fMRI

…more mechanistic analyses of M/EEG and fMRI data. Opening lectures by Rodrigo Cofré on whole-brain models and high-order interactions, Kaichao Wu on modelling brain criticality in health and disease, Yonatan Sanz Per

…monitoring and decision-making - using fMRI, structural MRI and diffusion-weighted imaging on the 3 T and

…shared task measured with EEG, MEG and fMRI, evidence about location, duration and inter-area communication, and the practical difficulties of interpreting mixed results across competing theories.

: In neuroscience, the question of how the brain combines individual visual concepts into more complex representations remains unresolved [1,2]. While previous studies have shown that artificial intelligence (AI) systems can encode and manipulate concepts through vector arithmetic (e.g., in language models), it is unclear whether the human brain operates in a similar, compositional manner for visual perception [2]. We tackle the question of whether brain activity patterns can be algebraically manipulated to represent compositional visual information, and if so, whether can we decode these pat

To discern speech or appreciate music, the human auditory system detects how pitch increases or decreases over time. However, the algorithms used to detect changes in pitch, or pitch motion, are incompletely understood. Here, using psychophysics, computational modeling, functional neuroimaging, and analysis of recorded speech, we ask if humans can detect pitch motion using computations analogous to those used by the visual system. We adapted visual stimuli to create novel auditory correlated noise stimuli that elicit robust pitch motion percepts. Crucially, these stimuli are inharmonic and pos

A central goal of sensory neuroscience is to build parsimonious computational models that can both predict neural responses to natural stimuli and reveal interpretable functional organization in the brain. Statistical “component” models can learn interpretable, low-dimensional structure across different brain regions and subjects, but lack an explicit “encoding model” that links these components to the stimuli that drive them, and thus cannot generate predictions for new stimuli or generalize across different experiments. The predictive power of sensory encoding models has improved substantial

Human perception is significantly influenced by sensory context. A classic demonstration is the tilt illusion, in which the perceived orientation of a center stimulus is altered by the orientation of a surround (Gibson \& Radner 1937). Previous research on the tilt illusion has primarily focused on how surround context alters the response characteristics of individual neurons (e.g., changes in gain and tuning curves), but connecting these observations at the level of single neurons to perceptual behavior remains challenging. More broadly, we still lack a functional and teleological account of

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

…neural circuitry to be monitored by fMRI. Using the reporter to perform functional imaging of synaptically defined neural populations in the rat somatosensory sys

…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

The spatio-temporal structure of brain dynamics at the macroscopic level has been hypothesized to reflect neuronal communication for cognitive processes. In particular, recent studies have focused on time-irreversibility and its relationship with consciousness [1,2]. Beyond this example of high-level cognitive function, this raises the question of the role of different ingredients that shape the structure-function relationship in whole-brain dynamics, which can be formalized using the Ornstein-Uhlenbeck (OU) process that combines input “noise” and effective connectivity as key ingredients. Her

Exploratory behaviors are crucial to the adaptation of organisms to uncertain environments. Human neuroscience studies have implicated the intraparietal sulcus (IPS), anterior cingulate cortex (ACC), and medial prefrontal cortex (mPFC) in exploratory behaviors. However, the roles of these regions in signaling specific aspects of exploratory behaviors remain unclear. Based on earlier studies, we classified exploratory behaviors into two types driven by different motives: internal and external explorations. Internal exploration involves encountering options with uncertain outcomes and seeking in

…of WM disconnections in a large fMRI dataset comprising individuals affected by varying degrees of white matter hyperintensities (WMH), in order to estimate the impact of WMH on brain dynamics in a data-driven manner. We use a virtual-lesioning (disconnectome [3]) approach in

Advancing therapies for neurological disorders relies on developing efficient, personalized whole-brain models that can capture mechanistic insights and pathological features. Current challenges in creating such computational models arise from managing a considerable number of adjustable parameters. The conventional manual exploration of this parameter space is a serious limitation for personalizing models to individual subject data [1]. This work aims to address this by approaching the problem as an optimization challenge in the context of developing brain models capable of characterizing in

Neuroimaging recordings during resting state (but also in tasks) reveal that brain-wide functional connectivity networks are highly dynamic, undergoing a structured flow of stochastic-like reconfiguration, with events of transient stabilization of connectivity, intertwined with "leaps" with high and fast variability. Markers of dynamic functional connectivity are increasingly being used as biomarkers of various pathologies and cognitive performance. Here we ask whether there should be some deep reason for Functional Connectivity to be as dynamic as it is observed to be. Functional Connectivit

Prior neuroimaging studies have shown that words associated with actions, like "grasp" and "kick," activate specific regions in the cortical motor system corresponding to the body parts involved in those actions. This activation follows the somatotopic mapping of these body parts. The variation in activation could suggest differences in how different types of symbols are processed semantically, or it might be a byproduct of activities related to imagining scenes associated with the meanings of these symbols. To address this debate, we simulated the semantic learning of realistic action words i

…functional Magnetic Resonance Imaging (fMRI) [3] (Fig. 1f) using several existing methods. Theoretical w

A range of behavioural phenomena such as food consumption, smoking, alcohol use, and altruism have been shown to ‘spread’ through human social networks. In particular, the spread of obesity in this manner has been identified as a contributing factor to the obesity epidemic. While specific neural reward systems such as the orbitofrontal cortex (OFC) have been implicated in social information processing, it is not well understood how more complex social factors, such as the motivation behind a social partner’s decision, are integrated into neural valuation signals that underlie one’s own prefere

…fMRI has reported that the amplitude of neural activity varies with the intensity of movement, but the correlation between movement and neural activity itself has not been reported in functional near-infrared spectroscopy (fNIRS). To investigate this, the present study performed simultaneous fMRI and fNIRS measureme

…Therefore, we used fMRI to study the neural correlates of cognitive map formation through touch and audition in individuals with congenital and acquired blindness, and sighted individuals a

…Dockree, & Kelly 2012), in an fMRI study. It involved a mental counting task assessing vi

…To address this question, we used fMRI and

…from 1003 participants with multiple fMRI sessions from the Human Connectome Project to gro

In the cortex of carnivore and primate model species, specific sensory features are encoded into functional maps. It remains unclear how the layout and interrelations of functional maps observed from surface layer activity generalize to deeper cortical layers. Cortical functional maps can be identified using electrophysiology with high temporal but spatially sparse sampling, impeding a holistic understanding of functional organization. Optical imaging can provide spatially extended sampling of cortical activity, but optical access is limited to about 1 mm depth, i.e. cortical layer 2/3 in larg