Cognitive and Non-Cognitive Outcomes Associated with Student Engagement in a Novel Brain Chemoarchitecture Mapping Course-Based Undergraduate Research Experience

Abstract

Course-based undergraduate research experiences (CUREs) engage emerging scholars in the authentic process of scientific discovery, and foster their development of content knowledge, motivation, and persistence in the science, technology, engineering, and mathematics (STEM) disciplines. Importantly, authentic research courses simultaneously offer investigators unique access to an extended population of students who receive education and mentoring in conducting scientifically relevant investigations and who are thus able to contribute effort toward big-data projects. While this paradigm benefits fields in neuroscience, such as atlas-based brain mapping of nerve cells at the tissue level, there are few documented cases of such laboratory courses offered in the domain. Here, we describe a curriculum designed to address this deficit, evaluate the scientific merit of novel student-produced brain atlas maps of immunohistochemically-identified nerve cell populations for the rat brain, and assess shifts in science identity, attitudes, and science communication skills of students engaged in the introductory-level Brain Mapping and Connectomics (BM&C) CURE. BM&C students reported gains in research and science process skills following participation in the course. Furthermore, BM&C students experienced a greater sense of science identity, including a greater likelihood to discuss course activities with non-class members compared to their non-CURE counterparts. Importantly, evaluation of student-generated brain atlas maps indicated that the course enabled students to produce scientifically valid products and make new discoveries to advance the field of neuroanatomy. Together, these findings support the efficacy of the BM&C course in addressing the relatively esoteric demands of chemoarchitectural brain mapping.

Keywords: CURE; active learning; brain atlas; collaboration; course-based undergraduate research experience; discovery; experimental design; introductory biology; laboratory instruction; mapping; neuroanatomy; neuroscience; science communication; science process skills.

Appendix II Figure 1

Companion Illustration to Appendix II Table.1. A. Sample images were obtained from Group 3 of Cohort 2 to illustrate student parcellation efforts of Nissl-stained tissue (×10 magnification). Clear labels are used throughout, and atlas and anatomical proficiency are evident in the boundaries drawn for gray and white matter regions. B. Immunofluorescence image of calbindin immunoreactivity from Group 3 files provide an example of student mounting and staining capabilities. Students aligned the image of the Nissl-stained section and its parcellation to the immunofluorescent single-channel images thus providing regional boundaries for the immunofluorescent section. The student indicated the atlas level(s) and region names within the delineated image. Note that mounting artifacts alter the registration of the parcellated-boundaries overlay with the main landmarks (white matter tracts, ventricle) in the underlying image. C. Signal identification and representation for the data for calbindin-immunoreactive cells is presented for Level 26. Note, in this example, students adopted the convention of using an open circle to denote cells that expressed calbindin immunoreactivity with clearance from the zone of the nucleus in lieu of using that notation to indicate weakly immunoreactive cells. The underlying atlas template is from Swanson (2004) and is reproduced and modified here under the conditions of a Creative Commons BY-NC 4.0 license (https://creativecommons.org/licenses/bync/4.0/legalcode).

Appendix II Figure 2

Scoring examples for the System and Logic category. Adobe Illustrator allows image files and objects (type, and vector paths) to be organized into data layers. These layers can be controlled independently (including locking or viewing), assigned descriptive titles, and assigned additional properties. As an example of the latter, the color bar depicted on the left can be switched to easily denote the pseudocolor that was used to represent a given IHC antigen (e.g., nNOS, MCH, and αMSH shown here from Cohort 1). In these screen captures of the data layer organization, first-pass efforts betray a less sophisticated understanding of the use of the software and/or the practical manipulation of the data layers than do second-pass efforts.

Appendix II Figure 3

Plane of section analysis in a parcellated image of a Nissl-stained section. This example, drawn from Cohort 3, provides an example of good consistency in demarcation (System and Logic category) and evidence of clear atlas and anatomical proficiency in all categories. Note the students’ ability to distinguish boundaries based on cytoarchitectural distinctions established in the image of the Nissl-stained section (×10 magnification) rather than arbitrarily attempting to overlay the representational atlas boundaries directly. A line (partially obscured by the blue frame) has been provided to indicate where the shift in atlas level takes place and the approximate direction in which plane-of-section shift occurs. Atlas levels have been correctly assigned based on key anatomical features (an example of an atlas plate for the level is provided and the region of interest is framed in blue). Key structures have been identified (region of interest = LHA, ZI), including white matter tracts used as fiducials. The atlas level in the inset is from Swanson (2004) (also available at https://larrywswanson.com) and is reproduced and modified here under the conditions of a Creative Commons BY-NC 4.0 license (https://creativecommons.org/licenses/bync/4.0/legalcode).

Appendix II Figure 4

Preliminary attempts of mapping prior to rubric-guided feedback. This example, drawn from Cohort 1, shows a preliminary attempt at parcellation and mapping for nNOS-immunoreactive cell bodies. Note that several discrepancies exist not only in data layer organization, but also in the consistency and accuracy of boundary placement. Note, too, the underrepresentation of cell bodies and the inaccurate migration of data onto the map as most clearly evident for cells drawn within the fornix in the atlas template. The atlas map is from Swanson (2004) (also available at https://larrywswanson.com) and is reproduced and modified here under the conditions of a Creative Commons BY-NC 4.0 license (https://creativecommons.org/licenses/bync/4.0/legalcode)

Appendix II Figure 5

Example of final data representation on an atlas level in comparison to the original data image. These images (reproduced from Figure 6 in the main text) illustrate that the distribution of cell somata and fibers are translated to the final map in a manner that preserves the positional relationships of signal within the depicted structures. The images at left was taken at ×10 magnification. The atlas level is from Swanson (2004) (also available at https://larrywswanson.com) and is reproduced and modified here under the conditions of a Creative Commons BY-NC 4.0 license (https://creativecommons.org/licenses/bync/4.0/legalcode).

Figure 1

Course Objectives and Outcomes. By enabling students to practice research skills to gain self-sufficiency, to gain proficiency in a specific set of technical skills through iteration, and by setting attainable research goals, the course objectives were designed to yield positive student affective outcomes. Specific benchmarks were identified to describe successful outcomes for each tier of objectives The mechanism employed to assess each outcome is provided. LCAS, Laboratory Course Assessment Survey; SCIID, Science Identity Scale; URSSA, Undergraduate Research Student Self-Assessment.

Figure 2

Alignment of Course Activities to the Five Dimensions of CUREs. Course activities were aligned to the five classic CURE elements: Scientific practices, iterative practices, discovery, collaboration, and broader relevance. Specifically, students iteratively engaged in authentic scientific practices within a tiered framework of collaboration. Their goal was to identify and contextualize novel chemoarchitectural patterns of neurons within hypothalamic regions of interest.

Figure 3

Students’ Perceptions of the Laboratory Environment. in a matched comparison of traditional introductory biology laboratory courses (non-CURE in gray) versus the BM&C (in blue) courses, BM&C students show significantly higher scores in collaboration (**p < 0.005), discovery, and iteration categories (*p < 0.05).

Figure 4

Student Networking Patterns. BM&C students (blue) engage in greater levels of external networking than their non-CURE (gray) peers (**p < 0.005).

Figure 5

Science Identity Scores. While no differences were seen among many of the categories assessed, BM&C students (blue) did self-identify as a scientist more frequently than non-CURE students (gray) (**p < 0.005).

Figure 6

Sample of Student Workflow. A. Students identified structures based on the Nissl-stained cytoarchitecture (photographed at ×10 magnification). B. Boundaries from the Nissl-stained section were overlaid onto immunohistochemical data images (tyrosine hydroxylase (TH), red; melanin concentrating hormone (MCH), blue; hypocretin/orexin (H/O), green. All images taken at ×10 magnification). C. Merged data files of student-generated maps representing fiber density trends and denoting immunoreactive cells within the modeled framework. The underlying atlas template is from Swanson (2004); also available at https://larrywswanson.com/) and is reproduced and modified here under the conditions of a Creative Commons BY-NC 4.0 license.

Figure 7

Post-semester URSSA Responses. Questions are grouped by the theme assessed. M = Mean; SEM = Standard Error of the Mean.