The ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same patterns capture fluctuations in attention within individuals remains unclear. Here, across five independent studies, we demonstrate that the sustained attention connectome-based predictive model (CPM), a validated model of sustained attention function, generalizes to predict attentional state from data collected across minutes, days, weeks, and months. Furthermore, the sustained attention CPM is sensitive to within-subject state changes induced by propofol as well as sevoflurane, such that individuals show functional connectivity signatures of stronger attentional states when awake than when under deep sedation and light anesthesia. Together, these results demonstrate that fluctuations in attentional state reflect variability in the same functional connectivity patterns that predict individual differences in sustained attention.
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Abstract The endocannabinoid system is an important regulator of emotional responses such as fear, and a number of studies have implicated endocannabinoid signaling in anxiety. The fatty acid amide hydrolase (FAAH) C385A polymorphism, which is associated with enhanced endocannabinoid signaling in the brain, has been identified across species as a potential protective factor from anxiety. In particular, adults with the variant FAAH 385A allele have greater fronto‐amygdala connectivity and lower anxiety symptoms. Whether broader network‐level differences in connectivity exist, and when during development this neural phenotype emerges, remains unknown and represents an important next step in understanding how the FAAH C385A polymorphism impacts neurodevelopment and risk for anxiety disorders. Here, we leveraged data from 3,109 participants in the nationwide Adolescent Brain Cognitive Development Study℠ (10.04 ± 0.62 years old; 44.23% female, 55.77% male) and a cross‐validated, data‐driven approach to examine associations between genetic variation and large‐scale resting‐state brain networks. Our findings revealed a distributed brain network, comprising functional connections that were both significantly greater (95% CI for
p values = [<0.001, <0.001]) and lesser (95% CI forp values = [0.006, <0.001]) in A‐allele carriers relative to non‐carriers. Furthermore, there was a significant interaction between genotype and the summarized connectivity of functional connections that were greater in A‐allele carriers, such that non‐carriers with connectivity more similar to A‐allele carriers (i.e., greater connectivity) had lower anxiety symptoms (β = −0.041,p = 0.030). These findings provide novel evidence of network‐level changes in neural connectivity associated with genetic variation in endocannabinoid signaling and suggest that genotype‐associated neural differences may emerge at a younger age than genotype‐associated differences in anxiety. -
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Abstract Introduction Connectome‐based predictive modeling (CPM) is a recently developed machine‐learning‐based framework to predict individual differences in behavior from functional brain connectivity (FC). In these models, FC was operationalized as Pearson's correlation between brain regions’ fMRI time courses. However, Pearson's correlation is limited since it only captures linear relationships. We developed a more generalized metric of FC based on information flow. This measure represents FC by abstracting the brain as a flow network of nodes that send bits of information to each other, where bits are quantified through an information theory statistic called transfer entropy.
Methods With a sample of individuals performing a sustained attention task and resting during functional magnetic resonance imaging (fMRI) (
n = 25), we use the CPM framework to build machine‐learning models that predict attention from FC patterns measured with information flow. Models trained onn − 1 participants’ task‐based patterns were applied to an unseen individual's resting‐state pattern to predict task performance. For further validation, we applied our model to two independent datasets that included resting‐state fMRI data and a measure of attention (Attention Network Task performance [n = 41] and stop‐signal task performance [n = 72]).Results Our model significantly predicted individual differences in attention task performance across three different datasets.
Conclusions Information flow may be a useful complement to Pearson's correlation as a measure of FC because of its advantages for nonlinear analysis and network structure characterization.
