Gray Matter Tissue Research Articles

Brain signatures based on structural MRI: Classification for MCI, PMCI, and AD.

Structural MRI (sMRI) provides valuable information for understanding neurodegenerative illnesses such as Alzheimer's Disease (AD) since it detects the brain's cerebral atrophy. The development of brain networks utilizing single imaging data—sMRI is an understudied area that has the potential to provide a network neuroscientific viewpoint on the brain. In this paper, we proposed a framework for constructing a brain network utilizing sMRI data, followed by the extraction of signature networks and important regions of interest (ROIs). To construct a brain network using sMRI, nodes are defined as regions described by the brain atlas, and edge weights are determined using a distance measure called the Sorensen distance between probability distributions of gray matter tissue probability maps. The brain signatures identified are based on the changes in the networks of disease and control subjects. To validate the proposed methodology, we first identified the brain signatures and critical ROIs associated with mild cognitive impairment (MCI), progressive MCI (PMCI), and Alzheimer's disease (AD) with 60 reference subjects (15 each of control, MCI, PMCI, and AD). Then, 200 examination subjects (50 each of control, MCI, PMCI, and AD) were selected to evaluate the identified signature patterns. Results demonstrate that the proposed framework is capable of extracting brain signatures and has a number of potential applications in the disciplines of brain mapping, brain communication, and brain network‐based applications.

White matter integrity changes and neurocognitive functioning in adult-late onset DM1: a follow-up DTI study

Myotonic Dystrophy Type 1 (DM1) is a multisystemic disease that affects gray and white matter (WM) tissues. WM changes in DM1 include increased hyperintensities and altered tract integrity distributed in a widespread manner. However, the precise temporal and spatial progression of the changes are yet undetermined. MRI data were acquired from 8 adult- and late-onset DM1 patients and 10 healthy controls (HC) at two different timepoints over 9.06 years. Fractional anisotropy (FA) and mean diffusivity (MD) variations were assessed with Tract-Based Spatial Statistics. Transversal and longitudinal intra- and intergroup analyses were conducted, along with correlation analyses with clinical and neuropsychological data. At baseline, reduced FA and increased MD values were found in patients in the uncinate, anterior-thalamic, fronto-occipital, and longitudinal tracts. At follow-up, the WM disconnection was shown to have spread from the frontal part to the rest of the tracts in the brain. Furthermore, WM lesion burden was negatively correlated with FA values, while visuo-construction and intellectual functioning were positively correlated with global and regional FA values at follow-up. DM1 patients showed a pronounced WM integrity loss over time compared to HC, with a neurodegeneration pattern that suggests a progressive anterior–posterior disconnection. The visuo-construction domain stands out as the most sensitive neuropsychological measure for WM microstructural impairment.

SARS-CoV-2 is associated with changes in brain structure in UK Biobank

There is strong evidence of brain-related abnormalities in COVID-191–13. However, it remains unknown whether the impact of SARS-CoV-2 infection can be detected in milder cases, and whether this can reveal possible mechanisms contributing to brain pathology. Here we investigated brain changes in 785 participants of UK Biobank (aged 51–81 years) who were imaged twice using magnetic resonance imaging, including 401 cases who tested positive for infection with SARS-CoV-2 between their two scans—with 141 days on average separating their diagnosis and the second scan—as well as 384 controls. The availability of pre-infection imaging data reduces the likelihood of pre-existing risk factors being misinterpreted as disease effects. We identified significant longitudinal effects when comparing the two groups, including (1) a greater reduction in grey matter thickness and tissue contrast in the orbitofrontal cortex and parahippocampal gyrus; (2) greater changes in markers of tissue damage in regions that are functionally connected to the primary olfactory cortex; and (3) a greater reduction in global brain size in the SARS-CoV-2 cases. The participants who were infected with SARS-CoV-2 also showed on average a greater cognitive decline between the two time points. Importantly, these imaging and cognitive longitudinal effects were still observed after excluding the 15 patients who had been hospitalised. These mainly limbic brain imaging results may be the in vivo hallmarks of a degenerative spread of the disease through olfactory pathways, of neuroinflammatory events, or of the loss of sensory input due to anosmia. Whether this deleterious effect can be partially reversed, or whether these effects will persist in the long term, remains to be investigated with additional follow-up.

Signatures of life course socioeconomic conditions in brain anatomy.

Socioeconomic status (SES) plays a significant role in health and disease. At the same time, early‐life conditions affect neural function and structure, suggesting the brain may be a conduit for the biological embedding of SES. Here, we investigate the brain anatomy signatures of SES in a large‐scale population cohort aged 45–85 years. We assess both gray matter morphometry and tissue properties indicative of myelin content. Higher life course SES is associated with increased volume in several brain regions, including postcentral and temporal gyri, cuneus, and cerebellum. We observe more widespread volume differences and higher myelin content in the sensorimotor network but lower myelin content in the temporal lobe associated with childhood SES. Crucially, childhood SES differences persisted in adult brains even after controlling for adult SES, highlighting the unique contribution of early‐life conditions to brain anatomy, independent of later changes in SES. These findings inform on the biological underpinnings of social inequality, particularly as they pertain to early‐life conditions.

Abstract 65: Assessment Of MR Blood-Oxygen-Level-Dependent Cerebrovascular Reactivity Under General Anaesthesia In Children With Moyamoya

Background: Moyamoya is a progressive arteriopathy condition characterized by steno-occlusion of the arteries of the circle of Willis. MRI performed with hypercapnic challenge can image blood-oxygen-level-dependent cerebrovascular reactivity (BOLD-CVR) thereby assessing cerebrovascular reserve. MRI studies of children <7 years of age, or with significant behavioural challenges require anesthesia. The purpose of this study was to validate the use of ventilator-induced hypercapnic challenge under general anaesthesia (GA). Methods: Children with moyamoya underwent two BOLD-CVR imaging in the same session under GA (GA-CVR). Differences in CVR estimates and intraclass correlation coefficient (ICC) between repeated scans were examined to determine repeatability across grey and white matter tissue and vascular territories. Bland-Altman plots were used to visulaize the overall variation between the scans. The associations with age, moyamoya types, and stroke presentation were also examined. Qualitative scoring by visual inspection was also conducted by trained neurologists. Results: Thirty-two paired GA-CVR studies (sixty-four scans in total) were analyzed (mean age: 7.07 (2.74 -17.95) years, 13 females). Forty-one percent (41%) were under 7 and 77%, under 10. No significant differences between repeated scans were found for any of the CVR estimates, when summarized by tissue and vascular territory. Of the paired studies, repeatability (ICC) for the whole-brain CVR estimates was excellent (≥0.74) in 14 (43.8%), good (≤0.59, >0.74) in 7 (21.9%), fair (≤0.41, >0.59) in 6 (18.8%) and poor (<0.41) in 5 (15.6%). Bland-Altman plots illustrated the overall variation of whole-brain CVR within 95% confidence interval level. Repeatability indices were not affected by children’s age and other clinical factors. On qualitative scoring, the Cohen weighted kappa showed substantial agreement in both right (0.75) and left (0.81) hemispheres. Conclusion: Our study support the clinical use of GA-CVR across all ages and disease conditions. The GA-CVR provides a feasible, repeatable, and reliably interpretable tool for the assessment of cerebrovascular reserve of very young and behaviourally challenged children with moyamoya.

Comprehensive Analysis of Brain Volume in REM Sleep Behavior Disorder with Mild Cognitive Impairment.

Rapid-eye-movement sleep behavior disorder (RBD) is a major risk factor for Parkinson's disease and dementia with Lewy bodies. More than a third of RBD patients have mild cognitive impairment (MCI), but their specific structural brain alterations remain poorly understood. This study aimed to investigate the local deformation and volume of gray and white matter tissue underlying MCI in RBD. Fifty-two idiopathic RBD patients, including 17 with MCI (33%), underwent polysomnography, neuropsychological, neurological, and magnetic resonance imaging assessments. MCI diagnosis was based on a subjective complaint, cognitive impairment on the neuropsychological battery, and preserved daily functioning. Forty-one controls were also included. Deformation-based morphometry (DBM), voxel-based morphometry (VBM), and regional volume analyses of the corpus callosum and cholinergic basal forebrain were performed. Multiple regression models were also computed using anatomical, cognitive (composite z scores), and motor parameters. Globally, patients with MCI displayed a widespread pattern of local deformation and volume atrophy in the cortical (bilateral insula, cingulate cortex, precuneus, frontal, temporal and occipital regions, right angular gyrus, and mid-posterior segment of the corpus callosum) and subcortical (brainstem, corona radiata, basal ganglia, thalamus, amygdala, and right hippocampus) regions compared to patients without MCI (DBM) or controls (DBM and VBM). Moreover, brain deformation (DBM) in patients were associated with lower performance in attention and executive functions, visuospatial abilities, and higher motor symptoms severity. The present study identified novel brain structural alterations in RBD patients with MCI which correlated with poorer cognitive performance. These results are consistent with those reported in patients with synucleinopathies-related cognitive impairment.

T1D -weighted ihMT imaging - Part I. Isolation of long- and short-T1D components by T1D -filtering.

To identify T1D -filtering methods, which can specifically isolate various ranges of T1D components as they may be sensitive to different microstructural properties. Modified Bloch-Provotorov equations describing a bi-T1D component biophysical model were used to simulate the inhomogeneous magnetization transfer (ihMT) signal from ihMTRAGE sequences at high RF power and low duty-cycle with different switching time values for the dual saturation experiment: Δt = 0.0, 0.8, 1.6, and 3.2 ms. Simulations were compared with experimental signals on the brain gray and white matter tissues of healthy mice at 7T. The lengthening of Δt created ihMT high-pass T1D -filters, which efficiently eliminated the signal from T1D components shorter than 1 ms, while partially attenuating that of longer components (≥ 1 ms). Subtraction of ihMTR images obtained with Δt = 0.0 ms and Δt = 0.8 ms generated a new ihMT band-pass T1D -filter isolating short-T1D components in the 100-µs to 1-ms range. Simulated ihMTR values in central nervous system tissues were confirmed experimentally. Long- and short-T1D components were successfully isolated with high RF power and low duty-cycle ihMT filters in the healthy mouse brain. Future studies should investigate the various T1D -range microstructural correlations in in vivo tissues.

Age-Specific Adult Rat Brain MRI Templates and Tissue Probability Maps.

Age-specific resources in human MRI mitigate processing biases that arise from structural changes across the lifespan. There are fewer age-specific resources for preclinical imaging, and they only represent developmental periods rather than adulthood. Since rats recapitulate many facets of human aging, it was hypothesized that brain volume and each tissue's relative contribution to total brain volume would change with age in the adult rat. Data from a longitudinal study of rats at 3, 5, 11, and 17 months old were used to test this hypothesis. Tissue volume was estimated from high resolution structural images using a priori information from tissue probability maps. However, existing tissue probability maps generated inaccurate gray matter probabilities in subcortical structures, particularly the thalamus. To address this issue, gray matter, white matter, and CSF tissue probability maps were generated by combining anatomical and signal intensity information. The effects of age on volumetric estimations were then assessed with mixed-effects models. Results showed that herein estimation of gray matter volumes better matched histological evidence, as compared to existing resources. All tissue volumes increased with age, and the tissue proportions relative to total brain volume varied across adulthood. Consequently, a set of rat brain templates and tissue probability maps from across the adult lifespan is released to expand the preclinical MRI community's fundamental resources.

Linking Multi-Modal MRI to Clinical Measures of Visual Field Loss After Stroke.

Loss of vision across large parts of the visual field is a common and devastating complication of cerebral strokes. In the clinic, this loss is quantified by measuring the sensitivity threshold across the field of vision using static perimetry. These methods rely on the ability of the patient to report the presence of lights in particular locations. While perimetry provides important information about the intactness of the visual field, the approach has some shortcomings. For example, it cannot distinguish where in the visual pathway the key processing deficit is located. In contrast, brain imaging can provide important information about anatomy, connectivity, and function of the visual pathway following stroke. In particular, functional magnetic resonance imaging (fMRI) and analysis of population receptive fields (pRF) can reveal mismatches between clinical perimetry and maps of cortical areas that still respond to visual stimuli after stroke. Here, we demonstrate how information from different brain imaging modalities—visual field maps derived from fMRI, lesion definitions from anatomical scans, and white matter tracts from diffusion weighted MRI data—provides a more complete picture of vision loss. For any given location in the visual field, the combination of anatomical and functional information can help identify whether vision loss is due to absence of gray matter tissue or likely due to white matter disconnection from other cortical areas. We present a combined imaging acquisition and visual stimulus protocol, together with a description of the analysis methodology, and apply it to datasets from four stroke survivors with homonymous field loss (two with hemianopia, two with quadrantanopia). For researchers trying to understand recovery of vision after stroke and clinicians seeking to stratify patients into different treatment pathways, this approach combines multiple, convergent sources of data to characterize the extent of the stroke damage. We show that such an approach gives a more comprehensive measure of residual visual capacity—in two particular respects: which locations in the visual field should be targeted and what kind of visual attributes are most suited for rehabilitation.

Neurodegenerative and functional signatures of the cerebellar cortex in m.3243A > G patients.

Mutations of the mitochondrial DNA are an important cause of inherited diseases that can severely affect the tissue’s homeostasis and integrity. The m.3243A > G mutation is the most commonly observed across mitochondrial disorders and is linked to multisystemic complications, including cognitive deficits. In line with in vitro experiments demonstrating the m.3243A > G’s negative impact on neuronal energy production and integrity, m.3243A > G patients show cerebral grey matter tissue changes. However, its impact on the most neuron dense, and therefore energy-consuming brain structure—the cerebellum—remains elusive. In this work, we used high-resolution structural and functional data acquired using 7 T MRI to characterize the neurodegenerative and functional signatures of the cerebellar cortex in m.3243A > G patients. Our results reveal altered tissue integrity within distinct clusters across the cerebellar cortex, apparent by their significantly reduced volume and longitudinal relaxation rate compared with healthy controls, indicating macroscopic atrophy and microstructural pathology. Spatial characterization reveals that these changes occur especially in regions related to the frontoparietal brain network that is involved in information processing and selective attention. In addition, based on resting-state functional MRI data, these clusters exhibit reduced functional connectivity to frontal and parietal cortical regions, especially in patients characterized by (i) a severe disease phenotype and (ii) reduced information-processing speed and attention control. Combined with our previous work, these results provide insight into the neuropathological changes and a solid base to guide longitudinal studies aimed to track disease progression.

Brain structure in subjective cognitive decline: A replication study

AbstractBackgroundAlzheimer’s disease (AD) is an incurable neurodegenerative disorder, which disproportionately affects women (Erol et al., 2015). Given that the current treatments for AD aim to prevent symptom progression, the primary objective of emerging research is to identify pre‐clinical biological markers. Investigating such biomarkers would be appropriate in those experiencing subjective cognitive decline (SCD). Individuals with SCD are thought to be the earliest along the cognitive continuum between healthy aging and AD, as their reported change in cognition is not yet measurable using standard neuropsychological assessment measures. Previous research has found differences to exist in brain function, but not structure between healthy controls and those with SCD (Parker et al., 2020). The current study represents a preliminary analysis of structural brain images in healthy women compared to women with SCD; no structural differences between groups were hypothesized.MethodThe 3T T1 anatomical magnetic resonance images (MRI) used in this analysis was a subset of participants obtained from the Women’s Healthy Ageing Project collected in 2012 (N = 170). This subset included 30 healthy women (mean age = 71.4; SD = 3.12) and 30 women with SCD (mean age = 70.5; SD = 2.23). Voxel‐based morphometry (VBM) analyses were conducted using FMRIB’s Software Library to examine cross sectional differences between healthy women and women with SCD.ResultWhole brain VBM analyses did not reveal any significant differences in grey matter tissue density between healthy women and women with SCD at p < 0.05 level with threshold free cluster enhancement (corrected for multiple comparisons).ConclusionAlthough the current findings did not reveal differences in grey matter between healthy women and women with SCD, this result replicates our previous findings and other reports in the literature that structural differences may not be detectable between healthy individuals and those with SCD. Follow‐up work will include investigation of differences in functional connectivity between these two groups using resting‐state functional MRI with the total sample collected in 2012 (N = 170). Identifying changes in brain function prior to structural atrophy and measurable decline on neuropsychological measures would represent a major advance in AD biomarker research.

Magnetic resonance T1w/T2w ratio and voxel-based morphometry in multiple system atrophy

Diagnosis of multiple system atrophy (MSA) may be improved by using multimodal imaging approaches. We investigated the use of T1-weighted/T2-weighted (T1w/T2w) images ratio combined with voxel-based morphometry to evaluate brain tissue integrity in MSA compared to Parkinson’s disease (PD) and healthy controls (HC). Twenty-six patients with MSA, 43 patients with PD and 56 HC were enrolled. Whole brain voxel-based and local regional analyses were performed to evaluate gray and white matter (GM and WM) tissue integrity and mean regional values were used for patients classification using logistic regression. Increased mean regional values of T1w/T2w in bilateral putamen were detected in MSA-P compared to PD and HC. The combined use of regional GM and T1w/T2w values in the right and left putamen showed the highest accuracy in discriminating MSA-P from PD and good accuracy in discriminating MSA from PD and HC. A good accuracy was also found in discriminating MSA from PD and HC by either combining regional GM and T1w/T2w values in the cerebellum or regional WM and T1w/T2w in the cerebellum and brainstem. The T1w/T2w image ratio alone or combined with validated MRI parameters can be further considered as a potential candidate biomarker for differential diagnosis of MSA.

Feasibility of Brain Imaging Using a Digital Surround Technology Body Coil: A Study Based on SRGAN-VGG Convolutional Neural Networks.

Brain imaging using conventional head coils presents several problems in routine magnetic resonance (MR) examination, such as anxiety and claustrophobic reactions during scanning with a head coil, photon attenuation caused by the MRI head coil in positron emission tomography (PET)/MRI, and coil constraints in intraoperative MRI or MRI-guided radiotherapy. In this paper, we propose a super resolution generative adversarial (SRGAN-VGG) network-based approach to enhance low-quality brain images scanned with body coils. Two types of T1 fluid-attenuated inversion recovery (FLAIR) images scanned with different coils were obtained in this study: joint images of the head-neck coil and digital surround technology body coil (H+B images) and body coil images (B images). The deep learning (DL) model was trained using images acquired from 36 subjects and tested in 4 subjects. Both quantitative and qualitative image quality assessment methods were performed during evaluation. Wilcoxon signed-rank tests were used for statistical analysis. Quantitative image quality assessment showed an improved structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) in gray matter and cerebrospinal fluid (CSF) tissues for DL images compared with B images (P <.01), while the mean square error (MSE) was significantly decreased (P <.05). The analysis also showed that the natural image quality evaluator (NIQE) and blind image quality index (BIQI) were significantly lower for DL images than for B images (P <.0001). Qualitative scoring results indicated that DL images showed an improved SNR, image contrast and sharpness (P<.0001). The outcomes of this study preliminarily indicate that body coils can be used in brain imaging, making it possible to expand the application of MR-based brain imaging.

The impact of laser-induced thermocoagulation effects on the optical spectra of brain tissues

In this work, the results of spectral and optical studies of the grey matter tissues of the brain by a laser hyperthermia action are presented. The coefficients of optical absorption and transport scattering in the spectral range of 250-1500 nm are determined, and the features of the spatial distribution of the laser radiation intensity in the studied objects are identified. It is shown that the coagulation effects lead to compaction and an increase in the concentration of endogenous chromophores and structural elements, which is reflected in the optical properties of biological objects.

Neural correlates of orbital telorism

Orbital telorism, the interocular distance, is clinically informative and in extremes is considered a minor physical anomaly. While its extremes, hypo- and hypertelorism, have been linked to disorders often related to cognitive ability, little is known about the neural correlates of normal variation of telorism within the general population. We derived measures of orbital telorism from cranial magnetic resonance imaging (MRI) by calculating the distance between the eyeball center of gravity in two population-based datasets (N = 5,653, N = 29,824; mean age 64.66, 63.75 years). This measure was found to be related to grey matter tissue density within numerous regions of the brain, including, but surprisingly not limited to, the frontal regions, in both positive and negative directions. Additionally, telorism was related to several cognitive functions, such as Purdue pegboard test (Beta, P-value (CI95%) −.02, 1.63 × 10−7 (−.03:-.01)) and fluid intelligence (.02, 4.75 × 10−6 (.01:0.02)), with some relationships driven by individuals with a smaller orbital telorism. This is reflective of the higher prevalence of hypotelorism in developmental disorders, specifically those that accompany lower cognitive lower functioning. This study suggests, despite previous links only made in clinical extremes, that orbital telorism holds some relation to structural brain development and cognitive function in the general population. This relationship is likely driven by shared developmental periods.

Evidence for microscopic kurtosis in neural tissue revealed by correlation tensor MRI.

PurposeThe impact of microscopic diffusional kurtosis (µK), arising from restricted diffusion and/or structural disorder, remains a controversial issue in contemporary diffusion MRI (dMRI). Recently, correlation tensor imaging (CTI) was introduced to disentangle the sources contributing to diffusional kurtosis, without relying on a‐priori multi‐gaussian component (MGC) or other microstructural assumptions. Here, we investigated µK in in vivo rat brains and assessed its impact on state‐of‐the‐art methods ignoring µK.Theory and MethodsCTI harnesses double diffusion encoding (DDE) experiments, which were here improved for speed and minimal bias using four different sets of acquisition parameters. The robustness of the improved CTI protocol was assessed via simulations. In vivo CTI acquisitions were performed in healthy rat brains using a 9.4T pre‐clinical scanner equipped with a cryogenic coil, and targeted the estimation of µK, anisotropic kurtosis, and isotropic kurtosis.ResultsThe improved CTI acquisition scheme substantially reduces scan time and importantly, also minimizes higher‐order‐term biases, thus enabling robust µK estimation, alongside Kaniso and Kiso metrics. Our CTI experiments revealed positive µK both in white and gray matter of the rat brain in vivo; µK is the dominant kurtosis source in healthy gray matter tissue. The non‐negligible µK substantially were found to bias prior MGC analyses of Kiso and Kaniso.ConclusionsCorrelation Tensor MRI offers a more accurate and robust characterization of kurtosis sources than its predecessors. µK is non‐negligible in vivo in healthy white and gray matter tissues and could be an important biomarker for future studies. Our findings thus have both theoretical and practical implications for future dMRI research.

Interpretable deep learning as a means for decrypting disease signature in multiple sclerosis

Objective. The mechanisms driving multiple sclerosis (MS) are still largely unknown, calling for new methods allowing to detect and characterize tissue degeneration since the early stages of the disease. Our aim is to decrypt the microstructural signatures of the Primary Progressive versus the Relapsing-Remitting state of disease based on diffusion and structural magnetic resonance imaging data. Approach. A selection of microstructural descriptors, based on the 3D-Simple Harmonics Oscillator Based Reconstruction and Estimation and the set of new algebraically independent Rotation Invariant spherical harmonics Features, was considered and used to feed convolutional neural networks (CNNs) models. Classical measures derived from diffusion tensor imaging, that are fractional anisotropy and mean diffusivity, were used as benchmark for diffusion MRI (dMRI). Finally, T1-weighted images were also considered for the sake of comparison with the state-of-the-art. A CNN model was fit to each feature map and layerwise relevance propagation (LRP) heatmaps were generated for each model, target class and subject in the test set. Average heatmaps were calculated across correctly classified patients and size-corrected metrics were derived on a set of regions of interest to assess the LRP contrast between the two classes. Main results. Our results demonstrated that dMRI features extracted in grey matter tissues can help in disambiguating primary progressive multiple sclerosis from relapsing-remitting multiple sclerosis patients and, moreover, that LRP heatmaps highlight areas of high relevance which relate well with what is known from literature for MS disease. Significance. Within a patient stratification task, LRP allows detecting the input voxels that mostly contribute to the classification of the patients in either of the two classes for each feature, potentially bringing to light hidden data properties which might reveal peculiar disease-state factors.

3D 31 P MRSI of the human brain at 9.4 Tesla: Optimization and quantitative analysis of metabolic images.

To present 31 P whole brain MRSI with a high spatial resolution to probe quantitative tissue analysis of 31 P MRSI at an ultrahigh field strength of 9.4 Tesla. The study protocol included a 31 P MRSI measurement with an effective resolution of 2.47 mL. For SNR optimization, the nuclear Overhauser enhancement at 9.4 Tesla was investigated. A sensitivity correction was achieved by applying a low rank approximation of the γ-adenosine triphosphate signal. Group analysis and regression on individual volunteers were performed to investigate quantitative concentration differences between different tissue types. Differences in gray and white matter tissue 31 P concentrations could be investigated for 12 different 31 P resonances. In addition, the first highly resolved quantitative MRSI images measured at B0 = 9.4 Tesla of 31 P detectable metabolites with high SNR could be presented. With an ultrahigh field strength B0 = 9.4 Tesla, 31 P MRSI moves further toward quantitative metabolic imaging, and subtle differences in concentrations between different tissue types can be detected.

Unraveling the MRI-Based Microstructural Signatures Behind Primary Progressive and Relapsing-Remitting Multiple Sclerosis Phenotypes.

BackgroundThe mechanisms driving primary progressive and relapsing–remitting multiple sclerosis (PPMS/RRMS) phenotypes are unknown. Magnetic resonance imaging (MRI) studies support the involvement of gray matter (GM) in the degeneration, highlighting its damage as an early feature of both phenotypes. However, the role of GM microstructure is unclear, calling for new methods for its decryption.PurposeTo investigate the morphometric and microstructural GM differences between PPMS and RRMS to characterize GM tissue degeneration using MRI.Study TypeProspective cross‐sectional study.SubjectsForty‐five PPMS (26 females) and 45 RRMS (32 females) patients.Field Strength/Sequence3T scanner. Three‐dimensional (3D) fast field echo T1‐weighted (T1‐w), 3D turbo spin echo (TSE) T2‐w, 3D TSE fluid‐attenuated inversion recovery, and spin echo‐echo planar imaging diffusion MRI (dMRI).AssessmentT1‐w and dMRI data were employed for providing information about morphometric and microstructural features, respectively. For dMRI, both diffusion tensor imaging and 3D simple harmonics oscillator based reconstruction and estimation models were used for feature extraction from a predefined set of regions. A support vector machine (SVM) was used to perform patients' classification relying on all these measures.Statistical TestsDifferences between MS phenotypes were investigated using the analysis of covariance and statistical tests (P < 0.05 was considered statistically significant).ResultsAll the dMRI indices showed significant microstructural alterations between the considered MS phenotypes, for example, the mode and the median of the return to the plane probability in the hippocampus. Conversely, thalamic volume was the only morphometric feature significantly different between the two MS groups. Ten of the 12 features retained by the selection process as discriminative across the two MS groups regarded the hippocampus. The SVM classifier using these selected features reached an accuracy of 70% and a precision of 69%.Data ConclusionWe provided evidence in support of the ability of dMRI to discriminate between PPMS and RRMS, as well as highlight the central role of the hippocampus.Level of Evidence2Technical Efficacy Stage3

Effect of age on brain metabotropic glutamate receptor subtype 5 measured with [18F]FPEB PET

ObjectiveMetabotropic glutamate receptor subtype 5 (mGluR5) is integral to the brain glutamatergic system and cognitive function. This study investigated whether aging is associated with decreased brain mGluR5 availability. MethodsCognitively normal participants (n = 45), aged 18 to 84 years, underwent [18F]FPEB positron emission tomography scans to quantify brain mGluR5. Distribution volume (VT) was computed using a venous or arterial input function and equilibrium modeling from 90 to 120 min. In the primary analysis, the association between age and VT in the hippocampus and association cortex was evaluated using a linear mixed model. Exploratory analyses assessed the association between age and VT in multiple brain regions. The contribution of gray matter tissue alterations and partial volume effects to associations with age was also examined. ResultsIn the primary analysis, older age was associated with lower [18F]FPEB binding to mGluR5 (P = 0.026), whereas this association was not significant after gray matter masking or partial volume correction to account for age-related tissue loss. Post hoc analyses revealed an age-related decline in mGluR5 availability in the hippocampus of 4.5% per decade (P = 0.007) and a non-significant trend in the association cortex (P = 0.085). An exploratory analysis of multiple brain regions revealed broader inverse associations of age with mGluR5 availability, but not after partial volume correction. ConclusionReductions in mGluR5 availability with age appear to be largely mediated by tissue loss. Quantification of [18F]FPEB binding to mGluR5 may expand our understanding of age-related molecular changes and the relationship with brain tissue loss.