Test-retest reliability and sample size estimates after MRI scanner relocation

Impact of COVID-19 on brain connectivity and rehabilitation outcome after stroke

Background

Coronavirus disease (COVID-19) may induce neurological issues, impacting brain structure and stroke recovery. Limited studies have explored its effects on post-stroke rehabilitation. Our study compares brain structure and connectivity, assessing rehabilitation outcomes based on pre-stroke COVID-19 infection.

Methods

A retrospective analysis of 299 post-stroke rehabilitation cases from May 2021 to January 2023 included two groups: those diagnosed with COVID-19 at least two weeks before stroke onset (COVID group) and those without (control group). Criteria involved first unilateral supratentorial stroke, <3 months post-onset, initial MR imaging, and pre- and post-rehabilitation clinical assessments. Propensity score matching ensured age, sex, and initial clinical assessment similarities. Using lesion mapping, tract-based statistical analysis, and group-independent component analysis MRI scans were assessed for structural and functional differences.

Results

After propensity score matching, 12 patients were included in each group. Patient demographics showed no significant differences. Analyses of MR imaging revealed no significant differences between COVID and control groups. Post-rehabilitation clinical assessments improved notably in both groups, however the intergroup analysis showed no significant difference.

Conclusions

Previous COVID-19 infection did not affect brain structure or connectivity nor outcomes after rehabilitation.

Harnessing the frontal aslant tract's structure to assess its involvement in cognitive functions: new insights from 7-T diffusion imaging

The first therapeutical goal followed by neurooncological surgeons dealing with prefrontal gliomas is attempting supramarginal tumor resection preserving relevant neurological function. Therefore, advanced knowledge of the frontal aslant tract (FAT) functional neuroanatomy in high-order cognitive domains beyond language and speech processing would help refine neurosurgeries, predicting possible relevant cognitive adverse events and maximizing the surgical efficacy. To this aim we performed the recently developed correlational tractography analyses to evaluate the possible relationship between FAT's microstructural properties and cognitive functions in 27 healthy subjects having ultra-high-field (7-Tesla) diffusion MRI. We independently assessed FAT segments innervating the dorsolateral prefrontal cortices (dlPFC-FAT) and the supplementary motor area (SMA-FAT). FAT microstructural robustness, measured by the tract's quantitative anisotropy (QA), was associated with a better performance in episodic memory, visuospatial orientation, cognitive processing speed and fluid intelligence but not sustained selective attention tests. Overall, the percentual tract volume showing an association between QA-index and improved cognitive scores (pQACV) was higher in the SMA-FAT compared to the dlPFC-FAT segment. This effect was right-lateralized for verbal episodic memory and fluid intelligence and bilateralized for visuospatial orientation and cognitive processing speed. Our results provide novel evidence for a functional specialization of the FAT beyond the known in language and speech processing, particularly its involvement in several higher-order cognitive domains. In light of these findings, further research should be encouraged to focus on neurocognitive deficits and their impact on patient outcomes after FAT damage, especially in the context of glioma surgery.

Neuroimaging for precision medicine in psychiatry

Although the lifetime burden due to mental disorders is increasing, we lack tools for more precise diagnosing and treating prevalent and disabling disorders such as major depressive disorder. We lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry, focusing on major depressive and anxiety disorders. We begin by outlining evidence for the use of functional neuroimaging to stratify the heterogeneity of these disorders, based on underlying circuit dysfunction. We then review the current landscape of how functional neuroimaging-derived circuit predictors can predict treatment outcomes and clinical trajectories in depression and anxiety. Future directions for advancing clinically appliable neuroimaging measures are considered. We conclude by considering the opportunities and challenges of translating neuroimaging measures into practice. As an illustration, we highlight one approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation.

Identifying tripartite relationship among cortical thickness, neuroticism, and mood and anxiety disorders

The number of young adults seeking help for emotional distress, subsyndromal-syndromal mood/anxiety symptoms, including those associated with neuroticism, is rising and can be an early manifestation of mood/anxiety disorders. Identification of gray matter (GM) thickness alterations and their relationship with neuroticism and mood/anxiety symptoms can aid in earlier diagnosis and prevention of risk for future mood and anxiety disorders. In a transdiagnostic sample of young adults (n = 252;177 females; age 21.7 ± 2), Hypothesis (H) 1:regularized regression followed by multiple regression examined relationships among GM cortical thickness and clinician-rated depression, anxiety, and mania/hypomania; H2:the neuroticism factor and its subfactors as measured by NEO Personality Inventory (NEO-PI-R) were tested as mediators. Analyses revealed positive relationships between left parsopercularis thickness and depression (B = 4.87, p = 0.002), anxiety (B = 4.68, p = 0.002), mania/hypomania (B = 6.08, p ≤ 0.001); negative relationships between left inferior temporal gyrus (ITG) thickness and depression (B = - 5.64, p ≤ 0.001), anxiety (B = - 6.77, p ≤ 0.001), mania/hypomania (B = - 6.47, p ≤ 0.001); and positive relationships between left isthmus cingulate thickness (B = 2.84, p = 0.011), and anxiety. NEO anger/hostility mediated the relationship between left ITG thickness and mania/hypomania; NEO vulnerability mediated the relationship between left ITG thickness and depression. Examining the interrelationships among cortical thickness, neuroticism and mood and anxiety symptoms enriches the potential for identifying markers conferring risk for mood and anxiety disorders and can provide targets for personalized intervention strategies for these disorders.

Early-phase amyloid PET reproduces metabolic signatures of cognitive decline in Parkinson's disease

INTRODUCTION

Recent work suggests that amyloid beta (Aβ) positron emission tomography (PET) tracer uptake shortly after injection ("early phase") reflects brain metabolism and perfusion. We assessed this modality in a predominantly amyloid-negative neurodegenerative condition, Parkinson's disease (PD), and hypothesized that early-phase 18F-florbetaben (eFBB) uptake would reproduce characteristic hypometabolism and hypoperfusion patterns associated with cognitive decline in PD.

METHODS

One hundred fifteen PD patients across the spectrum of cognitive impairment underwent dual-phase Aβ PET, structural and arterial spin labeling (ASL) magnetic resonance imaging (MRI), and neuropsychological assessments. Multiple linear regression models compared eFBB uptake to cognitive performance and ASL MRI perfusion.

RESULTS

Reduced eFBB uptake was associated with cognitive performance in brain regions previously linked to hypometabolism-associated cognitive decline in PD, independent of amyloid status. Furthermore, eFBB uptake correlated with cerebral perfusion across widespread regions.

DISCUSSION

EFBB uptake is a potential surrogate measure for cerebral perfusion/metabolism. A dual-phase PET imaging approach may serve as a clinical tool for assessing cognitive impairment.

Highlights

Images taken at amyloid beta (Aβ) positron emission tomography tracer injection may reflect brain perfusion and metabolism.Parkinson's disease (PD) is a predominantly amyloid-negative condition.Early-phase florbetaben (eFBB) in PD was associated with cognitive performance.eFBB uptake reflects hypometabolism-related cognitive decline in PD.eFBB correlated with arterial spin labeling magnetic resonance imaging measured cerebral perfusion.eFBB distinguished dementia from normal cognition and mild cognitive impairment.Findings were independent of late-phase Aβ burden.Thus, eFBB may serve as a surrogate measure for brain metabolism/perfusion.

Brain morphology predicts individual sensitivity to pain: a multicenter machine learning approach

ABSTRACT

Sensitivity to pain shows a remarkable interindividual variance that has been reported to both forecast and accompany various clinical pain conditions. Although pain thresholds have been reported to be associated to brain morphology, it is still unclear how well these findings replicate in independent data and whether they are powerful enough to provide reliable pain sensitivity predictions on the individual level. In this study, we constructed a predictive model of pain sensitivity (as measured with pain thresholds) using structural magnetic resonance imaging-based cortical thickness data from a multicentre data set (3 centres and 131 healthy participants). Cross-validated estimates revealed a statistically significant and clinically relevant predictive performance (Pearson r = 0.36, P < 0.0002, R2 = 0.13). The predictions were found to be specific to physical pain thresholds and not biased towards potential confounding effects (eg, anxiety, stress, depression, centre effects, and pain self-evaluation). Analysis of model coefficients suggests that the most robust cortical thickness predictors of pain sensitivity are the right rostral anterior cingulate gyrus, left parahippocampal gyrus, and left temporal pole. Cortical thickness in these regions was negatively correlated to pain sensitivity. Our results can be considered as a proof-of-concept for the capacity of brain morphology to predict pain sensitivity, paving the way towards future multimodal brain-based biomarkers of pain.

Replicable brain-phenotype associations require large-scale neuroimaging data

Numerous neuroimaging studies have investigated the neural basis of interindividual differences but the replicability of brain-phenotype associations remains largely unknown. We used the UK Biobank neuroimaging dataset (N = 37,447) to examine associations with six variables related to physical and mental health: age, body mass index, intelligence, memory, neuroticism and alcohol consumption, and assessed the improvement of replicability for brain-phenotype associations with increasing sampling sizes. Age may require only 300 individuals to provide highly replicable associations but other phenotypes required 1,500 to 3,900 individuals. The required sample size showed a negative power law relation with the estimated effect size. When only comparing the upper and lower quarters, the minimally required sample sizes for imaging decreased by 15-75%. Our findings demonstrate that large-scale neuroimaging data are required for replicable brain-phenotype associations, that this can be mitigated by preselection of individuals and that small-scale studies may have reported false positive findings.

Assessments of variability in cortical and subcortical measurements and within-network connectivity of the brain using test-retest data

The evaluation and diagnosis of structural changes in brain caused by disease or treatment over time has become one of the important applications of medical imaging methods, in particular MRI, and it is growing. It is critical to evaluate the reliability of the changes in measurements observed in an individual patient for any clinical decision-making. In this paper, we calculated the repeatability coefficient (RC) as a measure of uncertainty for MRI measurements of subcortical volumes and cortical thickness, and within-network connectivity using test-retest data of 20 healthy subjects. We also evaluated changes in 13 patients who received 20 sessions of transcranial magnetic stimulation as a treatment. The most reliable measure seems to be in the thickness of the left occipital with RC% of 3.5 and the least reliable measure is the brain connectivity within visual network using Yeo's atlas with RC% of 29.4. The most sensitive measure to the percentage of true changes in treated patients is the connectivity within subcortical network of AAL with 76.9%.Clinical Relevance- The results of this study can be useful for evaluating changes in the gray matter structures or functional connectivity of the brain due to a neurological disease such as Alzheimer's or Parkinson's. Also, the obtained results can be used to evaluate the changes caused by any intervention or treatment that may have any positive or negative effect on the brain.

The intracortical myelin content of impulsive choices: results from T1- and T2-weighted MRI myelin mapping

Delay discounting (DD) refers to a phenomenon that humans tend to choose small-sooner over large-later rewards during intertemporal choices. Steep discounting of delayed outcome is related to a variety of maladaptive behaviors and is considered as a transdiagnostic process across psychiatric disorders. Previous studies have investigated the association between brain structure (e.g. gray matter volume) and DD; however, it is unclear whether the intracortical myelin (ICM) influences DD. Here, based on a sample of 951 healthy young adults drawn from the Human Connectome Project, we examined the relationship between ICM, which was measured by the contrast of T1w and T2w images, and DD and further tested whether the identified associations were mediated by the regional homogeneity (ReHo) of brain spontaneous activity. Vertex-wise regression analyses revealed that steeper DD was significantly associated with lower ICM in the left temporoparietal junction (TPJ) and right middle-posterior cingulate cortex. Region-of-interest analysis revealed that the ReHo values in the left TPJ partially mediated the association of its myelin content with DD. Our findings provide the first evidence that cortical myelination is linked with individual differences in decision impulsivity and suggest that the myelin content affects cognitive performances partially through altered local brain synchrony.

Quality assessment, variability and reproducibility of anatomical measurements derived from T1-weighted brain imaging: The RIN-Neuroimaging Network case study

Initiatives for the collection of harmonized MRI datasets are growing continuously, opening questions on the reliability of results obtained in multi-site contexts. Here we present the assessment of the brain anatomical variability of MRI-derived measurements obtained from T1-weighted images, acquired according to the Standard Operating Procedures, promoted by the RIN-Neuroimaging Network. A multicentric dataset composed of 77 brain T1w acquisitions of young healthy volunteers (mean age = 29.7 ± 5.0 years), collected in 15 sites with MRI scanners of three different vendors, was considered. Parallelly, a dataset of 7 "traveling" subjects, each undergoing three acquisitions with scanners from different vendors, was also used. Intra-site, intra-vendor, and inter-site variabilities were evaluated in terms of the percentage standard deviation of volumetric and cortical thickness measures. Image quality metrics such as contrast-to-noise and signal-to-noise ratio in gray and white matter were also assessed for all sites and vendors. The results showed a measured global variability that ranges from 11% to 19% for subcortical volumes and from 3% to 10% for cortical thicknesses. Univariate distributions of the normalized volumes of subcortical regions, as well as the distributions of the thickness of cortical parcels appeared to be significantly different among sites in 8 subcortical (out of 17) and 21 cortical (out of 68) regions of i nterest in the multicentric study. The Bland-Altman analysis on "traveling" brain measurements did not detect systematic scanner biases even though a multivariate classification approach was able to classify the scanner vendor from brain measures with an accuracy of 0.60 ± 0.14 (chance level 0.33).

Early infant prefrontal gray matter volume is associated with concurrent and future infant emotionality

High levels of infant negative emotionality (NE) are associated with emotional and behavioral problems later in childhood. Identifying neural markers of high NE as well as low positive emotionality (PE) in infancy can provide neural markers to aid early identification of vulnerability, and inform interventions to help delay or even prevent psychiatric disorders before the manifestation of symptoms. Prefrontal cortical (PFC) subregions support the regulation of NE and PE, with each PFC subregion differentially specializing in distinct emotional regulation processes. Gray matter (GM) volume measures show good test-retest reliability, and thus have potential use as neural markers of NE and PE. Yet, while studies showed PFC GM structural abnormalities in adolescents and young adults with affective disorders, few studies examined how PFC subregional GM measures are associated with NE and PE in infancy. We aimed to identify relationships among GM in prefrontal cortical subregions at 3 months and caregiver report of infant NE and PE, covarying for infant age and gender and caregiver sociodemographic and clinical variables, in two independent samples at 3 months (Primary: n = 75; Replication sample: n = 40) and at 9 months (Primary: n = 44; Replication sample: n = 40). In the primary sample, greater 3-month medial superior frontal cortical volume was associated with higher infant 3-month NE (p < 0.05); greater 3-month ventrolateral prefrontal cortical volume predicted lower infant 9-month PE (p < 0.05), even after controlling for 3-month NE and PE. GM volume in other PFC subregions also predicted infant 3- and 9-month NE and PE, together with infant demographic factors, caregiver age, and/or caregiver affective instability and anxiety. These findings were replicated in the independent sample. To our knowledge, this is the first study to determine in primary and replication samples associations among infant PFC GM volumes and concurrent and prospective NE and PE, and identify promising, early markers of future psychopathology risk.

Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference

Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain's network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants' compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.

Choice of Voxel-based Morphometry processing pipeline drives variability in the location of neuroanatomical brain markers

Fundamental and clinical neuroscience has benefited tremendously from the development of automated computational analyses. In excess of 600 human neuroimaging papers using Voxel-based Morphometry (VBM) are now published every year and a number of different automated processing pipelines are used, although it remains to be systematically assessed whether they come up with the same answers. Here we examined variability between four commonly used VBM pipelines in two large brain structural datasets. Spatial similarity and between-pipeline reproducibility of the processed gray matter brain maps were generally low between pipelines. Examination of sex-differences and age-related changes revealed considerable differences between the pipelines in terms of the specific regions identified. Machine learning-based multivariate analyses allowed accurate predictions of sex and age, however accuracy differed between pipelines. Our findings suggest that the choice of pipeline alone leads to considerable variability in brain structural markers which poses a serious challenge for reproducibility and interpretation.

Four common processing pipelines tested on two Voxel-based Morphometry (VBM) datasets yield considerable variations in results, raising issues on the interpretability and robustness of VBM results.

Reproducibility and repeatability of magnetic resonance imaging in dementia

PURPOSE

Individualised predictive models of cognitive decline require disease-monitoring markers that are repeatable. For wide-spread adoption, such markers also need to be reproducible at different locations. This study assessed the repeatability and reproducibility of MRI markers derived from a dementia protocol.

METHODS

Six participants were scanned at three different sites with a 3T MRI scanner. The protocol employed: T1-weighted (T1w) imaging, resting state functional MRI (rsfMRI), arterial spin labelling (ASL), diffusion-weighted imaging (DWI), T2-weighted fluid attenuation inversion recovery (FLAIR), T2-weighted (T2w) imaging, and susceptibility weighted imaging (SWI). Participants were scanned repeatedly, up to six times over a maximum period of five years. One participant was also scanned a further three times on sequential days on one scanner. Fifteen derived metrics were computed from the seven different modalities.

RESULTS

Reproducibility (coefficient of variation; CoV, across sites) was best for T1w derived grey matter, white matter and hippocampal volume (CoV < 1.5%), compared to rsfMRI and SWI derived metrics (CoV, 19% and 21%). For a given metric, long-term repeatability (CoV across time) was comparable to reproducibility, with short-term repeatability considerably better.

CONCLUSIONS

Reproducibility and repeatability were assessed for a suite of markers calculated from a dementia MRI protocol. In general, structural markers were less variable than functional MRI markers. Variability over time on the same scanner was comparable to variability measured across different scanners. Overall, the results support the viability of multi-site longitudinal studies for monitoring cognitive decline.

A corticostriatal pathway mediating self-efficacy enhancement

Forming positive beliefs about one's ability to perform challenging tasks, often termed self-efficacy, is fundamental to motivation and emotional well-being. Self-efficacy crucially depends on positive social feedback, yet people differ in the degree to which they integrate such feedback into self-beliefs (i.e., positive bias). While diminished positive bias of this sort is linked to mood and anxiety, the neural processes by which positive feedback on public performance enhances self-efficacy remain unclear. To address this, we conducted a behavioral and fMRI study wherein participants delivered a public speech and received fictitious positive and neutral feedback on their performance in the MRI scanner. Before and after receiving feedback, participants evaluated their actual and expected performance. We found that reduced positive bias in updating self-efficacy based on positive social feedback associated with a psychopathological dimension reflecting symptoms of anxiety, depression, and low self-esteem. Analysis of brain encoding of social feedback showed that a positive self-efficacy update bias associated with a stronger reward-related response in the ventral striatum (VS) and stronger coupling of the VS with a temporoparietal region involved in self-processing. Together, our findings demarcate a corticostriatal circuit that promotes positive bias in self-efficacy updating based on social feedback, and highlight the centrality of such bias to emotional well-being.

Recovery of neural dynamics criticality in personalized whole-brain models of stroke

The critical brain hypothesis states that biological neuronal networks, because of their structural and functional architecture, work near phase transitions for optimal response to internal and external inputs. Criticality thus provides optimal function and behavioral capabilities. We test this hypothesis by examining the influence of brain injury (strokes) on the criticality of neural dynamics estimated at the level of single participants using directly measured individual structural connectomes and whole-brain models. Lesions engender a sub-critical state that recovers over time in parallel with behavior. The improvement of criticality is associated with the re-modeling of specific white-matter connections. We show that personalized whole-brain dynamical models poised at criticality track neural dynamics, alteration post-stroke, and behavior at the level of single participants.

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset

The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders.

Impact of physiological factors on longitudinal structural MRI measures of the brain

Longitudinal MRI is used in clinical research studies to examine illness progression, neurodevelopment, and the effect of medical interventions. Such studies typically report changes in brain volume of less than 5%. However, there is a concern that these findings could be obscured or confounded by small changes in brain volume estimates caused by physiological factors such as, dehydration, blood pressure, caffeine levels, and circadian rhythm. In this study, MRI scans using the ADNI-III protocol were acquired from 20 participants (11 female) at two time points (mean interval = 20.3 days). Hydration, systolic and diastolic blood pressure, caffeine intake, and time of day were recorded at both visits. Images were processed using FreeSurfer. Three a priori hypothesised brain regions (hippocampus, lateral ventricles, and total brain) were selected, and an exploratory analysis was conducted on FreeSurfer's auto-segmented brain regions. There was no significant effect of the physiological factors on changes in the hypothesised brain regions. We provide estimates for the maximum percentage change in regional brain volumes that could be expected to occur from normal variation in each of the physiological measures. In this study, normal variations in physiological parameters did not have a detectable effect on longitudinal changes in brain volume.

Spatial variation of perfusion MRI reflects cognitive decline in mild cognitive impairment and early dementia

Cerebral blood flow (CBF) measured with arterial spin labelling (ASL) magnetic resonance imaging (MRI) reflects cerebral perfusion, related to metabolism, and arterial transit time (ATT), related to vascular health. Our aim was to investigate the spatial coefficient of variation (sCoV) of CBF maps as a surrogate for ATT, in volunteers meeting criteria for subjective cognitive decline (SCD), amnestic mild cognitive impairment (MCI) and probable Alzheimer’s dementia (AD). Whole-brain pseudo continuous ASL MRI was performed at 3 T in 122 participants (controls = 20, SCD = 44, MCI = 45 and AD = 13) across three sites in New Zealand. From CBF maps that included all grey matter, sCoV progressively increased across each group with increased cognitive deficit. A similar overall trend was found when examining sCoV solely in the temporal lobe. We conclude that sCoV, a simple to compute imaging metric derived from ASL MRI, is sensitive to varying degrees of cognitive changes and supports the view that vascular health contributes to cognitive decline associated with Alzheimer’s disease.

Reproducibility of Longitudinal Changes in Cortical Thickness Determined by Surface-Based Morphometry Between Non-Accelerated and Accelerated MR Imaging

BACKGROUND

Scan acceleration such as parallel imaging reduces scan time, but shorter scan time may reduce the signal-to-noise ratio and affect image quality. The reproducibility of longitudinal changes in the brain structure between non-accelerated and accelerated imaging by surface-based analysis is unclear.

PURPOSE

To determine the reproducibility of longitudinal changes in cortical thickness, measured by surface-based morphometry, between non-accelerated and accelerated structural T₁ -weighted imaging in the healthy elderly and those with mild cognitive impairment (MCI) and Alzheimer's disease (AD).

STUDY TYPE

Retrospective.

SUBJECTS

Fifty healthy elderly subjects (age = 73 ± 5 years, 29 females, 21 males), 54 MCI patients (age = 71 ± 7 years, 23 females, 31 males), and 8 AD patients (age = 78 ± 6 years, 6 females, 2 males).

FIELD STRENGTH/SEQUENCE

3 T, magnetization-prepared rapid gradient-echo.

ASSESSMENT

Longitudinal changes in cortical thickness estimated by the longitudinal stream in FreeSurfer from 2-year interval data, and visual assessment of image quality by three radiologists.

STATISTICAL TESTS

Intraclass correlation coefficient (ICC) and Kruskal-Wallis test. A P value <0.05 was considered significant.

RESULTS

Healthy elderly subjects, MCI patients, and AD patients showed different patterns in the ICC maps. For the smoothing of 20 mm full width at half maximum, the mean ICC was 0.45 overall (healthy elderly, 0.33; MCI patients, 0.49; AD patients, 0.31). The within-subject SDs of the symmetrized percent changes were similar between healthy elderly subjects (mean, 1.3%/year) and MCI patients (mean, 1.3%/year) but larger in AD patients (mean, 1.7%/year). Image quality did not significantly differ per group (P = 0.18).

DATA CONCLUSION

The results of this study indicate the reproducibility of longitudinal changes in cortical thickness measured by surface-based morphometry between non-accelerated and accelerated imaging, and that the reproducibility varies by disease and region.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 1.

Reproducibility of Brain Volume Changes in Longitudinal Voxel-Based Morphometry Between Non-Accelerated and Accelerated Magnetic Resonance Imaging

Background: Scan acceleration techniques, such as parallel imaging, can reduce scan times, but reliability is essential to implement these techniques in neuroimaging. Objective: To evaluate the reproducibility of the longitudinal changes in brain morphology determined by longitudinal voxel-based morphometry (VBM) between non-accelerated and accelerated magnetic resonance images (MRI) in normal aging, mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Methods: Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 2 database, comprising subjects who underwent non-accelerated and accelerated structural T1-weighted MRI at screening and at a 2-year follow-up on 3.0 T Philips scanners, we examined the reproducibility of longitudinal gray matter volume changes determined by longitudinal VBM processing between non-accelerated and accelerated imaging in 50 healthy elderly subjects, 54 MCI patients, and eight AD patients. Results: The intraclass correlation coefficient (ICC) maps differed among the three groups. The mean ICC was 0.72 overall (healthy elderly, 0.63; MCI, 0.75; AD, 0.63), and the ICC was good to excellent (0.6–1.0) for 81.4%of voxels (healthy elderly, 64.8%; MCI, 85.0%; AD, 65.0%). The differences in image quality (head motion) were not significant (Kruskal–Wallis test, p = 0.18) and the within-subject standard deviations of longitudinal gray matter volume changes were similar among the groups. Conclusion: The results indicate that the reproducibility of longitudinal gray matter volume changes determined by VBM between non-accelerated and accelerated MRI is good to excellent for many regions but may vary between diseases and regions.

Comparison of structural MRI brain measures between 1.5 and 3 T: Data from the Lothian Birth Cohort 1936

Multi‐scanner MRI studies are reliant on understanding the apparent differences in imaging measures between different scanners. We provide a comprehensive analysis of T₁‐weighted and diffusion MRI (dMRI) structural brain measures between a 1.5 T GE Signa Horizon HDx and a 3 T Siemens Magnetom Prisma using 91 community‐dwelling older participants (aged 82 years). Although we found considerable differences in absolute measurements (global tissue volumes were measured as ~6–11% higher and fractional anisotropy [FA] was 33% higher at 3 T than at 1.5 T), between‐scanner consistency was good to excellent for global volumetric and dMRI measures (intraclass correlation coefficient [ICC] range: .612–.993) and fair to good for 68 cortical regions (FreeSurfer) and cortical surface measures (mean ICC: .504–.763). Between‐scanner consistency was fair for dMRI measures of 12 major white matter tracts (mean ICC: .475–.564), and the general factors of these tracts provided excellent consistency (ICC ≥ .769). Whole‐brain structural networks provided good to excellent consistency for global metrics (ICC ≥ .612). Although consistency was poor for individual network connections (mean ICCs: .275−.280), this was driven by a large difference in network sparsity (.599 vs. .334), and consistency was improved when comparing only the connections present in every participant (mean ICCs: .533–.647). Regression‐based k‐fold cross‐validation showed that, particularly for global volumes, between‐scanner differences could be largely eliminated (R² range .615–.991). We conclude that low granularity measures of brain structure can be reliably matched between the scanners tested, but caution is warranted when combining high granularity information from different scanners.

Cortical thickness in Parkinson's disease: a coordinate-based meta-analysis

Parkinson's disease (PD) is a common age-related neurodegenerative disease that affects the structural architecture of the cerebral cortex. Cortical thickness (CTh) via surface-based morphometry (SBM) analysis is a popular measure to assess brain structural alterations in the gray matter in PD. However, the results of CTh analysis in PD lack consistency and have not been systematically reviewed. We conducted a comprehensive coordinate-based meta-analysis (CBMA) of 38 CTh studies (57 comparison datasets) in 1,843 patients with PD using the latest seed-based d mapping software. Compared with 1,172 healthy controls, no significantly consistent CTh alterations were found in patients with PD, suggesting CTh as an unreliable neuroimaging marker for PD. The lack of consistent CTh alterations in PD could be ascribed to the heterogeneity in clinical populations, variations in imaging methods, and underpowered small sample sizes. These results highlight the need to control for potential confounding factors to produce robust and reproducible CTh results in PD.

Cortical Thickness in Migraine: A Coordinate-Based Meta-Analysis

Cortical thickness (CTh) via surface-based morphometry analysis is a popular method to characterize brain morphometry. Many studies have been performed to investigate CTh abnormalities in migraine. However, the results from these studies were not consistent and even conflicting. These divergent results hinder us to obtain a clear picture of brain morphometry regarding CTh alterations in migraine. Coordinate-based meta-analysis (CBMA) is a promising technique to quantitatively pool individual neuroimaging studies to identify consistent brain areas involved. Electronic databases (PubMed, EMBASE, Web of Science, China National Knowledge Infrastructure, WanFang, and SinoMed) and other sources (bioRxiv and reference lists of relevant articles and reviews) were systematically searched for studies that compared regional CTh differences between patients with migraine and healthy controls (HCs) up to May 15, 2020. A CBMA was performed using the Seed-based d Mapping with Permutation of Subject Images approach. In total, we identified 16 studies with 17 datasets reported that were eligible for the CBMA. The 17 datasets included 872 patients with migraine (average sample size 51.3, mean age 39.6 years, 721 females) and 949 HCs (average sample size 59.3, mean age 44.2 years, 680 females). The CBMA detected no statistically significant consistency of CTh alterations in patients with migraine relative to HCs. Sensitivity analysis and subgroup analysis verified this result to be robust. Metaregression analyses revealed that this CBMA result was not confounded by age, gender, aura, attack frequency per month, and illness duration. Our CBMA adds to the evidence of the replication crisis in neuroimaging research that is increasingly recognized. Many potential confounders, such as underpowered sample size, heterogeneous patient selection criteria, and differences in imaging collection and methodology, may contribute to the inconsistencies of CTh alterations in migraine, which merit attention before planning future research on this topic.