Recursive Partitioning Analysis of Fractional Low-Frequency Fluctuations in Narcolepsy With Cataplexy
To identify narcolepsy related regional brain activity alterations compared with matched healthy controls. To determine whether these changes can be used to distinguish narcolepsy from healthy controls by recursive partitioning analysis (RPA) and receiver operating characteristic (ROC) curve analysi...
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          | Published in | Frontiers in neurology Vol. 9; p. 936 | 
|---|---|
| Main Authors | , , , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
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          Frontiers Media S.A
    
        02.11.2018
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| Online Access | Get full text | 
| ISSN | 1664-2295 1664-2295  | 
| DOI | 10.3389/fneur.2018.00936 | 
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| Abstract | To identify narcolepsy related regional brain activity alterations compared with matched healthy controls. To determine whether these changes can be used to distinguish narcolepsy from healthy controls by recursive partitioning analysis (RPA) and receiver operating characteristic (ROC) curve analysis.
Fifty-one narcolepsy with cataplexy patients (26 adults and 25 juveniles) and sixty matched heathy controls (30 adults and 30 juveniles) were recruited. All subjects underwent a resting-state functional magnetic resonance imaging scan. Fractional low-frequency fluctuations (fALFF) was used to investigate narcolepsy induced regional brain activity alterations among adult and juveniles, respectively. Recursive partitioning analysis and Receiver operating curve analysis was used to seek the ability of fALFF values within brain regions in distinguishing narcolepsy from healthy controls.
Compared with healthy controls, both adult and juvenile narcolepsy had lower fALFF values in bilateral medial superior frontal gyrus, bilateral inferior parietal lobule and supra-marginal gyrus. Compared with healthy controls, both adult and juvenile narcolepsy had higher fALFF values in bilateral sensorimotor cortex and middle temporal gyrus. Also juvenile narcolepsy had higher fALFF in right putamen and right thalamus compared with healthy controls. Based on RPA and ROC curve analysis, in adult participants, fALFF differences in right medial superior frontal gyrus can discriminate narcolepsy from healthy controls with high degree of sensitivity (100%) and specificity (88.9%). In juvenile participants, fALFF differences in left superior frontal gyrus can discriminate narcolepsy from healthy controls with moderate degree of sensitivity (57.1%) and specificity (88.9%).
Compared with healthy controls, both the adult and juvenile narcolepsy showed overlap brain regions in fALFF differences after case-control comparison. Furthermore, we propose that fALFF value can be a helpful imaging biomarker in distinguishing narcolepsy from healthy controls among both adults and juveniles. | 
    
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| AbstractList | Objective: To identify narcolepsy related regional brain activity alterations compared with matched healthy controls. To determine whether these changes can be used to distinguish narcolepsy from healthy controls by recursive partitioning analysis (RPA) and receiver operating characteristic (ROC) curve analysis. Method: Fifty-one narcolepsy with cataplexy patients (26 adults and 25 juveniles) and sixty matched heathy controls (30 adults and 30 juveniles) were recruited. All subjects underwent a resting-state functional magnetic resonance imaging scan. Fractional low-frequency fluctuations (fALFF) was used to investigate narcolepsy induced regional brain activity alterations among adult and juveniles, respectively. Recursive partitioning analysis and Receiver operating curve analysis was used to seek the ability of fALFF values within brain regions in distinguishing narcolepsy from healthy controls. Results: Compared with healthy controls, both adult and juvenile narcolepsy had lower fALFF values in bilateral medial superior frontal gyrus, bilateral inferior parietal lobule and supra-marginal gyrus. Compared with healthy controls, both adult and juvenile narcolepsy had higher fALFF values in bilateral sensorimotor cortex and middle temporal gyrus. Also juvenile narcolepsy had higher fALFF in right putamen and right thalamus compared with healthy controls. Based on RPA and ROC curve analysis, in adult participants, fALFF differences in right medial superior frontal gyrus can discriminate narcolepsy from healthy controls with high degree of sensitivity (100%) and specificity (88.9%). In juvenile participants, fALFF differences in left superior frontal gyrus can discriminate narcolepsy from healthy controls with moderate degree of sensitivity (57.1%) and specificity (88.9%). Conclusion: Compared with healthy controls, both the adult and juvenile narcolepsy showed overlap brain regions in fALFF differences after case-control comparison. Furthermore, we propose that fALFF value can be a helpful imaging biomarker in distinguishing narcolepsy from healthy controls among both adults and juveniles. Objective: To identify narcolepsy related regional brain activity alterations compared with matched healthy controls. To determine whether these changes can be used to distinguish narcolepsy from healthy controls by recursive partitioning analysis (RPA) and receiver operating characteristic (ROC) curve analysis. Method: Fifty-one narcolepsy with cataplexy patients (26 adults and 25 juveniles) and sixty matched heathy controls (30 adults and 30 juveniles) were recruited. All subjects underwent a resting-state functional magnetic resonance imaging scan. Fractional low-frequency fluctuations (fALFF) was used to investigate narcolepsy induced regional brain activity alterations among adult and juveniles, respectively. Recursive partitioning analysis and Receiver operating curve analysis was used to seek the ability of fALFF values within brain regions in distinguishing narcolepsy from healthy controls. Results: Compared with healthy controls, both adult and juvenile narcolepsy had lower fALFF values in bilateral medial superior frontal gyrus, bilateral inferior parietal lobule and supra-marginal gyrus. Compared with healthy controls, both adult and juvenile narcolepsy had higher fALFF values in bilateral sensorimotor cortex and middle temporal gyrus. Also juvenile narcolepsy had higher fALFF in right putamen and right thalamus compared with healthy controls. Based on RPA and ROC curve analysis, in adult participants, fALFF differences in right medial superior frontal gyrus can discriminate narcolepsy from healthy controls with high degree of sensitivity (100%) and specificity (88.9%). In juvenile participants, fALFF differences in left superior frontal gyrus can discriminate narcolepsy from healthy controls with moderate degree of sensitivity (57.1%) and specificity (88.9%). Conclusion: Compared with healthy controls, both the adult and juvenile narcolepsy showed overlap brain regions in fALFF differences after case-control comparison. Furthermore, we propose that fALFF value can be a helpful imaging biomarker in distinguishing narcolepsy from healthy controls among both adults and juveniles.Objective: To identify narcolepsy related regional brain activity alterations compared with matched healthy controls. To determine whether these changes can be used to distinguish narcolepsy from healthy controls by recursive partitioning analysis (RPA) and receiver operating characteristic (ROC) curve analysis. Method: Fifty-one narcolepsy with cataplexy patients (26 adults and 25 juveniles) and sixty matched heathy controls (30 adults and 30 juveniles) were recruited. All subjects underwent a resting-state functional magnetic resonance imaging scan. Fractional low-frequency fluctuations (fALFF) was used to investigate narcolepsy induced regional brain activity alterations among adult and juveniles, respectively. Recursive partitioning analysis and Receiver operating curve analysis was used to seek the ability of fALFF values within brain regions in distinguishing narcolepsy from healthy controls. Results: Compared with healthy controls, both adult and juvenile narcolepsy had lower fALFF values in bilateral medial superior frontal gyrus, bilateral inferior parietal lobule and supra-marginal gyrus. Compared with healthy controls, both adult and juvenile narcolepsy had higher fALFF values in bilateral sensorimotor cortex and middle temporal gyrus. Also juvenile narcolepsy had higher fALFF in right putamen and right thalamus compared with healthy controls. Based on RPA and ROC curve analysis, in adult participants, fALFF differences in right medial superior frontal gyrus can discriminate narcolepsy from healthy controls with high degree of sensitivity (100%) and specificity (88.9%). In juvenile participants, fALFF differences in left superior frontal gyrus can discriminate narcolepsy from healthy controls with moderate degree of sensitivity (57.1%) and specificity (88.9%). Conclusion: Compared with healthy controls, both the adult and juvenile narcolepsy showed overlap brain regions in fALFF differences after case-control comparison. Furthermore, we propose that fALFF value can be a helpful imaging biomarker in distinguishing narcolepsy from healthy controls among both adults and juveniles. To identify narcolepsy related regional brain activity alterations compared with matched healthy controls. To determine whether these changes can be used to distinguish narcolepsy from healthy controls by recursive partitioning analysis (RPA) and receiver operating characteristic (ROC) curve analysis. Fifty-one narcolepsy with cataplexy patients (26 adults and 25 juveniles) and sixty matched heathy controls (30 adults and 30 juveniles) were recruited. All subjects underwent a resting-state functional magnetic resonance imaging scan. Fractional low-frequency fluctuations (fALFF) was used to investigate narcolepsy induced regional brain activity alterations among adult and juveniles, respectively. Recursive partitioning analysis and Receiver operating curve analysis was used to seek the ability of fALFF values within brain regions in distinguishing narcolepsy from healthy controls. Compared with healthy controls, both adult and juvenile narcolepsy had lower fALFF values in bilateral medial superior frontal gyrus, bilateral inferior parietal lobule and supra-marginal gyrus. Compared with healthy controls, both adult and juvenile narcolepsy had higher fALFF values in bilateral sensorimotor cortex and middle temporal gyrus. Also juvenile narcolepsy had higher fALFF in right putamen and right thalamus compared with healthy controls. Based on RPA and ROC curve analysis, in adult participants, fALFF differences in right medial superior frontal gyrus can discriminate narcolepsy from healthy controls with high degree of sensitivity (100%) and specificity (88.9%). In juvenile participants, fALFF differences in left superior frontal gyrus can discriminate narcolepsy from healthy controls with moderate degree of sensitivity (57.1%) and specificity (88.9%). Compared with healthy controls, both the adult and juvenile narcolepsy showed overlap brain regions in fALFF differences after case-control comparison. Furthermore, we propose that fALFF value can be a helpful imaging biomarker in distinguishing narcolepsy from healthy controls among both adults and juveniles.  | 
    
| Author | Jun, Zhang Dianjiang, Zhao Fang, Han Fulong, Xiao Qihong, Zou Chao, Lu Wei, Zhang  | 
    
| AuthorAffiliation | 5 Department of Neurology, Peking University People's Hospital , Beijing , China 2 Department of Radiology, Peking University International Hospital , Beijing , China 4 PKU-Upenn Sleep Center, Peking University International Hospital , Beijing , China 3 Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University , Beijing , China 1 Department of Respiratory and Critical Care Medicine, Sleep Medicine Center, Peking University People's Hospital , Beijing , China  | 
    
| AuthorAffiliation_xml | – name: 2 Department of Radiology, Peking University International Hospital , Beijing , China – name: 5 Department of Neurology, Peking University People's Hospital , Beijing , China – name: 1 Department of Respiratory and Critical Care Medicine, Sleep Medicine Center, Peking University People's Hospital , Beijing , China – name: 3 Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University , Beijing , China – name: 4 PKU-Upenn Sleep Center, Peking University International Hospital , Beijing , China  | 
    
| Author_xml | – sequence: 1 givenname: Xiao surname: Fulong fullname: Fulong, Xiao – sequence: 2 givenname: Lu surname: Chao fullname: Chao, Lu – sequence: 3 givenname: Zhao surname: Dianjiang fullname: Dianjiang, Zhao – sequence: 4 givenname: Zou surname: Qihong fullname: Qihong, Zou – sequence: 5 givenname: Zhang surname: Wei fullname: Wei, Zhang – sequence: 6 givenname: Zhang surname: Jun fullname: Jun, Zhang – sequence: 7 givenname: Han surname: Fang fullname: Fang, Han  | 
    
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| CitedBy_id | crossref_primary_10_1007_s40675_020_00185_9 crossref_primary_10_1093_sleep_zsaa018 crossref_primary_10_1093_texcom_tgaa073 crossref_primary_10_1016_j_acra_2023_08_026 crossref_primary_10_3389_fpsyt_2022_914039 crossref_primary_10_3389_fnhum_2022_874703 crossref_primary_10_3390_ijerph192315482 crossref_primary_10_1016_j_sleep_2025_02_036 crossref_primary_10_1093_sleep_zsab050  | 
    
| Cites_doi | 10.1523/JNEUROSCI.1004-10.2010 10.1016/j.neuroimage.2005.06.019 10.1038/nrn3857 10.1097/MD.0000000000000208 10.1007/s00408-013-9516-y 10.3389/fnhum.2013.00910 10.3389/fnhum.2016.00369 10.1093/sleep/31.6.777 10.5664/jcsm.5586 10.1016/j.ijrobp.2010.10.077 10.1016/j.neuroimage.2006.11.042 10.1371/journal.pone.0033525 10.1016/j.braindev.2006.07.002 10.1016/j.ejca.2014.03.003 10.1111/j.1748-1716.2009.02012.x 10.1016/j.sleep.2015.11.023 10.1146/annurev.neuro.25.112701.142826 10.1016/j.jcrc.2017.02.019 10.1002/ana.20212 10.1016/0021-9681(84)90041-9 10.1186/1471-2202-15-105 10.1136/jnnp.2009.175786 10.1002/mrm.1910350312 10.1371/journal.pone.0093813 10.1002/hbm.10053 10.2147/NDT.S73730 10.1212/01.wnl.0000218205.72668.ab 10.3389/fneur.2014.00105 10.3389/fneur.2017.00350 10.1016/j.neuroimage.2006.02.042 10.1073/pnas.1411011112 10.1523/JNEUROSCI.1103-13.2013 10.1016/j.jneumeth.2008.04.012 10.1016/j.neuroimage.2014.09.038 10.1007/s00415-005-0598-1 10.2176/nmc.53.474 10.1007/s12021-016-9299-4  | 
    
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| Copyright | Copyright © 2018 Fulong, Chao, Dianjiang, Qihong, Wei, Jun and Fang. 2018 Fulong, Chao, Dianjiang, Qihong, Wei, Jun and Fang | 
    
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| Keywords | fractional low-frequency fluctuations recursive partitioning analysis receiver operating characteristic curve analysis narcolepsy functional magnetic resonance imaging  | 
    
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Hiroshi Kadotani, Shiga University of Medical Science, Japan; Axel Steiger, Max-Planck-Institut für Psychiatrie, Germany; Benito de Celis Alonso, Benemérita Universidad Autónoma de Puebla, Mexico Edited by: Hengyi Rao, University of Pennsylvania, United States This article was submitted to Sleep and Chronobiology, a section of the journal Frontiers in Neurology  | 
    
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| Snippet | To identify narcolepsy related regional brain activity alterations compared with matched healthy controls. To determine whether these changes can be used to... Objective: To identify narcolepsy related regional brain activity alterations compared with matched healthy controls. To determine whether these changes can be...  | 
    
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| SubjectTerms | fractional low-frequency fluctuations functional magnetic resonance imaging narcolepsy Neurology receiver operating characteristic curve analysis recursive partitioning analysis  | 
    
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| Title | Recursive Partitioning Analysis of Fractional Low-Frequency Fluctuations in Narcolepsy With Cataplexy | 
    
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