Effects of anesthesia on resting state BOLD signals in white matter of non-human primates

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.

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.

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.

Combined functional and structural imaging of brain white matter reveals stage-dependent impairment in multiple system atrophy of cerebellar type

Advances in fMRI of brain white matter (WM) have established the feasibility of understanding how functional signals of WM evolve with brain diseases. By combining functional signals with structural features of WM, the current study characterizes functional and structural impairments of WM in cerebelar type multiple system atrophy, with the goal to derive new mechanistic insights into the pathological progression of this disease. Our analysis of 30 well-diagnosed patients revealed pronounced decreases in functional connectivity in WM bundles of the cerebellum and brainstem, and concomitant local structural alterations that depended on the disease stage. The novel findings implicate a critical time point in the pathological evolution of the disease, which could guide optimal therapeutic interventions. Furthermore, fMRI signals of impaired WM bundles exhibited superior sensitivity in differentiating initial disease development, which demonstrates great potential of using these signals to inform disease management.

A Mouse Holder for Awake Functional Imaging in Unanesthetized Mice: Applications in 31P Spectroscopy, Manganese-Enhanced Magnetic Resonance Imaging Studies, and Resting-State Functional Magnetic Resonance Imaging

Anesthesia is often used in preclinical imaging studies that incorporate mouse or rat models. However, multiple reports indicate that anesthesia has significant physiological impacts. Thus, there has been great interest in performing imaging studies in awake, unanesthetized animals to obtain accurate results without the confounding physiological effects of anesthesia. Here, we describe a newly designed mouse holder that is interfaceable with existing MRI systems and enables awake in vivo mouse imaging. This holder significantly reduces head movement of the awake animal compared to previously designed holders and allows for the acquisition of improved anatomical images. In addition to applications in anatomical T2-weighted magnetic resonance imaging (MRI), we also describe applications in acquiring 31P spectra, manganese-enhanced magnetic resonance imaging (MEMRI) transport rates and resting-state functional magnetic resonance imaging (rs-fMRI) in awake animals and describe a successful conditioning paradigm for awake imaging. These data demonstrate significant differences in 31P spectra, MEMRI transport rates, and rs-fMRI connectivity between anesthetized and awake animals, emphasizing the importance of performing functional studies in unanesthetized animals. Furthermore, these studies demonstrate that the mouse holder presented here is easy to construct and use, compatible with standard Bruker systems for mouse imaging, and provides rigorous results in awake mice.

Investigation of synapses in the cortical white matter in human temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is one of the most common focal pharmacotherapy-resistant epilepsy in adults. Previous studies have shown significantly higher numbers of neurons in the neocortical white matter in TLE patients than in controls. The aim of this work was to investigate whether white matter neurons are part of the neuronal circuitry. Therefore, we studied the distribution and density of synapses in surgically resected neocortical tissue of pharmacotherapy-resistant TLE patients. Neocortical white matter of temporal lobe from non-epileptic patients were used as controls. Synapses and neurons were visualized with immunohistochemistry using antibodies against synaptophysin and NeuN, respectively. The presence of synaptophysin in presynaptic terminals was verified by electron microscopy. Quantification of immunostaining was performed and the data of the patients' cognitive tests as well as clinical records were compared to the density of neurons and synapses. Synaptophysin density in the white matter of TLE patients was significantly higher than in controls. In TLE, a significant correlation was found between synaptophysin immunodensity and density of white matter neurons. Neuronal as well as synaptophysin density significantly correlated with scores of verbal memory of TLE patients. Neurosurgical outcome of TLE patients did not significantly correlate with histological data, although, higher neuronal and synaptophysin densities were observed in patients with favorable post-surgical outcome. Our results suggest that white matter neurons in TLE patients receive substantial synaptic input and indicate that white matter neurons may be integrated in epileptic neuronal networks responsible for the development or maintenance of seizures.

Imaging functional neuroplasticity in human white matter tracts

Magnetic resonance imaging (MRI) studies are sensitive to biological mechanisms of neuroplasticity in white matter (WM). In particular, diffusion tensor imaging (DTI) has been used to investigate structural changes. Historically, functional MRI (fMRI) neuroplasticity studies have been restricted to gray matter, as fMRI studies have only recently expanded to WM. The current study evaluated WM neuroplasticity pre-post motor training in healthy adults, focusing on motor learning in the non-dominant hand. Neuroplasticity changes were evaluated in two established WM regions-of-interest: the internal capsule and the corpus callosum. Behavioral improvements following training were greater for the non-dominant hand, which corresponded with MRI-based neuroplasticity changes in the internal capsule for DTI fractional anisotropy, fMRI hemodynamic response functions, and low-frequency oscillations (LFOs). In the corpus callosum, MRI-based neuroplasticity changes were detected in LFOs, DTI, and functional correlation tensors (FCT). Taken together, the LFO results converged as significant amplitude reductions, implicating a common underlying mechanism of optimized transmission through altered myelination. The structural and functional neuroplasticity findings open new avenues for direct WM investigations into mapping connectomes and advancing MRI clinical applications.

A Longitudinal Study of White Matter Functional Network in Mild Traumatic Brain Injury

Some patients after mild traumatic brain injury (mTBI) experience microstructural damages in the long-distance white matter (WM) connections, which disrupts the functional connectome of large-scale brain networks that support cognitive function. Patterns of WM structural damage following mTBI were well documented using diffusion tensor imaging (DTI). However, the functional organization of WM and its association with gray matter functional networks (GM-FNs) and its DTI metrics remain unknown. The present study adopted resting-state functional magnetic resonance imaging to explore WM functional properties in mTBI patients (108 acute patients, 48 chronic patients, 46 healthy controls [HCs]). Eleven large-scale WM functional networks (WM-FNs) were constructed by the k-means clustering algorithm of voxel-wise WM functional connectivity (FC). Compared with HCs, acute mTBI patients observed enhanced FC between inferior fronto-occipital fasciculus (IFOF) WM-FN and primary sensorimotor WM-FNs, and cortical primary sensorimotor GM-FNs. Further, acute mTBI patients showed increased DTI metrics (mean diffusivity, axial diffusivity, and radial diffusivity) in deep WM-FNs and higher-order cognitive WM-FNs. Moreover, mTBI patients demonstrated full recovery of FC and partial recovery of DTI metrics in the chronic stage. Additionally, enhanced FC between IFOF WM-FN and anterior cerebellar GM-FN was correlated with impaired information processing speed. Our findings provide novel evidence for functional and structural alteration of WM-FNs in mTBI patients. Importantly, the convergent damage of the IFOF network might imply its crucial role in our understanding of the pathophysiology mechanism of mTBI patients.

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.

Concomitant modulation of BOLD responses in white matter pathways and cortex

In response to a flickering visual stimulus, the BOLD response in primary visual cortex varies with the flickering frequency and is maximal when it is close to 8Hz. In previous studies we demonstrated that BOLD signals in specific white matter (WM) pathways covary with the alternations between stimulus conditions in a block design in similar manner to gray matter (GM) regions. Here we investigated whether WM tracts show varying responses to changes in flicker frequency and are modulated in the same manner as cortical areas. We used a Fourier analysis of BOLD signals to measure the signal amplitude and phase at the fundamental frequency of a block-design task in which flickering visual stimuli alternated with blank presentations, avoiding the assumption of any specific hemodynamic response function. The BOLD responses in WM pathways and the primary visual cortex were evaluated for flicker frequencies varying between 2 and 14Hz. The variations with frequency of BOLD signals in specific WM tracts followed closely those in primary visual cortex, suggesting that variations in cortical activation are directly coupled to corresponding BOLD signals in connected WM tracts. Statistically significant differences in the timings of BOLD responses were also measured between visual cortex and specific WM bundles. These results confirm that when cortical BOLD responses are modulated by selecting different task parameters, relevant WM tracts exhibit corresponding BOLD signals that are also affected.

Resting-state connectivity stratifies premanifest Huntington's disease by longitudinal cognitive decline rate

Patient stratification is critical for the sensitivity of clinical trials at early stages of neurodegenerative disorders. In Huntington’s disease (HD), genetic tests make cognitive, motor and brain imaging measurements possible before symptom manifestation (pre-HD). We evaluated pre-HD stratification models based on single visit resting-state functional MRI (rs-fMRI) data that assess observed longitudinal motor and cognitive change rates from the multisite Track-On HD cohort (74 pre-HD, 79 control participants). We computed longitudinal performance change on 10 tasks (including visits from the preceding TRACK-HD study when available), as well as functional connectivity density (FCD) maps in single rs-fMRI visits, which showed high test-retest reliability. We assigned pre-HD subjects to subgroups of fast, intermediate, and slow change along single tasks or combinations of them, correcting for expectations based on aging; and trained FCD-based classifiers to distinguish fast- from slow-progressing individuals. For robustness, models were validated across imaging sites. Stratification models distinguished fast- from slow-changing participants and provided continuous assessments of decline applicable to the whole pre-HD population, relying on previously-neglected white matter functional signals. These results suggest novel correlates of early deterioration and a robust stratification strategy where a single MRI measurement provides an estimate of multiple ongoing longitudinal changes.

Altered Resting State Functional Activity and Microstructure of the White Matter in Migraine With Aura

Introduction: Brain structure and function were reported to be altered in migraine. Importantly our earlier results showed that white matter diffusion abnormalities and resting state functional activity were affected differently in the two subtypes of the disease, migraine with and without aura. Resting fluctuation of the BOLD signal in the white matter was reported recently. The question arising whether the white matter activity, that is strongly coupled with gray matter activity is also perturbed differentially in the two subtypes of the disease and if so, is it related to the microstructural alterations of the white matter.

Methods: Resting state fMRI, 60 directional DTI images and high-resolution T1 images were obtained from 51 migraine patients and 32 healthy volunteers. The images were pre-processed and the white matter was extracted. Independent component analysis was performed to obtain white matter functional networks. The differential expression of the white matter functional networks in the two subtypes of the disease was investigated with dual-regression approach. The Fourier spectrum of the resting fMRI fluctuations were compared between groups. Voxel-wise correlation was calculated between the resting state functional activity fluctuations and white matter microstructural measures.

Results: Three white matter networks were identified that were expressed differently in migraine with and without aura. Migraineurs with aura showed increased functional connectivity and amplitude of BOLD fluctuation. Fractional anisotropy and radial diffusivity showed strong correlation with the expression of the frontal white matter network in patients with aura.

Discussion: Our study is the first to describe changes in white matter resting state functional activity in migraine with aura, showing correlation with the underlying microstructure. Functional and structural differences between disease subtypes suggest at least partially different pathomechanism, which may necessitate handling of these subtypes as separate entities in further studies.

A Web-Based Atlas Combining MRI and Histology of the Squirrel Monkey Brain

The squirrel monkey (Saimiri sciureus) is a commonly-used surrogate for humans in biomedical research. In the neuroimaging community, MRI and histological atlases serve as valuable resources for anatomical, physiological, and functional studies of the brain; however, no digital MRI/histology atlas is currently available for the squirrel monkey. This paper describes the construction of a web-based multi-modal atlas of the squirrel monkey brain. The MRI-derived information includes anatomical MRI contrast (i.e., T2-weighted and proton-density-weighted) and diffusion MRI metrics (i.e., fractional anisotropy and mean diffusivity) from data acquired both in vivo and ex vivo on a 9.4 Tesla scanner. The histological images include Nissl and myelin stains, co-registered to the corresponding MRI, allowing identification of cyto- and myelo-architecture. In addition, a bidirectional neuronal tracer, biotinylated dextran amine (BDA) was injected into the primary motor cortex, enabling highly specific identification of regions connected to the injection location. The atlas integrates the results of common image analysis methods including diffusion tensor imaging glyphs, labels of 57 white-matter tracts identified using DTI-tractography, and 18 cortical regions of interest identified from Nissl-revealed cyto-architecture. All data are presented in a common space, and all image types are accessible through a web-based atlas viewer, which allows visualization and interaction of user-selectable contrasts and varying resolutions. By providing an easy to use reference system of anatomical information, our web-accessible multi-contrast atlas forms a rich and convenient resource for comparisons of brain findings across subjects or modalities. The atlas is called the Combined Histology-MRI Integrated Atlas of the Squirrel Monkey (CHIASM). All images are accessible through our web-based viewer ( https://chiasm.vuse.vanderbilt.edu /), and data are available for download at ( https://www.nitrc.org/projects/smatlas/ ).

Functional MRI and resting state connectivity in white matter - a mini-review

Functional MRI (fMRI) signals are robustly detectable in white matter (WM) but they have been largely ignored in the fMRI literature. Their nature, interpretation, and relevance as potential indicators of brain function remain under explored and even controversial. Blood oxygenation level dependent (BOLD) contrast has for over 25 years been exploited for detecting localized neural activity in the cortex using fMRI. While BOLD signals have been reliably detected in grey matter (GM) in a very large number of studies, such signals have rarely been reported from WM. However, it is clear from our own and other studies that although BOLD effects are weaker in WM, using appropriate detection and analysis methods they are robustly detectable both in response to stimuli and in a resting state. BOLD fluctuations in a resting state exhibit similar temporal and spectral profiles in both GM and WM, and their relative low frequency (0.01-0.1 Hz) signal powers are comparable. They also vary with baseline neural activity e.g. as induced by different levels of anesthesia, and alter in response to a stimulus. In previous work we reported that BOLD signals in WM in a resting state exhibit anisotropic temporal correlations with neighboring voxels. On the basis of these findings, we derived functional correlation tensors that quantify the correlational anisotropy in WM BOLD signals. We found that, along many WM tracts, the directional preferences of these functional correlation tensors in a resting state are grossly consistent with those revealed by diffusion tensors, and that external stimuli tend to enhance visualization of specific and relevant fiber pathways. These findings support the proposition that variations in WM BOLD signals represent tract-specific responses to neural activity. We have more recently shown that sensory stimulations induce explicit BOLD responses along parts of the projection fiber pathways, and that task-related BOLD changes in WM occur synchronously with the temporal pattern of stimuli. WM tracts also show a transient signal response following short stimuli analogous to but different from the hemodynamic response function (HRF) characteristic of GM. Thus there is converging and compelling evidence that WM exhibits both resting state fluctuations and stimulus-evoked BOLD signals very similar (albeit weaker) to those in GM. A number of studies from other laboratories have also reported reliable observations of WM activations. Detection of BOLD signals in WM has been enhanced by using specialized tasks or modified data analysis methods. In this mini-review we report summaries of some of our recent studies that provide evidence that BOLD signals in WM are related to brain functional activity and deserve greater attention by the neuroimaging community.

Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages

High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative prognosis for this cohort can lead to better treatment planning. Conventional survival prediction based on clinical information is subjective and could be inaccurate. Recent radiomics studies have shown better prognosis by using carefully-engineered image features from magnetic resonance images (MRI). However, feature engineering is usually time consuming, laborious and subjective. Most importantly, the engineered features cannot effectively encode other predictive but implicit information provided by multi-modal neuroimages. We propose a two-stage learning-based method to predict the overall survival (OS) time of high-grade gliomas patient. At the first stage, we adopt deep learning, a recently dominant technique of artificial intelligence, to automatically extract implicit and high-level features from multi-modal, multi-channel preoperative MRI such that the features are competent of predicting survival time. Specifically, we utilize not only contrast-enhanced T1 MRI, but also diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI), for computing multiple metric maps (including various diffusivity metric maps derived from DTI, and also the frequency-specific brain fluctuation amplitude maps and local functional connectivity anisotropy-related metric maps derived from rs-fMRI) from 68 high-grade glioma patients with different survival time. We propose a multi-channel architecture of 3D convolutional neural networks (CNNs) for deep learning upon those metric maps, from which high-level predictive features are extracted for each individual patch of these maps. At the second stage, those deeply learned features along with the pivotal limited demographic and tumor-related features (such as age, tumor size and histological type) are fed into a support vector machine (SVM) to generate the final prediction result (i.e., long or short overall survival time). The experimental results demonstrate that this multi-model, multi-channel deep survival prediction framework achieves an accuracy of 90.66%, outperforming all the competing methods. This study indicates highly demanded effectiveness on prognosis of deep learning technique in neuro-oncological applications for better individualized treatment planning towards precision medicine.

Resting-state white matter-cortical connectivity in non-human primate brain

Numerous studies have used functional magnetic resonance imaging (fMRI) to characterize functional connectivity between cortical regions by analyzing correlations in blood oxygenation level dependent (BOLD) signals in a resting state. However, to date, there have been only a handful of studies reporting resting state BOLD signals in white matter. Nonetheless, a growing number of reports has emerged in recent years suggesting white matter BOLD signals can be reliably detected, though their biophysical origins remain unclear. Moreover, recent studies have identified robust correlations in a resting state between signals from cortex and specific white matter tracts. In order to further validate and interpret these findings, we studied a non-human primate model to investigate resting-state connectivity patterns between parcellated cortical volumes and specific white matter bundles. Our results show that resting-state connectivity patterns between white and gray matter structures are not randomly distributed but share notable similarities with diffusion- and histology-derived anatomic connectivities. This suggests that resting-state BOLD correlations between white matter fiber tracts and the gray matter regions to which they connect are directly related to the anatomic arrangement and density of WM fibers. We also measured how different levels of baseline neural activity, induced by varying levels of anesthesia, modulate these patterns. As anesthesia levels were raised, we observed weakened correlation coefficients between specific white matter tracts and gray matter regions while key features of the connectivity pattern remained similar. Overall, results from this study provide further evidence that neural activity is detectable by BOLD fMRI in both gray and white matter throughout the resting brain. The combined use of gray and white matter functional connectivity could also offer refined full-scale functional parcellation of the entire brain to characterize its functional architecture.

Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline

We measured and compared heritability estimates for measures of functional brain connectivity extracted using the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) rsfMRI analysis pipeline in two cohorts: the genetics of brain structure (GOBS) cohort and the HCP (the Human Connectome Project) cohort. These two cohorts were assessed using conventional (GOBS) and advanced (HCP) rsfMRI protocols, offering a test case for harmonization of rsfMRI phenotypes, and to determine measures that show consistent heritability for in-depth genome-wide analysis. The GOBS cohort consisted of 334 Mexican-American individuals (124M/210F, average age = 47.9 ± 13.2 years) from 29 extended pedigrees (average family size = 9 people; range 5-32). The GOBS rsfMRI data was collected using a 7.5-min acquisition sequence (spatial resolution = 1.72 × 1.72 × 3 mm³ ). The HCP cohort consisted of 518 twins and family members (240M/278F; average age = 28.7 ± 3.7 years). rsfMRI data was collected using 28.8-min sequence (spatial resolution = 2 × 2 × 2 mm³ ). We used the single-modality ENIGMA rsfMRI preprocessing pipeline to estimate heritability values for measures from eight major functional networks, using (1) seed-based connectivity and (2) dual regression approaches. We observed significant heritability (h² = 0.2-0.4, p < .05) for functional connections from seven networks across both cohorts, with a significant positive correlation between heritability estimates across two cohorts. The similarity in heritability estimates for resting state connectivity measurements suggests that the additive genetic contribution to functional connectivity is robustly detectable across populations and imaging acquisition parameters. The overarching genetic influence, and means to consistently detect it, provides an opportunity to define a common genetic search space for future gene discovery studies.

Effects of Tissue-Specific Functional Magnetic Resonance Imaging Signal Regression on Resting-State Functional Connectivity

Neuroimaging studies typically consider white matter as unchanging in different neural and metabolic states. However, a recent study demonstrated that white matter signal regression (WMSR) produced a similar loss of neurometabolic information to global (whole-brain) signal regression (GSR) in resting-state functional magnetic resonance imaging (R-fMRI) data. This was unexpected as the loss of information would normally be attributed to neural activity within gray matter correlating with the global R-fMRI signal. Indeed, WMSR has been suggested as an alternative to avoid such pitfalls in GSR. To address these concerns about tissue-specific regression in R-fMRI data analysis, we performed GSR, WMSR, and gray matter signal regression (GMSR) on R-fMRI data from the 1000 Functional Connectomes Project. We describe several regional and motion-related differences between different types of regressions. However, the overall effects of concern, particularly network-specific alteration of correlation coefficients, are present for all regressions. This suggests that tissue-specific regression is not an adequate strategy to counter pitfalls of GSR. Conversely, if GSR is desired, but the studied disease state excludes either gray matter or white matter from analysis (e.g., due to tissue atrophy), our results indicate that WMSR or GMSR may reproduce the gross effects of GSR.

Detection of synchronous brain activity in white matter tracts at rest and under functional loading

Functional MRI based on blood oxygenation level-dependent (BOLD) contrast is well established as a neuroimaging technique for detecting neural activity in the cortex of the human brain. While detection and characterization of BOLD signals, as well as their electrophysiological and hemodynamic/metabolic origins, have been extensively studied in gray matter (GM), the detection and interpretation of BOLD signals in white matter (WM) remain controversial. We have previously observed that BOLD signals in a resting state reveal structure-specific anisotropic temporal correlations in WM and that external stimuli alter these correlations and permit visualization of task-specific fiber pathways, suggesting variations in WM BOLD signals are related to neural activity. In this study, we provide further strong evidence that BOLD signals in WM reflect neural activities both in a resting state and under functional loading. We demonstrate that BOLD signal waveforms in stimulus-relevant WM pathways are synchronous with the applied stimuli but with various degrees of time delay and that signals in WM pathways exhibit clear task specificity. Furthermore, resting-state signal fluctuations in WM tracts show significant correlations with specific parcellated GM volumes. These observations support the notion that neural activities are encoded in WM circuits similarly to cortical responses.

An interspecies comparative study of invasive electrophysiological functional connectivity

INTRODUCTION

Resting-state connectivity patterns have been observed in humans and other mammal species, and can be recorded using a variety of different technologies. Functional connectivity has been previously compared between species using resting-state fMRI, but not in electrophysiological studies.

METHODS

We compared connectivity with implanted electrodes in humans (electrocorticography) to macaques and sheep (microelectrocorticography), which are capable of recording neural data at high frequencies with spatial precision. We specifically examined synchrony, implicated in functional integration between regions.

RESULTS

We found that connectivity strength was overwhelmingly similar in humans and monkeys for pairs of two different brain regions (prefrontal, motor, premotor, parietal), but differed more often within single brain regions. The two connectivity measures, correlation and phase locking value, were similar in most comparisons. Connectivity strength agreed more often between the species at higher frequencies. Where the species differed, monkey synchrony was stronger than human in all but one case. In contrast, human and sheep connectivity within somatosensory cortex diverged in almost all frequencies, with human connectivity stronger than sheep.

DISCUSSION

Our findings imply greater heterogeneity within regions in humans than in monkeys, but comparable functional interactions between regions in the two species. This suggests that monkeys may be effectively used to probe resting-state connectivity in humans, and that such findings can then be validated in humans. Although the discrepancy between humans and sheep is larger, we suggest that findings from sheep in highly invasive studies may be used to provide guidance for studies in other species.

Evidence for Functional Networks within the Human Brain's White Matter

Investigation of the functional macro-scale organization of the human cortex is fundamental in modern neuroscience. Although numerous studies have identified networks of interacting functional modules in the gray-matter, limited research was directed to the functional organization of the white-matter. Recent studies have demonstrated that the white-matter exhibits blood oxygen level-dependent signal fluctuations similar to those of the gray-matter. Here we used these signal fluctuations to investigate whether the white-matter is organized as functional networks by applying a clustering analysis on resting-state functional MRI (RSfMRI) data from white-matter voxels, in 176 subjects (of both sexes). This analysis indicated the existence of 12 symmetrical white-matter functional networks, corresponding to combinations of white-matter tracts identified by diffusion tensor imaging. Six of the networks included interhemispheric commissural bridges traversing the corpus callosum. Signals in white-matter networks correlated with signals from functional gray-matter networks, providing missing knowledge on how these distributed networks communicate across large distances. These findings were replicated in an independent subject group and were corroborated by seed-based analysis in small groups and individual subjects. The identified white-matter functional atlases and analysis codes are available at http://mind.huji.ac.il/white-matter.aspx Our results demonstrate that the white-matter manifests an intrinsic functional organization as interacting networks of functional modules, similarly to the gray-matter, which can be investigated using RSfMRI. The discovery of functional networks within the white-matter may open new avenues of research in cognitive neuroscience and clinical neuropsychiatry.SIGNIFICANCE STATEMENT In recent years, functional MRI (fMRI) has revolutionized all fields of neuroscience, enabling identifications of functional modules and networks in the human brain. However, most fMRI studies ignored a major part of the brain, the white-matter, discarding signals from it as arising from noise. Here we use resting-state fMRI data from 176 subjects to show that signals from the human white-matter contain meaningful information. We identify 12 functional networks composed of interacting long-distance white-matter tracts. Moreover, we show that these networks are highly correlated to resting-state gray-matter networks, highlighting their functional role. Our findings enable reinterpretation of many existing fMRI datasets, and suggest a new way to explore the white-matter role in cognition and its disturbances in neuropsychiatric disorders.

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc.

Functional connectivity and activity of white matter in somatosensory pathways under tactile stimulations

Functional MRI has proven to be effective in detecting neural activity in brain cortices on the basis of blood oxygenation level dependent (BOLD) contrast, but has relatively poor sensitivity for detecting neural activity in white matter. To demonstrate that BOLD signals in white matter are detectable and contain information on neural activity, we stimulated the somatosensory system and examined distributions of BOLD signals in related white matter pathways. The temporal correlation profiles and frequency contents of BOLD signals were compared between stimulation and resting conditions, and between relevant white matter fibers and background regions, as well as between left and right side stimulations. Quantitative analyses show that, overall, MR signals from white matter fiber bundles in the somatosensory system exhibited significantly greater temporal correlations with the primary sensory cortex and greater signal power during tactile stimulations than in a resting state, and were stronger than corresponding measurements for background white matter both during stimulations and in a resting state. The temporal correlation and signal power under stimulation were found to be twice those observed from the same bundle in a resting state, and bore clear relations with the side of stimuli. These indicate that BOLD signals in white matter fibers encode neural activity related to their functional roles connecting cortical volumes, which are detectable with appropriate methods.

Biophysical and neural basis of resting state functional connectivity: Evidence from non-human primates

Functional MRI (fMRI) has evolved from simple observations of regional changes in MRI signals caused by cortical activity induced by a task or stimulus, to task-free acquisitions of images in a resting state. Such resting state signals contain low frequency fluctuations which may be correlated between voxels, and strongly correlated regions are deemed to reflect functional connectivity within synchronized circuits. Resting state functional connectivity (rsFC) measures have been widely adopted by the neuroscience community, and are being used and interpreted as indicators of intrinsic neural circuits and their functional states in a broad range of applications, both basic and clinical. However, there has been relatively little work reported that validates whether inter-regional correlations in resting state fluctuations of fMRI (rsfMRI) signals actually measure functional connectivity between brain regions, or to establish how MRI data correlate with other metrics of functional connectivity. In this mini-review, we summarize recent studies of rsFC within mesoscopic scale cortical networks (100μm-10mm) within a well defined functional region of primary somatosensory cortex (S1), as well as spinal cord and brain white matter in non-human primates, in which we have measured spatial patterns of resting state correlations and validated their interpretation with electrophysiological signals and anatomic connections. Moreover, we emphasize that low frequency correlations are a general feature of neural systems, as evidenced by their presence in the spinal cord as well as white matter. These studies demonstrate the valuable role of high field MRI and invasive measurements in an animal model to inform the interpretation of human imaging studies.

Arterial spin labeling perfusion magnetic resonance imaging of non-human primates

Non-human primates (NHPs) resemble most aspects of humans in brain physiology and anatomy and are excellent animal models for translational research in neuroscience, biomedical research and pharmaceutical development. Cerebral blood flow (CBF) offers essential physiological information of the brain to examine the abnormal functionality in NHP models with cerebral vascular diseases and neurological disorders or dementia. Arterial spin labeling (ASL) perfusion MRI techniques allow for high temporal and spatial CBF measurement and are intensively used in studies of animals and humans. In this article, current high-resolution ASL perfusion MRI techniques for quantitative evaluation of brain physiology and function in NHPs are described and their applications and limitation are discussed as well.