Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections.

This paper explicates a solution to building correspondences between molecular-scale transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross-specimen/technology alignment where specimens per emerging technology remain sparse and conventional image representations cannot efficiently model the high dimensions from subcellular detection of thousands of genes. We address these challenges by representing spatial transcriptomics data as generalized functions encoding position and high-dimensional feature (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling regions as homogeneous random fields with unknown transcriptomic feature distribution. We solve simultaneously for the minimizing geodesic diffeomorphism of coordinates through LDDMM and for these latent feature densities. We map tissue-scale mouse brain atlases to gene-based and cell-based transcriptomics data from MERFISH and BARseq technologies and to histopathology and cross-species atlases to illustrate integration of diverse molecular and cellular datasets into a single coordinate system as a means of comparison and further atlas construction.

Preliminary evidence for chaotic signatures in host-microbe interactions.

Host-microbe interactions constitute dynamical systems that can be represented by mathematical formulations that determine their dynamic nature and are categorized as deterministic, stochastic, or chaotic. Knowing the type of dynamical interaction is essential for understanding the system under study. Very little experimental work has been done to determine the dynamical characteristics of host-microbe interactions, and its study poses significant challenges. The most straightforward experimental outcome involves an observation of time to death upon infection. However, in measuring this outcome, the internal parameters and the dynamics of each particular host-microbe interaction in a population of interactions are hidden from the experimentalist. To investigate whether a time-to-death (time-to-event) data set provides adequate information for searching for chaotic signatures, we first determined our ability to detect chaos in simulated data sets of time-to-event measurements and successfully distinguished the time-to-event distribution of a chaotic process from a comparable stochastic one. To do so, we introduced an inversion measure to test for a chaotic signature in time-to-event distributions. Next, we searched for chaos in the time-to-death of Caenorhabditis elegans and Drosophila melanogaster infected with Pseudomonas aeruginosa or Pseudomonas entomophila, respectively. We found suggestions of chaotic signatures in both systems but caution that our results are preliminary and highlight the need for more fine-grained and larger data sets in determining dynamical characteristics. If validated, chaos in host-microbe interactions would have important implications for the occurrence and outcome of infectious diseases, the reproducibility of experiments in the field of microbial pathogenesis, and the prediction of microbial threats.IMPORTANCEIs microbial pathogenesis a predictable scientific field? At a time when we are dealing with coronavirus disease 2019, there is intense interest in knowing about the epidemic potential of other microbial threats and new emerging infectious diseases. To know whether microbial pathogenesis will ever be a predictable scientific field requires knowing whether a host-microbe interaction follows deterministic, stochastic, or chaotic dynamics. If randomness and chaos are absent from virulence, there is hope for prediction in the future regarding the outcome of microbe-host interactions. Chaotic systems are inherently unpredictable, although it is possible to generate short-term probabilistic models, as is done in applications of stochastic processes and machine learning to weather forecasting. Information on the dynamics of a system is also essential for understanding the reproducibility of experiments, a topic of great concern in the biological sciences. Our study finds preliminary evidence for chaotic dynamics in infectious diseases.

Amidst an amygdala renaissance in Alzheimer's disease.

The amygdala was highlighted as an early site for neurofibrillary tau tangle pathology in Alzheimer's disease in the seminal 1991 article by Braak and Braak. This knowledge has, however, only received traction recently with advances in imaging and image analysis techniques. Here, we provide a cross-disciplinary overview of pathology and neuroimaging studies on the amygdala. These studies provide strong support for an early role of the amygdala in Alzheimer's disease and the utility of imaging biomarkers of the amygdala in detecting early changes and predicting decline in cognitive functions and neuropsychiatric symptoms in early stages. We summarize the animal literature on connectivity of the amygdala, demonstrating that amygdala nuclei that show the earliest and strongest accumulation of neurofibrillary tangle pathology are those that are connected to brain regions that also show early neurofibrillary tangle accumulation. Additionally, we propose an alternative pathway of neurofibrillary tangle spreading within the medial temporal lobe between the amygdala and the anterior hippocampus. The proposed existence of this pathway is strengthened by novel experimental data on human functional connectivity. Finally, we summarize the functional roles of the amygdala, highlighting the correspondence between neurofibrillary tangle accumulation and symptomatic profiles in Alzheimer's disease. In summary, these findings provide a new impetus for studying the amygdala in Alzheimer's disease and a unique perspective to guide further study on neurofibrillary tangle spreading and the occurrence of neuropsychiatric symptoms in Alzheimer's disease.

A Pilot Study of Art Museum-Based Small Group Learning for Pre-Health Students.

Introduction: Health professions educators are increasingly recognizing the fundamental role the arts and humanities play in professional identity formation; however, few reports exist of programs designed specifically for pre-health professional students. Methods: We designed and delivered four, 2.5-h sessions for pre-health professions students at a local museum in partnership with museum educators. Participants were invited to respond to a follow-up survey asking about their perceived insights from and importance of the session. We used descriptive statistics and thematic content analysis for quantitative and qualitative data, respectively. Results: Ten of the participants responded to the survey (n=10/23, response rate=43%) and all supported the integration of such an experience in their pre-health curriculum. The qualitative analysis of responses to the open-ended item about any insights gained from participation in the program revealed three themes: cultivation of the health professional, personal growth, and awareness and appreciation of multiple perspectives. Discussion: Participants who responded to our survey drew meaningful connections to the relevance of these sessions to their development as future professionals.

A Universal Method for Crossing Molecular and Atlas Modalities using Simplex-Based Image Varifolds and Quadratic Programming.

This paper explicates a solution to the problem of building correspondences between molecular-scale transcriptomics and tissue-scale atlases. The central model represents spatial transcriptomics as generalized functions encoding molecular position and high-dimensional transcriptomic-based (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling each atlas compartment as a homogeneous random field with unknown transcriptomic feature distribution. The algorithm presented solves simultaneously for the minimizing geodesic diffeomorphism of coordinates and latent atlas transcriptomic feature fractions by alternating LDDMM optimization for coordinate transformations and quadratic programming for the latent transcriptomic variables. We demonstrate the universality of the algorithm in mapping tissue atlases to gene-based and cell-based MERFISH datasets as well as to other tissue scale atlases. The joint estimation of diffeomorphisms and latent feature distributions allows integration of diverse molecular and cellular datasets into a single coordinate system and creates an avenue of comparison amongst atlas ontologies for continued future development.

Transformative experiences at art museums to support flourishing in medicine.

PURPOSE: We implemented and evaluated a hybrid 4-week arts-based elective for clinical medical students to support flourishing. MATERIALS AND METHODS: Five students participated in early 2022. Twelve sessions occurred in-person at art museums and other cultural centers, and five occurred online. Sessions incorporated varied arts-based learning activities, including Visual Thinking Strategies, a jazz seminar, and a mask-making workshop. We evaluated the course via weekly reflective essays, interviews 6 weeks after the course, and pre-post surveys that included four scales with clinical relevance: capacity for wonder (CfW), tolerance for ambiguity (TFA), interpersonal reactivity index, and openness to diversity. RESULTS: Qualitatively, the course helped learners: 1) reconnect with individual characteristics and interests that had been neglected during medical education; 2) better appreciate others' perspectives; 3) develop identities as physicians; and 4) engage in quiet reflection, renewing their sense of purpose. Quantitatively, pre-post mean totals increased for the CfW (32.0 [SD 6.8] vs 44.0 [SD 5.7], p=.006) and TFA scales (16.4 [SD 5.2] vs 24.2 [SD 6.9], p=.033). CONCLUSIONS: This elective facilitated learners' connecting with themselves, others, and their profession with improvement in clinically-relevant measures. This provides further evidence that arts-based education can foster professional identity formation and be transformative for students.

Early amygdala and ERC atrophy linked to 3D reconstruction of rostral neurofibrillary tau tangle pathology in Alzheimer's disease.

Previous research has emphasized the unique impact of Alzheimer's Disease (AD) pathology on the medial temporal lobe (MTL), a reflection that tau pathology is particularly striking in the entorhinal and transentorhinal cortex (ERC, TEC) early in the course of disease. However, other brain regions are affected by AD pathology during its early phases. Here, we use longitudinal diffeomorphometry to measure the atrophy rate from MRI of the amygdala compared with that in the ERC and TEC in cognitively unimpaired (CU) controls, CU individuals who progressed to mild cognitive impairment (MCI), and individuals with MCI who progressed to dementia of the AD type (DAT), using a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results show significantly higher atrophy rates of the amygdala in both groups of 'converters' (CU→MCI, MCI→DAT) compared to controls, with rates of volume loss comparable to rates of thickness loss in the ERC and TEC. We localize atrophy within the amygdala within each of these groups using fixed effects modeling. Controlling for the familywise error rate highlights the medial regions of the amygdala as those with significantly higher atrophy in both groups of converters than in controls. Using our recently developed method, referred to as Projective LDDMM, we map measures of neurofibrillary tau tangles (NFTs) from digital pathology to MRI atlases and reconstruct dense 3D spatial distributions of NFT density within regions of the MTL. The distribution of NFTs is consistent with the spatial distribution of MR measured atrophy rates, revealing high densities (and atrophy) in the amygdala (particularly medial), ERC, and rostral third of the MTL. The similarity of the location of NFTs in AD and shape changes in a well-defined clinical population suggests that amygdalar atrophy rate, as measured through MRI may be a viable biomarker for AD.

Projective Diffeomorphic Mapping of Molecular Digital Pathology with Tissue MRI.

Reconstructing dense 3D anatomical coordinates from 2D projective measurements has become a central problem in digital pathology for both animal models and human studies. Here we describe Projective Large Deformation Diffeomorphic Metric Mapping (LDDMM), a technique which projects diffeomorphic mappings of dense human magnetic resonance imaging (MRI) atlases at tissue scales onto sparse measurements at micrometre scales associated with histological and more general optical imaging modalities. We solve the problem of dense mapping surjectively onto histological sections by incorporating technologies for crossing modalities that use nonlinear scattering transforms to represent multiple radiomic-like textures at micron scales, together with a Gaussian mixture-model framework for modelling tears and distortions associated to each section. We highlight the significance of our method through incorporation of neuropathological measures and MRI, of relevance to the development of biomarkers for Alzheimer's disease and one instance of the integration of imaging data across the scales of clinical imaging and digital pathology.

From Picoscale Pathology to Decascale Disease: Image Registration with a Scattering Transform and Varifolds for Manipulating Multiscale Data.

Advances in neuroimaging have yielded extensive variety in the scale and type of data available. Effective integration of such data promises deeper understanding of anatomy and disease-with consequences for both diagnosis and treatment. Often catered to particular datatypes or scales, current computational tools and mathematical frameworks remain inadequate for simultaneously registering these multiple modes of "images" and statistically analyzing the ensuing menagerie of data. Here, we present (1) a registration algorithm using a "scattering transform" to align high and low resolution images and (2) a varifold-based modeling framework to compute 3D spatial statistics of multiscale data. We use our methods to quantify microscopic tau pathology across macroscopic 3D regions of the medial temporal lobe to address a major challenge in the diagnosis of Alzheimer's Disease-the reliance on invasive methods to detect microscopic pathology.

Macrophages use a bet-hedging strategy for antimicrobial activity in phagolysosomal acidification.

Microbial ingestion by a macrophage results in the formation of an acidic phagolysosome but the host cell has no information on the pH susceptibility of the ingested organism. This poses a problem for the macrophage and raises the fundamental question of how the phagocytic cell optimizes the acidification process to prevail. We analyzed the dynamical distribution of phagolysosomal pH in murine and human macrophages that had ingested live or dead Cryptococcus neoformans cells, or inert beads. Phagolysosomal acidification produced a range of pH values that approximated normal distributions, but these differed from normality depending on ingested particle type. Analysis of the increments of pH reduction revealed no forbidden ordinal patterns, implying that the phagosomal acidification process was a stochastic dynamical system. Using simulation modeling, we determined that by stochastically acidifying a phagolysosome to a pH within the observed distribution, macrophages sacrificed a small amount of overall fitness to gain the benefit of reduced variation in fitness. Hence, chance in the final phagosomal pH introduces unpredictability to the outcome of the macrophage-microbe, which implies a bet-hedging strategy that benefits the macrophage. While bet hedging is common in biological systems at the organism level, our results show its use at the organelle and cellular level.