Zhaohua Ding

Magnetic resonance images of normal brains were analyzed in order to clarify the relationship of resting state BOLD signals in white matter to cortical neural activity. We quantified the degree to which spontaneous activities in the cortex, which are reflected in low frequency fluctuations in BOLD signals from gray matter, modulate corresponding resting state BOLD signals in white matter. The similarity between the resting state BOLD signals from selected cortical regions and white matter voxels, measured using the inner product of their time series, was found to be directly proportional to the BOLD signal power in each cortical volume. From measurements of resting state correlations we find cortical networks supporting more basic level functions tend to contribute more to correlated fluctuations in white matter than those of higher level functions. In addition, each cortical network exhibits a distinct spatial pattern of modulating effects on white matter BOLD signals, and their magnitudes are strongly correlated with the myelination level of the cortical network. Our findings confirm that resting state BOLD signals in white matter encode the spontaneous activity of specific cortical networks and are affected by the cytomyeloarchitecture of the cortex.

Functional magnetic resonance imaging studies have traditionally focused on gray matter, overlooking white matter despite growing evidence that functional blood oxygenation-level dependent effects also occur there. In particular, functional coupling across the gray-white matter boundary, an interface between local and global processing, remains poorly understood. This study introduces two metrics: gray-white matter functional connectivity, which captures temporal synchrony across the boundary, and gray-white blood oxygenation-level dependent power ratio, which reflects differences in signal amplitude. Gray-white matter functional connectivity aligns with patterns of myelination, long-range connectivity, and sensorimotor organization, suggesting efficient signal transmission. In contrast, the power ratio shows an inverse pattern, with higher values in higher-order regions, possibly reflecting increased metabolic demands in white matter. It also increases with age (8 to 21 years), suggesting developmental shifts in energetic demands. Together, these metrics highlight distinct yet complementary roles of signal fidelity and energy modulation at the gray-white matter boundary.

The relationship of BOLD signals in white matter to cortical neural activity remains unclear. We quantified the degree to which spontaneous neural activities in the cortex, which are reflected in low frequency fluctuations in cortical BOLD signals, modulate BOLD signals in white matter. From measurements of resting state correlations we find cortical networks of more basic level functions tend to contribute more to correlated fluctuations in white matter than those of higher level functions. In addition, each cortical network exhibits distinct, structurally interpretable spatial distribution patterns of white matter projections. Moreover, the myelination level of cortical networks is found to be strongly correlated with the white matter projection of cortical BOLD signals. Our findings confirm that BOLD signals in white matter encode neural activity in proportion to the spontaneous activity of individual cortical networks, and with network-specific spatial distribution patterns, which could be mediated by the microstructure of the brain cortex.

Functional magnetic resonance imaging (fMRI) has been widely employed for brain function mapping by localizing neural activities evoked by conditional stimuli, with temporal variations in fMRI signals modeled as superpositions of linear hemodynamic responses to these stimuli. However, this standard model overlooks the brain’s nonlinear responses to external stimuli and inherent spatial organizations of brain activities. The dynamics of many natural systems can be well described by their eigenmodes, which are the fundamental resonant patterns determined by their internal structures. Here, we explore the potential of characterizing brain dynamics in a framework that integrates temporal and spatial profiles of brain activities using an eigenmode paradigm. Specifically, spatiotemporally coordinated neural dynamics are represented as local excitations of brain eigenmodes derived from functional connectomes that reflect the brain’s axonal connection structures. We found that eigenmode-represented signals can reliably characterize subtle spatiotemporal trajectories of brain dynamics. Through this representation, we further reveal the widespread existence of non-linear brain responses, which potentially bias the results of conventional analyses. To map brain functions with non-linearity explicitly considered, we exploited the similarity in eigenmode-represented signals measured under repeated stimuli to identify evoked brain responses. Our findings demonstrate that cognitive tasks elicit more extensive engagements of brain networks than those detected using linear models, unveiling previously underappreciated neural architectures underlying specific brain functions. Moreover, our results of function mapping exhibit significantly enhanced individual-level repeatability and accelerated convergence to group consensus, enabling investigations into personalized brain function and potentially advancing applications of fMRI that target individual brain variations.

Cognitive decline with aging involves multifactorial processes, including changes in brain structure and function. This study focuses on the role of white matter functional characteristics, as reflected in blood oxygenation level-dependent signals, in age-related cognitive deterioration. Building on previous research confirming the reproducibility and age-dependence of blood oxygenation level-dependent signals acquired via functional magnetic resonance imaging, we here employ mediation analysis to test if aging affects cognition through white matter blood oxygenation level-dependent signal changes, impacting various cognitive domains and specific white matter regions. We used independent component analysis of resting-state blood oxygenation level-dependent signals to segment white matter into coherent hubs, offering a data-driven view of white matter’s functional architecture. Through correlation analysis, we constructed a graph network and derived metrics to quantitatively assess regional functional properties based on resting-state blood oxygenation level-dependent fluctuations. Our analysis identified significant mediators in the age-cognition relationship, indicating that aging differentially influences cognitive functions by altering the functional characteristics of distinct white matter regions. These findings enhance our understanding of the neurobiological basis of cognitive aging, highlighting the critical role of white matter in maintaining cognitive integrity and proposing new approaches to assess interventions targeting cognitive decline in older populations.

Correlations between magnetic resonance imaging (MRI) blood oxygenation level–dependent (BOLD) signals from pairs of gray matter areas are used to infer their functional connectivity, but they are unable to describe how white matter is engaged in brain networks. Recently, evidence that BOLD signals in white matter are robustly detectable and are modulated by neural activities has accumulated. We introduce a three-way correlation between BOLD signals from pairs of gray matter volumes (nodes) and white matter bundles (edges) to define the communication connectivity through each white matter bundle. Using MRI images from publicly available databases, we show, for example, that the three-way connectivity is influenced by age. By integrating functional MRI signals from white matter as a third component in network analyses, more comprehensive descriptions of brain function may be obtained.

Recent studies have revealed the production of time-locked blood oxygenation level–dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to tasks, challenging the existence of sparse and localized brain functions and highlighting the pervasiveness of potential false negative fMRI findings. “Whole-brain” actually refers to gray matter, the only tissue traditionally studied with fMRI. However, several reports have demonstrated reliable detection of BOLD signals in white matter, which have previously been largely ignored. Using simple tasks and analyses, we demonstrate BOLD signal changes across the whole brain, in both white and gray matters, in similar manner to previous reports of whole brain studies. We investigated whether white matter displays time-locked BOLD signals across multiple structural pathways in response to a stimulus in a similar manner to the cortex. We find that both white and gray matter show time-locked activations across the whole brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that, just like all gray matter, essentially all white matter in the brain shows time-locked BOLD signal changes in response to multiple stimuli, challenging the idea of sparse functional localization and the prevailing wisdom of treating white matter BOLD signals as artifacts to be removed.

Seasonal variations have long been observed in various aspects of human life. While there is an abundance of research that has characterized seasonality effects in, for example, cognition, mood, and behavior, including studies of underlying biophysical mechanisms, direct measurements of seasonal variations of brain functional activities have not gained wide attention. We have quantified seasonal effects on functional connectivity as derived from MRI scans. A cohort of healthy human subjects was divided into four groups based on the seasons of their scanning dates as documented in the image database of the Human Connectome Project. Sinusoidal functions were used as regressors to determine whether there were significant seasonal variations in measures of brain activities. We began with the analysis of seasonal variations of the fractional amplitudes of low frequency fluctuations of regional functional signals, followed by the seasonal variations of functional connectivity in both global- and network-level. Furthermore, relevant environmental factors, including average temperature and daylength, were found to be significantly associated with brain functional activities, which may explain how the observed seasonal fluctuations arise. Finally, topological properties of the brain functional network also showed significant variations across seasons. All the observations accumulated revealed seasonality effects of human brain activities in a resting-state, which may have important practical implications for neuroimaging research.

White matter (WM) consists of fibers that transmit information from one brain region to another, and functional fiber clustering that combines diffusion and functional MRI provides a novel perspective for exploring the functional architecture of axonal fibers. However, existing methods only concern functional signals in gray matter (GM), whereas the connecting fibers may not transmit relevant functional signals. There has been growing evidence that neural activity is encoded in WM BOLD signals as well, which provides rich multimodal information for fiber clustering. In this paper, we develop a comprehensive Riemannian framework for functional fiber clustering using WM BOLD signals along fibers. Specifically, we derive a novel metric that is highly discriminative of different functional classes while reducing the variability within classes and, in the meantime, enables low-dimensional coding of high-dimensional data. Our in vivo experiments show that the proposed framework is able to achieve clustering results with inter-subject consistency and functional homogeneity. In addition, we develop an atlas of WM functional architecture for standardizable yet flexible use and exemplify a machine-learning-based application for the classification of autism spectrum disorders, which further demonstrates the great potential of our approach in practical applications.