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.
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.
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.
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.
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.
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.
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.
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.
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.
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.