Dataset Information

Baseline biomarkers of connectome disruption and atrophy predict future processing speed in early multiple sclerosis.

ABSTRACT: The development of accurate prognoses in multiple sclerosis is difficult, as the disease is characterized by heterogeneous patterns of brain abnormalities that relate in an unclear way to future impairments. Here, we use a statistical modeling approach to determine if the baseline pattern of connectome disruption due to T2-FLAIR lesions could predict a patient's future processing speed, as measured using the Symbol Digits Modality Test scores. Imaging data, demographics and Symbol Digits Modality Test scores were collected from 61 early relapsing remitting multiple sclerosis patients. The Network Modification Tool was used to estimate damage to the connectome by quantifying white matter abnormalities' effects on 1) global network properties, 2) regional connectivity and 3) connectivity between pairs of regions. MS subjects showed significant improvement of processing speed between baseline and follow-up (t?=?-2.6, p?=?0.0096); however, both baseline (t?=?-4.01, p?=?0.00012) and follow-up (t?=?-2.10, p?=?0.038) processing speed were significantly lower than age-matched healthy controls. Partial Least Squares Regression was used to create models that predict future processing speed from between baseline imaging metrics and demographics. The model based on region-pair disconnection and gray matter atrophy had the lowest AIC and highest prediction accuracy (R2?=?0.79) compared to models based on global (R2?=?0.41) or regional (R2?=?0.66) disconnection and gray matter atrophy, overlap with an ROI-based atlas and gray matter atrophy (R2?=?0.73) or gray matter atrophy alone (R2?=?0.71). We found that baseline measures of connectivity disruption in various parietal, temporal, occipital and subcortical regions and atrophy in the putamen were important predictors of future processing speed. We conclude that information about disruptions to pairwise brain connections is more informative of future processing speed than regional or global metrics or gray matter atrophy alone. The combination of quantitative disconnectome metrics, gray matter atrophy and statistical modeling approaches could enable clinicians in developing more accurate, individualized prognoses of future cognitive status in multiple sclerosis patients.

SUBMITTER: Kuceyeski A

PROVIDER: S-EPMC6019863 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Baseline biomarkers of connectome disruption and atrophy predict future processing speed in early multiple sclerosis.

Kuceyeski A A Monohan E E Morris E E Fujimoto K K Vargas W W Gauthier S A SA

NeuroImage. Clinical 20180508

The development of accurate prognoses in multiple sclerosis is difficult, as the disease is characterized by heterogeneous patterns of brain abnormalities that relate in an unclear way to future impairments. Here, we use a statistical modeling approach to determine if the baseline pattern of connectome disruption due to T2-FLAIR lesions could predict a patient's future processing speed, as measured using the Symbol Digits Modality Test scores. Imaging data, demographics and Symbol Digits Modalit ...[more]

PMID: 30013921

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Project 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): ? = 0.87, R 2 = 0.76, p < 0.001; GE(DMN): ? = 0.82, R 2 = 0.67, p < 0.001; GE(CBL): ? = 0.80, R 2 = 0.64, p < 0.001]. In CPMS the correlation between these measures was significantly lower [GE(CBL-DMN): ? = 0.51, R 2 = 0.26, p < 0.001; GE(DMN): ? = 0.48, R 2 = 0.23, p = 0.001; GE(CBL): ? = 0.52, R 2 = 0.27, p < 0.001] and SDMT scores correlated most with BPF (? = 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.

Dataset Information

Baseline biomarkers of connectome disruption and atrophy predict future processing speed in early multiple sclerosis.

Publications

Baseline biomarkers of connectome disruption and atrophy predict future processing speed in early multiple sclerosis.

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