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Jerome Barakos

Publications and source records attributed to Jerome Barakos.

5 recordsLinked to original sources
PubMed2006

Brain atrophy in long-term abstinent alcoholics who demonstrate impairment on a simulated gambling task.

We recently demonstrated impairment on the Simulated Gambling Task (SGT) in long-term abstinent alcoholics (AbsAlc). Brain regions that have been shown to be necessary for intact SGT performance are the ventromedial prefrontal cortex (VMPFC) and the amygdala; patients with VMPFC or amygdalar damage demonstrate SGT impairments similar to those of substance abusing populations. We examined these brain regions, using T1-weighted MRIs, in the 101 participants from our previous study using voxel-based morphometry (VBM). VBM was performed using a modification we developed [Fein, G., Landman, B., Tran, H., Barakos, J., Moon, K., Di Sclafani, V., Shumway, R., 2006. Statistical parametric mapping of brain morphology: sensitivity is dramatically increased by using brain-extracted images as inputs. Neuroimage] of Baron's procedure, [], in which we use skull-stripped images as input. We also restricted the analysis to a ROI consisting of the amygdala and VMPFC as defined by the Talairach Daemon resource. Compared to the controls, the AbsAlc participants had significant foci of reduced gray matter density within the amygdala. Thus, SGT decision-making deficits are associated with reduced gray matter in the amygdala, a brain region previously implicated in similar decision-making impairments in neurological samples. This structurally based abnormality may be the result of long-term alcohol abuse or dependence, or it may reflect a pre-existing factor that predisposes one to severe alcoholism. From an image analysis perspective, this work demonstrates the increased sensitivity that results from using skull-stripped inputs and from restricting the analysis to a ROI. Without both of these methodological advances, no statistically significant finding would have been forthcoming from this work.

Adult↗
PubMed2006

Statistical parametric mapping of brain morphology: sensitivity is dramatically increased by using brain-extracted images as inputs.

A major attraction of voxel-based morphometry (VBM) is that it allows researchers to explore large datasets with minimal human intervention. However, the validity and sensitivity of the Statistical Parametric Mapping (SPM2) approach to VBM are the subject of considerable debate. We visually inspected the SPM2 gray matter segmentations for 101 research participants and found a gross inclusion of non-brain tissue surrounding the entire brain as gray matter in five subjects and focal areas bordering the brain in which non-brain tissue was classified as gray matter in many other subjects. We also found many areas in which the cortical gray matter was incorrectly excluded from the segmentation of the brain. The major source of these errors was the misregistration of individual brain images with the reference T1-weighted brain template. These errors could be eliminated if SPM2 operated on images from which non-brain tissues (scalp, skull, and meninges) are removed (brain-extracted images). We developed a modified SPM2 processing pipeline that used brain-extracted images as inputs to test this hypothesis. We describe the modifications to the SPM2 pipeline that allow analysis of brain-extracted inputs. Using brain-extracted inputs eliminated the non-brain matter inclusions and the cortical gray matter exclusions noted above, reducing the residual mean square errors (RMSEs, the error term of the SPM2 statistical analyses) by over 30%. We show how this reduction in the RMSEs profoundly affects power analyses. SPM2 analyses of brain-extracted images may require sample sizes only half as great as analyses of non-brain-extracted images.

Adult↗
PubMed2004

Controlling for premorbid brain size in imaging studies: T1-derived cranium scaling factor vs. T2-derived intracranial vault volume.

Intracranial vault (ICV) volume, obtained from T2-weighted magnetic resonance imaging (MRI), is generally used to estimate premorbid brain size in imaging studies. T1-weighted sequences lack the signal characteristics for ICV measurements [they have poor contrast at the outer boundary of sulcal cranium scaling factor (CSF)] but are valuable in imaging studies due to their excellent gray vs. white matter contrast. Smith et al. [NeuroImage 17 (2002) 479] suggested a T1-derived cranium scaling factor as an alternative control variable for premorbid brain size in cross-sectional studies. This index, which is computed using the SIENAX software, is a scaling factor comparing an individual's skull to a template skull derived from the Montreal Neurological Institute (MNI) average of 152 T1 studies (the MNI152). SIENAX computes coarsely defined estimates for the individual and MNI skulls rather than well-defined volumes. To test how well this approach would work as a control variable for premorbid brain size in cross-sectional studies, we compared the T1-derived cranium scaling factor to T2-derived ICV measurements in a sample of 92 individuals: 39 white males, 22 white females, and 31 African-American males, with an age range of 26-78 years. The correlation between T1- and T2-derived variables was 0.94 and did not differ across subject groups. The T1-derived cranium scaling factor accounted for a statistically significant portion (87%) of the variance of the T2-derived ICV measure and thus is a good surrogate for ICV measurement of premorbid brain size as a reference measure in MRI atrophy studies. Furthermore, neither race, sex, nor age accounted for any additional variance in ICV, indicating that neither race-, gender-, nor age-associated cranial bone thickness effects were present in this data set.

Adult↗