The z-spectrum from human blood at 7T

Chemical Exchange Saturation Transfer (CEST) has been used to assess healthy and pathological tissue in both animals and humans. However, the CEST signal from blood has not been fully assessed. This paper presents the CEST and nuclear Overhauser enhancement (NOE) signals detected in human blood measured via z-spectrum analysis. We assessed the effects of blood oxygenation levels, haematocrit, cell structure and pH upon the z-spectrum in ex vivo human blood for different saturation powers at 7T. The data were analysed using Lorentzian difference (LD) model fitting and AREX (to compensate for changes in T1), which have been successfully used to study CEST effects in vivo. Full Bloch-McConnell fitting was also performed to provide an initial estimate of exchange rates and transverse relaxation rates of the various pools. CEST and NOE signals were observed at 3.5 ppm, -1.7ppm and -3.5 ppm and were found to originate primarily from the red blood cells (RBCs), although the amide proton transfer (APT) CEST effect, and NOEs showed no dependence upon oxygenation levels. Upon lysing, the APT and NOE signals fell significantly. Different pH levels in blood resulted in changes in both the APT and NOE (at -3.5ppm), which suggests that this NOE signal is in part an exchange relayed process. These results will be important for assessing in vivo z-spectra

Hand classification of fMRI ICA noise components

We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets

Pathophysiologisch-serologische, bildgebende und klinische Charakteristika der Neuromyelitis Optica

Hintergrund: Neuromyelitis optica-Spektrum-Erkrankungen (NMOSD) stellen eine Gruppe neuroinflammatorischer Erkrankungen dar, die mit dem klinischen Auftreten von Myelitiden und/oder Optikusneuritiden (ON) einhergeht. Aufgrund zahlreicher überlappender klinischer und paraklinischer Eigenschaften beim Nachweis verschiedener Antikörper, vor allem auch in Abgrenzung zur Multiplen Sklerose (MS), besteht weiterhin der Bedarf nach neuen Biomarkern. Methodik: In zwei Studien wurden NMOSD-Patienten mit positivem Nachweis für Aquaporin-4-Antikörper (AQP-4-Ak) mittels 7 Tesla (T) Magnetresonanztomografie (MRT) hinsichtlich der I) periventrikulären Venendichte (PVA) in T2*-gewichteten Aufnahmen und II) der Phasenverschiebung in suszeptibilitätsgewichteten Sequenzen untersucht. Als Vergleich dienten die Ergebnisse von Patienten mit MS und gesunden Kontrollen (HC). In einer dritten Arbeit (III) erfolgte eine retrospektive Auswertung visueller Parameter im Vergleich von AQP-4-Ak-positiven Patienten und Patienten mit Antikörpern gegen das Myelin-Oligodendrozyten-Glykoprotein (MOG) mittels Optischer Kohärenztomografie (OCT), Visuell Evozierter Potenziale (VEP) und der Fernvisus-Messung. Ergebnisse: Bildmorphologisch zeigte sich in den 7T-T2* gewichteten Aufnahmen bei Patienten mit AQP-4-Ak-positiver NMOSD eine normal große PVA (AQP-4-Ak: PVA = 133 mm2; MS: PVA = 117 mm2; HC: PVA =144 mm2) und überwiegend fehlende paramagnetische Phasenverschiebungen (107 von 112 Läsionen, 96%) in den SWI-Sequenzen. Hinsichtlich des Vergleichs von MOG-Ak- gegenüber von AQP4-Ak-positiven Patienten fiel eine größere absolute Schubrate (Mittelwert, Spannweite, MOG-Ak: 4.5, 1 - 13; APQ4-Ak: 2, 1 -4; p = 0.012), bei insgesamt ähnlichem Verlust der im OCT gemessenen peripapillären retinalen Nervenfaserschicht (pRNFL) der AQP-4-Ak-positiven NMOSD im Vergleich zu den MOG-Ak-positiven Patienten auf (Mittelwert Standardabweichung, MOG-Ak: 59 ± 23 µm, AQP-4-Ak: 59 ± 21 µm). Jedoch waren die Werte der pRNFL nach dem Erstereignis einer ON bei den Patienten mit AQP-4-Ak deutlich stärker reduziert, als bei den MOG-Ak-positiven Patienten (AQP-4-Ak: pRNFL-Verlust = 32.8 μm (p<0.001); MOG-Ak: pRNFL-Verlust = 12.8 μm (p=0.001)). Schlussfolgerung: Mit Hilfe von modernen diagnostischen Verfahren, wie dem Ultrahochfeld-MRT und dem OCT wird die bessere Charakterisierung von phänotypisch ähnlichen neuroinflammatorischen Krankheitsentitäten ermöglicht. Die hierfür zugrundeliegenden unterschiedlichen Pathomechanismen sind bisher nicht vollständig verstanden und bedürfen weiterer Untersuchungen.Introduction: Different neuroinflammatory entities define the group of Neuromyelitis optica spectrum disorders (NMOSD) and are usually associated with the presentation of myelitis and/or optic neuritis. Although various antibodies were verified, there is still the challenge of overlapping clinical and paraclinical phenotypes which ask for further new diagnostic parameters. Methods: By using 7 Tesla (T) magnetic resonance imaging (MRI) patients with aquaporin-4-antibodies (AQP-4-ab) were investigated concerning a) the periventricular venous area (PVA) at T2*-weighted images and b) the phase changes within brain lesions at susceptibility-weighted (SWI)-images. The findings were compared to patients with Multiple Sclerosis (MS) and healthy controls (HC). Further patients with AQP-4-ab and antibodies against myelin oligodendrocyte glycoprotein (MOG-ab) were faced by using retrospective data of retinal optical coherence tomography (OCT), visual acuity and visual evoked potentials (VEP). Results: Patients with AQP-4-ab presented equal results like HC concerning the PVA (AQP-4-ab: PVA = 133 mm2; MS: PVA = 117 mm2; HC: PVA =144 mm2) and predominantly missing phase changes in brain lesions at SWI-images (107 of 112 lesions, 96%). Both, AQP-4-ab- and MOGab-positive patients, presented a loss in peripapillary nerve fiber layer (pRNFL) thickness at the same extend (mean ± standard deviation, MOG-ab: 59 ± 23 ±m, AQP4-ab: 59 ± 21 ±m), while the number of episodes of optic neuritis (ON) was lower in AQP4-ab-positive patients (mean, range, MOG-ab: 4.5, 1 - 13; APQ4-ab: 2, 1 -4; p = 0.012). However, the loss of pRNFL thickness after the first episode of ON was greater in patients with AQP-4-ab (AQP-4-ab: pRNFL-loss = 32.8 µm (p<0.001); MOG-ab pRNFL-loss = 12.8 µm (p=0.001). Conclusion: With the help of novel diagnostic tools, like the ultrahighfield-MRI and OCT, it is possible to distinguish between neuroinflammatory entities with similar phenotypes. For a better understanding of the underlying pathomechanisms further investigations are still needed

Hippocampal subregion abnormalities in schizophrenia: A systematic review of structural and physiological imaging studies.

AimThe hippocampus is considered a key region in schizophrenia pathophysiology, but the nature of hippocampal subregion abnormalities and how they contribute to disease expression remain to be fully determined. This study reviews findings from schizophrenia hippocampal subregion volumetric and physiological imaging studies published within the last decade.MethodsThe PubMed database was searched for publications on hippocampal subregion volume and physiology abnormalities in schizophrenia and their findings were reviewed.ResultsThe main replicated findings include smaller CA1 volumes and CA1 hyperactivation in schizophrenia, which may be predictive of conversion in individuals at clinical high risk of psychosis, smaller CA1 and CA4/DG volumes in first-episode schizophrenia, and more widespread smaller hippocampal subregion volumes with longer duration of illness. Several studies have reported relationships between hippocampal subregion volumes and declarative memory or symptom severity.ConclusionsTogether these studies provide support for hippocampal formation circuitry models of schizophrenia. These initial findings must be taken with caution as the scientific community is actively working on hippocampal subregion method improvement and validation. Further improvements in our understanding of the nature of hippocampal formation subregion involvement in schizophrenia will require the collection of structural and physiological imaging data at submillimeter voxel resolution, standardization and agreement of atlases, adequate control for possible confounding factors, and multi-method validation of findings. Despite the need for cautionary interpretation of the initial findings, we believe that improved localization of hippocampal subregion abnormalities in schizophrenia holds promise for the identification of disease contributing mechanisms

Serial Correlations in Single-Subject fMRI with Sub-Second TR

When performing statistical analysis of single-subject fMRI data, serial correlations need to be taken into account to allow for valid inference. Otherwise, the variability in the parameter estimates might be under-estimated resulting in increased false-positive rates. Serial correlations in fMRI data are commonly characterized in terms of a first-order autoregressive (AR) process and then removed via pre-whitening. The required noise model for the pre-whitening depends on a number of parameters, particularly the repetition time (TR). Here we investigate how the sub-second temporal resolution provided by simultaneous multislice (SMS) imaging changes the noise structure in fMRI time series. We fit a higher-order AR model and then estimate the optimal AR model order for a sequence with a TR of less than 600 ms providing whole brain coverage. We show that physiological noise modelling successfully reduces the required AR model order, but remaining serial correlations necessitate an advanced noise model. We conclude that commonly used noise models, such as the AR(1) model, are inadequate for modelling serial correlations in fMRI using sub-second TRs. Rather, physiological noise modelling in combination with advanced pre-whitening schemes enable valid inference in single-subject analysis using fast fMRI sequences

Quantitative Susceptibility Mapping: Contrast Mechanisms and Clinical Applications.

Quantitative susceptibility mapping (QSM) is a recently developed MRI technique for quantifying the spatial distribution of magnetic susceptibility within biological tissues. It first uses the frequency shift in the MRI signal to map the magnetic field profile within the tissue. The resulting field map is then used to determine the spatial distribution of the underlying magnetic susceptibility by solving an inverse problem. The solution is achieved by deconvolving the field map with a dipole field, under the assumption that the magnetic field is a result of the superposition of the dipole fields generated by all voxels and that each voxel has its unique magnetic susceptibility. QSM provides improved contrast to noise ratio for certain tissues and structures compared to its magnitude counterpart. More importantly, magnetic susceptibility is a direct reflection of the molecular composition and cellular architecture of the tissue. Consequently, by quantifying magnetic susceptibility, QSM is becoming a quantitative imaging approach for characterizing normal and pathological tissue properties. This article reviews the mechanism generating susceptibility contrast within tissues and some associated applications

General technical remarks on ^{1}HMRS translational research in 7T

Purpose: The aim of the work was to share the practical experience of preclinical and clinical proton magnetic resonance spectroscopy (1HMRS) studies conducted using a 7-Tesla magnetic field strength scanner, taking into account the specificity of both settings in the context of translational research. Material and methods: 1HMRS volunteer studies conducted using a Discovery 950 GE 7T scanner, were carried out with PRESS sequence, and a VOI measuring 2.0 × 2.0 × 2.0 cm3 placed in the white matter at the parietal occipital lobe. Rodent spectra obtained using a 7T Bruker were measured with PRESS, with a VOI 2.0 × 2.0 × 5.5 mm3 placed over the hippocampus. Results: 1HMRS data from humans and rats show that the brain spectra obtained in the same field are characterised by a similar neurochemical structure and spectral resolution. Spectra obtained from rats demonstrate the following metabolites: NAA, Glu, Gln, Ins, Cho, Cr, PCr, Tau, GABA, Lac, NAAG, and Asp. In turn, spectra from humans allowed estimation of the following metabolites: Ala, NAA, Glu, Gln, Ins, Cho, Cr, PCr, Tau, GABA, Lac, NAAG, and Asp. Signals from Gln, Glu with chemical shift around 2.4 ppm, from Cr, PCr, and GABA at 3 ppm, and signals from Cho and Tau at approximately 3.2 ppm, can be properly separated and estimated both in humans and in rats. Conclusions: These results are promising in terms of broadening the knowledge of many neurological diseases by inducing them on animal models and then transferring this knowledge to clinical practice. In spite of this, important distinctions in the technical aspects and methodological differences of high-field 1HMRS in both preclinical and clinical conditions should be taken into account

Neuromyelitis optica does not impact periventricular venous density versus healthy controls: a 7.0 Tesla MRI clinical study

Objective: To quantify the periventricular venous density in neuromyelitis optica spectrum disease (NMOSD) in comparison to that in patients with multiple sclerosis (MS) and healthy control subjects. Materials and methods: Sixteen patients with NMOSD, 16 patients with MS and 16 healthy control subjects underwent 7.0-Tesla (7T) MRI. The imaging protocol included T2*-weighted (T2*w) fast low angle-shot (FLASH) and fluid-attenuated inversion recovery (FLAIR) sequences. The periventricular venous area (PVA) was manually determined by a blinded investigator in order to estimate the periventricular venous density in a region of interest-based approach. Results: No significant differences in periventricular venous density indicated by PVA were detectable in NMOSD versus healthy controls (p = 0.226). In contrast, PVA was significantly reduced in MS patients compared to healthy controls (p = 0.013). Conclusion: Unlike patients with MS, those suffering from NMOSD did not show reduced venous visibility. This finding may underscore primary and secondary pathophysiological differences between these two distinct diseases of the central nervous system