Synergizing functional brain imaging results in an era of information abundance & technological rise

A major challenge in neuroscience is understanding the organizational principles of brain structure and function that transcend scales and species. Identifying these generalizable principles is crucial for ensuring the reliability of new neuroscientific knowledge, especially in this era of rapid technological advancement. In this talk, I will explore the idea that we can make significant strides into uncovering simple and elegant principles governing the brain through the synthesis of cross-disciplinary and cross-species perspectives.

I will first highlight how we can leverage theoretical concepts from physics and engineering (e.g., scaling laws, resonant modes, and criticality), experimental techniques (e.g., imaging), and computational tools (e.g., modeling) to understand principles of brain organization and function at multiple scales—from cellular microcircuits to the whole brain. I will then argue that to fully grasp the universality of these principles, they must be understood in the context of evolution. By comparing neuroanatomy, molecular diversity, and functional organization across species, we can disentangle the fundamental constraints of brain structure and function from species-specific adaptations.

Together, I aim to show integrating knowledge across disciplines and species can the brain’s underlying simplicity. Such an approach not only deepens our understanding of brain complexity but also provides a framework for unifying the diverse methodologies and insights that define modern neuroscience.
 

This presentation will highlight the critical need for greater inclusion of rural populations in neuroimaging research. It will begin by examining the varying definitions of “rural” across countries and contexts. The discussion will then explore the ethical implications of under-sampling rural populations, focusing on urban-rural biases in connectome-based predictive modeling. Finally, the presentation will address the challenges and opportunities in recruiting rural research participants.
 

With a sea of diverse data types ranging from molecular to macroscale emerging across various fields of neuroscience, overarching frameworks are needed to integrate and derive coherent understandings. Network-based models have emerged as a powerful tool for this exact purpose, offering computational approaches that can map complex interactions across biological systems. My talk will demonstrate how network frameworks, such as network-based simulations of disease processes, have matured to the point where we can both generate and test hypotheses that cross spatiotemporal scales. These models, including sophisticated agent-based protein spreading models that track the propagation of molecular changes through biological systems, have been immensely successful in neurology, providing unprecedented insights into atrophy patterns and longitudinal progression across multiple neurodegenerative conditions. Critically, these computational approaches have now opened a path to application in enigmatic psychiatric disorders such as schizophrenia, bridging our understanding of complex neurological phenomena.

I will cover a series of novel studies by multiple different research groups using network simulations that combine macroscale data, such as in vivo diffusion imaging-based tractography, with molecular scales, such as ex vivo gene expression. These integrative approaches aim to comprehensively explain the pathophysiology of complex mental illnesses and uncover previously unknown mechanisms underlying psychiatric medications. By linking brain structural connectivity with molecular-level information, researchers can develop more nuanced models of disease progression and treatment response. I'll conclude by raising several critical future challenges, highlighting the field's trajectory from mechanistic explanation to prediction. This includes developing predictive models to guide treatment selection and transitioning from observational studies in psychiatric neuroscience to more powerful causal experimental designs, such as placebo-controlled randomized controlled trials, ultimately moving towards a more personalized and precise approach to understanding and treating complex mental health conditions.
 

This presentation will introduce brain signatures of wakefulness and drowsiness, examining how these signals influence fMRI data analyses and whether they can provide novel biomarkers. Additionally, this presentation will discuss how to capture shared variance across fMRI, EEG, and peripheral physiological signals; e.g., can we identify common patterns that span these modalities, and can we predict features of one modality from the other (fMRI/EEG, fMRI/physiology)?