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Livne O, Potter KW, Schuster RM, Gilman JM. Longitudinal Associations Between Cannabis Use and Cognitive Impairment in a Clinical Sample of Middle-Aged Adults Using Cannabis for Medical Symptoms. Cannabis Cannabinoid Res 2024; 9:e933-e938. [PMID: 37625034 DOI: 10.1089/can.2022.0310] [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] [Indexed: 08/27/2023] Open
Abstract
Introduction: Cannabis use to alleviate medical symptoms is increasing in middle-aged and older adults. Cognitive impairment associated with cannabis use may be especially detrimental to these understudied age groups. We hypothesized that among middle-aged and older adults who used cannabis for 12 months, frequent (≥3 days/week) compared with nonfrequent (≤2 days/week) use will be associated with cognitive impairment. Materials and Methods: We performed secondary analysis on data from a clinical trial of cannabis use for medical symptoms. Participants (n=62) were ≥45 years, and completed a baseline and at least one postbaseline visit. Cognitive domains were assessed through the Cambridge Neuropsychological Test Automated Battery. Cannabis use was assessed prospectively through daily smartphone diaries. Frequency of cannabis use was a binary predictor in a mixed-effects logistic regression model predicting cognitive impairment adjusted for baseline cognitive functioning. Results: At baseline, participants were primarily nonfrequent cannabis users; however, in all other time periods, most participants were frequent users (range: 55-58%). Cognitive outcomes did not differ between frequent and nonfrequent cannabis users. However, in sensitivity analyses, respondents with problematic cannabis use scored significantly worse on one cognitive domain compared to those without problematic cannabis use. Conclusions: In a clinical sample of adults aged ≥45 years, no longitudinal associations were found between cannabis use and cognitive functioning. However, a few significant associations were observed between problematic use and cognitive functioning. Further research is needed to assess the impact of cannabis use on adults, particularly those aged ≥65 years, and to investigate potential subtler influences of cannabis use on cognition. ClinicalTrials.gov ID: NCT03224468.
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Affiliation(s)
- Ofir Livne
- New York State Psychiatric Institute, New York, New York, USA
| | - Kevin W Potter
- Department of Psychiatry, Massachusetts General Hospital (MGH), Boston, Massachusetts, USA
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
| | - Randi M Schuster
- Department of Psychiatry, Massachusetts General Hospital (MGH), Boston, Massachusetts, USA
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
| | - Jodi M Gilman
- Department of Psychiatry, Massachusetts General Hospital (MGH), Boston, Massachusetts, USA
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
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Huang Y, Li Y, Yuan Y, Zhang X, Yan W, Li T, Niu Y, Xu M, Yan T, Li X, Li D, Xiang J, Wang B, Yan T. Beta-informativeness-diffusion multilayer graph embedding for brain network analysis. Front Neurosci 2024; 18:1303741. [PMID: 38525375 PMCID: PMC10957763 DOI: 10.3389/fnins.2024.1303741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 02/07/2024] [Indexed: 03/26/2024] Open
Abstract
Brain network analysis provides essential insights into the diagnosis of brain disease. Integrating multiple neuroimaging modalities has been demonstrated to be more effective than using a single modality for brain network analysis. However, a majority of existing brain network analysis methods based on multiple modalities often overlook both complementary information and unique characteristics from various modalities. To tackle this issue, we propose the Beta-Informativeness-Diffusion Multilayer Graph Embedding (BID-MGE) method. The proposed method seamlessly integrates structural connectivity (SC) and functional connectivity (FC) to learn more comprehensive information for diagnosing neuropsychiatric disorders. Specifically, a novel beta distribution mapping function (beta mapping) is utilized to increase vital information and weaken insignificant connections. The refined information helps the diffusion process concentrate on crucial brain regions to capture more discriminative features. To maximize the preservation of the unique characteristics of each modality, we design an optimal scale multilayer brain network, the inter-layer connections of which depend on node informativeness. Then, a multilayer informativeness diffusion is proposed to capture complementary information and unique characteristics from various modalities and generate node representations by incorporating the features of each node with those of their connected nodes. Finally, the node representations are reconfigured using principal component analysis (PCA), and cosine distances are calculated with reference to multiple templates for statistical analysis and classification. We implement the proposed method for brain network analysis of neuropsychiatric disorders. The results indicate that our method effectively identifies crucial brain regions associated with diseases, providing valuable insights into the pathology of the disease, and surpasses other advanced methods in classification performance.
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Affiliation(s)
- Yin Huang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Ying Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Yuting Yuan
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Xingyu Zhang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Wenjie Yan
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Ting Li
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yan Niu
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Mengzhou Xu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Ting Yan
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, China
| | - Xiaowen Li
- Computer Information Engineering Institute, Shanxi Technology and Business College, Taiyuan, China
| | - Dandan Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Bin Wang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Tianyi Yan
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
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Xie Z, Stallings-Smith S, Stetten N, Hamadi HY, Marlow NM. Marijuana use disorder among adults with functional disabilities-A US population-based cross-sectional study. Am J Addict 2024; 33:26-35. [PMID: 37821239 DOI: 10.1111/ajad.13485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 09/24/2023] [Accepted: 09/24/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Recent studies suggest a growing trend in marijuana use, compared to a stable prevalence of marijuana use disorder among US adults over the first 15 years of the 21st century. This study investigated the recent patterns of marijuana use disorder among people with disabilities (PWD). METHODS We extracted a nationally representative sample (N = 209,058) from the 2015-2019 National Survey on Drug Use and Health data set and examined associations by functional disability status (any disability, disability by type, and number of disabling limitations) with marijuana use disorder using a series of independent multivariable logistic regression models. We also performed trend analyses during the study period. RESULTS The prevalence of marijuana use disorder (from 1.7% to 2.3%) increased significantly among PWD between 2015 and 2019 (p-trend < .001). PWD were significantly more likely to report marijuana use disorder (odds ratio [OR], 1.37, 95% confidence interval [CI], 1.24-1.52) than people without disability (PWoD). Those with cognitive limitation only (OR, 1.78, 95% CI, 1.53-2.06) and ≥2 limitations (OR, 1.29, 95% CI, 1.10-1.51) were more likely to report marijuana use disorder than PWoD. DISCUSSION AND CONCLUSIONS PWD had a consistently higher prevalence of marijuana use disorder than PWoD. Additionally, the level of risk for marijuana use disorder varied by disability type and number of disabling limitations. SCIENTIFIC SIGNIFICANCE Our study provided new nuance on disparities in marijuana use disorder between PWD and PWoD and further revealed the varied risks for marijuana use disorder across different disability statuses.
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Affiliation(s)
- Zhigang Xie
- Department of Public Health, University of North Florida, Jacksonville, Florida, USA
| | | | - Nichole Stetten
- Department of Occupational Therapy, University of Florida, Gainesville, Florida, USA
| | - Hanadi Y Hamadi
- Department of Health Administration, University of North Florida, Jacksonville, Florida, USA
| | - Nicole M Marlow
- Department of Health Services Research, Management and Policy, University of Florida, Gainesville, Florida, USA
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Chen H, Xu J, Lv W, Hu Z, Ke Z, Qin R, Chen Y, Xu Y. Altered morphological connectivity mediated white matter hyperintensity-related cognitive impairment. Brain Res Bull 2023; 202:110714. [PMID: 37495024 DOI: 10.1016/j.brainresbull.2023.110714] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/06/2023] [Accepted: 07/22/2023] [Indexed: 07/28/2023]
Abstract
White matter hyperintensities (WMH) are widely observed in older adults and are closely associated with cognitive impairment. However, the underlying neuroimaging mechanisms of WMH-related cognitive dysfunction remain unknown. This study recruited 61 WMH individuals with mild cognitive impairment (WMH-MCI, n = 61), 48 WMH individuals with normal cognition (WMH-NC, n = 48) and 57 healthy control (HC, n = 57) in the final analyses. We constructed morphological networks by applying the Kullback-Leibler divergence to estimate interregional similarity in the distributions of regional gray matter volume. Based on morphological networks, graph theory was applied to explore topological properties, and their relationship to WMH-related cognitive impairment was assessed. There were no differences in small-worldness, global efficiency and local efficiency. The nodal local efficiency, degree centrality and betweenness centrality were altered mainly in the limbic network (LN) and default mode network (DMN). The rich-club analysis revealed that WMH-MCI subjects showed lower average strength of the feeder and local connections than HC (feeder connections: P = 0.034; local connections: P = 0.042). Altered morphological connectivity mediated the relationship between WMH and cognition, including language (total indirect effect: -0.010; 95 % CI: -0.024, -0.002) and executive (total indirect effect: -0.010; 95 % CI: -0.028, -0.002) function. The altered topological organization of morphological networks was mainly located in the DMN and LN and was associated with WMH-related cognitive impairment. The rich-club connection was relatively preserved, while the feeder and local connections declined. The results suggest that single-subject morphological networks may capture neurological dysfunction due to WMH and could be applied to the early imaging diagnostic protocol for WMH-related cognitive impairment.
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Affiliation(s)
- Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China; Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China; Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China; Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Jingxian Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Weiping Lv
- Nanjing Drum Tower Hospital Clinical College of Jiangsu University, Nanjing, China
| | - Zheqi Hu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhihong Ke
- Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China; Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China; Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Ying Chen
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China; Nanjing Drum Tower Hospital Clinical College of Jiangsu University, Nanjing, China; Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China; Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China; Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China; Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China.
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Lorenzetti V, McTavish E, Broyd S, van Hell H, Thomson D, Ganella E, Kottaram AR, Beale C, Martin J, Galettis P, Solowij N, Greenwood LM. Daily Cannabidiol Administration for 10 Weeks Modulates Hippocampal and Amygdalar Resting-State Functional Connectivity in Cannabis Users: A Functional Magnetic Resonance Imaging Open-Label Clinical Trial. Cannabis Cannabinoid Res 2023. [PMID: 37603080 DOI: 10.1089/can.2022.0336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023] Open
Abstract
Introduction: Cannabis use is associated with brain functional changes in regions implicated in prominent neuroscientific theories of addiction. Emerging evidence suggests that cannabidiol (CBD) is neuroprotective and may reverse structural brain changes associated with prolonged heavy cannabis use. In this study, we examine how an ∼10-week exposure of CBD in cannabis users affected resting-state functional connectivity in brain regions functionally altered by cannabis use. Materials and Methods: Eighteen people who use cannabis took part in a ∼10 weeks open-label pragmatic trial of self-administered daily 200 mg CBD in capsules. They were not required to change their cannabis exposure patterns. Participants were assessed at baseline and post-CBD exposure with structural magnetic resonance imaging (MRI) and a functional MRI resting-state task (eyes closed). Seed-based connectivity analyses were run to examine changes in the functional connectivity of a priori regions-the hippocampus and the amygdala. We explored if connectivity changes were associated with cannabinoid exposure (i.e., cumulative cannabis dosage over trial, and plasma CBD concentrations and Δ9-tetrahydrocannabinol (THC) plasma metabolites postexposure), and mental health (i.e., severity of anxiety, depression, and positive psychotic symptom scores), accounting for cigarette exposure in the past month, alcohol standard drinks in the past month and cumulative CBD dose during the trial. Results: Functional connectivity significantly decreased pre-to-post the CBD trial between the anterior hippocampus and precentral gyrus, with a strong effect size (d=1.73). Functional connectivity increased between the amygdala and the lingual gyrus pre-to-post the CBD trial, with a strong effect size (d=1.19). There were no correlations with cannabinoids or mental health symptom scores. Discussion: Prolonged CBD exposure may restore/reduce functional connectivity differences reported in cannabis users. These new findings warrant replication in a larger sample, using robust methodologies-double-blind and placebo-controlled-and in the most vulnerable people who use cannabis, including those with more severe forms of Cannabis Use Disorder and experiencing worse mental health outcomes (e.g., psychosis, depression).
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Affiliation(s)
- Valentina Lorenzetti
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Center, School of Health and Behavioral Sciences, Australian Catholic University, Melbourne, Victoria, Australia
| | - Eugene McTavish
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Center, School of Health and Behavioral Sciences, Australian Catholic University, Melbourne, Victoria, Australia
| | - Samantha Broyd
- School of Psychology and Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, New South Wales, Australia
- Illawarra Shoalhaven Local Health District, Wollongong, New South Wales, Australia
| | - Hendrika van Hell
- School of Psychology and Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, New South Wales, Australia
| | - Diny Thomson
- Turner Institute for Brain and Mental Health, School of Psychological Science, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia
| | - Eleni Ganella
- Melbourne Neuropsychiatry Center, Department of Psychiatry, The University of Melbourne, Carlton South, Victoria, Australia
- Orygen, the National Center of Excellence in Youth Mental Health, Parkville, Victoria, Australia
| | - Akhil Raja Kottaram
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Center, School of Health and Behavioral Sciences, Australian Catholic University, Melbourne, Victoria, Australia
- Melbourne Neuropsychiatry Center, Department of Psychiatry, The University of Melbourne, Carlton South, Victoria, Australia
| | - Camilla Beale
- School of Psychology and Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, New South Wales, Australia
| | - Jennifer Martin
- John Hunter Hospital, Newcastle, New South Wales, Australia
- Center for Drug Repurposing and Medicines Research, University of Newcastle and Hunter Medical Research Institute, Callaghan, New South Wales, Australia
- The Australian Center for Cannabinoid Clinical and Research Excellence (ACRE), New Lambton Heights, New South Wales, Australia
| | - Peter Galettis
- Center for Drug Repurposing and Medicines Research, University of Newcastle and Hunter Medical Research Institute, Callaghan, New South Wales, Australia
- The Australian Center for Cannabinoid Clinical and Research Excellence (ACRE), New Lambton Heights, New South Wales, Australia
| | - Nadia Solowij
- School of Psychology and Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, New South Wales, Australia
- The Australian Center for Cannabinoid Clinical and Research Excellence (ACRE), New Lambton Heights, New South Wales, Australia
| | - Lisa-Marie Greenwood
- The Australian Center for Cannabinoid Clinical and Research Excellence (ACRE), New Lambton Heights, New South Wales, Australia
- Research School of Psychology, The Australian National University, Canberra, Australian Capital Territory, Australia
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6
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Soleimani N, Kazemi K, Helfroush MS, Aarabi A. Altered brain structural and functional connectivity in cannabis users. Sci Rep 2023; 13:5847. [PMID: 37037859 PMCID: PMC10086048 DOI: 10.1038/s41598-023-32521-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/28/2023] [Indexed: 04/12/2023] Open
Abstract
Cannabis is one of the most used and commodified illicit substances worldwide, especially among young adults. The neurobiology mechanism of cannabis is yet to be identified particularly in youth. The purpose of this study was to concurrently measure alterations in brain structural and functional connectivity in cannabis users using resting-state functional magnetic resonance images (rs-fMRI) and diffusion-weighted images (DWI) from a group of 73 cannabis users (age 22-36, 19 female) in comparison with 73 healthy controls (age 22-36, 14 female) from Human Connectome Project (HCP). Several significant differences were observed in local structural/functional network measures (e.g. degree and clustering coefficient), being prominent in the insular and frontal opercular cortex and lateral/medial temporal cortex. The rich-club organization of structural networks revealed a normal trend, distributed within bilateral frontal, temporal and occipital regions. However, minor differences were found between the two groups in the superior and inferior temporal gyri. Functional rich-club nodes were mostly located within parietal and posterior areas, with minor differences between the groups found mainly in the centro-temporal and parietal regions. Regional network measures of structural/functional networks were associated with times used cannabis (TUC) in several regions. Although the structural/functional network in both groups showed small-world property, no differences between cannabis users and healthy controls were found regarding the global network measures, showing no association with cannabis use. After FDR correction, all of the significant associations between network measures and TUC were found to be insignificant, except for the association between degree and TUC within the presubiculum region. To recap, our findings revealed alterations in local topological properties of structural and functional networks in cannabis users, although their global brain network organization remained intact.
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Affiliation(s)
- Najme Soleimani
- Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Kamran Kazemi
- Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran.
| | | | - Ardalan Aarabi
- Faculty of Medicine, University of Picardie Jules Verne, Amiens, France
- Laboratory of Functional Neuroscience and Pathologies, University Research Center, University Hospital, Amiens, France
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7
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Piao S, Chen K, Wang N, Bao Y, Liu X, Hu B, Lu Y, Yang L, Geng D, Li Y. Modular Level Alterations Of Structural-Functional Connectivity Coupling in Mild Cognitive Impairment Patients and Interactions with Age Effect. J Alzheimers Dis 2023; 92:1439-1450. [PMID: 36911934 DOI: 10.3233/jad-220837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
BACKGROUND Structural-functional connectivity (SC- FC) coupling is related to various cognitive functions and more sensitive for the detection of subtle brain alterations. OBJECTIVE To investigate whether decoupling of SC-FC was detected in mild cognitive impairment (MCI) patients on a modular level, the interaction effect of aging and disease, and its relationship with network efficiency. METHODS 73 patients with MCI and 65 healthy controls were enrolled who underwent diffusion tensor imaging and resting-state functional MRI to generate structural and functional networks. Five modules were defined based on automated anatomical labeling 90 atlas, including default mode network (DMN), frontoparietal attention network (FPN), sensorimotor network (SMN), subcortical network (SCN), and visual network (VIS). Intra-module and inter-module SC-FC coupling were compared between two groups. The interaction effect of aging and group on modular SC-FC coupling was further analyzed by two-way ANOVA. The correlation between the coupling and network efficiency was finally calculated. RESULTS In MCI patients, aberrant intra-module coupling was noted in SMN, and altered inter-module coupling was found in the other four modules. Intra-module coupling exhibited significant age-by-group effects in DMN and SMN, and inter-module coupling showed significant age-by-group effects in DMN and FPN. In MCI patients, both positive or negative correlations between coupling and network efficiency were found in DMN, FPN, SCN, and VIS. CONCLUSION SC-FC coupling could reflect the association of SC and FC, especially in modular levels. In MCI, SC-FC coupling could be affected by the interaction effect of aging and disease, which may shed light on advancing the pathophysiological mechanisms of MCI.
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Affiliation(s)
- Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Keliang Chen
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Na Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yifang Bao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xueling Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Bin Hu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yucheng Lu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Liqin Yang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
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8
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Peng L, Chen Z, Gao X. Altered rich-club organization of brain functional network in autism spectrum disorder. Biofactors 2023. [PMID: 36785880 DOI: 10.1002/biof.1933] [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: 12/06/2022] [Accepted: 12/20/2022] [Indexed: 02/15/2023]
Abstract
Despite numerous research showing the association between brain network abnormalities and autism spectrum disorder (ASD), contrasting findings have been reported from broad functional underconnectivity to broad overconnectivity. Thus, the significance of rich-hub organizations in the brain functional connectome of individuals with ASD remains largely unknown. High-quality data subset of ASD (n = 45) and healthy controls (HC; n = 47) children (7-15 years old) were retrieved from the ABIDE data set, and rich-club organization and network-based statistic (NBS) were assessed from resting-state functional magnetic resonance imaging (rs-fMRI). The rich-club organization functional network (normalized rich-club coefficients >1) was observed in all subjects under a range of thresholds. Compared with HC, ASD patients had higher degree of feeder connections and lower degree of local connections (degree of feeder connections: ASD = 259.20 ± 32.97, HC = 244.98 ± 30.09, p = 0.041; degree of local connections: ASD = 664.02 ± 39.19, HC = 679.89 ± 34.05, p = 0.033) but had similar in rich-club connections. Further, nonparametric NBS analysis showed the presence of abnormal connectivity in the functional network of ASD individuals. Our findings indicated that local connection might be more vulnerable, and feeder connection may compensate for its disruption in ASD, enhancing our understanding on the mechanism of functional connectome dysfunction in ASD.
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Affiliation(s)
- Liling Peng
- Department of PET/MR, Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, People's Republic of China
| | - Zhuang Chen
- Department of Cardiology, The Fifth People's Hospital of Jinan, Jinan, Shandong, People's Republic of China
| | - Xin Gao
- Department of PET/MR, Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, People's Republic of China
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9
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Liu X, Qiu S, Wang X, Chen H, Tang Y, Qin Y. Aberrant dynamic Functional-Structural connectivity coupling of Large-scale brain networks in poststroke motor dysfunction. Neuroimage Clin 2023; 37:103332. [PMID: 36708666 PMCID: PMC10037213 DOI: 10.1016/j.nicl.2023.103332] [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: 10/05/2022] [Revised: 01/11/2023] [Accepted: 01/19/2023] [Indexed: 01/24/2023]
Abstract
BACKGROUND AND PURPOSE Stroke may lead to widespread functional and structural reorganization in the brain. Several studies have reported a potential correlation between functional network changes and structural network changes after stroke. However, it is unclear how functional-structural relationships change dynamically over the course of one resting-state fMRI scan in patients following a stroke; furthermore, we know little about their relationships with the severity of motor dysfunction. Therefore, this study aimed to investigate dynamic functional and structural connectivity (FC-SC) coupling and its relationship with motor function in subcortical stroke from the perspective of network dynamics. METHODS Resting-state functional magnetic resonance imaging and diffusion tensor imaging were obtained from 39 S patients (19 severe and 20 moderate) and 22 healthy controls (HCs). Brain structural networks were constructed by tracking fiber tracts in diffusion tensor imaging, and structural network topology metrics were calculated using a graph-theoretic approach. Independent component analysis, the sliding window method, and k-means clustering were used to calculate dynamic functional connectivity and to estimate different dynamic connectivity states. The temporal patterns and intergroup differences of FC-SC coupling were analyzed within each state. We also calculated dynamic FC-SC coupling and its relationship with functional network efficiency. In addition, the correlation between FC-SC coupling and the Fugl-Meyer assessment scale was analyzed. RESULTS For SC, stroke patients showed lower global efficiency than HCs (all P < 0.05), and severely affected patients had a higher characteristic path length (P = 0.003). For FC and FC-SC coupling, stroke patients predominantly showed lower local efficiency and reduced FC-SC coupling than HCs in state 2 (all P < 0.05). Furthermore, severely affected patients also showed lower local efficiency (P = 0.031) and reduced FC-SC coupling (P = 0.043) in state 3, which was markedly linked to the severity of motor dysfunction after stroke. In addition, FC-SC coupling was correlated with functional network efficiency in state 2 in moderately affected patients (r = 0.631, P = 0.004) but not significantly in severely affected patients. CONCLUSIONS Stroke patients show abnormal dynamic FC-SC coupling characteristics, especially in individuals with severe injuries. These findings may contribute to a better understanding of the anatomical functional interactions underlying motor deficits in stroke patients and provide useful information for personalized rehabilitation strategies.
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Affiliation(s)
- Xiaoying Liu
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China
| | - Shuting Qiu
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China
| | - Xiaoyang Wang
- Department of the Fujian Key Laboratory of Functional Imaging, Department of Radiology, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou 350025, China
| | - Hui Chen
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China
| | - Yuting Tang
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China
| | - Yin Qin
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China; Department of Rehabilitation Medicine, Fuzhou General Hospital (Dongfang Hospital), Xiamen University, Fuzhou 350025, China.
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10
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Guo J, Chen Y, Huang L, Liu W, Hu D, Lv Y, Kang H, Li N, Peng Y. Local structural-functional connectivity decoupling of caudate nucleus in infantile esotropia. Front Neurosci 2022; 16:1098735. [PMID: 36620443 PMCID: PMC9815444 DOI: 10.3389/fnins.2022.1098735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022] Open
Abstract
Abnormal brain structural and functional properties were demonstrated in patients with infantile esotropia (IE). However, few studies have investigated the interaction between structural and functional connectivity (SC-FC) in patients with IE. Structural network was generated with diffusion tensor imaging and functional network was constructed with resting-state functional magnetic resonance imaging for 18 patients with IE as well as 20 age- and gender- matched healthy subjects. The SC-FC coupling for global connectome, short connectome and long connectome were examined in IE patients and compared with those of healthy subjects. A linear mixed effects model was employed to examine the group-age interaction in terms of the coupling metrics. The Pearson correlation between coupling measures and strabismus degree was evaluated in IE patients, on which the regulatory effect of age was also investigated through hierarchical regression analysis. Significantly decreased SC-FC coupling score for short connections was observed in left caudate nucleus (CAU) in IE patients, whereas no brain regions exhibited altered coupling metrics for global connections or long connections. The group-age interaction was also evident in local coupling metrics of left CAU. The age-related regulatory effect on coupling-degree association was distinguishing between brain regions implicated in visual processing and cognition-related brain areas in IE patients. Local SC-FC decoupling in CAU was evident in patients with IE and was initiated in their early postnatal period, possibly interfering the visual cortico-striatal loop and subcortical optokinetic pathway subserving visual processing and nasalward optokinesis during neurodevelopment, which provides new insight into underlying neuropathological mechanism of IE.
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Affiliation(s)
- Jianlin Guo
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Yuanyuan Chen
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Lijuan Huang
- Department of Ophthalmology, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China,Department of Ophthalmology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Wen Liu
- Department of Ophthalmology, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Di Hu
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Yanqiu Lv
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Huiying Kang
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Ningdong Li
- Department of Ophthalmology, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China,Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing, China,*Correspondence: Ningdong Li,
| | - Yun Peng
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China,Yun Peng,
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11
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Thomson H, Labuschagne I, Greenwood LM, Robinson E, Sehl H, Suo C, Lorenzetti V. Is resting-state functional connectivity altered in regular cannabis users? A systematic review of the literature. Psychopharmacology (Berl) 2022; 239:1191-1209. [PMID: 34415377 DOI: 10.1007/s00213-021-05938-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 07/13/2021] [Indexed: 12/23/2022]
Abstract
RATIONALE Regular cannabis use has been associated with brain functional alterations within frontal, temporal, and striatal pathways assessed during various cognitive tasks. Whether such alterations are consistently reported in the absence of overt task performance needs to be elucidated to uncover the core neurobiological mechanisms of regular cannabis use. OBJECTIVES We aim to systematically review findings from studies that examine spontaneous fluctuations of brain function using functional Magnetic Resonance Imaging (fMRI) resting-state functional connectivity (rsFC) in cannabis users versus controls, and the association between rsFC and cannabis use chronicity, mental health symptoms, and cognitive performance. METHODS We conducted a PROSPERO registered systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and searched eight databases. RESULTS Twenty-one studies were included for review. Samples comprised 1396 participants aged 16 to 42 years, of which 737 were cannabis users and 659 were controls. Most studies found greater positive rsFC in cannabis users compared to controls between frontal-frontal, fronto-striatal, and fronto-temporal region pairings. The same region pairings were found to be preliminarily associated with varying measures of cannabis exposure. CONCLUSIONS The evidence to date shows that regular cannabis exposure is consistently associated with alteration of spontaneous changes in Blood Oxygenation Level-Dependent signal without any explicit cognitive input or output. These findings have implications for interpreting results from task-based fMRI studies of cannabis users, which may additionally tax overlapping networks. Future longitudinal rsFC fMRI studies are required to determine the clinical relevance of the findings and their link to the chronicity of use, mental health, and cognitive performance.
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Affiliation(s)
- Hannah Thomson
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Faculty of Health, Australian Catholic University, 17 Young Street, Fitzroy, VIC, 3065, Australia
| | - Izelle Labuschagne
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Faculty of Health, Australian Catholic University, 17 Young Street, Fitzroy, VIC, 3065, Australia
| | - Lisa-Marie Greenwood
- Research School of Psychology, Australian National University, Canberra, Australian Capital Territory, Australia.,The Australian Centre for Cannabinoid Clinical and Research Excellence (ACRE), New Lambton Heights, NSW, Australia
| | - Emily Robinson
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Faculty of Health, Australian Catholic University, 17 Young Street, Fitzroy, VIC, 3065, Australia
| | - Hannah Sehl
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Faculty of Health, Australian Catholic University, 17 Young Street, Fitzroy, VIC, 3065, Australia
| | - Chao Suo
- BrainPark, Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, VIC, Australia
| | - Valentina Lorenzetti
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Faculty of Health, Australian Catholic University, 17 Young Street, Fitzroy, VIC, 3065, Australia.
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12
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Yun JY, Kim YK. Graph theory approach for the structural-functional brain connectome of depression. Prog Neuropsychopharmacol Biol Psychiatry 2021; 111:110401. [PMID: 34265367 DOI: 10.1016/j.pnpbp.2021.110401] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 06/30/2021] [Accepted: 07/07/2021] [Indexed: 01/22/2023]
Abstract
To decipher the organizational styles of neural underpinning in major depressive disorder (MDD), the current article reviewed recent neuroimaging studies (published during 2015-2020) that applied graph theory approach to the diffusion tensor imaging data or functional brain activation data acquired during task-free resting state. The global network organization of resting-state functional connectivity network in MDD were diverse according to the onset age and medication status. Intra-modular functional connections were weaker in MDD compared to healthy controls (HC) for default mode and limbic networks. Weaker local graph metrics of default mode, frontoparietal, and salience network components in MDD compared to HC were also found. On the contrary, brain regions comprising the limbic, sensorimotor, and subcortical networks showed higher local graph metrics in MDD compared to HC. For the brain white matter-based structural connectivity network, the global network organization was comparable to HC in adult MDD but was attenuated in late-life depression. Local graph metrics of limbic, salience, default-mode, subcortical, insular, and frontoparietal network components in structural connectome were affected from the severity of depressive symptoms, burden of perceived stress, and treatment effects. Collectively, the current review illustrated changed global network organization of structural and functional brain connectomes in MDD compared to HC and were varied according to the onset age and medication status. Intra-modular functional connectivity within the default mode and limbic networks were weaker in MDD compared to HC. Local graph metrics of structural connectome for MDD reflected severity of depressive symptom and perceived stress, and were also changed after treatments. Further studies that explore the graph metrics-based neural correlates of clinical features, cognitive styles, treatment response and prognosis in MDD are required.
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Affiliation(s)
- Je-Yeon Yun
- Seoul National University Hospital, Seoul, Republic of Korea; Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Yong-Ku Kim
- Department of Psychiatry, College of Medicine, Korea University, Seoul, South Korea
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13
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Zhang X, Shi Y, Fan T, Wang K, Zhan H, Wu W. Analysis of Correlation Between White Matter Changes and Functional Responses in Post-stroke Depression. Front Aging Neurosci 2021; 13:728622. [PMID: 34707489 PMCID: PMC8542668 DOI: 10.3389/fnagi.2021.728622] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 09/20/2021] [Indexed: 11/28/2022] Open
Abstract
Objective: Post-stroke depression (PSD) is one of the most common neuropsychiatric symptoms with high prevalence, however, the mechanism of the brain network in PSD and the relationship between the structural and functional network remain unclear. This research applies graph theory to structural networks and explores the relationship between structural and functional networks. Methods: Forty-five patients with acute ischemic stroke were divided into the PSD group and post-stroke without depression (non-PSD) group respectively and underwent the magnetic resonance imaging scans. Network construction and Module analysis were used to explore the structural connectivity-functional connectivity (SC-FC) coupling of multi-scale brain networks in patients with PSD. Results: Compared with non-PSD, the structural network in PSD was related to the reduction of clustering and the increase of path length, but the degree of modularity was lower. Conclusions: The SC-FC coupling may serve as a biomarker for PSD. The similarity in SC and FC is associated with cognitive dysfunction, retardation, and desperation. Our findings highlighted the distinction in brain structural-functional networks in PSD. Clinical Trial Registration: https://www.clinicaltrials.gov/ct2/show/NCT03256305, NCT03256305.
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Affiliation(s)
- Xuefei Zhang
- Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yu Shi
- Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Tao Fan
- Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Kangling Wang
- Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Hongrui Zhan
- Department of Rehabilitation, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Wen Wu
- Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou, China.,Rehabilitation Medical School, Southern Medical University, Guangzhou, China
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14
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MacDonald B, Sadek J. Naturalistic exploratory study of the associations of substance use on ADHD outcomes and function. BMC Psychiatry 2021; 21:251. [PMID: 33980212 PMCID: PMC8117494 DOI: 10.1186/s12888-021-03263-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 04/29/2021] [Indexed: 03/06/2023] Open
Abstract
BACKGROUND Although Attention Deficit Hyperactivity Disorder (ADHD) is associated with an increased risk of substance use disorder (SUD), existing literature on how SUD interacts with ADHD outcomes is limited. This study investigates whether SUD among individuals with ADHD is associated with worse ADHD outcomes and prognosis, and the association between overall functioning and SUD. In addition, we seek to understand whether heavy cannabis use is a better predictor of poorer outcomes compared to SUD status alone. METHOD We conducted a retrospective analysis on 50 ADHD patient charts, which were allocated based on SUD status. Subgroup analysis was performed on the total sample population, with allocation based on heavy cannabis use. Mann-Whitney and Chi-Square tests were used for both the primary and subgroup analyses. RESULTS SUD status highly correlated with more ADHD-related cognitive impairments and poorer functional outcomes at the time of diagnosis. ADHD patients with comorbid ADHD-SUD scored significantly lower (p = < 0.0001) on objective cognitive testing (Integrated Auditory and Visual Continuous Performance Test (IVA/CPT)) than ADHD patients without SUD. The correlation with poorer ADHD outcomes was more pronounced when groups were allocated based on heavy cannabis use status; in addition to significantly lower IVA/CPT scores (p = 0.0011), heavy cannabis use was associated with more severe fine motor hyperactivity and self-reported hyperactivity/impulsivity scores (p = 0.0088 and 0.0172, respectively). CONCLUSION Future research is needed to determine how substance abuse can be a barrier to improved ADHD outcomes, and the effect cannabis and other substances have on cognitive function and pharmacotherapy of ADHD.
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Affiliation(s)
| | - Joseph Sadek
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
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15
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Ma J, Liu F, Yang B, Xue K, Wang P, Zhou J, Wang Y, Niu Y, Zhang J. Selective Aberrant Functional-Structural Coupling of Multiscale Brain Networks in Subcortical Vascular Mild Cognitive Impairment. Neurosci Bull 2020; 37:287-297. [PMID: 32975745 DOI: 10.1007/s12264-020-00580-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 05/30/2020] [Indexed: 01/04/2023] Open
Abstract
Subcortical vascular mild cognitive impairment (svMCI) is a common prodromal stage of vascular dementia. Although mounting evidence has suggested abnormalities in several single brain network metrics, few studies have explored the consistency between functional and structural connectivity networks in svMCI. Here, we constructed such networks using resting-state fMRI for functional connectivity and diffusion tensor imaging for structural connectivity in 30 patients with svMCI and 30 normal controls. The functional networks were then parcellated into topological modules, corresponding to several well-defined functional domains. The coupling between the functional and structural networks was finally estimated and compared at the multiscale network level (whole brain and modular level). We found no significant intergroup differences in the functional-structural coupling within the whole brain; however, there was significantly increased functional-structural coupling within the dorsal attention module and decreased functional-structural coupling within the ventral attention module in the svMCI group. In addition, the svMCI patients demonstrated decreased intramodular connectivity strength in the visual, somatomotor, and dorsal attention modules as well as decreased intermodular connectivity strength between several modules in the functional network, mainly linking the visual, somatomotor, dorsal attention, ventral attention, and frontoparietal control modules. There was no significant correlation between the altered module-level functional-structural coupling and cognitive performance in patients with svMCI. These findings demonstrate for the first time that svMCI is reflected in a selective aberrant topological organization in multiscale brain networks and may improve our understanding of the pathophysiological mechanisms underlying svMCI.
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Affiliation(s)
- Juanwei Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Bingbing Yang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Pinxiao Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jian Zhou
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yang Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yali Niu
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
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16
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Huang J, Wang M, Xu X, Jie B, Zhang D. A novel node-level structure embedding and alignment representation of structural networks for brain disease analysis. Med Image Anal 2020; 65:101755. [PMID: 32592983 DOI: 10.1016/j.media.2020.101755] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 06/08/2020] [Accepted: 06/11/2020] [Indexed: 10/24/2022]
Abstract
Brain networks based on various neuroimaging technologies, such as diffusion tensor image (DTI) and functional magnetic resonance imaging (fMRI), have been widely applied to brain disease analysis. Currently, there are several node-level structural measures (e.g., local clustering coefficients and node degrees) for representing and analyzing brain networks since they usually can reflect the topological structure of brain regions. However, these measures typically describe specific types of structural information, ignoring important network properties (i.e., small structural changes) that could further improve the performance of brain network analysis. To overcome this problem, in this paper, we first define a novel node-level structure embedding and alignment (nSEA) representation to accurately characterize the node-level structural information of the brain network. Different from existing measures that characterize a specific type of structural properties with a single value, our proposed nSEA method can learn a vector representation for each node, thus contain richer structure information to capture small structural changes. Furthermore, we develop an nSEA representation based learning (nSEAL) framework for brain disease analysis. Specifically, we first perform structural embedding to calculate node vector representations for each brain network and then align vector representations of all brain networks into the common space for two group-level network analyses, including a statistical analysis and brain disease classifications. Experiment results on a real schizophrenia dataset demonstrate that our proposed method not only discover disease-related brain regions that could help to better understand the pathology of brain diseases, but also improve the classification performance of brain diseases, compared with state-of-the-art methods.
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Affiliation(s)
- Jiashuang Huang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 210029, China.
| | - Mingliang Wang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 210029, China.
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University Nanjing, 210029, China.
| | - Biao Jie
- Department of Computer Science and Technology, Anhui Normal University, Wuhu 241000, China.
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 210029, China.
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17
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Chen H, Sheng X, Luo C, Qin R, Ye Q, Zhao H, Xu Y, Bai F. The compensatory phenomenon of the functional connectome related to pathological biomarkers in individuals with subjective cognitive decline. Transl Neurodegener 2020; 9:21. [PMID: 32460888 PMCID: PMC7254770 DOI: 10.1186/s40035-020-00201-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 05/20/2020] [Indexed: 01/01/2023] Open
Abstract
Background Subjective cognitive decline (SCD) is a preclinical stage along the Alzheimer’s disease (AD) continuum. However, little is known about the aberrant patterns of connectivity and topological alterations of the brain functional connectome and their diagnostic value in SCD. Methods Resting-state functional magnetic resonance imaging and graph theory analyses were used to investigate the alterations of the functional connectome in 66 SCD individuals and 64 healthy controls (HC). Pearson correlation analysis was computed to assess the relationships among network metrics, neuropsychological performance and pathological biomarkers. Finally, we used the multiple kernel learning-support vector machine (MKL-SVM) to differentiate the SCD and HC individuals. Results SCD individuals showed higher nodal topological properties (including nodal strength, nodal global efficiency and nodal local efficiency) associated with amyloid-β levels and memory function than the HC, and these regions were mainly located in the default mode network (DMN). Moreover, increased local and medium-range connectivity mainly between the bilateral parahippocampal gyrus (PHG) and other DMN-related regions was found in SCD individuals compared with HC individuals. These aberrant functional network measures exhibited good classification performance in the differentiation of SCD individuals from HC individuals at an accuracy up to 79.23%. Conclusion The findings of this study provide insight into the compensatory mechanism of the functional connectome underlying SCD. The proposed classification method highlights the potential of connectome-based metrics for the identification of the preclinical stage of AD.
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Affiliation(s)
- Haifeng Chen
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Xiaoning Sheng
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Caimei Luo
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Qing Ye
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, P. R. China. .,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China. .,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China. .,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China.
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18
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Cao R, Wang X, Gao Y, Li T, Zhang H, Hussain W, Xie Y, Wang J, Wang B, Xiang J. Abnormal Anatomical Rich-Club Organization and Structural-Functional Coupling in Mild Cognitive Impairment and Alzheimer's Disease. Front Neurol 2020; 11:53. [PMID: 32117016 PMCID: PMC7013042 DOI: 10.3389/fneur.2020.00053] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 01/14/2020] [Indexed: 12/17/2022] Open
Abstract
Emerging research indicates interruptions in the wiring organization of the brain network in Mild cognitive impairment (MCI) and Alzheimer's disease (AD). Due to the important role of rich-club organization in distinguishing abnormalities of AD patients and the close relationship between structural connectivity (SC) and functional connectivity (FC), our study examined whether changes in SC-FC coupling and the relationship with abnormal rich-club organizations during the development of diseases may contribute to the pathophysiology of AD. Structural diffusion-tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) were performed in 38 normal controls (NCs), 40 MCI patients and 19 AD patients. Measures of the rich-club structure and its role in global structural-functional coupling were administered. Our study found decreased levels of feeder and local connectivity in MCI and AD patients, which were the main contributing factors to the lower efficiency of the brain structural network. Another important finding was that we have more accurately characterized the changing pattern of functional brain dynamics. The enhanced coupling between SC and FC in MCI and AD patients might be due to disruptions in optimal structural organization. More interestingly, we also found increases in the SC-FC coupling for feeder and local connections in MCI and AD patients. SC-FC coupling also showed significant differences between MCI and AD patients, mainly between the abnormal feeder connections. The connection density and coupling strength were significantly correlated with clinical metrics in patients. The present findings enhanced our understanding of the neurophysiologic mechanisms associated with MCI and AD.
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Affiliation(s)
- Rui Cao
- College of Software, Taiyuan University of Technology, Taiyuan, China
| | - Xin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yuan Gao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ting Li
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Hui Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Waqar Hussain
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yunyan Xie
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jing Wang
- Department of Health management, Aerospace Center Hospital, Peking University Aerospace School of Clinical Medicine, Beijing, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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19
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Huang J, Zhu Q, Wang M, Zhou L, Zhang Z, Zhang D. Coherent Pattern in Multi-Layer Brain Networks: Application to Epilepsy Identification. IEEE J Biomed Health Inform 2020; 24:2609-2620. [PMID: 31899443 DOI: 10.1109/jbhi.2019.2962519] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Currently, how to conjointly fuse structural connectivity (SC) and functional connectivity (FC) for identifying brain diseases is a hot topic in the area of brain network analysis. Most of the existing works combine two types of connectivity in decision level, thus ignoring the underlying relationship between SC and FC. To solve this problem, in this paper, we model the brain network as the multi-layer network formed by the SC and FC, and then propose a coherent pattern to represent structural information of the multi-layer network for the brain disease identification. The proposed coherent pattern consists of a paired-subgraph extracted from the FC and SC within the same node-set. Compared with the previous methods, this coherent pattern not only describes the connectivity information of both SC and FC by subgraphs at each layer, but also reflects their intrinsic relationship by the co-occurrence pattern of the paired-subgraph. Based on this coherent pattern, we further develop a framework for identifying brain diseases. Specifically, we first construct multi-layer networks by using SC and FC for each subject and then mine coherent patterns that frequently appear in each group. Next, we select the discriminative coherent pattern from these frequent coherent patterns according to their frequency of occurrence. Finally, we construct a feature matrix for each subject based on the binary indicator vector and then use the support vector machine (SVM) as its classifier. Experimental results on real epilepsy datasets demonstrate that our method outperforms several state-of-the-art approaches in the tasks of brain disease classification.
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Burggren AC, Shirazi A, Ginder N, London ED. Cannabis effects on brain structure, function, and cognition: considerations for medical uses of cannabis and its derivatives. THE AMERICAN JOURNAL OF DRUG AND ALCOHOL ABUSE 2019; 45:563-579. [PMID: 31365275 PMCID: PMC7027431 DOI: 10.1080/00952990.2019.1634086] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 06/14/2019] [Accepted: 06/17/2019] [Indexed: 12/16/2022]
Abstract
Background: Cannabis is the most widely used illicit substance worldwide, and legalization for recreational and medical purposes has substantially increased its availability and use in the United States.Objectives: Decades of research have suggested that recreational cannabis use confers risk for cognitive impairment across various domains, and structural and functional differences in the brain have been linked to early and heavy cannabis use.Methods: With substantial evidence for the role of the endocannabinoid system in neural development and understanding that brain development continues into early adulthood, the rising use of cannabis in adolescents and young adults raises major concerns. Yet some formulations of cannabinoid compounds are FDA-approved for medical uses, including applications in children.Results: Potential effects on the trajectory of brain morphology and cognition, therefore, should be considered. The goal of this review is to update and consolidate relevant findings in order to inform attitudes and public policy regarding the recreational and medical use of cannabis and cannabinoid compounds.Conclusions: The findings point to considerations for age limits and guidelines for use.
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Affiliation(s)
- Alison C Burggren
- Robert and Beverly Lewis Center for Neuroimaging, University of Oregon, Eugene, OR, USA
| | - Anaheed Shirazi
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, USA
| | - Nathaniel Ginder
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, USA
| | - Edythe D. London
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, USA
- Department of Molecular and Medical Pharmacology, and the Brain Research Institute, University of California at Los Angeles, Los Angeles, CA, USA
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Kim DJ, Schnakenberg Martin AM, Shin YW, Jo HJ, Cheng H, Newman SD, Sporns O, Hetrick WP, Calkins E, O'Donnell BF. Aberrant structural-functional coupling in adult cannabis users. Hum Brain Mapp 2018; 40:252-261. [PMID: 30203892 DOI: 10.1002/hbm.24369] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 08/09/2018] [Accepted: 08/11/2018] [Indexed: 12/13/2022] Open
Abstract
Cellular studies indicate that endocannabinoid type-1 retrograde signaling plays a major role in synaptic plasticity. Disruption of these processes by delta-9-tetrahydrocannabinol (THC) could produce alterations either in structural and functional brain connectivity or in their association in cannabis (CB) users. Graph theoretic structural and functional networks were generated with diffusion tensor imaging and resting-state functional imaging in 37 current CB users and 31 healthy non-users. The primary outcome measures were coupling between structural and functional connectivity, global network characteristics, association between the coupling and network properties, and measures of rich-club organization. Structural-functional (SC-FC) coupling was globally preserved showing a positive association in current CB users. However, the users had disrupted associations between SC-FC coupling and network topological characteristics, most perturbed for shorter connections implying region-specific disruption by CB use. Rich-club analysis revealed impaired SC-FC coupling in the hippocampus and caudate of users. This study provides evidence of the abnormal SC-FC association in CB users. The effect was predominant in shorter connections of the brain network, suggesting that the impact of CB use or predispositional factors may be most apparent in local interconnections. Notably, the hippocampus and caudate specifically showed aberrant structural and functional coupling. These structures have high CB1 receptor density and may also be associated with changes in learning and habit formation that occur with chronic cannabis use.
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Affiliation(s)
- Dae-Jin Kim
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
| | | | - Yong-Wook Shin
- Department of Psychiatry, Ulsan University School of Medicine, ASAN Medical Center, Seoul, South Korea
| | - Hang Joon Jo
- Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota
| | - Hu Cheng
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana.,Imaging Research Facility, Indiana University, Bloomington, Indiana
| | - Sharlene D Newman
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana.,Imaging Research Facility, Indiana University, Bloomington, Indiana
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana.,Indiana University Network Science Institute, Indiana University, Bloomington, Indiana
| | - William P Hetrick
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
| | - Eli Calkins
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
| | - Brian F O'Donnell
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
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