Topology predicts long-term functional outcome in early psychosis.

Early intervention in psychosis is crucial to improving patient response to treatment and the functional deficits that critically affect their long-term quality of life. Stratification tools are needed to personalize functional deficit prevention strategies at an early stage. In the present study, we applied topological tools to analyze symptoms of early psychosis patients, and detected a clear stratification of the cohort into three groups. One of the groups had a significantly better psychosocial outcome than the others after a 3-year clinical follow-up. This group was characterized by a metabolic profile indicative of an activated antioxidant response, while that of the groups with poorer outcome was indicative of oxidative stress. We replicated in a second cohort the finding that the three distinct clinical profiles at baseline were associated with distinct outcomes at follow-up, thus validating the predictive value of this new stratification. This approach could assist in personalizing treatment strategies.

Digital Reconstruction of the Neuro-Glia-Vascular Architecture.

Astrocytes connect the vasculature to neurons mediating the supply of nutrients and biochemicals. They are involved in a growing number of physiological and pathophysiological processes that result from biophysical, physiological, and molecular interactions in this neuro-glia-vascular ensemble (NGV). The lack of a detailed cytoarchitecture severely restricts the understanding of how they support brain function. To address this problem, we used data from multiple sources to create a data-driven digital reconstruction of the NGV at micrometer anatomical resolution. We reconstructed 0.2 mm3 of the rat somatosensory cortex with 16 000 morphologically detailed neurons, 2500 protoplasmic astrocytes, and its microvasculature. The consistency of the reconstruction with a wide array of experimental measurements allows novel predictions of the NGV organization, allowing the anatomical reconstruction of overlapping astrocytic microdomains and the quantification of endfeet connecting each astrocyte to the vasculature, as well as the extent to which they cover the latter. Structural analysis showed that astrocytes optimize their positions to provide uniform vascular coverage for trophic support and signaling. However, this optimal organization rapidly declines as their density increases. The NGV digital reconstruction is a resource that will enable a better understanding of the anatomical principles and geometric constraints, which govern how astrocytes support brain function.

Two-Tier Mapper, an unbiased topology-based clustering method for enhanced global gene expression analysis.

MOTIVATION: Unbiased clustering methods are needed to analyze growing numbers of complex datasets. Currently available clustering methods often depend on parameters that are set by the user, they lack stability, and are not applicable to small datasets. To overcome these shortcomings we used topological data analysis, an emerging field of mathematics that discerns additional feature and discovers hidden insights on datasets and has a wide application range. RESULTS: We have developed a topology-based clustering method called Two-Tier Mapper (TTMap) for enhanced analysis of global gene expression datasets. First, TTMap discerns divergent features in the control group, adjusts for them, and identifies outliers. Second, the deviation of each test sample from the control group in a high-dimensional space is computed, and the test samples are clustered using a new Mapper-based topological algorithm at two levels: a global tier and local tiers. All parameters are either carefully chosen or data-driven, avoiding any user-induced bias. The method is stable, different datasets can be combined for analysis, and significant subgroups can be identified. It outperforms current clustering methods in sensitivity and stability on synthetic and biological datasets, in particular when sample sizes are small; outcome is not affected by removal of control samples, by choice of normalization, or by subselection of data. TTMap is readily applicable to complex, highly variable biological samples and holds promise for personalized medicine. AVAILABILITY AND IMPLEMENTATION: TTMap is supplied as an R package in Bioconductor. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Objective Morphological Classification of Neocortical Pyramidal Cells.

A consensus on the number of morphologically different types of pyramidal cells (PCs) in the neocortex has not yet been reached, despite over a century of anatomical studies, due to the lack of agreement on the subjective classifications of neuron types, which is based on expert analyses of neuronal morphologies. Even for neurons that are visually distinguishable, there is no common ground to consistently define morphological types. The objective classification of PCs can be achieved with methods from algebraic topology, and the dendritic arborization is sufficient for the reliable identification of distinct types of cortical PCs. Therefore, we objectively identify 17 types of PCs in the rat somatosensory cortex. In addition, we provide a solution to the challenging problem of whether 2 similar neurons belong to different types or to a continuum of the same type. Our topological classification does not require expert input, is stable, and helps settle the long-standing debate on whether cell-types are discrete or continuous morphological variations of each other.

The unfolding argument: Why IIT and other causal structure theories cannot explain consciousness.

How can we explain consciousness? This question has become a vibrant topic of neuroscience research in recent decades. A large body of empirical results has been accumulated, and many theories have been proposed. Certain theories suggest that consciousness should be explained in terms of brain functions, such as accessing information in a global workspace, applying higher order to lower order representations, or predictive coding. These functions could be realized by a variety of patterns of brain connectivity. Other theories, such as Information Integration Theory (IIT) and Recurrent Processing Theory (RPT), identify causal structure with consciousness. For example, according to these theories, feedforward systems are never conscious, and feedback systems always are. Here, using theorems from the theory of computation, we show that causal structure theories are either false or outside the realm of science.

Sacral Insufficiency Fractures are Common After High-dose Radiation for Sacral Chordomas Treated With or Without Surgery.

BACKGROUND: Surgery with high-dose radiation and high-dose radiation alone for sacral chordomas have shown promising local control rates. However, we have noted frequent sacral insufficiency fractures and perceived this rate to be higher than previously reported. QUESTIONS/PURPOSES: We wished (1) to characterize the incidence of sacral insufficiency fractures in patients with chordomas of the sacrum who received high-dose radiation, and (2) to determine whether patients treated with surgery plus high-dose radiation or high-dose radiation alone are more likely to experience a sacral fracture, and to compare time to fracture in these groups. METHODS: Sixty-two patients who received high-dose radiation for sacral chordomas with (n = 44) or without surgical resection (n = 18) between 1992 and 2013 were included in this retrospective study. At our institution, sacral chordomas generally are treated by preoperative radiotherapy, followed by en bloc resection, and postoperative radiotherapy. Radiation alone, with an intent to cure, is offered to patients who otherwise are not good surgical candidates or patients who elect radiotherapy based on tumor location and the anticipated morbidity after surgery (such as sexual, bowel, or bladder dysfunction). MRI and CT scans were evaluated for evidence of sacral insufficiency fractures. Complete followup was available at a minimum of 2 years (or until fracture or death) for all 18 patients who underwent radiation alone, whereas 14% (six of 44 patients) in the surgery plus radiation group (9% [three of 33] after high sacrectomy and 27% [three of 11] after low sacrectomy) were lost to followup before 2 years. RESULTS: Sacral insufficiency fractures occurred in 29 of the 62 patients (47%). A total of 25 of 33 patients (76%) with high sacrectomy had fractures develop compared with zero of 11 (0%) after low sacrectomy, and four of the 18 patients (22%) who had high-dose radiation alone (p < 0.001). The fracture rate was greater in the high sacrectomy group than in the low sacrectomy group (p < 0.001) and the radiation only group (p < 0.001). There was no difference with the numbers evaluated in fracture probability between patients in the low-sacrectomy group and those treated with radiation alone (p = 0.112). The fracture-free survival probability was 0.99 for the low sacrectomy group at all times as there were no insufficiency fractures in this group; the 1-year fracture-free survival probability was 0.53 (95% CI, 0.35-0.69) after high sacrectomy, 0.83 (95% CI, 0.57-0.94) after radiation alone; the 2-year fracture-free survival probability was 0.36 (95% CI, 0.19-0.52) after high sacrectomy and 0.77 (95% CI, 0.50-0.91) after radiation alone; and the 5-year fracture-free survival probability was 0.14 (95% CI, 0.04-0.30) after high sacrectomy and 0.77 (95% CI, 0.50-0.91) after radiation alone. CONCLUSIONS: Acknowledging the limitations of potential differences in baseline and followup among treatment groups in our study, we found that almost ½ of our patients experienced an insufficiency fracture. We found that the fracture rate was greater in the surgery group compared with the radiation alone group and that high sacrectomy accounted for all fractures in the surgery group. These findings can be used to inform patients and also support the need for further research to elucidate the influence of high-dose radiation on bone quality. LEVEL OF EVIDENCE: Level III, therapeutic study.

How Does the Level of Sacral Resection for Primary Malignant Bone Tumors Affect Physical and Mental Health, Pain, Mobility, Incontinence, and Sexual Function?

BACKGROUND: En bloc resection for treatment of sacral tumors is the approach of choice for patients with resectable tumors who are well enough to undergo surgery, and studies describe patient survival, postoperative complications, and recurrence rates associated with this treatment. However, most of these studies do not provide patient-reported functional outcomes other than binary metrics for bowel and bladder function postresection. QUESTIONS/PURPOSES: The purpose of this study was to use validated patient-reported outcomes tools to compare quality of life based on level of sacral resection in terms of (1) physical and mental health; (2) pain; (3) mobility; and (4) incontinence and sexual function. METHODS: Our analysis included 33 patients (19 men, 14 women) who had a mean age of 53 years (range, 22-72 years) with a quality-of-life survey administered at a mean postoperative followup of 41 months (range, 6-123 months). The majority of patient-reported quality-of-life outcome surveys for this study were taken from the National Institute of Health's Patient Reported Outcome Measurement Information System (PROMIS) system. To assess physical and mental health, the PROMIS Global Items Survey with physical and mental subscores, Anxiety, and Depression scores were used. Pain outcomes were assessed using PROMIS Pain Intensity and Pain Interference surveys. Patient-reported lower extremity function was assessed using the PROMIS Mobility Survey. Patient-reported quality of life for sexual function was assessed using the PROMIS Sex Interest and Orgasm survey, whereas incontinence was measured using the International Continence Society Voiding and Incontinence scores and the Modified Obstruction and Defecation Score. Surveys were collected prospectively during clinic visits in the postoperative period. Patients were grouped by the level of osteotomy as determined by review of postoperative MRI or CT and half levels were grouped with the more cephalad level. This resulted in the inclusion of total sacrectomy (N = 6), S1 (N = 8), S2 (N = 10), S3 (N = 5), and S4 (N = 4). One-way analysis of variance tests on means or ranks were used to conduct statistical analysis between levels. RESULTS: Patients with more caudal resections had higher physical health (95% confidence interval [CI] total sacrectomy 36-42 versus S4 50-64, p < 0.001), less intense pain (95% CI total sacrectomy 47-60 versus S4 28-37, p < 0.001), less interference resulting from pain (95% CI total sacrectomy 58-69 versus S4 36-51, p = 0.004), higher mobility (95% CI total sacrectomy 24-46 versus S4 59-59, p = 0.002), and were more functionally able to achieve orgasm (95% CI S1 1-1 versus S4 2.2-5.3, p = 0.043). No difference was found for PROMIS Global Item Mental Health Subscore, Sex Interest, Sex Satisfaction, modified obstruction and defecation score, and International Continence Society Voiding and Incontinence although this could be the result of an inadequate sample size. CONCLUSIONS: Our analysis on patient-reported quality of life based on the level of bony resection in patients who underwent resection for primary sacral tumor indicates that patients with higher resections have more pain and loss of physical function in comparison to patients with lower resections. Additionally, use of the PROMIS outcomes allows for comparisons to normative data. LEVEL OF EVIDENCE: Level III, therapeutic study.

Persistent Homology With Improved Locality Information for More Effective Delineation.

Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results. However, existing methods are very global and ignore the location of topological features. In this paper, we remedy this by introducing a new filtration function that fuses two earlier approaches: thresholding-based filtration, previously used to train deep networks to segment medical images, and filtration with height functions, typically used to compare 2D and 3D shapes. We experimentally demonstrate that deep networks trained using our PH-based loss function yield reconstructions of road networks and neuronal processes that reflect ground-truth connectivity better than networks trained with existing loss functions based on PH.

Dynamics of CLIMP-63 S-acylation control ER morphology.

The complex architecture of the endoplasmic reticulum (ER) comprises distinct dynamic features, many at the nanoscale, that enable the coexistence of the nuclear envelope, regions of dense sheets and a branched tubular network that spans the cytoplasm. A key player in the formation of ER sheets is cytoskeleton-linking membrane protein 63 (CLIMP-63). The mechanisms by which CLIMP-63 coordinates ER structure remain elusive. Here, we address the impact of S-acylation, a reversible post-translational lipid modification, on CLIMP-63 cellular distribution and function. Combining native mass-spectrometry, with kinetic analysis of acylation and deacylation, and data-driven mathematical modelling, we obtain in-depth understanding of the CLIMP-63 life cycle. In the ER, it assembles into trimeric units. These occasionally exit the ER to reach the plasma membrane. However, the majority undergoes S-acylation by ZDHHC6 in the ER where they further assemble into highly stable super-complexes. Using super-resolution microscopy and focused ion beam electron microscopy, we show that CLIMP-63 acylation-deacylation controls the abundance and fenestration of ER sheets. Overall, this study uncovers a dynamic lipid post-translational regulation of ER architecture.

Intact Drosophila central nervous system cellular quantitation reveals sexual dimorphism.

Establishing with precision the quantity and identity of the cell types of the brain is a prerequisite for a detailed compendium of gene and protein expression in the central nervous system (CNS). Currently, however, strict quantitation of cell numbers has been achieved only for the nervous system of Caenorhabditis elegans. Here, we describe the development of a synergistic pipeline of molecular genetic, imaging, and computational technologies designed to allow high-throughput, precise quantitation with cellular resolution of reporters of gene expression in intact whole tissues with complex cellular constitutions such as the brain. We have deployed the approach to determine with exactitude the number of functional neurons and glia in the entire intact larval Drosophila CNS, revealing fewer neurons and more glial cells than previously predicted. We also discover an unexpected divergence between the sexes at this juvenile developmental stage, with the female CNS having significantly more neurons than that of males. Topological analysis of our data establishes that this sexual dimorphism extends to deeper features of CNS organisation. We additionally extended our analysis to quantitate the expression of voltage-gated potassium channel family genes throughout the CNS and uncover substantial differences in abundance. Our methodology enables robust and accurate quantification of the number and positioning of cells within intact organs, facilitating sophisticated analysis of cellular identity, diversity, and gene expression characteristics.

Computational synthesis of cortical dendritic morphologies.

Neuronal morphologies provide the foundation for the electrical behavior of neurons, the connectomes they form, and the dynamical properties of the brain. Comprehensive neuron models are essential for defining cell types, discerning their functional roles, and investigating brain-disease-related dendritic alterations. However, a lack of understanding of the principles underlying neuron morphologies has hindered attempts to computationally synthesize morphologies for decades. We introduce a synthesis algorithm based on a topological descriptor of neurons, which enables the rapid digital reconstruction of entire brain regions from few reference cells. This topology-guided synthesis generates dendrites that are statistically similar to biological reconstructions in terms of morpho-electrical and connectivity properties and offers a significant opportunity to investigate the links between neuronal morphology and brain function across different spatiotemporal scales. Synthesized cortical networks based on structurally altered dendrites associated with diverse brain pathologies revealed principles linking branching properties to the structure of large-scale networks.

A tool for mapping microglial morphology, morphOMICs, reveals brain-region and sex-dependent phenotypes.

Environmental cues influence the highly dynamic morphology of microglia. Strategies to characterize these changes usually involve user-selected morphometric features, which preclude the identification of a spectrum of context-dependent morphological phenotypes. Here we develop MorphOMICs, a topological data analysis approach, which enables semiautomatic mapping of microglial morphology into an atlas of cue-dependent phenotypes and overcomes feature-selection biases and biological variability. We extract spatially heterogeneous and sexually dimorphic morphological phenotypes for seven adult mouse brain regions. This sex-specific phenotype declines with maturation but increases over the disease trajectories in two neurodegeneration mouse models, with females showing a faster morphological shift in affected brain regions. Remarkably, microglia morphologies reflect an adaptation upon repeated exposure to ketamine anesthesia and do not recover to control morphologies. Finally, we demonstrate that both long primary processes and short terminal processes provide distinct insights to morphological phenotypes. MorphOMICs opens a new perspective to characterize microglial morphology.

Extracellular Vesicle MicroRNA in Malignant Pleural Effusion.

Lung and breast cancer are the two most common causes of malignant pleural effusion (MPE). MPE diagnosis plays a crucial role in determining staging and therapeutic interventions in these cancers. However, our understanding of the pathogenesis and progression of MPE at the molecular level is limited. Extracellular Vesicles (EVs) and their contents, including microRNAs (miRNAs), can be isolated from all bodily fluids, including pleural fluid. This study aims to compare EV-miRNA patterns of expression in MPE caused by breast (BA-MPE) and lung (LA-MPE) adenocarcinomas compared to the control group of heart-failure-induced effusions (HF-PE). We conducted an analysis of 24 pleural fluid samples (8 LA-MPE, 8 BA-MPE, and 8 HF-PE). Using NanoString technology, we profiled miRNAs within EVs isolated from 12 cases. Bioinformatic analysis demonstrated differential expression of miR-1246 in the MPE group vs. HF-PE group and miR-150-5p and miR-1246 in the BA-MPE vs. LA-MPE group, respectively. This difference was demonstrated and validated in an independent cohort using real-time PCR (RT-PCR). miRNA-1246 demonstrated 4-fold increased expression (OR: 3.87, 95% CI: 0.43, 35) in the MPE vs. HF-PE group, resulting in an area under the curve of 0.80 (95% CI: 0.60, 0.99). The highest accuracy for differentiating MPE vs. HF-PE was seen with a combination of miRNAs compared to each miRNA alone. Consistent with prior studies, this study demonstrates dysregulation of specific EV-based miRNAs in breast and lung cancer; pleural fluid provides direct access for the analysis of these EV-miRNAs as biomarkers and potential targets and may provide insight into the underlying pathogenesis of tumor progression. These findings should be explored in large prospective studies.

Topological exploration of artificial neuronal network dynamics.

One of the paramount challenges in neuroscience is to understand the dynamics of individual neurons and how they give rise to network dynamics when interconnected. Historically, researchers have resorted to graph theory, statistics, and statistical mechanics to describe the spatiotemporal structure of such network dynamics. Our novel approach employs tools from algebraic topology to characterize the global properties of network structure and dynamics. We propose a method based on persistent homology to automatically classify network dynamics using topological features of spaces built from various spike train distances. We investigate the efficacy of our method by simulating activity in three small artificial neural networks with different sets of parameters, giving rise to dynamics that can be classified into four regimes. We then compute three measures of spike train similarity and use persistent homology to extract topological features that are fundamentally different from those used in traditional methods. Our results show that a machine learning classifier trained on these features can accurately predict the regime of the network it was trained on and also generalize to other networks that were not presented during training. Moreover, we demonstrate that using features extracted from multiple spike train distances systematically improves the performance of our method.

High-Throughput Screening Approach for Nanoporous Materials Genome Using Topological Data Analysis: Application to Zeolites.

The materials genome initiative has led to the creation of a large (over a million) database of different classes of nanoporous materials. As the number of hypothetical materials that can, in principle, be experimentally synthesized is infinite, a bottleneck in the use of these databases for the discovery of novel materials is the lack of efficient computational tools to analyze them. Current approaches use brute-force molecular simulations to generate thermodynamic data needed to predict the performance of these materials in different applications, but this approach is limited to the analysis of tens of thousands of structures due to computational intractability. As such, it is conceivable and even likely that the best nanoporous materials for any given application have yet to be discovered both experimentally and theoretically. In this article, we seek a computational approach to tackle this issue by transitioning away from brute-force characterization to high-throughput screening methods based on big-data analysis, using the zeolite database as an example. For identifying and comparing zeolites, we used a topological data analysis-based descriptor (TD) recognizing pore shapes. For methane storage and carbon capture applications, our analyses seeking pairs of highly similar zeolites discovered good correlations between performance properties of a seed zeolite and the corresponding pair, which demonstrates the capability of TD to predict performance properties. It was also shown that when some top zeolites are known, TD can be used to detect other high-performing materials as their neighbors with high probability. Finally, we performed high-throughput screening of zeolites based on TD. For methane storage (or carbon capture) applications, the promising sets from our screenings contained high-percentages of top-performing zeolites: 45% (or 23%) of the top 1% zeolites in the entire set. This result shows that our screening approach using TD is highly efficient in finding high-performing materials. We expect that this approach could easily be extended to other applications by simply adjusting one parameter, the size of the target gas molecule.

A Topological Representation of Branching Neuronal Morphologies.

Many biological systems consist of branching structures that exhibit a wide variety of shapes. Our understanding of their systematic roles is hampered from the start by the lack of a fundamental means of standardizing the description of complex branching patterns, such as those of neuronal trees. To solve this problem, we have invented the Topological Morphology Descriptor (TMD), a method for encoding the spatial structure of any tree as a "barcode", a unique topological signature. As opposed to traditional morphometrics, the TMD couples the topology of the branches with their spatial extents by tracking their topological evolution in 3-dimensional space. We prove that neuronal trees, as well as stochastically generated trees, can be accurately categorized based on their TMD profiles. The TMD retains sufficient global and local information to create an unbiased benchmark test for their categorization and is able to quantify and characterize the structural differences between distinct morphological groups. The use of this mathematically rigorous method will advance our understanding of the anatomy and diversity of branching morphologies.

Evaluating the Extent of Clinical Variability Among Treatment Options for Patients With Adult Spinal Deformity.

STUDY DESIGN: This is a surgical strategy survey. OBJECTIVE: The purpose of this study was to evaluate the variability in in surgical strategy planning of adult spinal deformity (ASD) based on patients' clinical and radiographic data. BACKGROUND: Literature guiding the management of ASD consists primarily of studies with low levels of evidence. Recent studies have demonstrated good agreement among surgeons about the factors influencing surgical decision but poor agreement about the need for surgery. Therefore there is a relative lack of consensus and guidelines in the clinical practice and treatment of ASD. METHODS: A total of 28 adult deformity surgeons were asked to fulfill an online survey of 10 spinal deformity cases. Case presentation included a clinical vignette with photographs, Oswestry Disability Index and Visual Analog Scale scores and imaging with radiographic measurements. For each case, the surgeons were asked whether surgical management would be beneficial and if so, their surgical plan (approach, staging, need for fusion, osteotomy or decompression and the techniques used). Intraobserver and interobserver reliability were studied using average Cohen and Feiss Kappa statistics, respectively. Descriptive statistics were calculated to evaluate the frequency of each of the alternatives in surgical planning. RESULTS: Average intrarater and interrater agreement for surgical strategy were evaluated to be substantial (κ=0.62) and fair (κ=0.24), respectively. Detailed interrater statistics demonstrates that there was only slight agreement on the need for surgery (κ=0.15), the approach (κ=0.15), and the need for fusion (κ=0.16) while moderate agreement was reached for the need for decompression (κ=0.42) and osteotomy (κ=0.29). CONCLUSIONS: Among surgeons, agreement about the need for surgery and the surgical strategy for ASD is limited. Findings from this survey highlight the need for comprehensive classifications for ASD, higher-level studies including randomized trials to set guidelines and lessen the variability in clinical practices, which would then hopefully lead to improved outcomes.