Three decades of advancements in osteoarthritis research: insights from transcriptomic, proteomic, and metabolomic studies.

OBJECTIVE: Osteoarthritis (OA) is a complex disease involving contributions from both local joint tissues and systemic sources. Patient characteristics, encompassing sociodemographic and clinical variables, are intricately linked with OA rendering its understanding challenging. Technological advancements have allowed for a comprehensive analysis of transcripts, proteomes and metabolomes in OA tissues/fluids through omic analyses. The objective of this review is to highlight the advancements achieved by omic studies in enhancing our understanding of OA pathogenesis over the last three decades. DESIGN: We conducted an extensive literature search focusing on transcriptomics, proteomics and metabolomics within the context of OA. Specifically, we explore how these technologies have identified individual transcripts, proteins, and metabolites, as well as distinctive endotype signatures from various body tissues or fluids of OA patients, including insights at the single-cell level, to advance our understanding of this highly complex disease. RESULTS: Omic studies reveal the description of numerous individual molecules and molecular patterns within OA-associated tissues and fluids. This includes the identification of specific cell (sub)types and associated pathways that contribute to disease mechanisms. However, there remains a necessity to further advance these technologies to delineate the spatial organization of cellular subtypes and molecular patterns within OA-afflicted tissues. CONCLUSIONS: Leveraging a multi-omics approach that integrates datasets from diverse molecular detection technologies, combined with patients' clinical and sociodemographic features, and molecular and regulatory networks, holds promise for identifying unique patient endophenotypes. This holistic approach can illuminate the heterogeneity among OA patients and, in turn, facilitate the development of tailored therapeutic interventions.

Evolution and advancements in genomics and epigenomics in OA research: How far we have come.

OBJECTIVE: Osteoarthritis (OA) is the most prevalent musculoskeletal disease affecting articulating joint tissues, resulting in local and systemic changes that contribute to increased pain and reduced function. Diverse technological advancements have culminated in the advent of high throughput "omic" technologies, enabling identification of comprehensive changes in molecular mediators associated with the disease. Amongst these technologies, genomics and epigenomics - including methylomics and miRNomics, have emerged as important tools to aid our biological understanding of disease. DESIGN: In this narrative review, we selected articles discussing advancements and applications of these technologies to OA biology and pathology. We discuss how genomics, deoxyribonucleic acid (DNA) methylomics, and miRNomics have uncovered disease-related molecular markers in the local and systemic tissues or fluids of OA patients. RESULTS: Genomics investigations into the genetic links of OA, including using genome-wide association studies, have evolved to identify 100+ genetic susceptibility markers of OA. Epigenomic investigations of gene methylation status have identified the importance of methylation to OA-related catabolic gene expression. Furthermore, miRNomic studies have identified key microRNA signatures in various tissues and fluids related to OA disease. CONCLUSIONS: Sharing of standardized, well-annotated omic datasets in curated repositories will be key to enhancing statistical power to detect smaller and targetable changes in the biological signatures underlying OA pathogenesis. Additionally, continued technological developments and analysis methods, including using computational molecular and regulatory networks, are likely to facilitate improved detection of disease-relevant targets, in-turn, supporting precision medicine approaches and new treatment strategies for OA.

Nile Red fluorescence spectroscopy reports early physicochemical changes in myelin with high sensitivity.

The molecular composition of myelin membranes determines their structure and function. Even minute changes to the biochemical balance can have profound consequences for axonal conduction and the synchronicity of neural networks. Hypothesizing that the earliest indication of myelin injury involves changes in the composition and/or polarity of its constituent lipids, we developed a sensitive spectroscopic technique for defining the chemical polarity of myelin lipids in fixed frozen tissue sections from rodent and human. The method uses a simple staining procedure involving the lipophilic dye Nile Red, whose fluorescence spectrum varies according to the chemical polarity of the microenvironment into which the dye embeds. Nile Red spectroscopy identified histologically intact yet biochemically altered myelin in prelesioned tissues, including mouse white matter following subdemyelinating cuprizone intoxication, as well as normal-appearing white matter in multiple sclerosis brain. Nile Red spectroscopy offers a relatively simple yet highly sensitive technique for detecting subtle myelin changes.

Multicenter Evaluation of AI-generated DIR and PSIR for Cortical and Juxtacortical Multiple Sclerosis Lesion Detection.

Background Cortical multiple sclerosis lesions are clinically relevant but inconspicuous at conventional clinical MRI. Double inversion recovery (DIR) and phase-sensitive inversion recovery (PSIR) are more sensitive but often unavailable. In the past 2 years, artificial intelligence (AI) was used to generate DIR and PSIR from standard clinical sequences (eg, T1-weighted, T2-weighted, and fluid-attenuated inversion-recovery sequences), but multicenter validation is crucial for further implementation. Purpose To evaluate cortical and juxtacortical multiple sclerosis lesion detection for diagnostic and disease monitoring purposes on AI-generated DIR and PSIR images compared with MRI-acquired DIR and PSIR images in a multicenter setting. Materials and Methods Generative adversarial networks were used to generate AI-based DIR (n = 50) and PSIR (n = 43) images. The number of detected lesions between AI-generated images and MRI-acquired (reference) images was compared by randomized blinded scoring by seven readers (all with >10 years of experience in lesion assessment). Reliability was expressed as the intraclass correlation coefficient (ICC). Differences in lesion subtype were determined using Wilcoxon signed-rank tests. Results MRI scans of 202 patients with multiple sclerosis (mean age, 46 years ± 11 [SD]; 127 women) were retrospectively collected from seven centers (February 2020 to January 2021). In total, 1154 lesions were detected on AI-generated DIR images versus 855 on MRI-acquired DIR images (mean difference per reader, 35.0% ± 22.8; P < .001). On AI-generated PSIR images, 803 lesions were detected versus 814 on MRI-acquired PSIR images (98.9% ± 19.4; P = .87). Reliability was good for both DIR (ICC, 0.81) and PSIR (ICC, 0.75) across centers. Regionally, more juxtacortical lesions were detected on AI-generated DIR images than on MRI-acquired DIR images (495 [42.9%] vs 338 [39.5%]; P < .001). On AI-generated PSIR images, fewer juxtacortical lesions were detected than on MRI-acquired PSIR images (232 [28.9%] vs 282 [34.6%]; P = .02). Conclusion Artificial intelligence-generated double inversion-recovery and phase-sensitive inversion-recovery images performed well compared with their MRI-acquired counterparts and can be considered reliable in a multicenter setting, with good between-reader and between-center interpretative agreement. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Zivadinov and Dwyer in this issue.

Sensorimotor network dynamics predict decline in upper and lower limb function in people with multiple sclerosis.

BACKGROUND: Upper and lower limb disabilities are hypothesized to have partially independent underlying (network) disturbances in multiple sclerosis (MS). OBJECTIVE: This study investigated functional network predictors and longitudinal network changes related to upper and lower limb progression in MS. METHODS: Two-hundred fourteen MS patients and 58 controls underwent functional magnetic resonance imaging (fMRI), dexterity (9-Hole Peg Test) and mobility (Timed 25-Foot Walk) measurements (baseline and 5 years). Patients were stratified into progressors (>20% decline) or non-progressors. Functional network efficiency was calculated using static (over entire scan) and dynamic (fluctuations during scan) approaches. Baseline measurements were used to predict progression; significant predictors were explored over time. RESULTS: In both limbs, progression was related to supplementary motor area and caudate efficiency (dynamic and static, respectively). Upper limb progression showed additional specific predictors; cortical grey matter volume, putamen static efficiency and posterior associative sensory (PAS) cortex, putamen, primary somatosensory cortex and thalamus dynamic efficiency. Additional lower limb predictors included motor network grey matter volume, caudate (dynamic) and PAS (static). Only the caudate showed a decline in efficiency over time in one group (non-progressors). CONCLUSION: Disability progression can be predicted using sensorimotor network measures. Upper and lower limb progression showed unique predictors, possibly indicating different network disturbances underlying these types of progression in MS.

Regional healthy brain activity, glioma occurrence and symptomatology.

It is unclear why exactly gliomas show preferential occurrence in certain brain areas. Increased spiking activity around gliomas leads to faster tumour growth in animal models, while higher non-invasively measured brain activity is related to shorter survival in patients. However, it is unknown how regional intrinsic brain activity, as measured in healthy controls, relates to glioma occurrence. We first investigated whether gliomas occur more frequently in regions with intrinsically higher brain activity. Second, we explored whether intrinsic cortical activity at individual patients' tumour locations relates to tumour and patient characteristics. Across three cross-sectional cohorts, 413 patients were included. Individual tumour masks were created. Intrinsic regional brain activity was assessed through resting-state magnetoencephalography acquired in healthy controls and source-localized to 210 cortical brain regions. Brain activity was operationalized as: (i) broadband power; and (ii) offset of the aperiodic component of the power spectrum, which both reflect neuronal spiking of the underlying neuronal population. We additionally assessed (iii) the slope of the aperiodic component of the power spectrum, which is thought to reflect the neuronal excitation/inhibition ratio. First, correlation coefficients were calculated between group-level regional glioma occurrence, as obtained by concatenating tumour masks across patients, and group-averaged regional intrinsic brain activity. Second, intrinsic brain activity at specific tumour locations was calculated by overlaying patients' individual tumour masks with regional intrinsic brain activity of the controls and was associated with tumour and patient characteristics. As proposed, glioma preferentially occurred in brain regions characterized by higher intrinsic brain activity in controls as reflected by higher offset. Second, intrinsic brain activity at patients' individual tumour locations differed according to glioma subtype and performance status: the most malignant isocitrate dehydrogenase-wild-type glioblastoma patients had the lowest excitation/inhibition ratio at their individual tumour locations as compared to isocitrate dehydrogenase-mutant, 1p/19q-codeleted glioma patients, while a lower excitation/inhibition ratio related to poorer Karnofsky Performance Status, particularly in codeleted glioma patients. In conclusion, gliomas more frequently occur in cortical brain regions with intrinsically higher activity levels, suggesting that more active regions are more vulnerable to glioma development. Moreover, indices of healthy, intrinsic excitation/inhibition ratio at patients' individual tumour locations may capture both tumour biology and patients' performance status. These findings contribute to our understanding of the complex and bidirectional relationship between normal brain functioning and glioma growth, which is at the core of the relatively new field of 'cancer neuroscience'.

Cellular Substrates of Functional Network Integration and Memory in Temporal Lobe Epilepsy.

Temporal lobe epilepsy (TLE) patients are at risk of memory deficits, which have been linked to functional network disturbances, particularly of integration of the default mode network (DMN). However, the cellular substrates of functional network integration are unknown. We leverage a unique cross-scale dataset of drug-resistant TLE patients (n = 31), who underwent pseudo resting-state functional magnetic resonance imaging (fMRI), resting-state magnetoencephalography (MEG) and/or neuropsychological testing before neurosurgery. fMRI and MEG underwent atlas-based connectivity analyses. Functional network centrality of the lateral middle temporal gyrus, part of the DMN, was used as a measure of local network integration. Subsequently, non-pathological cortical tissue from this region was used for single cell morphological and electrophysiological patch-clamp analysis, assessing integration in terms of total dendritic length and action potential rise speed. As could be hypothesized, greater network centrality related to better memory performance. Moreover, greater network centrality correlated with more integrative properties at the cellular level across patients. We conclude that individual differences in cognitively relevant functional network integration of a DMN region are mirrored by differences in cellular integrative properties of this region in TLE patients. These findings connect previously separate scales of investigation, increasing translational insight into focal pathology and large-scale network disturbances in TLE.

Axon-Myelin Unit Blistering as Early Event in MS Normal Appearing White Matter.

OBJECTIVE: Multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease of unknown etiology. Although the prevalent view regards a CD4+ -lymphocyte autoimmune reaction against myelin at the root of the disease, recent studies propose autoimmunity as a secondary reaction to idiopathic brain damage. To gain knowledge about this possibility we investigated the presence of axonal and myelinic morphological alterations, which could implicate imbalance of axon-myelin units as primary event in MS pathogenesis. METHODS: Using high resolution imaging histological brain specimens from patients with MS and non-neurological/non-MS controls, we explored molecular changes underpinning imbalanced interaction between axon and myelin in normal appearing white matter (NAWM), a region characterized by normal myelination and absent inflammatory activity. RESULTS: In MS brains, we detected blister-like swellings formed by myelin detachment from axons, which were substantially less frequently retrieved in non-neurological/non-MS controls. Swellings in MS NAWM presented altered glutamate receptor expression, myelin associated glycoprotein (MAG) distribution, and lipid biochemical composition of myelin sheaths. Changes in tethering protein expression, widening of nodes of Ranvier and altered distribution of sodium channels in nodal regions of otherwise normally myelinated axons were also present in MS NAWM. Finally, we demonstrate a significant increase, compared with controls, in citrullinated proteins in myelin of MS cases, pointing toward biochemical modifications that may amplify the immunogenicity of MS myelin. INTERPRETATION: Collectively, the impaired interaction of myelin and axons potentially leads to myelin disintegration. Conceptually, the ensuing release of (post-translationally modified) myelin antigens may elicit a subsequent immune attack in MS. ANN NEUROL 2021;89:711-725.

Altered functional brain states predict cognitive decline 5 years after a clinically isolated syndrome.

BACKGROUND: Cognitive impairment occurs in the earliest stages of multiple sclerosis (MS) together with altered functional connectivity (FC). OBJECTIVE: The aim of this study was to investigate the evolution of dynamic FC states in early MS and their role in shaping cognitive decline. METHODS: Overall, 32 patients were enrolled after their first neurological episode suggestive of MS and underwent cognitive evaluation and resting-state functional MRI (fMRI) over 5 years. In addition, 28 healthy controls were included at baseline. RESULTS: Cognitive performance was stable during the first year and declined after 5 years.At baseline, the number of transitions between states was lower in MS compared to controls (p = 0.01). Over time, frequency of high FC states decreased in patients (p = 0.047) and increased in state with low FC (p = 0.035). Cognitive performance at Year 5 was best predicted by the mean connectivity of high FC state at Year 1. CONCLUSION: Patients with early MS showed reduced functional network dynamics at baseline. Longitudinal changes showed longer time spent in a state of low FC but less time spent and more connectivity disturbance in more integrative states with high within- and between-network FC. Disturbed FC within this more integrative state was predictive of future cognitive decline.

Artificial double inversion recovery images for (juxta)cortical lesion visualization in multiple sclerosis.

BACKGROUND: Cortical lesions are highly inconspicuous on magnetic resonance imaging (MRI). Double inversion recovery (DIR) has a higher sensitivity than conventional clinical sequences (i.e. T1, T2, FLAIR) but is difficult to acquire, leading to overseen cortical lesions in clinical care and clinical trials. OBJECTIVE: To evaluate the usability of artificially generated DIR (aDIR) images for cortical lesion detection compared to conventionally acquired DIR (cDIR). METHODS: The dataset consisted of 3D-T1 and 2D-proton density (PD) T2 images of 73 patients (49RR, 20SP, 4PP) at 1.5 T. Using a 4:1 train:test-ratio, a fully convolutional neural network was trained to predict 3D-aDIR from 3D-T1 and 2D-PD/T2 images. Randomized blind scoring of the test set was used to determine detection reliability, precision and recall. RESULTS: A total of 626 vs 696 cortical lesions were detected on 15 aDIR vs cDIR images (intraclass correlation coefficient (ICC) = 0.92). Compared to cDIR, precision and recall were 0.84 ± 0.06 and 0.76 ± 0.09, respectively. The frontal and temporal lobes showed the largest differences in discernibility. CONCLUSION: Cortical lesions can be detected with good reliability on artificial DIR. The technique has potential to broaden the availability of DIR in clinical care and provides the opportunity of ex post facto implementation of cortical lesions imaging in existing clinical trial data.

A randomized trial predicting response to cognitive rehabilitation in multiple sclerosis: Is there a window of opportunity?

BACKGROUND: Cognitive training elicits mild-to-moderate improvements in cognitive functioning in people with multiple sclerosis (PwMS), although response heterogeneity limits overall effectiveness. OBJECTIVE: To identify patient characteristics associated with response and non-response to cognitive training. METHODS: Eighty-two PwMS were randomized into a 7-week attention training (n = 58, age = 48.4 ± 10.2 years) or a waiting-list control group (n = 24, age = 48.5 ± 9.4 years). Structural and functional magnetic resonance imaging (MRI) was obtained at baseline and post-intervention. Twenty-one healthy controls (HCs, age = 50.27 ± 10.15 years) were included at baseline. Responders were defined with a reliable change index of 1.64 on at least 2/6 cognitive domains. General linear models and logistic regression were applied. RESULTS: Responders (n = 36) and non-responders (n = 22) did not differ on demographics, clinical variables and baseline cognition and structural MRI. However, non-responders exhibited a higher baseline functional connectivity (FC) between the default-mode network (DMN) and the ventral attention network (VAN), compared with responders (p = 0.018) and HCs (p = 0.001). Conversely, responders exhibited no significant baseline differences in FC compared with HCs. Response to cognitive training was predicted by lower DMN-VAN FC (p = 0.004) and DMN-frontoparietal FC (p = 0.029) (Nagelkerke R2 = 0.25). CONCLUSION: An intact pre-intervention FC is associated with cognitive training responsivity in pwMS, suggesting a window of opportunity for successful cognitive interventions.

Disability in multiple sclerosis is related to thalamic connectivity and cortical network atrophy.

BACKGROUND: Thalamic atrophy is proposed to be a major predictor of disability progression in multiple sclerosis (MS), while thalamic function remains understudied. OBJECTIVES: To study how thalamic functional connectivity (FC) is related to disability and thalamic or cortical network atrophy in two large MS cohorts. METHODS: Structural and resting-state functional magnetic resonance imaging (fMRI) was obtained in 673 subjects from Amsterdam (MS: N = 332, healthy controls (HC): N = 96) and Graz (MS: N = 180, HC: N = 65) with comparable protocols, including disability measurements in MS (Expanded Disability Status Scale, EDSS). Atrophy was measured for the thalamus and seven well-recognized resting-state networks. Static and dynamic thalamic FC with these networks was correlated with disability. Significant correlates were included in a backward multivariate regression model. RESULTS: Disability was most strongly related (adjusted R2 = 0.57, p < 0.001) to higher age, a progressive phenotype, thalamic atrophy and increased static thalamic FC with the sensorimotor network (SMN). Static thalamus-SMN FC was significantly higher in patients with high disability (EDSS ⩾ 4) and related to network atrophy but not thalamic atrophy or lesion volumes. CONCLUSION: The severity of disability in MS was related to increased static thalamic FC with the SMN. Thalamic FC changes were only related to cortical network atrophy, but not to thalamic atrophy.

Inhibitory synaptic loss drives network changes in multiple sclerosis: An ex vivo to in silico translational study.

BACKGROUND: Synaptic and neuronal loss contribute to network dysfunction and disability in multiple sclerosis (MS). However, it is unknown whether excitatory or inhibitory synapses and neurons are more vulnerable and how their losses impact network functioning. OBJECTIVE: To quantify excitatory and inhibitory synapses and neurons and to investigate how synaptic loss affects network functioning through computational modeling. METHODS: Using immunofluorescent staining and confocal microscopy, densities of glutamatergic and GABAergic synapses and neurons were compared between post-mortem MS and non-neurological control cases. Then, a corticothalamic biophysical model was employed to study how MS-induced excitatory and inhibitory synaptic loss affect network functioning. RESULTS: In layer VI of normal-appearing MS cortex, excitatory and inhibitory synaptic densities were significantly lower than controls (reductions up to 14.9%), but demyelinated cortex showed larger losses of inhibitory synapses (29%). In our computational model, reducing inhibitory synapses impacted the network most, leading to a disinhibitory increase in neuronal activity and connectivity. CONCLUSION: In MS, excitatory and inhibitory synaptic losses were observed, predominantly for inhibitory synapses in demyelinated cortex. Inhibitory synaptic loss affected network functioning most, leading to increased neuronal activity and connectivity. As network disinhibition relates to cognitive impairment, inhibitory synaptic loss seems particularly relevant in MS.

Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis.

Quantitative MRI provides biophysical measures of the microstructural integrity of the CNS, which can be compared across CNS regions, patients, and centres. In patients with multiple sclerosis, quantitative MRI techniques such as relaxometry, myelin imaging, magnetization transfer, diffusion MRI, quantitative susceptibility mapping, and perfusion MRI, complement conventional MRI techniques by providing insight into disease mechanisms. These include: (i) presence and extent of diffuse damage in CNS tissue outside lesions (normal-appearing tissue); (ii) heterogeneity of damage and repair in focal lesions; and (iii) specific damage to CNS tissue components. This review summarizes recent technical advances in quantitative MRI, existing pathological validation of quantitative MRI techniques, and emerging applications of quantitative MRI to patients with multiple sclerosis in both research and clinical settings. The current level of clinical maturity of each quantitative MRI technique, especially regarding its integration into clinical routine, is discussed. We aim to provide a better understanding of how quantitative MRI may help clinical practice by improving stratification of patients with multiple sclerosis, and assessment of disease progression, and evaluation of treatment response.

Functional correlates of motor control impairments in multiple sclerosis: A 7 Tesla task functional MRI study.

Upper and lower limb impairments are common in people with multiple sclerosis (pwMS), yet difficult to clinically identify in early stages of disease progression. Tasks involving complex motor control can potentially reveal more subtle deficits in early stages, and can be performed during functional MRI (fMRI) acquisition, to investigate underlying neural mechanisms, providing markers for early motor progression. We investigated brain activation during visually guided force matching of hand or foot in 28 minimally disabled pwMS (Expanded Disability Status Scale (EDSS) < 4 and pyramidal and cerebellar Kurtzke Functional Systems Scores ≤ 2) and 17 healthy controls (HC) using ultra-high field 7-Tesla fMRI, allowing us to visualise sensorimotor network activity in high detail. Task activations and performance (tracking lag and error) were compared between groups, and correlations were performed. PwMS showed delayed (+124 s, p = .002) and more erroneous (+0.15 N, p = .001) lower limb tracking, together with lower cerebellar, occipital and superior parietal cortical activation compared to HC. Lower activity within these regions correlated with worse EDSS (p = .034), lower force error (p = .006) and higher lesion load (p < .05). Despite no differences in upper limb task performance, pwMS displayed lower inferior occipital cortical activation. These results demonstrate that ultra-high field fMRI during complex hand and foot tracking can identify subtle impairments in lower limb movements and upper and lower limb brain activity, and differentiates upper and lower limb impairments in minimally disabled pwMS.

Complement-associated loss of CA2 inhibitory synapses in the demyelinated hippocampus impairs memory.

The complement system is implicated in synapse loss in the MS hippocampus, but the functional consequences of synapse loss remain poorly understood. Here, in post-mortem MS hippocampi with demyelination we find that deposits of the complement component C1q are enriched in the CA2 subfield, are linked to loss of inhibitory synapses and are significantly higher in MS patients with cognitive impairments compared to those with preserved cognitive functions. Using the cuprizone mouse model of demyelination, we corroborated that C1q deposits are highest within the demyelinated dorsal hippocampal CA2 pyramidal layer and co-localized with inhibitory synapses engulfed by microglia/macrophages. In agreement with the loss of inhibitory perisomatic synapses, we found that Schaffer collateral feedforward inhibition but not excitation was impaired in CA2 pyramidal neurons and accompanied by intrinsic changes and a reduced spike output. Finally, consistent with excitability deficits, we show that cuprizone-treated mice exhibit impaired encoding of social memories. Together, our findings identify CA2 as a critical circuit in demyelinated intrahippocampal lesions and memory dysfunctions in MS.

Meningeal inflammation in multiple sclerosis induces phenotypic changes in cortical microglia that differentially associate with neurodegeneration.

Meningeal inflammation strongly associates with demyelination and neuronal loss in the underlying cortex of progressive MS patients, thereby contributing significantly to clinical disability. However, the pathological mechanisms of meningeal inflammation-induced cortical pathology are still largely elusive. By extensive analysis of cortical microglia in post-mortem progressive MS tissue, we identified cortical areas with two MS-specific microglial populations, termed MS1 and MS2 cortex. The microglial population in MS1 cortex was characterized by a higher density and increased expression of the activation markers HLA class II and CD68, whereas microglia in MS2 cortex showed increased morphological complexity and loss of P2Y12 and TMEM119 expression. Interestingly, both populations associated with inflammation of the overlying meninges and were time-dependently replicated in an in vivo rat model for progressive MS-like chronic meningeal inflammation. In this recently developed animal model, cortical microglia at 1-month post-induction of experimental meningeal inflammation resembled microglia in MS1 cortex, and microglia at 2 months post-induction acquired a MS2-like phenotype. Furthermore, we observed that MS1 microglia in both MS cortex and the animal model were found closely apposing neuronal cell bodies and to mediate pre-synaptic displacement and phagocytosis, which coincided with a relative sparing of neurons. In contrast, microglia in MS2 cortex were not involved in these synaptic alterations, but instead associated with substantial neuronal loss. Taken together, our results show that in response to meningeal inflammation, microglia acquire two distinct phenotypes that differentially associate with neurodegeneration in the progressive MS cortex. Furthermore, our in vivo data suggests that microglia initially protect neurons from meningeal inflammation-induced cell death by removing pre-synapses from the neuronal soma, but eventually lose these protective properties contributing to neuronal loss.

The subcellular arrangement of alpha-synuclein proteoforms in the Parkinson's disease brain as revealed by multicolor STED microscopy.

Various post-translationally modified (PTM) proteoforms of alpha-synuclein (aSyn)-including C-terminally truncated (CTT) and Serine 129 phosphorylated (Ser129-p) aSyn-accumulate in Lewy bodies (LBs) in different regions of the Parkinson's disease (PD) brain. Insight into the distribution of these proteoforms within LBs and subcellular compartments may aid in understanding the orchestration of Lewy pathology in PD. We applied epitope-specific antibodies against CTT and Ser129-p aSyn proteoforms and different aSyn domains in immunohistochemical multiple labelings on post-mortem brain tissue from PD patients and non-neurological, aged controls, which were scanned using high-resolution 3D multicolor confocal and stimulated emission depletion (STED) microscopy. Our multiple labeling setup highlighted a consistent onion skin-type 3D architecture in mature nigral LBs in which an intricate and structured-appearing framework of Ser129-p aSyn and cytoskeletal elements encapsulates a core enriched in CTT aSyn species. By label-free CARS microscopy we found that enrichments of proteins and lipids were mainly localized to the central portion of nigral aSyn-immunopositive (aSyn+) inclusions. Outside LBs, we observed that 122CTT aSyn+ punctae localized at mitochondrial membranes in the cytoplasm of neurons in PD and control brains, suggesting a physiological role for 122CTT aSyn outside of LBs. In contrast, very limited to no Ser129-p aSyn immunoreactivity was observed in brains of non-neurological controls, while the alignment of Ser129-p aSyn in a neuronal cytoplasmic network was characteristic for brains with (incidental) LB disease. Interestingly, Ser129-p aSyn+ network profiles were not only observed in neurons containing LBs but also in neurons without LBs particularly in donors at early disease stage, pointing towards a possible subcellular pathological phenotype preceding LB formation. Together, our high-resolution and 3D multicolor microscopy observations in the post-mortem human brain provide insights into potential mechanisms underlying a regulated LB morphogenesis.

The cerebellum and its network: Disrupted static and dynamic functional connectivity patterns and cognitive impairment in multiple sclerosis.

BACKGROUND: The impact of cerebellar damage and (dys)function on cognition remains understudied in multiple sclerosis. OBJECTIVE: To assess the cognitive relevance of cerebellar structural damage and functional connectivity (FC) in relapsing-remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS). METHODS: This study included 149 patients with early RRMS, 81 late RRMS, 48 SPMS and 82 controls. Cerebellar cortical imaging included fractional anisotropy, grey matter volume and resting-state functional magnetic resonance imaging (MRI). Cerebellar FC was assessed with literature-based resting-state networks, using static connectivity (that is, conventional correlations), and dynamic connectivity (that is, fluctuations in FC strength). Measures were compared between groups and related to disability and cognition. RESULTS: Cognitive impairment (CI) and cerebellar damage were worst in SPMS. Only SPMS showed cerebellar connectivity changes, compared to early RRMS and controls. Lower static FC was seen in fronto-parietal and default-mode networks. Higher dynamic FC was seen in dorsal and ventral attention, default-mode and deep grey matter networks. Cerebellar atrophy and higher dynamic FC together explained 32% of disability and 24% of cognitive variance. Higher dynamic FC was related to working and verbal memory and to information processing speed. CONCLUSION: Cerebellar damage and cerebellar connectivity changes were most prominent in SPMS and related to worse CI.

Cortical axonal loss is associated with both gray matter demyelination and white matter tract pathology in progressive multiple sclerosis: Evidence from a combined MRI-histopathology study.

BACKGROUND: Neuroaxonal degeneration is one of the hallmarks of clinical deterioration in progressive multiple sclerosis (PMS). OBJECTIVE: To elucidate the association between neuroaxonal degeneration and both local cortical and connected white matter (WM) tract pathology in PMS. METHODS: Post-mortem in situ 3T magnetic resonance imaging (MRI) and cortical tissue blocks were collected from 16 PMS donors and 10 controls. Cortical neuroaxonal, myelin, and microglia densities were quantified histopathologically. From diffusion tensor MRI, fractional anisotropy, axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) were quantified in normal-appearing white matter (NAWM) and white matter lesions (WML) of WM tracts connected to dissected cortical regions. Between-group differences and within-group associations were investigated through linear mixed models. RESULTS: The PMS donors displayed significant axonal loss in both demyelinated and normal-appearing (NA) cortices (p < 0.001 and p = 0.02) compared with controls. In PMS, cortical axonal density was associated with WML MD and AD (p = 0.003; p = 0.02, respectively), and NAWM MD and AD (p = 0.04; p = 0.049, respectively). NAWM AD and WML AD explained 12.6% and 22.6%, respectively, of axonal density variance in NA cortex. Additional axonal loss in demyelinated cortex was associated with cortical demyelination severity (p = 0.002), explaining 34.4% of axonal loss variance. CONCLUSION: Reduced integrity of connected WM tracts and cortical demyelination both contribute to cortical axonal loss in PMS.