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Several concepts, which in the aggregate get might be used to account for "resilience" against age- and disease-related changes, have been the subject of much research. These include brain reserve, cognitive reserve, and brain maintenance. However, different investigators have use these terms in different ways, and there has never been an attempt to arrive at consensus on the definition of these concepts. Furthermore, there has been confusion regarding the measurement of these constructs and the appropriate ways to apply them to research. Therefore the reserve, resilience, and protective factors professional interest area, established under the auspices of the Alzheimer's Association, established a whitepaper workgroup to develop consensus definitions for cognitive reserve, brain reserve, and brain maintenance. The workgroup also evaluated measures that have been used to implement these concepts in research settings and developed guidelines for research that explores or utilizes these concepts. The workgroup hopes that this whitepaper will form a reference point for researchers in this area and facilitate research by supplying a common language., (© 2019 the Alzheimer's Association.)

Impairment in executive function (EF) is commonly found in Alzheimer's disease (AD) and mild cognitive impairment (MCI). Atlas-based diffusion tensor imaging (DTI) methods may be useful in relating regional integrity to EF measures in MCI and AD. Sixty-six participants (25 normal controls, 22 MCI, and 19 AD) received DTI scans and clinical evaluation. DTI scans were applied to a pre-segmented atlas and fractional anisotropy (FA) and mean diffusivity (MD) were calculated. ANOVA was used to assess group differences in frontal, parietal, and cerebellar regions. For regions differing between groups (p < 0.01), linear regression examined the relationship between EF scores and regional FA and MD. Anisotropy and diffusivity in frontal and parietal lobe white matter structures were associated with EF scores in MCI and only frontal lobe structures in AD. EF was more strongly associated with FA than MD. The relationship between EF and anisotropy and diffusivity was strongest in MCI. These results suggest that regional white matter integrity is compromised in MCI and AD and that FA may be a better correlate of EF than MD.

6-[(18)F]-Fluoro-L-dopa (FDOPA) has been widely used as a biomarker for catecholamine synthesis, storage, and metabolism--its intense uptake in the striatum, and fainter uptake in other brain regions, is correlated with the symptoms and pathophysiology of Parkinson's disease (PD). 6-[(18)F]fluoro-m-tyrosine (FMT), which also targets L-amino acid decarboxylase, has potential advantages over FDOPA as a radiotracer because it does not form catechol-O-methyltransferase (COMT) metabolites. The purpose of the present study was to compare the regional distribution of these radiotracers in the brains of PD patients. Fifteen Parkinson's patients were studied with FMT and FDOPA positron emission tomography (PET) as well as high-resolution structural magnetic resonance imaging (MRI). MRI's were automatically parcellated into neuroanatomical regions of interest (ROIs) in Freesurfer (http://surfer.nmr.mgh.harvard.edu); region-specific uptake rate constants (Kocc) were generated from coregistered PET using a Patlak graphical approach. The essential findings were as follows: (1) regional Kocc were highly correlated between the radiotracers and in agreement with a previous FDOPA studies that used different ROI selection techniques; (2) FMT Kocc were higher in extrastriatal regions of relatively large uptake such as amygdala, pallidum, brainstem, hippocampus, entorhinal cortex, and thalamus, whereas cortical Kocc were similar between radiotracers; (3) while subcortical uptake of both radiotracers was related to disease duration and severity, cortical uptake was not. These results suggest that FMT may have advantages for examining pathologic changes within allocortical loop structures, which may contribute to cognitive and emotional symptoms of PD., (© 2014 Wiley Periodicals, Inc.)

There is growing body of research devoted to designing imaging-based biomarkers that identify Alzheimer's disease (AD) in its prodromal stage using statistical machine learning methods. Recently several authors investigated how clinical trials for AD can be made more efficient (i.e., smaller sample size) using predictive measures from such classification methods. In this paper, we explain why predictive measures given by such SVM type objectives may be less than ideal for use in the setting described above. We give a solution based on a novel deep learning model, randomized denoising autoencoders (rDA), which regresses on training labels y while also accounting for the variance, a property which is very useful for clinical trial design. Our results give strong improvements in sample size estimates over strategies based on multi-kernel learning. Also, rDA predictions appear to more accurately correlate to stages of disease. Separately, our formulation empirically shows how deep architectures can be applied in the large d, small n regime--the default situation in medical imaging. This result is of independent interest.

Recent studies suggest that white matter abnormalities contribute to both motor and non-motor symptoms of Parkinson’s disease. The present study was designed to investigate the degree to which diffusion tensor magnetic resonance imaging (DTI) indices are related to executive function in Parkinson’s patients. We used tract-based spatial statistics to compare DTI data from 15 patients to 15 healthy, age- and education-matched controls. We then extracted mean values of fractional anisotropy (FA) and mean diffusivity (MD) within an a priori frontal mask. Executive function composite Z scores were regressed against these DTI indices, age, and total intracranial volume. In Parkinson’s patients, FA was related to executive composite scores, and both indices were related to Stroop interference scores. We conclude that white matter microstructural abnormalities contribute to cognitive deficits in Parkinson’s disease. Further work is needed to determine whether these white matter changes reflect the pathological process or a clinically important comorbidity.