Alzheimer’s disease (AD) is the most common type of dementia, affecting 1 in 8 people (13%) aged 65 or older. AD is a neurodegenerative disease characterized by memory loss in its early stages, followed by a progressive decline in other behavioral and cognitive functions. Recent therapeutic efforts have focused on early mild cognitive impairment (eMCI) to enable earlier treatment of individuals with heightened risk of developing AD. Identifying biomarkers in these patients that might predict brain tissue loss is vital for drug trial enrichment, and to help identify those most likely to decline. Image-based predictors of decline may also offer new leads for understanding the development and pathogenesis of AD.
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a large multi-site longitudinal study to evaluate measures that may help to track or predict disease progression in AD. In addition to the more widely-accepted measures from anatomical MRI, PET, and CSF measures of pathology, ADNI recently added additional neuroimaging measures including diffusion tensor imaging (DTI), arterial spin labeling, and resting state functional MRI. The primary goal of ADNI is to identify sensitive biomarkers of very early AD progression to help monitor disease progression and treatment efficacy with greater precision.
MRI-based image analysis methods have long been used to track structural atrophy of the brain. Diffusion tensor imaging (DTI) is sensitive to microscopic white matter (WM) injury not always detectable with standard anatomical MRI [1
]. Diffusion imaging can be used to track the highly anisotropic diffusion of water along axons, revealing microstructural fiber bundles connecting cortical and subcortical regions. In diseases such as AD, and even in MCI subjects at risk for AD, these connections progressively deteriorate.
By combining DTI with standard MRI, we can measure the integrity and connectivity of white matter tracts. Connectivity mapping is a relatively recent direction in neuroimaging, and variations in the degree and extent of connections may be useful as measures of disease burden. Recent models of AD suggest that cognitive deficits arise from the progressive disconnection of cortical and subcortical regions, promoted by neuronal loss and white matter injury [2
]. The two accepted pathological markers of AD, amyloid plaques and neurofibrillary tangles, tend to affect association cortices early in the disease. From these cortical regions, long pathways of association fibers are linked to many other brain regions, made up of large populations of pyramidal neurons that support connections within and between hemispheres [3
]. AD patients also show a decrease in the volume and integrity of WM commissures such as the corpus callosum, as well as pathways such as the cingulum and superior longitudinal fasciculus [5
], suggesting an ongoing disruption of connectivity.
Recently, graph theory has been used to describe anatomical networks and characterize connectivity patterns based on signals in brain images. Structural brain networks are modeled as graphs where nodes
designate elements (i.e., brain regions) linked by edges
representing physical connections. A recent study found that AD patients have abnormal “small-world” architecture in large-scale brain networks, with increased clustering and longer shortest paths linking individual regions, implying a less optimal network topology [8
]. However, as far as we know, small-world global network measures, such as characteristic path length (CPL) and mean clustering coefficient (MCC), have not yet been used to predict future
WM disruption in AD.
Here we assessed a group of 19 patients with early signs of cognitive impairment (also termed “early MCI”). We first examined whether baseline average fractional anisotropy (FA) measures in the corpus callosum (CC) were predictive of changes in white matter integrity, as measured by changes in FA, after a 6-month follow-up interval. When we found that this was not the case, we further examined whether small-world architecture measures calculated from baseline connectivity maps, derived from both MRI and DTI data, were able to predict changes in white matter integrity after 6 months. We found that global network measures may offer a potentially useful biomarker in predicting white matter changes at this critical time before the onslaught of AD.