Christian Langkammer

Bio: Christian Langkammer is an academic researcher from Medical University of Graz. The author has contributed to research in topics: Quantitative susceptibility mapping & Clinically isolated syndrome. The author has an hindex of 32, co-authored 82 publications receiving 3969 citations. Previous affiliations of Christian Langkammer include University of Graz & Harvard University.

Quantitative MR Imaging of Brain Iron: A Postmortem Validation Study

Abstract: The results of this study show that transverse MR relaxation rates are valid in vivo measures of brain iron concentration, with R2* as the preferred parameter because it is more sensitive than R2 to variations in iron concentration and can depict differences in white matter.

Quantitative Susceptibility Mapping in Multiple Sclerosis

Abstract: We have showed that quantitative susceptibility mapping has superior sensitivity in the assessment of multiple sclerosis-related tissue changes in the basal ganglia than does R2* relaxation rate mapping, with susceptibility changes already observable in patients with clinically isolated syndrome.

Differential developmental trajectories of magnetic susceptibility in human brain gray and white matter over the lifespan

Abstract: As indicated by several recent studies, magnetic susceptibility of the brain is influenced mainly by myelin in the white matter and by iron deposits in the deep nuclei. Myelination and iron deposition in the brain evolve both spatially and temporally. This evolution reflects an important characteristic of normal brain development and ageing. In this study, we assessed the changes of regional susceptibility in the human brain in vivo by examining the developmental and ageing process from 1 to 83 years of age. The evolution of magnetic susceptibility over this lifespan was found to display differential trajectories between the gray and the white matter. In both cortical and subcortical white matter, an initial decrease followed by a subsequent increase in magnetic susceptibility was observed, which could be fitted by a Poisson curve. In the gray matter, including the cortical gray matter and the iron-rich deep nuclei, magnetic susceptibility displayed a monotonic increase that can be described by an exponential growth. The rate of change varied according to functional and anatomical regions of the brain. For the brain nuclei, the age-related changes of susceptibility were in good agreement with the findings from R2* measurement. Our results suggest that magnetic susceptibility may provide valuable information regarding the spatial and temporal patterns of brain myelination and iron deposition during brain maturation and ageing. Hum Brain Mapp 35:2698–2713, 2014. © 2013 Wiley Periodicals, Inc.

The role of iron in brain ageing and neurodegenerative disorders

Abstract: Summary In the CNS, iron in several proteins is involved in many important processes such as oxygen transportation, oxidative phosphorylation, myelin production, and the synthesis and metabolism of neurotransmitters. Abnormal iron homoeostasis can induce cellular damage through hydroxyl radical production, which can cause the oxidation and modification of lipids, proteins, carbohydrates, and DNA. During ageing, different iron complexes accumulate in brain regions associated with motor and cognitive impairment. In various neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, changes in iron homoeostasis result in altered cellular iron distribution and accumulation. MRI can often identify these changes, thus providing a potential diagnostic biomarker of neurodegenerative diseases. An important avenue to reduce iron accumulation is the use of iron chelators that are able to cross the blood–brain barrier, penetrate cells, and reduce excessive iron accumulation, thereby affording neuroprotection.

Neurofilaments as biomarkers in neurological disorders

Abstract: Neuroaxonal damage is the pathological substrate of permanent disability in various neurological disorders. Reliable quantification and longitudinal follow-up of such damage are important for assessing disease activity, monitoring treatment responses, facilitating treatment development and determining prognosis. The neurofilament proteins have promise in this context because their levels rise upon neuroaxonal damage not only in the cerebrospinal fluid (CSF) but also in blood, and they indicate neuroaxonal injury independent of causal pathways. First-generation (immunoblot) and second-generation (enzyme-linked immunosorbent assay) neurofilament assays had limited sensitivity. Third-generation (electrochemiluminescence) and particularly fourth-generation (single-molecule array) assays enable the reliable measurement of neurofilaments throughout the range of concentrations found in blood samples. This technological advancement has paved the way to investigate neurofilaments in a range of neurological disorders. Here, we review what is known about the structure and function of neurofilaments, discuss analytical aspects and knowledge of age-dependent normal ranges of neurofilaments and provide a comprehensive overview of studies on neurofilament light chain as a marker of axonal injury in different neurological disorders, including multiple sclerosis, neurodegenerative dementia, stroke, traumatic brain injury, amyotrophic lateral sclerosis and Parkinson disease. We also consider work needed to explore the value of this axonal damage marker in managing neurological diseases in daily practice.

An overview of deep learning in medical imaging focusing on MRI

Abstract: What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

Oxygenic photosynthesis as a protection mechanism for cyanobacteria against iron-encrustation in environments with high Fe2+ concentrations

Abstract: If O2 is available at circumneutral pH, Fe2+ is rapidly oxidized to Fe3+, which precipitates as FeO(OH). Neutrophilic iron oxidizing bacteria have evolved mechanisms to prevent self-encrustation in iron. Hitherto, no mechanism has been proposed for cyanobacteria from Fe2+-rich environments; these produce O2 but are seldom found encrusted in iron. We used two sets of illuminated reactors connected to two groundwater aquifers with different Fe2+ concentrations (0.9 μM vs. 26 μM) in the Aspo Hard Rock Laboratory (HRL), Sweden. Cyanobacterial biofilms developed in all reactors and were phylogenetically different between the reactors. Unexpectedly, cyanobacteria growing in the Fe2+-poor reactors were encrusted in iron, whereas those in the Fe2+-rich reactors were not. In-situ microsensor measurements showed that O2 concentrations and pH near the surface of the cyanobacterial biofilms from the Fe2+-rich reactors were much higher than in the overlying water. This was not the case for the biofilms growing at low Fe2+ concentrations. Measurements with enrichment cultures showed that cyanobacteria from the Fe2+-rich environment increased their photosynthesis with increasing Fe2+ concentrations, whereas those from the low Fe2+ environment were inhibited at Fe2+ > 5 μM. Modeling based on in-situ O2 and pH profiles showed that cyanobacteria from the Fe2+-rich reactor were not exposed to significant Fe2+ concentrations. We propose that, due to limited mass transfer, high photosynthetic activity in Fe2+-rich environments forms a protective zone where Fe2+ precipitates abiotically at a non-lethal distance from the cyanobacteria. This mechanism sheds new light on the possible role of cyanobacteria in precipitation of banded iron formations.

Serum Neurofilament light: A biomarker of neuronal damage in multiple sclerosis.

Abstract: OBJECTIVE Neurofilament light chains (NfL) are unique to neuronal cells, are shed to the cerebrospinal fluid (CSF), and are detectable at low concentrations in peripheral blood. Various diseases causing neuronal damage have resulted in elevated CSF concentrations. We explored the value of an ultrasensitive single-molecule array (Simoa) serum NfL (sNfL) assay in multiple sclerosis (MS). METHODS sNfL levels were measured in healthy controls (HC, n = 254) and two independent MS cohorts: (1) cross-sectional with paired serum and CSF samples (n = 142), and (2) longitudinal with repeated serum sampling (n = 246, median follow-up = 3.1 years, interquartile range [IQR] = 2.0-4.0). We assessed their relation to concurrent clinical, imaging, and treatment parameters and to future clinical outcomes. RESULTS sNfL levels were higher in both MS cohorts than in HC (p < 0.001). We found a strong association between CSF NfL and sNfL (β = 0.589, p < 0.001). Patients with either brain or spinal (43.4pg/ml, IQR = 25.2-65.3) or both brain and spinal gadolinium-enhancing lesions (62.5pg/ml, IQR = 42.7-71.4) had higher sNfL than those without (29.6pg/ml, IQR = 20.9-41.8; β = 1.461, p = 0.005 and β = 1.902, p = 0.002, respectively). sNfL was independently associated with Expanded Disability Status Scale (EDSS) assessments (β = 1.105, p < 0.001) and presence of relapses (β = 1.430, p < 0.001). sNfL levels were lower under disease-modifying treatment (β = 0.818, p = 0.003). Patients with sNfL levels above the 80th, 90th, 95th, 97.5th, and 99th HC-based percentiles had higher risk of relapses (97.5th percentile: incidence rate ratio = 1.94, 95% confidence interval [CI] = 1.21-3.10, p = 0.006) and EDSS worsening (97.5th percentile: OR = 2.41, 95% CI = 1.07-5.42, p = 0.034). INTERPRETATION These results support the value of sNfL as a sensitive and clinically meaningful blood biomarker to monitor tissue damage and the effects of therapies in MS. Ann Neurol 2017;81:857-870.