Brain age estimation using multi-feature-based networks

Profiling cortical morphometric similarity in perinatal brains: Insights from development, sex difference, and inter-individual variation

The topological organization of the macroscopic cortical networks important for the development of complex brain functions. However, how the cortical morphometric organization develops during the third trimester and whether it demonstrates sexual and individual differences at this particular stage remain unclear. Here, we constructed the morphometric similarity network (MSN) based on morphological and microstructural features derived from multimodal MRI of two independent cohorts (cross-sectional and longitudinal) scanned at 30-44 postmenstrual weeks (PMW). Sex difference and inter-individual variations of the MSN were also examined on these cohorts. The cross-sectional analysis revealed that both network integration and segregation changed in a nonlinear biphasic trajectory, which was supported by the results obtained from longitudinal analysis. The community structure showed remarkable consistency between bilateral hemispheres and maintained stability across PMWs. Connectivity within the primary cortex strengthened faster than that within high-order communities. Compared to females, male neonates showed a significant reduction in the participation coefficient within prefrontal and parietal cortices, while their overall network organization and community architecture remained comparable. Furthermore, by using the morphometric similarity as features, we achieved over 65 % accuracy in identifying an individual at term-equivalent age from images acquired after birth, and vice versa. These findings provide comprehensive insights into the development of morphometric similarity throughout the perinatal cortex, enhancing our understanding of the establishment of neuroanatomical organization during early life.

Graph kernel of brain networks considering functional similarity measures

As a tool of brain network analysis, the graph kernel is often used to assist the diagnosis of neurodegenerative diseases. It is used to judge whether the subject is sick by measuring the similarity between brain networks. Most of the existing graph kernels calculate the similarity of brain networks based on structural similarity, which can better capture the topology of brain networks, but all ignore the functional information including the lobe, centers, left and right brain to which the brain region belongs and functions of brain regions in brain networks. The functional similarities can help more accurately locate the specific brain regions affected by diseases so that we can focus on measuring the similarity of brain networks. Therefore, a multi-attribute graph kernel for the brain network is proposed, which assigns multiple attributes to nodes in the brain network, and computes the graph kernel of the brain network according to Weisfeiler-Lehman color refinement algorithm. In addition, in order to capture the interaction between multiple brain regions, a multi-attribute hypergraph kernel is proposed, which takes into account the functional and structural similarities as well as the higher-order correlation between the nodes of the brain network. Finally, the experiments are conducted on real data sets and the experimental results show that the proposed methods can significantly improve the performance of neurodegenerative disease diagnosis. Besides, the statistical test shows that the proposed methods are significantly different from compared methods.

Improving brain age prediction with anatomical feature attention-enhanced 3D-CNN

Currently, significant progress has been made in predicting brain age from structural Magnetic Resonance Imaging (sMRI) data using deep learning techniques. However, despite the valuable structural information they contain, the traditional engineering features known as anatomical features have been largely overlooked in this context. To address this issue, we propose an attention-based network design that integrates anatomical and deep convolutional features, leveraging an anatomical feature attention (AFA) module to effectively capture salient anatomical features. In addition, we introduce a fully convolutional network, which simplifies the extraction of deep convolutional features and overcomes the high computational memory requirements associated with deep learning. Our approach outperforms several widely-used models on eight publicly available datasets (n = 2501), with a mean absolute error (MAE) of 2.20 years in predicting brain age. Comparisons with deep learning models lacking the AFA module demonstrate that our fusion model effectively improves overall performance. These findings provide a promising approach for combining anatomical and deep convolutional features from sMRI data to predict brain age, with potential applications in clinical diagnosis and treatment, particularly for populations with age-related cognitive decline or neurological disorders.

A review on brain age prediction models

Brain age in neuroimaging has emerged over the last decade and reflects the estimated age based on the brain MRI scan from a person. As a person ages, their brain structure will change, and these changes will be exclusive to males and females and will differ for each. White matter and grey matter density have a deeper relationship with brain aging. Hence, if the white matter and grey matter concentrations vary, the rate at which the brain ages will also vary. Neurodegenerative illnesses can be detected using the biomarker known as brain age. The development of deep learning has made it possible to analyze structural neuroimaging data in new ways, notably by predicting brain ages. We introduce the techniques and possible therapeutic uses of brain age prediction in this cutting-edge review. Creating a machine learning regression model to analyze age-related changes in brain structure among healthy individuals is a typical procedure in studies focused on brain aging. Subsequently, this model is employed to forecast the aging of brains in new individuals. The concept of the "brain-age gap" refers to the difference between an individual's predicted brain age and their actual chronological age. This score may serve as a gauge of the general state of the brain's health while also reflecting neuroanatomical disorders. It may help differential diagnosis, prognosis, and therapy decisions as well as early identification of brain-based illnesses. The following is a summary of the many forecasting techniques utilized over the past 11 years to estimate brain age. The study's conundrums and potential outcomes of the brain age predicted by current models will both be covered.

A deep neural network estimation of brain age is sensitive to cognitive impairment and decline

The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62×10-32; T1: r=0.61, p=1.45×10-26, FW+T1: r=0.77, p=6.48×10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: β=-1.094, p=6.32×10-7; T1: β=-1.331, p=6.52×10-7; FW+T1: β=-1.476, p=2.53×10-10; executive function, FW: β=-1.276, p=1.46×10-9; T1: β=-1.337, p=2.52×10-7; FW+T1: β=-1.850, p=3.85×10-17) and longitudinal cognition (memory, FW: β=-0.091, p=4.62×10-11; T1: β=-0.097, p=1.40×10-8; FW+T1: β=-0.101, p=1.35×10-11; executive function, FW: β=-0.125, p=1.20×10-10; T1: β=-0.163, p=4.25×10-12; FW+T1: β=-0.158, p=1.65×10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.

Gingival shape analysis using surface curvature estimation of the intraoral scans

Background

Despite many advances in dentistry, no objective and quantitative method is available to evaluate gingival shape. The surface curvature of the optical scans represents an unexploited possibility. The present study aimed to test surface curvature estimation of intraoral scans for objective evaluation of gingival shape.

Methods

The method consists of four main steps, i.e., optical scanning, surface curvature estimation, region of interest (ROI) definition, and gingival shape analysis. Six different curvature measures and three different diameters were tested for surface curvature estimation on central (n = 78) and interdental ROI (n = 88) of patients with advanced periodontitis to quantify gingiva with a novel gingival shape parameter (GS). The reproducibility was evaluated by repeating the method on two consecutive intraoral scans obtained with a scan-rescan process of the same patient at the same time point (n = 8).

Results

Minimum and mean curvature measures computed at 2 mm diameter seem optimal GS to quantify shape at central and interdental ROI, respectively. The mean (and standard deviation) of the GS was 0.33 ± 0.07 and 0.19 ± 0.09 for central ROI using minimum, and interdental ROI using mean curvature measure, respectively, computed at a diameter of 2 mm. The method’s reproducibility evaluated on scan-rescan models for the above-mentioned ROI and curvature measures was 0.02 and 0.01, respectively.

Conclusions

Surface curvature estimation of the intraoral optical scans presents a precise and highly reproducible method for the objective gingival shape quantification enabling the detection of subtle changes. A careful selection of parameters for surface curvature estimation and curvature measures is required.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02322-y.