Detecting and visualizing differences in brain structures with SPHARM and functional data analysis.

A new procedure for classifying brain structures described by SPHARM is presented. We combine a dimension reduction technique (functional principal component analysis or functional independent component analysis) with stepwise variable selection for linear discriminant classification. This procedure is compared with many well-known methods in a novel classification problem in neuroeducation, where the reversal error (a common error in mathematical problem solving) is analyzed by using the left and right putamens of 33 participants. The comparison shows that our proposal not only provides outstanding performance in terms of predictive power, but it is also valuable in terms of interpretation, since it yields a linear discriminant function for 3D structures.

Brain-anatomy image data set for problem solving associated with reversal error: Volumetric data.

Reversal Error (RE) is a common error in algebra problem solving. This error occurs when students recognize the information in the statement but make mistakes when translating some sentences from natural language to algebraic language, reversing the relationship between two variables in comparison word problems. Structural Magnetic Resonance Image (sMRI) data were collected with the purpose of identifying brain anatomical regions related to the RE phenomenon. The aim of the research was to investigate the brain anatomy differences between participants who failed more than 50% of the answers on the task (N=15) and those who responded correctly 100% of the time (N=18). sMRI analysis revealed differences between the two groups, and details about these data can be found in Ventura-Campos et al. (2022) [1]. This data set contains the sMRI (raw data, pre-processed images), and an excel file with personal information such as age and gender, the scanner with which their sMRI were collected, and the group to which each of the 33 subjects belonged.

Neural differences between monolinguals and early bilinguals in their native language during comprehension.

Research has shown that semantic processing of sentences engages more activity in the bilingual compared to the monolingual brain and, more specifically, in the inferior frontal gyrus. The present study aims to extend those results and examines whether semantic and also grammatical sentence processing involve different cerebral structures when testing in the native language. In this regard, highly proficient Spanish/Catalan bilinguals and Spanish monolinguals made grammatical and semantic judgments in Spanish while being scanned. Results showed that both types of judgments recruited more cerebral activity for bilinguals in language-related areas including the superior and middle temporal gyri. Such neural differences co-occurred with similar performance at the behavioral level. Taken together, these data suggest that early bilingualism shapes the brain and cognitive processes in sentence comprehension even in their native language; on the other hand, they indicate that brain over activation in bilinguals is not constrained to a specific area.

A new window to understanding individual differences in reward sensitivity from attentional networks.

Existing evidence suggests that the presence of reward cues modifies the activity in attentional networks, however, the nature of these influences remains poorly understood. Here, we performed independent component analysis (ICA) in two fMRI datasets corresponding to two incentive delay tasks, which compared the response to reward (money and erotic pictures) and neutral cues, and yielded activations in the ventral striatum using a general linear model approach. Across both experiments, ICA revealed that both the right frontoparietal network and default mode network time courses were positively and negatively modulated by reward cues, respectively. Moreover, this dual neural response pattern was enhanced in individuals with strong reward sensitivity. Therefore, ICA may be a complementary tool to investigate the relevant role of attentional networks on reward processing, and to investigate reward sensitivity in normal and pathological populations.