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Abstract Objective Monosynaptically cortically innervated α‐motoneurons are early and strongly involved in amyotrophic lateral sclerosis (ALS). Consequently, the muscles that receive the strongest direct corticomotoneuronal input are the clinically most affected. To objectify this concept in vivo through morphological image correlates, whole‐body magnetic resonance imaging (MRI) with muscle signal analysis was performed in patients with ALS compared to healthy controls. Methods Modified Dixon‐based whole‐body MRI was acquired in patients with ALS (n = 33) and matched healthy controls (n = 30). Manual labeling of limb muscle MRI was performed, and a specific subset of nine muscles, selected as pairs of muscle groups with different corticomotoneuronal input, was analyzed per subject based on their volume, fat fraction, and functional remaining muscle area (fRMA). Results Statistical analysis of 978 muscles in total revealed significantly decreased volumes, decreased fRMA, and increased fat fraction in the muscles of patients with ALS compared to controls. The clinical degree of pareses of directly innervated muscles was significantly worse than that of less directly innervated muscles in each comparison. The muscles receiving stronger direct corticomotoneuronal input showed more pronounced morphological involvement compared to those with less monosynaptic corticomotoneuronal input (fRMA, significant in three pairwise comparisons). Interpretation In conclusion, whole‐body MRI‐based muscle analysis provided additional evidence for a characteristic pattern of pareses in ALS. This technical approach (parameterization and quantification of muscle alterations from MRI) to patients with ALS could pave the way for the future establishment of a diagnostic algorithm of muscle MRI for ALS and may serve as a biomarker.

Progressive supranuclear palsy (PSP) is an atypical Parkinsonian syndrome characterized initially by falls and eye movement impairment. This multimodal imaging study aimed at eliciting structural and functional disease-specific brain alterations. T1-weighted and resting-state functional MRI were applied in multi-centric cohorts of PSP and matched healthy controls. Midbrain, cerebellum, and cerebellar peduncles showed severely low gray/white matter volume, whereas thinner cortical gray matter was observed in cingulate cortex, medial and temporal gyri, and insula. Eigenvector centrality analyses revealed regionally specific alterations. Multivariate pattern recognition classified patients correctly based on gray and white matter segmentations with up to 98 % accuracy. Highest accuracies were obtained when restricting feature selection to the midbrain. Eigenvector centrality indices yielded an accuracy around 70 % in this comparison; however, this result did not reach significance. In sum, the study reveals multimodal, widespread brain changes in addition to the well-known midbrain atrophy in PSP. Alterations in brain structure seem to be superior to eigenvector centrality parameters, in particular for prediction with machine learning approaches.

Abstract The hypothalamus is a small structure of the brain with an essential role in metabolic homeostasis, sleep regulation, and body temperature control. Some neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and dementia syndromes are reported to be related to hypothalamic volume alterations. Despite its crucial role in human body regulation, neuroimaging studies of this structure are rather scarce due to work-intensive operator-dependent manual delineations from MRI and lack of automated segmentation tools. In this study we present a fully automatic approach based on deep convolutional neural networks (CNN) for hypothalamic segmentation and volume quantification. We applied CNN of U-Net architecture with EfficientNetB0 backbone to allow for accurate automatic hypothalamic segmentation in seconds on a GPU. We further applied our approach for the quantification of the normalized hypothalamic volumes to a large neuroimaging dataset of 432 ALS patients and 112 healthy controls (without the ground truth labels). Using the automated volumetric analysis, we could reproduce hypothalamic atrophy findings associated with ALS by detecting significant volume differences between ALS patients and controls at the group level. In conclusion, a fast and unbiased AI-assisted hypothalamic quantification method is introduced in this study (whose acceptance rate based on the outlier removal strategy was estimated to be above 95%) and made publicly available for researchers interested in the conduction of hypothalamus studies at a large scale.

Background: Paraneoplastic ischemic stroke has a poor prognosis. We have recently reported an algorithm based on the number of ischemic territories, C-reactive protein (CRP), lactate dehydrogenase (LDH), and granulocytosis to predict the underlying active cancer in a case-control setting. However, co-occurrence of cancer and stroke might also be merely incidental. Objective: To detect cancer-associated ischemic stroke in a large, unselected cohort of consecutive stroke patients by detailed analysis of ischemic stroke associated with specific cancer subtypes and comparison to patients with bacterial endocarditis. Methods: Retrospective single-center cohort study of consecutive 1612 ischemic strokes with magnetic resonance imaging, CRP, LDH, and relative granulocytosis data was performed, including identification of active cancers, history of now inactive cancers, and the diagnosis of endocarditis. The previously developed algorithm to detect paraneoplastic cancer was applied. Tumor types associated with paraneoplastic stroke were used to optimize the diagnostic algorithm. Results: Ischemic strokes associated with active cancer, but also endocarditis, were associated with more ischemic territories as well as higher CRP and LDH levels. Our previous algorithm identified active cancer-associated strokes with a specificity of 83% and sensitivity of 52%. Ischemic strokes associated with lung, pancreatic, and colorectal (LPC) cancers but not with breast and prostate cancers showed more frequent and prominent characteristics of paraneoplastic stroke. A multiple logistic regression model optimized to identify LPC cancers detected active cancer with a sensitivity of 77.8% and specificity of 81.4%. The positive predictive value (PPV) for all active cancers was 13.1%. Conclusion: Standard clinical examinations can be employed to identify suspect paraneoplastic stroke with an adequate sensitivity, specificity, and PPV when it is considered that the association of ischemic stroke with breast and prostate cancers in the stroke-prone elderly population might be largely incidental.

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

Abstract Background Phelan-McDermid syndrome (PMS) is a rare multisystem disease with global developmental delay and autistic features. Genetically, the disease is based on a heterozygous deletion of chromosome 22q13.3 with involvement of at least part of the SHANK3 gene or heterozygous pathogenic variants in SHANK3. Pathophysiologically, this syndrome has been regarded as a synaptopathy, but current data suggest an additional concept, since axonal functions of neurons are also impaired, thus, the specific pathophysiological processes in this disease are not yet fully understood. Since symptoms of the autism spectrum, regression, and stagnation in development occur, we investigated whether neuroinflammatory and neurodegenerative processes may also play a role. To this end, we analysed biomarkers in cerebrospinal fluid (CSF) and parameters from magnetic resonance imaging with high-resolution structural T1w volumetry and diffusion tensor imaging analysis in 19 Phelan-McDermid syndrome patients. Results CSF showed no inflammation but abnormalities in tau protein and amyloid-ß concentrations, however, with no typical biomarker pattern as in Alzheimer’s disease. It could be demonstrated that these CSF changes were correlated with integrity losses of the fibres in the corticospinal tract as well as in the splenium and dorsal part of the cingulum. High CSF levels of tau protein were associated with loss of integrity of fibres in the corticospinal tract; lower levels of amyloid-ß were associated with decreasing integrity of fibre tracts of the splenium and posterior cingulate gyrus. Volumetric investigations showed global atrophy of the white matter, but not the grey matter, and particularly not in temporal or mesiotemporal regions, as is typical in later stages of Alzheimer’s disease. Conclusions In summary, alterations of neurodegenerative CSF markers in PMS individuals could be demonstrated which were correlated with structural connectivity losses of the corticospinal tract, the splenium, and the dorsal part of the cingulum, which can also be associated with typical clinical symptoms in these patients. These findings might represent a state of dysfunctional processes with ongoing degenerative and regenerative processes or a kind of accelerated aging. This study should foster further clinical diagnostics like tau- and amyloid-PET imaging as well as novel scientific approaches especially in basic research for further mechanistic proof.

Objective: The corticospinal tract (CST) reveals progressive microstructural alterations in ALS measurable by DTI. The aim of this study was to evaluate fractional anisotropy (FA) along the CST as a longitudinal marker of disease progression in ALS. Methods: The study cohort consisted of 114 patients with ALS and 110 healthy controls from the second prospective, longitudinal, multicentre study of the Canadian ALS Neuroimaging Consortium (CALSNIC-2). DTI and clinical data from a harmonized protocol across 7 centres were collected. Thirty-nine ALS patients and 61 controls completed baseline and two follow-up visits and were included for longitudinal analyses. Whole brain-based spatial statistics and hypothesis-guided tract-of-interest analyses were performed for cross-sectional and longitudinal analyses. Results: FA was reduced at baseline and longitudinally in the CST, mid-corpus callosum (CC), frontal lobe, and other ALS-related tracts, with alterations most evident in the CST and mid-CC. CST and pontine FA correlated with functional impairment (ALSFRS-R), upper motor neuron function, and clinical disease progression rate. Reduction in FA was largely located in the upper CST; however, the longitudinal decline was greatest in the lower CST. Effect sizes were dependent on region, resulting in study group sizes between 17 and 31 per group over a 9-month interval. Cross-sectional effect sizes were maximal in the upper CST; whereas, longitudinal effect sizes were maximal in mid-callosal tracts. Conclusions: Progressive microstructural alterations in ALS are most prominent in the CST and CC. DTI can provide a biomarker of cerebral degeneration in ALS, with longitudinal changes in white matter demonstrable over a reasonable observation period, with a feasible number of participants, and within a multicentre framework.

Background: Dose optimization of sublingual apomorphine (SL-APO), a dopamine agonist for the treatment of OFF episodes in patients with Parkinson’s disease (PD), has been performed under clinical supervision in clinical trials. SL-APO may be a candidate for home dosing optimization which would be less burdensome for patients. Objectives: To evaluate the feasibility and safety of home optimization of SL-APO in patients with PD and OFF episodes. Design: A multicenter, randomized, crossover study comparing SL-APO with subcutaneous apomorphine was conducted, comprising an open-label dose-optimization phase and a treatment phase. This non-comparative analysis focuses on the outcomes of the dose-optimization phase with SL-APO only. Methods: Patients with PD and OFF episodes received SL-APO at an initial dose of 10 mg in the clinic (open-label). Further optimization could continue at home in 5 mg increments during subsequent OFF episodes (maximum dose of 30 mg). Optimization and tolerability were assessed daily by patient-reported feedback via telephone. Patients reporting a FULL ON returned to the clinic for a dose-confirmation visit (DCV). In patients with inadequate response as determined during the DCV, the dose could be further optimized at home. Results: Home optimization was continued by 81.4% (83/102) of patients. Of these, 80.7% identified an effective, tolerable dose. Mean time between initial clinic visit and DCV 1 was 6.8 days, and the final optimized dose of SL-APO was 30 mg (mode). In total, 62.7% of patients reported ⩾1 adverse event; the most common included nausea (31.4%), dizziness (9.8%), somnolence (8.8%), dyskinesia (7.8%), and fatigue (5.9%). The safety profile in this study in which most patients performed home dose optimization was consistent with the study utilizing clinic-based optimization. Conclusion: After the first clinic dose, home dose optimization of SL-APO appears feasible in patients with PD and OFF episodes, with most patients identifying their optimal SL-APO dose at home. Trial registration: This study is registered with EudraCT (2016-003456-7): Clinical Trials register – Search for eudract_number:2016-003456-70.

Summary: Background: People with complex symptomatology but unclear diagnosis presenting to a centre for rare diseases (CRD) may present with mental (co-)morbidity. We hypothesised that combining an expert in somatic medicine with a mental health specialist working in tandem will improve the diagnostic outcome. Methods: Patients aged 12 years and older who presented to one of the 11 participating German CRDs with an unknown diagnosis were recruited into this prospective cohort trial with a two-phase cohort design. From October 1, 2018 to September 30, 2019, participants were allocated to standard care (SC, N = 684), and from October 1, 2019 to January 31, 2021 to innovative care (IC, N = 695). The cohorts consisted mainly of adult participants with only a minority of children included (N = 67). IC included the involvement of a mental health specialist in all aspects of care (e.g., assessing medical records, clinic visits, telehealth care, and case conferences). Clinicaltrials.gov identifier: NCT03563677. Findings: The proportion of patients with diagnoses established within 12 months after the first visit to the CRD explaining the entire symptomatology (primary outcome) was 19% (N = 131 of 672) in the SC and 42% (N = 286 of 686) in the IC cohort (OR adjusted for centre effects 3.45 [95% CrI: 1.99–5.65]). The difference was mainly due to a higher prevalence of mental disorders and non-rare somatic diseases in the IC cohort. The median time to explaining diagnoses was one month shorter with IC (95% CrI: 1–2), and significantly more patients could be referred to local regular care in the IC (27.5%; N = 181 of 659) compared to the SC (12.3%; N = 81 of 658) cohort (OR adjusted for centre effects 2.70 [95% CrI: 2.02–3.60]). At 12-month follow-up, patient satisfaction with care was significantly higher in the IC compared to the SC cohort, while quality of life was not different between cohorts. Interpretation: Our findings suggested that including a mental health specialist in the entire evaluation process of CRDs for undiagnosed adolescents and adults should become an integral part of the assessment of individuals with a suspected rare disease. Funding: The study was funded by the Global Innovation Fund from the Joint Federal Committee in Germany (Innovationsfonds des Gemeinsamen Bundesausschusses), grant number 01NVF17031.

Il-13 is expressed in neurons and IL-13 ko causes memory impairment. Here, authors show that IL-13 and its receptor IL-13Ra1 are pre- and post-synaptic proteins, respectively, involved in synaptic signaling, plasticity and neuroprotection.

Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized by four-repeat tau deposition in various cell types and anatomical regions, and can manifest as several clinical phenotypes, including the most common phenotype, Richardson’s syndrome. The limited availability of biomarkers for PSP relates to the overlap of clinical features with other neurodegenerative disorders, but identification of a growing number of biomarkers from imaging is underway. One way to increase the reliability of imaging biomarkers is to combine different modalities for multimodal imaging. This review aimed to provide an overview of the current state of PSP hybrid imaging by combinations of positron emission tomography (PET) and magnetic resonance imaging (MRI). Specifically, combined PET and MRI studies in PSP highlight the potential of [18F]AV-1451 to detect tau, but also the challenge in differentiating PSP from other neurodegenerative diseases. Studies over the last years showed a reduced synaptic density in [11C]UCB-J PET, linked [11C]PK11195 and [18F]AV-1451 markers to disease progression, and suggested the potential role of [18F]RO948 PET for identifying tau pathology in subcortical regions. The integration of quantitative global and regional gray matter analysis by MRI may further guide the assessment of reduced cortical thickness or volume alterations, and diffusion MRI could provide insight into microstructural changes and structural connectivity in PSP. Challenges in radiopharmaceutical biomarkers and hybrid imaging require further research targeting markers for comprehensive PSP diagnosis.

Abstract Background The underlying neuropathological process of amyotrophic lateral sclerosis (ALS) can be classified in a four‐stage sequential pTDP‐43 cerebral propagation scheme. Using diffusion tensor imaging (DTI), in vivo imaging of these stages has already been shown to be feasible for the specific corticoefferent tract systems. Because both cognitive and oculomotor dysfunctions are associated with microstructural changes at the brain level in ALS, a cognitive and an oculomotor staging classification were developed, respectively. The association of these different in vivo staging schemes has not been attempted to date. Methods A total of 245 patients with ALS underwent DTI, video‐oculography, and cognitive testing using Edinburgh Cognitive and Behavioral ALS Screen (ECAS). A set of tract‐related diffusion metrics, cognitive, and oculomotor parameters was selected for further analysis. Hierarchical and k‐means clustering algorithms were used to obtain an optimal cluster solution. Results According to cluster analysis, differentiation of patients with ALS into four clusters resulted: Cluster A showed the highest fractional anisotropy (FA) values and thereby the best performances in executive oculomotor tasks and cognitive tests, whereas cluster D showed the lowest FA values, the lowest ECAS scores, and the worst executive oculomotor performance across all clusters. Clusters B and C showed intermediate results regarding parameter values. Discussion In a multimodal dataset of technical assessments of brain structure and function in ALS, an artificial intelligence‐based cluster analysis showed high congruence of DTI, executive oculomotor function, and neuropsychological performance for mapping in vivo correlates of neuropathological spreading.

BackgroundFor recurrent high-grade glioma, especially glioblastoma, no standard of care treatment exists. Due to the prolongation of progression-free survival and a cortiocosteroid-sparing effect, bevacizumab is often used in this condition. Despite initial clinical responses, there is growing evidence that bevacizumab may potentiate microstructural alterations which may cause cognitive decline, mostly affecting learning and memory.MethodsTo investigate bevacizumab-associated microstructural damage of defined regions of interest (ROIs) in the white matter, diffusion tensor imaging (DTI) was performed in 10 patients with a case history or third-party report for neurological dysfunction concerning cognitive performance. Serial DTI data before and under bevacizumab were collected and longitudinal changes of fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) were assessed in mesiotemporal (hippocampal), frontal, and occipital regions.ResultsThe longitudinal DTI data under bevacizumab compared to DTI prior to bevacizumab demonstrated a significant decrease in FA and increase in AD and RD both in mesiotemporal (hippocampal) regions and in frontal regions, whereas occipital regions showed no significant alterations in DTI metrics.ConclusionThe regionally impaired microstructure in mesiotemporal (hippocampal) regions and in frontal regions is in line with the fact that neurocognitive impairment in learning and memory is mostly related to hippocampal integrity and attentional control in frontal regions. Further studies could investigate the potential of DTI to assess bevacizumab-associated microstructural damages in vulnerable brain regions.

The incidence of obesity in pet population increased over the last decades. Cats have been suggested as model for human obesity because of similar co-morbidities as diabetes and dyslipidaemia. Aim of this study were to quantify the distribution of visceral and subcutaneous adipose tissue (VAT, SAT respectively) in healthy adult cats during feeding-induced body weight (BW) gain by MRI, and to correlate it to the increased hepatic fat fraction (HFF). Cats received a commercial dry food ad libitum for 40 weeks and were longitudinally scanned three times. VAT and SAT were determined from Dixon MRI data by a dedicated software solution (ATLAS, established in human and rodents). HFF was quantified from a commercially available sequence. At both individual and group level, normalized adipose tissue volumes significantly increased longitudinally, with median VAT/SAT ratio always < 1. With increased BW, more than proportional increased total adipose tissue was observed together with more than proportional increased HFF. HFF is disproportionately high in overweight cats compared to SAT and VAT accumulation in the 40 weeks observation period. Quantitative unbiased MRI examination of different body fat components is useful in longitudinal monitoring of obesity in cats.

IntroductionAmyotrophic Lateral Sclerosis (ALS) is characterized by progressive motoneuron degeneration through cell autonomous and non-cell autonomous mechanisms; and the involvement of the innate and adaptive immune system has been hypothesized based on human and murine model data. We have explored if B-cell activation and IgG responses, as detected by IgG Oligoclonal bands (OCB) in serum and cerebrospinal fluid, were associated with ALS or with a subgroup of patients with distinct clinical features.MethodsIgG OCB were determined in patients affected by ALS (n=457), Alzheimer Disease (n=516), Mild Cognitive Impairment (n=91), Tension-type Headache (n=152) and idiopathic Facial Palsy (n=94). For ALS patients, clinico-demographic and survival data were prospectively collected in the Register Schabia.ResultsThe prevalence of IgG OCB is comparable in ALS and the four neurological cohorts. When the OCB pattern was considered (highlighting either intrathecal or systemic B-cells activation), no effect of OCB pattern on clinic-demographic parameters and overall. ALS patients with intrathecal IgG synthesis (type 2 and 3) were more likely to display infectious, inflammatory or systemic autoimmune conditions.DiscussionThese data suggest that OCB are not related to ALS pathophysiology but rather are a finding possibly indicative a coincidental infectious or inflammatory comorbidity that merits further investigation.

Summary: Background: The emergence of potentially effective new therapies for genetic forms of amyotrophic lateral sclerosis (ALS) necessitates the identification of biomarkers to facilitate early treatment, prior to the onset of motor symptoms. Here, we sought to investigate whether metabolic alterations are detectable in presymptomatic ALS gene mutation carriers, and whether such alterations precede neurofilament light chain (NfL) changes in serum. Methods: Between 02/2014 and 11/2021, we prospectively studied 60 presymptomatic ALS gene mutation carriers (40% male, age 48.7 ± 14.9; 28 C9orf72, 22 SOD1, 10 other) compared to 73 individuals from the same families (47% male, age 47.4 ± 12.9) without pathogenic mutations as controls. Bioimpedance analysis (BIA) and indirect calorimetry were performed, and Body Mass Index (BMI), Fat Mass (FM), Body Fat Percentage, Body Water (BW), Lean Body Mass (LBM), Extracellular Mass (ECM), Body Cell Mass (BCM), ECM/BCM ratio, Cells Percentage, Phase Angle, Resting Metabolic Rate (RMR), Metabolic Ratio (MR), and NfL were measured. Participants and evaluators were blinded regarding gene carrier status. Findings: Presymptomatic ALS gene carriers showed reduced LBM (p = 0.02), BCM (p = 0.004), Cells Percentage (p = 0.04), BW (p = 0.02), Phase Angle (p = 0.04), and increased ECM/BCM ratio (p = 0.04), consistently indicating a loss of metabolically active body cells. While in C9orf72 mutation carriers all tissue masses were reduced, only metabolically active tissue was affected in SOD1 mutation carriers. Unexpectedly, RMR (p = 0.009) and MR (p = 0.01) were lower in presymptomatic ALS gene carriers compared to non-carriers. NfL serum levels were similar in mutation carriers and non-carriers (p = 0.60). Interpretation: The observed metabolic phenomena might reflect reduced physical activity and/or preemptive, insufficient compensatory mechanisms to prepare for the later hypermetabolic state. As pre-symptomatic biomarkers we propose ECM/BCM ratio and Phase Angle for SOD1, and a 4-compartment affection in BIA for C9orf72 mutation carriers. Funding: This work was an investigator-initiated trial. On the German side, there was no institutional or industrial funding. On the Swedish side, this work was supported by grants from the Swedish Brain Foundation (grants nr. 2013-0279, 2016-0303, 2018-0310, 2020-0353), the Swedish Research Council (grants nr. 2012-3167, 2017-03100), the Knut and Alice Wallenberg Foundation (grants nr. 2012.0091, 2014.0305, 2020.0232), the Ulla-Carin Lindquist Foundation, Umeå University (223-2808-12, 223-1881-13, 2.1.12-1605-14) and the Västerbotten County Council (grants nr 56103-7002829), King Gustaf V:s and Queen Victoria's Freemason's Foundation.

Abstract Importance The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context. Objective Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging. Design, setting, and participants Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes. Interventions N.A. Main outcomes and measures Cohen’s kappa, accuracy, and F1-score to assess model performance. Results Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy. Conclusions and relevance Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.

Abstract The objective of this study was to automate the discrimination and quantification of human abdominal body fat compartments into subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) from T1-weighted MRI using encoder-decoder convolutional neural networks (CNN) and to apply the algorithm to a diseased patient sample, i.e., patients with amyotrophic lateral sclerosis (ALS). One-hundred-and-fifty-five participants (74 patients with ALS and 81 healthy controls) were split in training (50%), validation (6%), and test (44%) data. SAT and VAT volumes were determined by a novel automated CNN-based algorithm of U-Net like architecture in comparison with an established protocol with semi-automatic assessment as the reference. The dice coefficients between the CNN predicted masks and the reference segmentation were 0.87 ± 0.04 for SAT and 0.64 ± 0.17 for VAT in the control group and 0.87 ± 0.08 for SAT and 0.68 ± 0.15 for VAT in the ALS group. The significantly increased VAT/SAT ratio in the ALS group in comparison to controls confirmed the previous results. In summary, the CNN approach using CNN of U-Net architecture for automated segmentation of abdominal adipose tissue substantially facilitates data processing and offers the opportunity to automatically discriminate abdominal SAT and VAT compartments. Within the research field of neurodegenerative disorders with body composition alterations like ALS, the unbiased analysis of body fat components might pave the way for these parameters as a potential biological marker or a secondary read-out for clinical trials.

Background: Due to the absence of robust biomarkers, and the low sensitivity and specificity of routine imaging techniques, the differential diagnosis between Parkinson’s disease (PD) and multiple system atrophy (MSA) is challenging. High-field magnetic resonance imaging (MRI) opened up new possibilities regarding the analysis of pathological alterations associated with neurodegenerative processes. Recently, we have shown that quantitative susceptibility mapping (QSM) enables visualization and quantification of two major histopathologic hallmarks observed in MSA: reduced myelin density and iron accumulation in the basal ganglia of a transgenic murine model of MSA. It is therefore emerging as a promising imaging modality on the differential diagnosis of Parkinsonian syndromes. Objectives: To assess QSM on high-field MRI for the differential diagnosis of PD and MSA. Methods: We assessed 23 patients (nine PDs and 14 MSAs) and nine controls using QSM on 3T and 7T MRI scanners at two academic centers. Results: We observed increased susceptibility in MSA at 3T in prototypical subcortical and brainstem regions. Susceptibility measures of putamen, pallidum, and substantia nigra reached excellent diagnostic accuracy to separate both synucleinopathies. Increase toward 100% sensitivity and specificity was achieved using 7T MRI in a subset of patients. Magnetic susceptibility correlated with age in all groups, but not with disease duration in MSA. Sensitivity and specificity were particularly high for possible MSA, and reached 100% in the putamen. Conclusion: Putaminal susceptibility measures, in particular on ultra-high-field MRI, may distinguish MSA patients from both, PD and controls, allowing an early and sensitive diagnosis of MSA.

Background: ALS patients with hexanucleotide expansion in C9orf72 are characterized by a specific clinical phenotype, including more aggressive disease course and cognitive decline. Computerized multiparametric MRI with gray matter volumetry and diffusion tensor imaging (DTI) to analyze white matter structural connectivity is a potential in vivo biomarker. Objective: The objective of this study was to develop a multiparametric MRI signature in a large cohort of ALS patients with C9orf72 mutations. The aim was to investigate how morphological features of C9orf72-associated ALS differ in structural MRI and DTI compared to healthy controls and ALS patients without C9orf72 mutations. Methods: Atlas-based volumetry (ABV) and whole brain-based DTI-based analyses were performed in a cohort of n = 51 ALS patients with C9orf72 mutations and compared with both n = 51 matched healthy controls and n = 51 C9orf72 negative ALS patients, respectively. Subsequently, Spearman correlation analysis of C9orf72 ALS patients‘ data with clinical parameters (age of onset, sex, ALS-FRS-R, progression rate, survival) as well as ECAS and p-NfH in CSF was performed. Results: The whole brain voxel-by-voxel comparison of fractional anisotropy (FA) maps between C9orf72 ALS patients and controls showed significant bilateral alterations in axonal structures of the white matter at group level, primarily along the corticospinal tracts and in fibers projecting to the frontal lobes. For the frontal lobes, these alterations were also significant between C9orf72 positive and C9orf72 negative ALS patients. In ABV, patients with C9orf72 mutations showed lower volumes of the frontal, temporal, and parietal lobe, with the lowest values in the gray matter of the superior frontal and the precentral gyrus, but also in hippocampi and amygdala. Compared to C9orf72 negative ALS, the differences were shown to be significant for cerebral gray matter (p = 0.04), especially in the frontal (p = 0.01) and parietal lobe (p = 0.01), and in the thalamus (p = 0.004). A correlation analysis between ECAS and averaged regional FA values revealed significant correlations between cognitive performance in ECAS and frontal association fibers. Lower FA values in the frontal lobes were associated with worse performance in all cognitive domains measured (language, verbal fluency, executive functions, memory and spatial perception). In addition, there were significant negative correlations between age of onset and atlas-based volumetry results for gray matter. Conclusions: This study demonstrates a distinct pattern of DTI alterations of the white matter and ubiquitous volume reductions of the gray matter early in the disease course of C9orf72-associated ALS. Alterations were closely linked to a more aggressive cognitive phenotype. These results are in line with an expected pTDP43 propagation pattern of cortical affection and thus strengthen the hypothesis that an underlying developmental disorder is present in ALS with C9orf72 expansions. Thus, multiparametric MRI could contribute to the assessment of the disease as an in vivo biomarker even in the early phase of the disease.

Introduction: Dementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI). Methods: Atlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer’s disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification). Results: The binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration. Discussion: Results suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer’s disease.

Background: The pathophysiology of idiopathic intracranial hypertension (IIH), a condition characterized by raised intracranial pressure, is not well understood. Objectives: We hypothesized that the hypothalamus might exhibit alterations in patients with IIH, based on its established association with obesity and the potential role of hormonal and metabolic factors in IIH. Design: Retrospective single-center cohort study. Methods: Thirty-three individuals with IIH and 40 matched healthy individuals were studied, including levels of the hormones and proteins leptin, adiponectin, ghrelin, insulin, growth/differentiation factor 15 (GDF15), somatostatin, and melatonin. In vivo high-resolution T1-weighted magnetic resonance imaging (MRI) data were analyzed by quantification of hypothalamic volumes using a well-established segmentation method, separate for the anterior and the posterior hypothalamic subvolumes. An additional analysis was performed using age, gender, and BMI-matched subgroups of 20 IIH patients and 20 controls. Results: The analysis of laboratory values showed significantly increased insulin, leptin, and melatonin levels for IIH patients in comparison to controls, while adiponectin levels were decreased in IIH; however, only melatonin level differences could be confirmed in the analysis with BMI matching. There was no statistical difference in total hypothalamus volumes between IIH and controls, but the hypothalamic morphology was altered in IIH patients with a lower volume of the anterior part of the hypothalamus and a higher volume of the posterior part; these results were identical in the analysis of the BMI-matched groups. The correlation analyses between hormonal changes and hypothalamic morphology did not achieve significant results. Conclusion: In this exploratory study, morphological abnormalities of the hypothalamus were observed to be associated with the IIH complex, although the mechanism remains to be unraveled. These findings expand the metabolic phenotype of IIH, but further functional studies are necessary to corroborate these data.

Abstract The dysregulation of peripheral immunity in Parkinson’s Disease (PD) includes changes in both the relative numbers and gene expression of T cells. The presence of peripheral T-cell abnormalities in PD is well-documented, but less is known about their association to clinical parameters, such as age, age of onset, progression rate or severity of the disease. We took a detailed look at T-cell numbers, gene expression and activation in cross-sectional cohorts of PD patients and age-matched healthy controls by means of flow cytometry and NanoString gene expression assay. We show that the well-pronounced decrease in relative T-cell numbers in PD blood is mostly driven by a decrease of CD8+ cytotoxic T cells and is primarily associated with the severity of the disease. In addition, we demonstrate that the expression of inflammatory genes in T cells from PD patients is also associated with disease severity. PD T cells presented with increased activation upon stimulation with phytohemagglutinin that also correlated with disease severity. In summary, our data suggest that the consequences of disease severity account for the changes in PD T cells, rather than age, age of onset, duration or the disease progression rate.

Background: Systemic and neuroinflammatory processes play key roles in neurodegenerative diseases such as Parkinson's disease (PD). Physical trauma which induces considerable systemic inflammatory responses, represents an evident environmental factor in aging. However, little is known about the impact of physical trauma, on the immuno-pathophysiology of PD. Especially blunt chest trauma which is associated with a high morbidity and mortality rate in the elderly population, can induce a strong pulmonary and systemic inflammatory reaction. Hence, we sought out to combine a well-established thoracic trauma mouse model with a well-established PD mouse model to characterize the influence of physical trauma to neurodegenerative processes in PD. Methods: To study the influence of peripheral trauma in a PD mouse model we performed a highly standardized blunt thorax trauma in a well-established PD mouse model and determined the subsequent local and systemic response. Results: We could show that blunt chest trauma leads to a systemic inflammatory response which is quantifiable with increased inflammatory markers in bronchoalveolar fluids (BALF) and plasma regardless of the presence of a PD phenotype. A difference of the local inflammatory response in the brain between the PD group and non-PD group could be detected, as well as an increase in the formation of oligomeric pathological alpha-Synuclein (asyn) suggesting an interplay between peripheral thoracic trauma and asyn pathology in PD. Conclusion: Taken together this study provides evidence that physical trauma is associated with increased asyn oligomerization in a PD mouse model underlining the relevance of PD pathogenesis under traumatic settings.

Human aging is characterized by progressive loss of physiological functions. To assess changes in the brain that occur with increasing age, the concept of brain aging has gained momentum in neuroimaging with recent advancements in statistical regression and machine learning (ML). A common technique to assess the brain age of a person is, first, fitting a regression model to neuroimaging data from a group of healthy subjects, and then, using the resulting model for age prediction. Although multiparametric MRI-based models generally perform best, models solely based on diffusion tensor imaging have achieved similar results, with the benefits of faster data acquisition and better replicability across scanners and field strengths. In the present study, we developed an artificial neural network (ANN) for brain age prediction based upon tract-based fractional anisotropy (FA). Consequently, we investigated if this age-prediction model could also be used for non-linear age correction of white matter diffusion metrics in healthy adults. The brain age prediction accuracy of the ANN (R2 = 0.47) was similar to established multimodal models. The comparison of the ANN-based age-corrected FA with the tract-wise linear age-corrected FA resulted in an R2 value of 0.90 [0.82; 0.93] and a mean difference of 0.00 [−0.04; 0.05] for all tract systems combined. In conclusion, this study demonstrated the applicability of complex ANN models to non-linear age correction of tract-based diffusion metrics as a proof of concept.

Neuroimaging assessment of motor neuron disease has turned into a cornerstone of its clinical workup. Amyotrophic lateral sclerosis (ALS), as a paradigmatic motor neuron disease, has been extensively studied by advanced neuroimaging methods, including molecular imaging by MRI and PET, furthering finer and more specific details of the cascade of ALS neurodegeneration and symptoms, facilitated by multicentric studies implementing novel methodologies. With an increase in multimodal neuroimaging data on ALS and an exponential improvement in neuroimaging technology, the need for harmonization of protocols and integration of their respective findings into a consistent model becomes mandatory. Integration of multimodal data into a model of a continuing cascade of functional loss also calls for the best attempt to correlate the different molecular imaging measurements as performed at the shortest inter-modality time intervals possible. As outlined in this perspective article, simultaneous PET/MRI, nowadays available at many neuroimaging research sites, offers the perspective of a one-stop shop for reproducible imaging biomarkers on neuronal damage and has the potential to become the new gold standard for characterizing motor neuron disease from the clinico-radiological and neuroscientific perspectives.

IntroductionDiffusion tensor imaging (DTI) can be used to map disease progression in amyotrophic lateral sclerosis (ALS) and therefore is a promising candidate for a biomarker in ALS. To this end, longitudinal study protocols need to be optimized and validated regarding group sizes and time intervals between visits. The objective of this study was to assess the influences of sample size, the schedule of follow-up measurements, and measurement uncertainties on the statistical power to optimize longitudinal DTI study protocols in ALS.Patients and methodsTo estimate the measurement uncertainty of a tract-of–interest-based DTI approach, longitudinal test-retest measurements were applied first to a normal data set. Then, DTI data sets of 80 patients with ALS and 50 healthy participants were analyzed in the simulation of longitudinal trajectories, that is, longitudinal fractional anisotropy (FA) values for follow-up sessions were simulated for synthetic patient and control groups with different rates of FA decrease in the corticospinal tract. Monte Carlo simulations of synthetic longitudinal study groups were used to estimate the statistical power and thus the potentially needed sample sizes for a various number of scans at one visit, different time intervals between baseline and follow-up measurements, and measurement uncertainties.ResultsFrom the simulation for different longitudinal FA decrease rates, it was found that two scans per session increased the statistical power in the investigated settings unless sample sizes were sufficiently large and time intervals were appropriately long. The positive effect of a second scan per session on the statistical power was particularly pronounced for FA values with high measurement uncertainty, for which the third scan per session increased the statistical power even further.ConclusionWith more than one scan per session, the statistical power of longitudinal DTI studies can be increased in patients with ALS. Consequently, sufficient statistical power can be achieved even with limited sample sizes. An improved longitudinal DTI study protocol contributes to the detection of small changes in diffusion metrics and thereby supports DTI as an applicable and reliable non-invasive biomarker in ALS.

Objectives: Multiparametric magnetic resonance imaging (MRI) is established as a technical instrument for the characterisation of patients with amyotrophic lateral sclerosis (ALS). The contribution of relaxation-weighted sodium ( 23 NaR) MRI remains to be defined. The aim of this study is to apply 23 NaR MRI to investigate brain sodium homeostasis and map potential alterations in patients with ALS as compared with healthy controls. Materials and Methods: Seventeen patients with ALS (mean age 61.1 ± 11.4 years, m/f = 9/8) and 10 healthy control subjects (mean age 60.3 ± 15.3 years, m/f = 6/4) were examined by 23 NaR MRI at 3 T. Regional sodium maps were obtained by the calculation of the weighted difference from two image data sets with different echo times (TE 1 = 0.3 ms, TE 2 = 25 ms). Voxel-based analysis of the relaxation-weighted maps, together with 23 Na concentration maps for comparison, was performed. Results: ROI-based analyses of relaxation-weighted brain sodium concentration maps demonstrated increased sodium concentrations in the upper corticospinal tracts and in the frontal lobes in patients with ALS; no differences between ALS patients and controls were found in reference ROIs, where no involvement in ALS-associated neurodegeneration could be anticipated. Conclusion: 23 NaR MRI mapped regional alterations within disease-relevant areas in ALS which correspond to the stages of the central nervous system (CNS) pathology, providing evidence that the technique is a potential biological marker of the cerebral neurodegenerative process in ALS.

Abstract Background Increased catabolism has recently been recognized as a clinical manifestation of amyotrophic lateral sclerosis (ALS). The hypothalamic systems have been shown to be involved in the metabolic dysfunction in ALS, but the exact extent of hypothalamic circuit alterations in ALS is yet to be determined. Here we explored the integrity of large-scale cortico-hypothalamic circuits involved in energy homeostasis in murine models and in ALS patients. Methods The rAAV2-based large-scale projection mapping and image analysis pipeline based on Wholebrain and Ilastik software suites were used to identify and quantify projections from the forebrain to the lateral hypothalamus in the SOD1(G93A) ALS mouse model (hypermetabolic) and the FusΔNLS ALS mouse model (normo-metabolic). 3 T diffusion tensor imaging (DTI)-magnetic resonance imaging (MRI) was performed on 83 ALS and 65 control cases to investigate cortical projections to the lateral hypothalamus (LHA) in ALS. Results Symptomatic SOD1(G93A) mice displayed an expansion of projections from agranular insula, ventrolateral orbitofrontal and secondary motor cortex to the LHA. These findings were reproduced in an independent cohort by using a different analytic approach. In contrast, in the FusΔNLS ALS mouse model hypothalamic inputs from insula and orbitofrontal cortex were maintained while the projections from motor cortex were lost. The DTI-MRI data confirmed the disruption of the orbitofrontal-hypothalamic tract in ALS patients. Conclusion This study provides converging murine and human data demonstrating the selective structural disruption of hypothalamic inputs in ALS as a promising factor contributing to the origin of the hypermetabolic phenotype.

Mutations in the RNA binding protein FUS are associated with ALS. Here the authors show that in FUS knock-in mice there is a progressive increase in neuronal activity in the frontal cortex which is associated with altered synaptic gene expression.

Abstract The identification of cerebral microinfarctions with magnetic resonance imaging (MRI) and histological methods remains challenging in aging and dementia. Here, we matched pathological changes in the microvasculature of cortical cerebral microinfarcts to MRI signals using single 100 μm-thick histological sections scanned with ultra-high-resolution 11.7 T MRI. Histologically, microinfarcts were located in superficial or deep cortical layers or transcortically, compatible with the pattern of layer-specific arteriolar blood supply of the cerebral cortex. Contrary to acute microinfarcts, at chronic stages the core region of microinfarcts showed pallor with extracellular accumulation of lipofuscin and depletion of neurons, a dense meshwork of collagen 4-positive microvessels with numerous string vessels, CD68-positive macrophages and glial fibrillary acidic protein (GFAP)-positive astrocytes. In MRI scans, cortical microinfarcts at chronic stages, called chronic cortical microinfarcts here, gave hypointense signals in T1-weighted and hyperintense signals in T2-weighted images when thinning of the tissue and cavitation and/or prominent iron accumulation were present. Iron accumulation in chronic microinfarcts, histologically verified with Prussian blue staining, also produced strong hypointense T2*-weighted signals. In summary, the microinfarct core was occupied by a dense microvascular meshwork with string vessels, which was invaded by macrophages and astroglia and contained various degrees of iron accumulation. While postmortem ultra-high-resolution single-section imaging improved MRI-histological matching and the structural characterization of chronic cortical cerebral microinfarcts, miniscule microinfarcts without thinning or iron accumulation could not be detected with certainty in the MRI scans. Moreover, string vessels at the infarct margin indicate disturbances in the microcirculation in and around microinfarcts, which might be exploitable in the diagnostics of cortical cerebral microinfarcts with MRI in vivo.

Abstract Objective Heterozygous SHANK3 mutations or partial deletions of the long arm of chromosome 22, also known as Phelan–McDermid syndrome, result in a syndromic form of the autism spectrum as well as in global developmental delay, intellectual disability, and several neuropsychiatric comorbidities. The exact pathophysiological mechanisms underlying the disease are still far from being deciphered but studies of SHANK3 models have contributed to the understanding of how the loss of the synaptic protein SHANK3 affects neuronal function. Methods and results Diffusion tensor imaging‐based and automatic volumetric brain mapping were performed in 12 SHANK3‐deficient participants (mean age 19 ± 15 years) versus 14 age‐ and gender‐matched controls (mean age 29 ± 5 years). Using whole brain–based spatial statistics, we observed a highly significant pattern of white matter alterations in participants with SHANK3 mutations with focus on the long association fiber tracts, particularly the uncinate tract and the inferior fronto‐occipital fasciculus. In contrast, only subtle gray matter volumetric abnormalities were detectable. In a back‐translational approach, we observed similar white matter alterations in heterozygous isoform–specific Shank3 knockout (KO) mice. Here, in the baseline data sets, the comparison of Shank3 heterozygous KO vs wildtype showed significant fractional anisotropy reduction of the long fiber tract systems in the KO model. The multiparametric Magnetic Resonance Imaging (MRI) analysis by DTI and volumetry demonstrated a pathology pattern with severe white matter alterations and only subtle gray matter changes in the animal model. Interpretation In summary, these translational data provide strong evidence that the SHANK3‐deficiency–associated pathomechanism presents predominantly with a white matter disease. Further studies should concentrate on the role of SHANK3 during early axonal pathfinding/wiring and in myelin formation.

Background and Objective: Diffusion tensor imaging (DTI) can be used to tract-wise map correlates of the sequential disease progression and, therefore, to assess disease stages of amyotrophic lateral sclerosis (ALS) in vivo. According to a threshold-based sequential scheme, a classification of ALS patients into disease stages is possible, however, several patients cannot be staged for methodological reasons. This study aims to implement a multivariate Bayesian classification algorithm for disease stage prediction at an individual ALS patient level based on DTI metrics of involved tract systems to improve disease stage mapping. Methods: The analysis of fiber tracts involved in each stage of ALS was performed in 325 ALS patients and 130 age- and gender-matched healthy controls. Based on Bayes’ theorem and in accordance with the sequential disease progression, a multistage classifier was implemented. Patients were categorized into in vivo DTI stages using the threshold-based method and the Bayesian algorithm. By the margin of confidence, the reliability of the Bayesian categorizations was accessible. Results: Based on the Bayesian multistage classifier, 88% of all ALS patients could be assigned into an ALS stage compared to 77% using the threshold-based staging scheme. Additionally, the confidence of all classifications could be estimated. Conclusions: By the application of the multi-stage Bayesian classifier, an individualized in vivo cerebral staging of ALS patients was possible based on the sequentially involved tract systems and, furthermore, the reliability of the respective classifications could be determined. The Bayesian classification algorithm is an improvement of the threshold-based staging method and could provide a framework for extending the DTI-based in vivo cerebral staging in ALS.

Introduction: Within the core neuroimaging signature of amyotrophic lateral sclerosis (ALS), the corpus callosum (CC) is increasingly recognized as a consistent feature. The aim of this study was to investigate the sensitivity and specificity of the microstructural segmental CC morphology, assessed by diffusion tensor imaging (DTI) and high-resolution T1-weighted (T1w) imaging, in a large cohort of ALS patients including different clinical phenotypes. Methods: In a single-centre study, 575 patients with ALS (classical phenotype, N = 432; restricted phenotypes primary lateral sclerosis (PLS) N = 55, flail arm syndrome (FAS) N = 45, progressive bulbar palsy (PBP) N = 22, lower motor neuron disease (LMND) N = 21) and 112 healthy controls underwent multiparametric MRI, i.e. volume-rendering T1w scans and DTI. Tract-based fractional anisotropy statistics (TFAS) was applied to callosal tracts of CC areas I-V, identified from DTI data (tract-of-interest (TOI) analysis), and texture analysis was applied to T1w data. In order to further specify the callosal alterations, a support vector machine (SVM) algorithm was used to discriminate between motor neuron disease patients and controls. Results: The analysis of white matter integrity revealed predominantly FA reductions for tracts of the callosal areas I, II, and III (with highest reductions in callosal area III) for all ALS patients and separately for each phenotype when compared to controls; texture analysis demonstrated significant alterations of the parameters entropy and homogeneity for ALS patients and all phenotypes for the CC areas I, II, and III (with again highest reductions in callosal area III) compared to controls. With SVM applied on multiparametric callosal parameters of area III, a separation of all ALS patients including phenotypes from controls with 72% sensitivity and 73% specificity was achieved. These results for callosal area III parameters could be improved by an SVM of six multiparametric callosal parameters of areas I, II, and III, achieving a separation of all ALS patients including phenotypes from controls with 84% sensitivity and 85% specificity. Discussion: The multiparametric MRI texture and DTI analysis demonstrated substantial alterations of the frontal and central CC with most significant alterations in callosal area III (motor segment) in ALS and separately in all investigated phenotypes (PLS, FAS, PBP, LMND) in comparison to controls, while no significant differences were observed between ALS and its phenotypes. The combination of the texture and the DTI parameters in an unbiased SVM-based approach might contribute as a neuroimaging marker for the assessment of the CC in ALS, including subtypes.

Currently, no treatments available for Parkinson’s disease (PD) can slow PD progression. At the early stage of the disease, only a subset of individuals with PD progress quickly, while the majority have a slowly progressive form of the disease. In developing treatments that aim to slow PD progression, clinical trials aim to include individuals who are likely to progress faster, such that a treatment effect, if one exists, can be identified easier and earlier. The aim of the present study was to identify baseline predictors of clinical progression in early PD. We analyzed 12-month data acquired from the PASADENA trial Part 1 (NCT03100149, n = 76 participants who were allocated to the placebo arm and did not start symptomatic therapy) and the Parkinson’s Progression Markers Initiative (PPMI) study (n = 139 demographically and clinically matched participants). By using ridge regression models including clinical characteristics, imaging, and non-imaging biomarkers, we found that Hoehn and Yahr stage and dopamine transporter single-photon emission computed tomography specific binding ratios (Dat-SPECT SBR) in putamen ipsilateral to the side of motor symptom onset predicted PD progression at the early stage of the disease. Further studies are needed to confirm the validity of these predictors to identify with high accuracy individuals with early PD with a faster progression phenotype.

The potential of multiparametric quantitative neuroimaging has been extensively discussed as a diagnostic tool in amyotrophic lateral sclerosis (ALS). In the past, the integration of multimodal, quantitative data into a useful diagnostic classifier was a major challenge. With recent advances in the field, machine learning in a data driven approach is a potential solution: neuroimaging biomarkers in ALS are mainly observed in the cerebral microstructure, with diffusion tensor imaging (DTI) and texture analysis as promising approaches. We set out to combine these neuroimaging markers as age-corrected features in a machine learning model with a cohort of 502 subjects, divided into 404 patients with ALS and 98 healthy controls. We calculated a linear support vector classifier (SVC) which is a very robust model and then verified the results with a multilayer perceptron (MLP)/neural network. Both classifiers were able to separate ALS patients from controls with receiver operating characteristic (ROC) curves showing an area under the curve (AUC) of 0.87–0.88 (“good”) for the SVC and 0.88–0.91 (“good” to “excellent”) for the MLP. Among the coefficients of the SVC, texture data contributed the most to a correct classification. We consider these results as a proof of concept that demonstrated the power of machine learning in the application of multiparametric quantitative neuroimaging data to ALS.

Background: The regional distribution of the widespread cerebral morphological alterations in progressive supranuclear palsy (PSP) is considered to include segmental parts of the corpus callosum (CC).Objective: The study was designed to investigate the regional white matter (WM) of the CC by T1 weighted magnetic resonance imaging (T1w MRI) data combined with diffusion tensor imaging (DTI) data in PSP patients, differentiated in the variants Richardson syndrome and PSP-parkinsonism, and to compare them with Parkinson’s Disease (PD) patients and healthy controls, in order to identify macro- and micro-structural alterations in vivo.Methods: MRI-based WM mapping was used to perform an operator-independent segmentation for the different CC segments in 66 PSP patients vs. 66 PD patients vs. 44 matched healthy controls. The segmentation was followed by both planimetric and texture analysis of the separated CC areas for the comparison of the three groups. Results were complemented by a DTI-based tract-of-interest analysis of the associated callosal tracts.Results: Significant alterations of the parameters entropy and homogeneity compared to controls were observed for PSP as well as for PD for the CC areas I, II, and III. The inhomogeneity in area II in the PSP cohort was the highest and differed significantly from PD. A combined score was defined as a potential marker for the different types of neurodegenerative parkinsonism; receiver operating characteristics (ROC) curves were calculated with areas under the curve values of 0.86 for PSP vs. controls, 0.72 for PD vs. controls, and 0.69 for PSP vs. PD, respectively.Conclusion: The multiparametric MRI texture and DTI analysis demonstrated extensive alterations of the frontal CC in neurodegenerative parkinsonism, whereas regional CC atrophy cannot be regarded as a constant neuroimaging feature of PSP. Specifically, the comparison PSP vs. PD revealed significant alterations in callosal area II. The combination of the texture and the DTI parameters might contribute as a neuroimaging marker for the assessment of the CC in PSP, including the differentiation vs. PD.

Background: With the advances in neuroimaging in amyotrophic lateral sclerosis (ALS), it has been speculated that multiparametric magnetic resonance imaging (MRI) is capable to contribute to early diagnosis. Machine learning (ML) can be regarded as the missing piece that allows for the useful integration of multiparametric MRI data into a diagnostic classifier. The major challenges in developing ML classifiers for ALS are limited data quantity and a suboptimal sample to feature ratio which can be addressed by sound feature selection. Methods: We conducted a systematic review to collect MRI biomarkers that could be used as features by searching the online database PubMed for entries in the recent 4 years that contained cross-sectional neuroimaging data of subjects with ALS and an adequate control group. In addition to the qualitative synthesis, a semi-quantitative analysis was conducted for each MRI modality that indicated which brain regions were most commonly reported. Results: Our search resulted in 151 studies with a total of 221 datasets. In summary, our findings highly resembled generally accepted neuropathological patterns of ALS, with degeneration of the motor cortex and the corticospinal tract, but also in frontal, temporal, and subcortical structures, consistent with the neuropathological four-stage model of the propagation of pTDP-43 in ALS. Conclusions: These insights are discussed with respect to their potential for MRI feature selection for future ML-based neuroimaging classifiers in ALS. The integration of multiparametric MRI including DTI, volumetric, and texture data using ML may be the best approach to generate a diagnostic neuroimaging tool for ALS.

Background: Currently available treatments for Parkinson's disease (PD) do not slow clinical progression nor target alpha-synuclein, a key protein associated with the disease.Objective: The study objective was to evaluate the efficacy and safety of prasinezumab, a humanized monoclonal antibody that binds aggregated alpha-synuclein, in individuals with early PD.Methods: The PASADENA study is a multicenter, randomized, double-blind, placebo-controlled treatment study. Individuals with early PD, recruited across the US and Europe, received monthly intravenous doses of prasinezumab (1,500 or 4,500 mg) or placebo for a 52-week period (Part 1), followed by a 52-week extension (Part 2) in which all participants received active treatment. Key inclusion criteria were: aged 40–80 years; Hoehn & Yahr (H&Y) Stage I or II; time from diagnosis ≤2 years; having bradykinesia plus one other cardinal sign of PD (e.g., resting tremor, rigidity); DAT-SPECT imaging consistent with PD; and either treatment naïve or on a stable monoamine oxidase B (MAO-B) inhibitor dose. Study design assumptions for sample size and study duration were built using a patient cohort from the Parkinson's Progression Marker Initiative (PPMI). In this report, baseline characteristics are compared between the treatment-naïve and MAO-B inhibitor-treated PASADENA cohorts and between the PASADENA and PPMI populations.Results: Of the 443 patients screened, 316 were enrolled into the PASADENA study between June 2017 and November 2018, with an average age of 59.9 years and 67.4% being male. Mean time from diagnosis at baseline was 10.11 months, with 75.3% in H&Y Stage II. Baseline motor and non-motor symptoms (assessed using Movement Disorder Society—Unified Parkinson's Disease Rating Scale [MDS-UPDRS]) were similar in severity between the MAO-B inhibitor-treated and treatment-naïve PASADENA cohorts (MDS-UPDRS sum of Parts I + II + III [standard deviation (SD)]; 30.21 [11.96], 32.10 [13.20], respectively). The overall PASADENA population (63.6% treatment naïve and 36.4% on MAO-B inhibitor) showed a similar severity in MDS-UPDRS scores (e.g., MDS-UPDRS sum of Parts I + II + III [SD]; 31.41 [12.78], 32.63 [13.04], respectively) to the PPMI cohort (all treatment naïve).Conclusions: The PASADENA study population is suitable to investigate the potential of prasinezumab to slow disease progression in individuals with early PD.Trial Registration: NCT03100149.

Introduction: Imaging studies showed affection of the corpus callosum (CC) in amyotrophic lateral sclerosis (ALS). Here, we sought to determine whether these structural alterations reflect on the functional level, using transcranial magnetic stimulation (TMS). Methods: In 31 ALS patients and 12 controls, we studied mirror movements (MM) and transcallosal inhibition (TI) using TMS. Structural integrity of transcallosal fibres was assessed using diffusion tensor imaging. Results: TI was pathologic in 25 patients (81%), 22 (71%) showed MM. Loss of TI was observed in very early stages (disease duration

Background: Computed tomography (CT) scans are the first-line imaging technique in acute stroke patients based on the argument of rapid feasibility. Using magnetic resonance imaging (MRI) as the first-line imaging technique is the exception to the rule, although it provides much more diagnostic information and avoids exposure to radiation. We evaluated whether an MRI-based acute stroke concept is fast, suitable, and useful to improve recanalization rates and patient outcomes. Methods: We performed a retrospective observational cohort study comparing patients treated at a comprehensive stroke center (Ulm/Germany) applying an MRI-based acute stroke concept with patients recorded in a large comprehensive stroke registry in Baden-Württemberg (Germany). We analyzed the quality indicators of acute stroke treatment, patient’s outcome, and the rate of transient ischemic attack (TIA) at discharge. Results: A total of 2182 patients from Ulm and 82,760 patients from the Baden-Württemberg (BW) stroke registry (including 29,575 patients of comprehensive stroke centers (BWc)) were included. Intravenous thrombolysis rate was higher in Ulm than in BW or the BWc stroke centers (Ulm 27.4% versus BW 20.9% versus BWc 26.1; p

Traumatic brain injury has a poorer prognosis in elderly patients, possibly because of the enhanced inflammatory response characteristic of advanced age, known as “inflammaging.” Recently, reduced activation of the TANK-Binding-Kinase 1 (Tbk1) pathway has been linked to age-associated neurodegeneration and neuroinflammation. Here we investigated how the blockade of Tbk1 and of the closely related IKK-ε by the small molecule Amlexanox could modify the microglial and immune response to cortical stab-wound injury in mice. We demonstrated that Tbk1/IKK-ε inhibition resulted in a massive expansion of microglial cells characterized by the TMEM119+/CD11c+ phenotype, expressing high levels of CD68 and CD317, and with the upregulation of Cst7a, Prgn and Ccl4 and the decrease in the expression levels of Tmem119 itself and P2yr12, thus a profile close to Disease-Associated Microglia (DAM, a subset of reactive microglia abundant in Alzheimer’s Disease and other neurodegenerative conditions). Furthermore, Tbk1/IKK-ε inhibition increased the infiltration of CD3+ lymphocytes, CD169+ macrophages and CD11c+/CD169+ cells. The enhanced immune response was associated with increased expression of Il-33, Ifn-g, Il-17, and Il-19. This upsurge in the response to the stab wound was associated with the expanded astroglial scars and increased deposition of chondroitin-sulfate proteoglycans at 7 days post injury. Thus, Tbk1/IKK-ε blockade results in a massive expansion of microglial cells with a phenotype resembling DAM and with the substantial enhancement of neuroinflammatory responses. In this context, the induction of DAM is associated with a detrimental outcome such as larger injury-related glial scars. Thus, the Tbk1/IKK-ε pathway is critical to repress neuroinflammation upon stab-wound injury and Tbk1/IKK-ε inhibitors may provide an innovative approach to investigate the consequences of DAM induction.

BackgroundLongitudinal brain MRI monitoring in neurodegeneration potentially provides substantial insights into the temporal dynamics of the underlying biological process, but is time- and cost-intensive and may be a burden to patients with disabling neurological diseases. Thus, the conceptualization of follow-up time-intervals in longitudinal MRI studies is an essential challenge and substantial for the results. The objective of this work is to discuss the association of time-intervals and the results of longitudinal trends in the frequently used design of one baseline and two follow-up scans.MethodsDifferent analytical approaches for calculating the linear trend of longitudinal parameters were studied in simulations including their performance of dealing with outliers; these simulations were based on the longitudinal striatum atrophy in MRI data of Huntington’s disease patients, detected by atlas-based volumetry (ABV).ResultsFor the design of one baseline and two follow-up visits, the simulations with outliers revealed optimum results for identical time-intervals between baseline and follow-up scans. However, identical time-intervals between the three acquisitions lead to the paradox that, depending on the fit method, the first follow-up scan results do not influence the final results of a linear trend analysis.ConclusionsThis theoretical study analyses how the design of longitudinal imaging studies with one baseline and two follow-up visits influences the results. Suggestions for the analysis of longitudinal trends are provided.

Background: Over the life span, the diffusion metrics in brain MRI show different, partly nonlinear changes. These age-dependent changes also seem to exhibit regional differences with respect to the brain anatomy. The age correction of a study cohort's diffusion metrics might thus require consideration of age-related factors.Methods: Diffusion tensor imaging data sets were acquired from 219 healthy participants at ages between 19 and 81 years. Fractional anisotropy (FA), mean diffusivity (MD), and axial and radial diffusivity (AD and RD, respectively) maps were analyzed by a tract of interest-based fiber tracking approach. To describe diffusion metrics as a function of the participant age, linear splines were used to perform curve fitting in 21 specific tract systems covering different functional areas and diffusion directions.Results: In the majority of tracts, an interpolation with a change of alteration rate during adult life described the diffusion properties more accurately than a linear model. Consequently, the diffusion properties remained relatively stable until a decrease (of FA) or increase (of MD, AD, and RD) started at a region-specific time point, whereas a uniform change of diffusion properties was observed only in a few tracts. Single tracts, e.g., located in the cerebellum, remained nearly unaltered throughout the ages between 19 and 81 years.Conclusions: Age corrections of diffusion properties should not be applied to all white matter regions and all age spans in the same way. Therefore, we propose three different approaches for age correction based on fiber tracking techniques, i.e., no correction for areas that do not experience age-related changes and two variants of an age correction depending on the age range of the cohort and the tracts considered.

Abstract Background In vivo diffusion tensor imaging (DTI) of the mouse brain was used to identify TDP-43 associated alterations in a mouse model for amyotrophic lateral sclerosis (ALS). Methods Ten mice with TDP-43 G298S overexpression under control of the Thy1.2 promoter and 10 wild type (wt) underwent longitudinal DTI scans at 11.7 T, including one baseline and one follow-up scan with an interval of about 5 months. Whole brain-based spatial statistics (WBSS) of DTI-based parameter maps was used to identify longitudinal alterations of TDP-43 G298S mice compared to wt at the cohort level. Results were supplemented by tractwise fractional anisotropy statistics (TFAS) and histological evaluation of motor cortex for signs of neuronal loss. Results Alterations at the cohort level in TDP-43 G298S mice were observed cross-sectionally and longitudinally in motor areas M1/M2 and in transcallosal fibers but not in the corticospinal tract. Neuronal loss in layer V of motor cortex was detected in TDP-43 G298S at the later (but not at the earlier) timepoint compared to wt. Conclusion DTI mapping of TDP-43 G298S mice demonstrated progression in motor areas M1/M2. WBSS and TFAS are useful techniques to localize TDP-43 G298S associated alterations over time in this ALS mouse model, as a biological marker.

Abstract Background IgG-class autoantibodies to N-Methyl-D-Aspartate (NMDA)-type glutamate receptors define a novel entity of autoimmune encephalitis. Studies examining the prevalence of NMDA IgA/IgM antibodies in patients with Parkinson disease with/without dementia produced conflicting results. We measured NMDA antibodies in a large, well phenotyped sample of Parkinson patients without and with cognitive impairment (n = 296) and controls (n = 295) free of neuropsychiatric disease. Detailed phenotyping and large numbers allowed statistically meaningful correlation of antibody status with diagnostic subgroups as well as quantitative indicators of disease severity and cognitive impairment. Methods NMDA antibodies were analysed in the serum of patients and controls using well established validated assays. We used anti-NMDA antibody positivity as the main independent variable and correlated it with disease status and phenotypic characteristics. Results The frequency of NMDA IgA/IgM antibodies was lower in Parkinson patients (13%) than in controls (22%) and higher than in previous studies in both groups. NMDA IgA/IgM antibodies were neither significantly associated with diagnostic subclasses of Parkinson disease according to cognitive impairment, nor with quantitative indicators of disease severity and cognitive impairment. A positive NMDA antibody status was positively correlated with age in controls but not in Parkinson patients. Conclusion It is unlikely albeit not impossible that NMDA antibodies play a significant role in the pathogenesis or progression of Parkinson disease e.g. to Parkinson disease with dementia, while NMDA IgG antibodies define a separate disease of its own.

Cerebrospinal fluid (CSF) and serum biomarkers are critical for clinical decision making in neurological diseases. In cerebral small vessel disease (CSVD), white matter hyperintensities (WMH) are an important neuroimaging biomarker, but more blood-based biomarkers capturing different aspects of CSVD pathology are needed. In 42 sporadic CSVD patients, we prospectively analysed WMH on magnetic resonance imaging (MRI) and the biomarkers neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), chitinase3-like protein 1 (CHI3L1), Tau and Aβ1-42 in CSF and NfL and GFAP in serum. GFAP and CHI3L1 expression was studied in post-mortem brain tissue in additional cases. CSVD cases with higher serum NfL and GFAP levels had a higher modified Rankin Scale (mRS) and NIHSS score and lower CSF Aβ1-42 levels, whereas the CSF NfL and CHI3L1 levels were positively correlated with the WMH load. Moreover, the serum GFAP levels significantly correlated with the neurocognitive functions. Pathological analyses in CSVD revealed a high density of GFAP-immunoreactive fibrillary astrocytic processes in the periventricular white matter and clusters of CHI3L1-immunoreactive astrocytes in the basal ganglia and thalamus. Thus, besides NfL, serum GFAP is a highly promising fluid biomarker of sporadic CSVD, because it does not only correlate with the clinical severity but also correlates with the cognitive function in patients.