Mingzhou Ding

Sensory cortical disinhibition is required for long-term fear memory in humans

This study uses HD-tACS and neural decoding to show that sensory cortical disinhibition is necessary for long-term fear memory in humans.

Neural Mechanisms of Willed Attention Control

This study investigates the neural mechanisms of willed attention control using fMRI and EEG data, finding distinct activation patterns and decoding capabilities in the frontoparietal decision network.

From Attention Control to Stimulus Selection: Neural Mechanisms Revealed by Multivariate Pattern and Functional Connectivity Analyses

This study uses fMRI and multivariate analysis to show how spatial attention templates guide stimulus selection through neural connectivity and decoding patterns.

Visual Emotion Perception in a Deep Neural Network Model with Both Bottom-Up and Top-Down Connections

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 Ventral Attention Network Mediates Attentional Reorienting to Cross-Modal Expectancy Violations: Evidence from EEG and fMRI

This study investigates the role of the Ventral Attention Network (VAN) in attentional reorienting during cross-modal expectancy violations using EEG and fMRI.

Biologically Inspired Deep Neural Network Models for Visual Emotion Processing

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.

Is there a ubiquitous spectrolaminar motif of local field potential power across primate neocortex?

This paper evaluates the universality and reliability of a spectrolaminar motif in primate neocortex, finding it not universal and methods unreliable.

Rhythmic Sampling and Competition of Target and Distractor in a Motion Detection Task

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.

Emotional Responses to Naturalistic and AI-generated Affective Pictures: A Systematic Comparison

This study compares emotional responses to AI-generated and naturalistic affective pictures, finding that AI pictures elicit similar self-reported and physiological responses to original stimuli.

Representational Competition of Spatially and Temporally Overlapped Target and Distractor

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

Neural representation of emotional valence in human amygdala

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.

Concept2Brain: An AI model for predicting subject-level neurophysiological responses to text and pictures

Concept2Brain generates synthetic electrophysiological responses to text and pictures using a deep learning model, enabling reproducible EEG datasets and AI-driven brain activity modeling.

Stimulus repetition induces a two-stage learning process in primary visual cortex

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.

Alpha-frequency stimulation strengthens coupling between temporal fluctuations in alpha oscillation power and default mode network connectivity

Robust single-trial estimates of electrocortical generalized aversive conditioning: Validation of a Bayesian multilevel learning model

The paper validates a Bayesian multilevel learning model for estimating single-trial electrocortical responses during aversive conditioning, showing improved accuracy and handling of missing data.

Decoding in the fourth dimension: Classification of temporal patterns and their generalization across locations

The paper introduces a decoding method using temporal features from neural time series to classify experimental conditions and assess generalization across sensor locations.

Brain Aging Among Individuals with Classical Trigeminal Neuralgia

The paper assesses brain aging differences in classical and secondary/idiopathic trigeminal neuralgia subtypes compared to controls, finding accelerated brain aging in classical TN but not in secondary/idiopathic TN.

Perceptual Expertise and Attention: An Exploration using Deep Neural Networks

This study uses CNNs to explore how perceptual expertise and attention interact in object recognition, finding that attention enhances performance within expertise domains but not outside.

Hierarchical Organization of Visual Feature Attention Control

This paper investigates the hierarchical organization of visual feature attention control using EEG and multivariate decoding, finding that broad feature dimensions are biased before specific attributes.

Quantifying Population-level Neural Tuning Functions Using Ricker Wavelets and the Bayesian Bootstrap

This paper introduces a method using Ricker wavelets and Bayesian bootstrap to quantify neural tuning functions in human EEG data during aversive learning, showing improved fit and interpretability compared to traditional models.

Emergence of Emotion Selectivity in A Deep Neural Network Trained to Recognize Visual Objects

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.

Decoding Visual Spatial Attention Control

The paper analyzes attention-related baseline activity using fMRI and two analytic approaches, finding that multivariate pattern analysis reveals top-down attentional biases across the visual hierarchy, which better predict behavioral performance than univariate activation.

Decoding fMRI Data: A Comparison Between Support Vector Machines and Deep Neural Networks

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

Transcranial stimulation of alpha oscillations modulates brain state dynamics in sustained attention

This study uses HD-tACS and fMRI to show that alpha oscillations modulate dynamic brain states during sustained attention, enhancing attention and suppressing specific brain states.

Imaging the Neural Substrate of Trigeminal Neuralgia Pain Using Deep Learning

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

Top-down control of the left visual field bias in cued visual spatial attention

The paper investigates how attentional control modulates a left visual field bias in cued visual spatial attention using pupillometry and fMRI.

Decoding the temporal dynamics of affective scene processing

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.

Transcranial stimulation of alpha oscillations upregulates the default mode network

This study shows that transcranial alpha-frequency stimulation increases default mode network connectivity by enhancing alpha oscillations.

Role of Inferior Frontal Junction (IFJ) in the Control of Feature vs Spatial Attention

This study investigates the role of the inferior frontal junction (IFJ) in feature versus spatial attention using fMRI, finding that right IFJ is involved in feature attention but not spatial attention.

Effects of Frontoparietal Theta tACS on Verbal Working Memory: Behavioral and Neurophysiological Analysis

This study examines how theta tACS affects verbal working memory, finding load- and subject-dependent improvements in performance and frontoparietal theta synchrony.