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Whether orientation-selectivity is discernable via fMRI remains unclear. Here, by analyzing a public dataset of responses to natural scenes using neurally-inspired image-computable models, the authors isolate and characterize a coarse-scale orientation map and demonstrate that orientation-selective BOLD responses reflect multiple distinct computations at a range of spatial scales.

How the pulvinar represents complex visual information, its functional topography, and its relationship to cortical processing of visually presented objects remains unclear. Here authors show that responses to natural scenes in the human pulvinar reveal organized spatial maps for both low-level visual features, such as local contrast, as well as high-level visual features, such as bodies and faces.

The authors released NSD-synthetic, a dataset of 7T fMRI responses from the same eight NSD participants for 284 out-of-distribution synthetic images, to facilitate the development of more robust models of visual processing.

Doerig, Kietzmann and colleagues show that the brain’s response to visual scenes can be modelled using language-based AI representations. By linking brain activity to caption-based embeddings from large language models, the study reveals a way to quantify complex visual understanding.

Head movements during eye tracking in fMRI frequently distort gaze signals. This paper presents motion-corrected eye tracking, a method that corrects head motion-induced errors to produce accurate gaze data in fMRI experiments.

A mode of brain organization that connects visual and bodily reference frames may translate raw sensory impressions into more abstract formats that are useful for action, social cognition and semantic processing.

It is unknown whether spatial processing in the ventral (‘what’) stream contributes to high-level visual recognition. Here the authors show that spatial processing in face-selective regions directly contributes to whole face recognition behavior.

Through controlled model-to-brain comparisons across a large-scale survey of deep neural networks, the authors show the data models are trained on matters far more for downstream brain prediction than design factors such as architecture and training task.

Recent functional magnetic resonance imaging (fMRI) studies have shown that it is possible to deduce simple features in the visual scene or to which category it belongs. A decoding method based on quantitative receptive field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas has now been developed. These models make it possible to identify, out of a large set of completely novel complex images, which specific image was seen by an observer.

Whether or not deep neural networks require hierarchical representations to predict brain activity is not known. Here, the authors show that a multi-branch deep neural network can predict neural activity independently in visual areas in the absence of hierarchical representations.

Prediction of high-level visual representations in the human brain may benefit from multimodal sources in network training and the incorporation of complex datasets. Wang and colleagues show that language pretraining and a large, diverse dataset together build better models of higher-level visual cortex compared to earlier models.

How hippocampal area CA1 and the entorhinal cortex preserve temporal memories over long timescales is not known. Here, the authors show using 7T fMRI, that temporal context memory for scene images is predicted by the re-expression of CA1 and entorhinal cortex activity patterns during subsequent encounters over a period of months.

The authors measured high-resolution fMRI activity from eight individuals who saw and memorized thousands of annotated natural images over 1 year. This massive dataset enables new paths of inquiry in cognitive neuroscience and artificial intelligence.

Precision functional mapping shows that the frontostriatal salience network occupies nearly twice as much of the cortex in people with depression, and this was unaffected by mood changes and detected in children before onset of symptoms.

There is a need for extensive neuroimaging datasets to facilitate the study of dynamic human visual perception. Here, the authors present a repository of whole-brain fMRI responses to over 1000 short naturalistic video clips across ten human subjects.

Temporal decomposition through manifold fitting (TDM) is an analysis technique that decomposes blood oxygenation level dependent (BOLD) responses in task-based fMRI into different components that likely correspond to microvasculature- and macrovasculature-driven signals.

Tuckute et al. use a machine learning approach to identify sentences that either maximally or minimally activate the human language processing network.

LiFE is an algorithm that evaluates human connectome models derived from magnetic resonance imaging (MRI) and tractography methods. The algorithm achieves this goal by assessing the contribution of all the fiber tracts in a connectome to predict the measured MRI signal.

The ATLAS Collaboration reports the observation of the electroweak production of two jets and a Z-boson pair. This process is related to vector-boson scattering and allows the nature of electroweak symmetry breaking to be probed.

The ATLAS Collaboration reports a measurement of the ratio of the decay rates of W bosons to τ leptons and muons, in agreement with universal lepton couplings as postulated in the standard model of particle physics.

The ATLAS experiment at the Large Hadron Collider reports a search for charged-lepton-flavour violation in decays of Z bosons into a τ lepton and an electron or muon of opposite charge.

Previous studies have attempted to decode functional imaging data to infer the perceptual state of an observer, but the level of detail has been limited. A new decoding study reconstructs accurate pictures of what an observer has seen.

Ten years after the discovery of the Higgs boson, the ATLAS  experiment at CERN probes its kinematic properties with a significantly larger dataset from 2015–2018 and provides further insights on its interaction with other known particles.

There is an urgent need for quantitative magnetic resonance approaches for assessing brain development, as well as for studying the effects of drugs on neural tissue inflammation. Aviv Mezer and colleagues have developed a neuroimaging method for the quantification of local tissue volume and tissue-surface interaction, producing reliable quantitative measurements across a range of scanners. They apply their method to both the healthy brain and individuals with relapsing-remitting multiple sclerosis.

Recognition memory decisions are predicted by the relative similarity of parietal and occipitotemporal representations of a probe stimulus to past versus future events.