Biology subjects

Momeni, A.

Publications and source records attributed to Momeni, A..

5 recordsLinked to original sources

bioRxiv2025

Cognitive modes involved in emotion regulation identified using Constrained Principal Component Analysis for fMRI (fMRI-CPCA)

Meta-analyses of functional magnetic resonance imaging (fMRI) studies have identified networks of widely distributed brain regions supporting emotion regulation. These overlap with attentional or cognitive control brain networks. The literature is short on data speaking to specific neurocognitive functions of these broad brain networks in reappraisal - a key emotion regulatory strategy involving the reframing of an event according to a goal to increase or decrease experienced emotion. We address this gap by examining both the spatial configuration and temporal profile of event-related blood oxygenation level dependent (BOLD) responses during a task requiring reappraisal. We analysed fMRI datasets obtained from 84 participants (51% female) who were instructed to increase or decrease their emotional response to unpleasant images. We extracted spatial maps and their estimated temporal event-related BOLD signal changes of four components with the highest loadings. Neurocognitive functions were derived by mapping each component onto templates of previously identified task-based cognitive modes. This analysis yielded four cognitive modes: 1) "multiple demand" 2) "response", 3) "re-evaluation", and 4) "default mode". The temporal profiles showed particularly prominent patterns for the increase and decrease conditions in "multiple demand" (mode 1) and "re-evaluation" (mode 3) respectively. These findings highlight a central role for specific neurocognitive processes linked to attentional control ("multiple demand") and switching ("re-evaluation"), as part of the broad brain networks supporting reappraisal. Moreover, the level of neural engagement of these cognitive modes varies depending on the regulatory goal. These findings provide tangible targets for neurocognitive interventions such as neurostimulation when emotion regulation is compromised.

neuroscience↗

bioRxiv2025

Cognitive Mode Detectable with Task-Based fMRI: Default Mode B (DMB)

In the context of task-based functional magnetic resonance imaging (fMRI), cognitive modes can be defined as task-general cognitive/sensory/motor processes which reliably elicit specific blood-oxygen-level-dependent (BOLD) signal pattern configurations. A number of cognitive modes are detectable with task-based fMRI, and here we focus on Default Mode B (DMB), a task-negative and late-trial peaking cognitive mode. The BOLD signal configurations associated with DMB are modulated by a range of tasks, and here we present eight. For each task, we report: (1) specific pattern-based (as opposed to coordinate-based) anatomical details essential for distinguishing DMB from other BOLD-based cognitive modes, and (2) task-induced BOLD signal changes associated with DMB over a range of task conditions. In order to facilitate recognition, we nick-named the anatomical patterns specific to DMB as follows: (1) In Flight, (2) Medial Temporal Dots, (3) Snowman Nose, (4) Angel Wings, and (5) Tripod. Evidence for DMB was derived from the timing and magnitude of task-induced BOLD signal changes induced by the following tasks: working memory, spatial capacity, semantic association, evidence integration, Ravens matrices, autobiographical event simulation, meditation and social perception. It was observed that deactivations in DMB were sensitive to cognitive load during attention to specific features of the external environment, based on evidence from working memory, spatial capacity, semantic association, evidence integration, and Ravens matrices. It was also observed that activations in DMB involved a cognitive process for engaging in mental projection into self-relevant social narratives, based on evidence from autobiographical event simulation, meditation, and social perception. Future research may explore DMB activation over a wider range of tasks in larger samples.

neuroscience↗

bioRxiv2021

Xenogeneic Skin Transplantation Promotes Angiogenesis and Tissue Regeneration Through Vitamin D-Activated Trem2+ Macrophages

Skin allo- and xenotransplantation are the standard treatment for major burns when donor sites for autografts are not available and have been shown to significantly accelerate wound healing. Although the cellular elements of foreign grafts are rejected, the extracellular matrix components integrate into the wound and may underlie their beneficial effects on wound healing. The molecular mechanisms defining the relationship between the immune response to foreign grafts and their impact on wound healing have not been fully elucidated. Here, we investigated changes in collagen architecture after xenogeneic implantation of clinically available human biologic scaffolds. We show that collagen deposition in response to the implantation of human split-thickness skin grafts (hSTSG) containing live cells recapitulates normal skin architecture, whereas human acellular dermal matrix (ADM) grafts led to highly aligned collagen deposition, characteristic of fibrosis and scar. Using single-cell RNA-sequencing, we show that macrophage differentiation in response to hSTSG is driven by vitamin D (VD) signaling toward Trem2+ subpopulations with an enrichment of pro-angiogenic and anti-fibrotic transcriptomic programs. We subsequently induced this regenerative subpopulation in vitro by treating bone marrow-derived cells with vitamin D3 and found that hydrogel delivery of Trem2+ macrophages significantly accelerated wound closure in a human-like murine excisional wound model. Our study identifies the preclinical therapeutic potential of Trem2+ macrophages to mitigate fibrosis and promote wound healing and provides a novel effective strategy to develop advanced cell therapies for complex wounds. One Sentence SummaryVitamin D-activated Trem2+ macrophages promote angiogenesis and mitigate fibrosis, providing a novel effective strategy to develop advanced cell therapies for complex wounds.

immunology↗

bioRxiv2018

Deep Recurrent Attention Models for Histopathological Image Analysis

Histopathology defines the gold standard in oncology. Automatic analysis of pathology images could thus have a significant impact on diagnoses, prognoses and treatment decisions for cancer patients. Recently, convolutional neural networks (CNNs) have shown strong performance in computational histopathology tasks. However, given it remains intractable to process pathology slides in their entirety, CNNs have traditionally performed inference on small individual patches extracted from the image. This often requires a significant amount of computation and can result in ignoring potentially relevant spatial and contextual information. Being able to process larger input patches and locating discriminatory regions more efficiently could help improve both computational and task specific performance. Inspired by the recent success of Deep Recurrent Attention Models (DRAMs) in image recognition tasks, we propose a novel attention-based architecture for classification in histopathology. Similar to CNNs, DRAMs have a degree of translation invariance built-in, but the amount of computation performed can be controlled independently from the input image size. The model is a deep recurrent neural network trained with reinforcement learning to attend to the most relevant areas of large input patches. We evaluate our model on histological and molecular subtype classification tasks for the glioma cohorts of The Cancer Genome Atlas (TCGA). Our results suggest that the DRAM has comparable performance to state-of-the-art CNNs despite only processing a select number of patches.

bioinformatics↗

bioRxiv2018

Dropout-Enabled Ensemble Learning for Multi-Scale Biomedical Data

Leveraging information from multiple scales is crucial to understanding complex diseases such as cancer where this could have a significant impact in improving diagnoses, patient management and treatment decisions. Recent advances in Convolutional Neural Networks (CNNs) have enabled major breakthroughs in biomedical image analysis, in particular for histopathology and radiology images. Our main contribution is a methodology to combine independent CNN models built for these two types of images in order to improve diagnostic accuracy. We train separate CNN models and combine them using a Dropout-Enabled meta-classifier. Our framework achieved second place in the MICCAI 2018 Computational Precision Medicine Challenge.

bioinformatics↗