ABSTRACT Sustained attention is essential for daily life and can be directed to information from different perceptual modalities, including audition and vision. Recently, cognitive neuroscience has aimed to identify neural predictors of behavior that generalize across datasets. Prior work has shown strong generalization of models trained to predict individual differences in sustained attention performance from patterns of fMRI functional connectivity. However, it is an open question whether predictions of sustained attention are specific to the perceptual modality in which they are trained. In the current study, we test whether connectome-based models predict performance on attention tasks performed in different modalities. We show first that a predefined network trained to predict adults’ visual sustained attention performance generalizes to predict auditory sustained attention performance in three independent datasets ( N 1 = 29, N 2 = 60, N 3 = 17). Next, we train new network models to predict performance on visual and auditory attention tasks separately. We find that functional networks are largely modality general, with both model-unique and shared model features predicting sustained attention performance in independent datasets regardless of task modality. Results support the supposition that visual and auditory sustained attention rely on shared neural mechanisms and demonstrate robust generalizability of whole-brain functional network models of sustained attention. AUTHOR SUMMARY While previous work has demonstrated external validity of functional connectivity-based networks for the prediction of cognitive and attentional performance, testing generalization across visual and auditory perceptual modalities has been limited. The current study demonstrates robust prediction of sustained attention performance, regardless of perceptual modality models are trained or tested in. Results demonstrate that connectivity-based models may generalize broadly capturing variance in sustained attention performance, which is agnostic to the perceptual modality of model training.

Although practicing a task generally benefits later performance on that same task, there are individual differences in practice effects. One avenue to model such differences comes from research showing that brain networks extract functional advantages from operating in the vicinity of criticality, a state in which brain network activity is more scale-free. We hypothesized that higher scale-free signal from fMRI data, measured with the Hurst exponent ( H ), indicates closer proximity to critical states. We tested whether individuals with higher H during repeated task performance would show greater practice effects. In Study 1, participants performed a dual-n-back task (DNB) twice during MRI ( n = 56). In Study 2, we used two runs of n-back task (NBK) data from the Human Connectome Project sample ( n = 599). In Study 3, participants performed a word completion task (CAST) across six runs ( n = 44). In all three studies, multivariate analysis was used to test whether higher H was related to greater practice-related performance improvement. Supporting our hypothesis, we found patterns of higher H that reliably correlated with greater performance improvement across participants in all three studies. However, the predictive brain regions were distinct, suggesting that the specific spatial H ↑ patterns are not task-general. Author Summary Individuals vary in the degree to which they improve on a task upon repeating it, referred to as practice effects. Research has shown a relationship between scale-free brain activity and advantageous functional properties for the brain networks. Therefore, we asked if practice effects are related to these scale-free brain dynamics. We hypothesized that individuals with more scale-free fMRI activity, measured with Hurst exponent ( H ), when repeating tasks would show larger practice effects. Across three datasets including different tasks and individuals, we found that higher fMRI H was associated with greater task performance improvement. We conclude that exhibiting more efficient information processing when performing a cognitive task may be indexed by higher fMRI scale-free activity and predict further improvement in task performance.

The Hurst exponent ( H ) isolated in fractal analyses of neuroimaging time series is implicated broadly in cognition. Within this literature, H is associated with multiple mental disorders, suggesting that H is transdimensionally associated with psychopathology. Here, we unify these results and demonstrate a pattern of decreased H with increased general psychopathology and attention-deficit/hyperactivity factor scores during a working memory task in 1,839 children. This pattern predicts current and future cognitive performance in children and some psychopathology in 703 adults. This pattern also defines psychological and functional axes associating psychopathology with an imbalance in resource allocation between fronto-parietal and sensorimotor regions, driven by reduced resource allocation to fronto-parietal regions. This suggests the hypothesis that impaired working memory function in psychopathology follows from a reduced cognitive resource pool and a reduction in resources allocated to the task at hand. Author Summary We study how the complexity of brain signals is associated with psychopathology. Using a measure of the fractalness of temporal brain dynamics, we find that psychopathology is associated with less fractal and less complex brain signals globally. In addition to this global pattern, we investigate spatial variations in the fractalness of brain signals. We find that disruptions in cognitive resources during a working memory task come from reductions in resources allocated to frontal regions alone. In addition, we find that these patterns of resource allocation are predictive of cognitive performance on multiple working memory tasks and a future working memory task. These findings suggest that future investigations of fractal brain dynamics in other contexts may help researchers better understand the mechanisms behind psychopathology-associated cognitive impairment.