Biology subjects

Aly, M.

Publications and source records attributed to Aly, M..

6 recordsLinked to original sources

bioRxiv2018

Focusing on what matters: Modulation of the human hippocampus by relational attention

Hippocampal episodic memory is fundamentally relational, consisting of links between events and the spatial and temporal contexts in which they occurred. Such relations are also important over much shorter time periods, during online visual perception. For example, how do we assess the relative spatial positions of objects, their temporal order, or the relationship between their features? Here, we investigate the role of the hippocampus in such online relational processing by manipulating visual attention to different kinds of relations in a dynamic display. While undergoing high-resolution fMRI, participants viewed two images in rapid succession on each trial and performed one of three relational tasks, judging the images relative: spatial positions, temporal onsets, or sizes. As a control, they sometimes also judged whether one image was tilted, irrespective of the other; this served as a baseline item task with no demands on relational processing. All hippocampal regions of interest (CA1, CA2/3/DG, subiculum) showed reliable deactivation when participants attended to relational vs. item information. Attention to temporal relations was associated with more robust deactivation than the other conditions. One possible interpretation of such deactivation is that it reflects hippocampal disengagement. If true, there should be reduced information content and noisier, less reliable patterns of activity in the hippocampus for the temporal vs. other tasks. Instead, analyses of multivariate activity patterns revealed more stable hippocampal representations in the temporal task. Additional analyses showed that this increased pattern similarity was not simply a reflection of the lower univariate activity. Thus, the hippocampus differentiates between relational and item processing even during online visual perception, and its representations of temporal relations in particular are robust and stable. Together, these findings suggest that the relational computations of the hippocampus, known to be important for memory, extend beyond this purpose, enabling the rapid online extraction of relational information in visual perception.

neuroscience↗

bioRxiv2018

Large-Scale Annotation of Histopathology Images from Social Media

Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic is 0.805-0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k=1 = 0.7618{+/-}0.0018 (chance 0.397{+/-}0.004, mean{+/-}stdev). The classifiers find texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g. cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, pre-neoplastic/benign/ low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e. from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data through pathobotology.org. We expect our project to cultivate a more connected world of physicians and improve patient care worldwide.

pathology↗

bioRxiv2018

Cell-free DNA profiling of metastatic prostate cancer reveals microsatellite instability, structural rearrangements and clonal hematopoiesis

BackgroundThere are multiple existing and emerging therapeutic avenues for metastatic prostate cancer, with a common denominator, which is the need for predictive biomarkers. Circulating tumor DNA (ctDNA) has the potential to cost-efficiently accelerate precision medicine trials to improve clinical efficacy and diminish costs and toxicity. However, comprehensive ctDNA profiling in metastatic prostate cancer to date has been limited.\n\nMethodsA combination of targeted- and low-pass whole genome sequencing was performed on plasma cell-free DNA and matched white blood cell germline DNA in 364 blood samples from 217 metastatic prostate cancer patients.\n\nResultsctDNA was detected in 85.9% of baseline samples, correlated to line of therapy and was mirrored by circulating tumor cell enumeration of synchronous blood samples. Comprehensive profiling of the androgen receptor (AR) revealed a continuous increase in the fraction of patients with intra-AR structural variation, from 15.4% during first line mCRPC therapy to 45.2% in fourth line, indicating a continuous evolution of AR during the course of the disease. Patients displayed frequent alterations in DNA repair deficiency genes (18.0%). Additionally, the microsatellite instability phenotype was identified in 3.81% of eligible samples ([≥]0.1 ctDNA fraction). Sequencing of non-repetitive intronic- and exonic regions of PTEN, RB1 and TP53 detected biallelic inactivation in 47.5%, 20.3% and 44.1% of samples with [≥]0.2 ctDNA fraction, respectively. Only one patient carried a clonal high-impact variant without a detectable second hit. Intronic high-impact structural variation was twice as common as exonic mutations in PTEN and RB1. Finally, 14.6% of patients presented false positive variants due to clonal hematopoiesis, commonly ignored in commercially available assays.\n\nConclusionsctDNA profiles appear to mirror the genomic landscape of metastatic prostate cancer tissue and may cost-efficiently provide somatic information in clinical trials designed to identify predictive biomarkers. However, intronic sequencing of the interrogated tumor suppressors challenge the ubiquitous focus on coding regions and is vital, together with profiling of synchronous white blood cells, to minimize erroneous assignments which in turn may confound results and impede true associations in clinical trials.

genomics↗

bioRxiv2017

Learning naturalistic temporal structure in the posterior medial network

The posterior medial network is at the apex of a temporal integration hierarchy in the brain, integrating information over many seconds of viewing intact, but not scrambled, movies. This has been interpreted as an effect of temporal structure. Such structure in movies depends on pre-existing event schemas, but temporal structure can also arise de novo from learning. Here we examined the relative role of schema-consistent temporal structure and arbitrary but consistent temporal structure on the human posterior medial network. We tested whether, with repeated viewing, the network becomes engaged by scrambled movies with temporal structure. Replicating prior studies, posterior medial regions were immediately locked to stimulus structure upon exposure to intact but not scrambled movies. However, for temporally structured scrambled movies, functional coupling within the network increased across stimulus repetitions, rising to the level of intact movies. Thus, temporal structure is a key determinant of network dynamics and function.

neuroscience↗

bioRxiv2017

Perceptual decisions result from the continuous accumulation of memory and sensory evidence

Expectations can inform fast, accurate decisions. But what informs expectations? Here we test the hypothesis that expectations are set by dynamic inference from memory. Participants performed a cue-guided perceptual decision task with independently-varying memory and sensory evidence. Cues established expectations by reminding participants of past stimulus-stimulus pairings, which predicted the likely target in a subsequent noisy image stream. Participants responses used both memory and sensory information, in accordance to their relative reliability. Formal model comparison showed that the sensory inference was best explained when its parameters were set dynamically at each trial by evidence sampled from memory. Supporting this model, neural pattern analysis revealed that responses to the probe were modulated by the specific content and fidelity of memory reinstatement that occurred before the probe appeared. Together, these results suggest that perceptual decisions arise from the continuous sampling of memory and sensory evidence.

neuroscience↗

bioRxiv2016

A genetic risk score to guide age-specific, personalized prostate cancer screening

BackgroundProstate-specific-antigen (PSA) screening resulted in reduced prostate cancer (PCa) mortality in a large clinical trial, but due to a high false-positive rate, among other concerns, many guidelines do not endorse universal screening and instead recommend an individualized decision based on each patients risk. Genetic risk may provide key information to guide the decisions of whether and at what age to screen an individual man for PCa.\n\nMethodsGenotype, PCa status, and age from 34,444 men of European ancestry from the PRACTICAL consortium database were analyzed to select single-nucleotide polymorphisms (SNPs) associated with prostate cancer diagnosis. These SNPs were then incorporated into a survival analysis to estimate their effects on age at PCa diagnosis. The resulting polygenic hazard score (PHS) is an assessment of individual genetic risk. The final model was validated in an independent dataset comprised of 6,417 men with screening PSA and genotype data. PHS was calculated for these men to test for prediction of PCa-free survival. PHS was also combined with age-specific PCa incidence data from the U.S. population to generate a PCa-Risk (PCaR) age that relates a given mans risk to that of the population average. PHS and PCaR age were evaluated for prediction of positive predictive value (PPV) of PSA screening.\n\nFindingsPHS calculated from 54 SNPs was very highly predictive of age at PCa diagnosis for men in the validation set (p =10-53). PPV of PSA screening varied from 0.18 to 0.52 for men with low and high genetic risk, respectively. PHS modulates PCa-free survival curves by an estimated 20 years between men in the 1st or 99th percentiles of genetic risk.\n\nInterpretationPolygenic hazard scores give personalized genetic risk estimates and can inform the decisions of whether and at what age to screen a man for PCa.\n\nFundingDepartment of Defense #W81XWH-13-1-0391

genetics↗