How are different neural networks related to consciousness?

Objective We aimed to investigate the roles of different resting‐state networks in predicting both the actual level of consciousness and its recovery in brain injury patients. Methods We investigated resting‐state functional connectivity within different networks in patients with varying levels of c...

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Veröffentlicht in:	Annals of neurology Jg. 78; H. 4; S. 594 - 605
Hauptverfasser:	Qin, Pengmin, Wu, Xuehai, Huang, Zirui, Duncan, Niall W., Tang, Weijun, Wolff, Annemarie, Hu, Jin, Gao, Liang, Jin, Yi, Wu, Xing, Zhang, Jianfeng, Lu, Lu, Wu, Chunping, Qu, Xiaoying, Mao, Ying, Weng, Xuchu, Zhang, Jun, Northoff, Georg
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Blackwell Publishing Ltd 01.10.2015 Wiley Subscription Services, Inc
Schlagworte:	Adult Brain - pathology Brain - physiopathology Brain Injuries - diagnosis Brain Injuries - physiopathology Consciousness Consciousness - physiology Female Humans Male Middle Aged Nerve Net - pathology Nerve Net - physiopathology Patients Persistent Vegetative State - diagnosis Persistent Vegetative State - physiopathology Rest - physiology
ISSN:	0364-5134, 1531-8249, 1531-8249
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Abstract	Objective We aimed to investigate the roles of different resting‐state networks in predicting both the actual level of consciousness and its recovery in brain injury patients. Methods We investigated resting‐state functional connectivity within different networks in patients with varying levels of consciousness: unresponsive wakefulness syndrome (UWS; n = 56), minimally conscious state (MCS; n = 29), and patients with brain lesions but full consciousness (BL; n = 48). Considering the actual level of consciousness, we compared the strength of network connectivity among the patient groups. We then checked the presence of connections between specific regions in individual patients and calculated the frequency of this in the different patient groups. Considering the recovery of consciousness, we split the UWS group into 2 subgroups according to recovery: those who emerged from UWS (UWS‐E) and those who remained in UWS (UWS‐R). The above analyses were repeated on these 2 subgroups. Results Functional connectivity strength in salience network (SN), especially connectivity between the supragenual anterior cingulate cortex (SACC) and left anterior insula (LAI), was reduced in the unconscious state (UWS) compared to the conscious state (MCS and BL). Moreover, at the individual level, SACC‐LAI connectivity was more present in MCS than in UWS. Default‐mode network (DMN) connectivity strength, especially between the posterior cingulate cortex (PCC) and left lateral parietal cortex (LLPC), was reduced in UWS‐R compared with UWS‐E. Furthermore, PCC‐LLPC connectivity was more present in UWS‐E than in UWS‐R. Interpretation Our findings show that SN (SACC‐LAI) connectivity correlates with behavioral signs of consciousness, whereas DMN (PCC‐LLPC) connectivity instead predicts recovery of consciousness. Ann Neurol 2015;78:594–605
AbstractList	We aimed to investigate the roles of different resting-state networks in predicting both the actual level of consciousness and its recovery in brain injury patients.OBJECTIVEWe aimed to investigate the roles of different resting-state networks in predicting both the actual level of consciousness and its recovery in brain injury patients.We investigated resting-state functional connectivity within different networks in patients with varying levels of consciousness: unresponsive wakefulness syndrome (UWS; n = 56), minimally conscious state (MCS; n = 29), and patients with brain lesions but full consciousness (BL; n = 48). Considering the actual level of consciousness, we compared the strength of network connectivity among the patient groups. We then checked the presence of connections between specific regions in individual patients and calculated the frequency of this in the different patient groups. Considering the recovery of consciousness, we split the UWS group into 2 subgroups according to recovery: those who emerged from UWS (UWS-E) and those who remained in UWS (UWS-R). The above analyses were repeated on these 2 subgroups.METHODSWe investigated resting-state functional connectivity within different networks in patients with varying levels of consciousness: unresponsive wakefulness syndrome (UWS; n = 56), minimally conscious state (MCS; n = 29), and patients with brain lesions but full consciousness (BL; n = 48). Considering the actual level of consciousness, we compared the strength of network connectivity among the patient groups. We then checked the presence of connections between specific regions in individual patients and calculated the frequency of this in the different patient groups. Considering the recovery of consciousness, we split the UWS group into 2 subgroups according to recovery: those who emerged from UWS (UWS-E) and those who remained in UWS (UWS-R). The above analyses were repeated on these 2 subgroups.Functional connectivity strength in salience network (SN), especially connectivity between the supragenual anterior cingulate cortex (SACC) and left anterior insula (LAI), was reduced in the unconscious state (UWS) compared to the conscious state (MCS and BL). Moreover, at the individual level, SACC-LAI connectivity was more present in MCS than in UWS. Default-mode network (DMN) connectivity strength, especially between the posterior cingulate cortex (PCC) and left lateral parietal cortex (LLPC), was reduced in UWS-R compared with UWS-E. Furthermore, PCC-LLPC connectivity was more present in UWS-E than in UWS-R.RESULTSFunctional connectivity strength in salience network (SN), especially connectivity between the supragenual anterior cingulate cortex (SACC) and left anterior insula (LAI), was reduced in the unconscious state (UWS) compared to the conscious state (MCS and BL). Moreover, at the individual level, SACC-LAI connectivity was more present in MCS than in UWS. Default-mode network (DMN) connectivity strength, especially between the posterior cingulate cortex (PCC) and left lateral parietal cortex (LLPC), was reduced in UWS-R compared with UWS-E. Furthermore, PCC-LLPC connectivity was more present in UWS-E than in UWS-R.Our findings show that SN (SACC-LAI) connectivity correlates with behavioral signs of consciousness, whereas DMN (PCC-LLPC) connectivity instead predicts recovery of consciousness.INTERPRETATIONOur findings show that SN (SACC-LAI) connectivity correlates with behavioral signs of consciousness, whereas DMN (PCC-LLPC) connectivity instead predicts recovery of consciousness. Objective We aimed to investigate the roles of different resting‐state networks in predicting both the actual level of consciousness and its recovery in brain injury patients. Methods We investigated resting‐state functional connectivity within different networks in patients with varying levels of consciousness: unresponsive wakefulness syndrome (UWS; n = 56), minimally conscious state (MCS; n = 29), and patients with brain lesions but full consciousness (BL; n = 48). Considering the actual level of consciousness, we compared the strength of network connectivity among the patient groups. We then checked the presence of connections between specific regions in individual patients and calculated the frequency of this in the different patient groups. Considering the recovery of consciousness, we split the UWS group into 2 subgroups according to recovery: those who emerged from UWS (UWS‐E) and those who remained in UWS (UWS‐R). The above analyses were repeated on these 2 subgroups. Results Functional connectivity strength in salience network (SN), especially connectivity between the supragenual anterior cingulate cortex (SACC) and left anterior insula (LAI), was reduced in the unconscious state (UWS) compared to the conscious state (MCS and BL). Moreover, at the individual level, SACC‐LAI connectivity was more present in MCS than in UWS. Default‐mode network (DMN) connectivity strength, especially between the posterior cingulate cortex (PCC) and left lateral parietal cortex (LLPC), was reduced in UWS‐R compared with UWS‐E. Furthermore, PCC‐LLPC connectivity was more present in UWS‐E than in UWS‐R. Interpretation Our findings show that SN (SACC‐LAI) connectivity correlates with behavioral signs of consciousness, whereas DMN (PCC‐LLPC) connectivity instead predicts recovery of consciousness. Ann Neurol 2015;78:594–605 Objective We aimed to investigate the roles of different resting-state networks in predicting both the actual level of consciousness and its recovery in brain injury patients. Methods We investigated resting-state functional connectivity within different networks in patients with varying levels of consciousness: unresponsive wakefulness syndrome (UWS; n=56), minimally conscious state (MCS; n=29), and patients with brain lesions but full consciousness (BL; n=48). Considering the actual level of consciousness, we compared the strength of network connectivity among the patient groups. We then checked the presence of connections between specific regions in individual patients and calculated the frequency of this in the different patient groups. Considering the recovery of consciousness, we split the UWS group into 2 subgroups according to recovery: those who emerged from UWS (UWS-E) and those who remained in UWS (UWS-R). The above analyses were repeated on these 2 subgroups. Results Functional connectivity strength in salience network (SN), especially connectivity between the supragenual anterior cingulate cortex (SACC) and left anterior insula (LAI), was reduced in the unconscious state (UWS) compared to the conscious state (MCS and BL). Moreover, at the individual level, SACC-LAI connectivity was more present in MCS than in UWS. Default-mode network (DMN) connectivity strength, especially between the posterior cingulate cortex (PCC) and left lateral parietal cortex (LLPC), was reduced in UWS-R compared with UWS-E. Furthermore, PCC-LLPC connectivity was more present in UWS-E than in UWS-R. Interpretation Our findings show that SN (SACC-LAI) connectivity correlates with behavioral signs of consciousness, whereas DMN (PCC-LLPC) connectivity instead predicts recovery of consciousness. Ann Neurol 2015;78:594-605 We aimed to investigate the roles of different resting-state networks in predicting both the actual level of consciousness and its recovery in brain injury patients. We investigated resting-state functional connectivity within different networks in patients with varying levels of consciousness: unresponsive wakefulness syndrome (UWS; n = 56), minimally conscious state (MCS; n = 29), and patients with brain lesions but full consciousness (BL; n = 48). Considering the actual level of consciousness, we compared the strength of network connectivity among the patient groups. We then checked the presence of connections between specific regions in individual patients and calculated the frequency of this in the different patient groups. Considering the recovery of consciousness, we split the UWS group into 2 subgroups according to recovery: those who emerged from UWS (UWS-E) and those who remained in UWS (UWS-R). The above analyses were repeated on these 2 subgroups. Functional connectivity strength in salience network (SN), especially connectivity between the supragenual anterior cingulate cortex (SACC) and left anterior insula (LAI), was reduced in the unconscious state (UWS) compared to the conscious state (MCS and BL). Moreover, at the individual level, SACC-LAI connectivity was more present in MCS than in UWS. Default-mode network (DMN) connectivity strength, especially between the posterior cingulate cortex (PCC) and left lateral parietal cortex (LLPC), was reduced in UWS-R compared with UWS-E. Furthermore, PCC-LLPC connectivity was more present in UWS-E than in UWS-R. Our findings show that SN (SACC-LAI) connectivity correlates with behavioral signs of consciousness, whereas DMN (PCC-LLPC) connectivity instead predicts recovery of consciousness.
Author	Mao, Ying Gao, Liang Duncan, Niall W. Hu, Jin Jin, Yi Wu, Chunping Lu, Lu Huang, Zirui Zhang, Jianfeng Northoff, Georg Weng, Xuchu Tang, Weijun Wolff, Annemarie Qu, Xiaoying Wu, Xuehai Wu, Xing Qin, Pengmin Zhang, Jun
Author_xml	– sequence: 1 givenname: Pengmin surname: Qin fullname: Qin, Pengmin organization: Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan – sequence: 2 givenname: Xuehai surname: Wu fullname: Wu, Xuehai organization: Neurosurgical Department of Huashan Hospital, Fudan University, Shanghai, China – sequence: 3 givenname: Zirui surname: Huang fullname: Huang, Zirui organization: Institute of Mental Health Research, University of Ottawa, Ontario, Ottawa, Canada – sequence: 4 givenname: Niall W. surname: Duncan fullname: Duncan, Niall W. organization: Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan – sequence: 5 givenname: Weijun surname: Tang fullname: Tang, Weijun organization: Radiologic Department of Huashan Hospital, Fudan University, Shanghai, China – sequence: 6 givenname: Annemarie surname: Wolff fullname: Wolff, Annemarie organization: Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan – sequence: 7 givenname: Jin surname: Hu fullname: Hu, Jin organization: Neurosurgical Department of Huashan Hospital, Fudan University, Shanghai, China – sequence: 8 givenname: Liang surname: Gao fullname: Gao, Liang organization: Neurosurgical Department of Huashan Hospital, Fudan University, Shanghai, China – sequence: 9 givenname: Yi surname: Jin fullname: Jin, Yi organization: Neurosurgical Department of Huashan Hospital, Fudan University, Shanghai, China – sequence: 10 givenname: Xing surname: Wu fullname: Wu, Xing organization: Neurosurgical Department of Huashan Hospital, Fudan University, Shanghai, China – sequence: 11 givenname: Jianfeng surname: Zhang fullname: Zhang, Jianfeng organization: Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China – sequence: 12 givenname: Lu surname: Lu fullname: Lu, Lu organization: Radiologic Department of Huashan Hospital, Fudan University, Shanghai, China – sequence: 13 givenname: Chunping surname: Wu fullname: Wu, Chunping organization: Radiologic Department of Huashan Hospital, Fudan University, Shanghai, China – sequence: 14 givenname: Xiaoying surname: Qu fullname: Qu, Xiaoying organization: Radiologic Department of Huashan Hospital, Fudan University, Shanghai, China – sequence: 15 givenname: Ying surname: Mao fullname: Mao, Ying organization: Neurosurgical Department of Huashan Hospital, Fudan University, Shanghai, China – sequence: 16 givenname: Xuchu surname: Weng fullname: Weng, Xuchu organization: Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China – sequence: 17 givenname: Jun surname: Zhang fullname: Zhang, Jun organization: Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, China – sequence: 18 givenname: Georg surname: Northoff fullname: Northoff, Georg email: maoying@fudan.edu.cn organization: Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/26290126$$D View this record in MEDLINE/PubMed
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PublicationTitle	Annals of neurology
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References	Vanhaudenhuyse A, Demertzi A, Schabus M, et al. Two distinct neuronal networks mediate the awareness of environment and of self. J Cogn Neurosci 2011;23:570-578. Youden WJ. Index for rating diagnostic tests. Cancer 1950;3:32-35. Horovitz SG, Fukunaga M, de Zwart JA, et al. Low frequency BOLD fluctuations during resting wakefulness and light sleep: a simultaneous EEG-fMRI study. Hum Brain Mapp 2008;29:671-682. Huang Z, Dai R, Wu X, et al. The self and its resting state in consciousness: an investigation of the vegetative state. Hum Brain Mapp 2013;35:1997-2008. Yan C, Liu D, He Y, et al. Spontaneous brain activity in the default mode network is sensitive to different resting-state conditions with limited cognitive load. PloS One 2009;4:e5743. Yan CG, Craddock RC, He Y, Milham MP. Addressing head motion dependencies for small-world topologies in functional connectomics. Front Hum Neurosci 2013;7:910. Qin P, Di H, Liu Y, et al. Anterior cingulate activity and the self in disorders of consciousness. Hum Brain Mapp 2010;31:1993-2002. Coleman MR, Rodd JM, Davis MH, et al. Do vegetative patients retain aspects of language comprehension? Evidence from fMRI. Brain 2007;130:2494-2507. Boly M, Tshibanda L, Vanhaudenhuyse A, et al. Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient. Hum Brain Mapp 2009;30:2393-2400. Crone JS, Holler Y, Bergmann J, et al. Self-related processing and deactivation of cortical midline regions in disorders of consciousness. Front Hum Neurosci 2013;7:504. Beissner F, Meissner K, Bar KJ, Napadow V. The autonomic brain: an activation likelihood estimation meta-analysis for central processing of autonomic function. J Neurosci 2013;33:10503-10511. Northoff G. Unlocking the brain. Volume II: Consciousness. Oxford, UK: Oxford University Press, 2014. Seeley WW, Menon V, Schatzberg AF, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 2007;27:2349-2356. Vanhaudenhuyse A, Noirhomme Q, Tshibanda LJ, et al. Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients. Brain 2010;133(pt 1):161-171. Rumsey DJ. Statistics for dummies. 2nd ed. Hoboken, NJ: Wiley, 2011. Boly M, Balteau E, Schnakers C, et al. Baseline brain activity fluctuations predict somatosensory perception in humans. Proc Natl Acad Sci U S A 2007;104:12187-12192. Di Perri C, Heine L, Amico E, et al. Technology-based assessment in patients with disorders of consciousness. Ann Ist Super Sanita 2014;50:209-220. Van Dijk KR, Sabuncu MR, Buckner RL. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 2012;59:431-438. Medford N, Critchley HD. Conjoint activity of anterior insular and anterior cingulate cortex: awareness and response. Brain Struct Funct 2010;214:535-549. Qin P, Northoff G. How is our self related to midline regions and the default-mode network? Neuroimage 2011;57:1221-1233. Voss HU, Heier LA, Schiff ND. Multimodal imaging of recovery of functional networks associated with reversal of paradoxical herniation after cranioplasty. Clin Imaging 2011;35:253-258. Mukamel EA, Pirondini E, Babadi B, et al. A transition in brain state during propofol-induced unconsciousness. J Neurosci 2014;34:839-845. Giacino JT, Fins JJ, Laureys S, Schiff ND. Disorders of consciousness after acquired brain injury: the state of the science. Nat Rev Neurol 2014;10:99-114. Di Perri C, Bastianello S, Bartsch AJ, et al. Limbic hyperconnectivity in the vegetative state. Neurology 2013;81:1417-1424. Fransson P. Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Hum Brain Mapp 2005;26:15-29. Demertzi A, Gomez F, Crone JS, et al. Multiple fMRI system-level baseline connectivity is disrupted in patients with consciousness alterations. Cortex 2014;52:35-46. Langsjo JW, Alkire MT, Kaskinoro K, et al. Returning from oblivion: imaging the neural core of consciousness. J Neurosci 2012;32:4935-4943. Stender J, Gosseries O, Bruno MA, et al. Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: a clinical validation study. Lancet 2014;384:514-522. Boly M, Phillips C, Tshibanda L, et al. Intrinsic brain activity in altered states of consciousness: how conscious is the default mode of brain function? Ann N Y Acad Sci 2008;1129:119-129. Andronache A, Rosazza C, Sattin D, et al. Impact of functional MRI data preprocessing pipeline on default-mode network detectability in patients with disorders of consciousness. Front Neuroinform 2013;7:16. Satterthwaite TD, Elliott MA, Gerraty RT, et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage 2013;64:240-256. Cauda F, Micon BM, Sacco K, et al. Disrupted intrinsic functional connectivity in the vegetative state. J Neurol Neurosurg Psychiatry 2009;80:429-431. Norton L, Hutchison RM, Young GB, et al. Disruptions of functional connectivity in the default mode network of comatose patients. Neurology 2012;78:175-181. Desikan RS, Segonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006;31:968-980. Shulman RG, Hyder F, Rothman DL. Baseline brain energy supports the state of consciousness. Proc Natl Acad Sci U S A 2009;106:11096-11101. Mason MF, Norton MI, Van Horn JD, et al. Wandering minds: the default network and stimulus-independent thought. Science 2007;315:393-395. Stender J, Kupers R, Rodell A, et al. Quantitative rates of brain glucose metabolism distinguish minimally conscious from vegetative state patients. J Cereb Blood Flow Metab 2015;35:58-65. Giacino JT, Kalmar K, Whyte J. The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility. Arch Phys Med Rehabil 2004;85:2020-2029. Saad ZS, Gotts SJ, Murphy K, et al. Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect 2012;2:25-32. Boveroux P, Vanhaudenhuyse A, Bruno MA, et al. Breakdown of within- and between-network resting state functional magnetic resonance imaging connectivity during propofol-induced loss of consciousness. Anesthesiology 2010;113:1038-1053. Fox MD, Snyder AZ, Vincent JL, et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 2005;102:9673-9678. Vincent JL, Patel GH, Fox MD, et al. Intrinsic functional architecture in the anaesthetized monkey brain. Nature 2007;447:83-86. Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 1996;29:162-173. Schiff ND. Recovery of consciousness after brain injury: a mesocircuit hypothesis. Trends Neurosci 2010;33:1-9. Casali AG, Gosseries O, Rosanova M, et al. A theoretically based index of consciousness independent of sensory processing and behavior. Sci Transl Med 2013;5:198ra05. Martuzzi R, Ramani R, Qiu M, et al. Functional connectivity and alterations in baseline brain state in humans. Neuroimage 2010;49:823-834. Schilbach L, Eickhoff SB, Rotarska-Jagiela A, et al. Minds at rest? Social cognition as the default mode of cognizing and its putative relationship to the "default system" of the brain. Conscious Cogn 2008;17:457-467. Cordes D, Haughton V, Carew JD, et al. Hierarchical clustering to measure connectivity in fMRI resting-state data. Magn Reson Imaging 2002;20:305-317. Raichle ME, MacLeod AM, Snyder AZ, et al. A default mode of brain function. Proc Natl Acad Sci U S A 2001;98:676-682. Crone JS, Schurz M, Holler Y, et al. Impaired consciousness is linked to changes in effective connectivity of the posterior cingulate cortex within the default mode network. Neuroimage 2015;110:101-109. Crone JS, Soddu A, Holler Y, et al. Altered network properties of the fronto-parietal network and the thalamus in impaired consciousness. Neuroimage Clin 2013;4:240-248. Sadaghiani S, Scheeringa R, Lehongre K, et al. Intrinsic connectivity networks, alpha oscillations, and tonic alertness: a simultaneous electroencephalography/functional magnetic resonance imaging study. J Neurosci 2010;30:10243-10250. Power JD, Barnes KA, Snyder AZ, et al. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 2012;59:2142-2154. Heine L, Soddu A, Gomez F, et al. Resting state networks and consciousness: alterations of multiple resting state network connectivity in physiological, pharmacological, and pathological consciousness States. Front Psychol 2012;3:295. Greicius MD, Kiviniemi V, Tervonen O, et al. Persistent default-mode network connectivity during light sedation. Hum Brain Mapp 2008;29:839-847. Schrouff J, Perlbarg V, Boly M, et al. Brain functional integration decreases during propofol-induced loss of consciousness. Neuroimage 2011;57:198-205. 2015; 35 2007; 104 2006; 31 2013; 4 2009; 80 2013; 64 2011; 57 2012; 59 2013; 7 2005; 26 2013; 5 1996; 29 2005; 102 2008; 29 2007; 130 2010; 113 2011; 23 2014; 52 2008; 1129 2014; 50 2010; 30 1950; 3 2014; 10 2007; 27 2001; 98 2010; 33 2004; 85 2010; 31 2007; 447 2011 2008; 17 2011; 35 2012; 78 2012; 32 2009; 30 2010; 49 2012; 2 2012; 3 2007; 315 2013; 33 2002; 20 2013; 35 2010; 214 2015; 110 2010; 133 2013; 81 2014 2009; 4 2014; 384 2014; 34 2009; 106 e_1_2_8_28_1 e_1_2_8_24_1 e_1_2_8_47_1 e_1_2_8_26_1 e_1_2_8_49_1 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_9_1 e_1_2_8_20_1 e_1_2_8_43_1 e_1_2_8_22_1 e_1_2_8_45_1 Rumsey DJ (e_1_2_8_42_1) 2011 e_1_2_8_41_1 e_1_2_8_17_1 e_1_2_8_19_1 e_1_2_8_36_1 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_57_1 Perri C (e_1_2_8_13_1) 2014; 50 Northoff G (e_1_2_8_3_1) 2014 e_1_2_8_32_1 e_1_2_8_55_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_53_1 e_1_2_8_51_1 e_1_2_8_30_1 e_1_2_8_29_1 e_1_2_8_25_1 e_1_2_8_46_1 e_1_2_8_27_1 e_1_2_8_48_1 e_1_2_8_2_1 e_1_2_8_4_1 e_1_2_8_6_1 e_1_2_8_8_1 e_1_2_8_21_1 e_1_2_8_23_1 e_1_2_8_44_1 e_1_2_8_40_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_16_1 e_1_2_8_37_1 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_56_1 e_1_2_8_12_1 e_1_2_8_33_1 e_1_2_8_54_1 e_1_2_8_52_1 e_1_2_8_50_1
References_xml	– reference: Vanhaudenhuyse A, Noirhomme Q, Tshibanda LJ, et al. Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients. Brain 2010;133(pt 1):161-171. – reference: Boly M, Tshibanda L, Vanhaudenhuyse A, et al. Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient. Hum Brain Mapp 2009;30:2393-2400. – reference: Beissner F, Meissner K, Bar KJ, Napadow V. The autonomic brain: an activation likelihood estimation meta-analysis for central processing of autonomic function. J Neurosci 2013;33:10503-10511. – reference: Langsjo JW, Alkire MT, Kaskinoro K, et al. Returning from oblivion: imaging the neural core of consciousness. J Neurosci 2012;32:4935-4943. – reference: Yan C, Liu D, He Y, et al. Spontaneous brain activity in the default mode network is sensitive to different resting-state conditions with limited cognitive load. PloS One 2009;4:e5743. – reference: Voss HU, Heier LA, Schiff ND. Multimodal imaging of recovery of functional networks associated with reversal of paradoxical herniation after cranioplasty. Clin Imaging 2011;35:253-258. – reference: Andronache A, Rosazza C, Sattin D, et al. Impact of functional MRI data preprocessing pipeline on default-mode network detectability in patients with disorders of consciousness. Front Neuroinform 2013;7:16. – reference: Crone JS, Soddu A, Holler Y, et al. Altered network properties of the fronto-parietal network and the thalamus in impaired consciousness. Neuroimage Clin 2013;4:240-248. – reference: Cordes D, Haughton V, Carew JD, et al. Hierarchical clustering to measure connectivity in fMRI resting-state data. Magn Reson Imaging 2002;20:305-317. – reference: Rumsey DJ. Statistics for dummies. 2nd ed. Hoboken, NJ: Wiley, 2011. – reference: Casali AG, Gosseries O, Rosanova M, et al. A theoretically based index of consciousness independent of sensory processing and behavior. Sci Transl Med 2013;5:198ra05. – reference: Youden WJ. Index for rating diagnostic tests. Cancer 1950;3:32-35. – reference: Yan CG, Craddock RC, He Y, Milham MP. Addressing head motion dependencies for small-world topologies in functional connectomics. Front Hum Neurosci 2013;7:910. – reference: Di Perri C, Heine L, Amico E, et al. Technology-based assessment in patients with disorders of consciousness. Ann Ist Super Sanita 2014;50:209-220. – reference: Schiff ND. Recovery of consciousness after brain injury: a mesocircuit hypothesis. Trends Neurosci 2010;33:1-9. – reference: Heine L, Soddu A, Gomez F, et al. Resting state networks and consciousness: alterations of multiple resting state network connectivity in physiological, pharmacological, and pathological consciousness States. Front Psychol 2012;3:295. – reference: Martuzzi R, Ramani R, Qiu M, et al. 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Anesthesiology 2010;113:1038-1053. – reference: Crone JS, Holler Y, Bergmann J, et al. Self-related processing and deactivation of cortical midline regions in disorders of consciousness. Front Hum Neurosci 2013;7:504. – reference: Demertzi A, Gomez F, Crone JS, et al. Multiple fMRI system-level baseline connectivity is disrupted in patients with consciousness alterations. Cortex 2014;52:35-46. – reference: Saad ZS, Gotts SJ, Murphy K, et al. Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect 2012;2:25-32. – reference: Huang Z, Dai R, Wu X, et al. The self and its resting state in consciousness: an investigation of the vegetative state. Hum Brain Mapp 2013;35:1997-2008. – reference: Desikan RS, Segonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. 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Hum Brain Mapp 2010;31:1993-2002. – reference: Northoff G. Unlocking the brain. Volume II: Consciousness. Oxford, UK: Oxford University Press, 2014. – reference: Mukamel EA, Pirondini E, Babadi B, et al. A transition in brain state during propofol-induced unconsciousness. J Neurosci 2014;34:839-845. – reference: Stender J, Gosseries O, Bruno MA, et al. Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: a clinical validation study. Lancet 2014;384:514-522. – reference: Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 1996;29:162-173. – reference: Schrouff J, Perlbarg V, Boly M, et al. Brain functional integration decreases during propofol-induced loss of consciousness. Neuroimage 2011;57:198-205. – reference: Van Dijk KR, Sabuncu MR, Buckner RL. The influence of head motion on intrinsic functional connectivity MRI. 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Snippet	Objective We aimed to investigate the roles of different resting‐state networks in predicting both the actual level of consciousness and its recovery in brain... We aimed to investigate the roles of different resting-state networks in predicting both the actual level of consciousness and its recovery in brain injury... Objective We aimed to investigate the roles of different resting-state networks in predicting both the actual level of consciousness and its recovery in brain...
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SubjectTerms	Adult Brain - pathology Brain - physiopathology Brain Injuries - diagnosis Brain Injuries - physiopathology Consciousness Consciousness - physiology Female Humans Male Middle Aged Nerve Net - pathology Nerve Net - physiopathology Patients Persistent Vegetative State - diagnosis Persistent Vegetative State - physiopathology Rest - physiology
Title	How are different neural networks related to consciousness?
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