Brief mock-scan training reduces head motion during real scanning for children: A growth curve study

Pediatric neuroimaging datasets are rapidly increasing in scales. Despite strict protocols in data collection and preprocessing focused on improving data quality, the presence of head motion still impedes our understanding of neurodevelopmental mechanisms. Large head motion can lead to severe noise and artifacts in magnetic resonance imaging (MRI) studies, inflating correlations between adjacent brain areas and decreasing correlations between spatial distant territories, especially in children and adolescents. Here, by leveraging mock-scans of 123 Chinese children and adolescents, we demonstrated the presence of increased head motion in younger participants. Critically, a 5.5-minute training session in an MRI mock scanner was found to effectively suppress the head motion in the children and adolescents. Therefore, we suggest that mock scanner training should be part of the quality assurance routine prior to formal MRI data collection, particularly in large-scale population-level neuroimaging initiatives for pediatrics.     

Global motion detection and censoring in high‐density diffuse optical tomography

Motion‐induced artifacts can significantly corrupt optical neuroimaging, as in most neuroimaging modalities. For high‐density diffuse optical tomography (HD‐DOT) with hundreds to thousands of source‐detector pair measurements, motion detection methods are underdeveloped relative to both functional magnetic resonance imaging (fMRI) and standard functional near‐infrared spectroscopy (fNIRS). This limitation restricts the application of HD‐DOT in many challenging imaging situations and subject populations (e.g., bedside monitoring and children). Here, we evaluated a new motion detection method for multi‐channel optical imaging systems that leverages spatial patterns across measurement channels. Specifically, we introduced a global variance of temporal derivatives (GVTD) metric as a motion detection index. We showed that GVTD strongly correlates with external measures of motion and has high sensitivity and specificity to instructed motion—with an area under the receiver operator characteristic curve of 0.88, calculated based on five different types of instructed motion. Additionally, we showed that applying GVTD‐based motion censoring on both hearing words task and resting state HD‐DOT data with natural head motion results in an improved spatial similarity to fMRI mapping. We then compared the GVTD similarity scores with several commonly used motion correction methods described in the fNIRS literature, including correlation‐based signal improvement (CBSI), temporal derivative distribution repair (TDDR), wavelet filtering, and targeted principal component analysis (tPCA). We find that GVTD motion censoring on HD‐DOT data outperforms other methods and results in spatial maps more similar to those of matched fMRI data.     

Higher body mass index is associated with reduced posterior default mode connectivity in older adults

Obesity is a complex neurobehavioral disorder that has been linked to changes in brain structure and function. However, the impact of obesity on functional connectivity and cognition in aging humans is largely unknown. Therefore, the association of body mass index (BMI), resting‐state network connectivity, and cognitive performance in 712 healthy, well‐characterized older adults of the Leipzig Research Center for Civilization Diseases (LIFE) cohort (60–80 years old, mean BMI 27.6 kg/m² ± 4.2 SD, main sample: n = 521, replication sample: n = 191) was determined. Statistical analyses included a multivariate model selection approach followed by univariate analyses to adjust for possible confounders. Results showed that a higher BMI was significantly associated with lower default mode functional connectivity in the posterior cingulate cortex and precuneus. The effect remained stable after controlling for age, sex, head motion, registration quality, cardiovascular, and genetic factors as well as in replication analyses. Lower functional connectivity in BMI‐associated areas correlated with worse executive function. In addition, higher BMI correlated with stronger head motion. Using 3T neuroimaging in a large cohort of healthy older adults, independent negative associations of obesity and functional connectivity in the posterior default mode network were observed. In addition, a subtle link between lower resting‐state connectivity in BMI‐associated regions and cognitive function was found. The findings might indicate that obesity is associated with patterns of decreased default mode connectivity similar to those seen in populations at risk for Alzheimer's disease. Hum Brain Mapp 38:3502–3515, 2017. © 2017 Wiley Periodicals, Inc.     

Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion

Motion‐contaminated T1‐weighted (T1w) magnetic resonance imaging (MRI) results in misestimates of brain structure. Because conventional T1w scans are not collected with direct measures of head motion, a practical alternative is needed to identify potential motion‐induced bias in measures of brain anatomy. Head movements during functional MRI (fMRI) scanning of 266 healthy adults (20–89 years) were analyzed to reveal stable features of in‐scanner head motion. The magnitude of head motion increased with age and exhibited within‐participant stability across different fMRI scans. fMRI head motion was then related to measurements of both quality control (QC) and brain anatomy derived from a T1w structural image from the same scan session. A procedure was adopted to “flag” individuals exhibiting excessive head movement during fMRI or poor T1w quality rating. The flagging procedure reliably reduced the influence of head motion on estimates of gray matter thickness across the cortical surface. Moreover, T1w images from flagged participants exhibited reduced estimates of gray matter thickness and volume in comparison to age‐ and gender‐matched samples, resulting in inflated effect sizes in the relationships between regional anatomical measures and age. Gray matter thickness differences were noted in numerous regions previously reported to undergo prominent atrophy with age. Recommendations are provided for mitigating this potential confound, and highlight how the procedure may lead to more accurate measurement and comparison of anatomical features. Hum Brain Mapp 38:472–492, 2017. © 2016 Wiley Periodicals, Inc.     

Neuroanatomical correlates of food addiction symptoms and body mass index in the general population

The food addiction model suggests neurobiological similarities between substance‐related and addictive disorders and obesity. While structural brain differences have been consistently reported in these conditions, little is known about the neuroanatomical correlates of food addiction. We therefore aimed to determine whether symptoms of food addiction related to body mass index (BMI), personality, and brain structure in a large population‐based sample. Participants of the LIFE‐Adult study (n = 625; 20–59 years old, 45% women) answered the Yale Food Addiction Scale (YFAS) and further personality measures, underwent anthropometric assessments and high‐resolution 3T‐neuroimaging. A higher YFAS symptom score correlated with higher BMI, eating behavior traits, neuroticism, and stress. Higher BMI predicted significantly lower thickness of (pre)frontal, temporal and occipital cortex and increased volume of left nucleus accumbens. In a whole‐brain analysis, YFAS symptom score was not associated with significant differences in cortical thickness or subcortical gray matter volumes. A hypothesis‐driven Bayes factor analysis suggested a small, additional contribution of YFAS symptom score to lower right lateral orbitofrontal cortex thickness over the effect of BMI. Our study indicates that symptoms of food addiction do not account for the major part of the structural brain differences associated with BMI in the general population. Yet, symptoms of food addiction might explain additional variance in orbitofrontal cortex, a hub area of the reward network. Longitudinal studies implementing both anatomical and functional MRI could further disentangle the neural mechanisms of addictive eating behaviors.     

Subtle in‐scanner motion biases automated measurement of brain anatomy from in vivo MRI

While the potential for small amounts of motion in functional magnetic resonance imaging (fMRI) scans to bias the results of functional neuroimaging studies is well appreciated, the impact of in‐scanner motion on morphological analysis of structural MRI is relatively under‐studied. Even among “good quality” structural scans, there may be systematic effects of motion on measures of brain morphometry. In the present study, the subjects' tendency to move during fMRI scans, acquired in the same scanning sessions as their structural scans, yielded a reliable, continuous estimate of in‐scanner motion. Using this approach within a sample of 127 children, adolescents, and young adults, significant relationships were found between this measure and estimates of cortical gray matter volume and mean curvature, as well as trend‐level relationships with cortical thickness. Specifically, cortical volume and thickness decreased with greater motion, and mean curvature increased. These effects of subtle motion were anatomically heterogeneous, were present across different automated imaging pipelines, showed convergent validity with effects of frank motion assessed in a separate sample of 274 scans, and could be demonstrated in both pediatric and adult populations. Thus, using different motion assays in two large non‐overlapping sets of structural MRI scans, convergent evidence showed that in‐scanner motion—even at levels which do not manifest in visible motion artifact—can lead to systematic and regionally specific biases in anatomical estimation. These findings have special relevance to structural neuroimaging in developmental and clinical datasets, and inform ongoing efforts to optimize neuroanatomical analysis of existing and future structural MRI datasets in non‐sedated humans. Hum Brain Mapp 37:2385–2397, 2016. © 2016 Wiley Periodicals, Inc .     

A comparison of denoising pipelines in high temporal resolution task‐based functional magnetic resonance imaging data

It has been known for decades that head motion/other artifacts affect the blood oxygen level‐dependent signal. Recent recommendations predominantly focus on denoising resting state data, which may not apply to task data due to the different statistical relationships that exist between signal and noise sources. Several blind‐source denoising strategies (FIX and AROMA) and more standard motion parameter (MP) regression (0, 12, or 24 parameters) analyses were therefore compared across four sets of event‐related functional magnetic resonance imaging (erfMRI) and block‐design (bdfMRI) datasets collected with multiband 32‐ (repetition time [TR] = 460 ms) or older 12‐channel (TR = 2,000 ms) head coils. The amount of motion varied across coil designs and task types. Quality control plots indicated small to moderate relationships between head motion estimates and percent signal change in both signal and noise regions. Blind‐source denoising strategies eliminated signal as well as noise relative to MP24 regression; however, the undesired effects on signal depended both on algorithm (FIX > AROMA) and design (bdfMRI > erfMRI). Moreover, in contrast to previous results, there were minimal differences between MP12/24 and MP0 pipelines in both erfMRI and bdfMRI designs. MP12/24 pipelines were detrimental for a task with both longer block length (30 ± 5 s) and higher correlations between head MPs and design matrix. In summary, current results suggest that there does not appear to be a single denoising approach that is appropriate for all fMRI designs. However, even nonaggressive blind‐source denoising approaches appear to remove signal as well as noise from task‐related data at individual subject and group levels.     

Real-time motion monitoring improves functional MRI data quality in infants

Imaging the infant brain with MRI has improved our understanding of early neurodevelopment. However, head motion during MRI acquisition is detrimental to both functional and structural MRI scan quality. Though infants are typically scanned while asleep, they commonly exhibit motion during scanning causing data loss. Our group has shown that providing MRI technicians with real-time motion estimates via Framewise Integrated Real-Time MRI Monitoring (FIRMM) software helps obtain high-quality, low motion fMRI data. By estimating head motion in real time and displaying motion metrics to the MR technician during an fMRI scan, FIRMM can improve scanning efficiency. Here, we compared average framewise displacement (FD), a proxy for head motion, and the amount of usable fMRI data (FD ≤ 0.2 mm) in infants scanned with (n = 407) and without FIRMM (n = 295). Using a mixed-effects model, we found that the addition of FIRMM to current state-of-the-art infant scanning protocols significantly increased the amount of usable fMRI data acquired per infant, demonstrating its value for research and clinical infant neuroimaging.     

软、硬质钴铬合金及金合金对磁共振成像伪影的影响★

背景：磁共振成像具有高对比度、无骨伪影、任意方位断层等优点，被广泛应用于头颈外科、神经外科和口腔颌面外科疾病的诊断。但某些金属材料在磁共振成像中形成的伪影严重影响了图像质量，这些材料的使用限制了磁共振成像在头颈部的应用和准确诊断。 目的：观察硬质钴铬铸造合金、软质钴铬铸造合金、金合金3种金属材料在5种不同头颈部扫描序列中的伪影。 方法：采用5种头颈常规及快速成像序列，分别对放置于圆柱型水模中心的3种金属试样进行轴位扫描，测量伪影，并进行统计分析。 结果与结论：3种金属在磁共振成像相同成像序列中，金合金产生的伪影最小，硬质钴铬形成的伪影最大，各组间比较差异有显著性意义(P均 < 0.05)；相同金属材料，自旋回波序列产生伪影最小，梯度回波序列产生伪影较大，平面回波序列产生伪影最大并使图像变形。 结果提示，与非贵金属口腔修复材料相比，以贵金属作为口腔修复材料，在头颈部磁共振成像过程中产生的伪影最小，因此通过合理选择口腔金属修复材料及成像技术，可得到最佳磁共振成像。 关键词：口腔金属材料；核磁共振；伪影；数字化医学；医学植入物     

Event‐related fMRI of tasks involving brief motion

The assessment of brain function by blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) for tasks involving motion near the field of view is compromised by artifacts arising from the motion. The aim of this study is to demonstrate that these artifacts can be reduced by acquiring the average response from a brief stimulus (a “single‐trial,” or “event‐related,” paradigm) as opposed to alternating blocks of repeated task with rest (a “block‐trial” paradigm). The basis of this technique is that the NMR signal changes from neuronal activation are delayed relative to the motion due to a slow hemodynamic response. By acquiring the average response from a brief stimulus, motion‐induced signal changes occur prior to neuronal activation‐induced signal changes, and the two can thus be distinguished. This technique is applied to the tasks of speaking out loud, swallowing, jaw clenching, and tongue movement. Functional activation maps derived from the single‐trial paradigm contain significantly less artifact than functional activation maps derived from a more traditional block‐trial paradigm. Hum. Brain Mapping 7:106–114, 1999. © 1999 Wiley‐Liss, Inc.     

Inferring pathobiology from structural MRI in schizophrenia and bipolar disorder: Modeling head motion and neuroanatomical specificity

Despite over 400 peer‐reviewed structural MRI publications documenting neuroanatomic abnormalities in bipolar disorder and schizophrenia, the confounding effects of head motion and the regional specificity of these defects are unclear. Using a large cohort of individuals scanned on the same research dedicated MRI with broadly similar protocols, we observe reduced cortical thickness indices in both illnesses, though less pronounced in bipolar disorder. While schizophrenia (n = 226) was associated with wide‐spread surface area reductions, bipolar disorder (n = 227) and healthy comparison subjects (n = 370) did not differ. We replicate earlier reports that head motion (estimated from time‐series data) influences surface area and cortical thickness measurements and demonstrate that motion influences a portion, but not all, of the observed between‐group structural differences. Although the effect sizes for these differences were small to medium, when global indices were covaried during vertex‐level analyses, between‐group effects became nonsignificant. This analysis raises doubts about the regional specificity of structural brain changes, possible in contrast to functional changes, in affective and psychotic illnesses as measured with current imaging technology. Given that both schizophrenia and bipolar disorder showed cortical thickness reductions, but only schizophrenia showed surface area changes, and assuming these measures are influenced by at least partially unique sets of biological factors, then our results could indicate some degree of specificity between bipolar disorder and schizophrenia. Hum Brain Mapp 38:3757–3770, 2017. © 2017 Wiley Periodicals, Inc.     

Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank

Increasing interest in the structural and functional organisation of the human brain encourages the acquisition of big data sets comprising multiple neuroimaging modalities, often accompanied by additional information obtained from health records, cognitive tests, biomarkers and genotypes. Diffusion weighted magnetic resonance imaging data enables a range of promising imaging phenotypes probing structural connections as well as macroanatomical and microstructural properties of the brain. The reliability and biological sensitivity and specificity of diffusion data depend on processing pipeline. A state‐of‐the‐art framework for data processing facilitates cross‐study harmonisation and reduces pipeline‐related variability. Using diffusion magnetic resonance imaging (MRI) data from 218 individuals in the UK Biobank, we evaluate the effects of different processing steps that have been suggested to reduce imaging artefacts and improve reliability of diffusion metrics. In lack of a ground truth, we compared diffusion metric sensitivity to age between pipelines. By comparing distributions and age sensitivity of the resulting diffusion metrics based on different approaches (diffusion tensor imaging, diffusion kurtosis imaging and white matter tract integrity), we evaluate a general pipeline comprising seven postprocessing blocks: noise correction; Gibbs ringing correction; evaluation of field distortions; susceptibility, eddy‐current and motion‐induced distortion corrections; bias field correction; spatial smoothing and final diffusion metric estimations. Based on this evaluation, we suggest an optimised processing pipeline for diffusion weighted MRI data.     

"移动战伤单元"16排移动CT头部扫描试验报告

目的探讨国产16排移动CT在救护车运载条件下进行头部扫描成像的可行性。 方法选取陆军某部训练基地的志愿者健康官兵95人,随机分为3组：Ⅰ组（77人）,救护车在停止行驶状态下进行头部扫描;Ⅱ组（11人）,救护车在行驶状态下进行头部扫描;Ⅲ组（7人）,设置电磁波干扰环境。分析对比3组受检者在不同状态下,移动CT扫描成像质量、运动伪影、数据采集及信息传输情况。 结果骨窗成像：3组受检者颅骨、眼眶、鼻蝶窦等成像清晰。脑组织窗成像：3组受检者均可清晰显示眼球、视神经、脑干、脑皮质及脑白质等结构,但颅底部分层面均存在不同程度的运动伪影,其中Ⅰ组16.88%（13/77）受检者有较轻微的线状运动伪影,Ⅱ组81.82%（9/11）受检者有较明显的运动伪影;Ⅲ组14.29%（1/7）受检者有轻微线状运动伪影。经χ²检验,Ⅰ组、Ⅲ组分别与Ⅱ组比较,差异均有统计学意义（χ²=21.645、7.901,均P<0.05）;Ⅰ组与Ⅲ组比较,差异无统计学意义（χ²=0.031,P>0.05）。在电磁干扰状态下,移动CT数据采集及信息传输正常,3组辐射剂量CTDIvol均为36.27 mGy。 结论16排移动CT在救护车行驶及电磁干扰状态下扫描成像稳定安全,数据采集及信息传输正常。     

Imaging brain activity in conscious animals using functional MRI

Functional magnetic resonance imaging (fMRI) in humans has helped improve our understanding of the neuroanatomical organization of behavior. Unfortunately, fMRI in animal studies has not kept pace with the human work. Experiments are limited because animals must be anesthetized to prevent motion artifacts, precluding most studies involving neuroimaging of brain activity during behavior. The present study tested a newly developed head and body holder for performing fMRI in fully conscious animals. Significant changes in signal intensities were observed in the somatosensory cortex of conscious rats in response to electrical shock of the hindpaw. These changes in evoked signal ranged between 4 and 19% and were accompanied by significant increases in local cerebral blood flow. The fMRI study was performed with a 2.0-Tesla spectrometer. Using this non-invasive method of imaging brain activity in conscious animals, it is now possible to perform developmental studies in animal models of neurological and psychiatric disorders.     

Automated brain extraction of multisequence MRI using artificial neural networks

Brain extraction is a critical preprocessing step in the analysis of neuroimaging studies conducted with magnetic resonance imaging (MRI) and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD‐BET) relying on artificial neural networks that aim to overcome these limitations. We demonstrate that HD‐BET outperforms six popular, publicly available brain extraction algorithms in several large‐scale neuroimaging datasets, including one from a prospective multicentric trial in neuro‐oncology, yielding state‐of‐the‐art performance with median improvements of +1.16 to +2.50 points for the Dice coefficient and ?0.66 to ?2.51 mm for the Hausdorff distance. Importantly, the HD‐BET algorithm, which shows robust performance in the presence of pathology or treatment‐induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility, the HD‐BET prediction algorithm is made freely available ( www.neuroAI-HD.org ) and may become an essential component for robust, automated, high‐throughput processing of MRI neuroimaging data.     

The Utility of Conductive Plastic Electrodes in Prolonged ICU EEG Monitoring

We investigated the feasibility and utilization of conductive plastic electrodes (CPEs) in patients undergoing continuous video-electroencephalographic (EEG) monitoring in the intensive care unit (ICU), and assessed the quality of brain magnetic resonance imaging (MRI) and computed tomography (CT) images obtained during this period. A total of 54 patients were monitored. Seizures were recorded in 16 patients. Twenty-five patients had neuroimaging performed with electrodes in place; 15 MRI and 23 CT scans were performed. All patients had excellent quality anatomical images without clinically significant artifacts, and without any signs or symptoms that raised safety concerns. Recording quality of the EEG was indistinguishable to that achieved with standard gold electrodes. The use of CPEs allowed for uninterrupted EEG recording of patients who required urgent neuroimaging, and decreased the amount of time spent by the technologists required to remove and reattach leads.     

Assessment and quantification of head motion in neuropsychiatric functional imaging research as applied to schizophrenia.

Differing degrees of head motion have long been recognized as a potential confound in functional neuroimaging studies comparing neuropsychiatric populations to healthy normal volunteers, and studies often cite excessive head motion as a possible reason for the different patterns of functional activation frequently observed between groups. We empirically tested the degree of head motion in 16 patients with chronic schizophrenia and 16, age- and education-matched controls during the acquisition of functional magnetic resonance imaging data. We examined the degree of motion across three different indices (total motion, relative motion, task-correlated motion) during a complex attentional task and the effect of entering the motion parameters as additional regressors in a general linear model analysis. Results indicate that individuals with schizophrenia did not exhibit more task-correlated or total motion compared with controls. Moreover, the residual error term from the general linear model analysis was similar for both groups of subjects. In conclusion, current results suggest that stable patients with schizophrenia are capable of controlling head motion compared with matched normal controls. However, a direct comparison of the motion parameters is an essential step for any quality assurance protocol to determine whether additional corrective techniques need to be implemented.     

Retrospective motion correction protocol for high-resolution anatomical MRI

Modern computational brain morphology methods require that anatomical images be acquired at high resolution and with a high signal-to-noise ratio. This often translates into long acquisition times (>20 minutes) and images susceptible to head motion. In this study we tested retrospective motion correction (RMC), common for functional MRI (fMRI) and PET image motion correction, as a means to improve the quality of high-resolution 3-D anatomical MR images. RMC methods are known to be effective for correcting interscan motion; therefore, a single high-resolution 3-D MRI brain study was divided into six shorter acquisition segments to help shift intrascan motion into interscan motion. To help reduce intrascan head motion, each segment image was reviewed for motion artifacts and repeated if necessary. Interscan motion correction was done by spatially registering images to the third image and forming a single average motion-corrected image. RMC was tested on 35 subjects who were considered at high risk for head motion. Our results show that RMC provided better contrast-to-noise ratio and boundary detail when compared to nonmotion-corrected averaged images.     

Automated quality assessment of structural magnetic resonance images in children: Comparison with visual inspection and surface‐based reconstruction

Motion‐related artifacts are one of the major challenges associated with pediatric neuroimaging. Recent studies have shown a relationship between visual quality ratings of T₁ images and cortical reconstruction measures. Automated algorithms offer more precision in quantifying movement‐related artifacts compared to visual inspection. Thus, the goal of this study was to test three different automated quality assessment algorithms for structural MRI scans. The three algorithms included a Fourier‐, integral‐, and a gradient‐based approach which were run on raw T₁‐weighted imaging data collected from four different scanners. The four cohorts included a total of 6,662 MRI scans from two waves of the Generation R Study, the NIH NHGRI Study, and the GUSTO Study. Using receiver operating characteristics with visually inspected quality ratings of the T₁ images, the area under the curve (AUC) for the gradient algorithm, which performed better than either the integral or Fourier approaches, was 0.95, 0.88, and 0.82 for the Generation R, NHGRI, and GUSTO studies, respectively. For scans of poor initial quality, repeating the scan often resulted in a better quality second image. Finally, we found that even minor differences in automated quality measurements were associated with FreeSurfer derived measures of cortical thickness and surface area, even in scans that were rated as good quality. Our findings suggest that the inclusion of automated quality assessment measures can augment visual inspection and may find use as a covariate in analyses or to identify thresholds to exclude poor quality data.     

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple‐acquisition MRI

Fractal analysis represents a promising new approach to structural neuroimaging data, yet systematic evaluation of the fractal dimension (FD) as a marker of structural brain complexity is scarce. Here we present in‐depth methodological assessment of FD estimation in structural brain MRI. On the computational side, we show that spatial scale optimization can significantly improve FD estimation accuracy, as suggested by simulation studies with known FD values. For empirical evaluation, we analyzed two recent open‐access neuroimaging data sets (MASSIVE and Midnight Scan Club), stratified by fundamental image characteristics including registration, sequence weighting, spatial resolution, segmentation procedures, tissue type, and image complexity. Deviation analyses showed high repeated‐acquisition stability of the FD estimates across both data sets, with differential deviation susceptibility according to image characteristics. While less frequently studied in the literature, FD estimation in T2‐weighted images yielded robust outcomes. Importantly, we observed a significant impact of image registration on absolute FD estimates. Applying different registration schemes, we found that unbalanced registration induced (a) repeated‐measurement deviation clusters around the registration target, (b) strong bidirectional correlations among image analysis groups, and (c) spurious associations between the FD and an index of structural similarity, and these effects were strongly attenuated by reregistration in both data sets. Indeed, differences in FD between scans did not simply track differences in structure per se, suggesting that structural complexity and structural similarity represent distinct aspects of structural brain MRI. In conclusion, scale optimization can improve FD estimation accuracy, and empirical FD estimates are reliable yet sensitive to image characteristics.