fMRI activation in late-life anxious depression: a potential biomarker.

OBJECTIVE AND METHODS: The neurobiology of late-life anxious depression (LLAD) is poorly characterized despite evidence that this is a common and severe subtype of late-life depression. To identify the neuroanatomical substrate of LLAD, we examined event-related fMRI data collected in eight subjects with late-life depression, half of whom had high levels of comorbid anxiety. Subjects were trained on the Preparing to Overcome Prepotency (POP) task, which is an executive control task that reliably activates the lateral prefrontal cortex-anterior cingulate cortex (ACC) cognitive control circuit. RESULTS: Time series analysis showed that, when compared with elderly depressed subjects, elderly subjects with anxious depression performing the POP task produced a significantly greater and more sustained signal in three regions: BA24 (dorsal anterior cingulate), BA31 (posterior cingulate), and BA6 (prefrontal cortex). While elderly subjects with pure depression presented a bimodal activation curve in the dorsal anterior cingulate and the posterior cingulate, elderly subjects with anxious depression presented a sustained unimodal activation pattern. CONCLUSIONS: Our preliminary results suggest specific activation patterns unique to anxious depression that may suggest greater and more sustained efforts of the ACC to carry out cognitive control tasks. Further research is needed to clarify the neuroanatomical basis of LLAD.

A fully automated method for quantifying and localizing white matter hyperintensities on MR images.

White matter hyperintensities (WMH), commonly found on T2-weighted FLAIR brain MR images in the elderly, are associated with a number of neuropsychiatric disorders, including vascular dementia, Alzheimer's disease, and late-life depression. Previous MRI studies of WMHs have primarily relied on the subjective and global (i.e., full-brain) ratings of WMH grade. In the current study we implement and validate an automated method for quantifying and localizing WMHs. We adapt a fuzzy-connected algorithm to automate the segmentation of WMHs and use a demons-based image registration to automate the anatomic localization of the WMHs using the Johns Hopkins University White Matter Atlas. The method is validated using the brain MR images acquired from eleven elderly subjects with late-onset late-life depression (LLD) and eight elderly controls. This dataset was chosen because LLD subjects are known to have significant WMH burden. The volumes of WMH identified in our automated method are compared with the accepted gold standard (manual ratings). A significant correlation of the automated method and the manual ratings is found (P<0.0001), thus demonstrating similar WMH quantifications of both methods. As has been shown in other studies (e.g. [Taylor, W.D., MacFall, J.R., Steffens, D.C., Payne, M.E., Provenzale, J.M., Krishnan, K.R., 2003. Localization of age-associated white matter hyperintensities in late-life depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 27 (3), 539-544.]), we found there was a significantly greater WMH burden in the LLD subjects versus the controls for both the manual and automated method. The effect size was greater for the automated method, suggesting that it is a more specific measure. Additionally, we describe the anatomic localization of the WMHs in LLD subjects as well as in the control subjects, and detect the regions of interest (ROIs) specific for the WMH burden of LLD patients. Given the emergence of large NeuroImage databases, techniques, such as that described here, will allow for a better understanding of the relationship between WMHs and neuropsychiatric disorders.