Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Stereoscopic three-dimensional visualization applied to multimodal brain images: Clinical applications and a functional connectivity atlas

Effective visualization is central to the exploration and comprehension of brain imaging data. While MRI data are acquired in three-dimensional space, the methods for visualizing such data have rarely taken advantage of three-dimensional stereoscopic technologies. We present here results of stereoscopic visualization of clinical data, as well as an atlas of whole-brain functional connectivity. In comparison with traditional 3D rendering techniques, we demonstrate the utility of stereoscopic visualizations to provide an intuitive description of the exact location and the relative sizes of various brain landmarks, structures and lesions. In the case of resting state fMRI, stereoscopic 3D visualization facilitated comprehension of the anatomical position of complex large-scale functional connectivity patterns. Overall, stereoscopic visualization improves the intuitive visual comprehension of image contents, and brings increased dimensionality to visualization of traditional MRI data, as well as patterns of functional connectivity

White Matter Structural Connectivity is Associated with Sensorimotor Function in Stroke Survivors

Purpose Diffusion tensor imaging (DTI) provides functionally relevant information about white matter structure. Local anatomical connectivity information combined with fractional anisotropy (FA) and mean diffusivity (MD) may predict functional outcomes in stroke survivors. Imaging methods for predicting functional outcomes in stroke survivors are not well established. This work uses DTI to objectively assess the effects of a stroke lesion on white matter structure and sensorimotor function. Methods A voxel-based approach is introduced to assess a stroke lesion\u27s global impact on motor function. Anatomical T1-weighted and diffusion tensor images of the brain were acquired for nineteen subjects (10 post-stroke and 9 age-matched controls). A manually selected volume of interest was used to alleviate the effects of stroke lesions on image registration. Images from all subjects were registered to the images of the control subject that was anatomically closest to Talairach space. Each subject\u27s transformed image was uniformly seeded for DTI tractography. Each seed was inversely transformed into the individual subject space, where DTI tractography was conducted and then the results were transformed back to the reference space. A voxel-wise connectivity matrix was constructed from the fibers, which was then used to calculate the number of directly and indirectly connected neighbors of each voxel. A novel voxel-wise indirect structural connectivity (VISC) index was computed as the average number of direct connections to a voxel\u27s indirect neighbors. Voxel-based analyses (VBA) were performed to compare VISC, FA, and MD for the detection of lesion-induced changes in sensorimotor function. For each voxel, a t-value was computed from the differences between each stroke brain and the 9 controls. A series of linear regressions was performed between Fugl-Meyer (FM) assessment scores of sensorimotor impairment and each DTI metric\u27s log number of voxels that differed from the control group. Results Correlation between the logarithm of the number of significant voxels in the ipsilesional hemisphere and total Fugl-Meyer score was moderate for MD (R2 = 0.512), and greater for VISC (R2 = 0.796) and FA (R2 = 0.674). The slopes of FA (p = 0.0036), VISC (p = 0.0005), and MD (p = 0.0199) versus the total FM score were significant. However, these correlations were driven by the upper extremity motor component of the FM score (VISC: R2 = 0.879) with little influence of the lower extremity motor component (FA: R2 = 0.177). Conclusion The results suggest that a voxel-wise metric based on DTI tractography can predict upper extremity sensorimotor function of stroke survivors, and that supraspinal intraconnectivity may have a less dominant role in lower extremity function

Getting the best outcomes from epilepsy surgery.

Neurosurgery is an underutilized treatment that can potentially cure drug-refractory epilepsy. Careful, multidisciplinary presurgical evaluation is vital for selecting patients and to ensure optimal outcomes. Advances in neuroimaging have improved diagnosis and guided surgical intervention. Invasive electroencephalography allows the evaluation of complex patients who would otherwise not be candidates for neurosurgery. We review the current state of the assessment and selection of patients and consider established and novel surgical procedures and associated outcome data. We aim to dispel myths that may inhibit physicians from referring and patients from considering neurosurgical intervention for drug-refractory focal epilepsies. Ann Neurol 2018;83:676-690

Methods for improved mapping of brain lesion connectivity

Recent advances over the past two decades in neuroimaging methods have enabled us to map the connectivity of the brain. In parallel, pathophysiological models of brain disease have shifted from an emphasis on understanding pathology in specific brain regions to characterizing disruptions to interconnected neural networks. Nevertheless, these recent methods for mapping brain connectivity are still under development. Every step of the mapping process becomes a potential source for additional error due to noise or artifacts that could impact final analyses. Segmentation, parcellation, registration, and tractography are some of the steps where this occurs. Moreover, mapping the connectivity in a brain lesion is even more susceptible to errors in these steps. In this body of work, I describe multiple new methods for improving the accuracy of mapping lesion connectivity by reducing errors at the tractography stage which is the most error prone stage. First, we develop an approach for directly normalizing streamlines into a template space that avoids performing tractography in the normalized template space, reducing the error of connectomes constructed in the template space with respect to the ground truth native space connectome. Second, we develop a rapid approach for performing shortest path tractography and constructing shortest path probability weighted connectomes which increases the connection specificity relative to local streamline tracking approaches. We then demonstrate how our shortest path tractography approach can be used construct a disconnectome, a connectivity map of the proportion of connections lost due to intersecting a lesion. We then develop a fast, greedy graph-theoretic algorithm that extracts the maximally disconnected subgraph containing brain regions with the greatest shared loss of connectivity. Finally, we demonstrate how combining methods from diffusion based image inpainting and optimal estimation can be used to restore or inpaint corrupted fiber diffusion models in lesioned white matter tissue, enabling tractography and the study of lesion connectivity and modeling of microstructural measures in the patient’s native space

Computer-Assisted Planning and Robotics in Epilepsy Surgery

Epilepsy is a severe and devastating condition that affects ~1% of the population. Around 30% of these patients are drug-refractory. Epilepsy surgery may provide a cure in selected individuals with drug-resistant focal epilepsy if the epileptogenic zone can be identified and safely resected or ablated. Stereoelectroencephalography (SEEG) is a diagnostic procedure that is performed to aid in the delineation of the seizure onset zone when non-invasive investigations are not sufficiently informative or discordant. Utilizing a multi-modal imaging platform, a novel computer-assisted planning (CAP) algorithm was adapted, applied and clinically validated for optimizing safe SEEG trajectory planning. In an initial retrospective validation study, 13 patients with 116 electrodes were enrolled and safety parameters between automated CAP trajectories and expert manual plans were compared. The automated CAP trajectories returned statistically significant improvements in all of the compared clinical metrics including overall risk score (CAP 0.57 +/- 0.39 (mean +/- SD) and manual 1.00 +/- 0.60, p < 0.001). Assessment of the inter-rater variability revealed there was no difference in external expert surgeon ratings. Both manual and CAP electrodes were rated as feasible in 42.8% (42/98) of cases. CAP was able to provide feasible electrodes in 19.4% (19/98), whereas manual planning was able to generate a feasible electrode in 26.5% (26/98) when the alternative generation method was not feasible. Based on the encouraging results from the retrospective analysis a prospective validation study including an additional 125 electrodes in 13 patients was then undertaken to compare CAP to expert manual plans from two neurosurgeons. The manual plans were performed separately and blindly from the CAP. Computer-generated trajectories were found to carry lower risks scores (absolute difference of 0.04 mm (95% CI = -0.42-0.01), p = 0.04) and were subsequently implanted in all cases without complication. The pipeline has been fully integrated into the clinical service and has now replaced manual SEEG planning at our institution. Further efforts were then focused on the distillation of optimal entry and target points for common SEEG trajectories and applying machine learning methods to develop an active learning algorithm to adapt to individual surgeon preferences. Thirty-two patients were prospectively enrolled in the study. The first 12 patients underwent prospective CAP planning and implantation following the pipeline outlined in the previous study. These patients were used as a training set and all of the 108 electrodes after successful implantation were normalized to atlas space to generate ‘spatial priors’, using a K-Nearest Neighbour (K-NN) classifier. A subsequent test set of 20 patients (210 electrodes) were then used to prospectively validate the spatial priors. From the test set, 78% (123/157) of the implanted trajectories passed through both the entry and target spatial priors defined from the training set. To improve the generalizability of the spatial priors to other neurosurgical centres undertaking SEEG and to take into account the potential for changing institutional practices, an active learning algorithm was implemented. The K-NN classifier was shown to dynamically learn and refine the spatial priors. The progressive refinement of CAP SEEG planning outlined in this and previous studies has culminated in an algorithm that not only optimizes the surgical heuristics and risk scores related to SEEG planning but can also learn from previous experience. Overall, safe and feasible trajectory schema were returning in 30% of the time required for manual SEEG planning. Computer-assisted planning was then applied to optimize laser interstitial thermal therapy (LITT) trajectory planning, which is a minimally invasive alternative to open mesial temporal resections, focal lesion ablation and anterior 2/3 corpus callosotomy. We describe and validate the first CAP algorithm for mesial temporal LITT ablations for epilepsy treatment. Twenty-five patients that had previously undergone LITT ablations at a single institution and with a median follow up of 2 years were included. Trajectory parameters for the CAP algorithm were derived from expert consensus to maximize distance from vasculature and ablation of the amygdalohippocampal complex, minimize collateral damage to adjacent brain structures whilst avoiding transgression of the ventricles and sulci. Trajectory parameters were also optimized to reduce the drilling angle to the skull and overall catheter length. Simulated cavities attributable to the CAP trajectories were calculated using a 5-15 mm ablation diameter. In comparison to manually planned and implemented LITT trajectories,CAP resulted in a significant increase in the percentage ablation of the amygdalohippocampal complex (manual 57.82 +/- 15.05% (mean +/- S.D.) and unablated medial hippocampal head depth (manual 4.45 +/- 1.58 mm (mean +/- S.D.), CAP 1.19 +/- 1.37 (mean +/- S.D.), p = 0.0001). As LITT ablation of the mesial temporal structures is a novel procedure there are no established standards for trajectory planning. A data-driven machine learning approach was, therefore, applied to identify hitherto unknown CAP trajectory parameter combinations. All possible combinations of planning parameters were calculated culminating in 720 unique combinations per patient. Linear regression and random forest machine learning algorithms were trained on half of the data set (3800 trajectories) and tested on the remaining unseen trajectories (3800 trajectories). The linear regression and random forest methods returned good predictive accuracies with both returning Pearson correlations of ρ = 0.7 and root mean squared errors of 0.13 and 0.12 respectively. The machine learning algorithm revealed that the optimal entry points were centred over the junction of the inferior occipital, middle temporal and middle occipital gyri. The optimal target points were anterior and medial translations of the centre of the amygdala. A large multicenter external validation study of 95 patients was then undertaken comparing the manually planned and implemented trajectories, CAP trajectories targeting the centre of the amygdala, the CAP parameters derived from expert consensus and the CAP trajectories utilizing the machine learning derived parameters. Three external blinded expert surgeons were then selected to undertake feasibility ratings and preference rankings of the trajectories. CAP generated trajectories result in a significant improvement in many of the planning metrics, notably the risk score (manual 1.3 +/- 0.1 (mean +/- S.D.), CAP 1.1 +/- 0.2 (mean +/- S.D.), p<0.000) and overall ablation of the amygdala (manual 45.3 +/- 22.2 % (mean +/- S.D.), CAP 64.2 +/- 20 % (mean +/- S.D.), p<0.000). Blinded external feasibility ratings revealed that manual trajectories were less preferable than CAP planned trajectories with an estimated probability of being ranked 4th (lowest) of 0.62. Traditional open corpus callosotomy requires a midline craniotomy, interhemispheric dissection and disconnection of the rostrum, genu and body of the corpus callosum. In cases where drop attacks persist a completion corpus callosotomy to disrupt the remaining fibres in the splenium is then performed. The emergence of LITT technology has raised the possibility of being able to undertake this procedure in a minimally invasive fashion and without the need for a craniotomy using two or three individual trajectories. Early case series have shown LITT anterior two-thirds corpus callosotomy to be safe and efficacious. Whole-brain probabilistic tractography connectomes were generated utilizing 3-Tesla multi-shell imaging data and constrained spherical deconvolution (CSD). Two independent blinded expert neurosurgeons with experience of performing the procedure using LITT then planned the trajectories in each patient following their current clinical practice. Automated trajectories returned a significant reduction in the risk score (manual 1.3 +/- 0.1 (mean +/- S.D.), CAP 1.1 +/- 0.1 (mean +/- S.D.), p<0.000). Finally, we investigate the different methods of surgical implantation for SEEG electrodes. As an initial study, a systematic review and meta-analysis of the literature to date were performed. This revealed a wide variety of implantation methods including traditional frame-based, frameless, robotic and custom-3D printed jigs were being used in clinical practice. Of concern, all comparative reports from institutions that had changed from one implantation method to another, such as following the introduction of robotic systems, did not undertake parallel-group comparisons. This suggests that patients may have been exposed to risks associated with learning curves and potential harms related to the new device until the efficacy was known. A pragmatic randomized control trial of a novel non-CE marked robotic trajectory guidance system (iSYS1) was then devised. Before clinical implantations began a series of pre-clinical investigations utilizing 3D printed phantom heads from previously implanted patients was performed to provide pilot data and also assess the surgical learning curve. The surgeons had comparatively little clinical experience with the new robotic device which replicates the introduction of such novel technologies to clinical practice. The study confirmed that the learning curve with the iSYS1 devices was minimal and the accuracies and workflow were similar to the conventional manual method. The randomized control trial represents the first of its kind for stereotactic neurosurgical procedures. Thirty-two patients were enrolled with 16 patients randomized to the iSYS1 intervention arm and 16 patients to the manual implantation arm. The intervention allocation was concealed from the patients. The surgical and research team could be not blinded. Trial management, independent data monitoring and trial steering committees were convened at four points doing the trial (after every 8 patients implanted). Based on the high level of accuracy required for both methods, the main distinguishing factor would be the time to achieve the alignment to the prespecified trajectory. The primary outcome for comparison, therefore, was the time for individual SEEG electrode implantation. Secondary outcomes included the implantation accuracy derived from the post-operative CT scan, infection, intracranial haemorrhage and neurological deficit rates. Overall, 32 patients (328 electrodes) completed the trial (16 in each intervention arm) and the baseline demographics were broadly similar between the two groups. The time for individual electrode implantation was significantly less with the iSYS1 device (median of 3.36 (95% CI 5.72 to 7.07) than for the PAD group (median of 9.06 minutes (95% CI 8.16 to 10.06), p=0.0001). Target point accuracy was significantly greater with the PAD (median of 1.58 mm (95% CI 1.38 to 1.82) compared to the iSYS1 (median of 1.16 mm (95% CI 1.01 to 1.33), p=0.004). The difference between the target point accuracies are not clinically significant for SEEG but may have implications for procedures such as deep brain stimulation that require higher placement accuracy. All of the electrodes achieved their respective intended anatomical targets. In 12 of 16 patients following robotic implantations, and 10 of 16 following manual PAD implantations a seizure onset zone was identified and resection recommended. The aforementioned systematic review and meta-analysis were updated to include additional studies published during the trial duration. In this context, the iSYS1 device entry and target point accuracies were similar to those reported in other published studies of robotic devices including the ROSA, Neuromate and iSYS1. The PAD accuracies, however, outperformed the previously published results for other frameless stereotaxy methods. In conclusion, the presented studies report the integration and validation of a complex clinical decision support software into the clinical neurosurgical workflow for SEEG planning. The stereotactic planning platform was further refined by integrating machine learning techniques and also extended towards optimisation of LITT trajectories for ablation of mesial temporal structures and corpus callosotomy. The platform was then used to seamlessly integrate with a novel trajectory planning software to effectively and safely guide the implantation of the SEEG electrodes. Through a single-blinded randomised control trial, the ISYS1 device was shown to reduce the time taken for individual electrode insertion. Taken together, this work presents and validates the first fully integrated stereotactic trajectory planning platform that can be used for both SEEG and LITT trajectory planning followed by surgical implantation through the use of a novel trajectory guidance system

In vivo analyses of the correlates of cortical and white matter pathology in patients with multiple sclerosis by quantitative 7 Tesla and 3 Tesla MRI and molecular imaging.

This thesis is divided in two sections. The first reports the results from a project regarding the application of ultra-high-field quantitative MRI for the study of cortical grey matter pathology in patients with early multiple sclerosis (MS). Applying a Combined Myelin Estimation method, obtained by 7 Tesla magnetic resonance imaging, we aimed at characterizing cortical microstructural abnormalities related to myelin content in cortical lesions and normal-appearing cortex, to assess their evolution at 1-year follow-up and to relate cortical myelin changes to clinical and radiological disease burden. Data obtained from 25 patients with early MS and 19 healthy volunteers showed overall abnormally low myelin content in cortical lesions and several areas of normal-appearing cortex. Myelin content along the cortex correlated with neurological impairment. Individual cortical lesion analysis revealed heterogenous patterns, ranging from extensive to partial demyelination, or even measurements comparable to the healthy group. At 1-year follow-up, cortical myelin was overall decreased in cortical lesions and scattered areas in the normal-appearing cortex. Diffusion metrics obtained in the same regions did not show the presence of cortical neural loss accompanying early demyelination. The second section reports results from a study in which we combined 11C-PBR28 positron-emission tomography, marking activated microglia, with the more recently validated synthetic magnetic resonance imaging for myelin content assessment, aiming at researching the presence of correlates of pathology in the white matter of patients with multiple sclerosis at different disease stages, and assessing the interplay between neuroinflammation and demyelination. We found abnormal increase of microglia activation in MS patients compared to healthy volunteers in several areas across the white matter, correlating with clinical and radiological disease burden. An individual analysis of white matter lesions showed the presence of active lesions in the early phases of the disease, evolving in inactive or peripherally active lesions in the late disease phases, the latter correlating with clinical disability. Myelin content in the normal-appearing white matter of MS patients correlated with neurological impairment and lesion load. Higher microglia activation in the white matter lesions was related to lower myelin content in the non-lesioned white matter

Imaging of cognitive outcomes in patients with autoimmune encephalitis

Die Autoimmunenzephalitis ist eine kürzlich beschriebene entzündliche Erkrankung des zentralen Nervensystems, die Gedächtnisdefizite, Psychosen, oder epileptische Anfälle hervorrufen kann. Derzeit ist hingegen noch nicht ausreichend verstanden, welche pathologischen Veränderungen zu den kognitiven Defiziten führen und welche neuropsychologischen und bildgebenden Langzeitoutcomes zu erwarten sind. Anhand von strukturellen und funktionellen Bildgebungsanalysen zeigt diese Dissertation, dass kognitive Defizite auch nach der akuten Phase der Autoimmunenzephalitis fortbestehen können. Bei der LGI1-Enzephalitis gehen Gedächtnisdefizite mit fokalen strukturellen Läsionen im Hippocampus einher. Durch eine funktionelle Störung der Resting-State-Konnektivität des Default-Mode- und Salienznetzwerkes beeinträchtigen diese Hippocampusläsionen auch Hirnregionen außerhalb des limbischen Systems. Bei Patient:innen mit NMDA-Rezeptor-Enzephalitis finden sich in der longitudinalen neuropsychologischen Untersuchung trotz guter allgemeiner Genesung auch noch mehrere Jahre nach der Akutphase persistierende Defizite des Gedächtnisses und exekutiver Funktionen. Zuletzt zeigt eine transdiagnostische Analyse, dass der anteriore Hippocampus eine erhöhte Vulnerabilität gegenüber immunvermittelten pathologischen Prozessen aufweist. Diese Ergebnisse legen nahe, dass kognitive Symptome auch noch nach der Entlassung aus der stationären Behandlung fortbestehen können. Sowohl umschriebene strukturelle Hippocampusläsionen als auch Veränderungen in makroskopischen funktionellen Hirnnetzwerken tragen zur pathophysiologischen Erklärung dieser Symptome bei. Zudem erlauben diese Ergebnisse einen Einblick in neuroplastische Veränderungen des Gehirns und haben weitreichende Implikationen für die Langzeitversorgung und das Design zukünftiger klinischer Studien.Autoimmune encephalitis is a recently described inflammatory disease of the central nervous system that can cause memory deficits, psychosis, or seizures. The trajectory of cognitive dysfunction and the underlying long-term imaging correlates are, however, not yet fully understood. By using advanced structural and functional neuroimaging, this thesis shows that cognitive deficits persist beyond the acute phase. In LGI1 encephalitis, MRI postprocessing revealed that memory deficits are related to focal structural hippocampal lesions. These hippocampal lesions propagate to brain areas outside the limbic system through aberrant resting-state connectivity of the default mode network (DMN) and the salience network. In NMDA receptor encephalitis, a longitudinal analysis of neuropsychological data describes persistent cognitive deficits, especially in the memory and executive domains, despite good physical recovery several years after the acute disease. Lastly, a transdiagnostic analysis reveals that the anterior hippocampus is particularly vulnerable to immune-mediated damage. In conclusion, these results demonstrate that cognitive symptoms in autoimmune encephalitis can persist beyond discharge from neurological care. Both discrete structural hippocampal damage and changes in macroscopic functional networks shed light on the pathophysiological basis of these symptoms. These findings help to explain how the brain responds to pathological damage and have substantial implications for long-term patient care and the design of future clinical studies

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction☆

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Deciphering and fine-tuning of myeloid cells in CNS demyelinating conditions

Demyelination in the central nervous system (CNS) is a characteristic of various neurological disorders, such as multiple sclerosis (MS), neuromyelitis optica (NMO), subacute combined degeneration (SCD), tabes dorsalis (syphilitic myelopathy), and more. Although the causes vary, CNS demyelination is often associated with a significant buildup of inflammatory activated myeloid cells, mainly consisting of CNS resident microglia and infiltrating monocyte-derived macrophages. On one hand, these myeloid cells can contribute to inflammation in the CNS and damage myelin, but on the other hand, they play a role in clearing myelin debris and releasing substances that facilitate myelin regeneration, a process known as remyelination. Therefore, it is crucial to determine the signals that control their specific functions and develop methods for regulating their activity. In this thesis, we investigated the role of TGF-β signaling in regulating myeloid cell function. By using cell-specific targeting mouse tools, we discovered that when the CNS lacks microglia in a specific experimental setting, where peripheral monocytes can enter the CNS and repopulate the microglia pool by transforming into microglia-like macrophages, deleting the TGF-β receptor (TGFBR2) on monocytes prevents their entry into the CNS. Furthermore, when monocyte-derived macrophages are engrafted in the CNS, the depletion of TGFBR2 causes their abnormal activation and failure to adopt a microglia-like signature, leading to spontaneous demyelination in the spinal cord and a progressive, fatal motor disease. The loss of TGF- β signaling in microglia or monocyte-derived microglia-like cells preferentially targets myelin in the dorsal column of the spinal cord, and a subpopulation of microglia closely associated with myelin loss is identified in the dorsal column. We further characterized that this microglial TGF-β signaling loss-induced disease is more severe in female and older mice and uncovered potential molecular mechanisms underlying these gender and age differences in response to the loss of TGF-β signaling. In addition to deciphering the mechanisms governing myeloid function, we also conducted translational studies aiming to provide therapeutic insights for demyelinating diseases. By using a drug screening tool and performing in vitro validation, as well as experiments in mouse models with CNS inflammation and ensuing demyelination, we confirmed the regulatory effect of topotecan, a topoisomerase 1 inhibitor, on myeloid cells, leading to improved disease outcome. We further developed a DNA nanostructure-based drug delivery system to encapsule topotecan and achieve specific targeting of TOP1 in myeloid cells, demonstrating that myeloid cell-specific inhibition of TOP1 could alleviate neuroinflammation. In the final study, within a non-inflammation-driven demyelinating context, we studied the role of TRPV1 activation in remyelination and revealed that the TRPV1 activator capsaicin could enhance microglial clearance of myelin debris following demyelination and promote remyelination. My thesis work thus provides mechanistic understanding of how myeloid cells regulate myelin health and CNS homeostasis, while also providing regulatory strategies for fine-tuning these cells