Edited by:
Reviewed by:
*Correspondence:
†
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Neural disruptions during emotion regulation are common of generalized anxiety disorder (GAD). Identifying distinct functional and effective connectivity patterns in GAD may provide biomarkers for their diagnoses. This study aims to investigate the differences of features of brain network connectivity between GAD patients and healthy controls (HC), and to assess whether those differences can serve as biomarkers to distinguish GAD from controls. Independent component analysis (ICA) with hierarchical partner matching (HPM-ICA) was conducted on resting-state functional magnetic resonance imaging data collected from 20 GAD patients with medicine-free and 20 matched HC, identifying nine highly reproducible and significantly different functional brain connectivity patterns across diagnostic groups. We then utilized Granger causality (GC) to study the effective connectivity between the regions that identified by HPM-ICA. The linear discriminant analysis was finally used to distinguish GAD from controls with these measures of neural connectivity. The GAD patients showed stronger functional connectivity in amygdala, insula, putamen, thalamus, and posterior cingulate cortex, but weaker in frontal and temporal cortex compared with controls. Besides, the effective connectivity in GAD was decreased from the cortex to amygdala and basal ganglia. Applying the ICA and GC features to the classifier led to a classification accuracy of 87.5%, with a sensitivity of 90.0% and a specificity of 85.0%. These findings suggest that the presence of emotion dysregulation circuits may contribute to the pathophysiology of GAD, and these aberrant brain features may serve as robust brain biomarkers for GAD.
Generalized anxiety disorder (GAD) is one of the most common mental disorders characterized by excessive anxiety and worry that is not focused on a specific situation or object. The GAD is highly prevalent in the general population. Patients with GAD often suffer from a variety of anxiety-related physical symptoms like difficulty concentrating, irritability, muscle tension, and disturbed sleep which impair their quality of life and social functioning (
Previous brain imaging studies have revealed neural differences in GAD compared with HC in brain areas associated with the emotion dysregulation, cognitive deficits, and/or reward processing, which may be important in the pathophysiology of GAD. Individuals with GAD exhibited over-engagement of amygdala and inferior frontal gyrus during the viewing of negative images on an emotion regulation task, compared to HC (
Resting-state fMRI has been widely used to study the mechanism of brain function, especially in the clinical study, due to simple operation, no task stimulation, as well as the stable and reliable results. Resting-state fMRI studies have reported decreased functional connectivity (FC) between amygdala and prefrontal cortex in adults (
Machine learning techniques have played an important role in exploring the brain differences between patients with anxiety disorder and HC. Recently, one study has employed resting-state functional MRI data to investigate multivariate classification of social anxiety disorder by using whole-brain FC (
Independent component analysis (ICA) is a multivariate method for blind source separation which has been widely applied for analyzing neuroimaging data (
At present, there are many methods to measure the effective connectivity such as structural equation model (SEM), dynamic causal model, and Granger causality (GC). The SEM and dynamic causal model does not contain time information, which also need to select interaction region in advance. A mistake on the model assumption will lead to a wrong result. GC overcomes these limitations effectively. GC analysis is very consistent with the actual situation because it does not require any prior knowledge and considers the effect of the time on the results (
The aim of the current study was to examine whether brain functional and effective connectivity networks differ in GAD. We applied the ICA with hierarchical partner matching (HPM-ICA) to assess the brain FC networks. The causal influence between the IC was estimated by utilizing GC method. The machine learning was finally used to distinguish the GAD from HC by combining the features of functional and effective connectivity brain networks. We hypothesized that we would detect brain connectivity differences in GAD within the cortical–subcortical neural systems that support emotion dysregulation and these feature patterns could be served as biomarkers for GAD.
Twenty participants with GAD who met the criteria for DSM-IV (13 females, 7 males, mean age 41.5 ± 10.7 years, range 30–50 years) were recruited from the psychological outpatient clinic at the Qilu Hospital of Shandong University. Twenty group matched by age and sex HC (13 females, 7 males, mean age 40.1 ± 9.8 years, range 30–49 years) were recruited by public advertisement to take part in the study. All GAD participants had been diagnosed by a licensed psychiatrist before enrolment. All participants were right-handed, native Chinese speakers. Exclusion criteria were: age younger than 18 years, lifetime history of increased intracranial pressure, seizure disorder, stroke, brain tumor, multiple sclerosis or brain surgery, cognitive impairment, history of substance-use disorder, an active autoimmune, endocrine, vascular disorder affecting the brain, any unstable cardiac disease, hypertension, and severe renal, liver insufficiency. All patients and controls were medication free for at least 1 month before enrolment. The safety screening form and informed consent form were approved by the Institutional Review Board of Qilu Hospital of Shandong University. We obtained written informed consent from all participants.
Psychiatric diagnoses were assessed using the Structured Clinical Interview for DSM-IV (SCID-I). We evaluated anxiety severity on the day of MRI scan using the assessment of the Hamilton Rating Scale for Anxiety (
Images were acquired on a Siemens Verio 3.0 Tesla MRI scanner (Siemens, Erlangen, Germany) using a 32-channel head coil at the Qilu Hospital of Shandong University. We used earplugs to reduce scanner noise and restraining foam pads to reduce head motion. Participants were instructed to rest with their eyes closed but not to fall asleep during scanning. When the scanning process was completed, we asked all the participants whether they were asleep during scanning. In addition, the participants had involuntary movement of body if they were asleep, leading to the motion artifact of fMRI data. The two rules controlled the participants who fall asleep during scanning were excluded in this study. Resting-state fMRI data were acquired using a single-shot gradient-echo planar imaging (EPI) sequence. Thirty-six contiguous axial slices were acquired along the AC–PC plane, with a 64 × 64 matrix and 20 cm field of view (voxel size = 3.4375 × 3.4375 × 3.0 mm3, repetition time = 2000 ms, echo time = 30 ms, flip angle = 90 degrees, slice thickness = 3 mm). The sequence took 8 min, resulting in a total of 240 volumes.
Firstly, we preprocessed the resting-state fMRI images by using SPM12 (Welcome Department of Imaging Neuroscience, London, United Kingdom) that was run under MATLAB. The functional scans were slice timing-corrected, spatially realigned to the first scan to correct for head movements, normalized to the Montreal Neurological Institute (MNI) coordinate system and spatially smoothed using an isotropic 8 mm full-width at half-maximum (FWHM) Gaussian kernel.
Secondly, we applied the ICA with HPM-ICA, which we proposed and published previously (
Finally, group-level statistical analysis was applied to detect random effects of group difference in FC between GAD and HC. We entered the
Granger causality was applied to estimate the effective connectivity or causal influences between the IC that were identified using the HPM-ICA method. The time courses of these components were used to compute GC indices (GCIs) between them. We then used two-sample
A feature selection technique is often performed before classification to avoid the curse of dimensionality and enhance generalization of the classifier. The central premise when using feature selection is that the data contain some features that are either redundant or irrelevant, and can thus be removed without incurring much loss of information. In this study the functional and effective connectivity were considered together to serve as features and two-sample
We also assessed the classification contribution of each feature in the MLDA classifier by using the coefficients of the discrimination hyperplane which measured the weights of the features. The selected features were consistent in each iteration of LOOCV, but feature weights were based on a slightly different subset of the data. Therefore, the final feature weights were the average across all folds of LOOCV.
We applied HPM-ICA to identify eight clusters of IC that were significantly reproducible in their spatial patterns across GAD and control participants. The general linear model in SPM was used to perform a one-sample
Comparisons of FC between GAD and HC. The first three columns display the random-effect group activity maps detected from the GAD. The first column is a coronal view, the second is a sagittal view, and the third is an axial view. The second three columns display the group activity maps detected from the HC. Each row displays one group activity map generated by applying a one-sample
Regional locations and significant comparisons of the independent component maps between GAD and healthy controls.
Brain areas | Location |
Peak location |
||||
---|---|---|---|---|---|---|
Side | BA | |||||
Amygdala | L | NA | –24 | –4 | –19 | +2.94 |
Insula | R | 16 | 46 | –1 | 1 | +2.72 |
Putamen | R | NA | 26 | 8 | 7 | +2.65 |
Thalamus | R | NA | 17 | –18 | 13 | +2.34 |
Posterior cingulate cortex (PCC) | R | 23 | 3 | –40 | 13 | +3.19 |
Middle frontal gyrus (MFG) | L | 9 | –27 | 13 | 60 | –3.23 |
Superior frontal gyrus (SFG) | R | 8 | 3 | 53 | 6 | –2.97 |
Middle temporal gyrus (MTG) | L | 21 | –48 | –56 | 13 | –3.06 |
Correlation of
We used GCIs to assess the effective connectivity between the IC involved in emotion dysregulation brain networks, including amygdala, insula, putamen, thalamus, PCC, MFG, SFG, and MTG. Basal ganglia are involved in many neuronal pathways including emotional, motivational, associative, and cognitive functions. The striatum (caudate nucleus, putamen, and nucleus accumbens) receive inputs from all cortical areas (top-down) and, throughout the thalamus, project principally to frontal lobe areas (bottom-up) (
Comparisons of statistically significant GCIs of the interregional connections of the reproducible IC.
GAD | HC | GAD vs. HC | |
---|---|---|---|
MFG → Amygdala | 0.125 ± 0.054, |
0.225 ± 0.108, |
|
SFG → Amygdala | 0.120 ± 0.063, |
0.224 ± 0.108, |
|
MTG → Amygdala | 0.061 ± 0.046, |
0.141 ± 0.147, |
|
Amygdala → Insula | 0.071 ± 0.049, |
0.021 ± 0.013, |
|
MFG → Putamen | 0.039 ± 0.023, |
0.083 ± 0.055, |
|
SFG → Putamen | 0.031 ± 0.034, |
0.067 ± 0.032, |
|
MFG → Thalamus | 0.187 ± 0.060, |
0.276 ± 0.144, |
|
Putamen → PCC via Thalamus | 0.017 ± 0.028, |
0.066 ± 0.086, |
|
Putamen → SFG via Thalamus | 0.027 ± 0.027, |
0.084 ± 0.073, |
Brain circuit that involved in emotion dysregulation in GAD. The image shows the significant interregional causal connections as estimated by the Granger causality index (GCI) and the comparison of GCIs between the GAD and HC. Yellow lines represent causal influences from region
We performed MLDA classifier with ICs and GCIs features, achieving a classification accuracy of 87.5%, with a sensitivity of 90.0% and a specificity of 85.0%. The weights of the features were shown in
Feature weights in the classification.
In the present study, we applied HPM-ICA and GC methods on resting-state fMRI data to investigate the intrinsic differences in functional and effective connectivity between GAD and HC. Machine learning was then used to assess if these brain features can serve as biomarkers for GAD. We found that GAD subjects showed stronger FC in the amygdala, insula, putamen, PCC, and thalamus, while weaker FC in the prefrontal cortex and MTG. GC influences were generally weaker in GAD than in controls in connections from the prefrontal cortex to amygdala and subcortical regions. The results indicated that abnormal functional and effective connectivity in emotion-related brain networks was related to the generic risk of GAD, which makes it a potential endophenotype for GAD.
The amygdala plays a primary role in the processing of emotional reactions in humans and other animals (
The insula is marked as an important component of salience network (
The GAD patients exhibited decreased connectivity between the prefrontal cortex and amygdala, which replicates previous neuroimaging findings (
We also found decreased connectivity between the MTG and the amygdala in GAD patients compared with HC. Evidence has shown that the medial temporal lobe and its interactions with the amygdala are critically implicated in enhancing memory for intrinsically threatening stimuli (
We found robust and reproducible increased connectivity in thalamus in patients with GAD, which is the central core in cortical–subcortical neurocircuitry. The thalamus is an important relay station for sensory information transmission and has widely spread functional connections with cortical and subcortical areas in the brain. Previous neuroimaging studies have suggested that the thalamus plays an important role not only in the filtering of sensory information, but also involved in the process of senior cognition and emotion regulation (
The putamen showed the expected pattern of increased FC in GAD patients compared with HC. Previous studies have observed increased amygdala FC with the putamen (
Additionally, we found increased connectivity in PCC which is the central core of the default mode network (DMN). The DMN is most commonly shown to be active during passive rest and mind-wandering (
Lastly, high classification accuracy with features of altered connectivity in amygdala, insula, prefrontal cortex, temporal cortex, thalamus, and putamen could across verify that pathological change of these regions may be the neural substrates underlying the occurrence of GAD. To our knowledge, this is the first study to show that GAD can be accurately classified from HC based on functional resting-state biomarkers. In addition, the future work will focus on how to improve the classification performance by larger sample size and incorporating information from different imaging modalities.
The current study has several limitations. First, the
This study was carried out in accordance with the recommendations of DSM-IV, the Institutional Review Board of Qilu Hospital of Shandong University with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Institutional Review Board of Qilu Hospital of Shandong University.
Conceived and designed the experiments: JQ, AL, and GX. Performed the experiments: JQ, AL, and GX. Analyzed the data: JQ, ZW, and JS. Contributed reagents/materials/analysis tools: JQ and ZW. Wrote the paper: JQ, ZW, CC, and GX.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.