Background and PurposeIn Alzheimer's continuum (a comprehensive of preclinical Alzheimer's disease [AD], mild cognitive impairment [MCI] due to AD, and AD dementia), cognitive dysfunctions are often related to cortical atrophy in specific brain regions. The purpose of this study was to investigate the association between anatomical pattern of cortical atrophy and specific neuropsychological deficits.MethodsA total of 249 participants with Alzheimer's continuum (125 AD dementia, 103 MCI due to AD, and 21 preclinical AD) who were confirmed to be positive for amyloid deposits were collected from the memory disorder clinic in the department of neurology at Samsung Medical Center in Korea between September 2013 and March 2018. To analyze neuropsychological test-specific neural correlates representing the relationship between cortical atrophy measured by cortical thickness and performance in specific neuropsychological tests, a linear regression analysis was performed. Two neural correlates acquired by 2 different standardized scores in neuropsychological tests were also compared.ResultsCortical atrophy in several specific brain regions was associated with most neuropsychological deficits, including digit span backward, naming, drawing-copying, verbal and visual recall, semantic fluency, phonemic fluency, and response inhibition. There were a few differences between 2 neural correlates obtained by different z-scores.ConclusionsThe poor performance of most neuropsychological tests is closely related to cortical thinning in specific brain areas in Alzheimer's continuum. Therefore, the brain atrophy pattern in patients with Alzheimer's continuum can be predict by an accurate analysis of neuropsychological tests in clinical practice.

Background In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patterns of atrophy among three FTD clinical syndromes including behavioral variant FTD (bvFTD), non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA) give rise to the need for classification models at the individual level. In this study, we aimed to classify each individual subject into one of the diagnostic categories in a hierarchical manner by employing a machine learning-based classification method. Methods We recruited 143 patients with FTD, 50 patients with Alzheimer's disease (AD) dementia, and 146 cognitively normal subjects. All subjects underwent a three-dimensional volumetric brain magnetic resonance imaging (MRI) scan, and cortical thickness was measured using FreeSurfer. We applied the Laplace Beltrami operator to reduce noise in the cortical thickness data and to reduce the dimension of the feature vector. Classifiers were constructed by applying both principal component analysis and linear discriminant analysis to the cortical thickness data. For the hierarchical classification, we trained four classifiers using different pairs of groups: Step 1 - CN vs. FTD + AD, Step 2 - FTD vs. AD, Step 3 - bvFTD vs. PPA, Step 4 - svPPA vs. nfvPPA. To evaluate the classification performance for each step, we used a10-fold cross-validation approach, performed 1000 times for reliability. Results The classification accuracy of the entire hierarchical classification tree was 75.8%, which was higher than that of the non-hierarchical classifier (73.0%). The classification accuracies of steps 1–4 were 86.1%, 90.8%, 86.9%, and 92.1%, respectively. Changes in the right frontotemporal area were critical for discriminating behavioral variant FTD from PPA. The left frontal lobe discriminated nfvPPA from svPPA, while the bilateral anterior temporal regions were critical for identifying svPPA. Conclusions In the present study, our automated classifier successfully classified FTD clinical subtypes with good to excellent accuracy. Our classifier may help clinicians diagnose FTD subtypes with subtle cortical atrophy and facilitate appropriate specific interventions.

Although the association between apolipoprotein E (APOE) genotype and disease progression is well characterized in patients with Alzheimer’s disease, such a relationship is unknown in patients with subcortical vascular cognitive impairment. We evaluated whether APOE genotype is associated with disease progression in subcortical vascular mild cognitive impairment (svMCI) patients. We prospectively recruited 72 svMCI patients (19 APOE4 carriers, 42 APOE3 homozygotes, and 11 APOE2 carriers). Patients were annually followed-up with brain MRI and neuropsychological tests for three years and underwent a second Pittsburgh compound B (PiB)-PET at a mean interval of 32.3 months. Amyloid-ß burden was quantified by PiB standardized uptake value ratio (SUVR), and the amount of small vessel disease was quantified by number of lacune and small vessel disease score on MRI. We also measured cortical thickness. During the three years of follow-up, compared to the APOE3 homozygotes, there was less increase in PiB SUVR among APOE2 carriers (p = 0.023), while the APOE genotype did not show significant effects on small vessel disease progression. APOE2 carriers also showed less cortical thinning (p = 0.023) and a slower rate of cognitive decline (p = 0.009) compared to those with APOE3 homozygotes. Our findings suggest that, in svMCI patients, APOE2 has protective effects against amyloid-ß accumulation, cortical thinning, and cognitive decline.

Amyloid positron emission tomography ([18F] florbetaben (FBB) PET) can be used to determine concomitant Alzheimer's disease (AD) in idiopathic normal pressure hydrocephalus (iNPH) patients. FBB PET scans and the tap test were performed in 31 patients with clinically suspected iNPH, and amyloid positive (iNPH/FBB+) and negative (iNPH/FBB-) groups were compared with respect to clinical characteristics. We evaluated prognostic value of FBB PET scans by analyzing the response to the tap test using a linear mixed model. We also performed a multivariable regression analysis to investigate whether amyloid PET positivity can predict the positive tap test response independent of other AD biomarkers. The results showed that the iNPH/FBB+ group (7/31, 22.6%) had a higher percentage of APOE4 carriers, lower Aβ42, higher CSF t-tau, and p-tau/Aβ42 ratio than the iNPH/FBB- group (24/31, 77.4%), while the two groups did not differ in imaging characteristics. The iNPH/FBB- group had a higher percentage of tap responders and showed a greater improvement in gait scores after the tap test than the iNPH/FBB+ group (group-tap test effect interaction, p = 0.035). A multivariable logistic regression analysis showed that amyloid positivity on PET scans (OR 0.03, p = 0.029) and CSF p-tau (OR 0.87, p = 0.044) were independently associated with the positive tap test response. Among 21 tap responders in the iNPH/FBB- group, 14 patients received shunt surgery and 12/14 (85.7%) patients showed symptom improvement. Our findings suggest that amyloid PET scans can help determine which iNPH patients will benefit from shunt surgery by discriminating concomitant AD.