1
|
Ma X, Li J, Yang Y, Qiu X, Sheng J, Han N, Wu C, Xu G, Jiang G, Tian J, Weng X, Wang J. Enhanced cerebral blood flow similarity of the somatomotor network in chronic insomnia: Transcriptomic decoding, gut microbial signatures and phenotypic roles. Neuroimage 2024; 297:120762. [PMID: 39089603 DOI: 10.1016/j.neuroimage.2024.120762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 07/24/2024] [Accepted: 07/26/2024] [Indexed: 08/04/2024] Open
Abstract
Chronic insomnia (CI) is a complex disease involving multiple factors including genetics, gut microbiota, and brain structure and function. However, there lacks a unified framework to elucidate how these factors interact in CI. By combining data of clinical assessment, sleep behavior recording, cognitive test, multimodal MRI (structural, functional, and perfusion), gene, and gut microbiota, this study demonstrated that enhanced cerebral blood flow (CBF) similarities of the somatomotor network (SMN) acted as a key mediator to link multiple factors in CI. Specifically, we first demonstrated that only CBF but not morphological or functional networks exhibited alterations in patients with CI, characterized by increases within the SMN and between the SMN and higher-order associative networks. Moreover, these findings were highly reproducible and the CBF similarity method was test-retest reliable. Further, we showed that transcriptional profiles explained 60.4 % variance of the pattern of the increased CBF similarities with the most correlated genes enriched in regulation of cellular and protein localization and material transport, and gut microbiota explained 69.7 % inter-individual variance in the increased CBF similarities with the most contributions from Negativicutes and Lactobacillales. Finally, we found that the increased CBF similarities were correlated with clinical variables, accounted for sleep behaviors and cognitive deficits, and contributed the most to the patient-control classification (accuracy = 84.4 %). Altogether, our findings have important implications for understanding the neuropathology of CI and may inform ways of developing new therapeutic strategies for the disease.
Collapse
Affiliation(s)
- Xiaofen Ma
- Department of Nuclear Medicine, Jinan University Affiliated Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Yuping Yang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Xiaofan Qiu
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Jintao Sheng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ningke Han
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Changwen Wu
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Guang Xu
- Department of Neurology, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Guihua Jiang
- Department of Nuclear Medicine, Jinan University Affiliated Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Junzhang Tian
- Department of Nuclear Medicine, Jinan University Affiliated Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Xuchu Weng
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China.
| |
Collapse
|
2
|
Wang J, Lai Q, Han J, Qin P, Wu H. Neuroimaging biomarkers for the diagnosis and prognosis of patients with disorders of consciousness. Brain Res 2024; 1843:149133. [PMID: 39084451 DOI: 10.1016/j.brainres.2024.149133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 05/29/2024] [Accepted: 07/25/2024] [Indexed: 08/02/2024]
Abstract
The progress in neuroimaging and electrophysiological techniques has shown substantial promise in improving the clinical assessment of disorders of consciousness (DOC). Through the examination of both stimulus-induced and spontaneous brain activity, numerous comprehensive investigations have explored variations in brain activity patterns among patients with DOC, yielding valuable insights for clinical diagnosis and prognostic purposes. Nonetheless, reaching a consensus on precise neuroimaging biomarkers for patients with DOC remains a challenge. Therefore, in this review, we begin by summarizing the empirical evidence related to neuroimaging biomarkers for DOC using various paradigms, including active, passive, and resting-state approaches, by employing task-based fMRI, resting-state fMRI (rs-fMRI), electroencephalography (EEG), and positron emission tomography (PET) techniques. Subsequently, we conducted a review of studies examining the neural correlates of consciousness in patients with DOC, with the findings holding potential value for the clinical application of DOC. Notably, previous research indicates that neuroimaging techniques have the potential to unveil covert awareness that conventional behavioral assessments might overlook. Furthermore, when integrated with various task paradigms or analytical approaches, this combination has the potential to significantly enhance the accuracy of both diagnosis and prognosis in DOC patients. Nonetheless, the stability of these neural biomarkers still needs additional validation, and future directions may entail integrating diagnostic and prognostic methods with big data and deep learning approaches.
Collapse
Affiliation(s)
- Jiaying Wang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Qiantu Lai
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Junrong Han
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China; Pazhou Lab, Guangzhou 510330, China.
| | - Hang Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China.
| |
Collapse
|
3
|
Dagnino PC, Escrichs A, López-González A, Gosseries O, Annen J, Sanz Perl Y, Kringelbach ML, Laureys S, Deco G. Re-awakening the brain: Forcing transitions in disorders of consciousness by external in silico perturbation. PLoS Comput Biol 2024; 20:e1011350. [PMID: 38701063 PMCID: PMC11068192 DOI: 10.1371/journal.pcbi.1011350] [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: 07/14/2023] [Accepted: 03/31/2024] [Indexed: 05/05/2024] Open
Abstract
A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with DoC. The model-free approach allowed us to characterize brain states in DoC and healthy controls as a probabilistic metastable substate (PMS) space. The PMS of each group was defined by a repertoire of unique patterns (i.e., metastable substates) with different probabilities of occurrence. In the model-based approach, we adjusted the PMS of each DoC group to a causal whole-brain model. This allowed us to explore optimal strategies for promoting transitions by applying off-line in silico probing. Furthermore, this approach enabled us to evaluate the impact of local perturbations in terms of their global effects and sensitivity to stimulation, which is a model-based biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions were obtained in a synchronous protocol, in which the somatomotor network, thalamus, precuneus and insula were the most sensitive areas to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness.
Collapse
Affiliation(s)
- Paulina Clara Dagnino
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Ane López-González
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau 2, University Hospital of Liège, Liège, Belgium
| | - Jitka Annen
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau 2, University Hospital of Liège, Liège, Belgium
| | - Yonatan Sanz Perl
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Steven Laureys
- Joint International Research Unit on Consciousness, CERVO Brain Research Centre, University of Laval, Québec, Québec, Canada
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
| |
Collapse
|
4
|
Watters H, Davis A, Fazili A, Daley L, LaGrow TJ, Schumacher EH, Keilholz S. Infraslow dynamic patterns in human cortical networks track a spectrum of external to internal attention. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590625. [PMID: 38712098 PMCID: PMC11071428 DOI: 10.1101/2024.04.22.590625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Early efforts to understand the human cerebral cortex focused on localization of function, assigning functional roles to specific brain regions. More recent evidence depicts the cortex as a dynamic system, organized into flexible networks with patterns of spatiotemporal activity corresponding to attentional demands. In functional MRI (fMRI), dynamic analysis of such spatiotemporal patterns is highly promising for providing non-invasive biomarkers of neurodegenerative diseases and neural disorders. However, there is no established neurotypical spectrum to interpret the burgeoning literature of dynamic functional connectivity from fMRI across attentional states. In the present study, we apply dynamic analysis of network-scale spatiotemporal patterns in a range of fMRI datasets across numerous tasks including a left-right moving dot task, visual working memory tasks, congruence tasks, multiple resting state datasets, mindfulness meditators, and subjects watching TV. We find that cortical networks show shifts in dynamic functional connectivity across a spectrum that tracks the level of external to internal attention demanded by these tasks. Dynamics of networks often grouped into a single task positive network show divergent responses along this axis of attention, consistent with evidence that definitions of a single task positive network are misleading. Additionally, somatosensory and visual networks exhibit strong phase shifting along this spectrum of attention. Results were robust on a group and individual level, further establishing network dynamics as a potential individual biomarker. To our knowledge, this represents the first study of its kind to generate a spectrum of dynamic network relationships across such an axis of attention.
Collapse
Affiliation(s)
| | - Aleah Davis
- Agnes Scott College
- Georgia Institute of Technology School of Psychology
| | | | - Lauren Daley
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - TJ LaGrow
- Georgia Institute of Technology School of Electrical and Computer Engineering
| | | | - Shella Keilholz
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| |
Collapse
|
5
|
Yang H, Wu H, Kong L, Luo W, Xie Q, Pan J, Quan W, Hu L, Li D, Wu X, Liang H, Qin P. Precise detection of awareness in disorders of consciousness using deep learning framework. Neuroimage 2024; 290:120580. [PMID: 38508294 DOI: 10.1016/j.neuroimage.2024.120580] [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: 01/19/2024] [Revised: 03/14/2024] [Accepted: 03/16/2024] [Indexed: 03/22/2024] Open
Abstract
Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites. We developed a cascade 3D EfficientNet-B3-based deep learning framework tailored for discriminating MCS from UWS patients, referred to as "DeepDOC", and compared its performance against five state-of-the-art machine learning models. We also included an independent dataset consists of 11 DOC patients to test whether our model could identify patients with cognitive motor dissociation (CMD), in which DOC patients were behaviorally diagnosed unconscious but could be detected conscious by brain computer interface (BCI) method. Our results demonstrate that DeepDOC outperforms the five machine learning models, achieving an area under curve (AUC) value of 0.927 and accuracy of 0.861 for distinguishing MCS from UWS patients. More importantly, DeepDOC excels in CMD identification, achieving an AUC of 1 and accuracy of 0.909. Using gradient-weighted class activation mapping algorithm, we found that the posterior cortex, encompassing the visual cortex, posterior middle temporal gyrus, posterior cingulate cortex, precuneus, and cerebellum, as making a more substantial contribution to classification compared to other brain regions. This research offers a convenient and accurate method for detecting covert awareness in patients with MCS and CMD using rs-fMRI data.
Collapse
Affiliation(s)
- Huan Yang
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China
| | - Hang Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Lingcong Kong
- Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China
| | - Wen Luo
- The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 528199, China
| | - Qiuyou Xie
- Joint Research Center for disorders of consciousness, Department of Rehabilitation, Zhujiang Hospital, School of Rehabilitation Sciences, Southern Medical University, Guangzhou 510220, China
| | - Jiahui Pan
- School of Software, South China Normal University, Foshan 528225, China; Pazhou Lab, Guangzhou 510330, China
| | - Wuxiu Quan
- Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China
| | - Lianting Hu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China
| | - Dantong Li
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China
| | - Xuehai Wu
- Pazhou Lab, Guangzhou 510330, China; Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200433, China; Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key laboratory of Brain Function Restoration and Neural Regeneration, Neurosurgical Institute of Fudan University, Shanghai 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200433, China
| | - Huiying Liang
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China.
| | - Pengmin Qin
- Pazhou Lab, Guangzhou 510330, China; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China.
| |
Collapse
|
6
|
Li J, Zou Y, Kong X, Leng Y, Yang F, Zhou G, Liu B, Fan W. Exploring functional connectivity alterations in sudden sensorineural hearing loss: A multilevel analysis. Brain Res 2024; 1824:148677. [PMID: 37979604 DOI: 10.1016/j.brainres.2023.148677] [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: 08/31/2023] [Revised: 11/02/2023] [Accepted: 11/13/2023] [Indexed: 11/20/2023]
Abstract
Sudden sensorineural hearing loss (SSNHL) constitutes an urgent otologic emergency, marked by a rapid decline of at least 30 dB across three consecutive frequencies within 72 h. While previous studies have noted brain region alterations encompassing both auditory and non-auditory areas, this research examines functional connectivity changes across integrity, network, and edge levels in SSNHL. The cohort included 184 participants: 107 SSNHL patients and 77 age- and sex-matched healthy controls. Our investigation comprises: (1) characterization of overall functional connectivity degree across 55 nodes in nine networks (p < 0.05, corrected for false discovery rate), exposing integrity level changes; (2) identification of reduced intranetwork connectivity strength within sensory and attention networks (somatomotor network, auditory network, ventral attention network, dorsal attention network) in SSNHL individuals (p < 0.05, Bonferroni corrected), and reduced internetwork connectivity across twelve distinct subnetwork pairs (p < 0.05, FDR corrected); (3) revelation of increased internetwork connectivity in SSNHL patients, primarily spanning dorsal attention network, fronto parietal network, default mode network, and limbic network, alongside widespread reductions in connectivity patterns among the nine distinct resting-state brain networks. The study further uncovers negative correlations between SSNHL duration and intranetwork connectivity of the auditory network (p < 0.001, R = -0.474), and between Tinnitus Handicap Inventory (THI) scores and internetwork connections linking auditory network and dorsal attention network (p < 0.001, R = -0.331). These observed alterations provide crucial insights into the neural mechanisms underpinning SSNHL and extend our comprehension of the brain's network-level responses to sensory loss. By unveiling the intricate interplay between sensory deprivation, adaptation, and cognitive processes, this study lays the groundwork for future research targeting enhanced diagnosis, treatment, and rehabilitation approaches for individuals afflicted by SSNHL.
Collapse
Affiliation(s)
- Jing Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China.
| | - Yan Zou
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China.
| | - Xiangchuang Kong
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China.
| | - Yangming Leng
- Department of Otorhinolaryngology Head and Neck Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Fan Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Guofeng Zhou
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China.
| | - Bo Liu
- Department of Otorhinolaryngology Head and Neck Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Wenliang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China.
| |
Collapse
|
7
|
Han J, Xie Q, Wu X, Huang Z, Tanabe S, Fogel S, Hudetz AG, Wu H, Northoff G, Mao Y, He S, Qin P. The neural correlates of arousal: Ventral posterolateral nucleus-global transient co-activation. Cell Rep 2024; 43:113633. [PMID: 38159279 DOI: 10.1016/j.celrep.2023.113633] [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: 01/10/2023] [Revised: 11/21/2023] [Accepted: 12/14/2023] [Indexed: 01/03/2024] Open
Abstract
Arousal and awareness are two components of consciousness whose neural mechanisms remain unclear. Spontaneous peaks of global (brain-wide) blood-oxygenation-level-dependent (BOLD) signal have been found to be sensitive to changes in arousal. By contrasting BOLD signals at different arousal levels, we find decreased activation of the ventral posterolateral nucleus (VPL) during transient peaks in the global signal in low arousal and awareness states (non-rapid eye movement sleep and anesthesia) compared to wakefulness and in eyes-closed compared to eyes-open conditions in healthy awake individuals. Intriguingly, VPL-global co-activation remains high in patients with unresponsive wakefulness syndrome (UWS), who exhibit high arousal without awareness, while it reduces in rapid eye movement sleep, a state characterized by low arousal but high awareness. Furthermore, lower co-activation is found in individuals during N3 sleep compared to patients with UWS. These results demonstrate that co-activation of VPL and global activity is critical to arousal but not to awareness.
Collapse
Affiliation(s)
- Junrong Han
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Qiuyou Xie
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, Guangdong, China; Joint Research Centre for Disorders of Consciousness, Guangzhou, Guangdong, China
| | - Xuehai Wu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zirui Huang
- Department of Anesthesiology, Center for Consciousness Science, University of Michigan, Ann Arbor, MI, USA
| | - Sean Tanabe
- Department of Anesthesiology, Center for Consciousness Science, University of Michigan, Ann Arbor, MI, USA
| | - Stuart Fogel
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Anthony G Hudetz
- Department of Anesthesiology, Center for Consciousness Science, University of Michigan, Ann Arbor, MI, USA
| | - Hang Wu
- Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, Guangdong, China
| | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada; Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Sheng He
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
| | - Pengmin Qin
- Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, Guangdong, China; Pazhou Lab, Guangzhou 510335, China.
| |
Collapse
|
8
|
Xie M, Huang Y, Cai W, Zhang B, Huang H, Li Q, Qin P, Han J. Neurobiological Underpinnings of Hyperarousal in Depression: A Comprehensive Review. Brain Sci 2024; 14:50. [PMID: 38248265 PMCID: PMC10813043 DOI: 10.3390/brainsci14010050] [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: 12/11/2023] [Revised: 12/26/2023] [Accepted: 12/30/2023] [Indexed: 01/23/2024] Open
Abstract
Patients with major depressive disorder (MDD) exhibit an abnormal physiological arousal pattern known as hyperarousal, which may contribute to their depressive symptoms. However, the neurobiological mechanisms linking this abnormal arousal to depressive symptoms are not yet fully understood. In this review, we summarize the physiological and neural features of arousal, and review the literature indicating abnormal arousal in depressed patients. Evidence suggests that a hyperarousal state in depression is characterized by abnormalities in sleep behavior, physiological (e.g., heart rate, skin conductance, pupil diameter) and electroencephalography (EEG) features, and altered activity in subcortical (e.g., hypothalamus and locus coeruleus) and cortical regions. While recent studies highlight the importance of subcortical-cortical interactions in arousal, few have explored the relationship between subcortical-cortical interactions and hyperarousal in depressed patients. This gap limits our understanding of the neural mechanism through which hyperarousal affects depressive symptoms, which involves various cognitive processes and the cerebral cortex. Based on the current literature, we propose that the hyperconnectivity in the thalamocortical circuit may contribute to both the hyperarousal pattern and depressive symptoms. Future research should investigate the relationship between thalamocortical connections and abnormal arousal in depression, and explore its implications for non-invasive treatments for depression.
Collapse
Affiliation(s)
- Musi Xie
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (M.X.); (Y.H.)
| | - Ying Huang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (M.X.); (Y.H.)
| | - Wendan Cai
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (W.C.); (B.Z.); (H.H.)
| | - Bingqi Zhang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (W.C.); (B.Z.); (H.H.)
| | - Haonan Huang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (W.C.); (B.Z.); (H.H.)
| | - Qingwei Li
- Department of Psychiatry, Tongji Hospital, Tongji University School of Medicine, Shanghai 200065, China;
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (M.X.); (Y.H.)
- Pazhou Laboratory, Guangzhou 510330, China
| | - Junrong Han
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (W.C.); (B.Z.); (H.H.)
| |
Collapse
|
9
|
Wang J, Gao X, Xiang Z, Sun F, Yang Y. Evaluation of consciousness rehabilitation via neuroimaging methods. Front Hum Neurosci 2023; 17:1233499. [PMID: 37780959 PMCID: PMC10537959 DOI: 10.3389/fnhum.2023.1233499] [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: 06/02/2023] [Accepted: 08/30/2023] [Indexed: 10/03/2023] Open
Abstract
Accurate evaluation of patients with disorders of consciousness (DoC) is crucial for personalized treatment. However, misdiagnosis remains a serious issue. Neuroimaging methods could observe the conscious activity in patients who have no evidence of consciousness in behavior, and provide objective and quantitative indexes to assist doctors in their diagnosis. In the review, we discussed the current research based on the evaluation of consciousness rehabilitation after DoC using EEG, fMRI, PET, and fNIRS, as well as the advantages and limitations of each method. Nowadays single-modal neuroimaging can no longer meet the researchers` demand. Considering both spatial and temporal resolution, recent studies have attempted to focus on the multi-modal method which can enhance the capability of neuroimaging methods in the evaluation of DoC. As neuroimaging devices become wireless, integrated, and portable, multi-modal neuroimaging methods will drive new advancements in brain science research.
Collapse
Affiliation(s)
| | | | | | - Fangfang Sun
- College of Automation, Hangzhou Dianzi University, Hangzhou, China
| | | |
Collapse
|
10
|
Plosnić G, Raguž M, Deletis V, Chudy D. Dysfunctional connectivity as a neurophysiologic mechanism of disorders of consciousness: a systematic review. Front Neurosci 2023; 17:1166187. [PMID: 37539385 PMCID: PMC10394244 DOI: 10.3389/fnins.2023.1166187] [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: 02/14/2023] [Accepted: 07/05/2023] [Indexed: 08/05/2023] Open
Abstract
Introduction Disorders of consciousness (DOC) has been an object of numbers of research regarding the diagnosis, treatment and prognosis in last few decades. We believe that the DOC could be considered as a disconnection syndrome, although the exact mechanisms are not entirely understood. Moreover, different conceptual frameworks highly influence results interpretation. The aim of this systematic review is to assess the current knowledge regarding neurophysiological mechanisms of DOC and to establish possible influence on future clinical implications and usage. Methods We have conducted a systematic review according to PRISMA guidelines through PubMed and Cochrane databases, with studies being selected for inclusion via a set inclusion and exclusion criteria. Results Eighty-nine studies were included in this systematic review according to the selected criteria. This includes case studies, randomized controlled trials, controlled clinical trials, and observational studies with no control arms. The total number of DOC patients encompassed in the studies cited in this review is 1,533. Conclusion Connectomics and network neuroscience offer quantitative frameworks for analysing dynamic brain connectivity. Functional MRI studies show evidence of abnormal connectivity patterns and whole-brain topological reorganization, primarily affecting sensory-related resting state networks (RSNs), confirmed by EEG studies. As previously described, DOC patients are identified by diminished global information processing, i.e., network integration and increased local information processing, i.e., network segregation. Further studies using effective connectivity measurement tools instead of functional connectivity as well as the standardization of the study process are needed.
Collapse
Affiliation(s)
- Gabriela Plosnić
- Department of Pediatrics, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Marina Raguž
- Department of Neurosurgery, Dubrava University Hospital, Zagreb, Croatia
- School of Medicine, Catholic University of Croatia, Zagreb, Croatia
| | - Vedran Deletis
- Albert Einstein College of Medicine, New York, NY, United States
| | - Darko Chudy
- Department of Neurosurgery, Dubrava University Hospital, Zagreb, Croatia
- School of Medicine, University of Zagreb, Zagreb, Croatia
| |
Collapse
|
11
|
Wu H, Xie Q, Pan J, Liang Q, Lan Y, Guo Y, Han J, Xie M, Liu Y, Jiang L, Wu X, Li Y, Qin P. Identifying Patients with Cognitive Motor Dissociation Using Resting-state Temporal Stability. Neuroimage 2023; 272:120050. [PMID: 36963740 DOI: 10.1016/j.neuroimage.2023.120050] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 02/04/2023] [Accepted: 03/21/2023] [Indexed: 03/26/2023] Open
Abstract
Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.
Collapse
Affiliation(s)
- Hang Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China
| | - Qiuyou Xie
- Joint Center for disorders of consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510220, China; Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, 510010, China
| | - Jiahui Pan
- School of Software, South China Normal University, Foshan, 528225, China; Pazhou Lab, Guangzhou, 510330, China
| | - Qimei Liang
- Joint Center for disorders of consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510220, China
| | - Yue Lan
- Joint Center for disorders of consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510220, China
| | - Yequn Guo
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, 510010, China
| | - Junrong Han
- Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, China; Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Musi Xie
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China
| | - Yueyao Liu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China
| | - Liubei Jiang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China
| | - Xuehai Wu
- Pazhou Lab, Guangzhou, 510330, China; Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200433, China; Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200433, China.
| | - Yuanqing Li
- Pazhou Lab, Guangzhou, 510330, China; School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China; Pazhou Lab, Guangzhou, 510330, China.
| |
Collapse
|
12
|
Northoff G, Scalabrini A, Fogel S. Topographic-dynamic reorganisation model of dreams (TRoD) - A spatiotemporal approach. Neurosci Biobehav Rev 2023; 148:105117. [PMID: 36870584 DOI: 10.1016/j.neubiorev.2023.105117] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 12/13/2022] [Accepted: 02/28/2023] [Indexed: 03/06/2023]
Abstract
Dreams are one of the most bizarre and least understood states of consciousness. Bridging the gap between brain and phenomenology of (un)conscious experience, we propose the Topographic-dynamic Re-organization model of Dreams (TRoD). Topographically, dreams are characterized by a shift towards increased activity and connectivity in the default-mode network (DMN) while they are reduced in the central executive network, including the dorsolateral prefrontal cortex (except in lucid dreaming). This topographic re-organization is accompanied by dynamic changes; a shift towards slower frequencies and longer timescales. This puts dreams dynamically in an intermediate position between awake state and NREM 2/SWS sleep. TRoD proposes that the shift towards DMN and slower frequencies leads to an abnormal spatiotemporal framing of input processing including both internally- and externally-generated inputs (from body and environment). In dreams, a shift away from temporal segregation to temporal integration of inputs results in the often bizarre and highly self-centric mental contents as well as hallucinatory-like states. We conclude that topography and temporal dynamics are core features of the TroD, which may provide the connection of neural and mental activity, e.g., brain and experience during dreams as their "common currency".
Collapse
Affiliation(s)
- Georg Northoff
- Faculty of Medicine, Centre for Neural Dynamics, The Royal's Institute of Mental Health Research, Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada; Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, China; Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China.
| | - Andrea Scalabrini
- Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy.
| | - Stuart Fogel
- Sleep and Neuroscience, The Royal's Institute of Mental Health Research, Brain and Mind Research Institute and Faculty of Social Sciences, University of Ottawa, Ottawa, ON, Canada.
| |
Collapse
|
13
|
Zang D, Zhao X, Qiao Y, Huo J, Wu X, Wang Z, Xu Z, Zheng R, Qi Z, Mao Y, Zhang L. Enhanced brain parcellation via abnormality inpainting for neuroimage-based consciousness evaluation of hydrocephalus patients by lumbar drainage. Brain Inform 2023; 10:3. [PMID: 36656455 PMCID: PMC9852379 DOI: 10.1186/s40708-022-00181-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/01/2022] [Indexed: 01/20/2023] Open
Abstract
Brain network analysis based on structural and functional magnetic resonance imaging (MRI) is considered as an effective method for consciousness evaluation of hydrocephalus patients, which can also be applied to facilitate the ameliorative effect of lumbar cerebrospinal fluid drainage (LCFD). Automatic brain parcellation is a prerequisite for brain network construction. However, hydrocephalus images usually have large deformations and lesion erosions, which becomes challenging for ensuring effective brain parcellation works. In this paper, we develop a novel and robust method for segmenting brain regions of hydrocephalus images. Our main contribution is to design an innovative inpainting method that can amend the large deformations and lesion erosions in hydrocephalus images, and synthesize the normal brain version without injury. The synthesized images can effectively support brain parcellation tasks and lay the foundation for the subsequent brain network construction work. Specifically, the novelty of the inpainting method is that it can utilize the symmetric properties of the brain structure to ensure the quality of the synthesized results. Experiments show that the proposed brain abnormality inpainting method can effectively aid the brain network construction, and improve the CRS-R score estimation which represents the patient's consciousness states. Furthermore, the brain network analysis based on our enhanced brain parcellation method has demonstrated potential imaging biomarkers for better interpreting and understanding the recovery of consciousness in patients with secondary hydrocephalus.
Collapse
Affiliation(s)
- Di Zang
- grid.411405.50000 0004 1757 8861Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 China ,National Center for Neurological Disorders, Shanghai, 200040 China ,grid.22069.3f0000 0004 0369 6365Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040 China ,grid.8547.e0000 0001 0125 2443Neurosurgical Institute of Fudan University, Shanghai, 200040 China ,grid.411405.50000 0004 1757 8861Shanghai Clinical Medical Center of Neurosurgery, Shanghai, 200040 China ,grid.8547.e0000 0001 0125 2443State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200040 China
| | - Xiangyu Zhao
- grid.16821.3c0000 0004 0368 8293School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030 China
| | - Yuanfang Qiao
- grid.16821.3c0000 0004 0368 8293School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030 China
| | - Jiayu Huo
- grid.16821.3c0000 0004 0368 8293School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030 China
| | - Xuehai Wu
- grid.411405.50000 0004 1757 8861Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 China ,National Center for Neurological Disorders, Shanghai, 200040 China ,grid.22069.3f0000 0004 0369 6365Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040 China ,grid.8547.e0000 0001 0125 2443Neurosurgical Institute of Fudan University, Shanghai, 200040 China ,grid.411405.50000 0004 1757 8861Shanghai Clinical Medical Center of Neurosurgery, Shanghai, 200040 China ,grid.8547.e0000 0001 0125 2443State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200040 China
| | - Zhe Wang
- grid.411405.50000 0004 1757 8861Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 China ,National Center for Neurological Disorders, Shanghai, 200040 China ,grid.22069.3f0000 0004 0369 6365Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040 China ,grid.8547.e0000 0001 0125 2443Neurosurgical Institute of Fudan University, Shanghai, 200040 China ,grid.411405.50000 0004 1757 8861Shanghai Clinical Medical Center of Neurosurgery, Shanghai, 200040 China ,grid.8547.e0000 0001 0125 2443State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200040 China
| | - Zeyu Xu
- grid.411405.50000 0004 1757 8861Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 China ,National Center for Neurological Disorders, Shanghai, 200040 China ,grid.22069.3f0000 0004 0369 6365Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040 China ,grid.8547.e0000 0001 0125 2443Neurosurgical Institute of Fudan University, Shanghai, 200040 China ,grid.411405.50000 0004 1757 8861Shanghai Clinical Medical Center of Neurosurgery, Shanghai, 200040 China ,grid.8547.e0000 0001 0125 2443State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200040 China
| | - Ruizhe Zheng
- grid.411405.50000 0004 1757 8861Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 China ,National Center for Neurological Disorders, Shanghai, 200040 China ,grid.22069.3f0000 0004 0369 6365Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040 China ,grid.8547.e0000 0001 0125 2443Neurosurgical Institute of Fudan University, Shanghai, 200040 China ,grid.411405.50000 0004 1757 8861Shanghai Clinical Medical Center of Neurosurgery, Shanghai, 200040 China ,grid.8547.e0000 0001 0125 2443State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200040 China
| | - Zengxin Qi
- grid.411405.50000 0004 1757 8861Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 China ,National Center for Neurological Disorders, Shanghai, 200040 China ,grid.22069.3f0000 0004 0369 6365Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040 China ,grid.8547.e0000 0001 0125 2443Neurosurgical Institute of Fudan University, Shanghai, 200040 China ,grid.411405.50000 0004 1757 8861Shanghai Clinical Medical Center of Neurosurgery, Shanghai, 200040 China ,grid.8547.e0000 0001 0125 2443State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200040 China
| | - Ying Mao
- grid.411405.50000 0004 1757 8861Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040 China ,National Center for Neurological Disorders, Shanghai, 200040 China ,grid.22069.3f0000 0004 0369 6365Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, 200040 China ,grid.8547.e0000 0001 0125 2443Neurosurgical Institute of Fudan University, Shanghai, 200040 China ,grid.411405.50000 0004 1757 8861Shanghai Clinical Medical Center of Neurosurgery, Shanghai, 200040 China ,grid.8547.e0000 0001 0125 2443State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200040 China
| | - Lichi Zhang
- grid.16821.3c0000 0004 0368 8293School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030 China
| |
Collapse
|
14
|
Li H, Zhang X, Sun X, Dong L, Lu H, Yue S, Zhang H. Functional networks in prolonged disorders of consciousness. Front Neurosci 2023; 17:1113695. [PMID: 36875660 PMCID: PMC9981972 DOI: 10.3389/fnins.2023.1113695] [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: 12/01/2022] [Accepted: 01/25/2023] [Indexed: 02/19/2023] Open
Abstract
Prolonged disorders of consciousness (DoC) are characterized by extended disruptions of brain activities that sustain wakefulness and awareness and are caused by various etiologies. During the past decades, neuroimaging has been a practical method of investigation in basic and clinical research to identify how brain properties interact in different levels of consciousness. Resting-state functional connectivity within and between canonical cortical networks correlates with consciousness by a calculation of the associated temporal blood oxygen level-dependent (BOLD) signal process during functional MRI (fMRI) and reveals the brain function of patients with prolonged DoC. There are certain brain networks including the default mode, dorsal attention, executive control, salience, auditory, visual, and sensorimotor networks that have been reported to be altered in low-level states of consciousness under either pathological or physiological states. Analysis of brain network connections based on functional imaging contributes to more accurate judgments of consciousness level and prognosis at the brain level. In this review, neurobehavioral evaluation of prolonged DoC and the functional connectivity within brain networks based on resting-state fMRI were reviewed to provide reference values for clinical diagnosis and prognostic evaluation.
Collapse
Affiliation(s)
- Hui Li
- Rehabilitation Center, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.,Department of Neurorehabilitation, China Rehabilitation Research Center, Beijing, China.,University of Health and Rehabilitation Sciences, Qingdao, Shandong, China
| | - Xiaonian Zhang
- Department of Neurorehabilitation, China Rehabilitation Research Center, Beijing, China
| | - Xinting Sun
- Department of Neurorehabilitation, China Rehabilitation Research Center, Beijing, China
| | - Linghui Dong
- Rehabilitation Center, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.,Department of Neurorehabilitation, China Rehabilitation Research Center, Beijing, China.,University of Health and Rehabilitation Sciences, Qingdao, Shandong, China
| | - Haitao Lu
- Department of Neurorehabilitation, China Rehabilitation Research Center, Beijing, China
| | - Shouwei Yue
- Rehabilitation Center, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.,University of Health and Rehabilitation Sciences, Qingdao, Shandong, China
| | - Hao Zhang
- Rehabilitation Center, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.,Department of Neurorehabilitation, China Rehabilitation Research Center, Beijing, China.,University of Health and Rehabilitation Sciences, Qingdao, Shandong, China
| |
Collapse
|
15
|
Aberrant brain functional hubs convergence in the acute severe traumatic brain injury patients with rapidly recovering. Neuroradiology 2023; 65:145-155. [PMID: 36056968 DOI: 10.1007/s00234-022-03048-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/27/2022] [Indexed: 01/10/2023]
Abstract
PURPOSE We aimed to identify the aberrant functional hubs in patients with acute severe traumatic brain injury (sTBI) and investigate whether they could help inform prognosis. METHODS Twenty-eight sTBI patients and health controls underwent imaging scanning. The graph-theoretical measure of degree centrality (DC) was applied to identify the abnormal brain functional hubs and conjoined with regions of interest-based analysis to investigate their interaction and impact on whole-brain. We further split sTBI patients into two subgroups according to their recovery to explore whether the fractional amplitude of low-frequency fluctuation (fALFF) roles in functional connectivity (FC) differential areas to help inform the patients' long-term prognosis. RESULTS We identified the part of prefrontal cortex (PFC), precentral and postcentral gyrus (Pre-/Post-CG), cingulate gyrus (CgG), posterior medial cortex (PMC), and brainstem that could be core hubs whose DC was significantly increased in patients with acute sTBI. The interaction strength of the paired hubs could be enhanced (CG-PFC, CgG-PFC, CG-brainstem, CgG-brainstem, PMC-brainstem, and PFC-brainstem) and weakened (CG-CgG, CG-PMC, CgG-PMC, and PMC-PFC), compared with healthy controls. We also found abnormal FC in 5 hubs to whole-brain. The spontaneous brain activities in the FC differential regions [e.g., the fALFF and mean fALFF value] were valid to predict outcome at 6-month in patients with sTBI. CONCLUSION We demonstrated a compensatory mechanism that part of brain regions will converge into abnormal functional hubs in patients with acute sTBI, which provides a potential approach to objectively predicting patients' long-term outcome.
Collapse
|
16
|
Chen C, Han J, Zheng S, Zhang X, Sun H, Zhou T, Hu S, Yan X, Wang C, Wang K, Hu Y. Dynamic Changes of Brain Activity in Different Responsive Groups of Patients with Prolonged Disorders of Consciousness. Brain Sci 2022; 13:brainsci13010005. [PMID: 36671987 PMCID: PMC9856292 DOI: 10.3390/brainsci13010005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/07/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022] Open
Abstract
As medical technology continues to improve, many patients diagnosed with brain injury survive after treatments but are still in a coma. Further, multiple clinical studies have demonstrated recovery of consciousness after transcranial direct current stimulation. To identify possible neurophysiological mechanisms underlying disorders of consciousness (DOCs) improvement, we examined the changes in multiple resting-state EEG microstate parameters after high-definition transcranial direct current stimulation (HD-tDCS). Because the left dorsolateral prefrontal cortex is closely related to consciousness, it is often chosen as a stimulation target for tDCS treatment of DOCs. A total of 21 patients diagnosed with prolonged DOCs were included in this study, and EEG microstate analysis of resting state EEG datasets was performed on all patients before and after interventions. Each of them underwent 10 anodal tDCS sessions of the left dorsolateral prefrontal cortex over 5 consecutive working days. According to whether the clinical manifestations improved, DOCs patients were divided into the responsive (RE) group and the non-responsive (N-RE) group. The dynamic changes of resting state EEG microstate parameters were also analyzed. After multiple HD-tDCS interventions, the duration and coverage of class C microstates in the RE group were significantly increased. This study also found that the transition between microstates A and C increased, while the transition between microstates B and D decreased in the responsive group. However, these changes in EEG microstate parameters in the N-RE group have not been reported. Our findings suggest that EEG neural signatures have the potential to assess consciousness states and that improvement in the dynamics of brain activity was associated with the recovery of DOCs. This study extends our understanding of the neural mechanism of DOCs patients in consciousness recovery.
Collapse
Affiliation(s)
- Chen Chen
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei 230032, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230032, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China
| | - Jinying Han
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei 230032, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230032, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China
| | - Shuang Zheng
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei 230032, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230032, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China
| | - Xintong Zhang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei 230032, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230032, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China
| | - Haibo Sun
- The First Clinical College of Anhui Medical University, Hefei 230032, China
| | - Ting Zhou
- Department of Neurology, The Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230001, China
| | - Shunyin Hu
- Department of Neurorehabilitation, Hefei Anhua Trauma Rehabilitation Hospital, Hefei 230011, China
| | - Xiaoxiang Yan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
| | - Changqing Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei 230032, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230032, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China
- Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei 230032, China
| | - Yajuan Hu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei 230032, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230032, China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China
- Correspondence: ; Tel.: +139-5691-2105
| |
Collapse
|
17
|
Brain Metabolic Connectivity Patterns in Patients with Prolonged Disorder of Consciousness after Hypoxic-Ischemic Injury: A Preliminary Study. Brain Sci 2022; 12:brainsci12070892. [PMID: 35884699 PMCID: PMC9313214 DOI: 10.3390/brainsci12070892] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 12/07/2022] Open
Abstract
Understanding the patterns of brain glucose metabolism and connectivity in hypoxic-ischemic encephalopathy (HIE) patients with prolonged disorders of consciousness (DOC) may be of positive significance to the accurate assessment of consciousness and the optimization of neuromodulation strategy. We retrospectively analyzed the brain glucose metabolism pattern and its correlation with clinical Coma Recovery Scale-Revised (CRS-R) score in six HIE patients with prolonged DOC who had undergone 18F-deoxyglucose brain positron emission tomography scanning (FDG-PET). We also compared the differences in global metabolic connectivity patterns and the characteristics of several brain networks between HIE patients and healthy controls (HC). The metabolism of multiple brain regions decreased significantly in HIE patients, and the degree of local metabolic preservation was correlated with CRS-R score. The internal metabolic connectivity of occipital lobe and limbic system in HIE patients decreased, and their metabolic connectivity with frontal lobe, parietal lobe and temporal lobe also decreased. The metabolic connectivity patterns of default mode network, dorsal attention network, salience network, executive control network and subcortex network of HIE also changed compared with HC. The present study suggested that pattern of cerebral glucose metabolism and network connectivity of HIE patients with prolonged DOC were significantly different from those of healthy people.
Collapse
|
18
|
Zeng F, Hong W, Zha R, Li Y, Jin C, Liu Y, Liu H, Liu M, Liu M, Xu F, Hu D, Song H, Wu H, Yu Y, Zhang X. Smoking related attention alteration in chronic obstructive pulmonary disease-smoking comorbidity. BMC Pulm Med 2022; 22:182. [PMID: 35524207 PMCID: PMC9078025 DOI: 10.1186/s12890-022-01964-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 04/22/2022] [Indexed: 11/22/2022] Open
Abstract
Background Chronic obstructive pulmonary disease (COPD) is a respiratory disease that causes a wide range of cognitive impairments. Although COPD-Smoking comorbidity is common, the relationship between smoking and cognitive function in COPD-Smoking comorbidity remains unclear. This study aimed to investigate the effect of smoking on cognitive function like attention in COPD-Smoking patients. Methods In this study, we used the Montreal Cognitive Assessment (MoCA) scale and resting-state functional magnetic resonance imaging (fMRI) to explore the effect of smoking on attention in patients with COPD. Results Behavioral analysis revealed that among patients with COPD the smokers had a shorter course of COPD and showed a worse attention performance than the non-smokers. Resting-state fMRI analysis revealed that among patients with COPD smokers showed lower regional homogeneity (ReHo) value of the fusiform gyrus than non-smokers. Importantly, the ReHo of the fusiform gyrus is positively associated with attention and mediates the effect of smoking on attention in COPD. Conclusions In summary, our study provides behavioral and neurobiological evidence supporting the positive effect of smoking on attention in COPD. This may be helpful for understanding and treating COPD and even other diseases comorbid with smoking. Supplementary Information The online version contains supplementary material available at 10.1186/s12890-022-01964-6.
Collapse
Affiliation(s)
- Feiyan Zeng
- Department of Radiology, the First Affiliated Hospital of USTC, Department of Psychology, School of Humanities & Social Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Wei Hong
- Department of Radiology, the First Affiliated Hospital of USTC, Department of Psychology, School of Humanities & Social Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Rujing Zha
- Department of Radiology, the First Affiliated Hospital of USTC, Department of Psychology, School of Humanities & Social Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China.
| | - Ying Li
- Department of Radiology, the First Affiliated Hospital of USTC, Department of Psychology, School of Humanities & Social Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Chen Jin
- Department of Radiology, the First Affiliated Hospital of USTC, Department of Psychology, School of Humanities & Social Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Ying Liu
- Department of Radiology, the First Affiliated Hospital of USTC, Department of Psychology, School of Humanities & Social Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Hao Liu
- School of Earth and Space Science, University of Science & Technology of China, Hefei, 230027, China
| | - Mengqiu Liu
- Department of Radiology, the First Affiliated Hospital of USTC, Department of Psychology, School of Humanities & Social Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Mei Liu
- Department of Radiology, the First Affiliated Hospital of USTC, Department of Psychology, School of Humanities & Social Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Fei Xu
- Department of Radiology, the First Affiliated Hospital of USTC, Department of Psychology, School of Humanities & Social Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Daiju Hu
- Department of Radiology, the First Affiliated Hospital of USTC, Department of Psychology, School of Humanities & Social Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Hongwen Song
- Department of Radiology, the First Affiliated Hospital of USTC, Department of Psychology, School of Humanities & Social Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Haiyan Wu
- Centre for Cognitive and Brain Sciences (CCBS), University of Macau (UM), Macau SAR, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
| | - Xiaochu Zhang
- Department of Radiology, the First Affiliated Hospital of USTC, Department of Psychology, School of Humanities & Social Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China. .,Application Technology Center of Physical Therapy to Brain Disorders, Institute of Advanced Technology, University of Science & Technology of China, Hefei, 230031, China. .,Hefei Medical Research Center on Alcohol Addiction, Affiliated Psychological Hospital of Anhui Medical University, Hefei Fourth People's Hospital, Anhui Mental Health Center, Hefei, 230017, China. .,Biomedical Sciences and Health Laboratory of Anhui Province, University of Science & Technology of China, Hefei, 230027, China.
| |
Collapse
|
19
|
Ping L, Zhou C, Sun S, Wang W, Zheng Q, You Z. Alterations in resting-state whole-brain functional connectivity pattern similarity in bipolar disorder patients. Brain Behav 2022; 12:e2580. [PMID: 35451228 PMCID: PMC9120726 DOI: 10.1002/brb3.2580] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 02/04/2022] [Accepted: 03/20/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Previous neuroimaging studies have extensively demonstrated many signs of functionally spontaneous local neural activity abnormalities in bipolar disorder (BD) patients using resting-state functional magnetic resonance imaging (rs-fMRI). However, how to identify the changes of voxel-wise whole-brain functional connectivity pattern and its corresponding functional connectivity changes remain largely unclear in BD patients. The current study aimed to investigate the voxel-wise changes of functional connectivity patterns in BD patients using publicly available data from the UCLA CNP LA5c Study. METHODS A total of 45 BD patients and 115 healthy control subjects were finally included and whole-brain functional connectivity homogeneity (FcHo) was calculated from their rs-fMRI. Moreover, the alterations of corresponding functional connectivity were subsequently identified using seed-based resting-state functional connectivity analysis. RESULTS Individuals with BD exhibited significantly lower FcHo values in the left middle temporal gyrus (MTG) when compared with controls. Functional connectivity findings further indicated decreased functional connectivities between left MTG and cluster 1 (left superior temporal gyrus, extend to middle temporal gyrus, rolandic operculum), cluster 2 (right postcentral, extend to right precentral) in BD patients. The mean FcHo values of left MTG were positively correlated with insomnia, middle scores and appetite increase scores. The mean functional connectivities of left MTG to cluster 1 were negatively correlated with grandiose delusions scores. While the functional connections between left MTG with cluster 2 were negatively correlated with delusions of reference and positively correlated with insomnia, middle scores in BD patients. CONCLUSIONS Our findings suggested that abnormal FcHo and functional connections in those areas of the brain involving DMN and SMN networks might play a crucial role in the neuropathology of BD.
Collapse
Affiliation(s)
- Liangliang Ping
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Cong Zhou
- School of Mental Health, Jining Medical University, Jining, China
| | - Shan Sun
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Wenqiang Wang
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Qi Zheng
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Zhiyi You
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| |
Collapse
|
20
|
Wu H, Qi Z, Wu X, Zhang J, Wu C, Huang Z, Zang D, Fogel S, Tanabe S, Hudetz AG, Northoff G, Mao Y, Qin P. Anterior precuneus related to the recovery of consciousness. Neuroimage Clin 2022; 33:102951. [PMID: 35134706 PMCID: PMC8856921 DOI: 10.1016/j.nicl.2022.102951] [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: 09/21/2021] [Revised: 01/22/2022] [Accepted: 01/25/2022] [Indexed: 11/28/2022]
Abstract
Degree centrality of anterior precuneus correlated with Glasgow Outcome Scale scores. Anterior precuneus was shown as a hub in multiple recoverable unconscious states. Anterior precuneus had similar connectivity pattern in recoverable unconscious states.
The neural mechanism that enables the recovery of consciousness in patients with unresponsive wakefulness syndrome (UWS) remains unclear. The aim of the current study is to characterize the cortical hub regions related to the recovery of consciousness. In the current fMRI study, voxel-wise degree centrality analysis was adopted to identify the cortical hubs related to the recovery of consciousness, for which a total of 27 UWS patients were recruited, including 13 patients who emerged from UWS (UWS-E), and 14 patients who remained in UWS (UWS-R) at least three months after the experiment performance. Furthermore, other recoverable unconscious states were adopted as validation groups, including three independent N3 sleep datasets (n = 12, 9, 9 respectively) and three independent anesthesia datasets (n = 27, 14, 6 respectively). Spatial similarity of the hub characteristic with the validation groups between the UWS-E and UWS-R was compared using the dice coefficient. Finally, with the cortical regions persistently shown as hubs across UWS-E and validation states, functional connectivity analysis was further performed to explore the connectivity patterns underlying the recovery of consciousness. The results identified four cortical hubs in the UWS-E, which showed significantly higher degree centrality for UWS-E than UWS-R, including the anterior precuneus, left inferior parietal lobule, left inferior frontal gyrus, and left middle frontal gyrus, of which the degree centrality value also positively correlated with the patients’ Glasgow Outcome Scale (GOS) score that assessed global brain functioning outcome after a brain injury. Furthermore, the anterior precuneus was found with significantly higher similarity of hub characteristics as well as functional connectivity patterns between UWS-E and the validation groups. The results suggest that the recovery of consciousness may be relevant to the integrity of cortical hubs in the recoverable unconscious states, especially the anterior precuneus. The identified cortical hub regions could serve as potential treatment targets for patients with UWS.
Collapse
Affiliation(s)
- Hang Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong 510631, China
| | - Zengxin Qi
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200433, China; Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200433, China
| | - Xuehai Wu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200433, China; Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200433, China; Pazhou Lab, Guangzhou 510335, China
| | - Jun Zhang
- Department of Anesthesiology, Fudan University Shanghai Cancer Center Shanghai, 200433, China
| | - Changwei Wu
- Research Center for Brain and Consciousness, Taipei Medical University, Taipei 11031, Taiwan; Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei 11031, Taiwan; Shuang-Ho Hospital, Taipei Medical University, New Taipei 23561, Taiwan
| | - Zirui Huang
- Department of Anesthesiology and Center for Consciousness Science, University of Michigan, Ann Arbor, MI 48105, USA
| | - Di Zang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200433, China; Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200433, China
| | - Stuart Fogel
- School of Psychology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Sean Tanabe
- Department of Anesthesiology and Center for Consciousness Science, University of Michigan, Ann Arbor, MI 48105, USA
| | - Anthony G Hudetz
- Department of Anesthesiology and Center for Consciousness Science, University of Michigan, Ann Arbor, MI 48105, USA
| | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario, ON K1Z 7K4, Canada; Mental Health Centre, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200433, China; Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200433, China.
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong 510631, China; Pazhou Lab, Guangzhou 510335, China.
| |
Collapse
|
21
|
Dynamic reconfiguration of human brain networks across altered states of consciousness. Behav Brain Res 2022; 419:113685. [PMID: 34838931 DOI: 10.1016/j.bbr.2021.113685] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 10/29/2021] [Accepted: 11/20/2021] [Indexed: 01/01/2023]
Abstract
Consciousness is supported by rich neuronal dynamics to orchestrate behaviors and conscious processing can be disrupted by general anesthetics. Previous studies suggested that dynamic reconfiguration of large-scale functional network is critical for learning and higher-order cognitive function. During altered states of consciousness, how brain functional networks are dynamically changed and reconfigured at the whole-brain level is still unclear. To fill this gap, using multilayer network approach and functional magnetic resonance imaging (fMRI) data of 21 healthy subjects, we investigated the dynamic network reconfiguration in three different states of consciousness: wakefulness, dexmedetomidine-induced sedation, and recovery. Applying time-varying community detection algorithm, we constructed multilayer modularity networks to track and quantify dynamic interactions among brain areas that span time and space. We compared four high-level network features (i.e., switching, promiscuity, integration, and recruitment) derived from multilayer modularity across the three conditions. We found that sedation state is primarily characterized by increased switching rates as well as decreased integration, representing a whole-brain pattern with higher modular dynamics and more fragmented communication; such alteration can be mostly reversed after the recovery of consciousness. Thus, our work can provide additional insights to understand the modular network reconfiguration across different states of consciousness and may provide some clinical implications for disorders of consciousness.
Collapse
|
22
|
Liang P, Xu H, Li S, Ren L, Zhao X. Virtual Reality-Based Sensory Stimulation for Pediatric Disorders of Consciousness: A Pilot Study. Front Pediatr 2022; 10:879422. [PMID: 35769213 PMCID: PMC9234395 DOI: 10.3389/fped.2022.879422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 05/16/2022] [Indexed: 11/21/2022] Open
Abstract
OBJECTIVE The purpose of this study was to determine whether virtual reality-based sensory stimulation has the ability to improve the level of consciousness in pediatric disorders of consciousness compared with general rehabilitation. METHODS Thirty subjects were divided into a virtual reality (VR) group (n = 15) and a control group (n = 15). Subjects in the VR group received both general rehabilitation and exposure to VR videos; the control group received only general rehabilitation. The Glasgow Coma Scale (GCS), Coma Recovery Scale-Revised (CRS-R), and amplitude-integrated electroencephalogram (EEG) (aEEG) were used to measure the clinical behavioral response and neuroelectrophysiology before and after the treatment. The Glasgow Outcome Scale Extended Pediatric Revised (GOS-E Peds) was used to measure the social and personal functional ability after 3 months. RESULTS After 2 weeks of treatment, the CRS-R and GCS improved in both groups. However, the VR group had better results than the control group in the CRS-R (p = 0.003) and GCS (p = 0.045). There were no significant differences on aEEG in the two groups after treatment. According to the GOS-E Peds, the improvement of social and personal functional ability had no significant differences in the two groups. Additionally, there were no obvious adverse reactions in the two group during the treatment. CONCLUSIONS This pilot study indicates potential benefit from the addition of VR to standard rehabilitation in pediatric disorders of consciousness. To further explore the efficacy of VR, a large-sample randomized controlled trial is warranted.
Collapse
Affiliation(s)
- Piao Liang
- Department of Rehabilitation, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Hong Xu
- Department of Rehabilitation, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Sinan Li
- Department of Rehabilitation, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Lei Ren
- Department of Nursing, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoke Zhao
- Department of Rehabilitation, Children's Hospital of Nanjing Medical University, Nanjing, China
| |
Collapse
|
23
|
Xin Y, Bai T, Zhang T, Chen Y, Wang K, Yu S, Liu N, Tian Y. Electroconvulsive therapy modulates critical brain dynamics in major depressive disorder patients. Brain Stimul 2022; 15:214-225. [DOI: 10.1016/j.brs.2021.12.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/03/2021] [Accepted: 12/20/2021] [Indexed: 01/04/2023] Open
|
24
|
Wu J, Wu J, Guo R, Chu L, Li J, Zhang S, Ren H. The decreased connectivity in middle temporal gyrus can be used as a potential neuroimaging biomarker for left temporal lobe epilepsy. Front Psychiatry 2022; 13:972939. [PMID: 36032260 PMCID: PMC9399621 DOI: 10.3389/fpsyt.2022.972939] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 07/08/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE We aimed to explore voxel-mirrored homotopic connectivity (VMHC) abnormalities between the two brain hemispheres in left temporal lobe epilepsy (lTLE) patients and to determine whether these alterations could be leveraged to guide lTLE diagnosis. MATERIALS AND METHODS Fifty-eight lTLE patients and sixty healthy controls (HCs) matched in age, sex, and education level were recruited to receive resting state functional magnetic resonance imaging (rs-fMRI) scan. Then VHMC analyses of bilateral brain regions were conducted based on the results of these rs-fMRI scans. The resultant imaging data were further analyzed using support vector machine (SVM) methods. RESULTS Compared to HCs, patients with lTLE exhibited decreased VMHC values in the bilateral middle temporal gyrus (MTG) and middle cingulum gyrus (MCG), while no brain regions in these patients exhibited increased VMHC values. SVM analyses revealed the diagnostic accuracy of reduced bilateral MTG VMHC values to be 75.42% (89/118) when differentiating between lTLE patients and HCs, with respective sensitivity and specificity values of 74.14% (43/58) and 76.67% (46/60). CONCLUSION Patients with lTLE exhibit abnormal VMHC values corresponding to the impairment of functional coordination between homotopic regions of the brain. These altered MTG VMHC values may also offer value as a robust neuroimaging biomarker that can guide lTLE patient diagnosis.
Collapse
Affiliation(s)
- Jinlong Wu
- Department of Imaging Center, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China.,Key Laboratory of Occupational Hazards and Identification, Wuhan University of Science and Technology, Wuhan, China
| | - Jun Wu
- Department of Neurosurgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruimin Guo
- Department of Imaging Center, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Linkang Chu
- Department of Imaging Center, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Jun Li
- Department of Neurosurgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng Zhang
- Liyuan Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongwei Ren
- Department of Imaging Center, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| |
Collapse
|