Biology subjects

Khodaei, M.

Publications and source records attributed to Khodaei, M..

7 recordsLinked to original sources

bioRxiv2025

Spatial entropy of brain network landscapes: a novel method to assess spatial disorder in brain networks

In this work, we introduce a method for mapping the spatial entropy of functional brain network community structure images in brain space. Entropy maps indicate the extent to which the network communities present in a local area are ordered or disordered. We demonstrate how spatial entropy can be quantified for each voxel in the brain according to the network community affiliations of surrounding voxels. This process results in interpretable maps of brain network entropy. We show that local entropy decreases in predictable brain regions during working memory and music-listening tasks. We suggest that these regional entropy reductions reflect self-organization of neural processes in support of functionally localized cognitive tasks. Analyses in this work provide a framework for future analyses of spatial entropy in complex networks that can be mapped to Euclidean space - both within the brain and in other contexts. Significance StatementWe introduce an approach for quantifying the spatial entropy of functional brain network community structure. We demonstrate the biological relevance of the measure in three independent datasets. This approach for analyzing brain network data is data-driven, easy to implement, and highly interpretable. It also allows investigators to visualize complex data by mapping values into the brain rather than storing values in extremely high-dimensional and abstract data structures. We believe this will make the method highly accessible even to investigators with minimal experience analyzing human neuroimaging data.

neuroscience↗

bioRxiv2025

The Effects of Mindfulness on Brain Network Dynamics Following an Acute Stressor in a Population of Moderate to Heavy Drinkers

Previous research has found that mindfulness-based techniques are beneficial for reducing stress in heavy drinking individuals. However, the underlying neurobiology of these stress-reducing effects are unclear. Moreover, much of the research examining neurobiological correlates of mindfulness have used static functional connectivity, suggesting brain activity goes unchanged for the entire length of an MRI scan. In the current study, we used a state-based dynamic functional connectivity model to examine brain states during either a 10-minute mindfulness session or resting control that followed an individually tailored stress imagery task. Using a Hidden Semi-Markov Model (HSMM), six brain states and the associated dynamics of state traversal were estimated for the population. Participants that experienced the mindfulness session had more transitions and longer time spent in states in which the salience network was more active. Participants assigned to the control group had more transitions and increased time spent in states in which nodes of the default mode network were more active. Moreover, for control participants, increased occupancy time to SN-dominant states were associated with lower perceived stress. Using HSMM provided unique insight into network connectivity during mindful states; we believe it offers a novel approach to testing and optimizing the content of mindful-based therapies.

neuroscience↗

bioRxiv2025

Dynamic Resting-State Network Markers of Disruptive Behavior Problems in Youth

BackgroundChildhood disruptive behavior problems are linked to aberrant integrity within large-scale cognitive control networks. However, it is unclear if transitory or dynamic variation in the functional brain architecture is a marker of disruptive behavior problems. The current study tested whether functional connectivity across dynamic networks is distinctly associated with the transdiagnostic symptom domain of disruptive behavior problems in children. MethodsParticipants were aged 9-10 years from the Adolescent Brain Cognitive Development (ABCD) Study, who completed resting-state fMRI (N=877). We employed a dynamic connectivity approach leveraging a hidden semi-Markov model (HSMM) to identify transient properties of brain networks and states. Models estimated the time spent in each state (occupancy time) and the number of consecutive timepoints in a state (dwell time) for each participant. Linear regression models were utilized to identify distinct associations between dynamic properties (occupancy and sojourn times) and severity of disruptive behavior problems, accounting for other commonly co-occurring symptoms. ResultsDynamic network markers of disruptive behavior problems included increased time in network states characterized by globally aberrant connectivity patterns in circuitry involved in cognitive control including frontoparietal and dorsal attention networks. Replication of findings was found in a held-out sample of resting-state fMRI runs in which greater severity of disruptive behavior problems was uniquely linked to greater occupancy time in similarly characterized brain states. ConclusionTransdiagnostic, dynamic resting-state markers of disruptive behavior problems in youth may assist in the development of brain-based biomarkers for monitoring treatment outcomes, assessing circuit target engagement and informing clinical decisions.

neuroscience↗

bioRxiv2024

MIND-Map; A Comprehensive Toolbox for Estimating Brain Dynamic States

Studying dynamic brain states has offered new insights into understanding functional connectivity. One of the promising approaches for estimating these brain states is the hidden semi-Markov model (HSMM). However, despite its potential, its adoption in the neuroscience community has been limited due to its complexity. We developed the Markov Inference Dynamic Mapping (MIND-Map) toolbox to overcome this limitation. This interactive user-friendly toolbox leverages HSMM to identify brain states, analyze their dynamics, and perform two-sample statistical comparisons of network dynamics. Furthermore, it introduces a new approach, not yet used in conjunction with these models, for determining the optimal number of states, addressing a key challenge in the field. We assessed the performance of the HSMM and our method for identifying the optimal number of states using two datasets, including a unique dataset explicitly developed for this purpose.

neuroscience↗

bioRxiv2024

Triple network dynamics and future alcohol consumption in adolescents

BackgroundHuman neuroimaging increasingly suggests that the brain is best modeled as a highly interconnected and dynamic system. However, novel methodology for studying functional brain network dynamics have never been applied to the study of adolescent alcohol consumption. We sought to determine whether brain network dynamics are related to future drinking behavior in teenagers. MethodsResting-state functional magnetic resonance imaging (fMRI) time series from 17-year-old non/low drinking participants (n=295) of the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study were used to fit a Hidden Semi-Markov Model (HSMM). Regions of the default mode network (DMN), salience network (SN), and central executive network (CEN), collectively known as the Triple Network, were included in modeling. The HSMM identified the most-likely state sequence for each participant, a trajectory through distinct brain network states over the course of their fMRI scan. Poisson regression models were used to assess relationships between state sequence metrics and future drinking frequency. Potential sex differences in state sequence metrics or the relationship between sequence metrics and future drinking were assessed with permutation testing and interactions in regression models. ResultsNo sex differences in state sequence metrics were observed. However, the relationship between occupancy times and future drinking frequency differed by sex for two brain states. In the full sample, occupancy time in a state characterized by high interconnectivity between the SN and CEN was negatively associated with drinking. Occupancy time in a separate state characterized by high activation in the DMN and SN, but low activation in the CEN, was negatively associated with future drinking. ConclusionsBrain network dynamics may be useful neural markers of predisposition to drinking in adolescents. Brain states which make teens vulnerable or resilient to drinking may differ between sexes.

neuroscience↗

bioRxiv2023

Multilocus Phylogeny Estimation Using Probabilistic Topic Modeling

AO_SCPLOWBSTRACTC_SCPLOWMethods for rapidly inferring the evolutionary history of species or populations with genome-wide data are progressing, but computational constraints still limit our abilities in this area. We developed an alignment-free method to infer genome-wide phylogenies and implemented it in the Python package TO_SCPLOWOPICC_SCPLOWCO_SCPLOWONTMLC_SCPLOW. The method uses probabilistic topic modeling (specifically, Latent Dirichlet Allocation or LDA) to extract topic frequencies from k-mers, which are derived from multilocus DNA sequences. These extracted frequencies then serve as an input for the program CO_SCPLOWONTMLC_SCPLOW in the PHYLIP package, which is used to generate a species tree. We evaluated the performance of TO_SCPLOWOPICC_SCPLOWCO_SCPLOWONTMLC_SCPLOW on simulated datasets with gaps and three biological datasets: (1) 14 DNA sequence loci from two Australian bird species distributed across nine populations, (2) 5162 loci from 80 mammal species, and (3) raw, unaligned, non-orthologous PO_SCPLOWACC_SCPLOWBO_SCPLOWIOC_SCPLOW sequences from 12 bird species. Our empirical results and simulated data suggest that our method is efficient and statistically robust. We also assessed the uncertainty of the estimated relationships among clades using a bootstrap procedure.

evolutionary biology↗

bioRxiv2022

Geodesics to characterize the phylogenetic landscape

Phylogenetic trees are fundamental for understanding evolutionary history. However, finding maximum likelihood trees is challenging due to the complexity of the likelihood landscape and the size of tree space. Based on the Billera-Holmes-Vogtmann (BHV) distance between trees, we describe a method to generate intermediate trees on the shortest path between two trees, called pathtrees. These pathtrees give a structured way to generate and visualize treespace in an area of interest. They allow investigating intermediate regions between trees of interest, exploring locally optimal trees in topological clusters of treespace, and potentially finding trees of high likelihood unexplored by tree search algorithms. We compared our approach against other tree search tools (PO_SCPLOWAUPC_SCPLOW*, RAxML, and RO_SCPLOWEVC_SCPLOWBO_SCPLOWAYESC_SCPLOW) in terms of generated highest likelihood trees, new topology proportions, and consistency of generated treespace. We assess our method using two datasets. The first consists of 23 primate species (CytB, 1141 bp), leading to well-resolved relationships. The second is a dataset of 182 milksnakes (CytB, 1117 bp), containing many similar sequences and complex relationships among individuals. Our method visualizes the treespace using log likelihood as a fitness function. It finds similarly optimal trees as heuristic methods and presents the likelihood landscape at different scales. It revealed that we could find trees that were not found with MCMC methods. The validation measures indicated that our method performed well mapping treespace into lower dimensions. Our method complements heuristic search analyses, and the visualization allows the inspection of likelihood terraces and exploration of treespace areas not visited by heuristic searches.

evolutionary biology↗