Diffusion Tensor Imaging Measures of Brain Connectivity for the Early Diagnosis of Alzheimer's Disease

Structural But Not Functional Connectivity Differences within Default Mode Network Indicate Conversion to Dementia

BACKGROUND

Malfunctioning of the default mode network (DMN) has been consistently related to mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, evidence on differences in this network between MCI converters (MCI-c) and non-converters (MCI-nc), which could mark progression to AD, is still inconsistent.

OBJECTIVE

To multimodally investigate the DMN in the AD continuum.

METHODS

We measured gray matter (GM) volume, white matter (WM) integrity, and functional connectivity (FC) at rest in healthy elderly controls, MCI-c, MCI-nc, and AD patients, matched on sociodemographic variables.

RESULTS

Significant differences between AD patients and controls were found in the structure of most of the regions of the DMN. MCI-c only differed from MCI-nc in GM volume of the left parahippocampus and bilateral hippocampi and middle frontal gyri, as well as in WM integrity of the parahippocampal cingulum connecting the left hippocampus and precuneus. We found significant correlations between integrity in some of those regions and global neuropsychological status, as well as an excellent discrimination ability between converters and non-converters for the sum of GM volume of left parahippocampus, bilateral hippocampi, and middle frontal gyri, and WM integrity of left parahippocampal cingulum. However, we found no significant differences in FC.

CONCLUSION

These results further support the relationship between abnormalities in the DMN and AD, and suggest that structural measures could be more accurate than resting-state estimates as markers of conversion from MCI to AD.

Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer's Disease

Identifying biomarkers that can assess the risk of developing Alzheimer's Disease (AD) remains a significant challenge. In this study, we investigated the integrity levels of brain white matter in 34 patients with mild cognitive impairment (MCI) who later converted to AD and 53 stable MCI patients. We used diffusion tensor imaging (DTI) and automated fiber quantification to obtain the diffusion properties of 20 major white matter tracts. To identify which tracts and diffusion measures are most relevant to AD conversion, we used support vector machines (SVMs) to classify the AD conversion and non-conversion MCI patients based on the diffusion properties of each tract individually. We found that diffusivity measures from seven white matter tracts were predictive of AD conversion with axial diffusivity being the most predictive diffusion measure. Additional analyses revealed that white matter changes in the central and parahippocampal terminal regions of the right cingulate hippocampal bundle, central regions of the right inferior frontal occipital fasciculus, and posterior and anterior regions of the left inferior longitudinal fasciculus were the best predictors of conversion from MCI to AD. An SVM based on these white matter tract regions achieved an accuracy of 0.75. These findings provide additional potential biomarkers of AD risk in MCI patients.

Disrupted balance of long and short-range functional connectivity density in Alzheimer's disease (AD) and mild cognitive impairment (MCI) patients: a resting-state fMRI study

Background

Alzheimer’s disease (AD) is an age-progressive neurodegenerative disorder that affects cognitive function. There have been several functional connectivity (FC) strengths; however, FC density needs more development in AD. Therefore, this study wanted to determine the alternations in resting-state functional connectivity density (FCD) induced by Alzheimer’s and mild cognitive impairment (MCI).

Methods

One hundred and eleven AD patients, 29 MCI patients, and 73 healthy controls (age- and sex-matched) were recruited and assessed using resting-state functional magnetic resonance imaging (MRI) scanning. The ultra-fast graph theory called FCD mapping was used to calculate the voxel-wise short- and long-range FCD values of the brain. We performed voxel-based between-group comparisons of FCD values to show the cerebral regions with significant FCD alterations. We performed Pearson’s correlation analyses between aberrant functional connectivity densities and several clinical variables with adjustment for age and sex.

Results

Patients with cognition decline showed significantly abnormal long-range FCD in the cerebellum crus I, right insula, left inferior frontal gyrus, left superior frontal gyrus, left inferior frontal gyrus, and right middle frontal gyrus. The short-range FCD changed in the cerebellum crus I, left inferior frontal gyrus, left superior occipital gyrus, and right middle frontal gyrus. The long- and short-range functional connectivity in the left inferior frontal gyrus was positively correlated with Mini-mental State Examination (MMSE) scores.

Conclusions

FCD in the identified regions reflects mechanism and compensation for loss of cognitive function. These findings could improve the pathology of AD and MCI and supply a neuroimaging marker for AD and MCI.

Anisotropy of Anomalous Diffusion Improves the Accuracy of Differentiating and Grading Alzheimer's Disease Using Novel Fractional Motion Model

Background and Purpose: Recent evidence shows that the fractional motion (FM) model may be a more appropriate model for describing the complex diffusion process of water in brain tissue and has shown to be beneficial in clinical applications of Alzheimer's disease (AD). However, the FM model averaged the anomalous diffusion parameter values, which omitted the impacts of anisotropy. This study aimed to investigate the potential feasibility of anisotropy of anomalous diffusion using the FM model for distinguishing and grading AD patients. Methods: Twenty-four patients with AD and 11 matched healthy controls were recruited, diffusion MRI was obtained from all participants and analyzed using the FM model. Generalized fractional anisotropy (gFA), an anisotropy metric, was introduced and the gFA values of FM-related parameters, Noah exponent (α) and the Hurst exponent (H), were calculated and compared between the healthy group and AD group and between the mild AD group and moderate AD group. The receiver-operating characteristic (ROC) analysis and the multivariate logistic regression analysis were used to assess the diagnostic performances of the anisotropy values and the directionally averaged values. Results: The gFA(α) and gFA(H) values of the moderate AD group were higher than those of the mild AD group in left hippocampus. The gFA(α) value of the moderate AD group was significantly higher than that of the healthy control group in both the left and right hippocampus. The gFA(ADC) values of the moderate AD group were significantly lower than those of the mild AD group and healthy control group in the right hippocampus. Compared with the gFA(α), gFA(H), α, and H, the ROC analysis showed larger areas under the curves for combination of α + gFA(α) and the combination of H + gFA(H) in differentiating the mild AD and moderate AD groups, and larger area under the curves for combination of α + gFA(α) in differentiating the healthy controls and AD groups. Conclusion: The anisotropy of anomalous diffusion could significantly differentiate and grade patients with AD, and the diagnostic performance was improved when the anisotropy metric was combined with commonly used directionally averaged values. The utility of anisotropic anomalous diffusion may provide novel insights to profoundly understand the neuropathology of AD.

Cognitive, Genetic, Brain Volume, and Diffusion Tensor Imaging Markers as Early Indicators of Dementia

BACKGROUND

Although associated with dementia and cognitive impairment, microstructural white matter integrity is a rarely used marker of preclinical dementia.

OBJECTIVE

We aimed to evaluate the individual and combined effects of multiple markers, with special focus on microstructural white matter integrity, in detecting individuals with increased dementia risk.

METHODS

A dementia-free subsample (n = 212, mean age = 71.33 years) included in the population-based Swedish National Study on Aging and Care (SNAC-K) underwent magnetic resonance imaging (T1-weighted, fluid-attenuated inversion recovery, diffusion tensor imaging), neuropsychological testing (perceptual speed, episodic memory, semantic memory, letter and category fluency), and genotyping (APOE). Incident dementia was assessed during six years of follow-up.

RESULTS

A global model (global cognition, APOE, total brain tissue volume: AUC = 0.920) rendered the highest predictive value for future dementia. Of the models based on specific markers, white matter integrity of the forceps major tract was included in the most predictive model, in combination with perceptual speed and hippocampal volume (AUC = 0.911).

CONCLUSION

Assessment of microstructural white matter integrity may improve the early detection of dementia, although the added benefit in this study was relatively small.

The accuracy of hippocampal volumetry and glucose metabolism for the diagnosis of patients with suspected Alzheimer's disease, using automatic quantitative clinical tools

The hippocampus is one of the earliest sites involved in the pathology of Alzheimer's disease (AD). Therefore, we specifically investigated the sensitivity and specificity of hippocampal volume and glucose metabolism in patients being evaluated for AD, using automated quantitative tools (NeuroQuant - magnetic resonance imaging [MRI] and Scenium - positron emission tomography [PET]) and clinical evaluation.This retrospective study included adult patients over the age of 45 years with suspected AD, who had undergone fluorodeoxyglucose positron emission tomography-computed tomography (FDG-PET-CT) and MRI. FDG-PET-CT images were analyzed both qualitatively and quantitatively. In quantitative volumetric MRI analysis, the percentage of the total intracranial volume of each brain region, as well as the total hippocampal volume, were considered in comparison to an age-adjusted percentile. The remaining brain regions were compared between groups according to the final diagnosis.Thirty-eight patients were included in this study. After a mean follow-up period of 23 ± 11 months, the final diagnosis for 16 patients was AD or high-risk mild cognitive impairment (MCI). Out of the 16 patients, 8 patients were women, and the average age of all patients was 69.38 ± 10.98 years. Among the remaining 22 patients enrolled in the study, 14 were women, and the average age was 67.50 ± 11.60 years; a diagnosis of AD was initially excluded, but the patients may have low-risk MCI. Qualitative FDG-PET-CT analysis showed greater accuracy (0.87), sensitivity (0.76), and negative predictive value (0.77), when compared to quantitative PET analysis, hippocampal MRI volumetry, and specificity. The positive predictive value of FDG-PET-CT was similar to the MRI value.The performance of FDG-PET-CT qualitative analysis was significantly more effective compared to MRI volumetry. At least in part, this observation could corroborate the sequential hypothesis of AD pathophysiology, which posits that functional changes (synaptic dysfunction) precede structural changes (atrophy).