Deep Gray Matter Research Articles

Abstract 4136290: Toward Quantitative Assessment of Cerebrovascular Autoregulation in Neonates using Ultrafast Ultrasound Power Doppler

Introduction: Newborns with congenital heart diseases requiring cardiopulmonary bypass are at risk of neurodevelopmental impairment. The impact of deep hypothermia during cardiopulmonary bypass (DH-CPB) on cerebrovascular autoregulation (CAR) that controls brain perfusion in the presence of variable blood pressure is not well understood. Recently, Ultrafast Power Doppler (UPD) showed potential to study CAR in neonates based on the measurement of cerebral blood volume (CBV). However, since CAR relies mainly on the arterial vasoconstriction/vasodilation, the interpretation of brain perfusion based on CBV requires further separation of arterial CBV from total CBV. This study aims to use UPD to monitor CAR during DH-CPB in neonates. Methods: An ultrasonic probe (5.7 MHz) was placed on the anterior fontanel of 6 newborns before, during and after DH-CPB. Using ultrafast sequences generated by a Verasonic Vantage research system, mid-coronal plane images were acquired (PRF of 9 kHz, 5 compounded diverging waves). An adaptive spatiotemporal singular value decomposition was used to separate the blood signal from tissue signal and obtain UPD. CBV was computed in the deep gray matter (DGM). This region was chosen due to its higher susceptibility to hypoxic-ischemic injury. Arterial CBV was isolated from total CBV based on the upward arterial flow obtained using signed-power Doppler (See Fig A). Pearson correlation ® was computed between mean arterial blood pressure (MAP) and arterial CBV to study CAR. Results: Before and after DH-CPB, negative correlations were found between arterial CBV and MAP: -0.30 and -0.43 respectively. This indicates that an increase in MAP is associated with a decrease in cerebral arterial blood volume. This decrease in arterial CBV likely arises from arterial vasoconstriction, suggesting an active CAR response. Conversely, during DH-CPB, no correlation was found (r = -0.02). This means that despite the increase in MAP, arterial CBV remains unchanged. This lack of variation of arterial CBV is likely due to absent arterial vasoconstriction, suggesting an impaired CAR response. Conclusion: Our findings highlight the potential of UPD for continuous monitoring of CAR, paving the way for improving neurovascular management strategies during DH- CPB.

Brain connectomes in youth at risk for serious mental illness: a longitudinal perspective.

Identifying biomarkers for serious mental illnesses (SMI) has significant implications for prevention and early intervention. In the current study, changes in whole brain structural and functional connectomes were investigated in youth at transdiagnostic risk over a one-year period. Based on clinical assessments, participants were assigned to one of 5 groups: healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9). Constrained spherical deconvolution was used to generate whole brain tractography maps, which were then used to calculate connectivity matrices for graph theory analysis. Graph theory was also used to analyze correlations of functional magnetic resonance imaging (fMRI) signal between pairs of brain regions. Linear mixed models revealed structural and functional abnormalities in global metrics of small world lambda, and resting state networks involving the fronto-parietal, default mode, and deep grey matter networks, along with the visual and dorsal attention networks. Machine learning analysis additionally identified changes in nodal metrics of betweenness centrality in the angular gyrus and bilateral temporal gyri as potential features which can discriminate between the groups. Our findings further support the view that abnormalities in large scale networks (particularly those involving fronto-parietal, temporal, default mode,and deep grey matter networks) may underlie transdiagnostic risk for SMIs.

Imaging Transcriptomics of Brain Functional Alterations in MS and Neuromyelitis Optica Spectrum Disorder.

The underlying transcriptomic signatures driving brain functional alterations in MS and neuromyelitis optica spectrum disorder (NMOSD) are still unclear. Regional fractional amplitude of low-frequency fluctuation (fALFF) values were obtained and compared among 209 patients with MS, 90 patients with antiaquaporin-4 antibody (AQP4)+ NMOSD, 49 with AQP4- NMOSD, and 228 healthy controls from a discovery cohort. We used partial least squares (PLS) regression to identify the gene transcriptomic signatures associated with disease-related fALFF alterations. The biologic process and cell type-specific signature of the identified PLS genes were explored by enrichment analysis. The correlation between PLS genes and clinical variables was explored. A prospective independent cohort was used to validate the brain fALFF alterations and the repeatability of identified genes. MS, AQP4+ NMOSD, and AQP4- NMOSD showed decreased fALFF in cognition-related regions and deep gray matter, while NMOSD (both AQP4+ and AQP4-) additionally demonstrated lower fALFF in the visual region. The overlapping PLS1- genes (indicating that the genes were overexpressed as regional fALFF decreased) were enriched in response to regulation of the immune response in all diseases, and the PLS1- genes were specifically enriched in the epigenetics profile in MS, membrane disruption and cell adhesion in AQP4+ NMOSD, and leukocyte activation in AQP4- NMOSD. For the cell type transcriptional signature, microglia and astrocytes accounted for the decreased fALFF. The fALFF-associated PLS1- genes directly correlated with Expanded Disability Status Scale of MS and disease duration across disorders. We revealed the functional activity alterations and their underlying shared and specific gene transcriptional signatures in MS, AQP4+ NMOSD, and AQP4- NMOSD.

Characterizing T1 in the fetal brain and placenta over gestational age at 0.55T.

T1 mapping and T1-weighted contrasts have a complimentary but currently under utilized role in fetal MRI. Emerging clinical low field scanners are ideally suited for fetal T1 mapping. The advantages are lower T1 values which results in higher efficiency and reduced field inhomogeneities resulting in a decreased requirement for specialist tools. In addition the increased bore size associated with low field scanners provides improved patient comfort and accessibility. This study aims to demonstrate the feasibility of fetal brain T1 mapping at 0.55T. An efficient slice-shuffling inversion-recovery echo-planar imaging (EPI)-based T1-mapping and postprocessing was demonstrated for the fetal brain at 0.55T in a cohort of 38 fetal MRI scans. Robustness analysis was performed and placental measurements were taken for validation. High-quality T1 maps allowing the investigation of subregions in the brain were obtained and significant correlation with gestational age was demonstrated for fetal brain T1 maps ( ) as well as regions-of-interest in the deep gray matter and white matter. Efficient, quantitative T1 mapping in the fetal brain was demonstrated on a clinical 0.55T MRI scanner, providing foundations for both future research and clinical applications including low-field specific T1-weighted acquisitions.

Epstein-Barr Virus Encephalitis Involving the Bilateral Corticospinal Tract: Wine Glass Sign

Epstein-Barr virus (EBV) causes a variety of central nervous system infections, including encephalitis. EBV encephalitis accounts for approximately 5.8% of all viral encephalitis, with fever, headache, convulsions, and decreased consciousness as common symptoms. Brain magnetic resonance imaging (MRI) in EBV encephalitis shows high signal intensity mainly in the parieto-occipital cortex, subcortical white matter, and deep gray matter nuclei with reversibility. In this report, we present a case of bilateral corticospinal tract involvement (wineglass sign) identified on brain MRI in a patient with diagnosed with EBV encephalitis.

Optimal timing of ultra-early diffusion-weighted MRI in out-of-hospital cardiac arrest patients based on a retrospective multicenter cohort study

Diffusion-weighted magnetic resonance imaging (DW-MRI) performed before target temperature management, within 6 h of return of spontaneous circulation (ROSC), is defined as ultra-early DW-MRI. In previous studies, high-signal intensity (HSI) on ultra-early DW-MRI can predict poor neurological outcomes (Cerebral Performance Category 3–5 at 6-months post-ROSC). We aimed to assess the optimal-timing for ultra-early DW-MRI to avoid false-negative outcomes post out-of-hospital cardiac arrest, considering cardiopulmonary resuscitation (CPR) factors. The primary outcomes were HSI in the cerebral cortex or deep gray matter on ultra-early DW-MRI. The impact of CPR factors and ROSC to DW-MRI scan-interval on HSI-presence was assessed. Of 206 included patients, 108 exhibited HSI-presence, exclusively associated with poor neurological outcomes. In multivariate regression analysis, ROSC to DW-MRI scan-interval (adjusted odds ratio [aOR], 1.509; 95% confidence interval (CI): 1.113–2.046; P = 0.008), low-flow time (aOR, 1.176; 95%CI: 1.121–1.233; P < 0.001), and non-shockable rhythm (aOR, 9.974; 95%CI: 3.363–29.578; P < 0.001) were independently associated with HSI-presence. ROSC to DW-MRI scan-interval cutoff of ≥ 2.2 h was particularly significant in low-flow time ≤ 21 min or shockable rhythm group. In conclusion, short low-flow time and shockable rhythm require a longer ROSC to DW-MRI scan-interval. Prolonged low-flow time and non-shockable rhythm reduce the need to consider scan-interval.

Iron load in the normal aging brain measured with QSM and at 7T: findings of the SENIOR cohort.

Iron accumulates in the brain during aging and is the focus of intensive research as an abnormal load, particularly in Deep Gray Matter (DGM), is related to neurodegeneration. Magnetic Resonance Imaging (MRI) metrics such as Quantitative Susceptibility Mapping (QSM) and apparent transverse relaxation rate can be used to follow up iron in vivo. While the influence of age and sex on iron levels has already been reported, a careful consideration of neuronal risk factors, as well as for an enhanced sensitivity, is needed to define the normal evolution. QSM and at ultra-high field MRI are used to study iron in DGM using a carefully-characterized cohort of the healthy aging brain (SENIOR). Seventy-seven cognitively healthy elders (from 54 to 78 y/o) with clinical, biology, genetics, and cardiovascular risk factors careful evaluation. Differences linked with age, sex, cardiovascular risk factors and weight are studied. Age and sex have an influence on the brain iron deposition measured by QSM and in a context of normal aging, without appearance of a pathological neurodegenerative process. Iron deposition shows higher values in the caudate and the putamen in older participants. Female participants present a higher level of iron in the amygdala, and males in the thalamus. Female participants also present differences in the accumbens, caudate and hippocampus when evaluating the joint age and sex effect. Participants with higher cardiovascular risk factors showed higher values of the iron, even without any impairment in their cognitive capability. An overweight is related with a higher iron load in the putamen for QSM and in female participants. We controlled that these modifications of iron deposition are not related to a specific profile in the genotype of ApoE loci. Establishing baseline values of QSM and as iron probes in the context of aging is essential to determine differences in the process of neurodegeneration. Age and sex of participants are important factors that affect brain iron normal values. On the other hand, the presence of cardiovascular risk factors, which can be associated with age related diseases, can also potentially be linked with the iron deposition in the brain.

Deep learning reconstruction of diffusion-weighted brain MRI for evaluation of patients with acute neurologic symptoms

Purpose: We aimed to evaluate whether the deep-learning (DL) accelerated diffusion weighted image (DWI) is clinically feasible for evaluating patients with acute neurologic symptoms, regarding its shorter study time and acceptable image quality. Materials and methods: In this retrospective study, brain images obtained at DWI with a b-value of 0 s/mm2 and DWI with a b-value of 1000 s/mm2 (DWI 1000) from 321 consecutive patients with acute stroke-like symptom were reconstructed with and without DL algorithm. We compare the diagnostic performance between DL-DWI and conventional DWI for detecting brain lesions, including acute infarction. We assessed the diagnostic accuracy of conventional DWI and DL-DWI and compared the results. Qualitative analysis based on image quality was assessed and compared using a five-point visual scoring system. Apparent diffusion coefficients (ADCs) from DWI with and without DL were also compared. Results: The mean acquisition time for the DL-DWI (49 s) was significantly shorter (P < 0.001) than conventional DWI (165 s). Both DWI with and without DL showed similar performance in diagnosing brain lesions especially sensitivity (98.8% in both DWI and DL-DWI) and specificity (99.5% in both DWI and DL-DWI). Overall image quality, gray-white matter and deep gray matter differentiation of two sequences were similar. DL DWI showed more artifacts than DWI. Lesion conspicuity, especially smaller than 5 mm, was better with DL DWI than conventional DWI (p = 0.03). ADC values of white matter, deep gray matter, and pons with DL were lower than conventional DWI. Conclusions: Compared to conventional DWI, DL-DWI achieved comparable image quality and brain lesion visualization for acute neurological symptoms, with a significantly shorter scan time.

Early regional cerebral grey matter damage predicts long-term cognitive impairment phenotypes in multiple sclerosis: a 20-year study.

Despite grey matter atrophy in cortical and subcortical regions has been related to cognitive impairment in multiple sclerosis, only a few studies evaluated its predictive value for alterations in the long-term. We aimed to determine early predictors of cognitive status after 20 years of multiple sclerosis. In this longitudinal retrospective study, participants underwent a 1.5 T MRI scanning at diagnosis (T0) and after two years (T2), which included the evaluation of regional grey matter volume loss patterns. All individuals with multiple sclerosis underwent a comprehensive neuropsychological assessment at the end of the study and were classified considering their global and specific cognitive domains status (memory, attention/information processing speed, executive functioning). Clinical and MRI characteristics were assessed as predictors of long-term cognitive impairment. Analysis of covariance, t-test, unadjusted and adjusted (for age, sex, disease duration, volume of white matter lesions, volume of cortical lesions) logistic regression were conducted. One hundred seventy-five people with multiple sclerosis (118 females; mean ± SD age at the end of study = 47.7 ± 9.4 years) clinically followed for 20 years from onset (mean ± SD = 19.9 ± 5.1) were evaluated. At the end of the study, 81 (47%) were classified as cognitively impaired: 38 as mildly impaired (22%), and 43 as severely impaired (25%). In particular, 46 were impaired in memory (27%), 66 were impaired in attention/information processing speed (38%), and 71 were impaired in executive functioning (41%). Regression models identified precuneus (adjusted odds ratio = 3.37; P < 0.001), insula (adjusted odds ratio = 2.33; P = 0.036), parahippocampal gyrus (adjusted odds ratio = 2.07; P < 0.001) and cingulate (adjusted odds ratio = 1.81; P = 0.009) as the most associated regions with global cognitive impairment and domains-specific cognitive alterations after a mean of 20 years of multiple sclerosis, after adjusting for demographic and clinical variables as well as for focal white matter and grey matter damage. Early grey matter volume loss of specific cortical and deep grey matter regions predicts global and domain cognitive alterations after 20 years from multiple sclerosis diagnosis.

Diffusion and functional MRI reveal microstructural and network connectivity impairment in adult-onset neuronal intranuclear inclusion disease.

Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disorder lacking reliable neuroimaging biomarkers. This study aimed to evaluate microstructural and functional connectivity alterations using diffusion kurtosis imaging (DKI) and resting-state fMRI (rs-fMRI), and to investigate their diagnostic potential as biomarkers. Twenty-three patients with NIID and 40 matched healthy controls (HCs) were recruited. Firstly, gray matter (GM) and white matter (WM) changes were assessed by voxel-based analysis (VBA) and tract-based spatial statistics (TBSS). Then we explored modifications in brain functional networks connectivity by independent component analysis. And the relationship between the altered DKI parameters and neuropsychological evaluation was analyzed. Finally, a receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of different gray matter and white matter parameters. Compared with the HCs, NIID patients showed reduced mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), and kurtosis fractional anisotropy (KFA) values in deep gray matter regions. Significantly decreased MK, RK, AK, KFA and fractional anisotropy (FA), and increased mean diffusivity (MD) values were observed in extensive white matter fiber tracts. Notable alterations in functional connectivity were also detected. Among all DKI parameters, the diagnostic efficiency of AK in GM and FA in WM regions was the highest. Adult-onset NIID patients exhibited altered microstructure and functional network connectivity. Our findings suggest that DKI parameters may serve as potential imaging biomarkers for diagnosing adult-onset NIID.

Dynamics of choroid plexus volume is associated with the presence and development of fatigue in multiple sclerosis

BackgroundImmune-mediated processes are implicated in the pathogenesis of fatigue, a common symptom in multiple sclerosis (MS). The choroid plexus (CP) regulates central nervous system (CNS) immune homeostasis and undergoes volumetric...

Quantitative Susceptibility Mapping Values Quantification in Deep Gray Matter Structures for Relapsing-Remitting Multiple Sclerosis: A Systematic Review and Meta-Analysis.

This systematic review and meta-analysis aimed to investigate the role of magnetic susceptibility (χ) in deep gray matter (DGM) structures, including the putamen (PUT), globus pallidus (GP), caudate nucleus (CN), and thalamus, in the most common types of multiple sclerosis (MS) and relapsing-remitting MS (RRMS), using quantitative susceptibility mapping (QSM). The literature was systematically reviewed up to November 2023, adhering to PRISMA guidelines. This study was conducted using a random-effects model to calculate the standardized mean difference (SMD) in QSM values between patients with RRMS and healthy controls (HCs). Publication bias and risk of bias were also assessed. Nine studies involving 1074 RRMS patients with RRMS and 640 HCs were included in the meta-analysis. The results showed significantly higher QSM (χ) values in the PUT (SMD = 0.40, 95% confidence interval [CI] = 0.22-0.59, p =.000), GP (SMD = 0.60, 95% CI = 0.50-0.70, p =.00), and CN (SMD = 0.40, 95% CI = 0.15-0.66, p =.005) of RRMS patients compared to HCs. However, there were no significant differences in the QSM values in the thalamus between patients with RRMS and HCs (SMD = -0.33, 95% CI -0.67-0.01, p =.026). Age- and sex-based subgroup analysis demonstrated that younger patients (<40 years) in the PUT, GP, and CN groups and larger male populations (>25%) in the PUT and GP groups had more significant χ. Interestingly, thalamic QSM values were found to decrease in RRMS patients over 40 years of age and in higher male populations. Sex-based subgroup analysis indicated higher iron levels in the PUT and GP of RRMS patients regardless of sex. QSM values were higher in certain brain regions (PUT, GP, and CN) during the early stages (disease duration <9.6 years) of RRMS, but lower in the thalamus during the later stages (disease duration >9.6 years) than HCs. QSM may serve as a biomarker for understanding χ value alterations such as iron dysregulation and its contribution to neurodegeneration in RRMS, especially in the basal ganglia nuclei including PUT, GP, and CN.

DC-CRP: Deep Cerebral Venous Thrombosis, Clinicoradiological Profile, and Treatment Outcome in Indian Population-A Tertiary Care Experience.

Deep cerebral venous thrombosis (DCVT) is an uncommon cause of stroke with diverse predisposing factors, clinical presentations, imaging findings, and functional outcomes, which makes the diagnosis of DCVT even more challenging. Retrospective observational study (December 2018 to January 2023). Cases with imaging data suggestive of DCVT were included. The neuroradiological assessment was performed using multimodality imaging to determine the location of parenchymal changes and the number of deep veins involved in isolation or with the superficial dural venous system. Clinical variables were tabulated. Of the 206 cases with CVT in the study period, 27 had DCVT (13.1%), of which four (14.8%) had isolated DCVT (male-to-female ratio 13:14; mean age = 33.4 years). Hyperhomocysteinemia (n = 11) is the most common risk factor associated with it. The most common presentations were headaches (n = 27) and focal motor deficits (n = 13). Raised intracranial tension (ICT) was present in almost half of the study population (n = 14). Mean and median modified Rankins score (mRS) at the time of discharge were 2.0 and 1, respectively. The most common deep vein involved was the straight sinus (SS) (n = 25), followed by the internal cerebral vein (n = 23). The mean and median mRS after 3 months from discharge were 0.3 and 0, respectively. A knowledge of diverse clinical presentations in DCVT, its neurovascular anatomy, and imaging characteristics with prompt diagnosis and timely interventions can assist in attenuating the risk of acute complications and long-term sequelae. Extensive deep grey matter involvement in DCVT is associated with neurological manifestations like altered sensorium and motor deficits, with increased severity of illness as measured by the mRS score.

Comparative analysis of in situ and ex situ postmortem brain MRI: Evaluating volumetry, DTI, and relaxometry

AbstractPurposeTo compare postmortem in situ with ex situ MRI parameters, including volumetry, diffusion tensor imaging (DTI), and relaxometry for assessing methodology‐induced alterations, which is a crucial prerequisite when performing MRI biomarker validation.MethodsMRI whole‐brain scans of five deceased patients with amyotrophic lateral sclerosis were performed at 3 T. In situ scans were conducted within 32 h after death (SD 18 h), and ex situ scans after brain extraction and 3 months of formalin fixation. The imaging protocol included MP2RAGE, DTI, and multi‐contrast spin‐echo and multi‐echo gradient‐echo sequences. Volumetry, fractional anisotropy, mean diffusivity, T1, T2, and have been assessed for specific brain regions.ResultsWhen comparing ex situ to in situ values, the following results were obtained. Deep gray matter as well as the thalamus and the hippocampus showed a reduced volume. Fractional anisotropy was reduced in the cortex and the whole brain. Mean diffusivity was decreased in white matter and deep gray matter. T1 and T2 were reduced in all investigated structures, whereas was increased in the cortex.ConclusionThe results of this study show that the volumes and MRI parameters of several brain regions are potentially affected by tissue extraction and subsequent formalin fixation, suggesting that methodological alterations are present in ex situ MRI. To avoid overlap of indistinguishable methodological and disease‐related changes, we recommend performing in situ postmortem MRI as an additional intermediate step for in vivo MRI biomarker validation.

Multimodal magnetic resonance longitudinal study on the deep gray matter in multiple sclerosis patients with teriflunomide

Disease-modifying therapies (DMTs) are used in an increasing number of patients with multiple sclerosis (MS). However, whether DMTs have intrinsic effects on deep gray matter (DGM) microstructure and atrophy is still poorly understood. In this study, we described the quantitative susceptibility values (QSV) and diffusion kurtosis imaging (DKI) metrics of DGM in relapsing–remitting MS (RRMS) patients and their association with cognitive deficits. We recruited 62 patients with RRMS receiving DMTs and 30 patients with RRMS not receiving DMTs underwent MRI on a 3T scanner. Fractional anisotropy (FA), kurtosis fractional anisotropy (KFA), mean diffusivity (MD), mean kurtosis (MK), QSV and volumes of bilateral caudate nucleus (CAU), amygdala (AMYG), putamen (PUT), hippocampus (Hipp), globus pallidus (GP) and thalamus (THA) were measured. Correlation analysis was performed between those image indexes with longitudinal significant changes and clinical neurological scores, including Expanded Disability Status Scale (EDSS), Digit Span Testand (DST), Symbol Digit Modalities Test (SDMT), Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Significant longitudinal increases in FA, KFA and MK values were found in both groups in bilateral CAU, AMYG, PUT, Hipp, GP and THA (all p < 0.005). MD values of the right of CAU in the two groups were significant longitudinal increase (p = 0.009, p = 0.047); MD values of the right of GP (p = 0.042), the left of THA (p = 0.003), the right of THA (p = 0.001) in treated MS were significant longitudinal decrease; There were no significant longitudinal changes between treated and untreated groups in normalized deep gray matter volume. For QSV, longitudinal increase in the right of PUT (p = 0.022) in the treated MS group and in the left of Hipp (p = 0.045) in the untreated MS group. The QSV and DKI measures were highly correlated with cognitive and disability tests. The treated RRMS patients showed different longitudinal changes of MD value and QSV with untreated in several DGM regions, and these differences were correlated with cognitive and microstructural integrity.

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning.

Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.

Nuclear-Magnetic-Resonance-Spectroscopy-Derived Serum Biomarkers of Metabolic Vulnerability Are Associated with Disability and Neurodegeneration in Multiple Sclerosis.

Metabolic vulnerabilities can exacerbate inflammatory injury and inhibit repair in multiple sclerosis (MS). The purpose was to evaluate whether blood biomarkers of inflammatory and metabolic vulnerability are associated with MS disability and neurodegeneration. Proton nuclear magnetic resonance spectra were obtained from serum samples from 153 healthy controls, 187 relapsing-remitting, and 91 progressive MS patients. The spectra were analyzed to obtain concentrations of lipoprotein sub-classes, glycated acute-phase proteins, and small-molecule metabolites, including leucine, valine, isoleucine, alanine, and citrate. Composite indices for inflammatory vulnerability, metabolic malnutrition, and metabolic vulnerability were computed. MS disability was measured on the Expanded Disability Status Scale. MRI measures of lesions and whole-brain and tissue-specific volumes were acquired. Valine, leucine, isoleucine, alanine, the Inflammatory Vulnerability Index, the Metabolic Malnutrition Index, and the Metabolic Vulnerability Index differed between healthy control and MS groups in regression analyses adjusted for age, sex, and body mass index. The Expanded Disability Status Scale was associated with small HDL particle levels, inflammatory vulnerability, and metabolic vulnerability. Timed ambulation was associated with inflammatory vulnerability and metabolic vulnerability. Greater metabolic vulnerability and inflammatory vulnerability were associated with lower gray matter, deep gray matter volumes, and greater lateral ventricle volume. Serum-biomarker-derived indices of inflammatory and metabolic vulnerability are associated with disability and neurodegeneration in MS.

Quantitative and Radiological Assessment of Post-cardiac-Arrest Comatose Patients with Diffusion-Weighted Magnetic Resonance Imaging.

Although magnetic resonance imaging, particularly diffusion-weighted imaging, has increasingly been used as part of a multimodal approach to prognostication in patients who are comatose after cardiac arrest, the performance of quantitative analysis of apparent diffusion coefficient (ADC) maps, as compared to standard radiologist impression, has not been well characterized. This retrospective study evaluated quantitative ADC analysis to the identification of anoxic brain injury by diffusion abnormalities on standard clinical magnetic resonance imaging reports. The cohort included 204 previously described comatose patients after cardiac arrest. Clinical outcome was assessed by (1) 3-6 month post-cardiac-arrest cerebral performance category and (2) coma recovery to following commands. Radiological evaluation was obtained from clinical reports and characterized as diffuse, cortex only, deep gray matter structures only, or no anoxic injury. Quantitative analyses of ADC maps were obtained in specific regions of interest (ROIs), whole cortex, and whole brain. A subgroup analysis of 172 was performed after eliminating images with artifacts and preexisting lesions. Radiological assessment outperformed quantitative assessment over all evaluated regions (area under the curve [AUC] 0.80 for radiological interpretation and 0.70 for the occipital region, the best performing ROI, p = 0.011); agreement was substantial for all regions. Radiological assessment still outperformed quantitative analysis in the subgroup analysis, though by smaller margins and with substantial to near-perfect agreement. When assessing for coma recovery only, the difference was no longer significant (AUC 0.83 vs. 0.81 for the occipital region, p = 0.70). Although quantitative analysis eliminates interrater differences in the interpretation of abnormal diffusion imaging and avoids bias from other prediction modalities, clinical radiologist interpretation has a higher predictive value for outcome. Agreement between radiological and quantitative analysis improved when using high-quality scans and when assessing for coma recovery using following commands. Quantitative assessment may thus be more subject to variability in both clinical management and scan quality than radiological assessment.

Quantitative Susceptibility Mapping in Adults with Persistent-Post Concussion Symptoms after Mild Traumatic Brain Injury: An Exploratory Study.

It is estimated that 18-30% of concussion sufferers experience symptoms lasting more than 1 month, known as persistent post-concussion symptoms (PPCS). Symptoms can be debilitating, and include headache, dizziness, nausea, problems with memory and concentration, sleep and mood disruption, and exercise intolerance. Previous studies have used quantitative susceptibility mapping (QSM) to show altered tissue susceptibility levels in adults acutely following concussion, however this finding has yet to be investigated in participants with PPCS. In this exploratory case-controlled study, we measured tissue susceptibility using quantitative susceptibility mapping (QSM) in 24 participants with PPCS following mild traumatic brain injury (mTBI) and 23 healthy controls with no history of concussion. We compute tissue susceptibility for seven white matter tracts and three deep grey matter regions and compare tissue susceptibility between groups using ANCOVA models controlling for age and sex. We also assess the relationship between regional tissue susceptibility and symptoms. There were no significant differences between tissue susceptibility in participants with PPCS compared to control subjects in any of the evaluated regions. However, we show lower tissue susceptibility across four white matter tracts was generally associated with worse symptoms in the PPCS group. Specifically, we saw relationships between white matter susceptibility and headache (p=0.006), time since injury (p=0.03), depressive symptoms (p=0.021) and daytime fatigue (p=0.01) in participants with PPCS. These results provide evidence in support of persistent changes in the brain months-to-years following injury and highlight the need to further understand the pathophysiology of PPCS, to determine effective prevention and treatment options. ATR: Anterior Thalamic Radiation; Caud: Caudate; CCB: Corpus Callosum Body; CCG: Corpus Callosum Genus; CCS: Corpus Callosum Splenium; CH: Cingulum; DHI: Dizziness Handicap Inventory; ESS: Epworth Sleepiness Scale; FM: Forceps Minor; FSS: Fatigue Severity Scale; GAD: Generalized Anxiety Disorder; HIT-6: Headache Impact Test 6; IFOF: Inferior Fronto-Occipital Fasciculus; ILF: Inferior Longitudinal Fasciculus; mTBI: mild traumatic brain injury; Pal: Pallidum; PPCS: Persistent Post-Concussive Symptoms; PCSC: Postconcussional Syndrone Checklist; PHQ: Patient Health Questionnaire; Put: Putamen; RPQ: Rivermead Post Concussion Symptoms Questionnaire; SLF: Superior Longitudinal Fasciculus; QSM: Quantitative Susceptibility Mapping.

Pooled analysis of multiple sclerosis findings on multisite 7 Tesla MRI: Protocol and initial observations.

Although 7 T MRI research has contributed much to our understanding of multiple sclerosis (MS) pathology, most prior data has come from small, single-center studies with varying methods. In order to truly know if such findings have widespread applicability, multicenter methods and studies are needed. To address this, members of the North American Imaging in MS (NAIMS) Cooperative worked together to create a multicenter collaborative study of 7 T MRI in MS. In this manuscript, we describe the methods we have developed for the purpose of pooling together a large, retrospective dataset of 7 T MRIs acquired in multiple MS studies at five institutions. To date, this group has contributed five-hundred and twenty-eight 7 T MRI scans from 350 individuals with MS to a common data repository, with plans to continue to increase this sample size in the coming years. We have developed unified methods for image processing for data harmonization and lesion identification/segmentation. We report here our initial observations on intersite differences in acquisition, which includes site/device differences in brain coverage and image quality. We also report on the development of our methods and training of image evaluators, which resulted in median Dice Similarity Coefficients for trained raters' annotation of cortical and deep gray matter lesions, paramagnetic rim lesions, and meningeal enhancement between 0.73 and 0.82 compared to final consensus masks. We expect this publication to act as a resource for other investigators aiming to combine multicenter 7 T MRI datasets for the study of MS, in addition to providing a methodological reference for all future analysis projects to stem from the development of this dataset.