Impact of acquisition protocols and processing streams on tissue segmentation of T1 weighted MR images

Unraveling schizophrenia replicable functional connectivity disruption patterns across sites

Functional connectivity (FC) disruption is a remarkable characteristic of schizophrenia. However, heterogeneous patterns reported across sites severely hindered its clinical generalization. Based on qualified nodal-based FC of 340 schizophrenia patients (SZ) and 348 normal controls (NC) acquired from seven different scanners, this study compared four commonly used site-effect correction methods in removing the site-related heterogeneities, and then tried to cluster the abnormal FCs into several replicable and independent disrupted subnets across sites, related them to clinical symptoms, and evaluated their potentials in schizophrenia classification. Among the four site-related heterogeneity correction methods, ComBat harmonization (F1 score: 0.806 ± 0.145) achieved the overall best balance between sensitivity and false discovery rate in unraveling the aberrant FCs of schizophrenia in the local and public data sets. Hierarchical clustering analysis identified three replicable FC disruption subnets across the local and public data sets: hypo-connectivity within sensory areas (Net1), hypo-connectivity within thalamus, striatum, and ventral attention network (Net2), and hyper-connectivity between thalamus and sensory processing system (Net3). Notably, the derived composite FC within Net1 was negatively correlated with hostility and disorientation in the public validation set (p < .05). Finally, the three subnet-specific composite FCs (Best area under the receiver operating characteristic curve [AUC] = 0.728) can robustly and meaningfully discriminate the SZ from NC with comparable performance with the full identified FCs features (best AUC = 0.765) in the out-of-sample public data set (Z = -1.583, p = .114). In conclusion, ComBat harmonization was most robust in detecting aberrant connectivity for schizophrenia. Besides, the three subnet-specific composite FC measures might be replicable neuroimaging markers for schizophrenia.

Standardizing T1-w/T2-w ratio images in trigeminal neuralgia to estimate the degree of demyelination in vivo

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The T1-w/T2-w ratio image or “myelin-sensitive map (MM)” is a non-invasive tool that can estimate myelin content in different regions of the brain and between different patients in vivo.

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T1-w and T2-w images are standardized post-hoc using histogram matching algorithms to provide tissue-specific intensity information and facilitate MM analysis.

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Analysis of MM intensities demonstrate reduced myelin content in MS plaques compared to its corresponding pontine regions in CTN patients and its surrounding NAWM in MSTN patients.

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MM has the potential to distinguish changes in myelin of NAWM before MS plaques are detectable on conventional MR images.

Background

Novel magnetic resonance (MR) imaging techniques have led to the development of T1-w/T2-w ratio images or “myelin-sensitive maps (MMs)” to estimate and compare myelin content in vivo. Currently, raw image intensities in conventional MR images are unstandardized, preventing meaningful quantitative comparisons. We propose an improved workflow to standardize the MMs, which was applied to patients with classic trigeminal neuralgia (CTN) and trigeminal neuralgia secondary to multiple sclerosis (MSTN), to assess the validity and feasibility of this clinical tool.

Methods

T1-w and T2-w images were obtained for 17 CTN patients and 17 MSTN patients using a 3 T scanner. Template images were obtained from ICBM152. Multiple sclerosis (MS) plaques in the pons were labelled in MSTN patients. For each patient image, a Gaussian curve was fitted to the histogram of its intensity distribution, and transformed to match the Gaussian curve of its template image.

Results

After standardization, the structural contrast of the patient image and its histogram more closely resembled the ICBM152 template. Moreover, there was reduced variability in the histogram peaks of the gray and white matter between patients after standardization (p < 0.001). MM intensities were decreased within MS plaques, compared to normal-appearing white matter (NAWM) in MSTN patients (p < 0.001) and its corresponding regions in CTN patients (p < 0.001).

Conclusions

Images intensities are calibrated according to a mathematic relationship between the intensities of the patient image and its template. Reduced variability among histogram peaks allows for interpretation of tissue-specific intensity and facilitates quantitative analysis. The resultant MMs facilitate comparisons of myelin content between different regions of the brain and between different patients in vivo. MM analysis revealed reduced myelin content in MS plaques compared to its corresponding regions in CTN patients and its surrounding NAWM in MSTN patients. Thus, the standardized MM serves as a non-invasive, easily-automated tool that can be feasibly applied to clinical populations for quantitative analyses of myelin content.

Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners

•We present Neuroharmony, a harmonization tool for images from unseen scanners. •We developed Neuroharmony using a total of 15,026 sMRI images. •The tool was able to reduce scanner-related bias from unseen scans. •Neuroharmony represents a significant step towards imaging-based clinical tools. •Neuroharmony is available at https://github.com/garciadias/Neuroharmony .

Inter-scanner reproducibility of brain volumetry: influence of automated brain segmentation software

Background

The inter-scanner reproducibility of brain volumetry is important in multi-site neuroimaging studies, where the reliability of automated brain segmentation (ABS) tools plays an important role. This study aimed to evaluate the influence of ABS tools on the consistency and reproducibility of the quantified brain volumetry from different scanners.

Methods

We included fifteen healthy volunteers who were scanned with 3D isotropic brain T1-weighted sequence on three different 3.0 Tesla MRI scanners (GE, Siemens and Philips). For each individual, the time span between image acquisitions on different scanners was limited to 1 h. All the T1-weighted images were processed with FreeSurfer v6.0, FSL v5.0 and AccuBrain^® with default settings to obtain volumetry of brain tissues (e.g. gray matter) and substructures (e.g. basal ganglia structures) if available. Coefficient of variation (CV) was calculated to test inter-scanner variability in brain volumetry of various structures as quantified by these ABS tools.

Results

The mean inter-scanner CV values per brain structure among three MRI scanners ranged from 6.946 to 12.29% (mean, 9.577%) for FreeSurfer, 7.245 to 20.98% (mean, 12.60%) for FSL and 1.348 to 8.800% (mean value, 3.546%) for AccuBrain^®. In addition, AccuBrain^® and FreeSurfer achieved the lowest mean values of region-specific CV between GE and Siemens scanners (from 0.818 to 5.958% for AccuBrain^®, and from 0.903 to 7.977% for FreeSurfer), while FSL-FIRST had the lowest mean values of region-specific CV between GE and Philips scanners (from 2.603 to 16.310%). AccuBrain^® also had the lowest mean values of region-specific CV between Siemens and Philips scanners (from 1.138 to 6.615%).

Conclusion

There is a large discrepancy in the inter-scanner reproducibility of brain volumetry when using different processing software. Image acquisition protocols and selection of ABS tool for brain volumetry quantification have impact on the robustness of results in multi-site studies.

Whole Brain and Cranial Size Adjustments in Volumetric Brain Analyses of Sex- and Age-Related Trends

Our goal was to determine the influence of sex, age and the head/brain size on the compartmental brain volumes in the radiologically verified healthy population (96 subjects; 54 women and 42 men) from the Upper Silesia region in Poland. The MRI examinations were done using 3T Philips Achieva with the same T1-weighted and T2-weighted protocols. The image segmentation procedures were performed with SPM (Statistical Parameter Mapping) and FSL-FIRST software. The volumes of 14 subcortical structures for the left and right hemispheres and 4 overall volumes were calculated. The General Linear Models (GLM) analysis was used with and without the Total Brain Volume (TBV) and Intracranial Volume (ICV) parameters as the covariates to study the regional vs. global brain atrophy. After the ICV/TBV adjustments, the majority of sex differences in the specific volumes of interest (VOIs) revealed to be linked to the difference in the head/brain size parameters. The analysis also confirmed the significant effect of the aging process on the brain loss. After the TBV adjustment, the age- and sex-related volumetric trends for the gray and white matter volumes were observed: the negative age dependence of the gray matter volume is more pronounced in the males, while in case of the white matter the positive age-related trend in the female group is weaker. The local losses of the left caudate nucleus and the right thalamus are more advanced than the global brain atrophy. Different head-size correction strategies are not interchangeable and may yield various volumetric results, but when used together, facilitate studies on the regional dependencies inherent to a healthy, but aging, brain.

DeepHarmony: A deep learning approach to contrast harmonization across scanner changes

Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks reproducibility between protocols and scanners. It has been shown that even when care is taken to standardize acquisitions, any changes in hardware, software, or protocol design can lead to differences in quantitative results. This greatly impacts the quantitative utility of MRI in multi-site or long-term studies, where consistency is often valued over image quality. We propose a method of contrast harmonization, called DeepHarmony, which uses a U-Net-based deep learning architecture to produce images with consistent contrast. To provide training data, a small overlap cohort (n = 8) was scanned using two different protocols. Images harmonized with DeepHarmony showed significant improvement in consistency of volume quantification between scanning protocols. A longitudinal MRI dataset of patients with multiple sclerosis was also used to evaluate the effect of a protocol change on atrophy calculations in a clinical research setting. The results show that atrophy calculations were substantially and significantly affected by protocol change, whereas such changes have a less significant effect and substantially reduced overall difference when using DeepHarmony. This establishes that DeepHarmony can be used with an overlap cohort to reduce inconsistencies in segmentation caused by changes in scanner protocol, allowing for modernization of hardware and protocol design in long-term studies without invalidating previously acquired data.

Big GABA II: Water-referenced edited MR spectroscopy at 25 research sites

Accurate and reliable quantification of brain metabolites measured in vivo using ¹H magnetic resonance spectroscopy (MRS) is a topic of continued interest. Aside from differences in the basic approach to quantification, the quantification of metabolite data acquired at different sites and on different platforms poses an additional methodological challenge. In this study, spectrally edited γ-aminobutyric acid (GABA) MRS data were analyzed and GABA levels were quantified relative to an internal tissue water reference. Data from 284 volunteers scanned across 25 research sites were collected using GABA+ (GABA + co-edited macromolecules (MM)) and MM-suppressed GABA editing. The unsuppressed water signal from the volume of interest was acquired for concentration referencing. Whole-brain T₁-weighted structural images were acquired and segmented to determine gray matter, white matter and cerebrospinal fluid voxel tissue fractions. Water-referenced GABA measurements were fully corrected for tissue-dependent signal relaxation and water visibility effects. The cohort-wide coefficient of variation was 17% for the GABA + data and 29% for the MM-suppressed GABA data. The mean within-site coefficient of variation was 10% for the GABA + data and 19% for the MM-suppressed GABA data. Vendor differences contributed 53% to the total variance in the GABA + data, while the remaining variance was attributed to site- (11%) and participant-level (36%) effects. For the MM-suppressed data, 54% of the variance was attributed to site differences, while the remaining 46% was attributed to participant differences. Results from an exploratory analysis suggested that the vendor differences were related to the unsuppressed water signal acquisition. Discounting the observed vendor-specific effects, water-referenced GABA measurements exhibit similar levels of variance to creatine-referenced GABA measurements. It is concluded that quantification using internal tissue water referencing is a viable and reliable method for the quantification of in vivo GABA levels.

Design and construction of an innovative brain phantom prototype for MRI

PURPOSE

The purpose of this project was to construct a physical brain phantom for MRI, mimicking structure and T₁ relaxation properties of white matter (WM) and gray matter (GM).

METHODS

The phantom design comprised 2 compartments, 1 resembling the WM and 1 resembling the GM. Their T₁ relaxation times, as assessed using an inversion recovery turbo spin echo sequence, were reproduced using an agar gel doped with contrast agent (CA) and their folding patterns were simulated through a molding-casting procedure using 3D-printed casts and flexible silicone molds. Three versions of the assembling procedure were adopted to build: Phantom1 without any separation; Phantom2 with a varnish layer; and Phantom3 with a thin wax layer between the compartments.

RESULTS

Phantom1 was characterized by an immediate diffusion of CA between the 2 compartments. Phantom2 and Phantom3, instead, showed relaxation times and shape comparable with the target ones identified in a healthy control subject (WM: 754 ± 40 ms; GM: 1277 ± 96 ms). Moreover, both compartments revealed intact gyri and sulci. However, the diffusion of CA made Phantom2 stable only for a short period of time. Phantom3 showed stability within a time window of several days but the wax layer between the WM and GM was visible in the MRI.

CONCLUSION

Structural and intensity properties of the constructed phantoms are useful in evaluating and validating steps from image acquisition to image processing. Moreover, the described constructing procedure and its modular design make it adjustable to a variety of applications.

Whole-brain atrophy assessed by proportional- versus registration-based pipelines from 3T MRI in multiple sclerosis

BACKGROUND AND PURPOSE

Whole-brain atrophy is a standard outcome measure in multiple sclerosis (MS) clinical trials as assessed by various software tools. The effect of processing method on the validity of such data obtained from high-resolution 3T MRI is not known. We compared two commonly used methods of quantifying whole-brain atrophy.

METHODS

Three-dimensional T1-weighted and FLAIR images were obtained at 3T in MS (n = 61) and normal control (NC, n = 30) groups. Whole-brain atrophy was assessed by two automated pipelines: (a) SPM8 to derive brain parenchymal fraction (BPF, proportional-based method); (b) SIENAX to derive normalized brain parenchymal volume (BPV, registration method). We assessed agreement between BPF and BPV, as well their relationship to Expanded Disability Status Scale (EDSS) score, timed 25-foot walk (T25FW), cognition, and cerebral T2 (FLAIR) lesion volume (T2LV).

RESULTS

Brain parenchymal fraction and BPV showed only partial agreement (r = 0.73) in the MS group, and r = 0.28 in NC. Both methods showed atrophy in MS versus NC (BPF p < 0.01, BPV p < 0.05). Within MS group comparisons, BPF (p < 0.05) but not BPV (p > 0.05) correlated with EDSS score. BPV (p = 0.03) but not BPF (p = 0.08) correlated with T25FW. Both metrics correlated with T2LV (p < 0.05) and cognitive subscales. BPF (p < 0.05) but not BPV (p > 0.05) showed lower brain volume in cognitively impaired (n = 23) versus cognitively preserved (n = 38) patients. However, direct comparisons of BPF and BPV sensitivities to atrophy and clinical correlations were not statistically significant.

CONCLUSION

Whole-brain atrophy metrics may not be interchangeable between proportional- and registration-based automated pipelines from 3T MRI in patients with MS.

A semi-automated algorithm for hypothalamus volumetry in 3 Tesla magnetic resonance images

The hypothalamus, a small diencephalic gray matter structure, is part of the limbic system. Volumetric changes of this structure occur in psychiatric diseases, therefore there is increasing interest in precise volumetry. Based on our detailed volumetry algorithm for 7 Tesla magnetic resonance imaging (MRI), we developed a method for 3 Tesla MRI, adopting anatomical landmarks and work in triplanar view. We overlaid T1-weighted MR images with gray matter-tissue probability maps to combine anatomical information with tissue class segmentation. Then, we outlined regions of interest (ROIs) that covered potential hypothalamus voxels. Within these ROIs, seed growing technique helped define the hypothalamic volume using gray matter probabilities from the tissue probability maps. This yielded a semi-automated method with short processing times of 20-40 min per hypothalamus. In the MRIs of ten subjects, reliabilities were determined as intraclass correlations (ICC) and volume overlaps in percent. Three raters achieved very good intra-rater reliabilities (ICC 0.82-0.97) and good inter-rater reliabilities (ICC 0.78 and 0.82). Overlaps of intra- and inter-rater runs were very good (≥ 89.7%). We present a fast, semi-automated method for in vivo hypothalamus volumetry in 3 Tesla MRI.

Longitudinally and inter-site consistent multi-atlas based parcellation of brain anatomy using harmonized atlases

As longitudinal and multi-site studies become increasingly frequent in neuroimaging, maintaining longitudinal and inter-scanner consistency of brain parcellation has become a major challenge due to variation in scanner models and/or image acquisition protocols across scanners and sites. We present a new automated segmentation method specifically designed to achieve a consistent parcellation of anatomical brain structures in such heterogeneous datasets. Our method combines a site-specific atlas creation strategy with a state-of-the-art multi-atlas anatomical label fusion framework. Site-specific atlases are computed such that they preserve image intensity characteristics of each site's scanner and acquisition protocol, while atlas pairs share anatomical labels in a way consistent with inter-scanner acquisition variations. This harmonization of atlases improves inter-study and longitudinal consistency of segmentations in the subsequent consensus labeling step. We tested this approach on a large sample of older adults from the Baltimore Longitudinal Study of Aging (BLSA) who had longitudinal scans acquired using two scanners that vary with respect to vendor and image acquisition protocol. We compared the proposed method to standard multi-atlas segmentation for both cross-sectional and longitudinal analyses. The harmonization significantly reduced scanner-related differences in the age trends of ROI volumes, improved longitudinal consistency of segmentations, and resulted in higher across-scanner intra-class correlations, particularly in the white matter.

Reproducibility of Brain MRI Segmentation Algorithms: Empirical Comparison of Local MAP PSTAPLE, FreeSurfer, and FSL-FIRST

BACKGROUND AND PURPOSE

Segmentation of human brain structures is crucial for the volumetric quantification of brain disease. Advances in algorithmic approaches have led to automated techniques that save time compared to interactive methods. Recently, the utility and accuracy of template library fusion algorithms, such as Local MAP PSTAPLE (PSTAPLE), have been demonstrated but there is little guidance regarding its reproducibility compared to single template-based algorithms such as FreeSurfer and FSL-FIRST.

METHODS

Eight repeated magnetic resonance imagings of 20 subjects were segmented using FreeSurfer, FSL-FIRST, and PSTAPLE. We reported the reproducibility of segmentation-derived volume measurements for brain structures and calculated sample size estimates for detecting hypothetical rates of tissue atrophy given the observed variances.

RESULTS

PSTAPLE had the most reproducible volume measurements for hippocampus, putamen, thalamus, caudate, pallidum, amygdala, Accumbens area, and cortical regions. FreeSurfer was most reproducible for brainstem. PSTAPLE was the most accurate algorithm in terms of several metrics include Dice's coefficient. The sample size estimates showed that a study utilizing PSTAPLE would require tens to hundreds less subjects than the other algorithms for detecting atrophy rates typically observed in brain disease.

CONCLUSIONS

PSTAPLE is a useful tool for automatic human brain segmentation due to its precision and accuracy, which enable the detection of the size of the effect typically reported for neurological disorders with a substantially reduced sample size, in comparison to the other tools we assessed. This enables randomized controlled trials to be executed with reduced cost and duration, in turn, facilitating the assessment of new therapeutic interventions.

Patch Based Synthesis of Whole Head MR Images: Application to EPI Distortion Correction

Different magnetic resonance imaging pulse sequences are used to generate image contrasts based on physical properties of tissues, which provide different and often complementary information about them. Therefore multiple image contrasts are useful for multimodal analysis of medical images. Often, medical image processing algorithms are optimized for particular image contrasts. If a desirable contrast is unavailable, contrast synthesis (or modality synthesis) methods try to "synthesize" the unavailable constrasts from the available ones. Most of the recent image synthesis methods generate synthetic brain images, while whole head magnetic resonance (MR) images can also be useful for many applications. We propose an atlas based patch matching algorithm to synthesize T₂-w whole head (including brain, skull, eyes etc) images from T₁-w images for the purpose of distortion correction of diffusion weighted MR images. The geometric distortion in diffusion MR images due to in-homogeneous B₀ magnetic field are often corrected by non-linearly registering the corresponding b = 0 image with zero diffusion gradient to an undistorted T₂-w image. We show that our synthetic T₂-w images can be used as a template in absence of a real T₂-w image. Our patch based method requires multiple atlases with T₁ and T₂ to be registeLowRes to a given target T₁. Then for every patch on the target, multiple similar looking matching patches are found on the atlas T₁ images and corresponding patches on the atlas T₂ images are combined to generate a synthetic T₂ of the target. We experimented on image data obtained from 44 patients with traumatic brain injury (TBI), and showed that our synthesized T₂ images produce more accurate distortion correction than a state-of-the-art registration based image synthesis method.

Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling

Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here, we analyzed the % of error introduced by automating the lesion segmentation and filling processes in the tissue segmentation of 70 clinically isolated syndrome patient images. First of all, images were processed using the LST and SLS toolkits with different pipeline combinations that differed in either automated or manual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM) and white matter (WM) using SPM8, and compared with the same images where expert lesion annotations were filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error in GM and WM volume on images of MS patients, and performed similarly to the images where expert lesion annotations were masked before segmentation. In all the pipelines, the amount of misclassified lesion voxels was the main cause in the observed error in GM and WM volume. However, the % of error was significantly lower when automatically estimated lesions were filled and not masked before segmentation. These results are relevant and suggest that LST and SLS toolboxes allow the performance of accurate brain tissue volume measurements without any kind of manual intervention, which can be convenient not only in terms of time and economic costs, but also to avoid the inherent intra/inter variability between manual annotations.

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SLS and LST pipelines incorporate fully automated lesion segmentation and filling.

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Both pipelines reduced the % of error in GM and WM volume of MS patient images.

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Performance was similar to images with lesion annotations masked before segmentation.

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The amount of misclassified lesion voxels was the main factor in the % of error.

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SLS/LST can reduce time, economic costs, and the variability between manual annotations.

A voxel-based morphometry comparison of the 3.0T ADNI-1 and ADNI-2 volumetric MRI protocols

OBJECTIVES

The Alzheimer's Disease Neuroimaging Initiative 3.0 T MRI image acquisition scheme changed between the original ADNI-1 grant and the two subsequent grants (ADNI-GO and ADNI-2). The aim of the current study was to investigate the compatibility of the 3.0 T ADNI-1 and ADNI-2 T1-weighted volumes using voxel-based morphometry (VBM).

METHODS

T1-weighted images of 30 subjects were acquired using a 3T GE Signa HDx using the ADNI-1 and ADNI-2 T1-weighted volume sequences. Images were pre-processed and analysed using SPM8. Global grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were compared, as well as voxel-by-voxel differences in GM and WM.

RESULTS

Correlation coefficients and percentage differences for each tissue type between ADNI-1 and ADNI-2 were as follows: ((GM: intraclass correlation coefficient (ICC) = 0.86, ADNI-1 3.09% < ADNI-2) (WM: ICC = 0.91, ADNI-1 2.92% > ADNI-2) (CSF: ICC = 0.90, ADNI-1 1.94% > ADNI-2)). For ADNI-2, widespread increases in GM were found relative to ADNI-1 (cerebellum and pre-central gyrus), with localised decreases along the midline and temporal lobes. For ADNI-1, widespread increases in WM were found relative to ADNI-2 (cerebellum and pre-central gyrus), together with localised decreases in the temporal gyrus.

CONCLUSIONS

The widespread increase in GM and localised decrease in WM in ADNI-2 compared to ADNI-1 images suggests that the image acquisition protocols are not directly comparable using SPM-8. Total volumes of GM, WM and CSF also differed between the protocols in the following order of magnitude: GM > WM > CSF. This has implications for studies aiming to analyse images acquired using the ADNI-1 and ADNI-2 protocols under VBM.

The SIENA/FSL whole brain atrophy algorithm is no more reproducible at 3T than 1.5 T for Alzheimer's disease

The back-to-back (BTB) acquisition of MP-RAGE MRI scans of the Alzheimer׳s Disease Neuroimaging Initiative (ADNI1) provides an excellent data set with which to check the reproducibility of brain atrophy measures. As part of ADNI1, 131 subjects received BTB MP-RAGEs at multiple time points and two field strengths of 3T and 1.5 T. As a result, high quality data from 200 subject-visit-pairs was available to compare the reproducibility of brain atrophies measured with FSL/SIENA over 12 to 18 month intervals at both 3T and 1.5 T. Although several publications have reported on the differing performance of brain atrophy measures at 3T and 1.5 T, no formal comparison of reproducibility has been published to date. Another goal was to check whether tuning SIENA options, including -B, -S, -R and the fractional intensity threshold (f) had a significant impact on the reproducibility. The BTB reproducibility for SIENA was quantified by the 50th percentile of the absolute value of the difference in the percentage brain volume change (PBVC) for the BTB MP-RAGES. At both 3T and 1.5 T the SIENA option combination of "-B f=0.2", which is different from the default values of f=0.5, yielded the best reproducibility as measured by the 50th percentile yielding 0.28 (0.23-0.39)% and 0.26 (0.20-0.32)%. These results demonstrated that in general 3T had no advantage over 1.5 T for the whole brain atrophy measure - at least for SIENA. While 3T MRI is superior to 1.5 T for many types of measurements, and thus worth the additional cost, brain atrophy measurement does not seem to be one of them.

Simulation-based partial volume correction for dopaminergic PET imaging: Impact of segmentation accuracy

AIM

Partial volume correction (PVC) is an essential step for quantitative positron emission tomography (PET). In the present study, PVELab, a freely available software, is evaluated for PVC in (18)F-FDOPA brain-PET, with a special focus on the accuracy degradation introduced by various MR-based segmentation approaches.

METHODS

Four PVC algorithms (M-PVC; MG-PVC; mMG-PVC; and R-PVC) were analyzed on simulated (18)F-FDOPA brain-PET images. MR image segmentation was carried out using FSL (FMRIB Software Library) and SPM (Statistical Parametric Mapping) packages, including additional adaptation for subcortical regions (SPML). Different PVC and segmentation combinations were compared with respect to deviations in regional activity values and time-activity curves (TACs) of the occipital cortex (OCC), caudate nucleus (CN), and putamen (PUT). Additionally, the PVC impact on the determination of the influx constant (Ki) was assessed.

RESULTS

Main differences between tissue-maps returned by three segmentation algorithms were found in the subcortical region, especially at PUT. Average misclassification errors in combination with volume reduction was found to be lowest for SPML (PUT < 30%) and highest for FSL (PUT > 70%). Accurate recovery of activity data at OCC is achieved by M-PVC (apparent recovery coefficient varies between 0.99 and 1.10). The other three evaluated PVC algorithms have demonstrated to be more suitable for subcortical regions with MG-PVC and mMG-PVC being less prone to the largest tissue misclassification error simulated in this study. Except for M-PVC, quantification accuracy of Ki for CN and PUT was clearly improved by PVC.

CONCLUSIONS

The regional activity value of PUT was appreciably overcorrected by most of the PVC approaches employing FSL or SPM segmentation, revealing the importance of accurate MR image segmentation for the presented PVC framework. The selection of a PVC approach should be adapted to the anatomical structure of interest. Caution is recommended in subsequent interpretation of Ki values. The possible different change of activity concentrations due to PVC in both target and reference regions tends to alter the corresponding TACs, introducing bias to Ki determination. The accuracy of quantitative analysis was improved by PVC but at the expense of precision reduction, indicating the potential impropriety of applying the presented framework for group comparison studies.

Magnetic Resonance Image Example-Based Contrast Synthesis

The performance of image analysis algorithms applied to magnetic resonance images is strongly influenced by the pulse sequences used to acquire the images. Algorithms are typically optimized for a targeted tissue contrast obtained from a particular implementation of a pulse sequence on a specific scanner. There are many practical situations, including multi-institution trials, rapid emergency scans, and scientific use of historical data, where the images are not acquired according to an optimal protocol or the desired tissue contrast is entirely missing. This paper introduces an image restoration technique that recovers images with both the desired tissue contrast and a normalized intensity profile. This is done using patches in the acquired images and an atlas containing patches of the acquired and desired tissue contrasts. The method is an example-based approach relying on sparse reconstruction from image patches. Its performance in demonstrated using several examples, including image intensity normalization, missing tissue contrast recovery, automatic segmentation, and multimodal registration. These examples demonstrate potential practical uses and also illustrate limitations of our approach.

PATCH BASED INTENSITY NORMALIZATION OF BRAIN MR IMAGES

Magnetic resonance (MR) imaging (MRI) is widely used to study the structure of human brains. Unlike computed tomography (CT), MR image intensities do not have a tissue specific interpretation. Thus images of the same subject obtained with either the same imaging sequence on different scanners or with differing parameters have widely varying intensity scales. This inconsistency introduces errors in segmentation, and other image processing tasks, thus necessitating image intensity standardization. Compared to previous intensity normalization methods using histogram transformations-which try to find a global one-to-one intensity mapping based on histograms-we propose a patch based generative model for intensity normalization between images acquired under different scanners or different pulse sequence parameters. Our method outperforms histogram based methods when normalizing phantoms simulated with various parameters. Additionally, experiments on real data, acquired under a variety of scanners and acquisition parameters, have more consistent segmentations after our normalization.

Accuracy and reliability of automated gray matter segmentation pathways on real and simulated structural magnetic resonance images of the human brain

Automated gray matter segmentation of magnetic resonance imaging data is essential for morphometric analyses of the brain, particularly when large sample sizes are investigated. However, although detection of small structural brain differences may fundamentally depend on the method used, both accuracy and reliability of different automated segmentation algorithms have rarely been compared. Here, performance of the segmentation algorithms provided by SPM8, VBM8, FSL and FreeSurfer was quantified on simulated and real magnetic resonance imaging data. First, accuracy was assessed by comparing segmentations of twenty simulated and 18 real T1 images with corresponding ground truth images. Second, reliability was determined in ten T1 images from the same subject and in ten T1 images of different subjects scanned twice. Third, the impact of preprocessing steps on segmentation accuracy was investigated. VBM8 showed a very high accuracy and a very high reliability. FSL achieved the highest accuracy but demonstrated poor reliability and FreeSurfer showed the lowest accuracy, but high reliability. An universally valid recommendation on how to implement morphometric analyses is not warranted due to the vast number of scanning and analysis parameters. However, our analysis suggests that researchers can optimize their individual processing procedures with respect to final segmentation quality and exemplifies adequate performance criteria.

Aerobic fitness relates to learning on a virtual Morris Water Task and hippocampal volume in adolescents

In rodents, exercise increases hippocampal neurogenesis and allows for better learning and memory performance on water maze tasks. While exercise has also been shown to be beneficial for the brain and behavior in humans, no study has examined how exercise impacts spatial learning using a directly translational water maze task, or if these relationships exist during adolescence--a developmental period which the animal literature has shown to be especially vulnerable to exercise effects. In this study, we investigated the influence of aerobic fitness on hippocampal size and subsequent learning and memory, including visuospatial memory using a human analogue of the Morris Water Task, in 34 adolescents. Results showed that higher aerobic fitness predicted better learning on the virtual Morris Water Task and larger hippocampal volumes. No relationship between virtual Morris Water Task memory recall and aerobic fitness was detected. Aerobic fitness, however, did not relate to global brain volume or verbal learning, which might suggest some specificity of the influence of aerobic fitness on the adolescent brain. This study provides a direct translational approach to the existing animal literature on exercise, as well as adds to the sparse research that exists on how aerobic exercise impacts the developing human brain and memory.

Consistent segmentation using a Rician classifier

Several popular classification algorithms used to segment magnetic resonance brain images assume that the image intensities, or log-transformed intensities, satisfy a finite Gaussian mixture model. In these methods, the parameters of the mixture model are estimated and the posterior probabilities for each tissue class are used directly as soft segmentations or combined to form a hard segmentation. It is suggested and shown in this paper that a Rician mixture model fits the observed data better than a Gaussian model. Accordingly, a Rician mixture model is formulated and used within an expectation maximization (EM) framework to yield a new tissue classification algorithm called Rician Classifier using EM (RiCE). It is shown using both simulated and real data that RiCE yields comparable or better performance to that of algorithms based on the finite Gaussian mixture model. As well, we show that RiCE yields more consistent segmentation results when used on images of the same individual acquired with different T1-weighted pulse sequences. Therefore, RiCE has the potential to stabilize segmentation results in brain studies involving heterogeneous acquisition sources as is typically found in both multi-center and longitudinal studies.

A compressed sensing approach for MR tissue contrast synthesis

The tissue contrast of a magnetic resonance (MR) neuroimaging data set has a major impact on image analysis tasks like registration and segmentation. It has been one of the core challenges of medical imaging to guarantee the consistency of these tasks regardless of the contrasts of the MR data. Inconsistencies in image analysis are attributable in part to variations in tissue contrast, which in turn arise from operator variations during image acquisition as well as software and hardware differences in the MR scanners. It is also a common problem that images with a desired tissue contrast are completely missing in a given data set for reasons of cost, acquisition time, forgetfulness, or patient comfort. Absence of this data can hamper the detailed, automatic analysis of some or all data sets in a scientific study. A method to synthesize missing MR tissue contrasts from available acquired images using an atlas containing the desired contrast and a patch-based compressed sensing strategy is described. An important application of this general approach is to synthesize a particular tissue contrast from multiple studies using a single atlas, thereby normalizing all data sets into a common intensity space. Experiments on real data, obtained using different scanners and pulse sequences, show improvement in segmentation consistency, which could be extremely valuable in the pooling of multi-site multi-scanner neuroimaging studies.

Longitudinal gray matter changes in multiple sclerosis--differential scanner and overall disease-related effects

Voxel-based morphometry (VBM) has been used repeatedly in single-center studies to investigate regional gray matter (GM) atrophy in multiple sclerosis (MS). In multi-center trials, across-scanner variations might interfere with the detection of disease-specific structural abnormalities, thereby potentially limiting the use of VBM. Here we evaluated longitudinally inter-site differences and inter-site comparability of regional GM in MS using VBM. Baseline and follow up 3D T1-weighted magnetic resonance imaging (MRI) data of 248 relapsing-remitting (RR) MS patients, recruited in two clinical centers, (center1/2: n = 129/119; mean age 42.6 ± 10.7/43.3 ± 9.3; male:female 33:96/44:75; median disease duration 150 [72-222]/116 [60-156]) were acquired on two different 1.5T MR scanners. GM volume changes between baseline and year 2 while controlling for age, gender, disease duration, and global GM volume were analyzed. The main effect of time on regional GM volume was larger in data of center two as compared to center one in most of the brain regions. Differential effects of GM volume reductions occurred in a number of GM regions of both hemispheres, in particular in the fronto-temporal and limbic cortex (cluster P corrected <0.05). Overall disease-related effects were found bilaterally in the cerebellum, uncus, inferior orbital gyrus, paracentral lobule, precuneus, inferior parietal lobule, and medial frontal gyrus (cluster P corrected <0.05). The differential effects were smaller as compared to the overall effects in these regions. These results suggest that the effects of different scanners on longitudinal GM volume differences were rather small and thus allow pooling of MR data and subsequent combined image analysis.

Effects of gradient non-linearity correction and intensity non-uniformity correction in longitudinal studies using structural image evaluation using normalization of atrophy (SIENA)

PURPOSE

To evaluate the effects of gradient nonlinearity correction and intensity nonuniformity correction on longitudinal (two-year) changes in global and regional brain volumes.

MATERIALS AND METHODS

A total of 208 subjects (70 females and 138 males, age range = 38.1-83.0 years) were included in this study. Each subject was scanned twice, at an interval of approximately two years (range = 1.5-2.3 years). Three-dimensional fast spoiled-gradient recalled acquisition in the steady state (3D-FSPGR) images corrected for gradient nonlinearity and/or intensity nonuniformity were compared with uncorrected 3D-FSPGR images with use of structural image evaluation using normalization of atrophy 2.6 (SIENA).

RESULTS

The mean absolute deviations of percentage brain volume change (PBVC) values in the gradient nonlinearity +/- intensity nonuniformity corrected images were significantly less than that in the uncorrected images, and the difference in the mean absolute deviation of PBVC was the most significant between the uncorrected images and the images corrected for both gradient nonlinearity and intensity nonuniformity. Voxel-wise comparisons showed large significant differences between the uncorrected images and the corrected images.

CONCLUSION

Correction for gradient nonlinearity and intensity nonuniformity reduces the variance of measured longitudinal changes in brain volumes and will improve accuracy for detecting subtle brain changes.

Mapping reliability in multicenter MRI: voxel-based morphometry and cortical thickness

Multicenter structural MRI studies can have greater statistical power than single-center studies. However, across-center differences in contrast sensitivity, spatial uniformity, etc., may lead to tissue classification or image registration differences that could reduce or wholly offset the enhanced statistical power of multicenter data. Prior work has validated volumetric multicenter MRI, but robust methods for assessing reliability and power of multisite analyses with voxel-based morphometry (VBM) and cortical thickness measurement (CORT) are not yet available. We developed quantitative methods to investigate the reproducibility of VBM and CORT to detect group differences and estimate heritability when MRI scans from different scanners running different acquisition protocols in a multicenter setup are included. The method produces brain maps displaying information such as lowest detectable effect size (or heritability) and effective number of subjects in the multicenter study. We applied the method to a five-site multicenter calibration study using scanners from four different manufacturers, running different acquisition protocols. The reliability maps showed an overall good comparability between the sites, providing a reasonable gain in sensitivity in most parts of the brain. In large parts of the cerebrum and cortex scan pooling improved heritability estimates, with "effective-N" values upto the theoretical maximum. For some areas, "optimal-pool" maps indicated that leaving out a site would give better results. The reliability maps also reveal which brain regions are in any case difficult to measure reliably (e.g., around the thalamus). These tools will facilitate the design and analysis of multisite VBM and CORT studies for detecting group differences and estimating heritability.

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.

A robust method to estimate the intracranial volume across MRI field strengths (1.5T and 3T)

As population-based studies may obtain images from scanners with different field strengths, a method to normalize regional brain volumes according to intracranial volume (ICV) independent of field strength is needed. We found systematic differences in ICV estimation, tested in a cohort of healthy subjects (n = 5) that had been imaged using 1.5T and 3T scanners, and confirmed in two independent cohorts. This was related to systematic differences in the intensity of cerebrospinal fluid (CSF), with higher intensities for CSF located in the ventricles compared with CSF in the cisterns, at 3T versus 1.5T, which could not be removed with three different applied bias correction algorithms. We developed a method based on tissue probability maps in MNI (Montreal Neurological Institute) space and reverse normalization (reverse brain mask, RBM) and validated it against manual ICV measurements. We also compared it with alternative automated ICV estimation methods based on Statistical Parametric Mapping (SPM5) and Brain Extraction Tool (FSL). The proposed RBM method was equivalent to manual ICV normalization with a high intraclass correlation coefficient (ICC = 0.99) and reliable across different field strengths. RBM achieved the best combination of precision and reliability in a group of healthy subjects, a group of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) and can be used as a common normalization framework.

Intra- and interscanner variability of automated voxel-based volumetry based on a 3D probabilistic atlas of human cerebral structures

The intra- and inter-scanner variability of an automated method for MRI-based volumetry was investigated. Using SPM5 algorithms and predefined masks derived from a probabilistic whole-brain atlas, this method allows to determine the volumes of various brain structures (e.g., hemispheres, lobes, cerebellum, basal ganglia, grey and white matter etc.) in single subjects in an observer-independent fashion. A healthy volunteer was scanned three times at six different MRI scanners (including different vendors and field strengths) to calculate intra- and inter-scanner volumetric coefficients of variation (CV). The mean intra-scanner CV values per brain structure ranged from 0.50% to 4.4% (median, 0.89%), while the inter-scanner CV results varied between 0.66% and 14.7% (median, 4.74%). The overall (=combined intra- and inter-scanner) variability of measurements was only marginally higher, with CV results of 0.87-15.1% (median, 4.80%). Furthermore, the minimum percentage volume difference for detecting a significant volume change between two volume measurements in the same subject was calculated for each substructure. For example, for the total brain volume, mean intra-scanner, inter-scanner, and overall CV results were 0.50%, 3.78%, and 3.80%, respectively, and the cut-offs for significant volume changes between two measurements in the same subject amounted to 1.4% for measurements on the same scanner and 10.5% on different scanners. These findings may be useful for planning and assessing volumetric studies in neurological diseases, for the differentiation of certain patterns of atrophy, or for longitudinal studies monitoring the course of a disease and potential therapeutic effects.

Evaluation of automated brain MR image segmentation and volumetry methods

We compare three widely used brain volumetry methods available in the software packages FSL, SPM5, and FreeSurfer and evaluate their performance using simulated and real MR brain data sets. We analyze the accuracy of gray and white matter volume measurements and their robustness against changes of image quality using the BrainWeb MRI database. These images are based on "gold-standard" reference brain templates. This allows us to assess between- (same data set, different method) and also within-segmenter (same method, variation of image quality) comparability, for both of which we find pronounced variations in segmentation results for gray and white matter volumes. The calculated volumes deviate up to >10% from the reference values for gray and white matter depending on method and image quality. Sensitivity is best for SPM5, volumetric accuracy for gray and white matter was similar in SPM5 and FSL and better than in FreeSurfer. FSL showed the highest stability for white (<5%), FreeSurfer (6.2%) for gray matter for constant image quality BrainWeb data. Between-segmenter comparisons show discrepancies of up to >20% for the simulated data and 24% on average for the real data sets, whereas within-method performance analysis uncovered volume differences of up to >15%. Since the discrepancies between results reach the same order of magnitude as volume changes observed in disease, these effects limit the usability of the segmentation methods for following volume changes in individual patients over time and should be taken into account during the planning and analysis of brain volume studies.

Size matters: cerebral volume influences sex differences in neuroanatomy

Biological and behavioral differences between the sexes range from obvious to subtle or nonexistent. Neuroanatomical differences are particularly controversial, perhaps due to the implication that they might account for behavioral differences. In this sample of 200 men and women, large effect sizes (Cohen's d > 0.8) were found for sex differences in total cerebral gray and white matter, cerebellum, and gray matter proportion (women had a higher proportion of gray matter). The only one of these sex differences that survived adjustment for the effect of cerebral volume was gray matter proportion. Individual differences in cerebral volume accounted for 21% of the difference in gray matter proportion, while sex accounted for an additional 4%. The relative size of the corpus callosum was 5% larger in women, but this difference was completely explained by a negative relationship between relative callosal size and cerebral volume. In agreement with Jancke et al., individuals with higher cerebral volume tended to have smaller corpora callosa. There were few sex differences in the size of structures in Broca's and Wernicke's area. We conclude that individual differences in brain volume, in both men and women, account for apparent sex differences in relative size.

Global average gray and white matter N-acetylaspartate concentration in the human brain

Since the amino acid derivative N-acetylaspartate (NAA) is almost exclusive to neuronal cells in the adult mammalian brain and its concentration has shown local (or global) abnormalities in most focal (or diffuse) neurological diseases, it is considered a specific neuronal marker. Yet despite its biological and clinical prominence, the relative NAA concentration in the gray and white matter (GM, WM) remains controversial, with each reported to be higher than, equal to, or less than the other. To help resolve the controversy and importantly, access the NAA in both compartments in their entirety, we introduce a new approach to distinguish and quantify the whole-brain average GM and WM NAA concentration by integrating MR-image segmentation, localized and non-localized quantitative (1)H-MRS. We demonstrate and validate the method in ten healthy volunteers (5 women) 27+/-6 years old (mean+/-standard-deviation) at 1.5T. The results show that the healthy adult human brain comprises significantly less WM, 39+/-3%, than GM 60+/-4% by volume (p<0.01). Furthermore, the average NAA concentration in the WM, 9.5+/-1.0 mM, is significantly lower than in GM, 14.3+/-1.1 mM (p<0.01).

FUZZY C-MEANS WITH VARIABLE COMPACTNESS

Fuzzy c-means (FCM) clustering has been extensively studied and widely applied in the tissue classification of biomedical images. Previous enhancements to FCM have accounted for intensity shading, membership smoothness, and variable cluster sizes. In this paper, we introduce a new parameter called "compactness" which captures additional information of the underlying clusters. We then propose a new classification algorithm, FCM with variable compactness (FCMVC), to classify three major tissues in brain MRIs by incorporating the compactness terms into a previously reported improvement to FCM. Experiments on both simulated phantoms and real magnetic resonance brain images show that the new method improves the repeatability of the tissue classification for the same subject with different acquisition protocols.

A geometric method for automatic extraction of sulcal fundi

Sulcal fundi are 3-D curves that lie in the depths of the cerebral cortex and, in addition to their intrinsic value in brain research, are often used as landmarks for downstream computations in brain imaging. In this paper, we present a geometric algorithm that automatically extracts the sulcal fundi from magnetic resonance images and represents them as spline curves lying on the extracted triangular mesh representing the cortical surface. The input to our algorithm is a triangular mesh representation of an extracted cortical surface as computed by one of several available software packages for performing automated and semi-automated cortical surface extraction. Given this input we first compute a geometric depth measure for each triangle on the cortical surface mesh, and based on this information we extract sulcal regions by checking for connected regions exceeding a depth threshold. We then identify endpoints of each region and delineate the fundus by thinning the connected region while keeping the endpoints fixed. The curves, thus, defined are regularized using weighted splines on the surface mesh to yield high-quality representations of the sulcal fundi. We present the geometric framework and validate it with real data from human brains. Comparisons with expert-labeled sulcal fundi are part of this validation process.

Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer's disease

We describe a new algorithm for the automated segmentation of the hippocampus (Hc) and the amygdala (Am) in clinical Magnetic Resonance Imaging (MRI) scans. Based on homotopically deforming regions, our iterative approach allows the simultaneous extraction of both structures, by means of dual competitive growth. One of the most original features of our approach is the deformation constraint based on prior knowledge of anatomical features that are automatically retrieved from the MRI data. The only manual intervention consists of the definition of a bounding box and positioning of two seeds; total execution time for the two structures is between 5 and 7 min including initialisation. The method is evaluated on 16 young healthy subjects and 8 patients with Alzheimer's disease (AD) for whom the atrophy ranged from limited to severe. Three aspects of the performances are characterised for validating the method: accuracy (automated vs. manual segmentations), reproducibility of the automated segmentation and reproducibility of the manual segmentation. For 16 young healthy subjects, accuracy is characterised by mean relative volume error/overlap/maximal boundary distance of 7%/84%/4.5 mm for Hc and 12%/81%/3.9 mm for Am; for 8 Alzheimer's disease patients, it is 9%/84%/6.5 mm for Hc and 15%/76%/4.5 mm for Am. We conclude that the performance of this new approach in data from healthy and diseased subjects in terms of segmentation quality, reproducibility and time efficiency compares favourably with that of previously published manual and automated segmentation methods. The proposed approach provides a new framework for further developments in quantitative analyses of the pathological hippocampus and amygdala in MRI scans.

Automatic segmentation of left and right cerebral hemispheres from MRI brain volumes using the graph cuts algorithm

An automated algorithm has been developed to segment stripped (non-brain tissue excluded) T1-weighted MRI brain volumes into left and right cerebral hemispheres and cerebellum+brainstem. The algorithm, which uses the Graph Cuts technique, performs a fully automated segmentation in approximately 30 s following pre-processing. It is robust and accurate and has been tested on datasets from two scanners using different field strengths and pulse sequences. We describe the Graph Cuts algorithm and compare the results of Graph Cuts segmentations against "gold standard" manual segmentations and segmentations produced by three popular software packages used by neuroimagers: BrainVisa, CLASP, and SurfRelax.

Age-related changes in prefrontal white matter volume across adolescence

Past research has suggested that white matter volume increases from childhood to adulthood; however, during adolescence, there is somewhat limited data to support this finding. In the present study, 65 typically developing adolescents underwent structural magnetic resonance imaging. Using magnetic resonance imaging, prefrontal white matter volumes were examined in relation to adolescent age and sex. Surprisingly, results suggested that prefrontal white matter volume decreased during late adolescence, particularly among the female sex. These findings are inconsistent with past research and suggest that perhaps some developmental processes in late adolescence are not yet fully explained. Possible methodological contributions and implications for the current findings are discussed.