scholarly journals Aberrant Interhemispheric Functional Organization in Children with Dyskinetic Cerebral Palsy

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Yun Qin ◽  
Bo Sun ◽  
Huali Zhang ◽  
Yanan Li ◽  
Tao Zhang ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Resting State ◽  
Functional Organization ◽  
Fundamental Principle ◽  
Pathophysiological Mechanism ◽  
Healthy Children ◽  
Resting State Networks ◽  
Frontoparietal Network ◽  
Interhemispheric Connectivity ◽  
Visual Motor

Background. Hemispheric asymmetry is one fundamental principle of neuronal organization. Interhemispheric connectivity and lateralization of intrinsic networks in the resting-state brain demonstrate the interhemispheric functional organization and can be affected by disease processes. This study aims to investigate the interhemispheric organization in children with dyskinetic cerebral palsy (DCP) based on resting-state functional MRI (fMRI). Methods. 24 children with DCP and 20 healthy children were included. Voxel-mirrored homotopic connectivity (VMHC) was calculated to detect the interhemispheric connectivity, and the lateralization of the resting-state networks was performed to examine the asymmetry of the intrinsic networks of brain. Results. Decreased interhemispheric connectivity was found at visual, motor, and motor-control related regions in children with DCP, while high cognitive related networks including the central executive network, the frontoparietal network, and the salience network represented decreased asymmetry in children with DCP. Abnormal VMHC in visual areas, as well as the altered lateralization in inferior parietal lobule and supplementary motor area, showed correlation with the gross motor function and activities of daily living in children with DCP. Conclusion. These findings indicate that the interhemispheric functional organization alteration exists in children with DCP, suggesting that abnormal interhemispheric interaction may be a pathophysiological mechanism of motor and cognitive dysfunction of CP.

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 Functional Organization within and between Resting-State Networks in Chronic Subcortical Infarction

2014 ◽  
Vol 34 (4) ◽  
pp. 597-605 ◽  
Author(s):  
Caihong Wang ◽  
Wen Qin ◽  
Jing Zhang ◽  
Tian Tian ◽  
Ying Li ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Resting State Networks ◽  
Imaging Data ◽  
Functional Magnetic Resonance ◽  
Magnetic Resonance Imaging Data ◽  
Stroke Patients ◽  
Frontoparietal Network ◽  
Normal Controls ◽  
Subcortical Infarction

This study aimed to investigate the changes in functional connectivity (FC) within each resting-state network (RSN) and between RSNs in subcortical stroke patients who were well recovered in global motor function. Eleven meaningful RSNs were identified via functional magnetic resonance imaging data from 25 subcortical stroke patients and 22 normal controls using independent component analysis. Compared with normal controls, stroke patients exhibited increased intranetwork FC in the sensorimotor (SMN), visual (VN), auditory (AN), dorsal attention (DAN), and default mode (DMN) networks; they also exhibited decreased intranetwork FC in the frontoparietal network (FPN) and anterior DMN. Stroke patients displayed a shift from no FC in controls to negative internetwork FC between the VN and AN as well as between the VN and SMN. Stroke patients also exhibited weakened positive (anterior and posterior DMN; posterior DMN and right FPN) or negative (AN and right FPN; posterior DMN and dorsal SMN) internetwork FC when compared with normal controls. We suggest that subcortical stroke may induce connectivity changes in multiple functional networks, affecting not only the intranetwork FC within RSNs but also the internetwork FC between these RSNs.

scholarly journals Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks

2018 ◽  
Author(s):  
Dόra Szabό ◽  
Kálmán Czeibert ◽  
Ádám Kettinger ◽  
Márta Gácsi ◽  
Attila Andics ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Large Scale ◽  
Functional Organization ◽  
Component Analysis ◽  
Independent Component ◽  
Group Level ◽  
Resting State Networks ◽  
Wide Range ◽  
Spatially Distributed

ABSTRACTResting-state networks are spatially distributed, functionally connected brain regions. Studying these networks gives us information about the large-scale functional organization of the brain and alternations in these networks are considered to play a role in a wide range of neurological conditions and aging. To describe resting-state networks in dogs, we measured 22 awake, unrestrained animals of either sex and carried out group-level spatial independent component analysis to explore whole-brain connectivity patterns. Using resting-state functional magnetic resonance imaging (rs-fMRI), in this exploratory study we found multiple resting-state networks in dogs, which resemble the pattern described in humans. We report the following dog resting-state networks: default mode network (DMN), visual network (VIS), sensorimotor network (SMN), combined auditory (AUD)-saliency (SAL) network and cerebellar network (CER). The DMN, similarly to Primates, but unlike previous studies in dogs, showed antero-posterior connectedness with involvement of hippocampal and lateral temporal regions. The results give us insight into the resting-state networks of awake animals from a taxon beyond rodents through a non-invasive method.

scholarly journals Abnormal Static and Dynamic Functional Network Connectivity in Patients With Presbycusis

2022 ◽  
Vol 13 ◽  
Author(s):  
Chunhua Xing ◽  
Yu-Chen Chen ◽  
Song’an Shang ◽  
Jin-Jing Xu ◽  
Huiyou Chen ◽  
...  
Keyword(s):  
Resting State ◽  
Network Connectivity ◽  
Functional Network ◽  
Resting State Networks ◽  
Cognitive Assessments ◽  
Functional Network Connectivity ◽  
Frontoparietal Network ◽  
Group Independent Component Analysis ◽  
Visual Network ◽  
Dynamic Functional Network Connectivity

Aim: This study aimed to investigate abnormal static and dynamic functional network connectivity (FNC) and its association with cognitive function in patients with presbycusis.Methods: In total, 60 patients with presbycusis and 60 age-, sex-, and education-matched healthy controls (HCs) underwent resting-state functional MRI (rs-fMRI) and cognitive assessments. Group independent component analysis (ICA) was carried out on the rs-fMRI data, and eight resting-state networks (RSNs) were identified. Static and dynamic FNCs (sFNC and dFNC) were then constructed to evaluate differences in RSN connectivity between the patients with presbycusis and the HCs. Furthermore, the correlations between these differences and cognitive scores were analyzed.Results: Patients with presbycusis had differences in sFNC compared with HCs, mainly reflected in decreased sFNC in the default mode network (DMN)-left frontoparietal network (LFPN) and attention network (AN)-cerebellum network (CN) pairs, but they had increased sFNC in the auditory network (AUN) between DMN domains. The decreased sFNC in the DMN-LFPN pair was negatively correlated with their TMT-B score (r = –0.441, p = 0.002). Patients with presbycusis exhibited aberrant dFNCs in State 2 and decreased dFNCs between the CN and AN and the visual network (VN). Moreover, the presbycusis group had a shorter mean dwell time (MDT) and fraction time (FT) in State 3 (p = 0.0027; p = 0.0031, respectively).Conclusion: This study highlighted differences in static and dynamic functional connectivity in patients with presbycusis and suggested that FNC may serve as an important biomarker of cognitive performance since abnormal alterations can better track cognitive impairment in presbycusis.

scholarly journals Abnormal Functional Resting-State Networks in ADHD: Graph Theory and Pattern Recognition Analysis of fMRI Data

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Anderson dos Santos Siqueira ◽  
Claudinei Eduardo Biazoli Junior ◽  
William Edgar Comfort ◽  
Luis Augusto Rohde ◽  
João Ricardo Sato
Keyword(s):  
Graph Theory ◽  
Resting State ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Centrality Measures ◽  
Support Vector ◽  
Healthy Children ◽  
Resting State Networks ◽  
Predictive Values ◽  
Predictive Information

The framework of graph theory provides useful tools for investigating the neural substrates of neuropsychiatric disorders. Graph description measures may be useful as predictor variables in classification procedures. Here, we consider several centrality measures as predictor features in a classification algorithm to identify nodes of resting-state networks containing predictive information that can discriminate between typical developing children and patients with attention-deficit/hyperactivity disorder (ADHD). The prediction was based on a support vector machines classifier. The analyses were performed in a multisite and publicly available resting-state fMRI dataset of healthy children and ADHD patients: the ADHD-200 database. Network centrality measures contained little predictive information for the discrimination between ADHD patients and healthy subjects. However, the classification between inattentive and combined ADHD subtypes was more promising, achieving accuracies higher than 65% (balance between sensitivity and specificity) in some sites. Finally, brain regions were ranked according to the amount of discriminant information and the most relevant were mapped. As hypothesized, we found that brain regions in motor, frontoparietal, and default mode networks contained the most predictive information. We concluded that the functional connectivity estimations are strongly dependent on the sample characteristics. Thus different acquisition protocols and clinical heterogeneity decrease the predictive values of the graph descriptors.

Assessing brain connectivity at rest is clinically relevant in early multiple sclerosis

2012 ◽  
Vol 18 (9) ◽  
pp. 1251-1258 ◽  
Author(s):  
Anthony Faivre ◽  
Audrey Rico ◽  
Wafaa Zaaraoui ◽  
Lydie Crespy ◽  
Françoise Reuter ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Independent Component Analysis ◽  
Functional Mri ◽  
Resting State ◽  
Surrogate Marker ◽  
Component Analysis ◽  
Independent Component ◽  
Resting State Networks ◽  
Frontoparietal Network ◽  
Multiple Sclerosis Patients

Objective: The present study aims to determine the clinical counterpart of brain resting-state networks reorganization recently evidenced in early multiple sclerosis. Methods: Thirteen patients with early relapsing–remitting multiple sclerosis and 14 matched healthy controls were included in a resting state functional MRI study performed at 3 T. Data were analyzed using group spatial Independent Component Analysis using concatenation approach (FSL 4.1.3) and double regression analyses (SPM5) to extract local and global levels of connectivity inside various resting state networks (RSNs). Differences in global levels of connectivity of each network between patients and controls were assessed using Mann–Whitney U-test. In patients, relationship between clinical data (Expanded Disability Status Scale and Multiple Sclerosis Functional Composite Score – MSFC) and global RSN connectivity were assessed using Spearman rank correlation. Results: Independent component analysis provided eight consistent neuronal networks involved in motor, sensory and cognitive processes. For seven RSNs, the global level of connectivity was significantly increased in patients compared with controls. No significant decrease in RSN connectivity was found in early multiple sclerosis patients. MSFC values were negatively correlated with increased RSN connectivity within the dorsal frontoparietal network ( r = −0.811, p = 0.001), the right ventral frontoparietal network ( r = − 0.587, p = 0.045) and the prefronto-insular network ( r = −0.615, p = 0.033). Conclusions: This study demonstrates that resting state networks reorganization is strongly associated with disability in early multiple sclerosis. These findings suggest that resting state functional MRI may represent a promising surrogate marker of disease burden.

scholarly journals Aberrant Functional Organization within and between Resting-State Networks in AD

PLoS ONE ◽  
2013 ◽  
Vol 8 (5) ◽  
pp. e63727 ◽  
Author(s):  
Jinyu Song ◽  
Wen Qin ◽  
Yong Liu ◽  
Yunyun Duan ◽  
Jieqiong Liu ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Resting State Networks

scholarly journals Reconstruction of resting-state networks from macaque electrocorticographic data

2017 ◽  
Author(s):  
R. Hindriks ◽  
C. Micheli ◽  
C.A. Bosman ◽  
R. Oostenveld ◽  
C. Lewis ◽  
...  
Keyword(s):  
Network Structure ◽  
Resting State ◽  
Functional Organization ◽  
Phase Locking ◽  
Simulated Data ◽  
Resting State Networks ◽  
Cortical Rhythms ◽  
Oscillatory Phase ◽  
Phase Lags ◽  
Band Limited

AbstractThe discovery of haemodynamic (BOLD-fMRI) resting-state networks (RSNs) has brought about a fundamental shift in our thinking about the role of intrinsic brain activity. The electrophysiological underpinnings of RSNs remain largely elusive and it has been shown only recently that electrophysiological cortical rhythms are organized into RSNs. Most electrophysiological studies into RSNs use magnetoencephalography (MEG) or electroencephalography (EEG), which limits the spatial scale on which RSNs can be investigated. Due to their close proximity to the cortical surface, electroencephalographic (ECoG) recordings can potentially provide a more detailed picture of the functional organization of resting-state cortical rhythms. In this study we propose using source-space independent component analysis for identifying generators of resting-state cortical rhythms as recorded with ECoG and reconstructing their network structure. Their network structure is characterized by two kinds of connectivity: instantaneous correlations between band-limited amplitude envelopes and oscillatory phase-locking. Using simulated data, we find that the reconstruction of oscillatory phase-locking is more challenging than that of amplitude correlations, particularly for low signal-to-noise levels. Specifically, phase-lags can both be over- and underestimated as a consequence of first-order and higher-order volume-conduction effects, which troubles the interpretation of interaction measures based on imaginary phase-locking or coherence. The methodology is applied to resting-state beta (15-30 Hz) rhythms within the motor system of a macaque monkey and leads to the identification of a functional network of seven cortical generators that are distributed across the sensorimotor system. The spatial extent of the identified generators, together with consistent phase-lags, suggests that these rhythms can be viewed as being spatially continuous with complex dynamics including traveling waves. Our findings illustrate the level of spatial detail attainable with source-projected ECoG and motivates wider use of the methodology for studying resting-state as well as event-related cortical dynamics in macaque and human.

scholarly journals The frontoparietal network: function, electrophysiology, and importance of individual precision mapping

2018 ◽  
Vol 20 (2) ◽  
pp. 133-140 ◽  
Keyword(s):  
Resting State ◽  
Resting State Networks ◽  
Individual Subject ◽  
Network Function ◽  
Hyperactivity Disorder ◽  
Frontoparietal Network ◽  
Functional Brain Networks ◽  
Network Coordination ◽  
Functional Topography ◽  
Human Brains

The frontoparietal network is critical for our ability to coordinate behavior in a rapid, accurate, and flexible goal-driven manner. In this review, we outline support for the framing of the frontoparietal network as a distinct control network, in part functioning to flexibly interact with and alter other functional brain networks. This network coordination likely occurs in a 4 Hz to13 Hz θ/α rhythm, both during resting state and task state. Precision mapping of individual human brains has revealed that the functional topography of the frontoparietal network is variable between individuals, underscoring the notion that group-average studies of the frontoparietal network may be obscuring important typical and atypical features. Many forms of psychopathology implicate the frontoparietal network, such as schizophrenia and attention-deficit/hyperactivity disorder. Given the interindividual variability in frontoparietal network organization, clinical studies will likely benefit greatly from acquiring more individual subject data to accurately characterize resting-state networks compromised in psychopathology

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