Dynamic Connectivity Patterns Research Articles

Real-time Classification of Brain States in Functional MRI Using Dynamic Connectivity Patterns and Machine Learning

Understanding real-time brain states facilitates deeper insights into cognitive processes, emotional responses, and neurological phenomena. It provides researchers with a dynamic view of brain function, aiding in the development of novel therapies, advancing neuroscience, and fostering innovations in brain-computer interfaces and artificial intelligence. The research aims to achieve real-time classification of brain states using dynamic connectivity patterns and Convolutional Neural Network (CNN) algorithms. It focuses on how demographic variables, such as brain volume and medication usage, influence classification accuracy. The study employs Python programming with FSL and Nilearn for preprocessing tasks like motion correction and dynamic connectivity pattern extraction. Feature selection and data partitioning are managed using Scikit-learn, ensuring standardized feature values and handling missing data. The CNN architecture is customized to handle spatial and temporal features in functional MRI (fMRI) data, with convolutional layers extracting spatial features representing local connectivity patterns. Dynamic connectivity matrices visualization aids in understanding brain network reconfigurations over time. This approach offers real-time insights into cognitive processes and neurological disorders, guiding personalized interventions for brain health. In result, connectivity matrices and 3D brain network visualizations are pivotal for unraveling brain state dynamics. Connectivity matrices unveil intricate interactions among brain regions across resting, task execution, and stress response states, offering quantitative insights into functional connectivity patterns. Meanwhile, 3D visualizations provide spatial representations, showcasing complex interplays and architectural changes across states.

Real-time Classification of Brain States in Functional MRI Using Dynamic Connectivity Patterns and Machine Learning

Understanding real-time brain states facilitates deeper insights into cognitive processes, emotional responses, and neurological phenomena. It provides researchers with a dynamic view of brain function, aiding in the development of novel therapies, advancing neuroscience, and fostering innovations in brain-computer interfaces and artificial intelligence. The research aims to achieve real-time classification of brain states using dynamic connectivity patterns and Convolutional Neural Network (CNN) algorithms. It focuses on how demographic variables, such as brain volume and medication usage, influence classification accuracy. The study employs Python programming with FSL and Nilearn for preprocessing tasks like motion correction and dynamic connectivity pattern extraction. Feature selection and data partitioning are managed using Scikit-learn, ensuring standardized feature values and handling missing data. The CNN architecture is customized to handle spatial and temporal features in functional MRI (fMRI) data, with convolutional layers extracting spatial features representing local connectivity patterns. Dynamic connectivity matrices visualization aids in understanding brain network reconfigurations over time. This approach offers real-time insights into cognitive processes and neurological disorders, guiding personalized interventions for brain health. In result, connectivity matrices and 3D brain network visualizations are pivotal for unraveling brain state dynamics. Connectivity matrices unveil intricate interactions among brain regions across resting, task execution, and stress response states, offering quantitative insights into functional connectivity patterns. Meanwhile, 3D visualizations provide spatial representations, showcasing complex interplays and architectural changes across states.

Altered dynamic functional network connectivity states in patients with acute basal ganglia ischemic stroke

BackgroundDynamic functional network connectivity (dFNC) patterns are successfully able to capture the time-varying features of intrinsic fluctuations throughout a scan. We explored dFNC alterations across the entire brain in patients with acute ischemic stroke (AIS) of the basal ganglia (BG). MethodResting-state functional magnetic resonance imaging data were acquired from 26 patients with first-ever AIS in the BG and 26 healthy controls (HCs). Independent component analysis, the sliding window method, and the K-means clustering method were used to obtain reoccurring dynamic network connectivity patterns. Moreover, temporal features across diverse dFNC states were compared between the two groups, and the local and global efficiencies across states were analyzed to explore the characteristics of the topological networks among states. ResultsFour dFNC states were characterized for comparison of dynamic brain network connectivity patterns. In contrast to the HC group, the AIS group spent a significantly higher fraction of time in State 1, which is characterized by a relatively weaker brain network connectome. Conversely, compared with HC, patients with AIS showed a lower mean dwell time in State 2, which was characterized by a relatively stronger brain network connectome. Additionally, functional networks exhibited variable efficiency of information transfer across 4 states. ConclusionsAIS not only altered the interaction between the different dynamic networks but also promoted characteristic alterations in the temporal and topological features of large-scale dynamic network connectivity.

Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study.

Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.

A Novel Hidden Markov Approach to Studying Dynamic Functional Connectivity States in Human Neuroimaging.

Introduction: Hidden Markov models (HMMs) are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse HMMs have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based [IB] states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: IB states are unlikely to provide comprehensive information about dynamic connectivity patterns. Methods: We addressed this problem by introducing a new HMM that defines states based on full functional connectivity (FFC) profiles among brain regions. We empirically explored the behavior of this new model in comparison to existing approaches based on IB or summed functional connectivity states using the Human Connectome Project unrelated 100 functional magnetic resonance imaging "resting-state" dataset. Results: Our FFC model discovered connectivity states with more distinguishable (i.e., unique and separable from each other) patterns than previous approaches, and recovered simulated connectivity-based states more faithfully than the other models tested. Discussion: Thus, if our goal is to extract and interpret connectivity states in neuroimaging data, our new model outperforms previous methods, which miss crucial information about the evolution of functional connectivity in the brain. Impact statement Hidden Markov models (HMMs) can be used to investigate brain states noninvasively. Previous models "recover" connectivity from intensity-based hidden states, or from connectivity "summed" across nodes. In this study, we introduce a novel connectivity-based HMM and show how it can reveal true connectivity hidden states under minimal assumptions.

Use of connectotyping on task functional MRI data reveals dynamic network level cross talking during task performance

Task-based functional MRI (fMRI) has greatly improved understanding of brain functioning, enabling the identification of brain areas associated with specific cognitive operations. Traditional analyses are limited to associating activation patterns in particular regions with specific cognitive operation, largely ignoring regional cross-talk or dynamic connectivity, which we propose is crucial for characterization of brain function in the context of task fMRI. We use connectotyping, which efficiently models functional brain connectivity to reveal the progression of temporal brain connectivity patterns in task fMRI. Connectotyping was employed on data from twenty-four participants (12 male, mean age 24.8 years, 2.57 std. dev) who performed a widely spaced event-related fMRI word vs. pseudoword decision task, where stimuli were presented every 20 s. After filtering for movement, we ended up with 15 participants that completed each trial and had enough usable data for our analyses. Connectivity matrices were calculated per participant across time for each stimuli type. A Repeated Measures ANOVA applied on the connectotypes was used to characterize differences across time for words and pseudowords. Our group level analyses found significantly different dynamic connectivity patterns during word vs. pseudoword processing between the Fronto-Parietal and Cingulo-Parietal Systems, areas involved in cognitive task control, memory retrieval, and semantic processing. Our findings support the presence of dynamic changes in functional connectivity during task execution and that such changes can be characterized using connectotyping but not with traditional Pearson’s correlations.

Dynamic connectivity patterns of resting-state brain functional networks in healthy individuals after acute alcohol intake.

AimsCurrently, there are only a few studies concerning brain functional alterations after acute alcohol exposure, and the majority of existing studies attach more importance to the spatial properties of brain function without considering the temporal properties. The current study adopted sliding window to investigate the resting-state brain networks in healthy volunteers after acute alcohol intake and to explore the dynamic changes in network connectivity.Materials and methodsTwenty healthy volunteers were enrolled in this study. Blood-oxygen-level-dependent (BOLD) data prior to drinking were obtained as control, while that 0.5 and 1 h after drinking were obtained as the experimental group. Reoccurring functional connectivity patterns (states) were determined following group independent component analysis (ICA), sliding window analysis and k-means clustering. Between-group comparisons were performed with respect to the functional connectivity states fractional windows, mean dwell time, and the number of transitions.ResultsThree optimal functional connectivity states were identified. The fractional windows and mean dwell time of 0.5 h group and 1 h group increased in state 3, while the fraction window and mean dwell time of 1 h group decreased in state 1. State 1 is characterized by strong inter-network connections between basal ganglia network (BGN) and sensorimotor network (SMN), BGN and cognitive executive network (CEN), and default mode network (DMN) and visual network (VN). However, state 3 is distinguished by relatively weak intra-network connections in SMN, VN, CEN, and DMN. State 3 was thought to be a characteristic connectivity pattern of the drunk brain. State 1 was believed to represent the brain’s main connection pattern when awake. Such dynamic changes in brain network connectivity were consistent with participants’ subjective feelings after drinking.ConclusionThe current study reveals the dynamic change in resting-state brain functional network connectivity before and after acute alcohol intake. It was discovered that there might be relatively independent characteristic functional network connection patterns under intoxication, and the corresponding patterns characterize the clinical manifestations of volunteers. As a valuable imaging biomarker, dynamic functional network connectivity (dFNC) offers a new approach and basis for further explorations on brain network alterations after alcohol consumption and the alcohol-related mechanisms for neurological damage.

Suboptimal states and frontoparietal network-centered incomplete compensation revealed by dynamic functional network connectivity in patients with post-stroke cognitive impairment.

BackgroundNeural reorganization occurs after a stroke, and dynamic functional network connectivity (dFNC) pattern is associated with cognition. We hypothesized that dFNC alterations resulted from neural reorganization in post-stroke cognitive impairment (PSCI) patients, and specific dFNC patterns characterized different pathological types of PSCI.MethodsResting-state fMRI data were collected from 16 PSCI patients with hemorrhagic stroke (hPSCI group), 21 PSCI patients with ischemic stroke (iPSCI group), and 21 healthy controls (HC). We performed the dFNC analysis for the dynamic connectivity states, together with their topological and temporal features.ResultsWe identified 10 resting-state networks (RSNs), and the dFNCs could be clustered into four reoccurring states (modular, regional, sparse, and strong). Compared with HC, the hPSCI and iPSCI patients showed lower standard deviation (SD) and coefficient of variation (CV) in the regional and modular states, respectively (p < 0.05). Reduced connectivities within the primary network (visual, auditory, and sensorimotor networks) and between the primary and high-order cognitive control domains were observed (p < 0.01).ConclusionThe transition trend to suboptimal states may play a compensatory role in patients with PSCI through redundancy networks. The reduced exploratory capacity (SD and CV) in different suboptimal states characterized cognitive impairment and pathological types of PSCI. The functional disconnection between the primary and high-order cognitive control network and the frontoparietal network centered (FPN-centered) incomplete compensation may be the pathological mechanism of PSCI. These results emphasize the flexibility of neural reorganization during self-repair.

Investigation on how dynamic effective connectivity patterns encode the fluctuating pain intensity in chronic migraine

Chronic migraine is characterised by persistent headaches for >15 days per month; the intensity of the pain is fluctuating over time. Here, we explored the dynamic interplay of connectivity patterns between regions known to be related to pain processing and their relation to the ongoing dynamic pain experience. We recorded EEG from 80 sessions (20 chronic migraine patients in 4 separate sessions of 25 min). The patients were asked to continuously rate the intensity of their endogenous headache. On different time-windows, a dynamic causal model (DCM) of cross spectral responses was inverted to estimate connectivity strengths. For each patient and session, the evolving dynamics of effective connectivity were related to pain intensities and to pain intensity changes by using a Bayesian linear model. Hierarchical Bayesian modelling was further used to examine which connectivity-pain relations are consistent across sessions and across patients.The results reflect the multi-facetted clinical picture of the disease. Across all sessions, each patient with chronic migraine exhibited a distinct pattern of pain intensity-related cortical connectivity. The diversity of the individual findings are accompanied by inconsistent relations between the connectivity parameters and pain intensity or pain intensity changes at group level. This suggests a rejection of the idea of a common neuronal core problem for chronic migraine.

The Clustering Analysis of Time Properties in Patients With Cerebral Small Vessel Disease: A Dynamic Connectivity Study.

PurposeThis study aimed to investigate the dynamic functional connectivity (DFC) pattern in cerebral small vessel disease (CSVD) and explore the relationships between DFC temporal properties and cognitive impairment in CSVD.MethodsFunctional data were collected from 67 CSVD patients, including 35 patients with subcortical vascular cognitive impairment (SVCI) and 32 cognitively unimpaired (CU) patients, as well as 35 healthy controls (HCs). The DFC properties were estimated by k-means clustering analysis. DFC strength analysis was used to explore the regional functional alterations between CSVD patients and HCs. Correlation analysis was used for DFC properties with cognition and SVD scores, respectively.ResultsThe DFC analysis showed three distinct connectivity states (state I: sparsely connected, state II: strongly connected, state III: intermediate pattern). Compared to HCs, CSVD patients exhibited an increased proportion in state I and decreased proportion in state II. Besides, CSVD patients dwelled longer in state I while dwelled shorter in state II. CSVD subgroup analyses showed that state I frequently occurred and dwelled longer in SVCI compared with CSVD-CU. Also, the internetwork (frontal-parietal lobe, frontal-occipital lobe) and intranetwork (frontal lobe, occipital lobe) functional activities were obviously decreased in CSVD. Furthermore, the fractional windows and mean dwell time (MDT) in state I were negatively correlated with cognition in CSVD but opposite to cognition in state II.ConclusionPatients with CSVD accounted for a higher proportion and dwelled longer mean time in the sparsely connected state, while presented lower proportion and shorter mean dwell time in the strongly connected state, which was more prominent in SVCI. The changes in the DFC are associated with altered cognition in CSVD. This study provides a better explanation of the potential mechanism of CSVD patients with cognitive impairment from the perspective of DFC.

Dynamic Effective Connectivity Patterns During Rapid Face Stimuli Presentation in Body Dysmorphic Disorder.

In individuals with body dysmorphic disorder (BDD), perceptual appearance distortions may be related to imbalances in global vs. local visual processing. Understanding the mechanistic brain effects of potential interventions is crucial for rational treatment development. The dorsal visual stream (DVS) is tuned to rapid image presentation, facilitating global/holistic processing, whereas the ventral visual stream (VVS), responsible for local/detailed processing, reduces activation magnitude with shorter stimulus duration. This study tested a strategy of rapid, short-duration face presentation on visual system connectivity. Thirty-eight unmedicated adults with BDD and 29 healthy controls viewed photographs of their faces for short (125 ms, 250 ms, 500 ms) and long (3000 ms) durations during fMRI scan. Dynamic effective connectivity in DVS and VVS was analyzed. BDD individuals exhibited weaker connectivity from occipital to parietal DVS areas than controls for all stimuli durations. Short compared with long viewing durations (125 ms vs. 3,000 ms and 500 ms vs. 3,000 ms) resulted in significantly weaker VVS connectivity from calcarine cortex to inferior occipital gyri in controls; however, there was only a trend for similar results in BDD. The DVS to VVS ratio, representing a balance between global and local processing, incrementally increased with shorter viewing durations in BDD, although it was not statistically significant. In sum, visual systems in those with BDD are not as responsive as in controls to rapid face presentation. Whether rapid face presentation could reduce connectivity in visual systems responsible for local/detailed processing in BDD may necessitate different parameters or strategies. These results provide mechanistic insights for perceptual retraining treatment designs.

P466. Genetic Signature of Brain Dynamic Connectivity Patterns Associated With Obsessive-Compulsive Symptoms in Healthy Children

Obsessive-compulsive symptoms during childhood predispose to obsessive-compulsive disorder (OCD) and have been linked to changes in brain circuits altered in OCD. Obsessive-compulsive symptoms may arise from altered glutamatergic neurotransmission, impairing cognitive oscillations, and promoting over-stable functional states.

ADHD detection using dynamic connectivity patterns of EEG data and ConvLSTM with attention framework

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental behavioral disorder. It is common in children, can be carried over into adulthood, and is associated with inattention, hyperactivity, and impulsive behavior. Physicians typically use the patient's description and questionnaires to diagnose this disorder. Due to its subjective nature, this procedure can lead to false diagnoses, which may cause irreparable distress to the patient's life. Since mental disorders are dependent on the brain function, researchers use biological signals such as electroencephalography (EEG) to help diagnose ADHD.In this study, we propose a new feature extraction scheme based on evaluating dynamic connectivity tensors among EEG channels for constructing the input formulation of the classification model. The tensors contain correlations among the EEG channels over different time frames. This method allows preserving both temporal and spatial structures of the EEG data while reducing the input dimensions of the model. We then employ a neural network model consisting of a convolutional long short-term memory (ConvLSTM) and an attention mechanism to classify ADHD patients and the control group. This model can encode the spatiotemporal representation of EEG recordings and identify dependencies between temporal segments. Convolution is responsible for encoding and finding spatial dependencies between electrodes. LSTM explores the relationships between different time blocks, and finally, attention focuses on the most relevant parts of the sequence. We evaluate the proposed approach by performing experiments on the EEG dataset, including 400 instances with 30 s length collected from 46 children with ADHD and 45 children in the control group. We achieved an average accuracy of 99.34% on this dataset, and our best model has an accuracy of 99.75%, both are the highest among the work done in this field.

Constrained Functional Connectivity Dynamics in Pediatric Surgical Patients Undergoing General Anesthesia

Functional connectivity in cortical networks is thought to be important for consciousness and can be disrupted during the anesthetized state. Recent work in adults has revealed dynamic connectivity patterns during stable general anesthesia, but whether similar connectivity state transitions occur in the developing brain remains undetermined. The hypothesis was that anesthetic-induced unconsciousness is associated with disruption of functional connectivity in the developing brain and that, as in adults, there are dynamic shifts in connectivity patterns during the stable maintenance phase of general anesthesia. This was a preplanned analysis of a previously reported single-center, prospective, cross-sectional study of healthy (American Society of Anesthesiologists status I or II) children aged 8 to 16 yr undergoing surgery with general anesthesia (n = 50) at Michigan Medicine. Whole-scalp (16-channel), wireless electroencephalographic data were collected from the preoperative period through the recovery of consciousness. Functional connectivity was measured using a weighted phase lag index, and discrete connectivity states were classified using cluster analysis. Changes in functional connectivity were associated with anesthetic state transitions across multiple regions and frequency bands. An increase in prefrontal-frontal alpha (median [25th, 75th]; baseline, 0.070 [0.049, 0.101] vs. maintenance 0.474 [0.286, 0.606]; P < 0.001) and theta connectivity (0.038 [0.029, 0.048] vs. 0.399 [0.254, 0.488]; P < 0.001), and decrease in parietal-occipital alpha connectivity (0.171 [0.145, 0.243] vs. 0.089 [0.055, 0.132]; P < 0.001) were among those with the greatest effect size. Contrary to the hypothesis, connectivity patterns during the maintenance phase of general anesthesia were dominated by stable theta and alpha prefrontal-frontal and alpha frontal-parietal connectivity and exhibited high between-cluster similarity (r = 0.75 to 0.87). Changes in functional connectivity are associated with anesthetic state transitions but, unlike in adults, connectivity patterns are constrained during general anesthesia in late childhood and early adolescence.

Torture exposure and the functional brain: investigating disruptions to intrinsic network connectivity using resting state fMRI

Torture has profound psychological and physiological consequences for survivors. While some brain structures and functions appear altered in torture survivors, it is unclear how torture exposure influences functional connectivity within and between core intrinsic brain networks. In this study, 37 torture survivors (TS) and 62 non-torture survivors (NTS) participated in a resting-state fMRI scan. Data-driven independent components analysis identified active intrinsic networks. Group differences in functional connectivity in the default mode network (DMN), salience network (SN) and central executive network (CEN) of the triple network model, as well any prefrontal network, were examined while controlling for PTSD symptoms and exposure to other potentially traumatic events. The analysis identified 25 networks; eight comprised our networks of interest. Within-network group differences were observed in the left CEN (lCEN), where the TS group showed less spectral power in the low-frequency band. Differential internetwork dynamic connectivity patterns were observed, where the TS group showed stronger positive coupling between the lCEN and anterior dorsomedial and ventromedial DMN, and stronger negative coupling between a lateral frontal network and the lCEN and anterior dorsomedial DMN (when contrasted with the NTS group). Group differences were not attributed to torture severity or dissociative symptoms. Torture survivors showed disrupted dynamic functional connectivity between a laterally-aligned lCEN that serves top-down control functions over external processes and the midline DMN that underpins internal self-referential processes, which may be an adaptive response to mitigate the worst effects of the torture experience. This study provides a critical step in mapping the neural signature of torture exposure to guide treatment development and selection.

Contributions of sex, depression, and cognition on brain connectivity dynamics in Parkinson\u2019s disease

Alterations in time-varying functional connectivity (FC) have been found in Parkinson’s disease (PD) patients. To date, very little is known about the influence of sex on brain FC in PD patients and how this could be related to disease severity. The first objective was to evaluate the influence of sex on dynamic FC characteristics in PD patients and healthy controls (HC), while the second aim was to investigate the temporal patterns of dynamic connectivity related to PD motor and non-motor symptoms. Ninety-nine PD patients and sixty-two HC underwent a neuropsychological and clinical assessment. Rs-fMRI and T1-weighted MRI were also acquired. Dynamic FC analyses were performed in the GIFT toolbox. Dynamic FC analyses identified two States: State I, characterized by within-network positive coupling; and State II that showed between-network connectivity, mostly involving somatomotor and visual networks. Sex differences were found in dynamic indexes in HC but these differences were not observed in PD. Hierarchical clustering analysis identified three phenotypically distinct PD subgroups: (1) Subgroup A was characterized by mild motor symptoms; (2) Subgroup B was characterized by depressive and motor symptoms; (3) Subgroup C was characterized by cognitive and motor symptoms. Results revealed that changes in the temporal properties of connectivity were related to the motor/non-motor outcomes of PD severity. Findings suggest that while in HC sex differences may play a certain role in dynamic connectivity patterns, in PD patients, these effects may be overcome by the neurodegenerative process. Changes in the temporal properties of connectivity in PD were mainly related to the clinical markers of PD severity.

RESTING STATE FUNCTIONAL CONNECTIVITY CHANGES IN NATURALISTIC STIMULATION STUDY

Naturalistic stimulation has had increasing influence on cognitive neuroscience in the last decade. Several studies have used naturalistic stimulation to investigate the connectivity dynamics under pathological condition or in healthy children for studying the developing brain. However, little is known about the connectivity changes during watching a movie in healthy, adult population. Our aim is to delineate the potential dynamic connectivity pattern of the healthy, mature brain during naturalistic movie watching compared to complete rest. In an observational case series, e leven healthy adult volunteers with no history of psychiatric or neurological illness underwent two magnetic resonance echo-planar imaging acquisitions. During the first acquisition they were asked to rest with their eyes closed and during the subsequent acquisition they watched a movie clip with slow human body movements. The Independent Component Analysis provided neural components which were further submitted to intra- and inter-network analyses. We found significantly increased intra-network connectivity during movie watching for the extrastriate visual and for the posterior visual networks. The inter-network analysis showed that for the extravisual and primary visual pair, the connectivity was lower during movie watching compared to rest condition. We also found significant uncoupling between the anterior Default Mode Network and the secondary somatosensory network during movie watching. These findings suggest greater visual network segregation during movie watching in order to support processing of complex stimuli. The decoupling of the secondary somatosensory network from the anterior Default Mode Network during movie watching suggest a somatosensory function segregation and its important role in processing information from observing the naturalistic movements of others. Brain imaging measures of major brain networks can contribute to early identification of risk for common psychiatric disorders hence facilitating preventive therapy. Keywords: naturalistic stimulation , rest, conectivity Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Times New Roman; mso-bidi-theme-font:minor-bidi; mso-ansi-language:EN-US; mso-fareast-language:EN-US;}

Neurogenetics of Dynamic Connectivity Patterns Associated With Obsessive-Compulsive Symptoms in Healthy Children

BackgroundObsessive-compulsive symptoms (OCSs) during childhood predispose to obsessive-compulsive disorder and have been associated with changes in brain circuits altered in obsessive-compulsive disorder samples. OCSs may arise from disturbed glutamatergic neurotransmission, impairing cognitive oscillations and promoting overstable functional states. MethodsA total of 227 healthy children completed the Obsessive Compulsive Inventory–Child Version and underwent a resting-state functional magnetic resonance imaging examination. Genome-wide data were obtained from 149 of them. We used a graph theory–based approach and characterized associations between OCSs and dynamic functional connectivity (dFC). dFC evaluates fluctuations over time in FC between brain regions, which allows characterizing regions with stable connectivity patterns (attractors). We then compared the spatial similarity between OCS-dFC correlation maps and mappings of genetic expression across brain regions to identify genes potentially associated with connectivity changes. In post hoc analyses, we investigated which specific single nucleotide polymorphisms of these genes moderated the association between OCSs and patterns of dFC. ResultsOCSs correlated with decreased attractor properties in the left ventral putamen and increased attractor properties in (pre)motor areas and the left hippocampus. At the specific symptom level, increased attractor properties in the right superior parietal cortex correlated with ordering symptoms. In the hippocampus, we identified two single nucleotide polymorphisms in glutamatergic neurotransmission genes (GRM7, GNAQ) that moderated the association between OCSs and attractor features. ConclusionsWe provide evidence that in healthy children, the association between dFC changes and OCSs may be mapped onto brain circuits predicted by prevailing neurobiological models of obsessive-compulsive disorder. Moreover, our findings support the involvement of glutamatergic neurotransmission in such brain network changes.

Differences in Disrupted Dynamic Functional Network Connectivity Among Children, Adolescents, and Adults With Attention Deficit/Hyperactivity Disorder: A Resting-State fMRI Study.

Attention deficit hyperactivity disorder (ADHD) is one of the most widespread mental disorders and often persists from childhood to adulthood, and its symptoms vary with age. In this study, we aim to determine the disrupted dynamic functional network connectivity differences in adult, adolescent, and child ADHD using resting-state functional magnetic resonance imaging (rs-fMRI) data consisting of 35 children (8.64 ± 0.81 years), 40 adolescents (14.11 ± 1.83 years), and 39 adults (31.59 ± 10.13 years). We hypothesized that functional connectivity is time-varying and that there are within- and between-network connectivity differences among the three age groups. Nine functional networks were identified using group ICA, and three FC-states were recognized based on their dynamic functional network connectivity (dFNC) pattern. Fraction of time, mean dwell time, transition probability, degree-in, and degree-out were calculated to measure the state dynamics. Higher-order networks including the DMN, SN, and FPN, and lower-order networks comprising the SMN, VN, SC, and AUD were frequently distributed across all states and were found to show connectivity differences among the three age groups. Our findings imply abnormal dynamic interactions and dysconnectivity associated with different ADHD, and these abnormalities differ between the three ADHD age groups. Given the dFNC differences between the three groups in the current study, our work further provides new insights into the mechanism subserved by age difference in the pathophysiology of ADHD and may set the grounds for future case-control studies in the individual age groups, as well as serving as a guide in the development of treatment strategies to target these specific networks in each age group.

Perturbing fMRI brain dynamics using transcranial direct current stimulation

The dynamic nature of resting-state functional magnetic resonance imaging (fMRI) brain activity and connectivity has drawn great interest in the past decade. Specific temporal properties of fMRI brain dynamics, including metrics such as occurrence rate and transitions, have been associated with cognition and behaviors, indicating the existence of mechanism distruption in neuropsychiatric disorders. The development of new methods to manipulate fMRI brain dynamics will advance our understanding of these pathophysiological mechanisms from native observation to experimental mechanistic manipulation. In the present study, we applied repeated transcranial direct current stimulation (tDCS) to the right dorsolateral prefrontal cortex (rDLPFC) and the left orbitofrontal cortex (lOFC), during multiple simultaneous tDCS-fMRI sessions from 81 healthy participants to assess the modulatory effects of stimulating target brain regions on fMRI brain dynamics. Using the rDLPFC and the lOFC as seeds, respectively, we first identified two reoccurring co-activation patterns (CAPs) and calculated their temporal properties (e.g., occurrence rate and transitions) before administering tDCS. The spatial maps of CAPs were associated with different cognitive and disease domains using meta-analytical decoding analysis. We then investigated how active tDCS compared to sham tDCS in the modulation of the occurrence rates of these different CAPs and perturbations of transitions between CAPs. We found that by enhancing neuronal excitability of the rDLPFC and the lOFC, the occurrence rate of one CAP was significantly decreased while that of another CAP was significantly increased during the first 6 min of stimulation. Furthermore, these tDCS-associated changes persisted over subsequent testing sessions (both during and before/after tDCS) across three consecutive days. Active tDCS could perturb transitions between CAPs and a non-CAP state (when the rDLPFC and the lOFC were not activated), but not the transitions within CAPs. These results demonstrate the feasibility of modulating fMRI brain dynamics, and open new possibilities for discovering stimulation targets and dynamic connectivity patterns that can ensure the propagation of tDCS-induced neuronal excitability, which may facilitate the development of new treatments for disorders with altered dynamics.