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• 1.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Functional Pharmacology.
Impaired Insulin Sensitivity as Indexed by the HOMA Score Is Associated With Deficits in Verbal Fluency and Temporal Lobe Gray Matter Volume in the Elderly2012In: Diabetes Care, ISSN 0149-5992, E-ISSN 1935-5548, Vol. 35, no 3, p. 488-494Article in journal (Refereed)

OBJECTIVE

Impaired insulin sensitivity is linked to cognitive deficits and reduced brain size. However, it is not yet known whether insulin sensitivity involves regional changes in gray matter volume. Against this background, we examined the association between insulin sensitivity, cognitive performance, and regional gray matter volume in 285 cognitively healthy elderly men and women aged 75 years from the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study.

RESEARCH DESIGN AND METHODS

Insulin sensitivity was calculated from fasting serum insulin and plasma glucose determinations using the homeostasis model assessment of insulin resistance (HOMA-IR) method. Cognitive performance was examined by a categorical verbal fluency. Participants also underwent a magnetic resonance imaging (MRI) brain scan. Multivariate analysis using linear regression was conducted, controlling for potential confounders (sex, education, serum LDL cholesterol, mean arterial blood pressure, and abdominal visceral fat volume).

RESULTS

The HOMA-IR was negatively correlated with verbal fluency performance, brain size (S1), and temporal lobe gray matter volume in regions known to be involved in speech production (Brodmann areas 21 and 22, respectively). No such effects were observed when examining diabetic (n = 55) and cognitively impaired (n = 27) elderly subjects as separate analyses.

CONCLUSIONS

These cross-sectional findings suggest that both pharmacologic and lifestyle interventions improving insulin signaling may promote brain health in late life but must be confirmed in patient studies.

• 2.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Functional Pharmacology.
Association between physical activity and brain health in older adults2013In: Neurobiology of Aging, ISSN 0197-4580, E-ISSN 1558-1497, Vol. 34, no 1, p. 83-90Article in journal (Refereed)

In the present cross-sectional study, we examined physical activity (PA) and its possible association with cognitive skills and brain structure in 331 cognitively healthy elderly. Based on the number of self-reported light and hard activities for at least 30 minutes per week, participants were assigned to 4 groups representing different levels of PA. The cognitive skills were assessed by the Mini Mental State Examination score, a verbal fluency task, and the Trail-making test as a measure of visuospatial orientation ability. Participants also underwent a magnetic resonance imaging of the brain. Multiple regression analysis revealed that greater PA was associated with a shorter time to complete the Trail-making test, and higher levels of verbal fluency. Further, the level of self-reported PA was positively correlated with brain volume, white matter, as well as a parietal lobe gray matter volume, situated bilaterally at the precuneus. These present cross-sectional results indicate that PA is a lifestyle factor that is linked to brain structure and function in late life.

• 3.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Functional Pharmacology.
Late-life obesity is associated with smaller global and regional gray matter volumes: a voxel-based morphometric study2013In: International Journal of Obesity, ISSN 0307-0565, E-ISSN 1476-5497, Vol. 37, no 2, p. 230-236Article in journal (Refereed)

OBJECTIVE:

Obesity adversely affects frontal lobe brain structure and function. Here we sought to show that people who are obese versus those who are of normal weight over a 5-year period have differential global and regional brain volumes.

DESIGN:

Using voxel-based morphometry, contrasts were done between those who were recorded as being either obese or of normal weight over two time points in the 5 years prior to the brain scan. In a post-hoc preliminary analysis, we compared scores for obese and normal weight people who completed the trail-making task.

SUBJECTS:

A total of 292 subjects were examined following exclusions (for example, owing to dementia, stroke and cortical infarcts) from the Prospective Investigation of the Vasculature in Uppsala Seniors cohort with a body mass index of normal weight (<25 kg m−2) or obese (30 kg m−2).

RESULTS:

People who were obese had significantly smaller total brain volumes and specifically, significantly reduced total gray matter (GM) volume (GMV) (with no difference in white matter or cerebrospinal fluid). Initial exploratory whole brain uncorrected analysis revealed that people who were obese had significantly smaller GMV in the bilateral supplementary motor area, bilateral dorsolateral prefrontal cortex (DLPFC), left inferior frontal gyrus and left postcentral gyrus. Secondary more stringent corrected analyses revealed a surviving cluster of GMV difference in the left DLPFC. Finally, post-hoc contrasts of scores on the trail-making task, which is linked to DLPFC function, revealed that obese people were significantly slower than those of normal weight.

CONCLUSION:

These findings suggest that in comparison with normal weight, people who are obese have smaller GMV, particularly in the left DLPFC. Our results may provide evidence for a potential working memory mechanism for the cognitive suppression of appetite that may lower the risk of developing obesity in later life.

• 4.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
SmartPaint: a tool for interactive segmentation of medical volume images2017In: Computer Methods In Biomechanics And Biomedical Engeineering-Imaging And Visualization, ISSN 2168-1163, Vol. 5, no 1, p. 36-44Article in journal (Refereed)

We present SmartPaint, a general-purpose method and software for interactive segmentation of medical volume images. SmartPaint uses a novel paint-brush interaction paradigm, where the user segments objects in the image by 'sweeping' over them with the mouse cursor. The key feature of SmartPaint is that the painting tools adapt to the image content, selectively sticking to objects of interest while avoiding other structures. This behaviour is achieved by modulating the effect of the tools by both the Euclidean distance and the range distance (difference in image intensity values) from the mouse cursor. We evaluate SmartPaint on three publicly available medical image datasets, covering different image modalities and segmentation targets. The results show that, with a limited user effort, SmartPaint can produce segmentations whose accuracy is comparable to both the state-of-the-art automatic segmentation methods and manual delineations produced by expert users. The SmartPaint software is freely available, and can be downloaded from the authors' web page (http://www.cb.uu.se/similar to filip/SmartPaint/).

• 5.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
Smart Paint: A New Interactive Segmentation Method\\ Applied to MR Prostate Segmentation2012In: Prostate MR Image Segmentation Grand Challenge (PROMISE'12), a MICCAI 2012 workshop, 2012Conference paper (Refereed)

This paper describes a general method for interactive segmentation, Smart Paint. The user interaction is inspired by the way an airbrush is used, objects are segmented by "sweeping" with the mouse cursor in the image. The user adds or removes details in 3D by the proposed segmentation tool and the user interface shows the segmentation result in 2D slices through the object. We use the novel method for prostate segmentation in transversal T2-weighted MR images from multiple centers and vendors and with differences in scanning protocol.

The method was evaluated on the training set obtained from http://promise12.grand-challenge.org. In the first round, all 50 volumes were segmented and the mean of Dice's coefficient was 0.82 with standard deviation 0.09. In a second round, the first 30 volumes were re-segmented by the same user and the result was slightly improved -- Dice's coefficient 0.86 $\pm$ 0.05 was obtained. For the training data, the mean time to segment a volume was 3 minutes and 30 seconds.

The proposed method is a generic tool for interactive image segmentation and this paper illustrates that it is well-suited for prostate segmentation.

• 6.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
Seeded Segmentation Based on Object Homogeneity2012In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR), 2012, p. 21-24Conference paper (Refereed)

Seeded segmentation methods attempt to solve the segmentation problem in the presence of prior knowledge in the form of a partial segmentation, where a small subset of the image elements (seed-points) have been assigned correct segmentation labels. Common for most of the leading methods in this area is that they seek to find a segmentation where the boundaries of the segmented regions coincide with sharp edges in the image. Here, we instead propose a method for seeded segmentation that seeks to divide the image into areas of homogeneous pixel values. The method is based on the computation of minimal cost paths in a discrete representation of the image, using a novel path-cost function. The utility of the proposed method is demonstrated in a case study on segmentation of white matter hyperintensitities in MR images of the human brain.

• 7.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
Analysis of Human Brain MRI: Contributions to Regional Volume Studies2014Doctoral thesis, comprehensive summary (Other academic)

Many disorders are associated with regional brain volumes. The analysis of these volumes from MR images often requires sequential processing steps such as localization and delineation. It is common to perform volumetric normalization using intracranial volume (ICV, the total volume inside the cranial cavity) when comparing regional brain volumes, since head size varies considerably between individuals. Multiple methods for estimating ICV and procedures for volume normalization exist.

A method for interhemispheric surface localization and extraction, using both intensity and symmetry information and without time consuming pre-processing, was developed. Evaluations of hemisphere division accuracy as well as suitability as a pre-processing step for interhemispheric structure localization were made. The performance of the method was comparable to that of methods focusing on either of these tasks, making it suited for use in many different studies.

Automated ICV estimations from Freesurfer and SPM were evaluated using 399 reference segmentations. Both methods overestimated ICV and estimations using Freesurfer contained errors associated with skull-size. Estimations from SPM contained errors associated with gender and atrophy. An experiment showed that the choice of method can affect study results.

Manual ICV estimation is very time consuming, but can be performed using only a subset of voxels in an image to increase speed and decrease manual labor. Segmenting every nth slice and stereology were evaluated in terms of required manual labor and estimation error, using the previously created ICV references. An illustration showing how much manual labor is required for a given estimation error using different combinations of n and stereology grid spacing was presented.

Finally, different procedures for ICV normalization of regional brain volumes when investigating gender related volume differences were theoretically explained and evaluated using both simulated and real data. Resulting volume differences were seen to depend on the procedure used. A suggested workflow for procedure selection was presented.

Methodological contributions that can aid the analysis of the human brain have been presented. The performed studies also contribute to the understanding of important methodological considerations for regional brain volume analysis.

1. Automated interhemispheric surface extraction in T1-weighted MRI using intensity and symmetry information
Open this publication in new window or tab >>Automated interhemispheric surface extraction in T1-weighted MRI using intensity and symmetry information
2014 (English)In: Journal of Neuroscience Methods, ISSN 0165-0270, E-ISSN 1872-678X, Vol. 222, p. 97-105Article in journal (Refereed) Published
##### Abstract [en]

Background: Localizing the human interhemispheric region is of interest in image analysis mainly because it can be used for hemisphere separation and as a preprocessing step for interhemispheric structure localization. Many existing methods focus on only one of these applications. New method: Here a new Intensity and Symmetry based Interhemispheric Surface extraction method (ISIS) that enables both applications is presented. A combination of voxel intensity and local symmetry is used to optimize a surface from T1-weighted MRI. Results: ISIS was evaluated in regard to cerebral hemisphere separation using manual segmentations. It was also evaluated in regard to being a preprocessing step for interhemispheric structure localization using manually placed landmarks. Comparison with existing methods: Results were compared to cerebral hemisphere separations by Brain-Visa and Freesurfer as well as to a midsagittal plane (MSP) extraction method. ISIS had less misclassified voxels than BrainVisa (ISIS: 0.119+/-0.114%, BrainVisa: 0.138+/-0.084%, p=0.020). Freesurfer had less misclassified voxels than ISIS for one dataset (ISIS: 0.063+/-0.056%, Freesurfer: 0.049+/-0.044%, p=0.019), but failed to produce usable results for another. Total voxel distance from all manual landmarks did not differ significantly between ISIS and the MSP method (ISIS: 4.00+/-1.88, MSP: 4.47+/-4.97). Conclusions: ISIS was found successful in both cerebral hemisphere separation and as a preprocessing step for interhemispheric structure localization. It needs no time consuming preprocessing and extracts the interhemispheric surface in less than 30 s.

##### Keywords
Interhemispheric surface, Graph cut, Hemisphere separation, Midsagittal plane, Anterior commissure, Posterior commissure
##### National Category
Medical and Health Sciences
##### Identifiers
urn:nbn:se:uu:diva-221919 (URN)10.1016/j.jneumeth.2013.11.007 (DOI)000331672000012 ()
Available from: 2014-04-07 Created: 2014-04-07 Last updated: 2017-12-05Bibliographically approved
2. Intracranial volume estimated with commonly used methods could introduce bias in studies including brain volume measurements
Open this publication in new window or tab >>Intracranial volume estimated with commonly used methods could introduce bias in studies including brain volume measurements
2013 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 83, p. 355-360Article in journal (Refereed) Published
##### Abstract [en]

In brain volumetric studies, intracranial volume (ICV) is often used as an estimate of pre-morbid brain size as well as to compensate for inter-subject variations in head size. However, if the estimated ICV is biased by for example gender or atrophy, it could introduce errors in study results. To evaluate how two commonly used methods for ICV estimation perform, computer assisted reference segmentations were created and evaluated. Segmentations were created for 399 MRI volumes from 75-year-old subjects, with 53 of these subjects having an additional scan and segmentation created at age 80. ICV estimates from Statistical Parametric Mapping (SPM, version 8) and Freesurfer (FS, version 5.1.0) were compared to the reference segmentations, and bias related to skull size (approximated with the segmentation measure), gender or atrophy were tested for. The possible ICV related effect on associations between normalized hippocampal volume and factors gender, education and cognition was evaluated by normalizing hippocampal volume with different ICV measures. Excellent agreement was seen for inter- (r=0.999) and intra- (r=0.999) operator reference segmentations. Both SPM and FS overestimated ICV. SPM showed bias associated with gender and atrophy while FS showed bias dependent on skull size. All methods showed good correlation between time points in the longitudinal data (reference: 0.998, SPM: 0.962, FS: 0.995). Hippocampal volume showed different associations with cognition and gender depending on which ICV measure was used for hippocampal volume normalization. These results show that the choice of method used for ICV estimation can bias results in studies including brain volume measurements.

##### National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
##### Identifiers
urn:nbn:se:uu:diva-207611 (URN)10.1016/j.neuroimage.2013.06.068 (DOI)000326953700032 ()23827332 (PubMedID)
Available from: 2013-09-17 Created: 2013-09-17 Last updated: 2017-12-06Bibliographically approved
3. Manual intracranial volume estimation from MRI: labor reduction and estimation error
Open this publication in new window or tab >>Manual intracranial volume estimation from MRI: labor reduction and estimation error
##### National Category
Medical and Health Sciences
##### Identifiers
urn:nbn:se:uu:diva-222374 (URN)
Available from: 2014-04-10 Created: 2014-04-10 Last updated: 2014-06-30
4. Intracranial volume normalization methods: considerations when investigating gender differences in regional brain volume
Open this publication in new window or tab >>Intracranial volume normalization methods: considerations when investigating gender differences in regional brain volume
##### National Category
Medical and Health Sciences
##### Identifiers
urn:nbn:se:uu:diva-222375 (URN)
Available from: 2014-04-10 Created: 2014-04-10 Last updated: 2017-02-21
• 8.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
Automated interhemispheric surface extraction in T1-weighted MRI using intensity and symmetry information2014In: Journal of Neuroscience Methods, ISSN 0165-0270, E-ISSN 1872-678X, Vol. 222, p. 97-105Article in journal (Refereed)

Background: Localizing the human interhemispheric region is of interest in image analysis mainly because it can be used for hemisphere separation and as a preprocessing step for interhemispheric structure localization. Many existing methods focus on only one of these applications. New method: Here a new Intensity and Symmetry based Interhemispheric Surface extraction method (ISIS) that enables both applications is presented. A combination of voxel intensity and local symmetry is used to optimize a surface from T1-weighted MRI. Results: ISIS was evaluated in regard to cerebral hemisphere separation using manual segmentations. It was also evaluated in regard to being a preprocessing step for interhemispheric structure localization using manually placed landmarks. Comparison with existing methods: Results were compared to cerebral hemisphere separations by Brain-Visa and Freesurfer as well as to a midsagittal plane (MSP) extraction method. ISIS had less misclassified voxels than BrainVisa (ISIS: 0.119+/-0.114%, BrainVisa: 0.138+/-0.084%, p=0.020). Freesurfer had less misclassified voxels than ISIS for one dataset (ISIS: 0.063+/-0.056%, Freesurfer: 0.049+/-0.044%, p=0.019), but failed to produce usable results for another. Total voxel distance from all manual landmarks did not differ significantly between ISIS and the MSP method (ISIS: 4.00+/-1.88, MSP: 4.47+/-4.97). Conclusions: ISIS was found successful in both cerebral hemisphere separation and as a preprocessing step for interhemispheric structure localization. It needs no time consuming preprocessing and extracts the interhemispheric surface in less than 30 s.

• 9.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
Intracranial volume normalization methods: Considerations when investigating gender differences in regional brain volume2015In: Psychiatry Research, ISSN 0165-1781, E-ISSN 1872-7123, Vol. 231, no 3, p. 227-235Article in journal (Refereed)

Intracranial volume (ICV) normalization of regional brain volumes (v) is common practice in volumetric studies of the aging brain. Multiple normalization methods exist and this study aimed to investigate when each method is appropriate to use in gender dimorphism studies and how differences in v are affected by the choice of method. A new method based on weighted ICV matching is also presented. Theoretical reasoning and simulated experiments were followed by an evaluation using real data comprising 400 subjects, all 75 years old, whose ICV was segmented with a gold standard method. The presented method allows good visualization of volume relation between gender groups. A different gender dimorphism in volume was found depending on the normalization method used for both simulated and real data. Method performance was also seen to depend on the slope (B) and intercept (m) from the linear relation between v and ICV (v=B·ICV+m) as well as gender distribution in the cohort. A suggested work-flow for selecting ICV normalization method when investigating gender related differences in regional brain volume is presented.

• 10.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
Intracranial volume estimated with commonly used methods could introduce bias in studies including brain volume measurements2013In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 83, p. 355-360Article in journal (Refereed)

In brain volumetric studies, intracranial volume (ICV) is often used as an estimate of pre-morbid brain size as well as to compensate for inter-subject variations in head size. However, if the estimated ICV is biased by for example gender or atrophy, it could introduce errors in study results. To evaluate how two commonly used methods for ICV estimation perform, computer assisted reference segmentations were created and evaluated. Segmentations were created for 399 MRI volumes from 75-year-old subjects, with 53 of these subjects having an additional scan and segmentation created at age 80. ICV estimates from Statistical Parametric Mapping (SPM, version 8) and Freesurfer (FS, version 5.1.0) were compared to the reference segmentations, and bias related to skull size (approximated with the segmentation measure), gender or atrophy were tested for. The possible ICV related effect on associations between normalized hippocampal volume and factors gender, education and cognition was evaluated by normalizing hippocampal volume with different ICV measures. Excellent agreement was seen for inter- (r=0.999) and intra- (r=0.999) operator reference segmentations. Both SPM and FS overestimated ICV. SPM showed bias associated with gender and atrophy while FS showed bias dependent on skull size. All methods showed good correlation between time points in the longitudinal data (reference: 0.998, SPM: 0.962, FS: 0.995). Hippocampal volume showed different associations with cognition and gender depending on which ICV measure was used for hippocampal volume normalization. These results show that the choice of method used for ICV estimation can bias results in studies including brain volume measurements.

• 11. Voevodskaya, Olga
The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer's disease2014In: Frontiers in Aging Neuroscience, ISSN 1663-4365, E-ISSN 1663-4365, Vol. 6, p. 264-Article in journal (Refereed)

In neurodegeneration research, normalization of regional volumes by intracranial volume (ICV) is important to estimate the extent of disease-driven atrophy. There is little agreement as to whether raw volumes, volume-to-ICV fractions or regional volumes from which the ICV factor has been regressed out should be used for volumetric brain imaging studies. Using multiple regional cortical and subcortical volumetric measures generated by Freesurfer (51 in total), the main aim of this study was to elucidate the implications of these adjustment approaches. Magnetic resonance imaging (MRI) data were analyzed from two large cohorts, the population-based PIVUS cohort (N = 406, all subjects age 75) and the Alzheimer disease Neuroimaging Initiative (ADNI) cohort (N = 724). Further, we studied whether the chosen ICV normalization approach influenced the relationship between hippocampus and cognition in the three diagnostic groups of the ADNI cohort (Alzheimer's disease, mild cognitive impairment, and healthy individuals). The ability of raw vs. adjusted hippocampal volumes to predict diagnostic status was also assessed. In both cohorts raw volumes correlate positively with ICV, but do not scale directly proportionally with it. The correlation direction is reversed for all volume-to-ICV fractions, except the lateral and third ventricles. Most gray matter fractions are larger in females, while lateral ventricle fractions are greater in males. Residual correction effectively eliminated the correlation between the regional volumes and ICV and removed gender differences. The association between hippocampal volumes and cognition was not altered by ICV normalization. Comparing prediction of diagnostic status using the different approaches, small but significant differences were found. The choice of normalization approach should be carefully considered when designing a volumetric brain imaging study.

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