Thalamic volume, resting-state activity, and their association with the efficacy of electroconvulsive therapy.

Electroconvulsive therapy (ECT) is the most effective antidepressant treatment. Biological predictors of clinical outcome to ECT are valuable. We aimed to examine multimodal magnetic resonance imaging (MRI) data that correlates to the efficacy of ECT. Structural and resting-state functional MRI data were acquired from 46 individuals (25 depressed individuals who received ECT, and 21 healthy controls). Whole-brain grey matter volume (GMV) and fractional amplitude of low frequency fluctuations (fALFF) were investigated to identify brain regions associated with post-ECT Hamilton Depression Rating Scale (HAM-D) total scores. GMV and fALFF values were compared with those in healthy controls using analysis of covariance (ANCOVA). Remission was defined by HAM-D ≤7. A multiple regression analysis revealed that pretreatment smaller GMV in the left thalamus was associated with worse response to ECT (i.e. higher post-ECT HAM-D). Pretreatment higher fALFF in the right anterior insula, and lower fALFF in the left thalamus and the cerebellum were associated with worse outcomes. The left thalamus was identified in both GMV and fALFF analyses. Nonremitters showed significantly smaller thalamic GMV compared to remitters and controls. We found that pretreatment thalamic volume and resting-state activity were associated with the efficacy of ECT. Our results highlight the importance of the thalamus as a possible biological predictor and its role in the underlying mechanisms of ECT action.

Analysis of changes in transcription start site distribution by a classification approach.

Change in transcription start site (TSS) usage is an important mechanism for the control of transcription process, and has a significant effect on the isoforms being transcribed. One of the goals in the study of TSS is the understanding of how and why their usage differs in different tissues or under different conditions. In light of recent efforts in the mapping of transcription start site landscape using high-throughput sequencing approaches, a quantitative and automated method is needed to process all the data that are being produced. In this work we propose a statistical approach that will classify changes in TSS distribution between different samples into several categories of changes that may have biological significance. Genes selected by the classifiers can then be analyzed together with additional supporting data to determine their biological significance. We use a set of time-course TSS data from mouse dendritic cells stimulated with lipopolysaccharide (LPS) to demonstrate the usefulness of our method.