Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing.

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.

AI-assisted automatic MRI-based tongue volume evaluation in motor neuron disease (MND).

PURPOSE: Motor neuron disease (MND) causes damage to the upper and lower motor neurons including the motor cranial nerves, the latter resulting in bulbar involvement with atrophy of the tongue muscle. To measure tongue atrophy, an operator independent automatic segmentation of the tongue is crucial. The aim of this study was to apply convolutional neural network (CNN) to MRI data in order to determine the volume of the tongue. METHODS: A single triplanar CNN of U-Net architecture trained on axial, coronal, and sagittal planes was used for the segmentation of the tongue in MRI scans of the head. The 3D volumes were processed slice-wise across the three orientations and the predictions were merged using different voting strategies. This approach was developed using MRI datasets from 20 patients with 'classical' spinal amyotrophic lateral sclerosis (ALS) and 20 healthy controls and, in a pilot study, applied to the tongue volume quantification to 19 controls and 19 ALS patients with the variant progressive bulbar palsy (PBP). RESULTS: Consensus models with softmax averaging and majority voting achieved highest segmentation accuracy and outperformed predictions on single orientations and consensus models with union and unanimous voting. At the group level, reduction in tongue volume was not observed in classical spinal ALS, but was significant in the PBP group, as compared to controls. CONCLUSION: Utilizing single U-Net trained on three orthogonal orientations with consequent merging of respective orientations in an optimized consensus model reduces the number of erroneous detections and improves the segmentation of the tongue. The CNN-based automatic segmentation allows for accurate quantification of the tongue volumes in all subjects. The application to the ALS variant PBP showed significant reduction of the tongue volume in these patients and opens the way for unbiased future longitudinal studies in diseases affecting tongue volume.

The central pattern of weakness of ALS: Morphological correlates in whole-body muscle MRI.

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.

Arterial Spin Labeling (ASL) in Neuroradiological Diagnostics - Methodological Overview and Use Cases.

BACKGROUND: Arterial spin labeling (ASL) is a magnetic resonance imaging (MRI)-based technique using labeled blood-water of the brain-feeding arteries as an endogenous tracer to derive information about brain perfusion. It enables the assessment of cerebral blood flow (CBF). METHOD: This review aims to provide a methodological and technical overview of ASL techniques, and to give examples of clinical use cases for various diseases affecting the central nervous system (CNS). There is a special focus on recent developments including super-selective ASL (ssASL) and time-resolved ASL-based magnetic resonance angiography (MRA) and on diseases commonly not leading to characteristic alterations on conventional structural MRI (e. g., concussion or migraine). RESULTS: ASL-derived CBF may represent a clinically relevant parameter in various pathologies such as cerebrovascular diseases, neoplasms, or neurodegenerative diseases. Furthermore, ASL has also been used to investigate CBF in mild traumatic brain injury or migraine, potentially leading to the establishment of imaging-based biomarkers. Recent advances made possible the acquisition of ssASL by selective labeling of single brain-feeding arteries, enabling spatial perfusion territory mapping dependent on blood flow of a specific preselected artery. Furthermore, ASL-based MRA has been introduced, providing time-resolved delineation of single intracranial vessels. CONCLUSION: Perfusion imaging by ASL has shown promise in various diseases of the CNS. Given that ASL does not require intravenous administration of a gadolinium-based contrast agent, it may be of particular interest for investigations in pediatric cohorts, patients with impaired kidney function, patients with relevant allergies, or patients that undergo serial MRI for clinical indications such as disease monitoring. KEY POINTS: · ASL is an MRI technique that uses labeled blood-water as an endogenous tracer for brain perfusion imaging.. · It allows the assessment of CBF without the need for administration of a gadolinium-based contrast agent.. · CBF quantification by ASL has been used in several pathologies including brain tumors or neurodegenerative diseases.. · Vessel-selective ASL methods can provide brain perfusion territory mapping in cerebrovascular diseases.. · ASL may be of particular interest in patient cohorts with caveats concerning gadolinium administration..

The gut microbiome modulates associations between adherence to a Mediterranean-style diet, abdominal adiposity, and C-reactive protein in population-level analysis.

BACKGROUND: Adherence to a Mediterranean-style dietary pattern is likely to have variable effects on body composition, but the impact of gut microbiome on this relationship is unknown. OBJECTIVES: To examine the potential mediating effect of the gut microbiome on the associations between Alternate Mediterranean Diet (aMed) scores, abdominal adiposity, and inflammation in population-level analysis. DESIGN: In a community-based sample aged 25 to 83 y (n = 620; 41% female) from Northern Germany, we assessed the role of the gut microbiome, sequenced from 16S rRNA genes, on the associations between aMed scores, estimated using validated food-frequency questionnaires, magnetic resonance imaging-determined visceral (VAT) and subcutaneous (SAT) adipose tissue and C-reactive protein (CRP). RESULTS: Higher aMed scores were associated with lower SAT (-0.86 L (95% CI: -1.56, -0.17), P = 0.01), VAT (-0.65 L (95% CI: -1.03,-0.27), P = 0.01) and CRP concentrations (-0.35 mg/L; ß: -20.1% (95% CI: 35.5, -1.09), P = 0.04) in the highest versus lowest tertile after multivariate adjustment. Of the taxa significantly associated with aMed scores, higher abundance of Porphyromonadaceae mediated 11.6%, 9.3%, and 8.7% of the associations with lower SAT, VAT, and CRP, respectively. Conversely, a lower abundance of Peptostreptococcaceae mediated 13.1% and 18.2% of the association with SAT and CRP levels. Of the individual components of the aMed score, moderate alcohol intake was associated with lower VAT (-0.2 (95% CI: -0.4, -0.1), P =0.01) with a higher abundance of Oxalobacteraceae and lower abundance of Burkholderiaceae explaining 8.3% and 9.6% of this association, respectively. CONCLUSION: These novel data suggest that abundance of specific taxa in the Porphyromonadaceae and Peptostreptococcaceae families may contribute to the association between aMed scores, lower abdominal adipose tissue, and inflammation.

AI-assisted quantification of hypothalamic atrophy in amyotrophic lateral sclerosis by convolutional neural network-based automatic segmentation.

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.

New Perspectives in Radiological and Radiopharmaceutical Hybrid Imaging in Progressive Supranuclear Palsy: A Systematic Review.

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.

Neurodegeneration or dysfunction in Phelan-McDermid syndrome? A multimodal approach with CSF and computational MRI.

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.

Structural and microstructural neuroimaging signature of C9orf72-associated ALS: A multiparametric MRI study.

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.

Bevacizumab is associated with cerebral microstructural alterations: a DTI study in high-grade glioma.

Background: For 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. Methods: To 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. Results: The 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. Conclusion: The 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.

Large diameter hemicraniectomy does not improve long-term outcome in malignant infarction.

INTRODUCTION: In malignant cerebral infarction decompressive hemicraniectomy has demonstrated beneficial effects, but the optimum size of hemicraniectomy is still a matter of debate. Some surgeons prefer a large-sized hemicraniectomy with a diameter of more than 14 cm (HC > 14). We investigated whether this approach is associated with reduced mortality and an improved long-term functional outcome compared to a standard hemicraniectomy with a diameter of less than 14 cm (HC ≤ 14). METHODS: Patients from the DESTINY (DEcompressive Surgery for the Treatment of malignant INfarction of the middle cerebral arterY) registry who received hemicraniectomy were dichotomized according to the hemicraniectomy diameter (HC ≤ 14 cm vs. HC > 14 cm). The primary outcome was modified Rankin scale (mRS) score ≤ 4 after 12 months. Secondary outcomes were in-hospital mortality, mRS ≤ 3 and mortality after 12 months, and the rate of hemicraniectomy-related complications. The diameter of the hemicraniectomy was examined as an independent predictor of functional outcome in multivariable analyses. RESULTS: Among 130 patients (32.3% female, mean (SD) age 55 (11) years), the mean hemicraniectomy diameter was 13.6 cm. 42 patients (32.3%) had HC > 14. There were no significant differences in the primary outcome and mortality by size of hemicraniectomy. Rate of complications did not differ (HC ≤ 14 27.6% vs. HC > 14 36.6%, p = 0.302). Age and infarct volume but not hemicraniectomy diameter were associated with outcome in multivariable analyses. CONCLUSION: In this post-hoc analysis, large hemicraniectomy was not associated with an improved outcome or lower mortality in unselected patients with malignant middle cerebral artery infarction. Randomized trials should further examine whether individual patients could benefit from a large-sized hemicraniectomy. CLINICAL TRIAL REGISTRATION INFORMATION: German Clinical Trials Register (URL: https://www.drks.de ; Unique Identifier: DRKS00000624).

MRI-based quantification of adipose tissue distribution in healthy adult cats during body weight gain.

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.

Presymptomatic grey matter alterations in ALS kindreds: a computational neuroimaging study of asymptomatic C9orf72 and SOD1 mutation carriers.

BACKGROUND: The characterisation of presymptomatic disease-burden patterns in asymptomatic mutation carriers has a dual academic and clinical relevance. The understanding of disease propagation mechanisms is of considerable conceptual interests, and defining the optimal time of pharmacological intervention is essential for improved clinical trial outcomes. METHODS: In a prospective, multimodal neuroimaging study, 22 asymptomatic C9orf72 GGGGCC hexanucleotide repeat carriers, 13 asymptomatic subjects with SOD1, and 54 "gene-negative" ALS kindreds were enrolled. Cortical and subcortical grey matter alterations were systematically appraised using volumetric, morphometric, vertex, and cortical thickness analyses. Using a Bayesian approach, the thalamus and amygdala were further parcellated into specific nuclei and the hippocampus was segmented into anatomically defined subfields. RESULTS: Asymptomatic GGGGCC hexanucleotide repeat carriers in C9orf72 exhibited early subcortical changes with the preferential involvement of the pulvinar and mediodorsal regions of the thalamus, as well as the lateral aspect of the hippocampus. Volumetric approaches, morphometric methods, and vertex analyses were anatomically consistent in capturing focal subcortical changes in asymptomatic C9orf72 hexanucleotide repeat expansion carriers. SOD1 mutation carriers did not exhibit significant subcortical grey matter alterations. In our study, none of the two asymptomatic cohorts exhibited cortical grey matter alterations on either cortical thickness or morphometric analyses. DISCUSSION: The presymptomatic radiological signature of C9orf72 is associated with selective thalamic and focal hippocampal degeneration which may be readily detectable before cortical grey matter changes ensue. Our findings confirm selective subcortical grey matter involvement early in the course of C9orf72-associated neurodegeneration.

Parkinson's disease or multiple system atrophy: potential separation by quantitative susceptibility mapping.

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.

Sequential alterations in diffusion metrics as correlates of disease severity in amyotrophic lateral sclerosis.

BACKGROUND AND OBJECTIVE: The neuropathology of amyotrophic lateral sclerosis (ALS) follows a regional distribution pattern in the brain with four stages. Using diffusion tensor imaging (DTI), this pattern can be translated into a tract-based staging scheme to assess cerebral progression in vivo. This study investigates the association between the sequential alteration pattern and disease severity in patients with ALS. METHODS: DTI data of 325 patients with ALS and 130 healthy controls were analyzed in a tract of interest (TOI)-based approach. Patients were categorized according to their ALS-FRS-R scores into groups with declining functionality. The fractional anisotropy (FA) values in the tracts associated with neuropathological stages were group-wise compared with healthy controls. RESULTS: The FA in the tracts associated with ALS stages showed a decrease which could be related to the disease severity stratification, i.e., at the group level, the lower the ALS-FRS-R of the categorized patient group, the higher was the effect size of the stage-related tract. In the patient group with the highest ALS-FRS-R, Cohen's d showed a medium effect size in the corticospinal tract and small effect sizes in the other stage-related tracts. Overall, the lower the ALS-FRS-R of the categorized patient group the higher was the effect size of the comparison with healthy controls. CONCLUSION: The progression of white matter alterations across tracts according to the model of sequential tract involvement is associated with clinical disease severity in patients with ALS, suggesting the use of staging-based DTI as a technical marker for disease progression.

Diffusion Tensor Imaging in Amyotrophic Lateral Sclerosis: Machine Learning for Biomarker Development.

Diffusion tensor imaging (DTI) allows the in vivo imaging of pathological white matter alterations, either with unbiased voxel-wise or hypothesis-guided tract-based analysis. Alterations of diffusion metrics are indicative of the cerebral status of patients with amyotrophic lateral sclerosis (ALS) at the individual level. Using machine learning (ML) models to analyze complex and high-dimensional neuroimaging data sets, new opportunities for DTI-based biomarkers in ALS arise. This review aims to summarize how different ML models based on DTI parameters can be used for supervised diagnostic classifications and to provide individualized patient stratification with unsupervised approaches in ALS. To capture the whole spectrum of neuropathological signatures, DTI might be combined with additional modalities, such as structural T1w 3-D MRI in ML models. To further improve the power of ML in ALS and enable the application of deep learning models, standardized DTI protocols and multi-center collaborations are needed to validate multimodal DTI biomarkers. The application of ML models to multiparametric MRI/multimodal DTI-based data sets will enable a detailed assessment of neuropathological signatures in patients with ALS and the development of novel neuroimaging biomarkers that could be used in the clinical workup.

Morphological alterations of the hypothalamus in idiopathic intracranial hypertension.

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.

Artificial neural networks for non-linear age correction of diffusion metrics in the brain.

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 (R 2 = 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 R 2 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.

Simultaneous PET/MRI: The future gold standard for characterizing motor neuron disease-A clinico-radiological and neuroscientific perspective.

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.

Simplification of 2D shear wave elastography by enlarged SWE box and multiple regions of interest in one acquisition.

Two-dimensional (2D) Shear Wave Elastography (SWE) is an easy to perform technique to evaluate the liver stiffness. To simplify the procedure and reduce the acquisition time we enlarged the size of the SWE-box and set ten regions of interest (ROI) in one acquisition. We compare the accuracy of this method to ten separate acquisitions in a small box each with a single ROI measurement. Sixty-nine volunteers with diffuse chronic liver disease were studied with 2D-SWE using a Canon Aplio i800 ultrasound system. The shear-wave-speed was measured in the right lobe in ten separate acquisitions and compared to one acquisition with increased size of the SWE-box and ten different ROI measurements. A Bland-Altmann plot was drawn and the interclass correlation coefficient (ICC) was calculated to compare both methods. Finally, 2D-SWE was successfully performed thru both methods in sixty-six participants. Between both methods the ICC is 0.82. The results of this study show a good reliability between ten separate measures and one grouped measure with ten ROI if the mean is below 1.6m/s (7.7kPa). For higher degrees of fibrosis (≥F2) further investigations are needed.