Decreased degree centrality values as a potential neuroimaging biomarker for migraine: A resting-state functional magnetic resonance imaging study and support vector machine analysis

Wang, Qian; Gao, Yujun; Zhang, Yuandong; Wang, Xi; Li, Xuying; Lin, Hang; Xiong, Li; Huang, Chunyan

doi:10.3389/fneur.2022.1105592

Cited by 8 publications

(5 citation statements)

References 43 publications

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“… Jorge-Hernandez et al (2014) developed a set of machine learning algorithms through graph theory analysis, demonstrating that the classification accuracies of migraine patients using NN and SVM classifiers were 92.86% and 87%, respectively. Wang Q. et al (2022) combined degree centrality (DC) and SVM analysis, revealing a significant decrease in the DC values of the bilateral inferior temporal gyrus (ITG) among migraine sufferers. A positive linear correlation was found between the left ITG and MIDAS scores.…”

Section: The Role Of Fmri Technology In Diagnosing Primary Headachesmentioning

confidence: 99%

A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis

Li,

Zhang,

Chen

et al. 2023

Front. Hum. Neurosci.

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Primary headache is a very common and burdensome functional headache worldwide, which can be classified as migraine, tension-type headache (TTH), trigeminal autonomic cephalalgia (TAC), and other primary headaches. Managing and treating these different categories require distinct approaches, and accurate diagnosis is crucial. Functional magnetic resonance imaging (fMRI) has become a research hotspot to explore primary headache. By examining the interrelationships between activated brain regions and improving temporal and spatial resolution, fMRI can distinguish between primary headaches and their subtypes. Currently the most commonly used is the cortical brain mapping technique, which is based on blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI). This review sheds light on the state-of-the-art advancements in data analysis based on fMRI technology for primary headaches along with their subtypes. It encompasses not only the conventional analysis methodologies employed to unravel pathophysiological mechanisms, but also deep-learning approaches that integrate these techniques with advanced statistical modeling and machine learning. The aim is to highlight cutting-edge fMRI technologies and provide new insights into the diagnosis of primary headaches.

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Section: The Role Of Fmri Technology In Diagnosing Primary Headachesmentioning

confidence: 99%

A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis

Li,

Zhang,

Chen

et al. 2023

Front. Hum. Neurosci.

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show abstract

“…Neuroimaging techniques combined with psychological scales, have been extensively employed in investigating structural and functional brain disorders (Gao et al, 2022 , 2023 ; Wang et al, 2022 ; Zhong et al, 2023a ). Meng et al found that abnormal connectivity patterns were observed in the left middle temporal gyrus in GAD, underscoring the significance of GAD pathophysiology.…”

Section: Psychological Scales and Brain Imagingmentioning

confidence: 99%

Editorial: Clinical application of psychiatric assessment and treatment in Psychosomatic diseases

Gao,

Han,

et al. 2023

Front. Psychol.

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“…Unlike task-based fMRI analyses, rs-fMRI permits the evaluation of individuals not performing complex tasks and is thus better suited to the assessment of individuals unable to complete cognitively demanding tasks due to neurological or psychiatric disorders (Takamura and Hanakawa, 2017). For these reasons, fMRI had been used to study patients with a range of diseases and disorders, including depression (Guo et al, 2022), bipolar disorder (Vargas et al, 2013), schizophrenia (Whitfield-Gabrieli et al, 2009), epilepsy (Gao et al, 2022b, attention deficit hyperactivity disorder, abnormal brain development (Jolles et al, 2011), migraine (Wang et al, 2022), mild cognitive impairment (Gao et al, 2022c), and Parkinson's Disease (Mi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Abnormal amplitude of low-frequency fluctuation values as a neuroimaging biomarker for major depressive disorder with suicidal attempts in adolescents: A resting-state fMRI and support vector machine analysis

Zhou¹,

Song

Chen³

et al. 2023

Front. Psychol.

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ObjectiveMajor depressive disorder (MDD) is associated with suicidal attempts (SAs) among adolescents, with suicide being the most common cause of mortality in this age group. This study explored the predictive utility of support vector machine (SVM)-based analyses of amplitude of low-frequency fluctuation (ALFF) results as a neuroimaging biomarker for aiding the diagnosis of MDD with SA in adolescents.MethodsResting-state functional magnetic resonance imaging (rs-fMRI) analyses of 71 first-episode, drug-naive adolescent MDD patients with SA and 54 healthy control individuals were conducted. ALFF and SVM methods were used to analyze the imaging data.ResultsRelative to healthy control individuals, adolescent MDD patients with a history of SAs showed reduced ALFF values in the bilateral medial superior frontal gyrus (mSFG) and bilateral precuneus. These lower ALFF values were also negatively correlated with child depression inventory (CDI) scores while reduced bilateral precuneus ALFF values were negatively correlated with Suicidal Ideation Questionnaire Junior (SIQ-JR) scores. SVM analyses showed that reduced ALFF values in the bilateral mSFG and bilateral precuneus had diagnostic accuracy levels of 76.8% (96/125) and 82.4% (103/125), respectively.ConclusionAdolescent MDD patients with a history of SA exhibited abnormal ALFF. The identified abnormalities in specific brain regions may be involved in the pathogenesis of this condition and may help identify at-risk adolescents. Specifically, reductions in the ALFF in the bilateral mSFG and bilateral precuneus may be indicative of MDD and SA in adolescent patients.

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Decreased degree centrality values as a potential neuroimaging biomarker for migraine: A resting-state functional magnetic resonance imaging study and support vector machine analysis

Cited by 8 publications

References 43 publications

A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis

A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis

Editorial: Clinical application of psychiatric assessment and treatment in Psychosomatic diseases

Abnormal amplitude of low-frequency fluctuation values as a neuroimaging biomarker for major depressive disorder with suicidal attempts in adolescents: A resting-state fMRI and support vector machine analysis

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