Altered amygdala effective connectivity in migraine without aura: evidence from resting‐state fMRI with Granger causality analysis

Huang, Xiaobin; Zhang, Di; Wang, Peng; Mao, Cunnan; Miao, Zhengfei; Liu, Chunmei; Xu, Chenjie; Yin, Xindao; Wu, Xinying

doi:10.1186/s10194-021-01240-8

Cited by 39 publications

(39 citation statements)

References 31 publications

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“…The study revealed that the SVM classifier demonstrated predictive ability with an accuracy of more than 89%, treating abnormal amygdala-related FC patterns and clinical characteristics as features of interest. The present study demonstrated that the amygdala showed stronger FCs with some higher cognitive regions, including the limbic system, visual cortex and prefrontal cortex in MwoA patients compared to the HCs, consistent with previous studies ( Wei et al, 2020b ; Huang et al, 2021 ). The higher cognitive regions are involved in the top-down pain control circuit.…”

Section: Discussionsupporting

confidence: 93%

“…The amygdala, a core region of the neurolimbic system, plays a crucial role in regulating the top-down nociceptive pathway ( Maizels et al, 2012 ; Minen et al, 2016 ). On the one hand, seed-based whole-brain functional connectivity (FC) and Granger causality analyses revealed that migraine sufferers exhibit aberrant connectivity between the amygdala and the higher cortex ( Wei et al, 2020b ; Huang et al, 2021 ). On the other hand, some studies suggested recurrent nociceptive input could affect the anatomical pattern of the amygdala, and these changes may explain the functional abnormalities in migraine patients ( Jia and Yu, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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Disrupted Functional Connectivity of the Amygdala Predicts the Efficacy of Non-steroidal Anti-inflammatory Drugs in Migraineurs Without Aura

Wei

Wang

et al. 2022

Front. Mol. Neurosci.

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Machine learning (ML) has been largely applied for predicting migraine classification. However, the prediction of efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) in migraine is still in the early stages. This study aims to evaluate whether the combination of machine learning and amygdala-related functional features could help predict the efficacy of NSAIDs in patients with migraine without aura (MwoA). A total of 70 MwoA patients were enrolled for the study, including patients with an effective response to NSAIDs (M-eNSAIDs, n = 35) and MwoA patients with ineffective response to NSAIDs (M-ieNSAIDs, n = 35). Furthermore, 33 healthy controls (HCs) were matched for age, sex, and education level. The study participants were subjected to resting-state functional magnetic resonance imaging (fMRI) scanning. Disrupted functional connectivity (FC) patterns from amygdala-based FC analysis and clinical characteristics were considered features that could promote classification through multivariable logistic regression (MLR) and support vector machine (SVM) for predicting the efficacy of NSAIDs. Further, receiver operating characteristic (ROC) curves were drawn to evaluate the predictive ability of the models. The M-eNSAIDs group exhibited enhanced FC with ipsilateral calcarine sulcus (CAL), superior parietal gyrus (SPG), paracentral lobule (PCL), and contralateral superior frontal gyrus (SFG) in the left amygdala. However, the M-eNSAIDs group showed decreased FC with ipsilateral caudate nucleus (CAU), compared to the M-ieNSAIDs group. Moreover, the M-eNSAIDs group showed higher FC with left pre-central gyrus (PreCG) and post-central gyrus (PoCG) compared to HCs. In contrast, the M-ieNSAIDs group showed lower FC with the left anterior cingulate cortex (ACC) and right SFG. Furthermore, the MwoA patients showed increased FC with the left middle frontal gyrus (MFG) in the right amygdala compared to HCs. The disrupted left amygdala-related FC patterns exhibited significant correlations with migraine characteristics in the M-ieNSAIDs group. The MLR and SVM models discriminated clinical efficacy of NSAIDs with an area under the curve (AUC) of 0.891 and 0.896, sensitivity of 0.971 and 0.833, and specificity of 0.629 and 0.875, respectively. These findings suggest that the efficacy of NSAIDs in migraine could be predicted using ML algorithm. Furthermore, this study highlights the role of amygdala-related neural function in revealing underlying migraine-related neuroimaging mechanisms.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Disrupted Functional Connectivity of the Amygdala Predicts the Efficacy of Non-steroidal Anti-inflammatory Drugs in Migraineurs Without Aura

Wei

Wang

et al. 2022

Front. Mol. Neurosci.

Self Cite

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“…It was possible that the identified temporal latency alteration of the STG/MTG may be related to sound hypersensitivity and disrupted salience response in patients with migraine. Our findings were partially supported by recent resting-state functional MRI studies that show aberrant effective connectivity of the STG/MTG [47] and sensorimotor cortex [48] in migraine patients. Furthermore, the absence of significant differences in STG/MTG and PoCG/PreCG lags between the two migraine groups indicated that atypical propagation of intrinsic activity in these two sensory processing-related brain regions were common features of individuals suffering from EM and CM.…”

Section: Discussionsupporting

confidence: 85%

Propagation Structure of Intrinsic Brain Activity in Migraine without Aura

Dai

Xiong

et al. 2022

Brain Sciences

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Previous studies have revealed highly reproducible patterns of temporally lagged brain activity in healthy human adults. However, it is unknown whether temporal organization of intrinsic activity is altered in migraines or if it relates to migraine chronification. In this resting-state functional magnetic resonance imaging study, temporal features of intrinsic activity were investigated using resting-state lag analysis, and 39 episodic migraine patients, 17 chronic migraine patients, and 35 healthy controls were assessed. Temporally earlier intrinsic activity in the hippocampal complex was revealed in the chronic migraine group relative to the other two groups. We also found earlier intrinsic activity in the medial prefrontal cortex in chronic compared with episodic migraines. Both migraine groups showed earlier intrinsic activity in the lateral temporal cortex and sensorimotor cortex compared with the healthy control group. Across all patients, headache frequency negatively correlated with temporal lag of the medial prefrontal cortex and hippocampal complex. Disrupted propagation of intrinsic activity in regions involved in sensory, cognitive and affective processing of pain may contribute to abnormal brain function during migraines. Decreased time latency in the lateral temporal cortex and sensorimotor cortex may be common manifestations in episodic and chronic migraines. The temporal features of the medial prefrontal cortex and hippocampal complex were associated with migraine chronification.

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“…In addition to NAc and mPFC, amygdala and hippocampus-both major nodes of the limbic brain ( 7 )-have been directly implicated in chronic pain conditions. Hence, amygdala functional connectivity is consistently altered in migraine patients ( 113 – 115 ). Amygdala volume and shape on the other hand are altered in CLBP patients ( 116 , 117 ).…”

Section: Beyond Sensory Perception: Chronic Pain and Aversive Learning A Conceptualization In Briefmentioning

confidence: 99%

Brain Imaging Biomarkers for Chronic Pain

2022

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The prevalence of chronic pain has reached epidemic levels. In addition to personal suffering chronic pain is associated with psychiatric and medical co-morbidities, notably substance misuse, and a huge a societal cost amounting to hundreds of billions of dollars annually in medical cost, lost wages, and productivity. Chronic pain does not have a cure or quantitative diagnostic or prognostic tools. In this manuscript we provide evidence that this situation is about to change. We first start by summarizing our current understanding of the role of the brain in the pathogenesis of chronic pain. We particularly focus on the concept of learning in the emergence of chronic pain, and the implication of the limbic brain circuitry and dopaminergic signaling, which underly emotional learning and decision making, in this process. Next, we summarize data from our labs and from other groups on the latest brain imaging findings in different chronic pain conditions focusing on results with significant potential for translation into clinical applications. The gaps in the study of chronic pain and brain imaging are highlighted in throughout the overview. Finally, we conclude by discussing the costs and benefits of using brain biomarkers of chronic pain and compare to other potential markers.

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Altered amygdala effective connectivity in migraine without aura: evidence from resting‐state fMRI with Granger causality analysis

Cited by 39 publications

References 31 publications

Disrupted Functional Connectivity of the Amygdala Predicts the Efficacy of Non-steroidal Anti-inflammatory Drugs in Migraineurs Without Aura

Disrupted Functional Connectivity of the Amygdala Predicts the Efficacy of Non-steroidal Anti-inflammatory Drugs in Migraineurs Without Aura

Propagation Structure of Intrinsic Brain Activity in Migraine without Aura

Brain Imaging Biomarkers for Chronic Pain

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