Identifying ADHD children using hemodynamic responses during a working memory task measured by functional near-infrared spectroscopy

Gu, Yue; Miao, Shuo; Han, Jifeng; Liang, Zhenhu; Ouyang, Gaoxiang; Yang, Jian; Li, Xiaoli

doi:10.1088/1741-2552/aa9ee9

Cited by 34 publications

(31 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5-fold CV was then performed on the permuted dataset and the procedure was repeated 10,000 times. If the classifier trained on real class labels had an accuracy exceeding the 95% confidence interval generated from the accuracies of the classifiers trained on randomly relabeled class labels, this classifier was considered to be well-performing (Gu et al, 2018; Noirhomme et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study

Shen

Gollub

et al. 2019

NeuroImage: Clinical

View full text Add to dashboard Cite

Chronic low back pain (cLBP) is associated with widespread functional and structural changes in the brain. This study aims to investigate the resting state functional connectivity (rsFC) changes of visual networks in cLBP patients and the feasibility of distinguishing cLBP patients from healthy controls using machine learning methods. cLBP ( n = 90) and control individuals ( n = 74) were enrolled and underwent resting-state BOLD fMRI scans. Primary, dorsal, and ventral visual networks derived from independent component analysis were used as regions of interest to compare resting state functional connectivity changes between the cLBP patients and healthy controls. We then applied a support vector machine classifier to distinguish the cLBP patients and control individuals. These results were further verified in a new cohort of subjects. We found that the functional connectivity between the primary visual network and the somatosensory/motor areas were significantly enhanced in cLBP patients. The rsFC between the primary visual network and S1 was negatively associated with duration of cLBP. In addition, we found that the rsFC of the visual network could achieve a classification accuracy of 79.3% in distinguishing cLBP patients from HCs, and these results were further validated in an independent cohort of subjects (accuracy = 66.7%). Our results demonstrate significant changes in the rsFC of the visual networks in cLBP patients. We speculate these alterations may represent an adaptation/self-adjustment mechanism and cross-model interaction between the visual, somatosensory, motor, attention, and salient networks in response to cLBP. Elucidating the role of the visual networks in cLBP may shed light on the pathophysiology and development of the disorder.

show abstract

Section: Methodsmentioning

confidence: 99%

Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study

Shen

Gollub

et al. 2019

NeuroImage: Clinical

View full text Add to dashboard Cite

show abstract

“…Support vector machines, in particular, are robust to the inclusion of noninformative channels, hence avoiding the arbitrary selection of channels of interest (McGettigan et al, 2012). Although fNIRS has fewer channels compared to fMRI voxels, multivariate analysis methods have been used successfully in studies in both adults and children (Bogler, Mehnert, Steinbrink, & Haynes, 2014;Gu et al, 2018;Heger, Mutter, Herff, Putze, & Schultz, 2013;Hosseini et al, 2011;Ichikawa et al, 2014;Luu & Chau, 2008;Misawa, Shimokawa, & Hirobayashi, 2014). Applying MVPA to infant fNIRS data is challenging given the low trial numbers, variability, and noise in the data.…”

Section: Introductionmentioning

confidence: 99%

Language Experience Impacts Brain Activation for Spoken and Signed Language in Infancy: Insights From Unimodal and Bimodal Bilinguals

Mercure

Evans

Pirazzoli

et al. 2020

Neurobiology of Language

View full text Add to dashboard Cite

Recent neuroimaging studies suggest that monolingual infants activate a left-lateralized frontotemporal brain network in response to spoken language, which is similar to the network involved in processing spoken and signed language in adulthood. However, it is unclear how brain activation to language is influenced by early experience in infancy. To address this question, we present functional near-infrared spectroscopy (fNIRS) data from 60 hearing infants (4 to 8 months of age): 19 monolingual infants exposed to English, 20 unimodal bilingual infants exposed to two spoken languages, and 21 bimodal bilingual infants exposed to English and British Sign Language (BSL). Across all infants, spoken language elicited activation in a bilateral brain network including the inferior frontal and posterior temporal areas, whereas sign language elicited activation in the right temporoparietal area. A significant difference in brain lateralization was observed between groups. Activation in the posterior temporal region was not lateralized in monolinguals and bimodal bilinguals, but right lateralized in response to both language modalities in unimodal bilinguals. This suggests that the experience of two spoken languages influences brain activation for sign language when experienced for the first time. Multivariate pattern analyses (MVPAs) could classify distributed patterns of activation within the left hemisphere for spoken and signed language in monolinguals (proportion correct = 0.68; p = 0.039) but not in unimodal or bimodal bilinguals. These results suggest that bilingual experience in infancy influences brain activation for language and that unimodal bilingual experience has greater impact on early brain lateralization than bimodal bilingual experience.

show abstract

“…To investigate functional alteration at the brain level in CSM patients, functional magnetic resonance imaging (fMRI), which is a highly effective method for investigating neurological and psychological conditions, has been widely applied (11)(12)(13)(14)(15). This technique measures blood-oxygenation-level-dependent (BOLD) signals and is used to study brain functional alterations in diseases like Alzheimer's disease (AD), Parkinson's disease (PD), epilepsy, attention-deficit hyperactivity disorder (ADHD), and mood disorders (16)(17)(18)(19)(20)(21)(22)(23). In neuroimaging studies of CSM, fMRI has illuminated functional alterations in motor cortices (4,7,10,24,25).…”

Section: Introductionmentioning

confidence: 99%

Neural Correlates of Cognitive Dysfunctions in Cervical Spondylotic Myelopathy Patients: A Resting-State fMRI Study

Zhao

et al. 2020

Front. Neurol.

View full text Add to dashboard Cite

Cervical spondylotic myelopathy (CSM) is a common disease of the elderly that is characterized by gait instability, sensorimotor deficits, etc. Recurrent symptoms including memory loss, poor attention, etc. have also been reported in recent studies. However, these have been rarely investigated in CSM patients. To investigate the cognitive deficits and their correlation with brain functional alterations, we conducted resting-state fMRI (rs-fMRI) signal variability. This is a novel indicator in the neuroimaging field for assessing the regional neural activity in CSM patients. Further, to explore the network changes in patients, functional connectivity (FC) and graph theory analyses were performed. Compared with the controls, the signal variabilities were significantly lower in the widespread brain regions especially at the default mode network (DMN), visual network, and somatosensory network. The altered inferior parietal lobule signal variability positively correlated with the cognitive function level. Moreover, the FC and the global efficiency of DMN increased in patients with CSM and positively correlated with the cognitive function level. According to the study results, (1) the cervical spondylotic myelopathy patients exhibited regional neural impairments, which correlated with the severity of cognitive deficits in the DMN brain regions, and (2) the increased FC and global efficiency of DMN can compensate for the regional impairment.

show abstract

Identifying ADHD children using hemodynamic responses during a working memory task measured by functional near-infrared spectroscopy

Abstract: This study demonstrated that, in a small sample, multivariate pattern analysis can effectively identify ADHD children from healthy controls based on fNIRS signals, which argues for the potential utility of fNIRS in future assessments.

Cited by 34 publications

References 59 publications

Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study

Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study

Language Experience Impacts Brain Activation for Spoken and Signed Language in Infancy: Insights From Unimodal and Bimodal Bilinguals

Neural Correlates of Cognitive Dysfunctions in Cervical Spondylotic Myelopathy Patients: A Resting-State fMRI Study

Contact Info

Product

Resources

About