Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that impacts how people communicate, interact, behave and learn. Neuroimaging techniques including MRI have been used to not only characterize biomarkers of ASD but also identify individuals with ASD based on analyzing neural structural and functional features, which may assist the precise diagnosis of ASD especially at an early age. Most existing neuroimaging methods for ASD identification focus on a single type of neural measure such as brain functional connections, but ignore the influence of other neural features such as regional activities. There is an increasing need for computational models to use complementary information from multi-modal data for identifying mental disorders. In this work, we propose a framework of graph convolution networks (GCNs) based on maximum entropy weighted independent set pooling (MEWISPool), called MEWISPool-GCN, which not only learns the functional connections and regional activities of the entire brain network, but also integrates non-imaging data such as demographics. Specifically, the graph structure of brain imaging is first downsampled by the MEWISPool method. This structure-adaptive pooling method considers the input graph structure as a noisy communication channel to maximize the mutual information between the input nodes and the pooled nodes. Then, a population graph is constructed in order to further recalibrate the distribution of extracted features using the non-imaging phenotypic information. The feature vectors obtained after pooling are embedded into the nodes of the population graph; and the similarities between non-imaging data are used as edges connecting the nodes. GCN is then employed to learn node embeddings. In the experiment of ASD identification using ABIDE-I dataset, MEWISPool-GCN achieved an accuracy of 87.68% and AUC of 92.89%, which outperformed other related classification methods.
This is the first study to conduct neuro-subtyping of autism spectrum disorder with a semi-supervised clustering method HYDRA. With the use of functional connectivity data from a large cohort of ASD, ABIDE, a multi-scale dimension-reduction method OPNNMF was first conducted to get a more robust and representable feature space with reduced dimensions. A three-layer procedure was conducted to obtain the optimal clustering in terms of better validity and reliability indices resulting two distinct clusters. By comparing with unsupervised clustering, the semi-supervised method showed more distinct connectivity patterns between clusters. Heterogenous brain-behavior relationships under various brain networks were observed across clusters indicating potential usage of ASD neuro-subtyping to detect reliable neuro-biomarkers assisting precise diagnosis and treatment in the future.
Individuals vary in behavior and cognition within a diseased or even “normal” population. Subtyping is critical for characterizing and understanding individual variations in behavior. Neuroimaging-based subtyping has emerged recently and shown great potential for clustering individuals with distinct neural patterns across sub-clusters. However, due to the high dimensional nature of neuroimaging data and relatively limited sample size, commonly used clustering methods in most existing studies such as k-mean may undermine the subtyping results due to the lack of power. Subspace clustering method with elastic net regularization is superior in the sense of jointly learning the sparse affinity matrix and its clustering to preserve reliable high dimension information during clustering limited number of samples. The current study aimed to introduce the Elastic Net subspace clustering to subtype brain structural connectivity matrices robustly. We have included 105 healthy young subjects and constructed structural connectivity matrices based on diffusion tensor imaging as input features for clustering. By calculating and optimizing two indices, “Silhouette Coefficient” and “Calinski-Harabasz” index, optimal parameters were selected to balance the low rank sparse subspace clustering and least square regression and determine the optimal number of clusters. Then the stability of clustering results was tested by subsampling all subjects and clustering each subgroup 3000 times, and the across- and within-subject clustering error rates were estimated. After receiving two robust clusters with high stability, we further explored and found different neural connectivity patterns between clusters. Results suggest that neurosubtyping has the potential to reveal underlying distinct neural patterns speaking to the variations in behavior and neurobehavioral relationships.
Accurate segmentation of infant brain magnetic resonance images is crucial for studying brain development. However, infant images even within a narrow age-range differ drastically in size and contrast. Here, we investigated whether deep-learning based methods evaluated in iSeg-2017 can be generalized to accurately segment brain data acquired from other cohorts with different acquisition and ages. ISeg-2017 and UNC infant datasets were used to investigate three methods: SemiDenseNet, HyperDenseNet, and 3D-DenseSeg. Results demonstrate that HyperDenseNet has better segmentation performance and generalizability. Moreover, we built a joint segmentation-registration method by applying HyperDenseNet for segmentation. Results show that the joint method produced better performance compared with only registration or segmentation.
The placenta is an important organ for the material exchange between the fetus and the mother. The abnormal placenta may lead to fetal intrauterine growth restriction, invasive placenta and other related diseases, thus endangering the health of both the mother and fetus. Accurate segmentation of placental tissue in fetal magnetic resonance images could help diagnose placental abnormalities. However, the manual segmentation of placenta is very time-consuming, and the semi automatic placental segmentation methods still require the operator’s interaction. In this paper, we proposed a fully automatic placental segmentation method, in which BiO-Net is used as the backbone network and is further improved by embedding Atrous Spatial Pyramid Pooling (ASPP) and attention mechanism, termed as BAA-Net. To retain more details of boundary information, the ASPP module was introduced to the encoder for capturing high-resolution feature maps to improve the placental segmentation performance. Because different feature channels from the encoder and decoder have different effects on the segmentation task, to make better use of the most useful features, four channel attention modules were introduced into the decoder to highlight the most relevant feature channels. To evaluate the performance of the proposed BAA-Net, MR images of 20 pregnant women were used for the experiments. The Dice and average symmetric surface distance (ASSD) obtained by our BAA-Net are 0.8674 and 2.8880 mm, respectively. The experimental results show that the proposed BAA-Net is effective for the automatic placental segmentation, comparing with the existing methods. Accurate placental segmentation is conducive to the diagnosis of placental abnormalities, which has important clinical significance.
Schizophrenia is a severe psychiatric disorder showing cognitive deficits among the early symptoms. Previous studies have reported that sex hormones are related to the cognitive dysfunction in schizophrenia. In this paper, we explore sex hormones as the potential biomarkers in differentiating first-episode schizophrenia patients from healthy controls, and investigate the most discriminative sex-hormone related biomarkers for the diagnosis of schizophrenia at the same time. Specifically, six sex hormones, which are follicle-stimulating hormone, luteinizing hormone, prolactin, estradiol, progesterone and testosterone, were examined as the basic features. Based on these sex hormones, the sex-hormone ratios and log-transformed sex-hormone ratios were computed for feature enhancement. T-test was further used for feature selection. In order to avoid the possible bias on sex hormones caused by female physiological factors, the classification experiment was performed on male participants using random forest. The effectiveness of different feature combination strategies was further studied. Among these strategies, the scheme fusing sex hormones, sex-hormone ratios and log-transformed sex-hormone ratios produced the best classification performance with an accuracy of 80.67%, which is comparable with the results obtained by complex neuroimaging data. Finally, the most discriminative features were further discussed according to the t-test results. Results show that the luteinizing hormone, prolactin, sex-hormone ratios and log-transformed sex-hormone ratios are the most significant features. This work supports the use of sex hormones as potential biomarkers for the diagnosis of schizophrenia. Given the convenient detection of these sex hormones in the routine examination, it could be a more straightforward and cost-saving way for the early diagnosis of schizophrenia patients.
Schizophrenia is associated with cognitive impairments. Exercise interventions including yoga and aerobic exercise have been shown to improve cognitive functioning in schizophrenic patients. Yet the underlying neural basis is not clear for this cognitive improvement. This work aimed to investigate the brain functional effect caused by exercise interventon in female patients with early psychosis. Resting-state fMRI was collected longitudinally from 71 patients who were randomized into three programs: yoga, aerobic exercise and waitlist control. Functional connectivity matrices of each individual at baseline and 12-week follow-up timepoint were estimated. Then the connectivity changes were calculated and used as potential predictors to classify the three groups. A machine learning method gcForest was used to train classification models on a subset and tested on the rest of the data. Classification performance was evaluated using multiple n-fold cross-validation to ensure a robust estimate of the accuracy. The classification accuracy ranges from 86.31 to 94.00. The most predictive features were examined in the brain, which include connectivity changes along several major pathways in high order functional networks including default mode and executive control networks. This is the first study showing that the connectivity alterations can successfully distinguish intervention from control groups, and also detect the two different types of intervention: yoga and aerobic exercise. Findings suggest that the altered functional connectivity may contribute to the cognitive improvement after intervention. Our work sheds light on the use of advanced neuroimaging and machine learning approaches to explore potential biomarkers for predicting outcomes of exercise intervention in psychosis.
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for development of future treatment. Magnetic resonance images (MRI) play important role to help understand the brain anatomical changes related to AD. Conventional methods extract the hand-crafted features such as gray matter volumes and cortical thickness and train a classifier to distinguish AD from other groups. Different from these methods, this paper proposes to construct multiple deep 3D convolutional neural networks (3D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. First, a number of local image patches are extracted from the whole brain image and a 3D-CNN is built upon each local patch to transform the local image into more compact high-level features. Then, the upper convolution and fully connected layers are fine-tuned to combine the multiple 3D-CNNs for image classification. The proposed method can automatically learn the generic features from imaging data for classification. Our method is evaluated using T1-weighted structural MR brain images on 428 subjects including 199 AD patients and 229 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 87.15% and an AUC (area under the ROC curve) of 92.26% for AD classification, demonstrating the promising classification performances.
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