The accuracy of hippocampal volumetry and glucose metabolism for the diagnosis of patients with suspected Alzheimer's disease, using automatic quantitative clinical tools

Abstract

The hippocampus is one of the earliest sites involved in the pathology of Alzheimer's disease (AD). Therefore, we specifically investigated the sensitivity and specificity of hippocampal volume and glucose metabolism in patients being evaluated for AD, using automated quantitative tools (NeuroQuant – magnetic resonance imaging [MRI] and Scenium – positron emission tomography [PET]) and clinical evaluation.This retrospective study included adult patients over the age of 45 years with suspected AD, who had undergone fluorodeoxyglucose positron emission tomography-computed tomography (FDG-PET-CT) and MRI. FDG-PET-CT images were analyzed both qualitatively and quantitatively. In quantitative volumetric MRI analysis, the percentage of the total intracranial volume of each brain region, as well as the total hippocampal volume, were considered in comparison to an age-adjusted percentile. The remaining brain regions were compared between groups according to the final diagnosis.Thirty-eight patients were included in this study. After a mean follow-up period of 23 ± 11 months, the final diagnosis for 16 patients was AD or high-risk mild cognitive impairment (MCI). Out of the 16 patients, 8 patients were women, and the average age of all patients was 69.38 ± 10.98 years. Among the remaining 22 patients enrolled in the study, 14 were women, and the average age was 67.50 ± 11.60 years; a diagnosis of AD was initially excluded, but the patients may have low-risk MCI. Qualitative FDG-PET-CT analysis showed greater accuracy (0.87), sensitivity (0.76), and negative predictive value (0.77), when compared to quantitative PET analysis, hippocampal MRI volumetry, and specificity. The positive predictive value of FDG-PET-CT was similar to the MRI value.The performance of FDG-PET-CT qualitative analysis was significantly more effective compared to MRI volumetry. At least in part, this observation could corroborate the sequential hypothesis of AD pathophysiology, which posits that functional changes (synaptic dysfunction) precede structural changes (atrophy).

Canine cognitive dysfunction patients have reduced total hippocampal volume compared with aging control dogs: A comparative magnetic resonance imaging study

“…However, our data suggest that hippocampal atrophy is not part of canine aging, as we could find no clear association between age and hippocampal volumes in either group. Hippocampal atrophy is a key and early feature of human AD, and loss of hippocampal neurons and synapses is strongly associated with cognitive decline in that disorder (Wang et al, 2015;Halliday, 2017;Ferrari et al, 2019). Hippocampal pathology has been documented in brains of dogs with CCD, but hippocampal atrophy has not been demonstrated for this disorder (Schmidt et al, 2015).…”

Glucose metabolism reflects local atrophy and tau pathology in symptomatic Alzheimer’s disease

“…Additionally, previous work in our lab found that tau-PET topography and magnitude are more predictive of longitudinal change in cortical thickness than cross-sectional cortical thickness. 60 It has been postulated that FDG-PET as a biomarker becomes abnormal earlier than MRI in Alzheimer's disease 17,[71][72][73][74] and other neurodegenerative disorders. 68,75 Taken together, these findings suggest that tau pathology may be more closely linked temporally to metabolic than structural abnormalities.…”

An update on magnetic resonance imaging markers in AD

“…In addition, the functional connectivity features of the regions of interest also predicted the severity of the neuropsychiatric symptoms, as well as the AD pathology (indexed by baseline and change of Aβ/phosphorylated tau ratio) with above 70% accuracy rate. Furthermore, measures of MRI at baseline (GM volumes of specific regions of interest, including the hippocampus) in combination with genetic [apolipoprotein E (APOE) ε4 allele and 19 single nucleotide polymorphisms (SNPs) significantly associated with AD] 35 or hypometabolism (qualitatively investigated with 18F-fluoro-2-deoxyglucose positron emission tomography – FDG-PET) 36 information could predict the progression to AD in MCI cases clinically followed up for 24 months, reaching accuracies higher than 80%.…”

“…Using different classification approaches, several studies demonstrated that brain structural MRI (particularly the medial temporal lobe), amyloid and FDG-PET, and genetic features (in particular the APOEε4 allele) better than other imaging and genetic features predicted the distinction between AD and healthy controls and the conversion from MCI to AD. 35 , 36 , 37 , 96 …”