Topic: fMRI

The BrAIN group recruits a postdoctoral researcher for the ERC-funded BrainAlign project, investigating whether human brain activity can improve language models’ long-timescale understanding. The researcher leads the computational neuroscience work, integrates heterogeneous fMRI datasets and develops models that learn from naturalistic language comprehension. The project also uses these models to investigate language processing in the brain. Candidates need a relevant PhD, strong computational or machine-learning skills and programming experience; fMRI expertise is particularly useful. Apply t

Our ability to recognize an object amongst many others is one of the most important features of the human mind. However, object recognition requires tremendous computational effort, as we need to solve a complex and recursive environment with ease and proficiency. This challenging feat is dependent on the implementation of an effective organization of knowledge in the brain. Here I put forth a novel understanding of how object knowledge is organized in the brain, by proposing that the organization of object knowledge follows key object-related dimensions, analogously to how sensory information

Behavior and cognition depend on the integrated action of neural structures and populations distributed throughout the brain. We recently developed a set of molecular imaging tools that enable multiregional processing and plasticity in neural networks to be studied at a brain-wide scale in rodents and nonhuman primates. Here we will describe how a novel genetically encoded activity reporter enables information flow in virally labeled neural circuitry to be monitored by fMRI. Using the reporter to perform functional imaging of synaptically defined neural populations in the rat somatosensory sys

This webinar convened researchers at the intersection of Artificial Intelligence and Neuroscience to investigate how large language models (LLMs) can serve as valuable “model organisms” for understanding human language processing. Presenters showcased evidence that brain recordings (fMRI, MEG, ECoG) acquired while participants read or listened to unconstrained speech can be predicted by representations extracted from state-of-the-art text- and speech-based LLMs. In particular, text-based LLMs tend to align better with higher-level language regions, capturing more semantic aspects, while speech

Dr. Nicholas Blauch will present on his work "Topoformer: Brain-like topographic organization in transformer language models through spatial querying and reweighting". Dr. Blauch is a postdoctoral fellow in the Harvard Vision Lab advised by Talia Konkle and George Alvarez. Paper link: https://openreview.net/pdf?id=3pLMzgoZSA Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri | https://groups.google.com/g/medarc-fmri).

Comprehending speech under acoustically challenging conditions is an everyday task that we can often execute with ease. However, accomplishing this requires the engagement of cognitive resources, such as auditory attention and working memory. The mechanisms that contribute to the robustness of speech comprehension are of substantial interest in the context of hearing mild to moderate hearing impairment, in which affected individuals typically report specific difficulties in understanding speech in background noise. Although hearing aids can help to mitigate this, they do not represent a univer

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 func

Lead author Mehdi Azabou will present on his work "POYO-1: A Unified, Scalable Framework for Neural Population Decoding" (https://poyo-brain.github.io/). Mehdi is an ML PhD student at Georgia Tech advised by Dr. Eva Dyer. Paper link: https://arxiv.org/abs/2310.16046 Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri | https://groups.google.com/g/medarc-fmri).

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). 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

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). This will be an informal journal club presentation, we do not have an author of the paper joining us. Title: Brain decoding: toward real-time reconstruction of visual perception Abstract: In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fidelity. This neuroimaging technique, however, suffers from

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: SwiFT: Swin 4D fMRI Transformer Abstract: Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dyna

Connor Lane will lead a journal club on the recent BrainLM preprint, a foundation model for fMRI trained using self-supervised masked autoencoder training. Preprint: https://www.biorxiv.org/content/10.1101/2023.09.12.557460v1 Tweeprint: https://twitter.com/david_van_dijk/status/1702336882301112631?t=Q2-U92-BpJUBh9C35iUbUA&s=19

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

Public journal club where this week Mihir will present on the 1.8 billion regressions paper (https://www.biorxiv.org/content/10.1101/2022.03.28.485868v2), where the authors use hundreds of pretrained model embeddings to best predict fMRI activity.

Dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) share similarities in pathology and clinical presentation and come under the umbrella term of Lewy body dementias (LBD). Fluctuating cognition is a key symptom in LBD and manifests as altered levels of alertness and attention, with a marked difference between best and worst performance. Cognition and alertness can change over seconds or minutes to hours and days of obtundation. Cognitive fluctuations can have significant impacts on the quality of life of people with LBD as well as potentially contribute to the exacerbation

Representational Connectivity Analysis (RCA) has gained mounting interest in the past few years. This is because, rather than conventional tracking of signal, RCA allows for the tracking of information across the brain. It can also provide insights into the content and potential transformations of the transferred information. This presentation explains several variations of the method in terms of implementation and how it can be adopted for different modalities (E/MEG and fMRI). I will also present caveats and nuances of the method which should be considered when using the RCA.

The ability to organise one's body for action without having to think about it is taken for granted, whether it is handwriting, typing on a smartphone or computer keyboard, tying a shoelace or playing the piano. When compromised, e.g. in stroke, neurodegenerative and developmental disorders, the individuals’ study, work and day-to-day living are impacted with high societal costs. Until recently, indirect methods such as invasive recordings in animal models, computer simulations, and behavioural markers during sequence execution have been used to study covert motor sequence planning in humans.

Faces and voices convey much of the non-verbal information that we use when communicating with other people. We look at faces and listen to voices to recognize others, understand how they are feeling, and decide how to act. Recent research in my lab aims to investigate whether there are similar coding mechanisms to represent faces and voices, and whether there are brain regions that integrate information across the visual and auditory modalities. In the first part of my talk, I will focus on an fMRI study in which we found that a region of the posterior STS exhibits modality-general representa

The development of circuit-based therapeutics to treat neurological and neuropsychiatric diseases require detailed localization and understanding of electrophysiological signals in the human brain. Electrodes can record and stimulate circuits in many ways, and we often rely on non-invasive imaging methods to predict the location to implant electrodes. However, electrophysiological and imaging signals measure the underlying tissue in a fundamentally different manner. To integrate multimodal data and benefit from these complementary measurements, I will describe an approach that considers how di

Our perception of time isn’t like a clock; it varies depending on other aspects of experience, such as what we see and hear in that moment. However, in everyday life, the properties of these simple features can change frequently, presenting a challenge to understanding real-world time perception based on simple lab experiments. We developed a computational model of human time perception based on tracking changes in neural activity across brain regions involved in sensory processing, using fMRI. By measuring changes in brain activity patterns across these regions, our approach accommodates the

How does our brain generate consciousness, that is, the subjective experience of what it is like to see face or hear a sound? Do we become aware of a stimulus during early sensory processing or only later when information is shared in a wide-spread fronto-parietal network? Neural correlates of consciousness are typically identified by comparing brain activity when a constant stimulus (e.g., a face) is perceived versus not perceived. However, in most previous experiments, conscious perception was systematically confounded with post-perceptual processes such as decision-making and report. In thi

Quantifying the timing (duration and frequency) of brief visual events is vital to human perception, multisensory integration and action planning. For example, this allows us to follow and interact with the precise timing of speech and sports. Here we investigate how visual event timing is represented and transformed across the brain’s hierarchy: from sensory processing areas, through multisensory integration areas, to frontal action planning areas. We hypothesized that the dynamics of neural responses to sensory events in sensory processing areas allows derivation of event timing representati

Classification of behavior from multi-regional neural data sequentially in time can help in the early detection of behavior, which may provide important ways to provide corrective neural stimulation before the onset of behavior. Recurrent Neural Networks (RNNs) are designed for time-series data: they take in sequential inputs and predict the class of the sequence using recurrent hidden states that are able to retain a memory of previous inputs. However, standard RNNs cannot guarantee the classification at earlier points of the sequence; they are traditionally designed to achieve correct classi

Elucidating the neural basis of cognition requires incorporating cognitive models into the modelling of neural data. A prevalent strategy in neuroimaging is to fit computational models of cognition to concurrent behavior, and then regress the latent variables of those models onto fMRI data. Though widely used, this approach is restricted to mapping computational variables that are in some way expressed in behavior. Here we introduce a computational parametric mapping (CPM), a framework which builds on and generalizes the Bayesian population receptive field framework. CPM offers three main adva