Interesting Works

[Article] Tentative fMRI signatures of perceptual echoes in early visual cortex.

Summary: Here, they conducted an EEG and fMRI experiment to investigate the neural basis of the impulse response function(IRF). They measured the IRF of each subject in the EEG session and then reconstructed an estimate of the EEG signal by convolving the IRF with the stimuli presented in the fMRI session. The envelope of reconstructed EEG signals in the theta, alpha, and beta bands was taken as regressors for the GLM. They found the envelope of the EEG alpha positively correlated with BOLD activity in V1 and V2, but not with activity in the retinotopically stimulated regions.

Luo, Canhuang, et al. “Tentative fMRI signatures of perceptual echoes in early visual cortex.” NeuroImage 237 (2021): 118053.

[Article] Aperiodic measures of neural excitability are associated with anticorrelated hemodynamic networks at rest: a combined EEG-fMRI study

Summary: They investigated periodic and aperiodic EEG parameters associated with distinct resting state networks and used simultaneous EEG-fMRI recording (resting state). They found that increases in aperiodic power is associated with an auditory-salience-cerebellar network and decreases in aperiodic power is associated with prefrontal regions. Also, they found that global neural excitability may reflect stimulus processing or arousal attributable to the uniqueness of the resting-state MR-scanner environment.

Jacob, Michael S., et al. “Aperiodic measures of neural excitability are associated with anticorrelated hemodynamic networks at rest: a combined EEG-fMRI study.” NeuroImage 245 (2021): 118705.

[Article] Utilizing deep learning towards multi-modal bio-sensing and vision-based affective computing

Summary: In this study, they applied novel feature extraction and deep-learning methods to 4 public datasets including DEAP and MAHNOB-HCI for multimodal emotion classification. They proposed utilization of pre-trained VGG-net to compensate for data shortage in bio-sensing field. A wide range of modalities was used including EEG, HRV, GSR and face videos. They evaluate accuracy of single modality, combination of datasets in feature level and transfer learning. Result outperformed previous studies.

Siddharth, S., Jung, T. P., & Sejnowski, T. J. (2019). Utilizing deep learning towards multi-modal bio-sensing and vision-based affective computing. IEEE Transactions on Affective Computing.

[Article] Cortical response to naturalistic stimuli is largely predictable with deep neural networks

Summary: Current encoding models have ignored the temporal dimension in naturalistic stimuli. In this paper, the authors introduced temporal (i.e., 1 vs. 20 s) and multimodal (i.e., unimodal vs. audiovisual) features in the DNN-based encoding models that predicted whole-brain activities. They found the audiovisual and temporally more extended model improved encoding accuracies, especially within high-order sensory regions.

Khosla, M., Ngo, G. H., Jamison, K., Kuceyeski, A., & Sabuncu, M. R. (2021). Cortical response to naturalistic stimuli is largely predictable with deep neural networks. Science Advances, 7(22), eabe7547.

[Article] Effects of eight neuropsychiatric copy number variants on human brain structure

Summary: Researchers aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. They analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers. Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy.

Modenato, Claudia, et al. “Effects of eight neuropsychiatric copy number variants on human brain structure.” Translational psychiatry 11.1 (2021): 1-10.

[Article] Is executive dysfunction a risk marker or consequence of psychopathology? A test of executive function as a prospective predictor and outcome of general psychopathology in the adolescent brain cognitive development study

Summary: In this paper, they used confirmatory factor analysis (CFA) to examine the relationship between the p-factor(from Michelini) and executive functions. Also, they examined the longitudinal measurement invariance of the p-factor over the 3 different time points, baseline, 1-year follow-up, and 2-year follow-up. They found negative cross-sectional relationships between executive functions and p-factor at the baseline and 2-year follow-up.

Romer, A. L., & Pizzagalli, D. A. (2021). Is executive dysfunction a risk marker or consequence of psychopathology? A test of executive function as a prospective predictor and outcome of general psychopathology in the adolescent brain cognitive development study®. Developmental cognitive neuroscience, 51, 100994.

[Article] Neural coding of cognitive control: The representational similarity analysis approach

Summary: In this review paper, they show representational similarity analysis (RSA) as a complementary approach that can powerfully inform representational components of cognitive control theories. Their aim is to illustrate how RSA can be incorporated into cognitive control investigations to shed new light on old questions.

Freund, M. C., Etzel, J. A., & Braver, T. S. “Neural coding of cognitive control: The representational similarity analysis approach.” Trends in Cognitive Sciences(2021),25(7), 622-638.

[Article] Investigating mechanisms of fast BOLD responses: The effects of stimulus intensity and of spatial heterogeneity of hemodynamics

Summary: Many studies had reported that fast fMRI can track neural activity well above the temporal limit of the canonical HRF model but the biophysical mechanisms under those techniques were not much investigated. In this study, they use visual and somatosensory tasks with simultaneous EEG-fMRI data to show the difference of the HRF’s timing and shapes by the differences of the stimulus intensity. Secondly, they show that as the spatial resolution of fMRI increases, voxel-wise HRFs begin to deviate from the canonical model, with a considerable portion of voxels exhibiting faster temporal dynamics than predicted by the canonical HRF.

Chen, Jingyuan E., et al. “Investigating mechanisms of fast BOLD responses: The effects of stimulus intensity and of spatial heterogeneity of hemodynamics.” NeuroImage 245 (2021): 118658.

[Article] Multidataset Independent Subspace Analysis With Application to Multimodal Fusion

Summary: They tried to take advantage of the complex multidimensional subspace structures that capture underlying modes of shared and unique variability across and within datasets. They designed a new method called multi dataset independent subspace analysis (MISA) that leverages joint information from multiple heterogeneous datasets in a flexible and synergistic fashion.

Silva, Rogers F., et al. “Multidataset independent subspace analysis with application to multimodal fusion.” IEEE Transactions on Image Processing 30 (2020): 588-602.

[Article] Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes

Summary: Researchers identified a large-scale association between multiple coordinated blood leukocyte gene coexpression modules and the multivariate fMRI response to speech. Associated coexpression modules were enriched in genes that are broadly expressed in the brain and many other tissues. These coexpression modules were also enriched in ASD-associated, prenatal, human-specific, and language-relevant genes. This work highlights distinctive neurobiology in ASD subtypes with different early language outcomes that is present well before such outcomes are known.

Lombardo, Michael V., et al. “Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes.” Nature neuroscience 21.12 (2018): 1680-1688.