Predictive modeling, cortical hierarchy, and their computational implications
Predictive modeling and dimensionality reduction of functional neuroimaging data have provided rich information about the representations and functional architectures of the human brain. While these approaches have been effective in many cases, we will discuss how neglecting the internal dynamics of the brain (e.g., spontaneous activity, global dynamics, effective connectivity) and its underlying computational principles may hinder our progress in understanding and modeling brain functions. By reexamining evidence from our previous and ongoing work, we will propose new hypotheses and directions for research that consider both internal dynamics and the computational principles that may govern brain processes.
Visualising time in the human brain
We all have a sense of time. Yet it is a particularly intangible sensation. So how is our “sense” of time represented in the brain? Functional neuroimaging studies have consistently identified a network of regions, including Supplementary Motor Area and basal ganglia, that are activated when participants make judgements about the duration of currently unfolding events. In parallel, left parietal cortex and cerebellum are activated when participants predict when future events are likely to occur. These structures are activated by temporal processing even when task goals are purely perceptual. So why should the perception of time be represented in regions of the brain that have more traditionally been implicated in motor function? One possibility is that we learn about time through action. In other words, action could provide the functional scaffolding for learning about time in childhood, explaining why it has come to be represented in motor circuits of the adult brain.
Neural mechanisms of altered states of consciousness under psychedelics
Interest in psychedelic compounds is growing due to their remarkable potential for understanding altered neural states and their breakthrough status to treat various psychiatric disorders. However, there are major knowledge gaps regarding how psychedelics affect the brain. The Computational Neuroscience Laboratory at the Turner Institute for Brain and Mental Health, Monash University, uses multimodal neuroimaging to test hypotheses of the brain’s functional reorganisation under psychedelics, informed by the accounts of hierarchical predictive processing, using dynamic causal modelling (DCM). DCM is a generative modelling technique which allows to infer the directed connectivity among brain regions using functional brain imaging measurements. In this webinar, Associate Professor Adeel Razi and PhD candidate Devon Stoliker will showcase a series of previous and new findings of how changes to synaptic mechanisms, under the control of serotonin receptors, across the brain hierarchy influence sensory and associative brain connectivity. Understanding these neural mechanisms of subjective and therapeutic effects of psychedelics is critical for rational development of novel treatments and for the design and success of future clinical trials. Associate Professor Adeel Razi is a NHMRC Investigator Fellow and CIFAR Azrieli Global Scholar at the Turner Institute of Brain and Mental Health, Monash University. He performs cross-disciplinary research combining engineering, physics, and machine-learning. Devon Stoliker is a PhD candidate at the Turner Institute for Brain and Mental Health, Monash University. His interest in consciousness and psychiatry has led him to investigate the neural mechanisms of classic psychedelic effects in the brain.
A macaque connectome for simulating large-scale network dynamics in The VirtualBrain
TheVirtualBrain (TVB; thevirtualbrain.org) is a software platform for simulating whole-brain network dynamics. TVB models link biophysical parameters at the cellular level with systems-level functional neuroimaging signals. Data available from animal models can provide vital constraints for the linkage across spatial and temporal scales. I will describe the construction of a macaque cortical connectome as an initial step towards a comprehensive multi-scale macaque TVB model. I will also describe our process of validating the connectome and show an example simulation of macaque resting-state dynamics using TVB. This connectome opens the opportunity for the addition of other available data from the macaque, such as electrophysiological recordings and receptor distributions, to inform multi-scale models of brain dynamics. Future work will include extensions to neurological conditions and other nonhuman primate species.
Precision and Temporal Stability of Directionality Inferences from Group Iterative Multiple Model Estimation (GIMME) Brain Network Models
The Group Iterative Multiple Model Estimation (GIMME) framework has emerged as a promising method for characterizing connections between brain regions in functional neuroimaging data. Two of the most appealing features of this framework are its ability to estimate the directionality of connections between network nodes and its ability to determine whether those connections apply to everyone in a sample (group-level) or just to one person (individual-level). However, there are outstanding questions about the validity and stability of these estimates, including: 1) how recovery of connection directionality is affected by features of data sets such as scan length and autoregressive effects, which may be strong in some imaging modalities (resting state fMRI, fNIRS) but weaker in others (task fMRI); and 2) whether inferences about directionality at the group and individual levels are stable across time. This talk will provide an overview of the GIMME framework and describe relevant results from a large-scale simulation study that assesses directionality recovery under various conditions and a separate project that investigates the temporal stability of GIMME’s inferences in the Human Connectome Project data set. Analyses from these projects demonstrate that estimates of directionality are most precise when autoregressive and cross-lagged relations in the data are relatively strong, and that inferences about the directionality of group-level connections, specifically, appear to be stable across time. Implications of these findings for the interpretation of directional connectivity estimates in different types of neuroimaging data will be discussed.