Diffusion Tensor Imaging and Gait in Elderly Persons With Cerebral Small Vessel Disease

Abstract

Background and Purpose-Although cerebral small vessel disease, including white matter lesions (WML) and lacunar infarcts, is associated with gait disturbances, not all individuals with small vessel disease have these disturbances. Identical-appearing WML on MRI could reflect different degrees of microstructural integrity. Moreover, conventional MRI does not assess the integrity of normal-appearing white matter (NAWM). We therefore investigated the relation between white matter integrity assessed by diffusion tensor imaging in WML, NAWM, several regions of interest, and gait. Methods-A total of 484 nondemented elderly persons between 50 and 85 years old with cerebral small vessel disease were included in this analysis and underwent MRI and diffusion tensor imaging scanning. Mean diffusivity and fractional anisotropy within WML, NAWM, and regions of interest were related to quantitative and semiquantitative gait parameters. Results-Mean diffusivity in the WML was inversely related with gait (velocity ␤ϭϪ0.15; Pϭ0.002). For the fractional anisotropy, this relation was less evident. The same was found in the NAWM (velocity ␤ϭϪ0.21; PϽ0.001) and for some parameters also after additional adjustment for WML and lacunar infarcts. Conclusion-This study indicates that integrity of both WML and NAWM, beyond the detection limit of conventional MRI, is associated with gait disturbances. (Stroke. 2011;42:373-379.)

White Matter Microstructural Damage on Diffusion Tensor Imaging in Cerebral Small Vessel Disease

“…Analyses for each cognitive domain showed the strongest relationship between (1) cingulum bundle microstructural integrity and verbal memory performance and (2) frontal WM and psychomotor speed. 8 However, in the same cohort, the main predictors for the development of incident dementia at 5 years were WM and hippocampal volumes, 34 whereas baseline WM integrity was not associated with decline in cognitive performances 35 In the Vascular Mild Cognitive Impairment Tuscany study, WM microstructural damage was more strongly reflected in Montreal cognitive assessment than mini mental status examination performances, 36 possibly for the presence in Montreal cognitive assessment of items reflecting executive functions and psychomotor speed.…”

The effect of age and microstructural white matter integrity on lap time variation and fast-paced walking speed

“…While initial DTI studies show that the disruption of WM microstructure is associated with variable and slow walking in patients with Parkinson’s disease, small vessel disease, and leukoaraiosis (Della Nave et al, 2007, de Laat et al, 2011a, de Laat et al, 2011b, Vercruysse et al, 2015), less is known about normal aging. Since subclinical age-related ischemic changes may contribute to the development of variable and slow walking, this issue is important to the field.…”

“…Pioneer DTI studies have identified specific regions of WM microstructure that are associated with variable and slow walking, particularly in patient populations (Della Nave et al, 2007, Bhadelia et al, 2009, de Laat et al, 2011a, de Laat et al, 2011b, Bruijn et al, 2014, Vercruysse et al, 2015). Regions of interest that can potentially affect walking extend beyond those directly linking the motor cortex.…”

Multimodal Action Recognition

“…In Fig. 4.6b, the dimension of mode-3 is fixed to be 20, and we can see that MDA gets the best result within dimensions of [15,15,20], which indicates the best subspace dimension of mode-1,2. MDA performs better than DATER, which means the mode-3 correlation plays an important role in the action silhouette sequence.…”

“…However, the vectorization process requires a huge amount of memory, and it is also very time consuming. Recently, tensor decomposition analysis (TDA) [1,9,29] has been successfully applied to various high-dimensional recognition related problems, such as action recognition [7,17,20], face recognition [3,8,27], and gait recognition [6,15,28]. TDA represents high-dimensional data as a multi-fold (mode) tensor, c fChengcheng Jia, Wei Pang, and Yun Fu j Springerg, f2015g.…”