01970d3ac83cdd739853f1f870f03b27 pmc_10766234.pdf ced9dea2eba0dce1a2ea8d2f4bc099c643f3b9e7 pmc_10766234.pdf 4390124d2eecdf3e7503c18f9372d8be35dc0435828f9e584148cd397ff71635 pmc_10766234.pdf Title: Prognostic value of single-subject grey matter networks in early multiple sclerosis Subject: DOI: 10.1093/brain/awad288; Brain, 147, 1, 2023-08-29.; Abstract: The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions.This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression.This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for 5 years (mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 health Keywords: relapsing-remitting multiple sclerosis; EDSS progression in MS; brain network measures; structural covariance; graph theory Author: Vinzenz Fleischer Creator: Servigistics Arbortext Advanced Print Publisher 11.1.4546/W-x64 Producer: PDFlib+PDI 9.0.7p3 (C++/Win64) CreationDate: Tue Jan 2 18:35:45 2024 CET ModDate: Wed Jan 3 15:08:25 2024 CET Custom Metadata: yes Metadata Stream: yes Tagged: yes UserProperties: no Suspects: no Form: AcroForm JavaScript: no Pages: 12 Encrypted: no Page size: 612.283 x 790.866 pts Page rot: 0 File size: 904715 bytes Optimized: no PDF version: 1.6 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- ATHMCU+CaeciliaLTStd-Bold Type 1C Custom yes yes yes 1078 0 FWEGSS+CaeciliaLTStd-Roman Type 1C Custom yes yes yes 1083 0 QLUQVL+STIXGeneral-Regular Type 1C Custom yes yes yes 1088 0 RIOWNO+CaeciliaLTStd-Italic Type 1C Custom yes yes yes 1093 0 ZIPBTL+CaeciliaLTStd-Heavy Type 1C Custom yes yes yes 56 0 CCNJMF+TimesNewRomanPSMT CID TrueType Identity-H yes yes yes 85 0 ZDFVVD+CaeciliaLTStd-BoldItalic Type 1C Custom yes yes yes 119 0 QSUABF+STIXGeneral-Bold Type 1C Custom yes yes yes 37 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-02-17 17:01:14 CET RepresentationInformation: pmc_10766234.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-02-13 02:31:50 CET Size: 904715 Format: PDF Version: 1.6 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf Profile: Tagged PDF PDFMetadata: Objects: 1188 FreeObjects: 5 IncrementalUpdates: 5 DocumentCatalog: ViewerPreferences: HideToolbar: false HideMenubar: false HideWindowUI: false FitWindow: false CenterWindow: false DisplayDocTitle: true NonFullScreenPageMode: UseNone Direction: L2R ViewArea: CropBox ViewClip: CropBox PrintArea: CropBox PageClip: CropBox PageLayout: OneColumn PageMode: UseOutlines Language: en Outlines: Item: Title: Prognostic value of single-subject grey matternetworks in early multiple sclerosis Children: Item: Title: Introduction Destination: secawad288-s0 Item: Title: Materials and methods Destination: secawad288-s1 Children: Item: Title: Patients and study design Destination: secawad288-s1.1 Item: Title: Clinical assessment Destination: secawad288-s1.2 Item: Title: Data acquisition Destination: secawad288-s1.3 Item: Title: White matter lesion load estimation and lesion filling Destination: secawad288-s1.4 Item: Title: Longitudinal MRI preprocessing Destination: secawad288-s1.5 Item: Title: Single-subject grey matter networks Destination: secawad288-s1.6 Item: Title: Network properties measures Destination: secawad288-s1.7 Item: Title: Statistical analysis Destination: secawad288-s1.8 Item: Title: Results Destination: secawad288-s2 Children: Item: Title: Demographic and clinical assessment Destination: secawad288-s2.1 Item: Title: Grey matter atrophy over 1 year and white matter lesion load Destination: secawad288-s2.2 Item: Title: Classifying grey matter network changes over 1 year based on subsequent EDSS progression status Destination: secawad288-s2.3 Item: Title: Grey matter network changes over 1 year based on NEDA/EDA status and subsequent EDSS progression Destination: secawad288-s2.4 Item: Title: Association of grey matter network changes over 1 year with EDSS changes over 5 years Destination: secawad288-s2.5 Item: Title: Discriminative power of longitudinal grey matter networks over 1 year in predicting EDSS progression Destination: secawad288-s2.6 Item: Title: Discussion Destination: secawad288-s3 Item: Title: Data availability Destination: secawad288-s4 Item: Title: Acknowledgements Destination: acknow Item: Title: Funding Destination: secawad288-s5 Item: Title: Competing interests Destination: secawad288-s6 Item: Title: Supplementary material Destination: secawad288-s7 Item: Title: References Destination: reflist Info: Title: Prognostic value of single-subject grey matter networks in early multiple sclerosis Author: Vinzenz Fleischer Subject: DOI: 10.1093/brain/awad288; Brain, 147, 1, 2023-08-29.; Abstract: The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such me愀猀甀爀攀猀 猀栀漀甀氀搀 洀愀欀攀 椀渀昀攀爀攀渀挀攀猀 愀琀 琀栀攀 椀渀搀椀瘀椀搀甀愀氀 氀攀瘀攀氀 琀漀 椀渀昀漀爀洀 挀氀椀渀椀挀愀氀 搀攀挀椀猀椀漀渀猀⸀吀栀椀猀 猀琀甀搀礀 椀渀瘀攀猀琀椀最愀琀攀搀 琀栀攀 瀀爀漀最渀漀猀琀椀挀 瘀愀氀甀攀of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with rel愀瀀猀椀渀最ⴀ爀攀洀椀琀琀椀渀最 䴀匀 刀刀䴀匀⸀ 圀攀 栀礀瀀漀琀栀攀猀椀稀攀搀 琀栀愀琀 渀攀琀眀漀爀欀 洀攀愀猀甀爀攀猀Ⰰ 搀攀爀椀瘀攀搀 昀爀漀洀 䴀刀䤀Ⰰ 漀甀琀瀀攀爀昀漀爀洀 挀漀渀瘀攀渀琀椀漀渀愀氀 䴀刀䤀 洀攀愀猀甀爀攀洀攀渀琀猀 t identifying patients at risk of developing disability progression.This longitudinal, multicentre study within the Magnetic Re猀漀渀愀渀挀攀 䤀洀愀最椀渀最 椀渀 䴀匀 䴀䄀䜀一䤀䴀匀 渀攀琀眀漀爀欀 椀渀挀氀甀搀攀搀 㐀　㘀 瀀愀琀椀攀渀琀猀 眀椀琀栀 刀刀䴀匀 洀攀愀渀 愀最攠ऀ㴠ऀ㌀㔀⸀㜠ऀ넠ऀ㤀⸀ㄠऀ礀攀愀爀猀 昀漀氀氀漀眀攀搀 甀瀀 昀漀爀 㔠ऀ礀攀愀爀猀(mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 health Keywords: relapsing-remitting multiple sclerosis; EDSS progression in MS; brain network measures; structural covariance; graph theory Creator: Servigistics Arbortext Advanced Print Publisher 11.1.4546/W-x64 Producer: PDFlib+PDI 9.0.7p3 (C++/Win64) CreationDate: Tue Jan 02 19:05:45 CET 2024 ModDate: Wed Jan 03 15:38:25 CET 2024 ID: 0x219151cd8c913a3766aac8907cd2ec07, 0xee97acd7e41c6741a65902dae3002bbe Filters: FilterPipeline: FlateDecode FilterPipeline: DCTDecode Images: Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 2236 ImageHeight: 254 BitsPerSample: 8 BitsPerSampleUnit: integer Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 1066 ImageHeight: 1323 BitsPerSample: 8 BitsPerSampleUnit: integer Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 1166 ImageHeight: 855 BitsPerSample: 8 BitsPerSampleUnit: integer Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 1166 ImageHeight: 395 BitsPerSample: 8 BitsPerSampleUnit: integer Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 1166 ImageHeight: 431 BitsPerSample: 8 BitsPerSampleUnit: integer Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 1166 ImageHeight: 365 BitsPerSample: 8 BitsPerSampleUnit: integer Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 650 ImageHeight: 708 BitsPerSample: 8 BitsPerSampleUnit: integer Fonts: Type0: Font: BaseFont: CCNJMF+TimesNewRomanPSMT Encoding: Identity-H ToUnicode: true Type1: Font: BaseFont: QLUQVL+STIXGeneral-Regular FontSubset: true FirstChar: 1 LastChar: 8 FontDescriptor: FontName: QLUQVL+STIXGeneral-Regular Flags: Nonsymbolic FontBBox: -995, -455, 2000, 1055 FontFile3: true EncodingDictionary: BaseEncoding: WinAnsiEncoding Differences: true ToUnicode: true Font: BaseFont: RIOWNO+CaeciliaLTStd-Italic FontSubset: true FirstChar: 1 LastChar: 41 FontDescriptor: FontName: RIOWNO+CaeciliaLTStd-Italic Flags: Nonsymbolic, Italic FontBBox: -166, -270, 1053, 915 FontFile3: true EncodingDictionary: BaseEncoding: WinAnsiEncoding Differences: true ToUnicode: true Font: BaseFont: QSUABF+STIXGeneral-Bold FontSubset: true FirstChar: 1 LastChar: 1 FontDescriptor: FontName: QSUABF+STIXGeneral-Bold Flags: Nonsymbolic, ForceBold FontBBox: -1030, -455, 2000, 1055 FontFile3: true EncodingDictionary: BaseEncoding: WinAnsiEncoding Differences: true ToUnicode: true Font: BaseFont: ATHMCU+CaeciliaLTStd-Bold FontSubset: true FirstChar: 1 LastChar: 71 FontDescriptor: FontName: ATHMCU+CaeciliaLTStd-Bold Flags: Nonsymbolic, ForceBold FontBBox: -166, -273, 1141, 938 FontFile3: true EncodingDictionary: BaseEncoding: WinAnsiEncoding Differences: true ToUnicode: true Font: BaseFont: ZDFVVD+CaeciliaLTStd-BoldItalic FontSubset: true FirstChar: 1 LastChar: 1 FontDescriptor: FontName: ZDFVVD+CaeciliaLTStd-BoldItalic Flags: Nonsymbolic, Italic, ForceBold FontBBox: -169, -273, 1082, 932 FontFile3: true EncodingDictionary: BaseEncoding: WinAnsiEncoding Differences: true ToUnicode: true Font: BaseFont: ZIPBTL+CaeciliaLTStd-Heavy FontSubset: true FirstChar: 1 LastChar: 31 FontDescriptor: FontName: ZIPBTL+CaeciliaLTStd-Heavy Flags: Nonsymbolic, ForceBold FontBBox: -170, -277, 1146, 970 FontFile3: true EncodingDictionary: BaseEncoding: WinAnsiEncoding Differences: true ToUnicode: true Font: BaseFont: FWEGSS+CaeciliaLTStd-Roman FontSubset: true FirstChar: 1 LastChar: 92 FontDescriptor: FontName: FWEGSS+CaeciliaLTStd-Roman Flags: Nonsymbolic FontBBox: -167, -270, 1136, 926 FontFile3: true EncodingDictionary: BaseEncoding: WinAnsiEncoding Differences: true ToUnicode: true CIDFontType2: Font: BaseFont: CCNJMF+TimesNewRomanPSMT CIDSystemInfo: Registry: Adobe Registry: Identity Supplement: 0 FontDescriptor: FontName: CCNJMF+TimesNewRomanPSMT Flags: Nonsymbolic FontBBox: -568, -307, 2046, 1040 FontFile2: true XMP: uuid:2c321119-b464-4359-9937-6e6900757f3b uuid:2C4DC7B6-BA17-B7FD-85E3-622D2BC9A9CC True relapsing-remitting multiple sclerosis; EDSS progression in MS; brain network measures; structural covariance; graph theory PDFlib+PDI 9.0.7p3 (C++/Win64) application/pdf 10.1093/brain/awad288 en Oxford University Press Prognostic value of single-subject grey matter networks in early multiple sclerosis Vinzenz Fleischer Gabriel Gonzalez-Escamilla Deborah Pareto Alex Rovira Jaume Sastre-Garriga Piotr Sowa Einar A. Høgestøl Hanne F. Harbo Barbara Bellenberg Carsten Lukas Serena Ruggieri Claudio Gasperini Tomas Uher Manuela Vaneckova Stefan Bittner Ahmed E. Othman Sara Collorone Ahmed T. Toosy Sven G. Meuth Frauke Zipp Frederik Barkhof Olga Ciccarelli Sergiu Groppa DOI: 10.1093/brain/awad288; Brain, 147, 1, 2023-08-29.; Abstract: The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions.This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression.This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for 5 years (mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 health © The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain. relapsing-remitting multiple sclerosis EDSS progression in MS brain network measures structural covariance graph theory Journal Brain © The Author(s) 2023. 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