Handling changes in MRI acquisition parameters in modeling whole brain lesion volume and atrophy data in multiple sclerosis subjects: Comparison of linear mixed-effect models

Modeling the impact of MRI acquisition bias on structural connectomes: Harmonizing structural connectomes

One way to increase the statistical power and generalizability of neuroimaging studies is to collect data at multiple sites or merge multiple cohorts. However, this usually comes with site-related biases due to the heterogeneity of scanners and acquisition parameters, negatively impacting sensitivity. Brain structural connectomes are not an exception: Being derived from T1-weighted and diffusion-weighted magnetic resonance images, structural connectivity is impacted by differences in imaging protocol. Beyond minimizing acquisition parameter differences, removing bias with postprocessing is essential. In this work we create, from the exhaustive Human Connectome Project Young Adult dataset, a resampled dataset of different b-values and spatial resolutions, modeling a cohort scanned across multiple sites. After demonstrating the statistical impact of acquisition parameters on connectivity, we propose a linear regression with explicit modeling of b-value and spatial resolution, and validate its performance on separate datasets. We show that b-value and spatial resolution affect connectivity in different ways and that acquisition bias can be reduced using a linear regression informed by the acquisition parameters while retaining interindividual differences and hence boosting fingerprinting performance. We also demonstrate the generative potential of our model, and its generalization capability in an independent dataset reflective of typical acquisition practices in clinical settings.

Linking maternal disrupted interaction and infant limbic volumes: The role of infant cortisol output

Despite a large animal literature documenting the role of low maternal nurturance and elevated glucocorticoid production on offspring limbic development, these pathways have not yet been assessed during human infancy. Informed by animal models, the present study examined whether 1) maternal disrupted interaction is related to infant cortisol levels, 2) infant cortisol levels are associated with infant limbic volumes, and 3) infant cortisol levels mediate associations between maternal disrupted interaction and infant limbic volumes. Participants included 57 mother-infant dyads. Infant saliva was measured at one time point before and two time points after the Still-Face Paradigm (SFP) at age 4 months. Five aspects of maternal disrupted interaction were coded during the SFP reunion episode. Between 4 and 25 months (M age = 11.74 months, SD = 6.12), under natural sleep, infants completed an MRI. Amygdala and hippocampal volumes were calculated via automated segmentation. Results indicated that 1) maternal disrupted interaction, and specifically disoriented interaction, with the infant was associated with higher infant salivary cortisol (AUCg) levels during the SFP, 2) higher infant AUCg was related to enlarged bilateral amygdala and hippocampal volumes, and 3) infant AUCg mediated the relation between maternal disrupted interaction and infant amygdala and hippocampal volumes. Findings are consistent with controlled animal studies and provide evidence of a link between increased cortisol levels and enlarged limbic volumes in human infants. Results further suggest that established interventions to decrease maternal disrupted interaction could impact both infant cortisol levels and infant limbic volumes.

Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions

Parkinson's disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease's structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task-specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discussed.

Influence of equipment changes on MRI measures of brain atrophy and brain microstructure in a placebo-controlled trial of ibudilast in progressive multiple sclerosis

Background

Hardware changes can be an unavoidable confound in imaging trials. Understanding the impact of such changes may play an important role in the analysis of imaging data.

Objective

To characterize the effect of equipment changes in a longitudinal, multi-site multiple sclerosis trial.

Methods

Using data from a clinical trial in progressive multiple sclerosis, we explored how major changes in imaging hardware affected data. We analyzed the extent to which these changes affected imaging biomarkers and the estimated treatment effects by including such changes as a time-dependent covariate.

Results

Significant differences whole brain atrophy (brain parenchymal fraction, BPF) and microstructure (transverse diffusivity, TD) between scans with and without changes were found and depended on the type of hardware change. A switch from GE HDxt to Siemens Skyra led to significant shifts in BPF (p < 0.04) and TD (p < 0.0001). However, we could not detect the influence of hardware changes on overall trial outcomes- differences between placebo and treatment arms in change over time of BPF and TD (p > 0.5).

Conclusions

The results suggest that differences among hardware types should be considered when planning and analyzing brain atrophy and diffusivity in a longitudinal clinical trial.

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.

Brain MRI Predicts Worsening Multiple Sclerosis Disability over 5 Years in the SUMMIT Study

BACKGROUND AND PURPOSE

Brain MRI-derived lesions and atrophy are related to multiple sclerosis (MS) disability. In the Serially Unified Multicenter MS Investigation (SUMMIT), from Brigham and Women's Hospital (BWH) and University of California, San Francisco (UCSF), we assessed whether MRI methodologic heterogeneity may limit the ability to pool multisite data sets to assess 5-year clinical-MRI associations.

METHODS

Patients with relapsing-remitting (RR) MS (n = 100 from each site) underwent baseline brain MRI and baseline and 5-year clinical evaluations. Patients were matched on sex (74 women each), age, disease duration, and Expanded Disability Status Scale (EDSS) score. MRI was performed with differences between sites in both acquisition (field strength, voxel size, pulse sequences), and postprocessing pipeline to assess brain parenchymal fraction (BPF) and T2 lesion volume (T2LV).

RESULTS

The UCSF cohort showed higher correlation than the BWH cohort between T2LV and disease duration. UCSF showed a higher inverse correlation between BPF and age than BWH. UCSF showed a higher inverse correlation than BWH between BPF and 5-year EDSS score. Both cohorts showed inverse correlations between BPF and T2LV, with no between-site difference. The pooled but not individual cohort data showed a link between a lower baseline BPF and the subsequent 5-year worsening in disability in addition to other stronger relationships in the data.

CONCLUSIONS

MRI acquisition and processing differences may result in some degree of heterogeneity in assessing brain lesion and atrophy measures in patients with MS. Pooling of data across sites is beneficial to correct for potential biases in individual data sets.

Standardized Brain MRI Acquisition Protocols Improve Statistical Power in Multicenter Quantitative Morphometry Studies

BACKGROUND AND PURPOSE

In this study, we used power analysis to calculate required sample sizes to detect group-level changes in quantitative neuroanatomical estimates derived from MRI scans obtained from multiple imaging centers. Sample size estimates were derived from (i) standardized 3T image acquisition protocols and (ii) nonstandardized clinically acquired images obtained at both 1.5 and 3T as part of the multicenter Human Epilepsy Project. Sample size estimates were compared to assess the benefit of standardizing acquisition protocols.

METHODS

Cortical thickness, hippocampal volume, and whole brain volume were estimated from whole brain T1-weighted MRI scans processed using Freesurfer v6.0. Sample sizes required to detect a range of effect sizes were calculated using (i) standard t-test based power analysis methods and (ii) a nonparametric bootstrap approach.

RESULTS

A total of 32 participants were included in our analyses, aged 29.9 ± 12.62 years. Standard deviation estimates were lower for all quantitative neuroanatomical metrics when assessed using standardized protocols. Required sample sizes per group to detect a given effect size were markedly reduced when using standardized protocols, particularly for cortical thickness changes <.2 mm and hippocampal volume changes <10%.

CONCLUSIONS

The use of standardized protocols yielded up to a five-fold reduction in required sample sizes to detect disease-related neuroanatomical changes, and is particularly beneficial for detecting subtle effects. Standardizing image acquisition protocols across scanners prior to commencing a study is a valuable approach to increase the statistical power of multicenter MRI studies.

Adaptive Bayesian label fusion using kernel-based similarity metrics in hippocampus segmentation

The effectiveness of brain magnetic resonance imaging (MRI) as a useful evaluation tool strongly depends on the performed segmentation of associated tissues or anatomical structures. We introduce an enhanced brain segmentation approach of Bayesian label fusion that includes the construction of adaptive target-specific probabilistic priors using atlases ranked by kernel-based similarity metrics to deal with the anatomical variability of collected MRI data. In particular, the developed segmentation approach appraises patch-based voxel representation to enhance the voxel embedding in spaces with increased tissue discrimination, as well as the construction of a neighborhood-dependent model that addresses the label assignment of each region with a different patch complexity. To measure the similarity between the target and training atlases, we propose a tensor-based kernel metric that also includes the training labeling set. We evaluate the proposed approach, adaptive Bayesian label fusion using kernel-based similarity metrics, in the specific case of hippocampus segmentation of five benchmark MRI collections, including ADNI dataset, resulting in an increased performance (assessed through the Dice index) as compared to other recent works.

Potential of a statistical approach for the standardization of multicenter diffusion tensor data: A phantom study

BACKGROUND

Diffusion tensor imaging (DTI) parameters, such as fractional anisotropy (FA), allow examining the structural integrity of the brain. However, the true value of these parameters may be confounded by variability in MR hardware, acquisition parameters, and image quality.

PURPOSE

To examine the effects of confounding factors on FA and to evaluate the feasibility of statistical methods to model and reduce multicenter variability.

STUDY TYPE

Longitudinal multicenter study.

PHANTOM

DTI single strand phantom (HQ imaging).

FIELD STRENGTH/SEQUENCE

3T diffusion tensor imaging.

ASSESSMENTS

Thirteen European imaging centers participated. DTI scans were acquired every 6 months and whenever maintenance or upgrades to the system were performed. A total of 64 scans were acquired in 2 years, obtained by three scanner vendors, using six individual head coils, and 12 software versions.

STATISTICAL TESTS

The variability in FA was assessed by the coefficients of variation (CoV). Several linear mixed effects models (LMEM) were developed and compared by means of the Akaike Information Criterion (AIC).

RESULTS

The CoV was 2.22% for mean FA and 18.40% for standard deviation of FA. The variables "site" (P = 9.26 × 10^-5 ), "vendor" (P = 2.18 × 10^-5 ), "head coil" (P = 9.00 × 10^-4 ), "scanner drift," "bandwidth" (P = 0.033), "TE" (P = 8.20 × 10^-6 ), "SNR" (P = 0.029) and "mean residuals" (P = 6.50 × 10^-4 ) had a significant effect on the variability in mean FA. The variables "site" (P = 4.00 × 10^-4 ), "head coil" (P = 2.00 × 10^-4 ), "software" (P = 0.014), and "mean voxel outlier intensity count" (P = 1.10 × 10^-4 ) had a significant effect on the variability in standard deviation of FA. The mean FA was best predicted by an LMEM that included "vendor" and the interaction term of "SNR" and "head coil" as model factors (AIC -347.98). In contrast, the standard deviation of FA was best predicted by an LMEM that included "vendor," "bandwidth," "TE," and the interaction term between "SNR" and "head coil" (AIC -399.81).

DATA CONCLUSION

Our findings suggest that perhaps statistical models seem promising to model the variability in quantitative DTI biomarkers for clinical routine and multicenter studies.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:955-965.

Automated segmentation of cerebral deep gray matter from MRI scans: effect of field strength on sensitivity and reliability

Background

The cerebral subcortical deep gray matter nuclei (DGM) are a common, early, and clinically-relevant site of atrophy in multiple sclerosis (MS). Robust and reliable DGM segmentation could prove useful to evaluate putative neuroprotective MS therapies. The objective of the study was to compare the sensitivity and reliability of DGM volumes obtained from 1.5T vs. 3T MRI.

Methods

Fourteen patients with MS [age (mean, range) 50.2 (32.0–60.8) years, disease duration 18.4 (8.2–35.5) years, Expanded Disability Status Scale score 3.1 (0–6), median 3.0] and 15 normal controls (NC) underwent brain 3D T1-weighted paired scan-rescans at 1.5T and 3T. DGM (caudate, thalamus, globus pallidus, and putamen) segmentation was obtained by the fully automated FSL-FIRST pipeline. Both raw and normalized volumes were derived.

Results

DGM volumes were generally higher at 3T vs. 1.5T in both groups. For raw volumes, 3T showed slightly better sensitivity (thalamus: p = 0.02; caudate: p = 0.10; putamen: p = 0.02; globus pallidus: p = 0.0004; total DGM: p = 0.01) than 1.5T (thalamus: p = 0.05; caudate: p = 0.09; putamen: p = 0.03; globus pallidus: p = 0.0006; total DGM: p = 0.02) for detecting DGM atrophy in MS vs. NC. For normalized volumes, 3T but not 1.5T detected atrophy in the globus pallidus in the MS group. Across all subjects, scan-rescan reliability was generally very high for both platforms, showing slightly higher reliability for some DGM volumes at 3T. Raw volumes showed higher reliability than normalized volumes. Raw DGM volume showed higher reliability than the individual structures.

Conclusions

These results suggest somewhat higher sensitivity and reliability of DGM volumes obtained from 3T vs. 1.5T MRI. Further studies should assess the role of this 3T pipeline in tracking potential MS neurotherapeutic effects.

Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis

BACKGROUND AND PURPOSE

MR imaging can be used to measure structural changes in the brains of individuals with multiple sclerosis and is essential for diagnosis, longitudinal monitoring, and therapy evaluation. The North American Imaging in Multiple Sclerosis Cooperative steering committee developed a uniform high-resolution 3T MR imaging protocol relevant to the quantification of cerebral lesions and atrophy and implemented it at 7 sites across the United States. To assess intersite variability in scan data, we imaged a volunteer with relapsing-remitting MS with a scan-rescan at each site.

MATERIALS AND METHODS

All imaging was acquired on Siemens scanners (4 Skyra, 2 Tim Trio, and 1 Verio). Expert segmentations were manually obtained for T1-hypointense and T2 (FLAIR) hyperintense lesions. Several automated lesion-detection and whole-brain, cortical, and deep gray matter volumetric pipelines were applied. Statistical analyses were conducted to assess variability across sites, as well as systematic biases in the volumetric measurements that were site-related.

RESULTS

Systematic biases due to site differences in expert-traced lesion measurements were significant (P < .01 for both T1 and T2 lesion volumes), with site explaining >90% of the variation (range, 13.0-16.4 mL in T1 and 15.9-20.1 mL in T2) in lesion volumes. Site also explained >80% of the variation in most automated volumetric measurements. Output measures clustered according to scanner models, with similar results from the Skyra versus the other 2 units.

CONCLUSIONS

Even in multicenter studies with consistent scanner field strength and manufacturer after protocol harmonization, systematic differences can lead to severe biases in volumetric analyses.

The effect of alcohol and red wine consumption on clinical and MRI outcomes in multiple sclerosis

BACKGROUND

Alcohol and in particular red wine have both immunomodulatory and neuroprotective properties, and may exert an effect on the disease course of multiple sclerosis (MS).

OBJECTIVE

To assess the association between alcohol and red wine consumption and MS course.

METHODS

MS patients enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB) who completed a self-administered questionnaire about their past year drinking habits at a single time point were included in the study. Alcohol and red wine consumption were measured as servings/week. The primary outcome was the Expanded Disability Status Scale (EDSS) at the time of the questionnaire. Secondary clinical outcomes were the Multiple Sclerosis Severity Score (MSSS) and number of relapses in the year before the questionnaire. Secondary MRI outcomes included brain parenchymal fraction and T2 hyperintense lesion volume (T2LV). Appropriate regression models were used to test the association of alcohol and red wine intake on clinical and MRI outcomes. All analyses were controlled for sex, age, body mass index, disease phenotype (relapsing vs. progressive), the proportion of time on disease modifying therapy during the previous year, smoking exposure, and disease duration. In the models for the MRI outcomes, analyses were also adjusted for acquisition protocol.

RESULTS

923 patients (74% females, mean age 47 ± 11 years, mean disease duration 14 ± 9 years) were included in the analysis. Compared to abstainers, patients drinking more than 4 drinks per week had a higher likelihood of a lower EDSS score (OR, 0.41; p = 0.0001) and lower MSSS (mean difference, - 1.753; p = 0.002) at the time of the questionnaire. Similarly, patients drinking more than 3 glasses of red wine per week had greater odds of a lower EDSS (OR, 0.49; p = 0.0005) and lower MSSS (mean difference, - 0.705; p = 0.0007) compared to nondrinkers. However, a faster increase in T2LV was observed in patients consuming 1-3 glasses of red wine per week compared to nondrinkers.

CONCLUSIONS

Higher total alcohol and red wine intake were associated with a lower cross-sectional level of neurologic disability in MS patients but increased T2LV accumulation. Further studies should explore a potential cause-effect neuroprotective relationship, as well as the underlying biological mechanisms.

Impact of immunoablation and autologous hematopoietic stem cell transplantation on gray and white matter atrophy in multiple sclerosis

Background:

Immunoablation and autologous hematopoietic stem cell transplantation (IA/aHSCT) halts relapses, white matter (WM) lesion formation, and pathological whole-brain (WB) atrophy in multiple sclerosis (MS) patients. Whether the latter was due to effects on gray matter (GM) or WM warranted further exploration.

Objective:

To model GM and WM volume changes after IA/aHSCT to further understand the effects seen on WB atrophy.

Methods:

GM and WM volume changes were calculated from serial baseline and follow-up magnetic resonance imaging (MRI) ranging from 1.5 to 10.5 years in 19 MS patients treated with IA/aHSCT. A mixed-effects model with two predictors (total busulfan dose and baseline T1-weighted WM lesion volume “T1LV”) characterized the time-courses after IA/aHSCT.

Results:

Accelerated short-term atrophy of 2.1% and 3.2% occurred in GM and WM, respectively, on average. Both busulfan dose and T1LV were significant predictors of WM atrophy, whereas only busulfan was a significant predictor of GM atrophy. Compared to baseline, a significant reduction in GM atrophy, not WM atrophy, was found. The average rates of long-term GM and WM atrophy were −0.18%/year (standard error (SE): 0.083) and −0.07%/year (SE: 0.14), respectively.

Conclusion:

Chemotherapy-related toxicity affected both GM and WM. WM was further affected by focal T1-weighted lesion-related pathologies. Long-term rates of GM and WM atrophy were comparable to those of normal-aging.

Spaceflight-induced neuroplasticity in humans as measured by MRI: what do we know so far?

Space travel poses an enormous challenge on the human body; microgravity, ionizing radiation, absence of circadian rhythm, confinement and isolation are just some of the features associated with it. Obviously, all of the latter can have an impact on human physiology and even induce detrimental changes. Some organ systems have been studied thoroughly under space conditions, however, not much is known on the functional and morphological effects of spaceflight on the human central nervous system. Previous studies have already shown that central nervous system changes occur during and after spaceflight in the form of neurovestibular problems, alterations in cognitive function and sensory perception, cephalic fluid shifts and psychological disturbances. However, little is known about the underlying neural substrates. In this review, we discuss the current limited knowledge on neuroplastic changes in the human central nervous system associated with spaceflight (actual or simulated) as measured by magnetic resonance imaging-based techniques. Furthermore, we discuss these findings as well as their future perspectives, since this can encourage future research into this delicate and intriguing aspect of spaceflight. Currently, the literature suffers from heterogeneous experimental set-ups and therefore, the lack of comparability of findings among studies. However, the cerebellum, cortical sensorimotor and somatosensory areas and vestibular-related pathways seem to be involved across different studies, suggesting that these brain regions are most affected by (simulated) spaceflight. Extending this knowledge is crucial, especially with the eye on long-duration interplanetary missions (e.g. Mars) and space tourism.

The Effects of Hemodynamic Changes on Pulse Wave Velocity in Cardiothoracic Surgical Patients

The effect of blood pressure on pulse wave velocity (PWV) is well established. However, PWV variability with acute hemodynamic changes has not been examined in the clinical setting. The aim of the present study is to investigate the effect of hemodynamic changes on PWV in patients who undergo cardiothoracic surgery. Using data from 25 patients, we determined blood pressure (BP), heart rate (HR), and the left ventricular outflow tract (LVOT) velocity-time integral. By superimposing the radial arterial waveform on the continuous wave Doppler waveform of the LVOT, obtained by transesophageal echo, we were able to determine pulse transit time and to calculate PWV, stroke volume (SV), cardiac output (CO), and systemic vascular resistance (SVR). Increases in BP, HR, and SVR were associated with higher values for PWV. In contrast increases in SV were associated with decreases in PWV. Changes in CO were not significantly associated with PWV.

Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease

Brain imaging is essential for the diagnosis and characterization of cerebral small vessel disease. Several magnetic resonance imaging markers have therefore emerged, providing new information on the diagnosis, progression, and mechanisms of small vessel disease. Yet, the reproducibility of these small vessel disease markers has received little attention despite being widely used in cross-sectional and longitudinal studies. This review focuses on the main small vessel disease-related markers on magnetic resonance imaging including: white matter hyperintensities, lacunes, dilated perivascular spaces, microbleeds, and brain volume. The aim is to summarize, for each marker, what is currently known about: (1) its reproducibility in studies with a scan-rescan procedure either in single or multicenter settings; (2) the acquisition-related sources of variability; and, (3) the techniques used to minimize this variability. Based on the results, we discuss technical and other challenges that need to be overcome in order for these markers to be reliably used as outcome measures in future clinical trials. We also highlight the key points that need to be considered when designing multicenter magnetic resonance imaging studies of small vessel disease.

Clinical relevance of brain atrophy assessment in multiple sclerosis. Implications for its use in a clinical routine

INTRODUCTION

Brain atrophy measurement in multiple sclerosis (MS) has become an important outcome for determining patients at risk for developing physical and cognitive disability.

AREAS COVERED

In this article, we discuss the methodological issues related to using this MRI metric routinely, in a clinical setting. Understanding trajectories of annualized whole brain, gray and white matter, thalamic volume loss, and enlargement of ventricular space in specific MS phenotypes is becoming increasingly important. Evidence is mounting that disease-modifying treatments exert a positive effect on slowing brain atrophy progression in MS. Expert Commentary: While there is a need to translate measurement of brain atrophy to clinical routine at the individual patient level, there are still a number of challenges to be met before this can actually happen, including how to account for biological confounding factors and pseudoatrophy, standardize acquisition and analyses parameters, which can influence the accuracy of the assessments.