Characteristic BOLD signals are detectable in white matter of the spinal cord at rest and after a stimulus.

We report the reliable detection of reproducible patterns of blood-oxygenation-level-dependent (BOLD) MRI signals within the white matter (WM) of the spinal cord during a task and in a resting state. Previous functional MRI studies have shown that BOLD signals are robustly detectable not only in gray matter (GM) in the brain but also in cerebral WM as well as the GM within the spinal cord, but similar signals in WM of the spinal cord have been overlooked. In this study, we detected BOLD signals in the WM of the spinal cord in squirrel monkeys and studied their relationships with the locations and functions of ascending and descending WM tracts. Tactile sensory stimulus -evoked BOLD signal changes were detected in the ascending tracts of the spinal cord using a general-linear model. Power spectral analysis confirmed that the amplitude at the fundamental frequency of the response to a periodic stimulus was significantly higher in the ascending tracts than the descending ones. Independent component analysis of resting-state signals identified coherent fluctuations from eight WM hubs which correspond closely to the known anatomical locations of the major WM tracts. Resting-state analyses showed that the WM hubs exhibited correlated signal fluctuations across spinal cord segments in reproducible patterns that correspond well with the known neurobiological functions of WM tracts in the spinal cord. Overall, these findings provide evidence of a functional organization of intraspinal WM tracts and confirm that they produce hemodynamic responses similar to GM both at baseline and under stimulus conditions.

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.

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.

White matter engagement in brain networks assessed by integration of functional and structural connectivity.

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.

Layer-specific BOLD effects in gradient and spin-echo acquisitions in somatosensory cortex.

PURPOSE: Previous studies have shown varied BOLD signals with gradient echo (GE) across cortical depth. To interpret these variations, and understand the effects of vascular geometry and size, the magnitudes and layer distributions of GE and spin-echo (SE) BOLD functional MRI signals were compared in the somatosensory cortex of squirrel monkeys during tactile stimulation and in a resting state at high spatial resolution and high field. METHODS: A block-design stimulation was used to identify tactile-evoked activation signals in somatosensory Areas 3b and 1. Layer-specific connectivities were calculated using resting-state data. Signal power spectra were compared by depth and pulse sequence. The measured ratios of transverse relaxation rate changes were compared with Anderson and Weiss's model. RESULTS: SE signals showed a 26% lower percentage signal change during tactile stimulation compared with GE, along with a slower time course. SE signals remained consistent but weaker in lower layers, whereas GE signals decreased with cortical depth. This pattern extended to resting-state power spectra. Resting-state functional connectivity indicated larger connectivity between the top layers of Area 3b and Area 1 for GE, with minimal changes for SE. Comparisons with theory suggest vessel diameters ranging from 19.4 to 9 microns are responsible for BOLD effects across cortical layers at 9.4 T. CONCLUSION: These results provide further evidence that at high field, SE BOLD signals are relatively free of contributions from sources other than microvascular changes in response to neural activity, whereas GE signals, even in the superficial layers, are not dominated by very large vessels.

Radiofrequency-transparent local B0 shimming coils using float traps.

PURPOSE: Static field (B0) inhomogeneities present a major challenge in high-field MRI. Multicoil shimming using independent, local, direct-current (DC) shim coils has emerged as a powerful and flexible technique to address this issue. However, many-turn DC coils can lead to significant mutual coupling with radiofrequency (RF) coils, causing transmit field (B1 +) distortions and signal-to-noise ratio degradation. METHODS: We introduce an innovative RF-transparent DC coil that performs B0 shimming while minimizing RF performance impact. The design incorporates float traps to maintain high RF impedance, allowing flexible placement relative to the RF coil without compromising signal-to-noise ratio or affecting B1 +. We fabricated square-shaped DC coils with float traps for 3T MRI and compared them with conventional DC coils. To demonstrate high ΔB0/Amp efficiency, we conducted a B0 shimming experiment around a metal hip implant. RESULTS: Bench tests and MRI experimental results demonstrated that the RF-transparent DC coil effectively minimized RF interference, preserved signal-to-noise ratio, and maintained B1 +, even when placed near the RF receive coil. Additionally, the DC coil significantly improved B0 homogeneity near metal implants and substantially reduced image distortion. CONCLUSION: The RF-transparent DC coil offers a flexible, effective solution for managing B0 inhomogeneities, paving the way for integrating multiturn DC coils in clinical MRI settings without extensive hardware modifications.

Detection of Treatment Response in Triple-Negative Breast Tumors to Paclitaxel Using MRI Cell Size Imaging.

BACKGROUND: Breast cancer treatment response evaluation using the response evaluation criteria in solid tumors (RECIST) guidelines, based on tumor volume changes, has limitations, prompting interest in novel imaging markers for accurate therapeutic effect determination. PURPOSE: To use MRI-measured cell size as a new imaging biomarker for assessing chemotherapy response in breast cancer. STUDY TYPE: Longitudinal; animal model. STUDY POPULATION: Triple-negative human breast cancer cell (MDA-MB-231) pellets (4 groups, n = 7) treated with dimethyl sulfoxide (DMSO) or 10 nM of paclitaxel for 24, 48, and 96 hours, and 29 mice with MDA-MB-231 tumors in right hind limbs treated with paclitaxel (n = 16) or DMSO (n = 13) twice weekly for 3 weeks. FIELD STRENGTH/SEQUENCE: Oscillating gradient spin echo and pulsed gradient spin echo sequences at 4.7 T. ASSESSMENT: MDA-MB-231 cells were analyzed using flowcytometry and light microscopy to assess cell cycle phases and cell size distribution. MDA-MB-231 cell pellets were MR imaged. Mice were imaged weekly, with 9, 6, and 14 being sacrificed for histology after MRI at weeks 1, 2, and 3, respectively. Microstructural parameters of tumors/cell pellets were derived by fitting diffusion MRI data to a biophysical model. STATISTICAL TESTS: One-way ANOVA compared cell sizes and MR-derived parameters between treated and control samples. Repeated measures 2-way ANOVA with Bonferroni post-tests compared temporal changes in MR-derived parameters. A P-value <0.05 was considered statistically significant. RESULTS: In vitro experiments showed that the mean MR-derived cell sizes of paclitaxel-treated cells increased significantly with a 24-hours treatment and decreased (P = 0.06) with a 96-hour treatment. For in vivo xenograft experiments, the paclitaxel-treated tumors showed significant decreases in cell size at later weeks. MRI observations were supported by flowcytometry, light microscopy, and histology. DATA CONCLUSIONS: MR-derived cell size may characterize the cell shrinkage during treatment-induced apoptosis, and may potentially provide new insights into the assessment of therapeutic response. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 4.

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.

Reductions in the white-gray functional connectome in preclinical Alzheimer's disease and their associations with amyloid and cognition.

BACKGROUND: The magnitudes and patterns of alterations of the white-gray matter (WM-GM) functional connectome in preclinical Alzheimer's disease (AD), and their associations with amyloid and cognition, remain unclear. METHODS: We compared regional WM-GM functional connectivity (FC) and network properties in subjects with preclinical AD (or AD dementia) and controls (total n = 344). Their associations with positron emission tomography AV45-measured amyloid beta (Aß) load and modified Preclinical Alzheimer Cognitive Composite (mPACC) scores were examined. RESULTS: Preclinical AD subjects showed lower FC in specific WM-GM pairs and reduced segregation of control, dorsal attention, and somatomotor networks. A major portion of the reduced FC and network segregations were linked to elevated Aß. Reduced FC of one WM-GM pair correlated with impaired mPACC. AD dementia exhibited broader reductions and stronger associations. DISCUSSION: The WM-GM functional connectome undergoes regional and systemic dysfunctions as early as in the preclinical stage, correlating with amyloid deposition and predicting cognitive impairment. HIGHLIGHTS: Preclinical Alzheimer's disease (AD) subjects showed lower functional connectivity in specific white-gray matter (WM-GM) pairs and reduced segregations of control, dorsal attention, and somatomotor networks. A major portion of the reduced connectivity and network segregations were linked to elevated amyloid beta load. Only one WM-GM pair's reduced connectivity was linearly correlated with impaired cognitive composite scores. AD dementia showed more extensive reductions in connectivity, network integration, and segregation, with stronger associations with amyloid elevation and cognitive impairment. The WM-GM functional connectome offers a distinct perspective for understanding changes in brain functional architecture throughout the AD continuum.

Differential Recovery of Submodality Touch Neurons and Interareal Communication in Sensory Input-Deprived Area 3b and S2 Cortices.

Cortical reactivation and regain of interareal functional connections have been linked to the recovery of hand grasping behavior after loss of sensory inputs in primates. We investigated contributions of neurons in two hierarchically organized somatosensory areas, 3b and S2, by characterizing local field potential (LFP) and multiunit spiking activity in five states (rest, stimulus-on, sustained, stimulus-off, and induced) and interareal communication after grasping behavior of dorsal column lesioned male squirrel monkeys had mostly recovered. Compared with normal cortex, fMRI, LFP, and spiking response magnitudes to step indentations were significantly weaker. The sustained component of the spiking recovered much better than the stimulus-off response. Correlation between overall spiking and γ LFP remained strong within each recovered areas 3b and S2. The interareal correlations of γ LFP were severely disrupted, except in the resting and stimulus-on periods. Interareal correlation of spiking was disrupted in the stimulus-off period only. In summary, submodality of low threshold mechanoreceptive neurons recovered differentially in input-deprived area 3b and S2 when impaired global hand grasping behavior returned. Slow-adapting-like neurons recovered, whereas rapid-adapting-like neurons did not. Interareal communications were also severely compromised. We propose that slow-adapting-like neurons and afferents in recovered area 3b and S2 mediate recovery of impaired grasping behavior after dorsal column tract lesion.SIGNIFICANCE STATEMENT Sensory feedback is essential for execution of hand grasping behavior in primates. Reactivations of somatosensory cortices have been attributed to recovery of such behavior after loss of sensory inputs via largely unknown mechanisms. In input-deprived area 3b and S2 cortex, after hand grasping behavior mostly recovered, we found slow-adapting-like neurons were greatly recovered, whereas rapid-adapting-like neurons did not. Communications between area 3b and S2 neurons were severely compromised. We suggest that recovery of slow-adapting-like neurons in input-deprived area 3b and S2 may mediate the recovery of hand grasping behavior.

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.

Diffusion time dependency of extracellular diffusion.

PURPOSE: To quantify the variations of the power-law dependences on diffusion time t or gradient frequency f $$ f $$ of extracellular water diffusion measured by diffusion MRI (dMRI). METHODS: Model cellular systems containing only extracellular water were used to investigate the t / f $$ t/f $$ dependence of D ex $$ {D}_{ex} $$ , the extracellular diffusion coefficient. Computer simulations used a randomly packed tissue model with realistic intracellular volume fractions and cell sizes. DMRI measurements were performed on samples consisting of liposomes containing heavy water(D2 O, deuterium oxide) dispersed in regular water (H2 O). D ex $$ {D}_{ex} $$ was obtained over a broad t $$ t $$ range (∼1-1000 ms) and then fit power-law equations D ex ( t ) = D const + const · t - ϑ t $$ {D}_{ex}(t)={D}_{\mathrm{const}}+\mathrm{const}\cdotp {t}^{-{\vartheta}_t} $$ and D ex ( f ) = D const + const · f ϑ f $$ {D}_{ex}(f)={D}_{\mathrm{const}}+\mathrm{const}\cdotp {f}^{\vartheta_f} $$ . RESULTS: Both simulated and experimental results suggest that no single power-law adequately describes the behavior of D ex $$ {D}_{ex} $$ over the range of diffusion times of most interest in practical dMRI. Previous theoretical predictions are accurate over only limited t $$ t $$ ranges; for example, θ t = θ f = - 1 2 $$ {\theta}_t={\theta}_f=-\frac{1}{2} $$ is valid only for short times, whereas θ t = 1 $$ {\theta}_t=1 $$ or θ f = 3 2 $$ {\theta}_f=\frac{3}{2} $$ is valid only for long times but cannot describe other ranges simultaneously. For the specific t $$ t $$ range of 5-70 ms used in typical human dMRI measurements, θ t = θ f = 1 $$ {\theta}_t={\theta}_f=1 $$ matches the data well empirically. CONCLUSION: The optimal power-law fit of extracellular diffusion varies with diffusion time. The dependency obtained at short or long t $$ t $$ limits cannot be applied to typical dMRI measurements in human cancer or liver. It is essential to determine the appropriate diffusion time range when modeling extracellular diffusion in dMRI-based quantitative microstructural imaging.

Detection of laser-associated heating in the brain during simultaneous fMRI and optogenetic stimulation.

PURPOSE: To calculate temperatures from T2 *-weighted images collected during optogenetic fMRI based on proton resonance frequency (PRF) shift thermometry, to monitor confounding heating effects and determine appropriate light parameters for optogenetic stimulation. METHODS: fMRI is mainly based on long-TE gradient-recalled echo acquisitions that are also suitable for measuring small temperature changes via the PRF shift. A motion- and respiration-robust processing pipeline was developed to calculate temperature changes based on the PRF shift directly from the T2 *-weighted images collected for fMRI with a two-shot 2D gradient-recalled echo-EPI sequence at 9.4T. Optogenetic fMRI protocols which differed in stimulation durations (3, 6 and 9 s) within a total block duration of 30 s were applied in a squirrel monkey to validate the methods with blue and green light (20 Hz, 30 mW) delivery interleaved between periods. General linear modeling was performed on the resulting time series temperature maps to verify if light delivery with each protocol resulted in significant heating in the brain around the optical fiber. RESULTS: The temperature SD was 0.05°C with the proposed imaging protocol and processing. Statistical analysis showed that the optogenetic stimulation protocol with a 3 s stimulation duration did not result in significant temperature rises. Significant temperature rises up to 0.13°C (p < 0. 05) were observed with 6 and 9 s stimulation durations for blue and green light. CONCLUSION: The proposed processing pipeline can be useful for the design of optogenetic stimulation protocols and for monitoring confounding heating effects.

Severity of polycystic kidney disease revealed by multiparametric MRI.

PURPOSE: We aimed to compare multiple MRI parameters, including relaxation rates ( R 1 $$ {R}_1 $$ , R 2 $$ {R}_2 $$ , and R 1 ρ $$ {R}_{1\rho } $$ ), ADC from diffusion weighted imaging, pool size ratio (PSR) from quantitative magnetization transfer, and measures of exchange from spin-lock imaging ( S ρ $$ {S}_{\rho } $$ ), for assessing and predicting the severity of polycystic kidney disease (PKD) over time. METHODS: Pcy/Pcy mice with CD1 strain, a mouse model of autosomal dominant PKD, were imaged at 5, 9, and 26 wk of age using a 7T MRI system. Twelve-week normal CD1 mice were used as controls. Post-mortem paraffin tissue sections were stained using hematoxylin and eosin and picrosirius red to identify histological changes. RESULTS: Histology detected segmental cyst formation in the early stage (week 5) and progression of PKD over time in Pcy kidneys. In T 2 $$ {T}_2 $$ -weighted images, small cysts appeared locally in cystic kidneys in week 5 and gradually extended to the whole cortex and outer stripe of outer medulla region from week 5 to week 26. Regional PSR, R 1 $$ {R}_1 $$ , R 2 $$ {R}_2 $$ , and R 1 ρ $$ {R}_{1\rho } $$ decreased consistently over time compared to normal kidneys, with significant changes detected in week 5. Among all the MRI measures, R 2 $$ {R}_2 $$ and R 1 ρ $$ {R}_{1\rho } $$ allow highest detectability to PKD, while PSR and R 1 $$ {R}_1 $$ have highest correlation with pathological indices of PKD. Using optimum MRI parameters as regressors, multiple linear regression provides reliable prediction of PKD progression. CONCLUSION: R 2 $$ {R}_2 $$ , R 1 $$ {R}_1 $$ , and PSR are sensitive indicators of the presence of PKD. Multiparametric MRI allows a comprehensive analysis of renal changes caused by cyst formation and expansion.

J-Difference editing (MEGA) of lactate in the human brain at 3T.

PURPOSE: The need to detect and quantify brain lactate accurately by MRS has stimulated the development of editing sequences based on J coupling effects. In J-difference editing of lactate, threonine can be co-edited and it contaminates lactate estimates due to the spectral proximity of the coupling partners of their methyl protons. We therefore implemented narrow-band editing 180° pulses (E180) in MEGA-PRESS acquisitions to resolve separately the 1.3-ppm resonances of lactate and threonine. METHODS: Two 45.3-ms rectangular E180 pulses, which had negligible effects 0.15-ppm away from the carrier frequency, were implemented in a MEGA-PRESS sequence with TE 139 ms. Three acquisitions were designed to selectively edit lactate and threonine, in which the E180 pulses were tuned to 4.1 ppm, 4.25 ppm, and a frequency far off resonance. Editing performance was validated with numerical analyses and acquisitions from phantoms. The narrow-band E180 MEGA and another MEGA-PRESS sequence with broad-band E180 pulses were evaluated in six healthy subjects. RESULTS: The 45.3-ms E180 MEGA offered a difference-edited lactate signal with lower intensity and reduced contamination from threonine compared to the broad-band E180 MEGA. The 45.3 ms E180 pulse had MEGA editing effects over a frequency range larger than seen in the singlet-resonance inversion profile. Lactate and threonine in healthy brain were both estimated to be 0.4 ± 0.1 mM, with reference to N-acetylaspartate at 12 mM. CONCLUSION: Narrow-band E180 MEGA editing minimizes threonine contamination of lactate spectra and may improve the ability to detect modest changes in lactate levels.

Correction of errors in estimates of T1ρ at low spin-lock amplitudes in the presence of B0 and B1 inhomogeneities.

Relaxation rates R1ρ in the rotating frame measured by spin-lock methods at very low locking amplitudes (≤ 100 Hz) are sensitive to the effects of water diffusion in intrinsic gradients and may provide information on tissue microvasculature, but accurate estimates are challenging in the presence of B0 and B1 inhomogeneities. Although composite pulse preparations have been developed to compensate for nonuniform fields, the transverse magnetization comprises different components and the spin-lock signals measured do not decay exponentially as a function of locking interval at low locking amplitudes. For example, during a typical preparation sequence, some of the magnetization in the transverse plane is nutated to the Z-axis and later tipped back, and so does not experience R1ρ relaxation. As a result, if the spin-lock signals are fit to a monoexponential decay with locking interval, there are residual errors in quantitative estimates of relaxation rates R1ρ and their dispersion with weak locking fields. We developed an approximate theoretical analysis to model the behaviors of the different components of the magnetization, which provides a means to correct these errors. The performance of this correction approach was evaluated both through numerical simulations and on human brain images at 3 T, and compared with a previous correction method using matrix multiplication. Our correction approach has better performance than the previous method at low locking amplitudes. Through careful shimming, the correction approach can be applied in studies using low spin-lock amplitudes to assess the contribution of diffusion to R1ρ dispersion and to derive estimates of microvascular sizes and spacings. The results of imaging eight healthy subjects suggest that R1ρ dispersion in human brain at low locking fields arises from diffusion among inhomogeneities that generate intrinsic gradients on a scale of capillaries (~7.4 ± 0.5 µm).

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.

Graph theory analysis identified two hubs that connect sensorimotor and cognitive and cortical and subcortical nociceptive networks in the non-human primate.

Pain perception involves multiple brain regions and networks. Understanding how these brain networks work together is fundamental for appreciating network-wise changes reported in patients with chronic pain disorders. Parcellating pain related networks and understanding their causal relationships is the first step to understand how painful information is processed, integrated, and modulated, and it requires direct manipulation of specific brain regions. Nonhuman primates (NHP) offer an ideal model system to achieve these goals because cortical and subcortical regions in the NHP brain are established based on a variety of different types of data collected in a way that is not feasible or, at least, extremely difficult in humans (i.e., histology data, tract-tracing, intracerebral recordings). In addition, different methodological techniques can also help characterize and further understand these brain cortical and subcortical regions over the course of development. Here we used a heat nociceptive stimulation that is proven to elicit activity of nociceptive neurons in the cortex to refine and parcellate the whole brain nociceptive functional networks, to identify key network hubs, and to characterize network-wise temporal dynamic signatures using high-resolution fMRI. We first functionally localized 24 cortical and subcortical regions that responded to heat nociceptive stimuli (somatosensory area 1/2, area 3a/3b, S2, posterior insula (pIns), anterior insula, area 7b, posterior parietal cortex, anterior cingulate cortex (ACC), prefrontal cortex, caudate, and mediodorsal (MD) and ventral posterior lateral (VPL) thalamic nuclei) and used them as seeds in resting state fMRI (rsfMRI) data analysis. We applied both hierarchical clustering and graph-theory analyses of the pairwise rsfMRI correlation metrics and identified five cortical and one subcortical sub-networks: strong resting state functional connectivity (rsFC) between ACC and prefrontal regions, parietal cortex and area 7b, S2 and posterior insula, areas 3a/3b and 1/2 within the S1 cortex, and thalamic MD and caudate nuclei. The rsFC strengths between cortical areas within each subnetwork were significantly stronger than those between subcortical regions. Regions within each sub-network also exhibited highly correlated temporal dynamics at rest, but the overall dynamic patterns varied drastically across sub-networks. Graph-theory analysis identified the MD nucleus as a hub that connects subcortical and cortical nociceptive sub-networks. The S2-pIns connection joins the sensory and affective/cognitive sub-networks.

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.

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.