Structural MR image processing using the BRAINS2 toolbox
Mental Health Clinical Research Center, The University of Iowa Hospitals and Clinics, Room 2911 JPP, 200 Hawkins Drive, Iowa City, IA 52242, USA. Computerized Medical Imaging and Graphics
(Impact Factor: 1.22).
07/2002; 26(4):251-64. DOI: 10.1016/S0895-6111(02)00011-3
Medical imaging has opened a new door into biomedical research. In order to study various diseases of the brain and detect their impact on brain structure, robust and user friendly image processing packages are required. These packages must be multi-faceted to distinguish variations in size, shape, volume, and the ability to detect longitudinal changes over the course of an illness. This paper describes the BRAINS2 image processing package, which contains both manual and automated tools for structural identification, methods for tissue classification and cortical surface generation. These features are described in detail, as well as the reliability of these procedures.
Available from: Michael M Plichta
- "Both hippocampus and amygdala were manually segmented by trained raters (Hipocampus: SP; Amygdala: RC). First, raw image data had to be converted to BRAINS2 (Magnotta et al., 2002) readable format using the MRIcro free software package. The employed standardized segmentation protocol for the hippocampus (Malykhin et al., 2007) is state of the art according to review articles (Geuze et al., 2005; Konrad et al., 2009). "
- "Automated processing of the anatomical images was performed using BRAINS2 software ( Magnotta et al . , 2002 ) . The BRAINS2 software includes automated AC – PC alignment , image alignment , image intensity standardization , tissue classification , and brain extraction . The BRAINS2 method for white matter segmentation has shown reliability with manual raters and adequately addresses concerns of partial volume contamination ( Harris et al . , "
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ABSTRACT: We sought to characterize the relationship between integrity of the white matter underlying the ventral anterior cingulate (vAC) and depressive symptoms in older adults with atherosclerotic vascular disease (AVD), a condition associated with preferential degeneration of the white matter. The ventral anterior cingulate was defined as including white matter underlying ventral Brodmann Area 24 and Brodmann Area 25, corresponding with the “subcallosal” and “subgenual” cingulate respectively. This region of interest was chosen based on the preponderance of evidence that the white matter in the region plays a critical role in the manifestation of depressive symptoms. Participants had current unequivocal diagnoses of AVD and were between 55 and 90 years old. Fractional anisotropy (FA) was used as an index of white matter integrity and organization. Whole-brain mean diffusivity (MD) was used as an index of global white matter lesion burden. Depressive symptoms were measured using the Symptom Checklist-90-Revised (SCL-90-R) Depression Scale. Depressive symptoms were significantly related to low FA in the right vAC (r=-.356, DF=30, p=.045) but not the left vAC (r=.024, DF=30, p=.896) after controlling for total brain MD (a statistical control for global white matter lesion burden). Further, depressive symptoms were significantly related to low FA in the right vAC (r=-0.361, DF=31, p=.039), but not the left vAC (r=.259, DF=31, p=.145) when controlled for the contralateral vAC FA. The correlation coefficients for this follow-up analysis were found to be significantly different between left and right vAC (Z=2.310, p=.021). Poor white matter health in the vAC may be a biological mechanism for depressive symptoms in older adults with vascular disease. Further studies may corroborate that the right vAC plays a unique role in depressive symptom manifestation in cases where the white matter is preferentially affected, as is the case in AVD. This could lead to future targeting of
Available from: Paula Suarez-Pinilla
- "Processing of the images was performed using BRAINS2 (Magnotta et al. 2002). The T1-weighted images were spatially normalized and resampled to 1.0-mm 3 voxels so that the anterior–posterior axis of the brain was realigned parallel to the anterior commissure/posterior commissure line and the interhemispheric fissure aligned on the other two axes. "
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ABSTRACT: Cortical thickness measurement offers an index of brain development processes. In healthy individuals, cortical thickness is reduced with increasing age and is related to cognitive decline. Cortical thinning has been reported in schizophrenia. Whether cortical thickness changes differently over time in patients and its impact on outcome remain unanswered.
Data were examined from 109 patients and 76 healthy controls drawn from the Santander Longitudinal Study of first-episode schizophrenia for whom adequate structural magnetic resonance imaging (MRI) data were available (n = 555 scans). Clinical and cognitive assessments and MRIs were acquired at three regular time points during a 3-year follow-up period. We investigated likely progressive cortical thickness changes in schizophrenia during the first 3 years after initiating antipsychotic treatment. The effects of cortical thickness changes on cognitive and clinical variables were also examined along with the impact of potential confounding factors.
There were significant diagnoses × scan time interaction main effects for total cortical thickness (F 1,309.1 = 4.60, p = 0.033) and frontal cortical thickness (F 1,310.6 = 5.30, p = 0.022), reflecting a lesser thinning over time in patients. Clinical and cognitive outcome was not associated with progressive cortical changes during the early years of the illness.
Cortical thickness abnormalities do not unswervingly progress, at least throughout the first years of the illness. Previous studies have suggested that modifiable factors may partly account for cortical thickness abnormalities. Therefore, the importance of implementing practical actions that may modify those factors and improve them over the course of the illness should be highlighted.
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