Information that is shared across brains is encoded in idiosyncratic fine-scale functional
topographies. Hyperalignment captures shared information by projecting pattern vectors for …

Decoding speech from brain activity is a long-awaited goal in both healthcare and
neuroscience. Invasive devices have recently led to major milestones in this regard: deep …

Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG)
and related techniques often have poor signal-to-noise ratios due to the presence of multiple …

Multi-subject fMRI data is critical for evaluating the generality and validity of findings across
subjects, and its effective utilization helps improve analysis sensitivity. We develop a shared …

In many biological and medical contexts, we construct a large labeled corpus by
aggregating many sources to use in target prediction tasks. Unfortunately, many of the …

Variation in cortical connectivity profiles is typically modeled as having a coarse spatial
scale parcellated into interconnected brain areas. We created a high-dimensional common …

How does one find dimensions in multivariate data that are reliably expressed across
repetitions? For example, in a brain imaging study one may want to identify combinations of …

Our understanding of the typical human brain has benefitted greatly from studying different
kinds of brains and their associated behavioral repertoires, including animal models and …

Background Functional magnetic resonance imaging (fMRI) capture aims at detecting
auditory-verbal hallucinations (AVHs) from continuously recorded brain activity. Establishing …

One of the most significant challenges in the neuroscience community is to understand how
the human brain works. Recent progress in neuroimaging techniques have validated that it …