1
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Huang BK, Zhou JH, Deng Y, Li CH, Ning BL, Ye ZY, Huang XC, Zhao MM, Dong D, Liu M, Zhang DL, Fu WB. Perceived stress and brain connectivity in subthreshold depression: Insights from eyes-closed and eyes-open states. Brain Res 2024; 1838:148947. [PMID: 38657887 DOI: 10.1016/j.brainres.2024.148947] [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: 03/14/2024] [Revised: 04/09/2024] [Accepted: 04/16/2024] [Indexed: 04/26/2024]
Abstract
Perceived stress is an acknowledged risk factor for subthreshold depression (StD), and fluctuations in perceived stress are thought to disrupt the harmony of brain networks essential for emotional and cognitive functioning. This study aimed to elucidate the relationship between eye-open (EO) and eye-closed (EC) states, perceived stress, and StD. We recruited 27 individuals with StD and 33 healthy controls, collecting resting state fMRI data under both EC and EO conditions. We combined intrinsic connectivity and seed-based functional connectivity analyses to construct the functional network and explore differences between EC and EO conditions. Graph theory analysis revealed weakened connectivity strength in the right superior frontal gyrus (SFG) and right median cingulate and paracingulate gyrus (MCC) among participants with StD, suggesting an important role for these regions in the stress-related emotions dysregulation. Notably, altered SFG connectivity was observed to significantly relate to perceived stress levels in StD, and the SFG connection emerges as a neural mediator potentially influencing the relationship between perceived stress and StD. These findings highlight the role of SFG and MCC in perceived stress and suggest that understanding EC and EO states in relation to these regions is important in the neurobiological framework of StD. This may offer valuable perspectives for early prevention and intervention strategies in mental health disorders.
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Affiliation(s)
- Bin-Kun Huang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou 510631, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Jun-He Zhou
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou 510631, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; Department of Acupuncture and Moxibustion, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Ying Deng
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Chang-Hong Li
- College of Teacher Education, Guangdong University of Education, Guangzhou 510303, China
| | - Bai-Le Ning
- Department of Acupuncture and Moxibustion, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Zi-Yu Ye
- Acupuncture and Rehabilitation Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Xi-Chang Huang
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Mi-Mi Zhao
- Acupuncture and Rehabilitation Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Dian Dong
- Acupuncture and Rehabilitation Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Ming Liu
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou 510631, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - De-Long Zhang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou 510631, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China.
| | - Wen-Bin Fu
- Department of Acupuncture and Moxibustion, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510000, China.
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2
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Ingram BT, Mayhew SD, Bagshaw AP. Brain state dynamics differ between eyes open and eyes closed rest. Hum Brain Mapp 2024; 45:e26746. [PMID: 38989618 PMCID: PMC11237880 DOI: 10.1002/hbm.26746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 04/25/2024] [Accepted: 05/08/2024] [Indexed: 07/12/2024] Open
Abstract
The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.
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Affiliation(s)
- Brandon T. Ingram
- Centre for Human Brain Health, School of PsychologyUniversity of BirminghamBirminghamUK
| | - Stephen D. Mayhew
- Institute of Health and NeurodevelopmentSchool of Psychology, Aston UniversityBirminghamUK
| | - Andrew P. Bagshaw
- Centre for Human Brain Health, School of PsychologyUniversity of BirminghamBirminghamUK
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3
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Cicero NG, Klimova M, Lewis LD, Ling S. Differential cortical and subcortical visual processing with eyes shut. J Neurophysiol 2024; 132:54-60. [PMID: 38810261 DOI: 10.1152/jn.00073.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 05/06/2024] [Accepted: 05/29/2024] [Indexed: 05/31/2024] Open
Abstract
Closing our eyes largely shuts down our ability to see. That said, our eyelids still pass some light, allowing our visual system to coarsely process information about visual scenes, such as changes in luminance. However, the specific impact of eye closure on processing within the early visual system remains largely unknown. To understand how visual processing is modulated when eyes are shut, we used functional magnetic resonance imaging (fMRI) to measure responses to a flickering visual stimulus at high (100%) and low (10%) temporal contrasts, while participants viewed the stimuli with their eyes open or closed. Interestingly, we discovered that eye closure produced a qualitatively distinct pattern of effects across the visual thalamus and visual cortex. We found that with eyes open, low temporal contrast stimuli produced smaller responses across the lateral geniculate nucleus (LGN), primary (V1) and extrastriate visual cortex (V2). However, with eyes closed, we discovered that the LGN and V1 maintained similar blood oxygenation level-dependent (BOLD) responses as the eyes open condition, despite the suppressed visual input through the eyelid. In contrast, V2 and V3 had strongly attenuated BOLD response when eyes were closed, regardless of temporal contrast. Our findings reveal a qualitatively distinct pattern of visual processing when the eyes are closed-one that is not simply an overall attenuation but rather reflects distinct responses across visual thalamocortical networks, wherein the earliest stages of processing preserve information about stimuli but are then gated off downstream in visual cortex.NEW & NOTEWORTHY When we close our eyes coarse luminance information is still accessible by the visual system. Using functional magnetic resonance imaging, we examined whether eyelid closure plays a unique role in visual processing. We discovered that while the LGN and V1 show equivalent responses when the eyes are open or closed, extrastriate cortex exhibited attenuated responses with eye closure. This suggests that when the eyes are closed, downstream visual processing is blind to this information.
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Affiliation(s)
- Nicholas G Cicero
- Graduate Program in Neuroscience, Boston University, Boston, Massachusetts, United States
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Michaela Klimova
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, United States
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Sam Ling
- Graduate Program in Neuroscience, Boston University, Boston, Massachusetts, United States
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, United States
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4
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Zhang Y, Han X, Ge X, Xu T, Wang Y, Mu J, Liu F. Modular brain network in volitional eyes closing: enhanced integration with a marked impact on hubs. Cereb Cortex 2024; 34:bhad464. [PMID: 38044477 DOI: 10.1093/cercor/bhad464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/12/2023] [Accepted: 11/13/2023] [Indexed: 12/05/2023] Open
Abstract
Volitional eyes closing would shift brain's information processing modes from the "exteroceptive" to "interoceptive" state. This transition induced by the eyes closing is underpinned by a large-scale reconfiguration of brain network, which is still not fully comprehended. Here, we investigated the eyes-closing-relevant network reconfiguration by examining the functional integration among intrinsic modules. Our investigation utilized a publicly available dataset with 48 subjects being scanned in both eyes closed and eyes open conditions. It was found that the modular integration was significantly enhanced during the eyes closing, including lower modularity index, higher participation coefficient, less provincial hubs, and more connector hubs. Moreover, the eyes-closing-enhanced integration was particularly noticeable in the hubs of network, mainly located in the default-mode network. Finally, the hub-dominant modular enhancement was positively correlated to the eyes-closing-reduced entropy of BOLD signal, suggesting a close connection to the diminished consciousness of individuals. Collectively, our findings strongly suggested that the enhanced modular integration with substantially reorganized hubs characterized the large-scale cortical underpinning of the volitional eyes closing.
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Affiliation(s)
- Yi Zhang
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Xiao Han
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Xuelian Ge
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Tianyong Xu
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Yanjie Wang
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Jiali Mu
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Fan Liu
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
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5
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Zapała D, Augustynowicz P, Tokovarov M, Iwanowicz P, Droździel P. Brief Visual Deprivation Effects on Brain Oscillations During Kinesthetic and Visual-motor Imagery. Neuroscience 2023; 532:37-49. [PMID: 37625688 DOI: 10.1016/j.neuroscience.2023.08.022] [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: 04/07/2023] [Revised: 08/10/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023]
Abstract
It is widely recognized that opening and closing the eyes can direct attention to external or internal stimuli processing. This has been confirmed by studies showing the effects of changes in visual stimulation changes on cerebral activity during different tasks, e.g., motor imagery and execution. However, an essential aspect of creating a mental representation of motion, such as imagery perspective, has not yet been investigated in the present context. Our study aimed to verify the effect of brief visual deprivation (under eyes open [EO] and eyes closed [EC] conditions) on brain wave oscillations and behavioral performance during kinesthetic imagery (KMI) and visual-motor imagery (VMI) tasks. We focused on the alpha and beta rhythms from visual- and motor-related EEG activity sources. Additionally, we used machine learning algorithms to establish whether the registered differences in brain oscillations might affect motor imagery brain-computer interface (MI-BCI) performance. The results showed that the occipital areas in the EC condition presented significantly stronger desynchronization during VMI tasks, which is typical for enhanced visual stimuli processing. Furthermore, the stronger desynchronization of alpha rhythms from motor areas in the EO, than EC condition confirmed previous effects obtained during real movements. It was also found that simulating movement under EC/EO conditions affected signal classification accuracy, which has practical implications for MI-BCI effectiveness. These findings suggest that shifting processing toward external or internal stimuli modulates brain rhythm oscillations associated with different perspectives on the mental representation of movement.
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Affiliation(s)
- Dariusz Zapała
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | - Paweł Augustynowicz
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | | | - Paulina Iwanowicz
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | - Paulina Droździel
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
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6
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Zhang J, Yang Y, Liu T, Shi Z, Pei G, Wang L, Wu J, Funahashi S, Suo D, Wang C, Yan T. Functional connectivity in people at clinical and familial high risk for schizophrenia. Psychiatry Res 2023; 328:115464. [PMID: 37690192 DOI: 10.1016/j.psychres.2023.115464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 09/03/2023] [Accepted: 09/04/2023] [Indexed: 09/12/2023]
Abstract
Patients diagnosed with schizophrenia (SZ) exhibit compromised functional connectivity within extensive brain networks. However, the precise development of this impairment during disease progression in the clinical high-risk (CHR) population and their relatives remains unclear. Our study leveraged data from 128 resting electroencephalography (EEG) channels acquired from 30 SZ patients, 21 CHR individuals, 17 unaffected healthy relatives (RSs; those at heightened SZ risk due to family history), and 31 healthy controls (HCs). These data were harnessed to establish functional connectivity patterns. By calculating the geometric distance between EEG sequences, we unveiled local and global nonlinear relationships within the entire brain. The process of dimensionality reduction led to low-dimensional representations, providing insights into high-dimensional EEG data. Our findings indicated that CHR participants exhibited aberrant functional connectivity across hemispheres, whereas RS individuals showcased anomalies primarily concentrated within hemispheres. In the realm of low-dimensional analysis, RS participants' third-dimensional occipital lobe values lay between those of the CHR individuals and HCs, significantly correlating with scale scores. This low-dimensional approach facilitated the visualization of brain states, potentially offering enhanced comprehension of brain structure, function, and early-stage functional impairment, such as occipital visual deficits, in RS individuals before cognitive decline onset.
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Affiliation(s)
- Jian Zhang
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Yaxin Yang
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Tiantian Liu
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Zhongyan Shi
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Guangying Pei
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Li Wang
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Jinglong Wu
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Shintaro Funahashi
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Dingjie Suo
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Changming Wang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China.
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7
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Cicero NG, Klimova M, Lewis LD, Ling S. Differential cortical and subcortical visual processing with eyes shut. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.557197. [PMID: 37745511 PMCID: PMC10515861 DOI: 10.1101/2023.09.11.557197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Closing our eyes largely shuts down our ability to see. That said, our eyelids still pass some light, allowing our visual system to coarsely process information about visual scenes, such as changes in luminance. However, the specific impact of eye closure on processing within the early visual system remains largely unknown. To understand how visual processing is modulated when eyes are shut, we used functional magnetic resonance imaging (fMRI) to measure responses to a flickering visual stimulus at high (100%) and low (10%) temporal contrasts, while participants viewed the stimuli with their eyes open or closed. Interestingly, we discovered that eye closure produced a qualitatively distinct pattern of effects across the visual thalamus and visual cortex. We found that with eyes open, low temporal contrast stimuli produced smaller responses, across the lateral geniculate nucleus (LGN), primary (V1) and extrastriate visual cortex (V2). However, with eyes closed, we discovered that the LGN and V1 maintained similar BOLD responses as the eyes open condition, despite the suppressed visual input through the eyelid. In contrast, V2 and V3 had strongly attenuated BOLD response when eyes were closed, regardless of temporal contrast. Our findings reveal a qualitative distinct pattern of visual processing when the eyes are closed - one that is not simply an overall attenuation, but rather reflects distinct responses across visual thalamocortical networks, wherein the earliest stages of processing preserves information about stimuli but is then gated off downstream in visual cortex.
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Affiliation(s)
- Nicholas G. Cicero
- Graduate Program in Neuroscience, Boston University
- Department of Biomedical Engineering, Boston University
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology
| | - Michaela Klimova
- Department of Psychological and Brain Sciences, Boston University
| | - Laura D. Lewis
- Department of Biomedical Engineering, Boston University
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital
| | - Sam Ling
- Graduate Program in Neuroscience, Boston University
- Department of Psychological and Brain Sciences, Boston University
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8
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Seifpour S, Šatka A. Tensor Decomposition Analysis of Longitudinal EEG Signals Reveals Differential Oscillatory Dynamics in Eyes-Closed and Eyes-Open Motor Imagery BCI: A Case Report. Brain Sci 2023; 13:1013. [PMID: 37508946 PMCID: PMC10377314 DOI: 10.3390/brainsci13071013] [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: 05/10/2023] [Revised: 06/15/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
Functional dissociation of brain neural activity induced by opening or closing the eyes has been well established. However, how the temporal dynamics of the underlying neuronal modulations differ between these eye conditions during movement-related behaviours is less known. Using a robotic-assisted motor imagery brain-computer interface (MI BCI), we measured neural activity over the motor regions with electroencephalography (EEG) in a stroke survivor during his longitudinal rehabilitation training. We investigated lateralized oscillatory sensorimotor rhythm modulations while the patient imagined moving his hemiplegic hand with closed and open eyes to control an external robotic splint. In order to precisely identify the main profiles of neural activation affected by MI with eyes-open (MIEO) and eyes-closed (MIEC), a data-driven approach based on parallel factor analysis (PARAFAC) tensor decomposition was employed. Using the proposed framework, a set of narrow-band, subject-specific sensorimotor rhythms was identified; each of them had its own spatial and time signature. When MIEC trials were compared with MIEO trials, three key narrow-band rhythms whose peak frequencies centred at ∼8.0 Hz, ∼11.5 Hz, and ∼15.5 Hz, were identified with differently modulated oscillatory dynamics during movement preparation, initiation, and completion time frames. Furthermore, we observed that lower and higher sensorimotor oscillations represent different functional mechanisms within the MI paradigm, reinforcing the hypothesis that rhythmic activity in the human sensorimotor system is dissociated. Leveraging PARAFAC, this study achieves remarkable precision in estimating latent sensorimotor neural substrates, aiding the investigation of the specific functional mechanisms involved in the MI process.
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Affiliation(s)
- Saman Seifpour
- RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
- Institute of Measurement Science, Slovak Academy of Sciences, Dubravska cesta 9, 84104 Bratislava, Slovakia
| | - Alexander Šatka
- Institute of Measurement Science, Slovak Academy of Sciences, Dubravska cesta 9, 84104 Bratislava, Slovakia
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9
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Zhou L, Xie Y, Wang R, Fan Y, Wu Y. Dynamic segregation and integration of brain functional networks associated with emotional arousal. iScience 2023; 26:106609. [PMID: 37250309 PMCID: PMC10214403 DOI: 10.1016/j.isci.2023.106609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 02/12/2023] [Accepted: 03/31/2023] [Indexed: 05/31/2023] Open
Abstract
The organization of brain functional networks dynamically changes with emotional stimuli, but its relationship to emotional behaviors is still unclear. In the DEAP dataset, we used the nested-spectral partition approach to identify the hierarchical segregation and integration of functional networks and investigated the dynamic transitions between connectivity states under different arousal conditions. The frontal and right posterior parietal regions were dominant for network integration whereas the bilateral temporal, left posterior parietal, and occipital regions were responsible for segregation and functional flexibility. High emotional arousal behavior was associated with stronger network integration and more stable state transitions. Crucially, the connectivity states of frontal, central, and right parietal regions were closely related to arousal ratings in individuals. Besides, we predicted the individual emotional performance based on functional connectivity activities. Our results demonstrate that brain connectivity states are closely associated with emotional behaviors and could be reliable and robust indicators for emotional arousal.
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Affiliation(s)
- Lv Zhou
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an 710049, China
| | - Yong Xie
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
| | - Rong Wang
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- College of Science, Xi’an University of Science and Technology, Xi’an 710054, China
| | - Yongchen Fan
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
| | - Ying Wu
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an 710049, China
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10
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Zhang X, Zang Z. Evaluate the efficacy and reliability of functional gradients in within-subject designs. Hum Brain Mapp 2023; 44:2336-2344. [PMID: 36661209 PMCID: PMC10028665 DOI: 10.1002/hbm.26213] [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: 09/20/2022] [Revised: 01/05/2023] [Accepted: 01/09/2023] [Indexed: 01/21/2023] Open
Abstract
The cerebral cortex is characterized as the integration of distinct functional principles that correspond to basic primary functions, such as vision and movement, and domain-general functions, such as attention and cognition. Diffusion embedding approach is a novel tool to describe transitions between different functional principles, and has been successively applied to investigate pathological conditions in between-group designs. What still lacking and urgently needed is the efficacy of this method to differentiate within-subject circumstances. In this study, we applied the diffusion embedding to eyes closed (EC) and eyes on (EO) resting-state conditions from 145 participants. We found significantly lower within-network dispersion of visual network (VN) (p = 7.3 × 10-4 ) as well as sensorimotor network (SMN) (p = 1 × 10-5 ) and between-network dispersion of VN (p = 2.3 × 10-4 ) under EC than EO, while frontoparietal network (p = 9.2 × 10-4 ) showed significantly higher between-network dispersion during EC than EO. Test-retest reliability analysis further displayed fair reliability (intraclass correlation coefficient [ICC] < 0.4) of the network dispersions (mean ICC = 0.116 ± 0.143 [standard deviation]) except for the within-network dispersion of SMN under EO (ICC = 0.407). And the reliability under EO was higher but not significantly higher than reliability under EC. Our study demonstrated that the diffusion embedding approach that shows fair reliability is capable of distinguishing EC and EO resting-state conditions, such that this method could be generalized to other within-subject designs.
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Affiliation(s)
- Xiaolong Zhang
- Department of Physiology, College of Basic Medical Sciences, Army Medical University, Chongqing, China
| | - Zhenxiang Zang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
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11
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Song I, Lee TH. Considering dynamic nature of the brain: the clinical importance of connectivity variability in machine learning classification and prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.26.525765. [PMID: 36747828 PMCID: PMC9901018 DOI: 10.1101/2023.01.26.525765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions as well as for predicting psychosocial characteristics. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) but less attention to temporal characteristics of connectivity changes (FC-variability). The primary goal of the current study was to investigate the effectiveness of using the FC-variability in classifying an individual's pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the FC-variability are reliable across various analysis procedures. To this end, three open public large resting-state fMRI datasets including individuals with Autism Spectrum Disorder (ABIDE; N = 1249), Schizophrenia disorder (COBRE; N = 145), and typical development (NKI; N = 672) were utilized for the machine learning (ML) classification and prediction based on their static-FC and the FC-variability metrics. To confirm the robustness of FC-variability utility, we benchmarked the ML classification and prediction with various brain parcellations and sliding window parameters. As a result, we found that the ML performances were significantly improved when the ML included FC-variability features in classifying pathological populations from controls (e.g., individuals with autism spectrum disorder vs. typical development) and predicting psychiatric severity (e.g., score of autism diagnostic observation schedule), regardless of parcellation selection and sliding window size. Additionally, the ML performance deterioration was significantly prevented with FC-variability features when excessive features were inputted into the ML models, yielding more reliable results. In conclusion, the current finding proved the usefulness of the FC-variability and its reliability.
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Affiliation(s)
- Inuk Song
- Department of Psychology, Virginia Tech
| | - Tae-Ho Lee
- Department of Psychology, Virginia Tech
- School of Neuroscience, Virginia Tech
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12
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Alaçam D, Miller R, Agcaoglu O, Preda A, Ford J, Calhoun V. A method for capturing dynamic spectral coupling in resting fMRI reveals domain-specific patterns in schizophrenia. Front Neurosci 2023; 17:1078995. [PMID: 37179560 PMCID: PMC10174238 DOI: 10.3389/fnins.2023.1078995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 04/03/2023] [Indexed: 05/15/2023] Open
Abstract
Introduction Resting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool for assessing functional brain connectivity. Recent studies have focused on shorter-term connectivity and dynamics in the resting state. However, most of the prior work evaluates changes in time-series correlations. In this study, we propose a framework that focuses on time-resolved spectral coupling (assessed via the correlation between power spectra of the windowed time courses) among different brain circuits determined via independent component analysis (ICA). Methods Motivated by earlier work suggesting significant spectral differences in people with schizophrenia, we developed an approach to evaluate time-resolved spectral coupling (trSC). To do this, we first calculated the correlation between the power spectra of windowed time-courses pairs of brain components. Then, we subgrouped each correlation map into four subgroups based on the connectivity strength utilizing quartiles and clustering techniques. Lastly, we examined clinical group differences by regression analysis for each averaged count and average cluster size matrices in each quartile. We evaluated the method by applying it to resting-state data collected from 151 (114 males, 37 females) people with schizophrenia (SZ) and 163 (117 males, 46 females) healthy controls (HC). Results Our proposed approach enables us to observe the change of connectivity strength within each quartile for different subgroups. People with schizophrenia showed highly modularized and significant differences in multiple network domains, whereas males and females showed less modular differences. Both cell count and average cluster size analysis for subgroups indicate a higher connectivity rate in the fourth quartile for the visual network in the control group. This indicates increased trSC in visual networks in the controls. In other words, this shows that the visual networks in people with schizophrenia have less mutually consistent spectra. It is also the case that the visual networks are less spectrally correlated on short timescales with networks of all other functional domains. Conclusions The results of this study reveal significant differences in the degree to which spectral power profiles are coupled over time. Importantly, there are significant but distinct differences both between males and females and between people with schizophrenia and controls. We observed a more significant coupling rate in the visual network for the healthy controls and males in the upper quartile. Fluctuations over time are complex, and focusing on only time-resolved coupling among time-courses is likely to miss important information. Also, people with schizophrenia are known to have impairments in visual processing but the underlying reasons for the impairment are still unknown. Therefore, the trSC approach can be a useful tool to explore the reasons for the impairments.
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Affiliation(s)
- Deniz Alaçam
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
- Department of Mathematics, Bursa Uludag University, Bursa, Türkiye
- *Correspondence: Deniz Alaçam
| | - Robyn Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Oktay Agcaoglu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Judith Ford
- San Francisco VA Medical Center, University of California, San Francisco, San Francisco, CA, United States
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
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13
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Khan DM, Yahya N, Kamel N, Faye I. A novel method for efficient estimation of brain effective connectivity in EEG. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 228:107242. [PMID: 36423484 DOI: 10.1016/j.cmpb.2022.107242] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 09/20/2022] [Accepted: 11/09/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Brain connectivity plays a pivotal role in understanding the brain's information processing functions by providing various details including magnitude, direction, and temporal dynamics of inter-neuron connections. While the connectivity may be classified as structural, functional and causal, a complete in-vivo directional analysis is guaranteed by the latter and is referred to as Effective Connectivity (EC). Two most widely used EC techniques are Directed Transfer Function (DTF) and Partial Directed Coherence (PDC) which are based on multivariate autoregressive models. The drawbacks of these techniques include poor frequency resolution and the requirement for experimental approach to determine signal normalization and thresholding techniques in identifying significant connectivities between multivariate sources. METHODS In this study, the drawbacks of DTF and PDC are addressed by proposing a novel technique, termed as Efficient Effective Connectivity (EEC), for the estimation of EC between multivariate sources using AR spectral estimation and Granger causality principle. In EEC, a linear predictive filter with AR coefficients obtained via multivariate EEG is used for signal prediction. This leads to the estimation of full-length signals which are then transformed into frequency domain by using Burg spectral estimation method. Furthermore, the newly proposed normalization method addressed the effect on each source in EEC using the sum of maximum connectivity values over the entire frequency range. Lastly, the proposed dynamic thresholding works by subtracting the first moment of causal effects of all the sources on one source from individual connections present for that source. RESULTS The proposed method is evaluated using synthetic and real resting-state EEG of 46 healthy controls. A 3D-Convolutional Neural Network is trained and tested using the PDC and EEC samples. The result indicates that compared to PDC, EEC improves the EEG eye-state classification accuracy, sensitivity and specificity by 5.57%, 3.15% and 8.74%, respectively. CONCLUSION Correct identification of all connections in synthetic data and improved resting-state classification performance using EEC proved that EEC gives better estimation of directed causality and indicates that it can be used for reliable understanding of brain mechanisms. Conclusively, the proposed technique may open up new research dimensions for clinical diagnosis of mental disorders.
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Affiliation(s)
- Danish M Khan
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia; Department of Telecommunications Engineering, NED University of Engineering & Technology, University Road, Karachi 75270, Pakistan.
| | - Norashikin Yahya
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia.
| | - Nidal Kamel
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia; VinUniversity, College of Engineering and Computer Science, Vinhomes Ocean Park, Gia Lam District, Hanoi, Vietnam
| | - Ibrahima Faye
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia
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14
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Frailty and functional brain connectivity (FBC) in older adults with mild cognitive impairment (MCI): baseline results from the SYNERGIC Trial. GeroScience 2022; 45:1033-1048. [PMID: 36539590 PMCID: PMC9767804 DOI: 10.1007/s11357-022-00702-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022] Open
Abstract
Functional brain connectivity (FBC), or areas that are anatomically separate but temporally synchronized in their activation, represent a sensitive biomarker for monitoring dementia progression. It is unclear whether frailty is associated with FBC in those at higher risk of progression to dementia (e.g., mild cognitive impairment -MCI-) and if sex plays a role. We used baseline data from the SYNERGIC trial, including participants with MCI that received brain MRI. In this cross-sectional analyses (n = 100), we measured frailty using a deficit accumulation frailty index. Using the CONN toolbox, we assessed FBC of networks and regions of interest across the entire connectome. We used Pearson's correlation to investigate the relationship between FBC and frailty index in the full sample and by sex. We also divided the full sample and each sex into tertiles based upon their frailty index score and then assessed between-tertile differences in FBC. The full sample (cluster: size = 291 p-FDR < 0.05) and males (cluster: size = 993 and 451 p-FDR < 0.01) demonstrated that increasing (stronger) connectivity between the right hippocampus and clusters in the temporal gyrus was positively correlated with increasing (worse) frailty. Males also demonstrated between-tertile differences in right hippocampus connectivity to clusters in the lateral occipital cortex (cluster: size = 289 p-FDR < 0.05). Regardless of frailty status, females demonstrated stronger within-network connectivity of the Default-Mode (p = 0.024). Our results suggest that increasing (worse) frailty was associated with increasing (stronger) connectivity between regions not typically linked, which may reflect a compensation tactic by the plastic brain. Furthermore, the relationship between the two variables appears to differ by sex. Our results may help elucidate why specific individuals progress to a dementia syndrome. NCT02808676. https://www.clinicaltrials.gov/ct2/show/NCT02808676.
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15
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Liu L, Ren J, Li Z, Yang C. A review of MEG dynamic brain network research. Proc Inst Mech Eng H 2022; 236:763-774. [PMID: 35465768 DOI: 10.1177/09544119221092503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.
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Affiliation(s)
- Lu Liu
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Jiechuan Ren
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhimei Li
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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16
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Bai P, Safikhani A, Michailidis G. A Fast Detection Method of Break Points in Effective Connectivity Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1017-1030. [PMID: 34822326 DOI: 10.1109/tmi.2021.3131142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
There is increasing interest in identifying changes in the underlying states of brain networks. The availability of large scale neuroimaging data creates a strong need to develop fast, scalable methods for detecting and localizing in time such changes and also identify their drivers, thus enabling neuroscientists to hypothesize about potential mechanisms. This paper presents a fast method for detecting break points in exceedingly long time series neurogimaging data, based on vector autoregressive (Granger causal) models. It uses a multi-step strategy based on a regularized objective function that leads to fast identification of candidate break points, followed by clustering steps to select the final set of break points and subsequent estimation with false positives control of the underlying Granger causal networks. The latter provide insights into key changes in network connectivity that led to the presence of break points. The proposed methodology is illustrated on synthetic data varying in their length, dimensionality, number of break points, strength of signal and also applied to EEG data related to visual tasks.
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17
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Yang H, Zhang H, Meng C, Wohlschläger A, Brandl F, Di X, Wang S, Tian L, Biswal B. Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: An fMRI study. Hum Brain Mapp 2022; 43:3792-3808. [PMID: 35475569 PMCID: PMC9294298 DOI: 10.1002/hbm.25884] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/05/2022] [Accepted: 04/05/2022] [Indexed: 11/09/2022] Open
Abstract
The resting‐state human brain is a dynamic system that shows frequency‐dependent characteristics. Recent studies demonstrate that coactivation pattern (CAP) analysis can identify recurring brain states with similar coactivation configurations. However, it is unclear whether and how CAPs depend on the frequency bands. The current study investigated the spatial and temporal characteristics of CAPs in the four frequency sub‐bands from slow‐5 (0.01–0.027 Hz), slow‐4 (0.027–0.073 Hz), slow‐3 (0.073–0.198 Hz), to slow‐2 (0.198–0.25 Hz), in addition to the typical low‐frequency range (0.01–0.08 Hz). In the healthy subjects, six CAP states were obtained at each frequency band in line with our prior study. Similar spatial patterns with the typical range were observed in slow‐5, 4, and 3, but not in slow‐2. While the frequency increased, all CAP states displayed shorter persistence, which caused more between‐state transitions. Specifically, from slow‐5 to slow‐4, the coactivation not only changed significantly in distributed cortical networks, but also increased in the basal ganglia as well as the amygdala. Schizophrenia patients showed significant alteration in the persistence of CAPs of slow‐5. Using leave‐one‐pair‐out, hold‐out and resampling validations, the highest classification accuracy (84%) was achieved by slow‐4 among different frequency bands. In conclusion, our findings provide novel information about spatial and temporal characteristics of CAP states at different frequency bands, which contributes to a better understanding of the frequency aspect of biomarkers for schizophrenia and other disorders.
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Affiliation(s)
- Hang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hong Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Afra Wohlschläger
- Department of Neuroradiology, TUM-Neuroimaging Center, Technical University of Munich (TUM), Munich, Germany
| | - Felix Brandl
- Department of Psychiatry, TUM-Neuroimaging Center, Technical University of Munich (TUM), Munich, Germany
| | - Xin Di
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Shuai Wang
- Department of Psychiatry, The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China
| | - Lin Tian
- Department of Psychiatry, The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
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18
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Hsu TY, Zhou JF, Yeh SL, Northoff G, Lane TJ. Intrinsic neural activity predisposes susceptibility to a body illusion. Cereb Cortex Commun 2022; 3:tgac012. [PMID: 35382092 PMCID: PMC8976633 DOI: 10.1093/texcom/tgac012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 02/21/2022] [Accepted: 03/01/2022] [Indexed: 11/13/2022] Open
Abstract
Susceptibility to the rubber hand illusion (RHI) varies. To date, however, there is no consensus explanation of this variability. Previous studies, focused on the role of multisensory integration, have searched for neural correlates of the illusion. But those studies have failed to identify a sufficient set of functionally specific neural correlates. Because some evidence suggests that frontal α power is one means of tracking neural instantiations of self, we hypothesized that the higher the frontal α power during the eyes-closed resting state, the more stable the self. As a corollary, we infer that the more stable the self, the less susceptible are participants to a blurring of boundaries—to feeling that the rubber hand belongs to them. Indeed, we found that frontal α amplitude oscillations negatively correlate with susceptibility. Moreover, since lower frequencies often modulate higher frequencies, we explored the possibility that this might be the case for the RHI. Indeed, some evidence suggests that high frontal α power observed in low-RHI participants is modulated by δ frequency oscillations. We conclude that while neural correlates of multisensory integration might be necessary for the RHI, sufficient explanation involves variable intrinsic neural activity that modulates how the brain responds to incompatible sensory stimuli.
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Affiliation(s)
- Tzu-Yu Hsu
- Graduate Institute of Mind, Brain, and Consciousness, Taipei Medical University, Taipei, Taiwan
- Brain and Consciousness Research Centre, TMU Shuang Ho Hospital, New Taipei City, Taiwan
| | - Ji-Fan Zhou
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hang Zhou, China
| | - Su-Ling Yeh
- Department of Psychology, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei, Taiwan
- Neurobiology and Cognitive Neuroscience Center, National Taiwan University, Taipei, Taiwan
- Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, Taiwan
| | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, Canada
| | - Timothy Joseph Lane
- Graduate Institute of Mind, Brain, and Consciousness, Taipei Medical University, Taipei, Taiwan
- Brain and Consciousness Research Centre, TMU Shuang Ho Hospital, New Taipei City, Taiwan
- Institute of European and American Studies, Academia Sinica, Taipei, Taiwan
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19
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Li YT, Chen JW, Yan LF, Hu B, Chen TQ, Chen ZH, Sun JT, Shang YX, Lu LJ, Cui GB, Wang W. Dynamic Alterations of Functional Connectivity and Amplitude of Low-Frequency Fluctuations in Patients with Unilateral Sudden Sensorineural Hearing Loss. Neurosci Lett 2022; 772:136470. [PMID: 35066092 DOI: 10.1016/j.neulet.2022.136470] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/26/2021] [Accepted: 01/17/2022] [Indexed: 02/05/2023]
Abstract
Unilateral sudden sensorineural hearing loss (SSNHL) adversely affects the quality of life, leading to increased risk of depression and cognitive decline. Our previous studies have mainly focused on the static brain function abnormalities in SSNHL patients. However, the dynamic features of brain activity in SSNHL patients are not elucidated. To explore the dynamic brain functional alterations in SSNHL patients, age- and sex- matched SSNHL patients (n=38) and healthy controls (HC, n=44) were enrolled. The dynamic functional connectivity (dFC) and dynamic amplitude of low-frequency fluctuation (dALFF) methods were used to compare the temporal features and dynamic neural activity between the two groups. In dFC analyses, the multiple functional connectivities (FCs) were clustered into 2 different states; a greater proportion of FCs in SSNHL patients showed sparse state compared with HC. In dALFF analyses, SSNHL individuals exhibited decreased dALFF variability in bilateral inferior occipital gyrus, middle occipital gyrus, calcarine, right lingual gyrus, and right fusiform gyrus. dALFF variability showed a negative correlation with activated partial thromboplatin time. The dynamic characteristics of SSNHL patients were different from static functional connectivity and static amplitude of low-frequency fluctuation, especially within the visual cortices. These findings suggest that SSNHL patients experience cross-modal plasticity and visual compensation, which may be closely related to the pathophysiology of SSNHL.
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Affiliation(s)
- Yu-Ting Li
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an 710038, Shaanxi, China
| | - Jia-Wei Chen
- Department of Otolaryngology Head and Neck Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an 710038, Shaanxi, China
| | - Lin-Feng Yan
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an 710038, Shaanxi, China
| | - Bo Hu
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an 710038, Shaanxi, China
| | - Tian-Qi Chen
- Institution of Basic Medicine, Fourth Military Medical University, 169 Changle Road, Xi'an 710032, Shaanxi, China
| | - Zhu-Hong Chen
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an 710038, Shaanxi, China
| | - Jing-Ting Sun
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an 710038, Shaanxi, China; Shaanxi University of Chinese Medicine, Middle Section of Century Avenue, Xianyang 712046, Shaanxi, China
| | - Yu-Xuan Shang
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an 710038, Shaanxi, China
| | - Lian-Jun Lu
- Department of Otolaryngology Head and Neck Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an 710038, Shaanxi, China.
| | - Guang-Bin Cui
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an 710038, Shaanxi, China.
| | - Wen Wang
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an 710038, Shaanxi, China.
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20
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Luo Y, Guo Y, Zhong L, Liu Y, Dang C, Wang Y, Zeng J, Zhang W, Peng K, Liu G. Abnormal dynamic brain activity and functional connectivity of primary motor cortex in blepharospasm. Eur J Neurol 2021; 29:1035-1043. [PMID: 34962021 DOI: 10.1111/ene.15233] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/19/2021] [Accepted: 12/17/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Accumulating evidence indicates that dynamic amplitude of low-frequency fluctuations (dALFF) or functional connectivity (dFC) can provide complementary information, distinct from static ALFF (sALFF) or FC (sFC), in detecting brain functional abnormalities in brain diseases. We aimed to examine whether dALFF and dFC can offer valuable information for the detection of functional brain abnormalities in patients with blepharospasm. METHODS We collected resting-state functional magnetic resonance imaging data from 46 patients each of blepharospasm, hemifacial spasm (HFS), and healthy controls (HCs). We examined inter-group differences in sALFF and dALFF to investigate abnormal regional brain activity in patients with blepharospasm. Based on the dALFF results, we conducted seed-based sFC and dFC analyses to identify static and dynamic connectivity changes in brain networks centered on areas showing abnormal temporal variability of local brain activity in patients with blepharospasm. RESULTS Compared with HCs, patients with blepharospasm displayed different brain functional change patterns characterized by increased sALFF in the left primary motor cortex (PMC) but increased dALFF variance in the right PMC. However, differences were not found between patients with HFS and HCs. Additionally, patients with blepharospasm exhibited decreased dFC strength, but no change in sFC, between right PMC and ipsilateral cerebellum compared with HCs; these findings were replicated when patients with blepharospasm were compared to those with HFS. CONCLUSIONS Our findings highlight that dALFF and dFC are complementary to sALFF and sFC and can provide valuable information for detecting brain functional abnormalities in blepharospasm. Blepharospasm may be a network disorder involving the cortico-ponto-cerebello-thalamo-cortical circuit.
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Affiliation(s)
- Yuhan Luo
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical, Department and Key Discipline of Neurology, No. 58, Zhongshan Road 2, Guangzhou, China
| | - Yaomin Guo
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical, Department and Key Discipline of Neurology, No. 58, Zhongshan Road 2, Guangzhou, China
| | - Linchang Zhong
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Ying Liu
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical, Department and Key Discipline of Neurology, No. 58, Zhongshan Road 2, Guangzhou, China
| | - Chao Dang
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical, Department and Key Discipline of Neurology, No. 58, Zhongshan Road 2, Guangzhou, China
| | - Ying Wang
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical, Department and Key Discipline of Neurology, No. 58, Zhongshan Road 2, Guangzhou, China
| | - Jinsheng Zeng
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical, Department and Key Discipline of Neurology, No. 58, Zhongshan Road 2, Guangzhou, China
| | - Weixi Zhang
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical, Department and Key Discipline of Neurology, No. 58, Zhongshan Road 2, Guangzhou, China
| | - Kangqiang Peng
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Gang Liu
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical, Department and Key Discipline of Neurology, No. 58, Zhongshan Road 2, Guangzhou, China.,Guangdong-HongKong, Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China
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21
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Jacob MS, Roach BJ, Sargent KS, Mathalon DH, Ford JM. Aperiodic measures of neural excitability are associated with anticorrelated hemodynamic networks at rest: A combined EEG-fMRI study. Neuroimage 2021; 245:118705. [PMID: 34798229 DOI: 10.1016/j.neuroimage.2021.118705] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 10/11/2021] [Accepted: 11/01/2021] [Indexed: 11/24/2022] Open
Abstract
The hallmark of resting EEG spectra are distinct rhythms emerging from a broadband, aperiodic background. This aperiodic neural signature accounts for most of total EEG power, although its significance and relation to functional neuroanatomy remains obscure. We hypothesized that aperiodic EEG reflects a significant metabolic expenditure and therefore might be associated with the default mode network while at rest. During eyes-open, resting-state recordings of simultaneous EEG-fMRI, we find that aperiodic and periodic components of EEG power are only minimally associated with activity in the default mode network. However, a whole-brain analysis identifies increases in aperiodic power correlated with hemodynamic activity in an auditory-salience-cerebellar network, and decreases in aperiodic power are correlated with hemodynamic activity in prefrontal regions. Desynchronization in residual alpha and beta power is associated with visual and sensorimotor hemodynamic activity, respectively. These findings suggest that resting-state EEG signals acquired in an fMRI scanner reflect a balance of top-down and bottom-up stimulus processing, even in the absence of an explicit task.
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Affiliation(s)
- Michael S Jacob
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
| | - Brian J Roach
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States.
| | - Kaia S Sargent
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States.
| | - Daniel H Mathalon
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
| | - Judith M Ford
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
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22
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Zhang P, Jiang Y, Liu G, Han J, Wang J, Ma L, Hu W, Zhang J. Altered brain functional network dynamics in classic trigeminal neuralgia: a resting-state functional magnetic resonance imaging study. J Headache Pain 2021; 22:147. [PMID: 34895135 PMCID: PMC8903588 DOI: 10.1186/s10194-021-01354-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 11/06/2021] [Indexed: 12/20/2022] Open
Abstract
Background Accumulating studies have indicated a wide range of brain alterations with respect to the structure and function of classic trigeminal neuralgia (CTN). Given the dynamic nature of pain experience, the exploration of temporal fluctuations in interregional activity covariance may enhance the understanding of pain processes in the brain. The present study aimed to characterize the temporal features of functional connectivity (FC) states as well as topological alteration in CTN. Methods Resting-state functional magnetic resonance imaging and three-dimensional T1-weighted images were obtained from 41 CTN patients and 43 matched healthy controls (HCs). After group independent component analysis, sliding window based dynamic functional network connectivity (dFNC) analysis was applied to investigate specific FC states and related temporal properties. Then, the dynamics of the whole brain topological organization were estimated by calculating the coefficient of variation of graph-theoretical properties. Further correlation analyses were performed between all these measurements and clinical data. Results Two distinct states were identified. Of these, the state 2, characterized by complicated coupling between default mode network (DMN) and cognitive control network (CC) and tight connections within DMN, was expressed more in CTN patients and presented as increased fractional windows and dwell time. Moreover, patients switched less frequently between states than HCs. Regarding the dynamic topological analysis, disruptions in global graph-theoretical properties (including network efficiency and small-worldness) were observed in patients, coupled with decreased variability in nodal efficiency of anterior cingulate cortex (ACC) in the salience network (SN) and the thalamus and caudate nucleus in the subcortical network (SC). The variation of topological properties showed negative correlation with disease duration and attack frequency. Conclusions The present study indicated disrupted flexibility of brain topological organization under persistent noxious stimulation and further highlighted the important role of “dynamic pain connectome” regions (including DMN/CC/SN) in the pathophysiology of CTN from the temporal fluctuation aspect. Additionally, the findings provided supplementary evidence for current knowledge about the aberrant cortical-subcortical interaction in pain development. Supplementary Information The online version contains supplementary material available at 10.1186/s10194-021-01354-z.
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Affiliation(s)
- Pengfei Zhang
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Yanli Jiang
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Guangyao Liu
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Jiao Han
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China
| | - Jun Wang
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Laiyang Ma
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Wanjun Hu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China. .,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, P. R. China.
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23
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Vishnubhotla RV, Radhakrishnan R, Kveraga K, Deardorff R, Ram C, Pawale D, Wu YC, Renschler J, Subramaniam B, Sadhasivam S. Advanced Meditation Alters Resting-State Brain Network Connectivity Correlating With Improved Mindfulness. Front Psychol 2021; 12:745344. [PMID: 34867626 PMCID: PMC8636330 DOI: 10.3389/fpsyg.2021.745344] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 10/15/2021] [Indexed: 11/28/2022] Open
Abstract
Purpose: The purpose of this study was to investigate the effect of an intensive 8-day Samyama meditation program on the brain functional connectivity using resting-state functional MRI (rs-fMRI). Methods: Thirteen Samyama program participants (meditators) and 4 controls underwent fMRI brain scans before and after the 8-day residential meditation program. Subjects underwent fMRI with a blood oxygen level dependent (BOLD) contrast at rest and during focused breathing. Changes in network connectivity before and after Samyama program were evaluated. In addition, validated psychological metrics were correlated with changes in functional connectivity. Results: Meditators showed significantly increased network connectivity between the salience network (SN) and default mode network (DMN) after the Samyama program (p < 0.01). Increased connectivity within the SN correlated with an improvement in self-reported mindfulness scores (p < 0.01). Conclusion: Samyama, an intensive silent meditation program, favorably increased the resting-state functional connectivity between the salience and default mode networks. During focused breath watching, meditators had lower intra-network connectivity in specific networks. Furthermore, increased intra-network connectivity correlated with improved self-reported mindfulness after Samyama. Clinical Trials Registration: [https://clinicaltrials.gov], Identifier: [NCT04366544]. Registered on 4/17/2020.
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Affiliation(s)
- Ramana V Vishnubhotla
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Rupa Radhakrishnan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Kestas Kveraga
- Department of Anesthesia, Critical Care and Pain Medicine, Sadhguru Center for a Conscious Planet, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Rachael Deardorff
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Chithra Ram
- Department of Radiology, School of Medicine, University of Louisville, Louisville, KY, United States
| | - Dhanashri Pawale
- Department of Anesthesia, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Janelle Renschler
- Department of Anesthesia, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Balachundhar Subramaniam
- Department of Anesthesia, Critical Care and Pain Medicine, Sadhguru Center for a Conscious Planet, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Senthilkumar Sadhasivam
- Department of Anesthesia, Indiana University School of Medicine, Indianapolis, IN, United States
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24
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Neuhaus E, Lowry SJ, Santhosh M, Kresse A, Edwards LA, Keller J, Libsack EJ, Kang VY, Naples A, Jack A, Jeste S, McPartland JC, Aylward E, Bernier R, Bookheimer S, Dapretto M, Van Horn JD, Pelphrey K, Webb SJ. Resting state EEG in youth with ASD: age, sex, and relation to phenotype. J Neurodev Disord 2021; 13:33. [PMID: 34517813 PMCID: PMC8439051 DOI: 10.1186/s11689-021-09390-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 08/17/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Identification of ASD biomarkers is a key priority for understanding etiology, facilitating early diagnosis, monitoring developmental trajectories, and targeting treatment efforts. Efforts have included exploration of resting state encephalography (EEG), which has a variety of relevant neurodevelopmental correlates and can be collected with minimal burden. However, EEG biomarkers may not be equally valid across the autism spectrum, as ASD is strikingly heterogeneous and individual differences may moderate EEG-behavior associations. Biological sex is a particularly important potential moderator, as females with ASD appear to differ from males with ASD in important ways that may influence biomarker accuracy. METHODS We examined effects of biological sex, age, and ASD diagnosis on resting state EEG among a large, sex-balanced sample of youth with (N = 142, 43% female) and without (N = 138, 49% female) ASD collected across four research sites. Absolute power was extracted across five frequency bands and nine brain regions, and effects of sex, age, and diagnosis were analyzed using mixed-effects linear regression models. Exploratory partial correlations were computed to examine EEG-behavior associations in ASD, with emphasis on possible sex differences in associations. RESULTS Decreased EEG power across multiple frequencies was associated with female sex and older age. Youth with ASD displayed decreased alpha power relative to peers without ASD, suggesting increased neural activation during rest. Associations between EEG and behavior varied by sex. Whereas power across various frequencies correlated with social skills, nonverbal IQ, and repetitive behavior for males with ASD, no such associations were observed for females with ASD. CONCLUSIONS Research using EEG as a possible ASD biomarker must consider individual differences among participants, as these features influence baseline EEG measures and moderate associations between EEG and important behavioral outcomes. Failure to consider factors such as biological sex in such research risks defining biomarkers that misrepresent females with ASD, hindering understanding of the neurobiology, development, and intervention response of this important population.
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Affiliation(s)
- Emily Neuhaus
- Center on Child Health, Behavior and Development, Seattle Children's Research Institute, 1920 Terry Ave, CURE-03, Seattle, WA, 98101, USA
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, USA
| | - Sarah J Lowry
- Center on Child Health, Behavior and Development, Seattle Children's Research Institute, 1920 Terry Ave, CURE-03, Seattle, WA, 98101, USA
| | - Megha Santhosh
- Center on Child Health, Behavior and Development, Seattle Children's Research Institute, 1920 Terry Ave, CURE-03, Seattle, WA, 98101, USA
| | - Anna Kresse
- Mailman School of Public Health, Columbia University, New York, USA
| | - Laura A Edwards
- School of Medicine, Emory University, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Jack Keller
- Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, USA
| | - Erin J Libsack
- Department of Psychology, Stony Brook University, Stony Brook, USA
| | - Veronica Y Kang
- Department of Special Education, University of Illinois at Chicago, Chicago, USA
| | - Adam Naples
- Yale Child Study Center, Yale University, New Haven, USA
| | - Allison Jack
- Department of Psychology, George Mason University, Fairfax, USA
| | - Shafali Jeste
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles School of Medicine, Los Angeles, USA
- Intellectual and Developmental Disabilities Research Center, University of California Los Angeles, Los Angeles, USA
| | | | - Elizabeth Aylward
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, USA
| | - Raphael Bernier
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, USA
| | - Susan Bookheimer
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles School of Medicine, Los Angeles, USA
- Intellectual and Developmental Disabilities Research Center, University of California Los Angeles, Los Angeles, USA
| | - Mirella Dapretto
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles School of Medicine, Los Angeles, USA
- Intellectual and Developmental Disabilities Research Center, University of California Los Angeles, Los Angeles, USA
| | - John D Van Horn
- Department of Psychology, University of Virginia, Charlottesville, USA
- School of Data Science, University of Virginia, Charlottesville, USA
| | - Kevin Pelphrey
- Department of Psychology, University of Virginia, Charlottesville, USA
- Department of Neurology, Brain Institute and School of Education and Human Development, University of Virginia, Charlottesville, USA
| | - Sara Jane Webb
- Center on Child Health, Behavior and Development, Seattle Children's Research Institute, 1920 Terry Ave, CURE-03, Seattle, WA, 98101, USA.
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, USA.
- Intellectual and Developmental Disabilities Research Center, University of Washington, Seattle, USA.
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25
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Raimondo L, Oliveira ĹAF, Heij J, Priovoulos N, Kundu P, Leoni RF, van der Zwaag W. Advances in resting state fMRI acquisitions for functional connectomics. Neuroimage 2021; 243:118503. [PMID: 34479041 DOI: 10.1016/j.neuroimage.2021.118503] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 08/16/2021] [Accepted: 08/22/2021] [Indexed: 01/21/2023] Open
Abstract
Resting state functional magnetic resonance imaging (rs-fMRI) is based on spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal, which occur simultaneously in different brain regions, without the subject performing an explicit task. The low-frequency oscillations of the rs-fMRI signal demonstrate an intrinsic spatiotemporal organization in the brain (brain networks) that may relate to the underlying neural activity. In this review article, we briefly describe the current acquisition techniques for rs-fMRI data, from the most common approaches for resting state acquisition strategies, to more recent investigations with dedicated hardware and ultra-high fields. Specific sequences that allow very fast acquisitions, or multiple echoes, are discussed next. We then consider how acquisition methods weighted towards specific parts of the BOLD signal, like the Cerebral Blood Flow (CBF) or Volume (CBV), can provide more spatially specific network information. These approaches are being developed alongside the commonly used BOLD-weighted acquisitions. Finally, specific applications of rs-fMRI to challenging regions such as the laminae in the neocortex, and the networks within the large areas of subcortical white matter regions are discussed. We finish the review with recommendations for acquisition strategies for a range of typical applications of resting state fMRI.
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Affiliation(s)
- Luisa Raimondo
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | - Ĺcaro A F Oliveira
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | - Jurjen Heij
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | | | - Prantik Kundu
- Hyperfine Research Inc, Guilford, CT, United States; Icahn School of Medicine at Mt. Sinai, New York, United States
| | - Renata Ferranti Leoni
- InBrain, Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, Brazil
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26
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Xu N, Shan W, Qi J, Wu J, Wang Q. Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity. Front Hum Neurosci 2021; 15:649074. [PMID: 34276321 PMCID: PMC8283278 DOI: 10.3389/fnhum.2021.649074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 06/07/2021] [Indexed: 11/21/2022] Open
Abstract
Epilepsy is caused by abnormal electrical discharges (clinically identified by electrophysiological recording) in a specific part of the brain [originating in only one part of the brain, namely, the epileptogenic zone (EZ)]. Epilepsy is now defined as an archetypical hyperexcited neural network disorder. It can be investigated through the network analysis of interictal discharges, ictal discharges, and resting-state functional connectivity. Currently, there is an increasing interest in embedding resting-state connectivity analysis into the preoperative evaluation of epilepsy. Among the various neuroimaging technologies employed to achieve brain functional networks, magnetoencephalography (MEG) with the excellent temporal resolution is an ideal tool for estimating the resting-state connectivity between brain regions, which can reveal network abnormalities in epilepsy. What value does MEG resting-state functional connectivity offer for epileptic presurgical evaluation? Regarding this topic, this paper introduced the origin of MEG and the workflow of constructing source-space functional connectivity based on MEG signals. Resting-state functional connectivity abnormalities correlate with epileptogenic networks, which are defined by the brain regions involved in the production and propagation of epileptic activities. This paper reviewed the evidence of altered epileptic connectivity based on low- or high-frequency oscillations (HFOs) and the evidence of the advantage of using simultaneous MEG and intracranial electroencephalography (iEEG) recordings. More importantly, this review highlighted that MEG-based resting-state functional connectivity has the potential to predict postsurgical outcomes. In conclusion, resting-state MEG functional connectivity has made a substantial progress toward serving as a candidate biomarker included in epileptic presurgical evaluations.
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Affiliation(s)
- Na Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wei Shan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jing Qi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianping Wu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Qun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neuromodulation, Beijing, China
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27
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Li W, Xu Q, Li Y, Li C, Wu F, Ji L. EEG characteristics in “eyes-open” versus “eyes-closed” condition during vibrotactile stimulation. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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28
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The indispensable role of the cerebellum in visual divergent thinking. Sci Rep 2020; 10:16552. [PMID: 33024190 PMCID: PMC7538600 DOI: 10.1038/s41598-020-73679-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 09/21/2020] [Indexed: 12/19/2022] Open
Abstract
Recent research has shown that the cerebellum is involved not only in motor control but also in higher-level activities, which are closely related to creativity. This study aimed to explore the role of the cerebellum in visual divergent thinking based on its intrinsic activity. To this end, we selected the resting-state fMRI data of high- (n = 22) and low-level creativity groups (n = 22), and adopted the voxel-wise, seed-wise, and dynamic functional connectivity to identify the differences between the two groups. Furthermore, the topological properties of the cerebello-cerebral network and their relations with visual divergent thinking were calculated. The voxel-wise functional connectivity results indicated group differences across the cerebellar (e.g. lobules VI, VIIb, Crus I, and Crus II) and cerebral regions (e.g. superior frontal cortex, middle frontal cortex, and inferior parietal gyrus), as well as the cerebellar lobules (e.g. lobules VIIIa, IX, and X) and the cerebral brain regions (the cuneus and precentral gyrus). We found a significant correlation between visual divergent thinking and activities of the left lobules VI, VIIb, Crus I, and Crus II, which are associated with executive functions. Our overall results provide novel insight into the important role of the cerebellum in visual divergent thinking.
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