Biology subjects

Xing, X.-X.

Publications and source records attributed to Xing, X.-X..

3 recordsLinked to original sources

bioRxiv2025

The Human Brain's Action Network Affinity

This study proposes a spatiotemporal connectome-based framework to characterize the human brains action network. Unlike temporal connectivity (TPC), this approach leverages full functional connectivity profiles derived from large-scale neural wave dynamics, termed spatial connectivity (SPC). We applied SPC to map the action network for the first time in a non-Western young adult cohort. Our results delineate the networks detailed functional architecture across the cerebral cortex, cerebellum, and subcortical nuclei. Compared to TPC, SPC is more robust to global signal regression when characterizing anticorrelation between the action and default networks. Critically, this intrinsic antagonism reflects a fundamental energy-saving balance in the brains dynamical system. Moreover, SPC reveals that the salience/parietal memory network resides at the interface of this antagonism, exhibiting weak but highly variable connectivity, a position that enables rapid state switching between external action and internal contemplation. These findings offer a mechanistic view of the brains resting "dark energy" and suggest a blueprint for brain-inspired AI. Embedding a dual-system opponent architecture, balanced by a flexible switching hub, may foster adaptive, energy-efficient intelligence. All derived high-resolution SPC patterns and computational code are publicly shared to promote open science (https://ccndc.scidb.cn/en).

neuroscience↗

bioRxiv2021

FMRI multi-scale cortical spontaneous activity: 7T vs. 3T

This paper describes the use of the Human Connectome Project (HCP) data for mapping the distribution of spontaneous activity in the human brain across different spatial scales, magnets and individuals. Specifically, the resting-state functional MRI signals acquired under the HCP 3 tesla (T) and 7T magnet protocols were measured by computational methods at multiple spatial scales across the cerebral cortex using: 1) an amplitude metric on a single measuring unit (ALFF), 2) a functional homogeneity metric on a set of neighboring measuring units (ReHo) and 3) a homotopic functional connectivity metric on pairs of symmetric measuring units between the two hemispheres (VMHC). Statistical assessments on these measurements revealed that all the raw metrics were enhanced by the higher magnetic field, highlighting their dependence on magnet field strength. Measurement reliability of these global measurements were moderate to high and comparable between between 3T and 7T magnets. The differences in these measurements introduced by the higher magnetic field were spatially dependent and varied according to specific cortical regions. Specifically, the spatial contrasts of ALFF were enhanced by the 7T magnet within the anterior cortex while weakened in the posterior cortex. This is opposite for ReHo and VMHC. This scale-dependent phenomena also held true for measurement reliabilities, which were enhanced by the 7T magnet for ReHo and VMHC and weakened for ALFF. These reliability differences were primarily located in high-order associate cortex, reflecting the corresponding changes of individual differences: higher between-subject variability and lower within-subject variability for ReHo and VMHC, lower between-subject variability and higher within-subject variability for ReHo and VMHC with respect to higher magnetic field strength. Our work, for the first time, demonstrates the spatial-scale dependence of spontaneous cortical activity measurements in the human brain and their test-retest reliability across different magnet strengths, and discussed about the statistical implications for experimental design using resting-state fMRI.

neuroscience↗

bioRxiv2020

DREAM: A Toolbox to Decode Rhythms of the Brain System

Rhythms of the brain are generated by neural oscillations across multiple frequencies, which can be observed with multiple modalities. Following the natural log linear law of frequency distribution, these oscillations can be decomposed into distinct frequency intervals associated with specific physiological processes. This perspective on neural oscillations has been increasingly applied to study human brain function and related behaviors. In practice, relevant signals are commonly measured as a discrete time series, and thus the sampling period and number of samples determine the number and ranges of decodable frequency intervals. However, these limits have been often ignored by researchers who instead decode measured oscillations into multiple frequency intervals using a fixed sample period and numbers of samples. One reason for such misuse is the lack of an easy-to-use toolbox to implement automatic decomposition of frequency intervals. We report on a toolbox with a graphical user interface for achieving local and remote decoding rhythms of the brain system (DREAM) which is accessible to the public via GitHub. We provide worked examples of DREAM used to investigate frequency-specific performance of both neural (spontaneous brain activity) and neurobehavioral (in-scanner head motion) oscillations. DREAM analyzed the head motion oscillations and found that younger children moved their heads more than older children across all five frequency intervals whereas boys moved more than girls in the age interval from 7 to 9 years. It is interesting that the higher frequency bands contains more head movements, and showed stronger age-motion associations but the weaker sex-motion interactions. Using the fast functional magnetic resonance imaging data from the Human Connectome Project, DREAM mapped the amplitude of these neural oscillations into multiple frequency bands and evaluated their test-retest reliability. A novel result indicated that the higher frequency bands exhibited more reliable amplitude measurements, implying more inter-individual variability of the amplitudes for the higher frequency bands. In summary, these findings demonstrated the applicability of DREAM for frequency-specific human brain mapping as well as the assessments on their measurement reliability and validity.

neuroscience↗