Summary: Summary: In this paper, the authors predicted the brain age of T1-weighted MRI images in UK Biobank dataset using 3D CNN architecture. They examined the relationship between predicted ages and UK Biobank variables which are categorized into lifestyle factors (exercise, alcohol, tobacco, …), physiological/medical measurements (bone, cardiac, eye, …), and medical history. They adopted attention gates as attention modules to observe activation of the CNN model, and they also visualized the model using saliency map for comparison.
Summary: The goal of study is to identify the differences in dynamic functional connectivity according to the trait mindfulness. They found the high mindfulness trait group showed more strong within-network connectivity in the DMN and the salience network than the low mindfulness trait group.
Summary: Collected knee biomechanics.using an MRI-compatible motion capture system. The study shows promise for the MRI-compatible system to capture lower-extremity biomechanical data collected concurrently during fMRI, and the present data identified potentially unique neural drivers of aberrant biomechanics.
Summary: The authors proposed a deep learning based training scheme, inspired by domain adaptation techniques. It is similar to my current study, but this study proposed a consecutive training process since they assumed that they may not know main task label (i.e., prediction target such as age, gender, p-factor) even they can know all available domain label (e.g., site, scanner). It would be good to present this paper because this paper has a similar, but different point of view for dealing with the scanner-effect.
Summary: The authors investigated the (category-selective) organization of macaque IT cortex using fMRI, microstimulation, electrophysiology. A total of 51 objects each presented at 24 views belonging to 6 different categories. The fc6 output of AlexNet for object classification was PCA decomposed and used as an object space. Pooling responses across face, body, NML, and stubby networks enabled reasonable reconstruction of general objects demonstrating the importance of these four networks in object representation.
Summary: This paper examines neural mechanisms for task switching in which task-relevant information involved perceptual uncertainty. As a result, the lateral prefrontal cortex (PFC) in the left hemisphere is associated with behavioral flexibility, and the lateral prefrontal cortex (PFC) in the right hemisphere is associated with the perception of ambiguous stimuli.
Summary: They proposed Independence and Structural sparsity canonical correlation analysis (ISCCA) for imaging genetics study. They combined ICA and CCA to reduce the collinear effects, which also incorporate graph structure of the data into the model to improve the accuracy of feature selection. This method helps identify risk genes and abnormal brain regions in schizophrenia.
Summary: Using deep neural networks(DNN) to classify diseases using genetic data is popular these days. In this article, they hypothesized that disease-relevant modules of genes can be discovered within the autoencoder (AE) representations. They compared shallow and deep AE with various node sizes, and showed deep AE works better. Also, they also showed that each different layer captures gradients of biology. By overlapping top 1000 genes for each disease with GWAS, they found a highly significant association for at least one layer in all tested diseases.
Summary: This paper showed the relationship between the activation of brain regions and sensory-motor semantic features. They defined five semantic features (sound, color, motion, shape, manipulation - which are related to sensory-motor experiences) for each word and observed brain regions which are associated with each of the semantic attributes. They found that four of the five attributes were related to the activation in corresponding sensory-motor regions.
Summary: The study into the relationship between physical function and working memory is actively underway. In this paper, they tried to find the relationships between cardiorespiratory fitness, gait speed, hand dexterity, muscular strength with N-back performance as a working memory task. The results illustrate that FPN and DMN are activated by task-evoked functional activity and the cardiorespiratory fitness and hand dexterity contribute to enhance this activation.