Brain Imaging

Annual meeting of the Organization for Human Brain Mapping at the Metro Toronto Convention Centre, bringing together researchers to share groundbreaking research, engage in educational forums, and foster collaboration across neuroimaging methods and applications.

1. Can we reconstruct images that a person saw, directly from their fMRI brain recordings? 2. Can we reconstruct the training data that a deep-network trained on, directly from the parameters of the network? The answer to both of these intriguing questions is “Yes!” In this talk I will present some of our work in both domains. I will then show how combining the power of Brains and Machines can lead to significant breakthroughs in both areas, and potentially bridge the gap between Minds and Machines. Finally, I will show how combining the power of Multiple Brains (with NO shared data) may lead to new breakthrough discoveries in Brain-Science, and allow mapping of information between different brains. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-02-18. Recording duration: 00:50:24.

How does the organization of neural information processing enable humans’ sophisticated cognition? Here we decompose functional interactions between brain regions into synergistic and redundant components, revealing their distinct information-processing roles. Combining functional, structural, and diffusion-weighted MRI with meta-analytic results from NeuroSynth, we demonstrate that redundant interactions are predominantly associated with structurally coupled, modular sensorimotor processing. Synergistic interactions instead support integrative processes and complex cognition across higher-order brain networks. The human brain leverages synergistic information to a greater extent than nonhuman primates, with high-synergy association cortices exhibiting the highest degree of evolutionary cortical expansion. Synaptic density mapping from in vivo positron emission tomography and convergent transcriptomic, molecular, and metabolic evidence demonstrate that synergistic interactions are supported by receptor diversity and human-accelerated genes underpinning synaptic function. This information-resolved approach provides analytic tools to illuminate how the human neurocognitive architecture navigates the trade-off between robustness and integration.

Visual restoration after decades of blindness is now becoming possible by means of retinal and cortical prostheses, as well as emerging stem cell and gene therapeutic approaches. After restoring visual perception, however, a key question remains. Are there optimal means and methods for retraining the visual cortex to process visual inputs, and for learning or relearning to “see”? Up to this point, it has been largely assumed that if the sensory loss is visual, then the rehabilitation focus should also be primarily visual. However, the other senses play a key role in visual rehabilitation due to the plastic repurposing of visual cortex during blindness by audition and somatosensation, and also to the reintegration of restored vision with the other senses. I will present multisensory neuroimaging results, cortical thickness changes, as well as behavioral outcomes for patients with Retinitis Pigmentosa (RP), which causes blindness by destroying photoreceptors in the retina. These patients have had their vision partially restored by the implantation of a retinal prosthesis, which electrically stimulates still viable retinal ganglion cells in the eye. Our multisensory and structural neuroimaging and behavioral results suggest a new, holistic concept of visual rehabilitation that leverages rather than neglects audition, somatosensation, and other sensory modalities.

The Brain Image Analysis Unit at the RIKEN Center for Brain Science seeks a postdoctoral researcher to develop deep-learning methods for large-scale microscopy and MRI of the marmoset brain within Brain/MINDS 2.0. The programme combines two-photon and bright-field microscopy, spatial transcriptomics, anterograde tracing and diffusion MRI into a whole-brain multimodal atlas. Research topics include neuron and axon tracing in dense 3D data, generative models for synthetic tracing ground truth, images exceeding GPU memory, cross-modal image registration, biomedical foundation models, and diffusion-MRI tractography validated against same-animal tracer data. Biomedical image-analysis experience is beneficial but is not mandatory. The role is based at Wako.

The Allen Institute has launched a new moonshot initiative on human brain health and disease called the Brain Health accelerator. This initiative aims to dramatically accelerate our understanding of human brain structure and function, identify the molecular, cellular and circuit basis of disease progression, and pioneer new therapeutic strategies targeting vulnerable and affected cell types. Our mission focuses on taking a human-centric approach to understanding and treating disease, combining a large-scale open science discovery approach across multiple diseases, AI-based disease modeling, and translational programs in specific diseases to move from discovery to clinical application. The major focus will be on neurodegenerative diseases including Alzheimer’s, Parkinson’s, Lewy Body Dementia, Huntington’s and ALS. We aim to make transformational change in understanding and treating brain disorders, the biggest health challenge of our time. We are seeking an outstanding individual to lead a program in proteomic and connectomic analysis of human brain tissues in health and disease, including the study of cell-type selective changes in synaptic connectivity associated with neurodegenerative disease. This will involve technological development such as optimization of methods for human tissue preparation for cleared and/or expanded tissues and light sheet microscopy, and biological studies of disease phenotypes. This role will work closely with teams working on proteomic analysis of human and mouse tissues, single cell and spatial genomics profiling, data analysis and modeling, and cell- and circuit-targeted therapeutic development. The successful candidate will be an established, recognized researcher in cellular and circuit biology with a strong track record of research and publication. Part of this research will focus on Alzheimer’s disease as part of the Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) consortium. The ideal candidate is a self-motivated team player who is willing to push the boundaries of human neuroscience and disease research through scientific leadership, individual and team contributions and continuing scholarship on the ever-changing landscape. The successful candidate will also have excellent communication skills that can represent our efforts to our stakeholders internally and in external forums. The Allen Institute offers exceptional working conditions, and nurtures team science in an intellectually rich and stimulating setting. We provide a highly collaborative research environment offering the opportunity to interface closely with our molecular and cell biologists, bioinformaticians, and partner Institutions. This is an Associate Investigator position comparable to an Associate Professor in a University/Medical School setting.

Over the past decade we have demonstrated that the fusion of subject-specific structural information of the human brain with mathematical dynamic models allows building biologically realistic brain network models, which have a predictive value, beyond the explanatory power of each approach independently. The network nodes hold neural population models, which are derived using mean field techniques from statistical physics expressing ensemble activity via collective variables. Our hybrid approach fuses data-driven with forward-modeling-based techniques and has been successfully applied to explain healthy brain function and clinical translation including aging, stroke and epilepsy. Here we illustrate the workflow along the example of epilepsy: we reconstruct personalized connectivity matrices of human epileptic patients using Diffusion Tensor weighted Imaging (DTI). Subsets of brain regions generating seizures in patients with refractory partial epilepsy are referred to as the epileptogenic zone (EZ). During a seizure, paroxysmal activity is not restricted to the EZ, but may recruit other healthy brain regions and propagate activity through large brain networks. The identification of the EZ is crucial for the success of neurosurgery and presents one of the historically difficult questions in clinical neuroscience. The application of latest techniques in Bayesian inference and model inversion, in particular Hamiltonian Monte Carlo, allows the estimation of the EZ, including estimates of confidence and diagnostics of performance of the inference. The example of epilepsy nicely underwrites the predictive value of personalized large-scale brain network models. The workflow of end-to-end modeling is an integral part of the European neuroinformatics platform EBRAINS and enables neuroscientists worldwide to build and estimate personalized virtual brains.