Early and Degressive Putamen Atrophy in Multiple Sclerosis

Concurrent inflammation-related brain reorganization in multiple sclerosis and depression

BACKGROUND

Neuroinflammation affects brain tissue integrity in multiple sclerosis (MS) and may have a role in major depressive disorder (MDD). Whether advanced magnetic resonance imaging characteristics of the gray-to-white matter border serve as proxy of neuroinflammatory activity in MDD and MS remain unknown.

METHODS

We included 684 participants (132 MDD patients with recurrent depressive episodes (RDE), 70 MDD patients with a single depressive episode (SDE), 222 MS patients without depressive symptoms (nMS), 58 MS patients with depressive symptoms (dMS), and 202 healthy controls (HC)). 3 T-T1w MRI-derived gray-to-white matter contrast (GWc) was used to reconstruct and characterize connectivity alterations of GWc-covariance networks by means of modularity, clustering coefficient, and degree. A cross-validated support vector machine was used to test the ability of GWc to stratify groups according to their depression symptoms, measured with BDI, at the single-subject level in MS and MDD independently.

FINDINGS

MS and MDD patients showed increased modularity (ANOVA partial-η2 = 0.3) and clustering (partial-η2 = 0.1) compared to HC. In the subgroups, a linear trend analysis attested a gradient of modularity increases in the form: HC, dMS, nMS, SDE, and RDE (ANOVA partial-η2 = 0.28, p < 0.001) while this trend was less evident for clustering coefficient. Reduced morphological integrity (GWc) was seen in patients with increased depressive symptoms (partial-η2 = 0.42, P < 0.001) and was associated with depression scores across patient groups (r = -0.2, P < 0.001). Depressive symptoms in MS were robustly classified (88 %).

CONCLUSIONS

Similar structural network alterations in MDD and MS exist, suggesting possible common inflammatory events like demyelination, neuroinflammation that are caught by GWc analyses. These alterations may vary depending on the severity of symptoms and in the case of MS may elucidate the occurrence of comorbid depression.

Multi-organ imaging-derived polygenic indexes for brain and body health

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Impaired lung function in multiple sclerosis: a single-center observational study in 371 persons

INTRODUCTION

Abnormal lung function in people with multiple sclerosis (PwMS) could be considered as the result of muscle weakness or MS-specific structural central nervous system (CNS) abnormalities as a precipitant factor for the worsening of motor impairment or cognitive symptoms.

METHODS

This is a cross-sectional observational study in PwMS. Forced spirometry was conducted, and normative metrics of forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), and the relation FEV1/FVC were calculated. Qualitative and quantitative brain magnetic resonance imaging (MRI) examinations were carried out.

RESULTS

A total of 371 PwMS were included in the study. Of those, 196 (53%) had RRMS, 92 (25%) SPMS, and 83 (22%) PPMS. Low FVC and FEV1 was present in 16 (8%), 16 (19%), and 23 (25%) of the patients in the RRMS, PPMS, and SPMS, respectively. PwMS with T2-FLAIR lesions involving the corpus callosum (CC) had a significantly higher frequency of abnormally low FVC and FEV1 (OR 3.62; 95% CI 1.33-9.83; p = 0.012) than patients without lesions in that region. This association remained significant in the RRMS group (OR 10.1; 95% CI 1.3-67.8; p 0.031) when the model excluded PPMS and SPMS. According to our study, for every increase of 1 z score of FVC, we observed an increase of 0.25 cm3 of hippocampal volume (β 0.25; 95% CI 0.03-0.47; p 0.023) and 0.43 cm3 of left hippocampus volume (β 0.43; 95% CI 0.16-0.71; p 0.002).

CONCLUSIONS

We observed an incremental prevalence of abnormally low pulmonary function tests that parallels a sequence from more early relapsing courses to long-standing progressive courses (RRMS to PPMS or SPMS).

Correlation of brain segmental volume changes with clinical parameters: a longitudinal study in multiple sclerosis patients

OBJECTIVE

 To measure the cranial volume differences from 15 different parts in the follow-up of relapsing-remitting multiple sclerosis (RRMS) patients and correlate them with clinical parameters.

METHODS

 Forty-seven patients with RRMS were included in the study. Patients were grouped into two categories; low Expanded Disability Status Scale (EDSS) (< 3; group 1), and moderate-high EDSS (≥ 3; group 2). Patients were evaluated with Beck Depression Inventory (BDI), Montreal Cognitive Assessment (MOCA), Symbol Digit Modalities Test (SDMT), Fatigue Severity Scale (FSS), and calculated Annualized Relapse Rate (ARR) scores. Magnetic resonance imaging (MRI) was performed with a 1.5T MRI device (Magnetom AERA, Siemens, Erlangen, Germany) twice in a 1-year period. Volumetric analysis was performed by a free, automated, online MRI brain volumetry software. The differences in volumetric values between the two MRI scans were calculated and correlated with the demographic and clinical parameters of the patients.

RESULTS

 The number of attacks, disease duration, BDI, and FSS scores were higher in group 2; SDMT was higher in group 1. As expected, volumetric analyses have shown volume loss in total cerebral white matter in follow-up patients (p < 0.001). In addition, putaminal volume loss was related to a higher number of attacks. Besides, a negative relation between FSS with total amygdala volumes, a link between atrophy of globus pallidus and ARR, and BDI scores was found with the aid of network analysis.

CONCLUSIONS

 Apart from a visual demonstration of volume loss, cranial MRI with volumetric analysis has a great potential for revealing covert links between segmental volume changes and clinical parameters.

White matter tracts that overlap with the thalamus and the putamen are protected against multiple sclerosis pathology

BACKGROUND

The thalamus and the putamen are highly connected hubs implicated in multiple sclerosis (MS) pathology. It remains unclear if white matter (WM) tracts, which pass through them, have a different susceptibility to MS pathology, and if so, if their impact on disability predominates over that exerted by disease in other WM tracts. We hypothesized that WM tracts connected to and passing through these hubs (subsequently termed hub+ tracts) would be more susceptible to MS-related pathology than tracts that do not pass through them (hub- tracts) due to retrograde and anterograde distant degeneration. Thus, we compared the lesion load and neurite orientation dispersion and density imaging (NODDI) derived metrics between hub+ and hub- tracts and assessed the relationship between these MRI metrics and those of physical impairment.

METHODS

Eighteen patients (mean age of 45.5 years, 12 females) had 3 Tesla MRI consisting of T1-weighted and T2-weighted Fluid Attenuated Inversion Recovery (FLAIR), and NODDI from which the orientation dispersion index (ODI), neurite density index (NDI), and isotropic volume fraction (IVF) were derived. Forty-nine WM tracts, i.e., 12 hub+ and 37 hub- tracts, were segmented out. Exploratory analyses of the differences in lesion burden, whole tract and normal appearing WM (NAWM) NODDI metrics were carried out between the two types of tracts using a Mann-Whitney U test. Correlations with physical impairment, quantified using the expanded disability status scale (EDSS) and timed 25-foot walk (T25FW) test were assessed using Spearman correlation analyses.

RESULTS

Hub- tracts had larger T1- (p<0.001) and T2-lesion (p<0.001) volumes; lower ODI (p<0.001), NDI (p<0.001) and higher IVF (p = 0.020) in comparison to hub+ tracts. Measures of tissue injury in hub+ tracts correlated with those of clinical disability, though less strongly than in hub- tracts.

CONCLUSIONS

Contrary to our hypothesis, our exploratory pilot study results suggest that WM tracts that overlap with the thalamus and the putamen have a lower degree of lesional and non-lesional tissue injury, suggesting a protective role of the hubs against MS pathology or a higher degree of vulnerability of those not passing through hub stations. We also show a weaker association between disability impairment and hub+ pathology, compared to that in hub- tracts. Our findings point to a potential role of disease location in relation to hubs as guidance for treatment personalization in MS.

Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis

Background

Brain volume loss (BVL) is widespread in MS and occurs throughout the disease course at a rate considerably greater than in the general population. In MS, brain volume correlates with and predicts future disability, making BVL a relevant measure of diffuse CNS damage leading to clinical disease progression, as well as serving as a useful outcome in evaluating MS therapies. The aim of our study was to evaluate the role of automated segmentation and quantification of deep grey matter structures and white matter lesions in Relapsing Remitting Multiple Sclerosis patients using MR images and to correlate the volumetric results with different degrees of disability based on expanded disability status scale (EDSS) scores.

Results

All the patients in our study showed relative atrophy of the thalamus and the putamen bilaterally when compared with the normal control group. Statistical analysis was significant for the thalamus and the putamen atrophy (P value < 0.05). On the other hand, statistical analysis was not significant for the caudate and the hippocampus (P value > 0.05); there was a significant positive correlation between the white matter lesions volume and EDSS scores (correlation coefficient of 0.7505). On the other hand, there was a significant negative correlation between the thalamus and putamen volumes, and EDSS scores (correlation coefficients < − 0.9), while the volumes of the caudate and the hippocampus had a very weak and non-significant correlation with the EDSS scores (correlation coefficients > − 0.35).

Conclusions

The automated segmentation and quantification tools have a great role in the assessment of brain structural changes in RRMS patients, and that it became essential to integrate these tools in the daily medical practice for the great value they add to the current evaluation measures.

Statistical shape analysis of putamen in early-onset Parkinson's disease

OBJECTIVE

To investigate the shape differences in the putamen of early-onset Parkinson's patients compared with healthy controls and to assess and to assess sub-regional brain abnormalities.

METHODS

This study was conducted using the 3-T MRI scans of 23 early-onset Parkinson's patients and age and gender matched control subjects. Landmark coordinate data obtained and Procrustes analysis was used to compare mean shapes. The relationships between the centroid sizes of the left and right putamen, and the durations of disease examined using growth curve models.

RESULTS

While there was a significant difference between the right putamen shape of control and patient groups, there was not found a significant difference in terms of left putamen. Sub-regional analyses showed that for the right putamen, the most prominent deformations were localized in the middle-posterior putamen and minimal deformations were seen in the anterior putamen.

CONCLUSION

Although they were not as pronounced as those in the right putamen, the deformations in the left putamen mimic the deformations in the right putamen which are found mainly in the middle-posterior putamen and at a lesser extend in the anterior putamen.

Neurostructural and Neurophysiological Correlates of Multiple Sclerosis Physical Fatigue: Systematic Review and Meta-Analysis of Cross-Sectional Studies

Fatigue is one of the most debilitating symptoms for people with multiple sclerosis (PwMS). By consolidating a diverse and conflicting evidence-base, this systematic review and meta-analysis aimed to gain new insights into the neurobiology of MS fatigue. MEDLINE, ProQuest, CINAHL, Web of Science databases and grey literature were searched using Medical Subject Headings. Eligible studies compared neuroimaging and neurophysiological data between people experiencing high (MS-HF) versus low (MS-LF) levels of perceived MS fatigue, as defined by validated fatigue questionnaire cut-points. Data were available from 66 studies, with 46 used for meta-analyses. Neuroimaging studies revealed lower volumetric measures in MS-HF versus MS-LF for whole brain (-22.74 ml; 95% CI: -37.72 to -7.76 ml; p = 0.003), grey matter (-18.81 ml; 95% CI: -29.60 to -8.03 ml; p < 0.001), putamen (-0.40 ml; 95% CI: -0.69 to -0.10 ml; p = 0.008) and acumbens (-0.09 ml; 95% CI: -0.15 to -0.03 ml; p = 0.003) and a higher volume of T1-weighted hypointense lesions (1.10 ml; 95% CI: 0.47 to 1.73 ml; p < 0.001). Neurophysiological data showed reduced lower-limb maximum voluntary force production (-19.23 N; 95% CI: -35.93 to -2.53 N; p = 0.02) and an attenuation of upper-limb (-5.77%; 95% CI:-8.61 to -2.93%; p < 0.0001) and lower-limb (-2.16%; 95% CI:-4.24 to -0.07%; p = 0.04) skeletal muscle voluntary activation, accompanied by more pronounced upper-limb fatigability (-5.61%; 95% CI: -9.57 to -1.65%; p = 0.006) in MS-HF versus MS-LF. Results suggest that MS fatigue is characterised by greater cortico-subcortical grey matter atrophy and neural lesions, accompanied by neurophysiological decrements, which include reduced strength and voluntary activation. Prospero registration Prospero registration number: CRD42016017934.

Early prediction of putamen imaging features in HIV-associated neurocognitive impairment syndrome

Background

To explore the correlation between the volume of putamen and brain cognitive impairment in patients with HIV and to predict the feasibility of early-stage HIV brain cognitive impairment through radiomics.

Method

Retrospective selection of 90 patients with HIV infection, including 36 asymptomatic neurocognitive impairment (ANI) patients and 54 pre-clinical ANI patients in Beijing YouAn Hospital. All patients received comprehensive neuropsychological assessment and MRI scanning. 3D Slicer software was used to acquire volume of interest (VOI) and radiomics features. Clinical variables and volume of putamen were compared between patients with ANI and pre-clinical ANI. The Kruskal Wallis test was used to analysis multiple comparisons between groups. The relationship between cognitive scores and VOI was compared using linear regression. For radiomics, principal component analysis (PCA) was used to reduce model overfitting and calculations and then a support vector machine (SVM) was used to build a binary classification model. For model performance evaluation, we used an accuracy, sensitivity, specificity and receiver operating characteristic curve (ROC).

Result

There were no significant differences in clinical variables between ANI group and pre-clinical-ANI group (P>0.05). The volume of bilateral putamen was significantly different between AHI group and pre-clinical group (P<0.05), but there was only a trend in the left putamen between ANI-treatment group and pre-clinical treatment group(P = 0.063). Reduced cognitive scores in Verbal Fluency, Attention/Working Memory, Executive Functioning, memory and Speed of Information Processing were negatively correlated with the increased VOI (P<0.05), but the correlation was relatively low. In diagnosing the ANI from pre-clinical ANI, the mean area under the ROC curves (AUC) were 0.85 ± 0.22, the mean sensitivity and specificity were 63.12 ± 5.51 and 94.25% ± 3.08%.

Conclusion

The volumes of putamen in patients with ANI may be larger than patients with pre-clinical ANI, the change of the volume of the putamen may have a certain process; there is a relationship between putamen and cognitive impairment, but the exact mechanism is unclear. Radiomics may be a useful tool for predicting early stage HAND in patients with HIV.

Detection of Cortical and Deep Gray Matter Lesions in Multiple Sclerosis Using DIR and FLAIR at 3T

BACKGROUND AND PURPOSE

The comparative detection rates of deep gray matter (GM) multiple sclerosis (MS) lesions using double inversion recovery (DIR) and fluid-attenuated inversion-recovery (FLAIR) on 3T MR imaging remain unknown. We aimed to assess the detectability of cortical and deep GM MS lesions using DIR and FLAIR on 3T clinical exams and evaluate the relationship between deep GM lesions and brain atrophy.

METHODS

One hundred fifty consecutive MS patients underwent routine brain MRI that included 3D DIR and 2D T2 FLAIR on the same 3T scanner. Three neuroradiologists independently reviewed all exams for cortical and deep GM lesions. Statistical parametric mapping (SPM) and FMRIB software library (FSL)-FIRST pipelines were used to determine normalized whole brain and deep GM volumes.

RESULTS

A total of 65 cortical and 98 deep lesions were detected on DIR versus 24 and 20, respectively, on FLAIR. Among all 150 patients, the number and percentage of patients with GM lesions on DIR and FLAIR were as follows: cortical 43 (28.7%) versus 24 (16.0%) (P < .001), thalamus 47 (31.3%) versus 20 (13.3%) (P < .001), putamen 10 (6.7%) versus 3 (2.0%) (P = .02), globus pallidus 9 (6.0%) versus 3 (1.3%) (P = .02), and caudate 5 (3.3%) versus 1 (0.7%) (P = .125). Presence of deep GM lesions weakly correlated with deep GM volume fractions.

CONCLUSION

Deep GM MS lesions can be detected using routine clinical brain MRI including DIR and FLAIR at 3T. Future studies to optimize these sequences may improve the detection rates of cortical and deep GM lesions. The presence of GM lesions showed weak correlation with GM atrophy.

Localised Grey Matter Atrophy in Multiple Sclerosis and Clinically Isolated Syndrome-A Coordinate-Based Meta-Analysis, Meta-Analysis of Networks, and Meta-Regression of Voxel-Based Morphometry Studies

Background: Atrophy of grey matter (GM) is observed in the earliest stages of multiple sclerosis (MS) and is associated with cognitive decline and physical disability. Localised GM atrophy in MS can be explored and better understood using magnetic resonance imaging and voxel-based morphometry (VBM). However, results are difficult to interpret due to methodological differences between studies. Methods: Coordinate-based analysis is a way to find the reliably observable results across multiple independent VBM studies. This work uses coordinate-based meta-analysis, meta-analysis of networks, and meta-regression to summarise the evidence from voxel-based morphometry of regional GM hanges in patients with MS and clinically isolated syndrome (CIS), and whether these measured changes are relatable to clinical features. Results: Thirty-four published articles reporting forty-four independent experiments using VBM for the assessment of GM atrophy between MS or CIS patients and healthy controls were identified. Analysis identified eight clusters of consistent cross-study reporting of localised GM atrophy involving both cortical and subcortical regions. Meta-network analysis identified a network-like pattern indicating that GM loss occurs with some symmetry between hemispheres. Meta-regression analysis indicates a relationship between disease duration or age and the magnitude of reported statistical effect in some deep GM structures. Conclusions: These results suggest consistency in MRI-detectible regional GM loss across multiple MS studies, and the estimated effect sizes and symmetries can help design prospective studies to test specific hypotheses.

The impact of brain iron accumulation on cognition: A systematic review

Iron is involved in many processes in the brain including, myelin generation, mitochondrial function, synthesis of ATP and DNA and the cycling of neurotransmitters. Disruption of normal iron homeostasis can result in iron accumulation in the brain, which in turn can partake in interactions which amplify oxidative damage. The development of MRI techniques for quantifying brain iron has allowed for the characterisation of the impact that brain iron has on cognition and neurodegeneration. This review uses a systematic approach to collate and evaluate the current literature which explores the relationship between brain iron and cognition. The following databases were searched in keeping with a predetermined inclusion criterion: Embase Ovid, PubMed and PsychInfo (from inception to 31^st March 2020). The included studies were assessed for study characteristics and quality and their results were extracted and summarised. This review identified 41 human studies of varying design, which statistically assessed the relationship between brain iron and cognition. The most consistently reported interactions were in the Caudate nuclei, where increasing iron correlated poorer memory and general cognitive performance in adulthood. There were also consistent reports of a correlation between increased Hippocampal and Thalamic iron and poorer memory performance, as well as, between iron in the Putamen and Globus Pallidus and general cognition. We conclude that there is consistent evidence that brain iron is detrimental to cognitive health, however, more longitudinal studies will be required to fully understand this relationship and to determine whether iron occurs as a primary cause or secondary effect of cognitive decline.

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.

Disrupted principal network organisation in multiple sclerosis relates to disability

Structural network-based approaches can assess white matter connections revealing topological alterations in multiple sclerosis (MS). However, principal network (PN) organisation and its clinical relevance in MS has not been explored yet. Here, structural networks were reconstructed from diffusion data in 58 relapsing-remitting MS (RRMS), 28 primary progressive MS (PPMS), 36 secondary progressive (SPMS) and 51 healthy controls (HCs). Network hubs' strengths were compared with HCs. Then, PN analysis was performed in each clinical subtype. Regression analysis was applied to investigate the associations between nodal strength derived from the first and second PNs (PN1 and PN2) in MS, with clinical disability. Compared with HCs, MS patients had preserved hub number, but some hubs exhibited reduced strength. PN1 comprised 10 hubs in HCs, RRMS and PPMS but did not include the right thalamus in SPMS. PN2 comprised 10 hub regions with intra-hemispheric connections in HCs. In MS, this subnetwork did not include the right putamen whilst in SPMS the right thalamus was also not included. Decreased nodal strength of the right thalamus and putamen from the PNs correlated strongly with higher clinical disability. These PN analyses suggest distinct patterns of disruptions in MS subtypes which are clinically relevant.

Imaging in mice and men: Pathophysiological insights into multiple sclerosis from conventional and advanced MRI techniques

Magnetic resonance imaging (MRI) is the most important tool for diagnosing multiple sclerosis (MS). However, MRI is still unable to precisely quantify the specific pathophysiological processes that underlie imaging findings in MS. Because autopsy and biopsy samples of MS patients are rare and biased towards a chronic burnt-out end or fulminant acute early stage, the only available methods to identify human disease pathology are to apply MRI techniques in combination with subsequent histopathological examination to small animal models of MS and to transfer these insights to MS patients. This review summarizes the existing combined imaging and histopathological studies performed in MS mouse models and humans with MS (in vivo and ex vivo), to promote a better understanding of the pathophysiology that underlies conventional MRI, diffusion tensor and magnetization transfer imaging findings in MS patients. Moreover, it provides a critical view on imaging capabilities and results in MS patients and mouse models and for future studies recommends how to combine those particular MR sequences and parameters whose underlying pathophysiological basis could be partly clarified. Further combined longitudinal in vivo imaging and histopathological studies on rationally selected, appropriate mouse models are required.

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.

Putamen gray matter volumes in neuropsychiatric and neurodegenerative disorders

Putamen is enriched with dopamine and associated with dopamine-related phenotypes including many neuropsychiatric and neurodegenerative disorders that manifest with motor impairment, impulsive behavior, and cognitive deficits. The gray matter volume of the putamen is age-dependent and genetically controlled. In most neuropsychiatric and neurodegenerative disorders, including Parkinson's spectrum disorders, Huntington's disease, dementia with Lewy bodies, Alzheimer's disease, multiple sclerosis, attention deficit hyperactivity disorder, developmental dyslexia, and major depression, the putamen volume is significantly reduced. On the other hand, in individuals with bipolar disorder, schizophrenia spectrum disorders, especially neuroleptics-medicated patients with schizophrenia, autism spectrum disorders, obsessive-compulsive spectrum disorders, and cocaine/amphetamine dependence, the putamen volume is significantly enlarged. Therefore, the putamen volume may serve as a structural neural marker for many neuropsychiatric and neurodegenerative disorders and a predictor of treatment outcomes in individuals afflicted with these conditions. We provided an overview of the genetic bases of putamen volume and explored potential mechanisms whereby altered putamen volume manifests in these neuropsychiatric and neurodegenerative conditions, with a specific focus on dopaminergic processes.

Baseline biomarkers of connectome disruption and atrophy predict future processing speed in early multiple sclerosis

The development of accurate prognoses in multiple sclerosis is difficult, as the disease is characterized by heterogeneous patterns of brain abnormalities that relate in an unclear way to future impairments. Here, we use a statistical modeling approach to determine if the baseline pattern of connectome disruption due to T2-FLAIR lesions could predict a patient's future processing speed, as measured using the Symbol Digits Modality Test scores. Imaging data, demographics and Symbol Digits Modality Test scores were collected from 61 early relapsing remitting multiple sclerosis patients. The Network Modification Tool was used to estimate damage to the connectome by quantifying white matter abnormalities' effects on 1) global network properties, 2) regional connectivity and 3) connectivity between pairs of regions. MS subjects showed significant improvement of processing speed between baseline and follow-up (t = −2.6, p = 0.0096); however, both baseline (t = −4.01, p = 0.00012) and follow-up (t = −2.10, p = 0.038) processing speed were significantly lower than age-matched healthy controls. Partial Least Squares Regression was used to create models that predict future processing speed from between baseline imaging metrics and demographics. The model based on region-pair disconnection and gray matter atrophy had the lowest AIC and highest prediction accuracy (R² = 0.79) compared to models based on global (R² = 0.41) or regional (R² = 0.66) disconnection and gray matter atrophy, overlap with an ROI-based atlas and gray matter atrophy (R² = 0.73) or gray matter atrophy alone (R² = 0.71). We found that baseline measures of connectivity disruption in various parietal, temporal, occipital and subcortical regions and atrophy in the putamen were important predictors of future processing speed. We conclude that information about disruptions to pairwise brain connections is more informative of future processing speed than regional or global metrics or gray matter atrophy alone. The combination of quantitative disconnectome metrics, gray matter atrophy and statistical modeling approaches could enable clinicians in developing more accurate, individualized prognoses of future cognitive status in multiple sclerosis patients.

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Atrophy and structural disconnection estimates via NeMo Tool were collected in MS.

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Future cognitive functioning in MS patients was predicted by baseline MRI measures.

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Measures of atrophy and disconnection between region-pairs had best goodness-of-fit.

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More caudate atrophy was significantly predictive of worse future cognition.

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Disconnections in parietal/temporal/occipital areas predicted worse future cognition.

Cortex Parcellation Associated Whole White Matter Parcellation in Individual Subjects

The investigation of specific white matter areas is a growing field in neurological research and is typically achieved through the use of atlases. However, the definition of anatomically based regions remains challenging for the white matter and thus hinders region-specific analysis in individual subjects. In this article, we focus on creating a whole white matter parcellation method for individual subjects where these areas can be associated to cortex regions. This is done by combining cortex parcellation and fiber tracking data. By tracking fibers out of each cortex region and labeling the fibers according to their origin, we populate a candidate image. We then derive the white matter parcellation by classifying each white matter voxel according to the distribution of labels in the corresponding voxel from the candidate image. The parcellation of the white matter with the presented method is highly reliable and is not as dependent on registration as with white matter atlases. This method allows for the parcellation of the whole white matter into individual cortex region associated areas and, therefore, associates white matter alterations to cortex regions. In addition, we compare the results from the presented method to existing atlases. The areas generated by the presented method are not as sharply defined as the areas in most existing atlases; however, they are computed directly in the DWI space of the subject and, therefore, do not suffer from distortion caused by registration. The presented approach might be a promising tool for clinical and basic research to investigate modalities or system specific micro structural alterations of white matter areas in a quantitative manner.

Putamen volume and its clinical and neurological correlates in primary HIV infection

OBJECTIVE

Little is known about the extent of cortical and subcortical volumetric alterations that may occur within the first year of HIV infection [primary HIV infection (PHI)].

DESIGN

We used structural MRI in this prospective cross-sectional neuroimaging study to determine the extent of volumetric changes in early HIV infection.

METHODS

Cerebrospinal fluid, blood, neuropsychological testing, and structural T1 MRI scans were acquired from 18 HIV and 47 PHI age-matched antiretroviral-naïve male participants. Using FreeSurfer 5.1, volumetric measurements were obtained from the caudate, amygdala, corpus callosum, ventricles, putamen, thalamus, cortical white matter, and total gray matter. Regional volumes were compared groupwise and related to biomarkers in cerebrospinal fluid (viral load, neopterin, and neurofilament light chain), blood (viral load, CD4, and CD8 T-cell count), and neuropsychometric tests (digit-symbol, grooved pegboard, finger-tapping, and timed gait).

RESULTS

A trend-level moderate reduction of putamen volume (P = 0.076, adjusted Cohen's d = 0.5 after controlling for age) was observed for PHI compared with HIV-uninfected individuals. Within the PHI group, putamen volume associated with CD4 cell count (P = 0.03), CD4/CD8 ratio (P = 0.045), infection duration (P = 0.009), and worsening psychomotor performance on the digit-symbol (P = 0.028), finger-tapping (P = 0.039), and timed gait (P = 0.009) tests.

CONCLUSION

Our volumetric results suggest that the putamen is preferentially susceptible to early HIV-associated processes. Examining the natural course of early HIV infection longitudinally will allow for mapping of the trajectory of HIV-associated central nervous system changes, enabling creation of improved interventional strategies to potentially stabilize or reverse these observed structural changes.