Neuroimaging, Brain Mapping & Computational Neuroscience

Session Overview

The capacity to measure the living human brain — its structure, function, connectivity, and molecular composition — with increasing spatial and temporal precision has transformed neuroscience from a discipline dependent on post-mortem observation into one capable of tracking biological change across the lifespan in health and disease. Neuroimaging technologies, computational modeling frameworks, and large-scale brain data initiatives are collectively enabling a systems-level understanding of brain organization and dysfunction that was inconceivable a generation ago. This session brings together neuroimaging scientists, computational neuroscientists, biomedical engineers, and clinician researchers to examine the methodological frontiers and translational applications of brain measurement and modeling in 2027.

This session features a keynote lecture, four oral presentations, and a poster presentation segment spanning structural and functional neuroimaging, advanced diffusion methods, computational circuit modeling, brain-computer interfaces, and the infrastructure of open and collaborative neuroscience.

Why This Session Matters Now

Several converging developments are making 2027 a particularly exciting moment for neuroimaging and computational neuroscience. Ultra-high field MRI is enabling submillimeter resolution imaging of cortical architecture and subcortical structures that were previously invisible to in vivo investigation. The maturation of large-scale neuroimaging consortia and open data sharing platforms is providing the statistical power and diversity needed to move beyond underpowered single-site findings toward reproducible, generalizable brain-behavior relationships. Machine learning and deep learning applied to neuroimaging data are enabling individualized prediction of diagnosis, prognosis, and treatment response with accuracy that begins to approach clinical utility. Brain-computer interface technology is advancing at pace, with implications ranging from motor rehabilitation in paralysis to direct neural communication in locked-in syndrome. The computational neuroscience community is developing increasingly sophisticated models of neural population dynamics and large-scale brain circuit behavior that bridge the gap between cellular and systems-level understanding.

Key Scientific & Technical Themes

Structural MRI, Diffusion Tensor Imaging & White Matter Architecture

Structural neuroimaging provides the anatomical foundation for understanding how brain organization relates to behavior, cognition, and disease vulnerability. Volumetric MRI analysis, cortical thickness mapping, and surface-based morphometry have generated a rich literature characterizing the structural brain correlates of neurological and psychiatric conditions, normal aging, and neurodevelopmental variation. Diffusion tensor imaging and its more advanced successors — including diffusion spectrum imaging, fixel-based analysis, and tractography — provide the primary in vivo window into white matter microstructure and long-range axonal connectivity, with applications in characterizing demyelination, axonal injury, and the structural connectivity disruptions associated with neurodegenerative and psychiatric disease. The development of brain atlases and normative structural datasets is enabling individualized assessment of deviation from population norms across the lifespan. This theme examines the methodological advances, analytical standards, and clinical applications of structural MRI and white matter imaging.

Functional MRI, Resting-State Connectivity & Brain Network Organization

Functional MRI has become the dominant tool for mapping the relationship between brain activity and cognitive, affective, and behavioral states, generating an extensive literature on the neural correlates of perception, memory, language, emotion regulation, and executive function. Resting-state fMRI, which characterizes the intrinsic functional architecture of the brain through the analysis of spontaneous BOLD signal fluctuations, has revealed a set of large-scale functional networks — including the default mode, salience, frontoparietal, and sensorimotor networks — whose organization and dynamics are reliably altered in neurological and psychiatric disease. Graph theory and network neuroscience frameworks are quantifying the topological properties of brain connectivity at the individual and group level, enabling characterization of how disease and treatment alter the efficiency, modularity, and resilience of brain network organization. This theme covers fMRI methodology, functional connectivity analysis, and the application of network neuroscience to understanding brain organization in health and disease.

EEG, MEG, Brain Oscillations & Neural Population Dynamics

Electroencephalography and magnetoencephalography provide the temporal resolution — in the millisecond range — that complements the spatial precision of fMRI, enabling characterization of the rapid neural dynamics that underlie cognition, perception, and behavior. Brain oscillations across frequency bands — from slow delta rhythms associated with deep sleep to high-frequency gamma activity associated with local cortical processing — encode information about neural population states that is directly relevant to understanding consciousness, memory consolidation, and the pathophysiology of epilepsy, sleep disorders, and psychiatric conditions. Neural population dynamics, examined through the lens of dimensionality reduction, manifold geometry, and latent variable models, are revealing the computational principles by which cortical circuits represent, transform, and communicate information. Computational modeling of neural circuit dynamics — from conductance-based single-neuron models to large-scale mean-field models of cortical columns — is providing mechanistic frameworks for understanding how synaptic and circuit-level changes generate the neural signatures observed in brain recordings. This theme examines temporal brain dynamics, oscillation science, and the computational modeling of neural population behavior.

Brain-Computer Interfaces, Neural Decoding & Optogenetics

Brain-computer interfaces represent the most direct translation of neuroscience into human application — systems that record neural activity, decode intended movements or communications, and deliver feedback or therapeutic stimulation in real time. The pace of advance in implantable and non-invasive BCI technology is accelerating, with high-density electrode arrays, neural decoding algorithms, and wireless transmission systems enabling communication and motor restoration in individuals with severe paralysis at a level of performance that was unimaginable a decade ago. Non-invasive approaches leveraging EEG and functional near-infrared spectroscopy are extending BCI applications to populations for whom surgical implantation is not appropriate, including applications in cognitive rehabilitation, attention training, and neurofeedback-based therapeutic interventions. Optogenetics — the use of light-activated ion channels to achieve millisecond-precision control of genetically defined neuronal populations — has transformed the experimental toolbox of circuit neuroscience, enabling causal interrogation of circuit function that observational methods cannot provide. This theme covers the technology, algorithms, clinical applications, and experimental science of neural interfaces and optogenetic circuit manipulation.

Open Neuroscience, Brain Atlases, Neurogenetics & Large-Scale Data Infrastructure

The reproducibility crisis in neuroimaging, arising from underpowered studies, variable analytical pipelines, and publication bias, has catalyzed a movement toward open data sharing, pre-registration, and collaborative multi-site research that is fundamentally changing the infrastructure of the field. Large-scale neuroimaging initiatives providing openly accessible datasets of brain imaging, genetic, cognitive, and behavioral data are enabling analyses at the scale required to detect small effects reliably and characterize the full population diversity of brain-behavior relationships. Brain atlas projects — mapping the cellular, molecular, and connectivity architecture of the brain at progressively finer resolution — are creating reference frameworks for integrating data across spatial scales from molecules to networks. Neurogenetics, examining how genetic variation shapes brain structure, function, and connectivity through imaging genetics and polygenic score analyses, is revealing the biological pathways through which genetic risk for neurological and psychiatric conditions manifests in brain phenotypes. This theme addresses open science infrastructure, neurogenetics, brain atlasing, and the methodological standards required for reproducible and generalizable neuroimaging research.

Research Landscape & Data Trends

Neuroimaging and computational neuroscience represent a field in methodological transition — moving from small-sample, single-site studies toward large-scale collaborative initiatives, and from descriptive brain-behavior correlations toward mechanistic and predictive models with clinical applicability. The literature has expanded enormously across all major imaging modalities, with particular growth in resting-state connectivity, diffusion imaging, and the application of machine learning to neuroimaging data. Reproducibility and generalizability have become central preoccupations of the field, driving investment in open data infrastructure, standardized preprocessing pipelines, and multi-site replication studies. Brain-computer interface research is one of the most rapidly expanding areas, with both implantable and non-invasive systems demonstrating performance advances that are creating genuine translational momentum. By 2027, the integration of neuroimaging with single-cell transcriptomics, spatial omics, and large-scale genetic data is expected to enable a multi-scale understanding of brain organization that bridges molecular and systems levels.

Who Should Attend

  • Neuroimaging scientists and MRI physicists developing and applying advanced brain imaging methodologies
  • Cognitive neuroscientists using fMRI, EEG, and MEG to map brain-behavior relationships
  • Computational neuroscientists developing models of neural circuit dynamics and large-scale brain function
  • Clinical neurologists and neuroradiologists applying advanced imaging in diagnostic and research contexts
  • Brain-computer interface researchers and engineers working on neural recording, decoding, and stimulation systems
  • Neuropsychologists and clinical researchers using imaging to characterize cognitive and behavioral dimensions of neurological conditions
  • Psychiatric neuroimaging researchers characterizing brain signatures of mental health conditions
  • Biomedical engineers developing next-generation neural recording and stimulation hardware
  • Neurogenetics researchers examining how genetic variation shapes brain structure and function
  • Open science advocates and data infrastructure developers building the collaborative platforms that define modern neuroscience

Session Perspective

Neuroimaging and computational neuroscience sit at the intersection of physics, engineering, computer science, and biology — a genuinely interdisciplinary enterprise whose progress depends on the quality of collaboration across these communities. The session reflects the conviction that methodological rigor, open science practices, and the integration of multiple levels of analysis are the foundations upon which clinically meaningful neuroscience must be built. Researchers who are advancing the technology, methodology, and translational application of brain measurement and modeling are invited to bring their work to a forum committed to the highest standards of scientific quality and reproducibility.

If your research aligns with this session, we invite you to submit an abstract for consideration.

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