Repeatability and reproducibility of FreeSurfer, FSL-SIENAX and SPM brain volumetric measurements and the effect of lesion filling in multiple sclerosis

Quantitative brain volume differences between COVID-19 patients and non-COVID-19 volunteers: A systematic review

BACKGROUND

The majority of COVID-19 neuroimaging literature focuses on the acute period after infection and clinically evident lesions. The chronic effects of COVID-19 on brain structure are less well defined. There are inconsistencies in the existing structural neuroimaging studies regarding differences in brain volumes after COVID-19 infection. It was thus our aim to systematically evaluate the structural neuroimaging literature focusing on volumetric differences between patients with COVID-19, and volunteers without COVID-19, at greater than 6 weeks post-infection.

METHODS

PubMed, Embase, and Scopus were searched in May 2023 with an updated search in September 2024, for studies with a quantitative comparison of brain volumes between COVID-19 patients and non-COVID-19 volunteers with MRI acquired more than 6-weeks after COVID-19. Exclusion criteria included COVID-19 patients selected for the presence of specific symptoms, case reports and case studies, and post-mortem studies. Forwards and backwards citation chasing were performed.

RESULTS

Sixteen studies met inclusion criteria. The majority of studies reported smaller grey matter volumes amongst COVID-19 patients compared to healthy volunteers. However, there were inconsistent findings, with 3 studies reporting larger grey matter volumes in the COVID-19 groups. Additionally, studies with COVID-19 cohorts with more severe presentations, characterized by admission to the hospital or the ICU, were more likely to report smaller grey matter volumes compared to healthy volunteers, than studies that were focused on patients who recovered at home.

CONCLUSION

A systematic review of the literature indicates that COVID-19 illness severity may explain some of the heterogeneity in brain volume differences between COVID-19 patients and healthy volunteers. More longitudinal follow-up studies are needed to assess the longitudinal course of COVID-19's effects on brain volumes.

FastSurfer-LIT: Lesion inpainting tool for whole-brain MRI segmentation with tumors, cavities, and abnormalities

Resection cavities, tumors, and other lesions can fundamentally alter brain structure and present as abnormalities in brain MRI. Specifically, quantifying subtle neuroanatomical changes in other, not directly affected regions of the brain is essential to assess the impact of tumors, surgery, chemo/radiotherapy, or drug treatments. However, only a limited number of solutions address this important task, while many standard analysis pipelines simply do not support abnormal brain images at all. In this paper, we present a method to perform sensitive neuroanatomical analysis of healthy brain regions in the presence of large lesions and cavities. Our approach called "FastSurfer Lesion Inpainting Tool" (FastSurfer-LIT) leverages the recently emerged Denoising Diffusion Probabilistic Models (DDPM) to fill lesion areas with healthy tissue that matches and extends the surrounding tissue. This enables subsequent processing with established MRI analysis methods such as the calculation of adjusted volume and surface measurements using FastSurfer or FreeSurfer. FastSurfer-LIT significantly outperforms previously proposed solutions on a large dataset of simulated brain tumors (N = 100) and synthetic multiple sclerosis lesions (N = 39) with improved Dice and Hausdorff measures, and also on a highly heterogeneous dataset with lesions and cavities in a manual assessment (N = 100). Finally, we demonstrate increased reliability to reproduce pre-operative cortical thickness estimates from corresponding post-operative temporo-mesial resection surgery MRIs. The method is publicly available at https://github.com/Deep-MI/LIT and will be integrated into the FastSurfer toolbox.

Post mortem cadaveric and imaging mapping analysis of the influence of cochlear implants on cMRI assessment regarding implant positioning and artifact formation

OBJECTIVES

In times of an aging society and considering the escalating health economic costs, the indications for imaging, particularly magnetic resonance imaging (MRI), must be carefully considered and strictly adhered to. This cadaver study aims to examine the influence of cochlear implant (CI) on the assessment of intracranial structures, artifact formation, and size in cranial MRI (cMRI). Furthermore, it seeks to evaluate the potential limitations in the interpretability and diagnostic value of cMRI in CI patients. Additionally, the study investigates the imaging of the brain stem and the internal ear canal and the feasibility of excluding cholesteatomas in cMRI for CI patients.

MATERIALS AND METHODS

Two cadaveric specimens were implanted with cochlear implants at varying angular positions (90°, 120°, and 135°), both unilaterally and bilaterally, with and without magnet in situ. MRI acquisition consisted of sequences commonly used in brain MRI scans (T1-MP-RAGE, T2-TSE, T1-TIRM, DWI, CISS). Subsequently, the obtained MRI images were manually juxtaposed with a reference brain from the Computational Anatomy Toolbox CAT12. The size and formation of artifacts were scrutinized to ascertain the assessability of 22 predefined intracranial structures. Furthermore, the internal auditory canal, middle ear and mastoid were evaluated.

RESULTS

The cadaveric head mapping facilitated the analysis of all 22 predefined intracranial structures. Artifacts were assessed in terms of their minimum and maximum impact on image comparability. Image quality and assessability were stratified into four categories (0-25%, 25-50%, 50-75%, and 75-100% of assessability restriction). The visualization of the central, temporal, parietal, and frontal lobes was contingent upon CI positioning and the choice of imaging sequence. Diffusion-weighted cMRI proved inadequate for monitoring cholesteatoma recurrence in ipsilateral CI patients, regardless of magnet presence. The ipsilateral internal auditory canal was inadequately visualized in both magnet-present and magnet-absent conditions. We divided our results into four categories. Category 3 (orange) indicates considerable limitations, while category 4 (red) indicates no interpretability, as the image is entirely obscured by artifacts.

CONCLUSION

This study provides detailed predictive power for the assessability and therefore the relevance of performing cMRIs in CI patients. We advocate consulting the relevant CI center if artifact overlay exceeds 50% (categories 3 and 4), to evaluate magnet explantation and reassess the necessity of cMRI. When suspecting cholesteatoma or cholesteatoma recurrences in patients with ipsilateral cochlear implants, diagnostic investigation should preferably be pursued surgically, as the necessary MRI sequences are prone to artifact interference, even in the absence of a magnet. The ipsilateral internal auditory canal remains inadequately evaluable with a magnet in situ, while without the magnet, only rudimentary assessments can be made across most sequences.

Validation of SynthSeg segmentation performance on CT using paired MRI from radiotherapy patients

INTRODUCTION

Manual segmentation of medical images is labor intensive and especially challenging for images with poor contrast or resolution. The presence of disease exacerbates this further, increasing the need for an automated solution. To this extent, SynthSeg is a robust deep learning model designed for automatic brain segmentation across various contrasts and resolutions. This study validates the SynthSeg robust brain segmentation model on computed tomography (CT), using a multi-center dataset.

METHODS

An open access dataset of 260 paired CT and magnetic resonance imaging (MRI) from radiotherapy patients treated in 5 centers was collected. Brain segmentations from CT and MRI were obtained with SynthSeg model, a component of the Freesurfer imaging suite. These segmentations were compared and evaluated using Dice scores and Hausdorff 95 distance (HD95), treating MRI-based segmentations as the ground truth. Brain regions that failed to meet performance criteria were excluded based on automated quality control (QC) scores.

RESULTS

Dice scores indicate a median overlap of 0.76 (IQR: 0.65-0.83). The mean volume difference is 7.79% (CI: 6.41%-9.18%), with CT segmentations typically smaller than MRI-based. The median HD95 is 2.95 mm (IQR: 1.73-5.39). QC score based thresholding improves median dice by 0.1 and median HD95 by 0.05 mm. Morphological differences related to sex and age, as detected by MRI, were also replicated with CT, with an approximate 17% difference between the CT and MRI results for sex and 10% difference between the results for age.

CONCLUSION

SynthSeg can be utilized for CT-based automatic brain segmentation, but only in applications where precision is not essential. CT performance is lower than MRI based on the integrated QC scores, but low-quality segmentations can be excluded with QC-based thresholding. Additionally, performing CT-based neuroanatomical studies is encouraged, as the results show correlations in sex- and age-based analyses similar to those found with MRI.

Glymphatic dysfunction in multiple sclerosis and its association with disease pathology and disability

BACKGROUND

The role of the glymphatic system in multiple sclerosis (MS)-related disability remains underexplored. Diffusion-tensor image analysis along the perivascular space (DTI-ALPS) offers a non-invasive method to assess glymphatic function.

OBJECTIVE

To evaluate glymphatic function in MS patients with lower and higher disability.

METHODS

This study included 118 MS patients who underwent structural, diffusion-weighted imaging, and clinical assessment. The participants were divided into lower (MS-L, n = 57) and higher disability (MS-H, n = 61) subgroups. Brain parenchymal fraction (BPF), lesion load (LL), and DTI-ALPS index were measured. Subgroup differences and correlations between DTI-ALPS index and other measures were explored. Logistic regression was performed to evaluate BPF, LL, and DTI-ALPS index in classifying lower and higher disability patients.

RESULTS

Significant differences in DTI-ALPS index between MS-H and MS-L (d = -0.71, false discovery rate-corrected p-value (p-FDR) = 0.001) were found. The DTI-ALPS index correlated significantly with disease duration (rp = -0.29, p-FDR = 0.002) and EDSS (rsp = -0.35, p-FDR = 0.0002). It also showed significant correlations with BPF and LL. DTI-ALPS index and LL were significant predictors of disability subgroup (DTI-ALPS: odds ratio (OR) = 1.77, p = 0.04, LL: OR = 0.94, p = 0.02).

CONCLUSION

Our findings highlight DTI-ALPS index as an imaging biomarker in MS, suggesting the involvement of glymphatic impairment in MS pathology, although further research is needed to elucidate its role in contributing to MS-related disability.

Slowly expanding lesions are associated with disease activity and gray matter loss in relapse-onset multiple sclerosis

BACKGROUND AND PURPOSE

Slowly expanding lesions (SELs) have been proposed as novel MRI markers of chronic active lesions in multiple sclerosis (MS). However, the mechanism through which SELs affect brain volume loss in patients with MS remains unknown. Additionally, the prevalence and significance of SELs in Asian patients with MS remain unclear. This study aimed to investigate the association between SELs and no evidence of disease activity (NEDA)-3 status as well as brain volume loss in Japanese patients.

METHODS

A total of 99 patients with relapse-onset MS were retrospectively evaluated. SELs were identified on brain MRI based on local deformation when consecutive scans were registered longitudinally. We developed a logistic regression model and generalized linear mixed models (GLMMs) to evaluate the association between the number of SELs and disease activity and changes in brain volume.

RESULTS

During the observation period (2.0 ± 0.22 years), 35 patients developed at least one SEL. Multivariable logistic regression analysis showed that ≥2 SELs were associated with 0.2 times the risk of achieving a NEDA-3 status. GLMMs revealed that the number of SELs was negatively associated with volume changes in the cortex (p = .00169) and subcortical gray matter (p = .00964) after correction for multiple comparisons.

CONCLUSION

SELs were identified in Japanese patients with MS during the 2-year observation period. The number of SELs is associated with disease activity and brain volume loss, suggesting that the number of SELs could be a biomarker of disease activity in MS.

Brain reserve and physical disability in secondary progressive multiple sclerosis

Background

The brain reserve hypothesis posits that larger maximal lifetime brain growth (MLBG) may confer protection against physical disability in multiple sclerosis (MS). Larger MLBG as a proxy for brain reserve, has been associated with reduced progression of physical disability in patients with early MS; however, it is unknown whether this association remains once in the secondary progressive phase of MS (SPMS). Our aim was to assess whether larger MLBG is associated with decreased physical disability progression in SPMS.

Methods

We conducted a post hoc analysis of participants in the MS-Secondary Progressive Multi-Arm Randomisation Trial (NCT01910259), a multicentre randomised placebo-controlled trial of the neuroprotective potential of three agents in SPMS. Physical disability was measured by Expanded Disability Status Scale (EDSS), 9-hole peg test (9HPT) and 25-foot timed walk test (T25FW) at baseline, 48 and 96 weeks. MLBG was estimated by baseline intracranial volume (ICV). Multivariable time-varying Cox regression models were used to investigate the association between MLBG and physical disability progression.

Results

383 participants (mean age 54.5 years, 298 female) were followed up over 96 weeks. Median baseline EDSS was 6.0 (range 4.0-6.5). Adjusted for covariates, larger MLBG was associated with a reduced risk of EDSS progression (HR 0.84,95% CI:0.72 to 0.99;p=0.04). MLBG was not independently associated with time to progression as measured by 9HPT or T25FW.

Conclusion

Larger MLBG is independently associated with physical disability progression over 96 weeks as measured by EDSS in SPMS. This suggests that MLBG as a proxy for brain reserve may continue to confer protection against disability when in the secondary progression phase of MS.

Trail registration number

NCT01910259.

Fatigue in early multiple sclerosis: MRI metrics of neuroinflammation, relapse and neurodegeneration

Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease affecting the brain and spinal cord. Fatigue is a common disabling symptom from MS onset, however the mechanisms by which underlying disease processes cause fatigue remain unclear. Improved pathophysiological understanding offers potential for improved treatments for MS-related fatigue. MRI provides insights into in vivo neuroinflammatory activity and neurodegeneration, although existing evidence for imaging correlates of MS fatigue is mixed. We explore associations between fatigue and MRI measures in the brain and spinal cord to identify neuroinflammatory and regional neurodegenerative substrates of fatigue in early relapsing-remitting MS (RRMS). Recently diagnosed (<6 months), treatment-naive people with RRMS (n = 440) were recruited to a longitudinal multi-centre nationally representative cohort study. Participants underwent 3-Tesla brain MRI at baseline and one year. We calculated global and regional white and grey matter volumes, white matter lesion (WML) load and upper cervical spinal cord cross-sectional area levels C2-3, and assessed new/enlarging WMLs visually. Participants were classed as fatigued or non-fatigued at baseline according to the Fatigue Severity Scale (>/≤36). Disability and depression were assessed with the expanded-disability status scale and Patient Health Questionnaire, respectively. MRI measures were compared between fatigue groups, both cross-sectionally and longitudinally, using regression analyses. Higher disability and depression scores were observed for participants with fatigue, with a higher number of fatigued participants receiving disease-modifying treatments at follow-up. Structural MRI data for brain were available for n = 313 (45% fatigued) and for spinal cord for n = 324 (46% fatigued). Cervical spinal cord cross-sectional area 2-3, white and grey matter volumes decreased, and WML volume increased, over time for both groups (q < 0.05). However, no significant between-group differences in these measures were found either cross-sectionally or longitudinally (q > 0.05). The presence of new/enlarging WMLs (49% in fatigued; 51% in non-fatigued) at follow-up also did not differ between groups (q > 0.05). Our results suggest that fatigue is not driven by neuroinflammation or neurodegeneration measurable by current structural MRI in early RRMS. This novel negative finding in a large multi-centre cohort of people with recently diagnosed RRMS helps to resolve uncertainty in existing literature. Notably, we showed that fatigue is prevalent in patients without brain radiological relapse, who may be considered to have inactive disease. This suggests that symptom detection and treatment should remain a clinical priority regardless of neuroinflammatory disease activity. More sensitive objective biomarkers are needed to elucidate fatigue mechanisms in RRMS, and ultimately facilitate development of effective targeted treatments for this important 'hidden disability'.

BrainAgeNeXt: Advancing Brain Age Modeling for Individuals with Multiple Sclerosis

Aging is associated with structural brain changes, cognitive decline, and neurodegenerative diseases. Brain age, an imaging biomarker sensitive to deviations from healthy aging, offers insights into structural aging variations and is a potential prognostic biomarker in neurodegenerative conditions. This study introduces BrainAgeNeXt, a novel convolutional neural network inspired by the MedNeXt framework, designed to predict brain age from T1-weighted magnetic resonance imaging (MRI) scans. BrainAgeNeXt was trained and validated on 11,574 MRI scans from 33 private and publicly available datasets of healthy volunteers, aged 5 to 95 years, imaged with 3T and 7T MRI. Performance was compared against three state-of-the-art brain age prediction methods. BrainAgeNeXt achieved a mean absolute error (MAE) of 2.78 ± 3.64 years, lower than the compared methods (MAE = 3.55, 3.59, and 4.16 years, respectively). We tested all methods also across different levels of image quality, and BrainAgeNeXt performed well even with motion artifacts and less common 7T MRI data. In three longitudinal multiple sclerosis (MS) cohorts (273 individuals), brain age was, on average, 4.21 ± 6.51 years greater than chronological age. Longitudinal analysis indicated that brain age increased by 1.15 years per chronological year in individuals with MS (95% CI = [1.05, 1.26]). Moreover, in early MS, individuals with worsening disability had a higher annual increase in brain age compared to those with stable clinical assessments (1.24 vs. 0.75, p < 0.01). These findings suggest that brain age is a promising prognostic biomarker for MS progression and potentially a valuable endpoint for clinical trials.

Deep learning-based super-resolution of structural brain MRI at 1.5 T: application to quantitative volume measurement

OBJECTIVE

This study investigated the feasibility of using deep learning-based super-resolution (DL-SR) technique on low-resolution (LR) images to generate high-resolution (HR) MR images with the aim of scan time reduction. The efficacy of DL-SR was also assessed through the application of brain volume measurement (BVM).

MATERIALS AND METHODS

In vivo brain images acquired with 3D-T1W from various MRI scanners were utilized. For model training, LR images were generated by downsampling the original 1 mm-2 mm isotropic resolution images. Pairs of LR and HR images were used for training 3D residual dense net (RDN). For model testing, actual scanned 2 mm isotropic resolution 3D-T1W images with one-minute scan time were used. Normalized root-mean-square error (NRMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used for model evaluation. The evaluation also included brain volume measurement, with assessments of subcortical brain regions.

RESULTS

The results showed that DL-SR model improved the quality of LR images compared with cubic interpolation, as indicated by NRMSE (24.22% vs 30.13%), PSNR (26.19 vs 24.65), and SSIM (0.96 vs 0.95). For volumetric assessments, there were no significant differences between DL-SR and actual HR images (p > 0.05, Pearson's correlation > 0.90) at seven subcortical regions.

DISCUSSION

The combination of LR MRI and DL-SR enables addressing prolonged scan time in 3D MRI scans while providing sufficient image quality without affecting brain volume measurement.

A deep learning model for brain segmentation across pediatric and adult populations

Automated quantification of brain tissues on MR images has greatly contributed to the diagnosis and follow-up of neurological pathologies across various life stages. However, existing solutions are specifically designed for certain age ranges, limiting their applicability in monitoring brain development from infancy to late adulthood. This retrospective study aims to develop and validate a brain segmentation model across pediatric and adult populations. First, we trained a deep learning model to segment tissues and brain structures using T1-weighted MR images from 390 patients (age range: 2-81 years) across four different datasets. Subsequently, the model was validated on a cohort of 280 patients from six distinct test datasets (age range: 4-90 years). In the initial experiment, the proposed deep learning-based pipeline, icobrain-dl, demonstrated segmentation accuracy comparable to both pediatric and adult-specific models across diverse age groups. Subsequently, we evaluated intra- and inter-scanner variability in measurements of various tissues and structures in both pediatric and adult populations computed by icobrain-dl. Results demonstrated significantly higher reproducibility compared to similar brain quantification tools, including childmetrix, FastSurfer, and the medical device icobrain v5.9 (p-value< 0.01). Finally, we explored the potential clinical applications of icobrain-dl measurements in diagnosing pediatric patients with Cerebral Visual Impairment and adult patients with Alzheimer's Disease.

White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) "OCD vs. healthy controls" (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) "unmedicated OCD vs. healthy controls" (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) "medicated OCD vs. unmedicated OCD" (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6-79.1 in adults; 35.9-63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.

ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

CAT: a computational anatomy toolbox for the analysis of structural MRI data

A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community.

Multimodal MRI study on the relation between WM integrity and connected GM atrophy and its effect on disability in early multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is characterized by pathology in white matter (WM) and atrophy of grey matter (GM), but it remains unclear how these processes are related, or how they influence clinical progression.

OBJECTIVE

To study the spatial and temporal relationship between GM atrophy and damage in connected WM in relapsing-remitting (RR) MS in relation to clinical progression.

METHODS

Healthy control (HC) and early RRMS subjects visited our center twice with a 1-year interval for MRI and clinical examinations, including the Expanded Disability Status Scale (EDSS) and Multiple Sclerosis Functional Composite (MSFC) scores. RRMS subjects were categorized as MSFC decliners or non-decliners based on ΔMSFC over time. Ten deep (D)GM and 62 cortical (C) GM structures were segmented and probabilistic tractography was performed to identify the connected WM. WM integrity was determined per tract with, amongst others, fractional anisotropy (FA), mean diffusivity (MD), neurite density index (NDI), and myelin water fraction (MWF). Linear mixed models (LMMs) were used to investigate GM and WM differences between HC and RRMS, and between MSFC decliners and non-decliners. LMM was also used to test associations between baseline WM z-scores and changes in connected GM z-scores, and between baseline GM z-scores and changes in connected WM z-scores, in HC/RRMS subjects and in MSFC decliners/non-decliners.

RESULTS

We included 13 HCs and 31 RRMS subjects with an average disease duration of 3.5 years and a median EDSS of 3.0. Fifteen RRMS subjects showed declining MSFC scores over time, and they showed higher atrophy rates and greater WM integrity loss compared to non-decliners. Lower baseline WM integrity was associated with increased CGM atrophy over time in RRMS, but not in HC subjects. This effect was only seen in MSFC decliners, especially when an extended WM z-score was used, which included FA, MD, NDI and MWF. Baseline GM measures were not significantly related to WM integrity changes over time in any of the groups.

DISCUSSION

Lower baseline WM integrity was related to more cortical atrophy in RRMS subjects that showed clinical progression over a 1-year follow-up, while baseline GM did not affect WM integrity changes over time. WM damage, therefore, seems to drive atrophy more than conversely.

Exploring the Potential of the Corpus Callosum Area as a Predictive Marker for Impaired Information Processing in Multiple Sclerosis

Early cognitive impairment (CI) detection is crucial in multiple sclerosis (MS). However, it can progress silently regardless of relapse activity and reach an advanced stage. We aimed to determine whether the corpus callosum area (CCA) is a sensitive and feasible marker for CI in MS compared to other neuroimaging markers. We assessed cognitive function in 77 MS patients using the Symbol Digit Modalities Test, Paced Auditory Serial Additions Task, Wechsler Adult Intelligence Scale-IV, and Wechsler Memory Scale-Revised. The neuroimaging markers included manually measured CCA, two diffusion tensor imaging markers, and nine volumetric measurements. Apart from volumes of the hippocampus and cerebellum, ten markers showed a significant correlation with all neuropsychological tests and significant differences between the groups. The normalized CCA demonstrated a moderate-to-strong correlation with all neuropsychological tests and successfully differentiated between the CI and cognitively normal groups with 80% sensitivity and 83% specificity. The marker had a large area under the curve and a high Youden index (0.82 and 0.63, respectively) and comparability with established cognitive markers. Therefore, the normalized CCA may serve as a reliable marker for CI in MS and can be easily implemented in clinical practice, providing a supportive diagnostic tool for CI in MS.

The effects of cytomegalovirus on brain structure following sport-related concussion

The neurotrophic herpes virus cytomegalovirus is a known cause of neuropathology in utero and in immunocompromised populations. Cytomegalovirus is reactivated by stress and inflammation, possibly explaining the emerging evidence linking it to subtle brain changes in the context of more minor disturbances of immune function. Even mild forms of traumatic brain injury, including sport-related concussion, are major physiological stressors that produce neuroinflammation. In theory, concussion could predispose to the reactivation of cytomegalovirus and amplify the effects of physical injury on brain structure. However, to our knowledge this hypothesis remains untested. This study evaluated the effect of cytomegalovirus serostatus on white and grey matter structure in a prospective study of athletes with concussion and matched contact-sport controls. Athletes who sustained concussion (n = 88) completed MRI at 1, 8, 15 and 45 days post-injury; matched uninjured athletes (n = 73) completed similar visits. Cytomegalovirus serostatus was determined by measuring serum IgG antibodies (n = 30 concussed athletes and n = 21 controls were seropositive). Inverse probability of treatment weighting was used to adjust for confounding factors between athletes with and without cytomegalovirus. White matter microstructure was assessed using diffusion kurtosis imaging metrics in regions previously shown to be sensitive to concussion. T1-weighted images were used to quantify mean cortical thickness and total surface area. Concussion-related symptoms, psychological distress, and serum concentration of C-reactive protein at 1 day post-injury were included as exploratory outcomes. Planned contrasts compared the effects of cytomegalovirus seropositivity in athletes with concussion and controls, separately. There was a significant effect of cytomegalovirus on axial and radial kurtosis in athletes with concussion but not controls. Cytomegalovirus positive athletes with concussion showed greater axial (P = 0.007, d = 0.44) and radial (P = 0.010, d = 0.41) kurtosis than cytomegalovirus negative athletes with concussion. Similarly, there was a significant association of cytomegalovirus with cortical thickness in athletes with concussion but not controls. Cytomegalovirus positive athletes with concussion had reduced mean cortical thickness of the right hemisphere (P = 0.009, d = 0.42) compared with cytomegalovirus negative athletes with concussion and showed a similar trend for the left hemisphere (P = 0.036, d = 0.33). There was no significant effect of cytomegalovirus on kurtosis fractional anisotropy, surface area, symptoms and C-reactive protein. The results raise the possibility that cytomegalovirus infection contributes to structural brain abnormalities in the aftermath of concussion perhaps via an amplification of concussion-associated neuroinflammation. More work is needed to identify the biological pathways underlying this process and to clarify the clinical relevance of this putative viral effect.

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.

How often does white matter hyperintensity volume regress in cerebral small vessel disease?

BACKGROUND AND OBJECTIVES

It has been suggested that white matter hyperintensity lesions (WMHs), which typically progress over time, can also regress, and that this might be associated with favorable cognitive performance. We determined the prevalence of WMH regression in patients with cerebral small vessel disease (SVD) and examined which demographic, clinical, and radiological markers were associated with this regression.

METHODS

We used semi-automated lesion marking methods to quantify WMH volume at multiple timepoints in three cohorts with symptomatic SVD; two with moderate-to-severe symptomatic SVD (the SCANS observational cohort and the control arm of the PRESERVE interventional trial) and one with mild-to-moderate SVD (the RUN DMC observational cohort). Mixed-effects ordered logistic regression models were used to test which factors predicted participants to show WMH regression.

RESULTS

No participants (0/98) in SCANS, 6/42 (14.3%) participants in PRESERVE, and 6/276 (2.2%) in RUN DMC showed WMH regression. On multivariate analysis, only lower WMH volume (OR: 0.36, 95% CI: 0.23-0.56) and better white matter microstructural integrity assessed by fractional anisotropy using diffusion tensor imaging (OR: 1.55, 95% CI: 1.07-2.24) predicted participant classification as regressor versus stable or progressor.

DISCUSSION

Only a small proportion of participants demonstrated WMH regression across the three cohorts, when a blinded standardized assessment method was used. Subjects who showed regression had less severe imaging markers of disease at baseline. Our results show that lesion regression is uncommon in SVD and unlikely to be a major factor affecting the use of WMH quantification as an outcome for clinical trials.

Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis

Multiple Sclerosis (MS) is an autoimmune demyelinating disease characterised by changes in iron and myelin content. These biomarkers are detectable by Quantitative Susceptibility Mapping (QSM), an advanced Magnetic Resonance Imaging technique detecting magnetic properties. When analysed with radiomic techniques that exploit its intrinsic quantitative nature, QSM may furnish biomarkers to facilitate early diagnosis of MS and timely assessment of progression. In this work, we explore the robustness of QSM radiomic features by varying the number of grey levels (GLs) and echo times (TEs), in a sample of healthy controls and patients with MS. We analysed the white matter in total and within six clinically relevant tracts, including the cortico-spinal tract and the optic radiation. After optimising the number of GLs (n = 64), at least 65% of features were robust for each Volume of Interest (VOI), with no difference (p > .05) between left and right hemispheres. Different outcomes in feature robustness among the VOIs depend on their characteristics, such as volume and variance of susceptibility values. This study validated the processing pipeline for robustness analysis and established the reliability of QSM-based radiomics features against GLs and TEs. Our results provide important insights for future radiomics studies using QSM in clinical applications.

Impact of brain segmentation methods on regional metabolism quantification in 18F-FDG PET/MR analysis

BACKGROUND

Accurate analysis of quantitative PET data plays a crucial role in studying small, specific brain structures. The integration of PET and MRI through an integrated PET/MR system presents an opportunity to leverage the benefits of precisely aligned structural MRI and molecular PET images in both spatial and temporal dimensions. However, in many clinical workflows, PET studies are often performed without the aid of individually matched structural MRI scans, primarily for the sake of convenience in the data collection and brain segmentation possesses. Currently, two commonly employed segmentation strategies for brain PET analysis are distinguished: methods with or without MRI registration and methods employing either atlas-based or individual-based algorithms. Moreover, the development of artificial intelligence (AI)-assisted methods for predicting brain segmentation holds promise but requires further validation of their efficiency and accuracy for clinical applications. This study aims to compare and evaluate the correlations, consistencies, and differences among the above-mentioned brain segmentation strategies in quantification of brain metabolism in 18F-FDG PET/MR analysis.

RESULTS

Strong correlations were observed among all methods (r = 0.932 to 0.999, P < 0.001). The variances attributable to subject and brain region were higher than those caused by segmentation methods (P < 0.001). However, intraclass correlation coefficient (ICC)s between methods with or without MRI registration ranged from 0.924 to 0.975, while ICCs between methods with atlas- or individual-based algorithms ranged from 0.741 to 0.879. Brain regions exhibiting significant standardized uptake values (SUV) differences due to segmentation methods were the basal ganglia nuclei (maximum to 11.50 ± 4.67%), and various cerebral cortexes in temporal and occipital regions (maximum to 18.03 ± 5.52%). The AI-based method demonstrated high correlation (r = 0.998 and 0.999, P < 0.001) and ICC (0.998 and 0.997) with FreeSurfer, substantially reducing the time from 8.13 h to 57 s on per subject.

CONCLUSIONS

Different segmentation methods may have impact on the calculation of brain metabolism in basal ganglia nuclei and specific cerebral cortexes. The AI-based approach offers improved efficiency and is recommended for its enhanced performance.

A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs

Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automated tool for segmenting and assessing the shape of the midCC from T1w, T2w, and FLAIR images. We train a UNet on images from multiple public datasets to obtain midCC segmentations. A quality control algorithm is also built-in, trained on the midCC shape features. We calculate intraclass correlations (ICC) and average Dice scores in a test-retest dataset to assess segmentation reliability. We test our segmentation on poor quality and partial brain scans. We highlight the biological significance of our extracted features using data from over 40,000 individuals from the UK Biobank; we classify clinically defined shape abnormalities and perform genetic analyses.

Cerebral volume is unaffected after pre-eclampsia

OBJECTIVES

Pre-eclampsia has been associated with cardiovascular, cerebrovascular and/or psychological complaints. Signs of altered brain morphology and more white-matter hyperintensities (WMHs) during and shortly after pre-eclampsia have been observed in some, but not all, studies. We compared volumes of cerebral structures and the number of WMHs between formerly pre-eclamptic women and those with normotensive gestational history and assessed the effect of age on brain volumes.

METHODS

Structural 7-Tesla magnetic resonance imaging of the brain was performed in 59 formerly pre-eclamptic women (aged 37 ± 6 years, 0.5-16 years postpartum) and 20 women with a history of normotensive pregnancy (aged 39 ± 5 years, 1-18 years postpartum). Fazekas scores were obtained to assess WMH load. Volumes of the whole brain, gray and white matter, brain lobes, and ventricular and pericortical cerebrospinal fluid (CSF) spaces were calculated after semiautomatic segmentation. Group differences were analyzed using ANCOVA and Bayes factors. Results were adjusted for age, educational attainment, presence of current hypertension and total intracranial volume. The effect of age on cerebral volumes was analyzed using linear regression analysis.

RESULTS

No changes in global and local brain volumes were observed between formerly pre-eclamptic and control women. Also, no difference in WMH load was observed. Independent of pre-eclamptic history, gray-matter volume significantly decreased with age, while ventricular and pericortical CSF space volumes significantly increased with age.

CONCLUSIONS

Volumetric changes of the cerebrum are age-related but are independent of pre-eclamptic history in the first two decades after childbirth. No evidence of greater WMH load after pre-eclampsia was found. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

A review on multiple sclerosis prognostic findings from imaging, inflammation, and mental health studies

Magnetic resonance imaging (MRI) of the brain is commonly used to detect where chronic and active lesions are in multiple sclerosis (MS). MRI is also extensively used as a tool to calculate and extrapolate brain health by way of volumetric analysis or advanced imaging techniques. In MS patients, psychiatric symptoms are common comorbidities, with depression being the main one. Even though these symptoms are a major determinant of quality of life in MS, they are often overlooked and undertreated. There has been evidence of bidirectional interactions between the course of MS and comorbid psychiatric symptoms. In order to mitigate disability progression in MS, treating psychiatric comorbidities should be investigated and optimized. New research for the prediction of disease states or phenotypes of disability have advanced, primarily due to new technologies and a better understanding of the aging brain.

Single‐value scores of memory‐related brain activity reflect dissociable neuropsychological and anatomical signatures of neurocognitive aging

Memory-related functional magnetic resonance imaging (fMRI) activations show age-related differences across multiple brain regions that can be captured in summary statistics like single-value scores. Recently, we described two single-value scores reflecting deviations from prototypical whole-brain fMRI activity of young adults during novelty processing and successful encoding. Here, we investigate the brain-behavior associations of these scores with age-related neurocognitive changes in 153 healthy middle-aged and older adults. All scores were associated with episodic recall performance. The memory network scores, but not the novelty network scores, additionally correlated with medial temporal gray matter and other neuropsychological measures including flexibility. Our results thus suggest that novelty-network-based fMRI scores show high brain-behavior associations with episodic memory and that encoding-network-based fMRI scores additionally capture individual differences in other aging-related functions. More generally, our results suggest that single-value scores of memory-related fMRI provide a comprehensive measure of individual differences in network dysfunction that may contribute to age-related cognitive decline.

SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry

Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutionize our understanding of many neurological diseases, but their morphometric analysis has not yet been possible due to their anisotropic resolution. We present an artificial intelligence technique, "SynthSR," that takes clinical brain MRI scans with any MR contrast (T1, T2, etc.), orientation (axial/coronal/sagittal), and resolution and turns them into high-resolution T1 scans that are usable by virtually all existing human neuroimaging tools. We present results on segmentation, registration, and atlasing of >10,000 scans of controls and patients with brain tumors, strokes, and Alzheimer's disease. SynthSR yields morphometric results that are very highly correlated with what one would have obtained with high-resolution T1 scans. SynthSR allows sample sizes that have the potential to overcome the power limitations of prospective research studies and shed new light on the healthy and diseased human brain.

Automatic analysis of skull thickness, scalp-to-cortex distance and association with age and sex in cognitively normal elderly

Personalized neurostimulation has been a potential treatment for many brain diseases, which requires insights into brain/skull geometry. Here, we developed an open source efficient pipeline BrainCalculator for automatically computing the skull thickness map, scalp-to-cortex distance (SCD), and brain volume based on T 1 -weighted magnetic resonance imaging (MRI) data. We examined the influence of age and sex cross-sectionally in 407 cognitively normal older adults (71.9±8.0 years, 60.2% female) from the ADNI. We demonstrated the compatibility of our pipeline with commonly used preprocessing packages and found that BrainSuite Skullfinder was better suited for such automatic analysis compared to FSL Brain Extraction Tool 2 and SPM12- based unified segmentation using ground truth. We found that the sphenoid bone and temporal bone were thinnest among the skull regions in both females and males. There was no increase in regional minimum skull thickness with age except in the female sphenoid bone. No sex difference in minimum skull thickness or SCD was observed. Positive correlations between age and SCD were observed, faster in females (0.307%/y) than males (0.216%/y) in temporal SCD. A negative correlation was observed between age and whole brain volume computed based on brain surface (females -1.031%/y, males -0.998%/y). In conclusion, we developed an automatic pipeline for MR-based skull thickness map, SCD, and brain volume analysis and demonstrated the sex-dependent association between minimum regional skull thickness, SCD and brain volume with age. This pipeline might be useful for personalized neurostimulation planning.

Effectiveness of ocrelizumab on clinical and MRI outcome measures in multiple sclerosis across black and white cohorts: A single-center retrospective study

OBJECTIVE

To examine differences in the therapeutic response to ocrelizumab in multiple sclerosis (MS) patients who self-identified as either White or Black, assessed longitudinally by expanded disability status scale (EDSS) progression and MRI brain volume loss.

METHODS

MS subjects treated with ocrelizumab were retrospectively identified. Clinical data were available for 229 subjects (White 146; Black 83) and MRI data from for 48 subjects (White 31; Black 17). Outcome measures were changes in the EDSS and brain volume over time. EDSS were analyzed as raw scores, ambulatory (EDSS <5.0) vs. ambulatory with assistance (5.5 ≤ EDSS ≤ 6.5) status, and EDSS severity (< 3.0, 3.0-5.0, and > 5.5 ≤ 6.5). General linear mixed model was used for statistical analysis. FreeSurfer was used for volumetric analysis.

RESULTS

The Black cohort had overrepresentation of females (78% vs. 62%, p = 0.013), lower age (median, 45 (IQR 39-51) vs. 49 (38-58), p = 0.08), lower Vitamin D levels (33 (21-45) vs. 40 (29-52), p = 0.002), and higher EDSS (4 (2-6) vs. 2.5 (1-6), p = 0.019). There was no progression of EDSS scores over the 2-year observation period. The covariates with significant influence on the baseline EDSS scores were older age, race, longer disease duration, prior MS treatment, and lower vitamin D levels. No differences were observed between the racial groups over time in the cortical, thalamic, caudate, putamen, and brainstem gray matter volumes nor in the cortical thickness or total lesion volume.

CONCLUSION

In this real-world clinical and radiological study, ocrelizumab treatment was highly effective in stabilizing clinical and MRI measures of disease progression in Blacks and Whites, despite higher baseline disability in the Black cohort.

Reliable brain morphometry from contrast-enhanced T1w-MRI in patients with multiple sclerosis

Brain morphometry is usually based on non-enhanced (pre-contrast) T1-weighted MRI. However, such dedicated protocols are sometimes missing in clinical examinations. Instead, an image with a contrast agent is often available. Existing tools such as FreeSurfer yield unreliable results when applied to contrast-enhanced (CE) images. Consequently, these acquisitions are excluded from retrospective morphometry studies, which reduces the sample size. We hypothesize that deep learning (DL)-based morphometry methods can extract morphometric measures also from contrast-enhanced MRI. We have extended DL+DiReCT to cope with contrast-enhanced MRI. Training data for our DL-based model were enriched with non-enhanced and CE image pairs from the same session. The segmentations were derived with FreeSurfer from the non-enhanced image and used as ground truth for the coregistered CE image. A longitudinal dataset of patients with multiple sclerosis (MS), comprising relapsing remitting (RRMS) and primary progressive (PPMS) subgroups, was used for the evaluation. Global and regional cortical thickness derived from non-enhanced and CE images were contrasted to results from FreeSurfer. Correlation coefficients of global mean cortical thickness between non-enhanced and CE images were significantly larger with DL+DiReCT (r = 0.92) than with FreeSurfer (r = 0.75). When comparing the longitudinal atrophy rates between the two MS subgroups, the effect sizes between PPMS and RRMS were higher with DL+DiReCT both for non-enhanced (d = -0.304) and CE images (d = -0.169) than for FreeSurfer (non-enhanced d = -0.111, CE d = 0.085). In conclusion, brain morphometry can be derived reliably from contrast-enhanced MRI using DL-based morphometry tools, making additional cases available for analysis and potential future diagnostic morphometry tools.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Analysis of the extent of limbic system changes in multiple sclerosis using FreeSurfer and voxel-based morphometry approaches

Background and purpose

The limbic brain is involved in diverse cognitive, emotional, and autonomic functions. Injury of the various parts of the limbic system have been correlated with clinical deficits in MS. The purpose of this study was to comprehensively examine different regions of the subcortical limbic system to assess the extent of damage within this entire system as it may be pertinent in correlating with specific aspects of cognitive and behavioral dysfunction in MS by using a fully automated, unbiased segmentation approach.

Methods

Sixty-seven subjects were included in this study, including 52 with multiple sclerosis (MS) and 15 healthy controls. Only patients with stable MS disease, without any relapses, MRI activity, or disability progression were included. Subcortical limbic system segmentation was performed using the FreeSurfer pipeline ScLimbic, which provides volumes for fornix, mammillary bodies, hypothalamus, septal nuclei, nucleus accumbens, and basal forebrain. Hippocampus and anterior thalamic nuclei were added as additional components of the limbic circuitry, also segmented through FreeSurfer. Whole limbic region mask was generated by combining these structures and used for Voxel-based morphometry (VBM) analysis.

Results

The mean [95% confidence interval] of the total limbic system volume was lower (0.22% [0.21–0.23]) in MS compared to healthy controls (0.27%, [0.25–0.29], p < .001). Pairwise comparisons of individual limbic regions between MS and controls was significant in the nucleus accumbens (0.046%, [0.043–0.050] vs. 0.059%, [0.051–0.066], p = .005), hypothalamus (0.062%, [0.059–0.065] vs. 0.074%, [0.068–0.081], p = .001), basal forebrain (0.038%, [0.036–0.040] vs. 0.047%, [0.042–0.051], p = .001), hippocampus (0.47%, [0.45–0.49] vs. 0.53%, [0.49–0.57], p = .004), and anterior thalamus (0.077%, [0.072–0.082] vs. 0.093%, [0.084–0.10], p = .001) after Bonferroni correction. Volume of several limbic regions was significantly correlated with T2 lesion burden and brain parenchymal fraction (BPF). Multiple regression model showed minimal influence of BPF on limbic brain volume and no influence of other demographic and disease state variables. VBM analysis showed cluster differences in the fornix and anterior thalamic nuclei at threshold p < 0.05 after adjusting for covariates but the results were insignificant after family-wise error corrections.

Conclusions

The results show evidence that brain volume loss is fairly extensive in the limbic brain. Given the significance of the limbic system in many disease states including MS, such volumetric analyses can be expanded to studying cognitive and emotional disturbances in larger clinical trials. FreeSurfer ScLimbic pipeline provided an efficient and reliable methodology for examining many of the subcortical structures related to the limbic brain.

Computational approaches to predicting treatment response to obesity using neuroimaging

Obesity is a worldwide disease associated with multiple severe adverse consequences and comorbid conditions. While an increased body weight is the defining feature in obesity, etiologies, clinical phenotypes and treatment responses vary between patients. These variations can be observed within individual treatment options which comprise lifestyle interventions, pharmacological treatment, and bariatric surgery. Bariatric surgery can be regarded as the most effective treatment method. However, long-term weight regain is comparably frequent even for this treatment and its application is not without risk. A prognostic tool that would help predict the effectivity of the individual treatment methods in the long term would be essential in a personalized medicine approach. In line with this objective, an increasing number of studies have combined neuroimaging and computational modeling to predict treatment outcome in obesity. In our review, we begin by outlining the central nervous mechanisms measured with neuroimaging in these studies. The mechanisms are primarily related to reward-processing and include "incentive salience" and psychobehavioral control. We then present the diverse neuroimaging methods and computational prediction techniques applied. The studies included in this review provide consistent support for the importance of incentive salience and psychobehavioral control for treatment outcome in obesity. Nevertheless, further studies comprising larger sample sizes and rigorous validation processes are necessary to answer the question of whether or not the approach is sufficiently accurate for clinical real-world application.

Cognitive Sequelae and Hippocampal Dysfunction in Chronic Kidney Disease following 5/6 Nephrectomy

Neurological disorders are prevalent in patients with chronic kidney disease (CKD). Vascular factors and uremic toxins are involved with cognitive impairment in CKD. In addition, vascular dementia-induced alterations in the structure and function of the hippocampus can lead to deficits in hippocampal synaptic plasticity and cognitive function. However, regardless of this clinical evidence, the pathophysiology of cognitive impairment in patients with CKD is not fully understood. We used male Sprague Dawley rats and performed 5/6 nephrectomy to observe the changes in behavior, field excitatory postsynaptic potential, and immunostaining of the hippocampus following CKD progression. We measured the hippocampus volume on magnetic resonance imaging scans in the controls (n = 34) and end-stage renal disease (ESRD) hemodialysis patients (n = 42). In four cognition-related behavior assays, including novel object recognition, Y-maze, Barnes maze, and classical contextual fear conditioning, we identified deficits in spatial working memory, learning and memory, and contextual memory, as well as the ability to distinguish familiar and new objects, in the rats with CKD. Immunohistochemical staining of Na⁺/H⁺ exchanger1 was increased in the hippocampus of the CKD rat models. We performed double immunofluorescent staining for aquaporin-4 and glial fibrillary acidic protein and then verified the high coexpression in the hippocampus of the CKD rat model. Furthermore, results from recoding of the field excitatory postsynaptic potential (fEPSP) in the hippocampus showed the reduced amplitude and slope of fEPSP in the CKD rats. ESRD patients with cognitive impairment showed a significant decrease in the hippocampus volume compared with ESRD patients without cognitive impairment or the controls. Our findings suggest that uremia resulting from decreased kidney function may cause the destruction of the blood–brain barrier and hippocampus-related cognitive impairment in CKD.

Automated Brain Volumetry in Patients With Memory Impairment: Comparison of Conventional and Ultrafast 3D T1-Weighted MRI Sequences Using Two Software Packages

BACKGROUND. Isotropic 3D T1-weighted imaging has long acquisition times, potentially leading to motion artifact and altered brain volume measurements. Acquisition times may be greatly shortened using an isotropic ultrafast 3D echo-planar imaging (EPI) T1-weighted sequence. OBJECTIVE. The purpose of this article was to compare automated brain volume measurements between conventional 3D T1-weighted imaging and ultrafast 3D EPI T1-weighted imaging. METHODS. This retrospective study included 36 patients (25 women, 11 men; mean age, 68.4 years) with memory impairment who underwent 3-T brain MRI. Examinations included both conventional 3D T1-weighted imaging using inversion recovery gradient-recalled echo sequence (section thickness, 1.0 mm; acquisition time, 3 minutes 4 seconds) and, in patients exhibiting motion, an isotropic ultrafast 3D EPI T1-weighted sequence (section thickness, 1.2 mm; acquisition time, 30 seconds). The 36-patient sample excluded five patients in whom severe motion artifact rendered the conventional sequence of insufficient quality for volume measurements. Automated brain volumetry was performed using NeuroQuant (version 3.0, CorTechs Laboratories) and FreeSurfer (version 7.1.1, Harvard University) software. Volume measurements were compared between sequences for nine regions in each hemisphere. RESULTS. Volumes showed substantial to almost perfect agreement between the two sequences for most regions bilaterally. However, most regions showed significant mean differences between sequences, and Bland-Altman analyses showed consistent systematic biases and wide limits of agreement (LOA). For example, for the left hemisphere using NeuroQuant, volume was significantly greater for the ultrafast sequence in four regions and significantly greater for the conventional sequence in three regions, whereas standardized effect size between sequences was moderate for four regions and large for one region. Using NeuroQuant, mean bias (ultrafast minus conventional) and 95% LOA were greatest in cortical gray matter bilaterally (-50.61 cm3 [-56.27 cm3, -44.94 cm3] for the left hemisphere; -50.02 cm3 [-54.88 cm3, -45.16 cm3] for the right hemisphere). The variation between the two sequences was observed in subset analyses of 16 patients with and 20 patients without Alzheimer disease. CONCLUSION. Brain volume measurements show significant differences and systematic biases between the conventional and ultrafast sequences. CLINICAL IMPACT. In patients in whom severe motion artifact precludes use of the conventional sequence, the ultrafast sequence may be useful to enable brain volume-try. However, the current conventional 3D T1-weighted sequence remains preferred in patients who can tolerate the standard examination.

Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®

OBJECTIVES

QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists.

METHODS

Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland-Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists.

RESULTS

The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes.

CONCLUSIONS

QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

KEY POINTS

• QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

Timed Up and Go in People with Subjective Cognitive Decline Is Associated with Faster Cognitive Deterioration and Cortical Thickness

INTRODUCTION

Early markers of neurodegeneration provide an opportunity to detect, monitor, and initiate interventions in individuals who have an increased risk of developing dementia. Here, we investigated whether the Timed Up and Go (TUG) test is associated with early brain neurodegeneration and whether the TUG test could be a marker of cognitive decline in people with subjective cognitive decline (SCD).

METHODS

This is a longitudinal analysis of the Dementia Disease Initiation Study, a prospective, community-based, cohort study from Norway, designed to investigate early markers of cognitive impairment and dementia. Participants were classified as SCD and healthy controls (HC). The main studied variables were the TUG test and cognition as measured by the Mini-Mental State Examination and the Consortium to Establish a Registry for Alzheimer's Disease memory composite score. Additionally, we investigated the cross-sectional association of brain morphology with the TUG using 1.5T-MRI.

RESULTS

The sample included 45 participants (SCD = 21, HC = 24) followed during a mean time of 1.50 ± 0.70 years. At baseline, the cognitive performance did not differ between the groups, but TUG was longer in SCD. Slower baseline TUG was associated with a faster cognitive decline in both groups and it was also associated with reduced cortical thickness especially in motor, executive, associative, and somatosensory cortical regions in people with SCD.

DISCUSSION/CONCLUSION

TUG predicted cognitive change in individuals with SCD, and there was a negative association between TUG and cortical thickness. TUG is a promising cheap and noninvasive marker of early cognitive decline and may help initiate interventions in individuals who have an increased risk of dementia.

The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging

Background

Graph theoretical network analysis with structural magnetic resonance imaging (MRI) of multiple sclerosis (MS) patients can be used to assess subtle changes in brain networks. However, the presence of multiple focal brain lesions might impair the accuracy of automatic tissue segmentation methods, and hamper the performance of graph theoretical network analysis. Applying “lesion filling” by substituting the voxel intensities of a lesion with the voxel intensities of nearby voxels, thus creating an image devoid of lesions, might improve segmentation and graph theoretical network analysis. This study aims to determine if brain networks are different between MS subtypes and healthy controls (HC) and if the assessment of these differences is affected by lesion filling.

Methods

The study included 49 MS patients and 19 HC that underwent a T1w, and T2w-FLAIR MRI scan. Graph theoretical network analysis was performed from grey matter fractions extracted from the original T1w-images and T1w-images after lesion filling.

Results

Artefacts in lesion-filled T1w images correlated positively with total lesion volume (r = 0.84, p < 0.001) and had a major impact on grey matter segmentation accuracy. Differences in sensitivity for network alterations were observed between original T1w data and after application of lesion filling: graph theoretical network analysis obtained from lesion-filled T1w images produced more differences in network organization in MS patients.

Conclusion

Lesion filling might reduce variability across subjects resulting in an increased detection rate of network alterations in MS, but also induces significant artefacts, and therefore should be applied cautiously especially in individuals with higher lesions loads.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-022-01198-4.

Simultaneous assessment of regional distributions of atrophy across the neuraxis in MS patients

BACKGROUND

The ability to assess brain and cord atrophy simultaneously would improve the efficiency of MRI to track disease evolution.

OBJECTIVE

To test a promising tool to simultaneously map the regional distribution of atrophy in multiple sclerosis (MS) patients across the brain and cord.

METHODS

Voxel-based morphometry combined with a statistical parametric mapping probabilistic brain-spinal cord (SPM-BSC) template was applied to standard T1-weighted magnetic resonance imaging (MRI) scans covering the brain and cervical cord from 37 MS patients and 20 healthy controls (HC). We also measured the cord area at C2-C3 with a semi-automatic segmentation method using (i) the same T1-weighted acquisitions used for the new voxel-based analysis and (ii) dedicated spinal cord phase sensitive inversion recovery (PSIR) acquisitions. Cervical cord findings derived from the three approaches were compared to each other and the goodness to fit to clinical scores was assessed by regression analyses.

RESULTS

The SPM-BSC approach revealed a severity-dependent pattern of atrophy across the cervical cord and thalamus in MS patients when compared to HCs. The magnitude of cord atrophy was confirmed by the semi-automatic extraction approach at C2-C3 using both standard brain T1-weighted and advanced cord dedicated acquisitions. Associations between atrophy of cord and thalamus with disability and cognition were demonstrated.

CONCLUSION

Atrophy in the brain and cervical cord of MS patients can be identified simultaneously and rapidly at the voxel-level. The SPM-BSC approach yields similar results as available standard processing tools with the added advantage of performing the analysis simultaneously and faster.

Diffusion Tensor Imaging Revealed Microstructural Changes in Normal-Appearing White Matter Regions in Relapsing–Remitting Multiple Sclerosis

Background

Axons and myelin sheaths are the physical foundation for white matter (WM) to perform normal functions. Our previous study found the metabolite abnormalities in frontal, parietal, and occipital normal-appearing white matter (NAWM) regions in relapsing–remitting multiple sclerosis (RRMS) patients by applying a 2D 1H magnetic resonance spectroscopic imaging method. Since the metabolite changes may associate with the microstructure changes, we used the diffusion tensor imaging (DTI) method to assess the integrity of NAWM in this study.

Method

Diffusion tensor imaging scan was performed on 17 clinically definite RRMS patients and 21 age-matched healthy controls on a 3.0-T scanner. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were extracted from 19 predefined regions of interest (ROIs), which were generated by removing a mask of manually drawn probabilistic lesion map from the Johns Hopkins University white-matter atlas. The mean values of FA, MD, AD, and RD were compared between different groups in the same ROIs.

Results

A probabilistic lesion map was successfully generated, and the lesion regions were eliminated from the WM atlas. We found that the RRMS patients had significantly lower FA in the entire corpus callosum (CC), bilateral of anterior corona radiata, and right posterior thalamic radiation (PTR). At the same time, RRMS patients showed significantly higher MD in the bilateral anterior corona radiata and superior corona radiata. Moreover, all AD values increased, and the bilateral external capsule, PTR, and left tapetum NAWM show statistical significance. What is more, all NAWM tracts showed increasing RD values in RRMS patients, and the bilateral superior corona radiata, the anterior corona radiata, right PTR, and the genu CC reach statistical significance.

Conclusion

Our study revealed widespread microstructure changes in NAWM in RRMS patients through a ready-made WM atlas and probabilistic lesion map. These findings support the hypothesis of demyelination, accumulation of inflammatory cells, and axonal injury in NAWM for RRMS. The DTI-based metrics could be considered as potential non-invasive biomarkers of disease severity.

A systematic analysis of diagnostic performance for Alzheimer's disease using structural MRI

Background

Alzheimer's disease (AD) is one of the most common neurodegenerative disorders in the elderly. Although numerous structural magnetic resonance imaging (sMRI) studies have reported diagnostic models that could distinguish AD from normal controls (NCs) with 80-95% accuracy, limited efforts have been made regarding the clinically practical computer-aided diagnosis (CAD) system for AD.

Objective

To explore the potential factors that hinder the clinical translation of the AD-related diagnostic models based on sMRI.

Methods

To systematically review the diagnostic models for AD based on sMRI, we identified relevant studies published in the past 15 years on PubMed, Web of Science, Scopus, and Ovid. To evaluate the heterogeneity and publication bias among those studies, we performed subgroup analysis, meta-regression, Begg's test, and Egger's test.

Results

According to our screening criterion, 101 studies were included. Our results demonstrated that high diagnostic accuracy for distinguishing AD from NC was obtained in recently published studies, accompanied by significant heterogeneity. Meta-analysis showed that many factors contributed to the heterogeneity of high diagnostic accuracy of AD using sMRI, which included but was not limited to the following aspects: (i) different datasets; (ii) different machine learning models, e.g. traditional machine learning or deep learning model; (iii) different cross-validation methods, e.g. k-fold cross-validation leads to higher accuracies than leave-one-out cross-validation, but both overestimate the accuracy when compared to validation in independent samples; (iv) different sample sizes; and (v) the publication times. We speculate that these complicated variables might be the adverse factor for developing a clinically applicable system for the early diagnosis of AD.

Conclusions

Our findings proved that previous studies reported promising results for classifying AD from NC with different models using sMRI. However, considering the many factors hindering clinical radiology practice, there would still be a long way to go to improve.

Improving segmentation reliability of multi-scanner brain images using a generative adversarial network

BACKGROUND

Magnetic resonance (MR) images generated by different scanners generally have inconsistent contrast properties, making it difficult to perform a combined quantitative analysis of images from a range of scanners. In this study, we aimed to develop an automatic brain image segmentation model to provide a more reliable analysis of MR images taken with different scanners.

METHODS

The spatially localized atlas network tiles-27 (SLANT-27) deep learning model was used to train the automatic segmentation module, based on a multi-center dataset of 1,917 three-dimensional (3D) T1-weighted MR images. Subsequently, a framework called Qbrain, consisting of a new generative adversarial network (GAN) image transfer module and the SLANT-27 segmentation module, was developed. Another 3D T1-weighted MRI interscan dataset of 48 participants who were scanned in 3 MRI scanners (1.5T Siemens Avanto, 3T Siemens Trio Tim, and 3T Philips Ingenia) on the same day was used to train and test the Qbrain model. Volumetric T1-weighted images were processed with Qbrain, SLANT-27, and FreeSurfer (FS). The automatic segmentation reliability across the scanners was assessed using test-retest variability (TRV).

RESULTS

The reproducibility of different segmentation methods across scanners showed a consistent trend in the greater reliability and robustness of QBrain compared to SLANT-27 which, in turn, showed greater reliability and robustness compared to FS. Furthermore, when the GAN image transfer module was added, the mean segmentation error of the TRV of the 3T Siemens vs. 1.5T Siemens, the 3T Philips vs. 1.5T Siemens, and the 3T Siemens vs. 3T Philips scanners was reduced by 1.57%, 2.01%, and 0.56%, respectively. In addition, the segmentation model improved intra-scanner variability (0.9-1.67%) compared with that of FS (2.47-4.32%).

CONCLUSIONS

The newly developed QBrain method combined with GAN image transfer module and a SLANT-27 segmentation module was shown to improve the reliability of whole-brain automatic structural segmentation results across multiple scanners, thus representing a suitable alternative quantitative method of comparative brain tissue analysis for individual patients.

A Comparison of Global Brain Volumetrics Obtained from CT versus MRI Using 2 Publicly Available Software Packages

BACKGROUND AND PURPOSE

Brain volumetrics have historically been obtained from MR imaging data. However, advances in CT, along with refined publicly available software packages, may support tissue-level segmentations of clinical CT images. Here, brain volumetrics obtained by applying two publicly available software packages to paired CT-MR data are compared.

MATERIALS AND METHODS

In a group of patients (n = 69; 35 men) who underwent both MR imaging and CT brain scans within 12 months of one another, brain tissue was segmented into WM, GM, and CSF compartments using 2 publicly available software packages: Statistical Parametric Mapping and FMRIB Software Library. A subset of patients with repeat imaging sessions was used to assess the repeatability of each segmentation. Regression analysis and Bland-Altman limits of agreement were used to determine the level of agreement between segmented volumes.

RESULTS

Regression analysis showed good agreement between volumes derived from MR images versus those from CT. The correlation coefficients between the 2 methods were 0.93 and 0.98 for Statistical Parametric Mapping and FMRIB Software Library, respectively. Differences between global volumes were significant (P < .05) for all volumes compared within a given segmentation pipeline. WM bias was 36% (SD, 38%) and 18% (SD, 18%) for Statistical Parametric Mapping and FMRIB Software Library, respectively, and 10% (SD, 30%) and 6% (SD, 20%) for GM (bias ± limits of agreement), with CT overestimating WM and underestimating GM compared with MR imaging. Repeatability was good for all segmentations, with coefficients of variation of <10% for all volumes.

CONCLUSIONS

The repeatability of CT segmentations using publicly available software is good, with good correlation with MR imaging. With careful study design and acknowledgment of measurement biases, CT may be a viable alternative to MR imaging in certain settings.

Alterations in the structural covariance network of the hypothalamus in patients with narcolepsy

PURPOSE

The hypothalamus plays a pivotal role in the pathogenesis of narcolepsy. This study aimed to evaluate the differences in the structural covariance network of thehypothalamus based on volume differences between patients with narcolepsy and healthy controls.

METHODS

We retrospectively enrolled 15 patients with narcolepsy and 19 healthy controls.All subjects underwent three-dimensional T1-weighted imaging using a 3-T magnetic resonance imaging scanner. Hypothalamic subunits were segmented, and the volumes of individual hypothalamic subunits were obtained using the FreeSurfer program. Subsequently, we conducted a structural covariance network analysis of the subunit volumes with graph theory using the BRAPH program in patients with narcolepsy and in healthy controls.

RESULTS

There were no significant differences in the volumes of the entire right and left hypothalamus nor in the hypothalamic subunit between patients with narcolepsy and healthy controls. However, we found significant differences in the structural covariance network in the hypothalamus between these groups. The characteristic path length was significantly lower in patients with narcolepsy than in healthy controls (1.698 vs. 2.831, p = 0.001). However, other network measures did not differ between patients with narcolepsy and healthy controls.

CONCLUSION

We found that the structural covariance network of the hypothalamus, as assessed from the subunit volumes of hypothalamic regions using a graph theoretical analysis, is different in patients with narcolepsy compared to healthy controls. These findings may contribute to the understanding of the pathogenesis of narcolepsy.

The effect of gadolinium-based contrast-agents on automated brain atrophy measurements by FreeSurfer in patients with multiple sclerosis

OBJECTIVE

To determine whether reliable brain atrophy measures can be obtained from post-contrast 3D T1-weighted images in patients with multiple sclerosis (MS) using FreeSurfer.

METHODS

Twenty-two patients with MS were included, in which 3D T1-weighted MR images were obtained during the same scanner visit, with the same acquisition protocol, before and after administration of gadolinium-based contrast agents (GBCAs). Two FreeSurfer versions (v.6.0.1 and v.7.1.1.) were applied to calculate grey matter (GM) and white matter (WM) volumes and global and regional cortical thickness. The consistency between measures obtained in pre- and post-contrast images was assessed by intra-class correlation coefficient (ICC), the difference was investigated by paired t-tests, and the mean percentage increase or decrease was calculated for total WM and GM matter volume, total deep GM and thalamus volume, and mean cortical thickness.

RESULTS

Good to excellent reliability was found between all investigated measures, with ICC ranging from 0.926 to 0.996, all p values < 0.001. GM volumes and cortical thickness measurements were significantly higher in post-contrast images by 3.1 to 17.4%, while total WM volume decreased significantly by 1.7% (all p values < 0.001).

CONCLUSION

The consistency between values obtained from pre- and post-contrast images was excellent, suggesting it may be possible to extract reliable brain atrophy measurements from T1-weighted images acquired after administration of GBCAs, using FreeSurfer. However, absolute values were systematically different between pre- and post-contrast images, meaning that such images should not be compared directly. Potential systematic effects, possibly dependent on GBCA dose or the delay time after contrast injection, should be investigated.

TRIAL REGISTRATION

Clinical trials.gov. identifier: NCT00360906.

KEY POINTS

• The influence of gadolinium-based contrast agents (GBCAs) on atrophy measurements is still largely unknown and challenges the use of a considerable source of historical and prospective real-world data. • In 22 patients with multiple sclerosis, the consistency between brain atrophy measurements obtained from pre- and post-contrast images was excellent, suggesting it may be possible to extract reliable atrophy measurements in T1-weighted images acquired after administration of GBCAs, using FreeSurfer. • Absolute values were systematically different between pre- and post-contrast images, meaning that such images should not be compared directly, and measurements extracted from certain regions (e.g., the temporal pole) should be interpreted with caution.

Chronic cluster headache: A study of the telencephalic and cerebellar cortical thickness

PURPOSE

Previous studies on brain morphological alterations in chronic cluster headache revealed inconsistent findings.

METHOD

The present cross-sectional explorative study determined telencephalic and cerebellar cortex thickness alterations in a relatively wide sample of chronic cluster headache patients (n = 28) comparing them to matched healthy individuals.

RESULTS

The combination of two highly robust state-of-the-art approaches for thickness estimation (Freesurfer, CERES), strengthened by functional characterization of the identified abnormal regions, revealed four main results: chronic cluster headache patients show 1) cortical thinning in the right middle cingulate cortex, left posterior insula, and anterior cerebellar lobe, regions involved in nociception's sensory and sensory-motor aspects and possibly in autonomic functions; 2) cortical thinning in the left anterior superior temporal sulcus and the left collateral/lingual sulcus, suggesting neuroplastic maladaptation in areas possibly involved in social cognition, which may promote psychiatric comorbidity; 3) abnormal functional connectivity among some of these identified telencephalic areas; 4) the identified telencephalic areas of cortical thinning present robust interaction, as indicated by the functional connectivity results, with the left posterior insula possibly playing a pivotal role.

CONCLUSION

The reported results constitute a coherent and robust picture of the chronic cluster headache brain. Our study paves the way for hypothesis-driven studies that might impact our understanding of the pathophysiology of this condition.

Brain Volumetric Measurements in Children With Attention Deficit Hyperactivity Disorder: A Comparative Study Between Synthetic and Conventional Magnetic Resonance Imaging

Objective: To investigate the profiles of brain volumetric measurements in children with attention deficit hyperactivity disorder (ADHD), and the consistency of these brain volumetric measurements derived from the synthetic and conventional T1 weighted MRI (SyMRI and cT1w MRI).

Methods: Brain SyMRI and cT1w images were prospectively collected for 38 pediatric patients with ADHD and 38 healthy children (HC) with an age range of 6–14 years. The gray matter volume (GMV), white matter volume (WMV), cerebrospinal fluid (CSF), non-WM/GM/CSF (NoN), myelin, myelin fraction (MYF), brain parenchyma volume (BPV), and intracranial volume (ICV) were automatically estimated from SyMRI data, and the four matching measurements (GMV, WMV, BPV, ICV) were extracted from cT1w images. The group differences of brain volumetric measurements were performed, respectively, using analysis of covariance. Pearson correlation analysis and interclass correlation coefficient (ICC) were applied to evaluate the association between synthetic and cT1w MRI-derived measurements.

Results: As for the brain volumetric measurements extracted from SyMRI, significantly decreased GMV, WMV, BPV, and increased NON volume (p < 0.05) were found in the ADHD group compared with HC; No group differences were found in ICV, CSF, myelin volume and MYF (p > 0.05). With regard to GMV, WMV, BPV, and ICV estimated from cT1w images, the group differences between ADHD and HC were consistent with the results estimated from SyMRI. And these four measurements showed noticeable correlation between the two approaches (r = 0.692, 0.643, 0.898, 0.789, respectively, p < 0.001; ICC values are 0.809, 0.782, 0.946, 0.873, respectively).

Conclusion: Our study demonstrated a global brain development disability, but normal whole-brain myelination in children with ADHD. Moreover, our results demonstrated the high consistency of brain volumetric indices between synthetic and cT1w MRI in children, which indicates the high reliability of SyMRI in the child-brain volumetric analysis.

Linking immune-mediated damage to neurodegeneration in multiple sclerosis: could network-based MRI help?

Inflammatory demyelination characterizes the initial stages of multiple sclerosis, while progressive axonal and neuronal loss are coexisting and significantly contribute to the long-term physical and cognitive impairment. There is an unmet need for a conceptual shift from a dualistic view of multiple sclerosis pathology, involving either inflammatory demyelination or neurodegeneration, to integrative dynamic models of brain reorganization, where, glia-neuron interactions, synaptic alterations and grey matter pathology are longitudinally envisaged at the whole-brain level. Functional and structural MRI can delineate network hallmarks for relapses, remissions or disease progression, which can be linked to the pathophysiology behind inflammatory attacks, repair and neurodegeneration. Here, we aim to unify recent findings of grey matter circuits dynamics in multiple sclerosis within the framework of molecular and pathophysiological hallmarks combined with disease-related network reorganization, while highlighting advances from animal models (in vivo and ex vivo) and human clinical data (imaging and histological). We propose that MRI-based brain networks characterization is essential for better delineating ongoing pathology and elaboration of particular mechanisms that may serve for accurate modelling and prediction of disease courses throughout disease stages.

Transcriptomic Signatures Associated With Regional Cortical Thickness Changes in Parkinson's Disease

Cortical atrophy is a common manifestation in Parkinson's disease (PD), particularly in advanced stages of the disease. To elucidate the molecular underpinnings of cortical thickness changes in PD, we performed an integrated analysis of brain-wide healthy transcriptomic data from the Allen Human Brain Atlas and patterns of cortical thickness based on T1-weighted anatomical MRI data of 149 PD patients and 369 controls. For this purpose, we used partial least squares regression to identify gene expression patterns correlated with cortical thickness changes. In addition, we identified gene expression patterns underlying the relationship between cortical thickness and clinical domains of PD. Our results show that genes whose expression in the healthy brain is associated with cortical thickness changes in PD are enriched in biological pathways related to sumoylation, regulation of mitotic cell cycle, mitochondrial translation, DNA damage responses, and ER-Golgi traffic. The associated pathways were highly related to each other and all belong to cellular maintenance mechanisms. The expression of genes within most pathways was negatively correlated with cortical thickness changes, showing higher expression in regions associated with decreased cortical thickness (atrophy). On the other hand, sumoylation pathways were positively correlated with cortical thickness changes, showing higher expression in regions with increased cortical thickness (hypertrophy). Our findings suggest that alterations in the balanced interplay of these mechanisms play a role in changes of cortical thickness in PD and possibly influence motor and cognitive functions.

Structural assessment of thalamus morphology in brain disorders: A review and recommendation of thalamic nucleus segmentation and shape analysis

The thalamus is a central brain structure crucially involved in cognitive, emotional, sensory, and motor functions and is often reported to be involved in the pathophysiology of neurological and psychiatric disorders. The functional subdivision of the thalamus warrants morphological investigation on the level of individual subnuclei. In addition to volumetric measures, the investigation of other morphological features may give additional insights into thalamic morphology. For instance, shape features offer a higher spatial resolution by revealing small, regional differences that are left undetected in volumetric analyses. In this review, we discuss the benefits and limitations of recent advances in neuroimaging techniques to investigate thalamic morphology in vivo, leading to our proposed methodology. This methodology consists of available pipelines for volume and shape analysis, focussing on the morphological features of volume, thickness, and surface area. We demonstrate this combined approach in a Parkinson's disease cohort to illustrate their complementarity. Considering our findings, we recommend a combined methodology as it allows for more sensitive investigation of thalamic morphology in clinical populations.