Antidepressant response to partial sleep deprivation in unipolar depression is not related to state anxiety.

One night of total or partial sleep deprivation (SD) produces a temporary remission in up to 60% of patients with major depression, yet mechanisms remain unclear. We investigated whether the antidepressant effects of SD are caused, even partially, by an improvement in anxiety. As part of a functional magnetic resonance imaging study, 19 unmedicated major depression patients and eight controls completed the Spielberger State/Trait Anxiety Inventory (STAI) (state version) at baseline and sleep-deprived scanning sessions. We found (1) greater anxiety in patients than controls; (2) no baseline or SD STAI difference between responders and nonresponders; (3) no STAI change with SD in any subject group; and (4) no significant correlation between any STAI and Hamilton Depression Rating Scale measures. Our findings did not provide support for an anxiolytic process associated with the antidepressant effects of SD.

Decreased perfusion in young alcohol-dependent women as compared with age-matched controls.

AIM: To use the superior spatial resolution of magnetic resonance imaging (MRI) to examine differences in cerebral perfusion between young alcohol dependent and normal women. METHODS: Eight alcohol dependent women and 8 controls (all ages 18-25) received single-slice resting perfusion-weighted MRI (directly proportional to brain blood flow), with slices located above the corpus callosum. RESULTS: Alcohol-dependent women had decreased perfusion in prefrontal and left parietal regions. CONCLUSIONS: Reduced perfusion has not previously been reported in young, physically healthy alcohol dependent females, yet is consistent with previously reported decreased cerebral activity in alcohol dependence.

A technique for the deidentification of structural brain MR images.

Due to the increasing need for subject privacy, the ability to deidentify structural MR images so that they do not provide full facial detail is desirable. A program was developed that uses models of nonbrain structures for removing potentially identifying facial features. When a novel image is presented, the optimal linear transform is computed for the input volume (Fischl et al. [2002]: Neuron 33:341-355; Fischl et al. [2004]: Neuroimage 23 (Suppl 1):S69-S84). A brain mask is constructed by forming the union of all voxels with nonzero probability of being brain and then morphologically dilated. All voxels outside the mask with a nonzero probability of being a facial feature are set to 0. The algorithm was applied to 342 datasets that included two different T1-weighted pulse sequences and four different diagnoses (depressed, Alzheimer's, and elderly and young control groups). Visual inspection showed none had brain tissue removed. In a detailed analysis of the impact of defacing on skull-stripping, 16 datasets were bias corrected with N3 (Sled et al. [1998]: IEEE Trans Med Imaging 17:87-97), defaced, and then skull-stripped using either a hybrid watershed algorithm (Ségonne et al. [2004]: Neuroimage 22:1060-1075, in FreeSurfer) or Brain Surface Extractor (Sandor and Leahy [1997]: IEEE Trans Med Imaging 16:41-54; Shattuck et al. [2001]: Neuroimage 13:856-876); defacing did not appreciably influence the outcome of skull-stripping. Results suggested that the automatic defacing algorithm is robust, efficiently removes nonbrain tissue, and does not unduly influence the outcome of the processing methods utilized; in some cases, skull-stripping was improved. Analyses support this algorithm as a viable method to allow data sharing with minimal data alteration within large-scale multisite projects.

Polysomnography and criteria for the antidepressant response to sleep deprivation.

BACKGROUND: One night of total or partial sleep deprivation (SD) produces a temporary remission in 40-60% of patients with major depression. Yet no attempts to determine the optimum response criterion(a) for the antidepressant response to SD have been published. METHODS: Twenty-three unmedicated major depression patients received polysomnography (PSG) on an adaptation night; a baseline night; a partial SD (PSD) night (awake after 3 a.m.); and a "recovery" night. Subjects received the Hamilton Depression Rating Scale (HDRS17) at standard times during baseline and PSD days and at 8 a.m. after the "recovery" night. Response was defined as percent decrease in the modified HDRS17 (HDRS17Mod) (omitting sleep and weight loss items) from baseline to the minimum following PSD. Using cutoffs of 30%, 35%, 40%, and 50% to dichotomize responders and nonresponders, PSG variables were analyzed for between-group differences. RESULTS: All cutoffs differentiated responders' and nonresponders' mood response to PSD despite similar baseline values. Sleep continuity measures most consistently differed between responders and nonresponders on baseline and recovery nights. None of the response cutoffs tested were clearly "best" in terms of detecting the most PSG differences between groups. LIMITATIONS: More subjects may be needed. CONCLUSIONS: Given the increasing interest in SD for clinical and research applications, as well as its proposed use for subtyping depression, further study to determine the optimal response criterion(a) for the antidepressant response to SD is warranted. Planned pooling of multisite data on standardized SD protocols could help determine the optimal cutpoint for response.

Improved anatomic delineation of the antidepressant response to partial sleep deprivation in medial frontal cortex using perfusion-weighted functional MRI.

This study used functional magnetic resonance imaging (fMRI) to clarify the sites of brain activity associated with the antidepressant effects of sleep deprivation (SD). We hypothesized: 1) depressed responders' baseline ventral anterior cingulate (AC) perfusion will be greater than that of nonresponders and controls; 2) following partial sleep deprivation (PSD), ventral AC perfusion will significantly decrease in responders only. Seventeen unmedicated outpatients with current major depression and eight controls received perfusion-weighted fMRI and structural MRI at baseline and following 1 night of late-night PSD. Talairach-transformed gray matter masks were merged with Talairach Daemon-based region of interest (ROI) templates. Baseline left ventral AC (LVAC) perfusion was greater in responders than nonresponders. There was no difference involving the medial frontal cortex. Responders' LVAC perfusion dropped from baseline to PSD scans compared with nonresponders and controls, as did perfusion in the right dorsal AC. In the patient group as a whole, decrease in LVAC perfusion from baseline to PSD scans correlated directly with the decrease in the modified 17-item Hamilton Depression Rating Scale (HDRS17) between baseline and PSD conditions. These data--the first using fMRI--show greater anatomic specificity than previous findings of SD and depression in linking decreased brain activity in this area with clinical improvement.

Does amygdalar perfusion correlate with antidepressant response to partial sleep deprivation in major depression?

This study used functional MRI (fMRI) to clarify the sites of brain activity associated with the antidepressant effects of sleep deprivation (SD). We hypothesized: (1) baseline perfusion in right and left amygdalae will be greater in responders than in nonresponders; (2) following partial sleep deprivation (PSD), perfusion in responders' right and left amygdalae would decrease. Seventeen unmedicated outpatients with current major depression and eight controls received perfusion-weighted fMRI and structural MRI at baseline and following 1 night of late-night PSD. Baseline bilateral amygdalar perfusion was greater in responders than nonresponders. Clusters involving both amygdalae decreased from baseline to PSD specifically in responders. Right amygdalar perfusion diverged with PSD, increasing in nonresponders and decreasing in responders. These novel amygdalar findings are consistent with the overarousal hypothesis of SD as well as other functional imaging studies showing increased baseline amygdalar activity in depression and decreased amygdalar activity with remission or antidepressant medications.

Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location.

Performance of automated methods to isolate brain from nonbrain tissues in magnetic resonance (MR) structural images may be influenced by MR signal inhomogeneities, type of MR image set, regional anatomy, and age and diagnosis of subjects studied. The present study compared the performance of four methods: Brain Extraction Tool (BET; Smith [2002]: Hum Brain Mapp 17:143-155); 3dIntracranial (Ward [1999] Milwaukee: Biophysics Research Institute, Medical College of Wisconsin; in AFNI); a Hybrid Watershed algorithm (HWA, Segonne et al. [2004] Neuroimage 22:1060-1075; in FreeSurfer); and Brain Surface Extractor (BSE, Sandor and Leahy [1997] IEEE Trans Med Imag 16:41-54; Shattuck et al. [2001] Neuroimage 13:856-876) to manually stripped images. The methods were applied to uncorrected and bias-corrected datasets; Legacy and Contemporary T1-weighted image sets; and four diagnostic groups (depressed, Alzheimer's, young and elderly control). To provide a criterion for outcome assessment, two experts manually stripped six sagittal sections for each dataset in locations where brain and nonbrain tissue are difficult to distinguish. Methods were compared on Jaccard similarity coefficients, Hausdorff distances, and an Expectation-Maximization algorithm. Methods tended to perform better on contemporary datasets; bias correction did not significantly improve method performance. Mesial sections were most difficult for all methods. Although AD image sets were most difficult to strip, HWA and BSE were more robust across diagnostic groups compared with 3dIntracranial and BET. With respect to specificity, BSE tended to perform best across all groups, whereas HWA was more sensitive than other methods. The results of this study may direct users towards a method appropriate to their T1-weighted datasets and improve the efficiency of processing for large, multisite neuroimaging studies.