Brain Imaging

The MIT Aging Brain Initiative brings together work in molecular imaging, cognition, chemistry, bioengineering, neurodegeneration, and artificial intelligence to address brain aging and Alzheimer's prevention.

Takaki Komiyama will discuss how cortical circuits maintain information about the value of behavioral options across trials and transform past experience into future choices. The talk draws on longitudinal imaging and circuit manipulation in mice to examine distinct neuronal populations and local and long-range interactions in history-dependent decision-making.

Takaki Komiyama presents longitudinal imaging and circuit-manipulation studies of history-dependent decision-making. The work identifies retrosplenial-cortex populations and local and long-range interactions that maintain option values across trials, update them after outcomes and translate past experience into future choices.

Jason Lerch presents a translational neuroimaging analysis spanning mouse models and human MRI cohorts to identify biologically informed subgroups across autism and related neurodevelopmental conditions.

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.

The Neurophysics department seeks a postdoctoral researcher to develop MRI microscopy and in-cell magnetometry methods for paramagnetic metals in the brain. The project integrates high-field MRI, diamond-based quantum sensing, and histology to study neurodegeneration. Duties include MRI protocol development, quantitative imaging, data analysis, and multimodal integration. A PhD in physics, biomedical engineering, neuroscience or a related field, MRI experience, programming skills, and English are required. The planned start is 1 January 2027 for an initial three years; appointment depends on project funding. Applications close on 30 September 2026.

The Sarvestani Lab at Cornell University is recruiting a postdoctoral scientist in systems neuroscience to study how visual and motor systems across the brain and body support perception and embodied cognition. Projects use cross-species behavioral experiments, multiphoton microscopy, electrophysiology, motion capture, and wireless sensors in tree shrews and rats.

The Neurophysics department seeks a postdoctoral researcher to develop MRI microscopy and in-cell magnetometry methods for paramagnetic metals in the brain. The project integrates high-field MRI, diamond-based quantum sensing, and histology to study neurodegeneration. Duties include MRI protocol development, quantitative imaging, data analysis, and multimodal integration. A PhD in physics, biomedical engineering, neuroscience or a related field, MRI experience, programming skills, and English are required. The planned start is 1 January 2027 for an initial three years; appointment depends on project funding. Applications close on 30 September 2026.

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.

Takaki Komiyama presents longitudinal imaging and circuit-manipulation studies of history-dependent decision-making. The work identifies retrosplenial-cortex populations and local and long-range interactions that maintain option values across trials, update them after outcomes and translate past experience into future choices.