Neurology · 2018
White matter integrity and processing speed in sickle cell anemia.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Stotesbury Hanne, Kirkham Fenella J, Kölbel Melanie, Balfour Philippa, Clayden Jonathan D, Sahota Sati, Sakaria Simrat, Saunders Dawn E, Howard Jo, Kesse-Adu Rachel, Inusa Baba, Pelidis Maria, Chakravorty Subarna, Rees David C, Awogbade Moji, Wilkey Olu, Layton Mark, Clark Christopher A, Kawadler Jamie M
Аннотация
OBJECTIVE: The purpose of this retrospective cross-sectional study was to investigate whether changes in white matter integrity are related to slower processing speed in sickle cell anemia.
METHODS: Thirty-seven patients with silent cerebral infarction, 46 patients with normal MRI, and 32 sibling controls (age range 8-37 years) underwent cognitive assessment using the Wechsler scales and 3-tesla MRI. Tract-based spatial statistics analyses of diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) parameters were performed.
RESULTS: Processing speed index (PSI) was lower in patients than controls by 9.34 points (95% confidence interval: 4.635-14.855, p = 0.0003). Full Scale IQ was lower by 4.14 scaled points (95% confidence interval: -1.066 to 9.551, p = 0.1), but this difference was abolished when PSI was included as a covariate (p = 0.18). There were no differences in cognition between patients with and without silent cerebral infarction, and both groups had lower PSI than controls (both p < 0.001). In patients, arterial oxygen content, socioeconomic status, age, and male sex were identified as predictors of PSI, and correlations were found between PSI and DTI scalars (fractional anisotropy r = 0.614, p < 0.00001; r = -0.457, p < 0.00001; mean diffusivity r = -0.341, p = 0.0016; radial diffusivity r = -0.457, p < 0.00001) and NODDI parameters (intracellular volume fraction r = 0.364, p = 0.0007) in widespread regions.
CONCLUSION: Our results extend previous reports of impairment that is independent of presence of infarction and may worsen with age. We identify processing speed as a vulnerable domain, with deficits potentially mediating difficulties across other domains, and provide evidence that reduced processing speed is related to the integrity of normal-appearing white matter using microstructure parameters from DTI and NODDI.
METHODS: Thirty-seven patients with silent cerebral infarction, 46 patients with normal MRI, and 32 sibling controls (age range 8-37 years) underwent cognitive assessment using the Wechsler scales and 3-tesla MRI. Tract-based spatial statistics analyses of diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) parameters were performed.
RESULTS: Processing speed index (PSI) was lower in patients than controls by 9.34 points (95% confidence interval: 4.635-14.855, p = 0.0003). Full Scale IQ was lower by 4.14 scaled points (95% confidence interval: -1.066 to 9.551, p = 0.1), but this difference was abolished when PSI was included as a covariate (p = 0.18). There were no differences in cognition between patients with and without silent cerebral infarction, and both groups had lower PSI than controls (both p < 0.001). In patients, arterial oxygen content, socioeconomic status, age, and male sex were identified as predictors of PSI, and correlations were found between PSI and DTI scalars (fractional anisotropy r = 0.614, p < 0.00001; r = -0.457, p < 0.00001; mean diffusivity r = -0.341, p = 0.0016; radial diffusivity r = -0.457, p < 0.00001) and NODDI parameters (intracellular volume fraction r = 0.364, p = 0.0007) in widespread regions.
CONCLUSION: Our results extend previous reports of impairment that is independent of presence of infarction and may worsen with age. We identify processing speed as a vulnerable domain, with deficits potentially mediating difficulties across other domains, and provide evidence that reduced processing speed is related to the integrity of normal-appearing white matter using microstructure parameters from DTI and NODDI.