Resting-state Functional Connectivity Networks Research Articles

Neural Substrates of Muscle Co-contraction during Dynamic Motor Adaptation.

As we learn to perform a motor task with novel dynamics, the central nervous system must adapt motor commands and modify sensorimotor transformations. The objective of the current research is to identify the neural mechanisms underlying the adaptive process. It has been shown previously that an increase in muscle co-contraction is frequently associated with the initial phase of adaptation and that co-contraction is gradually reduced as performance improves. Our investigation focused on the neural substrates of muscle co-contraction during the course of motor adaptation using a resting-state fMRI approach in healthy human subjects of both genders. We analyzed the functional connectivity in resting-state networks during three phases of adaptation, corresponding to different muscle co-contraction levels and found that change in the strength of functional connectivity in one brain network was correlated with a metric of co-contraction, and in another with a metric of motor learning. We identified the cerebellum as the key component for regulating muscle co-contraction, especially its connection to the inferior parietal lobule, which was particularly prominent in early stage adaptation. A neural link between cerebellum, superior frontal gyrus and motor cortical regions was associated with reduction of co-contraction during later stages of adaptation. We also found reliable changes in the functional connectivity of a network involving primary motor cortex, superior parietal lobule and cerebellum that were specifically related to the motor learning.SIGNIFICANCE STATEMENT It is well known that co-contracting muscles is an effective strategy for providing postural stability by modulating mechanical impedance and thereby allowing the central nervous system to compensate for unfamiliar or unexpected physical conditions until motor commands can be appropriately adapted. The present study elucidates the neural substrates underlying the ability to modulate the mechanical impedance of a limb as we learn during motor adaptation. Using resting-state fMRI analysis we demonstrate that a distributed cerebellar-parietal-frontal network functions to regulate muscle co-contraction with the cerebellum as its key component.

Altered resting-state functional network connectivity in profound sensorineural hearing loss infants within an early sensitive period: A group ICA study.

Data from both animal models and deaf children provide evidence for that the maturation of auditory cortex has a sensitive period during the first 2–4 years of life. During this period, the auditory stimulation can affect the development of cortical function to the greatest extent. Thus far, little is known about the brain development trajectory after early auditory deprivation within this period. In this study, independent component analysis (ICA) technique was used to detect the characteristics of brain network development in children with bilateral profound sensorineural hearing loss (SNHL) before 3 years old. Seven resting‐state networks (RSN) were identified in 50 SNHL and 36 healthy controls using ICA method, and further their intra‐and inter‐network functional connectivity (FC) were compared between two groups. Compared with the control group, SNHL group showed decreased FC within default mode network, while enhanced FC within auditory network (AUN) and salience network. No significant changes in FC were found in the visual network (VN) and sensorimotor network (SMN). Furthermore, the inter‐network FC between SMN and AUN, frontal network and AUN, SMN and VN, frontal network and VN were significantly increased in SNHL group. The results implicate that the loss and the compensatory reorganization of brain network FC coexist in SNHL infants. It provides a network basis for understanding the brain development trajectory after hearing loss within early sensitive period.

A research on resting-state functional network connectivity after rt-fMRI neurofeedback in insomnia

Using resting-state functional connectivity based on regions of interest to explore connectivity changes between specific brain regions and the whole brain in insomnia patients after real-time functional magnetic resonance neurofeedback (rt-fMRI NF) therapy has the limitation that the brain information used is not comprehensive enough. While the use of functional network connectivity (FNC) can more systematically and effectively explore the effect of neurofeedback intervention on brain activity in patients with insomnia. In this paper, we used independent component analysis (ICA) method to reveal the spatial properties of brain networks in the whole brain of insomnia patients, then examined and compared the FNC before and after rt-fMRI neurofeedback. The whole-brain FNC results show that the functional connectivity between the executive control network (ECN) and salience network (SAN), default mode network (DMN) were significantly decreased, while the connectivity between the DMN and the SAN was increased. In summary, rt-fMRI neurofeedback training for insomnia patients alters intrinsic functional network connectivity, which may provide objective neuroimage evidence for emotion and sleep improvement in insomnia patients.

Rapidly spreading seizures arise from large-scale functional brain networks in focal epilepsy

It remains unclear whether epileptogenic networks in focal epilepsy develop on physiological networks. This work aimed to explore the association between the rapid spread of ictal fast activity (IFA), a proposed biomarker for epileptogenic networks, and the functional connectivity or networks of healthy subjects. We reviewed 45 patients with focal epilepsy who underwent electrocorticographic (ECoG) recordings to identify the patients showing the rapid spread of IFA. IFA power was quantified as normalized beta-gamma band power. Using published resting-state functional magnetic resonance imaging databases, we estimated resting-state functional connectivity of healthy subjects (RSFC-HS) and resting-state networks of healthy subjects (RSNs-HS) at the locations corresponding to the patients' electrodes. We predicted the IFA power of each electrode based on RSFC-HS between electrode locations (RSFC-HS-based prediction) using a recently developed method, termed activity flow mapping. RSNs-HS were identified using seed-based and atlas-based methods. We compared IFA power with RSFC-HS-based prediction or RSNs-HS using non-parametric correlation coefficients. RSFC and seed-based RSNs of each patient (RSFC-PT and seed-based RSNs-PT) were also estimated using interictal ECoG data and compared with IFA power in the same way as RSFC-HS and seed-based RSNs-HS. Spatial autocorrelation-preserving randomization tests were performed for significance testing. Nine patients met the inclusion criteria. None of the patients had reflex seizures. Six patients showed pathological evidence of a structural etiology. In total, we analyzed 49 seizures (2–13 seizures per patient). We observed significant correlations between IFA power and RSFC-HS-based prediction, seed-based RSNs-HS, or atlas-based RSNs-HS in 28 (57.1%), 21 (42.9%), and 28 (57.1%) seizures, respectively. Thirty-two (65.3%) seizures showed a significant correlation with either seed-based or atlas-based RSNs-HS, but this ratio varied across patients: 27 (93.1%) of 29 seizures in six patients correlated with either of them. Among atlas-based RSNs-HS, correlated RSNs-HS with IFA power included the default mode, control, dorsal attention, somatomotor, and temporal-parietal networks. We could not obtain RSFC-PT and RSNs-PT in one patient due to frequent interictal epileptiform discharges. In the remaining eight patients, most of the seizures showed significant correlations between IFA power and RSFC-PT-based prediction or seed-based RSNs-PT. Our study provides evidence that the rapid spread of IFA in focal epilepsy can arise from physiological RSNs. This finding suggests an overlap between epileptogenic and functional networks, which may explain why functional networks in patients with focal epilepsy frequently disrupt.

Cortico‐subcortical functional connectivity modifications in fatigued multiple sclerosis patients treated with fampridine and amantadine

Fatigue in multiple sclerosis (MS) is common and disabling; medication efficacy is still not fully proven. The aim of this study was to investigate 4-week modifications of fatigue severity in 45 relapsing-remitting MS patients after different symptomatic treatments, and changes in concomitant resting state (RS) functional connectivity (FC). Patients were randomly, blindly assigned to treatment with fampridine (n=15), amantadine (n=15) or placebo (n=15), and underwent clinical assessment and 3-Tesla RS functional magnetic resonance imaging at baseline (t0) and after 4weeks (w4) of treatment. Fifteen healthy controls (HCs) were also studied. Changes in modified fatigue impact scale (MFIS) score and network RS FC were assessed. In MS, abnormalities of network RS FC at t0 did not differ between treatment groups and correlated with fatigue severity. At w4, global scores and subscores on the MFIS decreased in all groups, with no time-by-treatment interaction. At w4, all patient groups had changes in RS FC in several networks, with significant time-by-treatment interactions in basal ganglia, sensorimotor and default-mode networks in fampridine-treated patients versus the other groups, and in frontoparietal network in amantadine-treated patients. In the fampridine group, RS FC changes correlated with concurrently decreased MFIS score (r range=-0.75 to 0.74, p range=0.003-0.05). Fatigue improved in all MS groups, independently of treatment. Concomitant RS FC modifications were located in sensorimotor, inferior frontal and subcortical regions for fampridine- and amantadine-treated patients, and in associative sensory cortices for placebo-treated patients.

Default mode network functional connectivity negatively associated with trait openness to experience.

Evaluating associations between the five-factor personality domains and resting-state functional connectivity networks (e.g. default mode network, DMN) highlights distributed neurobiological systems linked to behaviorally relevant phenotypes. Establishing these associations can highlight a potential underlying role for these neural pathways in related clinical illness and treatment response. Here, we examined associations between within- and between-network resting-state functional connectivity with functional magnetic resonance imaging and the five-factor personality domains: Openness to experience (Openness), Extraversion, Neuroticism, Agreeableness and Conscientiousness. We included data from 470 resting-state scan sessions and personality assessments in 295 healthy participants. Within- and between-network functional connectivity from 32 a priori defined regions was computed across seven resting-state networks. The association between functional connectivity and personality traits was assessed using generalized least squares. Within-network DMN functional connectivity was significantly negatively associated with trait Openness (regression coefficient = −0.0010; [95% confidence interval] = [−0.0017, −0.0003]; PFWER = 0.033), seemingly driven by association with the Fantasy subfacet. Trait Extraversion was significantly negatively associated with functional connectivity between the visual and dorsal attention networks and positively associated with functional connectivity between the frontoparietal and language networks. Our findings provide evidence that resting-state DMN is associated with trait Openness and gives insight into personality neuroscience.

Neurocognitive disorders in the elderly: altered functional resting-state hyperconnectivities in postoperative delirium patients

Postoperative delirium (POD) represents a confusional state during days/weeks after surgery and is particularly frequent in elderly patients. Hardly any fMRI studies were conducted to understand the underlying pathophysiology of POD patients. This prospective observational cohort study aims to examine changes of specific resting-state functional connectivity networks across different time points (pre- and 3–5 months postoperatively) in delirious patients compared to no-POD patients. Two-hundred eighty-three elderly surgical patients underwent preoperative resting-state fMRI (46 POD). One-hundred seventy-eight patients completed postoperative scans (19 POD). For functional connectivity analyses, three functional connectivity networks with seeds located in the orbitofrontal cortex (OFC), nucleus accumbens (NAcc), and hippocampus were investigated. The relationship of POD and connectivity changes between both time points (course connectivity) were examined (ANOVA). Preoperatively, delirious patients displayed hyperconnectivities across the examined functional connectivity networks. In POD patients, connectivities within NAcc and OFC networks demonstrated a decrease in course connectivity [max. F = 9.03, p = 0.003; F = 4.47, p = 0.036, resp.]. The preoperative hyperconnectivity in the three networks in the patients at risk for developing POD could possibly indicate existing compensation mechanisms for subtle brain dysfunction. The observed pathophysiology of network function in POD patients at least partially involves dopaminergic pathways.

Association between metabolic syndrome and resting-state functional brain connectivity

The objective of this study is to examine whether metabolic syndrome (MetS), the clustering of 3 or more cardiovascular risk factors, disrupts the resting-state functional connectivity (FC) of the large-scale cortical brain networks. Resting-state functional magnetic resonance imaging data were collected from seventy-eight middle-aged and older adults living with and without MetS (27 MetS; 51 non-MetS). FC maps were derived from the time series of intrinsic activity in the large-scale brain networks by correlating the spatially averaged time series with all brain voxels using a whole-brain seed-based FC approach. Participants with MetS showed hyperconnectivity across the core brain regions with evidence of loss of modularity when compared with non-MetS individuals. Furthermore, patterns of higher between-network MetS-related effects were observed across most of the seed regions in both right and left hemispheres. These findings indicate that MetS is associated with altered intrinsic communication across core neural networks and disrupted between-network connections across the brain due to the co-occurring vascular risk factors in MetS.

Patterns of Infant Amygdala Connectivity Mediate the Impact of High Caregiver Affect on Reducing Infant Smiling: Discovery and Replication

BackgroundBehavioral research indicates that caregiver mood disorders and emotional instability in the early months following childbirth are associated with lower positive emotionality and higher negative emotionality in infants, but the neural mechanisms remain understudied. MethodsUsing resting-state functional connectivity as a measure of the functional architecture of the early infant brain, we aimed to determine the extent to which connectivity between the amygdala, a key region supporting emotional learning and perception, and large-scale neural networks mediated the association between caregiver affect and anxiety and early infant negative emotionality and positive emotionality. Two samples of infants (first sample: n = 58; second sample: n = 31) 3 months of age underwent magnetic resonance imaging during natural sleep. ResultsDuring infancy, greater resting-state functional connectivity between the amygdala and the salience network and, to a lesser extent, lower amygdala and executive control network resting-state functional connectivity mediated the effect of greater caregiver postpartum depression and trait anxiety on reducing infant smiling (familywise error–corrected p < .05). Furthermore, results from the first sample were replicated in the second, independent sample, to a greater extent for caregiver depression than for caregiver anxiety. ConclusionsWe provide evidence of early objective neural markers that can help identify infants who are more likely to be at risk from, versus those who might be protected against, the deleterious effects of caregiver depression and anxiety and reduced positive emotionality.

Detection of prenatal alcohol exposure using machine learning classification of resting-state functional network connectivity data.

Fetal Alcohol Spectrum Disorder (FASD), a wide range of physical and neurobehavioral abnormalities associated with prenatal alcohol exposure (PAE), is recognized as a significant public health concern. Advancements in the diagnosis of FASD have been hindered by a lack of consensus in diagnostic criteria and limited use of objective biomarkers. Previous research from our group utilized resting-state functional magnetic resonance imaging (fMRI) to measure functional network connectivity (FNC), which revealed several sex- and region-dependent alterations in FNC as a result of moderate PAE relative to controls. Considering that FNC is sensitive to moderate PAE, this study explored the use of FNC data and machine learning methods to detect PAE among a sample of rodents exposed to alcohol prenatally and controls. We utilized previously acquired resting state fMRI data collected from adult rats exposed to moderate levels of prenatal alcohol (PAE) or a saccharin control solution (SAC) to assess FNC of resting state networks extracted by spatial group independent component analysis (GICA). FNC data were subjected to binary classification using support vector machine (SVM) -based algorithms and leave-one-out-cross validation (LOOCV) in an aggregated sample of males and females (n=48; 12 male PAE, 12 female PAE, 12 male SAC, 12 female SAC), a males-only sample (n=24; 12 PAE, 12 SAC), and a females-only sample (n=24; 12 PAE, 12 SAC). Results revealed that a quadratic SVM (QSVM) kernel was significantly effective for PAE detection in females. QSVM kernel-based classification resulted in accuracy rates of 62.5% for all animals, 58.3% for males, and 79.2% for females. Additionally, qualitative evaluation of QSVM weights implicates an overarching theme of several hippocampal and cortical networks in contributing to the formation of correct classification decisions by QSVM. Our results suggest that binary classification using QSVM and adult female FNC data is a potential candidate for the translational development of novel and non-invasive techniques for the identification of FASD.

Working memory network plasticity after exercise intervention detected by task and resting-state functional MRI

ABSTRACT The current study examined the effects of an 11-week exercise intervention on brain activity during a working memory (WM) task and resting-state functional network connectivity in deaf children. Twenty-six deaf children were randomly assigned to either an 11-week exercise intervention or control conditions. Before and after the exercise intervention, all participants were scanned with functional magnetic resonance imaging (fMRI) during N-back task performance and a resting state. The behavioural results showed that the exercise intervention improved WM performance. Task activation analyses showed an increase in the parietal, occipital, and temporal gyri and hippocampus and hippocampus (HIP). In addition, WM performance improvements were associated with greater activation in the left HIP region. Resting-state functional connectivity (Rs-FC) between HIP and certain other brain areas shown a significant interaction of group (exercise versus no exercise) and time (pre- and postintervention). Moreover, connectivity between the left HIP and left middle frontal gyrus was related to improved WM performance. These data extend current knowledge by indicating that an exercise intervention can improve WM in deaf children, and these enhancements may be related to the WM network plasticity changes induced by exercise.

Impact of self-designed Ningxin Anshen Decoction on the resting-state network functional connectivity in patients with mild to moderate generalized anxiety disorders.

Generalized anxiety disorder (GAD) is a persistent chronic excessive anxiety that is hard to control. Our previous study indicated that self-designed Ningxin Anshen Formula (NXAS) was effective to treat mild to moderate GAD patients. This study is a randomized controlled clinical trial and aimed to investigate the imapct of self-designed NXAS on the resting-state functional connectivity (rsFC) in patients with mild to moderate GAD and explore the potential mechanisms. A total of 61 patients diagnosed with mild to moderate GAD were recruited and divided into two groups randomly: NXAS group (n=31) and placebo group (n=30). Before and after treatment, the rsFC was examined by resting-state functional MRI (rs-fMRI), the anxiety was assessed with HAMA, and the independent component analysis (ICA) was used to analyze the resting-state functional connectivity (rsFC). The correlation between HAMA score and abnormal rsFC was further evaluated. The default mode network (DMN) showed evident rsFC interaction in the PCUN in both groups before and after therapy. The salience network (SN) showed obvious rsFC interaction in the bilateral gyrus frontalis inferiors and bilateral gyri temporalis superiors before and after therapy. In the NXAS group, the rsFC interction reduced significantly in the left gyrus frontalis inferior, but remained unchanged in the right gyrus frontalis inferior and bilateral gyri temporalis superiors after therapy. In the control group, the rsFC interaction increased dramatically after treatment. In addition, the abnormal rsFC had no relationship with HAMA score. The self-designed NXAS can increase the rsFC in the PUCN on DMN and reduce rsFC in the orbIFG.L on SN to exert anti-anxiety effect.

Measuring functional connectivity in patients with strabismus using stationary functional magnetic resonance imaging: a resting-state network study.

Strabismus (STR) is a common eye disease characterized by abnormal eye movements and stereo vision. Neuroimaging studies have revealed that STR patients have impaired functional connectivity (FC) in the visual cortex and sensorimotor cortex. To investigate alterations in FC and connections within and between subnetworks of the visual network (VN), sensorimotor network (SMN), and default mode network (DMN) in patients with STR. A total of 32 patients with STR (24 men, 8 women) and 32 age- and sex-matched healthy controls (HCs) (24 men, 8 women) were recruited. Participants underwent resting-state functional magnetic resonance imaging scans. The resting-state network (RSN) was examined by independent component analysis, and differences in RSN FC between STR and HC groups were evaluated with the t test. Functional network connectivity (FNC) analysis was performed for the three RSNs. Compared to the HC group, the STR group showed increased FC in the VN and SMN (voxel-level P < 0.01; two-tailed Gaussian random field correction; cluster-level P < 0.05). There were no significant alterations in DMN FC between the two groups. FNC analysis of connections in the RSN revealed that one of the three connections in the VN was reduced, but no connectivity changes were observed in the SMN or DMN. FNC analysis of the connection between two RSNs showed that two had increased and one had a decreased connection value. The VN, SMN, and DMN are reorganized in patients with STR compared to HCs, providing novel insight into the neural substrates of STR.

Evaluation of Resting-State Functional Magnetic Resonance Imaging Findings of Patients with Social Anxiety Disorder.

The most prominent functional magnetic resonance imaging findings about social anxiety disorder are increased activity in emotional regulation areas (amygdala, insula, hippocampus, dorsal anterior cingulate cortex) and fear circuit, and altered activity in prefrontal cortex. This study aims to investigate network abnormalities during resting state. Resting state functional magnetic resonance images of 21 drug-free patients with social anxiety disorder and 21 healthy controls (matched on age, gender, and years of education) were recorded. Resting state functional connectivity networks were obtained with independent component analysis, and were compared by using the voxel based t-test between the two groups. Patients with social anxiety disorder displayed decreased intrinsic functional connectivity in the anterior component of the salience network (left orbitofrontal cortex) and increased intrinsic functional connectivity in the posterior component of the salience network (left supramarginal gyrus). Most of the studies about social anxiety disorder mainly focused on fear circuit and emotional regulation areas by using anxiety provoking tasks or by using seed based analysis of functional connectivity. By applying a whole-brain independent component analysis, we found altered functional connectivity in the salience network, but no significant difference was found in the fear circuit areas. Our results suggest that abnormal connectivity in the salience network might play a crucial role in the neurobiology of social anxiety disorder.

Resting-state functional network connectivity underlying eating disorder symptoms in healthy young adults

Previous neuroimaging research of eating disorders such as anorexia nervosa and bulimia nervosa has mainly focused on clinical patients, indicating the crucial role of intrinsic connectivity networks involved in aberrant behavioral control (i.e., executive control network), reward reactivity (i.e., basal ganglia network), and excessive self-focused and body-focused ruminations (i.e., default mode network) in the onset and maintenance of eating disturbances. However, examinations of large-scale resting-state networks that support the role of cognitive control, reward sensitivity, and self-directed thinking in disordered eating have rarely involved non-clinical samples from the general population. This study, involving a total of 693 healthy young adults (68.69% females; mean age, 18.37 years), investigated these issues by using pre-defined functional regions of interest from the executive control network, basal ganglia network, default mode network, and a seed-based region of interest-to-region of interest approach. After statistically controlling for differences in age, sex, body mass index, and head motion, we observed significant associations of higher levels of eating disorder symptoms, especially bulimia-type eating (i.e., binge eating and a combination of binge eating and compensatory behaviors, such as purging via self-induced vomiting or laxative use, and compulsive exercise), with weaker intra-network and inter-network functional synchrony. These results remained significant after excluding underweight, overweight, and obese participants. These findings suggest that these neural circuits may play a key role in the symptoms of disordered eating in healthy adults. They further reveal that the less efficient information exchange within and between intrinsic networks associated with self-referential thinking, inhibitory control, and reward sensitivity are strongly related to eating disorder symptoms.

Glioma-Induced Disruption of Resting-State Functional Connectivity and Amplitude of Low-Frequency Fluctuations in the Salience Network.

Cognitive challenges are prevalent in survivors of glioma, but their neurobiology is incompletely understood. The purpose of this study was to investigate the effect of glioma presence and tumor characteristics on resting-state functional connectivity and amplitude of low-frequency fluctuations of the salience network, a key neural network associated with cognition. Sixty-nine patients with glioma (mean age, 48.74 [SD, 14.32] years) who underwent resting-state fMRI were compared with 31 healthy controls (mean age, 49.68 [SD, 15.54] years). We identified 4 salience network ROIs: left/right dorsal anterior cingulate cortex and left/right anterior insula. Average salience network resting-state functional connectivity and amplitude of low-frequency fluctuations within the 4 salience network ROIs were computed. Patients with gliomas showed decreased overall salience network resting-state functional connectivity (P = .001) and increased amplitude of low-frequency fluctuations in all salience network ROIs (P < .01) except in the left dorsal anterior cingulate cortex. Compared with controls, patients with left-sided gliomas showed increased amplitude of low-frequency fluctuations in the right dorsal anterior cingulate cortex (P = .002) and right anterior insula (P < .001), and patients with right-sided gliomas showed increased amplitude of low-frequency fluctuations in the left anterior insula (P = .002). Anterior tumors were associated with decreased salience network resting-state functional connectivity (P < .001) and increased amplitude of low-frequency fluctuations in the right anterior insula, left anterior insula, and right dorsal anterior cingulate cortex. Patients with high-grade gliomas had decreased salience network resting-state functional connectivity compared with healthy controls (P < .05). The right anterior insula showed increased amplitude of low-frequency fluctuations in patients with grade II and IV gliomas compared with controls (P < .01). By demonstrating decreased resting-state functional connectivity and an increased amplitude of low-frequency fluctuations related to the salience network in patients with glioma, this study adds to our understanding of the neurobiology underpinning observable cognitive deficits in these patients. In addition to more conventional functional connectivity, amplitude of low-frequency fluctuations is a promising functional-imaging biomarker of tumor-induced vascular and neural pathology.

Recognition of Cognitive Impairment in Adult Moyamoya Disease: A Classifier Based on High-Order Resting-State Functional Connectivity Network.

Objective: Vascular cognitive impairment (VCI) is a common complication in adult patients with moyamoya disease (MMD), and is reversible by surgical revascularization in its early stage of mild VCI. However, accurate diagnosis of mild VCI is difficult based on neuropsychological examination alone. This study proposed a method of dynamic resting-state functional connectivity (FC) network to recognize global cognitive impairment in MMD.Methods: For MMD, 36 patients with VCI and 43 patients with intact cognition (Non-VCI) were included, as well as 26 normal controls (NCs). Using resting-state fMRI, dynamic low-order FC networks were first constructed with multiple brain regions which were generated through a sliding window approach and correlated in temporal dimension. In order to obtain more information of network interactions along the time, high-order FC networks were established by calculating correlations among each pair of brain regions. Afterwards, a sparse representation-based classifier was constructed to recognize MMD (experiment 1) and its cognitive impairment (experiment 2) with features extracted from both low- and high-order FC networks. Finally, the ten-fold cross-validation strategy was proposed to train and validate the performance of the classifier.Results: The three groups did not differ significantly in demographic features (p > 0.05), while the VCI group exhibited the lowest MMSE scores (p = 0.001). The Non-VCI and NCs groups did not differ significantly in MMSE scores (p = 0.054). As for the classification between MMD and NCs, the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the classifier reached 90.70, 88.57, 93.67, and 73.08%, respectively. While for the classification between VCI and Non-VCI, the AUC, accuracy, sensitivity, and specificity of the classifier reached 91.02, 84.81, 80.56, and 88.37%, respectively.Conclusion: This study not only develops a promising classifier to recognize VCI in adult MMD in its early stage, but also implies the significance of time-varying properties in dynamic FC networks.

Sex-specific differences in resting-state functional connectivity of large-scale networks in postconcussion syndrome

Concussions are associated with a range of cognitive, neuropsychological and behavioral sequelae that, at times, persist beyond typical recovery times and are referred to as postconcussion syndrome (PCS). There is growing support that concussion can disrupt network-based connectivity post-injury. To date, a significant knowledge gap remains regarding the sex-specific impact of concussion on resting state functional connectivity (rs-FC). The aims of this study were to (1) investigate the injury-based rs-FC differences across three large-scale neural networks and (2) explore the sex-specific impact of injury on network-based connectivity. MRI data was collected from a sample of 80 concussed participants who fulfilled the criteria for postconcussion syndrome and 31 control participants who did not have any history of concussion. Connectivity maps between network nodes and brain regions were used to assess connectivity using the Functional Connectivity (CONN) toolbox. Network based statistics showed that concussed participants were significantly different from healthy controls across both salience and fronto-parietal network nodes. More specifically, distinct subnetwork components were identified in the concussed sample, with hyperconnected frontal nodes and hypoconnected posterior nodes across both the salience and fronto-parietal networks, when compared to the healthy controls. Node-to-region analyses showed sex-specific differences across association cortices, however, driven by distinct networks. Sex-specific network-based alterations in rs-FC post concussion need to be examined to better understand the underlying mechanisms and associations to clinical outcomes.

Construction of embedded fMRI resting-state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling, Isometric Feature Mapping, Diffusion Maps, Locally Linear Embedding and kernel PCA. Furthermore, based on key global graph-theoretic properties of the embedded FCN, we compare their classification potential using machine learning. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the cross correlation metric. We show that diffusion maps with the cross correlation metric outperform the other combinations.

Prediction of Cognitive Task Activations via Resting-State Functional Connectivity Networks: An EEG Study

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> The resting state is an internal state that is closely related to neural activation and the performance of tasks. Studying the relationship between the resting and task states is helpful for understanding the organization of information processing. It remains unclear how information is translated between these two states. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> In this study, we focused on electroencephalography (EEG) data because its high time resolution allowed us to study processing both overall and in detail. Resting-state functional connectivity (FC) networks were constructed in the time and frequency domains. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> FC constructed by synchronization of signals in the time domain was suitable for predicting event-related potential activation. In addition, FC measured by phase distributions had superior prediction accuracy for predicting spectral power. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> Our findings suggest that there is intrinsic organization across the two states. Furthermore, the activity flow modeled in different domains could reflect different levels of neuronal activation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Significance:</i> Changes in neural activity across resting and task states on a subsecond time scale can be detected by EEG, which is helpful for understanding the underlying mechanisms of illness and therapeutic outcomes.