Prognostic value of single-subject grey matter networks in early multiple sclerosis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F24%3A10472569" target="_blank" >RIV/00064165:_____/24:10472569 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11110/24:10472569

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=awKWdIBfZx" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=awKWdIBfZx</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/brain/awad288" target="_blank" >10.1093/brain/awad288</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Prognostic value of single-subject grey matter networks in early multiple sclerosis

Popis výsledku v původním jazyce

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 healthy subjects (mean age = 35.0 +/- 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted.The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30.0, P &lt; 0.001] and global efficiency [H(2) = 31.3, P &lt; 0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1.5, P = 0.474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P-values &lt; 0.05).Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors.

Název v anglickém jazyce

Prognostic value of single-subject grey matter networks in early multiple sclerosis

Popis výsledku anglicky

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 healthy subjects (mean age = 35.0 +/- 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted.The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30.0, P &lt; 0.001] and global efficiency [H(2) = 31.3, P &lt; 0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1.5, P = 0.474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P-values &lt; 0.05).Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

<a href="/cs/project/LX22NPO5107" target="_blank" >LX22NPO5107: Národní ústav pro neurologický výzkum</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Brain

ISSN

0006-8950

e-ISSN

1460-2156

Svazek periodika

147

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

135-146

Kód UT WoS článku

001114878800001

EID výsledku v databázi Scopus

2-s2.0-85180593931