Whole Brain Volume Measured from 1.5T versus 3T MRI in Healthy Subjects and Patients with Multiple Sclerosis

Glial Activity Load on PET Reveals Persistent "Smoldering" Inflammation in MS Despite Disease-Modifying Treatment: 18 F-PBR06 Study

PURPOSE OF THE REPORT

18 F-PBR06-PET targeting 18-kDa translocator protein can detect abnormal microglial activation (MA) in multiple sclerosis (MS). The objectives of this study are to develop individualized mapping of MA using 18 F-PBR06, to determine the effect of disease-modifying treatment (DMT) efficacy on reducing MA, and to determine its clinical, radiological, and serological correlates in MS patients.

PATIENTS AND METHODS

Thirty 18 F-PBR06-PET scans were performed in 22 MS patients (mean age, 46 ± 13 years; 16 females) and 8 healthy controls (HCs). Logarithmically transformed "glial activity load on PET" scores (calculated as the sum of voxel-by-voxel z -scores ≥4), "lnGALP," were compared between MS and HC and between MS subjects on high-efficacy DMTs (H-DMT, n = 13) and those on no or lower-efficacy treatment, and correlated with clinical measures, serum biomarkers, and cortical thickness.

RESULTS

Cortical gray matter (CoGM) and white matter (WM) lnGALP scores were higher in MS versus HC (+33% and +48%, P < 0.001). In H-DMT group, CoGM and WM lnGALP scores were significantly lower than lower-efficacy treatment ( P < 0.01) but remained abnormally higher than in HC group ( P = 0.006). Within H-DMT patients, CoGM lnGALP scores correlated positively with physical disability, fatigue and serum glial fibrillary acid protein levels ( r = 0.65-0.79, all P 's < 0.05), and inversely with cortical thickness ( r = -0.66, P < 0.05).

CONCLUSIONS

High-efficacy DMTs decrease, but do not normalize, CoGM and WM MA in MS patients. Such "residual" MA in CoGM is associated with clinical disability, serum biomarkers, and cortical degeneration. Individualized mapping of translocator protein PET using 18 F-PBR06 is clinically feasible and can potentially serve as an imaging biomarker for evaluating "smoldering" inflammation in MS patients.

External evaluation of a deep learning-based approach for automated brain volumetry in patients with huntington's disease

A crucial step in the clinical adaptation of an AI-based tool is an external, independent validation. The aim of this study was to investigate brain atrophy in patients with confirmed, progressed Huntington's disease using a certified software for automated volumetry and to compare the results with the manual measurement methods used in clinical practice as well as volume calculations of the caudate nuclei based on manual segmentations. Twenty-two patients were included retrospectively, consisting of eleven patients with Huntington's disease and caudate nucleus atrophy and an age- and sex-matched control group. To quantify caudate head atrophy, the frontal horn width to intercaudate distance ratio and the intercaudate distance to inner table width ratio were obtained. The software mdbrain was used for automated volumetry. Manually measured ratios and automatically measured volumes of the groups were compared using two-sample t-tests. Pearson correlation analyses were performed. The relative difference between automatically and manually determined volumes of the caudate nuclei was calculated. Both ratios were significantly different between the groups. The automatically and manually determined volumes of the caudate nuclei showed a high level of agreement with a mean relative discrepancy of - 2.3 ± 5.5%. The Huntington's disease group showed significantly lower volumes in a variety of supratentorial brain structures. The highest degree of atrophy was shown for the caudate nucleus, putamen, and pallidum (all p < .0001). The caudate nucleus volume and the ratios were found to be strongly correlated in both groups. In conclusion, in patients with progressed Huntington's disease, it was shown that the automatically determined caudate nucleus volume correlates strongly with measured ratios commonly used in clinical practice. Both methods allowed clear differentiation between groups in this collective. The software additionally allows radiologists to more objectively assess the involvement of a variety of brain structures that are less accessible to standard semiquantitative methods.

Predictors of multiple sclerosis progression: A systematic review of conventional magnetic resonance imaging studies

INTRODUCTION

Multiple Sclerosis (MS) is a chronic neurodegenerative disorder that affects the central nervous system (CNS) and results in progressive clinical disability and cognitive decline. Currently, there are no specific imaging parameters available for the prediction of longitudinal disability in MS patients. Magnetic resonance imaging (MRI) has linked imaging anomalies to clinical and cognitive deficits in MS. In this study, we aimed to evaluate the effectiveness of MRI in predicting disability, clinical progression, and cognitive decline in MS.

METHODS

In this study, according to PRISMA guidelines, we comprehensively searched the Web of Science, PubMed, and Embase databases to identify pertinent articles that employed conventional MRI in the context of Relapsing-Remitting and progressive forms of MS. Following a rigorous screening process, studies that met the predefined inclusion criteria were selected for data extraction and evaluated for potential sources of bias.

RESULTS

A total of 3028 records were retrieved from database searching. After a rigorous screening, 53 records met the criteria and were included in this study. Lesions and alterations in CNS structures like white matter, gray matter, corpus callosum, thalamus, and spinal cord, may be used to anticipate disability progression. Several prognostic factors associated with the progression of MS, including presence of cortical lesions, changes in gray matter volume, whole brain atrophy, the corpus callosum index, alterations in thalamic volume, and lesions or alterations in cross-sectional area of the spinal cord. For cognitive impairment in MS patients, reliable predictors include cortical gray matter volume, brain atrophy, lesion characteristics (T2-lesion load, temporal, frontal, and cerebellar lesions), white matter lesion volume, thalamic volume, and corpus callosum density.

CONCLUSION

This study indicates that MRI can be used to predict the cognitive decline, disability progression, and disease progression in MS patients over time.

MRI features and disability in multiple sclerosis: A systematic review and meta-analysis

BACKGROUND

In this systematic review and meta-analysis, we aimed to investigate the correlation between disability in patients with Multiple sclerosis (MS) measured by the Expanded Disability Status Scale (EDSS) and brain Magnetic Resonance Imaging (MRI) features to provide reliable results on which characteristics in the MRI can predict disability and prognosis of the disease.

METHODS

A systematic literature search was performed using three databases including PubMed, Scopus, and Web of Science. The selected peer-reviewed studies must report a correlation between EDSS scores and MRI features. The correlation coefficients of included studies were converted to the Fisher's z scale, and the results were pooled.

RESULTS

Overall, 105 studies A total of 16,613 patients with MS entered our study. We found no significant correlation between total brain volume and EDSS assessment (95 % CI: -0.37 to 0.08; z-score: -0.15). We examined the potential correlation between the volume of T1 and T2 lesions and the level of disability. A positive significant correlation was found (95 % CI: 0.19 to 0.43; z-score: 0.31), (95 % CI: 0.17 to 0.33; z-score: 0.25). We observed a significant correlation between white matter volume and EDSS score in patients with MS (95 % CI: -0.37 to -0.03; z-score: -0.21). Moreover, there was a significant negative correlation between gray matter volume and disability (95 % CI: -0.025 to -0.07; z-score: -0.16).

CONCLUSION

In conclusion, this systematic review and meta-analysis revealed that disability in patients with MS is linked to extensive changes in different brain regions, encompassing gray and white matter, as well as T1 and T2 weighted MRI lesions.

Effect of magnet strength on fetal brain biometry - a single-center retrospective MRI-based cohort study

PURPOSE

Abnormal fetal brain measurements might affect clinical management and parental counseling. The effect of between-field-strength differences was not evaluated in quantitative fetal brain imaging until now. Our study aimed to compare fetal brain biometry measurements in 3.0 T with 1.5 T scanners.

METHODS

A retrospective cohort of 1150 low-risk fetuses scanned between 2012 and 2021, with apparently normal brain anatomy, were retrospectively evaluated for biometric measurements. The cohort included 1.5 T (442 fetuses) and 3.0 T scans (708 fetuses) of populations with comparable characteristics in the same tertiary medical center. Manually measured biometry included bi-parietal, fronto-occipital and trans-cerebellar diameters, length of the corpus-callosum, vermis height, and width. Measurements were then converted to centiles based on previously reported biometric reference charts. The 1.5 T centiles were compared with the 3.0 T centiles.

RESULTS

No significant differences between centiles of bi-parietal diameter, trans-cerebellar diameter, or length of the corpus callosum between 1.5 T and 3.0 T scanners were found. Small absolute differences were found in the vermis height, with higher centiles in the 3.0 T, compared to the 1.5 T scanner (54.6th-centile, vs. 39.0th-centile, p < 0.001); less significant differences were found in vermis width centiles (46.9th-centile vs. 37.5th-centile, p = 0.03). Fronto-occipital diameter was higher in 1.5 T than in the 3.0 T scanner (66.0th-centile vs. 61.8th-centile, p = 0.02).

CONCLUSIONS

The increasing use of 3.0 T MRI for fetal imaging poses a potential bias when using 1.5 T-based charts. We elucidate those biometric measurements are comparable, with relatively small between-field-strength differences, when using manual biometric measurements. Small inter-magnet differences can be related to higher spatial resolution with 3 T scanners and may be substantial when evaluating small brain structures, such as the vermis.

Reliability of brain atrophy measurements in multiple sclerosis using MRI: an assessment of six freely available software packages for cross-sectional analyses

PURPOSE

Volume measurement using MRI is important to assess brain atrophy in multiple sclerosis (MS). However, differences between scanners, acquisition protocols, and analysis software introduce unwanted variability of volumes. To quantify theses effects, we compared within-scanner repeatability and between-scanner reproducibility of three different MR scanners for six brain segmentation methods.

METHODS

Twenty-one people with MS underwent scanning and rescanning on three 3 T MR scanners (GE MR750, Philips Ingenuity, Toshiba Vantage Titan) to obtain 3D T1-weighted images. FreeSurfer, FSL, SAMSEG, FastSurfer, CAT-12, and SynthSeg were used to quantify brain, white matter and (deep) gray matter volumes both from lesion-filled and non-lesion-filled 3D T1-weighted images. We used intra-class correlation coefficient (ICC) to quantify agreement; repeated-measures ANOVA to analyze systematic differences; and variance component analysis to quantify the standard error of measurement (SEM) and smallest detectable change (SDC).

RESULTS

For all six software, both between-scanner agreement (ICCs ranging 0.4-1) and within-scanner agreement (ICC range: 0.6-1) were typically good, and good to excellent (ICC > 0.7) for large structures. No clear differences were found between filled and non-filled images. However, gray and white matter volumes did differ systematically between scanners for all software (p < 0.05). Variance component analysis yielded within-scanner SDC ranging from 1.02% (SAMSEG, whole-brain) to 14.55% (FreeSurfer, CSF); and between-scanner SDC ranging from 4.83% (SynthSeg, thalamus) to 29.25% (CAT12, thalamus).

CONCLUSION

Volume measurements of brain, GM and WM showed high repeatability, and high reproducibility despite substantial differences between scanners. Smallest detectable change was high, especially between different scanners, which hampers the clinical implementation of atrophy measurements.

Brain Atrophy as an Outcome of Disease-Modifying Therapy for Remitting-Relapsing Multiple Sclerosis

Introduction

Currently, clinical trials of DMTs strive to determine their effect on neuroinflammation and neurodegeneration. We aimed to determine the impact of currently used DMTs on brain atrophy and disability in RRMS. The main goal of this review is to evaluate the neuroprotective potential of MS therapy and assess its impact on disability.

Methods

We performed a systematic analysis of clinical trials that used brain atrophy as an outcome or performed post hoc analysis of volumetric MRI parameters to assess the neuroprotective potential of applied therapies. Trials between 2008 and 2019 that included published results of brain parenchymal fraction (BPF) change and brain volume loss (BVL) in the period from baseline to week 96 or longer were considered.

Results

Twelve from 146 clinical trials met the inclusion criteria and were incorporated into the analysis. DMTs that presented a large reduction in BVL also exhibited robust effects on clinical disability worsening, e.g., alemtuzumab with a 42% risk reduction in 6-month confirmed disability accumulation (p = 0.0084), ocrelizumab with a 40% risk reduction in 6-month confirmed disability progression (p = 0.003), and other DMTs (cladribine and teriflunomide) with moderate influence on brain atrophy were also associated with a marked impact on disability worsening. Dimethyl fumarate (DEFINE) and fingolimod (FREEDOMS I) initially exhibited significant effect on BVL; however, this effect was not confirmed in further clinical trials: CONFIRM and FREEDOMS II, respectively. Peg-IFN-β1a shows a modest effect on BVL and disability worsening.

Conclusion

Our results show that BVL in one of the components of clinical disability worsening, together with other variables (lesion volume and annualized relapse rate). Standardization of atrophy measurement technique as well as harmonization of disability worsening and progression criteria in further clinical trials are of utmost importance as they enable a reliable comparison of neuroprotective potential of DMTs.

Machine learning in neuro-oncology: toward novel development fields

PURPOSE

Artificial Intelligence (AI) involves several and different techniques able to elaborate a large amount of data responding to a specific planned outcome. There are several possible applications of this technology in neuro-oncology.

METHODS

We reviewed, according to PRISMA guidelines, available studies adopting AI in different fields of neuro-oncology including neuro-radiology, pathology, surgery, radiation therapy, and systemic treatments.

RESULTS

Neuro-radiology presented the major number of studies assessing AI. However, this technology is being successfully tested also in other operative settings including surgery and radiation therapy. In this context, AI shows to significantly reduce resources and costs maintaining an elevated qualitative standard. Pathological diagnosis and development of novel systemic treatments are other two fields in which AI showed promising preliminary data.

CONCLUSION

It is likely that AI will be quickly included in some aspects of daily clinical practice. Possible applications of these techniques are impressive and cover all aspects of neuro-oncology.

Alzheimer's Disease Diagnosis With Brain Structural MRI Using Multiview-Slice Attention and 3D Convolution Neural Network

Numerous artificial intelligence (AI) based approaches have been proposed for automatic Alzheimer's disease (AD) prediction with brain structural magnetic resonance imaging (sMRI). Previous studies extract features from the whole brain or individual slices separately, ignoring the properties of multi-view slices and feature complementarity. For this reason, we present a novel AD diagnosis model based on the multiview-slice attention and 3D convolution neural network (3D-CNN). Specifically, we begin by extracting the local slice-level characteristic in various dimensions using multiple sub-networks. Then we proposed a slice-level attention mechanism to emphasize specific 2D-slices to exclude the redundancy features. After that, a 3D-CNN was employed to capture the global subject-level structural changes. Finally, all these 2D and 3D features were fused to obtain more discriminative representations. We conduct the experiments on 1,451 subjects from ADNI-1 and ADNI-2 datasets. Experimental results showed the superiority of our model over the state-of-the-art approaches regarding dementia classification. Specifically, our model achieves accuracy values of 91.1 and 80.1% on ADNI-1 for AD diagnosis and mild cognitive impairment (MCI) convention prediction, respectively.

Predictive MRI Biomarkers in MS—A Critical Review

Background and Objectives: In this critical review, we explore the potential use of MRI measurements as prognostic biomarkers in multiple sclerosis (MS) patients, for both conventional measurements and more novel techniques such as magnetization transfer, diffusion tensor, and proton spectroscopy MRI. Materials and Methods: All authors individually and comprehensively reviewed each of the aspects listed below in PubMed, Medline, and Google Scholar. Results: There are numerous MRI metrics that have been proven by clinical studies to hold important prognostic value for MS patients, most of which can be readily obtained from standard 1.5T MRI scans. Conclusions: While some of these parameters have passed the test of time and seem to be associated with a reliable predictive power, some are still better interpreted with caution. We hope this will serve as a reminder of how vast a resource we have on our hands in this versatile tool—it is up to us to make use of it.

Structural volume and cortical thickness differences between males and females in cognitively normal, cognitively impaired and Alzheimer's dementia population

We investigated differences due to sex in brain structural volume and cortical thickness in older cognitively normal (N=742), cognitively impaired (MCI; N=540) and Alzheimer's Dementia (AD; N=402) individuals from the ADNI and AIBL datasets (861 Males and 823 Females). General linear models were used to control the effect of relevant covariates including age, intracranial volume, magnetic resonance imaging (MRI) scanner field strength and scanner types. Significant volumetric differences due to sex were observed within different cortical and subcortical regions of the cognitively normal group. The number of significantly different regions was reduced in the MCI group, and no region remained different in the AD group. Cortical thickness was overall thinner in males than females in the cognitively normal group, and likewise, the differences due to sex were reduced in the MCI and AD groups. These findings were sustained after including cerebrospinal fluid (CSF) Tau and phosphorylated tau (pTau) as additional covariates.

Gut Microbiome in Progressive Multiple Sclerosis

OBJECTIVE

This study was undertaken to investigate the gut microbiome in progressive multiple sclerosis (MS) and how it relates to clinical disease.

METHODS

We sequenced the microbiota from healthy controls and relapsing-remitting MS (RRMS) and progressive MS patients and correlated the levels of bacteria with clinical features of disease, including Expanded Disability Status Scale (EDSS), quality of life, and brain magnetic resonance imaging lesions/atrophy. We colonized mice with MS-derived Akkermansia and induced experimental autoimmune encephalomyelitis (EAE).

RESULTS

Microbiota β-diversity differed between MS patients and controls but did not differ between RRMS and progressive MS or differ based on disease-modifying therapies. Disease status had the greatest effect on the microbiome β-diversity, followed by body mass index, race, and sex. In both progressive MS and RRMS, we found increased Clostridium bolteae, Ruthenibacterium lactatiformans, and Akkermansia and decreased Blautia wexlerae, Dorea formicigenerans, and Erysipelotrichaceae CCMM. Unique to progressive MS, we found elevated Enterobacteriaceae and Clostridium g24 FCEY and decreased Blautia and Agathobaculum. Several Clostridium species were associated with higher EDSS and fatigue scores. Contrary to the view that elevated Akkermansia in MS has a detrimental role, we found that Akkermansia was linked to lower disability, suggesting a beneficial role. Consistent with this, we found that Akkermansia isolated from MS patients ameliorated EAE, which was linked to a reduction in RORγt+ and IL-17-producing γδ T cells.

INTERPRETATION

Whereas some microbiota alterations are shared in relapsing and progressive MS, we identified unique bacteria associated with progressive MS and clinical measures of disease. Furthermore, elevated Akkermansia in MS may be a compensatory beneficial response in the MS microbiome. ANN NEUROL 2021;89:1195-1211.

DeepGRAI (Deep Gray Rating via Artificial Intelligence): Fast, feasible, and clinically relevant thalamic atrophy measurement on clinical quality T2-FLAIR MRI in multiple sclerosis

BACKGROUND

Thalamic volume loss is a key marker of neurodegeneration in multiple sclerosis (MS). T2-FLAIR MRI is a common denominator in clinical routine MS imaging, but current methods for thalamic volumetry are not applicable to it.

OBJECTIVE

To develop and validate a robust algorithm to measure thalamic volume using clinical routine T2-FLAIR MRI.

METHODS

A dual-stage deep learning approach based on 3D U-net (DeepGRAI - Deep Gray Rating via Artificial Intelligence) was created and trained/validated/tested on 4,590 MRI exams (4288 2D-FLAIR, 302 3D-FLAIR) from 59 centers (80/10/10 train/validation/test split). As training/test targets, FIRST was used to generate thalamic masks from 3D T1 images. Masks were reviewed, corrected, and aligned into T2-FLAIR space. Additional validation was performed to assess inter-scanner reliability (177 subjects at 1.5 T and 3 T within one week) and scan-rescan-reliability (5 subjects scanned, repositioned, and then re-scanned). A longitudinal dataset including assessment of disability and cognition was used to evaluate the predictive value of the approach.

RESULTS

DeepGRAI automatically quantified thalamic volume in approximately 7 s per case, and has been made publicly available. Accuracy on T2-FLAIR relative to 3D T1 FIRST was 99.4% (r = 0.94, p < 0.001,TPR = 93.0%, FPR = 0.3%). Inter-scanner error was 3.21%. Scan-rescan error with repositioning was 0.43%. DeepGRAI-derived thalamic volume was associated with disability (r = -0.427,p < 0.001) and cognition (r = -0.537,p < 0.001), and was a significant predictor of longitudinal cognitive decline (R2 = 0.081, p = 0.024; comparatively, FIRST-derived volume was R2 = 0.080, p = 0.025).

CONCLUSIONS

DeepGRAI provides fast, reliable, and clinically relevant thalamic volume measurement on multicenter clinical-quality T2-FLAIR images. This indicates potential for real-world thalamic volumetry, as well as quantification on legacy datasets without 3D T1 imaging.

Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL

BACKGROUND AND PURPOSE

There are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software (Entelai Pic) for brain volume estimation and compare it to traditional software such as FreeSurfer, CAT12 and FSL in healthy controls (HC).

MATERIALS AND METHODS

Sixteen HC were scanned four times, two different days on two different MRI scanners (1.5 T and 3 T). Volumetric T1-weighted images were acquired and post-processed with FreeSurfer v6.0.0, Entelai Pic v2, CAT12 v12.5 and FSL v5.0.9. Whole-brain, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were calculated. Correlation and agreement between methods was assessed using intraclass correlation coefficient (ICC) and Bland Altman plots. Robustness was assessed using the coefficient of variation (CV).

RESULTS

Whole-brain volume estimation had better correlation between FreeSurfer and Entelai Pic (ICC (95% CI) 0.96 (0.94-0.97)) than FreeSurfer and CAT12 (0.92 (0.88-0.96)) and FSL (0.87 (0.79-0.91)). WM, GM and CSF showed a similar trend. Compared to FreeSurfer, Entelai Pic provided similarly robust segmentations of brain volumes both on same-scanner (mean CV 1.07, range 0.20-3.13% vs. mean CV 1.05, range 0.21-3.20%, p = 0.86) and on different-scanner variables (mean CV 3.84, range 2.49-5.91% vs. mean CV 3.84, range 2.62-5.13%, p = 0.96). Mean post-processing times were 480, 5, 40 and 5 min for FreeSurfer, Entelai Pic, CAT12 and FSL respectively.

CONCLUSION

Based on robustness and processing times, our CNN-based model is suitable for cross-sectional volumetry on clinical practice.

Regional microglial activation in the substantia nigra is linked with fatigue in MS

OBJECTIVE

The goal of our study is to assess the role of microglial activation in MS-associated fatigue (MSAF) using [F-18]PBR06-PET.

METHODS

Fatigue severity was measured using the Modified Fatigue Impact Scale (MFIS) in 12 subjects with MS (7 relapsing-remitting and 5 secondary progressive) and 10 healthy control participants who underwent [F-18]PBR06-PET. The MFIS provides a total fatigue score as well as physical, cognitive, and psychosocial fatigue subscale scores. Standardized Uptake Value (SUV) 60-90 minute frame PET maps were coregistered to 3T MRI. Voxel-by-voxel analysis using Statistical Parametric Mapping and atlas-based regional analyses were performed. SUV ratios (SUVRs) were global brain normalized.

RESULTS

Peak voxel-based level of significance for correlation between total fatigue score and PET uptake was localized to the right substantia nigra (T-score 4.67, p = 0.001). Similarly, SUVRs derived from atlas-based segmentation of the substantia nigra showed significant correlation with MFIS (r = 0.76, p = 0.004). On multiple regression, the right substantia nigra was an independent predictor of total MFIS (p = 0.02) and cognitive MFIS subscale values (p = 0.007), after adjustment for age, disability, and depression. Several additional areas of significant correlations with fatigue scores were identified, including the right parahippocampal gyrus, right precuneus, and juxtacortical white matter (all p < 0.05). There was no correlation between fatigue scores and brain atrophy and lesion load in patients with MS.

CONCLUSION

Substantia nigra microglial activation is linked to fatigue in MS. Microglial activation across key brain regions may represent a unifying mechanism for MSAF, and further evaluation of neuroimmunologic basis of MSAF is warranted.

Lasting consequences of concussion on the aging brain: Findings from the Baltimore Longitudinal Study of Aging

Studies suggest that concussions may be related to increased risk of neurodegenerative diseases, such as Chronic Traumatic Encephalopathy and Alzheimer’s Disease. Most neuroimaging studies show effects of concussionsin frontal and temporal lobes of the brain, yet the long-term impacts of concussions on the aging brain have not been well studied. We examined neuroimaging data from 51 participants (mean age at first imaging visit = 65.1±11.23) in the Baltimore Longitudinal Study of Aging (BLSA) who reported a concussion in their medical history an average of 23 years prior to the first imaging visit, and compared them to 150 participants (mean age at first imaging visit = 66.6 ± 10.97) with no history of concussion. Participants underwent serial structural MRI overa mean of 5.17 ± 6.14 years and DTI over a mean of 2.92 ± 2.22 years to measure brain structure, as well as ¹⁵O-water PET over a mean of 5.33 ± 2.19 years to measure brain function. A battery of neuropsychological tests was also administered over a mean of 11.62 ± 7.41 years. Analyses of frontal and temporal lobe regions were performed to examine differences in these measures between the concussion and control groups at first imaging visit and in change over time. Compared to those without concussion, participants with a prior concussion had greater brain atrophy in temporal lobe white matter and hippocampus at first imaging visit, which remained stable throughout the follow-up visits. Those with prior concussion also showed differences in white matter microstructure using DTI, including increased radial and axial diffusivity in the fornix/stria terminalis, anterior corona radiata, and superior longitudinal fasciculus at first imaging visit. In ¹⁵O-water PET, higher resting cerebral blood flow was seen at first imaging visit in orbitofrontal and lateral temporal regions, and both increases and decreases were seen in prefrontal, cingulate, insular, hippocampal, and ventral temporal regions with longitudinal follow-up. There were no significant differences in neuropsychological performance between groups. Most of the differences observed between the concussed and non-concussed groups were seen at the first imaging visit, suggesting that concussions can produce long-lasting structural and functional alterations in temporal and frontal regions of the brain in older individuals. These results also suggest that many of the reported short-term effects of concussion may still be apparent later in life.

Sex-Specific Differences in Life Span Brain Volumes in Multiple Sclerosis

BACKGROUND AND PURPOSE

Numerous sex-specific differences in multiple sclerosis (MS) susceptibility, disease manifestation, disability progression, inflammation, and neurodegeneration have been previously reported. Previous magnetic resonance imaging (MRI) studies have shown structural differences between female and male MS brain volumes. To determine sex-specific global and tissue-specific brain volume throughout the MS life span in a real-world large MRI database.

METHODS

A total of 2,199 MS patients (female/male ratio of 1,651/548) underwent structural MRI imaging on either a 1.5-T or 3-T scanner. Global and tissue-specific volumes of whole brain (WBV), white matter, and gray matter (GMV) were determined by utilizing Structural Image Evaluation using Normalisation of Atrophy Cross-sectional (SIENAX). Lateral ventricular volume (LVV) was determined with the Neurological Software Tool for REliable Atrophy Measurement (NeuroSTREAM). General linear models investigated sex and age interactions, and post hoc comparative sex analyses were performed.

RESULTS

Despite being age-matched with female MS patents, a greater proportion of male MS patients were diagnosed with progressive MS and had lower normalized WBV (P < .001), GMV (P < .001), and greater LVV (P < .001). In addition to significant stand-alone main effects, an interaction between sex and age had an additional effect on the LVV (F-statistics = 4.53, P = .033) and GMV (F-statistics = 4.59, P = .032). The sex and age interaction was retained in both models of LVV (F-statistics = 3.31, P = .069) and GMV (F-statistics = 6.1, P = .003) when disease subtype and disease-modifying treatment (DMT) were also included. Although male MS patients presented with significantly greater LVV and lower GMV during the early and midlife period when compared to their female counterparts (P < .001 for LVV and P < .019 for GMV), these differences were nullified in 60+ years old patients. Similar findings were seen within a subanalysis of MS patients that were not on any DMT at the time of enrollment.

CONCLUSION

There are sex-specific differences in the LVV and GMV over the MS life span.

Brain MRI Predicts Worsening Multiple Sclerosis Disability over 5 Years in the SUMMIT Study

BACKGROUND AND PURPOSE

Brain MRI-derived lesions and atrophy are related to multiple sclerosis (MS) disability. In the Serially Unified Multicenter MS Investigation (SUMMIT), from Brigham and Women's Hospital (BWH) and University of California, San Francisco (UCSF), we assessed whether MRI methodologic heterogeneity may limit the ability to pool multisite data sets to assess 5-year clinical-MRI associations.

METHODS

Patients with relapsing-remitting (RR) MS (n = 100 from each site) underwent baseline brain MRI and baseline and 5-year clinical evaluations. Patients were matched on sex (74 women each), age, disease duration, and Expanded Disability Status Scale (EDSS) score. MRI was performed with differences between sites in both acquisition (field strength, voxel size, pulse sequences), and postprocessing pipeline to assess brain parenchymal fraction (BPF) and T2 lesion volume (T2LV).

RESULTS

The UCSF cohort showed higher correlation than the BWH cohort between T2LV and disease duration. UCSF showed a higher inverse correlation between BPF and age than BWH. UCSF showed a higher inverse correlation than BWH between BPF and 5-year EDSS score. Both cohorts showed inverse correlations between BPF and T2LV, with no between-site difference. The pooled but not individual cohort data showed a link between a lower baseline BPF and the subsequent 5-year worsening in disability in addition to other stronger relationships in the data.

CONCLUSIONS

MRI acquisition and processing differences may result in some degree of heterogeneity in assessing brain lesion and atrophy measures in patients with MS. Pooling of data across sites is beneficial to correct for potential biases in individual data sets.

Standardized Brain MRI Acquisition Protocols Improve Statistical Power in Multicenter Quantitative Morphometry Studies

BACKGROUND AND PURPOSE

In this study, we used power analysis to calculate required sample sizes to detect group-level changes in quantitative neuroanatomical estimates derived from MRI scans obtained from multiple imaging centers. Sample size estimates were derived from (i) standardized 3T image acquisition protocols and (ii) nonstandardized clinically acquired images obtained at both 1.5 and 3T as part of the multicenter Human Epilepsy Project. Sample size estimates were compared to assess the benefit of standardizing acquisition protocols.

METHODS

Cortical thickness, hippocampal volume, and whole brain volume were estimated from whole brain T1-weighted MRI scans processed using Freesurfer v6.0. Sample sizes required to detect a range of effect sizes were calculated using (i) standard t-test based power analysis methods and (ii) a nonparametric bootstrap approach.

RESULTS

A total of 32 participants were included in our analyses, aged 29.9 ± 12.62 years. Standard deviation estimates were lower for all quantitative neuroanatomical metrics when assessed using standardized protocols. Required sample sizes per group to detect a given effect size were markedly reduced when using standardized protocols, particularly for cortical thickness changes <.2 mm and hippocampal volume changes <10%.

CONCLUSIONS

The use of standardized protocols yielded up to a five-fold reduction in required sample sizes to detect disease-related neuroanatomical changes, and is particularly beneficial for detecting subtle effects. Standardizing image acquisition protocols across scanners prior to commencing a study is a valuable approach to increase the statistical power of multicenter MRI studies.

Gray matter microglial activation in relapsing vs progressive MS: A [F-18]PBR06-PET study

Objective

To determine the value of [F-18]PBR06-PET for assessment of microglial activation in the cerebral gray matter in patients with MS.

Methods

Twelve patients with MS (7 relapsing-remitting and 5 secondary progressive [SP]) and 5 healthy controls (HCs) had standardized uptake value (SUV) PET maps coregistered to 3T MRI and segmented into cortical and subcortical gray matter regions. SUV ratios (SUVRs) were global brain normalized. Voxel-by-voxel analysis was performed using statistical parametric mapping (SPM). Normalized brain parenchymal volumes (BPVs) were determined from MRI using SIENAX.

Results

Cortical SUVRs were higher in the hippocampus, amygdala, midcingulate, posterior cingulate, and rolandic operculum and lower in the medial-superior frontal gyrus and cuneus in the MS vs HC group (all p < 0.05). Subcortical gray matter SUVR was higher in SPMS vs RRMS (+10.8%, p = 0.002) and HC (+11.3%, p = 0.055) groups. In the MS group, subcortical gray matter SUVR correlated with the Expanded Disability Status Scale (EDSS) score (r = 0.75, p = 0.005) and timed 25-foot walk (T25FW) (r = 0.70, p = 0.01). Thalamic SUVRs increased with increasing EDSS scores (r = 0.83, p = 0.0008) and T25FW (r = 0.65, p = 0.02) and with decreasing BPV (r = -0.63, p = 0.03). Putaminal SUVRs increased with increasing EDSS scores (0.71, p = 0.009) and with decreasing BPV (r = -0.67, p = 0.01). On SPM analysis, peak correlations of thalamic voxels with BPV were seen in the pulvinar and with the EDSS score and T25FW in the dorsomedial thalamic nuclei.

Conclusions

This study suggests that [F-18]PBR06-PET detects widespread abnormal microglial activation in the cerebral gray matter in MS. Increased translocator protein binding in subcortical gray matter regions is associated with brain atrophy and may link to progressive MS.

Lifespan normative data on rates of brain volume changes

We provide here normative values of yearly percentage brain volume change (PBVC/y) as obtained with Structural Imaging Evaluation, using Normalization, of Atrophy, a widely used open-source software, developing a PBVC/y calculator for assessing the deviation from the expected PBVC/y in patients with neurological disorders. We assessed multicenter (34 centers, 11 acquisition protocols) magnetic resonance imaging data of 720 healthy participants covering the whole adult lifespan (16-90 years). Data of 421 participants with a follow-up > 6 months were used to obtain the normative values for PBVC/y and data of 392 participants with a follow-up <1 month were selected to assess the intrasubject variability of the brain volume measurement. A mixed model evaluated PBVC/y dependence on age, sex, and magnetic resonance imaging parameters (scan vendor and magnetic field strength). PBVC/y was associated with age (p < 0.001), with 60- to 70-year-old participants showing twice more volume decrease than participants aged 30-40 years. PBVC/y was also associated with magnetic field strength, with higher decreases when measured by 1.5T than 3T scanners (p < 0.001). The variability of PBVC/y normative percentiles was narrower as the interscan interval was longer (e.g., 80th normative percentile was 50% smaller for participants with 2-year than with 1-year follow-up). The use of these normative data, eased by the freely available calculator, might help in better discriminating pathological from physiological conditions in the clinical setting.

The power of sample size through a multi-scanner approach in MR neuroimaging regression analysis: evidence from Alzheimer's disease with and without depression

The inconsistency of volumetric results often seen in MR neuroimaging studies can be partially attributed to small sample sizes and variable data analysis approaches. Increased sample size through multi-scanner studies can tackle the former, but combining data across different scanner platforms and field-strengths may introduce a variability factor capable of masking subtle statistical differences. To investigate the sample size effect on regression analysis between depressive symptoms and grey matter volume (GMV) loss in Alzheimer's disease (AD), a retrospective multi-scanner investigation was conducted. A cohort of 172 AD patients, with or without comorbid depressive symptoms, was studied. Patients were scanned with different imaging protocols in four different MRI scanners operating at either 1.5 T or 3.0 T. Acquired data were uniformly analyzed using the computational anatomy toolbox (CAT12) of the statistical parametric mapping (SPM12) software. Single- and multi-scanner regression analyses were applied to identify the anatomical pattern of correlation between GM loss and depression severity. A common anatomical pattern of correlation between GMV loss and increased depression severity, mostly involving sensorimotor areas, was identified in all patient subgroups imaged in different scanners. Analysis of the pooled multi-scanner data confirmed the above finding employing a more conservative statistical criterion. In the retrospective multi-scanner setting, a significant correlation was also exhibited for temporal and frontal areas. Increasing the sample size by retrospectively pooling multi-scanner data, irrespective of the acquisition platform and parameters employed, can facilitate the identification of anatomical areas with a strong correlation between GMV changes and depression symptoms in AD patients.

Quantitative assessment of field strength, total intracranial volume, sex, and age effects on the goodness of harmonization for volumetric analysis on the ADNI database

When analyzing large multicenter databases, the effects of multiple confounding covariates increase the variability in the data and may reduce the ability to detect changes due to the actual effect of interest, for example, changes due to disease. Efficient ways to evaluate the effect of covariates toward the data harmonization are therefore important. In this article, we showcase techniques to assess the "goodness of harmonization" of covariates. We analyze 7,656 MR images in the multisite, multiscanner Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We present a comparison of three methods for estimating total intracranial volume to assess their robustness and correct the brain structure volumes using the residual method and the proportional (normalization by division) method. We then evaluated the distribution of brain structure volumes over the entire ADNI database before and after accounting for multiple covariates such as total intracranial volume, scanner field strength, sex, and age using two techniques: (a) Zscapes, a panoramic visualization technique to analyze the entire database and (b) empirical cumulative distributions functions. The results from this study highlight the importance of assessing the goodness of data harmonization as a necessary preprocessing step when pooling large data set with multiple covariates, prior to further statistical data analysis.

The Impact of MRI T1 Hypointense Brain Lesions on Cerebral Deep Gray Matter Volume Measures in Multiple Sclerosis

BACKGROUND AND PURPOSE

Deep gray matter (DGM) atrophy has been shown at early stages of multiple sclerosis (MS) and reported as an informative marker of cognitive dysfunction and clinical progression. Therefore, accurate measurement of DGM structure volume is a key priority in MS research. Findings from prior studies have shown that hypointense T1 lesions may impact the accuracy of global brain volume measures; however, literature on the effects of hypointense T1 lesions on DGM structure volumes is sparse.

METHODS

We explored the effects of hypointense T1 lesions on data from 54 relapsing remitting MS patients. Lesions were segmented both manually and with a freely available automatic lesion segmentation/in-painting algorithm (Lesion Segmentation Tool-LST). Volumes of 14 DGM structures were calculated from non-in-painted and in-painted images and compared via paired t-tests, intraclass correlation coefficient, and Dice similarity coefficient.

RESULTS

There were no significant differences in DGM structural volumes between non-in-painted and in-painted images. Automatic lesion-segmentation/in-painting tool provided similar results to manual segmentation/in-painting.

CONCLUSIONS

Our results suggest that lesion in-painting has a negligible impact on DGM structure volume measurement although some regions are more vulnerable to the impact of lesions than others. Furthermore, manual lesion segmentation/in-painting can be replaced by an automatic segmentation/in-painting process.

Whole brain and deep gray matter atrophy detection over 5 years with 3T MRI in multiple sclerosis using a variety of automated segmentation pipelines

Background

Cerebral atrophy is common in multiple sclerosis (MS) and selectively involves gray matter (GM). Several fully automated methods are available to measure whole brain and regional deep GM (DGM) atrophy from MRI.

Objective

To assess the sensitivity of fully automated MRI segmentation pipelines in detecting brain atrophy in patients with relapsing-remitting (RR) MS and normal controls (NC) over five years.

Methods

Consistent 3D T1-weighted sequences were performed on a 3T GE unit in 16 mildly disabled patients with RRMS and 16 age-matched NC at baseline and five years. All patients received disease-modifying immunotherapy on-study. Images were applied to two pipelines to assess whole brain atrophy [brain parenchymal fraction (BPF) from SPM12; percentage brain volume change (PBVC) from SIENA] and two other pipelines (FSL-FIRST; FreeSurfer) to assess DGM atrophy (thalamus, caudate, globus pallidus, putamen). MRI change was compared by two sample t-tests. Expanded Disability Status Scale (EDSS) and timed 25-foot walk (T25FW) change was compared by repeated measures proportional odds models.

Results

Using FreeSurfer, the MS group had a ~10-fold acceleration in on-study volume loss than NC in the caudate (mean decrease 0.51 vs. 0.05 ml, p = 0.022). In contrast, caudate atrophy was not detected by FSL-FIRST (mean decrease 0.21 vs. 0.12 ml, p = 0.53). None of the other pipelines showed any difference in volume loss between groups, for whole brain or regional DGM atrophy (all p>0.38). The MS group showed on-study stability on EDSS (p = 0.47) but slight worsening of T25FW (p = 0.054).

Conclusions

In this real-world cohort of mildly disabled treated patients with RRMS, we identified ongoing atrophy of the caudate nucleus over five years, despite the lack of any significant whole brain atrophy, compared to healthy controls. The detectability of caudate atrophy was dependent on the MRI segmentation pipeline employed. These findings underscore the increased sensitivity gained when assessing DGM atrophy in monitoring MS.

Confused Connections? Targeting White Matter to Address Treatment Resistant Schizophrenia

Despite development of comprehensive approaches to treat schizophrenia and other psychotic disorders and improve outcomes, there remains a proportion (approximately one-third) of patients who are treatment resistant and will not have remission of psychotic symptoms despite adequate trials of pharmacotherapy. This level of treatment response is stable across all stages of the spectrum of psychotic disorders, including early phase psychosis and chronic schizophrenia. Our current pharmacotherapies are beneficial in decreasing positive symptomology in most cases, however, with little to no impact on negative or cognitive symptoms. Not all individuals with treatment resistant psychosis unfortunately, even benefit from the potential pharmacological reductions in positive symptoms. The existing pharmacotherapy for psychosis is targeted at neurotransmitter receptors. The current first and second generation antipsychotic medications all act on dopamine type 2 receptors with the second generation drugs also interacting significantly with serotonin type 1 and 2 receptors, and with varying pharmacodynamic profiles overall. This focus on developing dopaminergic/serotonergic antipsychotics, while beneficial, has not reduced the proportion of patients experiencing treatment resistance to date. Another pharmacological approach is imperative to address treatment resistance both for response overall and for negative symptoms in particular. There is research suggesting that changes in white matter integrity occur in schizophrenia and these may be more associated with cognition and even negative symptomology. Here we review the evidence that white matter abnormalities in the brain may be contributing to the symptomology of psychotic disorders. Additionally, we propose that white matter may be a viable pharmacological target for pharmacoresistant schizophrenia and discuss current treatments in development for schizophrenia that target white matter.

SUMMIT (Serially Unified Multicenter Multiple Sclerosis Investigation): creating a repository of deeply phenotyped contemporary multiple sclerosis cohorts

BACKGROUND

There is a pressing need for robust longitudinal cohort studies in the modern treatment era of multiple sclerosis.

OBJECTIVE

Build a multiple sclerosis (MS) cohort repository to capture the variability of disability accumulation, as well as provide the depth of characterization (clinical, radiologic, genetic, biospecimens) required to adequately model and ultimately predict a patient's course.

METHODS

Serially Unified Multicenter Multiple Sclerosis Investigation (SUMMIT) is an international multi-center, prospectively enrolled cohort with over a decade of comprehensive follow-up on more than 1000 patients from two large North American academic MS Centers (Brigham and Women's Hospital (Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB; BWH)) and University of California, San Francisco (Expression/genomics, Proteomics, Imaging, and Clinical (EPIC))). It is bringing online more than 2500 patients from additional international MS Centers (Basel (Universitätsspital Basel (UHB)), VU University Medical Center MS Center Amsterdam (MSCA), Multiple Sclerosis Center of Catalonia-Vall d'Hebron Hospital (Barcelona clinically isolated syndrome (CIS) cohort), and American University of Beirut Medical Center (AUBMC-Multiple Sclerosis Interdisciplinary Research (AMIR)).

RESULTS AND CONCLUSION

We provide evidence for harmonization of two of the initial cohorts in terms of the characterization of demographics, disease, and treatment-related variables; demonstrate several proof-of-principle analyses examining genetic and radiologic predictors of disease progression; and discuss the steps involved in expanding SUMMIT into a repository accessible to the broader scientific community.

Whole-brain atrophy assessed by proportional- versus registration-based pipelines from 3T MRI in multiple sclerosis

BACKGROUND AND PURPOSE

Whole-brain atrophy is a standard outcome measure in multiple sclerosis (MS) clinical trials as assessed by various software tools. The effect of processing method on the validity of such data obtained from high-resolution 3T MRI is not known. We compared two commonly used methods of quantifying whole-brain atrophy.

METHODS

Three-dimensional T1-weighted and FLAIR images were obtained at 3T in MS (n = 61) and normal control (NC, n = 30) groups. Whole-brain atrophy was assessed by two automated pipelines: (a) SPM8 to derive brain parenchymal fraction (BPF, proportional-based method); (b) SIENAX to derive normalized brain parenchymal volume (BPV, registration method). We assessed agreement between BPF and BPV, as well their relationship to Expanded Disability Status Scale (EDSS) score, timed 25-foot walk (T25FW), cognition, and cerebral T2 (FLAIR) lesion volume (T2LV).

RESULTS

Brain parenchymal fraction and BPV showed only partial agreement (r = 0.73) in the MS group, and r = 0.28 in NC. Both methods showed atrophy in MS versus NC (BPF p < 0.01, BPV p < 0.05). Within MS group comparisons, BPF (p < 0.05) but not BPV (p > 0.05) correlated with EDSS score. BPV (p = 0.03) but not BPF (p = 0.08) correlated with T25FW. Both metrics correlated with T2LV (p < 0.05) and cognitive subscales. BPF (p < 0.05) but not BPV (p > 0.05) showed lower brain volume in cognitively impaired (n = 23) versus cognitively preserved (n = 38) patients. However, direct comparisons of BPF and BPV sensitivities to atrophy and clinical correlations were not statistically significant.

CONCLUSION

Whole-brain atrophy metrics may not be interchangeable between proportional- and registration-based automated pipelines from 3T MRI in patients with MS.

Longitudinal changes in neuroimaging and neuropsychiatric status of post-deployment veterans: a CENC pilot study

PRIMARY OBJECTIVE

The purpose of this study was to evaluate preliminary data on longitudinal changes in psychiatric, neurobehavioural, and neuroimaging findings in Iraq and Afghanistan combat veterans following blast exposure.

RESEARCH DESIGN

Longitudinal observational analysis.

METHODS AND PROCEDURES

Participants were invited to participate in two research projects approximately 7 years apart. For each project, veterans completed the Structured Clinical Interview for DSM-IV Disorders and/or the Clinician-Administered PTSD Scale, Neurobehavioral Symptom Inventory, and magnetic resonance imaging (MRI).

MAIN OUTCOMES AND RESULTS

Chi-squared tests indicated no significant changes in current psychiatric diagnoses, traumatic brain injury (TBI) history, or blast exposure history between assessment visits. Wilcoxon signed-rank tests indicated significant increases in median neurobehavioural symptoms, total number of white matter hyperintensities (WMH), and total WMH volume between assessment visits. Spearman rank correlations indicated no significant associations between change in psychiatric diagnoses, TBI history, blast exposure history, or neurobehavioural symptoms and change in WMH.

CONCLUSION

MRI WMH changes were not associated with changes in psychiatric diagnoses or symptom burden, but were associated with severity of blast exposure. Future, larger studies might further evaluate presence and aetiology of long-term neuropsychiatric symptoms and MRI findings in blast-exposed populations.

Body composition skeletal muscle analysis in cancer cachexia studies: Is there a place for 3T MRI analysis?

Background: Cancer cachexia is a condition often seen in end stage Non-Small Cell Lung Cancer (NSCLC) patients. Recent developments include the use of pharmaceutical agents and/or exercise to induce stability/hypertrophy of muscle volume. This requires accurate assessment of the change in both quantity and quality of the muscle during cancer cachexia clinical studies. 3T Magnetic Resonance Imaging (MRI) is appropriately placed to address both of these factors. Methods: Auckland’s Cancer Cachexia evaluating Resistance Training (ACCeRT) study is a randomised controlled feasibility study investigating eicosapentaenoic acid (EPA) and cyclo-oxygenase-2 (COX-2) inhibitor (celebrex) (Arm A) versus EPA, COX-2 inhibitor (celebrex), Progressive Resistance Training (PRT) plus essential amino acids (EAAs) high in leucine (Arm B) in NSCLC cachectic patients. All participants underwent 3T MRI scanning at baseline and at last or end of trial (EOT) visit.Results: Analysis showed a mean total quadriceps muscle volume percentage change from baseline to EOT of +12.47% (Arm A), compared with -2.96% (Arm B). There was a difference in muscle volume between genders. Arm B participant data showed a percentage change of +4.23% within females (n=2) compared with ˗10.15% (n=2) within males at EOT visit. All EOT results suggests the use of EPA and celecoxib +/- PRT and EAAs could potentially preserve muscle volume loss during refractory cachexia.Conclusion: ACCeRT is the first study to utilise 3T MRI total quadriceps muscle volume within a cancer cachexia study, along with the first in an end-stage/refractory cachexia population. These results can be used for baseline/reference for future cancer cachexia studies targeting the anabolic muscle pathways in end˗stage/refractory cachexia patients.

MRI-based measurements of whole-brain global cerebral blood flow: Comparison and validation at 1.5T and 3T

BACKGROUND

Whole-brain global cerebral blood flow (CBF) determined by MRI techniques, calculated using total CBF (TCBF) from phase-contrast MRI (PC-MRI), and brain parenchyma volume (BPV) from T₁ -weighted image, have become increasingly popular in many applications.

PURPOSE/HYPOTHESIS

To determine if MRI-based measurements of whole-brain global CBF data obtained across different field strengths could be merged, TCBF and BPV data acquired at 1.5T and 3T were compared.

STUDY TYPE

Prospective study.

POPULATION

Seventeen healthy subjects (eight females, aged 21-29 years old).

FIELD STRENGTH/SEQUENCE

Fast spoiled gradient echo (FSPGR) and PC-MRI at both 1.5T and 3T.

ASSESSMENT

TCBF and BPV data acquired at 1.5T and 3T were compared.

STATISTICAL TESTS

The relationships of TCBF and whole-brain global CBF between two field strengths were examined by using the Pearson correlation coefficient analysis and intraclass correlation coefficient (ICC).

RESULTS

Regression analysis revealed a strong correlation between TCBF at two field strengths (R²  = 0.78, P < 0.001), and the ICC was 0.85, suggesting measurements of TCBF at 1.5T were comparable and correlated with those at 3T. There was a significant difference in BPV between field strengths, where the white matter estimate was significantly larger at 1.5T when compared with that at 3T (P < 0.001). When TCBF was further normalized to the brain parenchyma mass to obtain whole-brain global CBF, it only showed a moderate correlation between measurements at the two field strengths (R²  = 0.46, P = 0.003) and lower ICC of 0.66, reflecting the slightly higher interstrength variability in the whole-brain global CBF measurements.

DATA CONCLUSION

TCBF measurements could be performed equally well with comparable results at both field strengths, but specific attention should be given when TCBF is further normalized to BPV to obtain whole-brain global CBF.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1273-1280.

Feasibility of Brain Atrophy Measurement in Clinical Routine without Prior Standardization of the MRI Protocol: Results from MS-MRIUS, a Longitudinal Observational, Multicenter Real-World Outcome Study in Patients with Relapsing-Remitting MS

BACKGROUND AND PURPOSE

Feasibility of brain atrophy measurement in patients with MS in clinical routine, without prior standardization of the MRI protocol, is unknown. Our aim was to investigate the feasibility of brain atrophy measurement in patients with MS in clinical routine.

MATERIALS AND METHODS

Multiple Sclerosis and Clinical Outcome and MR Imaging in the United States (MS-MRIUS) is a multicenter (33 sites), retrospective study that included patients with relapsing-remitting MS who began treatment with fingolimod. Brain MR imaging examinations previously acquired at the baseline and follow-up periods on 1.5T or 3T scanners with no prior standardization were used, to resemble a real-world situation. Brain atrophy outcomes included the percentage brain volume change measured by structural image evaluation with normalization of atrophy on 2D-T1-weighted imaging and 3D-T1WI and the percentage lateral ventricle volume change, measured by VIENA on 2D-T1WI and 3D-T1WI and NeuroSTREAM on T2-fluid-attenuated inversion recovery examinations.

RESULTS

A total of 590 patients, followed for 16 months, were included. There were 585 (99.2%) T2-FLAIR, 425 (72%) 2D-T1WI, and 166 (28.2%) 3D-T1WI longitudinal pairs of examinations available. Excluding MR imaging examinations with scanner changes, the analyses were available on 388 (65.8%) patients on T2-FLAIR for the percentage lateral ventricle volume change, 259 and 257 (43.9% and 43.6%, respectively) on 2D-T1WI for the percentage brain volume change and the percentage lateral ventricle volume change, and 110 (18.6%) on 3D-T1WI for the percentage brain volume change and percentage lateral ventricle volume change. The median annualized percentage brain volume change was -0.31% on 2D-T1WI and -0.38% on 3D-T1WI. The median annualized percentage lateral ventricle volume change was 0.95% on 2D-T1WI, 1.47% on 3D-T1WI, and 0.90% on T2-FLAIR.

CONCLUSIONS

Brain atrophy was more readily assessed by estimating the percentage lateral ventricle volume change on T2-FLAIR compared with the percentage brain volume change or percentage lateral ventricle volume change using 2D- or 3D-T1WI in this observational retrospective study. Although measurement of the percentage brain volume change on 3D-T1WI remains the criterion standard and should be encouraged in future prospective studies, T2-FLAIR-derived percentage lateral ventricle volume change may be a more feasible surrogate when historical or other practical constraints limit the availability of percentage brain volume change on 3D-T1WI.

Automated segmentation of cerebral deep gray matter from MRI scans: effect of field strength on sensitivity and reliability

Background

The cerebral subcortical deep gray matter nuclei (DGM) are a common, early, and clinically-relevant site of atrophy in multiple sclerosis (MS). Robust and reliable DGM segmentation could prove useful to evaluate putative neuroprotective MS therapies. The objective of the study was to compare the sensitivity and reliability of DGM volumes obtained from 1.5T vs. 3T MRI.

Methods

Fourteen patients with MS [age (mean, range) 50.2 (32.0–60.8) years, disease duration 18.4 (8.2–35.5) years, Expanded Disability Status Scale score 3.1 (0–6), median 3.0] and 15 normal controls (NC) underwent brain 3D T1-weighted paired scan-rescans at 1.5T and 3T. DGM (caudate, thalamus, globus pallidus, and putamen) segmentation was obtained by the fully automated FSL-FIRST pipeline. Both raw and normalized volumes were derived.

Results

DGM volumes were generally higher at 3T vs. 1.5T in both groups. For raw volumes, 3T showed slightly better sensitivity (thalamus: p = 0.02; caudate: p = 0.10; putamen: p = 0.02; globus pallidus: p = 0.0004; total DGM: p = 0.01) than 1.5T (thalamus: p = 0.05; caudate: p = 0.09; putamen: p = 0.03; globus pallidus: p = 0.0006; total DGM: p = 0.02) for detecting DGM atrophy in MS vs. NC. For normalized volumes, 3T but not 1.5T detected atrophy in the globus pallidus in the MS group. Across all subjects, scan-rescan reliability was generally very high for both platforms, showing slightly higher reliability for some DGM volumes at 3T. Raw volumes showed higher reliability than normalized volumes. Raw DGM volume showed higher reliability than the individual structures.

Conclusions

These results suggest somewhat higher sensitivity and reliability of DGM volumes obtained from 3T vs. 1.5T MRI. Further studies should assess the role of this 3T pipeline in tracking potential MS neurotherapeutic effects.

The Role of T1-Weighted Derived Measures of Neurodegeneration for Assessing Disability Progression in Multiple Sclerosis

Introduction

Multiple sclerosis (MS) is characterised by the accumulation of permanent neurological disability secondary to irreversible tissue loss (neurodegeneration) in the brain and spinal cord. MRI measures derived from T1-weighted image analysis (i.e., black holes and atrophy) are correlated with pathological measures of irreversible tissue loss. Quantifying the degree of neurodegeneration in vivo using MRI may offer a surrogate marker with which to predict disability progression and the effect of treatment. This review evaluates the literature examining the association between MRI measures of neurodegeneration derived from T1-weighted images and disability in MS patients.

Methods

A systematic PubMed search was conducted in January 2017 to identify MRI studies in MS patients investigating the relationship between “black holes” and/or atrophy in the brain and spinal cord, and disability. Results were limited to human studies published in English in the previous 10 years.

Results

A large number of studies have evaluated the association between the previous MRI measures and disability. These vary considerably in terms of study design, duration of follow-up, size, and phenotype of the patient population. Most, although not all, have shown that there is a significant correlation between disability and black holes in the brain, as well as atrophy of the whole brain and grey matter. The results for brain white matter atrophy are less consistently positive, whereas studies evaluating spinal cord atrophy consistently showed a significant correlation with disability. Newer ways of measuring atrophy, thanks to the development of segmentation and voxel-wise methods, have allowed us to assess the involvement of strategic regions of the CNS (e.g., thalamus) and to map the regional distribution of damage. This has resulted in better correlations between MRI measures and disability and in the identification of the critical role played by some CNS structures for MS clinical manifestations.

Conclusion

The evaluation of MRI measures of atrophy as predictive markers of disability in MS is a highly active area of research. At present, measurement of atrophy remains within the realm of clinical studies, but its utility in clinical practice has been recognized and barriers to its implementation are starting to be addressed.

Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis

BACKGROUND AND PURPOSE

MR imaging can be used to measure structural changes in the brains of individuals with multiple sclerosis and is essential for diagnosis, longitudinal monitoring, and therapy evaluation. The North American Imaging in Multiple Sclerosis Cooperative steering committee developed a uniform high-resolution 3T MR imaging protocol relevant to the quantification of cerebral lesions and atrophy and implemented it at 7 sites across the United States. To assess intersite variability in scan data, we imaged a volunteer with relapsing-remitting MS with a scan-rescan at each site.

MATERIALS AND METHODS

All imaging was acquired on Siemens scanners (4 Skyra, 2 Tim Trio, and 1 Verio). Expert segmentations were manually obtained for T1-hypointense and T2 (FLAIR) hyperintense lesions. Several automated lesion-detection and whole-brain, cortical, and deep gray matter volumetric pipelines were applied. Statistical analyses were conducted to assess variability across sites, as well as systematic biases in the volumetric measurements that were site-related.

RESULTS

Systematic biases due to site differences in expert-traced lesion measurements were significant (P < .01 for both T1 and T2 lesion volumes), with site explaining >90% of the variation (range, 13.0-16.4 mL in T1 and 15.9-20.1 mL in T2) in lesion volumes. Site also explained >80% of the variation in most automated volumetric measurements. Output measures clustered according to scanner models, with similar results from the Skyra versus the other 2 units.

CONCLUSIONS

Even in multicenter studies with consistent scanner field strength and manufacturer after protocol harmonization, systematic differences can lead to severe biases in volumetric analyses.

Critical analysis on the present methods for brain volume measurements in multiple sclerosis

Objective

The treatment of multiple sclerosis (MS) has quickly evolved from a time when controlling clinical relapses would suffice, to the present day, when complete disease control is expected. Measurement of brain volume is still at an early stage to be indicative of therapeutic decisions in MS.

Methods

This paper provides a critical review of potential biases and artifacts in brain measurement in the follow-up of patients with MS.

Results

Clinical conditions (such as hydration or ovulation), time of the day, type of magnetic resonance machine (manufacturer and potency), brain volume artifacts and different platforms for volumetric assessment of the brain can induce variations that exceed the acceptable physiological rate of annual loss of brain volume.

Conclusion

Although potentially extremely valuable, brain volume measurement still has to be regarded with caution in MS.

Neurological software tool for reliable atrophy measurement (NeuroSTREAM) of the lateral ventricles on clinical-quality T2-FLAIR MRI scans in multiple sclerosis

Background

There is a need for a brain volume measure applicable to the clinical routine scans. Nearly every multiple sclerosis (MS) protocol includes low-resolution 2D T2-FLAIR imaging.

Objectives

To develop and validate cross-sectional and longitudinal brain atrophy measures on clinical-quality T2-FLAIR images in MS patients.

Methods

A real-world dataset from 109 MS patients from 62 MRI scanners was used to develop a lateral ventricular volume (LVV) algorithm with a longitudinal Jacobian-based extension, called NeuroSTREAM. Gold-standard LVV was calculated on high-resolution T1 1 mm, while NeuroSTREAM LVV was obtained on low-resolution T2-FLAIR 3 mm thick images. Scan-rescan reliability was assessed in 5 subjects. The variability of LVV measurement at different field strengths was tested in 76 healthy controls and 125 MS patients who obtained both 1.5T and 3T scans in 72 hours. Clinical validation of algorithm was performed in 176 MS patients who obtained serial yearly MRI 1.5T scans for 10 years.

Results

Correlation between gold-standard high-resolution T1 LVV and low-resolution T2-FLAIR LVV was r = 0.99, p < 0.001 and the scan-rescan coefficient of variation was 0.84%. Correlation between low-resolution T2-FLAIR LVV on 1.5T and 3T was r = 0.99, p < 0.001 and the scan-rescan coefficient of variation was 2.69% cross-sectionally and 2.08% via Jacobian integration. NeuroSTREAM showed comparable effect size (d = 0.39–0.71) in separating MS patients with and without confirmed disability progression, compared to SIENA and VIENA.

Conclusions

Brain atrophy measurement on clinical quality T2-FLAIR scans is feasible, accurate, reliable, and relates to clinical outcomes.

•

A robust algorithm for measuring lateral ventricular volume on clinical FLAIR scans is proposed.

•

The algorithm combines multi-atlas joint fusion labeling with level-set smoothness-constraining refinement.

•

Results show a similar relationship to disability progression as with established metrics on high-resolution scans.

Association Between Serum MicroRNAs and Magnetic Resonance Imaging Measures of Multiple Sclerosis Severity

Importance

MicroRNAs (miRNAs) are promising multiple sclerosis (MS) biomarkers. Establishing the association between miRNAs and magnetic resonance imaging (MRI) measures of disease severity will help define their significance and potential impact.

Objective

To correlate circulating miRNAs in the serum of patients with MS to brain and spinal MRI.

Design, Setting, and Participants

A cross-sectional study comparing serum miRNA samples with MRI metrics was conducted at a tertiary MS referral center. Two independent cohorts (41 and 79 patients) were retrospectively identified from the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital. Expression of miRNA was determined by locked nucleic acid-based quantitative real-time polymerase chain reaction. Spearman correlation coefficients were used to test the association between miRNA and brain lesions (T2 hyperintense lesion volume [T2LV]), the ratio of T1 hypointense lesion volume [T1LV] to T2LV [T1:T2]), brain atrophy (whole brain and gray matter), and cervical spinal cord lesions (T2LV) and atrophy. The study was conducted from December 2013 to April 2016.

Main Outcomes and Measures

miRNA expression.

Results

Of the 120 patients included in the study, cohort 1 included 41 participants (7 [17.1%] men), with mean (SD) age of 47.7 (9.5) years; cohort 2 had 79 participants (26 [32.9%] men) with a mean (SD) age of 43.0 (7.5) years. Associations between miRNAs and MRIs were both protective and pathogenic. Regarding miRNA signatures, a topographic specificity differed for the brain vs the spinal cord, and the signature differed between T2LV and atrophy/destructive measures. Four miRNAs showed similar significant protective correlations with T1:T2 in both cohorts, with the highest for hsa.miR.143.3p (cohort 1: Spearman correlation coefficient rs = -0.452, P = .003; cohort 2: rs = -0.225, P = .046); the others included hsa.miR.142.5p (cohort 1: rs = -0.424, P = .006; cohort 2: rs = -0.226, P = .045), hsa.miR.181c.3p (cohort 1: rs = -0.383, P = .01; cohort 2: rs = -0.222, P = .049), and hsa.miR.181c.5p (cohort 1: rs = -0.433, P = .005; cohort 2: rs = -0.231, P = .04). In the 2 cohorts, hsa.miR.486.5p (cohort 1: rs = 0.348, P = .03; cohort 2: rs = 0.254, P = .02) and hsa.miR.92a.3p (cohort 1: rs = 0.392, P = .01; cohort 2: rs = 0.222, P = .049) showed similar significant pathogenic correlations with T1:T2; hsa.miR.375 (cohort 1: rs = -0.345, P = .03; cohort 2: rs = -0.257, P = .022) and hsa.miR.629.5p (cohort 1: rs = -0.350, P = .03; cohort 2: rs = -0.269, P = .02) showed significant pathogenic correlations with brain atrophy. Although we found several miRNAs associated with MRI outcomes, none of these associations remained significant when correcting for multiple comparisons, suggesting that further validation of our findings is needed.

Conclusions and Relevance

Serum miRNAs may serve as MS biomarkers for monitoring disease progression and act as surrogate markers to identify underlying disease processes.

Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma

PURPOSE

To develop an image analysis technique that distinguishes pseudoprogression from true progression by analyzing tumour heterogeneity in T2-weighted images using topological descriptors of image heterogeneity called Minkowski functionals (MFs).

METHODS

Using a retrospective patient cohort (n = 50), and blinded to treatment response outcome, unsupervised feature estimation was performed to investigate MFs for the presence of outliers, potential confounders, and sensitivity to treatment response. The progression and pseudoprogression groups were then unblinded and supervised feature selection was performed using MFs, size and signal intensity features. A support vector machine model was obtained and evaluated using a prospective test cohort.

RESULTS

The model gave a classification accuracy, using a combination of MFs and size features, of more than 85% in both retrospective and prospective datasets. A different feature selection method (Random Forest) and classifier (Lasso) gave the same results. Although not apparent to the reporting radiologist, the T2-weighted hyperintensity phenotype of those patients with progression was heterogeneous, large and frond-like when compared to those with pseudoprogression.

CONCLUSION

Analysis of heterogeneity, in T2-weighted MR images, which are acquired routinely in the clinic, has the potential to detect an earlier treatment response allowing an early change in treatment strategy. Prospective validation of this technique in larger datasets is required.

Texture analysis of the developing human brain using customization of a knowledge-based system

Background: Pattern recognition software originally designed for geospatial and other technical applications could be trained by physicians and used as texture-analysis tools for evidence-based practice, in order to improve diagnostic imaging examination during pregnancy.Methods: Various machine-learning techniques and customized datasets were assessed for training of an integrable knowledge-based system (KBS), to determine a hypothetical methodology for texture classification of closely-related anatomical structures in fetal brain magnetic resonance (MR) images. Samples were manually categorized according to the magnetic field of the MRI scanner (i.e. 1.5-tesla (1.5T), 3-tesla (3T)), rotational planes (i.e. coronal, sagittal and axial), and signal weighting (i.e. spin-lattice, spin-spin, relaxation, proton density). In the machine-learning sessions, the operator manually selected relevant regions of interest (ROI) in 1.5/3T MR images. Semi-automatic procedures in MaZda/B11 were performed to determine optimal parameter sets for ROI classification. Four classes were defined: ventricles, thalamus, grey matter, and white matter. Various textures analysis methods were tested. The KBS performed automatic data pre-processing and semi-automatic classification of ROIs.Results: After testing 3456 ROIs, statistical binary classification revealed that combination of reduction techniques with linear discriminant algorithms (LDA) or nonlinear discriminant algorithms (NDA) yielded the best scoring in terms of sensitivity (both 100%, 95% CI: 99.79-100), specificity (both 100%, 95% CI: 99.79-100) and Fisher coefficient (≈E+4, ≈E+5, respectively). Conclusions: LDA and NDA in MaZda can be useful data mining tools for screening a population of interest subjected to a clinical test.

The Effect of Dimethyl Fumarate on Cerebral Gray Matter Atrophy in Multiple Sclerosis

INTRODUCTION

The objective of this pilot study was to compare cerebral gray matter (GM) atrophy over 1 year in patients starting dimethyl fumarate (DMF) for multiple sclerosis (MS) to that of patients on no disease-modifying treatment (noDMT). DMF is an established therapy for relapsing-remitting (RR) MS.

METHODS

We retrospectively analyzed 20 patients with RRMS at the start of DMF [age (mean ± SD) 46.1 ± 10.2 years, Expanded Disability Status Scale (EDSS) score 1.1 ± 1.2, timed 25-foot walk (T25FW) 4.6 ± 0.8 s] and eight patients on noDMT (age 42.5 ± 6.6 years, EDSS 1.7 ± 1.1, T25FW 4.4 ± 0.6 s). Baseline and 1-year 3D T1-weighted 3T MRI was processed with automated pipelines (SIENA, FSL-FIRST) to assess percentage whole brain volume change (PBVC) and deep GM (DGM) atrophy. Group differences were assessed by analysis of covariance, with time between MRI scans as a covariate.

RESULTS

Over 1 year, the DMF group showed a lower rate of whole brain atrophy than the noDMT group (PBVC: -0.37 ± 0.49% vs. -1.04 ± 0.67%, p = 0.005). The DMF group also had less change in putamen volume (-0.06 ± 0.22 vs. -0.32 ± 0.28 ml, p = 0.02). There were no significant on-study differences between groups in caudate, globus pallidus, thalamus, total DGM volume, T2 lesion volume, EDSS, or T25FW (all p > 0.20).

CONCLUSIONS

These results suggest a treatment effect of DMF on GM atrophy appearing at 1 year after starting therapy. However, due to the retrospective study design and sample size, these findings should be considered preliminary, and require confirmation in future investigations.

FUNDING

Biogen.

Brain MRI lesions and atrophy are associated with employment status in patients with multiple sclerosis

Multiple sclerosis (MS) commonly affects occupational function. We investigated the link between brain MRI and employment status. Patients with MS (n = 100) completed a Work Productivity and Activity Impairment (WPAI) (general health version) survey measuring employment status, absenteeism, presenteeism, and overall work and daily activity impairment. Patients “working for pay” were considered employed; “temporarily not working but looking for work,” “not working or looking for work due to age,” and “not working or looking for work due to disability” were considered not employed. Brain MRI T1 hypointense (T1LV) and T2 hyperintense (T2LV) lesion volumes were quantified. To assess lesional destructive capability, we calculated each subject’s ratio of T1LV to T2LV (T1/T2). Normalized brain parenchymal volume (BPV) assessed brain atrophy. The mean (SD) age was 45.5 (9.7) years; disease duration was 12.1 (8.1) years; 75 % were women, 76 % were relapsing-remitting, and 76 % were employed. T1LV, T1/T2, Expanded Disability Status Scale (EDSS) scores, and activity impairment were lower and BPV was higher in the employed vs. not employed group (Wilcoxon tests, p < 0.05). Age, disease duration, MS clinical subtype, and T2LV did not differ between groups (p > 0.05). In multivariable logistic regression modeling, adjusting for age, sex, and disease duration, higher T1LV predicted a lower chance of employment (p < 0.05). Pearson correlations showed that EDSS was associated with activity impairment (p < 0.05). Disease duration, age, and MRI measures were not correlated with activity impairment or other WPAI outcomes (p > 0.05). We report a link between brain atrophy and lesions, particularly lesions with destructive potential, to MS employment status.