Inter-Regional Proteomic Profiling of the Human Brain Using an Optimized Protein Extraction Method from Formalin-Fixed Tissue to Identify Signaling Pathways.

Proteomics offers vast potential for studying the molecular regulation of the human brain. Formalin fixation is a common method for preserving human tissue; however, it presents challenges for proteomic analysis. In this study, we compared the efficiency of two different protein-extraction buffers on three post-mortem, formalin-fixed human brains. Equal amounts of extracted proteins were subjected to in-gel tryptic digestion and LC-MS/MS. Protein, peptide sequence, and peptide group identifications; protein abundance; and gene ontology pathways were analyzed. Protein extraction was superior using lysis buffer containing tris(hydroxymethyl)aminomethane hydrochloride, sodium dodecyl sulfate, sodium deoxycholate, and Triton X-100 (TrisHCl, SDS, SDC, Triton X-100), which was then used for inter-regional analysis. Pre-frontal, motor, temporal, and occipital cortex tissues were analyzed by label free quantification (LFQ) proteomics, Ingenuity Pathway Analysis and PANTHERdb. Inter-regional analysis revealed differential enrichment of proteins. We found similarly activated cellular signaling pathways in different brain regions, suggesting commonalities in the molecular regulation of neuroanatomically-linked brain functions. Overall, we developed an optimized, robust, and efficient method for protein extraction from formalin-fixed human brain tissue for in-depth LFQ proteomics. We also demonstrate herein that this method is suitable for rapid and routine analysis to uncover molecular signaling pathways in the human brain.

Use of deep learning in the MRI diagnosis of Chiari malformation type I.

PURPOSE: To train deep learning convolutional neural network (CNN) models for classification of clinically significant Chiari malformation type I (CM1) on MRI to assist clinicians in diagnosis and decision making. METHODS: A retrospective MRI dataset of patients diagnosed with CM1 and healthy individuals with normal brain MRIs from the period January 2010 to May 2020 was used to train ResNet50 and VGG19 CNN models to automatically classify images as CM1 or normal. A total of 101 patients diagnosed with CM1 requiring surgery and 111 patients with normal brain MRIs were included (median age 30 with an interquartile range of 23-43; 81 women with CM1). Isotropic volume transformation, image cropping, skull stripping, and data augmentation were employed to optimize model accuracy. K-fold cross validation was used to calculate sensitivity, specificity, and the area under receiver operating characteristic curve (AUC) for model evaluation. RESULTS: The VGG19 model with data augmentation achieved a sensitivity of 97.1% and a specificity of 97.4% with an AUC of 0.99. The ResNet50 model achieved a sensitivity of 94.0% and a specificity of 94.4% with an AUC of 0.98. CONCLUSIONS: VGG19 and ResNet50 CNN models can be trained to automatically detect clinically significant CM1 on MRI with a high sensitivity and specificity. These models have the potential to be developed into clinical support tools in diagnosing CM1.

Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.

PURPOSE: To systematically review the literature regarding the application of machine learning (ML) of magnetic resonance imaging (MRI) radiomics in common sellar tumors. To identify future directions for application of ML in sellar tumor MRI. METHODS: PubMed, Medline, Embase, Google Scholar, Scopus, ArxIV, and bioRxiv were searched to identify relevant studies published between 2010 and September 2021. Studies were included if they specifically involved ML of MRI radiomics in the analysis of sellar masses. Risk of bias assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) Tool. RESULTS: Fifty-eight articles were identified for review. All papers utilized retrospective data, and a quantitative systematic review was performed for thirty-one studies utilizing a public dataset which compared pituitary adenomas, meningiomas, and gliomas. One of the analyzed architectures yielded the highest classification accuracy of 0.996. The remaining twenty-seven articles were qualitatively reviewed and showed promising findings in predicting specific tumor characteristics such as tumor consistency, Ki-67 proliferative index, and post-surgical recurrence. CONCLUSION: This review highlights the potential clinical application of ML using MRI radiomic data of the sellar region in diagnosis and predicting treatment outcomes. We describe future directions for practical application in the clinical care of patients with pituitary neuroendocrine and other sellar tumors.

Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning.

The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. In this chapter, we explore the fundamentals of multiparametric brain tumour characterisation, as an understanding of the strengths, limitations and applications of these tools allows clinicians to better develop and evaluate models with improved diagnostic and prognostic value in brain tumour patients.

The legacy of Renaissance surgeon Giovanni Andrea Dalla Croce on the history of military surgery and neurosurgery.

Giovanni Andrea Dalla Croce was a Venetian physician who lived in the 16th century and was famous for his treatment of wounds, which was surprisingly modern. He was the military surgeon of the Venetian Republic's naval fleet. In 1537, he published the Chirurgiae universalis opus absolutum (The absolute work on universal surgery) in Latin, then expanded and translated into vernacular Italian and published in 1574 with the title Cirugia universale e perfetta di tutte le parti pertinenti all'ottimo chirurgo (Universal and perfect surgery of all the parts necessary for the optimal surgeon). This monumental work was a comprehensive handbook of surgery, medicine, and the treatment of many kinds of wounds with techniques to be used on the battlefield. It is also notable for the inclusion of illustrations of various weapons and projectiles, for the most comprehensive description and illustrations of surgical instruments at that time, and for the first illustrations of a surgeon performing trephination of the skull in an operating room. Dalla Croce also considered the writings of his surgical forebears in formulating his own ideas. Dalla Croce was a leader of traumatology, a universal surgeon who exemplified the erudite Renaissance man, and left a tremendous legacy to military surgery of the 16th century and beyond.

Anchoring of a mental nerve stimulator for treatment of facial neuropathic pain: a case illustration.

INTRODUCTION: Mental nerve stimulation is recognised as a treatment option for neuropathic facial pain. Historically however, lead migration across the mobile temporomandibular joint has prevented this procedures utility. METHODS: We describe a new method of insertion and anchoring of a mental nerve stimulator for the management of refractory neuropathic pain in the distribution of the mental nerve. We anchored the stimulator lead to the mandibular body. RESULTS: Significant analgesic effect was achieved and no lead migration had occurred at 1 year post-operatively. CONCLUSIONS: This report describes in detail the procedure of mental nerve stimulator insertion, with a novel technique of mandibular anchoring of the lead.

SoftMatch: Comparing Scanpaths Using Combinatorial Spatio-Temporal Sequences with Fractal Curves.

Recent studies matching eye gaze patterns with those of others contain research that is heavily reliant on string editing methods borrowed from early work in bioinformatics. Previous studies have shown string editing methods to be susceptible to false negative results when matching mutated genes or unordered regions of interest in scanpaths. Even as new methods have emerged for matching amino acids using novel combinatorial techniques, scanpath matching is still limited by a traditional collinear approach. This approach reduces the ability to discriminate between free viewing scanpaths of two people looking at the same stimulus due to the heavy weight placed on linearity. To overcome this limitation, we here introduce a new method called SoftMatch to compare pairs of scanpaths. SoftMatch diverges from traditional scanpath matching in two different ways: firstly, by preserving locality using fractal curves to reduce dimensionality from 2D Cartesian (x,y) coordinates into 1D (h) Hilbert distances, and secondly by taking a combinatorial approach to fixation matching using discrete Fréchet distance measurements between segments of scanpath fixation sequences. These matching "sequences of fixations over time" are a loose acronym for SoftMatch. Results indicate high degrees of statistical and substantive significance when scoring matches between scanpaths made during free-form viewing of unfamiliar stimuli. Applications of this method can be used to better understand bottom up perceptual processes extending to scanpath outlier detection, expertise analysis, pathological screening, and salience prediction.

Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario.

PURPOSE: Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence, especially deep learning (DL), allow development of automatic segmentation that overcome the labor-intensive and operator-dependent manual segmentation. We aimed to evaluate the accuracy of the top-performing DL model from the 2018 Brain Tumor Segmentation (BraTS) challenge, the impact of missing MRI sequences, and whether a model trained on gliomas can accurately segment other brain tumor types. METHODS: We trained the model using Medical Decathlon dataset, applied it to the BraTS 2019 glioma dataset, and developed additional models using individual and multimodal MRI sequences. The Dice score was calculated to assess the model's accuracy compared to ground truth labels by neuroradiologists on BraTS dataset. The model was then applied to a local dataset of 105 brain tumors, performance of which was qualitatively evaluated. RESULTS: The DL model using pre- and post-gadolinium contrast T1 and T2 FLAIR sequences performed best, with a Dice score 0.878 for whole tumor, 0.732 tumor core, and 0.699 active tumor. Lack of T1 or T2 sequences did not significantly degrade performance, but FLAIR and T1C were important contributors. All segmentations performed by the model in the local dataset, including non-glioma cases, were considered accurate by a pool of specialists. CONCLUSION: The DL model could use available MRI sequences to optimize glioma segmentation and adopt transfer learning to segment non-glioma tumors, thereby serving as a useful tool to improve treatment planning and personalized surveillance of patients.

Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis.

Radiomics is an emerging field that involves extraction and quantification of features from medical images. These data can be mined through computational analysis and models to identify predictive image biomarkers that characterize intra-tumoral dynamics throughout the course of treatment. This is particularly difficult in gliomas, where heterogeneity has been well established at a molecular level as well as visually in conventional imaging. Thus, acquiring clinically useful features remains difficult due to temporal variations in tumor dynamics. Identifying surrogate biomarkers through radiomics may provide a non-invasive means of characterizing biologic activities of gliomas. We present an extensive literature review of radiomics-based analysis, with a particular focus on computational modeling, machine learning, and fractal-based analysis in improving differential diagnosis and predicting clinical outcomes. Novel strategies in extracting quantitative features, segmentation methods, and their clinical applications are producing promising results. Moreover, we provide a detailed summary of the morphometric parameters that have so far been proposed as a means of quantifying imaging characteristics of gliomas. Newly emerging radiomic techniques via machine learning and fractal-based analyses holds considerable potential for improving diagnostic and prognostic accuracy of gliomas. Key points• Radiomic features can be mined through computational analysis to produce quantitative imaging biomarkers that characterize intra-tumoral dynamics throughout the course of treatment.• Surrogate image biomarkers identified through radiomics could enable a non-invasive means of characterizing biologic activities of gliomas.• With novel analytic algorithms, quantification of morphological or sub-regional tumor features to predict survival outcomes is producing promising results.• Quantifying intra-tumoral heterogeneity may improve grading and molecular sub-classifications of gliomas.• Computational fractal-based analysis of gliomas allows geometrical evaluation of tumor irregularities and complexity, leading to novel techniques for tumor segmentation, grading, and therapeutic monitoring.

How I do it: 3D exoscopic endoscope-assisted microvascular decompression.

BACKGROUND: Microvascular decompression (MVD) is an effective treatment for drug-resistant trigeminal neuralgia and hemifacial spasm. However, failure of symptomatic improvement can arise from difficulties in identifying and/or decompressing the offending vessel. Microscopic and endoscopic techniques have been used to improve visualisation and safety of the procedure but there are limitations to each technique. METHOD: A 3D exoscopic endoscope-assisted MVD technique is described, including advice on potential pitfalls. CONCLUSION: Compared with the standard microscope-assisted techniques, the 3D exoscopic endoscope-assisted MVD offers an improved visualisation without compromising the field of view within and outside the surgical field.

Overweight, obesity and height as risk factors for meningioma, glioma, pituitary adenoma and nerve sheath tumor: a large population-based prospective cohort study.

BACKGROUND: In 2016, the International Agency for Research on Cancer (IARC) has announced that avoiding body fatness (i.e. overweight and obesity) contributes to prevent meningioma occurrence, but considered the available evidence for glioma inadequate. The association of body fatness with other CNS tumor subgroups is largely unknown. OBJECTIVES: To assess whether body fatness or body height are associated with risk for meningioma, glioma, pituitary adenoma (PA) or nerve sheath tumor (NST) in a large population-based Norwegian cohort. METHODS: In this prospective cohort study of 1.8 million Norwegian residents, weight and height were measured at baseline and incident intracranial tumors were subsequently identified by linkage to the Cancer Registry of Norway. Cox regression analyses were performed to estimate risk for each tumor subgroup in relation to anthropometric measures, stratified by sex and in different age groups. RESULTS: During 54 million person-years of follow-up 3335 meningiomas, 4382 gliomas, 1071 PAs and 759 NSTs were diagnosed. Obesity (BMI ≥30 kg/m2) was not associated with risk for meningioma or glioma, but was significantly associated with risk for PA (HR 1.43; 95% CI 1.09-1.88) compared with the reference group (BMI 20-24.9 kg/m2). For intracranial NSTs, obesity was associated with reduced tumor risk (HR 0.68; 95% CI 0.46-0.99). Body height was associated with increased risk for all four tumor subgroups. CONCLUSIONS: This study does not confirm overweight or obesity as risk factors for meningioma. Additionally, overweight and obesity can be quite confidently excluded as risk factors for glioma. However, this study indicates that body fatness increases the risk for PA, while it reduces the risk for NST.

Cerebrospinal fluid leaks in extended endoscopic transsphenoidal surgery: covering all the angles.

Following extended endoscopic transsphenoidal approach (EETSA), cerebrospinal fluid (CSF) leak rate has been reported in the range of 5-50 %. Novel closure techniques, such as the nasoseptal flap and other multilayered repairs improved the outcomes significantly but took most of our focus. Little attention, however, was given to other aspects of the equation such as nasal support-to support the heavy weight of such repairs-and lumbar drains. These are important because they diminish the forces acting on both sides of the repair, hence covering all the angles. We reviewed data of 98 consecutive patients who underwent an EETSA between 1999 and 2014. We analyzed the rates of CSF leak throughout the years and with every modification added to our closure technique. Common pathologies encountered were invasive adenomas, meningiomas, chordomas, and craniopharyngiomas. CSF leak occurred overall in five patients (5.1 %). The nasoseptal flap decreased the rate of CSF leak but not significantly (P = 0.112), while placing a nasal trumpet to support our repair resulted in significant decrease in CSF leak rate (P = 0.0013). In the last 2 years of our series, when all modifications took place and all angles were covered, there was one leak in 35 cases (2.8 %). A protocol that covers all the angles by a good multilayered repair (regardless of its type and materials) while diminishing the forces acting on both sides of the repair leads to a minimal rate of CSF leak. No principle alone is effective individually.

Arginine vasopressin (AVP): a review of its historical perspectives, current research and multifunctional role in the hypothalamo-hypophysial system.

INTRODUCTION: This publication reviews the function of arginine vasopressin and focuses on the morphologic and functional correlation between the hormone and its effect on stress, the hypophysial-adrenocortical axis, neuroimmune responses, renal function and corticotroph pituitary tumors. MATERIALS AND METHODS: A literature review was performed using various search engines for information regarding the morphology and the multifunctional role of arginine vasopressin. RESULTS: Although a large number of studies were published discussing these interactions, there are several important areas that are still obscure. CONCLUSION: The questions of how does arginine vasopressin affect the morphology and function of these various areas, and how does the secretion of ACTH and adrenocortical hormones influence the morphology of arginine vasopressin-producing cells and their hormone secretion requires further investigation.

Current status on histological classification in Cushing's disease.

INTRODUCTION: Managing Cushing's disease remains a challenge. Surgery is the first option of treatment and it offers a high success rate. Even in cases where biochemical remission is not achieved, it is crucial to obtain surgical tissue for morphological diagnosis because the therapeutic approach can be modified according to the findings. MATERIALS AND METHODS: A literature search was performed using PubMed for information regarding pathology and Cushing's disease. RESULTS: The histopathological features found in the pituitary gland of patients with Cushing's disease are presented. CONCLUSION: Different subtypes of ACTH-producing pituitary tumors are recognized and characterized. The significance of finding a normal pituitary gland with or without Crooke's changes is also discussed.

Chordoid meningiomas: incidence and clinicopathological features of a case series over 18 years.

Chordoid meningioma (CM) is a rare subtype of meningioma, classified as grade II, which exhibits a high rate of recurrence following subtotal resection. We retrospectively examined nine cases of chordoid meningioma over a case series of 1743 meningiomas (0.52%) operated upon at our institution from 1995 to 2013. All the reported clinicopathological findings were analyzed. Two hundred and twenty-one CM cases have been published to date worldwide and few single-center large case series have been issued. Seventy-five percent of the cases that underwent subtotal resection at our institution had recurrence within 1 year. Total resection of the tumor should be the major objective of surgery to reduce the possibility of tumor recurrence. The percentage of chordoid features within the tumor specimen could assist in predicting the pathogenesis of the lesion. The correlation of the index of proliferation to recurrence rate is still controversial. Much debate exists with regard to the role of adjuvant radiotherapy in CM cases. Immunohistochemical, cytological and ultrastructural studies should be used in combination to assure a correct diagnosis of CM. Owing to the rare occurrence of this meningioma subtype, larger case series are required to assist in providing a reference for diagnosis and to improve the therapeutic management of CM.

Endoscopic versus microscopic approach for surgical treatment of acromegaly.

Transsphenoidal surgery in the setting of acromegaly is quite challenging due to increased soft tissue mass, bony overgrowth, and bleeding. There is a debate on the endoscopic versus microscopic approach for these patients. The purpose of our study is to compare the outcomes for acromegaly after transsphenoidal surgery using both techniques. Retrospective review of 65 acromegalic patients who underwent transsphenoidal surgery in our department. Clinical remission was defined as resolution of typical acromegalic symptoms. Radiological resection was defined by volumetric criteria, and biochemical remission was defined as by the 2010 consensus on the criteria for remission of acromegaly. There was no significant difference in age, preoperative endocrine status, percent of macro adenomas, suprasellar, or infrasellar extension between both groups. Patients were assigned to both groups based on our existing referral pattern. Endoscopic approach was performed in 42 patients, while the microscopic approach was performed in 23 patients. No significant difference in remission rates was found between both groups (45.2 vs. 34.7 %, p = 0.40). The endoscopic group, however, had a significantly higher rate of gross total resections (61 vs. 42 %, p = 0.05). There was also a trend towards higher rates of gross total resections when cavernous sinus was present (48 vs. 14.2 %, p = 0.09). Postoperative diabetes insipidus occurred more in microscopic patients (34.7 vs. 17 %, p = 0.05), otherwise there was no significant difference in rates of complications. The median follow-up period was 56.6 months (range 6-156, mean 66.1). There is no significant difference in the rates of biochemical remission between the endoscopic and microscopic techniques. The endoscope technique, however, seems to be superior in achieving gross total resection especially with tumors invading the cavernous sinus.

Skull base embryology: a multidisciplinary review.

INTRODUCTION: The skull base represents a central and complex bone structure of the skull and forms the floor of the cranial cavity on which the brain lies. Anatomical knowledge of this particular region is important for understanding several pathologic conditions as well as for planning surgical procedures. Embryology of the cranial base is of great interest due to its pronounced impact on the development of adjacent regions including the brain, neck, and craniofacial skeleton. MATERIALS AND METHODS: Information from human and comparative anatomy, anthropology, embryology, surgery, and computed modelling was integrated to provide a perspective to interpret skull base formation and variability within the cranial functional and structural system. RESULTS AND CONCLUSIONS: The skull base undergoes an elaborate sequence of development stages and represents a key player in skull, face and brain development. Furthering our holistic understanding of the embryology of the skull base promises to expand our knowledge and enhance our ability to treat associated anomalies.

Evaluation of cerebral aneurysm wall thickness in experimental aneurysms: comparison of 3T-MR imaging with direct microscopic measurements.

BACKGROUND: Thin aneurysm wall thickness (AWT) is thought to portend an elevated risk of intracranial aneurysm rupture. Magnetic resonance imaging (MRI) is biased by AWT overestimations. Previously, this suspected bias has been qualitatively described but never quantified. We aimed to quantify the overestimation of AWT by MRI when compared to the gold standard of AWT as measured by light microscopy of fresh aneurysm specimens (without any embedding procedure). This analysis should help to define the clinical potential of MRI estimates of AWT. METHODS: 3-Tesla (3T) MRI (contrast-enhanced T1 Flash sequences; resolution: 0.4 x 0.4 x 1.5 mm(3)) was performed in 13 experimental aneurysms. After MR acquisition, the aneurysms were retrieved, longitudinally sectioned and calibrated micrographs were obtained immediately. AWT at the dome, AWT at the neck and parent vessel wall thickness (PVT) were measured on precisely correlated MR-images and histologic micrographs by blinded independent investigators. Parameters were statistically compared (Wilcoxon test, Spearman's correlation). RESULTS: AWT was assessed and reliably measured using MRI. Interobserver variability was not significant for either method. MR overestimation was only significant below the image resolution threshold: AWT at the dome (0.24 ± 0.06 mm vs. MR 0.30 ± 0.08 mm; p = 0.0078; R = 0.6125), AWT at the neck (0.25 ± 0.07 mm vs. MR 0.29 ± 0.07 mm; p = 0.0469; R = 0.7451), PVT (0.46 ± 0.06 mm vs. MR 0.48 ± 0.06 mm; p = 0.5; R = 0.8568). CONCLUSION: In this experimental setting, MR overestimations were minimal (mean 0.02 mm) above the image resolution threshold. When AWT is classified in ranges defined by the MR resolution threshold, clinical usage may be beneficial. Further quantitative and comparative experimental and human studies are warranted to confirm these findings.

Fractals, Pattern Recognition, Memetics, and AI: A Personal Journal in the Computational Neurosurgery.

In this chapter, the personal journey of the author in many countries, including Italy, Germany, Austria, the United Kingdom, Switzerland, the United States, Canada, and Australia, is summarized, aimed to merge different translational fields (such as neurosurgery and the clinical neurosciences in general, biomedical engineering, mathematics, computer science, and cognitive sciences) and lay the foundations of a new field defined computational neurosurgery, with fractals, pattern recognition, memetics, and artificial intelligence as the common key words of the journey.