Influence of the BDNF Genotype on Amygdalo-Prefrontal White Matter Microstructure is Linked to Nonconscious Attention Bias to Threat

Cognitive processing biases, such as increased attention to threat, are gaining recognition as causal factors in anxiety. Yet, little is known about the anatomical pathway by which threat biases cognition and how genetic factors might influence the integrity of this pathway, and thus, behavior. For 40 normative adults, we reconstructed the entire amygdalo-prefrontal white matter tract (uncinate fasciculus) using diffusion tensor weighted MRI and probabilistic tractography to test the hypothesis that greater fiber integrity correlates with greater nonconscious attention bias to threat as measured by a backward masked dot-probe task. We used path analysis to investigate the relationship between brain-derived nerve growth factor genotype, uncinate fasciculus integrity, and attention bias behavior. Greater structural integrity of the amygdalo-prefrontal tract correlates with facilitated attention bias to nonconscious threat. Genetic variability associated with brain-derived nerve growth factor appears to influence the microstructure of this pathway and, in turn, attention bias to nonconscious threat. These results suggest that the integrity of amygdalo-prefrontal projections underlie nonconscious attention bias to threat and mediate genetic influence on attention bias behavior. Prefrontal cognition and attentional processing in high bias individuals appear to be heavily influenced by nonconscious threat signals relayed via the uncinate fasciculus

Effects of Serotonin Transporter Gene Variation on Impulsivity Mediated by Default Mode Network: A Family Study of Depression

Serotonergic neurotransmission, potentially through effects on the brain’s default mode network (DMN), may regulate aspects of attention including impulse control. Indeed, genetic variants of the serotonin transporter (5-HTT) have been implicated in impulsivity and related psychopathology. Yet it remains unclear the mechanism by which the 5-HTT genetic variants contribute to individual variability in impulse control. Here, we tested whether DMN connectivity mediates an association between the 5-HTT genetic variants and impulsivity. Participants (N = 92) were from a family cohort study of depression in which we have previously shown a broad distribution of 5-HTT variants. We genotyped for 5-HTTLPR and rs25531 (stratified by transcriptional efficiency: 8 low/low, 53 low/high, and 31 high/high), estimated DMN structural connectivity using diffusion probabilistic tractography, and assessed behavioral measures of impulsivity (from 12 low/low, 48 low/high, and 31 high/high) using the Continuous Performance Task. We found that low transcriptional efficiency genotypes were associated with decreased connection strength between the posterior DMN and the superior frontal gyrus (SFG). Path modeling demonstrated that decreased DMN–SFG connectivity mediated the association between low-efficiency genotypes and increased impulsivity. Taken together, this study suggests a gene-brain-behavior pathway that perhaps underlies the role of the serotonergic neuromodulation in impulse control

SwiFT: Swin 4D fMRI Transformer

Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple large-scale resting-state fMRI datasets, including the Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD), and UK Biobank (UKB) datasets, to predict sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. Furthermore, by leveraging its end-to-end learning capability, we show that contrastive loss-based self-supervised pre-training of SwiFT can enhance performance on downstream tasks. Additionally, we employ an explainable AI method to identify the brain regions associated with sex classification. To our knowledge, SwiFT is the first Swin Transformer architecture to process dimensional spatiotemporal brain functional data in an end-to-end fashion. Our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI.Comment: NeurIPS 202