Cortical response to local perturbations validates spatial, but not temporal, neural field theory predictions
Poster, OHBM 2025, Brisbane, Queensland, Australia
Talks and presentations
Distinct contribution of brain geometry and connectivity for whole-cortex communication
Poster, OHBM Noose Workshop 2024, Noosa, Queensland, Australia
The brain mechanisms that describe how neural signals propagate throughout the cortex are poorly defined. This knowledge is essential to understand altered patterns of neural communications in mental disorders and develop effective and targeted therapeutic interventions. Recent findings suggest that the decomposition of neural signals with cortical geometry alone (cortical eigenmodes) can be used to understand the emergence of macroscopic cortical dynamics. We used novel methods to estimate cortical eigenmodes, describing the distinct contribution of cortical geometry and brain wiring (the connectome) in supporting cortical dynamics. We assessed how eigenmodes support and predict whole-cortex signal propagation using an empirical dataset of single-pulse transcranial magnetic stimulation (TMS) given to three cortical regions. Results show that eigenmodes can accurately capture how a local perturbation in neural activity evolves into a widespread cortical response. Furthermore, eigenmodes provide key insights into how perturbation size, location, timing and intensity affect the spread of changes in local neural signals. We also showed that long-range brain connections are required to fully account for the observed changes in whole-cortex activity. Our results advance knowledge of large-scale brain communication by highlighting the distinct contribution of brain geometry and anatomical connectivity to carry neural signals across the cortex. These findings are important to understand macroscopic changes in cortical activity observed across mental disorders and develop targeted neuromodulatory interventions to restore cortical communication.
Distinct contribution of brain geometry and connectivity for whole-cortex communication
Poster, Biological Psychiatry Australia, Cairns, Queensland, Australia
Distinct contribution of brain geometry and connectivity for whole-cortex communication
Talk, OHBM Australia 2023, Sydney, New South Wales, Australia
The brain mechanisms that describe how neural signals propagate throughout the cortex are poorly defined. This knowledge is essential to understand altered patterns of neural communications in mental disorders and develop effective and targeted therapeutic interventions. Recent findings suggest that the decomposition of neural signals with cortical geometry alone (cortical eigenmodes) can be used to understand the emergence of macroscopic cortical dynamics. We used novel methods to estimate cortical eigenmodes, describing the distinct contribution of cortical geometry and brain wiring (the connectome) in supporting cortical dynamics. We assessed how eigenmodes support and predict whole-cortex signal propagation using an empirical dataset of single-pulse transcranial magnetic stimulation (TMS) given to three cortical regions. Results show that eigenmodes can accurately capture how a local perturbation in neural activity evolves into a widespread cortical response. Furthermore, eigenmodes provide key insights into how perturbation size, location, timing and intensity affect the spread of changes in local neural signals. We also showed that long-range brain connections are required to fully account for the observed changes in whole-cortex activity. Our results advance knowledge of large-scale brain communication by highlighting the distinct contribution of brain geometry and anatomical connectivity to carry neural signals across the cortex. These findings are important to understand macroscopic changes in cortical activity observed across mental disorders and develop targeted neuromodulatory interventions to restore cortical communication.
Hundreds of Queenslanders could die as state reopens borders, modelling predicts
Talk, 4BC Drive with Scott Emerson, Brisbane, Queensland, Australia
4BC Drive radio interview with Scott Emerson about COVID modelling for the Queensland Government. See website here.
Eigenmodes in the brain explain how local perturbations evolve into long-range effects
Talk, Australian Brain and Psychological Sciences Meeting, Brisbane, Queensland, Australia
The brain is a complex system consisting of 10^11 neurons and over 10^14 connections. Theories of criticality from statistical physics can provide insight into systems of this size, and ask whether the brain is in fact critical. I outline my work where we developed numerical simulations of ideal models of criticality with the random field Ising model, and compared the observed dynamics to single-cell resolution calcium imaging data of the zebrafish brain. We investigated how sub-sampling and finite sized systems change self-organized criticality metrics such as power laws and universal scale.
Statistical Physics Models for Zebrafish Neural Dynamics
Talk, QIMR Berghofer Clinical Brain Network & Brain Modelling Group Lab Meeting, Brisbane, Queensland, Australia
The brain is a complex system consisting of 10^11 neurons and over 10^14 connections. Theories of criticality from statistical physics can provide insight into systems of this size, and ask whether the brain is in fact critical. I outline my work where we developed numerical simulations of ideal models of criticality with the random field Ising model, and compared the observed dynamics to single-cell resolution calcium imaging data of the zebrafish brain. We investigated how sub-sampling and finite sized systems change self-organized criticality metrics such as power laws and universal scale.
Predicting particle properties in optical traps with machine learning
Talk, SPIE Nanoscience + Engineering, 2020, San Diego, California, United States
Identifying a particle in an optical trap can be a difficult task, especially for biological samples with low contrast. The relationship of radius and refractive index to the stiffness of optical traps is non-intuitive, motivating a machine learning approach. We demonstrate methods for real-time estimates of the radius and refractive index of particles trapped by optical tweezers. This is achieved by analyzing the particle’s position and force with artificial neural networks. Our network achieved binary classification of experimental particles by sampling only milliseconds of force and position values. This demonstrates that real-time particle recognition is achievable with machine learning systems.