Membralin is required for maize development and defines a branch of the endoplasmic reticulum-associated degradation pathway in plants.

Endoplasmic reticulum (ER)-associated degradation (ERAD) plays key roles in controlling protein levels and quality in eukaryotes. The Ring Finger Protein 185 (RNF185)/membralin ubiquitin ligase complex was recently identified as a branch in mammals and is essential for neuronal function, but its function in plant development is unknown. Here, we report the map-based cloning and characterization of Narrow Leaf and Dwarfism 1 (NLD1), which encodes the ER membrane-localized protein membralin and specifically interacts with maize homologs of RNF185 and related components. The nld1 mutant shows defective leaf and root development due to reduced cell number. The defects of nld1 were largely restored by expressing membralin genes from Arabidopsis thaliana and mice, highlighting the conserved roles of membralin proteins in animals and plants. The excessive accumulation of ß-hydroxy ß-methylglutaryl-CoA reductase in nld1 indicates that the enzyme is a membralin-mediated ERAD target. The activation of bZIP60 mRNA splicing-related unfolded protein response signaling and marker gene expression in nld1, as well as DNA fragment and cell viability assays, indicate that membralin deficiency induces ER stress and cell death in maize, thereby affecting organogenesis. Our findings uncover the conserved, indispensable role of the membralin-mediated branch of the ERAD pathway in plants. In addition, ZmNLD1 contributes to plant architecture in a dose-dependent manner, which can serve as a potential target for genetic engineering to shape ideal plant architecture, thereby enhancing high-density maize yields.

Quantification of mediation effects of white matter functional characteristics on cognitive decline in aging.

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.

Assessing White Matter Engagement in Brain Networks through Functional and Structural Connectivity Mapping.

Understanding the intricate interplay between gray matter (GM) and white matter (WM) is crucial for deciphering the complex activities of the brain. While diffusion tensor imaging (DTI) has advanced the mapping of these structural pathways, the relationship between structural connectivity (SC) and functional connectivity (FC) remains inadequately understood. This study addresses the need for a more integrative approach by mapping the importance of the inter-GM functional link to its structural counterparts in WM. This mapping yields a spatial distribution of engagement that is not only highly reproducible but also aligns with direct structural, functional, and bioenergetic measures within WM, illustrating a notable interdependence between the function of GM and the characteristics of WM. Additionally, our research has uncovered a set of unique engagement modes through a clustering analysis of window-wise engagement maps, highlighting the dyanmic nature of the engagement. The engagement along with their temporal variations revealed significant differences across genders and age groups. These findings suggest the potential of WM engagement as a biomarker for neurological and cognitive conditions, offering a more nuanced understanding of individualized brain activity and connectivity patterns.

The missing third dimension-Functional correlations of BOLD signals incorporating white matter.

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.

Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis.

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 of functional connectivity of human brains.

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.

Biofortification of different maize cultivars with zinc, iron and selenium by foliar fertilizer applications.

Fertilizer-based biofortification is a strategy for combating worldwide malnutrition of zinc (Zn), iron (Fe) and selenium (Se). Field experiments were conducted to investigate the effects of foliar treatments on concentrations of Zn, Fe, Se, N and bioavailability of Zn and Fe in grains of three maize cultivars grown at three locations. We compared the efficacy of ZnO nanoparticles (ZnO-NPs), Zn complexed chitosan nanoparticles (Zn-CNPs), conventional ZnSO4 and a cocktail solution (containing Zn, Fe and Se). All treatments were foliar-applied at rate of 452 mg Zn L-1, plus urea. Applying ten-fold less Zn (at rate of 45.2 mg Zn L-1) plus urea in the form of ZnO-NPs, Zn-CNPs, or ZnSO4 resulted in no increase, or a negligible increase, in grain Zn concentration compared with deionized water. By contrast, among the different Zn sources plus urea applied by foliar sprays, conventional ZnSO4 was the most efficient in improving grain Zn concentration. Furthermore, foliar application of a cocktail solution effectively improved grain concentrations of Zn, Fe, Se and N simultaneously, without a grain yield trade-off. For example, the average grain concentrations were simultaneously increased from 13.8 to 22.1 mg kg-1 for Zn, from 17.2 to 22.1 mg kg-1for Fe, from 21.4 to 413.5 ug kg-1 for Se and from 13.8 to 14.7 g kg-1 for N by foliar application of a cocktail solution. Because grain yield was significantly negatively correlated with grain nutrient concentrations, the magnitude of increase in grain concentrations of Zn and Fe was most pronounced in the maize cultivar with the lowest grain yield (Zhengdan958 grown in Linyi). Foliar application of a cocktail solution also significantly decreased the phytic acid (PA) concentration, ratios of PA/Fe and PA/Zn in grains, indicating an increased bioavailability of Fe and Zn for human health. In conclusion, we found that a foliar application of a cocktail solution including Zn, Fe, Se and N was most effective for biofortification, but that the grains with the lowest yield contained the greatest concentration of these elements. This finding highlights the need to breed maize varieties that are capable of achieving both high grain yield and high grain nutritional quality to address food security and human health challenges.

Automatic Preprocessing Pipeline for White Matter Functional Analyses of Large-Scale Databases.

Recently, increasing evidence suggests that fMRI signals in white matter (WM), conventionally ignored as nuisance, are robustly detectable using appropriate processing methods and are related to neural activity, while changes in WM with aging and degeneration are also well documented. These findings suggest variations in patterns of BOLD signals in WM should be investigated. However, existing fMRI analysis tools, which were designed for processing gray matter signals, are not well suited for large-scale processing of WM signals in fMRI data. We developed an automatic pipeline for high-performance preprocessing of fMRI images with emphasis on quantifying changes in BOLD signals in WM in an aging population. At the image processing level, the pipeline integrated existing software modules with fine parameter tunings and modifications to better extract weaker WM signals. The preprocessing results primarily included whole-brain time-courses, functional connectivity, maps and tissue masks in a common space. At the job execution level, this pipeline exploited a local XNAT to store datasets and results, while using DAX tool to automatic distribute batch jobs that run on high-performance computing clusters. Through the pipeline, 5,034 fMRI/T1 scans were preprocessed. The intraclass correlation coefficient (ICC) of test-retest experiment based on the preprocessed data is 0.52 - 0.86 (N=1000), indicating a high reliability of our pipeline, comparable to previously reported ICC in gray matter experiments. This preprocessing pipeline highly facilitates our future analyses on WM functional alterations in aging and may be of benefit to a larger community interested in WM fMRI studies.

Functional alterations in bipartite network of white and grey matters during aging.

The effects of normal aging on functional connectivity (FC) within various brain networks of gray matter (GM) have been well-documented. However, the age effects on the networks of FC between white matter (WM) and GM, namely WM-GM FC, remains unclear. Evaluating crucial properties, such as global efficiency (GE), for a WM-GM FC network poses a challenge due to the absence of closed triangle paths which are essential for assessing network properties in traditional graph models. In this study, we propose a bipartite graph model to characterize the WM-GM FC network and quantify these challenging network properties. Leveraging this model, we assessed the WM-GM FC network properties at multiple scales across 1,462 cognitively normal subjects aged 22-96 years from three repositories (ADNI, BLSA and OASIS-3) and investigated the age effects on these properties throughout adulthood and during late adulthood (age ≥70 years). Our findings reveal that (1) heterogeneous alterations occurred in region-specific WM-GM FC over the adulthood and decline predominated during late adulthood; (2) the FC density of WM bundles engaged in memory, executive function and processing speed declined with age over adulthood, particularly in later years; and (3) the GE of attention, default, somatomotor, frontoparietal and limbic networks reduced with age over adulthood, and GE of visual network declined during late adulthood. These findings provide unpresented insights into multi-scale alterations in networks of WM-GM functional synchronizations during normal aging. Furthermore, our bipartite graph model offers an extendable framework for quantifying WM-engaged networks, which may contribute to a wide range of neuroscience research.

Changes in white matter functional networks across late adulthood.

Introduction: The aging brain is characterized by decreases in not only neuronal density but also reductions in myelinated white matter (WM) fibers that provide the essential foundation for communication between cortical regions. Age-related degeneration of WM has been previously characterized by histopathology as well as T2 FLAIR and diffusion MRI. Recent studies have consistently shown that BOLD (blood oxygenation level dependent) effects in WM are robustly detectable, are modulated by neural activities, and thus represent a complementary window into the functional organization of the brain. However, there have been no previous systematic studies of whether or how WM BOLD signals vary with normal aging. We therefore performed a comprehensive quantification of WM BOLD signals across scales to evaluate their potential as indicators of functional changes that arise with aging. Methods: By using spatial independent component analysis (ICA) of BOLD signals acquired in a resting state, WM voxels were grouped into spatially distinct functional units. The functional connectivities (FCs) within and among those units were measured and their relationships with aging were assessed. On a larger spatial scale, a graph was reconstructed based on the pair-wise connectivities among units, modeling the WM as a complex network and producing a set of graph-theoretical metrics. Results: The spectral powers that reflect the intensities of BOLD signals were found to be significantly affected by aging across more than half of the WM units. The functional connectivities (FCs) within and among those units were found to decrease significantly with aging. We observed a widespread reduction of graph-theoretical metrics, suggesting a decrease in the ability to exchange information between remote WM regions with aging. Discussion: Our findings converge to support the notion that WM BOLD signals in specific regions, and their interactions with other regions, have the potential to serve as imaging markers of aging.

Research Progress on the Mechanism of Salt Tolerance in Maize: A Classic Field That Needs New Efforts.

Maize is the most important cereal crop globally. However, in recent years, maize production faced numerous challenges from environmental factors due to the changing climate. Salt stress is among the major environmental factors that negatively impact crop productivity worldwide. To cope with salt stress, plants developed various strategies, such as producing osmolytes, increasing antioxidant enzyme activity, maintaining reactive oxygen species homeostasis, and regulating ion transport. This review provides an overview of the intricate relationships between salt stress and several plant defense mechanisms, including osmolytes, antioxidant enzymes, reactive oxygen species, plant hormones, and ions (Na+, K+, Cl-), which are critical for salt tolerance in maize. It addresses the regulatory strategies and key factors involved in salt tolerance, aiming to foster a comprehensive understanding of the salt tolerance regulatory networks in maize. These new insights will also pave the way for further investigations into the significance of these regulations in elucidating how maize coordinates its defense system to resist salt stress.

A Riemannian Framework for Structurally Curated Functional Clustering of Brain White Matter Fibers.

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.

Whole-brain, gray and white matter time-locked functional signal changes with simple tasks and model-free analysis.

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 a task, challenging the idea of sparse and localized brain functions, and highlighting the pervasiveness of potential false negative fMRI findings. In these studies, 'whole-brain' refers to gray matter regions only, which is the only tissue traditionally studied with fMRI. However, recent reports have also demonstrated reliable detection and analyses of BOLD signals in white matter which have been largely ignored in previous reports. Here, using model-free analysis and simple tasks, we investigate BOLD signal changes in both white and gray matters. We aimed to evaluate whether white matter also displays time-locked BOLD signals across all structural pathways in response to a stimulus. 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 very 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 the whole brain, including both white and gray matter, show time-locked activation to multiple stimuli, not only challenging the idea of sparse functional localization, but also the prevailing wisdom of treating white matter BOLD signals as artefacts to be removed.

Anomalous and heterogeneous characteristics of the BOLD hemodynamic response function in white matter.

Detailed knowledge of the BOLD hemodynamic response function (HRF) is crucial for accurate analyses and interpretation of functional MRI data. Considerable efforts have been made to characterize the HRF in gray matter (GM), but much less attention has been paid to BOLD effects in white matter (WM). However, several recent reports have demonstrated reliable detection and analyses of WM BOLD signals both after stimulation and in a resting state. WM and GM differ in composition, energy requirements, and blood flow, so their neurovascular couplings also may well be different. We aimed to derive a comprehensive characterization of the HRF in WM across a population, including accurate measurements of its shape and its variation along and between WM pathways, using resting-state fMRI acquisitions. Our results show that the HRF is significantly different between WM and GM. Features of the HRF, such as a prominent initial dip, show strong relationships with features of the tissue microstructure derived from diffusion imaging, and these relationships differ between WM and GM, consistent with BOLD signal fluctuations reflecting different energy demands and neurovascular couplings in tissues of different composition and function. We also show that the HRF varies in shape significantly along WM pathways and is different between different WM pathways, suggesting the temporal evolution of BOLD signals after an event vary in different parts of the WM. These features of the HRF in WM are especially relevant for interpretation of the biophysical basis of BOLD effects in WM.

Detection of functional activity in brain white matter using fiber architecture informed synchrony mapping.

A general linear model is widely used for analyzing fMRI data, in which the blood oxygenation-level dependent (BOLD) signals in gray matter (GM) evoked in response to neural stimulation are modeled by convolving the time course of the expected neural activity with a canonical hemodynamic response function (HRF) obtained a priori. The maps of brain activity produced reflect the magnitude of local BOLD responses. However, detecting BOLD signals in white matter (WM) is more challenging as the BOLD signals are weaker and the HRF is different, and may vary more across the brain. Here we propose a model-free approach to detect changes in BOLD signals in WM by measuring task-evoked increases of BOLD signal synchrony in WM fibers. The proposed approach relies on a simple assumption that, in response to a functional task, BOLD signals in relevant fibers are modulated by stimulus-evoked neural activity and thereby show greater synchrony than when measured in a resting state, even if their magnitudes do not change substantially. This approach is implemented in two technical stages. First, for each voxel a fiber-architecture-informed spatial window is created with orientation distribution functions constructed from diffusion imaging data. This provides the basis for defining neighborhoods in WM that share similar local fiber architectures. Second, a modified principal component analysis (PCA) is used to estimate the synchrony of BOLD signals in each spatial window. The proposed approach is validated using a 3T fMRI dataset from the Human Connectome Project (HCP) at a group level. The results demonstrate that neural activity can be reliably detected as increases in fMRI signal synchrony within WM fibers that are engaged in a task with high sensitivities and reproducibility.

Functional Parcellation of Human Brain Using Localized Topo-Connectivity Mapping.

The analysis of connectivity between parcellated regions of cortex provides insights into the functional architecture of the brain at a systems level. However, the derivation of functional structures from voxel-wise analyses at finer scales remains a challenge. We propose a novel method, called localized topo-connectivity mapping with singular-value-decomposition-informed filtering (or filtered LTM), to identify and characterize voxel-wise functional structures in the human brain from resting-state fMRI data. Here we describe its mathematical formulation and provide a proof-of-concept using simulated data that allow an intuitive interpretation of the results of filtered LTM. The algorithm has also been applied to 7T fMRI data acquired as part of the Human Connectome Project to generate group-average LTM images. Generally, most of the functional structures revealed by LTM images agree in the boundaries with anatomical structures identified by T1-weighted images and fractional anisotropy maps derived from diffusion MRI. In addition, the LTM images also reveal subtle functional variations that are not apparent in the anatomical structures. To assess the performance of LTM images, the subcortical region and occipital white matter were separately parcellated. Statistical tests were performed to demonstrate that the synchronies of fMRI signals in LTM-derived functional parcels are significantly larger than those with geometric perturbations. Overall, the filtered LTM approach can serve as a tool to investigate the functional organization of the brain at the scale of individual voxels as measured in fMRI.

Dynamic variations of resting-state BOLD signal spectra in white matter.

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.

Correlated functional connectivity and glucose metabolism in brain white matter revealed by simultaneous MRI/positron emission tomography.

PURPOSE: There has been converging evidence of reliable detections of blood oxygenation level dependent (BOLD) signals evoked by neural stimulation and in a resting state in white matter (WM), within which few studies examined the relationship between BOLD functional signals and tissue metabolism. The purpose of the present study was to explore whether such relationship exists using combined functional MRI and positron emission tomography (PET) measurements of glucose uptake. METHODS: Functional and metabolic imaging data from 25 right-handed healthy human adults (aged 18-23 years, 18 females) were analyzed. Measures, including average resting state functional connectivity (FC) with respect to 82 Brodmann areas, fractional amplitude of low-frequency fluctuations (FALFF), and average fluorodeoxyglucose (FDG) uptake by PET, were computed for 48 predefined WM bundles. Pearson correlations across the bundles and 25 subjects studied were calculated among these measures. Linear mixed effects models were used to estimate the variance explainable by a predictor variable in the absence of inter-subject variations. RESULTS: Analysis of six separate imaging intervals found that average FC the bundles was significantly correlated with local FDG uptake (r = 0.25, p < 0.001), and the FC also covaried significantly with FALFF (r = 0.41, p < 0.001). When random effects from inter-subject variations were controlled, these correlations appeared to be medium to strong (r = 0.41 for FC vs. FDG uptake, and r = 0.65 for FALFF vs. FC). CONCLUSION: This study indicates that BOLD signals in WM are directly related to variations in metabolic demand and engagement with cortical processing and suggests they should be incorporated into more complete models of brain function.

Power spectra reveal distinct BOLD resting-state time courses in white matter.

Accurate characterization of the time courses of blood-oxygen-level-dependent (BOLD) signal changes is crucial for the analysis and interpretation of functional MRI data. While several studies have shown that white matter (WM) exhibits distinct BOLD responses evoked by tasks, there have been no comprehensive investigations into the time courses of spontaneous signal fluctuations in WM. We measured the power spectra of the resting-state time courses in a set of regions within WM identified as showing synchronous signals using independent components analysis. In each component, a clear separation between voxels into two categories was evident, based on their power spectra: one group exhibited a single peak, and the other had an additional peak at a higher frequency. Their groupings are location specific, and their distributions reflect unique neurovascular and anatomical configurations. Importantly, the two categories of voxels differed in their engagement in functional integration, revealed by differences in the number of interregional connections based on the two categories separately. Taken together, these findings suggest WM signals are heterogeneous in nature and depend on local structural-vascular-functional associations.

Lower functional connectivity of white matter during rest and working memory tasks is associated with cognitive impairments in schizophrenia.

BACKGROUND: Schizophrenia can be understood as a disturbance of functional connections within brain networks. However, functional alterations that involve white matter (WM) specifically, or their cognitive correlates, have seldomly been investigated, especially during tasks. METHODS: Resting state and task fMRI images were acquired on 84 patients and 67 controls. Functional connectivities (FC) between 46 WM bundles and 82 cortical regions were compared between the groups under two conditions (i.e., resting state and during working memory retention period). The FC density of each WM bundle was then compared between groups. Associations of FC with cognitive scores were evaluated. RESULTS: FC measures were lower in schizophrenia relative to controls for external capsule, cingulum (cingulate and hippocampus), uncinate fasciculus, as well as corpus callosum (genu and body) under the rest or the task condition, and were higher in the posterior corona radiata and posterior thalamic radiation during the task condition. FC for specific WM bundles was correlated with cognitive performance assessed by working memory and processing speed metrics. CONCLUSIONS: The findings suggest that the functional abnormalities in patients' WM are heterogeneous, possibly reflecting several underlying mechanisms such as structural damage, functional compensation and excessive effort on task, and that WM FC disruption may contribute to the impairments of working memory and processing speed. This is the first report on WM FC abnormalities in schizophrenia relative to controls and their cognitive associates during both rest and task and highlights the need to consider WM functions as components of brain functional networks in schizophrenia.