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Tong X, Zhao K, Fonzo GA, Xie H, Carlisle NB, Keller C, Oathes DJ, Sheline Y, Nemeroff CB, Trivedi M, Etkin A, Zhang Y. Optimizing Antidepressant Efficacy: Generalizable Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.11.24305583. [PMID: 38645124 PMCID: PMC11030479 DOI: 10.1101/2024.04.11.24305583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages and demographic groups. Unfortunately, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates in a substantial number of patients. The development of effective therapies for MDD is hindered by the insufficiently understood heterogeneity within the disorder and its elusive underlying mechanisms. To address these challenges, we present a target-oriented multimodal fusion framework that robustly predicts antidepressant response by integrating structural and functional connectivity data (sertraline: R-squared = 0.31; placebo: R-squared = 0.22). Remarkably, the sertraline response biomarker is further tested on an independent escitalopram-medicated cohort of MDD patients, validating its generalizability (p = 0.01) and suggesting an overlap of psychopharmacological mechanisms across selective serotonin reuptake inhibitors. Through the model, we identify multimodal neuroimaging biomarkers of antidepressant response and observe that sertraline and placebo show distinct predictive patterns. We further decompose the overall predictive patterns into constitutive network constellations with generalizable structural-functional co-variation, which exhibit treatment-specific association with personality traits and behavioral/cognitive task performance. Our innovative and interpretable multimodal framework provides novel and reliable insights into the intricate neuropsychopharmacology of antidepressant treatment, paving the way for advances in precision medicine and development of more targeted antidepressant therapeutics.
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Zhang Q, Hudgins S, Struck AF, Ankeeta A, Javidi SS, Sperling MR, Hermann BP, Tracy JI. Association of Normative and Non-Normative Brain Networks With Cognitive Function in Patients With Temporal Lobe Epilepsy. Neurology 2024; 103:e209800. [PMID: 39250744 PMCID: PMC11385956 DOI: 10.1212/wnl.0000000000209800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/11/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Despite their temporal lobe pathology, a significant subgroup of patients with temporal lobe epilepsy (TLE) is able to maintain normative cognitive functioning. In this study, we identify patients with TLE with intact vs impaired neurocognitive profiles and interrogate for the presence of both normative and highly individual intrinsic connectivity networks (ICNs)-all toward understanding the transition from impaired to intact neurocognitive status. METHODS This retrospective cross-sectional study included patients with TLE and matched healthy controls (HCs) from the Thomas Jefferson Comprehensive Epilepsy Center. Functional MRI data were decomposed using independent component analysis to obtain individualized ICNs. In this article, we calculated the degree of match between individualized ICNs and canonical ICNs (e.g., 17 resting-state networks by Yeo et al.) and divided each participant's ICNs into normative or non-normative status based on the degree of match. RESULTS 100 patients with TLE (mean age 42.0 [SD: 13.7] years, 47 women) and 92 HCs were included in this study. We found that the individualized networks matched to the canonical networks less well in the cognitively impaired (n = 24) compared with the cognitively intact (n = 63) patients with TLE by 2-way mixed-measures analysis of variance (impaired vs intact mean difference [MD] -0.165 [-0.317, -0.013], p = 0.028). The cognitively impaired patients showed significant abnormalities in the profiles of both normative (impaired vs intact MD -0.537 [-0.998, -0.076], p = 0.017, intact vs HC MD -0.221 [-0.536, 0.924], p = 0.220, and impaired vs HC MD -0.759 [-1.200, -0.319], p < 0.001) and non-normative networks (impaired vs intact MD 0.484 [0.030, 0.937], p = 0.033, intact vs HC MD 0.369 [0.059, 0.678], p = 0.014, and impaired vs HC MD 0.853 [0.419, 1.286], p < 0.001) while the intact patients showed abnormalities only in non-normative networks. At the same time, we found that normative networks held a strong, positive association with the neuropsychological measures, with this association negative in non-normative networks. DISCUSSION Our data demonstrated that significant cognitive deficits are associated with the status of both canonical and highly individual ICNs, making clear that the transition from intact to impaired cognitive status is not simply the result of disruption to normative brain networks.
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Affiliation(s)
- Qirui Zhang
- From the Farber Institute for Neuroscience (Q.Z., A.A., S.S.J., M.R.S., J.I.T.), Department of Neurology, Thomas Jefferson University, Philadelphia; Department of Biomedical Engineering (S.H.), Drexel University, Philadelphia, PA; and Department of Neurology (A.F.S., B.P.H.), University of Wisconsin School of Medicine and Public Health, Madison
| | - Stacy Hudgins
- From the Farber Institute for Neuroscience (Q.Z., A.A., S.S.J., M.R.S., J.I.T.), Department of Neurology, Thomas Jefferson University, Philadelphia; Department of Biomedical Engineering (S.H.), Drexel University, Philadelphia, PA; and Department of Neurology (A.F.S., B.P.H.), University of Wisconsin School of Medicine and Public Health, Madison
| | - Aaron F Struck
- From the Farber Institute for Neuroscience (Q.Z., A.A., S.S.J., M.R.S., J.I.T.), Department of Neurology, Thomas Jefferson University, Philadelphia; Department of Biomedical Engineering (S.H.), Drexel University, Philadelphia, PA; and Department of Neurology (A.F.S., B.P.H.), University of Wisconsin School of Medicine and Public Health, Madison
| | - Ankeeta Ankeeta
- From the Farber Institute for Neuroscience (Q.Z., A.A., S.S.J., M.R.S., J.I.T.), Department of Neurology, Thomas Jefferson University, Philadelphia; Department of Biomedical Engineering (S.H.), Drexel University, Philadelphia, PA; and Department of Neurology (A.F.S., B.P.H.), University of Wisconsin School of Medicine and Public Health, Madison
| | - Sam S Javidi
- From the Farber Institute for Neuroscience (Q.Z., A.A., S.S.J., M.R.S., J.I.T.), Department of Neurology, Thomas Jefferson University, Philadelphia; Department of Biomedical Engineering (S.H.), Drexel University, Philadelphia, PA; and Department of Neurology (A.F.S., B.P.H.), University of Wisconsin School of Medicine and Public Health, Madison
| | - Michael R Sperling
- From the Farber Institute for Neuroscience (Q.Z., A.A., S.S.J., M.R.S., J.I.T.), Department of Neurology, Thomas Jefferson University, Philadelphia; Department of Biomedical Engineering (S.H.), Drexel University, Philadelphia, PA; and Department of Neurology (A.F.S., B.P.H.), University of Wisconsin School of Medicine and Public Health, Madison
| | - Bruce P Hermann
- From the Farber Institute for Neuroscience (Q.Z., A.A., S.S.J., M.R.S., J.I.T.), Department of Neurology, Thomas Jefferson University, Philadelphia; Department of Biomedical Engineering (S.H.), Drexel University, Philadelphia, PA; and Department of Neurology (A.F.S., B.P.H.), University of Wisconsin School of Medicine and Public Health, Madison
| | - Joseph I Tracy
- From the Farber Institute for Neuroscience (Q.Z., A.A., S.S.J., M.R.S., J.I.T.), Department of Neurology, Thomas Jefferson University, Philadelphia; Department of Biomedical Engineering (S.H.), Drexel University, Philadelphia, PA; and Department of Neurology (A.F.S., B.P.H.), University of Wisconsin School of Medicine and Public Health, Madison
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Wei L, Xu X, Su Y, Lan M, Wang S, Zhong S. Abnormal multimodal neuroimaging patterns associated with social deficits in male autism spectrum disorder. Hum Brain Mapp 2024; 45:e70017. [PMID: 39230055 PMCID: PMC11372822 DOI: 10.1002/hbm.70017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 07/24/2024] [Accepted: 08/20/2024] [Indexed: 09/05/2024] Open
Abstract
Atypical social impairments (i.e., impaired social cognition and social communication) are vital manifestations of autism spectrum disorder (ASD) patients, and the incidence rate of ASD is significantly higher in males than in females. Characterizing the atypical brain patterns underlying social deficits of ASD is significant for understanding the pathogenesis. However, there are no robust imaging biomarkers that are specific to ASD, which may be due to neurobiological complexity and limitations of single-modality research. To describe the multimodal brain patterns related to social deficits in ASD, we highlighted the potential functional role of white matter (WM) and incorporated WM functional activity and gray matter structure into multimodal fusion. Gray matter volume (GMV) and fractional amplitude of low-frequency fluctuations of WM (WM-fALFF) were combined by fusion analysis model adopting the social behavior. Our results revealed multimodal spatial patterns associated with Social Responsiveness Scale multiple scores in ASD. Specifically, GMV exhibited a consistent brain pattern, in which salience network and limbic system were commonly identified associated with all multiple social impairments. More divergent brain patterns in WM-fALFF were explored, suggesting that WM functional activity is more sensitive to ASD's complex social impairments. Moreover, brain regions related to social impairment may be potentially interconnected across modalities. Cross-site validation established the repeatability of our results. Our research findings contribute to understanding the neural mechanisms underlying social disorders in ASD and affirm the feasibility of identifying biomarkers from functional activity in WM.
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Affiliation(s)
- Long Wei
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, People's Republic of China
| | - Xin Xu
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, People's Republic of China
| | - Yuwei Su
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, People's Republic of China
| | - Min Lan
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, People's Republic of China
| | - Sifeng Wang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, People's Republic of China
| | - Suyu Zhong
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, People's Republic of China
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Stoyanov D, Khorev V, Paunova R, Kandilarova S, Kurkin S, Calhoun VD. Group independent components underpin responses to items from a depression scale. Acta Neuropsychiatr 2024; 36:9-16. [PMID: 37088536 DOI: 10.1017/neu.2023.22] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
OBJECTIVE The aim of the present study is to investigate the brain circuits or networks that underpin diagnostically specific tasks by means of group independent component analysis for FMRI toolbox (GIFT). We hypothesised that there will be neural network patterns of activation and deactivation, which correspond to real-time performance on clinical self-evaluation scales. METHODS In total, 20 healthy controls (HC) and 22 patients with major depressive episode have been included. All subjects were scanned with functional magnetic resonance imaging (fMRI) with paradigm composed of diagnostic clinical self-assessment depression scale contrasted to neutral scale. The data were processed with group independent component analysis for functional MRI toolbox and statistical parametric mapping. RESULTS The results have demonstrated that there exist positively or negatively modulated brain networks during processing of diagnostic specific task questions for depressive disorder. There have also been confirmed differences in the networks processing diagnostic versus off blocks between patients and controls in anterior cingulate cortex and middle frontal gyrus. Diagnostic conditions (depression scale) when contrasted to neutral conditions demonstrate differential activity of right superior frontal gyrus and right middle cingulate cortex in the comparison of patients with HC. CONCLUSION Potential neuroimaging of state-dependent biomarkers has been directly linked with clinical assessment self-evaluation scale, administered as stimuli simultaneously with the fMRI acquisition. It may be regarded as further evidence in support of the convergent capacity of both methods to distinguish groups by means of incremental translational cross-validation.
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Affiliation(s)
- Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
| | - Vladimir Khorev
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Rossitsa Paunova
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
| | - Semen Kurkin
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
- Neuroscience Research Institute, Samara State Medical University, Samara, Russia
| | - Vince D Calhoun
- Center for Translational Research in Neuroimaging and Data Science (TReNDS), The Georgia State University/Georgia Institute of Technology/Emory University, Atlanta, GA, USA
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Yuan X, Chen M, Ding P, Gan A, Gong A, Chu Z, Nan W, Fu Y, Cheng Y. Cross-Domain Identification of Multisite Major Depressive Disorder Using End-to-End Brain Dynamic Attention Network. IEEE Trans Neural Syst Rehabil Eng 2024; 32:33-42. [PMID: 38090844 DOI: 10.1109/tnsre.2023.3341923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Abstract
Establishing objective and quantitative imaging markers at individual level can assist in accurate diagnosis of Major Depressive Disorder (MDD). However, the clinical heterogeneity of MDD and the shift to multisite data decreased identification accuracy. To address these issues, the Brain Dynamic Attention Network (BDANet) is innovatively proposed, and analyzed bimodal scans from 2055 participants of the Rest-meta-MDD consortium. The end-to-end BDANet contains two crucial components. The Dynamic BrainGraph Generator dynamically focuses and represents topological relationships between Regions of Interest, overcoming limitations of static methods. The Ensemble Classifier is constructed to obfuscate domain sources to achieve inter-domain alignment. Finally, BDANet dynamically generates sample-specific brain graphs by downstream recognition tasks. The proposed BDANet achieved an accuracy of 81.6%. The regions with high attribution for classification were mainly located in the insula, cingulate cortex and auditory cortex. The level of brain connectivity in p24 region was negatively correlated ( [Formula: see text]) with the severity of MDD. Additionally, sex differences in connectivity strength were observed in specific brain regions and functional subnetworks ( [Formula: see text] or [Formula: see text]). These findings based on a large multisite dataset support the conclusion that BDANet can better solve the problem of the clinical heterogeneity of MDD and the shift of multisite data. It also illustrates the potential utility of BDANet for personalized accurate identification, treatment and intervention of MDD.
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Mellema CJ, Montillo AA. Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI. J Neural Eng 2023; 20:066023. [PMID: 37963396 DOI: 10.1088/1741-2552/ad0c5f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 11/14/2023] [Indexed: 11/16/2023]
Abstract
Objective.New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity (FC) between pairs of brain regions in functional MRI, such as correlation and partial correlation, fail to capture nonlinear aspects in the regional associations. We propose a new machine learning based measure of FC (ML.FC) which efficiently captures linear and nonlinear aspects.Approach.To capture directed information flow between brain regions, effective connectivity (EC) metrics, including dynamic causal modeling and structural equation modeling have been used. However, these methods are impractical to compute across the many regions of the whole brain. Therefore, we propose two new EC measures. The first, a machine learning based measure of effective connectivity (ML.EC), measures nonlinear aspects across the entire brain. The second, Structurally Projected Granger Causality (SP.GC) adapts Granger Causal connectivity to efficiently characterize and regularize the whole brain EC connectome to respect underlying biological structural connectivity. The proposed measures are compared to traditional measures in terms ofreproducibilityand theability to predict individual traitsin order to demonstrate these measures' internal validity. We use four repeat scans of the same individuals from the Human Connectome Project and measure the ability of the measures to predict individual subject physiologic and cognitive traits.Main results.The proposed new FC measure ofML.FCattains high reproducibility (mean intra-subjectR2of 0.44), while the proposed EC measure ofSP.GCattains the highest predictive power (meanR2across prediction tasks of 0.66).Significance.The proposed methods are highly suitable for achieving high reproducibility and predictiveness and demonstrate their strong potential for future neuroimaging studies.
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Affiliation(s)
- Cooper J Mellema
- Lyda Hill Department of Bioinformatics, Dallas, TX, United States of America
- Biomedical Engineering Department, Dallas, TX, United States of America
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, United States of America
| | - Albert A Montillo
- Lyda Hill Department of Bioinformatics, Dallas, TX, United States of America
- Biomedical Engineering Department, Dallas, TX, United States of America
- Advanced Imaging Research Center, Dallas, TX, United States of America
- Radiology Department, Dallas, TX, United States of America
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, United States of America
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Sui J, Zhi D, Calhoun VD. Data-driven multimodal fusion: approaches and applications in psychiatric research. PSYCHORADIOLOGY 2023; 3:kkad026. [PMID: 38143530 PMCID: PMC10734907 DOI: 10.1093/psyrad/kkad026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/08/2023] [Accepted: 11/21/2023] [Indexed: 12/26/2023]
Abstract
In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.
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Affiliation(s)
- Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - 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, United States
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Li Q, Dong F, Gai Q, Che K, Ma H, Zhao F, Chu T, Mao N, Wang P. Diagnosis of Major Depressive Disorder Using Machine Learning Based on Multisequence MRI Neuroimaging Features. J Magn Reson Imaging 2023; 58:1420-1430. [PMID: 36797655 DOI: 10.1002/jmri.28650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 02/03/2023] [Accepted: 02/04/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND Previous studies have found qualitative structural and functional brain changes in major depressive disorder (MDD) patients. However, most studies ignored the complementarity of multisequence MRI neuroimaging features and cannot determine accurate biomarkers. PURPOSE To evaluate machine-learning models combined with multisequence MRI neuroimaging features to diagnose patients with MDD. STUDY TYPE Prospective. SUBJECTS A training cohort including 111 patients and 90 healthy controls (HCs) and a test cohort including 28 patients and 22 HCs. FIELD STRENGTH/SEQUENCE A 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and single-shot echo-planar diffusion tensor imaging. ASSESSMENT Recruitment and integration were used to reflect the dynamic changes of functional networks, while gray matter volume and fractional anisotropy were used to reflect the changes in the morphological and anatomical network. We then fused features with significant differences in functional, morphological, and anatomical networks to evaluate a random forest (RF) classifier to diagnose patients with MDD. Furthermore, a support vector machine (SVM) classifier was used to verify the stability of neuroimaging features. Linear regression analyses were conducted to investigate the relationships among multisequence neuroimaging features and the suicide risk of patients. STATISTICAL TESTS The comparison of functional network attributes between patients and controls by two-sample t-test. Network-based statistical analysis was used to identify structural and anatomical connectivity changes between MDD and HCs. The performance of the model was evaluated by receiver operating characteristic (ROC) curves. RESULTS The performance of the RF model integrating multisequence neuroimaging features in the diagnosis of depression was significantly improved, with an AUC of 93.6%. In addition, we found that multisequence neuroimaging features could accurately predict suicide risk in patients with MDD (r = 0.691). DATA CONCLUSION The RF model fusing functional, morphological, and anatomical network features performed well in diagnosing patients with MDD and provided important insights into the pathological mechanisms of MDD. EVIDENCE LEVEL 1. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Qinghe Li
- Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, People's Republic of China
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, People's Republic of China
| | - Fanghui Dong
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Qun Gai
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Feng Zhao
- School of Compute Science and Technology, Shandong Technology and Business University, Yantai, Shandong, People's Republic of China
| | - Tongpeng Chu
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Peiyuan Wang
- Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, People's Republic of China
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Mohammadi S, Seyedmirzaei H, Salehi MA, Jahanshahi A, Zakavi SS, Dehghani Firouzabadi F, Yousem DM. Brain-based Sex Differences in Depression: A Systematic Review of Neuroimaging Studies. Brain Imaging Behav 2023; 17:541-569. [PMID: 37058182 PMCID: PMC10102695 DOI: 10.1007/s11682-023-00772-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/31/2023] [Indexed: 04/15/2023]
Abstract
Major depressive disorder (MDD) is a common psychiatric illness with a wide range of symptoms such as mood decline, loss of interest, and feelings of guilt and worthlessness. Women develop depression more often than men, and the diagnostic criteria for depression mainly rely on female patients' symptoms. By contrast, male depression usually manifests as anger attacks, aggression, substance use, and risk-taking behaviors. Various studies have focused on the neuroimaging findings in psychiatric disorders for a better understanding of their underlying mechanisms. With this review, we aimed to summarize the existing literature on the neuroimaging findings in depression, separated by male and female subjects. A search was conducted on PubMed and Scopus for magnetic resonance imaging (MRI), functional MRI (fMRI), and diffusion tensor imaging (DTI) studies of depression. After screening the search results, 15 MRI, 12 fMRI, and 4 DTI studies were included. Sex differences were mainly reflected in the following regions: 1) total brain, hippocampus, amygdala, habenula, anterior cingulate cortex, and corpus callosum volumes, 2) frontal and temporal gyri functions, along with functions of the caudate nucleus and prefrontal cortex, and 3) frontal fasciculi and frontal projections of corpus callosum microstructural alterations. Our review faces limitations such as small sample sizes and heterogeneity in populations and modalities. But in conclusion, it reflects the possible roles of sex-based hormonal and social factors in the depression pathophysiology.
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Affiliation(s)
- Soheil Mohammadi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Homa Seyedmirzaei
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, Tehran, Iran
| | | | - Ali Jahanshahi
- School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
| | - Seyed Sina Zakavi
- School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - David M Yousem
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institution, Baltimore, MD, USA.
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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11
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Vargas TG, Mittal VA. Brain morphometry points to emerging patterns of psychosis, depression, and anxiety vulnerability over a 2-year period in childhood. Psychol Med 2023; 53:3322-3334. [PMID: 37323064 PMCID: PMC10276191 DOI: 10.1017/s0033291721005304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Gray matter morphometry studies have lent seminal insights into the etiology of mental illness. Existing research has primarily focused on adults and then, typically on a single disorder. Examining brain characteristics in late childhood, when the brain is preparing to undergo significant adolescent reorganization and various forms of serious psychopathology are just first emerging, may allow for a unique and highly important perspective of overlapping and unique pathogenesis. METHODS A total of 8645 youth were recruited as part of the Adolescent Brain and Cognitive Development study. Magnetic resonance imaging scans were collected, and psychotic-like experiences (PLEs), depressive, and anxiety symptoms were assessed three times over a 2-year period. Cortical thickness, surface area, and subcortical volume were used to predict baseline symptomatology and symptom progression over time. RESULTS Some features could possibly signal common vulnerability, predicting progression across forms of psychopathology (e.g. superior frontal and middle temporal regions). However, there was a specific predictive value for emerging PLEs (lateral occipital and precentral thickness), anxiety (parietal thickness/area and cingulate), and depression (e.g. parahippocampal and inferior temporal). CONCLUSION Findings indicate common and distinct patterns of vulnerability for varying forms of psychopathology are present during late childhood, before the adolescent reorganization, and have direct relevance for informing novel conceptual models along with early prevention and intervention efforts.
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Affiliation(s)
- Teresa G Vargas
- Northwestern University, Swift Hall 102, 2029 Sheridan Road, Evanston, IL 60201, USA
| | - Vijay A Mittal
- Northwestern University, Swift Hall 102, 2029 Sheridan Road, Evanston, IL 60201, USA
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12
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Chen X, Dai Z, Lin Y. Biotypes of major depressive disorder identified by a multiview clustering framework. J Affect Disord 2023; 329:257-272. [PMID: 36863463 DOI: 10.1016/j.jad.2023.02.118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 02/11/2023] [Accepted: 02/22/2023] [Indexed: 03/04/2023]
Abstract
BACKGROUND The advances in resting-state functional magnetic resonance imaging techniques motivate parsing heterogeneity in major depressive disorder (MDD) through neurophysiological subtypes (i.e., biotypes). Based on graph theories, researchers have observed the functional organization of the human brain as a complex system with modular structures and have found wide-spread but variable MDD-related abnormality regarding the modules. The evidence implies the possibility of identifying biotypes using high-dimensional functional connectivity (FC) data in ways that suit the potentially multifaceted biotypes taxonomy. METHODS We proposed a multiview biotype discovery framework that involves theory-driven feature subspace partition (i.e., "view") and independent subspace clustering. Six views were defined using intra- and intermodule FC regarding three MDD focal modules (i.e., the sensory-motor system, default mode network, and subcortical network). For robust biotypes, the framework was applied to a large multisite sample (805 MDD participants and 738 healthy controls). RESULTS Two biotypes were stably obtained in each view, respectively characterized by significantly increased and decreased FC compared to healthy controls. These view-specific biotypes promoted the diagnosis of MDD and showed different symptom profiles. By integrating the view-specific biotypes into biotype profiles, a broad spectrum in the neural heterogeneity of MDD and its separation from symptom-based subtypes was further revealed. LIMITATIONS The power of clinical effects is limited and the cross-sectional nature cannot predict the treatment effects of the biotypes. CONCLUSIONS Our findings not only contribute to the understanding of heterogeneity in MDD, but also provide a novel subtyping framework that could transcend current diagnostic boundaries and data modality.
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Affiliation(s)
- Xitian Chen
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.
| | - Ying Lin
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.
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13
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Mestre-Bach G, Granero R, Fernández-Aranda F, Jiménez-Murcia S, Potenza MN. Independent component analysis for internet gaming disorder. DIALOGUES IN CLINICAL NEUROSCIENCE 2023; 25:14-23. [PMID: 36817972 PMCID: PMC9930851 DOI: 10.1080/19585969.2023.2168135] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 11/17/2022] [Indexed: 02/10/2023]
Abstract
Introduction: There is a growing interest in the study of the neurobiological correlates of internet gaming disorder (IGD), and new techniques are beginning to be implemented for this purpose, such as independent component analysis (ICA). Aims: The present narrative review aimed to explore the studies that had used ICA for the study of the different brain networks possibly associated with IGD. Methods: We specifically focussed on three of the main networks: default-mode network, executive-control and salience networks. Results: Most studies have identified alterations in these three brain networks in individuals with IGD, which may be involved in the development and maintenance of this disorder. Conclusion: More studies are needed to deepen an understanding of the specific role of each in the symptomatology and treatment of IGD.
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Affiliation(s)
- Gemma Mestre-Bach
- Facultad de Ciencias de la Salud, Universidad Internacional de la Rioja, La Rioja, Spain
- Institute for Culture and Society (ICS), University of Navarra, Pamplona, Spain
| | - Roser Granero
- Departament de Psicobiologia i Metodologia de les Ciències de la Salut, Universitat Autònoma de Barcelona, Barcelona, Spain
- Ciber Fisiopatología Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Psychoneurobiology of Eating and Addictive Behaviors Group, Neurosciences Programme, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Fernando Fernández-Aranda
- Ciber Fisiopatología Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Psychoneurobiology of Eating and Addictive Behaviors Group, Neurosciences Programme, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
- Behavioral Addictions Unit, Department of Psychiatry, Bellvitge University Hospital, Barcelona, Spain
- Department of Clinical Sciences, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Susana Jiménez-Murcia
- Ciber Fisiopatología Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Psychoneurobiology of Eating and Addictive Behaviors Group, Neurosciences Programme, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
- Behavioral Addictions Unit, Department of Psychiatry, Bellvitge University Hospital, Barcelona, Spain
- Department of Clinical Sciences, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Marc N. Potenza
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Council on Problem Gambling, Wethersfield, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
- Wu Tsai Institute, Yale University School of Medicine, New Haven, CT, USA
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14
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Tornero-Costa R, Martinez-Millana A, Azzopardi-Muscat N, Lazeri L, Traver V, Novillo-Ortiz D. Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review. JMIR Ment Health 2023; 10:e42045. [PMID: 36729567 PMCID: PMC9936371 DOI: 10.2196/42045] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/02/2022] [Accepted: 11/20/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. OBJECTIVE This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. METHODS A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. RESULTS A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). CONCLUSIONS These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.
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Affiliation(s)
- Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Ledia Lazeri
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
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15
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Cui H, Dai W, Zhu Y, Kan X, Gu AAC, Lukemire J, Zhan L, He L, Guo Y, Yang C. BrainGB: A Benchmark for Brain Network Analysis With Graph Neural Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:493-506. [PMID: 36318557 PMCID: PMC10079627 DOI: 10.1109/tmi.2022.3218745] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic study of how to design effective GNNs for brain network analysis. To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs. BrainGB standardizes the process by (1) summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and (2) modularizing the implementation of GNN designs. We conduct extensive experiments on datasets across cohorts and modalities and recommend a set of general recipes for effective GNN designs on brain networks. To support open and reproducible research on GNN-based brain network analysis, we host the BrainGB website at https://braingb.us with models, tutorials, examples, as well as an out-of-box Python package. We hope that this work will provide useful empirical evidence and offer insights for future research in this novel and promising direction.
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16
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Chen X, Xie H, Li Z, Cheng G, Leng M, Wang FL. Information fusion and artificial intelligence for smart healthcare: a bibliometric study. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2022.103113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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17
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Liu X, Tyler LK, Rowe JB, Tsvetanov KA. Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy aging subjects. Hum Brain Mapp 2022; 43:5490-5508. [PMID: 35855641 PMCID: PMC9704789 DOI: 10.1002/hbm.26025] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/24/2022] [Accepted: 07/07/2022] [Indexed: 01/15/2023] Open
Abstract
Brain aging is a complex process that requires a multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization, and vascular dynamics. However, most neuroimaging studies of aging have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and their relevance to cognitive decline in aging. Here, we used a data-driven multimodal approach, linked independent component analysis (ICA), to jointly analyze magnetic resonance imaging (MRI) of grey matter volume, cerebrovascular, and functional network topographies in relation to measures of fluid intelligence. Neuroimaging and cognitive data from the Cambridge Centre for Ageing and Neuroscience study were used, with healthy participants aged 18-88 years (main dataset n = 215 and secondary dataset n = 433). Using linked ICA, functional network activities were characterized in independent components but not captured in the same component as structural and cerebrovascular patterns. Split-sample (n = 108/107) and out-of-sample (n = 433) validation analyses using linked ICA were also performed. Global grey matter volume with regional cerebrovascular changes and the right frontoparietal network activity were correlated with age-related and individual differences in fluid intelligence. This study presents the insights from linked ICA to bring together measurements from multiple imaging modalities, with independent and additive information. We propose that integrating multiple neuroimaging modalities allows better characterization of brain pattern variability and changes associated with healthy aging.
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Affiliation(s)
- Xulin Liu
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Lorraine K. Tyler
- The Centre for Speech, Language and the Brain, Department of PsychologyUniversity of CambridgeCambridgeUK
| | - Cam‐CAN
- Cambridge Centre for Ageing and Neuroscience (Cam‐CAN), MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - James B. Rowe
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - Kamen A. Tsvetanov
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- The Centre for Speech, Language and the Brain, Department of PsychologyUniversity of CambridgeCambridgeUK
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18
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Li Y, Yu Z, Wu P, Chen J. Ability of an altered functional coupling between resting-state networks to predict behavioral outcomes in subcortical ischemic stroke: A longitudinal study. Front Aging Neurosci 2022; 14:933567. [PMID: 36185473 PMCID: PMC9520312 DOI: 10.3389/fnagi.2022.933567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 08/16/2022] [Indexed: 11/22/2022] Open
Abstract
Stroke can be viewed as an acute disruption of an individual's connectome caused by a focal or widespread loss of blood flow. Although individuals exhibit connectivity changes in multiple functional networks after stroke, the neural mechanisms that underlie the longitudinal reorganization of the connectivity patterns are still unclear. The study aimed to determine whether brain network connectivity patterns after stroke can predict longitudinal behavioral outcomes. Nineteen patients with stroke with subcortical lesions underwent two sessions of resting-state functional magnetic resonance imaging scanning at a 1-month interval. By independent component analysis, the functional connectivity within and between multiple brain networks (including the default mode network, the dorsal attention network, the limbic network, the visual network, and the frontoparietal network) was disrupted after stroke and partial recovery at the second time point. Additionally, regression analyses revealed that the connectivity between the limbic and dorsal attention networks at the first time point showed sufficient reliability in predicting the clinical scores (Fugl-Meyer Assessment and Neurological Deficit Scores) at the second time point. The overall findings suggest that functional coupling between the dorsal attention and limbic networks after stroke can be regarded as a biomarker to predict longitudinal clinical outcomes in motor function and the degree of neurological functional deficit. Overall, the present study provided a novel opportunity to improve prognostic ability after subcortical strokes.
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Affiliation(s)
- Yongxin Li
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Zeyun Yu
- Acupuncture and Tuina School/Third Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ping Wu
- Acupuncture and Tuina School/Third Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jiaxu Chen
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
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19
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Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders. J Pers Med 2022; 12:jpm12091403. [PMID: 36143188 PMCID: PMC9504356 DOI: 10.3390/jpm12091403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 01/10/2023] Open
Abstract
Depressive disorders are highly heterogeneous in nature. Previous studies have not been useful for the clinical diagnosis and prediction of outcomes of major depressive disorder (MDD) at the individual level, although they provide many meaningful insights. To make inferences beyond group-level analyses, machine learning (ML) techniques can be used for the diagnosis of subtypes of MDD and the prediction of treatment responses. We searched PubMed for relevant studies published until December 2021 that included depressive disorders and applied ML algorithms in neuroimaging fields for depressive disorders. We divided these studies into two sections, namely diagnosis and treatment outcomes, for the application of prediction using ML. Structural and functional magnetic resonance imaging studies using ML algorithms were included. Thirty studies were summarized for the prediction of an MDD diagnosis. In addition, 19 studies on the prediction of treatment outcomes for MDD were reviewed. We summarized and discussed the results of previous studies. For future research results to be useful in clinical practice, ML enabling individual inferences is important. At the same time, there are important challenges to be addressed in the future.
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20
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Abstract
BACKGROUND In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. METHODS This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis. RESULTS A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test. CONCLUSION The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.
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Affiliation(s)
- Sweta Bhadra
- Department of CS & IT, Cotton University, Guwahati, India
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21
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Wang Q, Li L, Qiao L, Liu M. Adaptive Multimodal Neuroimage Integration for Major Depression Disorder Detection. Front Neuroinform 2022; 16:856175. [PMID: 35571867 PMCID: PMC9100686 DOI: 10.3389/fninf.2022.856175] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
Major depressive disorder (MDD) is one of the most common mental health disorders that can affect sleep, mood, appetite, and behavior of people. Multimodal neuroimaging data, such as functional and structural magnetic resonance imaging (MRI) scans, have been widely used in computer-aided detection of MDD. However, previous studies usually treat these two modalities separately, without considering their potentially complementary information. Even though a few studies propose integrating these two modalities, they usually suffer from significant inter-modality data heterogeneity. In this paper, we propose an adaptive multimodal neuroimage integration (AMNI) framework for automated MDD detection based on functional and structural MRIs. The AMNI framework consists of four major components: (1) a graph convolutional network to learn feature representations of functional connectivity networks derived from functional MRIs, (2) a convolutional neural network to learn features of T1-weighted structural MRIs, (3) a feature adaptation module to alleviate inter-modality difference, and (4) a feature fusion module to integrate feature representations extracted from two modalities for classification. To the best of our knowledge, this is among the first attempts to adaptively integrate functional and structural MRIs for neuroimaging-based MDD analysis by explicitly alleviating inter-modality heterogeneity. Extensive evaluations are performed on 533 subjects with resting-state functional MRI and T1-weighted MRI, with results suggesting the efficacy of the proposed method.
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Affiliation(s)
- Qianqian Wang
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Long Li
- Taian Tumor Prevention and Treatment Hospital, Taian, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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22
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Dalvie S, Chatzinakos C, Al Zoubi O, Georgiadis F, Lancashire L, Daskalakis NP. From genetics to systems biology of stress-related mental disorders. Neurobiol Stress 2021; 15:100393. [PMID: 34584908 PMCID: PMC8456113 DOI: 10.1016/j.ynstr.2021.100393] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 07/22/2021] [Accepted: 09/08/2021] [Indexed: 01/20/2023] Open
Abstract
Many individuals will be exposed to some form of traumatic stress in their lifetime which, in turn, increases the likelihood of developing stress-related disorders such as post-traumatic stress disorder (PTSD), major depressive disorder (MDD) and anxiety disorders (ANX). The development of these disorders is also influenced by genetics and have heritability estimates ranging between ∼30 and 70%. In this review, we provide an overview of the findings of genome-wide association studies for PTSD, depression and ANX, and we observe a clear genetic overlap between these three diagnostic categories. We go on to highlight the results from transcriptomic and epigenomic studies, and, given the multifactorial nature of stress-related disorders, we provide an overview of the gene-environment studies that have been conducted to date. Finally, we discuss systems biology approaches that are now seeing wider utility in determining a more holistic view of these complex disorders.
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Affiliation(s)
- Shareefa Dalvie
- South African Medical Research Council (SAMRC), Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- South African Medical Research Council (SAMRC), Unit on Child & Adolescent Health, Department of Paediatrics and Child Health, University of Cape Town, Cape Town, South Africa
| | - Chris Chatzinakos
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
| | - Obada Al Zoubi
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
| | - Foivos Georgiadis
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
| | | | - Lee Lancashire
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
- Department of Data Science, Cohen Veterans Bioscience, New York, USA
| | - Nikolaos P. Daskalakis
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
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23
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Li M. Research on Extraction of Useful Tourism Online Reviews Based on Multimodal Feature Fusion. ACM T ASIAN LOW-RESO 2021. [DOI: 10.1145/3453694] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
To effectively identify the influencing factors of the perceived usefulness of multimodal data in online reviews of tourism products, this article explores the optimization method of online tourism products based on user-generated content and conducts feature fusion of multimodal data in online reviews of tourism products from the perspective of data fusion analysis. Therefore, based on the word vector model, this article proposes a method to select the seed word set of emotion dictionary. In this method, emotional words are represented in vector form and the distance between word vectors is calculated to form the selection criteria and classification basis of seed word set, and then the sentiment dictionary of online review is formed by category judgment. This article takes the real online review data of tourism products as the research object, carries out descriptive statistical analysis, uses machine learning and deep learning methods, carries out text vector embedding and image content recognition, integrates image and text feature vector, constructs multimodal online review usefulness classification model, and conducts model test. The experimental results show that, compared with the single-mode reviews containing only text or pictures, the multimodal reviews combined with text and pictures can better predict the usefulness of online reviews, improve the quality of online reviews, give full play to the potential value of user-generated content, provide optimization ideas for product providers, and provide decision support for product consumers.
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Affiliation(s)
- Meng Li
- Baotou Teachers’ College, Batou Inner Mongolia 014030 China
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24
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Timonidis N, Tiesinga PHE. Progress towards a cellularly resolved mouse mesoconnectome is empowered by data fusion and new neuroanatomy techniques. Neurosci Biobehav Rev 2021; 128:569-591. [PMID: 34119523 DOI: 10.1016/j.neubiorev.2021.06.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 04/02/2021] [Accepted: 06/05/2021] [Indexed: 10/21/2022]
Abstract
Over the past decade there has been a rapid improvement in techniques for obtaining large-scale cellular level data related to the mouse brain connectome. However, a detailed mapping of cell-type-specific projection patterns is lacking, which would, for instance, allow us to study the role of circuit motifs in cognitive processes. In this work, we review advanced neuroanatomical and data fusion techniques within the context of a proposed Multimodal Connectomic Integration Framework for augmenting the cellularly resolved mouse mesoconnectome. First, we emphasize the importance of registering data modalities to a common reference atlas. We then review a number of novel experimental techniques that can provide data for characterizing cell-types in the mouse brain. Furthermore, we examine a number of data integration strategies, which involve fine-grained cell-type classification, spatial inference of cell densities, latent variable models for the mesoconnectome and multi-modal factorisation. Finally, we discuss a number of use cases which depend on connectome augmentation techniques, such as model simulations of functional connectivity and generating mechanistic hypotheses for animal disease models.
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Affiliation(s)
- Nestor Timonidis
- Neuroinformatics department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands.
| | - Paul H E Tiesinga
- Neuroinformatics department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
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25
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Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain. Neuroinformatics 2021; 19:649-667. [PMID: 33704701 PMCID: PMC8566442 DOI: 10.1007/s12021-021-09511-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/06/2021] [Indexed: 11/16/2022]
Abstract
Finding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis. We identified representative and literature-validated cortico-midbrain and cortico-striatal projections, whose gene subsets were enriched with annotations for neuronal and synaptic function and related developmental and metabolic processes. The results were highly reproducible when including all available projections, as well as consistent with factorisations obtained using the Dictionary Learning and Sparse Coding technique. Hence, Linked ICA yielded reproducible independent components that were preserved under increasing data variance. Taken together, we have developed and validated a novel paradigm for linking gene expression and structural projection patterns in the mouse mesoconnectome, which can power future studies aiming to relate genes to brain function.
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26
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Eitel F, Schulz MA, Seiler M, Walter H, Ritter K. Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research. Exp Neurol 2021; 339:113608. [PMID: 33513353 DOI: 10.1016/j.expneurol.2021.113608] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 01/07/2021] [Accepted: 01/09/2021] [Indexed: 12/13/2022]
Abstract
By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables.
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Affiliation(s)
- Fabian Eitel
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Marc-André Schulz
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Moritz Seiler
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Henrik Walter
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Kerstin Ritter
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany.
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27
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Zhang YD, Dong Z, Wang SH, Yu X, Yao X, Zhou Q, Hu H, Li M, Jiménez-Mesa C, Ramirez J, Martinez FJ, Gorriz JM. Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2020; 64:149-187. [PMID: 32834795 PMCID: PMC7366126 DOI: 10.1016/j.inffus.2020.07.006] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/06/2020] [Accepted: 07/14/2020] [Indexed: 05/13/2023]
Abstract
Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.
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Affiliation(s)
- Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Zhengchao Dong
- Department of Psychiatry, Columbia University, USA
- New York State Psychiatric Institute, New York, NY 10032, USA
| | - Shui-Hua Wang
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- School of Architecture Building and Civil engineering, Loughborough University, Loughborough, LE11 3TU, UK
- School of Mathematics and Actuarial Science, University of Leicester, LE1 7RH, UK
| | - Xiang Yu
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Xujing Yao
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Qinghua Zhou
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Hua Hu
- Department of Psychiatry, Columbia University, USA
- Department of Neurology, The Second Affiliated Hospital of Soochow University, China
| | - Min Li
- Department of Psychiatry, Columbia University, USA
- School of Internet of Things, Hohai University, Changzhou, China
| | - Carmen Jiménez-Mesa
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Javier Ramirez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Francisco J Martinez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
- Department of Psychiatry, University of Cambridge, Cambridge CB21TN, UK
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28
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Lund MJ, Alnæs D, Schwab S, van der Meer D, Andreassen OA, Westlye LT, Kaufmann T. Differences in directed functional brain connectivity related to age, sex and mental health. Hum Brain Mapp 2020; 41:4173-4186. [PMID: 32613721 PMCID: PMC7502836 DOI: 10.1002/hbm.25116] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 06/07/2020] [Accepted: 06/16/2020] [Indexed: 01/09/2023] Open
Abstract
Functional interconnections between brain regions define the "connectome" which is of central interest for understanding human brain function. Resting-state functional magnetic resonance (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive abilities and psychiatric symptoms, yet little is known how these factors may alter the information flow. The commonly used approach infers functional brain connectivity using stationary coefficients yielding static estimates of the undirected connection strength between brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic coefficients reflecting directed temporal associations between nodes, and can yield novel insight into directed functional connectivity. Here, we leveraged this approach to test for associations between edge-wise estimates of direction flow across the functional connectome and age, sex, intellectual abilities and mental health. We applied DGM to investigate patterns of information flow in data from 984 individuals from the Human Connectome Project (HCP) and 10,249 individuals from the UK Biobank. Our analysis yielded patterns of directed connectivity in independent HCP and UK Biobank data similar to those previously reported, including that the cerebellum consistently receives information from other networks. We show robust associations between information flow and age and sex for several connections, with strongest effects of age observed in the sensorimotor network. Visual, auditory and sensorimotor nodes were also linked to mental health. Our findings support the use of DGM as a measure of directed connectivity in rsfMRI data and provide new insight into the shaping of the connectome during aging.
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Affiliation(s)
- Martina J. Lund
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
- Bjørknes University CollegeOsloNorway
| | - Simon Schwab
- Center for Reproducible Science (CRS) & Epidemiology, Biostatistics and Prevention Institute (EBPI)University of ZürichZurichSwitzerland
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
- KG Jebsen Centre for neurodevelopmental disorders, University of OsloOsloNorway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
- KG Jebsen Centre for neurodevelopmental disorders, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
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29
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Maglanoc LA, Kaufmann T, Jonassen R, Hilland E, Beck D, Landrø NI, Westlye LT. Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis. Hum Brain Mapp 2020; 41:241-255. [PMID: 31571370 PMCID: PMC7267936 DOI: 10.1002/hbm.24802] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 09/08/2019] [Accepted: 09/09/2019] [Indexed: 01/03/2023] Open
Abstract
Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting-state functional magnetic resonance imaging default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case-control status, and symptom loads for depression and anxiety with the resulting brain components. Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case-control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.
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Affiliation(s)
- Luigi A. Maglanoc
- Clinical Neuroscience Research Group, Department of PsychologyUniversity of OsloOsloNorway
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Rune Jonassen
- Faculty of Health SciencesOslo Metropolitan UniversityOsloNorway
| | - Eva Hilland
- Clinical Neuroscience Research Group, Department of PsychologyUniversity of OsloOsloNorway
- Division of PsychiatryDiakonhjemmet HospitalOsloNorway
| | - Dani Beck
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Nils Inge Landrø
- Clinical Neuroscience Research Group, Department of PsychologyUniversity of OsloOsloNorway
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
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30
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Wang L, Zhao Y, Edmiston EK, Womer FY, Zhang R, Zhao P, Jiang X, Wu F, Kong L, Zhou Y, Tang Y, Wei S. Structural and Functional Abnormities of Amygdala and Prefrontal Cortex in Major Depressive Disorder With Suicide Attempts. Front Psychiatry 2019; 10:923. [PMID: 31969839 PMCID: PMC6960126 DOI: 10.3389/fpsyt.2019.00923] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 11/20/2019] [Indexed: 11/13/2022] Open
Abstract
Finding neural features of suicide attempts (SA) in major depressive disorder (MDD) may be helpful in preventing suicidal behavior. The ventral and medial prefrontal cortex (PFC), as well as the amygdala form a circuit implicated in emotion regulation and the pathogenesis of MDD. The aim of this study was to identify whether patients with MDD who had a history of SA show structural and functional connectivity abnormalities in the amygdala and PFC relative to MDD patients without a history of SA. We measured gray matter volume in the amygdala and PFC and amygdala-PFC functional connectivity using structural and functional magnetic resonance imaging (MRI) in 158 participants [38 MDD patients with a history of SA, 60 MDD patients without a history of SA, and 60 healthy control (HC)]. MDD patients with a history of SA had decreased gray matter volume in the right and left amygdala (F = 30.270, P = 0.000), ventral/medial/dorsal PFC (F = 15.349, P = 0.000), and diminished functional connectivity between the bilateral amygdala and ventral and medial PFC regions (F = 22.467, P = 0.000), compared with individuals who had MDD without a history of SA, and the HC group. These findings provide evidence that the amygdala and PFC may be closely related to the pathogenesis of suicidal behavior in MDD and implicate the amygdala-ventral/medial PFC circuit as a potential target for suicide intervention.
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Affiliation(s)
- Lifei Wang
- Department of Psychiatry, China Medical University, Shenyang, China.,Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang, China.,Brain Function Research Section, First Affiliated Hospital, China Medical University, Shenyang, China
| | - Yimeng Zhao
- Department of Psychiatry, China Medical University, Shenyang, China.,Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang, China.,Brain Function Research Section, First Affiliated Hospital, China Medical University, Shenyang, China
| | - Elliot K Edmiston
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Fay Y Womer
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Ran Zhang
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang, China
| | - Pengfei Zhao
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang, China
| | - Xiaowei Jiang
- Brain Function Research Section, First Affiliated Hospital, China Medical University, Shenyang, China.,Department of Radiology, First Affiliated Hospital, China Medical University, Shenyang, China
| | - Feng Wu
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang, China
| | - Lingtao Kong
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang, China
| | - Yifang Zhou
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang, China.,Department of Geriatric Medicine, First Affiliated Hospital, China Medical University, Shenyang, China
| | - Yanqing Tang
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang, China.,Department of Geriatric Medicine, First Affiliated Hospital, China Medical University, Shenyang, China
| | - Shengnan Wei
- Brain Function Research Section, First Affiliated Hospital, China Medical University, Shenyang, China.,Department of Radiology, First Affiliated Hospital, China Medical University, Shenyang, China
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