Replay-triggered brain-wide activation in humans.

The consolidation of discrete experiences into a coherent narrative shapes the cognitive map, providing structured mental representations of our experiences. In this process, past memories are reactivated and replayed in sequence, fostering hippocampal-cortical dialogue. However, brain-wide engagement coinciding with sequential reactivation (or replay) of memories remains largely unexplored. In this study, employing simultaneous EEG-fMRI, we capture both the spatial and temporal dynamics of memory replay. We find that during mental simulation, past memories are replayed in fast sequences as detected via EEG. These transient replay events are associated with heightened fMRI activity in the hippocampus and medial prefrontal cortex. Replay occurrence strengthens functional connectivity between the hippocampus and the default mode network, a set of brain regions key to representing the cognitive map. On the other hand, when subjects are at rest following learning, memory reactivation of task-related items is stronger than that of pre-learning rest, and is also associated with heightened hippocampal activation and augmented hippocampal connectivity to the entorhinal cortex. Together, our findings highlight a distributed, brain-wide engagement associated with transient memory reactivation and its sequential replay.

The House-Tree-Person test is not valid for the prediction of mental health: An empirical study using deep neural networks.

As one of the projective drawing techniques, the House-Tree-Person test (HTP) has been widely used in psychological counseling. However, its validity in diagnosing mental health problems remains controversial. Here, we adopted two approaches to examine the validity of HTP in diagnosing mental health problems objectively. First, we summarized the diagnostic features reported in previous HTP studies and found no reliable association between the existing HTP indicators and mental health problems studied. Next, after obtaining HTP drawings and depression scores from 4196 Chinese children and adolescents (1890 females), we used the Deep Neural Networks (DNNs) to explore implicit features from entire HTP drawings that might have been missed in previous studies. We found that although the DNNs successfully learned to extract critical features of houses, trees, and persons in HTP drawings for object classification, it failed to classify the drawings of depressive individuals from those of non-depressive individuals. Taken together, our study casts doubts on the validity of the HTP in diagnosing mental health problems, and provides a practical paradigm of examining the validity of projective tests with deep learning.

An fMRI dataset for whole-body somatotopic mapping in humans.

The somatotopic representation of the body is a well-established organizational principle in the human brain. Classic invasive direct electrical stimulation for somatotopic mapping cannot be used to map the whole-body topographical representation of healthy individuals. Functional magnetic resonance imaging (fMRI) has become an indispensable tool for the noninvasive investigation of somatotopic organization of the human brain using voluntary movement tasks. Unfortunately, body movements during fMRI scanning often cause large head motion artifacts. Consequently, there remains a lack of publicly accessible fMRI datasets for whole-body somatotopic mapping. Here, we present public high-resolution fMRI data to map the somatotopic organization based on motor movements in a large cohort of healthy adults (N = 62). In contrast to previous studies that were mostly designed to distinguish few body representations, most body parts are considered, including toe, ankle, leg, finger, wrist, forearm, upper arm, jaw, lip, tongue, and eyes. Moreover, the fMRI data are denoised by combining spatial independent component analysis with manual identification to clean artifacts from head motion associated with body movements.

Implementation-Independent Representation for Deep Convolutional Neural Networks and Humans in Processing Faces.

Deep convolutional neural networks (DCNN) nowadays can match human performance in challenging complex tasks, but it remains unknown whether DCNNs achieve human-like performance through human-like processes. Here we applied a reverse-correlation method to make explicit representations of DCNNs and humans when performing face gender classification. We found that humans and a typical DCNN, VGG-Face, used similar critical information for this task, which mainly resided at low spatial frequencies. Importantly, the prior task experience, which the VGG-Face was pre-trained to process faces at the subordinate level (i.e., identification) as humans do, seemed necessary for such representational similarity, because AlexNet, a DCNN pre-trained to process objects at the basic level (i.e., categorization), succeeded in gender classification but relied on a completely different representation. In sum, although DCNNs and humans rely on different sets of hardware to process faces, they can use a similar and implementation-independent representation to achieve the same computation goal.