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Diffusion magnetic resonance imaging (dMRI) is widely used to probe tissue microstructure, and is currently the only non-invasive way to measure the brain's fiber architecture. While a large number of approaches to recover the intra-voxel fiber structure have been utilized in the scientific community, a direct, 3D, quantitative validation of these methods against relevant histological fiber geometries is lacking. In this study, we investigate how well different high angular resolution diffusion imaging (HARDI) models and reconstruction methods predict the ground-truth histologically defined fiber orientation distribution (FOD), as well as investigate their behavior over a range of physical and experimental conditions. The dMRI methods tested include constrained spherical deconvolution (CSD), Q-ball imaging (QBI), diffusion orientation transform (DOT), persistent angular structure (PAS), and neurite orientation dispersion and density imaging (NODDI) methods. Evaluation criteria focus on overall agreement in FOD shape, correct assessment of the number of fiber populations, and angular accuracy in orientation. In addition, we make comparisons of the histological orientation dispersion with the fiber spread determined from the dMRI methods. As a general result, no HARDI method outperformed others in all quality criteria, with many showing tradeoffs in reconstruction accuracy. All reconstruction techniques describe the overall continuous angular structure of the histological FOD quite well, with good to moderate correlation (median angular correlation coefficient > 0.70) in both single- and multiple-fiber voxels. However, no method is consistently successful at extracting discrete measures of the number and orientations of FOD peaks. The major inaccuracies of all techniques tend to be in extracting local maxima of the FOD, resulting in either false positive or false negative peaks. Median angular errors are ∼10° for the primary fiber direction and ∼20° for the secondary fiber, if present. For most methods, these results did not vary strongly over a wide range of acquisition parameters (number of diffusion weighting directions and b value). Regardless of acquisition parameters, all methods show improved successes at resolving multiple fiber compartments in a voxel when fiber populations cross at near-orthogonal angles, with no method adequately capturing low to moderate angle (<60°) crossing fibers. Finally, most methods are limited in their ability to capture orientation dispersion, resulting in low to moderate, yet statistically significant, correlation with histologically-derived dispersion with both HARDI and NODDI methodologies. Together, these results provide quantitative measures of the reliability and limitations of dMRI reconstruction methods and can be used to identify relative advantages of competing approaches as well as potential strategies for improving accuracy. •3D histological validation of diffusion MRI measures of fiber orientation.•All methods capture the overall structure of the FOD quite well.•Most inaccuracies occur when extracting discrete peaks from the FOD.•No method consistently resolves fibers crossing at low to moderate angles.•Measures of dispersion show modest correlation with histological measures.

The topological characteristics of functional networks, derived from measurements of resting-state connectivity in gray matter (GM), are associated with individual cognitive abilities or specific dysfunctions. However, blood oxygen level-dependent (BOLD) signals in white matter (WM) are usually ignored or even regressed out as nuisance factors in the data analyses that underlie network models. Recent studies have demonstrated reliable detection of WM BOLD signals and imply these reflect associated neural activities. Here we evaluate quantitatively the contributions of individual WM voxels to the identification of functional networks, which we term their engagement (or conceptually, their importance). We quantify the engagement by measuring the reductions of connectivity, produced by ignoring the signal fluctuations within each WM voxel, with respect to both the entire network (global) or a single GM node (local). We observed highly reproducible spatial distributions of global engagement maps, as well as a trend toward increased relevance of deep WM voxels at delayed times. Local engagement maps exhibit homogeneous spatial distributions with respect to internal nodes that constitute a well-recognized sub-functional network, but inhomogeneous distributions with respect to other nodes. WM voxels show distinct distributions of engagement depending on their anatomical locations. These findings demonstrate the important role of WM in network modeling, thus supporting the need for changes of conventional views that WM signal variations represent only physiological noise. •Contributions of WM BOLD signals to brain functional networks are evaluated.•Spatial distributions of WM engagement maps are found to be highly reproducible.•A trend toward increased engagement of deep WM at delayed times is observed.•WM voxels exhibit region-dependent distributions of engagement.

Hydrophobicity of macroscopic planar surface is conventionally characterized by the contact angle of water droplets. However, this engineering measurement cannot be directly extended to surfaces of proteins, due to the nanometer scale of amino acids and inherent nonplanar structures. To measure the hydrophobicity of side chains of proteins quantitatively, numerous parameters were developed to characterize behavior of hydrophobic solvation. However, consistency among these parameters is not always apparent. Herein, we demonstrate an alternative way of characterizing hydrophobicity of amino acid side chains in a protein environment by constructing a monolayer of amino acids (i.e., artificial planar peptide network) according to the primary and the β-sheet secondary structures of protein so that the conventional engineering measurement of the contact angle of a water droplet can be brought to bear. Using molecular dynamics simulations, contact angles θ of a water nanodroplet on the planar peptide network, together with excess chemical potentials of purely repulsive methane-sized Weeks–Chandler–Andersen solute, are computed. All of the 20 types of amino acids and the corresponding planar peptide networks are studied. Expectedly, all of the planar peptide networks with nonpolar amino acids are hydrophobic due to θ > 90°, whereas all of the planar peptide networks of the polar and charged amino acids are hydrophilic due to θ < 90°. Planar peptide networks of the charged amino acids exhibit complete-wetting behavior due to θ = 0°. This computational approach for characterization of hydrophobicity can be extended to artificial planar networks of other soft matter.

Background The leopard coral grouper ( Plectropomus leopardus ) is a commercially valuable tropical marine fish species known to be sensitive to low temperatures. A comprehensive understanding of the molecular mechanisms governing its response to acute cold stress is of great importance. However, there is a relative scarcity of fundamental research on low-temperature tolerance in the leopard coral grouper. Methods In this study, a cooling and rewarming experiment was conducted on 6-month-old leopard coral groupers. Within 24 h, we decreased the ambient temperature from 25 °C to 13 °C and subsequently allowed it to naturally return to 25 °C. During this process, a comprehensive investigation of serum hormone levels, enzyme activity, and brain transcriptome analysis was performed. Results P. leopardus displayed a noticeable adaptive response to the initial temperature decrease by temporarily reducing its life activities. Our transcriptome analysis revealed that the differentially expressed genes (DEGs) were primarily concentrated in crucial pathways including the blood-brain barrier (BBB), inflammatory response, and coagulation cascade. In situ hybridization of claudin 15a ( cldn15a ), a key gene for BBB maintaining, further confirmed that exposure to low temperatures led to the disruption of the blood-brain barrier and stimulated a pronounced inflammatory reaction within the brain. Upon rewarming, there was a recovery of BBB integrity accompanied by the persistence of inflammation within the brain tissue. Conclusions Our study reveals the complex interactions between blood-brain barrier function, inflammation, and recovery in P. leopardus during short-term temperature drops and rewarming. These findings provide valuable insights into the physiological responses of this species under cold stress conditions.

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. This study introduces two new measures of brain function at the gray–white matter boundary, revealing distinct patterns of signal transmission and metabolic activity that deepen our understanding of brain organization and development.

In vivo functional changes in white matter during the progression of Alzheimer's disease (AD) have not been previously reported. Our objectives are to measure changes in white matter functional connectivity (FC) in an elderly population undergoing cognitive decline as AD develops, to establish their relationship to neuropsychological scores of cognitive abilities, and to assess the performance in prediction of AD using white matter FC measures as features. Analyses were conducted using resting state functional MRI and neuropsychological data from 383 ADNI participants, including 136 cognitive normal (CN) controls, 46 with significant memory concern, 83 with early mild cognitive impairment (MCI), 37 with MCI, 46 with late MCI, and 35 with AD dementia. FC metrics between segregated white matter tracts and discrete gray matter volumes or between white matter tracts were quantitatively analyzed and characterized, along with their relationships to 6 cognitive measures. Finally, supervised machine learning was implemented on white matter FCs to classify the participants and performance of the classification was evaluated. Significant decreases in FC measures were found in white matter with prominent, specific, regional deficits appearing in late MCI and AD dementia patients from CN. These changes significantly correlated with neuropsychological measurements of impairments in cognition and memory. The sensitivity and specificity of distinguishing AD dementia and CN using white matter FCs were 0.83 and 0.81 respectively. The white matter FC decreased in late MCI and AD dementia patients compared to CN participants, and this decrease was correlated with cognitive measures. White matter FC is valuable in the prediction of AD. All these findings suggest that white matter FC may be a promising avenue for understanding functional impairments in white matter tracts during AD progression.

•Integrated functional and structural MRI maps white matter engagement.•White matter engagement correlates with other functional and structural properties.•Significant gender differences were found in white matter engagement.•Distribution of white matter engagement varies over time.•White matter engagement shows promise as a biomarker for neurological conditions. Current models of brain networks may potentially be improved by integrating our knowledge of structural connections, within and between circuits, with metrics of functional interactions between network nodes. The former may be obtained from diffusion MRI of white matter (WM), while the latter may be derived by measuring correlations between resting state BOLD signals from pairs of gray matter (GM) regions. From inspection of diffusion MRI data, it is clear that each WM voxel within a 3D image array may be traversed by multiple WM structural tracts, each of which connects a pair of GM nodes. We hypothesized that by appropriately weighting and then integrating the functional connectivity of each such connected pair, the overall engagement of any WM voxel in brain functions could be evaluated. This model introduces a structural constraint to earlier studies of WM engagement and addresses some limitations of previous efforts to relate structure and function. Using concepts derived from graph theory, we obtained spatial maps of WM engagement which highlight WM regions critical for efficient communications across the brain. The distributions of WM engagement are highly reproducible across subjects and depict a notable interdependence between the distribution of GM activities and the detailed organization of WM. Additionally, we provide evidence that the engagement varies over time and shows significant differences between genders. These findings suggest the potential of WM engagement as a measure of the integrity of normal brain functions and as a biomarker for neurological and cognitive disorders.

•Spectral patterns of WM BOLD signals could be categorized into five modes that recurred over time.•These modes showed distinct spatial distributions of their occurrences and durations.•One of the modes exhibited a strong coupling of its occurrence between GM and WM.•Two communities of WM voxels were identified according to the hierarchical structures of transitions among modes.•These modes are coupled to the shape of instantaneous HRFs Recent studies have demonstrated that the mathematical model used for analyzing and interpreting fMRI data in gray matter (GM) is inappropriate for detecting or describing blood-oxygenation-level-dependent (BOLD) signals in white matter (WM). In particular the hemodynamic response function (HRF) which serves as the regressor in general linear models is different in WM compared to GM. We recently reported measurements of the frequency contents of resting-state signal time courses in WM that showed distinct power spectra which depended on local structural-vascular-functional associations. In addition, multiple studies of GM have revealed how functional connectivity between regions, as measured by the correlation between BOLD time series, varies dynamically over time. We therefore investigated whether and how BOLD signals from WM in a resting state varied over time. We measured voxel-wise spectrograms, which reflect the time-varying spectral patterns of WM time courses. The results suggest that the spectral patterns are non-stationary but could be categorized into five modes that recurred over time. These modes showed distinct spatial distributions of their occurrences and durations, and the distributions were highly consistent across individuals. In addition, one of the modes exhibited a strong coupling of its occurrence between GM and WM across individuals, and two communities of WM voxels were identified according to the hierarchical structures of transitions among modes. Moreover, these modes are coupled to the shape of instantaneous HRFs. Our findings extend previous studies and reveal the non-stationary nature of spectral patterns of BOLD signals over time, providing a spatial-temporal-frequency characterization of resting-state signals in WM.

The utilization of anionic redox is one of the most prospective strategies to obtain high specific capacities in addition to the transition metal redox. The specific local environments around the oxygen atoms lead to the transference of the oxygen electrons with different difficulties for the excess capacity of the cathode materials. Nevertheless, the structural and performance degradation is closely related to the irreversible anionic redox, resulting in serious challenges for the commercialization of the cathode materials. Therefore, it is necessary to summarize the correlation between the oxygen configuration for the anionic redox and the resultant structural features to better utilize the anionic redox for the cathode materials with better performances. This review summarizes the latest advances of oxygen redox in the cathode materials in terms of their oxygen configurations. First, the fundamental mechanisms of anionic redox are discussed. On the basis of the local atoms and configurations, the recent strategies are then discussed that are promising to boost the reversibility of the anionic redox and mitigate the side reaction triggered by the oxygen redox. At last, an outlook is presented on the strategies to optimize the reversibility of the anionic redox for achieving the high energy‐density cathode materials. Anionic redox is one of the most promising strategies to boost the high specific capacity in addition to the conventional transition metal redox. The various configurations around oxygen atoms facilitate the transference of electrons with different difficulties for the oxygen redox in cathode materials. This review summarizes the mechanisms of the anionic redox with different configurations and discusses the possible strategies for boosting the reversibility of the anionic redox and mitigating the side reaction triggered by the oxygen redox.

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.