Ke Bo

This study uses finite impulse response modeling to reveal distinct temporal dynamics of amygdala subregions in response to negative affect, showing prolonged activation in centromedial regions.

This paper introduces a neuroimaging framework to map brain mitochondria non-invasively and finds age-related declines in mitochondrial density and respiratory capacity, along with disease-specific alterations and links to systemic stress and cognition.

The paper investigates how mitochondrial energy capacity affects brain function during cognitive, sensory, and affective tasks, finding that working memory is more vulnerable to energetic stress than sensory or affective processes.

The paper identifies convergent and selective functional zones in the insula using fMRI data, revealing a hierarchical convergence architecture across pain, appetitive, aversive, and cognitive control domains.

This paper introduces EmoFB, a deep neural network model that simulates emotional perception by integrating top-down feedback signals, showing improved recognition and alignment with human brain responses.

The paper examines the neural correlates of social interaction perception and theory of mind using fMRI, finding overlapping and distinct brain regions involved in these processes.

This paper introduces a biologically inspired deep neural network model for visual emotion processing and shows it accurately predicts human emotion ratings and aligns with amygdala activity.

The paper investigates rhythmic sampling of target and distractor information in a motion detection task and finds that their temporal phase relationship affects task performance.

The paper tests how distractor stimuli affect target stimulus processing by measuring decoding accuracy and behavioral performance in different brain regions.

This study compares univariate and multivariate models of emotional valence encoding in the human amygdala using fMRI, finding that core amygdala regions use global activation rather than distributed patterns.

The paper compares neural representations of imagined and experienced pain using fMRI, finding shared and distinct patterns across brain regions.

This study investigates how repeated stimulus exposure affects neural responses in the primary visual cortex, revealing a two-stage learning process involving fatigue and sharpening mechanisms.

This study shows that deep neural networks trained on visual object recognition develop emotion-selective neurons, supporting the idea that the visual system can intrinsically represent affective significance.

This study compares SVM and CNN performance on fMRI data, finding CNNs achieve higher decoding accuracy and use distinct brain activity patterns.

The paper uses deep learning methods to identify brain regions associated with trigeminal neuralgia pain from fMRI data.

The paper identifies four neural systems related to cognitive reappraisal using Bayes factors and systems-identification in fMRI data from two studies.

The paper uses EEG-fMRI data to decode the temporal dynamics of affective scene processing, finding distinct neural activity patterns for pleasant and unpleasant images.

This study uses EEG and fMRI with machine learning to show that affective pictures evoke valence-specific neural representations in retinotopic visual cortex, influenced by limbic and frontal connectivity.

This study shows that fear conditioning leads to sparser neural representations of threat cues in primary visual cortex, associated with changes in alpha oscillations and BOLD signals.

The paper uses multivariate pattern classification on multichannel ERPs to investigate attention control and selection, finding earlier and stronger attentional effects under instructional cueing and linking decoding accuracy to behavioral performance.