A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes

A Bayesian model of shape and appearance for subcortical brain segmentation

Automatic segmentation of subcortical structures in human brain MR images is an important but difficult task due to poor and variable intensity contrast. Clear, well-defined intensity features are absent in many places along typical structure boundaries and so extra information is required to achieve successful segmentation. A method is proposed here that uses manually labelled image data to provide anatomical training information. It utilises the principles of the Active Shape and Appearance Models but places them within a Bayesian framework, allowing probabilistic relationships between shape and intensity to be fully exploited. The model is trained for 15 different subcortical structures using 336 manually-labelled T1-weighted MR images. Using the Bayesian approach, conditional probabilities can be calculated easily and efficiently, avoiding technical problems of ill-conditioned covariance matrices, even with weak priors, and eliminating the need for fitting extra empirical scaling parameters, as is required in standard Active Appearance Models. Furthermore, differences in boundary vertex locations provide a direct, purely local measure of geometric change in structure between groups that, unlike voxel-based morphometry, is not dependent on tissue classification methods or arbitrary smoothing. In this paper the fully-automated segmentation method is presented and assessed both quantitatively, using Leave-One-Out testing on the 336 training images, and qualitatively, using an independent clinical dataset involving Alzheimer's disease. Median Dice overlaps between 0.7 and 0.9 are obtained with this method, which is comparable or better than other automated methods. An implementation of this method, called FIRST, is currently distributed with the freely-available FSL package.

Scan-rescan reliability of subcortical brain volumes derived from automated segmentation

Large-scale longitudinal studies of regional brain volume require reliable quantification using automated segmentation and labeling. However, repeated MR scanning of the same subject, even if using the same scanner and acquisition parameters, does not result in identical images due to small changes in image orientation, changes in prescan parameters, and magnetic field instability. These differences may lead to appreciable changes in estimates of volume for different structures. This study examined scan-rescan reliability of automated segmentation algorithms for measuring several subcortical regions, using both within-day and across-day comparison sessions in a group of 23 normal participants. We found that the reliability of volume measures including percent volume difference, percent volume overlap (Dice's coefficient), and intraclass correlation coefficient (ICC), varied substantially across brain regions. Low reliability was observed in some structures such as the amygdala (ICC = 0.6), with higher reliability (ICC = 0.9) for other structures such as the thalamus and caudate. Patterns of reliability across regions were similar for automated segmentation with FSL/FIRST and FreeSurfer (longitudinal stream). Reliability was associated with the volume of the structure, the ratio of volume to surface area for the structure, the magnitude of the interscan interval, and the method of segmentation. Sample size estimates for detecting changes in brain volume for a range of likely effect sizes also differed by region. Thus, longitudinal research requires a careful analysis of sample size and choice of segmentation method combined with a consideration of the brain structure(s) of interest and the magnitude of the anticipated effects.

Stochastic tractography study of Inferior Frontal Gyrus anatomical connectivity in schizophrenia

BACKGROUND

Abnormalities within language-related anatomical structures have been associated with clinical symptoms and with language and memory deficits in schizophrenia. Recent studies suggest disruptions in functional connectivity within the Inferior Frontal Gyrus (IFG) network in schizophrenia. However, due to technical challenges, anatomical connectivity abnormalities within this network and their involvement in clinical and cognitive deficits have not been studied.

MATERIAL AND METHODS

Diffusion and anatomical scans were obtained from 23 chronic schizophrenia patients and 23 matched controls. The IFG was automatically segmented, and its white matter connections extracted and measured with newly-developed stochastic tractography tools. Correlations between anatomical structures and measures of semantic processing were also performed.

RESULTS

White Matter connections between the IFG and posterior brain regions followed two distinct pathways: dorsal and ventral. Both demonstrated left lateralization, but ventral pathway abnormalities were only found in schizophrenia. IFG volumes also showed left lateralization and abnormalities in schizophrenia. Further, despite similar laterality and abnormality patterns, IFG volumes and white matter connectivity were not correlated with each other in either group. Interestingly, measures of semantic processing correlated with white matter connectivity in schizophrenia and with gray matter volumes in controls. Finally, hallucinations were best predicted by both gray matter and white matter measures together.

CONCLUSIONS

Our results suggest abnormalities within the ventral IFG network in schizophrenia, with white matter abnormalities better predicting semantic deficits. The lack of a statistical relationship between coexisting gray and white matter deficits might suggest their different origin and the necessity for a multimodal approach in future schizophrenia studies.

A learning-based wrapper method to correct systematic errors in automatic image segmentation: consistently improved performance in hippocampus, cortex and brain segmentation

We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation method. The wrapper method attempts to learn the intensity, spatial and contextual patterns associated with systematic segmentation errors produced by the host method on training data for which manual segmentations are available. The method then attempts to correct such errors in segmentations produced by the host method on new images. One practical use of the proposed wrapper method is to adapt existing segmentation tools, without explicit modification, to imaging data and segmentation protocols that are different from those on which the tools were trained and tuned. An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems. The wrapper method is evaluated with four host brain MRI segmentation methods: hippocampus segmentation using FreeSurfer (Fischl et al., 2002); hippocampus segmentation using multi-atlas label fusion (Artaechevarria et al., 2009); brain extraction using BET (Smith, 2002); and brain tissue segmentation using FAST (Zhang et al., 2001). The wrapper method generates 72%, 14%, 29% and 21% fewer erroneously segmented voxels than the respective host segmentation methods. In the hippocampus segmentation experiment with multi-atlas label fusion as the host method, the average Dice overlap between reference segmentations and segmentations produced by the wrapper method is 0.908 for normal controls and 0.893 for patients with mild cognitive impairment. Average Dice overlaps of 0.964, 0.905 and 0.951 are obtained for brain extraction, white matter segmentation and gray matter segmentation, respectively.

Suicidal attempts and increased right amygdala volume in schizophrenia

Suicide is a major cause of death in schizophrenia. Neurobiological studies suggest that suicidality is associated with abnormal brain structure and connectivity in fronto-temporo-limbic regions. However, it is still unclear whether suicidality in schizophrenia is related to volumetric abnormalities in subcortical structures that play a key role in emotion regulation, aggression and impulse control. Therefore, we aimed to examine whether the volume of selected subcortical regions is associated with previous suicidal attempts and self-aggression in schizophrenia. For this cross-sectional study, we recruited 50 outpatients with schizophrenia and 50 healthy controls (HC) matched for age and gender. Fourteen patients had a history of one or more suicide attempts. Different forms of aggression were assessed using the Modified Overt Aggression Scale. All participants underwent structural MR imaging at 3 Tesla. Physical volumetric measures were calculated for the lateral ventricles, thalamus, hippocampus, amygdala, caudate, putamen, pallidum and accumbens using an automatic segmentation method on T1-weighted high-resolution (voxel size 1×1×1mm(3)) images. Multivariate and follow-up univariate ANOVAs revealed a selective increase in volume in the right amygdala of patients with a history of suicidality compared both to patients without such a history and HC. Moreover, in the entire patient group increased right amygdala volume was related to increased self-aggression. Our findings suggest that right amygdala hypertrophy may be a risk factor for suicide attempts in patients with schizophrenia and this could be relevant for suicide prevention.

Obesity-mediated inflammation may damage the brain circuit that regulates food intake

Adiposity is associated with chronic low-grade systemic inflammation and increased inflammation in the hypothalamus, a key structure in feeding behavior. It remains unknown whether inflammation impacts other brain structures that regulate feeding behavior. We studied 44 overweight/obese and 19 lean individuals with MRI and plasma fibrinogen levels (marker of inflammation). We performed MRI-based segmentations of the medial and lateral orbitofrontal cortex (OFC) and hippocampal volumes. Gray matter (GM) volumes were adjusted for head size variability. We conducted logistic and hierarchical regressions to assess the association between fibrinogen levels and brain volumetric data. Using diffusion tensor imaging (DTI), we created apparent diffusion coefficient (ADC) maps and conducted voxelwise correlational analyses. Fibrinogen concentrations were higher among the overweight/obese (t[61] = -2.33, P = 0.023). Lateral OFC associated together with fibrinogen correctly classified those with excess of weight (accuracy = 76.2%, sensitivity = 95.5%, and specificity=31.6%). The lateral OFC volumes of overweight/obese were negatively associated with fibrinogen (r = -0.37, P = 0.016) and after accounting for age, hypertension, waist/hip ratio and lipid and sugar levels, fibrinogen significantly explained an additional 9% of the variance in the lateral OFC volume (β = -0.348, ΔR(2) = 0.093, ΔF P = 0.046). Among overweight/obese the associations between GM ADC and fibrinogen were significantly positive (P < 0.001) in the left and right amygdala and the right parietal region. Among lean individuals these associations were negative and located in the left prefrontal, the right parietal and the left occipital lobes. This is the first study to report that adiposity-related inflammation may reduce the integrity of some of the brain structures involved in reward and feeding behaviors.

Reduced thickness of medial orbitofrontal cortex in smokers

BACKGROUND

Structural deficiencies within the prefrontal cortex might be related to drug-taking behavior that prevails in smokers. Cortical thickness has been found to be a structural modulator of cerebral function and cognition and a subtle correlate of mental disorders. However, to date an analysis of cortical thickness in smokers compared with never-smokers has not been undertaken.

METHODS

We acquired high-resolution magnetic resonance imaging scans from 22 smokers and 21 never-smokers and used FreeSurfer to model the gray-white and pial surfaces for each individual cortex to compute the distance between these surfaces to obtain a measure of cortical thickness. The main cortical folds were aligned across individuals with FreeSurfer's surface-based averaging technique to compare whole brain differences in cortical thickness between smokers and never-smokers.

RESULTS

Relative to never-smokers, smokers showed greater cortical thinning in the left medial orbitofrontal cortex (mOFC). Cortical thickness measures extracted from mOFC correlated negatively with the amount of cigarettes consumed/day and the magnitude of lifetime exposure to tobacco smoke.

CONCLUSIONS

The brains of smokers are structurally different from those of never-smokers in a dose-dependent manner. The cortical thinning in mOFC in smokers relative to never-smokers might imply dysfunctions of the brain's reward, impulse control, and decision-making circuits. Related behavioral correlates are suggested to be relevant for smoking initiation and maintenance.

Preserved subcortical volumes and cortical thickness in women with sexual abuse-related PTSD

Posttraumatic stress disorder (PTSD) has been frequently associated with volumetric reductions of grey matter structures (e.g. hippocampus and anterior cingulate), but these results remain controversial, especially in female non-combat-related samples. The present study aimed at exploring whole-brain structures in women with sexual abuse-related PTSD on the basis of cortical and subcortical structure comparisons to a matched pair sample that was well-controlled. Seventeen young women who had experienced sexual abuse and who had a diagnosis of chronic PTSD based on the Clinician Administered PTSD Scale for DSM-IV and 17 healthy controls individually matched for age and years of education were consecutively recruited. Both groups underwent structural magnetic resonance imaging and psychiatric assessment of the main disorders according to Axis I of DSM-IV. The resulting scans were analyzed using automated cortical and subcortical volumetric quantifications. Compared with controls, PTSD subjects displayed normal global and regional brain volumes and cortical thicknesses. Our results indicate preserved subcortical volumes and cortical thickness in a sample of female survivors of sexual abuse with PTSD. The authors discuss potential differences between neural mechanisms of sexual abuse-related PTSD and war-related PTSD.

Testosterone modifies the effect of APOE genotype on hippocampal volume in middle-aged men

BACKGROUND

The APOE epsilon4 allele is an established risk factor for Alzheimer disease (AD), yet findings are mixed for how early its effects are manifest. One reason for the mixed results could be the presence of interaction effects with other AD risk factors. Increasing evidence indicates that testosterone may play a significant role in the development of AD. The aim of the present study was to examine the potential interaction of testosterone and APOE genotype with respect to hippocampal volume in middle age.

METHODS

Participants were men from the Vietnam Era Twin Study of Aging (n = 375). The mean age was 55.9 years (range 51-59). Between-group comparisons were performed utilizing a hierarchical linear mixed model that adjusted for the nonindependence of twin data.

RESULTS

A significant interaction was observed between testosterone and APOE genotype (epsilon4-negative vs epsilon4-positive). Those with both low testosterone (> or =1 SD below the mean) and an epsilon4-positive status had the smallest hippocampal volumes, although comparisons with normal testosterone groups were not significant. However, individuals with low testosterone and epsilon4-negative status had significantly larger hippocampal volumes relative to all other groups. A main effect of APOE genotype on hippocampal volume was observed, but only when the APOE-by-testosterone interaction was present.

CONCLUSIONS

These findings demonstrate an interaction effect between testosterone and the APOE epsilon4 allele on hippocampal volume in middle-aged men, and they may suggest 2 low testosterone subgroups. Furthermore, these results allude to potential gene-gene interactions between APOE and either androgen receptor polymorphisms or genes associated with testosterone production.

Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation

Quantitative magnetic resonance analysis often requires accurate, robust, and reliable automatic extraction of anatomical structures. Recently, template-warping methods incorporating a label fusion strategy have demonstrated high accuracy in segmenting cerebral structures. In this study, we propose a novel patch-based method using expert manual segmentations as priors to achieve this task. Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation. Validation with two different datasets is presented. In our experiments, the hippocampi of 80 healthy subjects and the lateral ventricles of 80 patients with Alzheimer's disease were segmented. The influence on segmentation accuracy of different parameters such as patch size and number of training subjects was also studied. A comparison with an appearance-based method and a template-based method was also carried out. The highest median kappa index values obtained with the proposed method were 0.884 for hippocampus segmentation and 0.959 for lateral ventricle segmentation.

Hippocampal atrophy in relapsing-remitting and primary progressive MS: a comparative study

Background: In multiple sclerosis (MS), demyelination and neuroaxonal damage are seen in the hippocampus, and MRI has revealed hippocampal atrophy.

Objectives: To investigate and compare hippocampal volume loss in patients with relapsing—remitting MS (RRMS) and primary progressive MS (PPMS) using manual volumetry, and explore its association with memory dysfunction.

Methods: Hippocampi were manually delineated on volumetric MRI of 34 patients with RRMS, 23 patients with PPMS and 18 controls. Patients underwent neuropsychological tests of verbal and visuospatial recall memory. Linear regression was used to compare hippocampal volumes between subject groups, and to assess the association with memory function.

Results: Hippocampal volumes were smaller in MS patients compared with controls, and were similar in patients with RRMS and PPMS. The mean decrease in hippocampal volume in MS patients was 317 mm3 (9.4%; 95% CI 86 to 549; p = 0.008) on the right and 284 mm3 (8.9%; 95% CI 61 to 508; p = 0.013) on the left. A borderline association of hippocampal volume with memory performance was observed only in patients with PPMS.

Conclusion: Hippocampal atrophy occurs in patients with RRMS and PPMS. Factors additional to hippocampal atrophy may impact on memory performance.

Amygdala and hippocampus enlargement during adolescence in autism

OBJECTIVE

The amygdala and hippocampus are key components of the neural system mediating emotion perception and regulation and are thought to be involved in the pathophysiology of autism. Although some studies in children with autism suggest that there is an enlargement of amygdala and hippocampal volume, findings in adolescence are sparse.

METHOD

We measured amygdala and hippocampus volume in a homogeneous group of adolescents with autism (12 through 18 years; n = 23) and compared them with an age-, sex-, and IQ-matched control group (n = 29) using a validated automated segmentation procedure in 1.5-T magnetic resonance images. All analyses were adjusted for total brain volume.

RESULTS

Repeated-measures analysis revealed a significant group x hemisphere x brain structure interaction (p = .038), even when corrected for total brain volume. Post-hoc analysis showed that the right amygdala and left hippocampus were significantly enlarged (p = .010; p = .015) in the autism compared with the control group. There were no significant correlations between age and amygdala or hippocampus volume.

CONCLUSIONS

The abnormal enlargement of the amygdala and hippocampus in adolescents with autism adds to previous findings of enlargement of these structures in children with autism. This may reflect increased activity of these structures and thereby altered emotion perception and regulation. Our results could therefore be interpreted in light of developmental adaptation of the autistic brain to a continuous overflow of emotional learning experiences.

Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion

We describe progress towards fully automatic segmentation of the hippocampus (HC) and amygdala (AG) in human subjects from MRI data. Three methods are described and tested with a set of MRIs from 80 young normal controls, using manual labeling of the HC and AG as a gold standard. The methods include: 1) our ANIMAL atlas-based method that uses non-linear registration to a pre-labeled non-linear average template (ICBM152). HC and AG labels, defined on the template are mapped through the inverse transformation to segment these structures on the subject's MRI. 2) We select the most similar MRI from the set of 80 labeled datasets to use as a template in the standard ANIMAL segmentation scheme. 3) We use label fusion techniques to combine segmentations from the 'n' most similar templates. The label fusion technique yields an optimal median Dice Kappa of 0.886 and similarity of 0.795 for HC, and 0.826 and 0.703 respectively for AG.

Manual validation of FreeSurfer's automated hippocampal segmentation in normal aging, mild cognitive impairment, and Alzheimer Disease subjects

Hippocampal volume is reduced in Alzheimer Disease (AD) and has been proposed as a possible surrogate biomarker to aid early diagnosis. Whilst automated methods to segment the hippocampus from magnetic resonance images are available, manual segmentation, in spite of being time-consuming and unsuitable for large samples, is still the standard. In order to study the validity of FreeSurfer's automated method, we compared hippocampal automated measures with manual tracing in a sample composed of healthy elderly (N=41), Mild Cognitive Impairment (MCI) (N=23), and AD (N=25) subjects. Percent volume overlap, percent volume difference, correlations, and Bland-Altman plots were studied. Automated measures were slightly larger than hand tracing ones (mean difference 10%). Percent volume overlap showed good results, but was far from perfect (78%). Manual and automated volume correlations were approximately 0.84 and the Bland-Altman analysis showed acceptable interchangeability of methods. Within-group analysis demonstrated that patient samples obtained smaller values in validity indexes than controls. Globally, FreeSurfer's automated hippocampal volumetry showed adequate validity when compared to manual tracing, with a tendency to overestimation. Nevertheless, the greater difference between automated and manual segmentation in atrophic brains suggests that studies in AD based on this software could be more likely to produce false negatives.

Reliability and validity of MRI-based automated volumetry software relative to auto-assisted manual measurement of subcortical structures in HIV-infected patients from a multisite study

The automated volumetric output of FreeSurfer and Individual Brain Atlases using Statistical Parametric Mapping (IBASPM), two widely used and well published software packages, was examined for accuracy and consistency relative to auto-assisted manual (AAM) tracings (i.e., manual correction of automated output) when measuring the caudate, putamen, amygdala, and hippocampus in the baseline scans of 120 HIV-infected patients (86.7% male, 47.3+/-6.3y.o., mean HIV duration 12.0+/-6.3years) from the NIH-funded HIV Neuroimaging Consortium (HIVNC) cohort. The data was examined for accuracy and consistency relative to auto-assisted manual tracing, and construct validity was assessed by correlating automated and AAM volumetric measures with relevant clinical measures of HIV progression. When results were averaged across all patients in the eight structures examined, FreeSurfer achieved lower absolute volume difference in five, higher sensitivity in seven, and higher spatial overlap in all eight structures. Additionally, FreeSurfer results exhibited less variability in all measures. Output from both methods identified discrepant correlations with clinical measures of HIV progression relative to AAM segmented data. Overall, FreeSurfer proved more effective in the context of subcortical volumetry in HIV-patients, particularly in a multisite cohort study such as this. These findings emphasize that regardless of the automated method used, visual inspection of segmentation output, along with manual correction if necessary, remains critical to ensuring the validity of reported results.

Atrophy patterns in Alzheimer's disease and semantic dementia: a comparison of FreeSurfer and manual volumetric measurements

Alzheimer's disease (AD) and semantic dementia (SD) are characterized by different patterns of global and temporal lobe atrophy which can be studied using magnetic resonance imaging (MRI). Manual delineation of regions of interest is time-consuming. FreeSurfer is a freely available automated technique which has a facility to label cortical and subcortical brain regions automatically. As with all automated techniques comparison with existing methods is important. Eight temporal lobe structures in each hemisphere were delineated using FreeSurfer and compared with manual segmentations in 10 control, 10 AD, and 10 SD subjects. The reproducibility errors for the manual segmentations ranged from 3% to 6%. Differences in protocols between the two methods led to differences in absolute volumes with the greatest differences between methods found bilaterally in the hippocampus, entorhinal cortex and fusiform gyrus (p<0.005). However, good correlations between the methods were found for most regions, with the highest correlations shown for the ventricles, whole brain and left medial-inferior temporal gyrus (r>0.9), followed by the bilateral amygdala and hippocampus, left superior temporal gyrus, right medial-inferior temporal gyrus and left temporal lobe (r>0.8). Overlap ratios differed between methods bilaterally in the amygdala, superior temporal gyrus, temporal lobe, left fusiform gyrus and right parahippocampal gyrus (p<0.01). Despite differences in protocol and volumes, both methods showed similar atrophy patterns in the patient groups compared with controls, and similar right-left differences, suggesting that both methods accurately distinguish between the three groups.

Putaminal volume and diffusion in early familial Creutzfeldt-Jakob disease

BACKGROUND

The putamen is centrally implicated in the pathophysiology of Creutzfeldt-Jakob Disease (CJD). To our knowledge, its volume has never been measured in this disease. We investigated whether gross putaminal atrophy can be detected by MRI in early stages, when the diffusion is already reduced.

METHODS

Twelve familial CJD patients with the E200K mutation and 22 healthy controls underwent structural and diffusion MRI scans. The putamen was identified in anatomical scans by two methods: manual tracing by a blinded investigator, and automatic parcellation by a computerized segmentation procedure (FSL FIRST). For each method, volume and mean Apparent Diffusion Coefficient (ADC) were calculated.

RESULTS

ADC was significantly lower in CJD patients (697+/-64 microm(2)/s vs. 750+/-31 microm(2)/s, p<0.005), as expected, but the volume was not reduced. The computerized FIRST delineation yielded comparable ADC values to the manual method, but computerized volumes were smaller than manual tracing values.

CONCLUSIONS

We conclude that significant diffusion reduction in the putamen can be detected by delineating the structure manually or with a computerized algorithm. Our findings confirm and extend previous voxel-based and observational studies. Putaminal volume was not reduced in our early-stage patients, thus confirming that diffusion abnormalities precede detectible atrophy in this structure.

Hippocampal volume assessment in temporal lobe epilepsy: How good is automated segmentation?

PURPOSE

Quantitative measurement of hippocampal volume using structural magnetic resonance imaging (MRI) is a valuable tool for detection and lateralization of mesial temporal lobe epilepsy with hippocampal sclerosis (mTLE). We compare two automated hippocampal volume methodologies and manual hippocampal volumetry to determine which technique is most sensitive for the detection of hippocampal atrophy in mTLE.

METHODS

We acquired a three-dimensional (3D) volumetric sequence in 10 patients with left-lateralized mTLE and 10 age-matched controls. Hippocampal volumes were measured manually, and using the software packages Freesurfer and FSL-FIRST. The sensitivities of the techniques were compared by determining the effect size for average volume reduction in patients with mTLE compared to controls. The volumes and spatial overlap of the automated and manual segmentations were also compared.

RESULTS

Significant volume reduction in affected hippocampi in mTLE compared to controls was detected by manual hippocampal volume measurement (p < 0.01, effect size 33.2%), Freesurfer (p < 0.01, effect size 20.8%), and FSL-FIRST (p < 0.01, effect size 13.6%) after correction for brain volume. Freesurfer correlated reasonably (r = 0.74, p << 0.01) with this manual segmentation and FSL-FIRST relatively poorly (r = 0.47, p << 0.01). The spatial overlap between manual and automated segmentation was reduced in affected hippocampi, suggesting the accuracy of automated segmentation is reduced in pathologic brains.

DISCUSSION

Expert manual hippocampal volumetry is more sensitive than both automated methods for the detection of hippocampal atrophy associated with mTLE. In our study Freesurfer was the most sensitive to hippocampal atrophy in mTLE and could be used if expert manual segmentation is not available.

Rebuttal to Hasan and Pedraza in comments and controversies: "Improving the reliability of manual and automated methods for hippocampal and amygdala volume measurements"

Here we address the critiques offered by Hasan and Pedraza to our recently published manuscript comparing the performance of two automated segmentation programs, FSL/FIRST and FreeSurfer (Morey R, Petty C, Xu Y, Pannu Hayes J, Wagner H, Lewis D, LaBar K, Styner M, McCarthy G. (2009): A comparison of automated segmentation and manual tracing for quantifying of hippocampal and amygdala volumes. Neuroimage 45:855-866). We provide an assessment and discussion of their specific critiques. Hasan and Pedraza bring up some important points concerning our omission of sample demographic features and inclusion of left and right hemisphere volumes as independent measures in correlational analyses. We present additional data on demographic attributes of our sample and correlations analyzed separately on left and right hemispheres of the amygdala and hippocampus. While their commentary aids the reader to more critically asses our study, it falls short of substantiating that our omissions ought to lead readers to significantly revise their interpretations. Further research will help to disentangle the advantages and limitations of the various freely-available automated segmentation software packages.