A cognitive neurogenetic approach to uncovering the structure of executive functions.

One central mission of cognitive neuroscience is to understand the ontology of complex cognitive functions. We addressed this question with a cognitive neurogenetic approach using a large-scale dataset of executive functions (EFs), whole-brain resting-state functional connectivity, and genetic polymorphisms. We found that the bifactor model with common and shifting-specific components not only was parsimonious but also showed maximal dissociations among the EF components at behavioral, neural, and genetic levels. In particular, the genes with enhanced expression in the middle frontal gyrus (MFG) and the subcallosal cingulate gyrus (SCG) showed enrichment for the common and shifting-specific component, respectively. Finally, High-dimensional mediation models further revealed that the functional connectivity patterns significantly mediated the genetic effect on the common EF component. Our study not only reveals insights into the ontology of EFs and their neurogenetic basis, but also provides useful tools to uncover the structure of complex constructs of human cognition.

Partitioning heritability analyses unveil the genetic architecture of human brain multidimensional functional connectivity patterns.

Resting-state functional connectivity profiles have been increasingly shown to be important endophenotypes that are tightly linked to human cognitive functions and psychiatric diseases, yet the genetic architecture of this multidimensional trait is barely understood. Using a unique sample of 1,704 unrelated, young and healthy Chinese Han individuals, we revealed a significant heritability of functional connectivity patterns in the whole brain and several subnetworks. We further proposed a partitioned heritability analysis for multidimensional functional connectivity patterns, which revealed the common and unique enrichment patterns of the genetic contributions to brain connectivity patterns for several gene sets linked to brain functions, including the genes expressed preferentially in the central nervous system and those associated with intelligence, educational attainment, attention-deficit/hyperactivity disorder, and schizophrenia. These results for the first time reveal the genetic architecture of multidimensional brain connectivity patterns across different networks and advance our understanding of the complex relationship between gene sets, neural networks, and behaviors.

Spaced Learning Enhances Episodic Memory by Increasing Neural Pattern Similarity Across Repetitions.

Spaced learning has been shown consistently to benefit memory compared with massed learning, yet the neural representations and processes underlying the spacing effect are still poorly understood. In particular, two influential models (i.e., the encoding variability hypothesis and the study-phase retrieval hypothesis) could both model behavioral performance very well, but they make opposite hypotheses regarding the spacing effect's neural mechanisms. The present study attempted to provide empirical neural evidence to adjudicate these competing hypotheses. Using spatiotemporal pattern similarity (STPS) analysis of EEG data, this study investigated whether and how repetition lags (massed/short-spaced/long-spaced) modulated the STPS's contribution to episodic memory encoding in male and female human participants. The results revealed that greater item-specific STPS in the right frontal electrodes at 543-727 ms after stimulus onset was associated with better memory performance. More importantly, this STPS was larger under the spaced-learning condition than the massed-learning condition and partially mediated the spacing effect on memory performance. In addition, we found that massed learning was associated with stronger repetition suppression in the N400 component that reflected momentary retrieval strength, but reduced activity in the late positive component that was associated with memory retrieval. These results suggest that spaced learning improves long-term memory by increasing retrieval effort and enhancing the pattern reinstatement of prior neural representations, which may be achieved by reducing the momentary retrieval strength as the extended repetition lags might help to eliminate the residual representation in working memory.SIGNIFICANCE STATEMENT As one of the most ubiquitous and fundamental phenomena in the history of memory research, the spacing effect provides an important window into understanding how enduring memory is formed in the brain and how different practice strategies could modulate these mechanisms to affect memory performance. By leveraging the neural representational analysis on scalp EEG data, the current study provides the first empirical data to show that spaced learning enhances memory by improving the spatiotemporal similarity that occurs at a late time window. Our results support the study-phase retrieval hypothesis but not the encoding variability hypothesis and emphasize the role of neural pattern reinstatement in strengthening memory via repeated study.