Multiple indices of diffusion identifies white matter damage in mild cognitive impairment and Alzheimer's disease

The study of multiple indices of diffusion, including axial (DA), radial (DR) and mean diffusion (MD), as well as fractional anisotropy (FA), enables WM damage in Alzheimer's disease (AD) to be assessed in detail. Here, tract-based spatial statistics (TBSS) were performed on scans of 40 healthy elders, 19 non-amnestic MCI (MCIna) subjects, 14 amnestic MCI (MCIa) subjects and 9 AD patients. Significantly higher DA was found in MCIna subjects compared to healthy elders in the right posterior cingulum/precuneus. Significantly higher DA was also found in MCIa subjects compared to healthy elders in the left prefrontal cortex, particularly in the forceps minor and uncinate fasciculus. In the MCIa versus MCIna comparison, significantly higher DA was found in large areas of the left prefrontal cortex. For AD patients, the overlap of FA and DR changes and the overlap of FA and MD changes were seen in temporal, parietal and frontal lobes, as well as the corpus callosum and fornix. Analysis of differences between the AD versus MCIna, and AD versus MCIa contrasts, highlighted regions that are increasingly compromised in more severe disease stages. Microstructural damage independent of gross tissue loss was widespread in later disease stages. Our findings suggest a scheme where WM damage begins in the core memory network of the temporal lobe, cingulum and prefrontal regions, and spreads beyond these regions in later stages. DA and MD indices were most sensitive at detecting early changes in MCIa

Using Support Vector Machines with Multiple Indices of Diffusion for Automated Classification of Mild Cognitive Impairment

Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy older subjects and subjects with mild cognitive impairment (MCI). Here we apply DTI to 40 healthy older subjects and 33 MCI subjects in order to derive values for multiple indices of diffusion within the white matter voxels of each subject. DTI measures were then used together with support vector machines (SVMs) to classify control and MCI subjects. Greater than 90% sensitivity and specificity was achieved using this method, demonstrating the potential of a joint DTI and SVM pipeline for fast, objective classification of healthy older and MCI subjects. Such tools may be useful for large scale drug trials in Alzheimer's disease where the early identification of subjects with MCI is critical

Sexual Dimorphism in Healthy Aging and Mild Cognitive Impairment: A DTI Study

Previous PET and MRI studies have indicated that the degree to which pathology translates into clinical symptoms is strongly dependent on sex with women more likely to express pathology as a diagnosis of AD, whereas men are more resistant to clinical symptoms in the face of the same degree of pathology. Here we use DTI to investigate the difference between male and female white matter tracts in healthy older participants (24 women, 16 men) and participants with mild cognitive impairment (21 women, 12 men). Differences between control and MCI participants were found in fractional anisotropy (FA), radial diffusion (DR), axial diffusion (DA) and mean diffusion (MD). A significant main effect of sex was also reported for FA, MD and DR indices, with male control and male MCI participants having significantly more microstructural damage than their female counterparts. There was no sex by diagnosis interaction. Male MCIs also had significantly less normalised grey matter (GM) volume than female MCIs. However, in terms of absolute brain volume, male controls had significantly more brain volume than female controls. Normalised GM and WM volumes were found to decrease significantly with age with no age by sex interaction. Overall, these data suggest that the same degree of cognitive impairment is associated with greater structural damage in men compared with women

Using diffusion tensor imaging and mixed-effects models to investigate primary and secondary white matter degeneration in Alzheimer's disease and mild cognitive impairment.

International audienceWhite matter (WM) degeneration in Alzheimer's disease (AD) and mild cognitive impairment (MCI) may be a key indicator of early damage in AD. Here, we analyzed WM diffusion tensor data using Tract-Based Spatial Statistics in conjunction with mixed-effects models. Four indices of diffusion were assessed in 61 healthy control, 19 non-amnestic MCIs, 14 amnestic MCIs, and 9 AD patients. The aim of the study was to use advanced mixed-effects models to investigate the retrogenesis hypothesis of AD, which suggests that tracts that are late to myelinate in ontogenetic development are the earliest to be affected in AD. Our results show that a number of late-myelinating pathways, including the parahippocampal region and the inferior longitudinal fasciculus, were predominantly affected by changes in WM volume. Conversely, early-myelinating pathways were found to be affected by a combination of both WM and gray matter (GM) atrophy. A model of the entire WM structure of the brain returned GM models for two indices of diffusion, suggesting that more complex regional landscapes of diffusion lie hidden beneath a global analysis of the entire brain. Our results warn against an explanation of white matter damage that points simply to one of two mechanisms: secondary degeneration or direct damage of myelin. We suggest that tracts may be affected by both mechanisms, with the balance depending on whether tracts are early or late-myelinating. A greater understanding of the pattern of WM changes in AD may prove useful for the early detection of AD

Diffusional changes in MCI subjects for FA and MD.

<p>(a) Significantly higher FA in MCIna relative to controls is shown in green. (b) Significantly higher MD in MCIa relative to controls is shown in red.</p

Demographic and Cognitive Characteristics of the Sample Groups.

<p>Values are mean (SD). Abbreviations: Con, control; MCI, Mild cognitive Impairment; MMSE, Mini-Mentral State Examination. Significant differences between male and female groups are marked in bold where p<0.05 using a t-test.</p

Differences in Multiple Indices of Diffusion between Control and MCI.

<p>FA (green) was significantly decreased in MCI subjects relative to controls, while DR (yellow), MD (blue) and DA (red) were significantly increased in MCI subjects relative to controls. The plots show values for each index of diffusion taken from the regions of the WM identified in the TBSS images. The TBSS images show results at p<0.05 corrected for multiple comparisons. The leftmost images show axial slices (z = 88), images in the central panel show coronal slices (y = 109) and the rightmost images show sagittal slices (x = 112).</p

Sensitivity, specificity, accuracy and the area under the curve for a receiver operating characteristic curve (ROC AUC) for Control, MCIna and MCIa classification.

<p>The values indicated are weighted averages for the three classes under consideration; control, MCIna and MCIa. Results are shown for the 7 datasets – 100 voxels, 250 voxels, 500 voxels, 750 voxels, 1000 voxels, 2000 voxels and 3000 voxels. The voxels comprising these reduced datasets were selected by the ReliefF algorithm.</p

Sex differences for normalised grey matter volume and normalised white matter volume in control and MCI subjects.

<p>(A) No significant differences in white matter volume were found between males and females for control or MCI conditions. (B) Male MCI subjects had significantly lower GM volume relative to male controls. *** p<0.001, with post-hoc Tukey test, following an ANOVA.</p

Sensitivity, specificity, accuracy and the area under the curve for a receiver operating characteristic curve (ROC AUC) for control and MCI classification.

<p>The values indicated are weighted averages for the two classes under consideration; i.e. control and MCI. Results are shown for 7 datasets – 100 voxels, 250 voxels, 500 voxels, 750 voxels, 1000 voxels, 2000 voxels and 3000 voxels. The voxels comprising these reduced datasets were selected by the ReliefF algorithm.</p