Prediction of post-earthquake depressive and anxiety symptoms: a longitudinal resting-state fMRI study.
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ABSTRACT:
SUBMITTER: Long J
PROVIDER: S-EPMC4168284 | biostudies-other | 2014
REPOSITORIES: biostudies-other
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Project description:Few longitudinal studies have focused on mental health problems among adolescents after earthquakes. We investigated changes in post-traumatic stress symptoms (PTSS) and depressive symptoms from 18 to 31 months after the 2015 earthquake in Nepal and explored potential risk factors associated with the change in psychiatric symptoms. This study comprised of 515 adolescents, aged 11-17 years from two earthquake-affected areas, one severely affected than the other. The psychiatric symptoms were assessed using the standardized Child Post-traumatic Stress Disorder Scale and the Depression Self-Rating Scale. No significant change was observed in the prevalence of PTSS and depressive symptoms from 18 to 31 months after the earthquake. Living in severely affected area and exposure to trauma after the earthquake were associated with adolescents who developed chronic or delayed PTSS and depressive symptoms. The study findings highlight the need for disaster preparedness and early interventions that strengthen support at various levels.
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Project description:BACKGROUND:Traumatized earthquake survivors may develop poor memory function. Resting-state functional magnetic resonance imaging (rs-fMRI) and machine learning techniques may one day aid the clinical assessment of individual psychiatric patients. This study aims to use machine learning with Rs-fMRI from the perspectives of neurophysiology and neuroimaging to explore the association between it and the individual memory function of trauma survivors. METHODS:Rs-fMRI data was acquired for eighty-nine survivors (male (33%), average age (SD):45.18(6.31) years) of Wenchuan earthquakes in 2008 each of whom was screened by experienced psychiatrists based on the clinician-administered post-traumatic stress disorder (PTSD) scale (CAPS), and their memory function scores were determined by the Wechsler Memory Scale-IV (WMS-IV). We explored which memory function scores were significantly associated with CAPS scores. Using simple multiple kernel learning (MKL), Rs-fMRI was used to predict the memory function scores that were associated with CAPS scores. A support vector machine (SVM) was also used to make classifications in trauma survivors with or without PTSD. RESULTS:Spatial addition (SA), which is defined by spatial working memory function, was negatively correlated with the total CAPS score (r?=?-?0.22, P?=?0.04). The use of simple MKL allowed quantitative association of SA scores with statistically significant accuracy (correlation?=?0.28, P?=?0.03; mean squared error?=?8.36; P?=?0.04). The left middle frontal gyrus and the left precuneus contributed the largest proportion to the simple MKL association frame. The SVM could not make a quantitative classification of diagnosis with statistically significant accuracy. LIMITATIONS:The use of the cross-sectional study design after exposure to an earthquake and the leave-one-out cross-validation (LOOCV) increases the risk of overfitting. CONCLUSION:Spontaneous brain activity of the left middle frontal gyrus and the left precuneus acquired by rs-fMRI may be a brain mechanism of visual working memory that is related to PTSD symptoms. Machine learning may be a useful tool in the identification of brain mechanisms of memory impairment in trauma survivors.
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Project description:Depressive symptoms are often elevated in acute and chronic HIV. Previous neuroimaging research identifies abnormalities in emotion-related brain regions in depression without HIV, including the anterior cingulate cortex (ACC) and amygdala. However, no studies have examined the neural signatures of depressive symptoms in acute HIV infection (AHI). Seed-based voxelwise resting-state functional connectivity (rsFC) for affective seed regions of interest (pregenual ACC, subgenual ACC [sgACC], bilateral amygdala) was computed for 74 Thai males with AHI and 30 Thai HIV-uninfected controls. Group analyses compared rsFC of ACC and amygdala seed regions between AHI and uninfected control groups. Within the AHI group, voxelwise regression analyses investigated the relationship between depressive symptoms and rsFC for these affective seed regions. Group analyses revealed alterations in rsFC of the amygdala in AHI versus uninfected controls. Depressive symptoms associated with decreased rsFC between ACC regions and posterior cingulate/precuneus, medial temporal, and lateral parietal regions in AHI. Symptoms of depression also correlated to increased rsFC between ACC regions and lateral prefrontal cortex, sgACC, and cerebellum in AHI. Similar to the ACC, depressive symptoms associated with decreased rsFC between amygdala and precuneus. Of blood biomarkers, only HIV RNA inversely correlated with rsFC between posterior sgACC and left uncus. We found that depressive symptoms in AHI associate with altered rsFC of ACC and amygdala regions previously implicated in depression. Longitudinal research in this cohort will be necessary to determine whether these early alterations in rsFC of affective network regions are related to persistent depressive symptoms after combination antiretroviral therapy.
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Project description:Resting-state functional magnetic resonance imaging (rs-fMRI) is used to investigate synchronous activations in spatially distinct regions of the brain, which are thought to reflect functional systems supporting cognitive processes. Analyses are often performed using seed-based correlation analysis, allowing researchers to explore functional connectivity between data in a seed region and the rest of the brain. Using scan-rescan rs-fMRI data, we investigate how well the subject-specific seed-based correlation map from the second replication of the study can be predicted using data from the first replication. We show that one can dramatically improve prediction of subject-specific connectivity by borrowing strength from the group correlation map computed using all other subjects in the study. Even more surprisingly, we found that the group correlation map provided a better prediction of a subject's connectivity than the individual's own data. While further discussion and experimentation are required to understand how this can be used in practice, results indicate that shrinkage-based methods that borrow strength from the population mean should play a role in rs-fMRI data analysis.
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Project description:Dysfunctions in bottom-up emotion processing (EP), as well as top-down emotion regulation (ER) are prominent features in pathophysiology of major depressive disorder (MDD). Nonetheless, it is not clear whether EP- and ER-related areas are regionally and/or connectively disturbed in MDD. In addition, it is yet to be known how EP- and ER-related areas are interactively linked to regulatory behavior, and whether this interaction is disrupted in MDD. In our study, regional amplitude of low frequency fluctuations (ALFF) and whole-brain functional connectivity (FC) of meta-analytic-driven EP- and ER-related areas were compared between 32 healthy controls (HC) and 20 MDD patients. Then, we aimed to investigate whether the EP-related areas can predict the ER-related areas and regulatory behavior in both groups. Finally, the brain-behavior correlations between the EP- and ER-related areas and depression severity were assessed. We found that: (a) affective areas are regionally and/or connectively disturbed in MDD; (b) EP-ER interaction seems to be disrupted in MDD; overburden of emotional reactivity in amygdala may inversely affect cognitive control processes in prefrontal cortices, which leads to diminished regulatory actions. (c) Depression severity is correlated with FC of affective areas. Our findings shed new lights on the neural underpinning of affective dysfunctions in depression.
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Project description:Neuroimaging techniques hold the promise that they may one day aid the clinical assessment of individual psychiatric patients. However, the vast majority of studies published so far have been based on average differences between groups. This study employed a multivariate approach to examine the potential of resting-state functional magnetic resonance imaging (MRI) data for making accurate predictions about psychopathology in survivors of the 2008 Sichuan earthquake at an individual level. Resting-state functional MRI data was acquired for 121 survivors of the 2008 Sichuan earthquake each of whom was assessed for symptoms of post-traumatic stress disorder (PTSD) using the 17-item PTSD Checklist (PCL). Using a multivariate analytical method known as relevance vector regression (RVR), we examined the relationship between resting-state functional MRI data and symptom scores. We found that the use of RVR allowed quantitative prediction of clinical scores with statistically significant accuracy (correlation=0.32, P=0.006; mean squared error=176.88, P=0.001). Accurate prediction was based on functional activation in a number of prefrontal, parietal, and occipital regions. This is the first evidence that neuroimaging techniques may inform the clinical assessment of trauma-exposed individuals by providing an accurate and objective quantitative estimation of psychopathology. Furthermore, the significant contribution of parietal and occipital regions to such estimation challenges the traditional view of PTSD as a disorder specific to the fronto-limbic network.
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Project description:ObjectiveThe purposes of this study are to explore (1) whether comorbid depressive symptoms in patients with chronic back pain (CBP) affect the pain matrix. And (2) whether the interaction of depression and CBP exacerbates impaired brain function.MethodsThirty-two patients with CBP without comorbid depressive symptoms and thirty patients with CBP with comorbid depressive symptoms were recruited. All subjects underwent functional magnetic resonance imaging (fMRI) scans. The graph theory analysis, mediation analysis, and functional connectivity (FC) analysis were included in this study. All subjects received the detection of clinical depressive symptoms and pain-related manifestations.ResultCompared with the CBP group, subjects in the CBP with comorbid depressive symptoms (CBP-D) group had significantly increased FC in the left medial prefrontal cortex and several parietal cortical regions. The results of the graph theory analyses showed that the area under the curve of small-world property (t = -2.175, p = 0.034), gamma (t = -2.332, p = 0.023), and local efficiency (t = -2.461, p = 0.017) in the CBP-D group were significantly lower. The nodal efficiency in the ventral posterior insula (VPI) (t = -3.581, p = 0.0007), and the network efficiency values (t = -2.758, p = 0.008) in the pain matrix were significantly lower in the CBP-D group. Both the topological properties and the FC values of these brain regions were significantly correlated with self-rating depression scale (SDS) scores (all FDR corrected) but not with pain intensity. Further mediation analyses demonstrated that pain intensity had a mediating effect on the relationship between SDS scores and Pain Disability Index scores. Likewise, the SDS scores mediated the relationship between pain intensity and PDI scores.ConclusionOur study found that comorbid depressive symptoms can aggravate the impairment of pain matrix function of CBP, but this impairment cannot directly lead to the increase of pain intensity, which may be because some brain regions of the pain matrix are the common neural basis of depression and CBP.
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