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Kyuragi Y, Oishi N, Hatakoshi M, Hirano J, Noda T, Yoshihara Y, Ito Y, Igarashi H, Miyata J, Takahashi K, Kamiya K, Matsumoto J, Okada T, Fushimi Y, Nakagome K, Mimura M, Murai T, Suwa T. Segmentation and Volume Estimation of the Habenula Using Deep Learning in Patients With Depression. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100314. [PMID: 38726037 PMCID: PMC11078767 DOI: 10.1016/j.bpsgos.2024.100314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/06/2024] [Accepted: 03/27/2024] [Indexed: 05/12/2024] Open
Abstract
Background The habenula is involved in the pathophysiology of depression. However, its small structure limits the accuracy of segmentation methods, and the findings regarding its volume have been inconsistent. This study aimed to create a highly accurate habenula segmentation model using deep learning, test its generalizability to clinical magnetic resonance imaging, and examine differences between healthy participants and patients with depression. Methods This multicenter study included 382 participants (patients with depression: N = 234, women 47.0%; healthy participants: N = 148, women 37.8%). A 3-dimensional residual U-Net was used to create a habenula segmentation model on 3T magnetic resonance images. The reproducibility and generalizability of the predictive model were tested on various validation cohorts. Thereafter, differences between the habenula volume of healthy participants and that of patients with depression were examined. Results A Dice coefficient of 86.6% was achieved in the derivation cohort. The test-retest dataset showed a mean absolute percentage error of 6.66, indicating sufficiently high reproducibility. A Dice coefficient of >80% was achieved for datasets with different imaging conditions, such as magnetic field strengths, spatial resolutions, and imaging sequences, by adjusting the threshold. A significant negative correlation with age was observed in the general population, and this correlation was more pronounced in patients with depression (p < 10-7, r = -0.59). Habenula volume decreased with depression severity in women even when the effects of age and scanner were excluded (p = .019, η2 = 0.099). Conclusions Habenula volume could be a pathophysiologically relevant factor and diagnostic and therapeutic marker for depression, particularly in women.
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Affiliation(s)
- Yusuke Kyuragi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Naoya Oishi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Momoko Hatakoshi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Jinichi Hirano
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Takamasa Noda
- Department of Psychiatry, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Yujiro Yoshihara
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuri Ito
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hiroyuki Igarashi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Jun Miyata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Psychiatry, Aichi Medical University, Aichi, Japan
| | - Kento Takahashi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kei Kamiya
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Junya Matsumoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Tomohisa Okada
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Masaru Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Taro Suwa
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Wang J, Li G, Ji G, Hu Y, Zhang W, Ji W, Yu J, Han Y, Cui G, Wang H, Manza P, Volkow ND, Wang GJ, Zhang Y. Habenula Volume and Functional Connectivity Changes Following Laparoscopic Sleeve Gastrectomy for Obesity Treatment. Biol Psychiatry 2024; 95:916-925. [PMID: 37480977 DOI: 10.1016/j.biopsych.2023.07.009] [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: 01/10/2023] [Revised: 06/18/2023] [Accepted: 07/13/2023] [Indexed: 07/24/2023]
Abstract
BACKGROUND Neuroimaging studies have revealed alterations in habenular (Hb) structure and functional connectivity (FC) in psychiatric conditions. The Hb plays a particularly critical role in regulating negative emotions, which trigger excessive food intake and obesity. However, obesity and weight loss intervention (i.e., laparoscopic sleeve gastrectomy [LSG])-associated changes in Hb structure and FC have not been studied. METHODS We used voxel-based morphometry analysis to measure changes in gray matter volume (GMV) in the Hb in 56 patients with obesity at pre-LSG and 12 months post-LSG and in 78 normal-weight (NW) control participants. Then, we conducted Hb seed-based resting-state FC (RSFC) to examine obesity-related and LSG-induced alterations in RSFC. Finally, we used mediation analysis to characterize the interrelationships among Hb GMV, RSFC, and behaviors. RESULTS Compared with NW participants, Hb GMV was smaller in patients at pre-LSG and increased at 12 months post-LSG to levels equivalent to that of NW; in addition, increases in Hb GMV were correlated with reduced body mass index (BMI). Compared with NW participants, pre-LSG patients showed greater RSFCs of the Hb-insula, Hb-precentral gyrus, and Hb-rolandic operculum and weaker RSFCs of the Hb-thalamus, Hb-hypothalamus, and Hb-caudate; LSG normalized these RSFCs. Decreased RSFC of the Hb-insula was correlated with reduced BMI, Yale Food Addiction Scale rating, and emotional eating; reduced hunger levels were correlated with increased RSFCs of the Hb-thalamus and Hb-hypothalamus; and reduced BMI and Yale Food Addiction Scale ratings were correlated with increased RSFCs of the Hb-thalamus and Hb-hypothalamus, respectively. The bidirectional relationships between Hb GMV and RSFC of the Hb-insula contributed to reduced BMI. CONCLUSIONS These findings indicate that LSG increased Hb GMV and that its related improvement in RSFC of the Hb-insula may mediate long-term benefits of LSG for eating behaviors and weight loss.
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Affiliation(s)
- Jia Wang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Guanya Li
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Gang Ji
- Department of Digestive Surgery, Xijing Hospital, Air Force Medical University, Xi'an, Shaanxi, China.
| | - Yang Hu
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Wenchao Zhang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Weibin Ji
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Juan Yu
- Department of Digestive Surgery, Xijing Hospital, Air Force Medical University, Xi'an, Shaanxi, China
| | - Yu Han
- Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, China
| | - Guangbin Cui
- Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, Air Force Medical University, Xi'an, Shaanxi, China
| | - Peter Manza
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, Maryland
| | - Nora D Volkow
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, Maryland
| | - Gene-Jack Wang
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, Maryland
| | - Yi Zhang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China; International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.
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Bian B, Hou L, Chai Y, Jiang Y, Pan X, Sun Y, Wang H, Qiu D, Yu Z, Zhao H, Zhang H, Meng F, Zhang L. Visualizing the Habenula Using 3T High-Resolution MP2RAGE and QSM: A Preliminary Study. AJNR Am J Neuroradiol 2024; 45:504-510. [PMID: 38453416 PMCID: PMC11288573 DOI: 10.3174/ajnr.a8156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 12/18/2023] [Indexed: 03/09/2024]
Abstract
BACKGROUND AND PURPOSE The habenula is a key node in the regulation of emotion-related behavior. Accurate visualization of the habenula and its reliable quantitative analysis is vital for the assessment of psychiatric disorders. To obtain high-contrast habenula images and allow them to be compatible with clinical applications, this preliminary study compared 3T MP2RAGE and quantitative susceptibility mapping with MPRAGE by evaluating the habenula segmentation performance. MATERIALS AND METHODS Ten healthy volunteers were scanned twice with 3T MPRAGE and MP2RAGE and once with quantitative susceptibility mapping. Image quality and visibility of habenula anatomic features were analyzed by 3 radiologists using a 5-point scale. Contrast assessments of the habenula and thalamus were also performed. The reproducibility of the habenula volume from MPRAGE and MP2RAGE was evaluated by manual segmentation and the Multiple Automatically Generated Template brain segmentation algorithm (MAGeTbrain). T1 values and susceptibility were measured in the whole habenula and habenula geometric subregion using MP2RAGE T1-mapping and quantitative susceptibility mapping. RESULTS The 3T MP2RAGE and quantitative susceptibility mapping demonstrated clear boundaries and anatomic features of the habenula compared with MPRAGE, with a higher SNR and contrast-to-noise ratio (all P < .05). Additionally, 3T MP2RAGE provided reliable habenula manual and MAGeTbrain segmentation volume estimates with greater reproducibility. T1-mapping derived from MP2RAGE was highly reliable, and susceptibility contrast was highly nonuniform within the habenula. CONCLUSIONS We identified an optimized sequence combination (3T MP2RAGE combined with quantitative susceptibility mapping) that may be useful for enhancing habenula visualization and yielding more reliable quantitative data.
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Affiliation(s)
- BingYang Bian
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Lin Hou
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - YaTing Chai
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - YueLuan Jiang
- MR Scientific Marketing, Diagnostic Imaging (Y.J.), Siemens Healthineers Ltd, Beijing, China
| | - XingChen Pan
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Yang Sun
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - HongChao Wang
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - DongDong Qiu
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - ZeChen Yu
- Siemens Healthineers Digital Technology (Shanghai) Co Ltd (Z.Y.), Shanghai, China
| | - Hua Zhao
- Department of Physiology (H. Zhao), College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - HuiMao Zhang
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - FanYang Meng
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Lei Zhang
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
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Bian B, Zhang B, Wong C, Dou L, Pan X, Wang H, Guo S, Zhang H, Zhang L. Recent Advances in Habenula Imaging Technology: A Comprehensive Review. J Magn Reson Imaging 2024; 59:737-746. [PMID: 37254969 DOI: 10.1002/jmri.28830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/13/2023] [Accepted: 05/16/2023] [Indexed: 06/01/2023] Open
Abstract
The habenula (Hb) is involved in many natural human behaviors, and the relevance of its alterations in size and neural activity to several psychiatric disorders and addictive behaviors has been presumed and investigated in recent years using magnetic resonance imaging (MRI). Although the Hb is small, an increasing number of studies have overcome the difficulties in MRI. Conventional structural-based imaging also has great defects in observing the Hb contrast with adjacent structures. In addition, more and more attention should be paid to the Hb's functional, structural, and quantitative imaging studies. Several advanced MRI methods have recently been employed in clinical studies to explore the Hb and its involvement in psychiatric diseases. This review summarizes the anatomy and function of the human Hb; moreover, it focuses on exploring the human Hb with noninvasive MRI approaches, highlighting strategies to overcome the poor contrast with adjacent structures and the need for multiparametric MRI to develop imaging markers for diagnosis and treatment follow-up. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- BingYang Bian
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, Changchun, Jilin, 130021, People's Republic of China
| | - Bei Zhang
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, Changchun, Jilin, 130021, People's Republic of China
| | - ChinTing Wong
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, 130021, People's Republic of China
| | - Le Dou
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, Changchun, Jilin, 130021, People's Republic of China
| | - XingChen Pan
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, Changchun, Jilin, 130021, People's Republic of China
| | - HongChao Wang
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, Changchun, Jilin, 130021, People's Republic of China
| | - ShiYu Guo
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, Changchun, Jilin, 130021, People's Republic of China
| | - HuiMao Zhang
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, Changchun, Jilin, 130021, People's Republic of China
| | - Lei Zhang
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, Changchun, Jilin, 130021, People's Republic of China
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Mao CP, Wu Y, Yang HJ, Qin J, Song QC, Zhang B, Zhou XQ, Zhang L, Sun HH. Altered habenular connectivity in chronic low back pain: An fMRI and machine learning study. Hum Brain Mapp 2023; 44:4407-4421. [PMID: 37306031 PMCID: PMC10318213 DOI: 10.1002/hbm.26389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 04/11/2023] [Accepted: 05/15/2023] [Indexed: 06/13/2023] Open
Abstract
The habenula has been implicated in the pathogenesis of pain and analgesia, while evidence concerning its function in chronic low back pain (cLBP) is sparse. This study aims to investigate the resting-state functional connectivity (rsFC) and effective connectivity of the habenula in 52 patients with cLBP and 52 healthy controls (HCs) and assess the feasibility of distinguishing cLBP from HCs based on connectivity by machine learning methods. Our results indicated significantly enhanced rsFC of the habenula-left superior frontal cortex (SFC), habenula-right thalamus, and habenula-bilateral insular pathways as well as decreased rsFC of the habenula-pons pathway in cLBP patients compared to HCs. Dynamic causal modelling revealed significantly enhanced effective connectivity from the right thalamus to right habenula in cLBP patients compared with HCs. RsFC of the habenula-SFC was positively correlated with pain intensities and Hamilton Depression scores in the cLBP group. RsFC of the habenula-right insula was negatively correlated with pain duration in the cLBP group. Additionally, the combination of the rsFC of the habenula-SFC, habenula-thalamus, and habenula-pons pathways could reliably distinguish cLBP patients from HCs with an accuracy of 75.9% by support vector machine, which was validated in an independent cohort (N = 68, accuracy = 68.8%, p = .001). Linear regression and random forest could also distinguish cLBP and HCs in the independent cohort (accuracy = 73.9 and 55.9%, respectively). Overall, these findings provide evidence that cLBP may be associated with abnormal rsFC and effective connectivity of the habenula, and highlight the promise of machine learning in chronic pain discrimination.
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Affiliation(s)
- Cui Ping Mao
- Department of Medical ImagingSecond Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiChina
| | - Yue Wu
- School of Computer Science and EngineeringXidian UniversityXi'anShaanxiChina
| | - Hua Juan Yang
- Department of Medical ImagingSecond Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiChina
| | - Jie Qin
- Department of Medical ImagingSecond Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiChina
| | - Qi Chun Song
- Department of Medical ImagingSecond Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiChina
| | - Bo Zhang
- Department of Medical ImagingSecond Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiChina
| | - Xiao Qian Zhou
- Department of Medical ImagingSecond Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiChina
| | - Liang Zhang
- School of Computer Science and EngineeringXidian UniversityXi'anShaanxiChina
| | - Hong Hong Sun
- Department of Medical ImagingSecond Affiliated Hospital of Xi'an Jiaotong UniversityXi'anShaanxiChina
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Gong L, Cheng F, Li X, Wang Z, Wang S, Xu R, Zhang B, Xi C. Abnormal functional connectivity in the habenula is associated with subjective hyperarousal state in chronic insomnia disorder. Front Neurol 2023; 14:1119595. [PMID: 37588671 PMCID: PMC10426801 DOI: 10.3389/fneur.2023.1119595] [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: 12/09/2022] [Accepted: 07/13/2023] [Indexed: 08/18/2023] Open
Abstract
Background The hyperarousal process model plays a central role in the physiology of chronic insomnia disorder (CID). Recent evidence has demonstrated that the habenula is involved in the arousal and sleep-wake cycle. However, whether the intrinsic habenular functional network contributes to the underlying mechanism of CID and its relationship to the arousal state in CID remains unclear. Methods This single-centered study included 34 patients with subjective CID and 22 matched good sleep control (GSC), and underwent a series of neuropsychological tests and resting-state functional magnetic resonance imaging scans. The habenular functional network was assessed using seed-based functional connectivity (FC) analysis. The subjective arousal state was evaluated with the hyperarousal scale (HAS). Alterations in the habenular FC network and their clinical significance in patients with CID were explored. Results Compared with the GSC group, the CID group showed decreased habenular FC in the left caudate nucleus and right inferior parietal lobule and increased FC in the right habenula, bilateral calcarine cortex, and posterior cingulate cortex. The decreased FC between the left habenula and caudate nucleus was associated with an increased arousal state in the CID group. Conclusion The present results provide evidence for a dysfunctional habenular network in patients with CID. These findings extend our understanding of the neuropathological mechanisms underlying the hyperarousal model in chronic insomnia.
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Affiliation(s)
- Liang Gong
- Department of Neurology, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
| | - Fang Cheng
- Department of Neurology, The Third Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xue Li
- Department of Neurology, The Third Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Zhiqi Wang
- Department of Neurology, The Third Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Shuo Wang
- Department of Neurology, The Third Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Ronghua Xu
- Department of Neurology, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
| | - Bei Zhang
- Department of Neurology, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
| | - Chunhua Xi
- Department of Neurology, The Third Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
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Yedavalli V, DiGiacomo P, Tong E, Zeineh M. High-resolution Structural Magnetic Resonance Imaging and Quantitative Susceptibility Mapping. Magn Reson Imaging Clin N Am 2021; 29:13-39. [PMID: 33237013 DOI: 10.1016/j.mric.2020.09.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
High-resolution 7-T imaging and quantitative susceptibility mapping produce greater anatomic detail compared with conventional strengths because of improvements in signal/noise ratio and contrast. The exquisite anatomic details of deep structures, including delineation of microscopic architecture using advanced techniques such as quantitative susceptibility mapping, allows improved detection of abnormal findings thought to be imperceptible on clinical strengths. This article reviews caveats and techniques for translating sequences commonly used on 1.5 or 3 T to high-resolution 7-T imaging. It discusses for several broad disease categories how high-resolution 7-T imaging can advance the understanding of various diseases, improve diagnosis, and guide management.
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Affiliation(s)
- Vivek Yedavalli
- Department of Radiology, Stanford University, 300 Pasteur Drive, Room S047, Stanford, CA 94305-5105, USA; Division of Neuroradiology, Johns Hopkins University, 600 N. Wolfe St. B-112 D, Baltimore, MD 21287, USA
| | - Phillip DiGiacomo
- Department of Bioengineering, Stanford University, Lucas Center for Imaging, Room P271, 1201 Welch Road, Stanford, CA 94305-5488, USA
| | - Elizabeth Tong
- Department of Radiology, 300 Pasteur Drive, Room S031, Stanford, CA 94305-5105, USA
| | - Michael Zeineh
- Department of Radiology, Stanford University, Lucas Center for Imaging, Room P271, 1201 Welch Road, Stanford, CA 94305-5488, USA.
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Jha MK, Kim JW, Kenny PJ, Chin Fatt C, Minhajuddin A, Salas R, Ely BA, Klein M, Abdallah CG, Xu J, Trivedi MH. Smoking status links habenular volume to glycated hemoglobin: Findings from the Human Connectome Project-Young Adult. Psychoneuroendocrinology 2021; 131:105321. [PMID: 34157587 DOI: 10.1016/j.psyneuen.2021.105321] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 06/07/2021] [Accepted: 06/08/2021] [Indexed: 11/24/2022]
Abstract
BACKGROUND The habenula-pancreas axis regulates the stimulatory effects of nicotine on blood glucose levels and may participate in the emergence of type 2 diabetes in human tobacco smokers. This secondary analysis of young adults from the Human Connectome Project (HCP-YA) evaluated whether smoking status links the relationship between habenular volume and glycated hemoglobin (HbA1c), a marker of long-term glycemic control. METHODS Habenula segmentation was performed using a fully-automated myelin content-based approach in HCP-YA participants and the results were inspected visually (n = 693; aged 22-37 years). A linear regression analysis was used with habenular volume as the dependent variable, the smoking-by-HbA1c interaction as the independent variable of interest, and age, gender, race, ethnicity, education, income, employment status, body mass index, and total gray matter volume as covariates. RESULTS Habenula volume and HbA1c were similar in smokers and nonsmokers. There was a significant interaction effect (F(1, 673)= 5.03, p = 0.025) indicating that habenular volume was related to HbA1c in a manner that depended on smoking status. Among participants who were smokers (n = 120), higher HbA1c was associated with apparently larger habenular volume (β = 6.74, standard error=2.36, p = 0.005). No such association between habenular volume and HbA1c was noted among participants who were nonsmokers (n = 573). DISCUSSION Blood glucose levels over an extended time period, reflected by HbA1c, were correlated with habenular volume in smokers, consistent with a relationship between the habenula and blood glucose homeostasis in smokers. Future studies are needed to evaluate how habenular function relates to glycemic control in smokers and nonsmokers.
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Affiliation(s)
- Manish K Jha
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Center for Depression Research and Clinical Care, UT Southwestern Medical Center, Dallas, TX, United States
| | - Joo-Won Kim
- Department of Radiology, Baylor College of Medicine, Houston, TX, United States; Department of Psychiatry, Baylor College of Medicine, Houston, TX, United States
| | - Paul J Kenny
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Cherise Chin Fatt
- Center for Depression Research and Clinical Care, UT Southwestern Medical Center, Dallas, TX, United States
| | - Abu Minhajuddin
- Center for Depression Research and Clinical Care, UT Southwestern Medical Center, Dallas, TX, United States
| | - Ramiro Salas
- Department of Psychiatry, Baylor College of Medicine, Houston, TX, United States; Michael E DeBakey VA Medical Center, Houston, TX, United States; The Menninger Clinic, Houston, TX, United States
| | - Benjamin A Ely
- Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, The Bronx, NY, United States
| | - Matthew Klein
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Chadi G Abdallah
- Department of Psychiatry, Baylor College of Medicine, Houston, TX, United States; Michael E DeBakey VA Medical Center, Houston, TX, United States
| | - Junqian Xu
- Department of Radiology, Baylor College of Medicine, Houston, TX, United States; Department of Psychiatry, Baylor College of Medicine, Houston, TX, United States
| | - Madhukar H Trivedi
- Center for Depression Research and Clinical Care, UT Southwestern Medical Center, Dallas, TX, United States.
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9
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Ely BA, Nguyen TNB, Tobe RH, Walker AM, Gabbay V. Multimodal Investigations of Reward Circuitry and Anhedonia in Adolescent Depression. Front Psychiatry 2021; 12:678709. [PMID: 34366915 PMCID: PMC8345280 DOI: 10.3389/fpsyt.2021.678709] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 06/15/2021] [Indexed: 02/01/2023] Open
Abstract
Depression is a highly prevalent condition with devastating personal and public health consequences that often first manifests during adolescence. Though extensively studied, the pathogenesis of depression remains poorly understood, and efforts to stratify risks and identify optimal interventions have proceeded slowly. A major impediment has been the reliance on an all-or-nothing categorical diagnostic scheme based solely on whether a patient endorses an arbitrary number of common symptoms for a sufficiently long period. This approach masks the well-documented heterogeneity of depression, a disorder that is highly variable in presentation, severity, and course between individuals and is frequently comorbid with other psychiatric conditions. In this targeted review, we outline the limitations of traditional diagnosis-based research and instead advocate an alternative approach centered around symptoms as unique dimensions of clinical dysfunction that span across disorders and more closely reflect underlying neurobiological abnormalities. In particular, we highlight anhedonia-the reduced ability to anticipate and experience pleasure-as a specific, quantifiable index of reward dysfunction and an ideal candidate for dimensional investigation. Anhedonia is a core symptom of depression but also a salient feature of numerous other conditions, and its severity varies widely within clinical and even healthy populations. Similarly, reward dysfunction is a hallmark of depression but is evident across many psychiatric conditions. Reward function is especially relevant in adolescence, a period characterized by exaggerated reward-seeking behaviors and rapid maturation of neural reward circuitry. We detail extensive work by our research group and others to investigate the neural and systemic factors contributing to reward dysfunction in youth, including our cumulative findings using multiple neuroimaging and immunological measures to study depressed adolescents but also trans-diagnostic cohorts with diverse psychiatric symptoms. We describe convergent evidence that reward dysfunction: (a) predicts worse clinical outcomes, (b) is associated with functional and chemical abnormalities within and beyond the neural reward circuitry, (c) is linked to elevated peripheral levels of inflammatory biomarkers, and (d) manifests early in the course of illness. Emphasis is placed on high-resolution neuroimaging techniques, comprehensive immunological assays, and data-driven analyses to fully capture and characterize the complex, interconnected nature of these systems and their contributions to adolescent reward dysfunction.
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Affiliation(s)
- Benjamin A. Ely
- Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Tram N. B. Nguyen
- Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Russell H. Tobe
- Department of Clinical Research, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Audrey M. Walker
- Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Vilma Gabbay
- Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY, United States
- Department of Clinical Research, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, United States
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10
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Lim SH, Yoon J, Kim YJ, Kang CK, Cho SE, Kim KG, Kang SG. Reproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 Tesla MRI. Sci Rep 2021; 11:13445. [PMID: 34188141 PMCID: PMC8241874 DOI: 10.1038/s41598-021-92952-z] [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] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 06/18/2021] [Indexed: 11/15/2022] Open
Abstract
The habenula is one of the most important brain regions for investigating the etiology of psychiatric diseases such as major depressive disorder (MDD). However, the habenula is challenging to delineate with the naked human eye in brain imaging due to its low contrast and tiny size, and the manual segmentation results vary greatly depending on the observer. Therefore, there is a great need for automatic quantitative analytic methods of the habenula for psychiatric research purposes. Here we propose an automated segmentation and volume estimation method for the habenula in 7 Tesla magnetic resonance imaging based on a deep learning-based semantic segmentation network. The proposed method, using the data of 69 participants (33 patients with MDD and 36 normal controls), achieved an average precision, recall, and dice similarity coefficient of 0.869, 0.865, and 0.852, respectively, in the automated segmentation task. Moreover, the intra-class correlation coefficient reached 0.870 in the volume estimation task. This study demonstrates that this deep learning-based method can provide accurate and quantitative analytic results of the habenula. By providing rapid and quantitative information on the habenula, we expect our proposed method will aid future psychiatric disease studies.
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Affiliation(s)
- Sang-Heon Lim
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Seongnam-si, Republic of Korea
- Department of Biomedical Engineering, College of Medicine, Gachon University, Seongnam-si, Republic of Korea
| | - Jihyun Yoon
- Department of Family Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Young Jae Kim
- Department of Biomedical Engineering, College of Medicine, Gachon University, Seongnam-si, Republic of Korea
| | - Chang-Ki Kang
- Department of Radiological Science, College of Health Science, Gachon University, Incheon, Republic of Korea
| | - Seo-Eun Cho
- Department of Psychiatry, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea
| | - Kwang Gi Kim
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Seongnam-si, Republic of Korea.
- Department of Biomedical Engineering, College of Medicine, Gachon University, Seongnam-si, Republic of Korea.
| | - Seung-Gul Kang
- Department of Psychiatry, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea.
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11
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Wu Z, Wang C, Ma Z, Pang M, Wu Y, Zhang N, Zhong Y. Abnormal functional connectivity of habenula in untreated patients with first-episode major depressive disorder. Psychiatry Res 2020; 285:112837. [PMID: 32044600 DOI: 10.1016/j.psychres.2020.112837] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 01/29/2020] [Accepted: 01/29/2020] [Indexed: 11/18/2022]
Abstract
Major depressive disorder (MDD) is associated with abnormalities in emotional/cognitive processing and low reward sensitivity. The habenula has a pivotal role in these processes that may contribute to depression. However, there has been little research on the abnormal connectivity between the habenula and whole brain of first-onset MDD. We aimed to explore the differences of functional connectivity between patients and healthy controls using functional magnetic resonance imaging. We used seed-based resting-state fMRI to examine functional connectivity between the habenula and whole-brain in 49 first-episode depressive patients and 25 healthy controls. Compared to controls, patients with MDD demonstrated significant increases in functional connectivity between the habenula and the dorsolateral prefrontal cortex (dlPFC). Furthermore, the receiver operating characteristic (ROC) curve proved that connectivity between the habenula and dlPFC was highly predictive. Additionally, there was a positive correlation between Hamilton Rating Scale for Depression (HAMD) score and functional connectivity between the habenula and right dlPFC. We found that the aberrant functional connectivity to the habenula and dlPFC can distinguish MDD patients from the normal.
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Affiliation(s)
- Zhou Wu
- School of Psychology, Nanjing Normal University, Nanjing 210097, China; Jiangsu Key Laboratory of Mental Health and Cognitive Science, Nanjing Normal University, Nanjing 210097, China
| | - Chun Wang
- Department of Psychiatry, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing 210029, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing 210029, China
| | - Zijuan Ma
- Department of Psychiatry, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China
| | - Manlong Pang
- Department of Psychiatry, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China
| | - Yun Wu
- Department of Psychiatry, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China
| | - Ning Zhang
- Department of Psychiatry, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing 210029, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing 210029, China
| | - Yuan Zhong
- School of Psychology, Nanjing Normal University, Nanjing 210097, China; Jiangsu Key Laboratory of Mental Health and Cognitive Science, Nanjing Normal University, Nanjing 210097, China.
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12
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Ely BA, Stern ER, Kim JW, Gabbay V, Xu J. Detailed mapping of human habenula resting-state functional connectivity. Neuroimage 2019; 200:621-634. [PMID: 31252057 PMCID: PMC7089853 DOI: 10.1016/j.neuroimage.2019.06.015] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 05/15/2019] [Accepted: 06/04/2019] [Indexed: 10/26/2022] Open
Abstract
The habenula (Hb) inhibits dopaminergic reward signaling in response to negative outcomes and has been linked to numerous functional domains relevant to mental health, including reward prediction, motivation, and aversion processing. Despite its important neuroscientific and clinical implications, however, the human Hb remains poorly understood due to its small size and the associated technical hurdles to in vivo functional magnetic resonance imaging (fMRI) investigation. Using high-resolution 3 T fMRI data from 68 healthy young adults acquired through the Human Connectome Project, we developed a rigorous approach for mapping the whole-brain resting-state functional connectivity of the human Hb. Our study combined an optimized strategy for defining subject-level connectivity seeds to maximize Hb blood-oxygen-level-dependent (BOLD) signal sensitivity with high-quality surface-based alignment for robust functional localization and cortical sensitivity. We identified significant positive Hb connectivity with: (i) conserved brainstem targets, including the dopaminergic ventral tegmental area, serotonergic raphe nuclei, and periaqueductal gray; (ii) subcortical structures related to reward and motor function, including the nucleus accumbens, dorsal striatum, pallidum, thalamus, and cerebellum; and (iii) cortical areas associated with the Salience Network and early sensory processing, including the dorsal anterior cingulate, anterior insula, and primary visual and auditory cortices. Hb connectivity was strongly biased towards task-positive brain regions, with weak or negative connectivity observed throughout the task-negative Default Mode Network. Our study provides a detailed characterization of Hb resting-state functional connectivity in healthy young adults, demonstrating both the feasibility and clinical potential of studying the human Hb using high-resolution 3 T fMRI.
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Affiliation(s)
- Benjamin A Ely
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Emily R Stern
- Department of Psychiatry, New York University Langone School of Medicine, New York, NY, USA; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Joo-Won Kim
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vilma Gabbay
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Junqian Xu
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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13
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Ma Z, Zhong Y, Hines CS, Wu Y, Li Y, Pang M, Li J, Wang C, Fox PT, Zhang N, Wang C. Identifying generalized anxiety disorder using resting state habenular circuitry. Brain Imaging Behav 2019; 14:1406-1418. [DOI: 10.1007/s11682-019-00055-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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14
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Kim JW, Naidich TP, Joseph J, Nair D, Glasser MF, O'halloran R, Doucet GE, Lee WH, Krinsky H, Paulino A, Glahn DC, Anticevic A, Frangou S, Xu J. Reproducibility of myelin content-based human habenula segmentation at 3 Tesla. Hum Brain Mapp 2018; 39:3058-3071. [PMID: 29582505 DOI: 10.1002/hbm.24060] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2017] [Revised: 03/16/2018] [Accepted: 03/16/2018] [Indexed: 02/06/2023] Open
Abstract
In vivo morphological study of the human habenula, a pair of small epithalamic nuclei adjacent to the dorsomedial thalamus, has recently gained significant interest for its role in reward and aversion processing. However, segmenting the habenula from in vivo magnetic resonance imaging (MRI) is challenging due to the habenula's small size and low anatomical contrast. Although manual and semi-automated habenula segmentation methods have been reported, the test-retest reproducibility of the segmented habenula volume and the consistency of the boundaries of habenula segmentation have not been investigated. In this study, we evaluated the intra- and inter-site reproducibility of in vivo human habenula segmentation from 3T MRI (0.7-0.8 mm isotropic resolution) using our previously proposed semi-automated myelin contrast-based method and its fully-automated version, as well as a previously published manual geometry-based method. The habenula segmentation using our semi-automated method showed consistent boundary definition (high Dice coefficient, low mean distance, and moderate Hausdorff distance) and reproducible volume measurement (low coefficient of variation). Furthermore, the habenula boundary in our semi-automated segmentation from 3T MRI agreed well with that in the manual segmentation from 7T MRI (0.5 mm isotropic resolution) of the same subjects. Overall, our proposed semi-automated habenula segmentation showed reliable and reproducible habenula localization, while its fully-automated version offers an efficient way for large sample analysis.
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Affiliation(s)
- Joo-Won Kim
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York.,Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Thomas P Naidich
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Joshmi Joseph
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Divya Nair
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Matthew F Glasser
- Department of Neuroscience, Washington University School of Medicine, Saint Louis, Missouri.,St. Luke's Hospital, Saint Louis, Missouri
| | - Rafael O'halloran
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Gaelle E Doucet
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Won Hee Lee
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Hannah Krinsky
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Alejandro Paulino
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.,Department of Psychology, Yale University School of Medicine, New Haven, Connecticut.,Olin Neuropsychiatric Research Center, Institute of Living, Hartford, Connecticut
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Junqian Xu
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York.,Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York
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15
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Schafer M, Kim JW, Joseph J, Xu J, Frangou S, Doucet GE. Imaging Habenula Volume in Schizophrenia and Bipolar Disorder. Front Psychiatry 2018; 9:456. [PMID: 30319463 PMCID: PMC6165901 DOI: 10.3389/fpsyt.2018.00456] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 09/03/2018] [Indexed: 02/01/2023] Open
Abstract
The habenula (Hb), a bilateral nucleus located next to the dorsomedial thalamus, is of particular relevance to psychiatric disorders based on preclinical evidence linking the Hb to depressive and amotivational states. However, studies in clinical samples are scant because segmentation of the Hb in neuroimaging data is challenging due to its small size and low contrast from the surrounding tissues. Negative affective states dominate the clinical course of schizophrenia and bipolar disorder and represent a major cause of disability. Diagnosis-related alterations in the volume of Hb in these disorders have therefore been hypothesized but remain largely untested. To probe this question, we used a recently developed objective and reliable semi-automated Hb segmentation method based on myelin-sensitive magnetic resonance imaging (MRI) data. We ascertained case-control differences in Hb volume from high resolution structural MRI data obtained from patients with schizophrenia (n = 95), bipolar disorder (n = 44) and demographically matched healthy individuals (n = 52). Following strict quality control of the MRI data, the final sample comprised 68 patients with schizophrenia, 32 with bipolar disorder and 40 healthy individuals. Regardless of diagnosis, age, sex, and IQ were not correlated with Hb volume. This was also the case for age of illness onset and medication (i.e., antipsychotic dose and lithium-treatment status). Case-control differences in Hb volume did not reach statistical significance; their effect size (Cohen's d) was negligible on the left (schizophrenia: 0.14; bipolar disorder: -0.03) and small on the right (schizophrenia: 0.34; bipolar disorder: 0.26). Nevertheless, variability in the volume of the right Hb was associated with suicidality in the entire patient sample (ρ = 0.29, p = 0.004) as well as in each patient group (bipolar disorder: ρ = 0.34, p = 0.04; schizophrenia: ρ = 0.25, p = 0.04). These findings warrant replication in larger samples and longitudinal designs and encourage more comprehensive characterization of Hb connectivity and function in clinical populations.
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Affiliation(s)
- Matthew Schafer
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Joo-Won Kim
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Joshmi Joseph
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Junqian Xu
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Gaelle E Doucet
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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