Recent brain imaging studies have provided new insight into how students are able to extend their previous problem solving skills to new but similar problems. It is still unclear, however, what the basis is of individual differences in their success at transfer. In this study, 75 subjects had been trained to solve a set of mathematical problems before they were put into the fMRI scanner, where they were challenged to solve modified versions of familiar problems. A hidden semi-Markov model identified the sequential structure of thought when solving the problems. Analyzing the patterns of brain activity over the sequence of states identified by the model, we observed that subjects who showed consistent brain patterns performed better. This consistency refers to both how consistently subjects respond to different problems (within-subject consistency), and how brain responses of a given subject deviate from the population average (between-subjects consistency). Early withinsubject consistency is particularly predictive of later performance in the experiment.

In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large background fluctuations and high spatial overlaps intrinsic to this recording modality. Here, we describe a new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest. We compared the proposed method against previous independent components analysis and constrained nonnegative matrix factorization approaches. On both simulated and experimental data recorded from mice, our method substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes. These advances can in turn significantly enhance the statistical power of downstream analyses, and ultimately improve scientific conclusions derived from microendoscopic data.