Neuroanatomical Signature of the Transition from Normal Cognition to MCI in Parkinson's Disease.

The progression of Parkinson's disease (PD) is often accompanied by cognitive decline. We had previously developed a brain age estimation program utilizing structural MRI data of 949 healthy individuals from publicly available sources. Structural MRI data of 244 PD patients who were cognitively normal at baseline was acquired from the Parkinson Progression Markers Initiative (PPMI). 192 of these showed stable normal cognitive function from baseline out to 5 years (PD-SNC), and the remaining 52 had unstable normal cognition and developed mild cognitive impairment within 5 years (PD-UNC). 105 healthy controls were also included in the analysis as a reference. First, we examined if there were any baseline differences in regional brain structure between PD-UNC and PD-SNC cohorts utilizing the three most widely used atrophy estimation pipelines, i.e., voxel-based morphometry (VBM), deformation-based morphometry and cortical thickness analyses. We then investigated if accelerated brain age estimation with our multivariate regressive machine learning algorithm was different across these groups (HC, PD-SNC, and PD-UNC). As per the VBM analysis, PD-UNC patients demonstrated a noticeable increase in GM volume in the posterior and anterior lobes of the cerebellum, sub-lobar, extra-nuclear, thalamus, and pulvinar regions when compared to PD-SNC at baseline. PD-UNC patients were observed to have significantly older brain age compared to both PD-SNC patients (p=0.009) and healthy controls (p<0.009). The increase in GM volume in the PD-UNC group could potentially indicate an inflammatory or neuronal hypertrophy response, which could serve as a biomarker for future cognitive decline among this population.

Analysis of Costs for Imaging-Assisted Pharmaceutical Intervention in Alzheimer's Disease with Lecanemab: Snapshot of the First 3 Years.

BACKGROUND: The approval of lecanemab for the treatment of Alzheimer's disease (AD) by the Food and Drug Administration in the United States has sparked controversy over issues of safety, cost, and efficacy. Furthermore, the prognostication of cognitive decline is prohibitively difficult with current methods. The inability to forecast incipient dementia in patients with biological AD suggests a prophylactic scenario wherein all patients with cognitive decline are prescribed anti-AD drugs at the earliest manifestations of dementia; however, most patients with mild cognitive impairment (approximately 77.7%) do not develop dementia over a 3-year period. Prophylactic response therefore constitutes unethical, costly, and unnecessary treatment for these patients. OBJECTIVE: We present a snapshot of the costs associated with the first 3 years of mass availability of anti-AD drugs in a variety of scenarios. METHODS: We consider multiple prognostication scenarios with varying sensitivities and specificities based on neuroimaging studies in patients with mild cognitive impairment to determine approximate costs for the large-scale use of lecanemab. RESULTS: The combination of fluorodeoxyglucose and magnetic resonance was determined to be the most cost-efficient at $177,000 for every positive outcome every 3 years under an assumed adjustment in the price of lecanemab to $9,275 per year. CONCLUSIONS: Imaging-assisted identification of cognitive status in patients with prodromal AD is demonstrated to reduce costs and prevent instances of unnecessary treatment in all cases considered. This highlights the potential of this technology for the ethical prescription of anti-AD medications under a paradigm of imaging-assisted early detection for pharmaceutical intervention in the treatment of AD.

Monitoring Alzheimer's Disease Progression in Mild Cognitive Impairment Stage Using Machine Learning-Based FDG-PET Classification Methods.

BACKGROUND: We previously introduced a machine learning-based Alzheimer's Disease Designation (MAD) framework for identifying AD-related metabolic patterns among neurodegenerative subjects. OBJECTIVE: We sought to assess the efficiency of our MAD framework for tracing the longitudinal brain metabolic changes in the prodromal stage of AD. METHODS: MAD produces subject scores using five different machine-learning algorithms, which include a general linear model (GLM), two different approaches of scaled subprofile modeling, and two different approaches of a support vector machine. We used our pre-trained MAD framework, which was trained based on metabolic brain features of 94 patients with AD and 111 age-matched cognitively healthy (CH) individuals. The MAD framework was applied on longitudinal independent test sets including 54 CHs, 51 stable mild cognitive impairment (sMCI), and 39 prodromal AD (pAD) patients at the time of the clinical diagnosis of AD, and two years prior. RESULTS: The GLM showed excellent performance with area under curve (AUC) of 0.96 in distinguishing sMCI from pAD patients at two years prior to the time of the clinical diagnosis of AD while other methods showed moderate performance (AUC: 0.7-0.8). Significant annual increment of MAD scores were identified using all five algorithms in pAD especially when it got closer to the time of diagnosis (p < 0.001), but not in sMCI. The increased MAD scores were also significantly associated with cognitive decline measured by Mini-Mental State Examination in pAD (q < 0.01). CONCLUSION: These results suggest that MAD may be a relevant tool for monitoring disease progression in the prodromal stage of AD.