Neuroimmune activation is associated with neurological outcome in anoxic and traumatic coma.

The pathophysiological underpinnings of critically disrupted brain connectomes resulting in coma are poorly understood. Inflammation is potentially an important but still undervalued factor. Here, we present a first-in-human prospective study using the 18-kDa translocator protein (TSPO) radioligand 18F-DPA714 for PET imaging to allow in vivo neuroimmune activation quantification in patients with coma (n = 17) following either anoxia or traumatic brain injuries in comparison with age- and sex-matched controls. Our findings yielded novel evidence of an early inflammatory component predominantly located within key cortical and subcortical brain structures that are putatively implicated in consciousness emergence and maintenance after severe brain injury (i.e. mesocircuit and frontoparietal networks). We observed that traumatic and anoxic patients with coma have distinct neuroimmune activation profiles, both in terms of intensity and spatial distribution. Finally, we demonstrated that both the total amount and specific distribution of PET-measurable neuroinflammation within the brain mesocircuit were associated with the patient's recovery potential. We suggest that our results can be developed for use both as a new neuroprognostication tool and as a promising biometric to guide future clinical trials targeting glial activity very early after severe brain injury.

Progressive Brain Atrophy in Multiple System Atrophy: A Longitudinal, Multicenter, Magnetic Resonance Imaging Study.

OBJECTIVE: To determine the rates of brain atrophy progression in vivo in patients with multiple system atrophy (MSA). BACKGROUND: Surrogate biomarkers of disease progression are a major unmet need in MSA. Small-scale longitudinal studies in patients with MSA using magnetic resonance imaging (MRI) to assess progression of brain atrophy have produced inconsistent results. In recent years, novel MRI post-processing methods have been developed enabling reliable quantification of brain atrophy in an automated fashion. METHODS: Serial 3D-T1-weighted MRI assessments (baseline and after 1 year of follow-up) of 43 patients with MSA were analyzed and compared to a cohort of early-stage Parkinson's disease (PD) patients and healthy controls (HC). FreeSurfer's longitudinal analysis stream was used to determine the brain atrophy rates in an observer-independent fashion. RESULTS: Mean ages at baseline were 64.4 ± 8.3, 60.0 ± 7.5, and 59.8 ± 9.2 years in MSA, PD patients and HC, respectively. A mean disease duration at baseline of 4.1 ± 2.5 years in MSA patients and 2.3 ± 1.4 years in PD patients was observed. Brain regions chiefly affected by MSA pathology showed progressive atrophy with annual rates of atrophy for the cerebellar cortex, cerebellar white matter, pons, and putamen of -4.24 ± 6.8%, -8.22 ± 8.8%, -4.67 ± 4.9%, and - 4.25 ± 4.9%, respectively. Similar to HC, atrophy rates in PD patients were minimal with values of -0.41% ± 1.8%, -1.47% ± 4.1%, -0.04% ± 1.8%, and -1.54% ± 2.2% for cerebellar cortex, cerebellar white matter, pons, and putamen, respectively. CONCLUSIONS: Patients with MSA show significant brain volume loss over 12 months, and cerebellar, pontine, and putaminal volumes were the most sensitive to change in mid-stage disease. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression.

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this causal depression network (CDN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis Principal Component Analysis (PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CDN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CDN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes (t = -2.35, p = 0.019). This evidence further supports that treatment interventions converge on a CDN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.

Galantamine combined with cognitive rehabilitation on post-stroke cognitive impairment: a proof-of-concept study.

PURPOSE: We conducted a proof-of-concept study to evaluate the effects of galantamine treatment versus placebo combined to cognitive rehabilitation (CR) after stroke. MATERIALS AND METHODS: In this 12-week, double blinded, randomized, controlled trial, patients were assigned to either combined approach of galantamine and CR (G-CR) or placebo and CR (P-CR). Primary outcome was the proportion of patients who crossed over from vascular cognitive disorder (VCD) to no-VCD at 12 weeks. Secondary outcomes included changes in cognition, mood, quality of life and the N-back fMRI paradigm, assessed at baseline, 6 and 12 weeks and after an 8-week washout period. RESULTS: Ten patients were allocated to G-RC group, 12 to the P-RC group. After 12 weeks, 40.1% of all patients converted to no-VCD with similar proportions between groups. Both groups showed improvements in episodic and working memory, executive and quality of life after 6 weeks of CR. Decreased depression and anxiety were noted, and all benefits persisted after the washout period. An interaction effect was observed in the right parietal lobule during the N-back task. CONCLUSIONS: Interventions lead to improved cognition and distinct cortical reorganization without being able to establish correlation between neural changes and behavioral measures.

Describing complex disease progression using joint latent class models for multivariate longitudinal markers and clinical endpoints.

Neurodegenerative diseases are characterized by numerous markers of progression and clinical endpoints. For instance, multiple system atrophy (MSA), a rare neurodegenerative synucleinopathy, is characterized by various combinations of progressive autonomic failure and motor dysfunction, and a very poor prognosis. Describing the progression of such complex and multi-dimensional diseases is particularly difficult. One has to simultaneously account for the assessment of multivariate markers over time, the occurrence of clinical endpoints, and a highly suspected heterogeneity between patients. Yet, such description is crucial for understanding the natural history of the disease, staging patients diagnosed with the disease, unravelling subphenotypes, and predicting the prognosis. Through the example of MSA progression, we show how a latent class approach modeling multiple repeated markers and clinical endpoints can help describe complex disease progression and identify subphenotypes for exploring new pathological hypotheses. The proposed joint latent class model includes class-specific multivariate mixed models to handle multivariate repeated biomarkers possibly summarized into latent dimensions and class-and-cause-specific proportional hazard models to handle time-to-event data. Maximum likelihood estimation procedure, validated through simulations is available in the lcmm R package. In the French MSA cohort comprising data of 598 patients during up to 13 years, five subphenotypes of MSA were identified that differ by the sequence and shape of biomarkers degradation, and the associated risk of death. In posterior analyses, the five subphenotypes were used to explore the association between clinical progression and external imaging and fluid biomarkers, while properly accounting for the uncertainty in the subphenotypes membership.

Neurofilament light levels predict clinical progression and death in multiple system atrophy.

Disease-modifying treatments are currently being trialled in multiple system atrophy. Approaches based solely on clinical measures are challenged by heterogeneity of phenotype and pathogenic complexity. Neurofilament light chain protein has been explored as a reliable biomarker in several neurodegenerative disorders but data on multiple system atrophy have been limited. Therefore, neurofilament light chain is not yet routinely used as an outcome measure in multiple system atrophy. We aimed to comprehensively investigate the role and dynamics of neurofilament light chain in multiple system atrophy combined with cross-sectional and longitudinal clinical and imaging scales and for subject trial selection. In this cohort study, we recruited cross-sectional and longitudinal cases in a multicentre European set-up. Plasma and CSF neurofilament light chain concentrations were measured at baseline from 212 multiple system atrophy cases, annually for a mean period of 2 years in 44 multiple system atrophy patients in conjunction with clinical, neuropsychological and MRI brain assessments. Baseline neurofilament light chain characteristics were compared between groups. Cox regression was used to assess survival; receiver operating characteristic analysis to assess the ability of neurofilament light chain to distinguish between multiple system atrophy patients and healthy controls. Multivariate linear mixed-effects models were used to analyse longitudinal neurofilament light chain changes and correlated with clinical and imaging parameters. Polynomial models were used to determine the differential trajectories of neurofilament light chain in multiple system atrophy. We estimated sample sizes for trials aiming to decrease neurofilament light chain levels. We show that in multiple system atrophy, baseline plasma neurofilament light chain levels were better predictors of clinical progression, survival and degree of brain atrophy than the neurofilament light chain rate of change. Comparative analysis of multiple system atrophy progression over the course of disease, using plasma neurofilament light chain and clinical rating scales, indicated that neurofilament light chain levels rise as the motor symptoms progress, followed by deceleration in advanced stages. Sample size prediction suggested that significantly lower trial participant numbers would be needed to demonstrate treatment effects when incorporating plasma neurofilament light chain values into multiple system atrophy clinical trials in comparison to clinical measures alone. In conclusion, neurofilament light chain correlates with clinical disease severity, progression and prognosis in multiple system atrophy. Combined with clinical and imaging analysis, neurofilament light chain can inform patient stratification and serve as a reliable biomarker of treatment response in future multiple system atrophy trials of putative disease-modifying agents.

Update on the Efficacy of Cognitive Rehabilitation After Moderate to Severe Traumatic Brain Injury: A Scoping Review.

OBJECTIVES: To identify, categorize, and analyze the methodological issues of cognitive rehabilitation of patients with moderate to severe traumatic brain injury and its efficacy. DATA SOURCES: Pubmed and PsycINFO were searched for studies published between 2015 and 2021 using keywords for cognitive intervention and traumatic brain injury. STUDY SELECTION: Two independent reviewers selected articles concerning cognitive rehabilitation for adults with traumatic brain injury. Of 458 studies, 97 full-text articles were assessed and 46 met the inclusion criteria. DATA EXTRACTION: Data were analyzed by 1 reviewer according to criteria concerning the methodological quality of studies. DATA SYNTHESIS: Results showed a large scope of 7 cognitive domains targeted by interventions, delivered mostly in individual sessions (83%) with an integrative cognitive approach (48%). Neuroimaging tools as a measure of outcome remained scarce, featuring in only 20% of studies. Forty-three studies reported significant effects of cognitive rehabilitation, among which 7 fulfilled a high methodological level of evidence. CONCLUSIONS: Advances and shortcomings in cognitive rehabilitation have both been highlighted and led us to develop methodological key points for future studies. The choice of outcome measures, the selection of control interventions, and the use of combined rehabilitation should be investigated in further studies.

Neuroinflammation PET imaging of the translocator protein (TSPO) in Alzheimer's disease: An update.

Neuroinflammation is a significant contributor to Alzheimer's disease (AD). Until now, PET imaging of the translocator protein (TSPO) has been widely used to depict the neuroimmune endophenotype of AD. The aim of this review was to provide an update to the results from 2018 and to advance the characterization of the biological basis of TSPO imaging in AD by re-examining TSPO function and expression and the methodological aspects of interest. Although the biological basis of the TSPO PET signal is obviously related to microglia and astrocytes in AD, the observed process remains uncertain and might not be directly related to neuroinflammation. Further studies are required to re-examine the cellular significance underlying a variation in the PET signal in AD.

Brain-age estimation accuracy is significantly increased using multishell free-water reconstruction.

Although free-water diffusion reconstruction for diffusion-weighted imaging (DWI) data can be applied to both single-shell and multishell data, recent finding in synthetic data suggests that the free-water indices from single-shell acquisition should be interpreted with care, as they are heavily influenced by initialization parameters and cannot discriminate between free-water and mean diffusivity modifications. However, whether using a longer multishell acquisition protocol significantly improve reconstruction for real human MRI data is still an open question. In this study, we compare canonical diffusion tensor imaging (DTI), single-shell and multishell free-water imaging (FW) indices derived from a short, clinical compatible diffusion protocol (b = 500 s/mm2 , b = 1,000 s/mm2 , 32 directions each) on their power to predict brain age. Age was chosen as it is well-known to be related to widespread modification of the white matter and because brain-age estimation has recently been found to be relevant to several neurodegenerative diseases. We used a previously developed and validated data-driven whole-brain machine learning pipeline to directly compare the precision of brain-age estimates in a sample of 89 healthy males between 20 and 85 years old. We found that multishell FW outperform DTI indices in estimating brain age and that multishell FW, even when using low (500 ms2 ) b-values secondary shell, outperform single-shell FW. Single-shell FW led to lower brain-age estimation accuracy even of canonical DTI indices, suggesting that single-shell FW indices should be used with caution. For all considered reconstruction algorithms, the most discriminant indices were those measuring free diffusion of water in the white matter.

Quality control of 3D MRSI data in glioblastoma: Can we do without the experts?

PURPOSE: Proton magnetic resonance spectroscopic imaging (1H MRSI) is a noninvasive technique for assessing tumor metabolism. Manual inspection is still the gold standard for quality control (QC) of spectra, but it is both time-consuming and subjective. The aim of the present study was to assess automatic QC of glioblastoma MRSI data using random forest analysis. METHODS: Data for 25 patients, acquired prospectively in a preradiotherapy examination, were submitted to postprocessing with syngo.MR Spectro (VB40A; Siemens) or Java-based magnetic resonance user interface (jMRUI) software. A total of 28 features were extracted from each spectrum for the automatic QC. Three spectroscopists also performed manual inspections, labeling each spectrum as good or poor quality. All statistical analyses, with addressing unbalanced data, were conducted with R 3.6.1 (R Foundation for Statistical Computing; https://www.r-project.org). RESULTS: The random forest method classified the spectra with an area under the curve of 95.5%, sensitivity of 95.8%, and specificity of 81.7%. The most important feature for the classification was Residuum_Lipids_Versus_Fit, obtained with syngo.MR Spectro. CONCLUSION: The automatic QC method was able to distinguish between good- and poor-quality spectra, and can be used by radiation oncologists who are not spectroscopy experts. This study revealed a novel set of MRSI signal features that are closely correlated with spectral quality.

Multimodal MRI-Based Whole-Brain Assessment in Patients In Anoxoischemic Coma by Using 3D Convolutional Neural Networks.

BACKGROUND: There is an unfulfilled need to find the best way to automatically capture, analyze, organize, and merge structural and functional brain magnetic resonance imaging (MRI) data to ultimately extract relevant signals that can assist the medical decision process at the bedside of patients in postanoxic coma. We aimed to develop and validate a deep learning model to leverage multimodal 3D MRI whole-brain times series for an early evaluation of brain damages related to anoxoischemic coma. METHODS: This proof-of-concept, prospective, cohort study was undertaken at the intensive care unit affiliated with the University Hospital (Toulouse, France), between March 2018 and May 2020. All patients were scanned in coma state at least 2 days (4 ± 2 days) after cardiac arrest. Over the same period, age-matched healthy volunteers were recruited and included. Brain MRI quantification encompassed both "functional data" from regions of interest (precuneus and posterior cingulate cortex) with whole-brain functional connectivity analysis and "structural data" (gray matter volume, T1-weighted, fractional anisotropy, and mean diffusivity). A specifically designed 3D convolutional neuronal network (CNN) was created to allow conscious state discrimination (coma vs. controls) by using raw MRI indices as the input. A voxel-wise visualization method based on the study of convolutional filters was applied to support CNN outcome. The Ethics Committee of the University Teaching Hospital of Toulouse, France (2018-A31) approved the study and informed consent was obtained from all participants. RESULTS: The final cohort consisted of 29 patients in postanoxic coma and 34 healthy volunteers. Coma patients were successfully discerned from controls by using 3D CNN in combination with different MR indices. The best accuracy was achieved by functional MRI data, in particular with resting-state functional MRI of the posterior cingulate cortex, with an accuracy of 0.96 (range 0.94-0.98) on the test set from 10-time repeated tenfold cross-validation. Even more satisfactory performances were achieved through the majority voting strategy, which was able to compensate for mistakes from single MR indices. Visualization maps allowed us to identify the most relevant regions for each MRI index, notably regions previously described as possibly being involved in consciousness emergence. Interestingly, a posteriori analysis of misclassified patients indicated that they may present some common functional MRI traits with controls, which suggests further favorable outcomes. CONCLUSIONS: A fully automated identification of clinically relevant signals from complex multimodal neuroimaging data is a major research topic that may bring a radical paradigm shift in the neuroprognostication of patients with severe brain injury. We report for the first time a successful discrimination between patients in postanoxic coma patients from people serving as controls by using 3D CNN whole-brain structural and functional MRI data. Clinical Trial Number http://ClinicalTrials.gov (No. NCT03482115).

Insular functional connectivity in migraine with aura.

INTRODUCTION: Insula plays an integrating role in sensory, affective, emotional, cognitive and autonomic functions in migraine, especially in migraine with aura (MA). Insula is functionally divided into 3 subregions, the dorsoanterior, the ventroanterior and the posterior insula respectively related to cognition, emotion, and somatosensory functions. This study aimed at investigating functional connectivity of insula subregions in MA. METHODS: Twenty-one interictal patients with MA were compared to 18 healthy controls (HC) and 12 interictal patients with migraine without aura (MO) and were scanned with functional MRI during the resting state. Functional coupling of the insula was comprehensively tested with 12 seeds located in the right and left, dorsal, middle, ventral, anterior and posterior insula, by using a seed-to-voxel analysis. RESULTS: Seed-to-voxel analysis revealed, in MA, a strong functional coupling of the right and left antero-dorsal insula with clusters located in the upper cerebellum. The overlap of these cerebellar clusters corresponded to the vermis VI. These functional couplings were not correlated to duration of MA, frequency of MA attacks nor time since last MA attack, and were not found in MO. DISCUSSION: The anterior insula and superior cerebellum, including vermis VI, are components of the central Autonomic Nervous System (ANS) network. As these regions are involved in the control of cardiovascular parasympathetic tone, we hypothesize that this connectivity may reflect the cardiovascular features of MA. CONCLUSION: The anterior dorsal insula is connected with vermis VI in MA patients in the resting state. This connectivity may reflect the cardiovascular features of MA. TRIAL REGISTRATION: NCT02708797.

Broad white matter impairment in multiple system atrophy.

Multiple system atrophy (MSA) is a rare neurodegenerative disorder characterized by the widespread aberrant accumulation of α-synuclein (α-syn). MSA differs from other synucleinopathies such as Parkinson's disease (PD) in that α-syn accumulates primarily in oligodendrocytes, the only source of white matter myelination in the brain. Previous MSA imaging studies have uncovered focal differences in white matter. Here, we sought to build on this work by taking a global perspective on whole brain white matter. In order to do this, in vivo structural imaging and diffusion magnetic resonance imaging were acquired on 26 MSA patients, 26 healthy controls, and 23 PD patients. A refined whole brain approach encompassing the major fiber tracts and the superficial white matter located at the boundary of the cortical mantle was applied. The primary observation was that MSA but not PD patients had whole brain deep and superficial white matter diffusivity abnormalities (p < .001). In addition, in MSA patients, these abnormalities were associated with motor (Unified MSA Rating Scale, Part II) and cognitive functions (Mini-Mental State Examination). The pervasive whole brain abnormalities we observe suggest that there is widespread white matter damage in MSA patients which mirrors the widespread aggregation of α-syn in oligodendrocytes. Importantly, whole brain white matter abnormalities were associated with clinical symptoms, suggesting that white matter impairment may be more central to MSA than previously thought.

Procedural learning and retention of audio-verbal temporal sequence is altered in children with developmental coordination disorder but cortical thickness matters.

Rhythmic abilities are impaired in developmental coordination disorder (DCD) but learning deficit of procedural skills implying temporal sequence is still unclear. Current contradictory results suggest that procedural learning deficits in DCD highly depend on learning conditions. The present study proposes to test the role of sensory modality of stimulations (visual or auditory) on synchronization, learning, and retention of temporal verbal sequences in children with and without DCD. We postulated a deficit in learning particularly with auditory stimulations, in association with atypical cortical thickness of three regions of interesting: sensorimotor, frontal and parietal regions. Thirty children with and without DCD (a) performed a synchronization task to a regular temporal sequence and (b) practiced and recalled a novel non-regular temporal sequences with auditory and visual modalities. They also had a magnetic resonance imaging to measure their cortical thickness. Results suggested that children with DCD presented a general deficit in synchronization of a regular temporal verbal sequence irrespective of the sensory modality, but a specific deficit in learning and retention of auditory non-regular verbal temporal sequence. Stability of audio-verbal synchronization during practice correlated with cortical thickness of the sensorimotor cortex. For the first time, our results suggest that synchronization deficits in DCD are not limited to manual tasks. This deficit persists despite repeated exposition and practice of an auditory temporal sequence, which suggests a possible alteration in audio-verbal coupling in DCD. On the contrary, control of temporal parameters with visual stimuli seems to be less affected, which opens perspectives for clinical practice.

Quantitative MRI markers in Parkinson's disease and parkinsonian syndromes.

PURPOSE OF REVIEW: In Parkinson's disease and parkinsonian disorders, the differential diagnosis is still challenging. We aim to review current developments in MRI quantitative markers and their potential in a clinical and neuroscientific setting. RECENT FINDINGS: There have been efforts to improve MRI acquisition methods and to explore new promising biomarkers. In parallel, technological advances in data analysis (i.e. deep learning) open new ways to use these biomarkers. The MRI markers may differ according to the brain structure investigated. Even if the newly adopted acquisition protocols served mainly the development of brainstem-related biomarkers (neuromelanin MRI, nigrosome sensitive MRI), more established markers (e.g. morphometric values) in basal ganglia, cortex and cerebellum demonstrate their relevance especially to differential diagnosis in parkinsonian syndromes. SUMMARY: We provide an overview on recent advances in MRI quantitative markers of Parkinson's disease that we divide for didactic purposes in three anatomical levels - cortical/cerebellum structures, basal ganglia and brainstem. We show the complementarity of new biomarkers sensitive to brain tissue properties to established morphometrics.

Functional and Structural Integrity of Frontoparietal Connectivity in Traumatic and Anoxic Coma.

OBJECTIVES: Recovery from coma might critically depend on the structural and functional integrity of frontoparietal networks. We aimed to measure this integrity in traumatic brain injury and anoxo-ischemic (cardiac arrest) coma patients by using an original multimodal MRI protocol. DESIGN: Prospective cohort study. SETTING: Three Intensive Critical Care Units affiliated to the University in Toulouse (France). PATIENTS: We longitudinally recruited 43 coma patients (Glasgow Coma Scale at the admission < 8; 29 cardiac arrest and 14 traumatic brain injury) and 34 age-matched healthy volunteers. Exclusion criteria were disorders of consciousness lasting more than 30 days and focal brain damage within the explored brain regions. Patient assessments were conducted at least 2 days (5 ± 2 d) after complete withdrawal of sedation. All patients were followed up (Coma Recovery Scale-Revised) 3 months after acute brain injury. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Functional and structural MRI data were recorded, and the analysis was targeted on the posteromedial cortex, the medial prefrontal cortex, and the cingulum. Univariate analyses and machine learning techniques were used to assess diagnostic and predictive values. Coma patients displayed significantly lower medial prefrontal cortex-posteromedial cortex functional connectivity (area under the curve, 0.94; 95% CI, 0.93-0.95). Cardiac arrest patients showed specific structural disturbances within posteromedial cortex. Significant cingulum architectural disturbances were observed in traumatic brain injury patients. The machine learning medial prefrontal cortex-posteromedial cortex multimodal classifier had a significant predictive value (area under the curve, 0.96; 95% CI, 0.95-0.97), best combination of subregions that discriminates a binary outcome based on Coma Recovery Scale-Revised). CONCLUSIONS: This exploratory study suggests that frontoparietal functional disconnections are specifically observed in coma and their structural counterpart provides information about brain injury mechanisms. Multimodal MRI biomarkers of frontoparietal disconnection predict 3-month outcome in our sample. These findings suggest that fronto-parietal disconnection might be particularly relevant for coma outcome prediction and could inspire innovative precision medicine approaches.

A Phase 1 Randomized Trial of Specific Active α-Synuclein Immunotherapies PD01A and PD03A in Multiple System Atrophy.

Multiple system atrophy (MSA) is a rare and fatal neurodegenerative disease with limited symptomatic treatment options. Aggregation of α-synuclein in oligodendrocytes is believed to be a central mechanism of the neurodegenerative process. PD01A and PD03A are 2 novel therapeutic vaccine candidates containing short peptides as antigenic moieties that are designed to induce a sustained antibody response, specifically targeting pathogenic assemblies of α-synuclein. The objectives of the current study were to evaluate primarily the safety and tolerability of PD01A and PD03A in patients with early MSA. Thirty patients (11 women) were randomized to receive 5 subcutaneous injections of either PD01A (n = 12), PD03A (n = 12), or placebo (n = 6) in this patient- and examiner-blinded, placebo-controlled, 52-week phase 1 clinical trial (ClinicalTrial.gov identifier: NCT02270489). Immunogenicity and clinical scores were assessed as secondary objectives. Twenty-nine patients reported a total of 595 treatment-emergent adverse events (mild or moderate, n = 555; severe, n = 40). Treatment-related adverse events included 190 injection-site reactions typically observed in vaccination trials with similar per-subject incidence in the treatment groups over time. Sustained IgG titers were observed in the PD01A-treated group, and 89% of treated patients developed a PD01-specific antibody response after receiving all injections. Induced antibodies displayed clear reactivity to the α-synuclein target epitope. Titers and antibody responder rate (58%) were lower in the PD03A-treated group. In conclusion, both PD01A and PD03A were safe and well tolerated. PD01A triggered a rapid and long-lasting antibody response that specifically targeted the α-synuclein epitope. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Neuromelanin Locus Coeruleus MRI Contrast in Migraine With Aura.

INTRODUCTION: The locus coeruleus (LC) is one of the brainstem nuclei that may be activated during migraine attack. As LC contains neuromelanin, a by-product of norepinephrine synthesis, it can be delineated in vivo using neuromelanin sensitive magnetic resonance imaging (MRI). The neuromelanin content in LC has been suggested to reflect previous LC activation. We investigated LC MRI contrast in patients with migraine with aura (MWA) and its correlation with migraine features. METHODS: This matched cohort study compared 23 MWA patients aged 30-55 and without comorbidity, to 23 sex- and age-matched healthy controls. The study was conducted in a University Hospital. LC contrast was measured with T1 neuromelanin-sensitive-weighted 3T MRI. Voxels were manually selected by 2 independent researchers and comparison was made twice using intersection and union of the voxels selected by the 2 observers. RESULTS: No difference was found in neuromelanin LC contrast between MWA patients and controls with both the INTER method (0.224 ± 0.042 vs 0.228 ± 0.048; difference: 0.0001 (95%CI: -0.032 to 0.026), P = .799) and UNION method (0.218 ± 0.043 vs 0.222 ± 0.047; difference: -0.0012 (95%CI: -0.031 to 0.026), P = .775). Global LC volume was also similar between the 2 groups with INTER method (15.087 ± 3.965 vs 13.739 ± 3.583; difference: 2 (95%CI: -1 to 4), P = .233) and UNION method (17.522 ± 4.440 vs 16.087 ± 4.274; difference: 1 (95%CI: -2 to 4), P = .270). Moreover, no correlations were found between neuromelanin LC contrast and migraine features (duration of migraine and frequency of attacks). CONCLUSION: These negative findings do not support the use of neuromelanin LC contrast as a biomarker of MA.

Topological disintegration of resting state functional connectomes in coma.

Graph theory has been playing an increasingly important role in understanding the organizational properties of brain networks, subsequently providing new tools for the search of neural correlates of consciousness, particularly in the context of patients recovering from severe brain injury. However, this approach is not without challenges, as it usually relies on arbitrarily fixing a threshold in order to retain the strongest connections proportionally equal across subjects. This method increases the comparability between individuals or groups but it risks the inclusion of false positive and therefore spurious connections, especially in the context of brain disorders. Resting state data acquired in 25 coma patients and 22 healthy subjects was compared. We obtained a representative fixed density of significant connections by first applying a p-value-based threshold on healthy subjects' networks and then choosing a threshold at which all individuals exhibited meaningful connections. The obtained threshold (i.e. 10%) was used to construct graphs in the patient group. The findings showed that coma patients have lower number of significant connections with approximately 50% of them not fulfilling the criteria of the fixed density threshold. The remaining patients with relatively preserved global functional connectivity had sufficient significant connections between regions, but showed signs of major whole-brain network reorganization. These results warrant careful consideration in the construction of functional connectomes in patients with disorders of consciousness and set the scene for future studies investigating potential clinical implications of such an approach.

Discriminating between neurofibromatosis-1 and typically developing children by means of multimodal MRI and multivariate analyses.

Neurofibromatosis Type 1 leads to brain anomalies involving both gray and white matter. The extent and granularity of these anomalies, together with their possible impact on brain activity, is still unknown. In this multicentric cross-sectional study we submitted a sample of 42 typically developing and 38 neurofibromatosis-1 children to a multimodal MRI assessment including T1, diffusion weighted and resting state functional sequences. We used a pipeline involving several features selection steps coupled with multivariate statistical analysis (supporting vector machine) to discriminate between the two groups while having interpretable models. We used MRI indexes measuring macro (gray matter volume) and microstructural (fractional anisotropy, mean diffusivity) characteristics of the brain, as well as indexes of brain activity (fractional amplitude of low frequency fluctuations) and connectivity (local and global correlation) at rest. We found that structural indexes could discriminate between the two groups, with the mean diffusivity leading to performance as high as the combination of all structural indexes combined (accuracy = 0.86), while functional indexes had worse performances. The MRI signature of NF1 brain pathology is a combination of gray and white matter abnormalities, as measured with gray matter volume, fractional anisotropy, and mean diffusivity.