Cerebral regional and network characteristics in asthma patients: a resting-state fMRI study

Abstract

Asthma is a serious health problem that involves not only the respiratory system but also the central nervous system. Previous studies identified either regional or network alterations in patients with asthma, but inconsistent results were obtained. A key question remains unclear: are the regional and neural network deficits related or are they two independent characteristics in asthma? Answering this question is the aim of this study. By collecting resting-state functional magnetic resonance imaging from 39 patients with asthma and 40 matched health controls, brain functional measures including regional activity (amplitude of low-frequency fluctuations) and neural network function (degree centrality (DC) and functional connectivity) were calculated to systematically characterize the functional alterations. Patients exhibited regional abnormities in the left angular gyrus, right precuneus, and inferior temporal gyrus within the default mode network. Network abnormalities involved both the sensorimotor network and visual network with key regions including the superior frontal gyrus and occipital lobes. Altered DC in the lingual gyrus was correlated with the degree of airway obstruction. This study elucidated different patterns of regional and network changes, thereby suggesting that the two parameters reflect different brain characteristics of asthma. These findings provide evidence for further understanding the potential cerebral alterations in the pathophysiology of asthma.

Keywords

asthma / brain / regional activation / functional connectivity / resting-state fMRI

Introduction

Asthma is one of the most serious and chronic respiratory diseases that also affects the central nervous system [¹–⁵]. Previous studies have illustrated functional changes and their implications in the brains of asthma patients by using functional MRI (fMRI), which is a noninvasive method that aims to improve our understanding of human brain function in vivo [⁶]. Regional functional changes, including altered activation in the insula, anterior cingulate cortex, and superior frontal gyrus, were observed in patients with asthma, and these specific functional changes are related to emotional pathway and pulmonary symptoms in the respiratory system [⁵,⁷,⁸]. In addition to regional function, fMRI studies have demonstrated a close connection between lung function and neural network changes in patients with asthma. For example, altered degree centrality (DC) values, which are the measure of the node influence across whole brain functional connections at the voxel level [⁹], have been detected in various brain regions in patients with asthma [¹⁰]. Functional connectivity (FC) changes across limbic circuits were also found during perceived dyspnea and pain in patients with asthma [¹¹].

The abovementioned evidence suggests that brain functional changes involve both regional and network levels in asthma. However, the relationship between regional functional changes and network disorganization remains unclear. Different patterns of changes have been detected using varying analytic approaches. Regional functional changes have been detected in various brain areas, including the anterior cingulate cortex, insular lobe, and frontal lobe [¹,⁷,⁸], while altered neural networks are located in the default mode network, cortical-basal ganglia network, frontoparietal network, and sensorimotor network, which affect areas including the temporal gyrus, parietal lobe, basal ganglia nuclei, and part of the cerebellum [¹⁰,¹²,¹³]. The varied findings at both regional and network levels may be affected by studied samples, methodologies, and therapy selections. Moreover, regional and network changes show associations with different clinical ratings. Functional changes in the anterior cingulate cortex and insular cortex are closely related to emotional performance with depression and stress [¹,⁸], while the neural network in the brainstem, periaqueductal gray, and limbic system is correlated with airway inflammation, dyspnea, and pain of the respiratory system in asthma [¹¹,¹⁴]. Regional functional changes can influence the global network in brain disorders such as schizophrenia [¹⁵–¹⁹]. Different regional and network patterns reflect specific pathophysiology mechanisms and can potentially be used as biomarkers and treatment targets for schizophrenia. However, whether regional functional changes also lead to neural network deficits in asthma remains largely unknown, and this information is crucial to clarify the biological characteristics of the disease and further develop clinical diagnosis and treatment.

Therefore, the current resting-state fMRI (rs-fMRI) study will explore brain functional changes at both the regional and network levels in patients with asthma. Metrics include amplitude of low-frequency fluctuation (ALFF, reflecting the spontaneous neural activity of the cerebral cortex) [20], DC, and FC. The relationship between functional measures and asthma-related clinical symptoms was analyzed to determine associations between functional brain changes and clinical ratings. Emotional correlation analysis with fMRI metrics was carried out based on the previous findings with processing of emotional information in patients with asthma [¹]. We hypothesized that a potential difference existed in brain functional changes and clinical performance at the regional and whole brain levels in patients with asthma, and regional abnormalities may be associated with network disorganization.

Materials and methods

Participants

This study was approved by the Ethical Committee of West China Hospital of Sichuan University. All participants volunteered for the study and provided written informed consent. We recruited 39 patients who demonstrated evidence of either bronchodilator reversibility or bronchial hyperresponsiveness and were diagnosed with asthma according to the Global Initiative for Asthma (GINA) criteria. Forty healthy controls (HCs) matched in age and sex without a history of asthma or other significant medical conditions were also enrolled. The inclusion criteria for patients were as follows: (1) age between 18 and 60 years, (2) right-handed, (3) airway hyperresponsiveness with inhaled methacholine (provocative dose causing a 20% fall in the forced expiratory volume in the first second (FEV₁)) or bronchodilator reversibility in the FEV₁ more than 12% predicted and more than 200 mL, and (4) moderate asthma classified according to the GINA criteria. Exclusion of criteria for all participants included the following: (1) age of younger than 18 years or older than 60 years, (2) history of cancer or other chronic diseases, (3) current pregnancy or breastfeeding, (4) history of psychiatric disorders as revealed by using the Structured Clinical Interview for DSM-IV (SCID)-Patient Version, (5) currently experiencing an acute asthma exacerbation, and (6) history of substance abuse or dependence.

Demographic and clinical data acquisition

All of the participants were interviewed to collect demographic data, including age, gender, years of education, and body mass index (BMI). For recruited patients with asthma, additional clinical information, including illness duration and severity of asthma, was recorded. Lung function measures, including FEV₁ and forced vital capacity (FVC), were tested by a spirometer (Master-Screen Body, Jaeger, Germany). To determine whether brain functional alterations in patients with asthma may contribute to the increased risk for psychiatric moods, we collected the Hamilton Rating Scale for Depression and Anxiety (HRSD and HRSA, respectively) to measure the emotional status of patients.

Image data acquisition

MRI data were collected on a whole-body 3.0 T MR scanner (Siemens Trio, Erlangen, Germany) with a 12-channel phase array head coil. Rs-fMRI data were acquired with an EPI protocol: TR= 2000 ms, TE= 30 ms, flip angle= 90°, matrix= 64×64, field of view= 240 mm×240 mm, 30 slices with 5 mm slice thickness, and voxel size= 3.75 mm×3.75 mm×5 mm. The duration of rs-fMRI scanning was 7 min, and each functional run contained 200 volumes. A high-resolution T1-weighted anatomical image was acquired using a 3D-spoiled gradient echo sequence (SPGR; TR= 8.5 ms, TE= 3.93 ms, flip angle= 12°, 156 sagittal slices with thickness= 1 mm, FOV= 240 mm×240 mm, and data matrix= 256×256) for normalization of rs-fMRI data.

Data preprocessing

We preprocessed rs-fMRI data using Data Processing Assistant for Resting-State fMRI (version 2.3) software, and this software was implemented within the MATLAB toolbox. The first 10 volumes were discarded to achieve steady-state longitudinal magnetization, and the remaining data were processed with the following steps: slice timing, head motion correction, spatial normalization, and smoothing with a Gaussian kernel of 4 mm full width at half maximum (FWHM). After the above steps were completed, fMRI images were spatially normalized to Montreal Neurological Institute space using the Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra to resample the images and achieve a voxel resolution of 3 mm×3 mm×3 mm [²¹]. We removed the linear trend to correct the scanner drift, and the bandpass filtering range was set at 0.01–0.08 Hz to reduce the effects of low-frequency drift and high-frequency physiological noise.

To obtain ALFF values, we transformed the time series to the frequency domain to achieve the power spectrum by using fast Fourier transformation. The measure of the ALFF from each voxel was yielded by transforming the square root from power and averaging from 0.01 Hz to 0.08 Hz. Finally, the ALFF of each voxel was divided by the global mean ALFF value, which was standardized across all subjects.

DC is a reliable centrality metric across the entire brain network. DC values were calculated using REST software (Resting-State fMRI Data Analysis Toolkit V1.8) [²²]. First, we computed the time series of all brain voxels using Pearson’s correlation coefficients (r). Second, by setting a predefined gray matter mask, we obtained an FC correlation matrix between the time series of one voxel and the time courses of all other voxels. Third, we defined the thresholding of each correlation at r>0.25 to remove the effects caused by noises [²³]. Finally, we converted the individual voxel-wise DC into a z-score map, and yielded DC data were smoothed spatially with a Gaussian kernel of 6 mm FWHM.

FC revealed the specific configurations of connected regions [²⁴]. FC values were computed by using the same software with DC values in the REST package toolkit. First, we selected the regions of interest (ROIs) based on the comparison results of ALFF and DC values between patients with asthma and HCs. Second, the representative time series of each ROI was obtained by averaging the preprocessed rs-fMRI data from all voxels in the ROI in each individual. Finally, FC values were computed using Pearson correlation coefficients between the time series of each ROI and whole brain, resulting in a z-score map of FC in each subject.

Statistical analysis

Statistical analytic processes of the comparison of ALFF, DC, and FC values between patients with asthma and HCs were performed in REST software with two-sample T test analysis. Additionally, age, gender, and education years were added as covariates. In all image statistical analyses, AlphaSim was used to correct for multiple comparisons when testing for statistical significance. The AlphaSim calculation was also performed in REST software on the basis of the statistical map of two groups and a predefined mask file. An actual smoothness kernel was estimated to ensure the accuracy of the results. The threshold of a false-positive detection for our study was set to P<0.05 using a minimum cluster size of five contiguous voxels.

Peak values of functional measures of brain regions that were significantly different between groups were extracted. Pearson correlation analysis between altered functional metrics and asthma-specific clinical indices, including illness duration, severity of illness score, FEV₁, FVC, FEV₁/FVC, and psychiatric score of HRSD and HRSA, was conducted using SPSS Statistics software (version 24.0). The significance level was set to P<0.05.

Results

Demographics

The demographic information showed no significant differences in age (patients, 34.54; HCs, 33.90), gender (male/female: patients, 11/28; HCs, 10/30), education years (patients, 11.46; HCs, 12.80), and BMI (patients, 23.1; HCs, 21.41) between patients with asthma and HCs (P>0.05). In patients with asthma, the mean FEV₁ and FVC were 2.28 and 3.12, respectively, and the mean percentage of FEV₁/FVC was 71.88. The mean duration of illness for patients was 5.88 years, and the mean severity of illness score was 2.29. HRSD and HRSA were evaluated separately, resulting in mean scores of 7.62 and 6.03, respectively, in patients with asthma (Table 1).

Imaging findings

In rs-fMRI analysis, we found regional functional alterations in the cerebrum within the default mode network measured with ALFF values. Compared with HCs, patients with asthma showed decreased ALFF values in the left angular gyrus and right precuneus, while ALFF values were increased in the right inferior temporal gyrus (P<0.05, corrected by AlphaSim calculation; Table 2 and Fig. 1).

Network alterations were evaluated with DC values. Compared with HCs, patients had decreased DC changes in the left caudate, left lingual gyrus, and right middle occipital gyrus extending to the right inferior occipital gyrus (P<0.05, corrected by AlphaSim calculation; Table 2 and Fig. 2). DC alterations showed no overlap of anatomic areas with regional functional changes.

To further determine the difference between regional and network changes, we performed FC analysis by selecting seeds based on brain regions with altered ALFF or DC values. By comparing patients with asthma with HCs in subsequent network analysis based on DC results, we detected widespread decreased FC consistent with DC findings. Decreased FC was found between the left caudate seed and left superior frontal gyrus and right inferior occipital gyrus in patients with asthma and between the left lingual gyrus seed and left superior frontal gyrus, as well as between the right middle occipital gyrus seed and left superior frontal gyrus (P<0.05, corrected by AlphaSim calculation; Table 3 and Fig. 3), which mostly involved the sensorimotor network and visual network. No significant FC was detected between seeds with regional ALFF changes and other brain regions.

Correlation with clinical ratings

A positive correlation was found between DC of the left lingual gyrus and FEV₁/FVC (r = 0.442, P = 0.009; Fig. 4), indicating that decreased DC was associated with severe airway obstruction. No significant correlation was detected between other rs-fMRI measures and asthmatic clinical data and emotional data.

Discussion

By analyzing the metrics of ALFF, DC, and FC, our rs-fMRI study revealed that regional functional changes were located in regions that differed from those with network changes. Regional functional changes included both decreased and increased activation within the default mode network in patients with asthma, while the network deficits included the cortical cortex and subcortical regions involving the left caudate, lingual gyrus, and right middle occipital gyrus. No significant correlation between ALFF and clinical symptoms was detected. Further FC analysis strengthened the decreased network alterations in asthma with decreased FC between DC-based seeds and the left superior frontal gyrus and right occipital gyrus in the sensorimotor network and visual network, respectively. Moreover, we detected the relationship between brain fMRI measures and lung functional index with a positive correlation between DC in the left lingual gyrus and FEV₁/FVC. Altogether, these findings suggested that regional and neural network deficits are independent characteristics in patients with asthma, which may reflect different aspects of the pathophysiology of asthma.

ALFF is a reliable metric with good performance in demonstrating spontaneous activities of the brain cortex at rest [²⁰,²⁵]. We observed regional brain functional changes with decreased ALFF in the left angular gyrus and right precuneus and increased ALFF located in the right inferior temporal gyrus. The angular gyrus, precuneus, and temporal lobe are usually classified as the default mode network in many network organizations [²⁶–²⁸]. The default mode network is considered a higher-level cognitive network and consists of brain regions that typically activate during resting-state but deactivate during the performance of a particular task [²⁹,³⁰]. The altered regional abnormalities within the default mode network implied spontaneous changes in the central nervous system affected by asthma. Previous studies demonstrated reduced gray matter volume in the angular gyrus within patients with asthma and reduced gray matter volume and cortical thickness in the precuneus in patients with chronic obstructive pulmonary disease (COPD) [³¹–³³]. We also detected the regional changes in the left angular gyrus with fractional ALFF (Supplementary Table S1), which further demonstrated the stable change of this area affected by asthma. The consistent alterations in brain structure and function may imply their important roles in the participation of pathophysiology in asthma-related cerebral changes. The only increased ALFF alteration was located in the right inferior temporal gyrus; thus, the high activation in patients with asthma was similar to the changes in the COPD-related brain function study [³⁴]. This area is correlated with chronic pain symptoms, such as chronic back pain [³⁵,³⁶], which is considered a key component engaging in the chronic pain sensory process [¹³]. This finding might explain the association of lasting symptoms in patients with asthma. Regional abnormalities exhibited independent changes within the default mode network, which suggested that the spontaneous changes in cerebral regional function in the central nervous system originated from asthma illness.

Unlike regional functional changes, the network deficits reflect cortical and subcortical changes involving the left caudate, lingual gyrus, and right occipital gyrus. No overlapping brain areas in ALFF and DC illustrated the inconsistency between regional and network changes in patients with asthma. The widespread decreased brain connectivity provided evidence for the impaired central nervous system in patients with asthma. Altered brain functional changes in the lingual gyrus have been reported among high-altitude residents [³⁷–³⁹], and brain connectivity changes in the lingual gyrus could be related to hypoxia in asthma. We also found a positive correlation between DC of the left lingual gyrus and FEV₁/FVC, which suggested that decreased DC was correlated with heavy airway obstruction. The direct relationship between brain functional measures and the lung functional index strengthens asthma-related impairments in the central nervous system. The caudate is believed to play a key role in cognitive neural circuits [⁴⁰], which may be correlated with degenerated cognitive function in patients with asthma [⁴¹]. Structural studies in COPD also reported decreased gray matter volume and density in the caudate [⁴²,⁴³]. Simultaneous changes in caudate structure and function suggested consistent subcortical alterations in asthma. Abnormalities in the bilateral occipital gyrus were detected in both CT and MRI examinations during asthma attacks [⁴⁴–⁴⁶], and visible structural changes enhance the influence of asthma on the cerebrum. The functional alterations in asthma and patients with COPD by using regional homogeneity (reflecting regional signal synchrony strength) [⁴⁷] and FC analysis were consistent with our findings [⁴⁸]. These aberrant functional changes demonstrated the crucial network nodes influenced by asthma.

We further calculated the seed-based FC to explore whether the ALFF or DC can affect the connectivity strength, which reflects the relationship between regional and network function in an intuitive way. We did not find significant FC based on seeds with ALFF alteration, which implied that regional function alterations exerted limited effects on network function. Moreover, widespread FC alterations in patients with asthma based on seeds with DC alteration provided evidence supporting decreased network function. Selected FC seeds (including the left caudate, lingual gyrus, and right middle occipital gyrus) from the DC results and the network exerted decreased FC, mainly involving the superior frontal gyrus and occipital lobe. The superior frontal gyrus is an important brain region in the sensorimotor network [⁴⁹,⁵⁰], and an altered sensorimotor network is associated with altered respiratory amplitude in patients with asthma [⁵¹]. We detected decreased FC in three seeds from the DC results and the superior frontal gyrus, which highlighted the crucial role of the superior frontal gyrus in asthma-specific neural abnormalities. Reduced gray matter volume in the superior frontal gyrus has been elucidated [³¹] and locally segregated but less efficiently integrated into the frontal lobe with structural organization [⁵²]. Respiratory system measures, including ratings of breathlessness, airway inflammation, and asthma control degree, are all associated with brain functional alterations in this area [³¹,⁵³]. Patients with COPD also demonstrated activation in the same brain area [³⁴], indicating the interrelation between respiratory system damage and functional changes in the superior frontal gyrus within the sensorimotor network. Except for the relation between network changes and clinical features, a previous study found direct evidence of neuroinflammation with increased levels of tumor necrosis factor a and interleukin-1 β in the superior frontal gyrus and significant neuronal loss in the asthmatic mouse brain [⁵⁴]. Our results also showed the importance of the occipital gyrus with decreased FC between the left caudate and right inferior occipital gyrus, as well as decreased FC between the right middle occipital gyrus and left superior frontal gyrus. The occipital cortex is associated with visual processing, and occipital degeneration participated in the visual deficits in some patients with asthma, as reflected in previous studies [⁴⁵,⁵⁵–⁵⁷]. The consistent results in our study hinted at the impaired visual network affected by asthma. Beyond this finding, the occipital lobe is a brain area with high vascular density. Therefore, functional changes may also be possible due to abnormal ventilation in poor asthma control patients [⁵⁸–⁶⁰]. The widespread decreased neural network exhibited different alterations with regional function, and the sensorimotor network and visual network are the core changes in the network influenced by asthma. These findings demonstrated different cerebral regional and network patterns, and those patterns reflected specific characteristics related to respiratory symptoms and secondary changes in patients with asthma, which could be potential biomarkers in helping us understand the neural pathophysiology of asthma and selection of treatment.

Although we found different regional and network characteristics with mostly widespread decreases in brain functional changes in asthma patients with ALFF, DC, and FC, some limitations should be considered in three perspectives. First, we recruited both patients and HCs with more females than males, and the gender bias could sway our results with more female features. Further studies with large sample sizes, including gender-balanced patients, are needed. Second, our cross-sectional study could not reflect asthma progression with dynamic functional abnormalities, and a longitudinal study is required in the future to display the progression of brain alterations in asthma. Finally, we did not exclude the confounding effects of medication, which could affect the asthma control condition exhibited in brain activities.

In conclusion, by using functional measures with ALFF, DC, and FC, this study reflected different patterns of brain functional changes at the regional and network levels in patients with asthma. Regional abnormalities reflected independent changes within the default mode network, which implied regional deficits with spontaneous changes in the central nervous system affected by asthma. Network characteristics were detected with widespread decreased functional alterations across the whole brain, and they were mostly involved with changes in sensorimotor and visual network. The correlation between network abnormalities and airway obstruction degree reflected that brain damage was associated with impaired respiratory system originating from asthma. This study is helpful in extending our understanding of the neuropathophysiology of asthma.

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