Cargando…

Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis

Cognitive impairment affects about 50% of multiple sclerosis (MS) patients, but the mechanisms underlying this remain unclear. The default mode network (DMN) has been linked with cognition, but in MS its role is still poorly understood. Moreover, within an extended DMN network including the cerebell...

Descripción completa

Detalles Bibliográficos
Autores principales: Savini, Giovanni, Pardini, Matteo, Castellazzi, Gloria, Lascialfari, Alessandro, Chard, Declan, D’Angelo, Egidio, Gandini Wheeler-Kingshott, Claudia A. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396736/
https://www.ncbi.nlm.nih.gov/pubmed/30853896
http://dx.doi.org/10.3389/fncel.2019.00021
_version_ 1783399315288358912
author Savini, Giovanni
Pardini, Matteo
Castellazzi, Gloria
Lascialfari, Alessandro
Chard, Declan
D’Angelo, Egidio
Gandini Wheeler-Kingshott, Claudia A. M.
author_facet Savini, Giovanni
Pardini, Matteo
Castellazzi, Gloria
Lascialfari, Alessandro
Chard, Declan
D’Angelo, Egidio
Gandini Wheeler-Kingshott, Claudia A. M.
author_sort Savini, Giovanni
collection PubMed
description Cognitive impairment affects about 50% of multiple sclerosis (MS) patients, but the mechanisms underlying this remain unclear. The default mode network (DMN) has been linked with cognition, but in MS its role is still poorly understood. Moreover, within an extended DMN network including the cerebellum (CBL-DMN), the contribution of cortico-cerebellar connectivity to MS cognitive performance remains unexplored. The present study investigated associations of DMN and CBL-DMN structural connectivity with cognitive processing speed in MS, in both cognitively impaired (CIMS) and cognitively preserved (CPMS) MS patients. 68 MS patients and 22 healthy controls (HCs) completed a symbol digit modalities test (SDMT) and had 3T brain magnetic resonance imaging (MRI) scans that included a diffusion weighted imaging protocol. DMN and CBL-DMN tracts were reconstructed with probabilistic tractography. These networks (DMN and CBL-DMN) and the cortico-cerebellar tracts alone were modeled using a graph theoretical approach with fractional anisotropy (FA) as the weighting factor. Brain parenchymal fraction (BPF) was also calculated. In CIMS SDMT scores strongly correlated with the FA-weighted global efficiency (GE) of the network [GE(CBL-DMN): ρ = 0.87, R(2) = 0.76, p < 0.001; GE(DMN): ρ = 0.82, R(2) = 0.67, p < 0.001; GE(CBL): ρ = 0.80, R(2) = 0.64, p < 0.001]. In CPMS the correlation between these measures was significantly lower [GE(CBL-DMN): ρ = 0.51, R(2) = 0.26, p < 0.001; GE(DMN): ρ = 0.48, R(2) = 0.23, p = 0.001; GE(CBL): ρ = 0.52, R(2) = 0.27, p < 0.001] and SDMT scores correlated most with BPF (ρ = 0.57, R(2) = 0.33, p < 0.001). In a multivariable regression model where SDMT was the independent variable, FA-weighted GE was the only significant explanatory variable in CIMS, while in CPMS BPF and expanded disability status scale were significant. No significant correlation was found in HC between SDMT scores, MRI or network measures. DMN structural GE is related to cognitive performance in MS, and results of CBL-DMN suggest that the cerebellum structural connectivity to the DMN plays an important role in information processing speed decline.
format Online
Article
Text
id pubmed-6396736
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-63967362019-03-08 Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis Savini, Giovanni Pardini, Matteo Castellazzi, Gloria Lascialfari, Alessandro Chard, Declan D’Angelo, Egidio Gandini Wheeler-Kingshott, Claudia A. M. Front Cell Neurosci Neuroscience Cognitive impairment affects about 50% of multiple sclerosis (MS) patients, but the mechanisms underlying this remain unclear. The default mode network (DMN) has been linked with cognition, but in MS its role is still poorly understood. Moreover, within an extended DMN network including the cerebellum (CBL-DMN), the contribution of cortico-cerebellar connectivity to MS cognitive performance remains unexplored. The present study investigated associations of DMN and CBL-DMN structural connectivity with cognitive processing speed in MS, in both cognitively impaired (CIMS) and cognitively preserved (CPMS) MS patients. 68 MS patients and 22 healthy controls (HCs) completed a symbol digit modalities test (SDMT) and had 3T brain magnetic resonance imaging (MRI) scans that included a diffusion weighted imaging protocol. DMN and CBL-DMN tracts were reconstructed with probabilistic tractography. These networks (DMN and CBL-DMN) and the cortico-cerebellar tracts alone were modeled using a graph theoretical approach with fractional anisotropy (FA) as the weighting factor. Brain parenchymal fraction (BPF) was also calculated. In CIMS SDMT scores strongly correlated with the FA-weighted global efficiency (GE) of the network [GE(CBL-DMN): ρ = 0.87, R(2) = 0.76, p < 0.001; GE(DMN): ρ = 0.82, R(2) = 0.67, p < 0.001; GE(CBL): ρ = 0.80, R(2) = 0.64, p < 0.001]. In CPMS the correlation between these measures was significantly lower [GE(CBL-DMN): ρ = 0.51, R(2) = 0.26, p < 0.001; GE(DMN): ρ = 0.48, R(2) = 0.23, p = 0.001; GE(CBL): ρ = 0.52, R(2) = 0.27, p < 0.001] and SDMT scores correlated most with BPF (ρ = 0.57, R(2) = 0.33, p < 0.001). In a multivariable regression model where SDMT was the independent variable, FA-weighted GE was the only significant explanatory variable in CIMS, while in CPMS BPF and expanded disability status scale were significant. No significant correlation was found in HC between SDMT scores, MRI or network measures. DMN structural GE is related to cognitive performance in MS, and results of CBL-DMN suggest that the cerebellum structural connectivity to the DMN plays an important role in information processing speed decline. Frontiers Media S.A. 2019-02-11 /pmc/articles/PMC6396736/ /pubmed/30853896 http://dx.doi.org/10.3389/fncel.2019.00021 Text en Copyright © 2019 Savini, Pardini, Castellazzi, Lascialfari, Chard, D’Angelo and Gandini Wheeler-Kingshott. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Savini, Giovanni
Pardini, Matteo
Castellazzi, Gloria
Lascialfari, Alessandro
Chard, Declan
D’Angelo, Egidio
Gandini Wheeler-Kingshott, Claudia A. M.
Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis
title Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis
title_full Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis
title_fullStr Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis
title_full_unstemmed Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis
title_short Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis
title_sort default mode network structural integrity and cerebellar connectivity predict information processing speed deficit in multiple sclerosis
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396736/
https://www.ncbi.nlm.nih.gov/pubmed/30853896
http://dx.doi.org/10.3389/fncel.2019.00021
work_keys_str_mv AT savinigiovanni defaultmodenetworkstructuralintegrityandcerebellarconnectivitypredictinformationprocessingspeeddeficitinmultiplesclerosis
AT pardinimatteo defaultmodenetworkstructuralintegrityandcerebellarconnectivitypredictinformationprocessingspeeddeficitinmultiplesclerosis
AT castellazzigloria defaultmodenetworkstructuralintegrityandcerebellarconnectivitypredictinformationprocessingspeeddeficitinmultiplesclerosis
AT lascialfarialessandro defaultmodenetworkstructuralintegrityandcerebellarconnectivitypredictinformationprocessingspeeddeficitinmultiplesclerosis
AT charddeclan defaultmodenetworkstructuralintegrityandcerebellarconnectivitypredictinformationprocessingspeeddeficitinmultiplesclerosis
AT dangeloegidio defaultmodenetworkstructuralintegrityandcerebellarconnectivitypredictinformationprocessingspeeddeficitinmultiplesclerosis
AT gandiniwheelerkingshottclaudiaam defaultmodenetworkstructuralintegrityandcerebellarconnectivitypredictinformationprocessingspeeddeficitinmultiplesclerosis