Widespread white matter aberrations are associated with phonemic verbal fluency impairment in chronic traumatic brain injury
Widespread white matter aberrations are associated with phonemic verbal fluency impairment in chronic traumatic brain injury
复制标题
广泛的白质畸变与慢性创伤性脑损伤中的音位语言流畅性损伤有关
DOI:
10.1089/neu.2019.6751
复制
发表时间:
2020
影响因子:
4.2
通讯作者:
Mimura M.
中科院分区:
文献类型:
--
作者:
57.Yamagata B;Ueda R;Tasato K;Aoki Y;Hotta S;Hirano J;Takamiya A;Nakaaki S;Tabuchi H;Mimura M.
Microstructural white matter (WM) disruption and resulting abnormal structural connectivity form a potential underlying pathology in traumatic brain injury (TBI). Herein, to determine the potential mechanism of cognitive deterioration in TBI, we examined the association of damage to specific WM tracts with cognitive function in TBI patients. We recruited 18 individuals with mild-to-moderate/severe TBI in the chronic phase and 17 age-matched controls. We determined the pattern of WM aberrations in TBI using tract-based spatial statistics (TBSS) and then examined the relationship between cognitive impairment and WM damage using the threshold-free cluster enhancement correction in TBSS. TBSS analysis showed that TBI patients exhibited WM aberrations in a wide range of brain regions. In the majority of these regions, lower fractional anisotropy (FA) largely overlapped with increased radial diffusivity, but not with axial diffusivity. Further, voxel-wise correction in TBSS demonstrated that higher FA values were associated with better performance in the phonemic verbal fluency task (VFT) in widespread WM regions, but not with the semantic VFT. Despite variation in the magnitude and location of brain injury between individual cases, chronic TBI patients exhibited widespread WM aberrations. We confirmed the findings of previous studies that WM integrity is lower across the spectrum of TBI severity in chronic subjects compared to controls. Further, phonemic VFT may be a more sensitive cognitive measure of executive dysfunction associated with WM aberrations in TBI compared with semantic VFT.