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Griffiths JD, McIntosh AR, Lefebvre J. A Connectome-Based, Corticothalamic Model of State- and Stimulation-Dependent Modulation of Rhythmic Neural Activity and Connectivity. Front Comput Neurosci 2020; 14:575143. [PMID: 33408622 PMCID: PMC7779529 DOI: 10.3389/fncom.2020.575143] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 11/19/2020] [Indexed: 11/13/2022] Open
Abstract
Rhythmic activity in the brain fluctuates with behaviour and cognitive state, through a combination of coexisting and interacting frequencies. At large spatial scales such as those studied in human M/EEG, measured oscillatory dynamics are believed to arise primarily from a combination of cortical (intracolumnar) and corticothalamic rhythmogenic mechanisms. Whilst considerable progress has been made in characterizing these two types of neural circuit separately, relatively little work has been done that attempts to unify them into a single consistent picture. This is the aim of the present paper. We present and examine a whole-brain, connectome-based neural mass model with detailed long-range cortico-cortical connectivity and strong, recurrent corticothalamic circuitry. This system reproduces a variety of known features of human M/EEG recordings, including spectral peaks at canonical frequencies, and functional connectivity structure that is shaped by the underlying anatomical connectivity. Importantly, our model is able to capture state- (e.g., idling/active) dependent fluctuations in oscillatory activity and the coexistence of multiple oscillatory phenomena, as well as frequency-specific modulation of functional connectivity. We find that increasing the level of sensory drive to the thalamus triggers a suppression of the dominant low frequency rhythms generated by corticothalamic loops, and subsequent disinhibition of higher frequency endogenous rhythmic behaviour of intracolumnar microcircuits. These combine to yield simultaneous decreases in lower frequency and increases in higher frequency components of the M/EEG power spectrum during states of high sensory or cognitive drive. Building on this, we also explored the effect of pulsatile brain stimulation on ongoing oscillatory activity, and evaluated the impact of coexistent frequencies and state-dependent fluctuations on the response of cortical networks. Our results provide new insight into the role played by cortical and corticothalamic circuits in shaping intrinsic brain rhythms, and suggest new directions for brain stimulation therapies aimed at state-and frequency-specific control of oscillatory brain activity.
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Affiliation(s)
- John D. Griffiths
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Anthony Randal McIntosh
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Jeremie Lefebvre
- Department of Biology, University of Ottawa, Ottawa, ON, Canada
- Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Department of Mathematics, University of Toronto, Toronto, ON, Canada
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52
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Gool JK, Cross N, Fronczek R, Lammers GJ, van der Werf YD, Dang-Vu TT. Neuroimaging in Narcolepsy and Idiopathic Hypersomnia: from Neural Correlates to Clinical Practice. CURRENT SLEEP MEDICINE REPORTS 2020. [DOI: 10.1007/s40675-020-00185-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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53
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Dimond D, Heo S, Ip A, Rohr CS, Tansey R, Graff K, Dhollander T, Smith RE, Lebel C, Dewey D, Connelly A, Bray S. Maturation and interhemispheric asymmetry in neurite density and orientation dispersion in early childhood. Neuroimage 2020; 221:117168. [DOI: 10.1016/j.neuroimage.2020.117168] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 06/15/2020] [Accepted: 07/12/2020] [Indexed: 12/13/2022] Open
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Yu J, Rawtaer I, Fam J, Feng L, Kua EH, Mahendran R. The individualized prediction of cognitive test scores in mild cognitive impairment using structural and functional connectivity features. Neuroimage 2020; 223:117310. [PMID: 32861786 DOI: 10.1016/j.neuroimage.2020.117310] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 07/31/2020] [Accepted: 08/20/2020] [Indexed: 11/16/2022] Open
Abstract
Neuropsychological assessments are essential in diagnosing age-related neurocognitive disorders. However, they are lengthy in duration and can be unreliable at times. To this end, we explored a modified connectome-based predictive modeling approach to estimating individualized scores from multiple cognitive domains using structural connectivity (SC) and functional connectivity (FC) features. Multi-shell HARDI and resting-state functional magnetic resonance imaging scans, and scores from 10 cognitive measures were acquired from 91 older adults with mild cognitive impairment. SC and FC matrices were derived from these scans and, in various combinations, entered into models along with demographic covariates to predict cognitive scores. Leave-one-out cross-validation was performed. Predictive accuracy was assessed via the correlation between predicted and observed scores (rpredicted-observed). Across all cognitive measures, significant rpredicted-observed (0.402 to 0.654) were observed from the best-predicting models. Six of these models consisted of multimodal features. For three cognitive measures, their best-predicting models' rpredicted-observed were similar to that of a model that included only demographic covariates- suggesting that SC and/or FC features did not contribute significantly on top of demographics. Cross-prediction models revealed that the best-predicting models were similarly accurate in predicting scores of related cognitive measures- suggesting their limited specificity in predicting cognitive scores. Generally, multimodal connectomes together with demographics, can be exploited as sensitive markers, though with limited specificity, to predict cognitive performance across a spectrum in multiple cognitive domains. In certain situations, it may not be worthwhile to acquire neuroimaging data, considering that demographics alone can be similarly accurate in predicting cognitive scores.
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Affiliation(s)
- Junhong Yu
- Department of Psychological Medicine, Mind Science Centre, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore.
| | - Iris Rawtaer
- Department of Psychological Medicine, Sengkang General Hospital, 110 Sengkang E way, Singapore 544886, Singapore
| | - Johnson Fam
- Department of Psychological Medicine, Mind Science Centre, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Lei Feng
- Department of Psychological Medicine, Mind Science Centre, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Ee-Heok Kua
- Department of Psychological Medicine, Mind Science Centre, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Rathi Mahendran
- Department of Psychological Medicine, Mind Science Centre, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore; Academic Development Department, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore.
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55
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Brain-wide resting-state connectivity regulation by the hippocampus and medial prefrontal cortex is associated with fluid intelligence. Brain Struct Funct 2020; 225:1587-1600. [PMID: 32333100 DOI: 10.1007/s00429-020-02077-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 04/18/2020] [Indexed: 10/24/2022]
Abstract
The connectivity hub property of the hippocampus (HIP) and the medial prefrontal cortex (MPFC) is essential for their widespread involvement in cognition; however, the cooperation mechanism between them is far from clear. Herein, using resting-state functional MRI and Gaussian Bayesian network to describe the directed organizing architecture of the HIP-MPFC pathway with regions in the brain, we demonstrated that the HIP and the MPFC have central roles as the driving hub and aggregating hub, respectively. The status of the HIP and the MPFC is dominant in communications between the HIP and the default-mode network, between the HIP and core neurocognitive networks, including the default-mode, frontoparietal, and salience networks, and between brain-wide representative regions, suggesting a strong and robust central position of the two regions in regulating the dynamics of large-scale brain activity. Furthermore, we found that the directed connectivity and flow from the right HIP to the MPFC is significantly linked to fluid intelligence. Together, these results clarify the different roles of the HIP and the MPFC that jointly contribute to network dynamics and cognitive ability from a data-driven insight via the use of the directed connectivity method.
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56
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Suárez LE, Markello RD, Betzel RF, Misic B. Linking Structure and Function in Macroscale Brain Networks. Trends Cogn Sci 2020; 24:302-315. [PMID: 32160567 DOI: 10.1016/j.tics.2020.01.008] [Citation(s) in RCA: 336] [Impact Index Per Article: 84.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 01/20/2020] [Accepted: 01/21/2020] [Indexed: 02/06/2023]
Abstract
Structure-function relationships are a fundamental principle of many naturally occurring systems. However, network neuroscience research suggests that there is an imperfect link between structural connectivity and functional connectivity in the brain. Here, we synthesize the current state of knowledge linking structure and function in macroscale brain networks and discuss the different types of models used to assess this relationship. We argue that current models do not include the requisite biological detail to completely predict function. Structural network reconstructions enriched with local molecular and cellular metadata, in concert with more nuanced representations of functions and properties, hold great potential for a truly multiscale understanding of the structure-function relationship.
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Affiliation(s)
- Laura E Suárez
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Ross D Markello
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Richard F Betzel
- Psychological and Brain Sciences, Program in Neuroscience, Cognitive Science Program, Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada.
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57
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Has Silemek AC, Fischer L, Pöttgen J, Penner IK, Engel AK, Heesen C, Gold SM, Stellmann JP. Functional and structural connectivity substrates of cognitive performance in relapsing remitting multiple sclerosis with mild disability. Neuroimage Clin 2020; 25:102177. [PMID: 32014828 PMCID: PMC6997626 DOI: 10.1016/j.nicl.2020.102177] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 12/06/2019] [Accepted: 01/11/2020] [Indexed: 01/10/2023]
Abstract
Multiple Sclerosis (MS) is the most common chronic inflammatory and neurodegenerative disease of the central nervous system (CNS), which can lead to severe cognitive impairment over time. Magnetic resonance imaging (MRI) is currently the best available biomarker to track MS pathophysiology in vivo and examine the link to clinical disability. However, conventional MRI metrics have limited sensitivity and specificity to detect direct associations between symptoms and their underlying CNS substrates. In this study, we aimed to investigate structural and resting state functional connectomes and subnetworks associated with neuropsychological (NP) performance using a graph theoretical approach. A comprehensive NP test battery was administered in a sample of patients with relapsing remitting MS (RRMS) and mild disability [n = 33, F/M = 20/13, age = 40.9 ± 9.7, median [Expanded Disability Status Scale] (EDSS) = 2, range =0-4] and compared to healthy controls (HC) [n = 29, F/M = 19/10, age = 41.0 ± 8.5] closely matched for age, sex, and level of education. The NP battery comprised the most relevant domains of cognitive dysfunction in MS including attention, processing speed, verbal and spatial learning and memory, and executive function. While standard MRI metrics showed good correlations with TAP Alertness test, disease duration and neurological exams, structural networks showed closer associations with 9-hole peg test and cognitive performances. Decreased graph strength was associated with two out of the 5 NP tests in the spatial learning and memory domain specified by BVMT [Sum 1-3] and BVMT [Recall], and with also SDMT which is one out of the 9 NP tests in the attention/processing speed domain, while no correlation was found between these scores and functional connectivity. Nodal strength was decreased in all subnetworks based on Yeo atlas in patients compared to HC; however, no difference was observed in nodal level of functional connectivity between the groups. The difference in structural and functional nodal connectivity between the groups was also observed in the relationship between structural and functional connectivity within the groups; the relationship between nodal degree and nodal strength was reversed in patients but positive in controls. On a nodal level, structural and functional networks (mainly the default mode network) were correlated with more than one cognitive domain rather than one specific network for each domain within patients. Interestingly, poorer cognitive performance was mostly correlated with increased functional connectivity but decreased structural connectivity in patients. Increased functional connectivity in the default mode network had both positive as well as negative associations with verbal and spatial learning and memory, possibly indicating adaptive and maladaptive mechanisms. In conclusion, our results suggest that cognitive performance, even in patients with RRMS and very mild disability, may reflect a loss of structural connectivity. In contrast, widespread increases in functional connectivity may be the result of maladaptive processes.
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Affiliation(s)
- Arzu Ceylan Has Silemek
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany.
| | - Lukas Fischer
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany
| | - Jana Pöttgen
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany; Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany
| | - Iris-Katharina Penner
- Klinik für Neurologie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf 40225, Germany; COGITO Zentrum für Angewandte Neurokognition und Neuropsychologische Forschung, Düsseldorf 40225, Germany
| | - Andreas K Engel
- Institut für Neurophysiologie und Pathophysiologie, Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany
| | - Christoph Heesen
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany; Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany
| | - Stefan M Gold
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany; Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health (BIH), Klinik für Psychiatrie & Psychotherapie und Medizinische Klinik m.S. Psychosomatik, Campus Benjamin Franklin (CBF), Hindenburgdamm 30, Berlin 12203, Germany
| | - Jan-Patrick Stellmann
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany; Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany; APHM, Hopital de la Timone, CEMEREM, Marseille, France; Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
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58
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Lee WH, Rodrigue A, Glahn DC, Bassett DS, Frangou S. Heritability and Cognitive Relevance of Structural Brain Controllability. Cereb Cortex 2019; 30:3044-3054. [PMID: 31838501 PMCID: PMC7197079 DOI: 10.1093/cercor/bhz293] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 09/20/2019] [Accepted: 10/30/2019] [Indexed: 01/09/2023] Open
Abstract
Cognition and behavior are thought to emerge from the connections and interactions among brain regions. The precise nature of these relationships remains elusive. Here we use tools provided by network control theory to determine how the structural connectivity profile of brain regions may shape individual variation in cognition. In a cohort of healthy young adults (n = 1066), we computed two fundamental brain regional control patterns, average and modal controllability, which index the degree of influence of a region over others. We first established that regional brain controllability measures were both reproducible and heritable. Regions with controllability profiles theoretically conducive to facilitating multiple cognitive operations were over-represented in higher-order resting-state networks. Finally, variation in regional controllability accounted for about 50% of interindividual variability in multiple cognitive domains. We conclude that controllability is a biologically plausible property of the structural connectome and provides a mechanistic explanation for how brain structural architecture may influence cognitive functions.
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Affiliation(s)
- Won Hee Lee
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Amanda Rodrigue
- Tommy Fuss Center for Neuropsychiatric Disease Research, Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - David C Glahn
- Tommy Fuss Center for Neuropsychiatric Disease Research, Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Physics and Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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59
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Li M, Gui S, Huang Q, Shi L, Lu J, Li P. Density center-based fast clustering of widefield fluorescence imaging of cortical mesoscale functional connectivity and relation to structural connectivity. NEUROPHOTONICS 2019; 6:045014. [PMID: 31853460 PMCID: PMC6917047 DOI: 10.1117/1.nph.6.4.045014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 11/20/2019] [Indexed: 05/09/2023]
Abstract
Spontaneous resting-state neural activity or hemodynamics has been used to reveal functional connectivity in the brain. However, most of the commonly used clustering algorithms for functional parcellation are time-consuming, especially for high-resolution imaging data. We propose a density center-based fast clustering (DCBFC) method that can rapidly perform the functional parcellation of isocortex. DCBFC was validated using both simulation data and the spontaneous calcium signals from widefield fluorescence imaging of excitatory neuron-expressing transgenic mice (Vglut2-GCaMP6s). Compared to commonly used clustering methods such as k-means, hierarchical, and spectral, DCBFC showed a higher adjusted Rand index when the signal-to-noise ratio was greater than - 8 dB for simulated data and higher silhouette coefficient for in vivo mouse data. The resting-state functional connectivity (RSFC) patterns obtained by DCBFC were compared with the anatomic axonal projection density (PDs) maps derived from the voxel-scale model. The results showed a high spatial correlation between RSFC patterns and PDs.
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Affiliation(s)
- Miaowen Li
- Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, Britton Chance Center for Biomedical Photonics, Wuhan, Hubei, China
- Huazhong University of Science and Technology, School of Engineering Sciences, MOE Key Laboratory for Biomedical Photonics, Wuhan, Hubei, China
| | - Shen Gui
- Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, Britton Chance Center for Biomedical Photonics, Wuhan, Hubei, China
- Huazhong University of Science and Technology, School of Engineering Sciences, MOE Key Laboratory for Biomedical Photonics, Wuhan, Hubei, China
| | - Qin Huang
- Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, Britton Chance Center for Biomedical Photonics, Wuhan, Hubei, China
- Huazhong University of Science and Technology, School of Engineering Sciences, MOE Key Laboratory for Biomedical Photonics, Wuhan, Hubei, China
| | - Liang Shi
- Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, Britton Chance Center for Biomedical Photonics, Wuhan, Hubei, China
- Huazhong University of Science and Technology, School of Engineering Sciences, MOE Key Laboratory for Biomedical Photonics, Wuhan, Hubei, China
| | - Jinling Lu
- Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, Britton Chance Center for Biomedical Photonics, Wuhan, Hubei, China
- Huazhong University of Science and Technology, School of Engineering Sciences, MOE Key Laboratory for Biomedical Photonics, Wuhan, Hubei, China
| | - Pengcheng Li
- Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, Britton Chance Center for Biomedical Photonics, Wuhan, Hubei, China
- Huazhong University of Science and Technology, School of Engineering Sciences, MOE Key Laboratory for Biomedical Photonics, Wuhan, Hubei, China
- HUST-Suzhou Institute for Brainsmatics, Suzhou, China
- Address all correspondence to Pengcheng Li, E-mail:
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60
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Ihara N, Wakaizumi K, Nishimura D, Kato J, Yamada T, Suzuki T, Hashiguchi S, Terasawa Y, Kosugi S, Morisaki H. Aberrant resting-state functional connectivity of the dorsolateral prefrontal cortex to the anterior insula and its association with fear avoidance belief in chronic neck pain patients. PLoS One 2019; 14:e0221023. [PMID: 31404104 PMCID: PMC6690512 DOI: 10.1371/journal.pone.0221023] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 07/29/2019] [Indexed: 11/23/2022] Open
Abstract
Chronic neck pain (CNP), a global health problem, involves a large amount of psychological and socioeconomic burdens. Not only physical causes but also behavioral disorders such as a fear-avoidance belief (FAB) can associate with the chronicity of neck pain. However, functional brain mechanisms underlying CNP and its related behavioral disorders remain unknown. The aim of the current resting-state functional magnetic resonance imaging (fMRI) study was to explore how the functional brain networks differed between CNP patients and age- and sex-matched healthy, pain-free controls (HCs). We also investigated whether these possible brain network changes in CNP patients were associated with fear avoidance belief (FAB) and the intensity of pain. We analyzed the resting-state fMRI data of 20 CNP patients and 20 HCs. FAB and the intensity of pain were assessed by Tampa Scale for Kinesiophobia (TSK) and Visual Analog Scale (VAS) of pain. The whole brain analysis showed that CNP patients had significant different functional connectivity (FC) compared with HCs, and the right dorsolateral prefrontal cortex (DLPFC) was a core hub of these altered functional networks. Furthermore, general linear model analyses showed that, in CNP patients, the increased FC between the right DLPFC and the right anterior insular cortex (aIC) significantly associated with increased TSK (p = 0.01, statistical significance after Bonferroni correction: p<0.025), and the FC between the right DLPFC and dorsal posterior cingulate cortex had a trend of inverse association with VAS (p = 0.04). Our findings suggest that aberrant FCs between the right DLPFC and aIC associated with CNP and its related FAB.
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Affiliation(s)
- Naho Ihara
- Department of Anesthesiology, Keio University School of Medicine, Tokyo, Japan
| | - Kenta Wakaizumi
- Department of Anesthesiology, Keio University School of Medicine, Tokyo, Japan
| | - Daisuke Nishimura
- Department of Anesthesiology, Keio University School of Medicine, Tokyo, Japan
| | - Jungo Kato
- Department of Anesthesiology, Keio University School of Medicine, Tokyo, Japan
| | - Takashige Yamada
- Department of Anesthesiology, Keio University School of Medicine, Tokyo, Japan
| | - Takeshi Suzuki
- Department of Anesthesiology, Keio University School of Medicine, Tokyo, Japan
| | - Saori Hashiguchi
- Department of Anesthesiology, Keio University School of Medicine, Tokyo, Japan
| | - Yuri Terasawa
- Department of Psychology, Keio University, Tokyo, Japan
| | - Shizuko Kosugi
- Department of Anesthesiology, Keio University School of Medicine, Tokyo, Japan
| | - Hiroshi Morisaki
- Department of Anesthesiology, Keio University School of Medicine, Tokyo, Japan
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