Background: The hippocampus and hippocampal subfields have been found to be diversely affected in Alzheimer’s Disease (AD) and early stages of Alzheimer’s disease by neuroimaging studies. However, our knowledge is still lacking about the trajectories of the hippocampus and hippocampal subfields atrophy with the progression of Alzheimer’s disease. Objective: To identify which subfields of the hippocampus differ in the trajectories of Alzheimer’s disease by magnetic resonance imaging (MRI) and to determine whether individual differences on memory could be explained by structural volumes of hippocampal subfields. Methods: Four groups of participants including 41 AD patients, 43 amnestic mild cognitive impairment (aMCI) patients, 35 subjective cognitive decline (SCD) patients and 42 normal controls (NC) received their structural MRI brain scans. Structural MR images were processed by the FreeSurfer 6.0 image analysis suite to extract the hippocampus and its subfields. Furthermore, we investigated relationships between hippocampal subfield volumes and memory test variables (AVLT-immediate recall, AVLT-delayed recall, AVLT-recognition) and the regression model analyses were controlled for age, gender, education and eTIV. Results: CA1, subiculum, presubiculum, molecular layer and fimbria showed the trend toward significant volume reduction among four groups with the progression of Alzheimer’s disease. Volume of left subiculum was most strongly and actively correlated with performance across AVLT measures. Conclusion: The trend changes in the hippocampus subfields and further illustrates that SCD is the preclinical stage of AD earlier than aMCI. Future studies should aim to associate the atrophy of the hippocampal subfields in SCD with possible conversion to aMCI or AD with longitudinal design.

Alzheimer's disease (AD) is a chronic neurodegenerative disease, and its long-term progression prediction is definitely important. The structural Magnetic Resonance Imaging (sMRI) can be used to characterize the cortical atrophy that is closely coupled with clinical symptoms in AD and its prodromal stages. Many existing methods have focused on predicting the cognitive scores at future time-points using a set of morphological features derived from sMRI. The 3D sMRI can provide more massive information than the cognitive scores. However, very few works consider to predict an individual brain MRI image at future time-points. In this paper, we propose a disease progression prediction framework that comprises a 3D multi-information generative adversarial network (mi-GAN) to predict what one's whole brain will look like with an interval, and a 3D DenseNet based multi-class classification network optimized with a focal loss to determine the clinical stage of the estimated brain. The mi-GAN can generate high-quality individual 3D brain MRI image conditioning on the individual 3D brain sMRI and multi-information at the baseline time-point. Experiments are implemented on the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our mi-GAN shows the state-of-the-art performance with the structural similarity index (SSIM) of 0.943 between the real MRI images at the fourth year and the generated ones. With mi-GAN and focal loss, the pMCI vs. sMCI accuracy achieves 6.04% improvement in comparison with conditional GAN and cross entropy loss.

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Previous studies have suggested that amnestic mild cognitive impairment (aMCI) is associated with changes in cortical morphological features, such as cortical thickness, sulcal depth, surface area, gray matter volume, metric distortion, and mean curvature. These features have been proven to have specific neuropathological and genetic underpinnings. However, most studies primarily focused on mass-univariate methods, and cortical features were generally explored in isolation. Here, we used a multivariate method to characterize the complex and subtle structural changing pattern of cortical anatomy in 24 aMCI human participants and 26 normal human controls. Six cortical features were extracted for each participant, and the spatial patterns of brain abnormities in aMCI were identified by high classification weights using a support vector machine method. The classification accuracy in discriminating the two groups was 76% in the left hemisphere and 80% in the right hemisphere when all six cortical features were used. Regions showing high weights were subtle, spatially complex, and predominately located in the left medial temporal lobe and the supramarginal and right inferior parietal lobes. In addition, we also found that the six morphological features had different contributions in discriminating the two groups even for the same region. Our results indicated that the neuroanatomical patterns that discriminated individuals with aMCI from controls were truly multidimensional and had different effects on the morphological features. Furthermore, the regions identified by our method could potentially be useful for clinical diagnosis.