No abstract

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 massunivariate 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.

Axin is a scaffold protein that controls multiple important pathways, including the canonical Wnt pathway and JNK signaling. Here we have identified an Axin-interacting protein, Aida, which blocks Axin-mediated JNK activation by disrupting Axin homodimerization. During investigation of in vivo functions of Axin/JNK signaling and aida in development, it was found that Axin, besides ventralizing activity by facilitating beta-catenin degradation, possesses a dorsalizing activity that is mediated by Axin-induced JNK activation. This dorsalizing activity is repressed when aida is overexpressed in zebrafish embryos. Whereas Aida-MO injection leads to dorsalized embryos, JNK-MO and MKK4-MO can ventralize embryos. The anti-dorsalization activity of aida is conferred by its ability to block Axin-mediated JNK activity. We further demonstrate that dorsoventral patterning regulated by Axin/JNK signaling is independent of maternal or zygotic Wnt signaling. We have thus identified a dorsalization pathway that is exerted by Axin/JNK signaling and its inhibitor Aida during vertebrate embryogenesis.

Precision medicine for Alzheimer's disease (AD) necessitates the development of personalized, reproducible, and neuroscientifically interpretable biomarkers, yet despite remarkable advances, few such biomarkers are available. Also, a comprehensive evaluation of the neurobiological basis and generalizability of the end‐to‐end machine learning system should be given the highest priority. For this reason, a deep learning model (3D attention network, 3DAN) that can simultaneously capture candidate imaging biomarkers with an attention mechanism module and advance the diagnosis of AD based on structural magnetic resonance imaging is proposed. The generalizability and reproducibility are evaluated using cross‐validation on in‐house, multicenter ( n = 716), and public ( n = 1116) databases with an accuracy up to 92%. Significant associations between the classification output and clinical characteristics of AD and mild cognitive impairment (MCI, a middle stage of dementia) groups provide solid neurobiological support for the 3DAN model. The effectiveness of the 3DAN model is further validated by its good performance in predicting the MCI subjects who progress to AD with an accuracy of 72%. Collectively, the findings highlight the potential for structural brain imaging to provide a generalizable, and neuroscientifically interpretable imaging biomarker that can support clinicians in the early diagnosis of AD.