Summary: A CNN model of the ventral stream was used to predict responses to the ROIs: FFA, PPA, and EBA. The model accurately predicted activations and the result was shown by synthesized stimuli using GAN. Moreover, they generated importance map for photographs where each ROI showed difference
Summary: This study uses their recently developed Spatio-temporal fMRI constrained EEG source imaging approach to perform source analysis on a simultaneous EEG-fMRI emotion regulation paradigm. They found cortical dynamics underlying negative emotional responses and reappraisal-based regulation using a newly developed multimodal EEG-fMRI integration technique. By using this method they could get detailed timecourses regional activities and causality analyses based on cortical current density could be identified.
Summary: They propose a novel framework to combine features corresponding to functional magnetic resonance imaging including dynamic functional network connectivity (dFNC) and SNP data from schizophrenia patients and healthy controls. For each subject, both the functional and SNP data are selected as features for a parallel ICA based imaging-genomic framework. They also assessed the correlations between the polygenic risk scores (PRS) and both dFNC and SNP components’ loading parameters.
Summary: They combined linear regression with CCA in a coupled manner to extract discriminative features for schizophrenia that are co-expressed in the fMRI and DNA methylation data (epigenetic data).
Summary: In this article, they tried to find a relationship between family conflict, parental monitoring and children’s behavior problems and cognitive scores. They utilized sMRI, CBCL and KSADS to get 20 behavioral problems scores and 10 cognitive scores. They visualized correlation between these criteria and showed structural differences according to these scores. Furthermore, they performed longitudinal association analysis with baseline & 1 year later data. Utilizing CBCL scores into account in my study later seems reasonable.
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.