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Magnetic resonance imaging (MRI) is the gold standard imaging technique for diagnosis and monitoring of many neurological diseases. However, the application of conventional MRI in clinical routine is mainly limited to the visual detection of macroscopic tissue pathology since mixed tissue contrasts depending on hardware and protocol parameters hamper its application for the assessment of subtle or diffuse impairment of the structural tissue integrity. Multiparametric quantitative (q)MRI determines tissue parameters quantitatively, enabling the detection of microstructural processes related to tissue remodeling in aging and neurological diseases. In contrast to measuring tissue atrophy via structural imaging, multiparametric qMRI allows for investigating biologically distinct microstructural processes, which precede changes of the tissue volume. This facilitates a more comprehensive characterization of tissue alterations by revealing early impairment of the microstructural integrity and specific disease-related patterns. So far, qMRI techniques have been employed in a wide range of neurological diseases, including in particular conditions with inflammatory, cerebrovascular and neurodegenerative pathology. Numerous studies suggest that qMRI might add valuable information, including the detection of microstructural tissue damage in areas appearing normal on conventional MRI and unveiling the microstructural correlates of clinical manifestations. This review will give an overview of current qMRI techniques, the most relevant tissue parameters and potential applications in neurological diseases, such as early (differential) diagnosis, monitoring of disease progression, and evaluating effects of therapeutic interventions.
Immune profile and radiological characteristics of progressive multifocal leukoencephalopathy
(2021)
Background and purpose: Progressive multifocal leukoencephalopathy (PML) constitutes a severe disease with increasing incidence, mostly in the context of immunosuppressive therapies. A detailed understanding of immune response in PML appears critical for the treatment strategy. The aim was a comprehensive immunoprofiling and radiological characterization of magnetic resonance imaging (MRI) defined PML variants.
Methods: All biopsy-confirmed PML patients (n = 15) treated in our department between January 2004 and July 2019 were retrospectively analysed. Data from MRI, histology as well as detailed clinical and outcome data were collected. The MRI-defined variants of classical (cPML) and inflammatory (iPML) PML were discriminated based on the intensity of gadolinium enhancement. In these PML variants, intensity and localization (perivascular vs. parenchymal) of inflammation in MRI and histology as well as the cellular composition by immunohistochemistry were assessed. The size of the demyelinating lesions was correlated with immune cell infiltration.
Results: Patients with MRI-defined iPML showed a stronger intensity of inflammation with an increased lymphocyte infiltration on histological level. Also, iPML was characterized by a predominantly perivascular inflammation. However, cPML patients also demonstrated certain inflammatory tissue alterations. Infiltration of CD163-positive microglia and macrophage (M/M) subtypes correlated with PML lesion size.
Conclusions: The non-invasive MRI-based discrimination of PML variants allows for an estimation of inflammatory tissue alterations, although exhibiting limitations in MRI-defined cPML. The association of a distinct phagocytic M/M subtype with the extent of demyelination might reflect disease progression.
Pathophysiological models are urgently needed for personalized treatments of mental disorders. However, most potential neural markers for psychopathology are limited by low interpretability, prohibiting reverse inference from brain measures to clinical symptoms and traits. Neural signatures—i.e. multivariate brain-patterns trained to be both sensitive and specific to a construct of interest—might alleviate this problem, but are rarely applied to mental disorders. We tested whether previously developed neural signatures for negative affect and discrete emotions distinguish between healthy individuals and those with mental disorders characterized by emotion dysregulation, i.e. Borderline Personality Disorder (BPD) and complex Post-traumatic Stress Disorder (cPTSD). In three different fMRI studies, a total sample of 192 women (49 BPD, 62 cPTSD, 81 healthy controls) were shown pictures of scenes with negative or neutral content. Based on pathophysiological models, we hypothesized higher negative and lower positive reactivity of neural emotion signatures in participants with emotion dysregulation. The expression of neural signatures differed strongly between neutral and negative pictures (average Cohen's d = 1.17). Nevertheless, a mega-analysis on individual participant data showed no differences in the reactivity of neural signatures between participants with and without emotion dysregulation. Confidence intervals ruled out even small effect sizes in the hypothesized direction and were further supported by Bayes factors. Overall, these results support the validity of neural signatures for emotional states during fMRI tasks, but raise important questions concerning their link to individual differences in emotion dysregulation.
A precise definition of a brain state has proven elusive. Here, we introduce the novel local-global concept of intrinsic ignition characterizing the dynamical complexity of different brain states. Naturally occurring intrinsic ignition events reflect the capability of a given brain area to propagate neuronal activity to other regions, giving rise to different levels of integration. The ignitory capability of brain regions is computed by the elicited level of integration for each intrinsic ignition event in each brain region, averaged over all events. This intrinsic ignition method is shown to clearly distinguish human neuroimaging data of two fundamental brain states (wakefulness and deep sleep). Importantly, whole-brain computational modelling of this data shows that at the optimal working point is found where there is maximal variability of the intrinsic ignition across brain regions. Thus, combining whole brain models with intrinsic ignition can provide novel insights into underlying mechanisms of brain states.