Biology subjects

Abrishami Moghaddam, H.

Publications and source records attributed to Abrishami Moghaddam, H..

7 recordsLinked to original sources

bioRxiv2026

Longitudinal Subject Pairing in Cross-Sectional Neonatal Data Reveals Asynchronous Structural and Functional Brain Maturation

The asynchronous development of structural and functional brain networks in early childhood remains largely unexamined, primarily due to the scarcity of longitudinal neuroimaging data. Resolving this temporal dimension is critical, as it promises to reshape our understanding of structural-functional (S-F) coupling, revealing not only whether brain architecture supports function, but also when and over what timescale its influence emerges. However, while rapid neonatal brain maturation and logistical constraints continue to hinder longitudinal data collection, large-scale cross-sectional multimodal datasets are currently available to bridge this gap. Here, we propose a longitudinal subject-pairing framework that reconstructs developmental trajectories from cross-sectional data. It pairs the infants with a predefined age gap while maximizing their similarity in both structural and functional features, thereby approximating the longitudinal trajectory of functional changes in relation to structural maturation. As a case study, we applied this framework to the perisylvian region in a subset of 505 neonates from the multimodal dHCP brain dataset. The myelination index was derived as a structural feature from MRI, and the fractional amplitude of low-frequency fluctuations (fALFF) was derived as a functional feature from resting-state functional MRI. A conventional cross-sectional analysis revealed a moderate S-F correlation magnitude (r = 0.34). In contrast, the proposed framework demonstrated a significant increase in S-F coupling to r = 0.46 when the structural maturation precedes functional maturation by approximately five days. These findings provide novel evidence of a functional maturation lag relative to structural brain development in neonates. Beyond elucidating S-F relationships in the early developing brain, this work establishes a framework for future longitudinal studies and advances in brain modeling across developmental trajectories, aging, and disease prediction.

neuroscience↗

bioRxiv2023

Assessment of resting state structural-functional relationships in perisylvian region during the early weeks after birth

This study investigates the structural-functional (S-F) relationships of the perisylvian region during the first weeks after birth in the resting state. Previous joint S-F studies of perisylvian development were mainly conducted on individual structural and functional metrics. By employing a weighted combination of metrics, joint S-F studies can enhance the understanding of perisylvian development in neonates, thereby providing valuable features for the prediction of neurodevelopmental disorders. To this end, we employed both structural and functional metrics derived from 16 perisylvian sub-regions (PSRs). Structural metrics included morphological and myelination measures, while functional metrics encompassed functional connectivity (FC) values between a designated seed PSR and the remaining PSRs. In addition, fractional amplitude of low-frequency fluctuations (fALFF) for all PSRs was computed and used as a distinct group of functional metrics. During statistical procedure, based on sparse canonical correlation analysis (CCA), the structural metrics were correlated with each respective group of functional metrics. Then, CCA was employed to delineate regional interdependencies and derive combined structural-functional features. The findings revealed that combined features outperform individual metrics for characterizing the normal development of the PSRs in term neonates. The myelination is prominently related to both fALFF and FCs, while morphological metrics of the PSRs have very limited contribution in combined structural features. Among the designated PSRs, the FCs of both the right insula and Heschls gyrus with other PSRs demonstrated a more robust correlation with the combined structural features.

bioengineering↗

bioRxiv2022

Skull and scalp segmentation in neonatal cerebral MRI using subject-specific probability models

This study presents a new approach for segmenting cranial bones in magnetic resonance images (MRIs) acquired from neonates in the gestational age range of 39 to 42 weeks. the approach uses subject-specific probability maps of the skull and scalp, created from atlas computed tomography (CT) images taken retrospectively from neonates in the same age range. the method also uses a subject-specific probability map of cerebrospinal fluid (CSF), constructed from retrospective atlas MRIs. To build skull, scalp, and CSF probability maps, a subject-specific bimodal MR-CT neonatal head template is employed. In the next step, the subject-specific probability maps are fed to the expectation maximization algorithm in conjunction with Markov random field method implemented in FSL software to segment the skull and scalp from the input MR image. The results of the proposed method were evaluated through various experiments. First, we employed our method as a brain tissue extractor and compared its results with public methods such as the Brain Extraction Tool (BET) and Brain Surface Extractor (BSE). Second, we calculated the similarity in shape between the frontal and occipital sutures (which had been reconstructed from segmented cranial bones) and the ground truth. For this purpose, modified versions of the Dice similarity coefficient (DSC) were adopted and used. Finally, retrospective data including MRI and CT images obtained from the same neonate within a short time interval were used. After aligning the two images, the DSC and modified Hausdorff distance (MHD) were used to compare the similarity of the cranial bones in the MR and CT images. Furthermore, the anterior fontanel size was compared to the normal size reported for neonates in the same age range. Cranial bone thickness was calculated and compared to normal values reported for healthy neonates. The results of these experiments demonstrated the success of our segmentation method. The algorithm for creating subject-specific atlases is publicly accessible through a graphical user interface at medvispy.ee.kntu.ac.ir.

bioengineering↗

bioRxiv2022

A correlational study between microstructural, macrostructural and functional age-related changes in the human visual cortex

Age-related changes in the human brain can be investigated from either structural or functional perspectives. Analysis of structural and functional age-related changes throughout the lifespan may help to understand the normal brain development process and monitor the structural and functional pathology of the brain. This study, combining dedicated electroencephalography (EEG) and magnetic resonance imaging (MRI) approaches in adults (20-78 years), highlights the complex relationship between micro/macrostructural properties and the functional responses to visual stimuli. Here, we aimed to relate age-related changes of the latency of visual evoked potentials (VEPs) to micro/macrostructural indexes and find any correlation between micro/macrostructural features, as well. We studied age-related structural changes in the brain, by using the MRI and diffusion-weighted imaging (DWI) as preferred imaging methods for extracting brain macrostructural parameters such as the cortical thickness, surface area, folding and curvature index, gray matter volume, and microstructural parameters such as mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD). All the mentioned features were significantly correlated with age in V1 and V2 regions of the visual cortex. Furthermore, we highlighted, negative correlations between structural features extracted from T1-weighted images and DWI. The latency and amplitude of the three dominants peaks (C1, P1, N1) of the VEP were considered as the brain functional features to be examined for correlation with age and structural features of the corresponding age. We observed significant correlations between mean C1 latency and GM volume averaged in V1 and V2. In hierarchical models, the structural index did not contributed to significant additional variance in the C1 latency after accounting for the variance associated with age. However, the age explained significant additional variance in the model after accounting for the variance associated with the structural feature.

bioengineering↗

bioRxiv2022

A Dynamic Bottom-Up Saliency Detection Method for Still Images

IntroductionExisting saliency detection algorithms in the literature have ignored the importance of time. They create a static saliency map for the whole recording time. However, bottom-up and top-down attention continuously compete and the salient regions change through time. In this paper, we propose an unsupervised algorithm to predict the dynamic evolution of bottom-up saliency in images. MethodWe compute the variation of low-level features within non-overlapping patches of the input image. A patch with higher variation is considered more salient. We use a threshold to ignore less salient parts and create a map. A weighted sum of this map and its center of mass is calculated to provide the saliency map. The threshold and weights are set dynamically. We use the MIT1003 and DOVES datasets for evaluation and break the recording to multiple 100ms or 500ms-time intervals. A separate ground-truth is created for each interval. Then, the predicted dynamic saliency map is compared to the ground-truth using Normalized Scanpath Saliency, Kullback-Leibler divergence, Similarity, and Linear Correlation Coefficient metrics. ResultsThe proposed method outperformed the competitors on DOVES dataset. It also had an acceptable performance on MIT1003 especially within 0-400ms after stimulus onset. ConclusionThis dynamic algorithm can predict an images salient regions better than the static methods as saliency detection is inherently a dynamic process. This method is biologically-plausible and in-line with the recent findings of the creation of a bottom-up saliency map in the primary visual cortex or superior colliculus.

bioengineering↗

bioRxiv2021

Normal development of the brain: a survey of joint structural-functional brain studies

Joint structural-functional (S-F) developmental studies present a novel approach to address the complex neuroscience questions on how the human brain works and how it matures. Joint S-F biomarkers have the inherent potential to model effectively the brains maturation, fill the information gap in temporal brain atlases, and demonstrate how the brains performance matures during the lifespan. This review presents the current state of knowledge on heterochronous and heterogeneous development of S-F links during the maturation period. The S-F relationship has been investigated in early-matured unimodal and prolonged-matured transmodal regions of the brain using a variety of structural and functional biomarkers and data acquisition modalities. Joint S-F unimodal studies have employed auditory and visual stimuli, while the main focus of joint S-F transmodal studies has been resting-state networks and working memory. However, non-significant associations between some structural and functional biomarkers and their maturation show that designing and developing effective S-F biomarkers is still a challenge in the field. Maturational characteristics of brain asymmetries have been poorly investigated by the joint S-F studies, and the results were inconsistent with previous non-joint ones. The inherent complexity of the brain performance can be modeled using multifactorial and nonlinear techniques as promising methods to simulate the impact of age on S-F relations considering their analysis challenges.

neuroscience↗

bioRxiv2018

Attention enhances LFP phase coherence in macaque visual cortex, improving sensory processing

Attention selectively routes the most behaviorally relevant information among the vast pool of sensory inputs through cortical regions. Previous studies have shown that visual attention samples the surrounding stimuli periodically. However, the neural mechanism underlying this sampling in the sensory cortex, and whether the brain actively uses these rhythms, has remained elusive. Here, we hypothesize that selective attention controls the phase of oscillatory synaptic activities to efficiently process the relevant information in the brain. We document an attentional modulation of pre-stimulus inter-trial phase coherence (a measure of deviation between instantaneous phases of trials) at low frequencies in macaque visual area MT. Our data reveal that phase coherence increases when attention is deployed towards the receptive field of the recorded neural population. We further show that the attentional enhancement of phase coherence is positively correlated with the attentional modulation of stimulus induced firing rate, and importantly, a higher phase coherence leads to a faster behavioral response. Our results suggest a functional utilization of intrinsic neural oscillatory activities for better processing upcoming environmental stimuli, generating the optimal behavior.

neuroscience↗