Cognitive status correlates with white matter alteration in Parkinson's disease

Abstract

Patients with Parkinson's disease (PD) can develop mild cognitive impairment (PD-MCI), frequently progressing to dementia (PDD). Here, we aimed to elucidate the relationship between white matter alteration and cognitive status in PD and dementia with Lewy bodies (DLB) by using diffusion tensor imaging. We also compared the progression patterns of white and gray matter and the cerebral perfusion. We enrolled patients with PD cognitively normal (PD-CogNL, n = 32), PD-MCI (n = 28), PDD (n = 25), DLB (n = 29), and age- and sex-matched healthy control subjects (n = 40). Fractional anisotropy (FA) map of a patient group was compared with that of control subjects by using tract-based spatial statistics. For the patient cohort, intersubject voxel-wise correlation was performed between FA values and Mini-Mental Status Examination (MMSE) scores. We also evaluated the gray matter and the cerebral perfusion by conducting a voxel-based analysis. There were significantly decreased FA values in many major tracts in patients with PD-MCI, PDD, and DLB, but not in PD-CogNL, compared with control subjects. FA values in the certain white matter areas, particularly the bilateral parietal white matter, were significantly correlated with MMSE scores in patients with PD. Patients with PDD and DLB had diffuse gray matter atrophy. All patient groups had occipital and posterior parietal hypoperfusion when compared with control subjects. Our results suggest that white matter damage underlies cognitive impairment in PD, and cognitive impairment in PD progresses with functional alteration (hypoperfusion) followed by structural alterations in which white matter alteration precedes gray matter atrophy.

Imaging changes associated with cognitive abnormalities in Parkinson’s disease

“…White matter changes in these regions were consistently reported in previous TBSS findings (Agosta et al 2013a, b;Deng et al 2013;Hattori et al 2012;Matsui et al 2007;Melzer et al 2013;Rae et al 2012). We detected group differences with an increase in MD (but not in FA) in our patients.…”

“…Our findings combined with previous studies (Agosta et al 2013b;Gallagher et al 2013;Hattori et al 2012;Melzer et al 2013) suggest that frontal white matter damage is a core pathological substrate of mild cognitive deficits in PD. White matter damage did not correlate UPDRS-III scores.…”

“…FA and MD in the whole-brain white matter can be assessed using voxel-based analysis (VBA) or tractbased statistical analysis (TBSS). Contrary to gray matter findings, in which regional changes are more common in non-demented PD, white matter changes are reported in more diffuse brain areas even in non-demented PD (Hattori et al 2012;Kim et al 2013;Melzer et al 2013;Theilmann et al 2013;Zheng et al 2014), and the frontal region was one of the most consistently reported regions for white matter changes (Agosta et al 2013b;Deng et al 2013;Gattellaro et al 2009;Rae et al 2012;Zhan et al 2012;Zhang et al 2011). Moreover, variability in FA and/ or MD was primarily associated with cognitive measures (Agosta et al 2013b;Gallagher et al 2013;Rae et al 2012;Theilmann et al 2013;Zheng et al 2014.…”

“…Furthermore, only correlations were demonstrated in PD without comparing structural data of PD with those of HCs (Lyoo et al 2011;Zheng et al 2014). To date, only a handful of studies have addressed which structural abnormalities account for different PD symptoms by investigating whole-brain gray and white matter changes (Agosta et al 2013a(Agosta et al , 2013bHattori et al 2012). Using both VBM and TBSS analyses, these studies have consistently concluded that white matter and not gray matter changes underlie cognitive impairment in PD (Agosta et al 2013a, b).…”

White Matter Alteration in Idiopathic Normal Pressure Hydrocephalus: Tract-Based Spatial Statistics Study

“…6 Recently, DTI has been used to quantitatively evaluate the white matter alteration in various neurodegenerative diseases in vivo. 7,8 Diffusion anisotropy is most commonly quantified by using FA values. 9 There are various methods of analyzing DTI data, such as region-of-interest analysis, tract-specific analysis, and voxelwise statistical analysis.…”