Biology subjects

Jahanikia, S.

Publications and source records attributed to Jahanikia, S..

3 recordsLinked to original sources

bioRxiv2026

A Systematic Approach Toward Implementing Machine Learning Techniques to Analyze Gut Microbiome Data

This study investigates the relationship between the gut microbiota and specific diseases. Data was collected from the Human Gut Microbiome Atlas, which examines regional variations across 20 countries on five continents, categorizing microbial species by taxonomy, from genus to species. The Atlas provides color-coded phylum classifications, numerical species counts within the same genus, and an analysis of dysbiosis-related associations with 23 diseases, as well as region-enriched species. The data stratified samples into distinct categories such as westernized, non-westernized, cancerous, and non-cancerous. The findings demonstrate that tree-based ensemble methods, such as Bagging and Boosting prediction methods, achieved the highest accuracies across all categories due to their robustness in handling the complex, high-dimensional data. The XGBoost model yielded the strongest predictive performance, achieving 91% accuracy for westernized cancer-associated samples, 84% accuracy for non-westernized cancer-associated samples, 92% accuracy for westernized samples, and 78% for non-westernized samples. Additionally, advanced topological data analysis was used to assess the global structure and underlying patterns within the dataset. ImportanceThis research aims to connect gut microbiome composition to diseases using global datasets from the Human Gut Microbiome Atlas. The goal was to evaluate how accurately different machine learning algorithms could classify microbiota species and diseases and predict disease associations by comparing westernized and non-westernized populations, including both cancerous and noncancerous groups. These findings can contribute to the future creation of population-specific and disease-specific microbial models.

bioinformatics↗

bioRxiv2025

Topological data analysis reveals rigid brain-state dynamics during self-viewing in trait rumination

Rumination--repetitive, negatively valenced, self-focused thought--is a maladaptive cognitive style linked to emotional dysregulation and psychiatric risk. To investigate its neural underpinnings in a naturalistic context, we developed an fMRI paradigm in which participants observed and reflected on videos of their own past group-based problem-solving sessions, a naturalistic self-relevant context rarely examined in fMRI studies. Thirty-two adults (mean age = 30.4 {+/-} 5.4 years; 13 F) were recorded during collaborative design-thinking tasks in triads. In a subsequent scanning session, each participant viewed two self-relevant team videos and one control team video, followed by a structured reflection period. We assessed trait rumination using the Rumination-Reflection Questionnaire (RRQ) and applied Topological Data Analysis (TDA) via the Mapper algorithm to model individual-level whole-brain dynamics during the task. Mapper shape graphs captured temporal transitions between brain states, allowing us to quantify the similarity of timepoints across the session. Individuals with higher trait rumination showed significantly higher temporal similarity, indicating reduced brain-state variability, during self-relevant conditions (r = 0.46, p = 0.018). This effect was not observed during the control condition. These findings suggest that rumination is associated with rigid brain dynamics during self-observation and evaluative processing. Traditional GLM and inter-subject correlation (ISC) analyses confirmed task engagement of key self-referential and social-evaluative regions, while Mapper revealed dynamic features not captured by static or group-averaged methods. Together, these findings demonstrate that trait rumination is associated with rigid large-scale brain dynamics during self-relevant cognition and highlight the value of combining naturalistic paradigms with topological approaches to capture behaviorally meaningful signatures. Significance StatementRumination--repetitive, self-focused thought--is a core cognitive feature of depression and anxiety. However, the neural dynamics underlying this mental inflexibility remain poorly understood, especially during ecologically valid, self-relevant experiences. Using topological data analysis and a naturalistic self-reflection fMRI paradigm, we identify a novel neural marker: individuals with higher rumination traits exhibit reduced flexibility in brain state transitions when watching videos of themselves. This signature is uniquely captured by Mapper, a data-driven geometric framework, and not detected by traditional neuroimaging methods. Our findings offer a new window into how maladaptive thought patterns manifest in brain dynamics, paving the way for personalized diagnostics and interventions in mood disorders.

neuroscience↗

bioRxiv2021

Spontaneous and deliberate modes of creativity: Multitask eigen-connectivity analysis captures latent cognitive modes during creative thinking

Despite substantial progress in the quest of demystifying the brain basis of creativity, several questions remain open. One such issue concerns the relationship between two latent cognitive modes during creative thinking, i.e., deliberate goal-directed cognition and spontaneous thought generation. Although an interplay between deliberate and spontaneous thinking is often indirectly implicated in the creativity literature (e.g., dual-process models), a bottom-up data-driven validation of the cognitive processes associated with creative thinking is still lacking. Here, we attempted to capture the latent modes of creative thinking by utilizing a data-driven approach on a novel continuous multitask paradigm (CMP) that widely sampled a hypothetical two-dimensional cognitive plane of deliberate and spontaneous thinking in a single fMRI session. The CMP consisted of eight task blocks ranging from undirected mind wandering to goal-directed working memory task, while also including two of the widely used creativity tasks, i.e., alternate uses task (AUT) and remote association task (RAT). Using data-driven eigen-connectivity (EC) analysis on the multitask whole-brain functional connectivity (FC) patterns, we embedded the multitask FCs into a low-dimensional latent space. The first two latent components, as revealed by the EC analysis, broadly mapped onto the two cognitive modes of deliberate and spontaneous thinking, respectively. Further, in this low-dimensional space, both creativity tasks were located in the upper right corner of high deliberate and spontaneous thinking (creative cognitive space). Neuroanatomically, the creative cognitive space was represented by not only increased intra-network connectivity within executive control and default mode networks, but also by a higher inter-network coupling between the two. Further, individual differences reflected in the low-dimensional connectivity embeddings were related to differences in deliberate and spontaneous thinking abilities. Altogether, using a continuous multitask paradigm and data-driven approach, we provide direct empirical evidence for the contribution of both deliberate and spontaneous modes of cognition during creative thinking.

neuroscience↗