Jianling Tan’s research while affiliated with Chongqing University of Posts and Telecommunications and other places

Effective visual search is essential for daily life, and attention orientation as well as inhibition of return play a significant role in visual search. Researches have established the involvement of dorsolateral prefrontal cortex in cognitive control during selective attention. However, neural evidence regarding dorsolateral prefrontal cortex modulates inhibition of return in visual search is still insufficient. In this study, we employed event-related functional magnetic resonance imaging and dynamic causal modeling to develop modulation models for two types of visual search tasks. In the region of interest analyses, we found that the right dorsolateral prefrontal cortex and temporoparietal junction were selectively activated in the main effect of search type. Dynamic causal modeling results indicated that temporoparietal junction received sensory inputs and only dorsolateral prefrontal cortex →temporoparietal junction connection was modulated in serial search. Such neural modulation presents a significant positive correlation with behavioral reaction time. Furthermore, theta burst stimulation via transcranial magnetic stimulation was utilized to modulate the dorsolateral prefrontal cortex region, resulting in the disappearance of the inhibition of return effect during serial search after receiving continuous theta burst stimulation. Our findings provide a new line of causal evidence that the top-down modulation by dorsolateral prefrontal cortex influences the inhibition of return effect during serial search possibly through the retention of inhibitory tagging via working memory storage.

During growth and aging, the role of the hippocampus in memory depends on its interactions with related brain regions. Particularly, two subregions, anterior hippocampus (aHipp) and posterior hippocampus (pHipp), play different and critical roles in memory processing. However, age-related changes of hippocampus subregions on structure and function are still unclear. Here, we investigated age-related structural and functional characteristics of 106 participants (7-85 years old) in resting state based on fractional anisotropy (FA) and functional connectivity (FC) in aHipp and pHipp in the lifespan. The correlation between FA and FC was also explored to identify the coupling. Furthermore, the Wechsler Abbreviated Scale of Intelligence (WASI) was used to explore the relationship between cognitive ability and hippocampal changes. Results showed that there was functional separation and integration in aHipp and pHipp, and the number of functional connections in pHipp was more than that in aHipp across the lifespan. The age-related FC changes showed four different trends (U-shaped/inverted U-shaped/linear upward/linear downward). And around the age of 40 was a critical period for transformation. Then, FA analyses indicated that all effects of age on the hippocampal structures were nonlinear, and the white matter integrity of pHipp was higher than that of aHipp. In the functional-structural coupling, we found that the age-related FA of the right aHipp (aHipp.R) was negatively related to the FC. Finally, through the WASI, we found that the age-related FA of the left aHipp (aHipp.L) was positively correlated with verbal IQ (VERB) and vocabulary comprehension (VOCAB.T), the FA of aHipp.R was only positively correlated with VERB, and the FA of the left pHipp (pHipp.L) was only positively correlated with VOCAB.T. These FC and FA results supported that age-related normal memory changes were closely related to the hippocampus subregions. We also provided empirical evidence that memory ability was altered with the hippocampus, and its efficiency tended to decline after age 40.

Visual joint attention, the ability to track gaze and recognize intent, plays a key role in the development of social and language skills in health humans, which is performed abnormally hard in autism spectrum disorder (ASD). The traditional convolutional neural network, EEGnet, is an effective model for decoding technology, but few studies have utilized this model to address attentional training in ASD patients. In this study, EEGNet was used to decode the P300 signal elicited by training and the saliency map method was used to visualize the cognitive properties of ASD patients during visual attention. The results showed that in the spatial distribution, the parietal lobe was the main region of classification contribution, especially for Pz electrode. In the temporal information, the time period from 300 to 500 ms produced the greatest contribution to the electroencephalogram (EEG) classification, especially around 300 ms. After training for ASD patients, the gradient contribution was significantly enhanced at 300 ms, which was effective only in social scenarios. Meanwhile, with the increase of joint attention training, the P300 latency of ASD patients gradually shifted forward in social scenarios, but this phenomenon was not obvious in non-social scenarios. Our results indicated that joint attention training could improve the cognitive ability and responsiveness of social characteristics in ASD patients.

Objectives The application of electroencephalography (EEG) to the study of major depressive disorder (MDD) is a common approach. However, there is no one-to-one correspondence between EEG and brain neural activity, and it is unclear whether single-trial EEG signals detect the cognitive neural activity of MDD. Methods Here, we used deep learning to explore this issue. Deep learning adopted in this paper was an end-to-end classification method named EEGNet which could update model parameters automatically based on the characteristics of the data and classify MDD and healthy subjects (HS) from single-trial EEG signals to obtain classification results above the chance level. Furthermore, the saliency map was used to analyze the neural network model and visualize the channels and time periods that contributed the most. Results Finally, EEGNet achieved an average classification accuracy of 61.4% in the four categories, and the result of feature visualization was consistent with the cognitive neural interpretation of existing studies. Conclusion The findings suggested that deep learning could help cognitive neuroscience explore neural activity.

Purpose: Emotion is the reflection of individual's perception and understanding of various things, which needs the synergy of various brain regions. A large number of emotion decoding methods based on electroencephalogram (EEG) have been proposed. But extracting the most discriminative and cognitive features to construct a model is yet to be determined. This paper aims to construct a model that can extract the most discriminative and cognitive features. Materials and methods: Here, we collected EEG signals from 24 subjects in a musical emotion induction experiment. Then, an end-to-end branch LSTM-CNN (BLCNN) was used to extract emotion features from the laboratory dataset and DEAP dataset for emotion decoding. Finally, the extracted features were visualized on the laboratory dataset using saliency map. Result: The classification results showed that the accuracy of the three classification of the laboratory dataset was 95.78% ± 1.70%, and the accuracy of the four classification of the DEAP dataset was 80.97% ± 7.99%. We found that the discriminating features of positive emotion were distributed in the left hemisphere, at the same time, negative emotion features were distributed in the right hemisphere, where mainly in the frontal, parietal and occipital lobes. Conclusion: In this paper, we proposed a neural network model, namely BLCNN. The model obtained good results in laboratory dataset and DEAP dataset. Through the visual analysis of the features extracted by BLCNN, it was found that the features were consistent with emotional cognition. Therefore, this paper provided a new perspective for the practical application of human-computer emotional interaction.