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‎content/2022_Fall.md

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# Fall 2022
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In Fall 2022, the students designed a study to explore mindwandering and affect using a naturalistic stimulus. Students scanned themselves watching the Jibaro episode from Season 3 of the Love Death + Robots Netflix series. Students were instructed to press a button everytime they had a thought they wanted to share with a friend. They then watched the same episode again to see how their brain responses changed after the first viewing. Data were preprocessed using fmriprep and analyzed using nltools in Python.
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## Probing default mode network activity and emotion with naturalistic stimuli: Intersubject and inter-run analyses
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Liesbeth Verheijn, Gretchen Carpenter, Grace Gallant, and Caroline Conway
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Past studies have yielded ambiguous information regarding the relationship between the default mode network (DMN), mind-wandering, and emotion. In this study, we aimed to investigate the relationship between the DMN and emotion in response to viewing naturalistic stimuli multiple times. Specifically, will subjects respond similarly to the same stimuli, and will their responses change upon a second viewing? Seven college-aged participants watched a television episode twice while in fMRI scanner. Six masks of interest were applied. For each mask, an inter-subject correlation (ISC) analysis was then performed between subjects within and across runs. To investigate relationships between networks, principal component analysis (PCA) was performed and the mean difference between masks over time was calculated. Finally, large changes in reward mask activity prompted additional analysis of reward activity during three scenes of interest to contextualize the large changes in activity within the naturalistic stimuli. These analyses informed us that there was significant correlation within runs for emotion for the most part and distinct DMN activity. Across runs there were differences for vicarious pain, DMN, and empathetic distress. PCA yielded three principle components isolating relations between networks and those that closely align (negative emotion masks) and those that do not (DMN with empathetic care/distress). Temporal analysis resulted in the isolation of significant increases and decreases in reward signaling corresponding with changes in emotional salience of the stimulus. We show that emotion responses to stimuli are similar between subjects and DMN responses are not. Differing responses across runs may be attributed to familiarity.
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See their {download}` presentation <../images/project_gallery/F2022_Jibaro_MultivariatePatternSimilarity.pdf>`.
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## Information processing in the default mode network
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Brandon Dormes, Megan Liu, Hannah Nicolson
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The Default Mode Network (DMN) has previously been shown to be involved in resting state cognition, internal attention, and more recently, the creation and updating of internal event models. This analysis uses naturalistic stimuli to examine whether event processing within the DMN differs when the stimulus presented is novel or known. While in an fMRI, subjects watched the same 14-minute video twice, and subsequent neural analysis focused on the regions within the DMN, comparing the event segmentation across subjects in the first viewing and the second viewing of the stimulus. Using a Hidden Markov Model, event boundaries were found to align with important narrative shifts and moments of surprise, confirming the role of the DMN in event segmentation. Particularly in the precuneus and posterior cingulate cortex (PCC), event segmentation in the first viewing was found to be less coherent and made with a greater emphasis on sensory input and very obvious narrative shifts, while event segmentation in the second viewing appeared to locate more nuanced conceptual narrative shifts. These results further confirm the role of the DMN (and in particular the PCC/precuneus) in event models and the integration of external stimuli with internal representations of the event at hand. This research speaks to the brain’s predictive capacities, and this study’s use of event segmentation also relates to the ability of the brain to continuously “chunk” ongoing experiences, which holds important implications for learning and memory.
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See their {download}` presentation <../images/project_gallery/F2022_Jibaro_EventSegmentation.pdf>`.
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## Jibaro: Synchrony and brain activation in regions of interest
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Evan Bloch, Sydney Burton, Christopher Cardillo, Tanner Randall
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Emotion centers and the default mode network (DMN) are crucial for the way that people develop shared experiences and perceive the world around them. This paper sets out to determine what the relationship is between emotion centers and the DMN between people when watching a particularly salient video. The study looks at possible changes in the way that emotion centers and the DMN may activate between participants when perceiving a naturalistic stimuli a first versus second time. Some evidence was found to support the idea that mind wandering, relying on the DMN, may have been more prevalent in the second watch as there were distinct periods of less synchrony between participants across the whole brain and in individual regions that weren’t seen in the first run. Additionally, evidence was found to support the notion that emotion and DMN centers may fire similarly across people when perceiving a salient negative stimuli. Similar activation patterns between emotion centers and the DMN are particularly interesting given their supposed contrasting purposes between heightened emotion and distraction. This study helped develop a better understanding of similarities and differences in how individuals react to confusing and emotionally distressing stimuli.
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See their {download}` presentation <../images/project_gallery/F2022_Jibaro_IntersubjectSynchrony.pdf>`.
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‎content/Download_Data.ipynb

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],
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"metadata": {
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"display_name": "Python 3.7.7 64-bit ('base': conda)",
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"display_name": "Python 3.9.13 64-bit",
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"language": "python",
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"name": "python3"
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‎content/GLM.ipynb

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],
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"metadata": {
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"display_name": "Python 3.9.13 64-bit",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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"toc": {
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}
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"nbformat": 4,

‎content/GLM_Single_Subject_Model.ipynb

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],
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"metadata": {
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"display_name": "Python 3.8.8",
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"name": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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"toc": {
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‎content/Group_Analysis.ipynb

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" os.mkdir('../data/localizer/derivatives/betas')\n",
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" \n",
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"for sub in tqdm(layout.get_subjects(scope='derivatives')):\n",
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" sub = layout.get_subjects(scope='derivatives')[0]\n",
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"\n",
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" data = Brain_Data(layout.get(subject=sub, scope='derivatives', suffix='bold', extension='nii.gz', return_type='file')[0])\n",
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" data = data.smooth(fwhm=fwhm)\n",
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" dm = load_bids_events(layout, sub)\n",

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