Brain activity

Nicole Provenza discusses deep brain stimulation for people with severe obsessive-compulsive disorder whose symptoms persist despite available treatments. Recordings from implanted devices allow researchers to examine neural activity associated with symptom changes, responses to stimulation and recovery. The talk connects these measurements with the longer-term goal of developing more precise, personalised treatments, using real-world recordings to understand how neuromodulation affects brain activity and behaviour.

…of existing approaches to measure brain activity, connectivity, genomics, or other aspects across the age spectrum of neurodevelopment. The overarching goal is to extend our understanding of brain development and aging, including studies of the neurodevelopmental origins of later health and disease, by improving repeated measures across longer epochs of the lifespan to better predict outcomes at later ages

…dynamics form a homeostatic target for brain activity. They discuss proposed signatures and testable predictions, controversies in earlier studies, and possible relationships among criticality, sleep and neural manifolds, drawing on their review of the experimental literature.

…disciplines and the interpretation of brain activity.

Sleep supports learning both by preparing the brain to encode information and by stabilizing memories afterward. This seminar examines the brain activity involved in sleep-dependent consolidation and how those mechanisms change across the lifespan, from childhood into older age.

Background: Biophysical network modeling (BNM) of the brain is a promising technique for bridging macro- and microscale levels of investigation, enabling inferences about latent features of brain activity such as excitation-inhibition balance. This approach allows personalized models of the brain to be fitted to individual subjects’ imaging data through parameter optimization [1, 2]. However, the process typically requires several thousand simulations per subject, making it computationally expensive and limiting its scalability to larger subject pools and more complex models. To address this,

In this study, we explore the influence of airway modulation on the phenomenon of breath-brain coupling in humans. Utilizing magnetoencephalography and concurrent breathing measurements, we conducted a comparative analysis of Respiratory-Modulated Brain Oscillations (RMBOs) during oral and nasal breathing. Our findings suggest an association between nasal breathing and the generation of RMBOs. This observation hints at the physiological mechanisms linking respiration and brain activity, highlighting the role of nasal breathing in modulating brain functions. Our work contributes to our understa

Virtual brain modeling is a data-driven approach that combines personalized anatomical information with mathematical models of brain activity to generate spatiotemporal patterns as observed in brain imaging signals. In this study our aim is to conduct inference and prediction on the local and global dynamics using virtual brain models, affected by disorders. Inference algorithms are necessary to efficiently estimate the unknown parameters (such as regional excitability parameters and global scaling factor on connectome), ideally incorporating measures of uncertainty. In this work, we provide f

…classes on children's learning and brain activities. Four subjects completed the experiments and were included for analysis. Teachers and students’ cerebral blood flow changes in pre

…Title: Brain-optimized inference improves reconstructions of fMRI brain activity Abstract: The release of large datasets and developments in AI have led to dramatic improvements in decoding methods that reconstruct seen images from human brain activity. We evaluate the prospect of further improving recent decoding methods by optimizing for consistency between reconstructions and brain activity during inference. We sample seed reconstructions from a base decoding method, then iteratively re

Sound waves can be used to modify brain activity safely and transiently with unprecedented precision even deep in the brain - unlike traditional brain stimulation methods. In a series of studies in humans and non-human primates, I will show that Transcranial Ultrasound Stimulation (TUS) can have medium- to long-lasting effects. Multiple read-outs allow us to conclude that TUS can perturb neuronal tissues up to 2h after intervention, including changes in local and distributed brain network configurations, behavioural changes, task-related neuronal changes and chemical changes in the sonicated f

Algonauts 2023 was a challenge to create the best model that predicts fMRI brain activity given a seen image. Huze team dominated the competition and released a preprint detailing their process. This journal club meeting will involve open discussion of the paper with Q/A with Huze. Paper: https://arxiv.org/pdf/2308.01175.pdf Related paper also from Huze that we can discuss: https://arxiv.org/pdf/2307.14021.pdf

Neurofeedback provides a feedback display which is linked with on-going brain activity and thus allows self-regulation of neural activity in specific brain regions associated with certain cognitive functions and is considered a promising tool for clinical interventions. Recent reviews of neurofeedback have stressed the importance of applying the “double-blind” experimental design where critically the patient is unaware of the neurofeedback treatment condition. An important question then becomes; is double-blind even possible? Or are subjects aware of the purposes of the neurofeedback experimen

…of the dress, the relationship between brain activity and conscious experience, and public participation in mapping the diversity of human perception.

Repetitive spatiotemporal patterns in resting-state brain activities have been widely observed in various species and regions, such as rat and cat visual cortices. Since they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. Moreover, spatiotemporal patterns involving whole-brain activities may also reflect a process that integrates information distributed over the entire brain, such as motor and visual information. Therefore, revealing such patterns may elucidate how the information is integrated to generate consc

Brain stimulation is an increasingly popular tool for both research and clinical use. However, the effects of brain stimulation are not entirely known, and neither are the mechanisms by which brain stimulation changes brain activity. One way to efficiently study the relationship between the brain and neuromodulation is to use computational models. Computational models allow researchers to run realistic simulations that show how the brain is influenced by neuromodulation or predict what brain stimulation will change in brain activity and/or behaviour. This talk will introduce how Wilson Cowan,

The view of human brain function has drastically shifted over the last decade, owing to the observation that the majority of brain activity is intrinsic rather than driven by external stimuli or cognitive demands. Specifically, all brain regions continuously communicate in spatiotemporally organized patterns that constitute the functional connectome, with consequences for cognition and behavior. In this talk, I will argue that another shift is underway, driven by new insights from synergistic interrogation of the functional connectome using different acquisition methods. The human functional c

Log-linear 1/f spectral densities are widely observed in the brain - across multiple species and at many levels of abstraction. However, the intrinsic relationship between the structural attributes of the brain and 1/f biorhythms remains largely unclear. Here, we explore a novel theoretical and generative framework, the white matter ephaptic coupling model (WMEC), and test its ability to explain the relationship between white matter morphology, spike propagation dynamics, and log-linear spectral densities observed in brain activity data.

…of affective charge on differences in brain activity evoked by sound versus unsound analogies. The

Every thought is associated with a unique pattern of brain activity. Thus, in principle, it should be possible to use these activity patterns as "brain fingerprints" for different thoughts and to read out what a person is thinking based on their brain activity alone. Indeed, using machine learning considerable progress has been made in such "brainreading" in recent years. It is now possible to decode which image a person is viewing, which film sequence they are watching, which emotional state they are in or which intentions they hold in mind. This talk will provide an overview of the current s

…We postulate that human brain representations share essential properties with these deep learning latent space