The neural markers of MRI to differentiate depression and panic disorder

Engeletin alleviates depressive-like behaviours by modulating microglial polarization via the LCN2/CXCL10 signalling pathway

Microglial polarization and associated inflammatory activity are the key mediators of depression pathogenesis. The natural Smilax glabra rhizomilax derivative engeletin has been reported to exhibit robust anti-inflammatory activity, but no studies to date have examined the mechanisms through which it can treat depressive symptoms. We showed that treatment for 21 days with engeletin significantly alleviated depressive-like behaviours in chronic stress social defeat stress (CSDS) model mice. T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) imaging revealed no significant differences between groups, but the bilateral prefrontal cortex of CSDS mice exhibited significant increases in apparent diffusion coefficient and T2 values relative to normal control mice, with a corresponding reduction in fractional anisotropy, while engeletin reversed all of these changes. CSDS resulted in higher levels of IL-1β, IL-6, and TNF-a production, enhanced microglial activation, and greater M1 polarization with a concomitant decrease in M2 polarization in the mPFC, whereas engeletin treatment effectively abrogated these CSDS-related pathological changes. Engeletin was further found to suppress the LCN2/C-X-C motif chemokine ligand 10 (CXCL10) signalling axis such that adeno-associated virus-induced LCN2 overexpression ablated the antidepressant effects of engeletin and reversed its beneficial effects on the M1/M2 polarization of microglia. In conclusion, engeletin can alleviate CSDS-induced depressive-like behaviours by regulating the LCN2/CXCL10 pathway and thereby altering the polarization of microglia. These data suggest that the antidepressant effects of engeletin are correlated with the polarization of microglia, highlighting a potential avenue for future design of antidepressant strategies that specifically target the microglia.

The difference in volumetric alternations of the orbitofrontal-limbic-striatal system between major depressive disorder and anxiety disorders: A systematic review and voxel-based meta-analysis

BACKGROUND

Major depressive disorder (MDD) and anxiety disorders (ANX) are psychiatric disorders with high mutual comorbidity rates that might indicate some shared neurobiological pathways between them, but they retain diverse phenotypes that characterize themselves specifically. However, no consistent evidence exists for common and disorder-specific gray matter volume (GMV) alternations between them.

METHODS

A systematic review and meta-analysis on voxel-based morphometry studies of patients with MDD and ANX were performed. The effect of comorbidity was explicitly controlled during disorder-specific analysis and particularly investigated in patient with comorbidity.

RESULTS

A total of 45 studies with 54 datasets comprising 2196 patients and 2055 healthy participants met the inclusion criteria. Deficits in the orbitofrontal cortex, striatum, and limbic regions were found in MDD and ANX. The disorder-specific analyses showed decreased GMV in the bilateral anterior cingulate cortex, right striatum, hippocampus, and cerebellum in MDD, while decreased GMV in the left striatum, amygdala, insula, and increased cerebellar volume in ANX. A totally different GMV alternation pattern was shown involving bilateral temporal and parietal gyri and left fusiform gyrus in patients with comorbidity.

LIMITATIONS

Owing to the design of included studies, only partial patients in the comorbid group had a secondary comorbidity diagnosis.

CONCLUSION

Patients with MDD and ANX shared a structural disruption in the orbitofrontal-limbic-striatal system. The disorder-specific effects manifested their greatest severity in distinct lateralization and directionality of these changes that differentiate MDD from ANX. The comorbid group showed a totally different GMV alternation pattern, possibly suggesting another illness subtype that requires further investigation.

Two-channel EEG based diagnosis of panic disorder and major depressive disorder using machine learning and non-linear dynamical methods

The current study aimed to investigate the possibility of rapid and accurate diagnoses of Panic disorder (PD) and Major depressive disorder (MDD) using machine learning. The support vector machine method was applied to 2-channel EEG signals from the frontal lobes (Fp1 and Fp2) of 149 participants to classify PD and MDD patients from healthy individuals using non-linear measures as features. We found significantly lower correlation dimension and Lempel-Ziv complexity in PD patients and MDD patients in the left hemisphere compared to healthy subjects at rest. Most importantly, we obtained a 90% accuracy in classifying MDD patients vs. healthy individuals, a 68% accuracy in classifying PD patients vs. controls, and a 59% classification accuracy between PD and MDD patients. In addition to demonstrating classification performance in a simplified setting, the observed differences in EEG complexity between subject groups suggest altered cortical processing present in the frontal lobes of PD patients that can be captured through non-linear measures. Overall, this study suggests that machine learning and non-linear measures using only 2-channel frontal EEGs are useful for aiding the rapid diagnosis of panic disorder and major depressive disorder.

Altered DNA Methylation of the Serotonin Transporter Gene Associated with Early Life Stress and White Matter Microalterations in Korean Patients with Panic Disorder

INTRODUCTION

Changes in the DNA methylation of 5-HTTLPR are associated with the pathophysiology of panic disorder (PD). This study was conducted to investigate the association between stressful life events and the level of 5-HTTLPR methylation in patients with PD. We also examined whether these factors were associated with white matter alterations in psychological trauma-related regions.

METHODS

The participants comprised 232 patients with PD and 93 healthy adults of Korean descent. DNA methylation levels of five cytosine-phosphate-guanine (CpG) sites in the 5-HTTLPR region were analyzed. Voxel-wise statistical analysis of diffusion tensor imaging data was performed within the trauma-related regions.

RESULTS

PD patients showed significantly lower levels of the DNA methylation at 5-HTTLPR 5 CpG sites than healthy controls. In patients with PD, the DNA methylation levels at 5-HTTLPR 5 CpG sites showed significant negative association with the parental separation-related psychological distress, and positive correlations with the fractional anisotropy values of the superior longitudinal fasciculus (SLF) which might be related to trait anxiety.

CONCLUSION

Early life stress was significantly associated with DNA methylation levels at 5-HTTLPR related to the decreased white matter integrity in the SLF region in PD. Decreased white matter connectivity in the SLF might be related to trait anxiety and is vital to the pathophysiology of PD.

Serum metabolomic profiling revealed potential diagnostic biomarkers in patients with panic disorder

BACKGROUND

Currently, specific metabolites and diagnostic biomarkers of panic disorder (PD) patients have not been identified in clinical practice. The aim of this study was to explore metabolites and metabolic pathways in serum through a metabolomics method.

METHODS

Fifty-five PD patients who completed 2 weeks of inpatient treatment and 55 healthy control subjects (HCs) matched for age, sex and BMI were recruited. Ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) was used to detect metabolites in serum. Multivariate Statistical Analysis was used to identify differential metabolites. The relevant biometabolic pathways were further identified by the online tool MetaboAnalyst 5.0.

RESULTS

43 different metabolites in PD patients compared to HCs (P < 0.05) were screened. Pathway analysis showed that these small molecules were mainly associated with amino acid metabolism. 14 metabolites were significantly changed after 2 weeks of drug treatment (P < 0.05), which were mainly associated with tryptophan metabolism.

CONCLUSION

In conclusion, our analysis of metabolomics of PD patients at baseline and two weeks after treatment screened for differential metabolites that could be potential diagnostic biomarkers involved in PD pathogenesis and influence some biometabolic pathways such as phenylalanine metabolism and tryptophan metabolism. In the future, we can summarize and observe the dynamic changes of differential metabolites that appear more frequently in similar studies to further explore the underlying mechanisms of PD evolution.

Achyranthes aspera ameliorates stress induced depression in mice by regulating neuroinflammatory cytokines

Background and aim

Achyranthes aspera Linn. (A. aspera) (family: Amaranthaceae) is highly recognized in ethnomedicine and traditional systems of Indian medicine as a nervine restorative for several psychiatric disorders. Study presented here was designed to appraise the antidepressant-like effects of A. aspera in murine model of chronic unpredictable mild stress (CUMS) induced depression.

Experimental procedures-

Rodents were exposed to different stressor in unpredictive manner during CUMS protocol once a day for 4 weeks. Mice were intraperitoneally injected with A. aspera extract (2.5, 5 and 10 mg/kg) or fluoxetine (10 mg/kg) or betaine (20 mg/kg) once daily during day 15–28 of the CUMS protocol. Sucrose preference, motivation and self-care, immobility latency and plasma corticosterone were evaluated after 24 h of last stressor. After behavioral assessments TNF-α, Il-6 and BDNF immunocontent was determined in hippocampus and prefrontal cortex.

Results and conclusion

A. aspera extract as well as betaine improved sucrose preference, increased grooming frequency and latency in splash test and ameliorated depression-like condition in CUMS mice in Porsolt test. A. aspera treatment decreased the elevated plasma corticosterone and reversed the effect of CUMS on TNF-α, Il-6 and BDNF immunocontent in mice. The results of the present study suggest A. aspera as a promising indigenous medicine for stress associated neurobehavioral and comorbid complications.

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Achyranthes aspera is a recognized medicine for psychiatric disorders.

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A. aspera improved sucrose preference, increased grooming frequency and latency in splash test in CUMS mice.

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A. aspera ameliorated depression-like condition in CUMS mice.

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A. aspera treatment decreased the elevated plasma corticosterone and reversed the effect of CUMS on TNF-α, Il-6 and BDNF immunocontent in mice.

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Results suggest A. aspera as a medicine for stress associated neurobehavioral and comorbid complications.

An interpretable radiomics model for the diagnosis of panic disorder with or without agoraphobia using magnetic resonance imaging

BACKGROUND

Early and accurate diagnosis of panic disorder with or without agoraphobia (PDA) is crucial to reducing disease burden and individual suffering. However, its diagnosis is challenging for lack of validated biomarkers. This study aimed to investigate whether radiomic features extracted from T1-weighted images (T1) of major fear-circuit structures (amygdala, insula, and anterior cingulate cortex [ACC]) could differentiate patients with PDA from healthy controls (HCs).

METHODS

The 213 participants (93 PDA, 120 HCs) were allocated to training (n = 149) and test (n = 64) sets after undergoing magnetic resonance imaging. Radiomic features (n = 1498) were extracted from T1 of the studied structures. Machine learning models were trained after feature selection and then validated in the test set. SHapley Additive exPlanations (SHAP) explored the model interpretability.

RESULTS

We identified 29 radiomic features to differentiate participants with PDA from HCs. The area under the curve, accuracy, sensitivity, and specificity of the best performing radiomics model in the test set were 0.84 (95% confidence interval: 0.74-0.95), 81.3%, 75.0%, and 86.1%, respectively. The SHAP model explanation suggested that the energy features extracted from the bilateral long insula gyrus and central sulcus of the insula and right ACC were highly associated with the risk of PDA.

LIMITATIONS

This was a cross-sectional study with a relatively small sample size, and the causality of changes in radiomic features and their biological and clinical meanings remained to be elucidated.

CONCLUSIONS

Our findings suggest that radiomic features from the fear-circuit structures could unveil hidden microstructural aberrations underlying the pathogenesis of PDA that could help identify PDA.

Effective Connectivity of Right Amygdala Subregions Predicts Symptom Improvement Following 12-Week Pharmacological Therapy in Major Depressive Disorder

The low rates of treatment response still exist in the pharmacological therapy of major depressive disorder (MDD). Exploring an optimal neurological predictor of symptom improvement caused by pharmacotherapy is urgently needed for improving response to treatment. The amygdala is closely related to the pathological mechanism of MDD and is expected to be a predictor of the treatment. However, previous studies ignored the heterogeneousness and lateralization of amygdala. Therefore, this study mainly aimed to explore whether the right amygdala subregion function at baseline can predict symptom improvement after 12-week pharmacotherapy in MDD patients. We performed granger causality analysis (GCA) to identify abnormal effective connectivity (EC) of right amygdala subregions in MDD and compared the EC strength before and after 12-week pharmacological therapy. The results show that the abnormal EC mainly concentrated on the frontolimbic circuitry and default mode network (DMN). With relief of the clinical symptom, these abnormal ECs also change toward normalization. In addition, the EC strength of right amygdala subregions at baseline showed significant predictive ability for symptom improvement using a regularized least-squares regression predict model. These findings indicated that the EC of right amygdala subregions may be functionally related in symptom improvement of MDD. It may aid us to understand the neurological mechanism of pharmacotherapy and can be used as a promising predictor for symptom improvement in MDD.

Systematic transdiagnostic review of magnetic-resonance imaging results: Depression, anxiety disorders and their co-occurrence

BACKGROUND

Major depressive disorder (MDD) and anxiety disorders (ANX) share core symptoms such as negative affect and often co-exist. Magnetic-resonance imaging (MRI) research suggests shared neuroanatomical/neurofunctional underpinnings. So far, studies considering transdiagnostic and disorder-specific neural alterations in MDD and ANX as well as the comorbid condition (COM) have not been reviewed systematically.

METHODS

Following PRISMA guidelines, the literature was screened and N = 247 articles were checked according to the PICOS criteria: MRI studies investigating transdiagnostic (across MDD, ANX, COM compared to healthy controls) and/or disorder-specific (between MDD, ANX, COM) neural alterations. N = 35, thereof n = 13 structural MRI and diffusion-tensor imaging studies and n = 22 functional MRI studies investigating emotional, cognitive deficits and resting state were included and quality coded.

RESULTS

Results indicated transdiagnostic structural/functional alterations in the orbitofrontal cortex/middle frontal cortex and in limbic regions (amygdala, cingulum, hippocampus). Few and inconsistent disorder-specific alterations were reported. However, depression-specific functional alterations were reported for the inferior frontal gyrus and dorsolateral prefrontal cortex during emotional tasks, and limbic regions at rest. Preliminary results for anxiety-specific functional alterations were found in the insula and frontal regions during emotional tasks, in the inferior parietal lobule, superior frontal gyrus and superior temporal gyrus during cognitive tasks, and (para)limbic alterations at rest.

CONCLUSIONS

This review provides evidence to support existing transdiagnostic fronto-limbic neural models in MDD and ANX. On top, it expands existing knowledge taking into account comorbidity and comparing MDD with ANX. Heterogeneous evidence exists for disorder-specific alterations. Research focusing on ANX sub-types, and the consideration of COM would contribute to a better understanding of basic neural underpinnings.

Magnetic resonance imaging in mood disorders: a bibliometric analysis from 1999 to 2020

Objective

Globally, mood disorders are highly prevalent, and are associated with increased morbidity and mortalities. Magnetic resonance imaging is widely used in the study of mood disorders. However, bibliometric analyses of the state of this field are lacking.

Methods

A literature search in the web of science core collection (WoSCC) for the period between 1945 and 2020 returned 3073 results. Data extracted from these publications include, publication year, journal names, countries of origin, institutions, author names and research areas. The bibliometric method, CiteSpace V and key words analysis were used to visualize the collaboration network and identify research trends, respectively.

Results

Since it was first reported in 1999, the use of magnetic resonance imaging in studies on mood disorders has been increasing. Biological psychiatry is the core journal that has extensively published on this topic, while the UNIV PITTSBURGH, USA, has the highest published papers on this topic. Keyword analysis indicated that studies on depression, bipolar disorders, and schizophrenia, with a focus on specific brain regions, including amygdala, prefrontal cortex and anterior cingulate cortex are key research topics.

Conclusion

Brain structure and network, sex differences, and treatment-associated brain changes are key topics of future research.

Fronto-limbic neuroimaging biomarkers for diagnosis and prediction of treatment responses in major depressive disorder

The neuroimaging is an important tool for understanding the biomarkers and predicting treatment responses in major depressive disorder (MDD). The potential biomarkers and prediction of treatment response in MDD will be addressed in the review article. The brain regions of cognitive control and emotion regulation, such as the frontal and limbic regions, might represent the potential targets for MDD biomarkers. The potential targets of frontal lobes might include anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). For the limbic system, hippocampus and amygdala might be the potentially promising targets for MDD. The potential targets of fronto-limbic regions have been found in the studies of several major neuroimaging modalities, such as the magnetic resonance imaging, near-infrared spectroscopy, electroencephalography, positron emission tomography, and single-photon emission computed tomography. Additional regions, such as brainstem and midbrain, might also play a part in the MDD biomarkers. For the prediction of treatment response, the gray matter volumes, white matter tracts, functional representations and receptor bindings of ACC, DLPFC, OFC, amygdala, and hippocampus might play a role in the prediction of antidepressant responses in MDD. For the response prediction of psychotherapies, the fronto-limbic, reward regions, and insula will be the potential targets. For the repetitive transcranial magnetic stimulation, the DLPFC, ACC, limbic, and visuospatial regions might represent the predictive targets for treatment. The neuroimaging targets of MDD might be focused in the fronto-limbic regions. However, the neuroimaging targets for the prediction of treatment responses might be inconclusive and beyond the fronto-limbic regions.

Initial white matter connectivity differences between remitters and non-remitters of patients with panic disorder after 6 months of pharmacotherapy

Panic disorder (PD) is a harmful mental condition that causes relapsed and persistent impairment. In the treatment of PD, the prognosis for PD should be considered. However, the relationship between pharmacotherapy and biomarkers, for predicting a better response through neuroimaging, is a little known. The purpose of the present study was to examine whether there would be the initial white matter (WM) regions associated with the remission in 6 months. A total of 104 patients with PD were investigated in the study. After six months, there were 17 remission patients with PD and 81 non-remission patients. The Panic Disorder Severity Scale, Albany Panic and Phobia Questionnaire, Anxiety Sensitivity Inventory-Revised, Beck Anxiety Inventory, and Beck Depression Inventory were assessed for all patients at baseline. We compared the diffusion indices between remission and non-remission group at 6 months using Tract-Based Spatial Statistics. The results showed that the fractional anisotropy (FA) values were significantly higher in the non-remitter group compared with those in the remitter group in the WM regions, such as the posterior corona radiata and superior longitudinal fasciculus, at the 6 month evaluation. The logistic regression analysis with clinical symptom severity and FA values of the WM regions as covariates showed that FA values in those regions and the Beck Depression Inventory-II predicted poor remission. This study suggests that posterior corona radiata and superior longitudinal fasciculus are related to potential predictive factors of 6-month remission in patients with PD. WM regions associated with the long-term remission should be verified with further investigations.

Biomarkers in Panic Disorder

Background:

Panic disorder (PD) is a kind of anxiety disorder that impacts the life quality and functional perspectives in patients. However, the pathophysiological study of PD seems still inadequate and many unresolved issues need to be clarified.

Objectives:

In this review article of biomarkers in PD, the investigator will focus on the findings of magnetic resonance imaging (MRI) of the brain in the pathophysiology study. The MRI biomarkers would be divided into several categories, on the basis of structural and functional perspectives.

Methods:

The structural category would include the gray matter and white matter tract studies. The functional category would consist of functional MRI (fMRI), resting-state fMRI (Rs-fMRI), and magnetic resonance spectroscopy (MRS). The PD biomarkers revealed by the above methodologies would be discussed in this article.

Results:

For the gray matter perspectives, the PD patients would have alterations in the volumes of fear network structures, such as the amygdala, parahippocampal gyrus, thalamus, anterior cingulate cortex, insula, and frontal regions. For the white matter tract studies, the PD patients seemed to have alterations in the fasciculus linking the fear network regions, such as the anterior thalamic radiation, uncinate fasciculus, fronto-occipital fasciculus, and superior longitudinal fasciculus. For the fMRI studies in PD, the significant results also focused on the fear network regions, such as the amygdala, hippocampus, thalamus, insula, and frontal regions. For the Rs-fMRI studies, PD patients seemed to have alterations in the regions of the default mode network and fear network model. At last, the MRS results showed alterations in neuron metabolites of the hippocampus, amygdala, occipital cortex, and frontal regions.

Conclusion:

The MRI biomarkers in PD might be compatible with the extended fear network model hypothesis in PD, which included the amygdala, hippocampus, thalamus, insula, frontal regions, and sensory-related cortex.

Task MRI-Based Functional Brain Network of Major Depression

This chapter will focus on task magnetic resonance imaging (MRI) to understand the biological mechanisms and pathophysiology of brain in major depressive disorder (MDD), which would have minor alterations in the brain function. Therefore, the functional study, such as task MRI functional connectivity, would play a crucial role to explore the brain function in MDD. Different kinds of tasks would determine the alterations in functional connectivity in task MRI studies of MDD. The emotion-related tasks are linked with alterations in anterior cingulate cortex, insula, and default mode network. The emotional memory task is linked with amygdala-hippocampus alterations. The reward-related task would be related to the reward circuit alterations, such as fronto-straital. The cognitive-related tasks would be associated with frontal-related functional connectivity alterations, such as the dorsolateral prefrontal cortex, anterior cingulate cortex, and other frontal regions. The visuo-sensory characteristics of tasks might be associated with the parieto-occipital alterations. The frontolimbic regions might be major components of task MRI-based functional connectivity in MDD. However, different scenarios and tasks would influence the representations of results.

Resting-State Functional Connectivity Patterns Predict Acupuncture Treatment Response in Primary Dysmenorrhea

Primary dysmenorrhea (PDM) is a common complaint in women throughout the menstrual years. Acupuncture has been shown to be effective in dysmenorrhea; however, there are large interindividual differences in patients’ responses to acupuncture treatment. Fifty-four patients with PDM were recruited and randomized into real or sham acupuncture treatment groups (over the course of three menstrual cycles). Pain-related functional connectivity (FC) matrices were constructed at baseline and post-treatment period. The different neural mechanisms altered by real and sham acupuncture were detected with multivariate analysis of variance. Multivariate pattern analysis (MVPA) based on a machine learning approach was used to explore whether the different FC patterns predicted the acupuncture treatment response in the PDM patients. The results showed that real but not sham acupuncture significantly relieved pain severity in PDM patients. Real and sham acupuncture displayed differences in FC alterations between the descending pain modulatory system (DPMS) and sensorimotor network (SMN), the salience network (SN) and SMN, and the SN and default mode network (DMN). Furthermore, MVPA found that these FC patterns at baseline could predict the acupuncture treatment response in PDM patients. The present study verified differentially altered brain mechanisms underlying real and sham acupuncture in PDM patients and supported the use of neuroimaging biomarkers for individual-based precise acupuncture treatment in patients with PDM.

Task MRI-Based Functional Brain Network of Anxiety

Magnetic resonance imaging (MRI) is a good tool for researchers to understand the biological mechanisms and pathophysiology of the brain due to the translational characteristics of MRI methods. For the psychiatric illness, this kind of mental disorders usually have minor alterations when compared to traditional neurological disorders. Therefore the functional study, such as functional connectivity, would play a significant role for understanding the pathophysiology of mental disorders. This chapter would focus on the discussion of task MRI-based functional network studies in anxiety. For social anxiety disorder, the limbic system, such as the temporal lobe, amygdala, and hippocampus, would show alterations in the functional connectivity with frontal regions, such as anterior cingulate, prefrontal, and orbitofrontal cortices. PD has anterior cingulate cortex-amygdala alterations in fear conditioning, frontoparietal alterations in attention network task, and limbic-prefrontal alterations in emotional task. A similar amygdala-based aberrant functional connectivity in specific phobia is observed. The mesocorticolimbic and limbic-prefrontal functional alterations are found in generalized anxiety disorder. The major components of task MRI-based functional connectivity in anxiety include limbic and frontal regions which might play a vital role for the origination of anxiety under different scenarios and tasks.

Promising Neuroimaging Biomarkers in Depression

The neuroimaging has been applied in the study of pathophysiology in major depressive disorder (MDD). In this review article, several kinds of methodologies of neuroimaging would be discussed to summarize the promising biomarkers in MDD. For the magnetic resonance imaging (MRI) and magnetoencephalography field, the literature review showed the potentially promising roles of frontal lobes, such as anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). In addition, the limbic regions, such as hippocampus and amygdala, might be the potentially promising biomarkers for MDD. The structures and functions of ACC, DLPFC, OFC, amygdala and hippocampus might be confirmed as the biomarkers for the prediction of antidepressant treatment responses and for the pathophysiology of MDD. The functions of cognitive control and emotion regulation of these regions might be crucial for the establishment of biomarkers. The near-infrared spectroscopy studies demonstrated that blood flow in the frontal lobe, such as the DLPFC and OFC, might be the biomarkers for the field of near-infrared spectroscopy. The electroencephalography also supported the promising role of frontal regions, such as the ACC, DLPFC and OFC in the biomarker exploration, especially for the sleep electroencephalogram to detect biomarkers in MDD. The positron emission tomography (PET) and single-photon emission computed tomography (SPECT) in MDD demonstrated the promising biomarkers for the frontal and limbic regions, such as ACC, DLPFC and amygdala. However, additional findings in brainstem and midbrain were also found in PET and SPECT. The promising neuroimaging biomarkers of MDD seemed focused in the fronto-limbic regions.

Gray matter changes in panic disorder: A voxel-based meta-analysis and meta-analytic connectivity modeling

Magnetic resonance imaging (MRI) studies of panic disorder (PD) have discovered various damaged brain regions, with heterogeneous results across studies. The present study used meta-analytic approaches to discover gray matter (GM) changes consistently detected in PD and to characterize the functional and connectivity profiles of these regions. In the present study we first conducted an activation likelihood estimation (ALE) meta-analysis of eight eligible whole-brain VBM studies. Then, meta-analytic connectivity modeling analyses (MACMs) were used to provide co-atrophy and co-activation profiles across all the experiments stored in BrainMap. Lastly, the co-atrophied and co-activated regions were analyzed using functional decoding to reveal their functions. Lower gray matter volume was found in the bilateral dorsomedial prefrontal cortex (DMPFC), left dorsolateral prefrontal cortex (DLPFC), right insula, right superior temporal gyrus (STG), right middle temporal gyrus (MTG) and right superior orbital frontal cortex (OFC). Significant co-atrophies were found in the STG, DMPFC and OFC and co-activations were found between the left DLPFC and bilateral DMPFC. Decreased gray matter volume in STG, OFC, DLPFC and DMPFC and their co-atrophy and co-activation patterns indicate the damaged higher cognitive functions in PD and suggest that cortical regions are important structural imaging biomarkers in PD.