The Mind Research Network

In practice, collecting auxiliary labeled data with same feature space from multiple domains is difficult. Thus, we focus on the heterogeneous transfer learning to address the problem of insufficient sample sizes in neuroimaging. Viewing subjects, time, and features as dimensions, brain activation and dynamic functional connectivity data can be treated as high-order heterogeneous data with heterogeneity arising from distinct feature space. To use the heterogeneous priori knowledge from the low-dimensional brain activation data to improve the classification performance of high-dimensional dynamic functional connectivity data, we propose a tensor dictionary-based heterogeneous transfer learning framework. It combines supervised tensor dictionary learning with heterogeneous transfer learning for enhance high-order heterogeneous knowledge sharing. The former can encode the underlying discriminative features in high-order data into dictionaries, while the latter can transfer heterogeneous knowledge encoded in dictionaries through feature transformation derived from mathematical relationship between domains. The primary focus of this paper is gender classification using fMRI data to identify emotion-related brain gender differences during adolescence. Additionally, experiments on simulated data and EEG data are included to demonstrate the generalizability of the proposed method. Experimental results indicate that incorporating prior knowledge significantly enhances classification performance. Further analysis of brain gender differences suggests that temporal variability in brain activity explains differences in emotion regulation strategies between genders. By adopting the heterogeneous knowledge sharing strategy, the proposed framework can capture the multifaceted characteristics of the brain, improve the generalization of the model, and reduce training costs. Understanding the gender specific neural mechanisms of emotional cognition helps to develop the gender-specific treatments for neurological diseases.

Facial expressions convey important social information and can initiate behavioral change through the processing and understanding of emotions. However, while this ability is known to evolve throughout development, it remains unclear whether this ability differs between girls and boys or how other variables such as level of anxiety can modulate it. Furthermore, understanding the underlying neural mechanisms of facial expression processing and how they are linked by sex and anxiety during development is essential, as alterations in this processing have been associated with psychiatric disorders. Herein, 191 typically developing youth (6- to 15-years old) completed an implicit face processing task involving three facial expressions (angry, happy, and neutral) during functional magnetic resonance imaging. We conducted linear models on the fMRI data to investigate the impact sex and anxiety on brain responses to emotional faces, accounting for age. Our findings indicated a significant anxiety-by-sex interaction in a posterior network covering bilateral visual and medial temporal cortices during the happy > neutral contrast. Specifically, girls with higher anxiety showed weaker activation while boys showed the opposite pattern. These findings suggest that the inter-subject variability reported in typically developing individuals in response to facial emotions may be related to many factors, including sex and anxiety level.

The Uniform Data Set (UDS) neuropsychological battery, administered across Alzheimer’s Disease Centers (ADC), includes memory tests but lacks a list‐learning paradigm. ADCs often supplement the UDS with their own preferred list‐learning task. Given the importance of list‐learning for characterizing memory, we aimed to develop a harmonized memory score that incorporates UDS memory tests while allowing centers to contribute differing list‐learning tasks.

We applied item‐banking confirmatory factor analysis to develop a composite memory score in 5,287 participants (mean age 67.1; SD = 12.2) recruited through 18 ADCs and four consortia (DiverseVCID, MarkVCID, ALLFTD, LEADS) who completed UDS memory tasks (used as linking‐items) and one of five list‐learning tasks. All analyses used linear regression. We tested whether memory scores were affected by which list‐learning task was administered. To assess construct validity, we tested associations of memory scores with demographics, disease severity (CDR Box Score), an independent memory task (TabCAT Favorites, n = 675), and hippocampal volume (n = 811). We compared performances between cognitively unimpaired (n = 279), AD‐biomarker+ MCI (n = 26), and AD‐biomarker+ dementia (n = 98). In a subsample with amyloid‐ and tau‐PET (n = 49), we compared memory scores from participants with positive vs negative scans determined using established quantitative cutoffs.

Model fit indices were excellent (e.g., CFI = 0.998) and factor loadings were strong (0.43‐0.93). Differences in list‐learning task had a negligible effect on scores (average Cohen’s d = 0.11). Higher memory scores were significantly (p’s<.001) correlated with younger age (β = ‐0.18), lower CDR Box Scores (β = ‐0.63), female sex (β = 0.12), higher education (β = 0.19), larger hippocampal volume (β = 0.42), and an independent memory task (β = 0.71, p<0.001). The memory composite declined in a stepwise fashion by diagnosis (cognitively unimpaired>MCI>AD dementia, p<0.001). On average, amyloid‐PET positivity was associated with lower composite scores, but was not statistically significant (β = ‐0.34; p = 0.25; d = 0.40). Tau‐PET positivity was associated with worse performance, demonstrating a large effect size (β = ‐0.75; p<0.002; d = 0.91).

The harmonized memory score developed in a large national sample was stable regardless of contributing list‐learning task and its validity for cross‐cohort ADRD research is supported by expected associations with demographics, clinical measures, and Alzheimer’s biomarkers. A processing script will be made available to enhance cross‐cohort ADRD research.