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Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis.
Front Cell Neurosci 2019; 13:21FC

Abstract

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.

Authors+Show Affiliations

Department of Physics, University of Milan, Milan, Italy.Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Genoa, Italy. Ospedale Policlinico S. Martino, Genoa, Italy.Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy. NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, Institute of Neurology, University College London, London, United Kingdom.Department of Physics, University of Milan, Milan, Italy.NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, Institute of Neurology, University College London, London, United Kingdom. National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, London, United Kingdom.Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy. Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, Institute of Neurology, University College London, London, United Kingdom. Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy. Brain MRI 3T Mondino Research Center, IRCCS Mondino Foundation, Pavia, Italy.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

30853896

Citation

Savini, Giovanni, et al. "Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis." Frontiers in Cellular Neuroscience, vol. 13, 2019, p. 21.
Savini G, Pardini M, Castellazzi G, et al. Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis. Front Cell Neurosci. 2019;13:21.
Savini, G., Pardini, M., Castellazzi, G., Lascialfari, A., Chard, D., D'Angelo, E., & Gandini Wheeler-Kingshott, C. A. M. (2019). Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis. Frontiers in Cellular Neuroscience, 13, p. 21. doi:10.3389/fncel.2019.00021.
Savini G, et al. Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis. Front Cell Neurosci. 2019;13:21. PubMed PMID: 30853896.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis. AU - Savini,Giovanni, AU - Pardini,Matteo, AU - Castellazzi,Gloria, AU - Lascialfari,Alessandro, AU - Chard,Declan, AU - D'Angelo,Egidio, AU - Gandini Wheeler-Kingshott,Claudia A M, Y1 - 2019/02/11/ PY - 2018/07/02/received PY - 2019/01/17/accepted PY - 2019/3/12/entrez PY - 2019/3/12/pubmed PY - 2019/3/12/medline KW - cerebellum KW - connectomics KW - default mode network (DMN) KW - diffusion weighted imaging (DWI) KW - magnetic resonance imaging (MRI) KW - multiple sclerosis (MS) KW - symbol digit modalities test (SDMT) KW - tractography SP - 21 EP - 21 JF - Frontiers in cellular neuroscience JO - Front Cell Neurosci VL - 13 N2 - 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. SN - 1662-5102 UR - https://www.unboundmedicine.com/medline/citation/30853896/Default_Mode_Network_Structural_Integrity_and_Cerebellar_Connectivity_Predict_Information_Processing_Speed_Deficit_in_Multiple_Sclerosis_ L2 - https://dx.doi.org/10.3389/fncel.2019.00021 DB - PRIME DP - Unbound Medicine ER -