Support vector machine learning and diffusion-derived structural networks predict amyloid quantity and cognition in adults with Down's syndrome.

Down's syndrome results from trisomy of chromosome 21, a genetic change which also confers a probable 100% risk for the development of Alzheimer's disease neuropathology (amyloid plaque and neurofibrillary tangle formation) in later life. We aimed to assess the effectiveness of diffusion-weighted imaging and connectomic modelling for predicting brain amyloid plaque burden, baseline cognition and longitudinal cognitive change using support vector regression. Ninety-five participants with Down's syndrome successfully completed a full Pittsburgh Compound B (PiB) PET-MR protocol and memory assessment at two timepoints. Our findings indicate that graph theory metrics of node degree and strength based on the structural connectome are effective predictors of global amyloid deposition. We also show that connection density of the structural network at baseline is a promising predictor of current cognitive performance. Directionality of effects were mainly significant reductions in the white matter connectivity in relation to both PiB+ status and greater rate of cognitive decline. Taken together, these results demonstrate the integral role of the white matter during neuropathological progression and the utility of machine learning methodology for non-invasively evaluating Alzheimer's disease prognosis.

Measuring cerebral perfusion with [11C]-PiB R1 in Down syndrome: associations with amyloid burden and longitudinal cognitive decline.

Positron emission tomography imaging of glucose hypometabolism and amyloid deposition are two well-established methods to evaluate preclinical changes in Alzheimer's disease and people with Down syndrome. However, the use of both imaging modalities may overburden participants, particularly those with intellectual disabilities and cognitive impairment. The relative tracer delivery of the [11C]-Pittsburgh Compound B has been proposed as a viable surrogate for cerebral perfusion. Here, we studied the impact of amyloid pathology on perfusion changes in Down syndrome and evaluated its associations with cognitive impairment. In total, 47 adults with Down syndrome underwent the [11C]-Pittsburgh Compound B imaging and structural imaging. The structural data were processed with Freesurfer to obtain anatomical segmentations and cortical thickness. The relative tracer delivery from [11C]-Pittsburgh Compound B was derived using a simplified reference tissue model. The sample was stratified into those with minimal amyloid burden (n = 25) and those with elevated amyloid (n = 22). We found significant and widespread reductions of cerebral perfusion in those with elevated amyloid burden, independent of age, gender, cognitive function and cortical thickness. In addition, cerebral perfusion was associated with the cognitive impairment among the Down syndrome group with elevated amyloid burden. These findings highlight the promising utility of the relative tracer delivery of the [11C]-Pittsburgh Compound B as a surrogate index in clinical trials for monitoring disease progression or tracking physiologic changes over time in Down syndrome.