Reduced prefrontal-temporal cortical activation during verbal fluency task in obsessive-compulsive disorder: A multi-channel near-infrared spectroscopy study

Liao, Jinmin; Li, Tian; Dong, Wei; Wang, J.; Tian, Ju; Liu, Jin; Quan, Wenxiang; Yan, Junhao

doi:10.1016/j.jpsychires.2018.11.006

Cited by 16 publications

(16 citation statements)

References 54 publications

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“…Second, OCD patients with TD exhibited significantly lower activation in a wide range of PFC areas during the VFT compared with patients without TD and HC. In previous studies, patients with OCD were found to exhibit reduced brain activation in the PFC during the VFT, assessed using NIRS, compared with HC 18,19 . Thus, the current results support the notion that OCD patients with TD may have more severe impairment of PFC function during the VFT.…”

Section: Discussionsupporting

confidence: 84%

“…Recently, several near‐infrared spectroscopy (NIRS) studies conducted during neuropsychological tasks consistently found significantly less PFC activation in patients with OCD compared with healthy controls (HC) 16–19 . In our previous study, 18 we used NIRS to compare brain activation in OCD patients with that of HC during three cognitive tasks in which impairment has previously been reported in OCD patients 20 : the Verbal Fluency Task (VFT), the Trail‐Making Task (TMT) and the Tower of London (ToL) Task.…”

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confidence: 99%

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Evaluation of hemodynamic changes using near‐infrared spectroscopy in patients with tic‐related obsessive–compulsive disorder

Mukai

Matsuura

Miyauchi

et al. 2021

Psychiatry Clin Neurosci

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Aim A tic‐related specifier is included in the DSM‐5 diagnostic criteria to identify a clinically specific obsessive–compulsive disorder (OCD) subtype. The current study sought to evaluate hemodynamic changes during executive function tasks among OCD patients with and without a lifetime history of tic disorder (TD) and healthy controls, and to investigate the relation between brain activation and clinical variables in each group using structured equation modeling. Methods Twenty‐nine OCD patients diagnosed according to the DSM‐IV‐TR and 15 healthy controls were recruited. Patients were divided into two groups according to the presence or absence of a lifetime history of TD (TD+, n = 11; TD−, n = 18). Prefrontal hemodynamic changes were measured using multi‐channel near‐infrared spectroscopy during the Verbal Fluency Task, Trail‐Making Task, and Tower of London (ToL) Task. Results There were significant brain activation differences in the frontopolar cortex between OCD patients with and without TD during Verbal Fluency Task and ToL performance. Brain activation in the dorsolateral prefrontal cortex (DLPFC) during the ToL Task in OCD patients with TD exerted a direct causal effect on the severity of compulsions. In addition, we detected a direct causal effect of the severity of obsessions in OCD patients without TD on brain activation in the DLPFC during the ToL Task. Conclusion Brain activation in the frontopolar cortex exhibits different hemodynamics depending on the task, and DLPFC function may play a different role in the neural basis of developing OCD symptoms between OCD patients with and without TD.

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Section: Discussionsupporting

confidence: 84%

mentioning

confidence: 99%

Evaluation of hemodynamic changes using near‐infrared spectroscopy in patients with tic‐related obsessive–compulsive disorder

Mukai

Matsuura

Miyauchi

et al. 2021

Psychiatry Clin Neurosci

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“…Notably, patients with obsessive-compulsive disorder also have abnormalities in these two microstate classes [61]; that is, the occurrence of these microstates was significantly different from those of HC. Previous studies have also suggested similarities in the manifestation of visual and salience network networks in patients with obsessive-compulsive disorder and addiction [62][63][64][65][66][67]. Therefore, abnormalities in these brain networks may partly explain the alteration in the parameters in microstates B and C in these two populations [22].…”

Section: Microstatementioning

confidence: 96%

Disrupted brain network dynamics and cognitive functions in methamphetamine use disorder: insights from EEG microstates

Chen

Zhong

et al. 2020

BMC Psychiatry

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Background Dysfunction in brain network dynamics has been found to correlate with many psychiatric disorders. However, there is limited research regarding resting electroencephalogram (EEG) brain network and its association with cognitive process for patients with methamphetamine use disorder (MUD). This study aimed at using EEG microstate analysis to determine whether brain network dynamics in patients with MUD differ from those of healthy controls (HC). Methods A total of 55 MUD patients and 27 matched healthy controls were included for analysis. The resting brain activity was recorded by 64-channel electroencephalography. EEG microstate parameters and intracerebral current sources of each EEG microstate were compared between the two groups. Generalized linear regression model was used to explore the correlation between significant microstates with drug history and cognitive functions. Results MUD patients showed lower mean durations of the microstate classes A and B, and a higher global explained variance of the microstate class C. Besides, MUD patients presented with different current density power in microstates A, B, and C relative to the HC. The generalized linear model showed that MA use frequency is negatively correlated with the MMD of class A. Further, the generalized linear model showed that MA use frequency, scores of Two-back task, and the error rate of MA word are correlated with the MMD and GEV of class B, respectively. Conclusions Intracranial current source densities of resting EEG microstates are disrupted in MUD patients, hence causing temporal changes in microstate topographies, which are correlated with attention bias and history of drug use.

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“…These advantages outweigh the merits of other neuroimaging techniques in the study of neurological disorders and psychiatric disorders such as dementia and brain-related disorders. Many studies have examined the validity of using fNIRS to compare the hemodynamic response in HC and patients with AD, and demonstrated that patients with AD showed lower activation in specific brain regions during various cognitive tasks than normal people’s ( Beishon et al, 2017 ; Vermeij et al, 2017 ; Liao et al, 2019 ). Hock et al (1997) reported that there was a decrease in HbO and Total Hemoglobin (HbT) given a verbal fluency task (VFT) in the parietal cortex of patients with AD.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Multilevel Classification of Alzheimer’s Disease Using Non-invasive Functional Near-Infrared Spectroscopy

Kim

Jeon

et al. 2022

Front. Aging Neurosci.

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The timely diagnosis of Alzheimer’s disease (AD) and its prodromal stages is critically important for the patients, who manifest different neurodegenerative severity and progression risks, to take intervention and early symptomatic treatments before the brain damage is shaped. As one of the promising techniques, functional near-infrared spectroscopy (fNIRS) has been widely employed to support early-stage AD diagnosis. This study aims to validate the capability of fNIRS coupled with Deep Learning (DL) models for AD multi-class classification. First, a comprehensive experimental design, including the resting, cognitive, memory, and verbal tasks was conducted. Second, to precisely evaluate the AD progression, we thoroughly examined the change of hemodynamic responses measured in the prefrontal cortex among four subject groups and among genders. Then, we adopted a set of DL architectures on an extremely imbalanced fNIRS dataset. The results indicated that the statistical difference between subject groups did exist during memory and verbal tasks. This presented the correlation of the level of hemoglobin activation and the degree of AD severity. There was also a gender effect on the hemoglobin changes due to the functional stimulation in our study. Moreover, we demonstrated the potential of distinguished DL models, which boosted the multi-class classification performance. The highest accuracy was achieved by Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) using the original dataset of three hemoglobin types (0.909 ± 0.012 on average). Compared to conventional machine learning algorithms, DL models produced a better classification performance. These findings demonstrated the capability of DL frameworks on the imbalanced class distribution analysis and validated the great potential of fNIRS-based approaches to be further contributed to the development of AD diagnosis systems.

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Reduced prefrontal-temporal cortical activation during verbal fluency task in obsessive-compulsive disorder: A multi-channel near-infrared spectroscopy study

Cited by 16 publications

References 54 publications

Evaluation of hemodynamic changes using near‐infrared spectroscopy in patients with tic‐related obsessive–compulsive disorder

Evaluation of hemodynamic changes using near‐infrared spectroscopy in patients with tic‐related obsessive–compulsive disorder

Disrupted brain network dynamics and cognitive functions in methamphetamine use disorder: insights from EEG microstates

Deep Learning-Based Multilevel Classification of Alzheimer’s Disease Using Non-invasive Functional Near-Infrared Spectroscopy

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