Interesting Works

[Article] Transdiagnostic dimensions of psychopathology explain individuals’ unique deviations from normative neurodevelopment in brain structure.

Summary: Heterogeneity and case-control approaches to mental disorders have made it hard to link dimensions of psychopathology to abnormalities of neurodevelopment. In this study, they tried to find psychiatric biomarkers with normative modeling and machine learning using cortical volume. They showed that modeling cortical volume as deviations from normative models of neurodevelopment improved the prediction of overall psychopathology (p-factor). Also, they showed detailed group differences between MDD/ADHD and healthy groups, suggesting that the p-factor confounded case-control comparisons.

Parkes, L., Moore, T. M., Calkins, M. E., Cook, P. A., Cieslak, M., Roalf, D. R., … & Bassett, D. S. (2021). Transdiagnostic dimensions of psychopathology explain individuals’ unique deviations from normative neurodevelopment in brain structure. Translational psychiatry, 11(1), 1-13.

[Article] BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis.

summary : They proposed BrainGNN, which is a graph neural network framework to analyze fMRI and discover neurological biomarkers. BrainGNN contains novel ROI-aware graph convolutional (Ra-GConv) layers that leverage the topological and functional information of fMRI. They applied the BrainGNN framework on two independent fMRI datasets(ASD fMRI dataset and HCP 900 subject release).

Li, Xiaoxiao, et al. “Braingnn: Interpretable brain graph neural network for fmri analysis.” Medical Image Analysis 74 (2021): 102233.

[Article] Emotion recognition based on convolutional neural networks and heterogeneous bio-signal data sources.

summary : This paper uses multi-modal bio-signals such as EEG, Eye data (Pupil diameter, eye gaze coordinates), and Facial data (using a depth camera) to recognize an individual’s emotions in valence and arousal. This paper suggests the multi-branch convolutional neural network (MBCNN) which shows the best accuracy among the state-of-the-art models while using multi-modal data, especially depth data.

Ngai, Wang Kay, et al. “Emotion recognition based on convolutional neural networks and heterogeneous bio-signal data sources.” Information Fusion 77 (2022): 107-117.

[Article] Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures.

summary : They examine the link between brain-wide gene expression and morphometric changes in individuals with MDD. They observe that the expression of MDD-associated genes spatially correlates with MSN differences.

Li, Jiao, et al. “Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures.” Nature communications 12.1 (2021): 1-14.

[Article] Genetic Association of Attention-Deficit/Hyperactivity Disorder and Major Depression With Suicidal Ideation and Attempts in Children: The Adolescent Brain Cognitive Development Study.

Summary: Suicidal attempt or ideation is well known associated with various environmental factors and psychopathology. In this study, they examined whether genetic susceptibility to major psychiatric disorders is associated with suicidal behaviors. Using polygenic risk scores and suicide risk measures from ABCD KSADS data, they found that MDD and ADHD are heavily associated with suicidal risks, internalizing and externalizing respectively.

Lee, P. H., Doyle, A. E., Li, X., Silberstein, M., Jung, J. Y., Gollub, R. L., … & Fava, M. (2021). Genetic Association of Attention-Deficit/Hyperactivity Disorder and Major Depression With Suicidal Ideation and Attempts in Children: The Adolescent Brain Cognitive Development Study. Biological Psychiatry.

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

Summary: The aim of study is to examine the association between dispositional mindfulness and PFC neural activity during working memory and identify the dispositional mindfulness from AAMS (Adult and Adolescent Mindfulness Scale) that would be associated with greater working memory performance. The result showed the decreased BOLD signal in the right vlPFC related to higher Attention and Awareness score and reduced FC between right vlPFC and dmPFC related to higher Nonreactivity.

Stein, J. A., Bray, S., MacMaster, F. P., Tomfohr-Madsen, L., & Kopala-Sibley, D. C. (2022). Adolescents with High Dispositional Mindfulness Show Altered Right Ventrolateral Prefrontal Cortex Activity During a Working Memory Task. Mindfulness, 13(1), 198-210.

[Article] A simulation-based approach to improve decoded neurofeedback performance.

Summary: in real-time neurofeedback experiments, there is not yet to be an empirical justification of the timing and data processing parameters. theses parameters and timing is important. so, they investigate how design parameters of decoded neurofeedback experiments affect accuracy and neurofeedback performance. and they demonstrate the usefulness of offline simulation to improve the success of real-time neurofeedback experiments.

Oblak, Ethan F., James S. Sulzer, and Jarrod A. Lewis-Peacock. “A simulation-based approach to improve decoded neurofeedback performance.” NeuroImage 195 (2019): 300-310.

[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.