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Abstract Degeneracy in biological systems refers to a many-to-one mapping between physical structures and their functional (including psychological) outcomes. Despite the ubiquity of the phenomenon, traditional analytical tools for modeling degeneracy in neuroscience are extremely limited. In this study, we generated synthetic datasets to describe three situations of degeneracy in fMRI data to demonstrate the limitations of the current univariate approach. We describe a novel computational approach for the analysis referred to as neural topographic factor analysis (NTFA). NTFA is designed to capture variations in neural activity across task conditions and participants. The advantage of this discovery-oriented approach is to reveal whether and how experimental trials and participants cluster into task conditions and participant groups. We applied NTFA on simulated data, revealing the appropriate degeneracy assumption in all three situations and demonstrating NTFA’s utility in uncovering degeneracy. Lastly, we discussed the importance of testing degeneracy in fMRI data and the implications of applying NTFA to do so. 

Rapid and accurate detection of the pathogens, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for COVID-19, is critical for mitigating the COVID-19 pandemic. Current state-of-the-art pathogen tests for COVID-19 diagnosis are done in a liquid medium and take 10–30 min for rapid antigen tests and hours to days for polymerase chain reaction (PCR) tests. Herein we report novel accurate pathogen sensors, a new test method, and machine-learning algorithms for a breathalyzer platform for fast detection of SARS-CoV-2 virion particles in the aerosol in 30 s. The pathogen sensors are based on a functionalized molecularly-imprinted polymer, with the template molecules being the receptor binding domain spike proteins for different variants of SARS-CoV-2. Sensors are tested in the air and exposed for 10 s to the aerosols of various types of pathogens, including wild-type, D614G, alpha, delta, and omicron variant SARS-CoV-2, BSA (Bovine serum albumin), Middle East respiratory syndrome–related coronavirus (MERS-CoV), influenza, and wastewater samples from local sewage. Our low-cost, fast-responsive pathogen sensors yield accuracy above 99% with a limit-of-detection (LOD) better than 1 copy/μL for detecting the SARS-CoV-2 virus from the aerosol. The machine-learning algorithm supporting these sensors can accurately detect the pathogens, thereby enabling a new and unique breathalyzer platform for rapid COVID-19 tests with unprecedented speeds.