Vocal pattern detection of depression among older adults.

Depression is a serious problem for many older adults but is too often undetected by the person, family or providers. Although vocal patterns have been successfully used to detect and predict depression in adults aged 18 to 65 years, no studies to date have included older adults. The study purpose was to determine whether vocal patterns associated with clinical depression in younger people also signify depression in older adults. An observational, repeated measures design was used to enroll 46 volunteer older adults who completed a semi-structured interview composed the 9-item Patient Health Questionnaire or PHQ-9 depression scale and selected speech measures. Recorded interviews were analysed by machine learning algorithms to evaluate whether vocal patterns may predict presence of depression in older adults. In this study, using the PHQ-9 and a supervised machine learning algorithm accurately predicted high and low depression scores between 86% and 92% of the time. Change in raw PHQ-9 scores between interview cycles was predicted within 1.17 points. These results provide strong and promising evidence that vocal patterns can be used effectively to detect clinical depression in adults who are 65 years and older.

Test-retest and between-site reliability in a multicenter fMRI study.

In the present report, estimates of test-retest and between-site reliability of fMRI assessments were produced in the context of a multicenter fMRI reliability study (FBIRN Phase 1, www.nbirn.net). Five subjects were scanned on 10 MRI scanners on two occasions. The fMRI task was a simple block design sensorimotor task. The impulse response functions to the stimulation block were derived using an FIR-deconvolution analysis with FMRISTAT. Six functionally-derived ROIs covering the visual, auditory and motor cortices, created from a prior analysis, were used. Two dependent variables were compared: percent signal change and contrast-to-noise-ratio. Reliability was assessed with intraclass correlation coefficients derived from a variance components analysis. Test-retest reliability was high, but initially, between-site reliability was low, indicating a strong contribution from site and site-by-subject variance. However, a number of factors that can markedly improve between-site reliability were uncovered, including increasing the size of the ROIs, adjusting for smoothness differences, and inclusion of additional runs. By employing multiple steps, between-site reliability for 3T scanners was increased by 123%. Dropping one site at a time and assessing reliability can be a useful method of assessing the sensitivity of the results to particular sites. These findings should provide guidance toothers on the best practices for future multicenter studies.