Altered regional and circuit resting-state activity in patients with occult spastic diplegic cerebral palsy

Mu, Xuetao; Wang, Zhi­qun; Nie, Binbin; Duan, Shaofeng; Ma, Qiaozhi; Dai, Guohao; Wu, Changming; Dong, Yuru; Shan, Baoci; Ma, Lin

doi:10.1016/j.pedneo.2017.10.003

Cited by 15 publications

(15 citation statements)

References 29 publications

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“…Li et al [ 56 ] reported that GSR strengthens association between resting-state functional connectivity and behavior in young healthy adults. And in some studies [ 57 , 58 ], the authors regressed out global signal as a nuisance variable to reduce the effects of nonneuronal BOLD fluctuations.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

Shi

Zhang

et al. 2021

Disease Markers

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Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy. However, the conventional ML which concatenated the features into a longer feature vector could not be sufficiently effective to combine different features from different modalities. There are considerable controversies over the use of global signal regression (GSR), and few studies have explored the role of GSR in ML in diagnosing neurological diseases. The current study utilized fMRI and sMRI data to implement a new method named multimodal imaging and multilevel characterization with multiclassifier (M3) to classify SZs and healthy controls (HCs) and investigate the influence of GSR in SZ classification. We found that when we used Brainnetome 246 atlas and without performed GSR, our method obtained a classification accuracy of 83.49%, with a sensitivity of 68.69%, a specificity of 93.75%, and an AUC of 0.8491, respectively. We also got great classification performances with different processing methods (with/without GSR and different brain parcellation schemes). We found that the accuracy and specificity of the models without GSR were higher than that of the models with GSR. Our findings indicate that the M3 method is an effective tool to distinguish SZs from HCs, and it can identify discriminative regions to detect SZ to explore the neural mechanisms underlying SZ. The global signal may contain important neuronal information; it can improve the accuracy and specificity of SZ detection.

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Section: Discussionmentioning

confidence: 99%

Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

Shi

Zhang

et al. 2021

Disease Markers

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“…In contrast, the resting-state fMRI scanning is simple and easy to execute for these children [ 31 ]. Functional connectivity, referring to an analysis for identifying spatial patterns of coherent BOLD activity in distributed brain regions, has been used to detect the abnormal pathways (e.g., somatosensory, motor, and thalamocortical pathway) in children with CP using resting-state fMRI data [ 22 , 32 , 33 ]. For defining distinct modes of long-distance interactions, independent component analysis (ICA) has been a popular method to generate resting-state networks (RSNs) [ 34 , 35 ], and resting-state functional network connectivity (FNC) [ 36 ] can be used to represent the temporal correlation among these RSNs.…”

Section: Introductionmentioning

confidence: 99%

Functional Connectivity Alterations in Children with Spastic and Dyskinetic Cerebral Palsy

Qin

Li²,

Sun

et al. 2018

Neural Plasticity

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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.

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Section: Introductionmentioning

confidence: 99%

“…Over the past decade, rs-fMRI has provided unique insights on how the perinatal brain injuries in youth with CP impact the integrity of the RSNs [25][26][27][28][29]. These studies have revealed that youth with a spastic diplegic presentation have higher FC within the sensorimotor cortices than their neurotypical peers [25,26]. In contrast, FC between the sensorimotor cortex and the cerebellum, contralateral sensorimotor cortex, cingulate motor area, visual cortices and parietal lobes have been described as weaker in youth with CP compared to controls [25,28,30,31].…”

Section: Introductionmentioning

confidence: 99%

“…The purpose of this investigation was to use rs-fMRI to map the SMN, AUD and VIS networks in youth with CP, and to determine if the FC emerging from these RSNs is linked with their altered spatiotemporal gait biomechanics. Based on prior rs-fMRI work [26,28], we hypothesized that the youth with CP would display weaker FC for the SMN, AUD, and VIS networks. Lastly, we hypothesized that youth with CP who have weaker FC in the SMN and VIS networks would be more likely to have greater alterations in their spatiotemporal gait biomechanics.…”

Section: Introductionmentioning

confidence: 99%

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Weaker Connectivity of the Cortical Networks Is Linked with the Uncharacteristic Gait in Youth with Cerebral Palsy

et al. 2021

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Cerebral palsy (CP) is the most prevalent pediatric neurologic impairment and is associated with major mobility deficiencies. This has led to extensive investigations of the sensorimotor network, with far less research focusing on other major networks. The aim of this study was to investigate the functional connectivity (FC) of the main sensory networks (i.e., visual and auditory) and the sensorimotor network, and to link FC to the gait biomechanics of youth with CP. Using resting-state functional magnetic resonance imaging, we first identified the sensorimotor, visual and auditory networks in youth with CP and neurotypical controls. Our analysis revealed reduced FC among the networks in the youth with CP relative to the controls. Notably, the visual network showed lower FC with both the sensorimotor and auditory networks. Furthermore, higher FC between the visual and sensorimotor cortices was associated with larger step length (r = 0.74, pFDR = 0.04) in youth with CP. These results confirm that CP is associated with functional brain abnormalities beyond the sensorimotor network, suggesting abnormal functional integration of the brain’s motor and primary sensory systems. The significant association between abnormal visuo-motor FC and gait could indicate a link with visuomotor disorders in this patient population.

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Altered regional and circuit resting-state activity in patients with occult spastic diplegic cerebral palsy

Abstract: Resting-state regional brain activities and FC changes in the patients with occult SDCP exhibited a special distribution pattern, which is consistent with the pathology of the disease.

Cited by 15 publications

References 29 publications

Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

Functional Connectivity Alterations in Children with Spastic and Dyskinetic Cerebral Palsy

Weaker Connectivity of the Cortical Networks Is Linked with the Uncharacteristic Gait in Youth with Cerebral Palsy

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