Distinct networks coupled with parietal cortex for spatial representations inside and outside the visual field

Motion-corrected eye tracking (MoCET) improves gaze accuracy during visual fMRI experiments

Human eye movements are essential for understanding cognition, yet achieving high-precision eye tracking in fMRI remains challenging. Even slight head shifts from the initial calibration position can introduce drift in eye tracking data, leading to substantial gaze inaccuracies. To address this, we introduce Motion-Corrected Eye Tracking (MoCET), a novel approach that corrects drift using head motion parameters derived from the preprocessing of fMRI data. MoCET requires no additional hardware and can be applied retrospectively to existing datasets. We show that it outperforms traditional detrending methods with respect to accuracy of gaze estimation and offers higher spatial and temporal precision compared to MR-based eye tracking approaches. By overcoming a key limitation in integrating eye tracking with fMRI, MoCET facilitates investigations of naturalistic vision and cognition in fMRI research.

Coherence-based channel selection and Riemannian geometry features for magnetoencephalography decoding

Magnetoencephalography (MEG) records the extremely weak magnetic fields on the surface of the scalp through highly sensitive sensors. Multi-channel MEG data provide higher spatial and temporal resolution when measuring brain activities, and can be applied for brain-computer interfaces as well. However, a large number of channels leads to high computational complexity and can potentially impact decoding accuracy. To improve the accuracy of MEG decoding, this paper proposes a new coherence-based channel selection method that effectively identifies task-relevant channels, reducing the presence of noisy and redundant information. Riemannian geometry is then used to extract effective features from selected channels of MEG data. Finally, MEG decoding is achieved by training a support vector machine classifier with the Radial Basis Function kernel. Experiments were conducted on two public MEG datasets to validate the effectiveness of the proposed method. The results from Dataset 1 show that Riemannian geometry achieves higher classification accuracy (compared to common spatial patterns and power spectral density) in the single-subject visual decoding task. Moreover, coherence-based channel selection significantly (P = 0.0002) outperforms the use of all channels. Moving on to Dataset 2, the results reveal that coherence-based channel selection is also significantly (P <0.0001) superior to all channels and channels around C3 and C4 in cross-session mental imagery decoding tasks. Additionally, the proposed method outperforms state-of-the-art methods in motor imagery tasks.

Mixed Selectivity Coding of Content-Temporal Detail by Dorsomedial Posterior Parietal Neurons

The dorsomedial posterior parietal cortex (dmPPC) is part of a higher-cognition network implicated in elaborate processes underpinning memory formation, recollection, episode reconstruction, and temporal information processing. Neural coding for complex episodic processing is however under-documented. Here, we recorded extracellular neural activities from three male rhesus macaques (Macaca mulatta) and revealed a set of neural codes of "neuroethogram" in the primate parietal cortex. Analyzing neural responses in macaque dmPPC to naturalistic videos, we discovered several groups of neurons that are sensitive to different categories of ethogram items, low-level sensory features, and saccadic eye movement. We also discovered that the processing of category and feature information by these neurons is sustained by the accumulation of temporal information over a long timescale of up to 30 s, corroborating its reported long temporal receptive windows. We performed an additional behavioral experiment with additional two male rhesus macaques and found that saccade-related activities could not account for the mixed neuronal responses elicited by the video stimuli. We further observed monkeys' scan paths and gaze consistency are modulated by video content. Taken altogether, these neural findings explain how dmPPC weaves fabrics of ongoing experiences together in real time. The high dimensionality of neural representations should motivate us to shift the focus of attention from pure selectivity neurons to mixed selectivity neurons, especially in increasingly complex naturalistic task designs.

EEG Network Analysis of Depressive Emotion Interference Spatial Cognition Based on a Simulated Robotic Arm Docking Task

Depressive emotion (DE) refers to clinically relevant depressive symptoms without meeting the diagnostic criteria for depression. Studies have demonstrated that DE can cause spatial cognition impairment. However, the brain network mechanisms underlying DE interference spatial cognition remain unclear. This study aimed to reveal the differences in brain network connections between DE and healthy control (HC) groups during resting state and a spatial cognition task. The longer operation time of the DE group during spatial cognition task indicated DE interference spatial cognition. In the resting state stage, the DE group had weaker network connections in theta and alpha bands than the HC group had. Specifically, the electrodes in parietal regions were hubs of the differential networks, which are related to spatial attention. Moreover, in docking task stages, the left frontoparietal network connections in delta, beta, and gamma bands were stronger in the DE group than those of the HC group. The enhanced left frontoparietal connections in the DE group may be related to brain resource reorganization to compensate for spatial cognition decline and ensure the completion of spatial cognition tasks. Thus, these findings might provide new insights into the neural mechanisms of depressive emotion interference spatial cognition.

A dataset of human fMRI/MEG experiments with eye tracking for spatial memory research using virtual reality

A dataset consisting of whole-brain fMRI (functional magnetic resonance imaging)/MEG (magnetoencephalography) images, eye tracking files, and behavioral records from healthy adult human participants when they performed a spatial-memory paradigm in a virtual environment was collected to investigate the neural representation of the cognitive map defined by unique spatial relationship of three objects, as well as the neural dynamics of the cognitive map following the task demand from localizing self-location to remembering the target location relative to the self-body. The dataset, including both fMRI and MEG, was also used to investigate the neural networks involved in representing a target within and outside the visual field. The dataset included 19 and 12 university students at Peking University for fMRI and MEG experiments, respectively (fMRI: 12 women, 7 men; MEG: 4 women, 8 men). The average ages of those participants were 24.9 years (MRI: 18–30 years) and 22.5 years (MEG: 19–25 years), respectively. fMRI BOLD and T1-weighted images were acquired using a 3T Siemens Prisma scanner (Siemens, Erlangen, Germany) equipped with a 20-channel receiver head coil. MEG neuromagnetic data were acquired using a 275-channel MEG system (CTF MEG, Canada). The dataset could be further used to investigate a range of neural mechanisms involved in human spatial cognition or to develop a bioinspired deep neural network to enhance machines' abilities in spatial processing.