Altered hypothalamic functional connectivity in post-traumatic headache after mild traumatic brain injury

Lu, Liangliang; Li, Fengfang; Wang, Peng; Chen, Huiyou; Chen, Yu‐Chen; Yin, Xindao

doi:10.1186/s10194-020-01164-9

Cited by 21 publications

(17 citation statements)

References 40 publications

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“…Our study's finding that VMHC was increased in this area might be explained by enhanced interhemispheric coordination between the bilateral middle frontal gyrus, which might strengthen the capacity of an individual's cognitiveevaluative modulation of unpleasant menstrual pain. In agreement with this, reports have shown that the middle frontal gyrus is related to the cognitive evaluation and modulation of pain in post-traumatic headache (30) and chronic lower back-related leg pain (19).…”

Section: Increased Vmhc and Fc In The Cognitive-evaluative Dimension ...supporting

confidence: 79%

Abnormal interhemispheric functional connectivity in patients with primary dysmenorrhea: a resting-state functional MRI study

Zhang¹,

Huang²,

Liu³

et al. 2022

Quant Imaging Med Surg

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Background: Neuroimaging studies have confirmed that functional connectivity (FC) disruption of painrelated brain networks may contribute to the cerebral pathophysiology of primary dysmenorrhea (PDM).However, it remains unclear whether FC of symmetrical regions of bilateral hemispheres associated with PDM is abnormal. This functional MRI study aimed to explore the changes of voxel-mirrored homotopic connectivity (VMHC) and seed-based FC in patients with PDM.Methods: A cohort comprising patients with PDM (n=35) and healthy controls (HCs) (n=41) underwent resting-state functional MRI scans during their menstrual phase. Interhemispheric FC was compared between the two groups using VMHC analysis. Brain areas with significant group differences in VMHC were selected as seed regions for FC analysis. Correlation analysis was also conducted to examine the relationship between abnormal connectivity of brain regions and clinical measures of pain and anxiety.Results: Compared with healthy individuals, patients with PDM showed significantly enhanced VMHC in the bilateral orbital part of the superior frontal gyrus and the bilateral middle frontal gyrus. Subsequent seedbased FC analysis showed enhanced connectivity between the aforementioned areas and pain-related brain structures. Hyperconnectivity between the left middle frontal gyrus and the right cingulate gyrus in patients was negatively correlated with an increase in the visual analogue score (VAS) for pain (r=−0.341, P<0.05). Conclusions:Our findings indicate that ongoing dysmenorrhea is accompanied by abnormal interhemispheric functional coordination and enhanced connectivity in pain-related regions, attention networks, and the reward system. These findings may provide a novel perspective on the central mechanism of pain caused by PDM.

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Section: Increased Vmhc and Fc In The Cognitive-evaluative Dimension ...supporting

confidence: 79%

Abnormal interhemispheric functional connectivity in patients with primary dysmenorrhea: a resting-state functional MRI study

Zhang¹,

Huang²,

Liu³

et al. 2022

Quant Imaging Med Surg

View full text Add to dashboard Cite

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“…[ 15 ] For example, headache frequency and headache intensity related with hypothalamic functional disconnections in mTBI with post-traumatic headache. [ 16 ] As a special type of traumatic brain injury, DAI patients may have extensive axonal injury that also involves the hypothalamic functional connectivity (FC) pathways and causes associated cognitive and behavioral symptoms. Increasing numbers of DAI studies have found disrupted regional, long-distance, and even network connectivity in patients with DAI.…”

Section: Introductionmentioning

confidence: 99%

Disrupted hypothalamic functional connectivity related to cognitive impairment after diffuse axonal injury

2021

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This study aims to investigate whether there is imaging evidence of disrupted hypothalamic functional connectivity (FC) in patients with diffuse axonal injury (DAI) and relationships with cognitive impairment. Resting-state functional magnetic resonance imaging (fMRI) data were acquired from acute patients with diagnosed DAI (n = 30) and healthy controls (HC) (n = 30). We first assessed hypothalamic FC with seed-based analysis. Furthermore, the lateral and medial hypothalamic seed was selected to show distinct functional connectivity in DAI. In addition, partial correlation was used to measure the clinical associations with the altered hypothalamic FC in DAI patients. Compared with HC, DAI group showed significantly increased hypothalamic FC with superior temporal gyrus, and the regions around the operculum. Furthermore, there was a significant negative correlation between the connectivity coefficient of hypothalamus to right and left superior temporal gyrus and the disability rating scale scores in DAI group. When the seed regions were divided into lateral and medial hypothalamus, except for increased connectivity of medial hypothalamus ( P < .01 with correction), we more observed that decreased left lateral hypothalamic connectivity was positively correlated with mini-mental state examination (MMSE) scores. Our results suggest that there are alterations of hypothalamic FC in DAI and offer further understanding of clinical symptoms including related cognitive impairment.

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“…Shi et al (2021) in a study on patients with mTBI showed an alteration of rs-fMRI measures in the cerebellum and temporal lobe regions and suggested that the FC abnormality is consistent with sensory perception, movement control, and micro-motor coordination in patients after injury. Also, it has been shown that motor network functional activation and connectivity in mTBI patients 10.3389/fnins.2022.1099560 with post-traumatic headache is related to patients' response to avoid movement during pain since physical movement may worsen the pain (Lu et al, 2020b).…”

Section: Discussionmentioning

confidence: 99%

Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques

et al. 2023

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Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67–100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42–93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level.Clinical trial registration[clinicaltrials.gov], identifier [NCT03241732].

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Altered hypothalamic functional connectivity in post-traumatic headache after mild traumatic brain injury

Cited by 21 publications

References 40 publications

Abnormal interhemispheric functional connectivity in patients with primary dysmenorrhea: a resting-state functional MRI study

Abnormal interhemispheric functional connectivity in patients with primary dysmenorrhea: a resting-state functional MRI study

Disrupted hypothalamic functional connectivity related to cognitive impairment after diffuse axonal injury

Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques

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