Brain Imaging seminars

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.

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.

Neuroimaging has greatly extended our capacity to study the workings of the human brain. Despite the wealth of knowledge this tool has generated however, there are still critical gaps in our understanding. While tremendous progress has been made in mapping areas of the brain that are specialized for particular stimuli, or cognitive processes, we still know very little about how large-scale interactions between different cortical networks facilitate the integration of information and the execution of complex tasks. Yet even the simplest behavioral tasks are complex, requiring integration over multiple cognitive domains. Our knowledge falls short not only in understanding how this integration takes place, but also in what drives the profound variation in behavior that can be observed on almost every task, even within the typically developing (TD) population. The search for the neural underpinnings of individual differences is important not only philosophically, but also in the service of precision medicine. We approach these questions using a three-pronged approach. First, we create a battery of behavioral tasks from which we can calculate objective measures for different aspects of the behaviors of interest, with sufficient variance across the TD population. Second, using these individual differences in behavior, we identify the neural variance which explains the behavioral variance at the network level. Finally, using covert neurofeedback, we perturb the networks hypothesized to correspond to each of these components, thus directly testing their casual contribution. I will discuss our overall approach, as well as a few of the new directions we are currently pursuing.

This webinar will showcase new approaches for electrophysiological recordings using our silicon neural probes and surface arrays combined with diverse optical methods such as wide-field or 2-photon imaging, fiber photometry, and optogenetic perturbations in awake, behaving mice. Multi-modal recording of single units and local field potentials across cortex, hippocampus and thalamus alongside calcium activity via GCaMP6F in cortical neurons in triple-transgenic animals or in hippocampal astrocytes via viral transduction are brought to bear to reveal hitherto inaccessible and under-appreciated aspects of coordinated dynamics in the brain.

Executive functions are cognitive processes that allow us to plan, monitor and execute our goals. Using fMRI, we investigated how early deafness influences crossmodal plasticity and the organisation of executive functions in the adult human brain. Results from a range of visual executive function tasks (working memory, task switching, planning, inhibition) show that deaf individuals specifically recruit superior temporal “auditory” regions during task switching. Neural activity in auditory regions predicts behavioural performance during task switching in deaf individuals, highlighting the functional relevance of the observed cortical reorganisation. Furthermore, language grammatical skills were correlated with the level of activation and functional connectivity of fronto-parietal networks. Together, these findings show the interplay between sensory and language experience in the organisation of executive processing in the brain.

We are very much looking forward to host Francisca Ferreira and Birte Forstmann on December 14th, 2023, at noon ET / 6PM CET. Francisca Ferreira is a PhD student and Neurosurgery trainee at the University College of London Queen Square Institute of Neurology and a Royal College of Surgeons “Emerging Leaders” program laureate. Her presentation title will be: “Microstructural and connectivity correlates of outcome variability in functional neurosurgery for movement disorders”. Birte Forstmann, PhD, is the Director of the Amsterdam Brain and Cognition Center, a Professor of Cognitive Neuroscience at the University of Amsterdam, and a Professor by Special Appointment of Neuroscientific Testing of Psychological Models at the University of Leiden. Besides her scientific presentation (“Imaging the human subcortex”), she will give us a glimpse at the “Person behind the science”. You can register via talks.stimulatingbrains.org to receive the (free) Zoom link!

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 focal volume. Combined with multiple neuroimaging techniques (resting state functional Magnetic Resonance Imaging [rsfMRI], Spectroscopy [MRS] and task-related fMRI changes), this talk will focus on recent human TUS studies.

Subsecond timing underlies nearly all sensory and motor activities across species and is critical to survival. While subsecond temporal information has been found across cortical and subcortical regions, it is unclear if it is generated locally and intrinsically or if it is a read out of a centralized clock-like mechanism. Indeed, mechanisms of subsecond timing at the circuit level are largely obscure. Primary sensory areas are well-suited to address these question as they have early access to sensory information and provide minimal processing to it: if temporal information is found in these regions, it is likely to be generated intrinsically and locally. We test this hypothesis by training mice to perform an audio-visual temporal pattern sensory discrimination task as we use 2-photon calcium imaging, a technique capable of recording population level activity at single cell resolution, to record activity in primary visual cortex (V1). We have found significant changes in network dynamics through mice’s learning of the task from naive to middle to expert levels. Changes in network dynamics and behavioral performance are well accounted for by an intrinsic model of timing in which the trajectory of q network through high dimensional state space represents temporal sensory information. Conversely, while we found evidence of other temporal encoding models, such as oscillatory activity, we did not find that they accounted for increased performance but were in fact correlated with the intrinsic model itself. These results provide insight into how subsecond temporal information is encoded mechanistically at the circuit level.

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.

On Thursday, October 26th, we will host Anastasia Yendiki and Suzanne Haber. Anastasia Yendiki, PhD, is an Associate Professor in Radiology at the Harvard Medical School and an Associate Investigator at the Massachusetts General Hospital and Athinoula A. Martinos Center. Suzanne Haber, PhD, is a Professor at the University of Rochester and runs a lab at McLean hospital at Harvard Medical School in Boston. She has received numerous awards for her work on neuroanatomy. Beside her scientific presentation, she will give us a glimpse at the “Person behind the science”. The talks will be followed by a shared discussion. You can register via talks.stimulatingbrains.org to receive the (free) Zoom link!

The large-scale activity of the human brain exhibits rich and complex patterns, but the spatiotemporal dynamics of these patterns and their functional roles in cognition remain unclear. Here by characterizing moment-by-moment fluctuations of human cortical functional magnetic resonance imaging signals, we show that spiral-like, rotational wave patterns (brain spirals) are widespread during both resting and cognitive task states. These brain spirals propagate across the cortex while rotating around their phase singularity centres, giving rise to spatiotemporal activity dynamics with non-stationary features. The properties of these brain spirals, such as their rotational directions and locations, are task relevant and can be used to classify different cognitive tasks. We also demonstrate that multiple, interacting brain spirals are involved in coordinating the correlated activations and de-activations of distributed functional regions; this mechanism enables flexible reconfiguration of task-driven activity flow between bottom-up and top-down directions during cognitive processing. Our findings suggest that brain spirals organize complex spatiotemporal dynamics of the human brain and have functional correlates to cognitive processing.

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 consciousness. In this talk, I will introduce our proposed method to estimate repetitive spatiotemporal patterns from resting-state brain activity data and show the spatiotemporal patterns estimated from human resting-state magnetoencephalography (MEG) and electroencephalography (EEG) data. Our analyses suggest that the patterns involved whole-brain propagating activities that reflected a process to integrate the information distributed over frequencies and networks. I will also introduce our current attempt to reveal signal flows and their roles in the spatiotemporal patterns using a big dataset. - Takeda et al., Estimating repetitive spatiotemporal patterns from resting-state brain activity data. NeuroImage (2016); 133:251-65. - Takeda et al., Whole-brain propagating patterns in human resting-state brain activities. NeuroImage (2021); 245:118711.

Identifying a structural brain lesion on MRI is the most important factor that correlates with seizure freedom after surgery in patients suffering from drug-resistant focal epilepsy. By providing better image contrast and higher spatial resolution, structural MRI at 7 Tesla (7T) can lead to lesion detection in about 25% of patients presenting with negative MRI at lower fields. In addition to a better detection/delineation/phenotyping of epileptogenic lesions, higher signal at ultra-high field also facilitates more detailed analyses of several functional and molecular alterations of tissues, susceptible to detect epileptogenic properties even in absence of visible lesions. These advantages but also the technical challenges of 7T MRI in practice will be presented and discussed.

Any sensory experience of the world, from the touch of a caress to the smile on our friend’s face, is embedded in time and it is often associated with the perception of the flow of it. The perception of time is therefore a peculiar sensory experience built without dedicated sensors. How the perception of time and the content of a sensory experience interact to give rise to this unique percept is unclear. A few empirical evidences show the existence of this interaction, for example the speed of a moving object or the number of items displayed on a computer screen can bias the perceived duration of those objects. However, to what extent the coding of time is embedded within the coding of the stimulus itself, is sustained by the activity of the same or distinct neural populations and subserved by similar or distinct neural mechanisms is far from clear. Addressing these puzzles represents a way to gain insight on the mechanism(s) through which the brain represents the passage of time. In my talk I will present behavioral and neuroimaging studies to show how concurrent changes of visual stimulus duration, speed, visual contrast and numerosity, shape and modulate brain’s and pupil’s responses and, in case of numerosity and time, influence the topographic organization of these features along the cortical visual hierarchy.

During development, the complex neuronal circuitry of the brain arises from limited information contained in the genome. After the genetic code instructs the birth of neurons, the emergence of brain regions, and the formation of axon tracts, it is believed that neuronal activity plays a critical role in shaping circuits for behavior. Current AI technologies are modeled after the same principle: connections in an initial weight matrix are pruned and strengthened by activity-dependent signals until the network can sufficiently generalize a set of inputs into outputs. Here, we challenge these learning-dominated assumptions by quantifying the contribution of neuronal activity to the development of visually guided swimming behavior in larval zebrafish. Intriguingly, dark-rearing zebrafish revealed that visual experience has no effect on the emergence of the optomotor response (OMR). We then raised animals under conditions where neuronal activity was pharmacologically silenced from organogenesis onward using the sodium-channel blocker tricaine. Strikingly, after washout of the anesthetic, animals performed swim bouts and responded to visual stimuli with 75% accuracy in the OMR paradigm. After shorter periods of silenced activity OMR performance stayed above 90% accuracy, calling into question the importance and impact of classical critical periods for visual development. Detailed quantification of the emergence of functional circuit properties by brain-wide imaging experiments confirmed that neuronal circuits came ‘online’ fully tuned and without the requirement for activity-dependent plasticity. Thus, contrary to what you learned on your mother's knee, complex sensory guided behaviors can be wired up innately by activity-independent developmental mechanisms.

It is increasingly recognized that epilepsy affects human brain organization across multiple scales, ranging from cellular alterations in specific regions towards macroscale network imbalances. My talk will overview an emerging paradigm that integrates cellular, neuroimaging, and network modelling approaches to faithful characterize the extent of structural and functional alterations in the common epilepsies. I will also discuss how multiscale framework can help to derive clinically useful biomarkers of dysfunction, and how these methods may guide surgical planning and prognostics.

Neurons in the rodent hippocampus appear to encode the position of the animal in physical space during movement. Individual ``place cells'' fire in restricted sub-regions of an environment, a feature often taken as evidence that the hippocampus encodes a map of space that subserves navigation. But these same neurons exhibit complex responses to many other variables that defy explanation by position alone, and the hippocampus is known to be more broadly critical for memory formation. Here we elaborate and test a theory of hippocampal coding which produces place cells as a general consequence of efficient memory coding. We constructed neural networks that actively exploit the correlations between memories in order to learn compressed representations of experience. Place cells readily emerged in the trained model, due to the correlations in sensory input between experiences at nearby locations. Notably, these properties were highly sensitive to the compressibility of the sensory environment, with place field size and population coding level in dynamic opposition to optimally encode the correlations between experiences. The effects of learning were also strongly biphasic: nearby locations are represented more similarly following training, while locations with intermediate similarity become increasingly decorrelated, both distance-dependent effects that scaled with the compressibility of the input features. Using virtual reality and 2-photon functional calcium imaging in head-fixed mice, we recorded the simultaneous activity of thousands of hippocampal neurons during virtual exploration to test these predictions. Varying the compressibility of sensory information in the environment produced systematic changes in place cell properties that reflected the changing input statistics, consistent with the theory. We similarly identified representational plasticity during learning, which produced a distance-dependent exchange between compression and pattern separation. These results motivate a more domain-general interpretation of hippocampal computation, one that is naturally compatible with earlier theories on the circuit's importance for episodic memory formation. Work done in collaboration with James Priestley, Lorenzo Posani, Marcus Benna, Attila Losonczy.

This set of short webinars will provide neuroscience researchers working in a neuroimaging setting with practical tips on strengthening credibility at different stages of the research project. Each webinar will be hosted by Cassandra Gould Van Praag from the Wellcome Centre for Integrative Neuroimaging.

This set of short webinars will provide neuroscience researchers working in a neuroimaging setting with practical tips on strengthening credibility at different stages of the research project. Each webinar will be hosted by Cassandra Gould Van Praag from the Wellcome Centre for Integrative Neuroimaging.

This set of short webinars will provide neuroscience researchers working in a neuroimaging setting with practical tips on strengthening credibility at different stages of the research project. Each webinar will be hosted by Cassandra Gould Van Praag from the Wellcome Centre for Integrative Neuroimaging.