45 research outputs found
Cortical thickness is not associated with current depression in a clinical treatment study
BackgroundReduced cortical thickness is a candidate biological marker of depression, although findings are inconsistent. This could reflect analytic heterogeneity, such as use of regionâwise cortical thickness based on the Freesurfer DesikanâKilliany (DK) atlas or surfaceâbased morphometry (SBM). The Freesurfer Destrieux (DS) atlas (more, smaller regions) has not been utilized in depression studies. This could also reflect differential gender and age effects.MethodsCortical thickness was collected from 170 currently depressed adults and 52 neverâdepressed adults. Visually inspected and approved Freesurferâgenerated surfaces were used to extract cortical thickness estimates according to the DK atlas (68 regions) and DS atlas (148 regions) for regionâwise analysis (216 total regions) and for SBM.ResultsOverall, except for small effects in a few regions, the two regionâwise approaches generally failed to discriminate depressed adults from nondepressed adults or current episode severity. Differential effects by age and gender were also rare and small in magnitude. Using SBM, depressed adults showed a significantly thicker cluster in the left supramarginal gyrus than nondepressed adults (Pâ=â0.047) but there were no associations with current episode severity.ConclusionsThree analytic approaches (i.e., DK atlas, DS atlas, and SBM) converge on the notion that cortical thickness is a relatively weak discriminator of current depression status. Differential age and gender effects do not appear to represent key moderators. Robust associations with demographic factors will likely hinder translation of cortical thickness into a clinically useful biomarker. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc. Hum Brain Mapp 38:4370â4385, 2017. © 2017 Wiley Periodicals, Inc.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138250/1/hbm23664_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138250/2/hbm23664.pd
Testâretest reliability of freesurfer measurements within and between sites: Effects of visual approval process
In the last decade, many studies have used automated processes to analyze magnetic resonance imaging (MRI) data such as cortical thickness, which is one indicator of neuronal health. Due to the convenience of image processing software (e.g., FreeSurfer), standard practice is to rely on automated results without performing visual inspection of intermediate processing. In this work, structural MRIs of 40 healthy controls who were scanned twice were used to determine the testâretest reliability of FreeSurferâderived cortical measures in four groups of subjectsâthose 25 that passed visual inspection (approved), those 15 that failed visual inspection (disapproved), a combined group, and a subset of 10 subjects (Travel) whose test and retest scans occurred at different sites. Testâretest correlation (TRC), intraclass correlation coefficient (ICC), and percent difference (PD) were used to measure the reliability in the Destrieux and DesikanâKilliany (DK) atlases. In the approved subjects, reliability of cortical thickness/surface area/volume (DK atlas only) were: TRC (0.82/0.88/0.88), ICC (0.81/0.87/0.88), PD (0.86/1.19/1.39), which represent a significant improvement over these measures when disapproved subjects are included. Travel subjectsâ results show that cortical thickness reliability is more sensitive to site differences than the cortical surface area and volume. To determine the effect of visual inspection on sample size required for studies of MRIâderived cortical thickness, the number of subjects required to show group differences was calculated. Significant differences observed across imaging sites, between visually approved/disapproved subjects, and across regions with different sizes suggest that these measures should be used with caution. Hum Brain Mapp 36:3472â3485, 2015. © 2015 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113142/1/hbm22856.pd
In vivo serotonin 1A receptor hippocampal binding potential in depression and reported childhood adversity
Abstract
Background
Reported childhood adversity (CA) is associated with development of depression in adulthood and predicts a more severe course of illness. Although elevated serotonin 1A receptor (5-HT1AR) binding potential, especially in the raphe nuclei, has been shown to be a trait associated with major depression, we did not replicate this finding in an independent sample using the partial agonist positron emission tomography tracer [11C]CUMI-101. Evidence suggests that CA can induce long-lasting changes in expression of 5-HT1AR, and thus, a history of CA may explain the disparate findings.
Methods
Following up on our initial report, 28 unmedicated participants in a current depressive episode (bipolar n = 16, unipolar n = 12) and 19 non-depressed healthy volunteers (HVs) underwent [11C]CUMI-101 imaging to quantify 5-HT1AR binding potential. Participants in a depressive episode were stratified into mild/moderate and severe CA groups via the Childhood Trauma Questionnaire. We hypothesized higher hippocampal and raphe nuclei 5-HT1AR with severe CA compared with mild/moderate CA and HVs.
Results
There was a group-by-region effect (p = 0.011) when considering HV, depressive episode mild/moderate CA, and depressive episode severe CA groups, driven by significantly higher hippocampal 5-HT1AR binding potential in participants in a depressive episode with severe CA relative to HVs (p = 0.019). Contrary to our hypothesis, no significant binding potential differences were detected in the raphe nuclei (p
-value
s > 0.05).
Conclusions
With replication in larger samples, elevated hippocampal 5-HT1AR binding potential may serve as a promising biomarker through which to investigate the neurobiological link between CA and depression
A Comprehensive Examination Of White Matter Tracts And Connectometry In Major Depressive Disorder
Background
Major depressive disorder (MDD) is a debilitating disorder characterized by widespread brain abnormalities. The literature is mixed as to whether or not white matter abnormalities are associated with MDD. This study sought to examine fractional anisotropy (FA) in white matter tracts in individuals with MDD using diffusion tensor imaging (DTI).
Methods
139 participants with MDD and 39 healthy controls (HC) in a multisite study were included. DTI scans were acquired in 64 directions and FA was determined in the brain using four methods: region of interest (ROI), tract-based spatial statistics (TBSS), and diffusion tractography. Diffusion connectometry was used to identify white matter pathways associated with MDD.
Results
There were no significant differences when comparing FA in MDD and HC groups using any method. In the MDD group, there was a significant relationship between depression severity and FA in the right medial orbitofrontal cortex, and between age of onset of MDD and FA in the right caudal anterior cingulate cortex using the ROI method. There was a significant relationship between age of onset and connectivity in the thalamocortical radiation, inferior longitudinal fasciculus, and cerebellar tracts using diffusion connectometry.
Conclusions
The lack of group differences in FA and connectometry analysis may result from the clinically heterogenous nature of MDD. However, the relationship between FA and depression severity may suggest a state biomarker of depression that should be investigated as a potential indicator of response. Age of onset may also be a significant clinical feature to pursue when studying white matter tracts
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A Comparison Of Structural Connectivity In Anxious Depression Versus Non-anxious Depression
Background: Major depressive disorder (MDD) and anxiety disorders are highly co-morbid. Research has shown conïŹicting evidence for white matter alteration and amygdala volume reduction in mood and anxiety disorders. To date, no studies have examined differences in structural connectivity between anxious depressed and non-anxious depressed individuals. This study compared fractional anisotropy (FA) and density of selected white matter tracts and amygdala volume between anxious depressed and non-anxious depressed individuals. Methods: 64- direction DTI and T1 scans were collected from 110 unmedicated subjects with MDD, 39 of whom had a co-morbid anxiety disorder diagnosis. Region of interest (ROI) and tractography methods were performed to calculate amygdala volume and FA in the uncinate fasciculus, respectively. Diffusion connectometry was performed to identify whole brain group differences in white matter health. Correlations were computed between biological and clinical measures. Results: Tractography and ROI analyses showed no signiïŹcant differences between bilateral FA values or bilateral amygdala volumes when comparing the anxious depressed and non-anxious depressed groups. The diffusion connectometry analysis showed no signiïŹcant differences in anisotropy between the groups. Furthermore, there were no signiïŹcant relationships between MRI-based and clinical measures. Conclusion: The lack of group differences could indicate that structural connectivity and amygdalae volumes of those with anxious-depression are not signiïŹcantly altered by a co-morbid anxiety disorder. Improving understanding of anxiety co-morbid with MDD would facilitate development of treatments that more accurately target the underlying networks
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Development And Evaluation Of A Multimodal Marker Of Major Depressive Disorder
This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniquesâpenalized logistic regression, random forest, and support vector machine (SVM)âwere used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r =â.36 correlation coefficient (p <â.001, continuous). Using SVM, R2 values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analysesâtwo dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results
Image-guided intraoperative brain deformation recovery
During neurosurgery, nonrigid brain deformation, referred to as brain shift, prevents preoperatively acquired images from accurately depicting the intraoperative brain. Image-guided surgical navigation systems, therefore, must account for this brain shift in order to provide accurate surgical guidance. However, the origins and complexity of this type of deformation prevent it from being entirely predicted preoperatively. Additionally, though volumetric images can be acquired at the time of intervention, this type of intraoperative imaging is either expensive, invasive or time intensive. A solution that overcomes these issues consists of warping preoperative images to reflect the intraoperative brain using sparse intraoperative information. One such source of intraoperative information, the exposed cortical surface, can be tracked optically, for example, using stereo vision. Unfortunately, however, these systems are often plagued with calibration error, which can corrupt the surface deformation estimation. In order to separate the effects of camera calibration and surface deformation, a framework is needed which can solve for disparate and often competing variables. In this work, game theory', which was developed specifically to handle decision making in this type of competitive environment, has been applied to the problem of cortical surface tracking and used to infer information about the physical processes of calibration and brain deformation. The specific application of this work is neocortical epilepsy, in which information about the surface deformation is the most critical. However, it is also shown that this type of surface deformation estimation can be extended to the volume through the use of a biomechanical model. As with any method that will be used in vivo, it was imperative to validate the algorithm results before patient application. For this purpose, a realistic brain phantom was constructed, which could simulate the brain shift experienced during surgery. The algorithms were tested both in simulation and using the realistic phantom. The result was a reliable intraoperative tracking method, which was tested on eight in vivo data sets. This ultimate goal of this project is to provide neurosurgeons with accurate surgical guidance, allowing better detection of pathologic tissue and decreased neurosurgical complications
Examining factors related to low performance of predicting remission in participants with major depressive disorder using neuroimaging data and other clinical features.
Major depressive disorder (MDD), a prevalent mental health issue, affects more than 8% of the US population, and almost 17% in the young group of 18-25 years old. Since Covid-19, its prevalence has become even more significant. However, the remission (being free of depression) rates of first-line antidepressant treatments on MDD are only about 30%. To improve treatment outcomes, researchers have built various predictive models for treatment responses and yet none of them have been adopted in clinical use. One reason is that most predictive models are based on data from subjective questionnaires, which are less reliable. Neuroimaging data are promising objective prognostic factors, but they are expensive to obtain and hence predictive models using neuroimaging data are limited and such studies were usually in small scale (N<100). In this paper, we proposed an advanced machine learning (ML) pipeline for small training dataset with large number of features. We implemented multiple imputation for missing data and repeated K-fold cross validation (CV) to robustly estimate predictive performances. Different feature selection methods and stacking methods using 6 general ML models including random forest, gradient boosting decision tree, XGBoost, penalized logistic regression, support vector machine (SVM), and neural network were examined to evaluate the model performances. All predictive models were compared using model performance metrics such as accuracy, balanced accuracy, area under ROC curve (AUC), sensitivity and specificity. Our proposed ML pipeline was applied to a training dataset and obtained an accuracy and AUC above 0.80. But such high performance failed while applying our ML pipeline using an external validation dataset from the EMBARC study which is a multi-center study. We further examined the possible reasons especially the site heterogeneity issue
Circadian rhythm biomarker from wearable device data is related to concurrent antidepressant treatment response
Abstract Major depressive disorder (MDD) is associated with circadian rhythm disruption. Yet, no circadian rhythm biomarkers have been clinically validated for assessing antidepressant response. In this study, 40 participants with MDD provided actigraphy data using wearable devices for one week after initiating antidepressant treatment in a randomized, double-blind, placebo-controlled trial. Their depression severity was calculated pretreatment, after one week and eight weeks of treatment. This study assesses the relationship between parametric and nonparametric measures of circadian rhythm and change in depression. Results show significant association between a lower circadian quotient (reflecting less robust rhythmicity) and improvement in depression from baseline following first week of treatment (estimateâ=â0.11, Fâ=â7.01, Pâ=â0.01). There is insufficient evidence of an association between circadian rhythm measures acquired during the first week of treatment and outcomes after eight weeks of treatment. Despite this lack of association with future treatment outcome, this scalable, cost-effective biomarker may be useful for timely mental health care through remote monitoring of real-time changes in current depression