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The paper examines neural individuality in human brain regions with evolutionary signatures, finding stronger individuality in more evolutionarily novel areas.

The paper uses deep learning models to show that the superior temporal sulcus represents naturalistic social interactions through multimodal integration of visual and auditory information.

This study shows that musical training leads to behavioral generalization but neural plasticity is specific to the learned melody, not the instrument.

This study investigates whether facial preferences can be influenced by context, finding that adding a decoy face increases selection of a target face by up to 7%.

The paper predicts group-level context effects in choices using neural similarity between options, outperforming traditional methods that rely on explicit attributes.

This study uses diffusion MRI to continuously monitor microstructural brain changes during motor learning, revealing dynamic neuroplasticity patterns in specific brain regions.

This study attempts to replicate and extend findings on habit formation in humans using multi-modal MRI, but finds no clear habit formation with training duration and identifies goal-directed neural patterns instead.

This study shows that functional connectivity from naturalistic stimuli fMRI better predicts brain activation and intelligence scores than resting-state fMRI.

The paper predicts individual traits from task-induced brain activation maps generated from resting-state fMRI data, showing improved accuracy over resting-state connectivity patterns.

The paper examines brain volumetric changes in a population before and after the COVID-19 outbreak and lockdown, finding increases in amygdala, putamen, and anterior temporal cortices, which diminished over time.

The paper predicts task-evoked brain activity in schizophrenia patients using resting-state fMRI from healthy controls, showing strong correlations and statistical significance.

This study uses diffusion MRI to show short-term structural brain plasticity after motor sequence learning, revealing dynamic changes in motor regions like the premotor cortex and cerebellum.