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Laverne Estanol

Publications and source records attributed to Laverne Estanol.

3 recordsLinked to original sources

Localizing gray matter deficits in late-onset depression using computational cortical pattern matching methods.

OBJECTIVE: The authors used magnetic resonance imaging and an image analysis technique known as cortical pattern matching to map cortical gray matter deficits in elderly depressed patients with an illness onset after age 60 (late-onset depression). METHOD: Seventeen patients with late-onset depression (11 women and six men; mean age=75.24, SD=8.52) and 17 group-matched comparison subjects (11 women and six men; mean age=73.88, SD=7.61) were included. Detailed spatial analyses of gray matter were conducted across the entire cortex by measuring local proportions of gray matter at thousands of homologous cortical surface locations in each subject, and these patterns were matched across subjects by using elastic transformations to align sulcal topography. To visualize regional changes, statistical differences were mapped at each cortical surface location in three dimensions. RESULTS: The late-onset depression group exhibited significant gray matter deficits in the right lateral temporal cortex and the right parietal cortex, where decreases were most pronounced in sensorimotor regions. The statistical maps also showed gray matter deficits in the same regions of the left hemisphere that approached significance after permutation testing. No significant group differences were detected in frontal cortices or any other anatomical region. CONCLUSIONS: Regionally specific decreases of gray matter occur in late-onset depression, supporting the hypothesis that this subset of elderly patients with major depression presents with certain unique neuroanatomical abnormalities that may differ from patients with an earlier onset of illness.

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Medical burden, cerebrovascular disease, and cognitive impairment in geriatric depression: modeling the relationships with the CART analysis.

Herein, the background information reflecting roles of medical burden, cerebrovascular disease and risk factors, and cognitive impairment in geriatric depression are reviewed. The authors then propose a nonparametric statistical approach to the data analysis of multiple putative causal variables for late-life depression, the Classification and Regression Tree Analysis. This analysis presents a useful approach to modeling nonlinear relationships and interactions among variables measuring physical and mental health, as well as magnetic resonance imaging and cognitive measures in depressed elderly. This method uncovers the existing interactions among multiple predictor variables, and provide thresholds for each variable, at which its predictive power becomes statistically significant. It presents a "hierarchy" of the predictors in a form of a decision tree by finding the best combination of predictors of an outcome. The authors present two models based on demographic variables, measures of vascular and nonvascular medical burden, neuroimaging indices, the Mini-Mental State Examination score, and neuropsychological test scores of 81 elderly depressed subjects. Cognitive tests of verbal fluency and executive function are identified as the best predictors of depression, followed by the frontal lobe volume and Mini-Mental State Examination. The authors observed that an interaction between frontal lobe volume, total lesion volume, and medical burden was predictive of depression.

Journal Article↗

Frontal white matter biochemical abnormalities in late-life major depression detected with proton magnetic resonance spectroscopy.

OBJECTIVE: Neuroanatomical abnormalities have been identified in patients with late-life mood disorders by using magnetic resonance imaging. This study examined the biochemical correlates of late-life major depression in the frontal gray and white matter by using single-voxel proton spectroscopy. METHOD: Twenty elderly patients with major depression and 18 comparison subjects similar in age and gender to the patients were scanned on a 1.5-T magnetic resonance scanner with head coil. Voxels were placed in the left dorsolateral white matter and bilaterally in the anterior cingulate gray matter. Absolute levels of N-acetylaspartate, choline, myo-inositol, and creatine were estimated with the LC-Model algorithm. Ratios of metabolite to creatine levels were computed from the absolute values. RESULTS: myo-Inositol/creatine and choline/creatine ratios were significantly higher in the frontal white matter in the major depression group than in the comparison group. The groups had no significant differences in the metabolite ratios in the gray matter. CONCLUSIONS: Biochemical changes in the white matter may provide some of the neurobiological substrates to late-life major depression.

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