Research Infrastructure: MRI: Track 1 Acquisition of a functional near-infrared spectroscopy (fNIRS) system for interdisciplinary cognitive neuroscience

Researchers at Lawrence Technological University (LTU) utilize NSF Major Research Instrumentation funding to purchase a NIRSport2 functional near-infrared spectroscopy (fNIRS) neuroimaging system from NIRx Medical Technologies, LLC. fNIRS provides a non-invasive measure of changes in oxygenated- and deoxygenated-hemoglobin levels in the human cerebral cortex, reflecting regional brain activity in response to perceptual or cognitive tasks. Acquisition of the fNIRS system alters LTU’s capacity for inter-disciplinary research in basic and applied cognitive science, enabling research collaborations between the principals in the LTU Psychology program and the wider LTU community.

This greatly increases ability to produce cutting-edge cognitive neuroscience research and to pursue funded projects in a wide range of domains. LTU students across several disciplines gain from hands-on experience collecting and interpreting functional neuroimaging data within a variety of contexts (e.g., business, engineering, computer science, psychology), as well as increased learning opportunities within classes at LTU.

The acquisition of fNIRS supports a wide range of planned collaborative, interdisciplinary research projects. These include the following. 1) Identifying neural markers of uncertainty to improve decision making (cognitive neuroscience). 2) Using machine learning to detect unsustainably high levels of cognitive workload (cognitive neuroscience). 3) Hyperscanning to understand neural synchrony in pairs during competitive social interactions (cognitive neuroscience). 4) Applying neurostimulation (tDCS) to examine causal contributions of motor and visual cortical regions to language processing (cognitive neuroscience). 5) Using machine learning to detect changing emotional states for brain-computer interface improvement (engineering). 6) Using fNIRS data to improve semi-supervised and unsupervised training of deep neural networks to classify engagement level during driving (computer science, machine learning), 7) Exploring the neural underpinnings of grit to enhance education and student achievement (decision science, education), 8) Understanding the neural basis for purchasing decisions in response to advertising (neuromarketing), and 9) Understanding prefrontal cortex contributions to the influence of brand novelty and information framing on purchasing decisions. (neuromarketing).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.