Town of Jay Annual Report Year Ending June 30, 2014

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Meta-analysis of regional white matter volume in bipolar disorder with replication in an independent sample using coordinates, T-maps, and individual MRI data

Converging evidence suggests that bipolar disorder (BD) is associated with white matter (WM) abnormalities. Meta-analyses of voxel based morphometry (VBM) data is commonly performed using published coordinates, however this method is limited since it ignores non-significant data. Obtaining statistical maps from studies (T-maps) as well as raw MRI datasets increases accuracy and allows for a comprehensive analysis of clinical variables. We obtained coordinate data (5-studies), T-Maps (12-studies, including unpublished data) and raw MRI datasets (5-studies) and analysed the 24 studies using Seed-based d Mapping (SDM). A VBM analysis was conducted to verify the results in an independent sample. The meta-analysis revealed decreased WM volume in the posterior corpus callosum extending to WM in the posterior cingulate cortex. This region was significantly reduced in volume in BD patients in the independent dataset (p=0.003) but there was no association with clinical variables. We identified a robust WM volume abnormality in BD patients that may represent a trait marker of the disease and used a novel methodology to validate the findings

Effective resting-state connectivity in severe unipolar depression before and after electroconvulsive therapy

Background Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depressive disorders. A recent multi-center study found no consistent changes in correlation-based (undirected) resting-state connectivity after ECT. Effective (directed) connectivity may provide more insight into the working mechanism of ECT. Objective We investigated whether there are consistent changes in effective resting-state connectivity. Methods This multi-center study included data from 189 patients suffering from severe unipolar depression and 59 healthy control participants. Longitudinal data were available for 81 patients and 24 healthy controls. We used dynamic causal modeling for resting-state functional magnetic resonance imaging to determine effective connectivity in the default mode, salience and central executive networks before and after a course of ECT. Bayesian general linear models were used to examine differences in baseline and longitudinal effective connectivity effects associated with ECT and its effectiveness. Results Compared to controls, depressed patients showed many differences in effective connectivity at baseline, which varied according to the presence of psychotic features and later treatment outcome. Additionally, effective connectivity changed after ECT, which was related to ECT effectiveness. Notably, treatment effectiveness was associated with decreasing and increasing effective connectivity from the posterior default mode network to the left and right insula, respectively. No effects were found using correlation-based (undirected) connectivity. Conclusions A beneficial response to ECT may depend on how brain regions influence each other in networks important for emotion and cognition. These findings further elucidate the working mechanisms of ECT and may provide directions for future non-invasive brain stimulation research.publishedVersio

Effective resting-state connectivity in severe unipolar depression before and after electroconvulsive therapy

BACKGROUND: Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depressive disorders. A recent multi-center study found no consistent changes in correlation-based (undirected) resting-state connectivity after ECT. Effective (directed) connectivity may provide more insight into the working mechanism of ECT. OBJECTIVE: We investigated whether there are consistent changes in effective resting-state connectivity. METHODS: This multi-center study included data from 189 patients suffering from severe unipolar depression and 59 healthy control participants. Longitudinal data were available for 81 patients and 24 healthy controls. We used dynamic causal modeling for resting-state functional magnetic resonance imaging to determine effective connectivity in the default mode, salience and central executive networks before and after a course of ECT. Bayesian general linear models were used to examine differences in baseline and longitudinal effective connectivity effects associated with ECT and its effectiveness. RESULTS: Compared to controls, depressed patients showed many differences in effective connectivity at baseline, which varied according to the presence of psychotic features and later treatment outcome. Additionally, effective connectivity changed after ECT, which was related to ECT effectiveness. Notably, treatment effectiveness was associated with decreasing and increasing effective connectivity from the posterior default mode network to the left and right insula, respectively. No effects were found using correlation-based (undirected) connectivity. CONCLUSIONS: A beneficial response to ECT may depend on how brain regions influence each other in networks important for emotion and cognition. These findings further elucidate the working mechanisms of ECT and may provide directions for future non-invasive brain stimulation research

Volume of the human hippocampus and clinical response following electroconvulsive therapy

BACKGROUND: Hippocampal enlargements are commonly reported after electroconvulsive therapy (ECT). To clarify mechanisms, we examined if ECT-induced hippocampal volume change relates to dose (number of ECT sessions and electrode placement) and acts as a biomarker of clinical outcome. METHODS: Longitudinal neuroimaging and clinical data from 10 independent sites participating in the Global ECT-Magnetic Resonance Imaging Research Collaboration (GEMRIC) were obtained for mega-analysis. Hippocampal volumes were extracted from structural magnetic resonance images, acquired before and after patients (n = 281) experiencing a major depressive episode completed an ECT treatment series using right unilateral and bilateral stimulation. Untreated nondepressed control subjects (n = 95) were scanned twice. RESULTS: The linear component of hippocampal volume change was 0.28% (SE 0.08) per ECT session (p < .001). Volume change varied by electrode placement in the left hippocampus (bilateral, 3.3 +/- 2.2%, d = 1.5; right unilateral, 1.6 +/- 2.1%, d = 0.8; p < .0001) but not the right hippocampus (bilateral, 3.0 +/- 1.7%, d = 1.8; right unilateral, 2.7 +/- 2.0%, d = 1.4; p = .36). Volume change for electrode placement per ECT session varied similarly by hemisphere. Individuals with greater treatment-related volume increases had poorer outcomes (Montgomery-Asberg Depression Rating Scale change -1.0 [SE 0.35], per 1% volume increase, p = .005), although the effects were not significant after controlling for ECT number (slope -0.69 [SE 0.38], p = .069). CONCLUSIONS: The number of ECT sessions and electrode placement impacts the extent and laterality of hippocampal enlargement, but volume change is not positively associated with clinical outcome. The results suggest that the high efficacy of ECT is not explained by hippocampal enlargement, which alone might not serve as a viable biomarker for treatment outcome

Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this causal depression network (CDN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis Principal Component Analysis (PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CDN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CDN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes (t = -2.35, p = 0.019). This evidence further supports that treatment interventions converge on a CDN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression

Quantitative DTI measures

Diffusion tensor imaging (DTI) has revolutionized the visualization of white matter in vivo. However, it is far more than a qualitative tool and can also be used to generate quantitative measures related to diffusion magnitude and its degree of anisotropy, which indirectly reflect microstructural organisation. Although highly sensitive to microstructural change, DTI measures lack specificity and are influenced by a wide range of biological and methodological factors. This makes the interpretation of DTI metric changes extremely challenging. This chapter introduces the most common DTI measures and how they relate to tissue microstructure. Important confounds are addressed, including how DTI metrics are influenced by biological factors such as ageing and pathology, and by methodological factors such as data acquisition, modeling, and analysis

DTI analysis methods : Voxel-based analysis

Voxel-based analysis (VBA) of diffusion tensor imaging (DTI) data permits the investigation of voxel-wise differences or changes in DTI metrics in every voxel of a brain dataset. It is applied primarily in the exploratory analysis of hypothesized group-level alterations in DTI parameters, as it does not require prior knowledge of where in the brain such changes may occur. Whilst VBA is a widely used, powerful preclinical research tool, there are a number of methodological issues that should be considered when applying the technique to study (pre)clinical populations. This chapter reviews the component steps of a typical VBA study pipeline and includes a comprehensive introduction to image registration, DTI template/atlas selection, smoothing, and statistical analysis. The popular tract-based spatial (TBSS) technique is introduced and contrasted with traditional VBA approaches. At each stage, guidance on optimizing parameter settings is presented along with the pros and cons of different methods to assist the reader in choosing the best approach for their application

Repeatability and sample size estimations for myelin water imaging

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Chemotherapy-induced Neurotoxicity in Pediatric Solid Non-CNS-tumor Patients: An Update on Current State of Research and Recommended Future Directions

Neurocognitive sequelae are known to be induced by cranial radiotherapy and central-nervous-system-directed chemotherapy in childhood Acute Lymphoblastic Leukemia (ALL) and brain tumor patients. However, less evidence exists for solid non-CNS-tumor patients. To get a better understanding of the potential neurotoxic mechanisms of non-CNS-directed chemotherapy during childhood, we performed a comprehensive literature review of this topic. Here, we provide an overview of preclinical and clinical studies investigating neurotoxicity associated with chemotherapy in the treatment of pediatric solid non-CNS tumors. Research to date suggests that chemotherapy has deleterious biological and psychological effects, with animal studies demonstrating histological evidence for neurotoxic effects of specific agents and human studies demonstrating acute neurotoxicity. Although the existing literature suggests potential neurotoxicity throughout neurodevelopment, research into the long-term neurocognitive sequelae in survivors of non-CNS cancers remains limited. Therefore, we stress the critical need for neurodevelopmental focused research in children who are treated for solid non-CNS tumors, since they are at risk for potential neurocognitive impairment.publisher: Elsevier articletitle: Chemotherapy-induced neurotoxicity in pediatric solid non-CNS tumor patients: An update on current state of research and recommended future directions journaltitle: Critical Reviews in Oncology/Hematology articlelink: http://dx.doi.org/10.1016/j.critrevonc.2016.05.001 content_type: article copyright: © 2016 Elsevier Ireland Ltd. All rights reserved.status: publishe