Interesting Works and Meetings

[Article] Integrated 3D motion analysis with functional magnetic resonance neuroimaging to identify neural correlates of lower extremity movement.

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.

Anand, M., Diekfuss, J. A., Slutsky-Ganesh, A. B., Grooms, D. R., Bonnette, S., Foss, K. D. B., … & Myer, G. D. (2021). Integrated 3D motion analysis with functional magnetic resonance neuroimaging to identify neural correlates of lower extremity movement. Journal of Neuroscience Methods, 355, 109108.

[Article] Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal.

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.

Dinsdale, N. K., Jenkinson, M., & Namburete, A. I. (2021). Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal. NeuroImage, 228, 117689.

[Article] A map of object space in primate inferotemporal cortex.

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.

Bao, P., She, L., McGill, M., & Tsao, D. Y. (2020). A map of object space in primate inferotemporal cortex. Nature, 583(7814), 103-108.

[Article] Cross-Hemispheric Complementary Prefrontal Mechanisms during Task Switching under Perceptual Uncertainty.

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.

Tsumura, Aoki, et al. (2021). “Cross-Hemispheric Complementary Prefrontal Mechanisms during Task Switching under Perceptual Uncertainty.” The Journal of Neuroscience, 41(10), 2197–2213.

[Article] Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity.

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.

Zhang, Yipu, et al. “Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity.” IEEE journal of biomedical and health informatics 24.9 (2020): 2621-2629.

[Article] Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder.

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.

Dwivedi, S. K., Tjärnberg, A., Tegnér, J., & Gustafsson, M. (2020). Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder. Nature communications, 11(1), 1-10.

[Article] Concept Representation Reflects Multimodal Abstraction: A Framework for Embodied Semantics.

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.

Fernandino, Leonardo, et al. “Concept representation reflects multimodal abstraction: A framework for embodied semantics.” Cerebral cortex 26.5 (2016): 2018-2034.

[Article] Identification of the brain networks that contribute to the interaction between physical function and working memory: an fMRI investigation with over 1,000 healthy adults

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.

Ishihara, Toru, et al. “Identification of the brain networks that contribute to the interaction between physical function and working memory: an fMRI investigation with over 1,000 healthy adults.” NeuroImage 221 (2020): 117152.

[Article] Neurofeedback of core language network nodes modulates connectivity with the default-mode network:a double-blind fMRI neurofeedback study on auditory verbal hallucinations.

Summary: The auditory-verbal hallucination by change of brain network function has been reported from schizophrenia patients. The goal of the paper is to investigate the modulation by neurofeedback in resting-state connectivity. They demonstrated the coupling increased between language and DMN node after the down-regulation NF. Also, they showed the possibility of NF as a therapeutic intervention

Zweerings, Jana, et al. (2019) “Neurofeedback of core language network nodes modulates connectivity with the default-mode network: a double-blind fMRI neurofeedback study on auditory verbal hallucinations.” NeuroImage 189 : 533-542.

[Article] natomical and functional properties of the foot and leg representation in areas 3b, 1 and 2 of primary somatosensory cortex in humans: A 7T fMRI study

Summary: In this paper, lower limb somatotopy mapping was investigated whether each mapped representation also responded to the stimulation of other body parts (i.e., response selectivity) and conducted dissimilarity analysis relating these anatomical and functional properties of S1 to the physical structure of the lower limbs. They found only minor differences between the properties of the three BAs of somasensory areas (i.e., BA 3,1,2), suggesting that S1 maps for the lower limbs differ from those described for the hand. Furthermore, this paper suggested a possible homology between the first digit of upper and lower extremity in humans, and report different patterns of selectivity in the foot representations (i.e. lower selectivity) compared to the other leg representations (i.e. greater selectivity)

Akselrod, Michel, et al. (2017) Anatomical and functional properties of the foot and leg representation in areas 3b, 1 and 2 of primary somatosensory cortex in humans: A 7T fMRI study. Neuroimage 159, 473-487.