Early remission in multiple sclerosis is linked to altered coherence of the Cerebellar Network

In dieser Arbeit haben wir die Veränderungen der funktionellen Konnektivität (FC) bei Patienten mit Multipler Sklerose (MS) nach dem ersten klinischen Schub untersucht. Die Entwicklung permanenter neurologischer Symptomatik bei MS ist von Patient zu Patient stark variabel, wobei die zugrundeliegenden Mechanismen noch nicht vollständig geklärt sind. Strukturelle Marker, wie z. B. die Läsionslast, spiegeln das Fortschreiten der Krankheit nicht akkurat wider. Daher untersuchten wir Veränderungen der funktionellen Netzwerkkohärenz bei MS-Patienten zwischen der ersten klinischen Episode und der anschließenden Remission, um mögliche Zusammenhänge zwischen der Reorganisation der Netzwerkstruktur und der Symptomatik zu ermitteln. Bei 18 neu diagnostizierten MS-Patienten wurde eine funktionelle Magnetresonaztomographie im Ruhezustand (rs-fMRI) durchgeführt, einmal während der ersten klinischen Episode (mit messbarer klinischer Symptomatik, definiert als Expanded Disability Status Scale, EDSS, >= 1.0) und einmal während der Remission ca. vier Wochen später (EDSS < Zeitpunkt 1). Wir analysierten Veränderungen der Netzwerkkohärenz von zehn prädefinierten funktionellen Netzwerken, die durch unabhängige Komponentenanalyse (ICA) identifiziert wurden. Dabei verglichen wir jeden Patienten mit einer gesunden Kontrollgruppe aus dem Human Connectome Project Test-Retest-Datensatz (N = 44). Für jedes Netzwerk und jeden Patienten identifizierten wir Regionen, deren Netzwerkbeitrag sich zwischen den beiden Scans veränderten. Bei verschiedenen Patienten beobachteten wir, dass sich die Topographie von verschiedenen Netzwerken änderte, wobei sich Regionen zeigten, die ihre Kohärenz mit einem betroffenen Netzwerk erhöhten oder verringerten. Zusätzlich beobachteten wir auch einige wenige Netzwerke, die bei allen Patienten gleichermaßen betroffen waren. Am interessantesten war jedoch, dass die Regionen, die ihre Kohärenz zu den betroffenen Netzwerken änderten, in der Regel ursprünglich nicht zu diesem Netzwerk gehörten (gemessen an den ICA-Beitragswerten der zehn prädefinierten Netzwerke). Durch die Kombination einer Ranking-Analyse der zehn Netzwerke mittels Monte-Carlo-Verfahren konnten wir feststellen, dass die Regionen, die Veränderungen aufwiesen, in erster Linie zum zerebellären Netzwerk gehörten, insbesondere bei Kohärenzerhöhungen. Diese unerwartete Gemeinsamkeit zwischen dem klinisch heterogenen Patientenkollektiv deutet darauf hin, dass das zerebelläre Netzwerk funktionell an der MS-Remission beteiligt ist (unabhängig davon, welche Netzwerke betroffen sind). Diese Ergebnisse erweitern die Erkenntnisse aus früheren Querschnittsuntersuchungen, die bereits eine protektive Rolle des Kleinhirns bei MSPatienten postulierten. Zukünftige Arbeiten können die Verbindung zwischen potenzieller synaptischer Plastizität (z. B. Langzeitpotenzierung und Langzeitdepression) des Kleinhirns und funktionellen Kohärenzveränderungen weiter untersuchen, um das Potential solche Konnektivitätsmessungen als Biomarker für die MS-Diagnostik und -Prognostik weiter zu untersuchen

Towards individualized cortical thickness assessment for clinical routine

Background Cortical thickness measures the width of gray matter of the human cortex. It can be calculated from T1-weighted magnetic resonance images (MRI). In group studies, this measure has been shown to correlate with the diagnosis/prognosis of a number of neurologic and psychiatric conditions, but has not been widely adapted for clinical routine. One of the reasons for this might be that there is no reference system which allows to rate individual cortical thickness data with respect to a control population. Methods To address this problem, this study compared different methods to assess statistical significance of cortical thinning, i.e. atrophy. All compared methods were nonparametric and encompassed rating an individual subject's data set with respect to a control data population. Null distributions were calculated using data from the Human Connectome Project (HCP, n = 1000), and an additional HCP data set (n = 113) was used to calculate sensitivity and specificity to compare the different methods, whereas atrophy was simulated for sensitivity assessment. Validation measures were calculated for the entire cortex ("cumulative") and distinct brain regions ("regional") where possible. Results The approach yielding the highest combination of specificity and sensitivity implemented generating null distributions for anatomically distinct brain regions, based on the most extreme values observed in the population. With that method, while regional variations were observed, cumulative specificity of 98.9% and cumulative sensitivity at 80% was achieved for simulated atrophy of 23%. Conclusions This study shows that validated rating of individual cortical thickness measures is possible, which can help clinicians in their daily routine to discover signs of atrophy before they become visually apparent on an unprocessed MRI. Furthermore, given different pathologies present with distinct atrophy patterns, the regional validation proposed here allows to detect distinct patterns of atrophy, which can further enhance differential diagnosis/prognosis

Mapping abnormality: Towards evaluating MRI scans for individualized diagnosis and validation of clinical course/progression in motor neuron disease

The present dissertation proposes a standardized assessment tool for meaningful interpretation of individual patients’ MRI data which may be used to infer on atrophy (or hypertrophy). Various MRI-based measures have been subject to a wide range of neuroscience studies and numerous associations between such measures and the diagnosis / prognosis with various pathological conditions have been identified. However, surprisingly, accurate individualized MRI-based assessment is still difficult, and most studies utilize group comparisons in order to describe group-based brain structural or functional differences. One of the reasons for this might be the lack of a standardized system which allows to evaluate individual MRI data. With this dissertation, it was aimed at passing this limit by describing a method to rate single subjects’ T1-weighted MRI data. The idea is straightforward, in that it rates single patient data with respect to a matched control population and uses nonparametric statistics to identify regions of unexpected thin (thick) cortical thickness. In this way, signs of atrophy (hypertrophy) can be localized for the individual. This thesis encompasses four original research papers which taken together describe and validate an individualized atrophy-assessment tool: first, the general procedure of rating an individual’s MRI data, specifically cortical thickness data, to a control population is investigated for sensitivity and specificity using simulations. The selected strategy was based on rating topographically distinct cortical regions (“mosaics”/”patches”) and is therefore referred to as “mosaic approach”. Given the reference groups were age- and gender matched, we investigated in a second study whether variance associated with these demographic variables was successfully eliminated, while maintaining information on clinical disability, studying a longitudinal data set of amyotrophic lateral sclerosis (ALS) patients (study 2). Finally, we explored the method for “external validity” in a dual approach: First, we tested whether the degree of cortical involvement is mirrored by our tool (which we hypothesized given it exclusively targets supratentorial gray matter regions) by contrasting different motor neuron diseases (MND) against each other (study 3). Second, we also explored if topographically distinct cortical pathology can be correctly localized with our tool, by including different patient subgroups from the frontotemporal dementia (FTD) spectrum. For FTD, as well as for MND, the “ground truth” localization of pathology is well-characterized by histological and previous (group-based) imaging findings, such that we could directly compare the individual results from the mosaic-approach to that knowledge. Another focus of this work was to provide an accessible visualization to easily identify regions of supposed atrophy (or hypertrophy) for the clinical user. Our results suggest that the here-proposed mosaic-approach which compares single patient’s cortical thickness data to matched control data is a viable approach to meaningfully detect signs of atrophy at the individual level; it is furthermore objective, reliable and valid. Despite clinical and methodological limitations, including small sample sizes of reference groups and varying acquisition parameters, which we are currently improving, we have high hopes that this tool can help clinical practice and can ultimately also be used as a novel endpoint for clinical trials

Functional Connectivity in Multiple Sclerosis: Recent Findings and Future Directions

Multiple sclerosis is a debilitating disorder resulting from scattered lesions in the central nervous system. Because of the high variability of the lesion patterns between patients, it is difficult to relate existing biomarkers to symptoms and their progression. The scattered nature of lesions in multiple sclerosis offers itself to be studied through the lens of network analyses. Recent research into multiple sclerosis has taken such a network approach by making use of functional connectivity. In this review, we briefly introduce measures of functional connectivity and how to compute them. We then identify several common observations resulting from this approach: (a) high likelihood of altered connectivity in deep-gray matter regions, (b) decrease of brain modularity, (c) hemispheric asymmetries in connectivity alterations, and (d) correspondence of behavioral symptoms with task-related and task-unrelated networks. We propose incorporating such connectivity analyses into longitudinal studies in order to improve our understanding of the underlying mechanisms affected by multiple sclerosis, which can consequently offer a promising route to individualizing imaging-related biomarkers for multiple sclerosis

Temporal Signal-to-Noise Changes in Combined Multislice- and In-Plane-Accelerated Echo-Planar Imaging with a 20- and 64-Channel Coil

Echo-planar imaging (EPI) is the most common method of functional MRI for acquiring the blood oxygenation level-dependent (BOLD) contrast, allowing the acquisition of an entire brain volume within seconds. However, because imaging protocols are limited by hardware (e.g., fast gradient switching), researchers must compromise between spatial resolution, temporal resolution, or whole-brain coverage. Earlier attempts to circumvent this problem included developing protocols in which slices of a volume were acquired faster (i.e., in-plane acceleration (S)) or simultaneously (i.e., multislice acceleration (M)). However, applying acceleration methods can lead to a reduction in the temporal signal-to-noise ratio (tSNR): a critical measure of signal stability over time. Using a 20- and 64-channel receiver coil, we show that enabling S-acceleration consistently yielded a substantial decrease in tSNR, regardless of the receiver coil, whereas M-acceleration yielded less pronounced tSNR decrease. Moreover, tSNR losses tended to occur in temporal, insular, and medial brain regions and were more noticeable with the 20-channel coil, while with the 64-channel coil, the tSNR in lateral frontoparietal regions remained relatively stable up to six-fold M-acceleration producing comparable tSNR to that of no acceleration. Such methodological explorations can guide researchers and clinicians in optimizing imaging protocols depending on the brain regions under investigation

Early remission in multiple sclerosis is linked to altered coherence of the Cerebellar Network

Background The development of permanent disability in multiple sclerosis (MS) is highly variable among patients, and the exact mechanisms that contribute to this disability remain unknown. Methods Following the idea that the brain has intrinsic network organization, we investigated changes of functional networks in MS patients to identify possible links between network reorganization and remission from clinical episodes in MS. Eighteen relapsing–remitting MS patients (RRMS) in their first clinical manifestation underwent resting-state functional MRI and again during remission. We used ten template networks, identified from independent component analysis, to compare changes in network coherence for each patient compared to those of 44 healthy controls from the Human Connectome Project test–retest dataset (two-sample t-test of pre-post differences). Combining a binomial test with Monte Carlo procedures, we tested four models of how functional coherence might change between the first clinical episode and remission: a network can change its coherence (a) with itself (“one-with-self”), (b) with another network (“one-with-other”), or (c) with a set of other networks (“one-with-many”), or (d) multiple networks can change their coherence with respect to one common network (“many-with-one”). Results We found evidence supporting two of these hypotheses: coherence decreased between the Executive Control Network and several other networks (“one-with-many” hypothesis), and a set of networks altered their coherence with the Cerebellar Network (“many-with-one” hypothesis). Conclusion Given the unexpected commonality of the Cerebellar Network’s altered coherence with other networks (a finding present in more than 70% of the patients, despite their clinical heterogeneity), we conclude that remission in MS may result from learning processes mediated by the Cerebellar Network

Indirect evidence for altered dopaminergic neurotransmission in very premature‐born adults

While animal models indicate altered brain dopaminergic neurotransmission after premature birth, corresponding evidence in humans is scarce due to missing molecular imaging studies. To overcome this limitation, we studied dopaminergic neurotransmission changes in human prematurity indirectly by evaluating the spatial co‐localization of regional alterations in blood oxygenation fluctuations with the distribution of adult dopaminergic neurotransmission. The study cohort comprised 99 very premature‐born (<32 weeks of gestation and/or birth weight below 1500 g) and 107 full‐term born young adults, being assessed by resting‐state functional MRI (rs‐fMRI) and IQ testing. Normative molecular imaging dopamine neurotransmission maps were derived from independent healthy control groups. We computed the co‐localization of local (rs‐fMRI) activity alterations in premature‐born adults with respect to term‐born individuals to different measures of dopaminergic neurotransmission. We performed selectivity analyses regarding other neuromodulatory systems and MRI measures. In addition, we tested if the strength of the co‐localization is related to perinatal measures and IQ. We found selectively altered co‐localization of rs‐fMRI activity in the premature‐born cohort with dopamine‐2/3‐receptor availability in premature‐born adults. Alterations were specific for the dopaminergic system but not for the used MRI measure. The strength of the co‐localization was negatively correlated with IQ. In line with animal studies, our findings support the notion of altered dopaminergic neurotransmission in prematurity which is associated with cognitive performance

Modeling and Bioinformatics Identify Responders to G-CSF in Patients With Amyotrophic Lateral Sclerosis

Objective: Developing an integrative approach to early treatment response classification using survival modeling and bioinformatics with various biomarkers for early assessment of filgrastim (granulocyte colony stimulating factor) treatment effects in amyotrophic lateral sclerosis (ALS) patients. Filgrastim, a hematopoietic growth factor with excellent safety, routinely applied in oncology and stem cell mobilization, had shown preliminary efficacy in ALS. Methods: We conducted individualized long-term filgrastim treatment in 36 ALS patients. The PRO-ACT database, with outcome data from 23 international clinical ALS trials, served as historical control and mathematical reference for survival modeling. Imaging data as well as cytokine and cellular data from stem cell analysis were processed as biomarkers in a non-linear principal component analysis (NLPCA) to identify individual response. Results: Cox proportional hazard and matched-pair analyses revealed a significant survival benefit for filgrastim-treated patients over PRO-ACT comparators. We generated a model for survival estimation based on patients in the PRO-ACT database and then applied the model to filgrastim-treated patients. Model-identified filgrastim responders displayed less functional decline and impressively longer survival than non-responders. Multimodal biomarkers were then analyzed by PCA in the context of model-defined treatment response, allowing identification of subsequent treatment response as early as within 3 months of therapy. Strong treatment response with a median survival of 3.8 years after start of therapy was associated with younger age, increased hematopoietic stem cell mobilization, less aggressive inflammatory cytokine plasma profiles, and preserved pattern of fractional anisotropy as determined by magnetic resonance diffusion tensor imaging (DTI-MRI). Conclusion: Long-term filgrastim is safe, is well-tolerated, and has significant positive effects on disease progression and survival in a small cohort of ALS patients. Developing and applying a model-based biomarker response classification allows use of multimodal biomarker patterns in full potential. This can identify strong individual treatment responders (here: filgrastim) at a very early stage of therapy and may pave the way to an effective individualized treatment option

Evaluation and categorisation of individual patients based on white matter profiles: Single-patient diffusion data interpretation in neurodegeneration

The majority of radiology studies in neurodegenerative conditions infer group-level imaging traits from group comparisons. While this strategy is helpful to define phenotype-specific imaging signatures for academic use, the meaningful interpretation of single scans of individual subjects is more important in everyday clinical practice. Accordingly, we present a computational method to evaluate individual subject diffusion tensor data to highlight white matter integrity alterations. Fifty white matter tracts were quantitatively evaluated in 132 patients with amyotrophic lateral sclerosis (ALS) with respect to normative values from 100 healthy subjects. Fractional anisotropy and radial diffusivity alterations were assessed individually in each patient. The approach was validated against standard tract-based spatial statistics and further scrutinised by the assessment of 78 additional data sets with a blinded diagnosis. Our z-score-based approach readily detected white matter degeneration in individual ALS patients and helped to categorise single subjects with a 'blinded diagnosis' as likely 'ALS' or 'control'. The group-level inferences from the z-score-based approach were analogous to the standard TBSS output maps. The benefit of the z-score-based strategy is that it enables the interpretation of single DTI datasets as well as the comparison of study groups. Outputs can be summarised either visually by highlighting the affected tracts, or, listing the affected tracts in a text file with reference to normative data, making it particularly useful for clinical applications. While individual diffusion data cannot be visually appraised, our approach provides a viable framework for single-subject imaging data interpretation