Gene-expression analysis of (un)conscious-related visual-attention and working memory paradigms
Poster No:
2004
Submission Type:
Abstract Submission
Authors:
Yaron Caspi1, Dimitar Ivanov2, Grace Hung1, Yiling Tseng3, Likai Huang4, Tzu-Yu Hsu5, Philip Tseng1, Po-Jang (Brown) Hsieh1
Institutions:
1Department of Psychology, National Taiwan University, Taipei, Taiwan, 2College of Natural Sciences, Minerva University, San Francisco, CA, 3Department of Clinical Psychology, Fu Jen Catholic University, New Taipei City, Taiwan, 4Dementia Center, Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan, 5Graduate Institute of Mind, Brain, and Consciousness, Taipei Medical University, Taipei, Taiwan
First Author:
Co-Author(s):
Likai Huang
Dementia Center, Department of Neurology, Shuang Ho Hospital, Taipei Medical University
New Taipei City, Taiwan
Dementia Center, Department of Neurology, Shuang Ho Hospital, Taipei Medical University
New Taipei City, Taiwan
Tzu-Yu Hsu
Graduate Institute of Mind, Brain, and Consciousness, Taipei Medical University
Taipei, Taiwan
Graduate Institute of Mind, Brain, and Consciousness, Taipei Medical University
Taipei, Taiwan
Introduction:
Brain activity, which results in behavior, is the product of cellular activity. Underlying the neuronal and glial cells' activities are fundamental biological processes executed by these cells' biological machinery. Hence, a logical question to ask is - are there biological processes and pathways that are correlated with a specific observable behavior?
Methods:
To answer this question, we followed the methodology from our previous publication (see (Tan et al., 2019)). We studied two commonly used experimental paradigms, namely, Attentional Blink (for attention) (Hommel et al., 2006) and Change Detection (for working memory) (Luck & Vogel, 2013). We used the expression data from the Allen Brain Human Atlas (Hawrylycz et al., 2012) together with self-collected published evidence about fMRI brain activity during the execution of these paradigms (see Fig. 1). As a control, we repeated the same analysis for data of specific functions that were obtained from the Neurosytnh and NeuroQuery databases (motor imagery, executive functions, metacognition, and visual word recognition).
Expression levels were obtained using ABAGEN (Markello et al., 2021). We used the meta-analysis NiAMARE tool (Salo et al., 2022) on the curated fMRI data to extract the Z-values related to the fMRI activity. We parceled the resulted Z-value and expression data using brain atlases for the cortical, subcortical, and cerebellar regions (cortical – (Kong et al., 2021) (aka Shaffaer100 and Shaffaer1000); subcortical – (Tian et al., 2020); cerebellum – (Buckner et al., 2011) and (Nettekoven et al., 2024)).
To identify pathways associated with Attentional Blink and Change Detection, we run a linear correlation analysis between the parceled RNA levels and the parceled fMRI Z-values. This analysis resulted in a list of genes and the associated correlation t-values for each of the two paradigms and for each brain region. Next, we used pathways-enrichment-based tools (g:Profiler (Kolberg et al., 2023), GSEA (Subramanian et al., 2005), and EnrichmentMap (Reimand et al., 2019)) to identify biological pathways that are enriched among the genes mostly correlated with the fMRI Z-values for each of the studied conditions.
We also collected published data about brain stimulation that influenced the execution of these two experimental paradigms. Next, we used a simulation tool (SimNIBS) (Thielscher et al., 2015) to simulate the expected electric field in the brain from published transcranial Magnetic and transcranial Direct Current stimulation experiments (TMS and tDCS). Finally, we repeated the same pipeline described above to deduce biological pathways associated with the stimulated brain regions.
Expression levels were obtained using ABAGEN (Markello et al., 2021). We used the meta-analysis NiAMARE tool (Salo et al., 2022) on the curated fMRI data to extract the Z-values related to the fMRI activity. We parceled the resulted Z-value and expression data using brain atlases for the cortical, subcortical, and cerebellar regions (cortical – (Kong et al., 2021) (aka Shaffaer100 and Shaffaer1000); subcortical – (Tian et al., 2020); cerebellum – (Buckner et al., 2011) and (Nettekoven et al., 2024)).
To identify pathways associated with Attentional Blink and Change Detection, we run a linear correlation analysis between the parceled RNA levels and the parceled fMRI Z-values. This analysis resulted in a list of genes and the associated correlation t-values for each of the two paradigms and for each brain region. Next, we used pathways-enrichment-based tools (g:Profiler (Kolberg et al., 2023), GSEA (Subramanian et al., 2005), and EnrichmentMap (Reimand et al., 2019)) to identify biological pathways that are enriched among the genes mostly correlated with the fMRI Z-values for each of the studied conditions.
We also collected published data about brain stimulation that influenced the execution of these two experimental paradigms. Next, we used a simulation tool (SimNIBS) (Thielscher et al., 2015) to simulate the expected electric field in the brain from published transcranial Magnetic and transcranial Direct Current stimulation experiments (TMS and tDCS). Finally, we repeated the same pipeline described above to deduce biological pathways associated with the stimulated brain regions.
Results:
After filtering out housekeeping pathways, we studied pathways that were statistically significantly correlated with the brain activity pattern of the two paradigms (see Fig. 2). For the left cortical hemisphere, we identified the ERK1/ERK2 pathway which was implicated in glial progenitor cell, as one that is associated with Attentional Blink, but not with Change Blindness (Nevertheless, it was implicated, though to a lesser extent in some control conditions). By contrast, Dopamine signaling was unique to the Change Detection paradigm. For the subcortical regions, we identify clathrin-coated pits and cilium-related functions as two pathways that were repeatedly implicated in the studied fMRI foci maps.
Conclusions:
We identified several candidate pathways correlating with brain activity for an attentional blink or change detection in specific brain regions (cortex, sub-cortex, or cerebellum). In some cases, pathways were unique or more strongly correlated with specific fMRI patterns. In other cases, some pathways were not unique for specific paradigms (control paradigms also implicated these pathways). Yet, pathways from the second group are not ones that are naively expected for behavioral-dependent brain activity. Hence, they might point to an interesting future research direction.
Genetics:
Transcriptomics
Genetics Other 2
Perception, Attention and Motor Behavior:
Attention: Visual 1
Consciousness and Awareness
Keywords:
Cerebellum
Consciousness
Cortex
FUNCTIONAL MRI
Perception
Phenotype-Genotype
Sub-Cortical
Vision
Other - Gene Expression; Working Memory;
1|2Indicates the priority used for review
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Provide references using APA citation style.
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