Cells and Molecules Underpinning Cannabis-Related Variations in Cortical Thickness during Adolescence.

During adolescence, cannabis experimentation is common, and its association with interindividual variations in brain maturation well studied. Cellular and molecular underpinnings of these system-level relationships are, however, unclear. We thus conducted a three-step study. First, we exposed adolescent male mice to Δ-9-tetrahydrocannabinol (THC) or a synthetic cannabinoid WIN 55,212-2 (WIN) and assessed differentially expressed genes (DEGs), spine numbers, and dendritic complexity in their frontal cortex. Second, in human (male) adolescents, we examined group differences in cortical thickness in 34 brain regions, using magnetic resonance imaging, between those who experimented with cannabis before age 16 (n = 140) and those who did not (n = 327). Finally, we correlated spatially these group differences with gene expression of human homologs of mouse-identified DEGs. The spatial expression of 13 THC-related human homologs of DEGs correlated with cannabis-related variations in cortical thickness, and virtual histology revealed coexpression patterns of these 13 genes with cell-specific markers of astrocytes, microglia, and a type of pyramidal cells enriched in dendrite-regulating genes. Similarly, the spatial expression of 18 WIN-related human homologs of DEGs correlated with group differences in cortical thickness and showed coexpression patterns with the same three cell types. Gene ontology analysis indicated that 37 THC-related human homologs are enriched in neuron projection development, while 33 WIN-related homologs are enriched in processes associated with learning and memory. In mice, we observed spine loss and lower dendritic complexity in pyramidal cells of THC-exposed animals (vs controls). Experimentation with cannabis during adolescence may influence cortical thickness by impacting glutamatergic synapses and dendritic arborization.

Neuropsychiatric symptoms: Risk factor or disease marker? A study of structural imaging biomarkers of Alzheimer's disease and incident cognitive decline.

Neuropsychiatric symptoms (NPS) are risk factors for Alzheimer's disease (AD) but can also manifest secondary to AD pathology. Mild behavioral impairment (MBI) refers to later-life emergent and persistent NPS that may mark early-stage AD. To distinguish MBI from NPS that are transient or which represent psychiatric conditions (non-MBI NPS), we investigated the effect of applying MBI criteria on NPS associations with AD structural imaging biomarkers and incident cognitive decline. Data for participants (n = 1273) with normal cognition (NC) or mild cognitive impairment (MCI) in the National Alzheimer's Coordinating Center Uniform Data Set were analyzed. NPS status (MBI, non-MBI NPS) was derived from the Neuropsychiatric Inventory Questionnaire and psychiatric history. Normalized measures of bilateral hippocampal (HPC) and entorhinal cortex (EC) volume, and AD meta-region of interest (ROI) mean cortical thickness were acquired from T1-weighted magnetic resonance imaging scans. Multivariable linear and Cox regressions examined NPS associations with imaging biomarkers and incident cognitive decline, respectively. MBI was associated with lower volume and cortical thickness in all ROIs in both NC and MCI, except for EC volume in NC. Non-MBI NPS were only associated with lower HPC volume in NC. Although both of the NPS groups showed higher hazards for MCI/dementia than No NPS, MBI participants showed more rapid decline. Although both types of NPS were linked to HPC atrophy, only NPS that emerged and persisted in later-life, consistent with MBI criteria, were related to AD neurodegenerative patterns beyond the HPC. Moreover, MBI predicted faster progression to dementia than non-MBI NPS.

Machine learning-based estimation of respiratory fluctuations in a healthy adult population using resting state BOLD fMRI and head motion parameters.

PURPOSE: External physiological monitoring is the primary approach to measure and remove effects of low-frequency respiratory variation from BOLD-fMRI signals. However, the acquisition of clean external respiratory data during fMRI is not always possible, so recent research has proposed using machine learning to directly estimate respiratory variation (RV), potentially obviating the need for external monitoring. In this study, we propose an extended method for reconstructing RV waveforms directly from resting state BOLD-fMRI data in healthy adult participants with the inclusion of both BOLD signals and derived head motion parameters. METHODS: In the proposed method, 1D convolutional neural networks (1D-CNNs) used BOLD signals and head motion parameters to reconstruct the RV waveform for the whole fMRI scan time. Resting-state fMRI data and associated respiratory records from the Human Connectome Project in Young Adults (HCP-YA) dataset are used to train and test the proposed method. RESULTS: Compared to using only BOLD-fMRI data for a CNN input, this approach yielded improvements of 14% in mean absolute error, 24% in mean square error, 14% in correlation, and 12% in dynamic time warping. When tested on independent datasets, the method demonstrated generalizability, even in data with different TRs and physiological conditions. CONCLUSION: This study shows that the respiratory variations could be reconstructed from BOLD-fMRI data in the young adult population, and its accuracy could be improved using supportive data such as head motion parameters. The method also performed well on independent datasets with different experimental conditions.

Fast magnetization transfer saturation imaging of the brain using MP2RAGE T1 mapping.

PURPOSE: Magnetization transfer saturation (MTsat) mapping is commonly used to examine the macromolecular content of brain tissue. This study compared variable flip angle (VFA) T1 mapping against compressed-sensing MP2RAGE (csMP2RAGE) T1 mapping for accelerating MTsat imaging. METHODS: VFA, MP2RAGE, and csMP2RAGE were compared against inversion-recovery T1 in an aqueous phantom at 3 T. The same 1-mm VFA, MP2RAGE, and csMP2RAGE protocols were acquired in 4 healthy subjects to compare T1 and MTsat. Bloch-McConnell simulations were used to investigate differences between the phantom and in vivo T1 results. Ten healthy controls were imaged twice with the csMP2RAGE MTsat protocol to quantify repeatability. RESULTS: The MP2RAGE and csMP2RAGE protocols were 13.7% and 32.4% faster than the VFA protocol, respectively. At these scan times, all approaches provided strong repeatability and accurate T1 times (< 5% difference) in the phantom, but T1 accuracy was more impacted by T2 for VFA than for MP2RAGE. In vivo, VFA estimated longer T1 times than MP2RAGE and csMP2RAGE. Simulations suggest that the differences in the T1 measured using VFA, MP2RAGE, and inversion recovery could be explained by the magnetization-transfer effects. In the test-retest experiment, we found that the csMP2RAGE has a minimum detectable change of 2.3% for T1 mapping and 7.8% for MTsat imaging. CONCLUSIONS: We demonstrated that MP2RAGE can be used in place of VFA T1 mapping in an MTsat protocol. Furthermore, a shorter scan time and high repeatability can be achieved using the csMP2RAGE sequence.

Direct machine learning reconstruction of respiratory variation waveforms from resting state fMRI data in a pediatric population.

In many functional magnetic resonance imaging (fMRI) studies, respiratory signals are unavailable or do not have acceptable quality due to issues with subject compliance, equipment failure or signal error. In large databases, such as the Human Connectome Projects, over half of the respiratory recordings may be unusable. As a result, the direct removal of low frequency respiratory variations from the blood oxygen level-dependent (BOLD) signal time series is not possible. This study proposes a deep learning-based method for reconstruction of respiratory variation (RV) waveforms directly from BOLD fMRI data in pediatric participants (aged 5 to 21 years old), and does not require any respiratory measurement device. To do this, the Lifespan Human Connectome Project in Development (HCP-D) dataset, which includes respiratory measurements, was used to both train a convolutional neural network (CNN) and evaluate its performance. Results show that a CNN can capture informative features from the BOLD signal time course and reconstruct accurate RV timeseries, especially when the subject has a prominent respiratory event. This work advances the use of direct estimation of physiological parameters from fMRI, which will eventually lead to reduced complexity and decrease the burden on participants because they may not be required to wear a respiratory bellows.

Detecting monocyte trafficking in an animal model of glioblastoma using R2* and quantitative susceptibility mapping.

BACKGROUND: The role of tumor-associated macrophages (TAMs) in glioblastoma (GBM) disease progression has received increasing attention. Recent advances have shown that TAMs can be re-programmed to exert a pro-inflammatory, anti-tumor effect to control GBMs. However, imaging methods capable of differentiating tumor progression from immunotherapy treatment effects have been lacking, making timely assessment of treatment response difficult. We showed that tracking monocytes using iron oxide nanoparticle (USPIO) with MRI can be a sensitive imaging method to detect therapy response directed at the innate immune system. METHODS: We implanted syngeneic mouse glioma stem cells into C57/BL6 mice and treated the animals with either niacin (a stimulator of innate immunity) or vehicle. Animals were imaged using an anatomical MRI sequence, R2* mapping, and quantitative susceptibility mapping (QSM) before and after USPIO injection. RESULTS: Compared to vehicles, niacin-treated animals showed significantly higher susceptibility and R2*, representing USPIO and monocyte infiltration into the tumor. We observed a significant reduction in tumor size in the niacin-treated group 7 days later. We validated our MRI results with flow cytometry and immunofluoresence, which showed that niacin decreased pro-inflammatory Ly6C high monocytes in the blood but increased CD16/32 pro-inflammatory macrophages within the tumor, consistent with migration of these pro-inflammatory innate immune cells from the blood to the tumor. CONCLUSION: MRI with USPIO injection can detect therapeutic responses of innate immune stimulating agents before changes in tumor size have occurred, providing a potential complementary imaging technique to monitor cancer immunotherapies. MANUSCRIPT HIGHLIGHT: We show that iron oxide nanoparticles (USPIOs) can be used to label innate immune cells and detect the trafficking of pro-inflammatory monocytes into the glioblastoma. This preceded changes in tumor size, making it a more sensitive imaging technique.

Optimization of acquisition parameters for cortical inhomogeneous magnetization transfer (ihMT) imaging using a rapid gradient echo readout.

PURPOSE: Imaging biomarkers with increased myelin specificity are needed to better understand the complex progression of neurological disorders. Inhomogeneous magnetization transfer (ihMT) imaging is an emergent technique that has a high degree of specificity for myelin content but suffers from low signal to-noise ratio (SNR). This study used simulations to determine optimal sequence parameters for ihMT imaging for use in high-resolution cortical mapping. METHODS: MT-weighted cortical image intensity and ihMT SNR were simulated using modified Bloch equations for a range of sequence parameters. The acquisition time was limited to 4.5 min/volume. A custom MT-weighted RAGE sequence with center-out k-space encoding was used to enhance SNR at 3 T. Pulsed MT imaging was studied over a range of saturation parameters, and the impact of the turbo factor on the effective ihMT resolution was investigated. 1 mm isotropic ihMTsat maps were generated in 25 healthy adults. RESULTS: Greater SNR was observed for larger number of bursts consisting of 6-8 saturation pulses each, combined with a high readout turbo factor. However, that protocol suffered from a point spread function that was more than twice the nominal resolution. For high-resolution cortical imaging, we selected a protocol with a higher effective resolution at the cost of a lower SNR. We present the first group-average ihMTsat whole-brain map at 1 mm isotropic resolution. CONCLUSION: This study presents the impact of saturation and excitation parameters on ihMTsat SNR and resolution. We demonstrate the feasibility of high-resolution cortical myelin imaging using ihMTsat in less than 20 min.

Limb Preference Changes after Focused-Ultrasound Thalamotomy for Tremor.

BACKGROUND: Magnetic resonance-guided focused-ultrasound (MRgFUS) thalamotomy is an effective treatment for essential and other tremors. It targets the ventrointermedius (Vim) nucleus, which is the thalamic relay in a proprioceptive pathway, and contains kinesthetic cells. Although MRgFUS thalamotomy reduces some risks associated with more invasive surgeries, it still has side effects, such as balance and gait disturbances; these may be caused by the lesion impacting proprioception. OBJECTIVES: Our aim was to quantitatively measure the effects of MRgFUS on proprioception and limb use in essential tremor patients. We hypothesized that this thalamotomy alters proprioception, because the sensorimotor Vim thalamus is lesioned. METHODS: Proprioception was measured using the Kinarm exoskeleton robot in 18 patients. Data were collected pre-operatively, and then 1 day, 3 months, and 1 year after surgery. Patients completed four tasks, assessing motor coordination and postural control, goal-directed movement and bimanual planning, position sense, and kinesthesia. RESULTS: Immediately after surgery there were changes in posture speed (indicating tremor improvement), and in bimanual hand use, with the untreated limb being preferred. However, these measures returned to pre-operative baseline over time. There were no changes in parameters related to proprioception. None of these measures correlated with lesion size or lesion-overlap with the dentato-rubro-thalamic tract. CONCLUSIONS: This is the first quantitative assessment of proprioception and limb preference following MRgFUS thalamotomy. Our results suggest that focused-ultrasound lesioning of the Vim thalamus does not degrade proprioception but alters limb preference. This change may indicate a required "relearning" in the treated limb, because the effect is transient. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks.

Quantitative susceptibility mapping (QSM) is an MRI post-processing technique that produces spatially resolved magnetic susceptibility maps from phase data. However, the traditional QSM reconstruction pipeline involves multiple non-trivial steps, including phase unwrapping, background field removal, and dipole inversion. These intermediate steps not only increase the reconstruction time but accumulates errors. This study aims to overcome existing limitations by developing a Laplacian-of-Trigonometric-functions (LoT) enhanced deep neural network for near-instant quantitative field and susceptibility mapping (i.e., iQFM and iQSM) from raw MRI phase data. The proposed iQFM and iQSM methods were compared with established reconstruction pipelines on simulated and in vivo datasets. In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the proposed neural networks. The proposed iQFM and iQSM methods in healthy subjects yielded comparable results to those involving the intermediate steps while dramatically improving reconstruction accuracies on intracranial hemorrhages with large susceptibilities. High susceptibility contrast between multiple sclerosis lesions and healthy tissue was also achieved using the proposed methods. Comparative studies indicated that the most significant contributor to iQFM and iQSM over conventional multi-step methods was the elimination of traditional Laplacian unwrapping. The reconstruction time on the order of minutes for traditional approaches was shortened to around 0.1 s using the trained iQFM and iQSM neural networks.

Permanent tissue damage in multiple sclerosis lesions is associated with reduced pre-lesion myelin and axon volume fractions.

BACKGROUND: The use of advanced magnetic resonance imaging (MRI) techniques in MS research has led to new insights in lesion evolution and disease outcomes. It has not yet been determined if, or how, pre-lesional abnormalities in normal-appearing white matter (NAWM) relate to the long-term evolution of new lesions. OBJECTIVE: To investigate the relationship between abnormalities in MRI measures of axonal and myelin volume fractions (AVF and MVF) in NAWM preceding development of black-hole (BH) and non-BH lesions in people with MS. METHODS: We obtained magnetization transfer and diffusion MRI at 6-month intervals in patients with MS to estimate MVF and AVF during lesion evolution. Lesions were classified as either BH or non-BH on the final imaging visit using T1 maps. RESULTS: Longitudinal data from 97 new T2 lesions from 9 participants were analyzed; 25 lesions in 8 participants were classified as BH 6-12 months after initial appearance. Pre-lesion MVF, AVF, and MVF/AVF were significantly lower, and T1 was significantly higher, in the lesions that later became BHs (p < 0.001) compared to those that did not. No significant pre-lesion abnormalities were found in non-BH lesions (p > 0.05). CONCLUSION: The present work demonstrated that pre-lesion abnormalities are associated with worse long-term lesion-level outcome.

Effects of prenatal alcohol exposure on neurobehavioural development and volume of rostral cingulate cortex subregions.

BACKGROUND: Maternal alcohol consumption during pregnancy can have widespread and long-lasting effects on children's cognition, behaviour, brain function and structure. The pregenual anterior cingulate cortex (ACC) and the anterior midcingulate cortex (MCC) mediate emotional and cognitive behaviours that are affected by prenatal alcohol exposure. However, the neurobehavioural development of the pregenual ACC and anterior MCC has not been examined in people with prenatal alcohol exposure. METHODS: We recruited 30 children and adolescents with prenatal alcohol exposure and 50 age- and gender-matched unexposed controls. We acquired structural MRI data sets on a 3 T scanner. We manually delineated 2 areas of the rostral cingulate cortex - the pregenual ACC and the anterior MCC - and compared them between groups. We measured behavioural and emotional problems using the Behaviour Assessment System for Children, 2nd Edition, Parent Rating Scale, and then explored their associations with rostral cingulate cortex volumes. RESULTS: Intracranial-normalized volumes of the right pregenual ACC and the right total rostral cingulate cortex were significantly smaller in individuals with prenatal alcohol exposure than in unexposed controls. The volume of the right anterior MCC had a significant positive association with scores on the Internalizing Problems scale in individuals with prenatal alcohol exposure. LIMITATIONS: This study was cross-sectional, and detailed information about the timing and amount of exposure was not always available. CONCLUSION: Prenatal alcohol exposure is associated with lower volumes in the right pregenual ACC. This finding may underlie some of the emotional and behavioural problems experienced by individuals with prenatal alcohol exposure.

Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction.

Quantitative susceptibility mapping (QSM) and R2* mapping are MRI post-processing methods that quantify tissue magnetic susceptibility and transverse relaxation rate distributions. However, QSM and R2* acquisitions are relatively slow, even with parallel imaging. Incoherent undersampling and compressed sensing reconstruction techniques have been used to accelerate traditional magnitude-based MRI acquisitions; however, most do not recover the full phase signal, as required by QSM, due to its non-convex nature. In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM and R2* acquisition. Magnitude, phase, R2*, and QSM results from DCRNet were compared with two iterative and one deep learning methods on retrospectively undersampled acquisitions from six healthy volunteers, one intracranial hemorrhage and one multiple sclerosis patients, as well as one prospectively undersampled healthy subject using a 7T scanner. Peak signal to noise ratio (PSNR), structural similarity (SSIM), root-mean-squared error (RMSE), and region-of-interest susceptibility and R2* measurements are reported for numerical comparisons. The proposed DCRNet method substantially reduced artifacts and blurring compared to the other methods and resulted in the highest PSNR, SSIM, and RMSE on the magnitude, R2*, local field, and susceptibility maps. Compared to two iterative and one deep learning methods, the DCRNet method demonstrated a 3.2% to 9.1% accuracy improvement in deep grey matter susceptibility when accelerated by a factor of four. The DCRNet also dramatically shortened the reconstruction time of single 2D brain images from 36-140 seconds using conventional approaches to only 15-70 milliseconds.

Efficient whole-brain tract-specific T1 mapping at 3T with slice-shuffled inversion-recovery diffusion-weighted imaging.

PURPOSE: Most voxels in white matter contain multiple fiber populations with different orientations and levels of myelination. Conventional T1 mapping measures 1 T1 value per voxel, representing a weighted average of the multiple tract T1 times. Inversion-recovery diffusion-weighted imaging (IR-DWI) allows the T1 times of multiple tracts in a voxel to be disentangled, but the scan time is prohibitively long. Recently, slice-shuffled IR-DWI implementations have been proposed to significantly reduce scan time. In this work, we demonstrate that we can measure tract-specific T1 values in the whole brain using simultaneous multi-slice slice-shuffled IR-DWI at 3T. METHODS: We perform simulations to evaluate the accuracy and precision of our crossing fiber IR-DWI signal model for various fiber parameters. The proposed sequence and signal model are tested in a phantom consisting of crossing asparagus pieces doped with gadolinium to vary T1 , and in 2 human subjects. RESULTS: Our simulations show that tract-specific T1 times can be estimated within 5% of the nominal fiber T1 values. Tract-specific T1 values were resolved in subvoxel 2 fiber crossings in the asparagus phantom. Tract-specific T1 times were resolved in 2 different tract crossings in the human brain where myelination differences have previously been reported; the crossing of the cingulum and genu of the corpus callosum and the crossing of the corticospinal tract and pontine fibers. CONCLUSION: Whole-brain tract-specific T1 mapping is feasible using slice-shuffled IR-DWI at 3T. This technique has the potential to improve the microstructural characterization of specific tracts implicated in neurodevelopment, aging, and demyelinating disorders.

MRI of healthy brain aging: A review.

We present a review of the characterization of healthy brain aging using MRI with an emphasis on morphology, lesions, and quantitative MR parameters. A scope review found 6612 articles encompassing the keywords "Brain Aging" and "Magnetic Resonance"; papers involving functional MRI or not involving imaging of healthy human brain aging were discarded, leaving 2246 articles. We first consider some of the biogerontological mechanisms of aging, and the consequences of aging in terms of cognition and onset of disease. Morphological changes with aging are reviewed for the whole brain, cerebral cortex, white matter, subcortical gray matter, and other individual structures. In general, volume and cortical thickness decline with age, beginning in mid-life. Prevalent silent lesions such as white matter hyperintensities, microbleeds, and lacunar infarcts are also observed with increasing frequency. The literature regarding quantitative MR parameter changes includes T1 , T2 , T2 *, magnetic susceptibility, spectroscopy, magnetization transfer, diffusion, and blood flow. We summarize the findings on how each of these parameters varies with aging. Finally, we examine how the aforementioned techniques have been used for age prediction. While relatively large in scope, we present a comprehensive review that should provide the reader with sound understanding of what MRI has been able to tell us about how the healthy brain ages.

xQSM: quantitative susceptibility mapping with octave convolutional and noise-regularized neural networks.

Quantitative susceptibility mapping (QSM) provides a valuable MRI contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill-posed dipole inversion process. In this study, a new deep learning method for QSM reconstruction, namely xQSM, was designed by introducing noise regularization and modified octave convolutional layers into a U-net backbone and trained with synthetic and in vivo datasets, respectively. The xQSM method was compared with two recent deep learning (QSMnet+ and DeepQSM) and two conventional dipole inversion (MEDI and iLSQR) methods, using both digital simulations and in vivo experiments. Reconstruction error metrics, including peak signal-to-noise ratio, structural similarity, normalized root mean squared error and deep gray matter susceptibility measurements, were evaluated for comparison of the different methods. The results showed that the proposed xQSM network trained with in vivo datasets achieved the best reconstructions of all the deep learning methods. In particular, it led to, on average, 32.3%, 25.4% and 11.7% improvement in the accuracy of globus pallidus susceptibility estimation for digital simulations and 39.3%, 21.8% and 6.3% improvements for in vivo acquisitions compared with DeepQSM, QSMnet+ and iLSQR, respectively. It also exhibited the highest linearity against different susceptibility intensity scales and demonstrated the most robust generalization capability to various spatial resolutions of all the deep learning methods. In addition, the xQSM method also substantially shortened the reconstruction time from minutes using MEDI to only a few seconds.

Cerebral OEF quantification: A comparison study between quantitative susceptibility mapping and dual-gas calibrated BOLD imaging.

PURPOSE: To compare regional oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen consumption (CMRO2 ) quantified from the microvascular quantitative susceptibility mapping (QSM) using a hypercapnic gas challenge with those measured by the dual-gas calibrated BOLD imaging (DGC-BOLD) in healthy subjects. METHODS: Ten healthy subjects were scanned using a 3T MR system. The QSM data were acquired with a multi-echo gradient-echo sequence at baseline and hypercapnia. Cerebral blood flow data were acquired using the pseudo-continuous arterial spin labeling technique. Baseline OEF and CMRO2 were calculated using QSM and cerebral blood flow measurements. The DGC-BOLD data were also collected under a hypercapnic and a hyperoxic condition to yield baseline OEF and CMRO2 . The QSM-OEF and CMRO2 maps were compared with DGC-BOLD OEF and CMRO2 maps using region of interest (vascular territories) analysis and Bland-Altman plots. RESULTS: Hypercapnia is a robust stimulus for mapping OEF in combination with QSM. Average OEF in 16 vascular territory regions of interest across 10 subjects was 0.40 ± 0.04 by QSM-OEF and 0.38 ± 0.09 by DGC-BOLD. The average CMRO2 was 176 ± 35 and 167 ± 53 µmol O2 /min/100g by QSM-OEF and DGC-BOLD, respectively. A Bland-Altman plot of regional OEF and CMRO2 in regions of interest revealed a statistically significant but small difference (OEF difference = 0.02, CMRO2 difference = 9 µmol O2 /min/100g, p < .05) between the 2 methods for the 10 healthy subjects. CONCLUSION: Hypercapnic challenge-assisted QSM-OEF is a feasible approach to quantify regional brain OEF and CMRO2 . Compared with DGC-BOLD, hypercapnia QSM-OEF results in smaller intersubject variability and requires only 1 gas challenge.

Quantification of brain oxygen extraction fraction using QSM and a hyperoxic challenge.

PURPOSE: To use hyperoxia in combination with QSM to quantify microvascular oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) in healthy subjects and to cross-validate results with those from hypercapnia QSM-OEF. METHODS: Ten healthy subjects were scanned on a 3T MRI scanner. At baseline normoxia and during hyperoxia (PetO2 = +300 mmHg), QSM data were acquired using a multi-echo gradient-echo (GRE) sequence, and cerebral blood flow data were acquired using a pseudocontinuous arterial spin labeling sequence. The OEF and CMRO2 maps were computed and compared with those from hypercapnia QSM-OEF, acquired in the same subjects, using correlation and Bland-Altman analysis in 16 vascular territories. RESULTS: Hyperoxia QSM-OEF produced physiologically reasonable OEF and CMRO2 values in all subjects (gray-matter region of interest average OEF = 0.42 ± 0.04, average CMRO2 = 181 ± 34 µmol O2 /min/100 g). When compared with hypercapnia QSM-OEF, Bland-Altman plots revealed small deviations (mean OEF difference = 0.015, mean CMRO2 difference = 4.9 µmol O2 /min/100 g, P < .05). Good and excellent correlations of regional OEF and CMRO2 were found for the two methods. In addition, hyperoxia had minimal impact on cerebral blood flow (average gray-matter cerebral blood flow was reduced by 7.5 ± 5.4%). CONCLUSIONS: Hyperoxia in combination with QSM is a robust approach to measure OEF. Compared with hypercapnia, hyperoxia is more comfortable and has minimal impact on cerebral blood flow.

Extracting more for less: multi-echo MP2RAGE for simultaneous T1 -weighted imaging, T1 mapping, R2∗ mapping, SWI, and QSM from a single acquisition.

PURPOSE: To demonstrate simultaneous T1 -weighted imaging, T1 mapping, R2∗ mapping, SWI, and QSM from a single multi-echo (ME) MP2RAGE acquisition. METHODS: A single-echo (SE) MP2RAGE sequence at 7 tesla was extended to ME with 4 bipolar gradient echo readouts. T1 -weighted images and T1 maps calculated from individual echoes were combined using sum of squares and averaged, respectively. ME-combined SWI and associated minimum intensity projection images were generated with TE-adjusted homodyne filters. A QSM reconstruction pipeline was used, including a phase-offsets correction and coil combination method to properly combine the phase images from the 32 receiver channels. Measurements of susceptibility, R2∗ , and T1 of brain tissue from ME-MP2RAGE were compared with those from standard ME-gradient echo and SE-MP2RAGE. RESULTS: The ME combined T1 -weighted, T1 map, SWI, and minimum intensity projection images showed increased SNRs compared to the SE results. The proposed coil combination method led to QSM results free of phase-singularity artifacts, which were present in the standard adaptive combination method. T1 -weighted, T1 , and susceptibility maps from ME-MP2RAGE were comparable to those obtained from SE-MP2RAGE and ME-gradient echo, whereas R2∗ maps showed increased blurring and reduced SNR. T1 , R2∗ , and susceptibility values of brain tissue from ME-MP2RAGE were consistent with those from SE-MP2RAGE and ME-gradient echo. CONCLUSION: High-resolution structural T1 weighted imaging, T1 mapping, R2∗ mapping, SWI, and QSM can be extracted from a single 8.5-min ME-MP2RAGE acquisition using a customized reconstruction pipeline. This method can be applied to replace separate SE-MP2RAGE and ME-gradient echo acquisitions to significantly shorten total scan time.

Interdatabase Variability in Cortical Thickness Measurements.

The phenomenon of cortical thinning with age has been well established; however, the measured rate of change varies between studies. The source of this variation could be image acquisition techniques including hardware and vendor specific differences. Databases are often consolidated to increase the number of subjects but underlying differences between these datasets could have undesired effects. We explore differences in cerebral cortex thinning between 4 databases, totaling 1382 subjects. We investigate several aspects of these databases, including: 1) differences between databases of cortical thinning rates versus age, 2) correlation of cortical thinning rates between regions for each database, and 3) regression bootstrapping to determine the effect of the number of subjects included. We also examined the effect of different databases on age prediction modeling. Cortical thinning rates were significantly different between databases in all 68 parcellated regions (ANCOVA, P < 0.001). Subtle differences were observed in correlation matrices and bootstrapping convergence. Age prediction modeling using a leave-one-out cross-validation approach showed varying prediction performance (0.64 < R2 < 0.82) between databases. When a database was used to calibrate the model and then applied to another database, prediction performance consistently decreased. We conclude that there are indeed differences in the measured cortical thinning rates between these large-scale databases.

Proprioception and motor performance after stroke: An examination of diffusion properties in sensory and motor pathways.

Proprioceptive and motor impairments commonly occur after stroke. Relationships between corticospinal tract (CST) fractional anisotropy (FA) and motor recovery have been identified. However, the relationship between sensory tract microstructure and proprioceptive recovery remains unexplored. Using probabilistic tractography, we examined the relationship between diffusion metrics in three tracts known to contain proprioceptive information (a) dorsal-column medial-lemniscal (DCML), (b) postcentral gyrus to supramarginal gyrus (POCG-SMG), (c) postcentral gyrus to Heschl's gyrus (POCG-HG) and proprioception at 1 (n = 26) and 6 months (n = 19) poststroke. Proprioception was assessed using two robotic tasks. Motor performance was also assessed robotically and compared to CST diffusion metrics. At 1-month poststroke, a nonsignificant relationship (r = -0.43, p = 0.05) was observed between DCML-FA and proprioceptive impairment. A moderate relationship was identified between POCG-SMG FA and POCG-HG FA and proprioceptive impairment (r = -0.47, p = 0.001 and r = -0.51, p = 0.008, respectively). No relationships were significant at 6 months poststroke. Similar to previous studies, lower CST-FA correlated with motor impairment at 1 month poststroke (r = -0.58, p = 0.002). While CST-FA is considered a predictor of motor impairment, our findings suggest that the relationship between FA and tracts containing proprioceptive information is not as straightforward and highlights the importance of sensory association areas in proprioception.