Measurement of Cortical Thickness and Volume of Subcortical Structures in Multiple Sclerosis: Agreement between 2D Spin-Echo and 3D MPRAGE T1-Weighted Images

Cerebrospinal fluid neurofilament light chains and CXCL13 as predictive factors for clinical course of multiple sclerosis

AIM

The aim of this study was to identify whether NfL and CXCL13 cerebrospinal fluid (CSF) concentrations at diagnostic lumbar puncture can predict the course of multiple sclerosis (MS) in terms of relapses, higher expanded disability status scale (EDSS) and magnetic resonance imaging (MRI) activity.

METHODS

We conducted a single-centre prospective observational cohort study at the MS center, University Hospital Ostrava, Czech Republic. CSF NfL (cNfL) and CXCL13 concentrations were examined (ELISA method) in patients with clinically isolated syndrome (CIS) and relapsing-remitting MS (RRMS) at the time of diagnostic lumbar puncture.

RESULTS

A total of 44 patients with CIS or early RRMS were enrolled, 31 (70.5%) of whom were women. The median age at the time of CSF sampling was 31.21 years (IQR 25.43-39.32), and the follow-up period was 54.6 months (IQR 44.03-59.48). In the simple and multiple logistic regression models, CXCL13 levels did not predict relapses, MRI activity or EDSS > 2.5. Similarly, cNfL concentrations did not predict relapses or MRI activity in either model. In the multiple regression, higher cNfL levels were associated with reaching EDSS > 2.5 (odds ratio [OR] 1.002, 95% confidence interval [CI] 1.000 to 1.003).

CONCLUSIONS

Our data did not confirm cNfL and/or CXCL13 CSF levels were predictive factors for disease activity such as relapses and MRI activity at the time of diagnostic lumbar puncture in patients with RRMS. While cNfL CSF levels predicted higher disability only after adjustment for other known risk factors, elevated CSF CXCL13 did not predict higher disability at all.

Cognitive impairment in paediatric onset multiple sclerosis and its relation to thalamic volume and cortical thickness of temporal lobe by magnetic resonance imaging

Background

Pediatric onset multiple sclerosis (POMS), defined as an age at onset younger than 18 years, which occurs in 5% of patients with MS. cognitive dysfunction is one of the prominent disabling sequelae of Multiple sclerosis. Brain volumetric studies by magnetic resonance images revealed the decline of whole and regional brain volumes along the disease course. This work aimed to investigate the relationship between cognitive impairment in pediatric MS patients with thalamic atrophy and cortical thickness of temporal lobe. This study included 50 patients who were diagnosed as POMS and 50 healthy control participants matched for age and sex. Both groups were compared for volumetric measurements of thalamic volumes and temporal lobes cortical thickness using a computerized program called FreeSurfer.MS group was evaluated for cognitive dysfunction using Arabic version of fifth edition of Standford–Benit test. A correlation between volumetric results and neuropsychological evaluation of MS group was done.

Results

Our study showed that the MS group has the lowest value regarding their thalamic volumes and their cortical thickness of temporal lobes in relation to the healthy control group, while there was a significant relation between cognitive impairment and decrease in thalamic volume and specific areas in cortical thickness, such as superior temporal thickness, middle temporal thickness, inferior temporal thickness, fusiform thickness and para hippocampal thickness of temporal lobe in pediatric onset MS patients.

Conclusions

POMS affects specific brain areas such as thalamus and cortical thickness of temporal lobes regarding their volume and thickness which influence the neuropsychological evaluation detected by Standford–Benit test.

Morphological analysis of the brain subcortical gray structures in restless legs syndrome

BACKGROUND

Although several studies have shown the involvement of specific structures of the central nervous system, the dopaminergic system, and iron metabolism in restless legs syndrome (RLS), the exact location and extent of its anatomical substrate is not yet known. The scope of this new study was to investigate the brain subcortical gray structures, by means of structural magnetic resonance imaging (MRI) studies, in RLS patients in order to assess the presence of any volume or shape abnormalities involving these structures.

METHODS

Thirty-three normal controls (24 females and nine males) and 45 RLS patients (34 females and 11 males) were retrospectively recruited and underwent a 1.5 Tesla MRI study with two-dimensional T1 sequences in the sagittal plane. Post-processing was performed by means of the Functional Magnetic Resonance Imaging of the Brain Analysis Group Integrated Registration and Segmentation Tool (FIRST) software, and both volumetric and morphological analyses of the thalamus, caudate, putamen, globus pallidus, brainstem, hippocampus, and amygdala, bilaterally, were carried out.

RESULTS

A statistically significant volumetric reduction in the left amygdala and left globus pallidus was found in subjects with RLS, as well as large surface morphological alterations affecting the amygdala bilaterally and other less widespread surface changes in both hippocampi, the right caudate, the left globus pallidus, and the left putamen.

CONCLUSIONS

These findings seem to indicate that the basic mechanisms of RLS might include a pathway involving not only the hypothalamus-spinal dopaminergic circuit (nucleus A11), but also pathways including the basal ganglia and structures that are part of the limbic system; moreover, structural alterations in RLS seem to concern the morphology as well as the volume of the above structures. The role of basal ganglia in the complex neurophysiological and neurochemical mechanism of RLS needs to carefully reconsidered.

Automated subcortical volume estimation from 2D MRI in epilepsy and implications for clinical trials

Purpose

Most techniques used for automatic segmentation of subcortical brain regions are developed for three-dimensional (3D) MR images. MRIs obtained in non-specialist hospitals may be non-isotropic and two-dimensional (2D). Automatic segmentation of 2D images may be challenging and represents a lost opportunity to perform quantitative image analysis. We determine the performance of a modified subcortical segmentation technique applied to 2D images in patients with idiopathic generalised epilepsy (IGE).

Methods

Volume estimates were derived from 2D (0.4 × 0.4 × 3 mm) and 3D (1 × 1x1mm) T1-weighted acquisitions in 31 patients with IGE and 39 healthy controls. 2D image segmentation was performed using a modified FSL FIRST (FMRIB Integrated Registration and Segmentation Tool) pipeline requiring additional image reorientation, cropping, interpolation and brain extraction prior to conventional FIRST segmentation. Consistency between segmentations was assessed using Dice coefficients and volumes across both approaches were compared between patients and controls. The influence of slice thickness on consistency was further assessed using 2D images with slice thickness increased to 6 mm.

Results

All average Dice coefficients showed excellent agreement between 2 and 3D images across subcortical structures (0.86–0.96). Most 2D volumes were consistently slightly lower compared to 3D volumes. 2D images with increased slice thickness showed lower agreement with 3D images with lower Dice coefficients (0.55–0.83). Significant volume reduction of the left and right thalamus and putamen was observed in patients relative to controls across 2D and 3D images.

Conclusion

Automated subcortical volume estimation of 2D images with a resolution of 0.4 × 0.4x3mm using a modified FIRST pipeline is consistent with volumes derived from 3D images, although this consistency decreases with an increased slice thickness. Thalamic and putamen atrophy has previously been reported in patients with IGE. Automated subcortical volume estimation from 2D images is feasible and most reliable at using in-plane acquisitions greater than 1 mm x 1 mm and provides an opportunity to perform quantitative image analysis studies in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00234-021-02811-x.

Corpus callosum index correlates with brain volumetry and disability in multiple sclerosis patients

OBJECTIVE

To analyze the correlation between corpus callosum index (CCI), brain volumetry, and disability in multiple sclerosis (MS) patients. The brain volumetry consists of the corpus callosum, cortical gray matter, subcortical gray matter, and white matter volumes.

METHODS

This was a retrospective cross-sectional study from October 2018 to February 2019 of 30 patients with MS aged 20 to 61 years old. Brain volumetry was performed using FreeSurfer software. The CCI were measured manually using conventional best mid-sagittal T1W brain MRI. The anterior, posterior, and medium segments were measured and divided to its greatest anteroposterior diameter. Higher CCI values indicated greater corpus callosum volumes. Clinical evaluation was comprised of MS subtype, age of onset, relapse frequency and Expanded Disability Status Scale (EDSS).

RESULTS

Thirty MS patients with median of age 22 years were included. Relapsing-remitting (RRMS) subtype were 73.3%. Very significant correlations were shown between the CCI and corpus callosum volume (CCV) (r=0.79; p<0.0001) and cerebral white matter volume (r=0.81; p<0.0001). Significant correlations were shown between the CCI and cortical gray matter volume (r=0.64; p<0.0001) and subcortical gray matter volume (r=0.69; p<0.0001). The CCI was positively correlated with age of onset and inversely with EDSS. The CCV and CCI were smaller in secondary progressive MS (SPMS).

CONCLUSION

The CCI is easy and fast to obtain in conventional MRI and significantly correlated with brain volumetry, age of onset and disability in MS patients.

Early putamen hypertrophy and ongoing hippocampus atrophy predict cognitive performance in the first ten years of relapsing-remitting multiple sclerosis

BACKGROUND

The first years of relapsing-remitting multiple sclerosis (RRMS) constitute the most vulnerable phase for the progression of cognitive impairment (CImp), due to a gradual decrease of compensatory mechanisms. In the first 10 years of RRMS, the temporal volumetric changes of deep gray matter structures must be clarified, since they could constitute reliable cognitive biomarkers for diagnostic, prognostic, and therapeutic purposes.

METHODS

Forty-five cognitively asymptomatic patients with RRMS lasting ≤ 10 years, and with a brain MRI performed in a year from the neuropsychological evaluation (Te-MRI), were included. They performed the Brief International Cognitive Assessment battery for MS. Thirty-one brain MRIs performed in the year of diagnosis (Td-MRI) and 13 brain MRIs of age- and sex-matched healthy controls (HCs) were also included in the study. The relationships between clinical features, cognitive performances, and Te- and Td-MRI volumes were statistically analyzed.

RESULTS

Cognitively preserved (CP) patients had significantly increased Td-L-putamen (P = 0.035) and Td-R-putamen volume (P = 0.027) with respect to cognitively impaired (CI) ones. CI patients had significantly reduced Te-L-hippocampus (P = 0.019) and Te-R-hippocampus volume (P = 0.042) compared, respectively, with Td-L-hippocampus and Td-R-hippocampus volume. Td-L-putamen volume (P = 0.011) and Te-L-hippocampus volume (P = 0.023) were independent predictors of the Symbol Digit Modalities Test score in all patients (r2 = 0.31, F = 6.175, P = 0.001).

CONCLUSION

In the first years of RRMS, putamen hypertrophy and hippocampus atrophy could represent promising indices of cognitive performance and reserve, and become potentially useful tools for diagnostic, prognostic, and therapeutic purposes.

Quantifying deep grey matter atrophy using automated segmentation approaches: A systematic review of structural MRI studies

The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a "silver standard" annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the "improved" segmentation from T1-sequences alone.

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

BACKGROUND AND PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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