related outcomes, although the mediated effects were small. INTERPRETATION Physical frailty is associated with diverse unfavourable health-related outcomes, which can be mediated by differences in brain structure. Our findings offer a framework for guiding preventative strategies targeting both frailty and psychiatric disorders. FUNDING National Institute of Mental Health, National Science Foundation.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Qinghao Liang
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA
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21
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Scheinost D, Pollatou A, Dufford AJ, Jiang R, Farruggia MC, Rosenblatt M, Peterson H, Rodriguez RX, Dadashkarimi J, Liang Q, Dai W, Foster ML, Camp CC, Tejavibulya L, Adkinson BD, Sun H, Ye J, Cheng Q, Spann MN, Rolison M, Noble S, Westwater ML. Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer. Biol Psychiatry 2023; 93:893-904. [PMID: 36759257 PMCID: PMC10259670 DOI: 10.1016/j.biopsych.2022.10.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 09/10/2022] [Accepted: 10/07/2022] [Indexed: 12/01/2022]
Abstract
Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.
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Affiliation(s)
- Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut; Child Study Center, Yale School of Medicine, New Haven, Connecticut; Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut.
| | - Angeliki Pollatou
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York
| | - Alexander J Dufford
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Michael C Farruggia
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Hannah Peterson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | | | | | - Qinghao Liang
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
| | - Maya L Foster
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Chris C Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Jean Ye
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Qi Cheng
- Departments of Neuroscience and Psychology, Smith College, Northampton, Massachusetts
| | - Marisa N Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York
| | - Max Rolison
- Child Study Center, Yale School of Medicine, New Haven, Connecticut
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Margaret L Westwater
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
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22
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Yang JY, Drury CF, Jiang R, Yang XM, Worth DE, Bittman S, Grant BB, Smith WN, Reid K. Simulating nitrogen balance in Canadian agricultural soils from 1981 to 2016. J Environ Manage 2023; 341:118015. [PMID: 37150173 DOI: 10.1016/j.jenvman.2023.118015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/18/2022] [Accepted: 04/24/2023] [Indexed: 05/09/2023]
Abstract
Agriculture produces food, fiber and biofuels for the world's growing population, however, agriculture can be a major contributor of nitrogen (N) losses including emissions of ammonia (NH3), nitrous oxide (N2O) and nitrate (NO3-) leaching and runoff. A Canadian Agricultural Nitrogen Budget for Reactive N (CANBNr) model was developed to estimate the soil N balance in 3487 soil landscape of Canada polygons from 1981 to 2016. The CANBNr model integrates NH3 emission from fertilizers, manure from housing, storage and field, as well as direct/indirect N2O emissions from fertilizers, manures, crop residues and soil organic matter. The NO3- leaching is estimated based on the residual soil N (RSN) at harvest and drainage derived with the DeNitrification-DeComposition (DNDC) model. From 1981 to 2016, the N input from fertilizer and N fixation increased at a greater rate than N removal in harvested crops in all provinces of Canada, resulting in an increase in the RSN and N losses. In 2016, the Prairie provinces had lower N losses (11.7 kg N ha-1) from N2O, NH3 and NO3- compared with 43.2 kg N ha-1 in central Canada, and 76.5 kg N ha-1 in Atlantic Canada. However, the Prairie provinces had 84.3% of the total Canadian farmland (74.3% of the total Canadian N input), while central Canada had 12.9% of Canadian farmland (21.7% of the total Canadian N input). In the Prairie provinces, the total N2O loss from fertilizer N ranged 4.4-8.6 Gg N whereas NH3 loss ranged from 17.1 to 44.6 Gg N and these values were influenced by both emission intensity and total land area. Total N2O losses from manure were highest in Alberta, Ontario and Quebec resulting in 4.8, 4.4, and 3.4 Gg N and NH3 losses from manure were also highest in these 3 provinces at 61.1, 45.2 and 40.4 Gg N, respectively. Nitrate leaching was impacted by drainage volumes, soil type and N inputs. In the non-growing season, NO3- leaching losses (36-yr average) were 63.3 Gg in Ontario and 57.5 Gg N in Quebec compared with 20.8 Gg N for Ontario and 35.5 Gg N for Quebec in the growing season. In contrast, the Prairie provinces showed higher NO3- leaching in the growing season (23.1-37.4 Gg N) than in the non-growing season (10.4-13.7 Gg N). In summary, total fertilizer N increased the most over the 36 years in the Prairies which resulted in increased RSN and N leaching losses that will require further intervention.
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Affiliation(s)
- J Y Yang
- Harrow Research and Development Centre, AAFC, 2585 County Road 20, Harrow, Ontario, N0R 1G0, Canada.
| | - C F Drury
- Harrow Research and Development Centre, AAFC, 2585 County Road 20, Harrow, Ontario, N0R 1G0, Canada
| | - R Jiang
- Harrow Research and Development Centre, AAFC, 2585 County Road 20, Harrow, Ontario, N0R 1G0, Canada; Institute of Plant Nutrition, Resources and Environment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
| | - X M Yang
- Harrow Research and Development Centre, AAFC, 2585 County Road 20, Harrow, Ontario, N0R 1G0, Canada
| | - D E Worth
- Ottawa Research and Development Centre, AAFC, 960 Carling Ave, Ottawa, K1A 0C5, Canada
| | - S Bittman
- Agassiz Research and Development Centre, AAFC, 6947 Highway 7, Agassiz, BC, V0M 1A0, Canada
| | - B B Grant
- Ottawa Research and Development Centre, AAFC, 960 Carling Ave, Ottawa, K1A 0C5, Canada
| | - W N Smith
- Ottawa Research and Development Centre, AAFC, 960 Carling Ave, Ottawa, K1A 0C5, Canada
| | - K Reid
- Harrow Research and Development Centre, AAFC, 2585 County Road 20, Harrow, Ontario, N0R 1G0, Canada
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23
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He H, Jiang R, Ren X, Jin L, Jiang Y. The safety of human embryos following long-term cryopreservation ( >6 years) on vitrification. Cryo Letters 2023; 44:178-184. [PMID: 37883171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
BACKGROUND Vitrification of embryos has become the basic means of assisted reproductive technology (ART) therapy in recent years. Concerns have also been raised about the safety of vitrification and the effect of cryopreservation time. Most of the previous studies were on the data within 6 years of cryopreservation. OBJECTIVE In this study, we aimed to evaluate the impact of long-term cryopreservation (>6 years) on pregnancy and neonatal outcomes. MATERIALS AND METHODS This research was a single-center, retrospective analysis, including 426 frozen-thawed embryo transfer (FET) cycles. Patients who participated in IVF-FET cycles between January 2013 to December 2020 were analyzed. Preferentially matched participants were divided into three groups according to storage time: group A (>72 months), group B (0-3 months, propensity score matching [PSM] according to the age of oocyte retrieval), and group C (0-3 months, PSM according to the age of embryo transfer). RESULTS Our results revealed that there were no significant differences in human chorionic gonadotropin [HCG] positive rate, clinical pregnancy rate, miscarriage rate, live birth rate, and neonatal outcomes when the embryo storage duration >72 months. But the proportion of high birth weight was higher in group A (>72 months) when matched according to age at embryo transfer. CONCLUSION The results of our study showed that long-term cryopreservation had no effect on the pregnancy and neonatal outcomes of vitrification. The results offer evidence for the safety of using long-term cryopreservation embryos after vitrification. DOI: 10.54680/fr23310110612.
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Affiliation(s)
- H He
- Department of Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - R Jiang
- Laboratory of Clinical Immunology, Wuhan No. 1 Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - X Ren
- Department of Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - L Jin
- Department of Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Y Jiang
- Department of Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China.
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Qi S, Calhoun VD, Zhang D, Miller J, Deng ZD, Narr KL, Sheline Y, McClintock SM, Jiang R, Yang X, Upston J, Jones T, Sui J, Abbott CC. Correction: Links between electroconvulsive therapy responsive and cognitive impairment multimodal brain networks in late-life major depressive disorder. BMC Med 2023; 21:113. [PMID: 36978111 PMCID: PMC10052797 DOI: 10.1186/s12916-023-02800-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/30/2023] Open
Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Jeremy Miller
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Zhi-De Deng
- Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Katherine L Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Yvette Sheline
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Shawn M McClintock
- Division of Psychology, Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - Rongtao Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xiao Yang
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Joel Upston
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Tom Jones
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
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25
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Jiang R, Dai LMJ, Sha YQ, Xia Y, Miao Y, Qin SC, Wu W, Qiu JY, Bi HL, Wang L, Fan L, Xu W, Li JY, Zhu HY. [Efficacy and safety of BTK inhibitor, venetoclax and rituximab in the treatment of chronic lymphocytic leukemia/small lymphocytic lymphoma]. Zhonghua Xue Ye Xue Za Zhi 2023; 44:247-250. [PMID: 37356988 DOI: 10.3760/cma.j.issn.0253-2727.2023.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Affiliation(s)
- R Jiang
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - L M J Dai
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - Y Q Sha
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - Y Xia
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - Y Miao
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - S C Qin
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - W Wu
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - J Y Qiu
- Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - H L Bi
- Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - L Wang
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - L Fan
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - W Xu
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - J Y Li
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - H Y Zhu
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
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Feng P, Jiang R, Wei L, Calhoun VD, Jing B, Li H, Sui J. Determining four confounding factors in individual cognitive traits prediction with functional connectivity: an exploratory study. Cereb Cortex 2023; 33:2011-2020. [PMID: 35567795 PMCID: PMC9977351 DOI: 10.1093/cercor/bhac189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/24/2022] [Accepted: 04/25/2022] [Indexed: 11/14/2022] Open
Abstract
Resting-state functional connectivity (RSFC) has been widely adopted for individualized trait prediction. However, multiple confounding factors may impact the predicted brain-behavior relationships. In this study, we investigated the impact of 4 confounding factors including time series length, functional connectivity (FC) type, brain parcellation choice, and variance of the predicted target. The data from Human Connectome Project including 1,206 healthy subjects were employed, with 3 cognitive traits including fluid intelligence, working memory, and picture vocabulary ability as the prediction targets. We compared the prediction performance under different settings of these 4 factors using partial least square regression. Results demonstrated appropriate time series length (300 time points) and brain parcellation (independent component analysis, ICA100/200) can achieve better prediction performance without too much time consumption. FC calculated by Pearson, Spearman, and Partial correlation achieves higher accuracy and lower time cost than mutual information and coherence. Cognitive traits with larger variance among subjects can be better predicted due to the well elaboration of individual variability. In addition, the beneficial effects of increasing scan duration to prediction partially arise from the improved test-retest reliability of RSFC. Taken together, the study highlights the importance of determining these factors in RSFC-based prediction, which can facilitate standardization of RSFC-based prediction pipelines going forward.
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Affiliation(s)
- Pujie Feng
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, 300 Cedar Street, New Haven, 06510 CT, United States
| | - Lijiang Wei
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekou Outer Street, Haidian District, 100875 Beijing, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Pl NE, Atlanta, 30303, GA, United States
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China
| | - Haiyun Li
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekou Outer Street, Haidian District, 100875 Beijing, China.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Pl NE, Atlanta, 30303, GA, United States
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Qin SC, Jiang R, Sha YQ, Qiu JY, Mi HL, Miao Y, Wu W, Wang L, Fan L, Xu W, Li JY, Zhu HY. [Efficacy and safety of BTK inhibitor combined with bendamustine and rituximab in the first-line treatment of chronic lymphocytic leukemia/small lymphocytic lymphoma]. Zhonghua Xue Ye Xue Za Zhi 2023; 44:158-161. [PMID: 36948873 PMCID: PMC10033273 DOI: 10.3760/cma.j.issn.0253-2727.2023.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Affiliation(s)
- S C Qin
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - R Jiang
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - Y Q Sha
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - J Y Qiu
- Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - H L Mi
- Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - Y Miao
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - W Wu
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - L Wang
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - L Fan
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - W Xu
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - J Y Li
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - H Y Zhu
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
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Li J, Ma Y, Zhang L, Cai C, Guo Y, Zhang Z, Li D, Tian Y, Kang X, Han R, Jiang R. Valgus-varus deformity induced abnormal tissue metabolism, inflammatory damage and apoptosis in broilers. Br Poult Sci 2023; 64:26-35. [PMID: 36102935 DOI: 10.1080/00071668.2022.2121640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
1. This study explored the tissue metabolic status and the relationship with inflammation in valgus-valgus deformity (VVD) broilers with increasing age.2. Tissue and blood from VVD and healthy broilers were collected at two, four and five weeks old. A fully automated biochemical analyser, real-time PCR, HE staining and enzyme-linked immunosorbent assay were used to detect tissue metabolic indexes, mRNA levels of inflammation and apoptosis cytokines in immune organs, histological changes and serum inflammation and immune-related protein contents in VVD broilers.3. The results showed that VVD increased the levels of total protein, albumin, alanine aminotransferase at five weeks of age, aspartate aminotransferase, urea and creatine kinase in blood at two weeks of age. It upregulated the gene expression of inflammatory factors IL-1β, IL-6, IL-8, TNF-α, NF-κB and TGF-β and apoptotic factors FAS, Bcl-2, caspase-3 and 9 in immune organs; increased levels of serum proteins TNF-α, IL-1β and IL-6 and decreased levels of serum immunoglobulins IgY and CD3+.4. In addition, with increasing age, IL-10 gene expression gradually increased in the BF and decreased in the spleen.5. In conclusion, VVD broilers have disorders of liver and kidney metabolism, inflammation and apoptosis of immune organs and increased levels of serum inflammatory factor proteins.
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Affiliation(s)
- J Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, Henan, China
| | - Y Ma
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, Henan, China
| | - L Zhang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, Henan, China
| | - C Cai
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, Henan, China
| | - Y Guo
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, Henan, China
| | - Z Zhang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, Henan, China
| | - D Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, Henan, China
| | - Y Tian
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, Henan, China
| | - X Kang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, Henan, China
| | - R Han
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, Henan, China
| | - R Jiang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
- Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou, Henan, China
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Liang C, Pearlson G, Bustillo J, Kochunov P, Turner JA, Wen X, Jiang R, Fu Z, Zhang X, Li K, Xu X, Zhang D, Qi S, Calhoun VD. Psychotic Symptom, Mood, and Cognition-associated Multimodal MRI Reveal Shared Links to the Salience Network Within the Psychosis Spectrum Disorders. Schizophr Bull 2023; 49:172-184. [PMID: 36305162 PMCID: PMC9810025 DOI: 10.1093/schbul/sbac158] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Schizophrenia (SZ), schizoaffective disorder (SAD), and psychotic bipolar disorder share substantial overlap in clinical phenotypes, associated brain abnormalities and risk genes, making reliable diagnosis among the three illness challenging, especially in the absence of distinguishing biomarkers. This investigation aims to identify multimodal brain networks related to psychotic symptom, mood, and cognition through reference-guided fusion to discriminate among SZ, SAD, and BP. Psychotic symptom, mood, and cognition were used as references to supervise functional and structural magnetic resonance imaging (MRI) fusion to identify multimodal brain networks for SZ, SAD, and BP individually. These features were then used to assess the ability in discriminating among SZ, SAD, and BP. We observed shared links to functional and structural covariation in prefrontal, medial temporal, anterior cingulate, and insular cortices among SZ, SAD, and BP, although they were linked with different clinical domains. The salience (SAN), default mode (DMN), and fronto-limbic (FLN) networks were the three identified multimodal MRI features within the psychosis spectrum disorders from psychotic symptom, mood, and cognition associations. In addition, using these networks, we can classify patients and controls and distinguish among SZ, SAD, and BP, including their first-degree relatives. The identified multimodal SAN may be informative regarding neural mechanisms of comorbidity for psychosis spectrum disorders, along with DMN and FLN may serve as potential biomarkers in discriminating among SZ, SAD, and BP, which may help investigators better understand the underlying mechanisms of psychotic comorbidity from three different disorders via a multimodal neuroimaging perspective.
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Affiliation(s)
- Chuang Liang
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Godfrey Pearlson
- Department of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Juan Bustillo
- Departments of Neurosciences and Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Xuyun Wen
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Rongtao Jiang
- Department of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Xiao Zhang
- Department of Psychiatry, Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
| | - Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Daoqiang Zhang
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Shile Qi
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Vince D Calhoun
- Department of Psychology, Georgia State University, Atlanta, GA, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Electrical and Computer Engineering, Georgia Tech University, Atlanta, GA, USA
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Li X, Towe SL, Bell RP, Jiang R, Hall SA, Calhoun VD, Meade CS, Sui J. The Individualized Prediction of Neurocognitive Function in People Living with HIV Based on Clinical and Multimodal Connectome Data. IEEE J Biomed Health Inform 2023; PP. [PMID: 37022271 PMCID: PMC10387132 DOI: 10.1109/jbhi.2023.3240508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Neurocognitive impairment continues to be common comorbidity for people living with HIV (PLWH). Given the chronic nature of HIV disease, identifying reliable biomarkers of these impairments is essential to advance our understanding of the underlying neural foundation and facilitate screening and diagnosis in clinical care. While neuroimaging provides immense potential for such biomarkers, to date, investigations in PLWH have been mostly limited to either univariate mass techniques or a single neuroimaging modality. In the present study, connectome-based predictive modeling (CPM) was proposed to predict individual differences of cognitive functioning in PLWH, using resting-state functional connectivity (FC), white matter structural connectivity (SC), and clinical relevant measures. We also adopted an efficient feature selection approach to identify the most predictive features, which achieved an optimal prediction accuracy of r = 0.61 in the discovery dataset (n = 102) and r = 0.45 in an independent validation HIV cohort (n = 88). Two brain templates and nine distinct prediction models were also tested for better modeling generalizability. Results show that combining multimodal FC and SC features enabled higher prediction accuracy of cognitive scores in PLWH, while adding clinical and demographic metrics may further improve the prediction by introducing complementary information, which may help better evaluate the individual-level cognitive performance in PLWH.
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Affiliation(s)
- Xiang Li
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Sheri L. Towe
- Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Ryan P. Bell
- Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, CT, USA
| | - Shana A. Hall
- Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Vince D. Calhoun
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Christina S. Meade
- Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Jing Sui
- Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University Georgia Institute of Technology, USA
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Qi S, Calhoun VD, Zhang D, Miller J, Deng ZD, Narr KL, Sheline Y, McClintock SM, Jiang R, Yang X, Upston J, Jones T, Sui J, Abbott CC. Links between electroconvulsive therapy responsive and cognitive impairment multimodal brain networks in late-life major depressive disorder. BMC Med 2022; 20:477. [PMID: 36482369 PMCID: PMC9733153 DOI: 10.1186/s12916-022-02678-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Although electroconvulsive therapy (ECT) is an effective treatment for depression, ECT cognitive impairment remains a major concern. The neurobiological underpinnings and mechanisms underlying ECT antidepressant and cognitive impairment effects remain unknown. This investigation aims to identify ECT antidepressant-response and cognitive-impairment multimodal brain networks and assesses whether they are associated with the ECT-induced electric field (E-field) with an optimal pulse amplitude estimation. METHODS A single site clinical trial focused on amplitude (600, 700, and 800 mA) included longitudinal multimodal imaging and clinical and cognitive assessments completed before and immediately after the ECT series (n = 54) for late-life depression. Another two independent validation cohorts (n = 84, n = 260) were included. Symptom and cognition were used as references to supervise fMRI and sMRI fusion to identify ECT antidepressant-response and cognitive-impairment multimodal brain networks. Correlations between ECT-induced E-field within these two networks and clinical and cognitive outcomes were calculated. An optimal pulse amplitude was estimated based on E-field within antidepressant-response and cognitive-impairment networks. RESULTS Decreased function in the superior orbitofrontal cortex and caudate accompanied with increased volume in medial temporal cortex showed covarying functional and structural alterations in both antidepressant-response and cognitive-impairment networks. Volume increases in the hippocampal complex and thalamus were antidepressant-response specific, and functional decreases in the amygdala and hippocampal complex were cognitive-impairment specific, which were validated in two independent datasets. The E-field within these two networks showed an inverse relationship with HDRS reduction and cognitive impairment. The optimal E-filed range as [92.7-113.9] V/m was estimated to maximize antidepressant outcomes without compromising cognitive safety. CONCLUSIONS The large degree of overlap between antidepressant-response and cognitive-impairment networks challenges parameter development focused on precise E-field dosing with new electrode placements. The determination of the optimal individualized ECT amplitude within the antidepressant and cognitive networks may improve the treatment benefit-risk ratio. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02999269.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Jeremy Miller
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Zhi-De Deng
- Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Katherine L Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Yvette Sheline
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Shawn M McClintock
- Division of Psychology, Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - Rongtao Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xiao Yang
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Joel Upston
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Tom Jones
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
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Sha YQ, Jiang R, Miao Y, Qiu TL, Qin SC, Qiu JY, Mi HL, Wu W, Qiao C, Wu YJ, Xia Y, Wang L, Fan L, Xu W, Li JY, Zhu HY. [Clonality relatedness and molecular characteristics of Richter transformation]. Zhonghua Xue Ye Xue Za Zhi 2022; 43:841-847. [PMID: 36709198 PMCID: PMC9669627 DOI: 10.3760/cma.j.issn.0253-2727.2022.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Indexed: 11/23/2022]
Abstract
Objective: To investigate the clinical, genetic, and clonality related aspects of individuals with Richter transformation (RT) . Methods: From January 2019 to December 2021, 18 RT patients with diagnoses at the First Affiliated Hospital of Nanjing Medical University (Pukou CLL center) were retrospectively examined. The immunoglobin heavy variable (IGHV) gene usage and IGHV-D-J rearrangement pattern of diagnosed CLL/SLL and transformed diffuse large B-cell lymphoma (DLBCL) were compared to determine the clonality relatedness. To investigate the risk factors of RT, Clinical and laboratory data from patients with newly diagnosed CLL/SLL and transformed DLBCL were gathered. Results: The median age of RT was 56.5 (41-75) years old. 17 patients transformed to DLBCL and 1 transformed to Hodgkin lymphoma (HL) . Of 17 individuals who had DLBCL transformation, 15 had CLL/SLL-related clonality and 2 had unrelated clonality. Next-generation sequencing (NGS) analysis of 11 paired initially diagnosed treatment-naive CLL/SLL and RT DLBCL found that EGR2、TP53 and NOTCH1 were among the most frequently mutated genes both in treatment-naive CLL/SLL and in RT DLBCL. In several cases, specific mutations were gained or lost throughout RT, indicating clonal evolution. Among 10 patients before exposure to BTK inhibitors before RT, four patients acquired BTK mutation. The aforementioned mutations should be considered high-risk variables for transformation; in addition, TP53 and EGR2 mutations could be linked to a poor prognosis following RT in patients receiving a cocktail of new medicines. Conclusion: Most RT DLBCL patients in our center are clonality related (15/17, 88.2% ) and we recommend all qualified centers to evaluate clonality relatedness of RT DLBCL patients. There was some variability in the mutational landscape between DLBCL that had undergone a transformation and initially diagnosed, treatment-naive CLL/SLL. The underlying molecular mechanism of RT needs more research.
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Affiliation(s)
- Y Q Sha
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - R Jiang
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Y Miao
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - T L Qiu
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - S C Qin
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - J Y Qiu
- Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - H L Mi
- Pukou CLL Center, Pukou Division of Jiangsu Province Hospital, Nanjing 211800, China
| | - W Wu
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - C Qiao
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Y J Wu
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Y Xia
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - L Wang
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - L Fan
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - W Xu
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - J Y Li
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - H Y Zhu
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
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Jiang R, Cheung C, Davies B, Cao J, Laksman Z, Krahn A. DETECTION OF CONGENITAL LONG QT SYNDROME WITH ARTIFICIAL INTELLIGENCE. Can J Cardiol 2022. [DOI: 10.1016/j.cjca.2022.08.081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
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34
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Jiang R, Westwater ML, Noble S, Rosenblatt M, Dai W, Qi S, Sui J, Calhoun VD, Scheinost D. Associations between grip strength, brain structure, and mental health in > 40,000 participants from the UK Biobank. BMC Med 2022; 20:286. [PMID: 36076200 PMCID: PMC9461129 DOI: 10.1186/s12916-022-02490-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 07/20/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Grip strength is a widely used and well-validated measure of overall health that is increasingly understood to index risk for psychiatric illness and neurodegeneration in older adults. However, existing work has not examined how grip strength relates to a comprehensive set of mental health outcomes, which can detect early signs of cognitive decline. Furthermore, whether brain structure mediates associations between grip strength and cognition remains unknown. METHODS Based on cross-sectional and longitudinal data from over 40,000 participants in the UK Biobank, this study investigated the behavioral and neural correlates of handgrip strength using a linear mixed effect model and mediation analysis. RESULTS In cross-sectional analysis, we found that greater grip strength was associated with better cognitive functioning, higher life satisfaction, greater subjective well-being, and reduced depression and anxiety symptoms while controlling for numerous demographic, anthropometric, and socioeconomic confounders. Further, grip strength of females showed stronger associations with most behavioral outcomes than males. In longitudinal analysis, baseline grip strength was related to cognitive performance at ~9 years follow-up, while the reverse effect was much weaker. Further, baseline neuroticism, health, and financial satisfaction were longitudinally associated with subsequent grip strength. The results revealed widespread associations between stronger grip strength and increased grey matter volume, especially in subcortical regions and temporal cortices. Moreover, grey matter volume of these regions also correlated with better mental health and considerably mediated their relationship with grip strength. CONCLUSIONS Overall, using the largest population-scale neuroimaging dataset currently available, our findings provide the most well-powered characterization of interplay between grip strength, mental health, and brain structure, which may facilitate the discovery of possible interventions to mitigate cognitive decline during aging.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA.
| | - Margaret L Westwater
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Wei Dai
- Department of Biostatistics, Yale University, New Haven, CT, 06520, USA
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, 30303, USA
| | - Jing Sui
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, 30303, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, 30303, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA.
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06520, USA.
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA.
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA.
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35
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Qi S, Sui J, Pearlson G, Bustillo J, Perrone-Bizzozero NI, Kochunov P, Turner JA, Fu Z, Shao W, Jiang R, Yang X, Liu J, Du Y, Chen J, Zhang D, Calhoun VD. Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network. Nat Commun 2022; 13:4929. [PMID: 35995794 PMCID: PMC9395379 DOI: 10.1038/s41467-022-32513-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 08/03/2022] [Indexed: 12/23/2022] Open
Abstract
Schizophrenia is a highly heritable psychiatric disorder characterized by widespread functional and structural brain abnormalities. However, previous association studies between MRI and polygenic risk were mostly ROI-based single modality analyses, rather than identifying brain-based multimodal predictive biomarkers. Based on schizophrenia polygenic risk scores (PRS) from healthy white people within the UK Biobank dataset (N = 22,459), we discovered a robust PRS-associated brain pattern with smaller gray matter volume and decreased functional activation in frontotemporal cortex, which distinguished schizophrenia from controls with >83% accuracy, and predicted cognition and symptoms across 4 independent schizophrenia cohorts. Further multi-disease comparisons demonstrated that these identified frontotemporal alterations were most severe in schizophrenia and schizo-affective patients, milder in bipolar disorder, and indistinguishable from controls in autism, depression and attention-deficit hyperactivity disorder. These findings indicate the potential of the identified PRS-associated multimodal frontotemporal network to serve as a trans-diagnostic gene intermediated brain biomarker specific to schizophrenia.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
| | - Godfrey Pearlson
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Juan Bustillo
- Departments of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Nora I Perrone-Bizzozero
- Departments of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | - Wei Shao
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Xiao Yang
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | - Yuhui Du
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Jiayu Chen
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA.
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
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36
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Xu M, Qi S, Calhoun V, Dai J, Yu B, Zhang K, Pei M, Li C, Wei Y, Jiang R, Zhi D, Huang Z, Qiu Z, Liang Z, Sui J. Aberrant brain functional and structural developments in MECP2 duplication rats. Neurobiol Dis 2022; 173:105838. [PMID: 35985556 PMCID: PMC9631682 DOI: 10.1016/j.nbd.2022.105838] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/22/2022] [Accepted: 08/11/2022] [Indexed: 12/02/2022] Open
Abstract
Transgenic animal models with homologous etiology provide a promising way to pursue the neurobiological substrates of the behavioral deficits in autism spectrum disorder (ASD). Gain-of-function mutations of MECP2 cause MECP2 duplication syndrome, a severe neurological disorder with core symptoms of ASD. However, abnormal brain developments underlying the autistic-like behavioral deficits of MECP2 duplication syndrome are rarely investigated. To this end, a human MECP2 duplication (MECP2-DP) rat model was created by the bacterial artificial chromosome transgenic method. Functional and structural magnetic resonance imaging (MRI) with high-field were performed on 16 male MECP2-DP rats and 15 male wildtype rats at postnatal 28 days, 42 days, and 56 days old. Multimodal fusion analyses guided by locomotor-relevant metrics and social novelty time separately were applied to identify abnormal brain networks associated with diverse behavioral deficits induced by MECP2 duplication. Aberrant functional developments of a core network primarily composed of the dorsal medial prefrontal cortex (dmPFC) and retrosplenial cortex (RSP) were detected to associate with diverse behavioral phenotypes in MECP2-DP rats. Altered developments of gray matter volume were detected in the hippocampus and thalamus. We conclude that gain-of-function mutations of MECP2 induce aberrant functional activities in the default-mode-like network and aberrant volumetric changes in the brain, resulting in autistic-like behavioral deficits. Our results gain critical insights into the biomarker of MECP2 duplication syndrome and the neurobiological underpinnings of the behavioral deficits in ASD.
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Affiliation(s)
- Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA 30303, USA
| | - Jiankun Dai
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Bin Yu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Kaiwei Zhang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mengchao Pei
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chenjian Li
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Peking University School of Life Sciences, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Beijing 100871, China
| | - Yusheng Wei
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Peking University School of Life Sciences, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Beijing 100871, China
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Dongmei Zhi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Zhimin Huang
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Peking University School of Life Sciences, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Beijing 100871, China
| | - Zilong Qiu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhifeng Liang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Jing Sui
- IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
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37
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Jiang R, Scheinost D, Zuo N, Wu J, Qi S, Liang Q, Zhi D, Luo N, Chung Y, Liu S, Xu Y, Sui J, Calhoun V. A Neuroimaging Signature of Cognitive Aging from Whole-Brain Functional Connectivity. Adv Sci (Weinh) 2022; 9:e2201621. [PMID: 35811304 PMCID: PMC9403648 DOI: 10.1002/advs.202201621] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 06/02/2022] [Indexed: 05/14/2023]
Abstract
Cognitive decline is amongst one of the most commonly reported complaints during normal aging. Despite evidence that age and cognition are linked with similar neural correlates, no previous studies have directly ascertained how these two constructs overlap in the brain in terms of neuroimaging-based prediction. Based on a long lifespan healthy cohort (CamCAN, aged 19-89 years, n = 567), it is shown that both cognitive function (domains spanning executive function, emotion processing, motor function, and memory) and human age can be reliably predicted from unique patterns of functional connectivity, with models generalizable in two external datasets (n = 533 and n = 453). Results show that cognitive decline and normal aging both manifest decrease within-network connections (especially default mode and ventral attention networks) and increase between-network connections (somatomotor network). Whereas dorsal attention network is an exception, which is highly predictive on cognitive ability but is weakly correlated with aging. Further, the positively weighted connections in predicting fluid intelligence significantly mediate its association with age. Together, these findings offer insights into why normal aging is often associated with cognitive decline in terms of brain network organization, indicating a process of neural dedifferentiation and compensational theory.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenCT06520USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenCT06520USA
- Interdepartmental Neuroscience ProgramYale UniversityNew HavenCT06520USA
- Department of Statistics and Data ScienceYale UniversityNew HavenCT06520USA
- Child Study CenterYale School of MedicineNew HavenCT06510USA
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190P. R. China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049P. R. China
| | - Jing Wu
- Department of Medical OncologyBeijing You‐An HospitalCapital Medical UniversityBeijing100069P. R. China
| | - Shile Qi
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing211106P. R. China
| | - Qinghao Liang
- Department of Biomedical EngineeringYale UniversityNew HavenCT06520USA
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100088P. R. China
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190P. R. China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049P. R. China
| | - Young‐Chul Chung
- Department of PsychiatryJeonbuk National University Medical SchoolJeonju54907Republic of Korea
- Department of PsychiatryChonbuk National University HospitalJeonju54907Republic of Korea
| | - Sha Liu
- Department of Psychiatry and MDT Center for Cognitive Impairment and Sleep DisordersFirst HospitalFirst Clinical Medical College of Shanxi Medical UniversityTaiyuan030001P. R. China
| | - Yong Xu
- Department of Psychiatry and MDT Center for Cognitive Impairment and Sleep DisordersFirst HospitalFirst Clinical Medical College of Shanxi Medical UniversityTaiyuan030001P. R. China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100088P. R. China
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia Institute of TechnologyEmory University and Georgia State UniversityAtlantaGA30303USA
| | - Vince Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia Institute of TechnologyEmory University and Georgia State UniversityAtlantaGA30303USA
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38
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Qi S, Fu Z, Wu L, Calhoun VD, Zhang D, Daughters SB, Hsu PC, Jiang R, Vergara VM, Sui J, Addicott MA. Cognition, Aryl Hydrocarbon Receptor Repressor Methylation, and Abstinence Duration-Associated Multimodal Brain Networks in Smoking and Long-Term Smoking Cessation. Front Neurosci 2022; 16:923065. [PMID: 35968362 PMCID: PMC9363622 DOI: 10.3389/fnins.2022.923065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 06/20/2022] [Indexed: 02/04/2023] Open
Abstract
Cigarette smoking and smoking cessation are associated with changes in cognition and DNA methylation; however, the neurobiological correlates of these effects have not been fully elucidated, especially in long-term cessation. Cognitive performance, percent methylation of the aryl hydrocarbon receptor repressor (AHRR) gene, and abstinence duration were used as references to supervise a multimodal fusion analysis of functional, structural, and diffusion magnetic resonance imaging (MRI) data, in order to identify associated brain networks in smokers and ex-smokers. Correlations among these networks and with smoking-related measures were performed. Cognition-, methylation-, and abstinence duration-associated networks discriminated between smokers and ex-smokers and correlated with differences in fractional amplitude of low frequency fluctuations (fALFF) values, gray matter volume (GMV), and fractional anisotropy (FA) values. Long-term smoking cessation was associated with more accurate cognitive performance, as well as lower fALFF and more GMV in the hippocampus complex. The methylation- and abstinence duration-associated networks positively correlated with smoking-related measures of abstinence duration and percent methylation, respectively, suggesting they are complementary measures. This analysis revealed structural and functional co-alterations linked to smoking abstinence and cognitive performance in brain regions including the insula, frontal gyri, and lingual gyri. Furthermore, AHRR methylation, a promising epigenetic biomarker of smoking recency, may provide an important complement to self-reported abstinence duration.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Stacey B. Daughters
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Ping-Ching Hsu
- Department of Environmental and Occupational Health, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Victor M. Vergara
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Merideth A. Addicott
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
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Jiang R, Woo CW, Qi S, Wu J, Sui J. Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging. IEEE Signal Process Mag 2022; 39:107-118. [PMID: 36712588 PMCID: PMC9880880 DOI: 10.1109/msp.2022.3155951] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding the interpretability of the results. Approaches to defining the specific contribution of functional connections, regions, or networks in prediction models are urgently needed, which may help explore the underlying mechanisms. In this article, we systematically review the methods and applications for interpreting brain signatures derived from predictive neuroimaging based on a survey of 326 research articles. Strengths, limitations, and the suitable conditions for major interpretation strategies are also deliberated. In-depth discussion of common issues in existing literature and the corresponding recommendations to address these pitfalls are provided. We highly recommend exhaustive validation on the reliability and interpretability of the biomarkers across multiple datasets and contexts, which thereby could translate technical advances in neuroimaging into concrete improvements in precision medicine.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA, 06520
| | - Choong-Wan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea, 16419
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea, 16419
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea, 16419
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China, 211106
| | - Jing Wu
- Department of Medical Oncology, Beijing You-An Hospital, Capital Medical University, Beijing, China, 100069
| | - Jing Sui
- State Key Laboratory of Brain Cognition and Learning, Beijing Normal University, Beijing, China, 100875
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40
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Abstract
Tooth agenesis is a common structural birth defect in humans that results from failure of morphogenesis during early tooth development. The homeobox transcription factor Msx1 and the canonical Wnt signaling pathway are essential for "bud to cap" morphogenesis and are causal factors for tooth agenesis. Our recent study suggested that Msx1 regulates Wnt signaling during early tooth development by suppressing the expression of Dkk2 and Sfrp2 in the tooth bud mesenchyme, and it demonstrated partial rescue of Msx1-deficient molar teeth by a combination of DKK inhibition and genetic inactivation of SFRPs. In this study, we found that Sostdc1/Wise, another secreted Wnt antagonist, is involved in regulating the odontogenic pathway downstream of Msx1. Whereas Sostdc1 expression in the developing tooth germ was not increased in Msx1-/- embryos, genetic inactivation of Sostdc1 rescued maxillary molar, but not mandibular molar, morphogenesis in Msx1-/- mice with full penetrance. Since the Msx1-/-;Sostdc1-/- embryos exhibited ectopic Dkk2 expression in the developing dental mesenchyme, similar to Msx1-/- embryos, we generated and analyzed tooth development in Msx1-/-;Dkk2-/- double and Msx1-/-;Dkk2-/-;Sostdc1-/- triple mutant mice. The Msx1-/-;Dkk2-/- double mutants showed rescued maxillary molar morphogenesis at high penetrance, with a small percentage also exhibiting mandibular molars that transitioned to the cap stage. Furthermore, tooth development was rescued in the maxillary and mandibular molars, with full penetrance, in the Msx1-/-;Dkk2-/-;Sostdc1-/- mice. Together, these data reveal 1) that a key role of Msx1 in driving tooth development through the bud-to-cap transition is to control the expression of Dkk2 and 2) that modulation of Wnt signaling activity by Dkk2 and Sostdc1 plays a crucial role in the Msx1-dependent odontogenic pathway during early tooth morphogenesis.
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Affiliation(s)
- J.-M. Lee
- Department of Oral Biology, School of Dental Medicine, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - C. Qin
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Ministry of Education Key Laboratory of Oral Biomedicine, and Department of Oral and Maxillofacial Surgery, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - O.H. Chai
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Anatomy, Jeonbuk National University Medical School, Jeonju, Korea
| | - Y. Lan
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Division of Plastic Surgery, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Departments of Pediatrics and Surgery, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - R. Jiang
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Division of Plastic Surgery, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Departments of Pediatrics and Surgery, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - H.-J.E. Kwon
- Department of Oral Biology, School of Dental Medicine, University at Buffalo, State University of New York, Buffalo, NY, USA
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
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41
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Feng A, Luo N, Zhao W, Calhoun VD, Jiang R, Zhi D, Shi W, Jiang T, Yu S, Xu Y, Liu S, Sui J. Multimodal brain deficits shared in early-onset and adult-onset schizophrenia predict positive symptoms regardless of illness stage. Hum Brain Mapp 2022; 43:3486-3497. [PMID: 35388581 PMCID: PMC9248316 DOI: 10.1002/hbm.25862] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/10/2022] [Accepted: 03/23/2022] [Indexed: 11/25/2022] Open
Abstract
Incidence of schizophrenia (SZ) has two predominant peaks, in adolescent and young adult. Early‐onset schizophrenia provides an opportunity to explore the neuropathology of SZ early in the disorder and without the confound of antipsychotic mediation. However, it remains unexplored what deficits are shared or differ between adolescent early‐onset (EOS) and adult‐onset schizophrenia (AOS) patients. Here, based on 529 participants recruited from three independent cohorts, we explored AOS and EOS common and unique co‐varying patterns by jointly analyzing three MRI features: fractional amplitude of low‐frequency fluctuations (fALFF), gray matter (GM), and functional network connectivity (FNC). Furthermore, a prediction model was built to evaluate whether the common deficits in drug‐naive SZ could be replicated in chronic patients. Results demonstrated that (1) both EOS and AOS patients showed decreased fALFF and GM in default mode network, increased fALFF and GM in the sub‐cortical network, and aberrant FNC primarily related to middle temporal gyrus; (2) the commonly identified regions in drug‐naive SZ correlate with PANSS positive significantly, which can also predict PANSS positive in chronic SZ with longer duration of illness. Collectively, results suggest that multimodal imaging signatures shared by two types of drug‐naive SZ are also associated with positive symptom severity in chronic SZ and may be vital for understanding the progressive schizophrenic brain structural and functional deficits.
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Affiliation(s)
- Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Wentao Zhao
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Vince D Calhoun
- Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical imaging, Yale University, New Haven, Connecticut, USA
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Weiyang Shi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yong Xu
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Sha Liu
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jing Sui
- Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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Flament F, Jacquet L, Ye C, Amar D, Kerob D, Jiang R, Zhang Y, Kroely C, Delaunay C, Passeron T. Artificial Intelligence analysis of over half a million European and Chinese women reveals striking differences in the facial skin aging process. J Eur Acad Dermatol Venereol 2022; 36:1136-1142. [DOI: 10.1111/jdv.18073] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/28/2022] [Accepted: 02/23/2022] [Indexed: 12/01/2022]
Affiliation(s)
- F. Flament
- L’Oréal Research and Innovation Clichy France
| | - L. Jacquet
- Vichy International Levallois‐Perret France
| | - C. Ye
- L’Oréal Research and Innovation Shanghai China
| | - D. Amar
- L’Oréal Research and Innovation Shanghai China
| | - D. Kerob
- Vichy International Levallois‐Perret France
| | - R. Jiang
- ModiFace – A L'Oréal Group Company Toronto Canada
| | - Y. Zhang
- ModiFace – A L'Oréal Group Company Toronto Canada
| | - C. Kroely
- L’Oréal CDO – Digital Service Factory Clichy France
| | - C. Delaunay
- L’Oréal Research and Innovation Clichy France
| | - T. Passeron
- Université Côte d’Azur CHU Nice Department of Dermatology Nice France
- Université Côte d’Azur INSERM U1065, C3M Nice France
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43
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Stokes W, Behera M, Jiang R, Gutman D, Huang Z, Burns A, Sebastian N, Sukhatme V, Lowe M, Ramalingam S, Sukhatme V, Moghanaki D. Effect of Antibiotic Therapy on Immunotherapy Outcomes for Non-Small Cell Lung Cancer: Analysis From the Veterans Health Administration. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2021.10.175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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44
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Chen T, Jiang R, Jiang L. Comment on "Influence of glycemic control and hypoglycemia on the risk of fracture in patients with diabetes mellitus: a systematic review and meta-analysis of observational studies". Osteoporos Int 2021; 32:2597-2598. [PMID: 34674022 DOI: 10.1007/s00198-021-06217-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 10/15/2021] [Indexed: 10/20/2022]
Affiliation(s)
- T Chen
- Department of TCM Orthopedics and Traumatology, Jiujiang Hospital of Traditional Chinese Medicine, Jiujiang, Jiangxi, 332005, China
| | - R Jiang
- Department of TCM Orthopedics and Traumatology, Jiujiang Hospital of Traditional Chinese Medicine, Jiujiang, Jiangxi, 332005, China
| | - L Jiang
- Department of TCM Orthopedics and Traumatology, Jiujiang Hospital of Traditional Chinese Medicine, Jiujiang, Jiangxi, 332005, China.
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He R, Jiao H, He N, Chang Y, Jiang H, Zhang Y, Li Y, Jiang R. Seasonal Variation of Zooplankton Communities and the Effects of Environmental Factors in the Seawater Near Taishan Nuclear Power Station. NEPT 2021. [DOI: 10.46488/nept.2021.v20i04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
In the seawater near Taishan Nuclear Power Station, Zooplankton community composition and abundance, the biomass of major taxa, vertical distribution pattern, together with several environmental factors were investigated to evaluate the variation tendency as the seasons change. The structure characteristics of the zooplankton community were analyzed by Margalef species richness (d), Shannon-wiener species diversity index (H’), Pielou evenness index(J’), zooplankton dominant (Y), and dominant species replacement rate (R). There are 48 species within 11 classes of zooplankton identified, including 32 species of copepods. Zooplankton species richness changed obviously in the four seasons, Spring saw the highest (8010.00 ind.m-3), followed by winter (5100.00 ind.m-3), autumn (1713.75 ind.m-3), and summer (1196.25 ind.m-3). Similar trends were observed for the wet biomass, which was highest in spring (215.90 mg.m-3), followed by winter (181.70 mg.m-3), summer (78.56 mg.m-3), and autumn (24.69 mg.m-3), which gave an annual average of 125.21 mg.m-3. The results indicate that the abundance and biomass in spring were significantly higher than those in other seasons. Altogether 8 dominant species were identified along the whole year: Acrocalanmus gibber, Bestiolina amoyensis, Paracalanus parvus, Acartia danae, Mesocyclops leuckarti, Noctiluca scientillans, Penilla avirostris, and Lucifer penicilliger. The annual average Shannon-Wiener diversity index, Margalef diversity index, Pielou evenness index were 1.75, 1.83, and 0.74, respectively. The effects of environmental factors on the zooplankton community were studied by R and canonical correspondence analysis (CCA). According to Pearson correlation analysis and canonical correspondence analysis, the most important environmental factors influencing the changes of zooplankton species composition, abundance and distribution were water temperature, salinity, and pH in the whole year.
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Qi S, Silva RF, Zhang D, Plis SM, Miller R, Vergara VM, Jiang R, Zhi D, Sui J, Calhoun VD. Three-way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data. Hum Brain Mapp 2021; 43:1280-1294. [PMID: 34811846 PMCID: PMC8837596 DOI: 10.1002/hbm.25720] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 10/17/2021] [Accepted: 11/07/2021] [Indexed: 01/24/2023] Open
Abstract
Advances in imaging acquisition techniques allow multiple imaging modalities to be collected from the same subject. Each individual modality offers limited yet unique views of the functional, structural, or dynamic temporal features of the brain. Multimodal fusion provides effective ways to leverage these complementary perspectives from multiple modalities. However, the majority of current multimodal fusion approaches involving functional magnetic resonance imaging (fMRI) are limited to 3D feature summaries that do not incorporate its rich temporal information. Thus, we propose a novel three‐way parallel group independent component analysis (pGICA) fusion method that incorporates the first‐level 4D fMRI data (temporal information included) by parallelizing group ICA into parallel ICA via a unified optimization framework. A new variability matrix was defined to capture subject‐wise functional variability and then link it to the mixing matrices of the other two modalities. Simulation results show that the three‐way pGICA provides highly accurate cross‐modality linkage estimation under both weakly and strongly correlated conditions, as well as comparable source estimation under different noise levels. Results using real brain imaging data identified one linked functional–structural–diffusion component associated to differences between schizophrenia and controls. This was replicated in an independent cohort, and the identified components were also correlated with major cognitive domains. Functional network connectivity revealed visual–subcortical and default mode‐cerebellum pairs that discriminate between schizophrenia and controls. Overall, both simulation and real data results support the use of three‐way pGICA to identify multimodal spatiotemporal links and to pursue the study of brain disorders under a single unifying multimodal framework.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Rogers F Silva
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Sergey M Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Robyn Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Victor M Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
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Zhi D, Calhoun VD, Wang C, Li X, Ma X, Lv L, Yan W, Yao D, Qi S, Jiang R, Zhao J, Yang X, Lin Z, Zhang Y, Chung YC, Zhuo C, Sui J. BNCPL: Brain-Network-based Convolutional Prototype Learning for Discriminating Depressive Disorders. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:1622-1626. [PMID: 34891596 PMCID: PMC9021005 DOI: 10.1109/embc46164.2021.9630010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Deep learning has shown great potential to adaptively learn hidden patterns from high dimensional neuroimaging data, so as to extract subtle group differences. Motivated by the convolutional neural networks and prototype learning, we developed a brain-network-based convolutional prototype learning model (BNCPL), which can learn representations that simultaneously maximize inter-class separation while minimize within-class distance. When applying BNCPL to distinguish 208 depressive disorders from 210 healthy controls using resting-state functional connectivity (FC), we achieved an accuracy of 71.0% in multi-site pooling classification (3 sites), with 2.4-7.2% accuracy increase compared to 3 traditional classifiers and 2 alternative deep neural networks. Saliency map was also used to examine the most discriminative FCs learned by the model; the prefrontal-subcortical circuits were identified, which were also correlated with disease severity and cognitive ability. In summary, by integrating convolutional prototype learning and saliency map, we improved both the model interpretability and classification performance, and found that the dysregulation of the functional prefrontal-subcortical circuit may play a pivotal role in discriminating depressive disorders from healthy controls.
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48
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Qi S, Schumann G, Bustillo J, Turner JA, Jiang R, Zhi D, Fu Z, Mayer AR, Vergara VM, Silva RF, Iraji A, Chen J, Damaraju E, Ma X, Yang X, Stevens M, Mathalon DH, Ford JM, Voyvodic J, Mueller BA, Belger A, Potkin SG, Preda A, Zhuo C, Xu Y, Chu C, Banaschewski T, Barker GJ, Bokde ALW, Quinlan EB, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Martinot JL, Paillère Martinot ML, Artiges E, Nees F, Orfanos DP, Paus T, Poustka L, Hohmann S, Fröhner JH, Smolka MN, Walter H, Whelan R, Calhoun VD, Sui J. Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker. Biol Psychiatry 2021; 90:529-539. [PMID: 33875230 PMCID: PMC8322149 DOI: 10.1016/j.biopsych.2021.01.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/28/2020] [Accepted: 01/04/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Dysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues. METHODS A subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n ≈1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels. RESULTS Multimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%. CONCLUSIONS Adolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS-associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging biomarker to predict disease risks or severity.
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Affiliation(s)
- Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia; Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, Georgia
| | - Rongtao Jiang
- University of Chinese Academy of Sciences, Beijing, China; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Dongmei Zhi
- University of Chinese Academy of Sciences, Beijing, China; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Andrew R Mayer
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Victor M Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Rogers F Silva
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Armin Iraji
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Jiayu Chen
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Eswar Damaraju
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiao Yang
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | | | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, California
| | - Judith M Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, California
| | - James Voyvodic
- Department of Radiology, Duke University, Durham, North Carolina
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Steven G Potkin
- Department of Psychiatry, University of California Irvine, Irvine, California
| | - Adrian Preda
- Department of Psychiatry, University of California Irvine, Irvine, California
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory, Nankai University Affiliated Anding Hospital, Tianjin, China
| | - Yong Xu
- Department of Humanities and Social Science, Shanxi Medical University, Taiyuan, China
| | - Congying Chu
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Erin Burke Quinlan
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Herta Flor
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, Vermont
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry," University Paris-Saclay, Paris, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry," University Paris-Saclay, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry," University Paris-Saclay, Paris, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | | | - Tomáš Paus
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital and Departments of Psychology and Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Berlin, Germany
| | - Robert Whelan
- PONS Research Group, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Humboldt University, Berlin, Germany
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia; Department of Psychology, Georgia State University, Atlanta, Georgia.
| | - Jing Sui
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia; University of Chinese Academy of Sciences, Beijing, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
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Wu J, Wang Y, Jiang R, Xue R, Yin X, Wu M, Meng Q. Ferroptosis in liver disease: new insights into disease mechanisms. Cell Death Discov 2021; 7:276. [PMID: 34611144 PMCID: PMC8492622 DOI: 10.1038/s41420-021-00660-4] [Citation(s) in RCA: 100] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 08/18/2021] [Accepted: 09/13/2021] [Indexed: 02/06/2023] Open
Abstract
Characterized by excessive iron accumulation and lipid peroxidation, ferroptosis is a novel form of iron-dependent cell death, which is morphologically, genetically, and biochemically distinct from other well-known cell death. In recent years, ferroptosis has been quickly gaining attention in the field of liver diseases, as the liver is predisposed to oxidative injury and generally, excessive iron accumulation is a primary characteristic of most major liver diseases. In the current review, we first delineate three cellular defense mechanisms against ferroptosis (GPx4 in the mitochondria and cytosol, FSP1 on plasma membrane, and DHODH in mitochondria), along with four canonical modulators of ferroptosis (system Xc−, nuclear factor erythroid 2-related factor 2, p53, and GTP cyclohydrolase-1). Next, we review recent progress of ferroptosis studies delineating molecular mechanisms underlying the pathophysiology of several common liver diseases including ischemia/reperfusion-related injury (IRI), nonalcoholic fatty liver disease (NAFLD), alcoholic liver disease (ALD), hemochromatosis (HH), drug-induced liver injury (DILI), and hepatocellular carcinoma (HCC). Furthermore, we also highlight both challenges and promises that emerged from recent studies that should be addressed and pursued in future investigations before ferroptosis regulation could be adopted as an effective therapeutic target in clinical practice.
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Affiliation(s)
- Jing Wu
- Department of Medical Oncology, Beijing You-An Hospital, Capital Medical University, Beijing, 100069, China
| | - Yi Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Ran Xue
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing, 100036, China
| | - Xuehong Yin
- Department of Medical Oncology, Beijing You-An Hospital, Capital Medical University, Beijing, 100069, China
| | - Muchen Wu
- Department of Medical Oncology, Beijing You-An Hospital, Capital Medical University, Beijing, 100069, China
| | - Qinghua Meng
- Department of Medical Oncology, Beijing You-An Hospital, Capital Medical University, Beijing, 100069, China.
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50
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Stokes W, Behera M, Jiang R, Gutman D, Huang Z, Giuste F, Burns A, Sebastian N, Ramalingam S, Sukhatme V, Lowe M, Ramalingam S, Sukhatme V, Moghanaki D. P53.08 Concomitant Fibrates and Immunotherapy in Non-Small Cell Lung Cancer Patients in the Veterans Health Administration. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.08.557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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