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Picchioni D, Yang FN, de Zwart JA, Wang Y, Mandelkow H, Ozbay PS, Chen G, Taylor PA, Lam N, Chappel-Farley MG, Chang C, Liu J, van Gelderen P, Duyn JH. Sleep defined by arousal threshold reveals decreases in corticocortical functional correlations independently from the conventional sleep stages. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.09.607376. [PMID: 39149368 PMCID: PMC11326234 DOI: 10.1101/2024.08.09.607376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Sleep research and sleep medicine have benefited from the use of polysomnography but have also suffered from an overreliance on the conventional, polysomnography-defined sleep stages. For example, reports of sleep-specific brain activity patterns have, with few exceptions, been constrained by assessing brain function as it relates to the conventional sleep stages. This limits the variety of sleep states and underlying activity patterns that one can discover. If undiscovered brain activity patterns exist during sleep, then removing the constraint of a stage-specific analysis may uncover them. The current study used all-night functional magnetic resonance imaging sleep data and defined sleep behaviorally with auditory arousal threshold (AAT) to begin to search for new brain states. It was hypothesized that, during sleep compared to wakefulness, corticocortical functional correlations would decrease. Functional correlation values calculated in a window immediately before the determination of AAT were entered into a linear mixed effects model, allowing multiple arousals across the night per subject into the analysis. The hypothesis was supported using both correlation matrices of brain networks and single seed-region analyses showing whole-brain maps. This represents a novel approach to studying the neuroanatomical correlates of sleep with high spatial resolution by defining sleep in a way that was independent from the conventional sleep stages. This work provides initial evidence to justify searching for sleep stages that are more neuroanatomically localized and unrelated to the conventional sleep stages.
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Han H, Jiang J, Gu L, Gan JQ, Wang H. Brain connectivity patterns derived from aging-related alterations in dynamic brain functional networks and their potential as features for brain age classification. J Neural Eng 2024; 21:026015. [PMID: 38479020 DOI: 10.1088/1741-2552/ad33b1] [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: 05/20/2023] [Accepted: 03/13/2024] [Indexed: 03/26/2024]
Abstract
Objective.Recent studies have demonstrated that the analysis of brain functional networks (BFNs) is a powerful tool for exploring brain aging and age-related neurodegenerative diseases. However, investigating the mechanism of brain aging associated with dynamic BFN is still limited. The purpose of this study is to develop a novel scheme to explore brain aging patterns by constructing dynamic BFN using resting-state functional magnetic resonance imaging data.Approach.A dynamic sliding-windowed non-negative block-diagonal representation (dNBDR) method is proposed for constructing dynamic BFN, based on which a collection of dynamic BFN measures are suggested for examining age-related differences at the group level and used as features for brain age classification at the individual level.Results.The experimental results reveal that the dNBDR method is superior to the sliding time window with Pearson correlation method in terms of dynamic network structure quality. Additionally, significant alterations in dynamic BFN structures exist across the human lifespan. Specifically, average node flexibility and integration coefficient increase with age, while the recruitment coefficient shows a decreased trend. The proposed feature extraction scheme based on dynamic BFN achieved the highest accuracy of 78.7% in classifying three brain age groups.Significance. These findings suggest that dynamic BFN measures, dynamic community structure metrics in particular, play an important role in quantitatively assessing brain aging.
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Affiliation(s)
- Hongfang Han
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, People's Republic of China
| | - Jiuchuan Jiang
- School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210003, Jiangsu, People's Republic of China
| | - Lingyun Gu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, People's Republic of China
| | - John Q Gan
- School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, People's Republic of China
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Zhao G, Zhan Y, Zha J, Cao Y, Zhou F, He L. Abnormal intrinsic brain functional network dynamics in patients with cervical spondylotic myelopathy. Cogn Neurodyn 2023; 17:1201-1211. [PMID: 37786665 PMCID: PMC10542087 DOI: 10.1007/s11571-022-09807-0] [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: 10/17/2021] [Revised: 03/15/2022] [Accepted: 04/01/2022] [Indexed: 11/03/2022] Open
Abstract
The specific topological changes in dynamic functional networks and their role in cervical spondylotic myelopathy (CSM) brain function reorganization remain unclear. This study aimed to investigate the dynamic functional connection (dFC) of patients with CSM, focusing on the temporal characteristics of the functional connection state patterns and the variability of network topological organization. Eighty-eight patients with CSM and 77 healthy controls (HCs) were recruited for resting-state functional magnetic resonance imaging. We applied the sliding time window analysis method and K-means clustering analysis to capture the dFC variability patterns of the two groups. The graph-theoretical approach was used to investigate the variance in the topological organization of whole-brain functional networks. All participants showed four types of dynamic functional connection states. The mean dwell time in state 2 was significantly different between the two groups. Particularly, the mean dwell time in state 2 was significantly longer in the CSM group than in the healthy control group. Among the four states, switching of relative brain networks mainly included the executive control network (ECN), salience network (SN), default mode network (DMN), language network (LN), visual network (VN), auditory network (AN), precuneus network (PN), and sensorimotor network (SMN). Additionally, the topological properties of the dynamic network were variable in patients with CSM. Dynamic functional connection states may offer new insights into intrinsic functional activities in CSM brain networks. The variance of topological organization may suggest instability of the brain networks in patients with CSM.
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Affiliation(s)
- Guoshu Zhao
- Department of Radiology, the First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Nanchang, Jiangxi 330006 People’s Republic of China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006 People’s Republic of China
| | - Yaru Zhan
- Department of Radiology, the First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Nanchang, Jiangxi 330006 People’s Republic of China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006 People’s Republic of China
| | - Jing Zha
- The 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force, Fuzhou, 330006 People’s Republic of China
| | - Yuan Cao
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, 610041 People’s Republic of China
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041 People’s Republic of China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006 People’s Republic of China
| | - Fuqing Zhou
- Department of Radiology, the First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Nanchang, Jiangxi 330006 People’s Republic of China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006 People’s Republic of China
| | - Laichang He
- Department of Radiology, the First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Nanchang, Jiangxi 330006 People’s Republic of China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006 People’s Republic of China
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Fan L, Li Y, Huang ZG, Zhang W, Wu X, Liu T, Wang J. Low-frequency repetitive transcranial magnetic stimulation alters the individual functional dynamical landscape. Cereb Cortex 2023; 33:9583-9598. [PMID: 37376783 DOI: 10.1093/cercor/bhad228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive approach to modulate brain activity and behavior in humans. Still, how individual resting-state brain dynamics after rTMS evolves across different functional configurations is rarely studied. Here, using resting state fMRI data from healthy subjects, we aimed to examine the effects of rTMS to individual large-scale brain dynamics. Using Topological Data Analysis based Mapper approach, we construct the precise dynamic mapping (PDM) for each participant. To reveal the relationship between PDM and canonical functional representation of the resting brain, we annotated the graph using relative activation proportion of a set of large-scale resting-state networks (RSNs) and assigned the single brain volume to corresponding RSN-dominant or a hub state (not any RSN was dominant). Our results show that (i) low-frequency rTMS could induce changed temporal evolution of brain states; (ii) rTMS didn't alter the hub-periphery configurations underlined resting-state brain dynamics; and (iii) the rTMS effects on brain dynamics differ across the left frontal and occipital lobe. In conclusion, low-frequency rTMS significantly alters the individual temporo-spatial dynamics, and our finding further suggested a potential target-dependent alteration of brain dynamics. This work provides a new perspective to comprehend the heterogeneous effect of rTMS.
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Affiliation(s)
- Liming Fan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Zi-Gang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Wenlong Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Xiaofeng Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
- The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, China
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5
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Coleman SC, Seedat ZA, Whittaker AC, Lenartowicz A, Mullinger KJ. Beyond the Beta Rebound: Post-Task Responses in Oscillatory Activity follow Cessation of Working Memory Processes. Neuroimage 2023; 265:119801. [PMID: 36496181 DOI: 10.1016/j.neuroimage.2022.119801] [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: 09/23/2022] [Revised: 11/23/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
Post-task responses (PTRs) are transitionary responses occurring for several seconds between the end of a stimulus/task and a period of rest. The most well-studied of these are beta band (13 - 30 Hz) PTRs in motor networks following movement, often called post-movement beta rebounds, which have been shown to differ in patients with schizophrenia and autism. Previous studies have proposed that beta PTRs reflect inhibition of task-positive networks to enable a return to resting brain activity, scaling with cognitive demand and reflecting cortical self-regulation. It is unknown whether PTRs are a phenomenon of the motor system, or whether they are a more general self-modulatory property of cortex that occur following cessation of higher cognitive processes as well as movement. To test this, we recorded magnetoencephalography (MEG) responses in 20 healthy participants to a working-memory task, known to recruit cortical networks associated with higher cognition. Our results revealed PTRs in the theta, alpha and beta bands across many regions of the brain, including the dorsal attention network (DAN) and lateral visual regions. These PTRs increased significantly (p < 0.05) in magnitude with working-memory load, an effect which is independent of oscillatory modulations occurring over the task period as well as those following individual stimuli. Furthermore, we showed that PTRs are functionally related to reaction times in left lateral visual (p < 0.05) and left parietal (p < 0.1) regions, while the oscillatory responses measured during the task period are not. Importantly, motor PTRs following button presses did not modulate with task condition, suggesting that PTRs in different networks are driven by different aspects of cognition. Our findings show that PTRs are not limited to motor networks but are widespread in regions which are recruited during the task. We provide evidence that PTRs have unique properties, scaling with cognitive load and correlating significantly with behaviour. Based on the evidence, we suggest that PTRs inhibit task-positive network activity to enable a transition to rest, however, further investigation is required to uncover their role in neuroscience and pathology.
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Affiliation(s)
- Sebastian C Coleman
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Zelekha A Seedat
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, NG7 2RD, UK; Young Epilepsy, St Pier's Lane, Dormansland, Lingfield, RH7 6PW, UK
| | - Anna C Whittaker
- Faculty of Health Sciences and Sport, University of Stirling, Stirling, UK
| | - Agatha Lenartowicz
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles
| | - Karen J Mullinger
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, NG7 2RD, UK; Centre for Human Brain Health, School of Psychology, University of Birmingham, UK.
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Peng J, Yang J, Li J, Lei D, Li N, Suo X, Duan L, Chen C, Zeng Y, Xi J, Jiang Y, Gong Q, Peng R. Disrupted Brain Functional Network Topology in Essential Tremor Patients With Poor Sleep Quality. Front Neurosci 2022; 16:814745. [PMID: 35360181 PMCID: PMC8960629 DOI: 10.3389/fnins.2022.814745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 01/14/2022] [Indexed: 11/30/2022] Open
Abstract
Sleep disturbances, especially poor quality of sleep (QoS), are common among essential tremor (ET) patients and may have adverse effects on their quality of life, but the etiology driving the poor QoS in these individuals remains inadequately understood. Few data are available on the neuroimaging alterations of ET with poor QoS. Thirty-eight ET patients with poor QoS (SleET), 48 ET patients with normal QoS (NorET), and 80 healthy controls (HCs) participated in this study. All subjects underwent a 3.0-T magnetic resonance imaging (MRI) scan for resting-state functional MRI data collection. Then, the whole-brain functional connectome was constructed by thresholding the partial correlation matrices of 116 brain regions. Graph theory and network-based statistical analyses were performed. We used a non-parametric permutation test for group comparisons of topological metrics. Partial correlation analyses between the topographical features and clinical characteristics were conducted. The SleET and NorET groups exhibited decreased clustering coefficients, global efficiency, and local efficiency and increased the characteristic path length. Both of these groups also showed reduced nodal degree and nodal efficiency in the left superior dorsolateral frontal gyrus, superior frontal medial gyrus (SFGmed), posterior cingulate gyrus (PCG), lingual gyrus, superior occipital gyrus, right middle occipital gyrus, and right fusiform gyrus. The SleET group additionally presented reduced nodal degrees and nodal efficiency in the right SFGmed relative to the NorET and HC groups, and nodal efficiency in the right SFGmed was negatively correlated with the Pittsburgh Sleep Quality Index score. The observed impaired topographical organizations of functional brain networks within the central executive network (CEN), default mode network (DMN), and visual network serve to further our knowledge of the complex interactions between tremor and sleep, adding to our understanding of the underlying neural mechanisms of ET with poor QoS.
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Affiliation(s)
- Jiaxin Peng
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Yang
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
| | - Junying Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Du Lei
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
| | - Nannan Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Xueling Suo
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
| | - Liren Duan
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Chaolan Chen
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Yan Zeng
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Xi
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Jiang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Qiyong Gong,
| | - Rong Peng
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
- Rong Peng,
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7
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Chang Q, Li C, Zhang J, Wang C. Dynamic brain functional network based on EEG microstate during sensory gating in schizophrenia. J Neural Eng 2022; 19. [PMID: 35130537 DOI: 10.1088/1741-2552/ac5266] [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: 08/18/2021] [Accepted: 02/07/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Cognitive impairment is one of the core symptoms of schizophrenia, with an emphasis on dysfunctional information processing. Sensory gating deficits have consistently been reported in schizophrenia, but the underlying physiological mechanism is not well-understood. We report the discovery and characterization of P50 dynamic brain connections based on microstate analysis. APPROACH We identify five main microstates associated with the P50 response and the difference between the first and second click presentation (S1-S2-P50) in first-episode schizophrenia patients (FESZ), ultra-high-risk individuals (UHR) and healthy controls (HC). The we used the signal segments composed of consecutive time points with the same microstate label to construct brain functional networks. MAIN RESULTS The microstate with a prefrontal extreme location during the response to the S1 of P50 are statistically different in duration, occurrence and coverage among the FESZ, UHR and HC groups. In addition, a microstate with anterior-posterior orientation was found to be associated with S1-S2-P50 and its coverage was found to differ among the FESZ, UHR and HC groups. Source location of microstates showed that activated brain regions were mainly concentrated in the right temporal lobe. Furthermore, the connectivities between brain regions involved in P50 processing of HC were widely different from those of FESZ and UHR. SIGNIFICANCE Our results indicate that P50 suppression deficits in schizophrenia may be due to both aberrant baseline sensory perception and adaptation to repeated stimulus. Our findings provide new insight into the mechanisms of P50 suppression in the early stage of schizophrenia.
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Affiliation(s)
- Qi Chang
- BeiHang University School of Biological Science and Medical Engineering, Xueyuan Road 37#, Haidian district, Beijing, 100191, P.R. China, Beijing, 100191, CHINA
| | - Cancheng Li
- School of Biological and Medical Engineering , Beihang University, Xueyuan Road 37#, Haidian district, Beijing, Beijing, 100083, CHINA
| | - Jicong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37#, Haidian district, Beijing, Beijing, 100083, CHINA
| | - Chuanyue Wang
- Beijing An Ding Hospital, 5 Ankang Hutong, Dewai Avenue, Xicheng District, Beijing, Beijing, 100088, CHINA
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Dong T, Huang Q, Huang S, Xin J, Jia Q, Gao Y, Shen H, Tang Y, Zhang H. Identification of Methamphetamine Abstainers by Resting-State Functional Magnetic Resonance Imaging. Front Psychol 2021; 12:717519. [PMID: 34526937 PMCID: PMC8435858 DOI: 10.3389/fpsyg.2021.717519] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/09/2021] [Indexed: 11/19/2022] Open
Abstract
Methamphetamine (MA) can cause brain structural and functional impairment, but there are few studies on whether this difference will sustain on MA abstainers. The purpose of this study is to investigate the correlation of brain networks in MA abstainers. In this study, 47 people detoxified for at least 14 months and 44 normal people took a resting-state functional magnetic resonance imaging (RS-fMRI) scan. A dynamic (i.e., time-varying) functional connectivity (FC) is obtained by applying sliding windows in the time courses on the independent components (ICs). The windowed correlation data for each IC were then clustered by k-means. The number of subjects in each cluster was used as a new feature for individual identification. The results show that the classifier achieved satisfactory performance (82.3% accuracy, 77.7% specificity, and 85.7% sensitivity). We find that there are significant differences in the brain networks of MA abstainers and normal people in the time domain, but the spatial differences are not obvious. Most of the altered functional connections (time-varying) are identified to be located at dorsal default mode network. These results have shown that changes in the correlation of the time domain may play an important role in identifying MA abstainers. Therefore, our findings provide valuable insights in the identification of MA and elucidate the pathological mechanism of MA from a resting-state functional integration point of view.
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Affiliation(s)
- Tingting Dong
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Qiuping Huang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Institute of Mental Health of Central South University, Chinese National Technology Institute on Mental Disorders, Changsha, China
| | - Shucai Huang
- The Fourth People’s Hospital of Wuhu, Wuhu, China
| | - Jiang Xin
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Qiaolan Jia
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Yang Gao
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hongxian Shen
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Institute of Mental Health of Central South University, Chinese National Technology Institute on Mental Disorders, Changsha, China
| | - Yan Tang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hao Zhang
- School of Computer Science and Engineering, Central South University, Changsha, China
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9
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Jing W, Xia Y, Li M, Cui Y, Chen M, Xue M, Guo D, Biswal BB, Yao D. State-independent and state-dependent patterns in the rat default mode network. Neuroimage 2021; 237:118148. [PMID: 33984491 DOI: 10.1016/j.neuroimage.2021.118148] [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: 02/03/2021] [Revised: 04/04/2021] [Accepted: 05/04/2021] [Indexed: 10/21/2022] Open
Abstract
Resting-state studies have typically assumed constant functional connectivity (FC) between brain regions, and these parameters of interest provide meaningful descriptions of the functional organization of the brain. A number of studies have recently provided evidence pointing to dynamic FC fluctuations in the resting brain, especially in higher-order regions such as the default mode network (DMN). The neural activities underlying dynamic FC remain poorly understood. Here, we recorded electrophysiological signals from DMN regions in freely behaving rats. The dynamic FCs between signals within the DMN were estimated by the phase locking value (PLV) method with sliding time windows across vigilance states [quiet wakefulness (QW) and slow-wave and rapid eye movement sleep (SWS and REMS)]. Factor analysis was then performed to reveal the hidden patterns within the DMN. We identified distinct spatial FC patterns according to the similarities between their temporal dynamics. Interestingly, some of these patterns were vigilance state-dependent, while others were independent across states. The temporal contributions of these patterns fluctuated over time, and their interactive relationships were different across vigilance states. These spatial patterns with dynamic temporal contributions and combinations may offer a flexible framework for efficiently integrating information to support cognition and behavior. These findings provide novel insights into the dynamic functional organization of the rat DMN.
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Affiliation(s)
- Wei Jing
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan 4030030, China
| | - Yang Xia
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China
| | - Yan Cui
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China
| | - Mingming Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Miaomiao Xue
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China
| | - Daqing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07103, United States.
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China.
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10
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Schumacher J, Taylor JP, Hamilton CA, Firbank M, Donaghy PC, Roberts G, Allan L, Durcan R, Barnett N, O'Brien JT, Thomas AJ. Functional connectivity in mild cognitive impairment with Lewy bodies. J Neurol 2021; 268:4707-4720. [PMID: 33928432 PMCID: PMC8563567 DOI: 10.1007/s00415-021-10580-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/23/2021] [Accepted: 04/24/2021] [Indexed: 12/13/2022]
Abstract
Previous resting-state fMRI studies in dementia with Lewy bodies have described changes in functional connectivity in networks related to cognition, motor function, and attention as well as alterations in connectivity dynamics. However, whether these changes occur early in the course of the disease and are already evident at the stage of mild cognitive impairment is not clear. We studied resting-state fMRI data from 31 patients with mild cognitive impairment with Lewy bodies compared to 28 patients with mild cognitive impairment due to Alzheimer’s disease and 24 age-matched controls. We compared the groups with respect to within- and between-network functional connectivity. Additionally, we applied two different approaches to study dynamic functional connectivity (sliding-window analysis and leading eigenvector dynamic analysis). We did not find any significant changes in the mild cognitive impairment groups compared to controls and no differences between the two mild cognitive impairment groups, using static as well as dynamic connectivity measures. While patients with mild cognitive impairment with Lewy bodies already show clear functional abnormalities on EEG measures, the fMRI analyses presented here do not appear to be sensitive enough to detect such early and subtle changes in brain function in these patients.
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Affiliation(s)
- Julia Schumacher
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK.
| | - John-Paul Taylor
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - Calum A Hamilton
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - Michael Firbank
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - Paul C Donaghy
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - Gemma Roberts
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - Louise Allan
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK.,Institute of Health Research, University of Exeter, Exeter, UK
| | - Rory Durcan
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - Nicola Barnett
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge School of Medicine, Cambridge, CB2 0SP, UK
| | - Alan J Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
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11
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Huang H, Zhang J, Zhu L, Tang J, Lin G, Kong W, Lei X, Zhu L. EEG-Based Sleep Staging Analysis with Functional Connectivity. SENSORS (BASEL, SWITZERLAND) 2021; 21:1988. [PMID: 33799850 PMCID: PMC7999974 DOI: 10.3390/s21061988] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/26/2021] [Accepted: 03/08/2021] [Indexed: 12/20/2022]
Abstract
Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To explore the electroencephalography (EEG)-based brain mechanisms of sleep stages through functional connectivity, especially from different frequency bands, we applied phase-locked value (PLV) to build the functional connectivity network and analyze the brain interaction during sleep stages for different frequency bands. Then, we performed the feature-level, decision-level and hybrid fusion methods to discuss the performance of different frequency bands for sleep stages. The results show that (1) PLV increases in the lower frequency band (delta and alpha bands) and vice versa during different stages of non-rapid eye movement (NREM); (2) alpha band shows a better discriminative ability for sleeping stages; (3) the classification accuracy of feature-level fusion (six frequency bands) reaches 96.91% and 96.14% for intra-subject and inter-subjects respectively, which outperforms decision-level and hybrid fusion methods.
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Affiliation(s)
- Hui Huang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Jianhai Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Li Zhu
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Jiajia Tang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Guang Lin
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Wanzeng Kong
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China;
- Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing 400715, China
| | - Lei Zhu
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China;
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12
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Higher-order sensorimotor circuit of the brain's global network supports human consciousness. Neuroimage 2021; 231:117850. [PMID: 33582277 DOI: 10.1016/j.neuroimage.2021.117850] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 12/29/2020] [Accepted: 02/08/2021] [Indexed: 12/17/2022] Open
Abstract
Consciousness is a mental characteristic of the human mind, whose exact neural features remain unclear. We aimed to identify the critical nodes within the brain's global functional network that support consciousness. To that end, we collected a large fMRI resting state dataset with subjects in at least one of the following three consciousness states: preserved (including the healthy awake state, and patients with a brain injury history (BI) that is fully conscious), reduced (including the N1-sleep state, and minimally conscious state), and lost (including the N3-sleep state, anesthesia, and unresponsive wakefulness state). We also included a unique dataset of subjects in rapid eye movement sleep state (REM-sleep) to test for the presence of consciousness with minimum movements and sensory input. To identify critical nodes, i.e., hubs, within the brain's global functional network, we used a graph-theoretical measure of degree centrality conjoined with ROI-based functional connectivity. Using these methods, we identified various higher-order sensory and motor regions including the supplementary motor area, bilateral supramarginal gyrus (part of inferior parietal lobule), supragenual/dorsal anterior cingulate cortex, and left middle temporal gyrus, that could be important hubs whose degree centrality was significantly reduced when consciousness was reduced or absent. Additionally, we identified a sensorimotor circuit, in which the functional connectivity among these regions was significantly correlated with levels of consciousness across the different groups, and remained present in the REM-sleep group. Taken together, we demonstrated that regions forming a higher-order sensorimotor integration circuit are involved in supporting consciousness within the brain's global functional network. That offers novel and more mechanism-guided treatment targets for disorders of consciousness.
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13
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Jiang W, Zhang H, Zeng L, Shen H, Qin J, Thung K, Yap P, Liu H, Hu D, Wang W, Shen D. Dynamic neural circuit disruptions associated with antisocial behaviors. Hum Brain Mapp 2021; 42:329-344. [PMID: 33064332 PMCID: PMC7776000 DOI: 10.1002/hbm.25225] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 08/28/2020] [Accepted: 09/22/2020] [Indexed: 12/14/2022] Open
Abstract
Antisocial behavior (ASB) is believed to have neural substrates; however, the association between ASB and functional brain networks remains unclear. The temporal variability of the functional connectivity (or dynamic FC) derived from resting-state functional MRI has been suggested as a useful metric for studying abnormal behaviors including ASB. This is the first study using low-frequency fluctuations of the dynamic FC to unravel potential system-level neural correlates with ASB. Specifically, we individually associated the dynamic FC patterns with the ASB scores (measured by Antisocial Process Screening Device) of the male offenders (age: 23.29 ± 3.36 years) based on machine learning. Results showed that the dynamic FCs were associated with individual ASB scores. Moreover, we found that it was mainly the inter-network dynamic FCs that were negatively associated with the ASB severity. Three major high-order cognitive functional networks and the sensorimotor network were found to be more associated with ASB. We further found that impaired behavior in the ASB subjects was mainly associated with decreased FC dynamics in these networks, which may explain why ASB subjects usually have impaired executive control and emotional processing functions. Our study shows that temporal variation of the FC could be a promising tool for ASB assessment, treatment, and prevention.
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Affiliation(s)
- Weixiong Jiang
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- Department of Information Science and EngineeringHunan First Normal UniversityChangshaHunanChina
| | - Han Zhang
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Ling‐Li Zeng
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaHunanChina
| | - Hui Shen
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaHunanChina
| | - Jian Qin
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaHunanChina
| | - Kim‐Han Thung
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Pew‐Thian Yap
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Huasheng Liu
- Department of Radiology, The Third Xiangya HospitalCentral South UniversityChangshaHunanChina
| | - Dewen Hu
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaHunanChina
| | - Wei Wang
- Department of Radiology, The Third Xiangya HospitalCentral South UniversityChangshaHunanChina
| | - Dinggang Shen
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- Department of Artificial IntelligenceKorea UniversitySeoulSouth Korea
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14
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Fan L, Zhong Q, Qin J, Li N, Su J, Zeng LL, Hu D, Shen H. Brain parcellation driven by dynamic functional connectivity better capture intrinsic network dynamics. Hum Brain Mapp 2020; 42:1416-1433. [PMID: 33283954 PMCID: PMC7927310 DOI: 10.1002/hbm.25303] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 11/19/2020] [Accepted: 11/19/2020] [Indexed: 01/04/2023] Open
Abstract
Until now, dynamic functional connectivity (dFC) based on functional magnetic resonance imaging is typically estimated on a set of predefined regions of interest (ROIs) derived from an anatomical or static functional atlas which follows an implicit assumption of functional homogeneity within ROIs underlying temporal fluctuation of functional coupling, potentially leading to biases or underestimation of brain network dynamics. Here, we presented a novel computational method based on dynamic functional connectivity degree (dFCD) to derive meaningful brain parcellations that can capture functional homogeneous regions in temporal variance of functional connectivity. Several spatially distributed but functionally meaningful areas that are well consistent with known intrinsic connectivity networks were identified through independent component analysis (ICA) of time‐varying dFCD maps. Furthermore, a systematical comparison with commonly used brain atlases, including the Anatomical Automatic Labeling template, static ICA‐driven parcellation and random parcellation, demonstrated that the ROI‐definition strategy based on the proposed dFC‐driven parcellation could better capture the interindividual variability in dFC and predict observed individual cognitive performance (e.g., fluid intelligence, cognitive flexibility, and sustained attention) based on chronnectome. Together, our findings shed new light on the functional organization of resting brains at the timescale of seconds and emphasized the significance of a dFC‐driven and voxel‐wise functional homogeneous parcellation for network dynamics analyses in neuroscience.
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Affiliation(s)
- Liangwei Fan
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Qi Zhong
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Jian Qin
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Na Li
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jianpo Su
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
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15
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Maleki Balajoo S, Asemani D, Khadem A, Soltanian-Zadeh H. Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions. Hum Brain Mapp 2020; 41:4264-4287. [PMID: 32643845 PMCID: PMC7502846 DOI: 10.1002/hbm.25124] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 06/21/2020] [Accepted: 06/22/2020] [Indexed: 11/10/2022] Open
Abstract
To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel-reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs-fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered-SWC (T-SWC) with different window lengths) based on both simulated and real rs-fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T-SWC (p-value = .001) and SWC (p-value <.001), respectively. Results of these different methods applied on real rs-fMRI data were investigated for two aspects: calculating the similarity between identified mean dynamic pattern and identifying dynamic pattern in default mode network (DMN). In 68% of subjects, the results of T-SWC with window length of 100 s, among different window lengths, demonstrated the highest similarity to those of KELLER. With regards to DMN, KELLER estimated previously reported dynamic connection pairs between dorsal and ventral DMN while SWC-based method was unable to detect these dynamic connections.
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Affiliation(s)
- Somayeh Maleki Balajoo
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Davud Asemani
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Ali Khadem
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Hamid Soltanian-Zadeh
- CIPCE, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.,School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.,Radiology Image Analysis Lab, Henry Ford Health System, Detroit, Michigan, USA
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16
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Ashinoff BK, Mayhew SD, Mevorach C. The same, but different: Preserved distractor suppression in old age is implemented through an age-specific reactive ventral fronto-parietal network. Hum Brain Mapp 2020; 41:3938-3955. [PMID: 32573907 PMCID: PMC7469802 DOI: 10.1002/hbm.25097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/12/2020] [Accepted: 06/02/2020] [Indexed: 01/15/2023] Open
Abstract
Previous studies have shown age-related impairments in the ability to suppress salient distractors. One possibility is that this is mediated by age-related impairments in the recruitment of the left intraparietal sulcus (Left IPS), which has been shown to mediate the suppression of salient distractors in healthy, young participants. Alternatively, this effect may be due to a shift in engagement from proactive control to reactive control, possibly to compensate for age-related impairments in proactive control. Another possibility is that this is due to changes in the functional specificity of brain regions that mediate salience suppression, expressed in changes in spontaneous connectivity of these regions. We assessed these possibilities by having participants engage in a proactive distractor suppression task while in an fMRI scanner. Although we did not find any age-related differences in behavior, the young (N = 15) and older (N = 15) cohorts engaged qualitatively distinctive brain networks to complete the task. Younger participants engaged the predicted proactive control network, including the Left IPS. On the other hand, older participants simultaneously engaged both a proactive and a reactive network, but this was not a consequence of reduced network specificity as resting state functional connectivity was largely comparable in both age groups. Furthermore, improved behavioral performance for older adults was associated with increased resting state functional connectivity between these two networks. Overall, the results of this study suggest that age-related differences in the recruitment of a left lateralized ventral fronto-parietal network likely reflect the specific recruitment of reactive control mechanisms for distractor inhibition.
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Affiliation(s)
- Brandon K. Ashinoff
- Centre for Human Brain Health (CHBH), School of PsychologyUniversity of BirminghamEdgbastonUK
| | - Stephen D. Mayhew
- Centre for Human Brain Health (CHBH), School of PsychologyUniversity of BirminghamEdgbastonUK
| | - Carmel Mevorach
- Centre for Human Brain Health (CHBH), School of PsychologyUniversity of BirminghamEdgbastonUK
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17
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Pasquini L, Toller G, Staffaroni A, Brown JA, Deng J, Lee A, Kurcyus K, Shdo SM, Allen I, Sturm VE, Cobigo Y, Borghesani V, Battistella G, Gorno-Tempini ML, Rankin KP, Kramer J, Rosen HH, Miller BL, Seeley WW. State and trait characteristics of anterior insula time-varying functional connectivity. Neuroimage 2020; 208:116425. [PMID: 31805382 PMCID: PMC7225015 DOI: 10.1016/j.neuroimage.2019.116425] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 11/28/2019] [Accepted: 11/30/2019] [Indexed: 12/12/2022] Open
Abstract
The human anterior insula (aINS) is a topographically organized brain region, in which ventral portions contribute to socio-emotional function through limbic and autonomic connections, whereas the dorsal aINS contributes to cognitive processes through frontal and parietal connections. Open questions remain, however, regarding how aINS connectivity varies over time. We implemented a novel approach combining seed-to-whole-brain sliding-window functional connectivity MRI and k-means clustering to assess time-varying functional connectivity of aINS subregions. We studied three independent large samples of healthy participants and longitudinal datasets to assess inter- and intra-subject stability, and related aINS time-varying functional connectivity profiles to dispositional empathy. We identified four robust aINS time-varying functional connectivity modes that displayed both "state" and "trait" characteristics: while modes featuring connectivity to sensory regions were modulated by eye closure, modes featuring connectivity to higher cognitive and emotional processing regions were stable over time and related to empathy measures.
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Affiliation(s)
- Lorenzo Pasquini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Gianina Toller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Adam Staffaroni
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jersey Deng
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Alex Lee
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Katarzyna Kurcyus
- Department of Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany; Department of Nuclear Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany
| | | | - Isabel Allen
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Virginia E Sturm
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Yann Cobigo
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Valentina Borghesani
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Giovanni Battistella
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | | | - Katherine P Rankin
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Joel Kramer
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Howard H Rosen
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA; Memory and Aging Center, Department of Pathology, University of California, San Francisco, CA, USA.
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18
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Gifford G, Crossley N, Kempton MJ, Morgan S, Dazzan P, Young J, McGuire P. Resting state fMRI based multilayer network configuration in patients with schizophrenia. Neuroimage Clin 2020; 25:102169. [PMID: 32032819 PMCID: PMC7005505 DOI: 10.1016/j.nicl.2020.102169] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 12/18/2019] [Accepted: 01/10/2020] [Indexed: 02/06/2023]
Abstract
Novel methods for measuring large-scale dynamic brain organisation are needed to provide new biomarkers of schizophrenia. Using a method for modelling dynamic modular organisation (Mucha et al., 2010), evidence suggests higher 'flexibility' (switching between multilayer network communities) to be a feature of schizophrenia (Braun et al., 2016). The current study compared flexibility between 55 patients with schizophrenia and 72 controls (the COBRE Dataset). In addition, novel methods of 'between resting state network synchronisation' (BRSNS) and the probability of transition from one community to another were used to further describe group differences in dynamic community structure. There was significantly higher schizophrenia group flexibility scores in cerebellar (F (1124) = 9.33, p (FDR) = 0.017), subcortical (F (1124) = 13.14, p (FDR) = 0.005), and fronto-parietal task control (F (1124) = 7.19, p (FDR) = 0.033) resting state networks (RSNs), as well as in the left thalamus (MNI XYZ: -2, -13, 12; F(1, 124) = 17.1, p (FDR) < 0.001) and the right crus I (MNI XYZ: 35, -67, -34; F (1, 124) = 19.65, p (FDR) < 0.001). Flexibility in the left thalamus reflected transitions between communities covering default mode and sensory-somatomotor RSNs. BRSNS scores suggested altered dynamic inter-RSN modular configuration in schizophrenia. This study suggests less stable community structure in a schizophrenia group at an RSN and node level and provides novel methods of exploring dynamic community structure. Mediation of group differences by mean time window correlation did however suggest flexibility to be no better as a schizophrenia biomarker than simpler measures and a range of methodological choices affected results.
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Affiliation(s)
- George Gifford
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK.
| | - Nicolas Crossley
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK; Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Diagonal Paraguay 362, Santiago 8330077, Chile
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Sarah Morgan
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK; The Alan Turing Institute, London NW1 2DB, UK
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Jonathan Young
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
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19
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Goldstein-Piekarski AN, Holt-Gosselin B, O'Hora K, Williams LM. Integrating sleep, neuroimaging, and computational approaches for precision psychiatry. Neuropsychopharmacology 2020; 45:192-204. [PMID: 31426055 PMCID: PMC6879628 DOI: 10.1038/s41386-019-0483-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 06/21/2019] [Accepted: 07/22/2019] [Indexed: 12/13/2022]
Abstract
In advancing precision psychiatry, we focus on what imaging technology and computational approaches offer for the future of diagnostic subtyping and personalized tailoring of interventions for sleep impairment in mood and anxiety disorders. Current diagnostic criteria for mood and anxiety tend to lump different forms of sleep disturbance together. Parsing the biological features of sleep impairment and brain circuit dysfunction is one approach to identifying subtypes within these disorders that are mechanistically coherent and offer targets for intervention. We focus on two large-scale neural circuits implicated in sleep impairment and in mood and anxiety disorders: the default mode network and negative affective network. Through a synthesis of existing knowledge about these networks, we pose a testable framework for understanding how hyper- versus hypo-engagement of these networks may underlie distinct features of mood and sleep impairment. Within this framework we consider whether poor sleep quality may have an explanatory role in previously observed associations between network dysfunction and mood symptoms. We expand this framework to future directions including the potential for connecting circuit-defined subtypes to more distal features derived from digital phenotyping and wearable technologies, and how new discovery may be advanced through machine learning approaches.
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Affiliation(s)
- Andrea N Goldstein-Piekarski
- Department of Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Road, Stanford, CA, 94305, USA
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA
| | - Bailey Holt-Gosselin
- Department of Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Road, Stanford, CA, 94305, USA
| | - Kathleen O'Hora
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Road, Stanford, CA, 94305, USA.
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA.
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20
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Yuan Y, Zhang L, Li L, Huang G, Anter A, Liang Z, Zhang Z. Distinct dynamic functional connectivity patterns of pain and touch thresholds: A resting-state fMRI study. Behav Brain Res 2019; 375:112142. [DOI: 10.1016/j.bbr.2019.112142] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 07/07/2019] [Accepted: 08/02/2019] [Indexed: 02/07/2023]
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21
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Yang Y, Wang L, Lei Y, Zhu Y, Shen H. Manifold Learning of Dynamic Functional Connectivity Reliably Identifies Functionally Consistent Coupling Patterns in Human Brains. Brain Sci 2019; 9:E309. [PMID: 31689958 PMCID: PMC6895885 DOI: 10.3390/brainsci9110309] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 10/31/2019] [Accepted: 11/01/2019] [Indexed: 12/21/2022] Open
Abstract
Most previous work on dynamic functional connectivity (dFC) has focused on analyzing temporal traits of functional connectivity (similar coupling patterns at different timepoints), dividing them into functional connectivity states and detecting their between-group differences. However, the coherent functional connectivity of brain activity among the temporal dynamics of functional connectivity remains unknown. In the study, we applied manifold learning of local linear embedding to explore the consistent coupling patterns (CCPs) that reflect functionally homogeneous regions underlying dFC throughout the entire scanning period. By embedding the whole-brain functional connectivity in a low-dimensional manifold space based on the Human Connectome Project (HCP) resting-state data, we identified ten stable patterns of functional coupling across regions that underpin the temporal evolution of dFC. Moreover, some of these CCPs exhibited significant neurophysiological meaning. Furthermore, we apply this method to HCP rsfMR and tfMRI data as well as sleep-deprivation data and found that the topological organization of these low-dimensional structures has high potential for predicting sleep-deprivation states (classification accuracy of 92.3%) and task types (100% identification for all seven tasks).In summary, this work provides a methodology for distilling coherent low-dimensional functional connectivity structures in complex brain dynamics that play an important role in performing tasks or characterizing specific states of the brain.
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Affiliation(s)
- Yuyuan Yang
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.
| | - Lubin Wang
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing 100850, China.
| | - Yu Lei
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing 100850, China.
| | - Yuyang Zhu
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing 100850, China.
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.
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22
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Xu T, Chen Z, Feng T. The preference for future outcomes correlates with the temporal variability of functional connectivity among brain regions. Behav Brain Res 2019; 375:112111. [PMID: 31404558 DOI: 10.1016/j.bbr.2019.112111] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 07/19/2019] [Accepted: 07/23/2019] [Indexed: 02/06/2023]
Abstract
People inevitably make decisions between short-term and long-term consequences across domains like education, health and economics. In this kind of decision, the tendency to discount the value of later-larger rewards with increasing delays is defined as delay discounting (DD). A recent review has suggested that three neural systems which respectively responsible for valuation, prospection and cognitive control (e.g., ventromedial prefrontal cortex [vmPFC], hippocampus, precuneus and dorsolateral prefrontal cortex [dlPFC]) could interact with each other flexibly to have impacts on DD. However, to date, there is little attention paid on the connection between the DD and the dynamic interaction of brain regions.To tackle this issue, we investigate the relationship between the DD and the time-varying connectivity among brain regions in two samples of young adults. Results in sample 1 found that the DD was negatively correlated with the temporal variability of functional connectivity [FC] between the vmPFC and precuneus, and between the vmPFC and the left superior frontal gyrus. And the temporal variabilities of FC between the ventral striatum and right dlPFC, and between the ventral striatum and dorsomedial prefrontal cortex were also negatively related to DD. Furthermore, the main results were well replicated and validated in another sample using different analysis parameters. Overall, our findings reveal that temporal fluctuation of FC within default mode and fronto-striatal circuits can favor for prospecting future, cognitive control and valuation of delayed incentives, and this flexible connectivity patterns generally have association with preference for future outcomes.
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Affiliation(s)
- Ting Xu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Zhiyi Chen
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Tingyong Feng
- Faculty of Psychology, Southwest University, Chongqing, China; Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, China.
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23
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Chowdhury MEH, Khandakar A, Mullinger KJ, Al-Emadi N, Bowtell R. Simultaneous EEG-fMRI: Evaluating the Effect of the EEG Cap-Cabling Configuration on the Gradient Artifact. Front Neurosci 2019; 13:690. [PMID: 31354408 PMCID: PMC6635558 DOI: 10.3389/fnins.2019.00690] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 06/18/2019] [Indexed: 01/11/2023] Open
Abstract
Electroencephalography (EEG) data recorded during simultaneous EEG-fMRI experiments are contaminated by large gradient artifacts (GA). The amplitude of the GA depends on the area of the wire loops formed by the EEG leads, as well as on the rate of switching of the magnetic field gradients, which are essential for MR imaging. Average artifact subtraction (AAS), the most commonly used method for GA correction, relies on the EEG amplifier having a large enough dynamic range to characterize the artifact voltages. Low-pass filtering (250 Hz cut-off) is generally used to attenuate the high-frequency voltage fluctuations of the GA, but even with this precaution channel saturation can occur, particularly during acquisition of high spatial resolution MRI data. Previous work has shown that the ribbon cable, used to connect the EEG cap and amplifier, makes a significant contribution to the GA, since the cable geometry produces large effective wire-loop areas. However, by appropriately connecting the wires of the ribbon cable to the EEG cap it should be possible to minimize the overall range and root mean square (RMS) amplitude of the GA by producing partial cancelation of the cap and cable contributions. Here by modifying the connections of the EEG cap to a 1 m ribbon cable we were able to reduce the range of the GA for a high-resolution coronal echo planar Imaging (EPI) acquisition by a factor of ∼ 1.6 and by a factor of ∼ 1.15 for a standard axial EPI acquisition. These changes could potentially be translated into a reduction in the required dynamic range, an increase in the EEG bandwidth or an increase in the achievable image resolution without saturation, all of which could be beneficially exploited in EEG-fMRI studies. The re-wiring could also prevent the system from saturating when small subject movements occur using the standard recording bandwidth.
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Affiliation(s)
- Muhammad E H Chowdhury
- Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar
| | - Karen J Mullinger
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom.,Birmingham University Imaging Centre, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Nasser Al-Emadi
- Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
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24
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Zhao Z, Yao S, Li K, Sindermann C, Zhou F, Zhao W, Li J, Lührs M, Goebel R, Kendrick KM, Becker B. Real-Time Functional Connectivity-Informed Neurofeedback of Amygdala-Frontal Pathways Reduces Anxiety. PSYCHOTHERAPY AND PSYCHOSOMATICS 2019; 88:5-15. [PMID: 30699438 DOI: 10.1159/000496057] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 12/03/2018] [Indexed: 11/19/2022]
Abstract
BACKGROUND Deficient emotion regulation and exaggerated anxiety represent a major transdiagnostic psychopathological marker. On the neural level these deficits have been closely linked to impaired, yet treatment-sensitive, prefrontal regulatory control over the amygdala. Gaining direct control over these pathways could therefore provide an innovative and promising intervention to regulate exaggerated anxiety. To this end the current proof-of-concept study evaluated the feasibility, functional relevance and maintenance of a novel connectivity-informed real-time fMRI neurofeedback training. METHODS In a randomized crossover sham-controlled design, 26 healthy subjects with high anxiety underwent real-time fMRI-guided neurofeedback training to enhance connectivity between the ventrolateral prefrontal cortex (vlPFC) and the amygdala (target pathway) during threat exposure. Maintenance of regulatory control was assessed after 3 days and in the absence of feedback. Training-induced changes in functional connectivity of the target pathway and anxiety ratings served as primary outcomes. RESULTS Training of the target, yet not the sham control, pathway significantly increased amygdala-vlPFC connectivity and decreased levels of anxiety. Stronger connectivity increases were significantly associated with higher anxiety reduction on the group level. At the follow-up, volitional control over the target pathway was maintained in the absence of feedback. CONCLUSIONS The present results demonstrate for the first time that successful self-regulation of amygdala-prefrontal top-down regulatory circuits may represent a novel intervention to control anxiety. As such, the present findings underscore both the critical contribution of amygdala-prefrontal circuits to emotion regulation and the therapeutic potential of connectivity-informed real-time neurofeedback.
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Affiliation(s)
- Zhiying Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuxia Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Keshuang Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Feng Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Weihua Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianfu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Michael Lührs
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,
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25
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Kung YC, Li CW, Chen S, Chen SCJ, Lo CYZ, Lane TJ, Biswal B, Wu CW, Lin CP. Instability of brain connectivity during nonrapid eye movement sleep reflects altered properties of information integration. Hum Brain Mapp 2019; 40:3192-3202. [PMID: 30941797 DOI: 10.1002/hbm.24590] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 03/05/2019] [Accepted: 03/11/2019] [Indexed: 02/01/2023] Open
Abstract
Nonrapid eye movement (NREM) sleep is associated with fading consciousness in humans. Recent neuroimaging studies have demonstrated the spatiotemporal alterations of the brain functional connectivity (FC) in NREM sleep, suggesting the changes of information integration in the sleeping brain. However, the common stationarity assumption in FC does not satisfactorily explain the dynamic process of information integration during sleep. The dynamic FC (dFC) across brain networks is speculated to better reflect the time-varying information propagation during sleep. Accordingly, we conducted simultaneous EEG-fMRI recordings involving 12 healthy men during sleep and observed dFC across sleep stages using the sliding-window approach. We divided dFC into two aspects: mean dFC (dFCmean ) and variance dFC (dFCvar ). A high dFCmean indicates stable brain network integrity, whereas a high dFCvar indicates instability of information transfer within and between functional networks. For the network-based dFC, the dFCvar were negatively correlated with the dFCmean across the waking and three NREM sleep stages. As sleep deepened, the dFCmean decreased (N0~N1 > N2 > N3), whereas the dFCvar peaked during the N2 stage (N0~N1 < N3 < N2). The highest dFCvar during the N2 stage indicated the unstable synchronizations across the entire brain. In the N3 stage, the overall disrupted network integration was observed through the lowest dFCmean and elevated dFCvar, compared with N0 and N1. Conclusively, when the network specificity (dFCmean ) breaks down, the consciousness dissipates with increasing variability of information exchange (dFCvar ).
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Affiliation(s)
- Yi-Chia Kung
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Chia-Wei Li
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Shuo Chen
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Sharon Chia-Ju Chen
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chun-Yi Z Lo
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Timothy J Lane
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Center, Shuang-Ho Hospital, New Taipei, Taiwan.,Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey.,Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Changwei W Wu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Center, Shuang-Ho Hospital, New Taipei, Taiwan.,Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Ching-Po Lin
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.,Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
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26
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Moehlman TM, de Zwart JA, Chappel-Farley MG, Liu X, McClain IB, Chang C, Mandelkow H, Özbay PS, Johnson NL, Bieber RE, Fernandez KA, King KA, Zalewski CK, Brewer CC, van Gelderen P, Duyn JH, Picchioni D. All-night functional magnetic resonance imaging sleep studies. J Neurosci Methods 2019; 316:83-98. [PMID: 30243817 PMCID: PMC6524535 DOI: 10.1016/j.jneumeth.2018.09.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 08/08/2018] [Accepted: 09/17/2018] [Indexed: 01/24/2023]
Abstract
BACKGROUND Previous functional magnetic resonance imaging (fMRI) sleep studies have been hampered by the difficulty of obtaining extended amounts of sleep in the sleep-adverse environment of the scanner and often have resorted to manipulations such as sleep depriving subjects before scanning. These manipulations limit the generalizability of the results. NEW METHOD The current study is a methodological validation of procedures aimed at obtaining all-night fMRI data in sleeping subjects with minimal exposure to experimentally induced sleep deprivation. Specifically, subjects slept in the scanner on two consecutive nights, allowing the first night to serve as an adaptation night. RESULTS/COMPARISON WITH EXISTING METHOD(S) Sleep scoring results from simultaneously acquired electroencephalography data on Night 2 indicate that subjects (n = 12) reached the full spectrum of sleep stages including slow-wave (M = 52.1 min, SD = 26.5 min) and rapid eye movement (REM, M = 45.2 min, SD = 27.9 min) sleep and exhibited a mean of 2.1 (SD = 1.1) nonREM-REM sleep cycles. CONCLUSIONS It was found that by diligently applying fundamental principles and methodologies of sleep and neuroimaging science, performing all-night fMRI sleep studies is feasible. However, because the two nights of the study were performed consecutively, some sleep deprivation from Night 1 as a cause of the Night 2 results is likely, so consideration should be given to replicating the current study with a washout period. It is envisioned that other laboratories can adopt the core features of this protocol to obtain similar results.
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Affiliation(s)
- Thomas M Moehlman
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
| | - Jacco A de Zwart
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
| | - Miranda G Chappel-Farley
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
| | - Xiao Liu
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA; Department of Biomedical Engineering, Pennsylvania State University, USA
| | - Irene B McClain
- Office of the Clinical Director, National Institute of Neurological Disorders and Stroke, USA
| | - Catie Chang
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA; Department of Electrical Engineering and Computer Science, Vanderbilt University, USA
| | - Hendrik Mandelkow
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
| | - Pinar S Özbay
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
| | - Nicholas L Johnson
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
| | - Rebecca E Bieber
- Audiology Unit, National Institute on Deafness and Other Communication Disorders, USA
| | - Katharine A Fernandez
- Section on Sensory Cell Biology, National Institute on Deafness and Other Communication Disorders, USA
| | - Kelly A King
- Audiology Unit, National Institute on Deafness and Other Communication Disorders, USA
| | | | - Carmen C Brewer
- Audiology Unit, National Institute on Deafness and Other Communication Disorders, USA
| | - Peter van Gelderen
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
| | - Jeff H Duyn
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA
| | - Dante Picchioni
- Advanced Magnetic Resonance Imaging Section, National Institute of Neurological Disorders and Stroke, USA; Section on Neuroadaptation and Protein Metabolism, National Institute of Mental Health, USA.
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27
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Duc NT, Lee B. Microstate functional connectivity in EEG cognitive tasks revealed by a multivariate Gaussian hidden Markov model with phase locking value. J Neural Eng 2019; 16:026033. [DOI: 10.1088/1741-2552/ab0169] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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28
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Mokhtari F, Akhlaghi MI, Simpson SL, Wu G, Laurienti PJ. Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state. Neuroimage 2019; 189:655-666. [PMID: 30721750 DOI: 10.1016/j.neuroimage.2019.02.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 01/10/2019] [Accepted: 02/01/2019] [Indexed: 12/22/2022] Open
Abstract
The sliding window correlation (SWC) analysis is a straightforward and common approach for evaluating dynamic functional connectivity. Despite the fact that sliding window analyses have been long used, there are still considerable technical issues associated with the approach. A great effort has recently been dedicated to investigate the window setting effects on dynamic connectivity estimation. In this direction, tapered windows have been proposed to alleviate the effect of sudden changes associated with the edges of rectangular windows. Nevertheless, the majority of the windows exploited to estimate brain connectivity tend to suppress dynamic correlations, especially those with faster variations over time. Here, we introduced a window named modulated rectangular (mRect) to address the suppressing effect associated with the conventional windows. We provided a frequency domain analysis using simulated time series to investigate how sliding window analysis (using the regular window functions, e.g. rectangular and tapered windows) may lead to unwanted spectral modulations, and then we showed how this issue can be alleviated through the mRect window. Moreover, we created simulated dynamic network data with altering states over time using simulated fMRI time series, to examine the performance of different windows in tracking network states. We quantified the state identification rate of different window functions through the Jaccard index, and observed superior performance of the mRect window compared to the conventional window functions. Overall, the proposed window function provides an approach that improves SWC estimations, and thus the subsequent inferences and interpretations based on the connectivity network analyses.
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Affiliation(s)
- Fatemeh Mokhtari
- Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA.
| | | | - Sean L Simpson
- Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA; Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Guorong Wu
- Biomedical Research Imaging Center, Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul J Laurienti
- Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA; Translational Science Center, Wake Forest University, Winston Salem, NC, USA
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29
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Vergara VM, Abrol A, Calhoun VD. An average sliding window correlation method for dynamic functional connectivity. Hum Brain Mapp 2019; 40:2089-2103. [PMID: 30659699 DOI: 10.1002/hbm.24509] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 12/05/2018] [Accepted: 01/02/2019] [Indexed: 01/06/2023] Open
Abstract
Sliding window correlation (SWC) is utilized in many studies to analyze the temporal characteristics of brain connectivity. However, spurious artifacts have been reported in simulated data using this technique. Several suggestions have been made through the development of the SWC technique. Recently, it has been proposed to utilize a SWC window length of 100 s given that the lowest nominal fMRI frequency is 0.01 Hz. The main pitfall is the loss of temporal resolution due to a large window length. In this work, we propose an average sliding window correlation (ASWC) approach that presents several advantages over the SWC. One advantage is the requirement for a smaller window length. This is important because shorter lengths allow for a more accurate estimation of transient dynamicity of functional connectivity. Another advantage is the behavior of ASWC as a tunable high pass filter. We demonstrate the advantages of ASWC over SWC using simulated signals with configurable functional connectivity dynamics. We present analytical models explaining the behavior of ASWC and SWC for several dynamic connectivity cases. We also include a real data example to demonstrate the application of the new method. In summary, ASWC shows lower artifacts and resolves faster transient connectivity fluctuations, resulting in a lower mean square error than in SWC.
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Affiliation(s)
- Victor M Vergara
- The Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico
| | - Anees Abrol
- The Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
| | - Vince D Calhoun
- The Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
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30
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Wilson RS, Mayhew SD, Rollings DT, Goldstone A, Hale JR, Bagshaw AP. Objective and subjective measures of prior sleep-wake behavior predict functional connectivity in the default mode network during NREM sleep. Brain Behav 2019; 9:e01172. [PMID: 30516035 PMCID: PMC6346660 DOI: 10.1002/brb3.1172] [Citation(s) in RCA: 5] [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: 04/20/2018] [Revised: 09/21/2018] [Accepted: 10/29/2018] [Indexed: 12/13/2022] Open
Abstract
INTRODUCTION Prior sleep behavior has been shown to correlate with waking resting-state functional connectivity (FC) in the default mode network (DMN). However, the impact of sleep history on FC during sleep has not been investigated. The aim of this study was to establish whether there is an association between intersubject variability in habitual sleep behaviors and the strength of FC within the regions of the DMN during non-rapid eye movement (NREM) sleep. METHODS Wrist actigraphy and sleep questionnaires were used as objective and subjective measures of habitual sleep behavior, and EEG-functional MRI during NREM sleep was used to quantify sleep. RESULTS There was a significant, regionally specific association between the interindividual variability in objective (total sleep time on the night before scanning) and subjective (Insomnia Severity Index) measures of prior sleep-wake behavior and the strength of DMN FC during subsequent wakefulness and NREM sleep. In several cases, FC was related to sleep measures independently of sleep stage, suggesting that previous sleep history effects sleep FC globally across the stages. CONCLUSIONS This work highlights the need to consider a subject's prior sleep history in studies utilizing FC analysis during wakefulness and sleep, and indicates the complexity of the impact of sleep on the brain both in the short and long term.
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Affiliation(s)
- Rebecca S Wilson
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK.,School of Psychology, University of Birmingham, Birmingham, UK
| | - Stephen D Mayhew
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK.,School of Psychology, University of Birmingham, Birmingham, UK
| | - David T Rollings
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK.,School of Psychology, University of Birmingham, Birmingham, UK.,Department of Neurophysiology, Queen Elizabeth Hospital, Birmingham, UK
| | - Aimee Goldstone
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK.,School of Psychology, University of Birmingham, Birmingham, UK.,Center for Health Sciences, SRI International, Menlo Park, California
| | - Joanne R Hale
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK.,School of Psychology, University of Birmingham, Birmingham, UK.,Clinical Physics and Bioengineering, University Hospital Coventry and Warwickshire, Coventry, UK
| | - Andrew P Bagshaw
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK.,School of Psychology, University of Birmingham, Birmingham, UK
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31
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Daniel AJ, Smith JA, Spencer GS, Jorge J, Bowtell R, Mullinger KJ. Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI. Hum Brain Mapp 2018; 40:578-596. [PMID: 30339731 PMCID: PMC6492138 DOI: 10.1002/hbm.24396] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 08/30/2018] [Accepted: 08/31/2018] [Indexed: 11/25/2022] Open
Abstract
Simultaneous EEG‐fMRI allows multiparametric characterisation of brain function, in principle enabling a more complete understanding of brain responses; unfortunately the hostile MRI environment severely reduces EEG data quality. Simply eliminating data segments containing gross motion artefacts [MAs] (generated by movement of the EEG system and head in the MRI scanner's static magnetic field) was previously believed sufficient. However recently the importance of removal of all MAs has been highlighted and new methods developed. A systematic comparison of the ability to remove MAs and retain underlying neuronal activity using different methods of MA detection and post‐processing algorithms is needed to guide the neuroscience community. Using a head phantom, we recorded MAs while simultaneously monitoring the motion using three different approaches: Reference Layer Artefact Subtraction (RLAS), Moiré Phase Tracker (MPT) markers and Wire Loop Motion Sensors (WLMS). These EEG recordings were combined with EEG responses to simple visual tasks acquired on a subject outside the MRI environment. MAs were then corrected using the motion information collected with each of the methods combined with different analysis pipelines. All tested methods retained the neuronal signal. However, often the MA was not removed sufficiently to allow accurate detection of the underlying neuronal signal. We show that the MA is best corrected using the RLAS combined with post‐processing using a multichannel, recursive least squares (M‐RLS) algorithm. This method needs to be developed further to enable practical utility; thus, WLMS combined with M‐RLS currently provides the best compromise between EEG data quality and practicalities of motion detection.
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Affiliation(s)
- Alexander J Daniel
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - James A Smith
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Glyn S Spencer
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom.,Department of Physics, Loughborough University, Leicestershire, United Kingdom
| | - João Jorge
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Karen J Mullinger
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom.,Birmingham University Imaging Centre, School of Psychology, University of Birmingham, Birmingham, United Kingdom
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32
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Xu H, Shen H, Wang L, Zhong Q, Lei Y, Yang L, Zeng LL, Zhou Z, Hu D, Yang Z. Impact of 36 h of total sleep deprivation on resting-state dynamic functional connectivity. Brain Res 2018; 1688:22-32. [DOI: 10.1016/j.brainres.2017.11.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 10/10/2017] [Accepted: 11/13/2017] [Indexed: 11/28/2022]
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33
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Mashour GA, Hudetz AG. Neural Correlates of Unconsciousness in Large-Scale Brain Networks. Trends Neurosci 2018; 41:150-160. [PMID: 29409683 PMCID: PMC5835202 DOI: 10.1016/j.tins.2018.01.003] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Revised: 12/12/2017] [Accepted: 01/09/2018] [Indexed: 12/21/2022]
Abstract
The biological basis of consciousness is one of the most challenging and fundamental questions in 21st century science. A related pursuit aims to identify the neural correlates and causes of unconsciousness. We review current trends in the investigation of physiological, pharmacological, and pathological states of unconsciousness at the level of large-scale functional brain networks. We focus on the roles of brain connectivity, repertoire, graph-theoretical techniques, and neural dynamics in understanding the functional brain disconnections and reduced complexity that appear to characterize these states. Persistent questions in the field, such as distinguishing true correlates, linking neural scales, and understanding differential recovery patterns, are also addressed.
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Affiliation(s)
- George A Mashour
- Neuroscience Graduate Program, Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, 1500 East Medical Center Drive, Ann Arbor, MI 48109, USA.
| | - Anthony G Hudetz
- Neuroscience Graduate Program, Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, 1500 East Medical Center Drive, Ann Arbor, MI 48109, USA
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34
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Xu H, Su J, Qin J, Li M, Zeng LL, Hu D, Shen H. Impact of global signal regression on characterizing dynamic functional connectivity and brain states. Neuroimage 2018; 173:127-145. [PMID: 29476914 DOI: 10.1016/j.neuroimage.2018.02.036] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Revised: 01/26/2018] [Accepted: 02/17/2018] [Indexed: 01/08/2023] Open
Abstract
Recently, resting-state functional magnetic resonance imaging (fMRI) studies have been extended to explore fluctuations in correlations over shorter timescales, referred to as dynamic functional connectivity (dFC). However, the impact of global signal regression (GSR) on dFC is not well established, despite the intensive investigations of the influence of GSR on static functional connectivity (sFC). This study aimed to examine the effect of GSR on the performance of the sliding-window correlation, a commonly used method for capturing functional connectivity (FC) dynamics based on resting-state fMRI and simultaneous electroencephalograph (EEG)-fMRI data. The results revealed that the impact of GSR on dFC was spatially heterogeneous, with some susceptible regions including the occipital cortex, sensorimotor area, precuneus, posterior insula and superior temporal gyrus, and that the impact was temporally modulated by the mean global signal (GS) magnitude across windows. Furthermore, GSR substantially changed the connectivity structures of the FC states responding to a high GS magnitude, as well as their temporal features, and even led to the emergence of new FC states. Conversely, those FC states marked by obvious anti-correlation structures associated with the default model network (DMN) were largely unaffected by GSR. Finally, we reported an association between the fluctuations in the windowed magnitude of GS and the time-varying EEG power within subjects, which implied changes in mental states underlying GS dynamics. Overall, this study suggested a potential neuropsychological basis, in addition to nuisance sources, for GS dynamics and highlighted the need for caution in applying GSR to sliding-window correlation analyses. At a minimum, the mental fluctuations of an individual subject, possibly related to ongoing vigilance, should be evaluated during the entire scan when the dynamics of FC is estimated.
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Affiliation(s)
- Huaze Xu
- College of Artificial Intelligence, National University of Defense Technology Changsha, Hunan, 410073, China
| | - Jianpo Su
- College of Artificial Intelligence, National University of Defense Technology Changsha, Hunan, 410073, China
| | - Jian Qin
- College of Artificial Intelligence, National University of Defense Technology Changsha, Hunan, 410073, China
| | - Ming Li
- College of Artificial Intelligence, National University of Defense Technology Changsha, Hunan, 410073, China
| | - Ling-Li Zeng
- College of Artificial Intelligence, National University of Defense Technology Changsha, Hunan, 410073, China
| | - Dewen Hu
- College of Artificial Intelligence, National University of Defense Technology Changsha, Hunan, 410073, China
| | - Hui Shen
- College of Artificial Intelligence, National University of Defense Technology Changsha, Hunan, 410073, China.
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35
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Steimke R, Nomi JS, Calhoun VD, Stelzel C, Paschke LM, Gaschler R, Goschke T, Walter H, Uddin LQ. Salience network dynamics underlying successful resistance of temptation. Soc Cogn Affect Neurosci 2017; 12:1928-1939. [PMID: 29048582 PMCID: PMC5716209 DOI: 10.1093/scan/nsx123] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2017] [Revised: 09/28/2017] [Accepted: 10/16/2017] [Indexed: 01/18/2023] Open
Abstract
Self-control and the ability to resist temptation are critical for successful completion of long-term goals. Contemporary models in cognitive neuroscience emphasize the primary role of prefrontal cognitive control networks in aligning behavior with such goals. Here, we use gaze pattern analysis and dynamic functional connectivity fMRI data to explore how individual differences in the ability to resist temptation are related to intrinsic brain dynamics of the cognitive control and salience networks. Behaviorally, individuals exhibit greater gaze distance from target location (e.g. higher distractibility) during presentation of tempting erotic images compared with neutral images. Individuals whose intrinsic dynamic functional connectivity patterns gravitate toward configurations in which salience detection systems are less strongly coupled with visual systems resist tempting distractors more effectively. The ability to resist tempting distractors was not significantly related to intrinsic dynamics of the cognitive control network. These results suggest that susceptibility to temptation is governed in part by individual differences in salience network dynamics and provide novel evidence for involvement of brain systems outside canonical cognitive control networks in contributing to individual differences in self-control.
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Affiliation(s)
- Rosa Steimke
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt - Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
- Berlin School of Mind and Brain
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106
- Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM 87131, USA
| | - Christine Stelzel
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt - Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- International Psychoanalytic University Berlin, Berlin, Germany
| | - Lena M Paschke
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt - Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Gaschler
- Department of Psychology, FernUniversität, Hagen, Hagen, Germany
| | - Thomas Goschke
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt - Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin School of Mind and Brain
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA
- Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, USA
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36
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Shakil S, Billings JC, Keilholz SD, Lee CH. Parametric Dependencies of Sliding Window Correlation. IEEE Trans Biomed Eng 2017; 65:254-263. [PMID: 29035206 DOI: 10.1109/tbme.2017.2762763] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
OBJECTIVE In this paper, we explore the dependence of sliding window correlation (SWC) results on different parameters of correlating signals. The SWC is extensively used to explore the dynamics of functional connectivity (FC) networks using resting-state functional MRI (rsfMRI) scans. These scanned signals often contain multiple amplitudes, frequencies, and phases. However, the exact values of these parameters are unknown. Two recent studies explored the relationship of window length and frequencies (minimum/maximum) in the correlating signals. METHODS We extend the findings of these studies by using two deterministic signals with multiple amplitudes, frequencies, and phases. Afterward, we modulate one of the signals to introduce dynamics (nonstationarity) in their relationship. We also explore the relationship of window length and frequency band for real rsfMRI data. RESULTS For deterministic signals, the spurious fluctuations due to the method itself minimize, and the SWC estimates the stationary correlation when frequencies in the signals have specific relationship. For dynamic relationship also, the undesirable frequencies were removed under specific conditions for the frequencies. For real rsfMRI data, the SWC results varied with frequencies and window length. CONCLUSION In the absence of any "ground truth" for different parameters in real rsfMRI signals, the SWC with a constant window size may not be a reliable method to study the dynamics of the FC. SIGNIFICANCE This study reveals the parametric dependencies of the SWC and its limitation as a method to analyze dynamics of FC networks in the absence of any ground truth.
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37
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Kafashan M, Palanca BJA, Ching S. Dimensionality reduction impedes the extraction of dynamic functional connectivity states from fMRI recordings of resting wakefulness. J Neurosci Methods 2017; 293:151-161. [PMID: 28947263 DOI: 10.1016/j.jneumeth.2017.09.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 09/19/2017] [Accepted: 09/20/2017] [Indexed: 12/29/2022]
Abstract
BACKGROUND Resting wakefulness is not a unitary state, with evidence accumulating that spontaneous reorganization of brain activity can be assayed through functional magnetic resonance imaging (fMRI). The dynamics of correlated fMRI signals among functionally-related brain regions, termed dynamic functional connectivity (dFC), may represent nonstationarity arising from underlying neural processes. However, given the dimensionality and noise inherent in such recordings, seeming fluctuations in dFC could be due to sampling variability or artifacts. NEW METHOD Here, we highlight key methodological considerations when evaluating dFC in resting-state fMRI data. COMPARISON WITH EXISTING METHOD In particular, we demonstrate how dimensionality reduction of fMRI data, a common practice often involving principal component analysis, may give rise to spurious dFC phenomenology due to its effect of decorrelating the underlying time-series. CONCLUSION We formalize a dFC assessment that avoids dimensionality reduction and use it to show the existence of at least two FC states in the resting-state.
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Affiliation(s)
- MohammadMehdi Kafashan
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Ben Julian A Palanca
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA; Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - ShiNung Ching
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA.
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38
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Thompson GJ. Neural and metabolic basis of dynamic resting state fMRI. Neuroimage 2017; 180:448-462. [PMID: 28899744 DOI: 10.1016/j.neuroimage.2017.09.010] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 08/30/2017] [Accepted: 09/06/2017] [Indexed: 02/07/2023] Open
Abstract
Resting state fMRI (rsfMRI) as a technique showed much initial promise for use in psychiatric and neurological diseases where diagnosis and treatment were difficult. To realize this promise, many groups have moved towards examining "dynamic rsfMRI," which relies on the assumption that rsfMRI measurements on short time scales remain relevant to the underlying neural and metabolic activity. Many dynamic rsfMRI studies have demonstrated differences between clinical or behavioral groups beyond what static rsfMRI measured, suggesting a neurometabolic basis. Correlative studies combining dynamic rsfMRI and other physiological measurements have supported this. However, they also indicate multiple mechanisms and, if using correlation alone, it is difficult to separate cause and effect. Hypothesis-driven studies are needed, a few of which have begun to illuminate the underlying neurometabolic mechanisms that shape observed differences in dynamic rsfMRI. While the number of potential noise sources, potential actual neurometabolic sources, and methodological considerations can seem overwhelming, dynamic rsfMRI provides a rich opportunity in systems neuroscience. Even an incrementally better understanding of the neurometabolic basis of dynamic rsfMRI would expand rsfMRI's research and clinical utility, and the studies described herein take the first steps on that path forward.
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Affiliation(s)
- Garth J Thompson
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China.
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39
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Mayhew SD, Bagshaw AP. Dynamic spatiotemporal variability of alpha-BOLD relationships during the resting-state and task-evoked responses. Neuroimage 2017; 155:120-137. [PMID: 28454820 DOI: 10.1016/j.neuroimage.2017.04.051] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 03/27/2017] [Accepted: 04/21/2017] [Indexed: 11/29/2022] Open
Abstract
Accurate characterization of the spatiotemporal relationship between two of the most prominent neuroimaging measures of neuronal activity, the 8-13Hz, occipito-parietal EEG alpha oscillation and the BOLD fMRI signal, must encompass the intrinsically dynamic nature of both alpha power and brain function. Here, during the eyes-open resting state, we use a 16s sliding-window analysis and demonstrate that the mean spatial network of dynamic alpha-BOLD correlations is highly comparable to the static network calculated over six minutes. However, alpha-BOLD correlations showed substantial spatiotemporal variability within-subjects and passed through many different configurations such that the static network was fully represented in only ~10% of 16s epochs, with visual and parietal regions (coherent on average) often opposingly correlated with each other or with alpha. We find that the common assumption of static-alpha BOLD correlations greatly oversimplifies temporal variation in brain network dynamics. Fluctuations in alpha-BOLD coupling significantly depended upon the instantaneous amplitude of alpha power, and primary and lateral visual areas were most strongly negatively correlated with alpha during different alpha power states, possibly suggesting the action of multiple alpha mechanisms. Dynamic alpha-BOLD correlations could not be explained by eye-blinks/movements, head motion or non-neuronal physiological variability. Individual's mean alpha power and frequency were found to contribute to between-subject variability in alpha-BOLD correlations. Additionally, application to a visual stimulation dataset showed that dynamic alpha-BOLD correlations provided functional information pertaining to the brain's response to stimulation by exhibiting spatiotemporal fluctuations related to variability in the trial-by-trial BOLD response magnitude. Significantly weaker visual alpha-BOLD correlations were found both preceding and following small amplitude BOLD response trials compared to large response trials.
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Affiliation(s)
- S D Mayhew
- Birmingham University Imaging Centre (BUIC), School of Psychology, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
| | - A P Bagshaw
- Birmingham University Imaging Centre (BUIC), School of Psychology, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
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40
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Zhang H, Lee A, Qiu A. A posterior-to-anterior shift of brain functional dynamics in aging. Brain Struct Funct 2017; 222:3665-3676. [PMID: 28417233 DOI: 10.1007/s00429-017-1425-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Accepted: 04/10/2017] [Indexed: 10/19/2022]
Abstract
Convergent evidence from task-based functional magnetic resonance imaging (fMRI) studies suggests a posterior-to-anterior shift as an adaptive compensatory scaffolding mechanism for aging. This study aimed to investigate whether brain functional dynamics at rest follow the same scaffolding mechanism for aging using a large Chinese sample aged from 22 to 79 years (n = 277). We defined a probability of brain regions being hubs over a period of time to characterize functional hub dynamic, and defined variability of the functional connectivity to characterize dynamic functional connectivity using resting-state fMRI. Our results revealed that both functional hub dynamics and dynamic functional connectivity posited an age-related posterior-to-anterior shift. Specifically, the posterior brain region showed attenuated dynamics, while the anterior brain regions showed augmented dynamics in aging. Interestingly, our analysis further indicated that the age-related episodic memory decline was associated with the age-related decrease in the brain functional dynamics of the posterior regions. Hence, these findings provided a new dimension to view the scaffolding mechanism for aging based on the brain functional dynamics.
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Affiliation(s)
- Han Zhang
- Department of Biomedical Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Annie Lee
- Department of Biomedical Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore, 117576, Singapore. .,Clinical Imaging Research Center, National University of Singapore, Singapore, 117456, Singapore. .,Singapore Institute for Clinical Sciences, The Agency for Science, Technology and Research, Singapore, 117609, Singapore.
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41
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Gao YR, Ma Y, Zhang Q, Winder AT, Liang Z, Antinori L, Drew PJ, Zhang N. Time to wake up: Studying neurovascular coupling and brain-wide circuit function in the un-anesthetized animal. Neuroimage 2016; 153:382-398. [PMID: 27908788 PMCID: PMC5526447 DOI: 10.1016/j.neuroimage.2016.11.069] [Citation(s) in RCA: 124] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 11/18/2016] [Accepted: 11/27/2016] [Indexed: 01/08/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) has allowed the noninvasive study of task-based and resting-state brain dynamics in humans by inferring neural activity from blood-oxygenation-level dependent (BOLD) signal changes. An accurate interpretation of the hemodynamic changes that underlie fMRI signals depends on the understanding of the quantitative relationship between changes in neural activity and changes in cerebral blood flow, oxygenation and volume. While there has been extensive study of neurovascular coupling in anesthetized animal models, anesthesia causes large disruptions of brain metabolism, neural responsiveness and cardiovascular function. Here, we review work showing that neurovascular coupling and brain circuit function in the awake animal are profoundly different from those in the anesthetized state. We argue that the time is right to study neurovascular coupling and brain circuit function in the awake animal to bridge the physiological mechanisms that underlie animal and human neuroimaging signals, and to interpret them in light of underlying neural mechanisms. Lastly, we discuss recent experimental innovations that have enabled the study of neurovascular coupling and brain-wide circuit function in un-anesthetized and behaving animal models.
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Affiliation(s)
- Yu-Rong Gao
- Neuroscience Graduate Program, Pennsylvania State University, University Park, PA 16802, Unidted States; Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16802, Unidted States
| | - Yuncong Ma
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, Unidted States
| | - Qingguang Zhang
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16802, Unidted States
| | - Aaron T Winder
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16802, Unidted States
| | - Zhifeng Liang
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, Unidted States
| | - Lilith Antinori
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, Unidted States
| | - Patrick J Drew
- Neuroscience Graduate Program, Pennsylvania State University, University Park, PA 16802, Unidted States; Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16802, Unidted States; Department of Neurosurgery, Pennsylvania State University, University Park, PA 16802, Unidted States; Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, Unidted States.
| | - Nanyin Zhang
- Neuroscience Graduate Program, Pennsylvania State University, University Park, PA 16802, Unidted States; Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, Unidted States.
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42
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Shakil S, Lee CH, Keilholz SD. Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states. Neuroimage 2016; 133:111-128. [PMID: 26952197 DOI: 10.1016/j.neuroimage.2016.02.074] [Citation(s) in RCA: 174] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Revised: 02/04/2016] [Accepted: 02/29/2016] [Indexed: 01/12/2023] Open
Abstract
A promising recent development in the study of brain function is the dynamic analysis of resting-state functional MRI scans, which can enhance understanding of normal cognition and alterations that result from brain disorders. One widely used method of capturing the dynamics of functional connectivity is sliding window correlation (SWC). However, in the absence of a "gold standard" for comparison, evaluating the performance of the SWC in typical resting-state data is challenging. This study uses simulated networks (SNs) with known transitions to examine the effects of parameters such as window length, window offset, window type, noise, filtering, and sampling rate on the SWC performance. The SWC time course was calculated for all node pairs of each SN and then clustered using the k-means algorithm to determine how resulting brain states match known configurations and transitions in the SNs. The outcomes show that the detection of state transitions and durations in the SWC is most strongly influenced by the window length and offset, followed by noise and filtering parameters. The effect of the image sampling rate was relatively insignificant. Tapered windows provide less sensitivity to state transitions than rectangular windows, which could be the result of the sharp transitions in the SNs. Overall, the SWC gave poor estimates of correlation for each brain state. Clustering based on the SWC time course did not reliably reflect the underlying state transitions unless the window length was comparable to the state duration, highlighting the need for new adaptive window analysis techniques.
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Affiliation(s)
- Sadia Shakil
- Georgia Institute of Technology, Electrical and Computer Engineering, Atlanta, GA, USA.
| | - Chin-Hui Lee
- Georgia Institute of Technology, Electrical and Computer Engineering, Atlanta, GA, USA.
| | - Shella Dawn Keilholz
- Georgia Institute of Technology, Biomedical Engineering, Atlanta, GA, USA; Emory University, Biomedical Engineering, Atlanta, GA, USA.
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43
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Nomi JS, Farrant K, Damaraju E, Rachakonda S, Calhoun VD, Uddin LQ. Dynamic functional network connectivity reveals unique and overlapping profiles of insula subdivisions. Hum Brain Mapp 2016; 37:1770-87. [PMID: 26880689 DOI: 10.1002/hbm.23135] [Citation(s) in RCA: 128] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Revised: 01/06/2016] [Accepted: 01/26/2016] [Indexed: 12/13/2022] Open
Abstract
The human insular cortex consists of functionally diverse subdivisions that engage during tasks ranging from interoception to cognitive control. The multiplicity of functions subserved by insular subdivisions calls for a nuanced investigation of their functional connectivity profiles. Four insula subdivisions (dorsal anterior, dAI; ventral, VI; posterior, PI; middle, MI) derived using a data-driven approach were subjected to static- and dynamic functional network connectivity (s-FNC and d-FNC) analyses. Static-FNC analyses replicated previous work demonstrating a cognition-emotion-interoception division of the insula, where the dAI is functionally connected to frontal areas, the VI to limbic areas, and the PI and MI to sensorimotor areas. Dynamic-FNC analyses consisted of k-means clustering of sliding windows to identify variable insula connectivity states. The d-FNC analysis revealed that the most frequently occurring dynamic state mirrored the cognition-emotion-interoception division observed from the s-FNC analysis, with less frequently occurring states showing overlapping and unique subdivision connectivity profiles. In two of the states, all subdivisions exhibited largely overlapping profiles, consisting of subcortical, sensory, motor, and frontal connections. Two other states showed the dAI exhibited a unique connectivity profile compared with other insula subdivisions. Additionally, the dAI exhibited the most variable functional connections across the s-FNC and d-FNC analyses, and was the only subdivision to exhibit dynamic functional connections with regions of the default mode network. These results highlight how a d-FNC approach can capture functional dynamics masked by s-FNC approaches, and reveal dynamic functional connections enabling the functional flexibility of the insula across time. Hum Brain Mapp 37:1770-1787, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, Florida
| | - Kristafor Farrant
- Department of Psychology, University of Miami, Coral Gables, Florida
| | | | | | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Department of ECE, the University of New Mexico, Albuquerque, New Mexico
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, Florida.,University of Miami Miller School of Medicine, Neuroscience Program, Miami, Florida
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44
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Shen H, Li Z, Qin J, Liu Q, Wang L, Zeng LL, Li H, Hu D. Changes in functional connectivity dynamics associated with vigilance network in taxi drivers. Neuroimage 2016; 124:367-378. [DOI: 10.1016/j.neuroimage.2015.09.010] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 08/23/2015] [Accepted: 09/06/2015] [Indexed: 12/15/2022] Open
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Hale JR, White TP, Mayhew SD, Wilson RS, Rollings DT, Khalsa S, Arvanitis TN, Bagshaw AP. Altered thalamocortical and intra-thalamic functional connectivity during light sleep compared with wake. Neuroimage 2015; 125:657-667. [PMID: 26499809 DOI: 10.1016/j.neuroimage.2015.10.041] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2015] [Revised: 09/16/2015] [Accepted: 10/16/2015] [Indexed: 01/14/2023] Open
Abstract
The transition from wakefulness into sleep is accompanied by modified activity in the brain's thalamocortical network. Sleep-related decreases in thalamocortical functional connectivity (FC) have previously been reported, but the extent to which these changes differ between thalamocortical pathways, and patterns of intra-thalamic FC during sleep remain untested. To non-invasively investigate thalamocortical and intra-thalamic FC as a function of sleep stage we recorded simultaneous EEG-fMRI data in 13 healthy participants during their descent into light sleep. Visual scoring of EEG data permitted sleep staging. We derived a functional thalamic parcellation during wakefulness by computing seed-based FC, measured between thalamic voxels and a set of pre-defined cortical regions. Sleep differentially affected FC between these distinct thalamic subdivisions and their associated cortical projections, with significant increases in FC during sleep restricted to sensorimotor connections. In contrast, intra-thalamic FC, both within and between functional thalamic subdivisions, showed significant increases with advancement into sleep. This work demonstrates the complexity and state-specific nature of functional thalamic relationships--both with the cortex and internally--over the sleep/wake cycle, and further highlights the importance of a thalamocortical focus in the study of sleep mechanisms.
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Affiliation(s)
- Joanne R Hale
- School of Psychology, University of Birmingham, Birmingham, UK.
| | - Thomas P White
- School of Psychology, University of Birmingham, Birmingham, UK
| | | | | | - David T Rollings
- School of Psychology, University of Birmingham, Birmingham, UK; Department of Neurophysiology, Queen Elizabeth Hospital, Birmingham, UK
| | - Sakhvinder Khalsa
- School of Psychology, University of Birmingham, Birmingham, UK; Department of Neuropsychiatry, The Barberry National Centre for Mental Health, Birmingham, UK
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Hale JR, Mayhew SD, Mullinger KJ, Wilson RS, Arvanitis TN, Francis ST, Bagshaw AP. Comparison of functional thalamic segmentation from seed-based analysis and ICA. Neuroimage 2015; 114:448-65. [PMID: 25896929 DOI: 10.1016/j.neuroimage.2015.04.027] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 04/02/2015] [Accepted: 04/07/2015] [Indexed: 01/07/2023] Open
Abstract
Information flow between the thalamus and cerebral cortex is a crucial component of adaptive brain function, but the details of thalamocortical interactions in human subjects remain unclear. The principal aim of this study was to evaluate the agreement between functional thalamic network patterns, derived using seed-based connectivity analysis and independent component analysis (ICA) applied separately to resting state functional MRI (fMRI) data from 21 healthy participants. For the seed-based analysis, functional thalamic parcellation was achieved by computing functional connectivity (FC) between thalamic voxels and a set of pre-defined cortical regions. Thalamus-constrained ICA provided an alternative parcellation. Both FC analyses demonstrated plausible and comparable group-level thalamic subdivisions, in agreement with previous work. Quantitative assessment of the spatial overlap between FC thalamic segmentations, and comparison of each to a histological "gold-standard" thalamic atlas and a structurally-defined thalamic atlas, highlighted variations between them and, most notably, differences with both histological and structural results. Whilst deeper understanding of thalamocortical connectivity rests upon identification of features common to multiple non-invasive neuroimaging techniques (e.g. FC, structural connectivity and anatomical localisation of individual-specific nuclei), this work sheds further light on the functional organisation of the thalamus and the varying sensitivities of complementary analyses to resolve it.
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Affiliation(s)
- Joanne R Hale
- School of Psychology, University of Birmingham, Birmingham, United Kingdom.
| | - Stephen D Mayhew
- School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Karen J Mullinger
- School of Psychology, University of Birmingham, Birmingham, United Kingdom; Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Rebecca S Wilson
- School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Theodoros N Arvanitis
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, United Kingdom
| | - Susan T Francis
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Andrew P Bagshaw
- School of Psychology, University of Birmingham, Birmingham, United Kingdom
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