scholarly journals Altered Functional Connectivity Within and Between Salience and Sensorimotor Networks in Patients With Functional Constipation

2021 ◽  
Vol 15 ◽  
Author(s):  
Shijun Duan ◽  
Lei Liu ◽  
Guanya Li ◽  
Jia Wang ◽  
Yang Hu ◽  
...  
Keyword(s):  
Abdominal Pain ◽  
Functional Connectivity ◽  
Treatment Strategies ◽  
Functional Constipation ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Anterior Cingulate ◽  
Precentral Gyrus ◽  
Sensorimotor Network ◽  
Brain Functions

Functional constipation (FCon) is a common functional gastrointestinal disorder. A considerable portion of patients with FCon is associated with anxiety/depressive status (FCAD). Previous neuroimaging studies mainly focused on patients with FCon without distinguishing FCAD from FCon patients without anxiety/depressive status (FCNAD). Differences in brain functions between these two subtypes remain unclear. Thus, we employed resting-state functional magnetic resonance imaging (RS-fMRI) and graph theory method to investigate differences in brain network connectivity and topology in 41 FCAD, 42 FCNAD, and 43 age- and gender-matched healthy controls (HCs). FCAD/FCNAD showed significantly lower normalized clustering coefficient and small-world-ness. Both groups showed altered nodal degree/efficiency mainly in the rostral anterior cingulate cortex (rACC), precentral gyrus (PreCen), supplementary motor area (SMA), and thalamus. In the FCAD group, nodal degree in the SMA was negatively correlated with difficulty of defecation, and abdominal pain was positively correlated with nodal degree/efficiency in the rACC, which had a lower within-module nodal degree. The salience network (SN) exhibited higher functional connectivity (FC) with the sensorimotor network (SMN) in FCAD/FCNAD, and FC between these two networks was negatively correlated with anxiety ratings in FCAD group. Additionally, FC of anterior insula (aINS)–rACC was only correlated with constipation symptom (i.e., abdominal pain) in the FCNAD group. In the FCAD group, FCs of dorsomedial prefrontal cortex–rACC, PreCen–aINS showed correlations with both constipation symptom (i.e., difficulty of defecation) and depressive status. These findings indicate the differences in FC of the SN–SMN between FCAD and FCNAD and provide neuroimaging evidence based on brain function, which portrays important clues for improving new treatment strategies.

scholarly journals Voxel-Wise Brain-Wide Functional Connectivity Abnormalities in Patients with Primary Blepharospasm at Rest

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Pan Pan ◽  
Shubao Wei ◽  
Huabing Li ◽  
Yangpan Ou ◽  
Feng Liu ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Anterior Cingulate ◽  
Support Vector ◽  
Pathophysiological Mechanism ◽  
Precentral Gyrus ◽  
Healthy Controls ◽  
Sensorimotor Network ◽  
Specificity And Sensitivity ◽  
Postcentral Gyrus ◽  
The Right

Background. Primary blepharospasm (BSP) is one of the most common focal dystonia and its pathophysiological mechanism remains unclear. An unbiased method was used in patients with BSP at rest to observe voxel-wise brain-wide functional connectivity (FC) changes. Method. A total of 48 subjects, including 24 untreated patients with BSP and 24 healthy controls, were recruited to undergo functional magnetic resonance imaging (fMRI). The method of global-brain FC (GFC) was adopted to analyze the resting-state fMRI data. We designed the support vector machine (SVM) method to determine whether GFC abnormalities could be utilized to distinguish the patients from the controls. Results. Relative to healthy controls, patients with BSP showed significantly decreased GFC in the bilateral superior medial prefrontal cortex/anterior cingulate cortex (MPFC/ACC) and increased GFC in the right postcentral gyrus/precentral gyrus/paracentral lobule, right superior frontal gyrus (SFG), and left paracentral lobule/supplement motor area (SMA), which were included in the default mode network (DMN) and sensorimotor network. SVM analysis showed that increased GFC values in the right postcentral gyrus/precentral gyrus/paracentral lobule could discriminate patients from controls with optimal accuracy, specificity, and sensitivity of 83.33%, 83.33%, and 83.33%, respectively. Conclusion. This study suggested that abnormal GFC in the brain areas associated with sensorimotor network and DMN might underlie the pathophysiology of BSP, which provided a new perspective to understand BSP. GFC in the right postcentral gyrus/precentral gyrus/paracentral lobule might be utilized as a latent biomarker to differentiate patients with BSP from controls.

scholarly journals Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory

2021 ◽  
Vol 11 (1) ◽  
pp. 111
Author(s):  
Farzad V. Farahani ◽  
Magdalena Fafrowicz ◽  
Waldemar Karwowski ◽  
Bartosz Bohaterewicz ◽  
Anna Maria Sobczak ◽  
...  
Keyword(s):  
Resting State ◽  
Network Architecture ◽  
Inferior Frontal Gyrus ◽  
Brain Network ◽  
Time Of Day ◽  
Precentral Gyrus ◽  
Network Organization ◽  
Diurnal Variability ◽  
Sleep Inertia ◽  
Brain Functions

Significant differences exist in human brain functions affected by time of day and by people’s diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of both time of day and the individual’s chronotype on whole-brain network organization. In this regard, 62 participants (39 women; mean age: 23.97 ± 3.26 years; half morning- versus half evening-type) were scanned about 1 and 10 h after wake-up time for morning and evening sessions, respectively. We found evidence for a time-of-day effect on connectivity profiles but not for the effect of chronotype. Compared with the morning session, we found relatively higher small-worldness (an index that represents more efficient network organization) in the evening session, which suggests the dominance of sleep inertia over the circadian and homeostatic processes in the first hours after waking. Furthermore, local graph measures were changed, predominantly across the left hemisphere, in areas such as the precentral gyrus, putamen, inferior frontal gyrus (orbital part), inferior temporal gyrus, as well as the bilateral cerebellum. These findings show the variability of the functional neural network architecture during the day and improve our understanding of the role of time of day in resting-state functional networks.

scholarly journals Altered Functional Connectivity after Epileptic Seizure Revealed by Scalp EEG

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yi Liang ◽  
Chunli Chen ◽  
Fali Li ◽  
Dezhong Yao ◽  
Peng Xu ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Local Area ◽  
Brain Network ◽  
Fuzzy Entropy ◽  
Clustering Coefficient ◽  
Characteristic Path Length ◽  
Functional Brain Network ◽  
Before And After

Epileptic seizures are considered to be a brain network dysfunction, and chronic recurrent seizures can cause severe brain damage. However, the functional brain network underlying recurrent epileptic seizures is still left unveiled. This study is aimed at exploring the differences in a related brain activity before and after chronic repetitive seizures by investigating the power spectral density (PSD), fuzzy entropy, and functional connectivity in epileptic patients. The PSD analysis revealed differences between the two states at local area, showing postseizure energy accumulation. Besides, the fuzzy entropies of preseizure in the frontal, central, and temporal regions are higher than that of postseizure. Additionally, attenuated long-range connectivity and enhanced local connectivity were also found. Moreover, significant correlations were found between network metrics (i.e., characteristic path length and clustering coefficient) and individual seizure number. The PSD, fuzzy entropy, and network analysis may indicate that the brain is gradually impaired along with the occurrence of epilepsy, and the accumulated effect of brain impairment is observed in individuals with consecutive epileptic bursts. The findings of this study may provide helpful insights into understanding the network mechanism underlying chronic recurrent epilepsy.

scholarly journals Brain Resting State Functional Networks in Infants with Prenatal Opioid Exposure

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Nicole Steinhardt ◽  
Ramana Vishnubhotla ◽  
Yi Zhao ◽  
David M. Haas ◽  
Gregory M. Sokol ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Functional Mri ◽  
Gestational Age ◽  
Resting State ◽  
Brain Regions ◽  
Brain Network ◽  
Anterior Cingulate ◽  
Global Network ◽  
Neurodevelopmental Outcomes ◽  
Distinct Component

Purpose: Infants of mothers with opioid and substance use can present with postnatal withdrawal symptoms and are at risk of poor neurodevelopmental outcomes in later childhood. Identifying methods to evaluate the consequences of substance exposure on the developing brain can help initiate proactive therapies to improve outcomes for opioid-exposed neonates. Additionally, early brain imaging in infancy has the potential to identify early brain developmental alterations that could prognosticate neurodevelopmental outcomes in these children. In this study, we aim to identify differences in global brain network connectivity in infants with prenatal opioid exposure compared to healthy control infants, using resting-state functional MRI performed at less than 2 months completed gestational age.   Materials and Methods: In this prospective, IRB-approved study, we recruited 20 infants with prenatal opioid exposure and 20 healthy, opioid naïve infants. Anatomic imaging and resting-state functional MRI were performed at less than 48 weeks corrected gestational age, and rs-fMRI images were co-registered to the UNC neonate brain template and 90 anatomic atlas-labelled regions. Covariate Assisted Principal (CAP) regression was performed to identify brain network functional connectivity that was significantly different among infants with prenatal opioid exposure compared to healthy neonates.   Results: Of the 5 significantly different CAP components identified, the most distinct component (CAP5, p= 3.86 x 10-6) spanned several brain regions, including the right inferior temporal gyrus, bilateral Hesch’s gyrus, left thalamus, left supramarginal gyrus, left inferior parietal lobule, left superior parietal gyrus, right anterior cingulate gyrus, right gyrus rectus, left supplementary motor area, and left pars triangularis. Functional connectivity in this network was lower in the infants with prenatal opioid exposure compared to non-opioid exposed infants.   Conclusion: This study demonstrates global network alterations in infants with prenatal opioid exposure compared to non-opioid exposed infants. Future studies should be aimed at identifying clinical significance of this altered connectivity.

scholarly journals Abnormal Functional Brain Network in Parkinson's Disease and the Effect of Acute Deep Brain Stimulation

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhibao Li ◽  
Chong Liu ◽  
Qiao Wang ◽  
Kun Liang ◽  
Chunlei Han ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Functional Connectivity ◽  
Frequency Band ◽  
Brain Regions ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Functional Brain ◽  
Frequency Bands ◽  
The Brain ◽  
Deep Brain

Objective: The objective of this study was to use functional connectivity and graphic indicators to investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) and the effect of acute deep brain stimulation (DBS) on those characteristics in patients with PD.Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) and 20 patients with PD who were in the DBS-OFF state and DBS-ON state during the resting state with eyes closed. A high-density EEG source connectivity method was used to identify functional brain networks. Power spectral density (PSD) analysis was compared between the groups. Functional connectivity was calculated for 68 brain regions in the theta (4–8 Hz), alpha (8–13 Hz), beta1 (13–20 Hz), and beta2 (20–30 Hz) frequency bands. Network estimates were measured at both the global (network topology) and local (inter-regional connection) levels.Results: Compared with HC, PSD was significantly increased in the theta (p = 0.003) frequency band and was decreased in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD. However, there were no differences in any frequency bands between patients with PD with DBS-OFF and DBS-ON. The clustering coefficient and local efficiency of patients with PD showed a significant decrease in the alpha, beta1, and beta2 frequency bands (p < 0.001). In addition, edgewise statistics showed a significant difference between the HC and patients with PD in all analyzed frequency bands (p < 0.005). However, there were no significant differences between the DBS-OFF state and DBS-ON state in the brain network, except for the functional connectivity in the beta2 frequency band (p < 0.05).Conclusion: Compared with HC, patients with PD showed the following characteristics: slowed EEG background activity, decreased clustering coefficient and local efficiency of the brain network, as well as both increased and decreased functional connectivity between different brain areas. Acute DBS induces a local response of the brain network in patients with PD, mainly showing decreased functional connectivity in a few brain regions in the beta2 frequency band.

scholarly journals A two-layered brain network model and its chimera state

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ling Kang ◽  
Changhai Tian ◽  
Siyu Huo ◽  
Zonghua Liu
Keyword(s):  
Cerebral Cortex ◽  
Network Model ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Coupling Phase ◽  
Chimera States ◽  
Chimera State ◽  
Brain Functions ◽  
Coupling Strengths ◽  
Left And Right

Abstract Based on the data of cerebral cortex, we present a two-layered brain network model of coupled neurons where the two layers represent the left and right hemispheres of cerebral cortex, respectively, and the links between the two layers represent the inter-couplings through the corpus callosum. By this model we show that abundant patterns of synchronization can be observed, especially the chimera state, depending on the parameters of system such as the coupling strengths and coupling phase. Further, we extend the model to a more general two-layered network to better understand the mechanism of the observed patterns, where each hemisphere of cerebral cortex is replaced by a highly clustered subnetwork. We find that the number of inter-couplings is another key parameter for the emergence of chimera states. Thus, the chimera states come from a matching between the structure parameters such as the number of inter-couplings and clustering coefficient etc and the dynamics parameters such as the intra-, inter-coupling strengths and coupling phase etc. A brief theoretical analysis is provided to explain the borderline of synchronization. These findings may provide helpful clues to understand the mechanism of brain functions.

Combined static and dynamic functional connectivity signatures differentiating bipolar depression from major depressive disorder

2020 ◽  
Vol 54 (8) ◽  
pp. 832-842 ◽  
Author(s):  
Yajing Pang ◽  
Huangbin Zhang ◽  
Qian Cui ◽  
Qi Yang ◽  
Fengmei Lu ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Functional Connectivity ◽  
Depressive Disorder ◽  
Sex Education ◽  
Orbitofrontal Cortex ◽  
Bipolar Depression ◽  
Test Group ◽  
Precentral Gyrus ◽  
Major Depressive ◽  
Sensorimotor Network

Objective: Bipolar disorder in the depressive phase (BDd) may be misdiagnosed as major depressive disorder (MDD), resulting in poor treatment outcomes. To identify biomarkers distinguishing BDd from MDD is of substantial clinical significance. This study aimed to characterize specific alterations in intrinsic functional connectivity (FC) patterns in BDd and MDD by combining whole-brain static and dynamic FC. Methods: A total of 40 MDD and 38 BDd patients, and 50 age-, sex-, education-, and handedness-matched healthy controls (HCs) were included in this study. Static and dynamic FC strengths (FCSs) were analyzed using complete time-series correlations and sliding window correlations, respectively. One-way analysis of variance was performed to test group effects. The combined static and dynamic FCSs were then used to distinguish BDd from MDD and to predict clinical symptom severity. Results: Compared with HCs, BDd patients showed lower static FCS in the medial orbitofrontal cortex and greater static FCS in the caudate, while MDD patients exhibited greater static FCS in the medial orbitofrontal cortex. BDd patients also demonstrated greater static and dynamic FCSs in the thalamus compared with both MDD patients and HCs, while MDD patients exhibited greater dynamic FCS in the precentral gyrus compared with both BDd patients and HCs. Combined static and dynamic FCSs yielded higher accuracy than either static or dynamic FCS analysis alone, and also predicted anhedonia severity in BDd patients and negative mood severity in MDD patients. Conclusion: Altered FC within frontal–striatal–thalamic circuits of BDd patients and within the default mode network/sensorimotor network of MDD patients accurately distinguishes between these disorders. These unique FC patterns may serve as biomarkers for differential diagnosis and provide clues to the pathogenesis of mood disorders.

scholarly journals Functional Connectivity Alterations in Children with Spastic and Dyskinetic Cerebral Palsy

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Yun Qin ◽  
Yanan Li ◽  
Bo Sun ◽  
Hui He ◽  
Rui Peng ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Functional Connectivity ◽  
Cognitive Dysfunction ◽  
Resting State ◽  
Network Connectivity ◽  
Brain Network ◽  
Spastic Cerebral Palsy ◽  
Pathophysiological Mechanism ◽  
Healthy Children ◽  
Sensorimotor Network

Cerebral palsy (CP) has long been investigated to be associated with a range of motor and cognitive dysfunction. As the two most common CP subtypes, spastic cerebral palsy (SCP) and dyskinetic cerebral palsy (DCP) may share common and distinct elements in their pathophysiology. However, the common and distinct dysfunctional characteristics between SCP and DCP on the brain network level are less known. This study aims to detect the alteration of brain functional connectivity in children with SCP and DCP based on resting-state functional MRI (fMRI). Resting-state networks (RSNs) were established based on the independent component analysis (ICA), and the functional network connectivity (FNC) was performed on the fMRI data from 16 DCP, 18 bilateral SCP, and 18 healthy children. Compared with healthy controls, altered functional connectivity within the cerebellum network, sensorimotor network (SMN), left frontoparietal network (LFPN), and salience network (SN) were found in DCP and SCP groups. Furthermore, the disconnections of the FNC consistently focused on the visual pathway; covariance of the default mode network (DMN) with other networks was observed both in DCP and SCP groups, while the DCP group had a distinct connectivity abnormality in motor pathway and self-referential processing-related connections. Correlations between the functional disconnection and the motor-related clinical measurement in children with CP were also found. These findings indicate functional connectivity impairment and altered integration widely exist in children with CP, suggesting that the abnormal functional connectivity is a pathophysiological mechanism of motor and cognitive dysfunction of CP.

scholarly journals Altered brain network functional connectivity patterns in patients with vestibular migraine diagnosed according to the diagnostic criteria of the Bárány Society and the International Headache Society

2021 ◽  
Author(s):  
Zhe-Yuan Li ◽  
Li-Hong Si ◽  
Bo Shen ◽  
Xu Yang
Keyword(s):  
Functional Connectivity ◽  
Diagnostic Criteria ◽  
International Headache Society ◽  
Brain Network ◽  
Vestibular Migraine ◽  
Caloric Test ◽  
Cingulate Gyrus ◽  
Sensorimotor Network ◽  
Connectivity Patterns ◽  
History Of

Abstract Background Vestibular migraine (VM) is considered one of the most common causes of episodic central vestibular disorders, the mechanism of VM is currently still unclear. The development of functional nuclear magnetic resonance (fMRI) in recent years offers the possibility to explore the altered functional connectivity patterns in patients with VM in depth. The study aimed to investigate altered patterns of brain network functional connectivity in patients with VM diagnosed based on the diagnostic criteria of the Bárány Society and the International Headache Society, and hope to provide a scientific theoretical basis for understanding whether VM is a no-structural central vestibular disease, i.e., functional central vestibular disease with altered brain function. Methods Seventeen patients with VM who received treatment in our hospital from December 2018 to December 2020 were enrolled. Eight patients with migraine and 17 health controls (HCs) were also included. Clinical data of all patients were collected. Blood pressure, blood routine tests and electrocardiography were conducted to exclude other diseases associated with chronic dizziness. Videonystagmography, the vestibular caloric test, the video head impulse test and vestibular-evoked myogenic potentials were measured to exclude peripheral vestibular lesions. MRI was utilized to exclude focal lesions and other neurological diseases. All subjects underwent fMRI. The independent component analysis was performed to explore changes in intra- and inter-network functional connectivity in patients with VM. Results Among 17 patients with VM, there were 7 males and 10 females with an average age of 39.47 ± 9.78 years old. All patients had a history of migraine. Twelve (70.6%) patients had recurrent spontaneous vertigo, 2 (11.7%) patients had visually induced vertigo, and 3 (17.6%) patients had head motion-induced vertigo. All 17 patients with VM reported worsening of dizziness vertigo during visual stimulation. The migraine-like symptoms were photophobia or phonophobia (n = 15, 88.2%), migraine-like headache (n = 8, 47.1%), visual aura during VM onset (n = 7, 41.2%). 5 (29.4%) patients with VM had hyperactive response during the caloric test, and 12 (70.6%) patients had caloric test intolerance. Eleven (64.7%) patients had a history of motion sickness. Totally 13 independent components were identified. Patients with VM showed decreased functional connectivity in the bilateral medial cingulate gyrus and paracingulate gyrus within sensorimotor network (SMN) compared with HCs. They also showed weakened functional connectivity between auditory network (AN) and anterior default mode network (aDMN) compared with HCs, and enhanced functional connectivity between AN and the salience network (SN) compared with patients with migraine. Conclusion Patients with vestibular migraine showed obvious altered functional connectivity in the bilateral medial cingulate gyrus and paracingulate gyrus within the SMN. The median cingulate and paracingulate gyri may be impaired, the disinhibition of sensorimotor network and vestibular cortical network may result in a hypersensitivity state (photophobia/phonophobia). Altered functional connectivity between AN and DMN, SN may lead to increased sensitivity to vestibular sensory processing.

scholarly journals Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Satoru Hiwa ◽  
Shogo Obuchi ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Working Memory ◽  
Functional Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Support Vector ◽  
Neural Basis ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Network Properties

Working memory (WM) load-dependent changes of functional connectivity networks have previously been investigated by graph theoretical analysis. However, the extraordinary number of nodes represented within the complex network of the human brain has hindered the identification of functional regions and their network properties. In this paper, we propose a novel method for automatically extracting characteristic brain regions and their graph theoretical properties that reflect load-dependent changes in functional connectivity using a support vector machine classification and genetic algorithm optimization. The proposed method classified brain states during 2- and 3-back test conditions based upon each of the three regional graph theoretical metrics (degree, clustering coefficient, and betweenness centrality) and automatically identified those brain regions that were used for classification. The experimental results demonstrated that our method achieved a >90% of classification accuracy using each of the three graph metrics, whereas the accuracy of the conventional manual approach of assigning brain regions was only 80.4%. It has been revealed that the proposed framework can extract meaningful features of a functional brain network that is associated with WM load from a large number of nodal graph theoretical metrics without prior knowledge of the neural basis of WM.

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