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Wang Y, Zhou J, Chen X, Liu R, Zhang Z, Feng L, Feng Y, Wang G, Zhou Y. Effects of escitalopram therapy on effective connectivity among core brain networks in major depressive disorder. J Affect Disord 2024; 350:39-48. [PMID: 38220106 DOI: 10.1016/j.jad.2024.01.115] [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: 10/09/2023] [Revised: 01/06/2024] [Accepted: 01/09/2024] [Indexed: 01/16/2024]
Abstract
BACKGROUND Patients with major depressive disorder (MDD) have abnormal functional interaction among large-scale brain networks, indicated by aberrant effective connectivity of the default mode network (DMN), salience network (SN), and dorsal attention network (DAN). However, it remains unclear whether antidepressants can normalize the altered effective connectivity in MDD. METHODS In this study, we collected resting-state functional magnetic resonance imaging data from 46 unmedicated patients with MDD at baseline and after 12 weeks of escitalopram treatment. We also collected data from 58 healthy controls (HCs) at the same time point with the same interval. Using spectral dynamic causal modeling and parametric empirical Bayes, we examined group differences, time effect and their interaction on the casual interactions among the regions of interest in the three networks. RESULTS Compared with HCs, patients with MDD showed increased positive (excitatory) connections within the DMN, decreased positive connections within the SN and DAN, decreased absolute value of negative (inhibitory) connectivity from the SN and DAN to the DMN, and decreased positive connections between the DAN and the SN. Furthermore, we found that six connections related to the DAN showed decreased group differences in effective connectivity between MDD and HCs during follow-up compared to the baseline. CONCLUSIONS Our findings suggest that escitalopram therapy can partly improve the disrupted effective connectivity among high-order brain functional networks in MDD. These findings deepened our understanding of the neural basis of antidepressants' effect on brain function in patients with MDD.
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Affiliation(s)
- Yun Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Jingjing Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Xiongying Chen
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Rui Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Zhifang Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Lei Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yuan Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Gang Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Yuan Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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2
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Chen H, Mao Q, Zhang Y, Shi M, Geng W, Ma Y, Chen Y, Yin X. Disrupted Effective Connectivity within the Fronto-Thalamic Circuit in Pontine Infarction: A Spectral Dynamic Causal Modeling Study. Brain Sci 2024; 14:45. [PMID: 38248260 PMCID: PMC10813776 DOI: 10.3390/brainsci14010045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 12/13/2023] [Accepted: 12/19/2023] [Indexed: 01/23/2024] Open
Abstract
This study aims to investigate alterations in effective connectivity (EC) within the fronto-thalamic circuit and their associations with motor and cognitive declines in pontine infarction (PI). A total of 33 right PI patients (RPIs), 38 left PI patients (LPIs), and 67 healthy controls (HCs) were recruited. The spectral dynamic causal modeling (spDCM) approach was used for EC analysis within the fronto-thalamic circuit, including the thalamus, caudate, supplementary motor area (SMA), medial prefrontal cortex (mPFC), and anterior cingulate cortex (ACC). The EC differences between different sides of the patients and HCs were assessed, and their correlations with motor and cognitive dysfunctions were analyzed. The LPIs showed increased EC from the mPFC to the R-SMA and decreased EC from the L-thalamus to the ACC, the L-SMA to the R-SMA, the R-caudate to the R-thalamus, and the R-thalamus to the ACC. For RPIs, the EC of the R-caudate to the mPFC, the L-thalamus and L-caudate to the L-SMA, and the L-caudate to the ACC increased obviously, while a lower EC strength was shown from the L-thalamus to the mPFC, the LSMA to the R-caudate, and the R-SMA to the L-thalamus. The EC from the R-caudate to the mPFC was negatively correlated with the MoCA score for RPIs, and the EC from the R-caudate to the R-thalamus was negatively correlated with the FMA score for LPIs. The results demonstrated EC within the fronto-thalamic circuit in PI-related functional impairments and reveal its potential as a novel imaging marker.
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Affiliation(s)
| | | | | | | | | | | | | | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China; (H.C.); (Q.M.); (Y.Z.); (M.S.); (W.G.); (Y.M.); (Y.C.)
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3
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Chen L, Sun J, Gao L, Wang J, Ma J, Xu E, Zhang D, Li L, Wu T. Dysconnectivity of the parafascicular nucleus in Parkinson's disease: A dynamic causal modeling analysis. Neurobiol Dis 2023; 188:106335. [PMID: 37890560 DOI: 10.1016/j.nbd.2023.106335] [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: 08/08/2023] [Revised: 10/24/2023] [Accepted: 10/24/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Recent animal model studies have suggested that the parafascicular nucleus has the potential to be an effective deep brain stimulation target for Parkinson's disease. However, our knowledge on the role of the parafascicular nucleus in Parkinson's disease patients remains limited. OBJECTIVE We aimed to investigate the functional alterations of the parafascicular nucleus projections in Parkinson's disease patients. METHODS We enrolled 72 Parkinson's disease patients and 60 healthy controls, then utilized resting-state functional MRI and spectral dynamic causal modeling to explore the effective connectivity of the bilateral parafascicular nucleus to the dorsal putamen, nucleus accumbens, and subthalamic nucleus. The associations between the effective connectivity of the parafascicular nucleus projections and clinical features were measured with Pearson partial correlations. RESULTS Compared with controls, the effective connectivity from the parafascicular nucleus to dorsal putamen was significantly increased, while the connectivity to the nucleus accumbens and subthalamic nucleus was significantly reduced in Parkinson's disease patients. There was a significantly positive correlation between the connectivity of parafascicular nucleus-dorsal putamen projection and motor deficits. The connectivity from the parafascicular nucleus to the subthalamic nucleus was negatively correlated with motor deficits and apathy, while the connectivity from the parafascicular nucleus to the nucleus accumbens was negatively associated with depression. CONCLUSION The present study demonstrates that the parafascicular nucleus-related projections are damaged and associated with clinical symptoms of Parkinson's disease. Our findings provide new insights into the impaired basal ganglia-thalamocortical circuits and give support for the parafascicular nucleus as a potential effective neuromodulating target of the disease.
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Affiliation(s)
- Lili Chen
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Junyan Sun
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Linlin Gao
- Department of General Medicine, Tianjin Union Medical Center, Tianjin, China
| | - Junling Wang
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jinghong Ma
- Department of Neurobiology, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Erhe Xu
- Department of Neurobiology, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Dongling Zhang
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Liang Li
- Brain Science Center, Beijing Institute of Basic Medical Sciences, China.
| | - Tao Wu
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China.
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4
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Amiri S, Arbabi M, Rahimi M, Parvaresh-Rizi M, Mirbagheri MM. Effective connectivity between deep brain stimulation targets in individuals with treatment-resistant depression. Brain Commun 2023; 5:fcad256. [PMID: 37901039 PMCID: PMC10600572 DOI: 10.1093/braincomms/fcad256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 06/27/2023] [Accepted: 10/06/2023] [Indexed: 10/31/2023] Open
Abstract
The therapeutic effect of deep brain stimulation on patients with treatment-resistant depression is strongly dependent on the connectivity of the stimulation region with other regions associated with depression. The aims of this study are to characterize the effective connectivity between the brain regions playing important roles in depression and further investigate the underlying pathophysiological mechanisms of treatment-resistant depression and the mechanisms involving deep brain stimulation. Thirty-three individuals with treatment-resistant depression and 29 healthy control subjects were examined. All subjects underwent resting-state functional MRI scanning. The coupling parameters reflecting the causal interactions among deep brain stimulation targets and medial prefrontal cortex were estimated using spectral dynamic causal modelling. Our results showed that compared to the healthy control subjects, in the left hemisphere of treatment-resistant depression patients, the nucleus accumbens was inhibited by the inferior thalamic peduncle and excited the ventral caudate and the subcallosal cingulate gyrus, which in turn excited the lateral habenula. In the right hemisphere, the lateral habenula inhibited the ventral caudate and the nucleus accumbens, both of which inhibited the inferior thalamic peduncle, which in turn inhibited the cingulate gyrus. The ventral caudate excited the lateral habenula and the cingulate gyrus, which excited the medial prefrontal cortex. Furthermore, these effective connectivity links varied between males and females, and the left and right hemispheres. Our findings suggest that intrinsic excitatory/inhibitory connections between deep brain stimulation targets are impaired in treatment-resistant depression patients, and that these connections are sex dependent and hemispherically lateralized. This knowledge can help to better understand the underlying mechanisms of treatment-resistant depression, and along with tractography, structural imaging, and other relevant clinical information, may assist to determine the appropriate region for deep brain stimulation therapy in each treatment-resistant depression patient.
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Affiliation(s)
- Saba Amiri
- Neuroscience Research Center, Shahid Beheshti University of Medical Science, Tehran 1983969367, Iran
| | - Mohammad Arbabi
- Psychiatry, Psychosomatic Medicine Research Center Department, Tehran University of Medical Sciences, Tehran 1419733141, Iran
| | - Milad Rahimi
- Medical Physics and Biomedical Engineering Group, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran 1461884513, Iran
| | - Mansour Parvaresh-Rizi
- Neurosurgery Department, Iran University of Medical Sciences (IUMS), Tehran 02166509120, Iran
| | - Mehdi M Mirbagheri
- Medical Physics and Biomedical Engineering Group, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran 1461884513, Iran
- Physical Medicine and Rehabilitation Department, Northwestern University, Chicago IL 60611, USA
- Neural Engineering and Rehabilitation Research Center, Tehran 1146733711, Iran
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5
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Li C, Li T, Chen Y, Zhang C, Ning M, Qin R, Li L, Wang X, Chen L. Sex differences of the triple network model in children with autism: A resting-state fMRI investigation of effective connectivity. Autism Res 2023; 16:1693-1706. [PMID: 37565548 DOI: 10.1002/aur.2991] [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: 04/22/2023] [Accepted: 07/06/2023] [Indexed: 08/12/2023]
Abstract
Autism spectrum disorder (ASD) has a pronounced male predominance, but the underlying neurobiological basis of this sex bias remains unclear. Gender incoherence (GI) theory suggests that ASD is more neurally androgynous than same-sex controls. Given its central role, altered structures and functions, and sex-dependent network differences in ASD, the triple network model, including the central executive network (CEN), default mode network (DMN), and salience network (SN), has emerged as a candidate for characterizing this sex difference. Here, we measured the sex-related effective connectivity (EC) differences within and between these three networks in 72 children with ASD (36 females, 8-14 years) and 72 typically developing controls (TCs) (36 females, 8-14 years) from 5 sites of the Autism Brain Imaging Data Exchange repositories using a 2 × 2 analysis of covariance factorial design. We also assessed brain-behavior relationships and the effects of age on EC. We found significant diagnosis-by-sex interactions on EC: females with ASD had significantly higher EC than their male counterparts within the DMN and between the SN and CEN. The interaction pattern supported the GI theory by showing that the higher EC observed in females with ASD reflected a shift towards the higher level of EC displayed in male TCs (neural masculinization), and the lower EC seen in males with ASD reflected a shift towards the lower level of EC displayed in female TCs (neural feminization). We also found significant brain-behavior correlations and significant effects of age on EC.
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Affiliation(s)
- Cuicui Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Tong Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Ying Chen
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Chunling Zhang
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Mingmin Ning
- Department of Neurology, Guangzhou Women and Children's Medical Center, China
| | - Rui Qin
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Lin Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Linglong Chen
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, China
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6
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Kyuragi Y, Oishi N, Yamasaki S, Hazama M, Miyata J, Shibata M, Fujiwara H, Fushimi Y, Murai T, Suwa T. Information flow and dynamic functional connectivity during electroconvulsive therapy in patients with depression. J Affect Disord 2023; 328:141-152. [PMID: 36801417 DOI: 10.1016/j.jad.2023.02.060] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/12/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023]
Abstract
BACKGROUND Electroconvulsive therapy is effectively used for treatment-resistant depression; however, its neural mechanism is largely unknown. Resting-state functional magnetic resonance imaging is promising for monitoring outcomes of electroconvulsive therapy for depression. This study aimed to explore the imaging correlates of the electroconvulsive therapy effects on depression using Granger causality analysis and dynamic functional connectivity analyses. METHODS We performed advanced analyses of resting-state functional magnetic resonance imaging data at the beginning and intermediate stages and end of the therapeutic course to identify neural markers that reflect or predict the therapeutic effects of electroconvulsive therapy on depression. RESULTS We demonstrated that information flow between the functional networks analyzed by Granger causality changes during electroconvulsive therapy, and this change was correlated with the therapeutic outcome. Information flow and the dwell time (an index reflecting the temporal stability of functional connectivity) before electroconvulsive therapy are correlated with depressive symptoms during and after treatment. LIMITATIONS First, the sample size was small. A larger group is needed to confirm our findings. Second, the influence of concomitant pharmacotherapy on our results was not fully addressed, although we expected it to be minimal because only minor changes in pharmacotherapy occurred during electroconvulsive therapy. Third, different scanners were used the groups, although the acquisition parameters were the same; a direct comparison between patient and healthy participant data was not possible. Thus, we presented the data of the healthy participants separately from that of the patients as a reference. CONCLUSIONS These results show the specific properties of functional brain connectivity.
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Affiliation(s)
- Yusuke Kyuragi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Naoya Oishi
- Medical Innovation Center, Kyoto University Graduate School of Medicine, Kyoto 606-8397, Japan.
| | - Shimpei Yamasaki
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Masaaki Hazama
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Jun Miyata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Mami Shibata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Hironobu Fujiwara
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan; Artificial Intelligence Ethics and Society Team, RIKEN Center for Advanced Intelligence Project, Saitama 351-0198, Japan; The General Research Division, Research Center on Ethical, Legal and Social Issues, Osaka University, Osaka 565-0871, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Taro Suwa
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
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Transcutaneous Electrical Cranial-Auricular Acupoint Stimulation Modulating the Brain Functional Connectivity of Mild-to-Moderate Major Depressive Disorder: An fMRI Study Based on Independent Component Analysis. Brain Sci 2023; 13:brainsci13020274. [PMID: 36831816 PMCID: PMC9953795 DOI: 10.3390/brainsci13020274] [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: 12/25/2022] [Revised: 01/28/2023] [Accepted: 02/01/2023] [Indexed: 02/10/2023] Open
Abstract
Evidence has shown the roles of taVNS and TECS in improving depression but few studies have explored their synergistic effects on MDD. Therefore, the treatment responsivity and neurological effects of TECAS were investigated and compared to escitalopram, a commonly used medication for depression. Fifty patients with mild-to-moderate MDD (29 in the TECAS group and 21 in another) and 49 demographically matched healthy controls were recruited. After an eight-week treatment, the outcomes of TECAS and escitalopram were evaluated by the effective rate and reduction rate based on the Montgomery-Asberg Depression Rating Scale, Hamilton Depression Rating Scale, and Hamilton Anxiety Rating Scale. Altered brain networks were analyzed between pre- and post-treatment using independent component analysis. There was no significant difference in clinical scales between TECAS and escitalopram but these were significantly decreased after each treatment. Both treatments modulated connectivity of the default mode network (DMN), dorsal attention network (DAN), right frontoparietal network (RFPN), and primary visual network (PVN), and the decreased PVN-RFPN connectivity might be the common brain mechanism. However, there was increased DMN-RFPN and DMN-DAN connectivity after TECAS, while it decreased in escitalopram. In conclusion, TECAS could relieve symptoms of depression similarly to escitalopram but induces different changes in brain networks.
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Cosío-Guirado R, Soriano-Mas C, Del Cerro I, Urretavizcaya M, Menchón JM, Soria V, Cañete-Massé C, Peró-Cebollero M, Guàrdia-Olmos J. Diagnosis of late-life depression using structural equation modeling and dynamic effective connectivity during resting fMRI. J Affect Disord 2022; 318:246-254. [PMID: 36096369 DOI: 10.1016/j.jad.2022.09.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/02/2022] [Accepted: 09/06/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Late-life depression (LLD) is characterized by cognitive and social impairments. Determining neurobiological alterations in connectivity in LLD by means of fMRI may lead to a better understanding of the neural basis underlying this disorder and more precise diagnostic markers. The primary objective of this paper is to identify a structural model that best explains the dynamic effective connectivity (EC) of the default mode network (DMN) in LLD patients compared to controls. METHODS Twenty-seven patients and 29 healthy controls underwent resting-state fMRI during a period of eight minutes. In both groups, jackknife correlation matrices were generated with six ROIs of the DMN that constitute the posterior DMN (pDMN). The different correlation matrices were used as input to estimate each structural equation model (SEM) for each subject in both groups incorporating dynamic effects. RESULTS The results show that the proposed LLD diagnosis algorithm achieves perfect accuracy in classifying LLD patients and controls. This differentiation is based on three aspects: the importance of ROIs 4 and 6, which seem to be the most distinctive among the subnetworks; the shape that the specific connections adopt in their networks, or in other words, the directed connections that are established among the ROIs in the pDMN for each group; and the number of dynamic effects that seem to be greater throughout the six ROIs studied [t = 54.346; df = 54; p < .001; 95 % CI difference = 5.486-5.906]. LIMITATIONS The sample size was moderate, and the participants continued their current medications. CONCLUSIONS The network models that we developed describe a pattern of dynamic activation in the pDMN that may be considered a possible biomarker for LLD, which may allow early diagnosis of this disorder.
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Affiliation(s)
- Raquel Cosío-Guirado
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain.
| | - Carles Soriano-Mas
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain; Bellvitge Biomedical Research Institute-IDIBELL, Department of Psychiatry, Bellvitge University Hospital, Barcelona, Spain; Network Center for Biomedical Research on Mental Health (CIBERSAM), Carlos III Health Institute (ISCIII), Madrid, Spain.
| | - Inés Del Cerro
- Bellvitge Biomedical Research Institute-IDIBELL, Department of Psychiatry, Bellvitge University Hospital, Barcelona, Spain
| | - Mikel Urretavizcaya
- Bellvitge Biomedical Research Institute-IDIBELL, Department of Psychiatry, Bellvitge University Hospital, Barcelona, Spain; Network Center for Biomedical Research on Mental Health (CIBERSAM), Carlos III Health Institute (ISCIII), Madrid, Spain; Department of Clinical Sciences, Bellvitge Campus, Universitat de Barcelona-UB, Barcelona, Spain
| | - José M Menchón
- Bellvitge Biomedical Research Institute-IDIBELL, Department of Psychiatry, Bellvitge University Hospital, Barcelona, Spain; Network Center for Biomedical Research on Mental Health (CIBERSAM), Carlos III Health Institute (ISCIII), Madrid, Spain; Department of Clinical Sciences, Bellvitge Campus, Universitat de Barcelona-UB, Barcelona, Spain
| | - Virginia Soria
- Bellvitge Biomedical Research Institute-IDIBELL, Department of Psychiatry, Bellvitge University Hospital, Barcelona, Spain; Network Center for Biomedical Research on Mental Health (CIBERSAM), Carlos III Health Institute (ISCIII), Madrid, Spain; Department of Clinical Sciences, Bellvitge Campus, Universitat de Barcelona-UB, Barcelona, Spain
| | - Cristina Cañete-Massé
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain; UB Institute of Complex Systems, Universitat de Barcelona, Spain
| | - Maribel Peró-Cebollero
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain; UB Institute of Complex Systems, Universitat de Barcelona, Spain; Institute of Neuroscience, Universitat de Barcelona, Spain
| | - Joan Guàrdia-Olmos
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain; UB Institute of Complex Systems, Universitat de Barcelona, Spain; Institute of Neuroscience, Universitat de Barcelona, Spain
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9
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Pilmeyer J, Huijbers W, Lamerichs R, Jansen JFA, Breeuwer M, Zinger S. Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects. J Neuroimaging 2022; 32:582-595. [PMID: 35598083 PMCID: PMC9540243 DOI: 10.1111/jon.13011] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/04/2022] [Accepted: 05/04/2022] [Indexed: 02/02/2023] Open
Abstract
Objective diagnosis and prognosis in major depressive disorder (MDD) remains a challenge due to the absence of biomarkers based on physiological parameters or medical tests. Numerous studies have been conducted to identify functional magnetic resonance imaging‐based biomarkers of depression that either objectively differentiate patients with depression from healthy subjects, predict personalized treatment outcome, or characterize biological subtypes of depression. While there are some findings of consistent functional biomarkers, there is still lack of robust data acquisition and analysis methodology. According to current findings, primarily, the anterior cingulate cortex, prefrontal cortex, and default mode network play a crucial role in MDD. Yet, there are also less consistent results and the involvement of other regions or networks remains ambiguous. We further discuss image acquisition, processing, and analysis limitations that might underlie these inconsistencies. Finally, the current review aims to address and discuss possible remedies and future opportunities that could improve the search for consistent functional imaging biomarkers of depression. Novel acquisition techniques, such as multiband and multiecho imaging, and neural network‐based cleaning approaches can enhance the signal quality in limbic and frontal regions. More comprehensive analyses, such as directed or dynamic functional features or the identification of biological depression subtypes, can improve objective diagnosis or treatment outcome prediction and mitigate the heterogeneity of MDD. Overall, these improvements in functional MRI imaging techniques, processing, and analysis could advance the search for biomarkers and ultimately aid patients with MDD and their treatment course.
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Affiliation(s)
- Jesper Pilmeyer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands
| | - Willem Huijbers
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Philips Research, Eindhoven, The Netherlands
| | - Rolf Lamerichs
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands.,Philips Research, Eindhoven, The Netherlands
| | - Jacobus F A Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands.,School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Marcel Breeuwer
- Philips Healthcare, Best, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Svitlana Zinger
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands
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10
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Abdul Wahab NS, Yahya N, Yusoff AN, Zakaria R, Thanabalan J, Othman E, Bee Hong S, Athi Kumar RK, Manan HA. Effects of Different Scan Duration on Brain Effective Connectivity among Default Mode Network Nodes. Diagnostics (Basel) 2022; 12:diagnostics12051277. [PMID: 35626432 PMCID: PMC9140862 DOI: 10.3390/diagnostics12051277] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/27/2022] [Accepted: 04/27/2022] [Indexed: 11/21/2022] Open
Abstract
Background: Resting-state functional magnetic resonance imaging (rs-fMRI) can evaluate brain functional connectivity without requiring subjects to perform a specific task. This rs-fMRI is very useful in patients with cognitive decline or unable to respond to tasks. However, long scan durations have been suggested to measure connectivity between brain areas to produce more reliable results, which are not clinically optimal. Therefore, this study aims to evaluate a shorter scan duration and compare the scan duration of 10 and 15 min using the rs-fMRI approach. Methods: Twenty-one healthy male and female participants (seventeen right-handed and four left-handed), with ages ranging between 21 and 60 years, were recruited. All participants underwent both 10 and 15 min of rs-fMRI scans. The present study evaluated the default mode network (DMN) areas for both scan durations. The areas involved were the posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), left inferior parietal cortex (LIPC), and right inferior parietal cortex (RIPC). Fifteen causal models were constructed and inverted using spectral dynamic causal modelling (spDCM). The models were compared using Bayesian Model Selection (BMS) for group studies. Result: The BMS results indicated that the fully connected model was the winning model among 15 competing models for both 10 and 15 min scan durations. However, there was no significant difference in effective connectivity among the regions of interest between the 10 and 15 min scans. Conclusion: Scan duration in the range of 10 to 15 min is sufficient to evaluate the effective connectivity within the DMN region. In frail subjects, a shorter scan duration is more favourable.
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Affiliation(s)
- Nor Shafiza Abdul Wahab
- Diagnostic Imaging & Radiotherapy Program, Centre for Diagnostic, Therapeutic and Investigative Studies, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, Kuala Lumpur 50300, Malaysia; (N.S.A.W.); (A.N.Y.)
- Makmal Pemprosesan Imej Kefungsian (Functional Image Processing Laboratory), Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur 56000, Malaysia;
- Department of Radiology and Intervency, Hospital Pakar Kanak-Kanak (Specialist Children Hospital), Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur 56000, Malaysia
| | - Noorazrul Yahya
- Diagnostic Imaging & Radiotherapy Program, Centre for Diagnostic, Therapeutic and Investigative Studies, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, Kuala Lumpur 50300, Malaysia; (N.S.A.W.); (A.N.Y.)
- Correspondence: (N.Y.); (H.A.M.)
| | - Ahmad Nazlim Yusoff
- Diagnostic Imaging & Radiotherapy Program, Centre for Diagnostic, Therapeutic and Investigative Studies, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, Kuala Lumpur 50300, Malaysia; (N.S.A.W.); (A.N.Y.)
| | - Rozman Zakaria
- Makmal Pemprosesan Imej Kefungsian (Functional Image Processing Laboratory), Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur 56000, Malaysia;
| | - Jegan Thanabalan
- Department of Neurosurgery, University Kebangsaan Malaysia Medical Centre, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur 56000, Malaysia; (J.T.); (R.K.A.K.)
| | - Elza Othman
- School of Medical Imaging, Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Kuala Terengganu 21300, Malaysia;
| | - Soon Bee Hong
- Department of Surgery, Pusat Perubatan Universiti Malaya, Lembah Pantai, Kuala Lumpur 59100, Malaysia;
| | - Ramesh Kumar Athi Kumar
- Department of Neurosurgery, University Kebangsaan Malaysia Medical Centre, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur 56000, Malaysia; (J.T.); (R.K.A.K.)
| | - Hanani Abdul Manan
- Makmal Pemprosesan Imej Kefungsian (Functional Image Processing Laboratory), Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur 56000, Malaysia;
- Department of Radiology and Intervency, Hospital Pakar Kanak-Kanak (Specialist Children Hospital), Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur 56000, Malaysia
- Correspondence: (N.Y.); (H.A.M.)
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11
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Jamieson AJ, Harrison BJ, Razi A, Davey CG. Rostral anterior cingulate network effective connectivity in depressed adolescents and associations with treatment response in a randomized controlled trial. Neuropsychopharmacology 2022; 47:1240-1248. [PMID: 34782701 PMCID: PMC9018815 DOI: 10.1038/s41386-021-01214-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 09/22/2021] [Accepted: 10/13/2021] [Indexed: 02/02/2023]
Abstract
The rostral anterior cingulate cortex (rACC) is consistently implicated in the neurobiology of depression. While the functional connectivity of the rACC has been previously associated with treatment response, there is a paucity of work investigating the specific directional interactions underpinning these associations. We compared the fMRI resting-state effective connectivity of 94 young people with major depressive disorder and 91 healthy controls. Following the fMRI scan, patients were randomized to receive cognitive behavioral therapy for 12 weeks, plus either fluoxetine or a placebo. Using spectral dynamic causal modelling, we examined the effective connectivity of the rACC with eight other regions implicated in depression: the left and right anterior insular cortex (AIC), amygdalae, and dorsolateral prefrontal cortex (dlPFC); and in the midline, the subgenual (sgACC) and dorsal anterior cingulate cortex (dACC). Parametric empirical Bayes was used to compare baseline differences between controls and patients and responders and non-responders to treatment. Depressed patients demonstrated greater inhibitory connectivity from the rACC to the dlPFC, AIC, dACC and left amygdala. Moreover, treatment responders illustrated greater inhibitory connectivity from the rACC to dACC, greater excitatory connectivity from the dACC to sgACC and reduced inhibitory connectivity from the sgACC to amygdalae at baseline. The inhibitory hyperconnectivity of the rACC in depressed patients aligns with hypotheses concerning the dominance of the default mode network over other intrinsic brain networks. Surprisingly, treatment responders did not demonstrate connectivity which was more similar to healthy controls, but rather distinct alterations that may have predicated their enhanced treatment response.
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Affiliation(s)
- Alec J Jamieson
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton, VIC, Australia.
| | - Ben J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton, VIC, Australia
| | - Adeel Razi
- Turner Institute for Brain and Mental Health & Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, ON, Canada
| | - Christopher G Davey
- Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia.
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12
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Rijpma MG, Yang WF, Toller G, Battistella G, Sokolov AA, Sturm VE, Seeley WW, Kramer JH, Miller BL, Rankin KP. Influence of periaqueductal gray on other salience network nodes predicts social sensitivity. Hum Brain Mapp 2022; 43:1694-1709. [PMID: 34981605 PMCID: PMC8886662 DOI: 10.1002/hbm.25751] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 11/22/2021] [Accepted: 11/28/2021] [Indexed: 11/09/2022] Open
Abstract
The intrinsic connectivity of the salience network (SN) plays an important role in social behavior, however the directional influence that individual nodes have on each other has not yet been fully determined. In this study, we used spectral dynamic causal modeling to characterize the effective connectivity patterns in the SN for 44 healthy older adults and for 44 patients with behavioral variant frontotemporal dementia (bvFTD) who have focal SN dysfunction. We examined the relationship of SN effective connections with individuals' socioemotional sensitivity, using the revised self-monitoring scale, an informant-facing questionnaire that assesses sensitivity to expressive behavior. Overall, average SN effective connectivity for bvFTD patients differs from healthy older adults in cortical, hypothalamic, and thalamic nodes. For the majority of healthy individuals, strong periaqueductal gray (PAG) output to right cortical (p < .01) and thalamic nodes (p < .05), but not PAG output to other central pattern generators contributed to sensitivity to socioemotional cues. This effect did not exist for the majority of bvFTD patients; PAG output toward other SN nodes was weak, and this lack of output negatively influenced socioemotional sensitivity. Instead, input to the left vAI from other SN nodes supported patients' sensitivity to others' socioemotional behavior (p < .05), though less effectively. The key role of PAG output to cortical and thalamic nodes for socioemotional sensitivity suggests that its core functions, that is, generating autonomic changes in the body, and moreover representing the internal state of the body, is necessary for optimal social responsiveness, and its breakdown is central to bvFTD patients' social behavior deficits.
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Affiliation(s)
- Myrthe G. Rijpma
- Department of Neurology, Memory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Winson F.Z. Yang
- Department of Neurology, Memory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of Psychological Sciences, College of Arts & SciencesTexas Tech UniversityLubbockTexasUSA
| | - Gianina Toller
- Department of Neurology, Memory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Giovanni Battistella
- Department of Neurology, Memory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Arseny A. Sokolov
- Département des Neurosciences Cliniques, Neuroscape@NeuroTech Platform, Service de Neuropsychologie et de NeuroréhabilitationCentre Hospitalier Universitaire Vaudois (CHUV)LausanneSwitzerland
- Wellcome Centre for Human Neuroimaging, Institute of NeurologyUniversity College LondonLondonUK
- Department of Neurology, Neuroscape CenterUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Virginia E. Sturm
- Department of Neurology, Memory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - William W. Seeley
- Department of Neurology, Memory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Joel H. Kramer
- Department of Neurology, Memory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Bruce L. Miller
- Department of Neurology, Memory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Katherine P. Rankin
- Department of Neurology, Memory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
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13
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Modulation of the brain's core-self network by self-appraisal processes. Neuroimage 2022; 251:118980. [PMID: 35143976 DOI: 10.1016/j.neuroimage.2022.118980] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/06/2022] [Accepted: 02/06/2022] [Indexed: 11/22/2022] Open
Abstract
The 'core' regions of the default mode network (DMN) - the medial prefrontal cortex (MPFC), the posterior cingulate cortex (PCC), and inferior parietal lobules (IPL) - show consistent involvement across mental states that involve self-oriented processing. Precisely how these regions interact in support of such processes remains an important unanswered question. In the current functional magnetic resonance imaging (fMRI) study, we examined dynamic interactions of the 'core-self' DMN regions during two forms of self-referential cognition: direct self-appraisal (thinking about oneself) and reflected self-appraisal (thinking about oneself from a third-person perspective). One-hundred and eleven participants completed our dual self-appraisal task during fMRI, and general linear models were used to characterize common and distinct neural responses to these conditions. Informed by these results, we then applied dynamic causal modelling to examine causal interactions among the 'core-self' regions, and how they were specifically modulated under the influence of direct and reflected self-appraisal. As a primary observation, this network modelling revealed a distinct inhibitory influence of the left IPL on the PCC during reflected compared to direct self-appraisal, which was accompanied by evidence of greater activation in both regions during the reflected self-appraisal condition. We suggest that the greater engagement posterior DMN regions during reflected self-appraisal is a function of the higher-order processing needed for this form of self-appraisal, with the left IPL supporting abstract self-related processes including episodic memory retrieval and shifts of perspective. Overall, we show that core DMN regions interact in functionally unique ways in support of self-referential processes, even when these processes are inter-related. Further characterization of DMN functional interactions across self-related mental states is likely to inform a deeper understanding of how this brain network orchestrates the self.
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14
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Varzaneh ZA, Orooji A, Erfannia L, Shanbehzadeh M. A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method. INFORMATICS IN MEDICINE UNLOCKED 2022; 28:100825. [PMID: 34977330 PMCID: PMC8712462 DOI: 10.1016/j.imu.2021.100825] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/13/2021] [Accepted: 12/14/2021] [Indexed: 12/14/2022] Open
Abstract
Background Predicting severe respiratory failure due to COVID-19 can help triage patients to higher levels of care, resource allocation and decrease morbidity and mortality. The need for this research derives from the increasing demand for innovative technologies to overcome complex data analysis and decision-making tasks in critical care units. Hence the aim of our paper is to present a new algorithm for selecting the best features from the dataset and developing Machine Learning(ML) based models to predict the intubation risk of hospitalized COVID-19 patients. Methods In this retrospective single-center study, the data of 1225 COVID-19 patients from February 9, 2020, to July 20, 2021, were analyzed by several ML algorithms which included, Decision Tree(DT), Support Vector Machine (SVM), Multilayer perceptron (MLP), and K-Nearest Neighbors(K-NN). First, the most important predictors were identified using the Horse herd Optimization Algorithm (HOA). Then, by comparing the ML algorithms' performance using some evaluation criteria, the best performing one was identified. Results Predictive models were trained using 12 validated features. Also, it found that proposed DT-based predictive model enables a reasonable level of accuracy (=93%) in predicting the risk of intubation among hospitalized COVID-19 patients. Conclusions The experimental results demonstrate the effectiveness of the proposed meta-heuristic feature selection technique in combining with DT model in predicting intubation risk for hospitalized patients with COVID-19. The proposed model have the potential to inform frontline clinicians with quantitative and non-invasive tool to assess illness severity and to identify high risk patients.
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Affiliation(s)
- Zahra Asghari Varzaneh
- Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Azam Orooji
- Department of Advanced Technologies, School of Medicine, North Khorasan University of Medical Science (NKUMS), North Khorasan, Iran
| | - Leila Erfannia
- Department of Health Information Management, School of Management and Medical Informatics, Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
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15
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Cui J, Wang Y, Liu R, Chen X, Zhang Z, Feng Y, Zhou J, Zhou Y, Wang G. Effects of escitalopram therapy on resting-state functional connectivity of subsystems of the default mode network in unmedicated patients with major depressive disorder. Transl Psychiatry 2021; 11:634. [PMID: 34903712 PMCID: PMC8668990 DOI: 10.1038/s41398-021-01754-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 11/21/2021] [Accepted: 11/30/2021] [Indexed: 11/09/2022] Open
Abstract
Antidepressants are often the first-line medications prescribed for patients with major depressive disorder (MDD). Given the critical role of the default mode network (DMN) in the physiopathology of MDD, the current study aimed to investigate the effects of antidepressants on the resting-state functional connectivity (rsFC) within and between the DMN subsystems. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data from 36 unmedicated MDD patients at baseline and after escitalopram treatment for 12 weeks. The rs-fMRI data were also collected from 61 matched healthy controls at the time point with the same interval. Then, we decomposed the DMN into three subsystems based on a template from previous studies and computed the rsFC within and between the three subsystems. Finally, repeated measures analysis of covariance was conducted to identify the main effect of group and time and their interaction effect. We found that the significantly reduced within-subsystem rsFC in the DMN core subsystem in patients with MDD at baseline was increased after escitalopram treatment and became comparable with that in the healthy controls, whereas the reduced within-subsystem rsFC persisted in the DMN dorsal medial prefrontal cortex (dMPFC) and medial temporal subsystems in patients with MDD following escitalopram treatment. In addition, the reduced between-subsystem rsFC between the core and dMPFC subsystem showed a similar trend of change after treatment in patients with MDD. Moreover, our main results were confirmed using the DMN regions from another brain atlas. In the current study, we found different effects of escitalopram on the rsFC of the DMN subsystems. These findings deepened our understanding of the neuronal basis of antidepressants' effect on brain function in patients with MDD. The trial name: appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement. URL: http://www.chictr.org.cn/showproj.aspx?proj=21377 . Registration number: ChiCTR-OOC-17012566.
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Affiliation(s)
- Jian Cui
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Yun Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Rui Liu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Xiongying Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Zhifang Zhang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Yuan Feng
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Jingjing Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Yuan Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Gang Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China.
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16
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Li J, Liu J, Zhong Y, Wang H, Yan B, Zheng K, Wei L, Lu H, Li B. Causal Interactions Between the Default Mode Network and Central Executive Network in Patients with Major Depression. Neuroscience 2021; 475:93-102. [PMID: 34487819 DOI: 10.1016/j.neuroscience.2021.08.033] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 08/25/2021] [Accepted: 08/26/2021] [Indexed: 11/15/2022]
Abstract
Two different but interacting neural systems exist in the human brain: the task positive networks and task negative networks. One of the most important task positive networks is the central executive network (CEN), while the task negative network generally refers to the default mode network (DMN), which usually demonstrates task-induced deactivation. Although previous studies have clearly shown the association of both the CEN and DMN with major depressive disorder (MDD), how the causal interactions between these two networks change in depressed patients remains unclear. In the current study, 99 subjects (43 patients with MDD and 56 healthy controls) were recruited with their resting-state fMRI data collected. After data preprocessing, spectral dynamic causal modeling (spDCM) was used to investigate the causal interactions within and between the DMN and CEN. Group commonalities and differences in causal interaction patterns within and between the CEN and DMN in patients and controls were assessed by a parametric empirical Bayes (PEB) model. Both subject groups demonstrated significant effective connectivity between regions of the CEN and DMN. In particular, we detected inhibitory influences from the CEN to the DMN with node-level PEB analyses, which may help to explain the anticorrelations between these two networks consistently reported in previous studies. Compared with healthy controls, patients with MDD showed increased effective connectivity within the CEN and decreased connectivity from regions of the CEN to DMN, suggesting impaired control of the DMN by the CEN in these patients. These findings might provide new insights into the neural substrates of MDD.
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Affiliation(s)
- Jiaming Li
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi 710032, China; School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Jian Liu
- Network Center, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Yufang Zhong
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Huaning Wang
- Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Baoyu Yan
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Kaizhong Zheng
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Lei Wei
- Network Center, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Hongbing Lu
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi 710032, China.
| | - Baojuan Li
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi 710032, China.
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17
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Wang D, Liang S. Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2299-2311. [PMID: 34714747 DOI: 10.1109/tnsre.2021.3123964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal responses. Previous reviews have highlighted the understanding of the conceptual basis behind DCM and its variants from different aspects. However, no detailed summary or classification research on the task-related effective connectivity of various brain regions has been made formally available so far, and there is also a lack of application analysis of DCM for hemodynamic and electrophysiological measurements. This review aims to analyze the effective connectivity of different brain regions using DCM for different measurement data. We found that, in general, most studies focused on the networks between different cortical regions, and the research on the networks between other deep subcortical nuclei or between them and the cerebral cortex are receiving increasing attention, but far from the same scale. Our analysis also reveals a clear bias towards some task types. Based on these results, we identify and discuss several promising research directions that may help the community to attain a clear understanding of the brain network interactions under different tasks.
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18
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Snyder AD, Ma L, Steinberg JL, Woisard K, Moeller FG. Dynamic Causal Modeling Self-Connectivity Findings in the Functional Magnetic Resonance Imaging Neuropsychiatric Literature. Front Neurosci 2021; 15:636273. [PMID: 34456665 PMCID: PMC8385130 DOI: 10.3389/fnins.2021.636273] [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: 12/01/2020] [Accepted: 06/07/2021] [Indexed: 11/15/2022] Open
Abstract
Dynamic causal modeling (DCM) is a method for analyzing functional magnetic resonance imaging (fMRI) and other functional neuroimaging data that provides information about directionality of connectivity between brain regions. A review of the neuropsychiatric fMRI DCM literature suggests that there may be a historical trend to under-report self-connectivity (within brain regions) compared to between brain region connectivity findings. These findings are an integral part of the neurologic model represented by DCM and serve an important neurobiological function in regulating excitatory and inhibitory activity between regions. We reviewed the literature on the topic as well as the past 13 years of available neuropsychiatric DCM literature to find an increasing (but still, perhaps, and inadequate) trend in reporting these results. The focus of this review is fMRI as the majority of published DCM studies utilized fMRI and the interpretation of the self-connectivity findings may vary across imaging methodologies. About 25% of articles published between 2007 and 2019 made any mention of self-connectivity findings. We recommend increased attention toward the inclusion and interpretation of self-connectivity findings in DCM analyses in the neuropsychiatric literature, particularly in forthcoming effective connectivity studies of substance use disorders.
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Affiliation(s)
- Andrew D Snyder
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University School of Medicine, Richmond, VA, United States.,Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, United States
| | - Liangsuo Ma
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University School of Medicine, Richmond, VA, United States.,Department of Radiology, Virginia Commonwealth University School of Medicine, Richmond, VA, United States
| | - Joel L Steinberg
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University School of Medicine, Richmond, VA, United States.,Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, United States
| | - Kyle Woisard
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University School of Medicine, Richmond, VA, United States.,Virginia Commonwealth University School of Medicine, Richmond, VA, United States
| | - Frederick G Moeller
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University School of Medicine, Richmond, VA, United States.,Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA, United States.,Department of Pharmacology and Toxicology, Virginia Commonwealth University School of Medicine, Richmond, VA, United States.,Department of Neurology, Virginia Commonwealth University School of Medicine, Richmond, VA, United States
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19
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Li G, Liu Y, Zheng Y, Wu Y, Li D, Liang X, Chen Y, Cui Y, Yap PT, Qiu S, Zhang H, Shen D. Multiscale neural modeling of resting-state fMRI reveals executive-limbic malfunction as a core mechanism in major depressive disorder. Neuroimage Clin 2021; 31:102758. [PMID: 34284335 PMCID: PMC8313604 DOI: 10.1016/j.nicl.2021.102758] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 06/30/2021] [Accepted: 07/03/2021] [Indexed: 11/15/2022]
Abstract
Major depressive disorder (MDD) represents a grand challenge to human health and society, but the underlying pathophysiological mechanisms remain elusive. Previous neuroimaging studies have suggested that MDD is associated with abnormal interactions and dynamics in two major neural systems including the default mode - salience (DMN-SAL) network and the executive - limbic (EXE-LIM) network, but it is not clear which network plays a central role and which network plays a subordinate role in MDD pathophysiology. To address this question, we refined a newly developed Multiscale Neural Model Inversion (MNMI) framework and applied it to test whether MDD is more affected by impaired circuit interactions in the DMN-SAL network or the EXE-LIM network. The model estimates the directed connection strengths between different neural populations both within and between brain regions based on resting-state fMRI data collected from normal healthy subjects and patients with MDD. Results show that MDD is primarily characterized by abnormal circuit interactions in the EXE-LIM network rather than the DMN-SAL network. Specifically, we observe reduced frontoparietal effective connectivity that potentially contributes to hypoactivity in the dorsolateral prefrontal cortex (dlPFC), and decreased intrinsic inhibition combined with increased excitation from the superior parietal cortex (SPC) that potentially lead to amygdala hyperactivity, together resulting in activation imbalance in the PFC-amygdala circuit that pervades in MDD. Moreover, the model reveals reduced PFC-to-hippocampus excitation but decreased SPC-to-thalamus inhibition in MDD population that potentially lead to hypoactivity in the hippocampus and hyperactivity in the thalamus, consistent with previous experimental data. Overall, our findings provide strong support for the long-standing limbic-cortical dysregulation model in major depression but also offer novel insights into the multiscale pathophysiology of this debilitating disease.
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Affiliation(s)
- Guoshi Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Yujie Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA; The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Yanting Zheng
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA; The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Ye Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Danian Li
- Cerebropathy Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Xinyu Liang
- The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Yaoping Chen
- The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Ying Cui
- Cerebropathy Center, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA.
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA.
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20
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Liu J, Tao J, Xia R, Li M, Huang M, Li S, Chen X, Wilson G, Park J, Zheng G, Chen L, Kong J. Mind-Body Exercise Modulates Locus Coeruleus and Ventral Tegmental Area Functional Connectivity in Individuals With Mild Cognitive Impairment. Front Aging Neurosci 2021; 13:646807. [PMID: 34194314 PMCID: PMC8236862 DOI: 10.3389/fnagi.2021.646807] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 05/11/2021] [Indexed: 11/13/2022] Open
Abstract
Mild cognitive impairment (MCI) is a common global health problem. Recently, the potential of mind-body intervention for MCI has drawn the interest of investigators. This study aims to comparatively explore the modulation effect of Baduanjin, a popular mind-body exercise, and physical exercise on the cognitive function, as well as the norepinephrine and dopamine systems using the resting state functional connectivity (rsFC) method in patients with MCI. 69 patients were randomized to the Baduanjin, brisk walking, or healthy education control group for 6 months. The Montreal Cognitive Assessment (MoCA) and magnetic resonance imaging (MRI) scans were applied at baseline and at the end of the experiment. Results showed that (1) compared to the brisk walking, the Baduanjin significantly increased MoCA scores; (2) Baduanjin significantly increased the right locus coeruleus (LC) and left ventral tegmental area (VTA) rsFC with the right insula and right amygdala compared to that of the control group; and the right anterior cingulate cortex (ACC) compared to that of the brisk walking group; (3) the increased right LC-right insula rsFC and right LC-right ACC rsFC were significantly associated with the corresponding MoCA score after 6-months of intervention; (4) both exercise groups experienced an increased effective connectivity from the right ACC to the left VTA compared to the control group; and (5) Baduanjin group experienced an increase in gray matter volume in the right ACC compared to the control group. Our results suggest that Baduanjin can significantly modulate intrinsic functional connectivity and the influence of the norepinephrine (LC) and dopamine (VTA) systems. These findings may shed light on the mechanisms of mind-body intervention and aid the development of new treatments for MCI.
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Affiliation(s)
- Jiao Liu
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.,National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou, China.,Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States.,Traditional Chinese Medicine Rehabilitation Research Center of State Administration of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Jing Tao
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.,Traditional Chinese Medicine Rehabilitation Research Center of State Administration of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.,Key Laboratory of Orthopedics & Traumatology of Traditional Chinese Medicine and Rehabilitation, Fujian University of Traditional Chinese Medicine, Ministry of Education, Fuzhou, China
| | - Rui Xia
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Moyi Li
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Maomao Huang
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Shuzhen Li
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Xiangli Chen
- Department of Rehabilitation Psychology and Special Education, University of Wisconsin, Madison, WI, United States
| | - Georgia Wilson
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Joe Park
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Guohua Zheng
- School of Nursing and Health Management, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Lidian Chen
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Jian Kong
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
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21
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Meng Y, Li H, Wang J, Xu Y, Wang B. Cognitive behavioral therapy for patients with mild to moderate depression: Treatment effects and neural mechanisms. J Psychiatr Res 2021; 136:288-295. [PMID: 33631654 DOI: 10.1016/j.jpsychires.2021.02.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 01/29/2021] [Accepted: 02/08/2021] [Indexed: 11/18/2022]
Abstract
In this study, we combined clinical assessment and magnetic resonance imaging (MRI) techniques to investigate the brain mechanisms in mild to moderate depression (MMD) patients following cognitive behavioral therapy (CBT). Data were collected from 30 MMD patients and 18 healthy controls, and we divided patients into two treatment periods (4 weeks, 8 weeks). Clinical assessment indicated that depression characteristics, as quantified by Hamilton Depression Rating Scale (HAMD), were significantly higher in MMD patients than in healthy controls. At the baseline, MRI data revealed abnormalities in the hippocampus and nucleus accumbens (NAc) of patients with MMD, e.g., smaller gray matter volumes of the hippocampus and nucleus accumbens (NAc), as well as weaker functional connectivity between NAc and the posterior cingulate cortex/precuneus. Moreover, the hippocampus and NAc volumes were negatively correlated with the HAMD scores in MMD patients. After CBT intervention, the HAMD scores decreased, and the structural and functional characteristics of NAc in MMD patients obtained at 8-week were improved; e.g., no significant differences in NAc volume or NAc-based functional connectivity between the two groups. Taken together, our results provided evidence suggesting that CBT is an effective treatment for MMD patients. Alterations of gray matter volume and resting-state functional connectivity after 8 weeks of CBT indicated a potential modulation mechanism in brain structural modifications and functional connectivity plasticity within the NAc in MMD patients.
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Affiliation(s)
- Yanjun Meng
- Nursing College, Shanxi Medical University, Taiyuan, China; Nursing College, Shanxi University of Chinese Medicine, Taiyuan, China
| | - Hong Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China; Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Junjie Wang
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Yong Xu
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Binquan Wang
- Nursing College, Shanxi Medical University, Taiyuan, China; Department of Otolaryngology, Head and Neck Surgery, First Hospital of Shanxi Medical University, Taiyuan, China.
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22
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Nakamura T, Tomita M, Horikawa N, Ishibashi M, Uematsu K, Hiraki T, Abe T, Uchimura N. Functional connectivity between the amygdala and subgenual cingulate gyrus predicts the antidepressant effects of ketamine in patients with treatment-resistant depression. Neuropsychopharmacol Rep 2021; 41:168-178. [PMID: 33615749 PMCID: PMC8340826 DOI: 10.1002/npr2.12165] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/21/2021] [Accepted: 01/23/2021] [Indexed: 12/28/2022] Open
Abstract
Aim Approximately one‐third of patients with major depressive disorder develop treatment‐resistant depression. One‐third of patients with treatment‐resistant depression demonstrate resistance to ketamine, which is a novel antidepressant effective for this disorder. The objective of this study was to examine the utility of resting‐state functional magnetic resonance imaging for the prediction of treatment response to ketamine in treatment‐resistant depression. Methods An exploratory seed‐based resting‐state functional magnetic resonance imaging analysis was performed to examine baseline resting‐state functional connectivity differences between ketamine responders and nonresponders before treatment with multiple intravenous ketamine infusions. Results Fifteen patients with treatment‐resistant depression received multiple intravenous subanesthetic (0.5 mg/kg/40 minutes) ketamine infusions, and nine were identified as responders. The exploratory resting‐state functional magnetic resonance imaging analysis identified a cluster of significant baseline resting‐state functional connectivity differences associating ketamine response between the amygdala and subgenual anterior cingulate gyrus in the right hemisphere. Using anatomical region of interest analysis of the resting‐state functional connectivity, ketamine response was predicted with 88.9% sensitivity and 100% specificity. The resting‐state functional connectivity of significant group differences between responders and nonresponders retained throughout the treatment were considered a trait‐like feature of heterogeneity in treatment‐resistant depression. Conclusion This study suggests the possible clinical utility of resting‐state functional magnetic resonance imaging for predicting the antidepressant effects of ketamine in treatment‐resistant depression patients and implicated resting‐state functional connectivity alterations to determine the trait‐like pathophysiology underlying treatment response heterogeneity in treatment‐resistant depression. This study illustrates that the alteration in the RSFC within the right AN in TRD patients reflects the antidepressant response to ketamine at baseline. The alteration remained throughout the 2‐week treatment with multiple ketamine infusions and seemed to reflect the trait‐like features underlying treatment heterogeneity in TRD. By employing an anatomical ROI of the sc/sgACC, the present study also suggests the possible clinical utility of the rsfMRI to predict the treatment response to ketamine in TRD patients.
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Affiliation(s)
- Tomoyuki Nakamura
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan
| | - Masaru Tomita
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan.,Elm-tree Mental Clinic, Ogori City, Japan
| | - Naoki Horikawa
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan.,Nozoe Hills Hospital, Kurume City, Japan
| | - Masatoshi Ishibashi
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan
| | - Ken Uematsu
- Uematsu Mental Clinic, Chikugo City, Japan.,Department of Pharmacology, Kurume University School of Medicine, Kurume City, Japan
| | - Teruyuki Hiraki
- Department of Anaesthesiology, Kurume University School of Medicine, Kurume City, Japan
| | - Toshi Abe
- Department of Radiology, Kurume University School of Medicine, Kurume City, Japan
| | - Naohisa Uchimura
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan
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23
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Figueroa-Jiménez MD, Cañete-Massé C, Carbó-Carreté M, Zarabozo-Hurtado D, Guàrdia-Olmos J. Structural equation models to estimate dynamic effective connectivity networks in resting fMRI. A comparison between individuals with Down syndrome and controls. Behav Brain Res 2021; 405:113188. [PMID: 33636235 DOI: 10.1016/j.bbr.2021.113188] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 01/21/2021] [Accepted: 02/10/2021] [Indexed: 11/17/2022]
Abstract
Emerging evidence suggests that an effective or functional connectivity network does not use a static process over time but incorporates dynamic connectivity that shows changes in neuronal activity patterns. Using structural equation models (SEMs), we estimated a dynamic component of the effective network through the effects (recursive and nonrecursive) between regions of interest (ROIs), taking into account the lag 1 effect. The aim of the paper was to find the best structural equation model (SEM) to represent dynamic effective connectivity in people with Down syndrome (DS) in comparison with healthy controls. Twenty-two people with DS were registered in a functional magnetic resonance imaging (fMRI) resting-state paradigm for a period of six minutes. In addition, 22 controls, matched by age and sex, were analyzed with the same statistical approach. In both groups, we found the best global model, which included 6 ROIs within the default mode network (DMN). Connectivity patterns appeared to be different in both groups, and networks in people with DS showed more complexity and had more significant effects than networks in control participants. However, both groups had synchronous and dynamic effects associated with ROIs 3 and 4 related to the upper parietal areas in both brain hemispheres as axes of association and functional integration. It is evident that the correct classification of these groups, especially in cognitive competence, is a good initial step to propose a biomarker in network complexity studies.
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Affiliation(s)
| | - Cristina Cañete-Massé
- Department of Social Psychology & Quantitative Psychology Faculty of Psychology, University of Barcelona, Spain; UB Institute of Complex Systems, University of Barcelona, Spain
| | - María Carbó-Carreté
- Serra Hunter Fellow, Department of Cognition, Developmental Psychology and Education, Faculty of Psychology, University of Barcelona, Spain; Institute of Neuroscience, University of Barcelona, Spain
| | - Daniel Zarabozo-Hurtado
- RIO Group Clinical Laboratory, Center for Research in Advanced Functional Neuro-Diagnosis CINDFA, Guadalajara, Mexico
| | - Joan Guàrdia-Olmos
- Department of Social Psychology & Quantitative Psychology Faculty of Psychology, University of Barcelona, Spain; UB Institute of Complex Systems, University of Barcelona, Spain; Institute of Neuroscience, University of Barcelona, Spain.
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24
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Do chronic pain and comorbidities affect brain function in sickle cell patients? A systematic review of neuroimaging and treatment approaches. Pain 2020; 160:1933-1945. [PMID: 31045749 DOI: 10.1097/j.pain.0000000000001591] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Sickle cell disease (SCD) is a medical condition in which chronic pain is common and negatively impacts psychosocial function and quality of life. Although the brain mechanisms underlying chronic pain are well studied in other painful conditions, the brain mechanisms underlying chronic pain and the associated psychosocial comorbidities are not well established in SCD. A growing literature demonstrates the effect of treatment of chronic pain, including pharmacological and nonpharmacological treatments, on brain function. The present systematic review aimed to (1) determine the effects of chronic pain and psychosocial comorbidities on brain function of patients with SCD; (2) summarize pharmacological and nonpharmacological approaches to treat these symptoms; and (3) identify areas for further investigation of potential beneficial effects of treatments on brain function. Titles were screened using predefined criteria, including SCD, and abstracts and full texts were reviewed by 2 independent reviewers. A total of 1167 SCD articles were identified, and 86 full articles were included covering 3 sections: chronic pain (4 studies), psychosocial comorbidities (11 studies), and pharmacological and nonpharmacological treatments (71 studies). Neuroimaging evidence demonstrates aberrant neural processing related to chronic pain and psychosocial comorbidities in SCD beyond ischemic stroke and cerebral hemorrhage. Although neuroimaging studies show an important role for psychological factors, pain management is nearly exclusively based on opioids. Behavior therapy seems useful to improve psychological symptoms as well as chronic pain and quality of life. Further investigation is required with larger cohorts, matched controls, and examination of treatment-related neural mechanisms.
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25
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Li G, Liu Y, Zheng Y, Li D, Liang X, Chen Y, Cui Y, Yap P, Qiu S, Zhang H, Shen D. Large-scale dynamic causal modeling of major depressive disorder based on resting-state functional magnetic resonance imaging. Hum Brain Mapp 2020; 41:865-881. [PMID: 32026598 PMCID: PMC7268036 DOI: 10.1002/hbm.24845] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2019] [Revised: 10/13/2019] [Accepted: 10/15/2019] [Indexed: 12/17/2022] Open
Abstract
Major depressive disorder (MDD) is a serious mental illness characterized by dysfunctional connectivity among distributed brain regions. Previous connectome studies based on functional magnetic resonance imaging (fMRI) have focused primarily on undirected functional connectivity and existing directed effective connectivity (EC) studies concerned mostly task-based fMRI and incorporated only a few brain regions. To overcome these limitations and understand whether MDD is mediated by within-network or between-network connectivities, we applied spectral dynamic causal modeling to estimate EC of a large-scale network with 27 regions of interests from four distributed functional brain networks (default mode, executive control, salience, and limbic networks), based on large sample-size resting-state fMRI consisting of 100 healthy subjects and 100 individuals with first-episode drug-naive MDD. We applied a newly developed parametric empirical Bayes (PEB) framework to test specific hypotheses. We showed that MDD altered EC both within and between high-order functional networks. Specifically, MDD is associated with reduced excitatory connectivity mainly within the default mode network (DMN), and between the default mode and salience networks. In addition, the network-averaged inhibitory EC within the DMN was found to be significantly elevated in the MDD. The coexistence of the reduced excitatory but increased inhibitory causal connections within the DMNs may underlie disrupted self-recognition and emotional control in MDD. Overall, this study emphasizes that MDD could be associated with altered causal interactions among high-order brain functional networks.
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Affiliation(s)
- Guoshi Li
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Yujie Liu
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
- The First School of Clinical MedicineGuangzhou University of Chinese MedicineGuangzhouChina
| | - Yanting Zheng
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
- The First School of Clinical MedicineGuangzhou University of Chinese MedicineGuangzhouChina
| | - Danian Li
- Cerebropathy CenterThe First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouChina
| | - Xinyu Liang
- The First School of Clinical MedicineGuangzhou University of Chinese MedicineGuangzhouChina
| | - Yaoping Chen
- The First School of Clinical MedicineGuangzhou University of Chinese MedicineGuangzhouChina
| | - Ying Cui
- Cerebropathy CenterThe Third Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Pew‐Thian Yap
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Shijun Qiu
- Department of RadiologyThe First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouChina
| | - Han Zhang
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Dinggang Shen
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulSouth Korea
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26
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Ishida T, Dierks T, Strik W, Morishima Y. Converging Resting State Networks Unravels Potential Remote Effects of Transcranial Magnetic Stimulation for Major Depression. Front Psychiatry 2020; 11:836. [PMID: 32973580 PMCID: PMC7468386 DOI: 10.3389/fpsyt.2020.00836] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 07/31/2020] [Indexed: 12/20/2022] Open
Abstract
Despite being a commonly used protocol to treat major depressive disorder (MDD), the underlying mechanism of repetitive transcranial magnetic stimulation (rTMS) on dorsolateral prefrontal cortex (DLPFC) remains unclear. In the current study, we investigated the resting-state fMRI data of 100 healthy subjects by exploring three overlapping functional networks associated with the psychopathologically MDD-related areas (the nucleus accumbens, amygdala, and ventromedial prefrontal cortex). Our results showed that these networks converged at the bilateral DLPFC, which suggested that rTMS over DLPFC might improve MDD by remotely modulating the MDD-related areas synergistically. Additionally, they functionally converged at the DMPFC and bilateral insula which are known to be associated with MDD. These two areas could also be potential targets for rTMS treatment. Dynamic causal modelling (DCM) and Granger causality analysis (GCA) revealed that all pairwise connections among bilateral DLPFC, DMPFC, bilateral insula, and three psychopathologically MDD-related areas contained significant causality. The DCM results also suggested that most of the functional interactions between MDD-related areas and bilateral DLPFC, DMPFC, and bilateral insula can predominantly be explained by the effective connectivity from the psychopathologically MDD-related areas to the rTMS stimulation sites. Finally, we found the conventional functional connectivity to be a more representative measure to obtain connectivity parameters compared to GCA and DCM analysis. Our research helped inspecting the convergence of the functional networks related to a psychiatry disorder. The results identified potential targets for brain stimulation treatment and contributed to the optimization of patient-specific brain stimulation protocols.
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Affiliation(s)
- Takuya Ishida
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Meguro-ku, Japan.,Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Kimiidera, Japan.,Division of Systems Neuroscience of Psychopathology, Translational Research Centre, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Thomas Dierks
- Division of Systems Neuroscience of Psychopathology, Translational Research Centre, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Werner Strik
- University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Yosuke Morishima
- Division of Systems Neuroscience of Psychopathology, Translational Research Centre, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
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27
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Baker CM, Burks JD, Briggs RG, Conner AK, Glenn CA, Sali G, McCoy TM, Battiste JD, O'Donoghue DL, Sughrue ME. A Connectomic Atlas of the Human Cerebrum-Chapter 1: Introduction, Methods, and Significance. Oper Neurosurg (Hagerstown) 2019; 15:S1-S9. [PMID: 30260422 DOI: 10.1093/ons/opy253] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 09/18/2018] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND As knowledge of the brain has increased, clinicians have learned that the cerebrum is composed of complex networks that interact to execute key functions. While neurosurgeons can typically predict and preserve primary cortical function through the primary visual and motor cortices, preservation of higher cognitive functions that are less well localized in regions previously deemed "silent" has proven more difficult. This suggests these silent cortical regions are more anatomically complex and redundant than our previous methods of inquiry can explain, and that progress in cerebral surgery will be made with an improved understanding of brain connectomics. Newly published parcellated cortex maps provide one avenue to study such connectomics in greater detail, and they provide a superior framework and nomenclature for studying cerebral function and anatomy. OBJECTIVE To describe the structural and functional aspects of the 180 distinct areas that comprise the human cortex model previously published under the Human Connectome Project (HCP). METHODS We divided the cerebrum into 8 macroregions: lateral frontal, motor/premotor, medial frontal, insular, temporal, lateral parietal, medial parietal, and occipital. These regions were further subdivided into their relevant parcellations based on the HCP cortical scheme. Connectome Workbench was used to localize parcellations anatomically and to demonstrate their functional connectivity. DSI studio was used to assess the structural connectivity for each parcellation. RESULTS The anatomy, functional connectivity, and structural connectivity of all 180 cortical parcellations identified in the HCP are compiled into a single atlas. Within each section of the atlas, we integrate this information, along with what is known about parcellation function to summarize the implications of these data on network connectivity. CONCLUSION This multipart supplement aims to build on the work of the HCP. We present this information in the hope that the complexity of cerebral connectomics will be conveyed in a more manageable format that will allow neurosurgeons and neuroscientists to accurately communicate and formulate hypotheses regarding cerebral anatomy and connectivity. We believe access to this information may provide a foundation for improving surgical outcomes by preserving lesser-known networks.
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Affiliation(s)
- Cordell M Baker
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Joshua D Burks
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Robert G Briggs
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Andrew K Conner
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Chad A Glenn
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Goksel Sali
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Tressie M McCoy
- Department of Physical Therapy, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - James D Battiste
- Department of Neurology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Daniel L O'Donoghue
- Department of Cell Biology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Michael E Sughrue
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma.,Department of Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia
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28
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Li X, Wang A, Xu J, Sun Z, Xia J, Wang P, Wang B, Zhang M, Tian J. Reduced Dynamic Interactions Within Intrinsic Functional Brain Networks in Early Blind Patients. Front Neurosci 2019; 13:268. [PMID: 30983956 PMCID: PMC6448007 DOI: 10.3389/fnins.2019.00268] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 03/07/2019] [Indexed: 11/16/2022] Open
Abstract
Neuroimaging studies in early blind (EB) patients have shown altered connections or brain networks. However, it remains unclear how the causal relationships are disrupted within intrinsic brain networks. In our study, we used spectral dynamic causal modeling (DCM) to estimate the causal interactions using resting-state data in a group of 20 EB patients and 20 healthy controls (HC). Coupling parameters in specific regions were estimated, including the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and inferior parietal lobule (IPC) in the default mode network (DMN); dorsal anterior cingulate cortex (dACC) and bilateral anterior insulae (AI) in the salience network (SN), and bilateral frontal eye fields (FEF) and superior parietal lobes (SPL) within the dorsal attention network (DAN). Statistical analyses found that all endogenous connections and the connections from the mPFC to bilateral IPCs in EB patients were significantly reduced within the DMN, and the effective connectivity from the PCC and lIPC to the mPFC, and from the mPFC to the PCC were enhanced. For the SN, all significant connections in EB patients were significantly decreased, except the intrinsic right AI connections. Within the DAN, more significant effective connections were observed to be reduced between the EB and HC groups, while only the connections from the right SPL to the left SPL and the intrinsic connection in the left SPL were significantly enhanced. Furthermore, discovery of more decreased effective connections in the EB subjects suggested that the disrupted causal interactions between specific regions are responsive to the compensatory brain plasticity in early deprivation.
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Affiliation(s)
- Xianglin Li
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Medical Imaging Research Institute, Binzhou Medical University, Yantai, China
| | - Ailing Wang
- Department of Clinical Laboratory, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
| | - Junhai Xu
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Artificial Intelligence, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Zhenbo Sun
- Medical Imaging Research Institute, Binzhou Medical University, Yantai, China
| | - Jikai Xia
- Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
| | - Peiyuan Wang
- Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
| | - Bin Wang
- Medical Imaging Research Institute, Binzhou Medical University, Yantai, China
| | - Ming Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jie Tian
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,School of Life Sciences and Technology, Xidian University, Xi'an, China
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29
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Structural networks analysis for depression combined with graph theory and the properties of fiber tracts via diffusion tensor imaging. Neurosci Lett 2018; 694:34-40. [PMID: 30465819 DOI: 10.1016/j.neulet.2018.11.025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 11/17/2018] [Accepted: 11/18/2018] [Indexed: 11/21/2022]
Abstract
Previous studies have suggested that major depressive disorder was associated with topological properties of impaired white matter. However, most related studies only use one property of nerve fibers to construct whole-brain structural brain network. Considering white matter changes variously, We hypothesized whether the alternations of white matter topological properties could reflect different impairment of white matter integrity. In addition, it is still unknown whether impaired integrity of the white matter fiber tracts has relationship with abnormal topological properties in MDD. This study investigated the impaired white matter by using graph theoretic analyses in a cohort of 37 MDD patients and 38 matched control subjects. In addition, we further investigated fiber tracts differences in three interregional connectivity matrixes of significant different topological regions in MDD. Our graph theoretic analyses demonstrated that 7 different regions were observed for the local measures in patients with MDD compared with control groups. These regions were the central nodes of cortical-limbic network, frontal-cingulate network, default mode network (DMN), cognitive control network(CCN)and affective network (AN). In addition, two impaired white matter pathways which included inferior longitudinal fasciculus (ILF) and cingulum were observed in MDD using fiber tracts analysis. We speculate impaired integrity of ILF is due to the alternations in the number of axons or myelination. The results further demonstrated that the number of fiber tracts of anterior cingulum was associated with the depression scores in MDD.
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30
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Bi K, Luo G, Tian S, Zhang S, Liu X, Wang Q, Lu Q, Yao Z. An enriched granger causal model allowing variable static anatomical constraints. Neuroimage Clin 2018; 21:101592. [PMID: 30448217 PMCID: PMC6411584 DOI: 10.1016/j.nicl.2018.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 10/08/2018] [Accepted: 11/03/2018] [Indexed: 01/08/2023]
Abstract
The anatomical connectivity constrains but does not fully determine functional connectivity, especially when one explores into the dynamics over the course of a trial. Therefore, an enriched granger causal model (GCM) integrated with anatomical prior information is proposed in this study, to describe the dynamic effective connectivity to distinguish the depression and explore the pathogenesis of depression. In the proposed frame, the anatomical information was converted via an optimized transformation model, which was then integrated into the normal GCM by variational bayesian model. Magnetoencephalography (MEG) signals and diffusion tensor imaging (DTI) of 24 depressive patients and 24 matched controls were utilized for performance comparison. Together with the sliding windowed MEG signals under sad facial stimuli, the enriched GCM was applied to calculate the regional-pair dynamic effective connectivity, which were repeatedly sifted via feature selection and fed into different classifiers. From the aspects of model errors and recognition accuracy rates, results supported the superiority of the enriched GCM with anatomical priors over the normal GCM. For the effective connectivity with anatomical priors, the best subject discrimination accuracy of SVM was 85.42% (the sensitivity was 87.50% and the specificity was 83.33%). Furthermore, discriminative feature analysis suggested that the enriched GCM that detect the variable anatomical constraint on function could better detect more stringent and less dynamic brain function in depression. The proposed approach is valuable in dynamic functional dysfunction exploration in depression and could be useful for depression recognition.
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Affiliation(s)
- Kun Bi
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Guoping Luo
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Shui Tian
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Siqi Zhang
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Xiaoxue Liu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Qiang Wang
- Medical School of Nanjing University, Nanjing University, Nanjing 210093, China
| | - Qing Lu
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China.
| | - Zhijian Yao
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China; Medical School of Nanjing University, Nanjing University, Nanjing 210093, China.
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31
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Smart O, Choi KS, Riva-Posse P, Tiruvadi V, Rajendra J, Waters AC, Crowell AL, Edwards J, Gross RE, Mayberg HS. Initial Unilateral Exposure to Deep Brain Stimulation in Treatment-Resistant Depression Patients Alters Spectral Power in the Subcallosal Cingulate. Front Comput Neurosci 2018; 12:43. [PMID: 29950982 PMCID: PMC6008542 DOI: 10.3389/fncom.2018.00043] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Accepted: 05/24/2018] [Indexed: 12/16/2022] Open
Abstract
Background: High-frequency Deep Brain Stimulation (DBS) of the subcallosal cingulate (SCC) region is an emerging strategy for treatment-resistant depression (TRD). This study examined changes in SCC local field potentials (LFPs). The LFPs were recorded from the DBS leads following transient, unilateral stimulation at the neuroimaging-defined optimal electrode contact. The goal was identifying a putative electrophysiological measure of target engagement during implantation. Methods: Fourteen consecutive patients underwent bilateral SCC DBS lead implantation. LFP recordings were collected from all electrodes during randomized testing of stimulation on each DBS contact (eight total). Analyses evaluated changes in spectral power before and after 3 min of unilateral stimulation at the contacts that later facilitated antidepressant response, as a potential biomarker of optimal contact selection in each hemisphere. Results: Lateralized and asymmetric power spectral density changes were detected in the SCC with acute unilateral SCC stimulation at those contacts subsequently selected for chronic, therapeutic stimulation. Left stimulation induced broadband ipsilateral decreases in theta, alpha, beta and gamma bands. Right stimulation effects were restricted to ipsilateral beta and gamma decreases. These asymmetric effects contrasted with identical white matter stimulation maps used in each hemisphere. More variable ipsilateral decreases were seen with stimulation at the adjacent "suboptimal" contacts, but changes were not statistically different from the "optimal" contact in either hemisphere despite obvious differences in impacted white matter bundles. Change in theta power was, however, most robust and specific with left-sided optimal stimulation, which suggested a putative functional biomarker on the left with no such specificity inferred on the right. Conclusion: Hemisphere-specific oscillatory changes can be detected from the DBS lead with acute intraoperative testing at contacts that later engender antidepressant effects. Our approach defined potential target engagement signals for further investigation, particularly left-sided theta decreases following initial exposure to stimulation. More refined models combining tractography, bilateral SCC LFP, and cortical recordings may further improve the precision and specificity of these putative biomarkers. It may also optimize and standardize the lead implantation procedure and provide input signals for next generation closed-loop therapy and/or monitoring technologies for TRD.
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Affiliation(s)
- Otis Smart
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, United States
| | - Ki S Choi
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Vineet Tiruvadi
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States.,Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Justin Rajendra
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Allison C Waters
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Andrea L Crowell
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Johnathan Edwards
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Robert E Gross
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, United States.,Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Helen S Mayberg
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
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Geng X, Xu J, Liu B, Shi Y. Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity. Front Neurosci 2018; 12:38. [PMID: 29515348 PMCID: PMC5825897 DOI: 10.3389/fnins.2018.00038] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 01/16/2018] [Indexed: 12/29/2022] Open
Abstract
Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (p < 0.0001) when using 19 effective connections and 89.36% when using 6,650 functional connections. The functional connections with high discriminative power were mainly located within or across the whole brain resting-state networks while the discriminative effective connections located in several specific regions, such as posterior cingulate cortex (PCC), ventromedial prefrontal cortex (vmPFC), dorsal cingulate cortex (dACC), and inferior parietal lobes (IPL). To further compare the discriminative power of functional connections and effective connections, a classification analysis only using the functional connections from those four networks was conducted and the highest accuracy achieved 78.33% (p < 0.0001). Our study demonstrated that the effective connectivity measures might play a more important role than functional connectivity in exploring the alterations between patients and health controls and afford a better mechanistic interpretability. Moreover, our results showed a diagnostic potential of the effective connectivity for the diagnosis of MDD patients with high accuracies allowing for earlier prevention or intervention.
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Affiliation(s)
- Xiangfei Geng
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Junhai Xu
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China
- Laboratory of Neural Imaging, Keck School of Medicine, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Baolin Liu
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China
- State Key Laboratory of Intelligent Technology and Systems, National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China
| | - Yonggang Shi
- Laboratory of Neural Imaging, Keck School of Medicine, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
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Kandilarova S, Stoyanov D, Kostianev S, Specht K. Altered Resting State Effective Connectivity of Anterior Insula in Depression. Front Psychiatry 2018; 9:83. [PMID: 29599728 PMCID: PMC5862800 DOI: 10.3389/fpsyt.2018.00083] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 02/28/2018] [Indexed: 12/11/2022] Open
Abstract
Depression has been associated with changes in both functional and effective connectivity of large scale brain networks, including the default mode network, executive network, and salience network. However, studies of effective connectivity by means of spectral dynamic causal modeling (spDCM) are still rare and the interaction between the different resting state networks has not been investigated in detail. Thus, we aimed at exploring differences in effective connectivity among eight right hemisphere brain areas-anterior insula, inferior frontal gyrus, middle frontal gyrus (MFG), frontal eye field, anterior cingulate cortex, superior parietal lobe, amygdala, and hippocampus, between a group of healthy controls (N = 20) and medicated depressed patients (N = 20). We found that patients not only had significantly reduced strength of the connection from the anterior insula to the MFG (i.e., dorsolateral prefrontal cortex) but also a significant connection between the amygdala and the anterior insula. Moreover, depression severity correlated with connectivity of the hippocampal node. In conclusion, the results from this resting state spDCM study support and enrich previous data on the role of the right anterior insula in the pathophysiology of depression. Furthermore, our findings add to the growing evidence of an association between depression severity and disturbances of the hippocampal function in terms of impaired connectivity with other brain regions.
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Affiliation(s)
- Sevdalina Kandilarova
- Research Complex for Translational Neuroscience, Medical University of Plovdiv (MUP), Plovdiv, Bulgaria.,Department of Psychiatry and Medical Psychology, Medical University of Plovdiv (MUP), Plovdiv, Bulgaria
| | - Drozdstoy Stoyanov
- Research Complex for Translational Neuroscience, Medical University of Plovdiv (MUP), Plovdiv, Bulgaria.,Department of Psychiatry and Medical Psychology, Medical University of Plovdiv (MUP), Plovdiv, Bulgaria
| | - Stefan Kostianev
- Research Complex for Translational Neuroscience, Medical University of Plovdiv (MUP), Plovdiv, Bulgaria.,Department of Pathophysiology, Medical University of Plovdiv (MUP), Plovdiv, Bulgaria
| | - Karsten Specht
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,Department of Education, The Arctic University of Norway (UiT), Tromsø, Norway
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34
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Li L, Li B, Bai Y, Liu W, Wang H, Leung H, Tian P, Zhang L, Guo F, Cui L, Yin H, Lu H, Tan Q. Abnormal resting state effective connectivity within the default mode network in major depressive disorder: A spectral dynamic causal modeling study. Brain Behav 2017; 7:e00732. [PMID: 28729938 PMCID: PMC5516606 DOI: 10.1002/brb3.732] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2016] [Revised: 03/31/2017] [Accepted: 04/14/2017] [Indexed: 01/13/2023] Open
Abstract
INTRODUCTION Understanding the neural basis underlying major depressive disorder (MDD) is essential for the diagnosis and treatment of this mental disorder. Aberrant activation and functional connectivity of the default mode network (DMN) have been consistently found in patients with MDD. It is not known whether effective connectivity within the DMN is altered in MDD. OBJECTS The primary object of this study is to investigate the effective connectivity within the DMN during resting state in MDD patients before and after eight weeks of antidepressant treatment. METHODS We defined four regions of the DMN (medial frontal cortex, posterior cingulate cortex, left parietal cortex, and right parietal cortex) for each participant using a group independent component analysis. The coupling parameters reflecting the causal interactions among the DMN regions were estimated using spectral dynamic causal modeling (DCM). RESULTS Twenty-seven MDD patients and 27 healthy controls were included in the statistical analysis. Our results showed declined influences from the left parietal cortex to other DMN regions in the pre-treatment patients as compared with healthy controls. After eight weeks of treatment, the influence from the right parietal cortex to the posterior cingulate cortex significantly decreased. CONCLUSION These findings suggest that the reduced excitatory causal influence of the left parietal cortex is the key alteration of the DMN in patients with MDD, and the disrupted causal influences that parietal cortex exerts on the posterior cingulate cortex is responsive to antidepressant treatment.
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Affiliation(s)
- Liang Li
- School of Biomedical EngineeringFourth Military Medical UniversityXi'anShaanxiChina
| | - Baojuan Li
- School of Biomedical EngineeringFourth Military Medical UniversityXi'anShaanxiChina
| | - Yuanhan Bai
- Department of PsychiatryXijing HospitalFourth Military Medical UniversityXi'anShaanxiChina
| | - Wenlei Liu
- School of Biomedical EngineeringFourth Military Medical UniversityXi'anShaanxiChina
| | - Huaning Wang
- Department of PsychiatryXijing HospitalFourth Military Medical UniversityXi'anShaanxiChina
| | - Hoi‐Chung Leung
- Department of PsychologyStony Brook UniversityStony BrookNYUSA
| | - Ping Tian
- Department of RadiologyXijing HospitalFourth Military Medical UniversityXi'anShaanxiChina
| | - Linchuan Zhang
- School of Biomedical EngineeringFourth Military Medical UniversityXi'anShaanxiChina
| | - Fan Guo
- Department of RadiologyXijing HospitalFourth Military Medical UniversityXi'anShaanxiChina
| | - Long‐Biao Cui
- Department of RadiologyXijing HospitalFourth Military Medical UniversityXi'anShaanxiChina
| | - Hong Yin
- Department of RadiologyXijing HospitalFourth Military Medical UniversityXi'anShaanxiChina
| | - Hongbing Lu
- School of Biomedical EngineeringFourth Military Medical UniversityXi'anShaanxiChina
| | - Qingrong Tan
- Department of PsychiatryXijing HospitalFourth Military Medical UniversityXi'anShaanxiChina
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