Dynamic Effective Connectivity Research Articles

Newborn's neural representation of instrumental and vocal music as revealed by fMRI: A dynamic effective brain connectivity study.

Music is ubiquitous, both in its instrumental and vocal forms. While speech perception at birth has been at the core of an extensive corpus of research, the origins of the ability to discriminate instrumental or vocal melodies is still not well investigated. In previous studies comparing vocal and musical perception, the vocal stimuli were mainly related to speaking, including language, and not to the non-language singing voice. In the present study, to better compare a melodic instrumental line with the voice, we used singing as a comparison stimulus, to reduce the dissimilarities between the two stimuli as much as possible, separating language perception from vocal musical perception. In the present study, 45 newborns were scanned, 10 full-term born infants and 35 preterm infants at term-equivalent age (mean gestational age at test = 40.17 weeks, SD = 0.44) using functional magnetic resonance imaging while listening to five melodies played by a musical instrument (flute) or sung by a female voice. To examine the dynamic task-based effective connectivity, we employed a psychophysiological interaction of co-activation patterns (PPI-CAPs) analysis, using the auditory cortices as seed region, to investigate moment-to-moment changes in task-driven modulation of cortical activity during an fMRI task. Our findings reveal condition-specific, dynamically occurring patterns of co-activation (PPI-CAPs). During the vocal condition, the auditory cortex co-activates with the sensorimotor and salience networks, while during the instrumental condition, it co-activates with the visual cortex and the superior frontal cortex. Our results show that the vocal stimulus elicits sensorimotor aspects of the auditory perception and is processed as a more salient stimulus while the instrumental condition activated higher-order cognitive and visuo-spatial networks. Common neural signatures for both auditory stimuli were found in the precuneus and posterior cingulate gyrus. Finally, this study adds knowledge on the dynamic brain connectivity underlying the newborns capability of early and specialized auditory processing, highlighting the relevance of dynamic approaches to study brain function in newborn populations.

A Deep Dynamic Causal Learning Model to Study Changes in Dynamic Effective Connectivity during Brain Development.

Brain dynamic effective connectivity (dEC), characterizes the information transmission patterns between brain regions that change over time, which provides insight into the biological mechanism underlying brain development. However, most existing methods predominantly capture fixed or temporally invariant EC, leaving dEC largely unexplored. Herein we propose a deep dynamic causal learning model specifically designed to capture dEC. It includes a dynamic causal learner to detect time-varying causal relationships from spatio-temporal data, and a dynamic causal discriminator to validate these findings by comparing original and reconstructed data. Our model outperforms established baselines in the accuracy of identifying dynamic causalities when tested on the simulated data. When applied to the Philadelphia Neurodevelopmental Cohort, the model uncovers distinct patterns in dEC networks across different age groups. Specifically, the evolution process of brain dEC networks in young adults is more stable than in children, and significant differences in information transfer patterns exist between them. This study highlights the brain's developmental trajectory, where networks transition from undifferentiated to specialized structures with age, in accordance with the improvement of an individual's cognitive and information processing capability. The proposed model consists of the identification and verification of dynamic causality, utilizing the spatio-temporal fusing information from fMRI. As a result, it can accurately detect dEC and characterize its evolution over age.

NHSMM-MAR-sdNC: A novel data-driven computational framework for state-dependent effective connectivity analysis

The brain exhibits intrinsic dynamics characterized by spontaneous spatiotemporal reorganization of neural activity or metastability, which is associated closely with functional integration and segregation. Compared to dynamic functional connectivity, state-dependent effective connectivity (i.e., dynamic effective connectivity) is more suitable for exploring the metastability as its ability to infer causalities between brain regions. However, methods for state-dependent effective connectivity are scarce and urgently needed. In this study, a novel data-driven computational framework, named NHSMM-MAR-sdNC integrating nonparametric hidden semi-Markov model combined with multivariate autoregressive model and state-dependent new causality, is proposed to investigate the state-dependent effective connectivity. The framework is not constrained by any biological assumptions. Furthermore, state number can be inferred from the observed data directly and the state duration distributions will be estimated explicitly rather than restricted by geometric form, which overcomes limitations of hidden Markov model. Experimental results of synthetic data show that the framework can identify the state number adaptively and the state-dependent causality networks accurately. The dynamics of state-related causality networks are also revealed by the new method on real-world resting-state fMRI data. Our method provides a new data-driven computational framework for identifying state-dependent effective connectivity, which will facilitate the identification and assessment of metastability and itinerant dynamics of the brain.

Functional and Effective Connectivity Underlying Semantic Verbal Fluency.

Semantic verbal fluency (SVF) impairment is present in several neurological disorders. Although activation in SVF-related areas has been reported, how these regions are connected and their functional roles in the network remain divergent. We assessed SVF static and dynamic functional connectivity (FC) and effective connectivity in healthy participants using functional magnetic resonance imaging. We observed activation in the inferior frontal (IFG), middle temporal (pMTG) and angular gyri (AG), anterior cingulate (AC), insular cortex, and regions of the superior, middle, and medial frontal gyri (SFG, MFG, MidFG). Our static FC analysis showed a highly interconnected task and resting state network. Increased connectivity of AC with the pMTG and AG was observed for the task. The dynamic FC analysis provided circuits with connections similarly modulated across time and regions related to category identification, language comprehension, word selection and recovery, word generation, inhibition of speaking, speech planning, and articulatory planning of orofacial movements. Finally, the effective connectivity analysis provided a network that best explained our data, starting at the AG and going to the pMTG, from which there was a division between the ventral and dorsal streams. The SFG and MFG regions were connected and modulated by the MidFG, while the inferior regions formed the ventral stream. Therefore, we successfully assessed the SVF network, exploring regions associated with the entire processing, from category identification to word generation. The methodological approach can be helpful for further investigation of the SVF network in neurological disorders.

Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis.

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.

Dynamic effective connectivity in the cerebellar dorsal dentate nucleus and the cerebrum, cognitive impairment, and clinical correlates in patients with schizophrenia

BackgroundSchizophrenia (SZ) is characterized by disconnected cerebral networks. Recent studies have shown that functional connectivity between the cerebellar dorsal dentate nucleus (dDN) and cerebrum is correlated with psychotic symptoms, and processing speed in SZ patients. Dynamic effective connectivity (dEC) is a reliable indicator of brain functional status. However, the dEC between the dDN and cerebrum in patients with SZ remains largely unknown. MethodsResting-state functional MRI data, symptom severity, and cognitive performance were collected from 74 SZ patients and 53 healthy controls (HC). Granger causality analysis and sliding time window methods were used to calculate dDN-based dEC maps for all subjects, and k-means clustering was performed to obtain several dEC states. Finally, between-group differences in dynamic effective connectivity variability (dECV) and clinical correlations were obtained using two-sample t-tests and correlation analysis. ResultsWe detected four dEC states from the cerebrum to the right dDN (IN states) and three dEC states from the right dDN to the cerebrum (OUT states), with SZ group having fewer transitions in the OUT states. SZ group had increased dECV from the right dDN to the right middle frontal gyrus (MFG) and left lingual gyrus (LG). Correlations were found between the dECV from the right dDN to the right MFG and symptom severity and between the dECV from the right dDN to the left LG and working memory performance. ConclusionsThis study reveals a dynamic causal relationship between cerebellar dDN and the cerebrum in SZ and provides new evidence for the involvement of cerebellar neural circuits in neurocognitive functions in SZ.

A new model for dynamic mapping of effective connectivity in task fMRI

Whole-brain dynamic functional connectivity is a growing area in neuroimaging research, encompassing data-driven methods for investigating how large-scale brain networks dynamically reorganize during resting states. However, this approach has been rarely applied to functional magnetic resonance imaging (fMRI) data acquired during task performance. In this study, we first combined the psychophysiological interactions (PPI) and sliding-window methods to analyze dynamic effective connectivity of fMRI data obtained from subjects performing the N-back task within the Human Connectome Project dataset. We then proposed a hypothetical model called Condition Activated Specific Trajectory (CAST) to represent a series of spatiotemporal synchronous changes in significantly activated connections across time windows, which we refer to as a trajectory. Our finding demonstrate that the CAST model outperforms other models in terms of intra-group consistency of individual spatial pattern of PPI connectivity, overall representational ability of temporal variability and hierarchy for individual task performance and cognitive traits. This dynamic view afforded by the CAST model reflects the intrinsic nature of coherent brain activities.

Altered dynamic functional and effective connectivity in drug-naive children with Tourette syndrome.

Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by repetitive, stereotyped, involuntary tics, the neurological basis of which remains unclear. Although traditional resting-state MRI (rfMRI) studies have identified abnormal static functional connectivity (FC) in patients with TS, dynamic FC (dFC) remains relatively unexplored. The rfMRI data of 54 children with TS and 46 typically developing children (TDC) were analyzed using group independent component analysis to obtain independent components (ICs), and a sliding-window approach to generate dFC matrices. All dFC matrices were clustered into two reoccurring states, the state transition metrics were obtained. We conducted Granger causality and nodal topological analyses to further investigate the brain regions that may play the most important roles in driving whole-brain switching between different states. We found that children with TS spent more time in state 2 (PFDR < 0.001), a state characterized by strong connectivity between ICs, and switched more quickly between states (PFDR = 0.025) than TDC. The default mode network (DMN) may play an important role in abnormal state transitions because the FC that changed the most between the two states was between the DMN and other networks. Additionally, the DMN had increased degree centrality, efficiency and altered causal influence on other networks. Certain alterations related to executive function (r = -0.309, P < 0.05) and tic symptom ratings (r = 0.282; 0.413, P < 0.05) may represent important aspects of the pathophysiology of TS. These findings facilitate our understanding of the neural basis for the clinical presentation of TS.

Altered dynamic effective connectivity of the default mode network in type 2 diabetes.

Altered functional connectivity of resting-state functional magnetic resonance imaging (rs-fMRI) within default mode network (DMN) regions has been verified to be closely associated with cognitive decline in patients with Type 2 diabetes mellitus (T2DM), but most studies neglected the fluctuations of brain activities-the dynamic effective connectivity (DEC) within DMN of T2DM is still unknown. For the current investigation, 40 healthy controls (HC) and 36 T2DM patients have been recruited as participants. To examine the variation of DEC between T2DM and HC, we utilized the methodologies of independent components analysis (ICA) and multivariate granger causality analysis (mGCA). We found altered DEC within DMN only show decrease in state 1. In addition, the causal information flow of diabetic patients major affected areas which are closely associated with food craving and metabolic regulation, and T2DM patients stayed longer in low activity level and exhibited decreased transition rate between states. Moreover, these changes related negatively with the MoCA scores and positively with HbA1C level. Our study may offer a fresh perspective on brain dynamic activities to understand the mechanisms underlying T2DM-related cognitive deficits.

MCAN: Multimodal Causal Adversarial Networks for Dynamic Effective Connectivity Learning From fMRI and EEG Data.

Dynamic effective connectivity (DEC) is the accumulation of effective connectivity in the time dimension, which can describe the continuous neural activities in the brain. Recently, learning DEC from functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data has attracted the attention of neuroinformatics researchers. However, the current methods fail to consider the gap between the fMRI and EEG modality, which can not precisely learn the DEC network from multimodal data. In this paper, we propose a multimodal causal adversarial network for DEC learning, named MCAN. The MCAN contains two modules: multimodal causal generator and multimodal causal discriminator. First, MCAN employs a multimodal causal generator with an attention-guided layer to produce a posterior signal and output a set of DEC networks. Then, the proposed method uses a multimodal causal discriminator to unsupervised calculate the joint gradient, which directs the update of the whole network. The experimental results on simulated data sets show that MCAN is superior to other state-of-the-art methods in learning the network structure of DEC and can effectively estimate the brain states. The experimental results on real data sets show that MCAN can better reveal abnormal patterns of brain activity and has good application potential in brain network analysis.

Dynamic Effective Connectivity Learning Based on Nonparametric State Estimation and GAN

Dynamic Effective Connectivity Learning Based on Nonparametric State Estimation and GAN

Large-scale network dynamics underlying the first few hundred milliseconds after stimulus presentation: An investigation of visual recognition memory using iEEG.

Recognition memory is the ability to recognize previously encountered objects. Even this relatively simple, yet extremely fast, ability requires the coordinated activity of large-scale brain networks. However, little is known about the sub-second dynamics of these networks. The majority of current studies into large-scale network dynamics is primarily based on imaging techniques suffering from either poor temporal or spatial resolution. We investigated the dynamics of large-scale functional brain networks underlying recognition memory at the millisecond scale. Specifically, we analyzed dynamic effective connectivity from intracranial electroencephalography while epileptic subjects (n = 18) performed a fast visual recognition memory task. Our data-driven investigation using Granger causality and the analysis of communities with the Louvain algorithm spotlighted a dynamic interplay of two large-scale networks associated with successful recognition. The first network involved the right visual ventral stream and bilateral frontal regions. It was characterized by early, predominantly bottom-up information flow peaking at 115 ms. It was followed by the involvement of another network with predominantly top-down connectivity peaking at 220 ms, mainly in the left anterior hemisphere. The transition between these two networks was associated with changes in network topology, evolving from a more segregated to a more integrated state. These results highlight that distinct large-scale brain networks involved in visual recognition memory unfold early and quickly, within the first 300 ms after stimulus onset. Our study extends the current understanding of the rapid network changes during rapid cognitive processes.

Dynamic interactions between anterior insula and anterior cingulate cortex link perceptual features and heart rate variability during movie viewing.

The dynamic integration of sensory and bodily signals is central to adaptive behaviour. Although the anterior cingulate cortex (ACC) and the anterior insular cortex (AIC) play key roles in this process, their context-dependent dynamic interactions remain unclear. Here, we studied the spectral features and interplay of these two brain regions using high-fidelity intracranial-EEG recordings from five patients (ACC: 13 contacts, AIC: 14 contacts) acquired during movie viewing with validation analyses performed on an independent resting intracranial-EEG dataset. ACC and AIC both showed a power peak and positive functional connectivity in the gamma (30-35 Hz) frequency while this power peak was absent in the resting data. We then used a neurobiologically informed computational model investigating dynamic effective connectivity asking how it linked to the movie's perceptual (visual, audio) features and the viewer's heart rate variability (HRV). Exteroceptive features related to effective connectivity of ACC highlighting its crucial role in processing ongoing sensory information. AIC connectivity was related to HRV and audio emphasising its core role in dynamically linking sensory and bodily signals. Our findings provide new evidence for complementary, yet dissociable, roles of neural dynamics between the ACC and the AIC in supporting brain-body interactions during an emotional experience.

Dynamic effective connectivity among large-scale brain networks mediates risk of anxiety.

Anxiety is characterized by altered brain networks. Directional information flows among dynamic brain networks concerning neuropathogenesis of anxiety have not yet been investigated. The role of directional influences between networks in gene-environment effects on anxiety remains to be further elucidated. In a large community sample, this resting-state functional MRI study estimated dynamic effective connectivity among large-scale brain networks based on a sliding-window approach and Granger causality analysis, providing dynamic and directional information for signal transmission in networks. We first explored altered effective connectivity among networks related to anxiety in distinct connectivity states. Due to the potential gene-environment effects on brain and anxiety, we further performed mediation and moderated mediation analyses to investigate the role of altered effective connectivity networks in relationships between polygenic risk scores, childhood trauma, and anxiety. State and trait anxiety scores showed correlations with altered effective connectivity among extensive networks in distinct connectivity states (p < .05, uncorrected). Only in a more frequent and strongly connected state, there were significant correlations between altered effective connectivity networks and trait anxiety (PFDR <0.05). Furthermore, mediation and moderated mediation analyses showed that the effective connectivity networks played a mediating role in the effects of childhood trauma and polygenic risk on trait anxiety. State-dependent effective connectivity changes among brain networks were significantly related to trait anxiety, and mediated gene-environment effects on trait anxiety. Our work sheds novel light on the neurobiological mechanisms underlying anxiety, and provides new insights into early objective diagnosis and intervention evaluation.

Dynamic Effective Connectivity using Physiologically informed Dynamic Causal Model with Recurrent Units: A functional Magnetic Resonance Imaging simulation study.

Functional MRI (fMRI) is an indirect reflection of neuronal activity. Using generative biophysical model of fMRI data such as Dynamic Causal Model (DCM), the underlying neuronal activities of different brain areas and their causal interactions (i.e., effective connectivity) can be calculated. Most DCM studies typically consider the effective connectivity to be static for a cognitive task within an experimental run. However, changes in experimental conditions during complex tasks such as movie-watching might result in temporal variations in the connectivity strengths. In this fMRI simulation study, we leverage state-of-the-art Physiologically informed DCM (P-DCM) along with a recurrent window approach and discretization of the equations to infer the underlying neuronal dynamics and concurrently the dynamic (time-varying) effective connectivities between various brain regions for task-based fMRI. Results from simulation studies on 3- and 10-region models showed that functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) responses and effective connectivity time-courses can be accurately predicted and distinguished from faulty graphical connectivity models representing cognitive hypotheses. In summary, we propose and validate a novel approach to determine dynamic effective connectivity between brain areas during complex cognitive tasks by combining P-DCM with recurrent units.

Characterising stationary and dynamic effective connectivity changes in the motor network during and after tDCS

The exact mechanisms behind the effects of transcranial direct current stimulation (tDCS) at a network level are still poorly understood, with most studies to date focusing on local (cortical) effects and changes in motor-evoked potentials or BOLD signal. Here, we explored stationary and dynamic effective connectivity across the motor network at rest in two experiments where we applied tDCS over the primary motor cortex (M1-tDCS) or the cerebellum (cb-tDCS) respectively. Two cohorts of healthy volunteers (n = 21 and n = 22) received anodal, cathodal, and sham tDCS sessions (counterbalanced) during 20 min of resting-state functional magnetic resonance imaging (fMRI). We used spectral Dynamic Causal Modelling (DCM) and hierarchical Parametrical Empirical Bayes (PEB) to analyze data after (compared to a pre-tDCS baseline) and during stimulation. We also implemented a novel dynamic (sliding windows) DCM/PEB approach to model the nature of network reorganisation across time. In both experiments we found widespread effects of tDCS that extended beyond the targeted area and modulated effective connectivity between cortex, thalamus, and cerebellum. These changes were characterised by unique nonlinear temporal fingerprints across connections and polarities. Our results support growing research challenging the classic notion of anodal and cathodal tDCS as excitatory and inhibitory respectively, as well as the idea of a cumulative effect of tDCS over time. Instead, they described a rich set of changes with specific spatial and temporal patterns. Our work provides a starting point for advancing our understanding of network-level tDCS effects and may guide future work to optimise its cognitive and clinical applications.

Altered dynamic functional architecture in type 2 diabetes mellitus.

Type 2 diabetes mellitus (T2DM) can accelerate cognitive decline and even dementia so that the underlying mechanism deserves further exploration. In the resting state, brain function is still changing dynamically. At present, it is still unknown whether the dynamic functional connectivity (dFC) between various brain regions is in a stable state. It is necessary to interpret brain changes from a new perspective, that is, the stability of brain architecture. In this study, we used a fixed dynamic time scale to explore the stability of dynamic functional architecture in T2DM, then the dynamic effective connectivity (dEC) was used to further explain how information flows through dynamically fluctuating brain architecture in T2DM. Two brain regions with decreased stability were found including the right supra-marginal gyrus (SMG) and the right median cingulate gyrus (MCG) in T2DM subjects. The dEC variation has increased between the left inferior frontal gyrus (IFG) and the right MCG. The direction of causal flow is from the right MCG to the left IFG. The combination of stability and dEC can not only show the stability of dynamic functional architecture in brain but also reflect the fluidity of brain information, which is an innovative and interesting attempt in the field of neuroimaging. The changes of dynamic architecture in T2DM patients may present an innovative perspective and explanation for their cognitive decline.

Diagnosis of late-life depression using structural equation modeling and dynamic effective connectivity during resting fMRI

BackgroundLate-life depression (LLD) is characterized by cognitive and social impairments. Determining neurobiological alterations in connectivity in LLD by means of fMRI may lead to a better understanding of the neural basis underlying this disorder and more precise diagnostic markers. The primary objective of this paper is to identify a structural model that best explains the dynamic effective connectivity (EC) of the default mode network (DMN) in LLD patients compared to controls. MethodsTwenty-seven patients and 29 healthy controls underwent resting-state fMRI during a period of eight minutes. In both groups, jackknife correlation matrices were generated with six ROIs of the DMN that constitute the posterior DMN (pDMN). The different correlation matrices were used as input to estimate each structural equation model (SEM) for each subject in both groups incorporating dynamic effects. ResultsThe results show that the proposed LLD diagnosis algorithm achieves perfect accuracy in classifying LLD patients and controls. This differentiation is based on three aspects: the importance of ROIs 4 and 6, which seem to be the most distinctive among the subnetworks; the shape that the specific connections adopt in their networks, or in other words, the directed connections that are established among the ROIs in the pDMN for each group; and the number of dynamic effects that seem to be greater throughout the six ROIs studied [t = 54.346; df = 54; p < .001; 95 % CI difference = 5.486–5.906]. LimitationsThe sample size was moderate, and the participants continued their current medications. ConclusionsThe network models that we developed describe a pattern of dynamic activation in the pDMN that may be considered a possible biomarker for LLD, which may allow early diagnosis of this disorder.

Dynamic neural reconfiguration for distinct strategies during competitive social interactions

Information exchange between brain regions is key to understanding information processing for social decision-making, but most analyses ignore its dynamic nature. New insights on this dynamic might help us to uncover the neural correlates of social cognition in the healthy population and also to understand the malfunctioning neural computations underlying dysfunctional social behavior in patients with mental disorders. In this work, we used a multi-round bargaining game to detect switches between distinct bargaining strategies in a cohort of 76 healthy participants. These switches were uncovered by dynamic behavioral modeling using the hidden Markov model. Proposing a novel model of dynamic effective connectivity to estimate the information flow between key brain regions, we found a stronger interaction between the right temporoparietal junction (rTPJ) and the right dorsolateral prefrontal cortex (rDLPFC) for the strategic deception compared with the social heuristic strategies. The level of deception was associated with the information flow from the Brodmann area 10 to the rTPJ, and this association was modulated by the rTPJ-to-rDLPFC information flow. These findings suggest that dynamic bargaining strategy is supported by dynamic reconfiguration of the rDLPFC-and-rTPJ interaction during competitive social interactions.

Neural and behavioral effects of modification of visual attention in body dysmorphic disorder

In individuals with body dysmorphic disorder (BDD), perceptual appearance distortions may be related to selective attention biases and aberrant visual scanning, contributing to imbalances in global vs. detailed visual processing. Treatments for the core symptom of perceptual distortions are underexplored in BDD; yet understanding their mechanistic effects on brain function is critical for rational treatment development. This study tested a behavioral strategy of visual-attention modification on visual system brain connectivity and eye behaviors. We acquired fMRI data in 37 unmedicated adults with BDD and 30 healthy controls. Participants viewed their faces naturalistically (naturalistic viewing), and holding their gaze on the image center (modulated viewing), monitored with an eye-tracking camera. We analyzed dynamic effective connectivity and visual fixation duration. Modulated viewing resulted in longer mean visual fixation duration compared to during naturalistic viewing, across groups. Further, modulated viewing resulted in stronger connectivity from occipital to parietal dorsal visual stream regions, also evident during the subsequent naturalistic viewing, compared with the initial naturalistic viewing, in BDD. Longer fixation duration was associated with a trend for stronger connectivity during modulated viewing. Those with more severe BDD symptoms had weaker dorsal visual stream connectivity during naturalistic viewing, and those with more negative appearance evaluations had weaker connectivity during modulated viewing. In sum, holding a constant gaze on a non-concerning area of one’s face may confer increased communication in the occipital/parietal dorsal visual stream, facilitating global/holistic visual processing. This effect shows persistence during subsequent naturalistic viewing. Results have implications for perceptual retraining treatment designs.