Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

Due to its complexity, graph learning-based multi-modal integration and classification is one of the most challenging obstacles for disease prediction. To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning). For the problem of negative impact between modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology

MRI-based Multi-task Decoupling Learning for Alzheimer's Disease Detection and MMSE Score Prediction: A Multi-site Validation

Accurately detecting Alzheimer's disease (AD) and predicting mini-mental state examination (MMSE) score are important tasks in elderly health by magnetic resonance imaging (MRI). Most of the previous methods on these two tasks are based on single-task learning and rarely consider the correlation between them. Since the MMSE score, which is an important basis for AD diagnosis, can also reflect the progress of cognitive impairment, some studies have begun to apply multi-task learning methods to these two tasks. However, how to exploit feature correlation remains a challenging problem for these methods. To comprehensively address this challenge, we propose a MRI-based multi-task decoupled learning method for AD detection and MMSE score prediction. First, a multi-task learning network is proposed to implement AD detection and MMSE score prediction, which exploits feature correlation by adding three multi-task interaction layers between the backbones of the two tasks. Each multi-task interaction layer contains two feature decoupling modules and one feature interaction module. Furthermore, to enhance the generalization between tasks of the features selected by the feature decoupling module, we propose the feature consistency loss constrained feature decoupling module. Finally, in order to exploit the specific distribution information of MMSE score in different groups, a distribution loss is proposed to further enhance the model performance. We evaluate our proposed method on multi-site datasets. Experimental results show that our proposed multi-task decoupled representation learning method achieves good performance, outperforming single-task learning and other existing state-of-the-art methods.Comment: 15 page

Exploring Contextual Relationships for Cervical Abnormal Cell Detection

Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references to identify its abnormality. To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection. Specifically, both contextual relationships between cells and cell-to-global images are exploited to enhance features of each region of interest (RoI) proposals. Accordingly, two modules, dubbed as RoI-relationship attention module (RRAM) and global RoI attention module (GRAM), are developed and their combination strategies are also investigated. We establish a strong baseline by using Double-Head Faster R-CNN with feature pyramid network (FPN) and integrate our RRAM and GRAM into it to validate the effectiveness of the proposed modules. Experiments conducted on a large cervical cell detection dataset reveal that the introduction of RRAM and GRAM both achieves better average precision (AP) than the baseline methods. Moreover, when cascading RRAM and GRAM, our method outperforms the state-of-the-art (SOTA) methods. Furthermore, we also show the proposed feature enhancing scheme can facilitate both image-level and smear-level classification. The code and trained models are publicly available at https://github.com/CVIU-CSU/CR4CACD.Comment: 10 pages, 14 tables, and 3 figure

Confirmatory study of time-dependent computed tomographic perfusion thresholds for use in acute ischemic stroke

Background and Purpose: Computed tomographic perfusion (CTP) thresholds associated with follow-up brain infarction may differ by time from symptom onset to imaging and reperfusion. We confirm CTP thresholds over time to imaging and reperfusion in patients with acute ischemic stroke from the HERMES collaboration (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) data. Methods: Patients with occlusion on CT angiography were acutely imaged with CTP. Noncontrast CT and magnetic resonance-diffusion weighted imaging at 24 to 48 hours defined follow-up infarction. Reperfusion was assessed on conventional angiogram. Tmax, cerebral blood flow (CBF), and cerebral blood volume maps were derived from delay-insensitive CTP postprocessing. These parameters were analyzed using receiver operator characteristics to derive optimal thresholds based on time from stroke onset-to-CTP or to reperfusion. ANOVA and linear regression were used to test whether the derived CTP thresholds were different by time. Results: One hundred thirty-seven patients were included. Tmax thresholds of >15.7 s and >15.8 s and absolute CBF thresholds of <8.9 and <7.5 mL·min−1·100 g−1 for gray matter and white matter respectively were associated with infarct if reperfusion was achieved <90 minutes from CTP with stroke onset-to-CTP <180 minutes. The discriminative ability of cerebral blood volume was modest. There were no statistically significant relationships between stroke onset-to-CTP time and Tmax, CBF, and cerebral blood volume thresholds (all P>0.05). A statistically significant relationship was observed between CTP-to-reperfusion time and the optimal thresholds for Tmax (P<0.001) and CBF (P<0.001). Similar but more modest relationship was noted for onset-to-reperfusion time and optimal thresholds for CBF (P≤0.01). Conclusions: CTP thresholds based on stroke onset and imaging time and taking into account time needed for reperfusion may improve infarct prediction in patients with acute ischemic stroke

The Homeobox Transcription Factor Barx2 Regulates Plasticity of Young Primary Myofibers

Adult mammalian muscle retains incredible plasticity. Muscle growth and repair involves the activation of undifferentiated myogenic precursors called satellite cells. In some circumstances, it has been proposed that existing myofibers may also cleave and produce a pool of proliferative cells that can re-differentiate into new fibers. Such myofiber dedifferentiation has been observed in the salamander blastema where it may occur in parallel with satellite cell activation. Moreover, ectopic expression of the homeodomain transcription factor Msx1 in differentiated C2C12 myotubes has been shown to induce their dedifferentiation. While it remains unclear whether dedifferentiation and redifferentiaton occurs endogenously in mammalian muscle, there is considerable interest in induced dedifferentiation as a possible regenerative tool.We previously showed that the homeobox protein Barx2 promotes myoblast differentiation. Here we report that ectopic expression of Barx2 in young immature myotubes derived from cell lines and primary mouse myoblasts, caused cleavage of the syncytium and downregulation of differentiation markers. Microinjection of Barx2 cDNA into immature myotubes derived from primary cells led to cleavage and formation of mononucleated cells that were able to proliferate. However, injection of Barx2 cDNA into mature myotubes did not cause cleavage. Barx2 expression in C2C12 myotubes increased the expression of cyclin D1, which may promote cell cycle re-entry. We also observed differential muscle gene regulation by Barx2 at early and late stages of muscle differentiation which may be due to differential recruitment of transcriptional activator or repressor complexes to muscle specific genes by Barx2.We show that Barx2 regulates plasticity of immature myofibers and might act as a molecular switch controlling cell differentiation and proliferation

Automated Collateral Scoring on CT Angiography of Patients with Acute Ischemic Stroke Using Hybrid CNN and Transformer Network

Collateral scoring plays an important role in diagnosis and treatment decisions of acute ischemic stroke (AIS). Most existing automated methods rely on vessel prominence and amount after vessel segmentation. The purpose of this study was to design a vessel-segmentation free method for automating collateral scoring on CT angiography (CTA). We first processed the original CTA via maximum intensity projection (MIP) and middle cerebral artery (MCA) region segmentation. The obtained MIP images were fed into our proposed hybrid CNN and Transformer model (MPViT) to automatically determine the collateral scores. We collected 154 CTA scans of patients with AIS for evaluation using five-folder cross validation. Results show that the proposed MPViT achieved an intraclass correlation coefficient of 0.767 (95% CI: 0.68–0.83) and a Kappa of 0.6184 (95% CI: 0.4954–0.7414) for three-point collateral score classification. For dichotomized classification (good vs. non-good and poor vs. non-poor), it also achieved great performance

Segmenting Ischemic Penumbra and Infarct Core Simultaneously on Non-Contrast CT of Patients with Acute Ischemic Stroke Using Novel Convolutional Neural Network

Differentiating between a salvageable Ischemic Penumbra (IP) and an irreversibly damaged Infarct Core (IC) is important for therapy decision making for acute ischemic stroke (AIS) patients. Existing methods rely on Computed Tomography Perfusion (CTP) or Diffusion-Weighted Imaging–Fluid Attenuated Inversion Recovery (DWI-FLAIR). We designed a novel Convolutional Neural Network named I2PC-Net, which relies solely on Non-Contrast Computed Tomography (NCCT) for the automatic and simultaneous segmentation of the IP and IC. In the encoder, Multi-Scale Convolution (MSC) blocks were proposed to capture effective features of ischemic lesions, and in the deep levels of the encoder, Symmetry Enhancement (SE) blocks were also designed to enhance anatomical symmetries. In the attention-based decoder, hierarchical deep supervision was introduced to address the challenge of differentiating between the IP and IC. We collected 197 NCCT scans from AIS patients to evaluate the proposed method. On the test set, I2PC-Net achieved Dice Similarity Scores of 42.76 ± 21.84%, 33.54 ± 24.13% and 65.67 ± 12.30% and lesion volume correlation coefficients of 0.95 (p p p < 0.001) for the IP, IC and IP + IC, respectively. The results indicated that NCCT could potentially be used as a surrogate technique of CTP for the quantitative evaluation of the IP and IC

Validation of an automated ASPECTS method on non-contrast computed tomography scans of acute ischemic stroke patients

Background The Alberta Stroke Program Early CT Score (ASPECTS) is a systematic method of assessing the extent of early ischemic change on non-contrast computed tomography in patients with acute ischemic stroke. Our objective was to validate an automated ASPECTS scoring method we recently developed on a large data set. Materials and methods We retrospectively collected 602 acute ischemic stroke patients’ non-contrast computed tomography scans. Expert ASPECTS readings on non-contrast computed tomography were compared to automated ASPECTS. Statistical analyses on the total ASPECTS, region level ASPECTS, and dichotomized ASPECTS (≤4 vs. >4) score were conducted. Results In total, 602 scans were evaluated and 6020 (602 × 10) ASPECTS regions were scored. Median time from stroke onset to computed tomography was 114 min (interquartile range: 73–183 min). Total ASPECTS for the 602 patients generated by the automated method agreed well with expert readings (intraclass correlation coefficient): 0.65 (95% confidence interval (CI): 0.60–0.69). Region level analysis showed that the automated method yielded accuracy of 81.25%, sensitivity of 61.13% (95% CI: 58.4%–63.8%), specificity of 86.56% (95% CI: 85.6%–87.5%), and area under curve of 0.74 (95% CI: 0.73–0.75). For dichotomized ASPECTS (≤4 vs. >4), the automated method demonstrated sensitivity 97.21% (95% CI: 95.4%–98.4%), specificity 57.81% (95% CI: 44.8%–70.1%), accuracy 93.02%, and area under the curve of 0.78 (95% CI: 0.74–0.81). For each individual region (M1–6, lentiform, insula, and caudate), the automated method demonstrated acceptable performance. Conclusion The automated system we developed approached the stroke expert in performance when scoring ASPECTS on non-contrast computed tomography scans of acute ischemic stroke patients