Neuroimaging Findings From Cerebral Structure and Function in Coronary Artery Disease.

An increasing number of evidence suggests that bidirectional communication between the cardiovascular system and the central nervous system (CNS), known as the heart-brain interaction, is crucial in understanding the impact of coronary artery disease (CAD) on brain health. The multifactorial role of CAD in the brain involves processes such as inflammation, oxidative stress, neuronal activity, neuroendocrine imbalances, and reduced cerebral perfusion, leading to various cerebral abnormalities. The mechanisms underlying the relationship between CAD and brain injury are complex and involve parallel pathways in the CNS, endocrine system, and immune system. Although the exact mechanisms remain partially understood, neuroimaging techniques offer valuable insights into subtle cerebral abnormalities in CAD patients. Neuroimaging techniques, including assessment of neural function, brain metabolism, white matter microstructure, and brain volume, provide information on the evolving nature of CAD-related cerebral abnormalities over time. This review provides an overview of the pathophysiological mechanisms of CAD in the heart-brain interaction and summarizes recent neuroimaging studies utilizing multiparametric techniques to investigate brain abnormalities associated with CAD. The application of advanced neuroimaging, particularly functional, diffusion, and perfusion advanced techniques, offers high resolution, multiparametric capabilities, and high contrast, thereby allowing for the early detection of changes in brain structure and function, facilitating further exploration of the intricate relationship between CAD and brain health. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.

Altered functional connectivity of the thalamus and salience network in patients with cluster headache: a pilot study.

BACKGROUND AND OBJECTIVE: Previous studies have shown that the salience network (SN) and the thalamus are involved in cluster headache (CH) attacks. However, very little is known regarding the altered thalamus-SN functional connectivity in CH. The aim of this study was to explore alterations of functional connectivity between the thalamus and the SN in patients with CH to further gain insight into the pathophysiology of CH. MATERIALS AND METHODS: The resting-state functional MRI (rs-fMRI) data of 21 patients with CH in the headache attack remission state during in-bout periods and 21 age- and sex-matched normal controls were obtained. The rs-fMRI data were analyzed by the independent component analysis (ICA) method, and the thalamus-SN functional connectivity in patients with right-sided and left-sided CH was compared with that in normal controls. RESULTS: Decreased functional connectivity was found between the thalamus, both ipsilateral and contralateral to the headache side, and the SN during headache remission state in both right-sided CH patients and left-sided CH patients. CONCLUSIONS: The findings suggest that the decreased functional connectivity between the thalamus and SN might be one of the pathologies underpinning the CH. This helps us to understand better the nature of the brain dysfunction in CH and the basic pathologies of CH, which implies that this deserves further investigation.

Generative Adversarial Network-based Noncontrast CT Angiography for Aorta and Carotid Arteries.

Background Iodinated contrast agents (ICAs), which are widely used in CT angiography (CTA), may cause adverse effects in humans, and their use is time-consuming and costly. Purpose To develop an ICA-free deep learning imaging model for synthesizing CTA-like images and to assess quantitative and qualitative image quality as well as the diagnostic accuracy of synthetic CTA (Syn-CTA) images. Materials and Methods A generative adversarial network (GAN)-based CTA imaging model was trained, validated, and tested on retrospectively collected pairs of noncontrast CT and CTA images of the neck and abdomen from January 2017 to June 2022, and further validated on an external data set. Syn-CTA image quality was evaluated using quantitative metrics. In addition, two senior radiologists scored the visual quality on a three-point scale (3 = good) and determined the vascular diagnosis. The validity of Syn-CTA images was evaluated by comparing the visual quality scores and diagnostic accuracy of aortic and carotid artery disease between Syn-CTA and real CTA scans. Results CT scans from 1749 patients (median age, 60 years [IQR, 50-68 years]; 1057 male patients) were included in the internal data set: 1137 for training, 400 for validation, and 212 for testing. The external validation set comprised CT scans from 42 patients (median age, 67 years [IQR, 59-74 years]; 37 male patients). Syn-CTA images had high similarity to real CTA images (normalized mean absolute error, 0.011 and 0.013 for internal and external test set, respectively; peak signal-to-noise ratio, 32.07 dB and 31.58 dB; structural similarity, 0.919 and 0.906). The visual quality of Syn-CTA and real CTA images was comparable (internal test set, P = .35; external validation set, P > .99). Syn-CTA showed reasonable to good diagnostic accuracy for vascular diseases (internal test set: accuracy = 94%, macro F1 score = 91%; external validation set: accuracy = 86%, macro F1 score = 83%). Conclusion A GAN-based model that synthesizes neck and abdominal CTA-like images without the use of ICAs shows promise in vascular diagnosis compared with real CTA images. Clinical trial registration no. NCT05471869 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Zhang and Turkbey in this issue.

Brain regions involved in fractional amplitude of low-frequency fluctuation in cluster headache patients: a resting-state functional MRI study.

BACKGROUND: We used resting-state functional magnetic resonance imaging (RS-fMRI) to assess the possible pathogenic role of fALFF in CH. A limited number of studies have reported on fractional amplitude of low-frequency fluctuation (fALFF) in cluster headache (CH). METHODS: RS-fMRI scans of 23 patients with CH were obtained (11with left-sided headache and 12 with right-sided headache), along with scans of 23 age- and sex-matched normal controls. The RS-fMRI data were analyzed to explore abnormal brain activity in the left CH and right CH patients during the non-painful state in one cluster period. fALFF was compared between patients and controls, and correlation analysis between the regional mean fALFF values and clinical characteristics was performed. RESULTS: A decrease in fALFF was detected in the left cerebellum, left lentiform nucleus, left frontal lobe, left anterior cingulate, and right postcentral gyrus in the left CH group compared to the controls, while a decrease of fALFF was detected in the right cerebellum, right cingulate gyrus, right superior parietal lobule, right inferior parietal lobule, right postcentral gyrus, and left precuneus in the right CH group. No patient had a region with increased fALFF. A moderate correlation was observed between some regional mean fALFF values and the clinical characteristics. CONCLUSIONS: We deduced that dysfunction in multiple brain areas is involved in the non-painful state of CH during a cluster period.

Enhanced functional connectivity between habenula and salience network in medication-overuse headache complicating chronic migraine positions it within the addiction disorders: an ICA-based resting-state fMRI study.

BACKGROUND: Medication-overuse headache (MOH) is a relatively frequently occurring secondary headache caused by overuse of analgesics and/or acute migraine medications. It is believed that MOH is associated with dependence behaviors and substance addiction, in which the salience network (SN) and the habenula may play an important role. This study aims to investigate the resting-state (RS) functional connectivity between the habenula and the SN in patients with MOH complicating chronic migraine (CM) compared with those with episodic migraine (EM) and healthy controls (HC). METHODS: RS-fMRI and 3-dimensional T1-weighted images of 17 patients with MOH + CM, 18 patients with EM and 30 matched healthy HC were obtained. The RS-fMRI data were analyzed using the independent component analysis (ICA) method to investigate the group differences of functional connectivity between the habenula and the SN in three groups. Correlation analysis was performed thereafter with all clinical variables by Pearson correlation. RESULTS: Increased functional connectivity between bilateral habenula and SN was detected in patients with MOH + CM compared with patients with EM and HC respectively. Correlation analysis showed significant correlation between medication overuse duration and habenula-SN connectivity in MOH + CM patients. CONCLUSIONS: The current study supported MOH to be lying within a spectrum of dependence and addiction disorder. The enhanced functional connectivity of the habenula with SN may correlate to the development or chronification of MOH. Furthermore, the habenula may be an indicator or treatment target for MOH for its integrative role involved in multiple aspects of MOH.

[Correlations between apparent diffusion coefficient in diffusion?weighted magnetic resonance imaging and molecular subtypes of invasive breast cancer masses].

OBJECTIVE: To study the correlation of apparent diffusion coefficient (ADC) measured by diffusion-weighted magnetic resonance imaging (MRI) with the molecular subtypes and biological prognostic factors of invasive breast cancer masses. METHODS: Breast MRI data (including dynamic enhanced and diffusion-weighted imaging) were collected from 64 patients with pathologically confirmed invasive breast cancer masses (a total of 69 lesions). The mean ADC values of the lesions were calculated and their correlations were analyzed with the 5 molecular subtypes of invasive breast cancer and the biological prognostic factors including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 (HER2), and Ki-67 index. RESULTS: The ADC values did not differ significantly among the 5 molecular subtypes of invasive breast cancer masses (P>0.05) or among lesions with different ER, PR, or HER2 status (P>0.05). The mean ADC values were significantly higher in Ki-67-positive lesions than in the negative lesions (P=0.023 and negatively correlated with the expressions of Ki-67 (r=-0.249). CONCLUSION: ADC value can not be used to identify the molecular subtypes of invasive breast cancer masses or to evaluate the biological prognosis of the lesions, but its correlation with Ki-67 expression may help in prognostic evaluation and guiding clinical therapy of the tumors.