Transcranial Direct Current Stimulation Reverses Stroke-Induced Network Alterations in Mice.

BACKGROUND: Beyond focal effects, stroke lesions impact the function of distributed networks. We here investigated (1) whether transcranial direct current stimulation (tDCS) alters the network changes induced by cerebral ischemia and (2) whether functional network parameters predict the therapeutic efficacy of tDCS in a mouse model of focal photothrombotic stroke. METHODS: Starting 3 days after stroke, cathodal tDCS (charge density=39.6 kC/m²) was applied over 10 days in male C57Bl/6J mice under light anesthesia over the lesioned sensory-motor cortex. Functional connectivity (resting-state functional magnetic resonance imaging) was evaluated for up to 28-day poststroke, with global graph parameters of network integration computed. RESULTS: Ischemia induced a subacute increase in connectivity accompanied by a significant reduction in characteristic path length, reversed by 10 days of tDCS. Early measures of functional network alterations and the network configuration at prestroke baseline predicted spontaneous and tDCS-augmented motor recovery. DISCUSSION: Stroke induces characteristic network changes throughout the brain that can be detected by resting-state functional magnetic resonance imaging. These network changes were, at least in part, reversed by tDCS. Moreover, early markers of a network impairment and the network configuration before the insult improve the prediction of motor recovery.

Network analysis of neuroimaging in mice.

Graph theory allows assessing changes of neuronal connectivity and interactions of brain regions in response to local lesions, e.g., after stroke, and global perturbations, e.g., due to psychiatric dysfunctions or neurodegenerative disorders. Consequently, network analysis based on constructing graphs from structural and functional MRI connectivity matrices is increasingly used in clinical studies. In contrast, in mouse neuroimaging, the focus is mainly on basic connectivity parameters, i.e., the correlation coefficient or fiber counts, whereas more advanced network analyses remain rarely used. This review summarizes graph theoretical measures and their interpretation to describe networks derived from recent in vivo mouse brain studies. To facilitate the entry into the topic, we explain the related mathematical definitions, provide a dedicated software toolkit, and discuss practical considerations for the application to rs-fMRI and DTI. This way, we aim to foster cross-species comparisons and the application of standardized measures to classify and interpret network changes in translational brain disease studies.

Reactive astrocytes prevent maladaptive plasticity after ischemic stroke.

Restoration of functional connectivity is a major contributor to functional recovery after stroke. We investigated the role of reactive astrocytes in functional connectivity and recovery after photothrombotic stroke in mice with attenuated reactive gliosis (GFAP-/-Vim-/-). Infarct volume and longitudinal functional connectivity changes were determined by in vivo T2-weighted magnetic resonance imaging (MRI) and resting-state functional MRI. Sensorimotor function was assessed with behavioral tests, and glial and neural plasticity responses were quantified in the peri-infarct region. Four weeks after stroke, GFAP-/-Vim-/- mice showed impaired recovery of sensorimotor function and aberrant restoration of global neuronal connectivity. These mice also exhibited maladaptive plasticity responses, shown by higher number of lost and newly formed functional connections between primary and secondary targets of cortical stroke regions and increased peri-infarct expression of the axonal plasticity marker Gap43. We conclude that reactive astrocytes modulate recovery-promoting plasticity responses after ischemic stroke.

Lesion Size- and Location-Dependent Recruitment of Contralesional Thalamus and Motor Cortex Facilitates Recovery after Stroke in Mice.

Brain lesions caused by cerebral ischemia or hemorrhage lead to a local breakdown of energy homeostasis followed by irreversible cell death and long-term impairment. Importantly, local brain lesions also generate remote functional and structural disturbances, which contribute to the behavioral deficit but also impact the recovery of function. While spontaneous recovery has been associated with endogenous repair mechanisms at the vascular, neural, and immune cell levels, the impact of structural plasticity on sensory-motor dysfunction and recovery thereof remains to be elucidated by longitudinal imaging in a mouse model. Here, we applied behavioral assessments, in vivo fiber tracking, and histological validation in a photothrombotic stroke mouse model. Atlas-based whole-brain structural connectivity analysis and ex vivo histology revealed secondary neurodegeneration in the ipsilesional brain areas, mostly in the dorsal sensorimotor area of the thalamus. Furthermore, we describe for the first time a lesion size-dependent increase in structural connectivity between the contralesional primary motor cortex and thalamus with the ipsilesional cortex. The involvement of the contralesional hemisphere was associated with improved functional recovery relative to lesion size. This study highlights the importance of in vivo fiber tracking and the role of the contralesional hemisphere during spontaneous functional improvement as a potential novel stroke biomarker and therapeutic targets.

Graph theoretical quantification of white matter reorganization after cortical stroke in mice.

Stroke is a devastating disease leading to cell death and disconnection between neurons both locally and remote, often resulting in severe long-term disability. Spontaneous reorganization of areas and pathways not primarily affected by ischemia is, however, associated with albeit limited recovery of function. Quantitative mapping of whole-brain changes of structural connectivity concerning the ischemia-induced sensorimotor deficit and recovery thereof would help to target structural plasticity in order to improve rehabilitation. Currently, only in vivo diffusion MRI can extract the structural whole-brain connectome noninvasively. This approach is, however, used primarily in human studies. Here, we applied atlas-based MRI analysis and graph theory to DTI in wild-type mice with cortical stroke lesions. Using a DTI network approach and graph theory, we aimed at gaining insights into the dynamics of the spontaneous reorganization after stroke related to the recovery of function. We found evidence for altered structural integrity of connections of specific brain regions, including the breakdown of connections between brain regions directly affected by stroke as well as long-range rerouting of intra- and transhemispheric connections related to improved sensorimotor behavior.

Atlas-based imaging data analysis tool for quantitative mouse brain histology (AIDAhisto).

Cell counting in neuroscience is a routine method of utmost importance to support descriptive in vivo findings with quantitative data on the cellular level. Although known to be error- and bias-prone, manual cell counting of histological stained brain slices remains the gold standard in the field. While the manual approach is limited to small regions-of-interest in the brain, automated tools are needed to up-scale translational approaches and generate whole mouse brain counts in an atlas framework. Our goal was to develop an algorithm which requires no pre-training such as machine learning algorithms, only minimal user input, and adjustable variables to obtain reliable cell counting results for stitched mouse brain slices registered to a common atlas such as the Allen Mouse Brain atlas. We adapted filter banks to extract the maxima from round-shaped cell nuclei and various cell structures. In a qualitative as well as quantitative comparison to other tools and two expert raters, AIDAhisto provides accurate and fast results for cell nuclei as well as immunohistochemical stainings of various types of cells in the mouse brain.

Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri).

Magnetic resonance imaging (MRI) is a key technology in multimodal animal studies of brain connectivity and disease pathology. In vivo MRI provides non-invasive, whole brain macroscopic images containing structural and functional information, thereby complementing invasive in vivo high-resolution microscopy and ex vivo molecular techniques. Brain mapping, the correlation of corresponding regions between multiple brains in a standard brain atlas system, is widely used in human MRI. For small animal MRI, however, there is no scientific consensus on pre-processing strategies and atlas-based neuroinformatics. Thus, it remains difficult to compare and validate results from different pre-clinical studies which were processed using custom-made code or individual adjustments of clinical MRI software and without a standard brain reference atlas. Here, we describe AIDAmri, a novel Atlas-based Imaging Data Analysis pipeline to process structural and functional mouse brain data including anatomical MRI, fiber tracking using diffusion tensor imaging (DTI) and functional connectivity analysis using resting-state functional MRI (rs-fMRI). The AIDAmri pipeline includes automated pre-processing steps, such as raw data conversion, skull-stripping and bias-field correction as well as image registration with the Allen Mouse Brain Reference Atlas (ARA). Following a modular structure developed in Python scripting language, the pipeline integrates established and newly developed algorithms. Each processing step was optimized for efficient data processing requiring minimal user-input and user programming skills. The raw data is analyzed and results transferred to the ARA coordinate system in order to allow an efficient and highly-accurate region-based analysis. AIDAmri is intended to fill the gap of a missing open-access and cross-platform toolbox for the most relevant mouse brain MRI sequences thereby facilitating data processing in large cohorts and multi-center studies.

Substrate elasticity induces quiescence and promotes neurogenesis of primary neural stem cells-A biophysical in vitro model of the physiological cerebral milieu.

In the brain, neural stem cells (NSC) are tightly regulated by external signals and biophysical cues mediated by the local microenvironment or "niche." In particular, the influence of tissue elasticity, known to fundamentally affect the function of various cell types in the body, on NSC remains poorly understood. We, accordingly, aimed to characterize the effects of elastic substrates on critical NSC functions. Primary rat NSC were grown as monolayers on polydimethylsiloxane- (PDMS-) based gels. PDMS-coated cell culture plates, simulating the physiological microenvironment of the living brain, were generated in various degrees of elasticity, ranging from 1 to 50 kPa; additionally, results were compared with regular glass plates as usually used in cell culture work. Survival of NSC on the PDMS-based substrates was unimpaired. The proliferation rate on 1 kPa PDMS decreased by 45% compared with stiffer PMDS substrates of 50 kPa (p < 0.05) whereas expression of cyclin-dependent kinase inhibitor 1B/p27Kip1 increased more than two fold (p < 0.01), suggesting NSC quiescence. NSC differentiation was accelerated on softer substrates and favored the generation of neurons (42% neurons on 1 kPa PDMS vs. 25% on 50 kPa PDMS; p < 0.05). Neurons generated on 1 kPa PDMS showed 29% longer neurites compared with those on stiffer PDMS substrates (p < 0.05), suggesting optimized neuronal maturation and an accelerated generation of neuronal networks. Data show that primary NSC are significantly affected by the mechanical properties of their microenvironment. Culturing NSC on a substrate of brain-like elasticity keeps them in their physiological, quiescent state and increases their neurogenic potential.

Cloud-based relational database for multimodal animal data.

Pre-clinical research builds on a large variety of in vivo and ex vivo tools such as non-invasive imaging, microscopy, and analysis of gene expression. To work efficiently with multimodal data and correlate results across scales, it is of particular importance to have easy access to all data points from different specimen, e.g. the magnetic resonance imaging (MRI) data from different time points, and the post-mortem histology. That requires an efficient data management, which is customizable and designed to relate all applied methods, raw data and analyses to one specific animal. Despite increasing demands to handle such complex data, most pre-clinical labs have not yet established such an electronic database. Here, we present a novel cloud-based relational database for multimodal animal data, which operates on commercial software. We have implemented data fields for various pre-clinical features such as MRI, histology and behaviour. Automated procedures replace manual and recurrent calculations. Pre-set plotting and printing features provide efficient analysis and documentation. The database template is useful for all labs working with laboratory animals and the adaption to specific research projects requires no prior scripting expertise. The database works operating-system independent through the web browser and allows multiple users to work simultaneously. The data entry is monitored and restricted for particular tests according to the user management in order to keep for example users during the experiment blinded for the experimental group. The database improves data accessibility, standardization of data recording and data handling efficiency in pre-clinical research.

Temporal Scalability of Dynamic Volume Data Using Mesh Compensated Wavelet Lifting.

Due to their high resolution, dynamic medical 2D+t and 3D+t volumes from computed tomography (CT) and magnetic resonance tomography (MR) reach a size which makes them very unhandy for teleradiologic applications. A lossless scalable representation offers the advantage of a down-scaled version which can be used for orientation or previewing, while the remaining information for reconstructing the full resolution is transmitted on demand. The wavelet transform offers the desired scalability. A very high quality of the lowpass sub-band is crucial in order to use it as a down-scaled representation. We propose an approach based on compensated wavelet lifting for obtaining a scalable representation of dynamic CT and MR volumes with very high quality. The mesh compensation is feasible to model the displacement in dynamic volumes which is mainly given by expansion and contraction of tissue over time. To achieve this, we propose an optimized estimation of the mesh compensation parameters to optimally fit for dynamic volumes. Within the lifting structure, the inversion of the motion compensation is crucial in the update step. We propose to take this inversion directly into account during the estimation step and can improve the quality of the lowpass sub-band by 0.63 and 0.43 dB on average for our tested dynamic CT and MR volumes at the cost of an increase of the rate by 2.4% and 1.2% on average.