A structural covariance network (SCN) has been used successfully in structural magnetic resonance imaging (sMRI) studies. However, most SCNs have been constructed by a unitary marker that is insensitive for discriminating different disease phases. The aim of this study was to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis. R2SNs were constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject. We further assessed the small-world properties of R2SNs, and we evaluated the reproducibility in different datasets and through test-retest analysis. The relationships between the R2SNs and general intelligence/interregional coexpression of enriched genes were also explored. R2SNs could be replicated in different datasets, regardless of the use of different feature subsets. R2SNs showed high reproducibility in the test-retest analysis (intraclass correlation coefficient >0.7). In addition, the small-word property (σ>2) and the high correlation between gene expression (R=0.29, P<0.001) and general intelligence were determined for R2SNs. Furthermore, the results have also been repeated in the Brainnetome atlas. R2SNs provide a novel, reliable and biologically plausible method to understand human morphological covariance based on sMRI.

Growing evidence indicates that amyloid-beta (Aβ) accumulation is one of the most common neurobiological biomarkers in Alzheimer’s disease (AD). The primary aim of this study was to explore whether the radiomic features of Aβ positron emission tomography (PET) images are used as predictors and provide a neurobiological foundation for AD. The radiomics features of Aβ PET imaging of each brain region of the Brainnetome Atlas were computed for classification and prediction using a support vector machine model. The results showed that the area under the receiver operating characteristic curve (AUC) was 0.93 for distinguishing AD (N = 291) from normal control (NC; N = 334). Additionally, the AUC was 0.83 for the prediction of mild cognitive impairment (MCI) converting (N = 88) (vs. no conversion, N = 100) to AD. In the MCI and AD groups, the systemic analysis demonstrated that the classification outputs were significantly associated with clinical measures (apolipoprotein E genotype, polygenic risk scores, polygenic hazard scores, cerebrospinal fluid Aβ, and Tau, cognitive ability score, the conversion time for progressive MCI subjects and cognitive changes). These findings provide evidence that the radiomic features of Aβ PET images can serve as new biomarkers for clinical applications in AD/MCI, further providing evidence for predicting whether MCI subjects will convert to AD.

Individuals with mild cognitive impairment (MCI) of different subtypes show distinct alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by employing a regional radiomics similarity network (R2SN). The second aim is to characterize the abnormality patterns associated with the clinical manifestations of each subtype. An individual‐level R2SN is constructed for N = 605 normal controls (NCs), N = 766 MCI patients, and N = 283 Alzheimer's disease (AD) patients. MCI patients’ R2SN profiles are clustered into two subtypes using nonnegative matrix factorization. The patterns of brain alterations, gene expression, and the risk of cognitive decline in each subtype are evaluated. MCI patients are clustered into “similar to the pattern of NCs” (N‐CI, N = 252) and “similar to the pattern of AD” (A‐CI, N = 514) subgroups. Significant differences are observed between the subtypes with respect to the following: 1) clinical measures; 2) multimodal neuroimaging; 3) the proportion of progression to dementia (61.54% for A‐CI and 21.77% for N‐CI) within three years; 4) enriched genes for potassium‐ion transport and synaptic transmission. Stratification into the two subtypes provides new insight for risk assessment and precise early intervention for MCI patients.

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Multi-spectral imaging (MSI) is a novel non-invasive tool for visualizing the entire span of the eye, from the internal limiting membrane to the choroid. However, spatial misalignments can be frequently observed in sequential MSI images because the eye saccade movement is usually faster than the MSI image acquisition speed. Therefore, registering MSI images is necessary for computer-based analysis of retinal degeneration via MSI. In this paper, we propose an early deep learning framework for achieving an accurate registration of MSI images in a group-wise fashion. The framework contains three parts: a template construction based on principal component analysis, a deformation field calculation, and a spatial transformation. The framework is uniquely capable of resolving two key challenges, i.e., the ''multimodal'' characteristics in MSI images for the acquisition with different spectra and the requirement of joint registration of the sequential images. Our experimental results demonstrate the superior performance of our framework compared to several representative state-of-the-art techniques in both speed and accuracy. INDEX TERMS Multi-spectral images, group-wise registration, deep learning, mono/multi-modal images.