Spring 2020

In Spring 2020, students were forced to take a remote version of the course due to the Covi19 pandemic. Unfortunately, this meant that students were unable to design and run their own original study. Instead, students developed original research questions and conducted secondary data analysis projects using data that was publicly available on OpenNeuro. One group of students worked with the naturalistic Sherlock dataset Chen et al., 2017. The other group worked with a dataset in which patient with depression and healthy controls listened to emotional music clips Lepping et al., 2016. Students were able to communicate with the authors of the study to get additional information that was not shared in the data repository to complete their analyses.

Self-Referencing With Memes

Lyrra Isanberg, Nina Kosowsky, Mia Newkirk, Kristen Soh, Sabrina Strauss

Memory is often regarded as an individualistic experience, with every person perceiving his or her world differently. To better understand the differences and similarities of human memory among individuals, we used the data collected by Chen et al. and performed several different analyses. We used contrast analyses, a representational similarity analysis (RSA), and an IS-RSA to answer several questions regarding brain activity during encoding and how that can be correlated with scene recall or semantic similarity. In our analyses, we provide answers to the following questions:

  1. Do participants who successfully recall a scene have different activations during the encoding period than participants who do not recall that particular scene?

  2. Are there common regions activated during encoding across subjects that are correlated with successful recall?

  3. How does the temporal pattern of activity in the brain throughout encoding correlate with subject similarity, based on their scene recall?

  4. How does the semantic similarity of scenes, based on their text similarity, correlate with the spatial representation of scenes during encoding?

See their presentation.

Music and Depression: A multivariate prediction/classification analysis and representational similarity analysis

Jada Brown, Kera Carey, Amanda Chen, Emily Chen, Mia Iqbal, Ephthalia Michael-Scwarzinger, Nathan Skinner, Bryce West

For years, music has been known to provoke a strong emotional response. Due to peoples’ tendencies to self-medicate emotions with music, it has been looked at as a treatment for individuals suffering from Major Depressive Disorder (MDD) or Post-Traumatic Stress Disorder (PTSD). Emotion-provoking music has been shown to act on the reward circuitry, as well as reactivate the Anterior Hippocampus. Individuals suffering from depression and PTSD have shown damage and decreased activity in the reward system, as well as in the Hippocampus. Newer results have proved that music could be used to reactivate the Anterior Cingulate Cortex, an area with decreased activity in depressed patients. These studies have been difficult to navigate, as the stimuli, as well as cognitive reactions experienced during musical therapy, have been poorly defined. In this project, our group came up with two research questions to test by reanalzing data from (Lepping et al, 2015). (1) Can we create a model to discriminate between MDD and ND participants using the contrast between positive and negative stimuli? And (2) Can we see the differences between MDD and ND participants when processing the same audio clip? While the main study was designed to investigate neural circuitry of emotion and reward in depression, we wanted to do more with that data. Instead of just doing contrasts, and ROIs, we performed a ​Multivoxel Pattern Analysis (MVPA) and a ​Representational Similarity Analysis​ (RSA) using the audio files.

See their presentation.