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Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input–output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study. [ABSTRACT FROM AUTHOR]

Background: Patients with anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis often experience severe symptoms. Resting-state functional MRI (rs-fMRI) has revealed widespread impairment of functional networks in patients. However, the changes in information flow remain unclear. This study aims to investigate the intrinsic functional connectivity (FC) both within and between resting-state networks (RSNs), as well as the alterations in effective connectivity (EC) between these networks. Methods: Resting-state functional MRI (rs-fMRI) data were collected from 25 patients with anti-NMDAR encephalitis and 30 healthy controls (HCs) matched for age, sex, and educational level. Changes in the intrinsic functional connectivity (FC) within and between RSNs were analyzed using independent component analysis (ICA). The functional interaction between RSNs was identified by granger causality analysis (GCA). Results: Compared to HCs, patients with anti-NMDAR encephalitis exhibited lower performance on the Wisconsin Card Sorting Test (WCST), both in terms of correct numbers and correct categories. Additionally, these patients demonstrated decreased scores on the Montreal Cognitive Assessment (MoCA). Neuroimaging studies revealed abnormal intra-FC within the default mode network (DMN), increased intra-FC within the visual network (VN) and dorsal attention network (DAN), as well as increased inter-FC between VN and the frontoparietal network (FPN). Furthermore, aberrant effective connectivity (EC) was observed among the DMN, DAN, FPN, VN, and somatomotor network (SMN). Conclusion: Patients with anti-NMDAR encephalitis displayed noticeable deficits in both memory and executive function. Notably, these patients exhibited widespread impairments in intra-FC, inter-FC, and EC. These results may help to explain the pathophysiological mechanism of anti-NMDAR encephalitis. [ABSTRACT FROM AUTHOR]

A major scientific objective of cognitive neuroscience is to define cortico-cortical functional connections supporting cognitive functions. Here, we use an original approach combining frequency-tagging and direct electrical stimulation (DES) to test for bidirectional and cross-hemispheric category-specific modulations within the human cortical face network. A unique patient bilaterally implanted with depth electrodes in multiple face-selective cortical regions of the ventral occipito-temporal cortex (VOTC) was shown 70 s sequences of variable natural object images at a 6 Hz rate, objectively identifying deviant face-selective neural activity at 1.2 Hz (i.e., every five images). Concurrent electrical stimulation was separately applied for 10 seconds on four independently defined face-selective sites in the right and left VOTC. Upon stimulation, we observed reduced or even abolished face-selective neural activity locally and, most interestingly, at distant VOTC recording sites. Remote DES effects were found up to the anterior temporal lobe (ATL) in both forward and backward directions along the VOTC, as well as across the two hemispheres. This reduction was specific to face-selective neural activity, with the general 6 Hz visual response being mostly unaffected. Overall, these results shed light on the functional connectivity of the cortical face-selective network, supporting its non-hierarchical organization as well as bidirectional effective category-selective connections between posterior 'core' regions and the ATL. They also pave the way for widespread and systematic development of this approach to better understand the functional and effective connectivity of human brain networks. [ABSTRACT FROM AUTHOR]

Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input–output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study.

Background: Sleep deprivation (SD) can impair an individual's alertness, which is the basis of attention and the mechanism behind continuous information processing. However, research concerning the effects of total sleep deprivation (TSD) on alertness networks is inadequate. In this study, we investigate the cognitive neural mechanism of alertness processing after TSD. Methods: Twenty-four college students volunteered to participate in the study. The resting-state electroencephalogram (EEG) data were collected under two conditions (rested wakefulness [RW], and TSD). We employed isolated effective coherence (iCoh) analysis and functional independent component analysis (fICA) to explore the effects of TSD on participants' alertness network. Results: This study found the existence of two types of effective connectivity after TSD, as demonstrated by iCoh: from the left cuneus to the right middle frontal gyrus in the β3 and γ bands, and from the left angular gyrus to the left insula in the δ, θ, α, β1, β3, and γ bands. Furthermore, Pearson correlation analysis showed that increased effective connectivity between all the bands had a positive correlation with increases in the response time in the psychomotor vigilance task (PVT). Finally, fICA revealed that the neural oscillations of the cuneus in the α2 bands increased, and of the angular gyrus in the α and β1 bands decreased in TSD. Conclusions: TSD impairs the alertness function among individuals. Increased effective connectivity from the cuneus to the middle frontal gyrus may represent overloads on the alertness network, resulting in participants strengthening top-down control of the attention system. Moreover, enhanced effective connectivity from the angular gyrus to the insula may indicate a special perception strategy in which individuals focus on salient and crucial environmental information while ignoring inessential stimuli to reduce the heavy burden on the alertness network. [ABSTRACT FROM AUTHOR]

Hao-Yu Lu,1,2,* Jia-Jia Wu,1,* Jun Shen,3,4,* Xiang-Xin Xing,1 Xu-Yun Hua,5 Mou-Xiong Zheng,5 Lian-Bo Xiao,3,4 Jian-Guang Xu1,2,6 1Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 2School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 3Department of Orthopedic, Guanghua Hospital of Integrative Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 4Arthritis Institute of Integrated Traditional Chinese and Western Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 5Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 6Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jian-Guang Xu, School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China, No. 1200 Cailun Road, Shanghai, People’s Republic of China, Tel +86 021-51322091, Fax +86 021-51322042, Email xjg@shutcm.edu.cn Lian-Bo Xiao, Department of Orthopedic, Guanghua Hospital of Integrative Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China, No. 540 Xinhua Road, Shanghai, People’s Republic of China, Tel/Fax +86 021-62805833, Email xiao_lianbo@163.comBackground: The chronic pain arising from knee osteoarthritis (KOA) is a prevalent clinical manifestation. As a traditional Chinese approach, electroacupuncture (EA) has a positive influence in relieving chronic pain from KOA. The study aims to explore functional connectivity (FC) and effective connectivity (EC) alterations induced by EA in anterior cruciate ligament transection (ACLT) rat model of KOA using resting-state functional magnetic resonance imaging (fMRI).Methods: After the establishment of ACLT, rats were randomly divided into the EA group and the sham-EA group. The EA group received EA intervention while the sham-EA group received sham-intervention for 3 weeks. Mechanical pain threshold (MPT) assessment was performed before and after intervention, and fMRI was conducted after intervention.Results: EA intervention effectively relieved pain in post-ACLT rats. Results of rest-state functional connectivity (rs-FC) analysis revealed that compared with the sham-EA group, the EA group had higher FC between the right raphe and the left auditory cortex, the left caudate_ putamen and the left internal capsule (IC), as well as the right zona incerta (ZI) and the left piriform cortex, but lower FC between the right raphe and the left hippocampus ventral, as well as the right septum and the left septum. Furthermore, Granger causality analysis (GCA) found the altered EC between the right septum and the left septum, as well as the left IC and the right septum.Conclusion: The results confirmed the effect of EA on analgesia in post- ACLT rats. The alterations of FC and EC, mainly involving basal ganglia and limbic system neural connections, might be one of the neural mechanisms underlying the effect of EA, providing novel information about connectomics plasticity of EA following ACLT.Keywords: knee osteoarthritis, anterior cruciate ligament transection, chronic pain, electroacupuncture, functional connectivity, effective connectivity

Introduction: Numerous modes or patterns of neural activity can be seen in the brain of individuals during the resting state. However, those functions do not persist long, and they are continuously altering in the brain. We have hypothesized that the brain activations during the resting state should themselves be responsible for this alteration of the activities. Methods: Using the resting-state fMRI data of 63 healthy young individuals, we estimated the causality effects of each resting-state activation map on all other networks. The resting-state networks were identified, their causality effects on the other components were extracted, the networks with the top 20% of the causality were chosen, and the networks which were under the influence of those causal networks were also identified. Results: Our results showed that the influence of each activation component over other components is different. The brain areas which showed the highest causality coefficients were subcortical regions, such as the brain stem, thalamus, and amygdala. On the other hand, nearly all the areas which were mostly under the causal effects were cortical regions. Discussion: In summary, our results suggest that subcortical brain areas exert a higher influence on cortical regions during the resting state, which could help in a better understanding the dynamic nature of brain functions. [ABSTRACT FROM AUTHOR]

Background: Normative childhood motor network resting-state fMRI effective connectivity is undefined, yet necessary for translatable dynamic resting-state-network-informed evaluation in pediatric cerebral palsy. Methods: Cross-spectral dynamic causal modeling of resting-state-fMRI was investigated in 50 neurotypically developing 5- to 13-year-old children. Fully connected six-node network models per hemisphere included primary motor cortex, striatum, subthalamic nucleus, globus pallidus internus, thalamus, and contralateral cerebellum. Parametric Empirical Bayes with exhaustive Bayesian model reduction and Bayesian modeling averaging informed the model; Purdue Pegboard Test scores of hand motor behavior were the covariate at the group level to determine the effective-connectivity-functional behavior relationship. Results: Although both hemispheres exhibited similar effective connectivity of motor cortico-basal ganglia-cerebellar networks, magnitudes were slightly greater on the right, except for left-sided connections of the striatum which were more numerous and of opposite polarity. Inter-nodal motor network effective connectivity remained consistent and robust across subjects. Age had a greater impact on connections to the contralateral cerebellum, bilaterally. Motor behavior, however, affected different connections in each hemisphere, exerting a more prominent effect on the left modulatory connections to the subthalamic nucleus, contralateral cerebellum, primary motor cortex, and thalamus. Discussion: This study revealed a consistent pattern of directed resting-state effective connectivity in healthy children aged 5-13 years within the motor network, encompassing cortical, subcortical, and cerebellar regions, correlated with motor skill proficiency. Both hemispheres exhibited similar effective connectivity within motor cortico-basal ganglia-cerebellar networks reflecting inter-nodal signal direction predicted by other modalities, mainly differing from task-dependent studies due to network differences at rest. Notably, age-related changes were more pronounced in connections to the contralateral cerebellum. Conversely, motor behavior distinctly impacted connections in each hemisphere, emphasizing its role in modulating left sided connections to the subthalamic nucleus, contralateral cerebellum, primary motor cortex, and thalamus. Motor network effective connectivity was correlated with motor behavior, validating its physiological significance. This study is the first to evaluate a normative effective connectivity model for the pediatric motor network using resting-state functional MRI correlating with behavior and serves as a foundation for identifying abnormal findings and optimizing targeted interventions like deep brain stimulation, potentially influencing future therapeutic approaches for children with movement disorders. [ABSTRACT FROM AUTHOR]

A major concept in cognitive neuroscience is that brains are “prediction machines”. Yet, conceptual frameworks on how perception and action become integrated still lack the concept of predictability and it is unclear how neural processes may implement predictive coding during dynamic perception-action integration. We show that distinct neurophysiological mechanisms of nonlinearly directed connectivities in the theta and alpha band between cortical structures underlie these processes. During the integration of perception and motor codes, especially theta band activity in the insular cortex and temporo-hippocampal structures is modulated by the predictability of upcoming information. Here, the insular cortex seems to guide processes. Conversely, the retrieval of such integrated perception-action codes during actions heavily relies on alpha band activity. Here, directed top-down influence of alpha band activity from inferior frontal structures on insular and temporo-hippocampal structures is key. This suggests that these top-down effects reflect attentional shielding of retrieval processes operating in the same neuroanatomical structures previously involved in the integration of perceptual and motor codes. Through neurophysiology, the present study connects predictive coding mechanisms with frameworks specifying the dynamic integration of perception and action.

The central autonomic network (CAN) serves as a regulatory hub with top-down regulatory control and integration of bottom-up physiological feedback via the autonomic nervous system. Heart rate variability (HRV)—the time variance of the heart's beat-to-beat intervals—is an index of the CAN's affective and behavioral regulatory capacity. Although neural functional connectivities that are associated with HRV and CAN have been well studied, no published report to date has studied effective (directional) connectivities (EC) that are associated with HRV and CAN. Better understanding of neural EC in the brain has the potential to improve our understanding of how the CAN sub-regions regulate HRV. To begin to address this knowledge gap, we employed resting-state functional magnetic resonance imaging and dynamic causal modeling (DCM) with parametric empirical Bayes analyses in 34 healthy adults (19 females; mean age= 32.68 years [SD= 14.09], age range 18–68 years) to examine the bottom-up and top-down neural circuits associated with HRV. Throughout the whole brain, we identified 12 regions associated with HRV. DCM analyses revealed that the ECs from the right amygdala to the anterior cingulate cortex and to the ventrolateral prefrontal cortex had a negative linear relationship with HRV and a positive linear relationship with heart rate. These findings suggest that ECs from the amygdala to the prefrontal cortex may represent a neural circuit associated with regulation of cardiodynamics.

Action understanding involves two distinct processing levels that engage separate neural mechanisms: perception of concrete kinematic information and recognition of abstract action intentions. The mirror neuron system and the mentalizing system have both been linked to concrete action and abstract information processing, but their specific roles remain debatable. Here, we conducted a functional magnetic resonance imaging study with 26 participants who passively observed expected and unexpected actions. We performed whole-brain activation, region of interest, and effective connectivity analyses to investigate the neural correlates of these actions. Whole-brain activation analyses revealed that expected actions were associated with increased activation in the left medial superior frontal gyrus, while unexpected actions were linked to heightened activity in the left supramarginal gyrus, left superior parietal lobule, right inferior temporal gyrus, and left middle frontal gyrus. Region of interest analyses demonstrated that the left ventral premotor cortex exhibited greater activation during the observation of expected actions compared to unexpected actions, while the left inferior frontal gyrus, left superior parietal lobule, and left precuneus showed stronger activation during the observation of unexpected actions. Effective connectivity was observed between the left ventral premotor cortex and the left angular gyrus, left intraparietal sulcus, left dorsal premotor cortex, and left ventromedial prefrontal cortex with the middle frontal gyrus when observing unexpected, but not expected, actions. These findings suggest that expected actions are primarily processed by the mirror neuron system, whereas unexpected actions engage both the mirror neuron system and the mentalizing system, with these systems playing complementary roles in the understanding of unexpected actions.

Abstract Background Visual-motor integration (VMI) is an essential skill in daily life. The present study aimed to use functional near-infrared spectroscopy (fNIRS) technology to explore the effective connectivity (EC) changes among brain regions during VMI activities of varying difficulty levels. Methods A total of 17 healthy participants were recruited for the study. Continuous Performance Test (CPT), Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A), and Beery VMI test were used to evaluate attention performance, executive function, and VMI performance. Granger causality analysis was performed for the VMI task data to obtain the EC matrix for all participants. One-way ANOVA analysis was used to identify VMI load-dependent EC values among different task difficulty levels from brain network and channel perspectives, and partial correlation analysis was used to explore the relationship between VMI load-dependent EC values and behavioral performance. Results We found that the EC values of dorsal attention network (DAN) → default mode network (DMN), DAN → ventral attention network (VAN), DAN → frontoparietal network (FPN), and DAN → somatomotor network (SMN) in the complex condition were higher than those in the simple and moderate conditions. Further channel analyses indicated that the EC values of the right superior parietal lobule (SPL) → right superior frontal gyrus (SFG), right middle occipital gyrus (MOG) → left SFG, and right MOG → right postcentral gyrus (PCG) in the complex condition were higher than those in the simple and moderate conditions. Subsequent partial correlation analysis revealed that the EC values from DAN to DMN, VAN, and SMN were positively correlated with executive function and VMI performance. Furthermore, the EC values of right MOG → left SFG and right MOG → right PCG were positively correlated with attention performance. Conclusions The DAN is actively involved during the VMI task and thus may play a critical role in VMI processes, in which two key brain regions (right SPL, right MOG) may contribute to the EC changes in response to increasing VMI load. Meanwhile, bilateral SFG and right PCG may also be closely related to the VMI performance.

Information theory explains how systems encode and transmit information. This article examines the neuronal system, which processes information via neurons that react to stimuli and transmit electrical signals. Specifically, we focus on transfer entropy to measure the flow of information between sequences and explore its use in determining effective neuronal connectivity. We analyze the causal relationships between two discrete time series, X : = X t : t ∈ Z and Y : = Y t : t ∈ Z , which take values in binary alphabets. When the bivariate process (X , Y) is a jointly stationary ergodic variable-length Markov chain with memory no larger than k, we demonstrate that the null hypothesis of the test—no causal influence—requires a zero transfer entropy rate. The plug-in estimator for this function is identified with the test statistic of the log-likelihood ratios. Since under the null hypothesis, this estimator follows an asymptotic chi-squared distribution, it facilitates the calculation of p-values when applied to empirical data. The efficacy of the hypothesis test is illustrated with data simulated from a neuronal network model, characterized by stochastic neurons with variable-length memory. The test results identify biologically relevant information, validating the underlying theory and highlighting the applicability of the method in understanding effective connectivity between neurons. [ABSTRACT FROM AUTHOR]

Introduction: Aberrant brain functional connectivity network is thought to be related to cognitive impairment in patients with type 2 diabetes mellitus (T2DM). This study aims to investigate the triple-network effective connectivity patterns in patients with T2DM within and between the default mode network (DMN), salience network (SN), and executive control network (ECN) and their associations with cognitive declines. Methods: In total, 92 patients with T2DM and 98 matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Spectral dynamic causal modeling (spDCM) was used for effective connectivity analysis within the triple network. The posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), lateral prefrontal cortex (LPFC), supramarginal gyrus (SMG), and anterior insula (AINS) were selected as the regions of interest. Group comparisons were performed for effective connectivity calculated using the fully connected model, and the relationships between effective connectivity alterations and cognitive impairment as well as clinical parameters were detected. Results: Compared to HCs, patients with T2DM exhibited increased or decreased effective connectivity patterns within the triple network. Furthermore, diabetes duration was significantly negatively correlated with increased effective connectivity from the r-LPFC to the mPFC, while body mass index (BMI) was significantly positively correlated with increased effective connectivity from the l-LPFC to the l-AINS (r = − 0.353, p = 0.001; r = 0.377, p = 0.004). Conclusion: These results indicate abnormal effective connectivity patterns within the triple network model in patients with T2DM and provide new insight into the neurological mechanisms of T2DM and related cognitive dysfunction. [ABSTRACT FROM AUTHOR]

• Concurrent recordings of brain function measured with frequency-tagging iEEG and DES. • Different faces presented at 6Hz perceived as the same during DES to right FFA. • DES to right FFA reduces concurrent face-selective neural activity at remote sites. • Frequency-tagging provides artifact-free measures during concurrent DES. • Cortical sites affected by DES effects are functionally connected. [ABSTRACT FROM AUTHOR]

Introduction: Donepezil enhances the function of cholinergic nerves by increasing the concentration of acetylcholine, thereby improving clinical symptoms in patients with Alzheimer’s disease (AD). However, the neural mechanisms of how donepezil modulates the effective connectivity (EC) network of cholinergic system in AD patients remain unknown. We speculated that the effective network of the cholinergic system changes in AD patients after donepezil intervention. Methods: We employed resting-state functional magnetic resonance imaging and Granger causality analysis approach to explore changes in the effective connectivity network of the basal forebrain in AD patients before and after donepezil intervention. This study included 32 participants, including 16 healthy controls (HCs) and 16 AD patients. In a 3T MRI scanner, the 16 AD patients were scanned before and after the donepezil intervention. To compare EC differences between the three groups of participants, ANOVA and post-hoc t-tests analysis were employed. Results: Compared to baseline status, AD patients after donepezil intervention had an increased EC from left middle occipital gyrus to right medial septum/diagonal bands. Compared to HCs, AD patients after donepezil intervention had an increased EC from right inferior frontal gyrus/orbit part to right medial septum/diagonal bands, AD patients before donepezil intervention had a reduced EC from right precuneus to right medial septum/diagonal bands. A significant positive correlation was found between EC values in right precuneus and Mini-Mental State Examination in pre-intervention AD patients (r = 0.7338, p = 0.0012). Discussion: Our study showed that effective connectivity of brain regions associated with the default mode network in the cholinergic pathway was enhanced after donepezil intervention. The results of this study will help us to better understand the neural mechanisms of donepezil intervention in AD and to find clinical targets for intervention. [ABSTRACT FROM AUTHOR]

Emotion regulation is a process by which individuals modulate their emotional responses to cope with different environmental demands, for example, by reappraising the emotional situation. Here, we tested whether effective connectivity of a reappraisal‐related neural network at rest is predictive of successfully regulating high‐ and low‐intensity negative emotions in an emotion‐regulation task. Task‐based and resting‐state functional magnetic resonance imaging (rs‐fMRI) data of 28 participants were collected using ultra‐high magnetic field strength at 7 Tesla during three scanning sessions. We used spectral dynamic causal modeling (spDCM) on the rs‐fMRI data within brain regions modulated by emotion intensity. We found common connectivity patterns for both high‐ and low‐intensity stimuli. Distinctive effective connectivity patterns in relation to low‐intensity stimuli were found from frontal regions connecting to temporal regions. Reappraisal success for high‐intensity stimuli was predicted by additional connections within the vlPFC and from temporal to frontal regions. Connectivity patterns at rest predicting reappraisal success were generally more pronounced for low‐intensity stimuli, suggesting a greater role of stereotyped patterns, potentially reflecting preparedness, when reappraisal was relatively easy to implement. The opposite was true for high‐intensity stimuli, which might require a more flexible recruitment of resources beyond what is reflected in resting state connectivity patterns. Resting‐state effective connectivity emerged as a robust predictor for successful reappraisal, revealing both shared and distinct network dynamics for high‐ and low‐intensity stimuli. These patterns signify specific preparatory states associated with heightened vigilance, attention, self‐awareness, and goal‐directed cognitive processing, particularly during reappraisal for mitigating the emotional impact of external stimuli. Our findings hold potential implications for understanding psychopathological alterations in brain connectivity related to affective disorders. [ABSTRACT FROM AUTHOR]

Background: Visual-motor integration (VMI) is an essential skill in daily life. The present study aimed to use functional near-infrared spectroscopy (fNIRS) technology to explore the effective connectivity (EC) changes among brain regions during VMI activities of varying difficulty levels. Methods: A total of 17 healthy participants were recruited for the study. Continuous Performance Test (CPT), Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A), and Beery VMI test were used to evaluate attention performance, executive function, and VMI performance. Granger causality analysis was performed for the VMI task data to obtain the EC matrix for all participants. One-way ANOVA analysis was used to identify VMI load-dependent EC values among different task difficulty levels from brain network and channel perspectives, and partial correlation analysis was used to explore the relationship between VMI load-dependent EC values and behavioral performance. Results: We found that the EC values of dorsal attention network (DAN) → default mode network (DMN), DAN → ventral attention network (VAN), DAN → frontoparietal network (FPN), and DAN → somatomotor network (SMN) in the complex condition were higher than those in the simple and moderate conditions. Further channel analyses indicated that the EC values of the right superior parietal lobule (SPL) → right superior frontal gyrus (SFG), right middle occipital gyrus (MOG) → left SFG, and right MOG → right postcentral gyrus (PCG) in the complex condition were higher than those in the simple and moderate conditions. Subsequent partial correlation analysis revealed that the EC values from DAN to DMN, VAN, and SMN were positively correlated with executive function and VMI performance. Furthermore, the EC values of right MOG → left SFG and right MOG → right PCG were positively correlated with attention performance. Conclusions: The DAN is actively involved during the VMI task and thus may play a critical role in VMI processes, in which two key brain regions (right SPL, right MOG) may contribute to the EC changes in response to increasing VMI load. Meanwhile, bilateral SFG and right PCG may also be closely related to the VMI performance. [ABSTRACT FROM AUTHOR]

Introduction: Repetitive transcranial magnetic stimulation (rTMS) is a non-pharmacological treatment for drug-resistant major depressive disorder (MDD) patients. Since the success rate of rTMS treatment is about 50%-55%, it is essential to predict the treatment outcome before starting based on electroencephalogram (EEG) signals, leading to identifying effective biomarkers and reducing the burden of health care centers. Methods: To this end, pretreatment EEG data with 19 channels in the resting state from 34 drug-resistant MDD patients were recorded. Then, all patients received 20 sessions of rTMS treatment, and a reduction of at least 50% in the total beck depression inventory (BDI-II) score before and after the rTMS treatment was defined as a reference. In the current study, effective brain connectivity features were determined by the direct directed transfer function (dDTF) method from patients' pretreatment EEG data in all frequency bands separately. Then, the brain functional connectivity patterns were modeled as graphs by the dDTF method and examined with the local graph theory indices, including degree, out-degree, in-degree, strength, outstrength, in-strength, and betweenness centrality. Results: The results indicated that the betweenness centrality index in the Fp2 node and the δ frequency band are the best biomarkers, with the highest area under the receiver operating characteristic curve value of 0.85 for predicting the rTMS treatment outcome in drug-resistant MDD patients. Conclusion: The proposed method investigated the significant biomarkers that can be used to predict the rTMS treatment outcome in drug-resistant MDD patients and help clinical decisions. [ABSTRACT FROM AUTHOR]

This study introduces a groundbreaking method to enhance the accuracy and reliability of emotion recognition systems by combining electrocardiogram (ECG) with electroencephalogram (EEG) data, using an eye-tracking gated strategy. Initially, we propose a technique to filter out irrelevant portions of emotional data by employing pupil diameter metrics from eye-tracking data. Subsequently, we introduce an innovative approach for estimating effective connectivity to capture the dynamic interaction between the brain and the heart during emotional states of happiness and sadness. Granger causality (GC) is estimated and utilized to optimize input for a highly effective pre-trained convolutional neural network (CNN), specifically ResNet-18. To assess this methodology, we employed EEG and ECG data from the publicly available MAHNOB-HCI database, using a 5-fold cross-validation approach. Our method achieved an impressive average accuracy and area under the curve (AUC) of 91.00 % and 0.97, respectively, for GC-EEG-ECG images processed with ResNet-18. Comparative analysis with state-of-the-art studies clearly shows that augmenting ECG with EEG and refining data with an eye-tracking strategy significantly enhances emotion recognition performance across various emotions.

IntroductionNumerous modes or patterns of neural activity can be seen in the brain of individuals during the resting state. However, those functions do not persist long, and they are continuously altering in the brain. We have hypothesized that the brain activations during the resting state should themselves be responsible for this alteration of the activities.MethodsUsing the resting-state fMRI data of 63 healthy young individuals, we estimated the causality effects of each resting-state activation map on all other networks. The resting-state networks were identified, their causality effects on the other components were extracted, the networks with the top 20% of the causality were chosen, and the networks which were under the influence of those causal networks were also identified.ResultsOur results showed that the influence of each activation component over other components is different. The brain areas which showed the highest causality coefficients were subcortical regions, such as the brain stem, thalamus, and amygdala. On the other hand, nearly all the areas which were mostly under the causal effects were cortical regions.DiscussionIn summary, our results suggest that subcortical brain areas exert a higher influence on cortical regions during the resting state, which could help in a better understanding the dynamic nature of brain functions.

Initial indications propose that repetitive transcranial magnetic stimulation (rTMS) could mitigate clinical manifestations in patients with autism spectrum disorder (ASD). Nevertheless, the precise mechanisms responsible for these therapeutic and behavioral outcomes remain elusive. We examined alterations in effective connectivity induced by rTMS using concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) in children with ASD. TMS-EEG data were acquired from 12 children diagnosed with ASD both before and following rTMS treatment. The rTMS intervention regimen included delivering 5-s trains at a frequency of 15 Hz, with 10-min intervals between trains, targeting the left parietal lobe. This was conducted on each consecutive weekday over 3 weeks, totaling 15 sessions. The dynamic EEG network analysis revealed that following the rTMS intervention, long-range feedback connections within the brains of ASD patients were strengthened (e.g., frontal to parietal regions, frontal to occipital regions, and frontal to posterior temporal regions), and short-range connections were weakened (e.g., between the bilateral occipital regions, and between the occipital and posterior temporal regions). In alignment with alterations in network connectivity, there was a corresponding amelioration in fundamental ASD symptoms, as assessed through clinical scales post-treatment. According to our findings, people with ASD may have increased long-range frontal-posterior feedback connection on application of rTMS to the parietal lobe.

The objective of this paper is to develop a novel emotion recognition system from electroencephalogram (EEG) signals using effective connectivity and deep learning methods. Emotion recognition is an important task for various applications such as human-computer interaction and, mental health diagnosis. The paper aims to improve the accuracy and robustness of emotion recognition by combining different effective connectivity (EC) methods and pre-trained convolutional neural networks (CNNs), as well as long short-term memory (LSTM). EC methods measure information flow in the brain during emotional states using EEG signals. We used three EC methods: transfer entropy (TE), partial directed coherence (PDC), and direct directed transfer function (dDTF). We estimated a fused image from these methods for each five-second window of 32-channel EEG signals. Then, we applied six pre-trained CNNs to classify the images into four emotion classes based on the two-dimensional valence-arousal model. We used the leave-one-subject-out cross-validation strategy to evaluate the classification results. We also used an ensemble model to select the best results from the best pre-trained CNNs using the majority voting approach. Moreover, we combined the CNNs with LSTM to improve recognition performance. We achieved the average accuracy and F-score of 98.76%, 98.86%, 98.66 and 98.88% for classifying emotions using DEAP and MAHNOB-HCI datasets, respectively. Our results show that fused images can increase the accuracy and that an ensemble and combination of pre-trained CNNs and LSTM can achieve high accuracy for automated emotion recognition. Our model outperformed other state-of-the-art systems using the same datasets for four-class emotion classification.

Previous studies have reported a role of alterations in the brain’s inhibitory control mechanism in addiction. Mounting evidence from neuroimaging studies indicates that its key components can be evaluated with brain oscillations and connectivity during inhibitory control. In this study, we developed an internet-related stop-signal task with electroencephalography (EEG) signal recorded to investigate inhibitory control. Healthy controls and participants with Internet addiction were recruited to participate in the internet-related stop-signal task with 19-channel EEG signal recording, and the corresponding event-related potentials and spectral perturbations were analyzed. Brain effective connections were also evaluated using direct directed transfer function. The results showed that, relative to the healthy controls, participants with Internet addiction had increased Stop-P3 during inhibitory control, suggesting that they have an altered neural mechanism in impulsive control. Furthermore, participants with Internet addiction showed increased low-frequency synchronization and decreased alpha and beta desynchronization in the middle and right frontal regions compared to healthy controls. Aberrant brain effective connectivity was also observed, with increased occipital-parietal and intra-occipital connections, as well as decreased frontal-paracentral connection in participants with Internet addiction. These results suggest that physiological signals are essential in future implementations of cognitive assessment of Internet addiction to further investigate the underlying mechanisms and effective biomarkers.

EMG-driven robot hand training can facilitate motor recovery in chronic stroke patients by restoring the interhemispheric balance between motor networks. However, the underlying mechanisms of reorganization between interhemispheric regions remain unclear. This study investigated the effective connectivity (EC) between the ventral premotor cortex (PMv), supplementary motor area (SMA), and primary motor cortex (M1) using Dynamic Causal Modeling (DCM) during motor tasks with the paretic hand. Nineteen chronic stroke subjects underwent 20 sessions of EMG-driven robot hand training, and their Action Reach Arm Test (ARAT) showed significant improvement ( $\beta $ =3.56, $\text{p} < 0.001$ ). The improvement was correlated with the reduction of inhibitory coupling from the contralesional M1 to the ipsilesional M1 (r=0.58, p=0.014). An increase in the laterality index was only observed in homotopic M1, but not in the premotor area. Additionally, we identified an increase in resting-state functional connectivity (FC) between bilateral M1 ( $\beta $ =0.11, p=0.01). Inter-M1 FC demonstrated marginal positive relationships with ARAT scores (r=0.402, p=0.110), but its changes did not correlate with ARAT improvements. These findings suggest that the improvement of hand functions brought about by EMG-driven robot hand training was driven explicitly by task-specific reorganization of motor networks. Particularly, the restoration of interhemispheric balance was induced by a reduction in interhemispheric inhibition from the contralesional M1 during motor tasks of the paretic hand. This finding sheds light on the mechanistic understanding of interhemispheric balance and functional recovery induced by EMG-driven robot training.

A major scientific objective of cognitive neuroscience is to define cortico-cortical functional connections supporting cognitive functions. Here, we use an original approach combining frequency-tagging and direct electrical stimulation (DES) to test for bidirectional and cross-hemispheric category-specific modulations within the human cortical face network. A unique patient bilaterally implanted with depth electrodes in multiple face-selective cortical regions of the ventral occipito-temporal cortex (VOTC) was shown 70 s sequences of variable natural object images at a 6 Hz rate, objectively identifying deviant face-selective neural activity at 1.2 Hz (i.e., every five images). Concurrent electrical stimulation was separately applied for 10 seconds on four independently defined face-selective sites in the right and left VOTC. Upon stimulation, we observed reduced or even abolished face-selective neural activity locally and, most interestingly, at distant VOTC recording sites. Remote DES effects were found up to the anterior temporal lobe (ATL) in both forward and backward directions along the VOTC, as well as across the two hemispheres. This reduction was specific to face-selective neural activity, with the general 6 Hz visual response being mostly unaffected. Overall, these results shed light on the functional connectivity of the cortical face-selective network, supporting its non-hierarchical organization as well as bidirectional effective category-selective connections between posterior ‘core’ regions and the ATL. They also pave the way for widespread and systematic development of this approach to better understand the functional and effective connectivity of human brain networks.

A prominent and robust finding in cognitive neuroscience is the strengthening of memories during nonrapid eye movement (NREM) sleep, with slow oscillations (SOs;

Objectives: Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are considered as the spectrum of preclinical Alzheimer's disease (AD), with abnormal brain network connectivity as the main neuroimaging feature. Repetitive transcranial magnetic stimulation (rTMS) has been proven to be an effective non-invasive technique for addressing neuropsychiatric disorders. This study aims to explore the potential of targeted rTMS to regulate effective connectivity within the default mode network (DMN) and the executive control network (CEN), thereby improving cognitive function. Methods: This study included 86 healthy controls (HCs), 72 SCDs, and 86 aMCIs. Among them, 10 SCDs and 11 aMCIs received a 2-week rTMS course of 5-day, once-daily. Cross-sectional analysis with the spectral dynamic causal model (spDCM) was used to analyze the DMN and CEN effective connectivity patterns of the three groups. Afterwards, longitudinal analysis was conducted on the changes in effective connectivity patterns and cognitive function before and after rTMS for SCD and aMCI, and the correlation between them was analyzed. Results: Cross-sectional analysis showed different effective connectivity patterns in the DMN and CEN among the three groups. Longitudinal analysis showed that the effective connectivity pattern of the SCD had changed, accompanied by improvements in episodic memory. Correlation analysis indicated a negative relationship between effective connectivity from the left angular gyrus (ANG) to the anterior cingulate gyrus and the ANG.R to the right middle frontal gyrus, with visuospatial and executive function, respectively. In patients with aMCI, episodic memory and executive function improved, while the effective connectivity pattern remained unchanged. Conclusion: This study demonstrates that PCUN-targeted rTMS in SCD regulates the abnormal effective connectivity patterns in DMN and CEN, thereby improving cognition function. Conversely, in aMCI, the mechanism of improvement may differ. Our findings further suggest that rTMS is more effective in preventing or delaying disease progression in the earlier stages of the AD spectrum. [ABSTRACT FROM AUTHOR]

Complex motor skills can be acquired while observing a model without physical practice. Transcranial direct-current stimulation (tDCS) applied to the primary motor cortex (M1) also facilitates motor learning. However, the effectiveness of observational practice for bimanual coordination skills is debated. We compared the behavioural and brain causal connectivity patterns following three interventions: primary motor cortex stimulation (M1-tDCS), action-observation (AO) and a combined group (AO+M1-tDCS) when acquiring a bimanual, two-ball juggling skill. Thirty healthy young adults with no juggling experience were randomly assigned to either video observation of a skilled juggler, anodal M1-tDCS or video observation combined with M1-tDCS. Thirty trials of juggling were performed and scored after the intervention. Resting-state EEG data were collected before and after the intervention. Information flow rate was applied to EEG source data to measure causal connectivity. The two observation groups were more accurate than the tDCS alone group. In the AO condition, there was strong information exchange from (L) parietal to (R) parietal regions, strong bidirectional information exchange between (R) parietal and (R) occipital regions and an extensive network of activity that was (L) lateralized. The M1-tDCS condition was characterized by bilateral long-range connections with the strongest information exchange from the (R) occipital region to the (R) temporal and (L) occipital regions. AO+M1-tDCS induced strong bidirectional information exchange in occipital and temporal regions in both hemispheres. Uniquely, it was the only condition that was characterized by information exchange between the (R) frontal and central regions. This study provides new results about the distinct network dynamics of stimulating the brain for skill acquisition, providing insights for motor rehabilitation. [ABSTRACT FROM AUTHOR]

Introduction: Many studies have collected normative developmental EEG data to better understand brain function in early life and associated changes during both aging and pathology. Higher cognitive functions of the brain do not normally stem from the workings of a single brain region that works but, rather, on the interaction between different brain regions. In this regard studying the connectivity between brain regions is of great importance towards understanding higher cognitive functions and its underlying mechanisms. Methods: In this study, EEG data of children (N = 253; 3-10 years old; 113 females, 140 males) from pre-school to schoolage was collected, and the weighted phase delay index and directed transfer function method was used to find the electrophysiological indicators of both functional connectivity and effective connectivity. A general linear model was built between the indicators and age, and the change trend of electrophysiological indicators analyzed for age. Results: The results showed an age trend for the functional and effective connectivity of the brain of children. Discussion: The results are of importance in understanding normative brain development and in defining those conditions that deviate from typical growth trajectories. [ABSTRACT FROM AUTHOR]

This study aims to investigate alterations in effective connectivity (EC) within the fronto-thalamic circuit and their associations with motor and cognitive declines in pontine infarction (PI). A total of 33 right PI patients (RPIs), 38 left PI patients (LPIs), and 67 healthy controls (HCs) were recruited. The spectral dynamic causal modeling (spDCM) approach was used for EC analysis within the fronto-thalamic circuit, including the thalamus, caudate, supplementary motor area (SMA), medial prefrontal cortex (mPFC), and anterior cingulate cortex (ACC). The EC differences between different sides of the patients and HCs were assessed, and their correlations with motor and cognitive dysfunctions were analyzed. The LPIs showed increased EC from the mPFC to the R-SMA and decreased EC from the L-thalamus to the ACC, the L-SMA to the R-SMA, the R-caudate to the R-thalamus, and the R-thalamus to the ACC. For RPIs, the EC of the R-caudate to the mPFC, the L-thalamus and L-caudate to the L-SMA, and the L-caudate to the ACC increased obviously, while a lower EC strength was shown from the L-thalamus to the mPFC, the LSMA to the R-caudate, and the R-SMA to the L-thalamus. The EC from the R-caudate to the mPFC was negatively correlated with the MoCA score for RPIs, and the EC from the R-caudate to the R-thalamus was negatively correlated with the FMA score for LPIs. The results demonstrated EC within the fronto-thalamic circuit in PI-related functional impairments and reveal its potential as a novel imaging marker. [ABSTRACT FROM AUTHOR]

BackgroundNormative childhood motor network resting-state fMRI effective connectivity is undefined, yet necessary for translatable dynamic resting-state-network-informed evaluation in pediatric cerebral palsy.MethodsCross-spectral dynamic causal modeling of resting-state-fMRI was investigated in 50 neurotypically developing 5- to 13-year-old children. Fully connected six-node network models per hemisphere included primary motor cortex, striatum, subthalamic nucleus, globus pallidus internus, thalamus, and contralateral cerebellum. Parametric Empirical Bayes with exhaustive Bayesian model reduction and Bayesian modeling averaging informed the model; Purdue Pegboard Test scores of hand motor behavior were the covariate at the group level to determine the effective-connectivity-functional behavior relationship.ResultsAlthough both hemispheres exhibited similar effective connectivity of motor cortico-basal ganglia-cerebellar networks, magnitudes were slightly greater on the right, except for left-sided connections of the striatum which were more numerous and of opposite polarity. Inter-nodal motor network effective connectivity remained consistent and robust across subjects. Age had a greater impact on connections to the contralateral cerebellum, bilaterally. Motor behavior, however, affected different connections in each hemisphere, exerting a more prominent effect on the left modulatory connections to the subthalamic nucleus, contralateral cerebellum, primary motor cortex, and thalamus.DiscussionThis study revealed a consistent pattern of directed resting-state effective connectivity in healthy children aged 5–13 years within the motor network, encompassing cortical, subcortical, and cerebellar regions, correlated with motor skill proficiency. Both hemispheres exhibited similar effective connectivity within motor cortico-basal ganglia-cerebellar networks reflecting inter-nodal signal direction predicted by other modalities, mainly differing from task-dependent studies due to network differences at rest. Notably, age-related changes were more pronounced in connections to the contralateral cerebellum. Conversely, motor behavior distinctly impacted connections in each hemisphere, emphasizing its role in modulating left sided connections to the subthalamic nucleus, contralateral cerebellum, primary motor cortex, and thalamus. Motor network effective connectivity was correlated with motor behavior, validating its physiological significance. This study is the first to evaluate a normative effective connectivity model for the pediatric motor network using resting-state functional MRI correlating with behavior and serves as a foundation for identifying abnormal findings and optimizing targeted interventions like deep brain stimulation, potentially influencing future therapeutic approaches for children with movement disorders.

Despite its widespread use, resting-state functional magnetic resonance imaging (rsfMRI) has been criticized for low test-retest reliability. To improve reliability, researchers have recommended using extended scanning durations, increased sample size, and advanced brain connectivity techniques. However, longer scanning runs and larger sample sizes may come with practical challenges and burdens, especially in rare populations. Here we tested if an advanced brain connectivity technique, dynamic causal modeling (DCM), can improve reliability of fMRI effective connectivity (EC) metrics to acceptable levels without extremely long run durations or extremely large samples. Specifically, we employed DCM for EC analysis on rsfMRI data from the Human Connectome Project. To avoid bias, we assessed four distinct DCMs and gradually increased sample sizes in a randomized manner across ten permutations. We employed pseudo true positive and pseudo false positive rates to assess the efficacy of shorter run durations (3.6, 7.2, 10.8, 14.4 min) in replicating the outcomes of the longest scanning duration (28.8 min) when the sample size was fixed at the largest (n = 160 subjects). Similarly, we assessed the efficacy of smaller sample sizes (n = 10, 20, …, 150 subjects) in replicating the outcomes of the largest sample (n = 160 subjects) when the scanning duration was fixed at the longest (28.8 min). Our results revealed that the pseudo false positive rate was below 0.05 for all the analyses. After the scanning duration reached 10.8 min, which yielded a pseudo true positive rate of 92%, further extensions in run time showed no improvements in pseudo true positive rate. Expanding the sample size led to enhanced pseudo true positive rate outcomes, with a plateau at n = 70 subjects for the targeted top one-half of the largest ECs in the reference sample, regardless of whether the longest run duration (28.8 min) or the viable run duration (10.8 min) was employed. Encouragingly, smaller sample sizes exhibited pseudo true positive rates of approximately 80% for n = 20, and 90% for n = 40 subjects. These data suggest that advanced DCM analysis may be a viable option to attain reliable metrics of EC when larger sample sizes or run times are not feasible.

The directional organization of multiple nociceptive regions, particularly within obscure operculoinsular areas, underlying multidimensional pain processing remains elusive. This study aims to establish the fundamental organization between somatosensory and insular cortices in routing nociceptive information. By employing an integrated multimodal approach of high-field fMRI, intracranial electrophysiology, and transsynaptic viral tracing in rats, we observed a hierarchically organized connection of S1/S2 → posterior insula → anterior insula in routing nociceptive information. The directional nociceptive pathway determined by early fMRI responses was consistent with that examined by early evoked LFP, intrinsic effective connectivity, and anatomical projection, suggesting fMRI could provide a valuable facility to discern directional neural circuits in animals and humans non-invasively. Moreover, our knowledge of the nociceptive hierarchical organization of somatosensory and insular cortices and the interface role of the posterior insula may have implications for the development of targeted pain therapies.

Visual working memory (WM) engages several nodes of a large-scale network that includes frontal, parietal, and visual regions; however, little is understood about how these regions interact to support WM behavior. In particular, it is unclear whether network dynamics during WM maintenance primarily represent feedforward or feedback connections. This question has important implications for current debates about the relative roles of frontoparietal and visual regions in WM maintenance. In the current study, we investigated the network activity supporting WM using MEG data acquired while healthy subjects performed a multi-item delayed estimation WM task. We used computational modeling of behavior to discriminate correct responses (high accuracy trials) from two different types of incorrect responses (low accuracy and swap trials), and dynamic causal modeling of MEG data to measure effective connectivity. We observed behaviorally dependent changes in effective connectivity in a brain network comprising frontoparietal and early visual areas. In comparison with high accuracy trials, frontoparietal and frontooccipital networks showed disrupted signals depending on type of behavioral error. Low accuracy trials showed disrupted feedback signals during early portions of WM maintenance and disrupted feedforward signals during later portions of maintenance delay, while swap errors showed disrupted feedback signals during the whole delay period. These results support a distributed model of WM that emphasizes the role of visual regions in WM storage and where changes in large scale network configurations can have important consequences for memory-guided behavior.

Aging is a major risk factor for neurodegenerative diseases like dementia and Alzheimer's disease. Even in non-pathological aging, decline in cognitive functioning is observed in the majority of the elderly population, necessitating the importance of studying the processes involved in healthy aging in order to identify brain biomarkers that promote the conservation of functioning. The default mode network (DMN) has been of special interest to aging research due to its vulnerability to atrophy and functional decline over the course of aging. Prior work has focused almost exclusively on functional (i.e. undirected) connectivity, yet converging findings are scarce. Therefore, we set out to use spectral dynamic causal modeling to investigate changes in the effective (i.e. directed) connectivity within the DMN and to discover changes in information flow in a sample of cognitively normal adults spanning from 48 to 89 years (n = 63). Age was associated to reduced verbal memory performance. Modeling of effective connectivity revealed a pattern of age-related downregulation of posterior DMN regions driven by inhibitory connections from the hippocampus and middle temporal gyrus. Additionally, there was an observed decline in the hippocampus’ susceptibility to network inputs with age, effectively disconnecting itself from other regions. The estimated effective connectivity parameters were robust and able to predict the age in out of sample estimates in a leave-one-out cross-validation. Attained education moderated the effects of aging, largely reversing the observed pattern of inhibitory connectivity. Thus, medial prefrontal cortex, hippocampus and posterior DMN regions formed an excitatory cycle of extrinsic connections related to the interaction of age and education. This suggests a compensatory role of years of education in effective connectivity, stressing a possible target for interventions. Our findings suggest a connection to the concept of cognitive reserve, which attributes a protective effect of educational level on cognitive decline in aging (Stern, 2009).

Trauma and posttraumatic stress are highly comorbid with chronic pain and are often antecedents to developing chronic pain conditions. Pain and trauma are associated with greater utilization of medical services, greater use of psychiatric medication, and increased total cost of treatment. Despite the high overlap in the clinic, the neural mechanisms of pain and trauma are often studied separately. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) scans were completed among a diagnostically heterogeneous sample of veterans with a range of back pain and trauma symptoms. Using Group Iterative Multiple Model Estimation (GIMME), an effective functional connectivity analysis, we explored an unsupervised model deriving subgroups based on path similarity in a priori defined regions of interest (ROIs) from brain regions implicated in the experience of pain and trauma. Three subgroups were identified by patterns in functional connection and differed significantly on several psychological measures despite similar demographic and diagnostic characteristics. The first subgroup was highly connected overall, was characterized by functional connectivity from the nucleus accumbens (NAc), the anterior cingulate cortex (ACC), and the posterior cingulate cortex (PCC) to the insula and scored low on pain and trauma symptoms. The second subgroup did not significantly differ from the first subgroup on pain and trauma measures but was characterized by functional connectivity from the ACC and NAc to the thalamus and from ACC to PCC. The third subgroup was characterized by functional connectivity from the thalamus and PCC to NAc and scored high on pain and trauma symptoms. Our results suggest that, despite demographic and diagnostic similarities, there may be neurobiologically dissociable biotypes with different mechanisms for managing pain and trauma. These findings may have implications for the determination of appropriate biotype-specific interventions that target these neurological systems.

Unlike other functional integration methods that examine the relationship and correlation between two channels, effective connection reports the direct effect of one channel on another and expresses their causal relationship. In this article, we investigate and classify electroencephalographic (EEG) signals based on effective connectivity. In this study, we leverage the Granger causality (GC) relationship, a method for measuring effective connectivity, to analyze EEG signals from both healthy individuals and those with autism. The EEG signals examined in this article were recorded during the presentation of abstract images. Given the nonstationary nature of EEG signals, a vector autoregression model has been employed to model the relationships between signals across different channels. GC is then used to quantify the influence of these channels on one another. Selecting regions of interest (ROI) is a critical step, as the quality of the time periods under consideration significantly impacts the outcomes of the connectivity analysis among the electrodes. By comparing these effects in the ROI and various areas, we have distinguished healthy subjects from those suffering from autism. Furthermore, through statistical analysis, we have compared the results between healthy individuals and those with autism. It has been observed that the causal relationship between these two hemispheres is significantly weaker in healthy individuals compared to those with autism.

A prominent finding of postmortem and molecular imaging studies on Alzheimer's disease (AD) is the accumulation of neuropathological proteins in brain regions of the default mode network (DMN). Molecular models suggest that the progression of disease proteins depends on the directionality of signaling pathways. At network level, effective connectivity (EC) reflects directionality of signaling pathways. We hypothesized a specific pattern of EC in the DMN of patients with AD, related to cognitive impairment. Metabolic connectivity mapping is a novel measure of EC identifying regions of signaling input based on neuroenergetics. We simultaneously acquired resting-state functional MRI and FDG-PET data from patients with early AD (n = 35) and healthy subjects (n = 18) on an integrated PET/MR scanner. We identified two distinct subnetworks of EC in the DMN of healthy subjects: an anterior part with bidirectional EC between hippocampus and medial prefrontal cortex and a posterior part with predominant input into medial parietal cortex. Patients had reduced input into the medial parietal system and absent input from hippocampus into medial prefrontal cortex (p

It is posited that cognitive and affective dysfunction in patients with major depression disorder (MDD) may be caused by dysfunctional signal propagation in the brain. By leveraging dynamic causal modeling, we investigated large-scale directed signal propagation (effective connectivity) among distributed large-scale brain networks with 43 MDD patients and 56 healthy controls. The results revealed the existence of two mutual inhibitory systems: the anterior default mode network, auditory network, sensorimotor network, salience network and visual networks formed an “emotional” brain, while the posterior default mode network, central executive networks, cerebellum and dorsal attention network formed a “rational brain”. These two networks exhibited excitatory intra-system connectivity and inhibitory inter-system connectivity. Patients were characterized by potentiated intra-system connections within the “emotional/sensory brain”, as well as over-inhibition of the “rational brain” by the “emotional/sensory brain”. The hierarchical architecture of the large-scale effective connectivity networks was then analyzed using a PageRank algorithm which revealed a shift of the controlling role of the “rational brain” to the “emotional/sensory brain” in the patients. These findings inform basic organization of distributed large-scale brain networks and furnish a better characterization of the neural mechanisms of depression, which may facilitate effective treatment.

Despite the prevalence of Parkinson’s disease (PD), there are no clinically-accepted neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or differentiate Parkinson’s disease from atypical progressive parkinsonian diseases. Since abnormal connectivity in the motor circuit and basal ganglia have been previously shown as early markers of neurodegeneration, we hypothesize that patterns of interregional connectivity could be useful to form patient-specific predictive models of disease state and of PD progression. We use fMRI data from subjects with Multiple System Atrophy (MSA), Progressive Supranuclear Palsy (PSP), idiopathic PD, and healthy controls to construct predictive models for motor and cognitive decline and differentiate between the four subgroups. Further, we identify the specific connections most informative for progression and diagnosis. When predicting the one-year progression in the MDS-UPDRS-III1*1* A summary of all acronyms used in this paper is presented in Appendix A. Supplementary Data. and Montreal Cognitive assessment (MoCA), we achieve new state-of-the-art mean absolute error performance. Additionally, the balanced accuracy we achieve in the diagnosis of PD, MSA, PSP, versus healthy controls surpasses that attained in most clinics, underscoring the relevance of the brain connectivity features. Our models reveal the connectivity between deep nuclei, motor regions, and the thalamus as the most important for prediction. Collectively these results demonstrate the potential of fMRI connectivity as a prognostic biomarker for PD and increase our understanding of this disease.

IntroductionPrevious studies have shown disrupted effective connectivity in the large-scale brain networks of individuals with major depressive disorder (MDD). However, it is unclear whether these changes differ between first-episode drug-naive MDD (FEDN-MDD) and recurrent MDD (R-MDD).MethodsThis study utilized resting-state fMRI data from 17 sites in the Chinese REST-meta-MDD project, consisting of 839 patients with MDD and 788 normal controls (NCs). All data was preprocessed using a standardized protocol. Then, we performed a granger causality analysis to calculate the effectivity connectivity (EC) within and between brain networks for each participant, and compared the differences between the groups.ResultsOur findings revealed that R-MDD exhibited increased EC in the fronto-parietal network (FPN) and decreased EC in the cerebellum network, while FEDN-MDD demonstrated increased EC from the sensorimotor network (SMN) to the FPN compared with the NCs. Importantly, the two MDD subgroups displayed significant differences in EC within the FPN and between the SMN and visual network. Moreover, the EC from the cingulo-opercular network to the SMN showed a significant negative correlation with the Hamilton Rating Scale for Depression (HAMD) score in the FEDN-MDD group.ConclusionThese findings suggest that first-episode and recurrent MDD have distinct effects on the effective connectivity in large-scale brain networks, which could be potential neural mechanisms underlying their different clinical manifestations.

This study discusses the effective connectivity in the brain and its time course in realizing perspective taking in verbal communication through electroencephalogram (EEG) associated with the understanding of Japanese utterances. We manipulated perspective taking in a sentence with the Japanese subsidiary verbs -ageru and -kureru, which mean "to give". We measured the EEG during the auditory présentation of the sentences with a multichannel electroencephalograph, and the partial directed coherence and its temporal variations were analyzed using the source localization method to examine causal interactions between nineteen régions of interest in the brain. Three différent processing stages were recognized on the basis of the connectivity hubs, direction of information flow, increase or decrease in flow, and temporal variation. We suggest that perspective taking in speech compréhension is realized by interactions between the mentalizing network, mirror neuron network, and executive control network. Furthermore, we found that individual différences in the sociality of typically developing adult speakers were systematically related to effective connectivity. In particular, attention switching was deeply concerned with perspective taking in real time, and the precuneus played a crucial rôle in implementing individual différences. [ABSTRACT FROM AUTHOR]

Background: Many studies have investigated the effect of total sleep deprivation (TSD) on resting-state functional networks, especially the default mode network (DMN) and sensorimotor network (SMN), using functional connectivity. While it is known that the activities of these networks differ based on eye state, it remains unclear how TSD affects them in different eye states. Therefore, we aimed to examine the effect of TSD on DMN and SMN in different eye states using effective functional connectivity via isolated effective coherence (iCoh) in exact lowresolution brain electromagnetic tomography (eLORETA). Methods: Resting-state electroencephalogram (EEG) signals were collected from 24 male college students, and each participant completed a psychomotor vigilance task (PVT) while behavioral data were acquired. Each participant underwent 36-h TSD, and the data were acquired in two sleep-deprivation times (rested wakefulness, RW: 0 h; and TSD: 36 h) and two eye states (eyes closed, EC; and eyes open, EO). Changes in neural oscillations and effective connectivity were compared based on paired t-test. Results: The behavioral results showed that PVT reaction time was significantly longer in TSD compared with that of RW. The EEG results showed that in the EO state, the activity of high-frequency bands in the DMN and SMN were enhanced compared to those of the EC state. Furthermore, when compared with the DMN and SMN of RW, in TSD, the activity of DMN was decreased, and SMN was increased. Moreover, the changed effective connectivity in the DMN and SMN after TSD was positively correlated with an increased PVT reaction time. In addition, the effective connectivity in the different network (EO-EC) of the SMN was reduced in the ß band after TSD compared with that of RW. Conclusion: These findings indicate that TSD impairs alertness and sensory information input in the SMN to a greater extent in an EO than in an EC state. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Different facets of divergent thinking (DT) are associated with connectivity between different cerebral areas. However, the causal interactions between the key DT nodes have yet to be explored. It is hypothesised that with creativity stimulation, changes in effective connectivity among regions will be observed. MATERIALS AND METHODS: By using control (n=26) and experimental (n=24) participants, this study aimed to investigate the effective connectivity between brain areas associated with divergent thinking that accentuate fluency, flexibility, and originality. The experimental participants attended a two-day creativity stimulation, followed by three task-based fMRI sessions for all participants, which included basic use (BU) identification, alternative use (AU) generation and unusual use (UU) determination tasks. Dynamic causal modelling (DCM) was used to determine the most optimal causal model representing the most possible modulatory influence on the connections between medial prefrontal cortices (mPFC), inferior frontal gyrus (IFG), and inferior parietal lobule (IPL). RESULTS: The experimental participants scored higher fluency and flexibility than the controls (p<0.05). At neuronal level, the control group showed similar intrinsic connections receiving modulatory influence for AU and UU tasks, while the experimental group preferred a different perturbation of connection between both tasks. These intergroup differences may be caused by different thinking strategies involving semantic and episodic memory retrieval, and integration of remotely associated ideas to construct new combination among the experimental participants. CONCLUSION: Different DT demands may influence the effective connectivity between mPFC, IFG and IPL especially among individuals with higher DT abilities, especially in fluency and flexibility versus originality. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the effective connectivity (EC) changes after multisite repetitive transcranial magnetic stimulation (rTMS) combined with cognitive training (COG). Method: We selected 51 patients with mild or moderate Alzheimer's disease (AD) and delivered 10Hz rTMS over the left dorsal lateral prefrontal cortex (DLPFC) and the lateral temporal lobe (LTL) combined with COG or sham stimulation for 4 weeks. The selected AD patients were divided into real (real rTMS+COG, n = 11) or sham (sham rTMS+COG, n = 8) groups to undergo neuropsychological assessment, resting-state fMRI, and 3D brain structural imaging before (T0), immediately at the end of treatment (T4), and 4 weeks after treatment (T8). A 2 × 3 factorial design with "time" as the within-subjects factor (three levels: T0, T4, and T8) and "group" as the between-subjects factor (two levels: real and sham) was used to investigate the EC changes related to the stimulation targets in the rest of the brain, as well as the causal interactions among seven resting-state networks based on Granger causality analysis (GCA). Results: At the voxel level, the EC changes from the left DLPFC out to the left inferior parietal lobe and the left superior frontal gyrus, as well as from the left LTL out to the left orbital frontal cortex, had a significant group × time interaction effect. At the network level, a significant interaction effect was identified in the increase in EC from the limbic network out to the default mode network. The decrease in EC at the voxel level and the increase in EC at the network level were both associated with the improved ability to perform activities of daily living and cognitive function. Conclusion: Multisite rTMS combined with cognitive training can modulate effective connectivity in patients with AD, resulting in improved ability to perform activities of daily living and cognitive function. [ABSTRACT FROM AUTHOR]

Neurosurgical intervention is the best available treatment for selected patients with drug resistant epilepsy. For these patients, surgical planning requires biomarkers that delineate the epileptogenic zone, the brain area that is indispensable for the generation of seizures. Interictal spikes recorded with electrophysiological techniques are considered key biomarkers of epilepsy. Yet, they lack specificity, mostly because they propagate across brain areas forming networks. Understanding the relationship between interictal spike propagation and functional connections among the involved brain areas may help develop novel biomarkers that can delineate the epileptogenic zone with high precision. Here, we reveal the relationship between spike propagation and effective connectivity among onset and areas of spread and assess the prognostic value of resecting these areas. We analysed intracranial EEG data from 43 children with drug resistant epilepsy who underwent invasive monitoring for neurosurgical planning. Using electric source imaging, we mapped spike propagation in the source domain and identified three zones: onset, early-spread and late-spread. For each zone, we calculated the overlap and distance from surgical resection. We then estimated a virtual sensor for each zone and the direction of information flow among them via Granger causality. Finally, we compared the prognostic value of resecting these zones, the clinically-defined seizure onset zone and the spike onset on intracranial EEG channels by estimating their overlap with resection. We observed a spike propagation in source space for 37 patients with a median duration of 95 ms (interquartile range: 34–206), a spatial displacement of 14 cm (7.5–22 cm) and a velocity of 0.5 m/s (0.3–0.8 m/s). In patients with good surgical outcome (25 patients, Engel I), the onset had higher overlap with resection [96% (40–100%)] than early-spread [86% (34–100%), P = 0.01] and late-spread [59% (12–100%), P = 0.002], and it was also closer to resection than late-spread [5 mm versus 9 mm, P = 0.007]. We found an information flow from onset to early-spread in 66% of patients with good outcomes, and from early-spread to onset in 50% of patients with poor outcome. Finally, resection of spike onset, but not area of spike spread or the seizure onset zone, predicted outcome with positive predictive value of 79% and negative predictive value of 56% (P = 0.04). Spatiotemporal mapping of spike propagation reveals information flow from onset to areas of spread in epilepsy brain. Surgical resection of the spike onset disrupts the epileptogenic network and may render patients with drug resistant epilepsy seizure-free without having to wait for a seizure to occur during intracranial monitoring. [ABSTRACT FROM AUTHOR]

Gamma-band (40-Hz) activity is critical for cortico-cortical transmission and the integration of information across neural networks during sensory and cognitive processing. Patients with schizophrenia show selective reductions in the capacity to support synchronized gamma-band oscillations in response to auditory stimulation presented 40-Hz. Despite widespread application of this 40-Hz auditory steady-state response (ASSR) as a translational electroencephalographic biomarker for therapeutic development for neuropsychiatric disorders, the spatiotemporal dynamics underlying the ASSR have not been fully characterized. In this study, a novel Granger causality analysis was applied to assess the propagation of gamma oscillations in response to 40-Hz steady-state stimulation across cortical sources in schizophrenia patients (n = 426) and healthy comparison subjects (n = 293). Both groups showed multiple ASSR source interactions that were broadly distributed across brain regions. Schizophrenia patients showed distinct, hierarchically sequenced connectivity abnormalities. During the response onset interval, patients exhibited abnormal increased connectivity from the inferior frontal gyrus to the superior temporal gyrus, followed by decreased connectivity from the superior temporal to the middle cingulate gyrus. In the later portion of the ASSR response (300-500 ms), patients showed significantly increased connectivity from the superior temporal to the middle frontal gyrus followed by decreased connectivity from the left superior frontal gyrus to the right superior and middle frontal gyri. These findings highlight both the orchestration of distributed multiple sources in response to simple gamma-frequency stimulation in healthy subjects as well as the patterns of deficits in the generation and maintenance of gamma-band oscillations across the temporo-frontal sources in schizophrenia patients.

Background: While single pulse electrical stimulation (SPES) is increasingly used to study effective connectivity, the effects of varying stimulation parameters on the resulting cortico-cortical evoked potentials (CCEPs) have not been systematically explored. Objective: We sought to understand the interacting effects of stimulation pulse width, current intensity, and charge on CCEPs through an extensive testing of this parameter space and analysis of several response metrics. Methods: We conducted SPES in 11 patients undergoing intracranial EEG monitoring using five combinations of current intensity (1.5, 2.0, 3.0, 5.0, and 7.5 mA) and pulse width at each of three charges (0.750, 1.125, and 1.500 μC/phase) to study how CCEP amplitude, distribution, latency, morphology, and stimulus artifact amplitude vary with each parameter. Results: Stimulations with a greater charge or a greater current intensity and shorter pulse width at a given charge generally resulted in greater CCEP amplitudes and spatial distributions, shorter latencies, and increased waveform correlation. These effects interacted such that stimulations with the lowest charge and highest current intensities resulted in greater response amplitudes and spatial distributions than stimulations with the highest charge and lowest current intensities. Stimulus artifact amplitude increased with charge, but this could be mitigated by using shorter pulse widths. Conclusions: Our results indicate that individual combinations of current intensity and pulse width, in addition to charge, are important determinants of CCEP magnitude, morphology, and spatial extent. Together, these findings suggest that high current intensity, short pulse width stimulations are optimal SPES settings for eliciting strong and consistent responses while minimizing charge.