ExPosition is a new comprehensive R package providing crisp graphics and implementing multivariate analysis methods based on the singular value decomposition (svd). The core techniques implemented in ...ExPosition are: principal components analysis, (metric) multidimensional scaling, correspondence analysis, and several of their recent extensions such as barycentric discriminant analyses (e.g., discriminant correspondence analysis), multi-table analyses (e.g.,multiple factor analysis, Statis, and distatis), and non-parametric resampling techniques (e.g., permutation and bootstrap). Several examples highlight the major differences between ExPosition and similar packages. Finally, the future directions of ExPosition are discussed.
Objective:Major depressive disorder is associated with aberrant resting-state functional connectivity across multiple brain networks supporting emotion processing, executive function, and reward ...processing. The purpose of this study was to determine whether patterns of resting-state connectivity between brain regions predict differential outcome to antidepressant medication (sertraline) compared with placebo.Methods:Participants in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study underwent structural and resting-state functional MRI at baseline. Participants were then randomly assigned to receive either sertraline or placebo treatment for 8 weeks (N=279). A region of interest–based approach was utilized to compute functional connectivity between brain regions. Linear mixed-model intent-to-treat analyses were used to identify brain regions that moderated (i.e., differentially predicted) outcomes between the sertraline and placebo arms.Results:Prediction of response to sertraline involved several within- and between-network connectivity patterns. In general, higher connectivity within the default mode network predicted better outcomes specifically for sertraline, as did greater between-network connectivity of the default mode and executive control networks. In contrast, both placebo and sertraline outcomes were predicted (in opposite directions) by between-network hippocampal connectivity.Conclusions:This study identified specific functional network–based moderators of treatment outcome involving brain networks known to be affected by major depression. Specifically, functional connectivity patterns of brain regions between and within networks appear to play an important role in identifying a favorable response for a drug treatment for major depressive disorder.
Major depressive disorder (MDD) is a serious, heterogeneous disorder accompanied by brain-related changes, many of which are still to be discovered or refined. Arterial spin labeling (ASL) is a ...neuroimaging technique used to measure cerebral blood flow (CBF; perfusion) to understand brain function and detect differences among groups. CBF differences have been detected in MDD, and may reveal biosignatures of disease-state. The current work aimed to discover and replicate differences in CBF between MDD participants and healthy controls (HC) as part of the EMBARC study. Participants underwent neuroimaging at baseline, prior to starting study medication, to investigate biosignatures in MDD. Relative CBF (rCBF) was calculated and compared between 106 MDD and 36 HC EMBARC participants (whole-brain Discovery); and 58 MDD EMBARC participants and 58 HC from the DLBS study (region-of-interest Replication). Both analyses revealed reduced rCBF in the right parahippocampus, thalamus, fusiform and middle temporal gyri, as well as the left and right insula, for those with MDD relative to HC. Both samples also revealed increased rCBF in MDD relative to HC in both the left and right inferior parietal lobule, including the supramarginal and angular gyri. Cingulate and prefrontal regions did not fully replicate. Lastly, significant associations were detected between rCBF in replicated regions and clinical measures of MDD chronicity. These results (1) provide reliable evidence for ASL in detecting differences in perfusion for multiple brain regions thought to be important in MDD, and (2) highlight the potential role of using perfusion as a biosignature of MDD.
•Accelerated brain aging is the difference between T1-predicted brain age and chronological age.•Higher body mass index (BMI) was associated with more accelerated brain aging.•Association between BMI ...and brain aging was stronger in males vs. females.
This report evaluated sex-specific differences in the association between brain aging and body mass index (BMI) in young adults using the publicly available data from the Human Connectome Project (HCP).
Participants of HCP with available structural imaging and BMI data were included n = 1112; mean age = 28.80 (SD = 3.70); mean BMI = 26.53 (SD = 5.20); males n = 507, females n = 605. Predicted brain age was generated using raw T1-weighted MRI scan and a Gaussian Processes regression model. The difference (Δ aging) between brain age predicted by structural imaging and chronological age was computed. A linear regression model was used with Δ aging as the dependent variable, and sex, BMI, and BMI-by-sex interaction as independent variables of interest, and race, ethnicity, income, and education as covariates.
There was a significant BMI-by-sex interaction for Δ aging (p = 0.041). Higher BMI was associated with greater brain aging in both sexes. However, this association was substantially stronger in males (β = 0.215; SE = 0.050; p < 0.0001) than in females (β = 0.122; SE = 0.035; p = 0.0005).
We found evidence suggesting that higher BMI is associated with greater brain aging in adults. Furthermore, the association between higher BMI and greater brain aging was stronger in males than in females. Future studies are needed to explore the mechanistic pathways that link higher BMI to greater brain aging and whether weight-loss interventions, such as exercise, can reverse higher BMI-associated greater brain aging.
Owing to the link between immune dysfunction and treatment-resistant depression (TRD) and the overwhelming evidence that the immune dysregulation and major depressive disorder (MDD) are associated ...with each other, using immune profiles to identify the biological distinct subgroup may be the step forward to understanding MDD and TRD. This report aims to briefly review the role of inflammation in the pathophysiology of depression (and TRD in particular), the role of immune dysfunction to guide precision medicine, tools used to understand immune function, and novel statistical techniques.
Diabetes has been linked to accelerated brain aging, i.e., neuroimaging-predicted age of brain is higher than chronological age. This report evaluated whether accelerated brain aging in diabetes is ...associated with higher levels of glycated hemoglobin (HbA1c) and increased mortality.
Brain age in Dallas Heart Study (n = 1949) was estimated using T1-weighted magnetic resonance imaging (MRI) scans and a previously-published Gaussian Processes Regression model. Accelerated brain aging (adjusted Δ brain age) was computed as follows: (brain age adjusted for chronological age)-minus-(chronological age). Mortality data until 12/31/2016 were obtained from the National Death Index. Associations of adjusted Δ brain age with diabetes in full sample and with HbA1c in individuals with diabetes were evaluated. Proportion of association between diabetes and all-cause mortality that was accounted for by adjusted Δ brain age were evaluated with mediation analyses. Covariates included Framingham 10-year risk score, race/ethnicity, income, body mass index, and history of myocardial infarction.
Diabetes was associated with higher adjusted Δ brain age estimate= 1.79; 95% confidence interval (CI): 0.889, 2.68. Among those with diabetes, higher HbA1c (log-base-2-transformed) was associated with higher adjusted Δ brain age (estimate=3.88; 95% CI: 1.47, 6.30). Over a median follow-up of 97.5 months, 24/246 (9.8%) with diabetes and 63/1703 (3.7%) without diabetes died. Adjusted Δ brain age accounted for 65.3 (95% CI: 39.3, 100.0)% of the association between diabetes and all-cause mortality.
Accelerated brain aging may be related to poor glycemic control in diabetes and partly account for the association between diabetes and all-cause mortality.
•Participants with diabetes had older-appearing brain age than those without.•Poorer glycemic control was associated with more accelerated brain aging.•Accelerated brain aging accounted for association between diabetes and mortality.
•The COVID-19 pandemic has been associated with concerns about worsening mental health.•In those with depression, symptoms of depression and anxiety were stable during the pandemic.•Disruption in ...routines and mental healthcare were associated with worse symptoms.
Emerging work has suggested worsening mental health in the general population during the COVID-19 pandemic, but there is minimal data on individuals with a prior history of depression.
Data regarding depression, anxiety and quality of life in adult participants with a history of a depressive disorder (n = 308) were collected before and during the COVID-19 pandemic. Mixed effects regression models were fit for these outcomes over the period May – August 2020, controlling for pre-pandemic depressive groups (none, mild, moderate-to-severe), demographic characteristics, and early COVID-19 related experiences (such as disruptions in routines, mental health treatment, and social supports).
In pre-to-early pandemic comparisons, the 3 pre-pandemic depressive categories varied significantly in anxiety (Fdf=2,197 = 7.93, p < 0.0005) and psychological QOL (Fdf=2,196 = 8.57, p = 0.0003). The mildly depressed group (Fdf=1,201 = 6.01, p = 0.02) and moderate-to-severely depressed group (Fdf=1,201 = 38.51, p < 0.0001) had a significant reduction in anxiety. There were no changes among the groups in any outcome from May to August 2020. However, early impact on mental health care access and disruption in routines predicted worse outcomes during this time.
Follow-up data were self-reported. Furthermore, the duration was a relatively short span into the pandemic.
Symptoms of depression, anxiety, and quality of life were generally stable from 2019 throughout August 2020 in adults with a history of depression. Disruption in mental health care access and routines in May 2020 predicted worse symptom outcomes through August 2020.
To address the clinical heterogeneity of Major Depressive Disorder (MDD), this investigation determined whether resting state functional magnetic resonance imaging (fMRI) could be deployed to ...identify circuit based homogeneous subgroups, and whether subgroups identified show differential treatment outcomes.
Pretreatment resting state fMRIs obtained from 278 outpatients with nonpsychotic MDD from Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression Study were used to create data-driven subgroups using CLICK clustering. These subgroups were then compared using baseline clinical data, as well as baseline-to-week 8 changes in depression severity measured using the 17-item Hamilton Rating Scale for Depression (HAMD17) and response/remission rates by treatment group.
Three subgroups were identified. Cluster-1 was characterized by overallhyperconnectivity coupled with profound hypoconnectivity between the supramarginal gyrus (executive control network; ECN) and the superior frontal cortex (dorsal attention network; DAN). Cluster-2 was characterized by overall hypoconnectivity coupled with hyperconnectivity between supramarginal gyrus (ECN) and superior frontal cortex (DAN). Cluster-3 showed hypoconnectivity, especially profound between the angular cortex (default mode network; DMN) and middle frontal cortex (ECN). While baseline clinical measures did not differentiate the three clusters, Cluster-3 had the remission rate (51.6%) compared to Cluster-1 and Cluster-2 (32.7% and 31.9%) when treated with sertraline.
Due to the exploratory nature of these analyses, there were no adjustments for multiple comparisons.
Baseline functional connectivity can be used to subgroup patients with MDD that differ in acute phase treatment outcomes. Measures of connectivity may address the heterogeneity of MDD.
Deaths due to suicide are one of the leading causes of mortality among youths and young adults. Active suicidal ideation (SI) is considered one of the strongest risk factors for suicide. Here, we ...evaluated the neurocircuitry of SI in a sample of youths and young adults (aged 10–26 years) with current or past diagnosis of either major depression or bipolar disorder who were enrolled in Texas Resilience Against Depression Study (T-RAD), and had neuroimaging and SI (assessed with the 3-item Suicidal Thoughts factor of Concise Health Risk Tracking self-report scale) data available (n = 72, 53 females). Resting-state functional connectivity (FC) was computed amongst 121 cortical and subcortical regions of interest resulting in 7260 FC pairs. Mean (SD) age and SI levels of participants were 19.6 years (4.01) and 1.48 (2.36) respectively. In univariate analyses, 34 out of the 7260 FC pairs were correlated with SI (p < .005). Stronger connectivity of default mode network (DMN) with striatum was associated with higher SI. Conversely, higher SI was associated with weaker connectivity of limbic network with hippocampus, DMN, dorsal attention network, and executive control network. In multivariate analyses, these 34 FC pairs together had an average correlation of 0.54 after five-fold cross-validation. In conclusion, SI was associated with distinct patterns of resting-state functional connectivity among youths and young adults with regions in DMN and the ventral striatum as key nodes.
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