The remarkable plasticity of the human connectome has inspired a new generation of interventions designed to modulate brain function, shaping the connectivity and dynamics of neural networks to promote learning and resilience throughout life. While traditional methods rely on a single intervention modality, emerging multimodal approaches integrate multiple methods to amplify their effects. Our research investigates the efficacy of these approaches across diverse populations, aiming to develop personalized interventions that are tailored to the needs of the individual and designed for specific performance goals.

The empirical literature on cognitive enhancement highlights two complementary approaches to intervention: (i) targeted methods that modulate specific brain networks and (ii) systemic approaches that drive global changes in brain function. Targeted interventions are designed to improve cognitive function by engaging specific brain networks. For example, skill-based cognitive training strengthens neural connections within these networks, while non-invasive brain stimulation (tDCS) modulates their cortical excitability. 

Systemic interventions, in contrast, enhance cognitive function by engaging global, system-wide mechanisms. For example, literacy interventions strengthen foundational cognitive domains – language processing, executive function, and memory – promoting lifelong intellectual engagement. Contemplative practices, such as mindfulness meditation, regulate stress-related neuroendocrine activity, reducing its widespread inhibitory effects on neuroplasticity and cognitive function. Physical activity and aerobic exercise influences multiple brain systems by increasing neurotrophic factors, such as BDNF, which enhances synaptic plasticity and neurovascular health. Similarly, dietary interventions impact systemic processes by regulating metabolism, modulating inflammation, and providing essential nutrients for brain function. While each of these approaches offer measurable benefits, their efficacy may be limited when applied in isolation, motivating modern approaches that seek to build a more comprehensive platform for cognitive enhancement. 

In this effort, multimodal approaches incorporate multiple methods of intervention to capitalize on their complementary effects. Our work suggests that combining cognitive training with physical fitness training enhances executive functions more effectively than cognitive training alone, as prior fitness training promotes synaptic plasticity and neurovascular function, creating a more optimal state for learning. Similarly, the cognitive benefits of fitness training are amplified when paired with nutritional intervention, highlighting the contributions of metabolic health to neuroplasticity. Additionally, pairing tDCS with cognitive training can accelerate learning, supporting the hypothesis that neurostimulation enhances task specific synaptic changes. These findings suggest that multimodal approaches integrating cognitive, physiological, and neurostimulation methods may provide a more effective framework for enhancing cognitive function.

Despite promising initial evidence, the effects of multimodal interventions are not yet well understood, making it difficult to determine how and when different interventions interact to promote cognitive performance. To investigate these effects, our research examines multimodal interventions in populations with distinct cognitive demands, including college students managing academic challenges, older adults seeking to maintain cognitive health, Division I athletes striving for peak performance, and elite military personnel enduring significant physical and psychological stress. Given individual variability in response to intervention, we apply statistical machine learning methods to design personalized protocols based on an individuals cognitive, neural, and physiological profile.

Our work in the DARPA TAILOR and DARPA MBA programs applies an ensemble learning framework to construct multivariate phenotypes that predict an individuals response to intervention. This framework combines Case-Based Reasoning to define individual difference phenotypes, Bayesian Causal Modeling to identify underlying mechanisms, and Bayesian Multitask Structure Learning to detect shared patterns across intervention outcomes. By leveraging these computational approaches, we develop multimodal interventions that are tailored to an individual’s unique performance phenotype. Together, these efforts aim to advance the field of cognitive enhancement by demonstrating the potential of multimodal interventions, gaining insight into their effects, and enhancing their benefits through a personalized, data driven approach.