Your browser doesn't support javascript.
loading
Resting-state functional connectivity networks associated with fatigue in multiple sclerosis with early age onset.
Stefancin, Patricia; Govindarajan, Sindhuja T; Krupp, Lauren; Charvet, Leigh; Duong, Timothy Q.
Afiliação
  • Stefancin P; Departments of Radiology, Stony Brook Medicine, Stony Brook, NY 11794, United States; Department of Neurology, New York University School of Medicine, New York, NY, United States.
  • Govindarajan ST; Departments of Radiology, Stony Brook Medicine, Stony Brook, NY 11794, United States; Department of Neurology, New York University School of Medicine, New York, NY, United States.
  • Krupp L; Departments of Radiology, Stony Brook Medicine, Stony Brook, NY 11794, United States; Department of Neurology, New York University School of Medicine, New York, NY, United States.
  • Charvet L; Departments of Radiology, Stony Brook Medicine, Stony Brook, NY 11794, United States; Department of Neurology, New York University School of Medicine, New York, NY, United States.
  • Duong TQ; Departments of Radiology, Stony Brook Medicine, Stony Brook, NY 11794, United States; Department of Neurology, New York University School of Medicine, New York, NY, United States. Electronic address: Tim.duong@stonybrook.edu.
Mult Scler Relat Disord ; 31: 101-105, 2019 Jun.
Article em En | MEDLINE | ID: mdl-30954931
ABSTRACT

BACKGROUND:

Fatigue is one of the most commonly experienced symptoms in multiple sclerosis (MS). The neural correlates of fatigue in MS, in general and specifically in early onset, remain poorly understood. This study employed resting-state fMRI (rsfMRI) to investigate the functional connectivity of fatigue in MS patients with early age onset.

METHODS:

Twenty-seven relapsing-remitting MS patients (20 ± 7yo at the age of diagnosis and 26.0 ±â€¯5.5yo at the time of study) were recruited and 22 patients were studied. Structural and rsfMRI sequences were performed on a 3-Tesla Seimens MRI scanner. Seed-based analysis was performed using CONN Functional Connectivity Toolbox for Statistic Parametric Mapping. The Fatigue Severity Scale (FSS) and the Modified Fatigue Impact scale (MFIS) as well as EDSS, Beck Depression Inventory, and symptomatology were measured. Non-fatigued (N = 12) and fatigued patients (N = 10) were separated based on FSS scores, with a score of 5 or greater being classified as fatigued. Group differences in rsfMRI between non-fatigued and fatigued patients were analyzed. Correlations between these functional connectivity differences and behavioral fatigue scores were also analyzed.

RESULTS:

Ages, disease duration, lesion load, lesion volume, and neurologic disability were not significantly different between non-fatigued and fatigued patients (p > 0.05). Fatigued patients showed significantly stronger connectivity between the right thalamus and right precentral gyrus (T = 4.58, p = 0.015), and a trending increase in connectivity between the left hippocampus and left precentral gyrus (T = 7.55, p = 0.051). Patients with fatigue showed significantly reduced connectivity between the right thalamus and left parietal operculum (T= -4.28, p = 0.0002), left thalamus and right superior frontal gyrus (T=-5.54, p = 0.046), and between the left insula and posterior cingulate (T=-9.4, p = 0.003). The connectivity between the left insula and posterior cingulate was significantly correlated with the cognitive score of MFIS (R2 = -0.471, p = 0.027) and FSS (R2 = -0.719, p = 0.0001). The connectivity between the right thalamus and left parietal operculum was significantly correlated with MFIS cognitive scores (R2 = -0.431, p = 0.045) and with FSS scores (R2 = 0.402, p = 0.006). Correlations remained significant after accounting for depression scores.

CONCLUSIONS:

rsfMRI identified Alterations in two distinct connections (the connectivity between insula and posterior cingulate gyrus and between the right thalamus and right precentral gyrus) that differed between fatigued and non-fatigued patients, as well as correlated with cognitive fatigue severity. These findings suggest that disruption of sensorimotor, high-order motor, and non-motor executive function likely contributes to the neural mechanism of fatigue in MS. Knowledge of the neural mechanisms of underlying MS fatigue could inform more effective treatment strategies.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Encéfalo / Esclerose Múltipla Recidivante-Remitente / Fadiga Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Female / Humans / Male Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Encéfalo / Esclerose Múltipla Recidivante-Remitente / Fadiga Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Female / Humans / Male Idioma: En Ano de publicação: 2019 Tipo de documento: Article