Anatomy and White Matter Connections of the Lingual Gyrus and Cuneus

Neurological disorders are the leading cause of disability and the second largest cause of death worldwide. Despite significant research efforts, neurology remains one of the most failure-prone areas of drug development. The complexity of the human brain, boundaries to examining the brain directly in vivo, and the significant evolutionary gap between animal models and humans, all serve to hamper translational success. Recent advances in microfluidic in vitro models have provided new opportunities to study human cells with enhanced physiological relevance. The ability to precisely micro-engineer cell-scale architecture, tailoring form and function, has allowed for detailed dissection of cell biology using microphysiological systems (MPS) of varying complexities from single cell systems to "Organ-on-chip" models. Simplified neuronal networks have allowed for unique insights into neuronal transport and neurogenesis, while more complex 3D heterotypic cellular models such as neurovascular unit mimetics and "Organ-on-chip" systems have enabled new understanding of metabolic coupling and blood-brain barrier transport. These systems are now being developed beyond MPS toward disease specific micro-pathophysiological systems, moving from "Organ-on-chip" to "Disease-on-chip." This review gives an outline of current state of the art in microfluidic technologies for neurological disease research, discussing the challenges and limitations while highlighting the benefits and potential of integrating technologies. We provide examples of where such toolsets have enabled novel insights and how these technologies may empower future investigation into neurological diseases.

Advances in microfluidic in vitro systems for neurological disease modeling

The surgical management of brain tumors is based on the principle that the extent of resection improves patient outcomes. Traditionally, neurosurgeons have considered that lesions in "non-eloquent" cerebrum can be more aggressively surgically managed compared to lesions in "eloquent" regions with more known functional relevance. Furthermore, advancements in multimodal imaging technologies have improved our ability to extend the rate of resection while minimizing the risk of inducing new neurologic deficits, together referred to as the "onco-functional balance." However, despite the common utilization of invasive techniques such as cortical mapping to identify eloquent tissue responsible for language and motor functions, glioma patients continue to present post-operatively with poor cognitive morbidity in higher-order functions. Such observations are likely related to the difficulty in interpreting the highly-dimensional information these technologies present to us regarding cognition in addition to our classically poor understanding of the functional and structural neuroanatomy underlying complex higher-order cognitive functions. Furthermore, reduction of the brain into isolated cortical regions without consideration of the complex, interacting brain networks which these regions function within to subserve higher-order cognition inherently prevents our successful navigation of true eloquent and non-eloquent cerebrum. Fortunately, recent large-scale movements in the neuroscience community, such as the Human Connectome Project (HCP), have provided updated neural data detailing the many intricate macroscopic connections between cortical regions which integrate and process the information underlying complex human behavior within a brain "connectome." Connectomic data can provide us better maps on how to understand convoluted cortical and subcortical relationships between tumor and human cerebrum such that neurosurgeons can begin to make more informed decisions during surgery to maximize the onco-functional balance. However, connectome-based neurosurgery and related applications for neurorehabilitation are relatively nascent and require further work moving forward to optimize our ability to add highly valuable connectomic data to our surgical armamentarium. In this manuscript, we review four concepts with detailed examples which will help us better understand post-operative cognitive outcomes and provide a guide for how to utilize connectomics to reduce cognitive morbidity following cerebral surgery.

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Reducing the Cognitive Footprint of Brain Tumor Surgery.

Novel microfluidic approaches to circulating tumor cell separation and sorting of blood cells: A review

Anatomy and White Matter Connections of the Middle Frontal Gyrus.

Data‐driven approaches to transcranial magnetic stimulation (TMS) might yield more consistent and symptom‐specific results based on individualized functional connectivity analyses compared to previous traditional approaches due to more precise targeting. We provide a proof of concept for an agile target selection paradigm based on using connectomic methods that can be used to detect patient‐specific abnormal functional connectivity, guide treatment aimed at the most abnormal regions, and optimize the rapid development of new hypotheses for future study.

An agile, data‐driven approach for target selection in rTMS therapy for anxiety symptoms: Proof of concept and preliminary data for two novel targets

Comparison of consistency between image guided and craniometric transcranial magnetic stimulation coil placement

An Eyebrow, Supracarotid Triangle Approach for Lesions at the Ventral Thalamopeduncular Junction: A Technical Report

Current limitations in boosted tree modelling prevent the effective scaling to datasets with a large feature number, particularly when investigating the magnitude and directionality of various features on classification. We present a novel methodology, Hollow-tree Super (HOTS), to resolve and visualize feature importance in boosted tree models involving a large number of features. Further, HOTS allows for investigation of the directionality and magnitude various features have on classification. Using the Iris dataset, we first compare HOTS to Gini Importance, Partial Dependence Plots, and Permutation Importance, and demonstrate how HOTS resolves the weaknesses present in these methods. We then show how HOTS can be utilized in high dimensional neuroscientific data, by taking 60 Schizophrenic subjects and applying the method to determine which brain regions were most important for classification of schizophrenia as determined by the PANSS. HOTS effectively replicated and supported the findings of Gini importance, Partial Dependence Plots and Permutation importance within the Iris dataset. When applied to the schizophrenic brain dataset, HOTS was able to resolve the top 10 most important features for classification, as well as their directionality for classification and magnitude compared to other features. Cross-validation supported that these same 10 features were consistently used in the decision-making process across multiple trees, and these features were localised primarily to the occipital and parietal cortices, commonly disturbed brain regions in those with Schizophrenia. It is imperative that a methodology is developed that is able to handle the demands of working with large datasets that contain a large number of features. HOTS represents a unique way to investigate both the directionality and magnitude of feature importance when working at scale with boosted-tree modelling.

Hollow-tree Super: a directional and scalable approach for feature importance in boosted tree models

Glioblastoma multiforme (GBM) is one of the most common primary brain tumors with an aggressive natural history consistent with a median survival of less than two years. Most clinical research has primarily focused on improving overall survival through aggressive cytoreductive surgery and adjuvant radiochemotherapy. However, far less clinical guidance has been given for unexpected instances of neurologic decline following safe glioma resection in the setting of vascular etiology. Here, we report a 50-year-old man who presented to our clinic with a seizure. His preoperative magnetic resonance imaging (MRI) demonstrated a left hippocampal glioblastoma. Ten months following total resection, the patient presented again with rapid loss of vision and hemorrhagic papilledema. An MRI demonstrated a recurrence of his glioma, which was partially resected with no complications. Eight days after surgery, the patient suddenly became unresponsive and imaging revealed moderate blood in the resection cavity, which was evacuated in the operating room. Follow-up scans showed a posterior cerebral artery infarction, and two days later, a middle cerebral artery infarction, upon which care was withdrawn. We do not propose a mechanism by which this delayed ischemia occurred, especially as the middle cerebral artery was not damaged during surgery, however, we note that delayed ischemia may be one mechanism of damage following glioma resection, which should be studied further to improve patient outcomes.

/pdf/delayed-progressive-multivessel-occlusion-after-resection-of-1hp3pq4p.pdf

Isabella M. Young

Papers

An agile, data‐driven approach for target selection in rTMS therapy for anxiety symptoms: Proof of concept and preliminary data for two novel targets

Comparison of consistency between image guided and craniometric transcranial magnetic stimulation coil placement

An Eyebrow, Supracarotid Triangle Approach for Lesions at the Ventral Thalamopeduncular Junction: A Technical Report

Hollow-tree Super: a directional and scalable approach for feature importance in boosted tree models

Delayed, Progressive Multivessel Occlusion After Resection of a Recurrent Glioma