Biology subjects

Ghaderi, A. H.

Publications and source records attributed to Ghaderi, A. H..

8 recordsLinked to original sources

bioRxiv2026

Network-Level Associations in Nonlinear Brain Dynamics Predict Transcendent Thinking in a Diverse Adolescent Sample

Transcendent thinking (TT) is an enduring affective and cognitive process characterized by abstract meaning-making, moral reflection, self-referential integration, and strong emotional engagement. Despite growing interest in its developmental and affective significance, the intrinsic neural dynamics that predict individual differences in disposition to TT remain poorly understood. Most prior work has relied on linear functional connectivity measures, which may be insufficient to capture the nonlinear and multiscale nature of brain dynamics underlying higher-order affective dispositions like TT. Here, we introduce a nonlinear functional brain network (FBN) framework based on multiscale entropy (MSE) to investigate whether intrinsic resting-state nonlinear brain dynamics predict disposition to TT in adolescents. Functional connectivity was defined as inter-regional similarity in MSE profiles derived from resting-state fMRI, yielding weighted networks that capture scale-dependent dynamical correspondence rather than linear synchrony. Graph-theoretical, spectral, and information-theoretic measures were computed and evaluated against signal-level and network-level null models. Predictive performance was assessed using machine-learning models and compared with conventional time series-based FBNs. Global intelligence (IQ) was examined as a control cognitive variable. MSE-based network features, particularly spectral energy and Shannon entropy, showed significant associations with TT and enabled reliable prediction of individual differences, whereas time series-based network measures failed to predict TT. No network measures reliably predicted IQ. Overall, these results indicate that intrinsic nonlinear brain dynamics carry predictive information about affective dispositions, rather than domainspecific or network-localized cognitive abilities such as IQ. This work demonstrates that nonlinear, multiscale network representations of resting-state brain activity provide a principled and predictive framework for modeling individual differences in enduring affective dispositions.

neuroscience↗

bioRxiv2026

Linear and Nonlinear Oscillatory Functional Brain Networks in Euthymic Bipolar Disorder Classification

Functional brain network (FBN) dysconnectivity has been repeatedly reported in bipolar disorder (BD). However, it remains unclear how this dysconnectivity manifests from the perspective of oscillatory FBNs, that is, which network measures and frequency bands most reliably capture this alteration. Moreover, it is unknown whether this dysconnection is predominantly expressed through linear or nonlinear interactions. Here, we investigated properties of oscillatory FBNs in individuals with euthymic BD. Networks were constructed using linear and nonlinear connectivity measures applied to source-localized resting-state electroencephalography (EEG) current density signals. We then quantified whole-FBN and nodal features using conventional and spectral graph theory methods to characterize disorder-related network mechanisms and evaluate their potential as biomarkers. Significant group differences between BD and control groups were observed in the theta and alpha1 bands. Dynamical whole-FBN alterations were detected primarily in linear oscillatory FBNs, with reduced Shannon entropy and energy in the BD group. These effects were replicated using machine learning, achieving 85% classification accuracy with entropy and energy as the most informative features. In contrast, nodal-level differences emerged mainly in nonlinear FBNs, revealing increased centrality in frontal, and decreased centrality across temporal, and limbic regions. These findings emphasize distinct, frequency-specific roles of linear and nonlinear oscillatory FBNs in BD, with global dysconnectivity reflected in linear FBNs and local alterations captured by nonlinear connectivity. Moreover, network measures related to synchronization stability and complexity more effectively capture BD-related dysconnectivity, suggesting that dynamic features of oscillatory FBNs may serve as potential biomarkers for BD.

neuroscience↗

bioRxiv2026

Modular Integration of Auditory Instructions and Visual Cues into the Cortical Reach Network

Humans often need to integrate bottom-up stimulus attributes (e.g., object shape) with top-down instructions (e.g., how to pick it up) for adaptive behavior. To investigate how such cues are integrated into the cortical reach network, we employed an fMRI paradigm where participants viewed a cube to their left or right (visual cue) and were verbally instructed to use a horizontal or vertical grip (auditory cue), in varying order. Univariate voxel-wise and region-of-interest analysis confirmed the expected order-dependent activation of sensory regions, followed by robust parietofrontal activation. Graph-theory analysis revealed two significant subnetworks (modules): an Occipital-Parietal module (likely integrating the visual cue into the reach plan) and a Temporal-Frontal module (likely integrating the auditory cue into the grasp plan), both converging in somatomotor cortex. Cue-order influenced modularity, i.e., the module corresponding to the first cue tended to dominate contralateral motor cortex, and conversely a classification model decoded cue-order based on modularity. Local network hubs were order-independent and occupied traditional reach areas, whereas global (between module) hubs were order-dependent and more distributed. Overall, these results suggest that multisensory integration of top-down and bottom-up cues for action is a network phenomenon that relies on both lateral and serial communication between parallel sensorimotor modules.

neuroscience↗

bioRxiv2025

Intersecting effects of social circumstances and transcendent thinking on mid-adolescents' longitudinal functional connectome development

Socially moderated variation in mid-adolescents functional brain network (FBN) development is insufficiently studied, especially within low-SES contexts. Alongside demographic factors, evidence suggests adolescents malleable psychological dispositions may contribute, with possible clinical and educational implications. We applied graph theory to a unique 2-year longitudinal fMRI dataset (N=65) with an ecologically valid 2-hour interview revealing an emergent mid-adolescent psychological disposition, "transcendent thinking" (TT)--adolescents tendency to consider systems-level, ethical, personal implications of social information. Overall, FBNs showed increased segregation and entropy but decreased integration and energy. Modularity increased particularly in somatosensory subnetworks. Classifying participants by two key demographic factors, parents education (PE) and community violence exposure (CVE), FBN changes differed by group, and TT moderated changes in high-CVE/low-PE participants, arguably the most vulnerable group. Machine learning revealed TT and CVE, but not IQ, as principal FBN change predictors across groups. Findings suggest social context and psychological dispositions influence low-SES adolescents FBN development.

neuroscience↗

bioRxiv2023

Exploring the Interplay between BOLD Signal Variability, Complexity, static and dynamic Functional Brain Network Features during Movie Viewing

As the brain is dynamic and complex, knowledge of brain signal variability and complexity is crucial in our understanding of brain function. Recent resting-fMRI studies revealed links between BOLD signal variability or complexity with static/dynamics features of functional brain networks (FBN). However, no study has examined the relationships between these brain metrics. The association between brain signal variability and complexity is still understudied. Here we investigated the association between movie naturalistic-fMRI BOLD signal variability/complexity and static/dynamic FBN features using graph theory analysis. We found that variability positively correlated with fine-scale complexity but negatively correlated with coarse-scale complexity. Hence, variability and coarse-scale complexity correlated with static FC oppositely. Specifically, regions with high centrality and clustering coefficient were related to less variable but more complex signal. Similar relationship persisted for dynamic FBN, but the associations with certain aspects of regional centrality dynamics became insignificant. Our findings demonstrate that the relationship between BOLD signal variability, static/dynamic FBN with BOLD signal complexity depends on the temporal scale of signal complexity. Additionally, altered correlation between variability and complexity with dynamic FBN features may indicate the complex, time-varying feature of FBN and reflect how BOLD signal variability and complexity co-evolve with dynamic FBN over time.

neuroscience↗

bioRxiv2021

Saccades and pre-saccadic stimulus repetition alter cortical network topology and dynamics: evidence from EEG and graph theoretical analysis

Parietal and frontal cortex are involved in saccade generation, but their output signals also modify visual signals throughout cortex. These signals produce well-documented behavioral phenomena (saccades, saccadic suppression, various perisaccadic perceptual distortions) but their underlying influence on cortical network dynamics is not known. Here, we combined electroencephalography (EEG) with frequency-dependent source localization and graph theory analysis (GTA) to understand how saccades and pre-saccadic visual stimuli interactively alter cortical network dynamics in humans. 21 participants viewed series of 1-3 vertical or horizontal grids, followed by grid with the opposite orientation just before a horizontal saccade or continued fixation. EEG signals from the presaccadic interval (cue + 200ms, or equivalent fixation period) were used for source localization. Source localization (saccade - fixation) identified bilateral dorsomedial frontoparietal activity across frequency bands, whereas stimulus repetition produced band-specific modulations in left prefrontal, posterior parietal, and central-superior frontal and/or parietal cortex, with significant saccade-repetition interactions in frontal and parietal regions. GTA analysis revealed a saccade-specific functional network with major hubs in inferior parietal cortex (alpha) and the frontal eye fields (beta), and major saccade-repetition interactions in left prefrontal (theta) and supramarginal gyrus (gamma). Overall, quantitative measures of whole-brain network topology and dynamics (segregation, integration, synchronization, complexity) were enhanced during the presaccadic interval, but repetition interactions reduced synchronization and complexity. These results show that presaccadic signals have widespread, coherent influence on cortical network dynamics, likely responsible for both saccade production and the perceptual phenomena associated with saccades. HighlightsO_LISource localization & graph theory were used to analyze presaccadic EEG signals C_LIO_LIPresaccadic signals produced band-specific modulations/hubs in parietofrontal cortex C_LIO_LIFrontal/parietal eye fields showed extensive functional connectivity across all lobes C_LIO_LIPresaccadic stimulus repetition further modulated functional network connectivity C_LIO_LISaccades and repetition both influenced network clustering, integration, & complexity C_LI

neuroscience↗

bioRxiv2020

Linear vector models of time perception account for saccade and stimulus novelty interactions

Various models (e.g., scalar, state-dependent network, and vector models) have been proposed to explain the global aspects of time perception, but they have not been tested against specific visual phenomena like perisaccadic time compression and novel stimulus time dilation. Here, in two separate experiments (N=31), we tested how the perceived duration of a novel stimulus is influenced by 1) a simultaneous saccade, in combination with 2) a prior series of repeated stimuli in human participants. This yielded a novel behavioral interaction: pre-saccadic stimulus repetition neutralizes perisaccadic time compression. We then tested these results against simulations of the above models. Our data yielded low correlations against scalar model simulations, high but non-specific correlations for our feedforward neural network, and correlations that were both high and specific for a vector model based on identity of objective and subjective time. These results demonstrate the power of global time perception models in explaining disparate empirical phenomena and suggest that subjective time has a similar essence to times physical vector.

neuroscience↗

bioRxiv2019

Synchrony and complexity in state-related EEG networks: an application of spectral graph theory

To characterize differences between different state-related brain networks, statistical graph theory approaches have been employed to identify informative, topological properties. However, dynamical properties have been studied little in this regard. Our goal here was to introduce spectral graph theory as a reliable approach to determine dynamic properties of functional brain networks and to find how topological versus dynamical features differentiate between such networks. To this goal, 45 participants performed no task with eyes open (EO) or closed (EC) while electroencephalography data were recorded. These data were used to create weighted adjacency matrices for each condition (rest state EO and rest state EC). Then, using the spectral graph theory approach and Shannon entropy, we identified dynamical properties for weighted graphs, and we compared these features with topological aspects of graphs. The results showed that spectral graph theory can distinguish different state-dependent neural networks with different synchronies. On the other hand, correlation analysis indicated that although dynamical and topological properties of random networks are completely independent, these network features can be related in the case of brain generated graphs. In conclusion, the spectral graph theory approach can be used to make inferences about various state-related brain networks, for healthy and clinical populations.\n\nAuthor SummeryBy considering functional communications across different brain regions, a complex network is achieved that is known as functional brain network. Topologically, this network is constructed by different nodes (activity of brain regions or signals over recording electrodes) and different edges (similarity, correlation or phase difference between nodes). Paths, clusters, hubs, and centrality of nodes are examples of topological properties of these networks. However, synchrony and stability of functional brain networks can not be revealed by consideration of topological properties. Alternatively, spectral graph theory (SGT) can demonstrate the dynamic, synchrony and stability of graphs. But this approach has been studied little in brain network analysis. Here, we employed SGT, as well as topological methods, to investigate which approaches are more reliable to find differences between distinct state-related brain networks. On the other hand, we investigated correlations between topology and dynamic in different type of networks (brain generated and random networks). We found that SGT measures can clearly distinguish between distinct state-related brain networks and it can reveal synchrony and complexity of these networks. Also, results show that although dynamic and topology of random-generated graph are completely independent, these properties exhibit several correlations in the case of functional brain networks.

neuroscience↗