Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior

Modeling the association between functional connectivity and lateralization with the activity flow framework

The human brain is localized and distributed. On the one hand, each cognitive function tends to involve one hemisphere more than the other, also known as the principle of lateralization. On the other hand, interactions among brain regions in the form of functional connectivity (FC) are indispensable for intact function. Recent years have seen growing interest in the association between lateralization and FC. However, FC metrics vary from spurious correlation to causal associations. If lateralization manifests local processing and causal network interactions, more causally valid FC metrics should predict lateralization index (LI) better than FC based on simple correlations. The present study directly investigates this hypothesis within the activity flow framework to compare the association between lateralization and four brain connectivity metrics: correlation-based FC, multiple-regression FC, partial-correlation FC, and combinedFC. We propose two modeling approaches: the one-step approach, which models the relationship between LI and FC directly, and the two-step approach, which predicts the brain activation and calculates the LI. Our results indicated that multiple-regression FC, partial-correlation FC, and combinedFC could significantly improve the model prediction compared to correlation-based FC, which was consistent in a spatial working memory task (typically right-lateralized) and a language task (typically left-lateralized). The one-step and two-step approach yielded similar conclusions. In addition, the finding was replicated in a clinical sample of schizophrenia (SZ), bipolar disorder (BP), and attention deficit hyperactivity disorder (ADHD). The present study suggests that the causal interactions among brain regions help shape the lateralization pattern.

Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations

Brain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions. However, these models have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. We progress from simple to complex FC measures, with each adding algorithmic details reflecting causal principles. This reflects many neuroscientists' preference for reduced FC measure complexity (to minimize assumptions, minimize compute time, and fully comprehend and easily communicate methodological details), which potentially trades off with causal validity. We start with Pearson correlation (the current field standard) to remain maximally relevant to the field, estimating causal validity across a range of FC measures using simulations and empirical fMRI data. Finally, we apply causal-FC-based activity flow modeling to a dorsolateral prefrontal cortex region (DLPFC), demonstrating distributed causal network mechanisms contributing to its strong activation during a working memory task. Notably, this fully distributed model is able to account for DLPFC working memory effects traditionally thought to rely primarily on within-region (i.e., not distributed) recurrent processes. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.

Generalizing prediction of task-evoked brain activity across datasets and populations

Predictions of task-based functional magnetic resonance imaging (fMRI) from task-free resting-state (rs) fMRI have gained popularity over the past decade. This method holds a great promise for studying individual variability in brain function without the need to perform highly demanding tasks. However, in order to be broadly used, prediction models must prove to generalize beyond the dataset they were trained on. In this work, we test the generalizability of prediction of task-fMRI from rs-fMRI across sites, MRI vendors and age-groups. Moreover, we investigate the data requirements for successful prediction. We use the Human Connectome Project (HCP) dataset to explore how different combinations of training sample sizes and number of fMRI datapoints affect prediction success in various cognitive tasks. We then apply models trained on HCP data to predict brain activations in data from a different site, a different MRI vendor (Phillips vs. Siemens scanners) and a different age group (children from the HCP-development project). We demonstrate that, depending on the task, a training set of approximately 20 participants with 100 fMRI timepoints each yields the largest gain in model performance. Nevertheless, further increasing sample size and number of timepoints results in significantly improved predictions, until reaching approximately 450-600 training participants and 800-1000 timepoints. Overall, the number of fMRI timepoints influences prediction success more than the sample size. We further show that models trained on adequate amounts of data successfully generalize across sites, vendors and age groups and provide predictions that are both accurate and individual-specific. These findings suggest that large-scale publicly available datasets may be utilized to study brain function in smaller, unique samples.

Neural representation dynamics reveal computational principles of cognitive task learning

During cognitive task learning, neural representations must be rapidly constructed for novel task performance, then optimized for robust practiced task performance. How the geometry of neural representations changes to enable this transition from novel to practiced performance remains unknown. We hypothesized that practice involves a shift from compositional representations (task-general activity patterns that can be flexibly reused across tasks) to conjunctive representations (task-specific activity patterns specialized for the current task). Functional MRI during learning of multiple complex tasks substantiated this dynamic shift from compositional to conjunctive representations, which was associated with reduced cross-task interference (via pattern separation) and behavioral improvement. Further, we found that conjunctions originated in subcortex (hippocampus and cerebellum) and slowly spread to cortex, extending multiple memory systems theories to encompass task representation learning. The formation of conjunctive representations hence serves as a computational signature of learning, reflecting cortical-subcortical dynamics that optimize task representations in the human brain.