A validation study of multicenter diffusion tensor imaging: reliability of fractional anisotropy and diffusivity values

Mediation of White Matter Alterations in the Association Between Ventricular Dilation and Cognitive Decline in Hydrocephalus Patients: An MRI Study

BACKGROUND

Cognitive impairment is commonly observed in hydrocephalus patients. Ventricular enlargement compresses brain parenchyma, especially the white matter (WM).

PURPOSE

To investigate whether the relationship between ventricular dilation and cognitive decline in hydrocephalus patients is mediated by WM alterations.

STUDY TYPE

Retrospective.

POPULATION

51 communicating hydrocephalus patients (median age, 54 years), 50 obstructive hydrocephalus patients (median age, 49 years), and 53 control subjects (median age, 50 years).

FIELD STRENGTH/SEQUENCE

Diffusion tensors imaging, 3D T1 BRAVO, 3D FIESTA, CUBE T2, and FLAIR sequences at 3T.

ASSESSMENT

DTI parameters (skeletonized fractional anisotropy (FA), skeletonized mean diffusivity (MD), and peak width of skeletonized mean diffusivity p(PSMD)) were extracted using FSL software. Global, periventricular, and deep white matter hyperintensity (WMH) volumes, degree of ventricular enlargement (Evans index), and other conventional imaging markers (number of lacunes and perivascular spaces, intracranial and brain volume) were extracted using united imaging intelligence. Cognitive tests included Montreal cognitive assessment (MoCA), clock drawing test (CDT), and vocabulary fluency test (VFT).

STATISTICAL TESTS

Multivariable linear regression analysis, mediation analyses, and dominance analysis. P-value <0.05 was considered significant.

RESULTS

The degree of ventricular dilation, DTI parameters, and cognitive function scores were interrelated. The skeletonized FA values (β = -0.0917, 95% confidence interval (CI): -0.205, -0.024) and normalized global WMH volume (β = -0.0635, 95% CI: -0.13, -0.0005) together mediated 37.2% of the association between Evans index and MoCA. A comparable causal pathway was found for periventricular WMHs but not for deep WMHs. Dominance analysis indicated skeletonized FA values had a greater impact on cognition than WMH volume. The skeletonized FA values also mediated the association between Evans index and CDT (β = -0.0897, 95% CI: -0.165, -0.026) and VFT (β = -0.1589, 95% CI: -0.27, -0.083).

CONCLUSION

WM alterations were causal mediators between ventricular dilation and cognitive decline in hydrocephalus patients.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 3.

Generalizing Diffusion Tensor Imaging of the Physis and Metaphysis

BACKGROUND

Current methods to predict height potential are inaccurate. Predicting height by using MRI of the physeal cartilage has shown promise but the applicability of this technique in different imaging setups has not been well-evaluated.

PURPOSE

To assess variability in diffusion tensor imaging of the physis and metaphysis (DTI-P/M) of the distal femur between different scanners, imaging parameters, tractography software, and resolution.

STUDY TYPE

Prospective.

POPULATION/SUBJECTS

Eleven healthy subjects (five males and six females ages 10-16.94).

FIELD STRENGTH/SEQUENCE

3 T; DTI single shot echo planar sequences.

ASSESSMENT

Physeal DTI tract measurements of the distal femur were compared between different scanners, imaging parameters, tractography settings, interpolation correction, and tractography software.

STATISTICAL TESTS

Bland-Altman, Spearman correlation, linear regression, and Shapiro-Wilk tests. Threshold for statistical significance was set at P = 0.05.

RESULTS

DTI tract values consistently showed low variability with different imaging and analysis settings. Vendor to vendor comparison exhibited strong correlation (ρ = 0.93) and small but significant bias (bias -5.76, limits of agreement [LOA] -24.31 to 12.78). Strong correlation and no significant difference were seen between technical replicates of the General Electric MRI scanner (ρ = 1, bias 0.17 [LOA -1.5 to 1.2], P = 0.42) and the Siemens MRI scanner (ρ = 0.89, bias = 0.56, P = 0.71). Different voxel sizes (1 × 1 × 2 mm3 vs. 2 × 2 × 3 mm3) did not significantly affect DTI values (bias = 1.4 [LOA -5.7 to 8.4], P = 0.35) but maintained a strong correlation (ρ = 0.82). Gap size (0 mm vs. 0.6 mm) significantly affects tract volume (bias = 1.8 [LOA -5.4 to 1.8]) but maintains a strong correlation (ρ = 0.93). Comparison of tractography algorithms generated significant differences in tract number, length, and volume while maintaining correlation (ρ = 0.86, 0.99, 0.93, respectively). Comparison of interobserver variability between different tractography software also revealed significant differences while maintaining high correlation (ρ = 0.85-0.98).

DATA CONCLUSION

DTI of the pediatric physis cartilage shows high reproducibility between different imaging and analytic parameters.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

High-frequency Quantitative Ultrasound Imaging of Human Rotator Cuff Muscles: Assessment of Repeatability and Reproducibility

This study evaluated the repeatability and reproducibility of using high-frequency quantitative ultrasound (QUS) measurement of backscatter coefficient (BSC), grayscale analysis, and gray-level co-occurrence matrix (GLCM) textural analysis, to characterize human rotator cuff muscles. The effects of varying scanner settings across two different operators and two US systems were investigated in a healthy volunteer with normal rotator cuff muscles and a patient with chronic massive rotator cuff injury and substantial muscle degeneration. The results suggest that BSC is a promising method for assessing rotator cuff muscles in both control and pathological subjects, even when operators were free to adjust system settings (depth, level of focus, and time-gain compensation). Measurements were repeatable and reproducible across the different operators and ultrasound imaging platforms. In contrast, grayscale and GLCM analyses were found to be less reliable in this setting, with significant measurement variability. Overall, the repeatability and reproducibility measurements of BSC indicate its potential as a diagnostic tool for rotator cuff muscle evaluation.

Effect of number of diffusion encoding directions in neonatal diffusion tensor imaging using Tract-Based Spatial Statistical analysis

Diffusion tensor imaging (DTI) has been used to study the developing brain in early childhood, infants and in utero studies. In infants, number of used diffusion encoding directions has traditionally been smaller in earlier studies down to the minimum of 6 orthogonal directions. Whereas the more recent studies often involve more directions, number of used directions remain an issue when acquisition time is optimized without compromising on data quality and in retrospective studies. Variability in the number of used directions may introduce bias and uncertainties to the DTI scalar estimates that affect cross-sectional and longitudinal study of the brain. We analysed DTI images of 133 neonates, each data having 54 directions after quality control, to evaluate the effect of number of diffusion weighting directions from 6 to 54 with interval of 6 to the DTI scalars with Tract-Based Spatial Statistics (TBSS) analysis. The TBSS analysis was applied to DTI scalar maps, and the mean region of interest (ROI) values were extracted using JHU atlas. We found significant bias in ROI mean values when only 6 directions were used (positive in fractional anisotropy [FA] and negative in fractional anisotropy [MD], axial diffusivity [AD] and fractional anisotropy [RD]), while when using 24 directions and above, the difference to scalar values calculated from 54 direction DTI was negligible. In repeated measures voxel-wise analysis, notable differences to 54 direction DTI were observed with 6, 12 and 18 directions. DTI measurements from data with at least 24 directions may be used in comparisons with DTI measurements from data with higher numbers of directions.

Image data harmonization tools for the analysis of post-traumatic epilepsy development in preclinical multisite MRI studies

Preclinical MRI studies have been utilized for the discovery of biomarkers that predict post-traumatic epilepsy (PTE). However, these single site studies often lack statistical power due to limited and homogeneous datasets. Therefore, multisite studies, such as the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), are developed to create large, heterogeneous datasets that can lead to more statistically significant results. EpiBioS4Rx collects preclinical data internationally across sites, including the United States, Finland, and Australia. However, in doing so, there are robust normalization and harmonization processes that are required to obtain statistically significant and generalizable results. This work describes the tools and procedures used to harmonize multisite, multimodal preclinical imaging data acquired by EpiBioS4Rx. There were four main harmonization processes that were utilized, including file format harmonization, naming convention harmonization, image coordinate system harmonization, and diffusion tensor imaging (DTI) metrics harmonization. By using Python tools and bash scripts, the file formats, file names, and image coordinate systems are harmonized across all the sites. To harmonize DTI metrics, values are estimated for each voxel in an image to generate a histogram representing the whole image. Then, the Quantitative Imaging Toolkit (QIT) modules are utilized to scale the mode to a value of one and depict the subsequent harmonized histogram. The standardization of file formats, naming conventions, coordinate systems, and DTI metrics are qualitatively assessed. The histograms of the DTI metrics were generated for all the individual rodents per site. For inter-site analysis, an average of the individual scans was calculated to create a histogram that represents each site. In order to ensure the analysis can be run at the level of individual animals, the sham and TBI cohort were analyzed separately, which depicted the same harmonization factor. The results demonstrate that these processes qualitatively standardize the file formats, naming conventions, coordinate systems, and DTI metrics of the data. This assists in the ability to share data across the study, as well as disseminate tools that can help other researchers to strengthen the statistical power of their studies and analyze data more cohesively.

Characterizing the penumbras of white matter hyperintensities in patients with cerebral small vessel disease

PURPOSE

The white matter hyperintensity penumbra (WMH-P) is the subtly changed normal-appearing white matter (NAWM) that surrounds white matter hyperintensities (WMHs). The goal of this study was to define WMH-P in cerebral small vessel disease (CSVD) by arterial spin labeling (ASL) and diffusion tensor imaging (DTI)/diffusion kurtosis imaging (DKI).

MATERIALS AND METHODS

We prospectively analyzed 42 patients with CSVD. To determine the range of cerebral blood flow (CBF) and DTI/DKI penumbras around white matter hyperintensities, we generated NAWM layer masks from periventricular WMHs (PVWMHs) and deep WMHs (DWMHs). Mean values of CBF, fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, mean kurtosis, axial kurtosis, and radial kurtosis within the WMHs and their corresponding NAWM layer masks were analyzed. Paired sample t tests were used for analysis, and differences were considered statistically significant if the associated p value was ≤ 0.05.

RESULTS

For DWMHs, the CBF penumbras were 13 mm, and the DTI/DKI penumbras were 8 mm. For PVWMHs, the CBF penumbras were 14 mm, and the DTI/DKI penumbras were 14 mm.

CONCLUSIONS

Our findings revealed that DTI/DKI and ASL can show structural and blood flow changes in brain tissue surrounding WMHs. In DWMHs, the blood flow penumbra was larger than the structural penumbra, while in PVWMHs, the blood flow penumbra was almost the same as the structural penumbra.

Microstructural and functional plasticity following repeated brain stimulation during cognitive training in older adults

The combination of repeated behavioral training with transcranial direct current stimulation (tDCS) holds promise to exert beneficial effects on brain function beyond the trained task. However, little is known about the underlying mechanisms. We performed a monocenter, single-blind randomized, placebo-controlled trial comparing cognitive training to concurrent anodal tDCS (target intervention) with cognitive training to concurrent sham tDCS (control intervention), registered at ClinicalTrial.gov (Identifier NCT03838211). The primary outcome (performance in trained task) and secondary behavioral outcomes (performance on transfer tasks) were reported elsewhere. Here, underlying mechanisms were addressed by pre-specified analyses of multimodal magnetic resonance imaging before and after a three-week executive function training with prefrontal anodal tDCS in 48 older adults. Results demonstrate that training combined with active tDCS modulated prefrontal white matter microstructure which predicted individual transfer task performance gain. Training-plus-tDCS also resulted in microstructural grey matter alterations at the stimulation site, and increased prefrontal functional connectivity. We provide insight into the mechanisms underlying neuromodulatory interventions, suggesting tDCS-induced changes in fiber organization and myelin formation, glia-related and synaptic processes in the target region, and synchronization within targeted functional networks. These findings advance the mechanistic understanding of neural tDCS effects, thereby contributing to more targeted neural network modulation in future experimental and translation tDCS applications.

A comprehensive evaluation of multicentric reliability of single-subject cortical morphological networks on traveling subjects

Despite the prevalence of research on single-subject cerebral morphological networks in recent years, whether they can offer a reliable way for multicentric studies remains largely unknown. Using two multicentric datasets of traveling subjects, this work systematically examined the inter-site test-retest (TRT) reliabilities of single-subject cerebral morphological networks, and further evaluated the effects of several key factors. We found that most graph-based network measures exhibited fair to excellent reliabilities regardless of different analytical pipelines. Nevertheless, the reliabilities were affected by choices of morphological index (fractal dimension > sulcal depth > gyrification index > cortical thickness), brain parcellation (high-resolution > low-resolution), thresholding method (proportional > absolute), and network type (binarized > weighted). For the factor of similarity measure, its effects depended on the thresholding method used (absolute: Kullback-Leibler divergence > Jensen-Shannon divergence; proportional: Jensen-Shannon divergence > Kullback-Leibler divergence). Furthermore, longer data acquisition intervals and different scanner software versions significantly reduced the reliabilities. Finally, we showed that inter-site reliabilities were significantly lower than intra-site reliabilities for single-subject cerebral morphological networks. Altogether, our findings propose single-subject cerebral morphological networks as a promising approach for multicentric human connectome studies, and offer recommendations on how to determine analytical pipelines and scanning protocols for obtaining reliable results.

Machine Learning for Brain MRI Data Harmonisation: A Systematic Review

BACKGROUND

Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise.

OBJECTIVE

This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions.

METHOD

This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications.

RESULTS

a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (n = 21) or an explicit (n = 20) way. Three MRI modalities were identified: structural MRI (n = 28), diffusion MRI (n = 7) and functional MRI (n = 6).

CONCLUSION

Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation.

Inter-vendor and inter-observer reliability of diffusion tensor imaging in the musculoskeletal system: a multiscanner MR study

BACKGROUND

To evaluate the inter-observer and inter-vendor reliability of diffusion tensor imaging parameters in the musculoskeletal system.

METHODS

This prospective study included six healthy volunteers three men (mean age: 42; range: 31-52 years) and three women (mean age: 36; range: 30-44 years). Each subject was scanned using different 3 Tesla magnetic resonance scanners from three different vendors at three different sites bilaterally. First, the intra-class correlation coefficient was used to determine between-observers agreement for overall measurements and clinical sites. Next, between-group comparisons were made through the nonparametric Friedman's test. Finally, the Bland-Altman method was used to determine agreement among the three scanner measurements, comparing them two by two.

RESULTS

A total of 792 measurement were calculated. ICC reported high levels of agreement between the two observers. ICC related to MD, FA, and RD measurements ranged from 0.88 (95% CI 0.85-0.90) to 0.95 (95% CI 0.94-0.96), from 0.85 (95% CI 0.81-0.88) to 0.95 (95% CI 0.93-0.96), and from 0.89 (0.85-0.90) to 0.92 (0.90-0.94). No statistically significant inter-vendor differences were observed. The Bland-Altmann method confirmed a high correlation between parameter values.

CONCLUSION

An excellent inter-observer and inter-vendor reliability was found in our study.

Sciatic nerve fractional anisotropy and neurofilament light chain protein are related to sensorimotor deficit of the upper and lower limbs in patients with type 2 diabetes

BACKGROUND

Diabetic sensorimotor polyneuropathy (DSPN) is one of the most prevalent and poorly understood diabetic microvascular complications. Recent studies have found that fractional anisotropy (FA), a marker for microstructural nerve integrity, is a sensitive parameter for the structural and functional nerve damage in DSPN. The aim of this study was to investigate the significance of proximal sciatic nerve's FA on different distal nerve fiber deficits of the upper and lower limbs and its correlation with the neuroaxonal biomarker, neurofilament light chain protein (NfL).

MATERIALS AND METHODS

Sixty-nine patients with type 2 diabetes (T2DM) and 30 healthy controls underwent detailed clinical and electrophysiological assessments, complete quantitative sensory testing (QST), and diffusion-weighted magnetic resonance neurography of the sciatic nerve. NfL was measured in the serum of healthy controls and patients with T2DM. Multivariate models were used to adjust for confounders of microvascular damage.

RESULTS

Patients with DSPN showed a 17% lower sciatic microstructural integrity compared to healthy controls (p<0.001). FA correlated with tibial and peroneal motor nerve conduction velocity (NCV) (r=0.6; p<0.001 and r=0.6; p<0.001) and sural sensory NCV (r=0.50; p<0.001). Participants with reduced sciatic nerve´s FA showed a loss of function of mechanical and thermal sensation of upper (r=0.3; p<0.01 and r=0.3; p<0.01) and lower (r=0.5; p<0.001 and r=0.3; p=<0.01) limbs and reduced functional performance of upper limbs (Purdue Pegboard Test for dominant hand; r=0.4; p<0.001). Increased levels of NfL and urinary albumin-creatinine ratio (ACR) were associated with loss of sciatic nerve´s FA (r=-0.5; p<0.001 and r= -0.3, p= 0.001). Of note, there was no correlation between sciatic FA and neuropathic symptoms or pain.

CONCLUSION

This is the first study showing that microstructural nerve integrity is associated with damage of different nerve fiber types and a neuroaxonal biomarker in DSPN. Furthermore, these findings show that proximal nerve damage is related to distal nerve function even before clinical symptoms occur. The microstructure of the proximal sciatic nerve and is also associated with functional nerve fiber deficits of the upper and lower limbs, suggesting that diabetic neuropathy involves structural changes of peripheral nerves of upper limbs too.

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.

The Role of School Environment in Brain Structure, Connectivity, and Mental Health in Children: A Multimodal Investigation

BACKGROUND

Much work has been dedicated to understanding the effects of adverse home environments on brain development. While the school social and learning environment plays a role in child development, little work has been done to investigate the impact of the school environment on the developing brain. The goal of the present study was to examine associations between the school environment, brain structure and connectivity, and mental health.

METHODS

In this preregistered study we investigated these questions in a large sample of adolescents (9-10 years of age) from the Adolescent Brain Cognitive Development (ABCD) Study. We examined the association between school environment and gray matter (n = 10,435) and white matter (n = 10,770) structure and functional connectivity (n = 9528). We then investigated multivariate relationships between school-associated brain measures and mental health.

RESULTS

School environment was associated with connectivity of the auditory and retrosplenial temporal network as well as of higher-order cognitive networks like the cingulo-opercular, default mode, ventral attention, and frontoparietal networks. Multivariate analyses revealed that connectivity of the cingulo-opercular and default mode networks was also associated with mental health.

CONCLUSIONS

Findings shed light on the neural mechanisms through which favorable school environments may contribute to positive mental health outcomes in children. Our findings have implications for interventions targeted at promoting positive youth functioning through improving school environments.

White Matter Correlates of Domain-Specific Working Memory

Prior evidence suggests domain-specific working memory (WM) buffers for maintaining phonological (i.e., speech sound) and semantic (i.e., meaning) information. The phonological WM buffer's proposed location is in the left supramarginal gyrus (SMG), whereas semantic WM has been related to the left inferior frontal gyrus (IFG), the middle frontal gyrus (MFG), and the angular gyrus (AG). However, less is known about the white matter correlates of phonological and semantic WM. We tested 45 individuals with left hemisphere brain damage on single word processing, phonological WM, and semantic WM tasks and obtained T1 and diffusion weighted neuroimaging. Virtual dissections were performed for each participants' arcuate fasciculus (AF), inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), middle longitudinal fasciculus (MLF), and uncinate fasciculus (UF), which connect the proposed domain-specific WM buffers with perceptual or processing regions. The results showed that the left ILF, MLF, IFOF, and the direct segment of the AF were related to semantic WM performance. Phonological WM was related to both the left ILF and the MLF. This work informs our understanding of the white matter correlates of WM, especially semantic WM, which has not previously been investigated. In addition, this work helps to adjudicate between theories of verbal WM, providing some evidence for separate pathways supporting phonological and semantic WM.

Diffusion Tensor Imaging of the Dentate Nucleus After Repeated Administration of Gadobutrol in Children

This study aimed to investigate possible signal changes in the dentate nucleus (DN) on diffusion tensor imaging (DTI) after administration of gadobutrol in a pediatric cohort. Total of 50 pediatric patients (mean age: 6.2 ± 4.3 years) with normal renal function exposed exclusively to the macrocyclic GBCA (mcGBCA) gadobutrol and 50 age- and sex-matched control patients with nonpathological neuroimaging findings (and no GBCA administration). Mean diffusivity (MD) and fractional anisotropy (FA) values were determined in the DN. A paired t test was performed to compare FA, MD values, and DN-to-middle cerebral peduncle (MCP) T1WI SI ratios between children exposed to gadobutrol and controls. Pearson correlation analysis was conducted to determine any correlation between FA and MD values as well as T1WI SI ratios and confounding parameters. The mean FA values of DN was significantly lower in children with mcGBCA than in the control group (p < 0.001; non-GBCA group, 0.299 ± 0.03; mcGBCA group, 0.254 ± 0.05), but no significant difference of the T1WI SI ratio was noted between the mcGBCA group (0.946 ± 0.06) and the control group (0.963 ± 0.05; p = 0.336). There was also a significant MD value difference between mcGBCA group and control group (p < 0.001; non-GBCA group, 0.152 ± 0.02 × 10^-3 mm²/s; mcGBCA group, 0.173 ± 0.03 × 10^-3 mm²/s). A significant correlation was identified between FA/MD values and the number of mcGBCA administration (FA; correlation coefficient =  - 0.355, p = 0.011 and MD; correlation coefficient = 0.334, p = 0.018). The administration of the gadobutrol was associated with higher MD and lower FA values in DN suggesting a difference in cerebellar tissue integrity between children exposed to mcGBCAs and control group.

Effect of number of diffusion encoding directions in Diffusion Metrics of 5-year-olds using Tract-Based Spatial Statistical analysis

Methodological aspects and effects of different imaging parameters on DTI (diffusion tensor imaging) results and their reproducibility have been recently studied comprehensively in adult populations. Although MR imaging of children's brains has become common, less interest has been focussed on researching whether adult‐based optimised parameters and pre‐processing protocols can be reliably applied to paediatric populations. Furthermore, DTI scalar values of preschool aged children are rarely reported. We gathered a DTI dataset from 5‐year‐old children (N = 49) to study the effect of the number of diffusion‐encoding directions on the reliability of resultant scalar values with TBSS (tract‐based spatial statistics) method. Additionally, the potential effect of within‐scan head motion on DTI scalars was evaluated. Reducing the number of diffusion‐encoding directions deteriorated both the accuracy and the precision of all DTI scalar values. To obtain reliable scalar values, a minimum of 18 directions for TBSS was required. For TBSS fractional anisotropy values, the intraclass correlation coefficient with two‐way random‐effects model (ICC[2,1]) for the subsets of 6 to 66 directions ranged between 0.136 [95%CI 0.0767;0.227] and 0.639 [0.542;0.740], whereas the corresponding values for subsets of 18 to 66 directions were 0.868 [0.815;0.913] and 0.995 [0.993;0.997]. Following the exclusion of motion‐corrupted volumes, minor residual motion did not associate with the scalar values. A minimum of 18 diffusion directions is recommended to result in reliable DTI scalar results with TBSS. We suggest gathering extra directions in paediatric DTI to enable exclusion of volumes with motion artefacts and simultaneously preserve the overall data quality.

Quantification of individual remyelination during short-term disease course by synthetic magnetic resonance imaging

MRI is an important diagnostic tool for evaluation of myelin content in multiple sclerosis and other CNS diseases, being especially relevant for studies investigating remyelinating pharmacotherapies. In this study, we evaluated a new synthetic MRI–based myelin estimation in methylenetetrahydrofolate reductase deficiency as a treatable primary demyelinating disorder and compared this method with established diffusion tensor imaging in both methylenetetrahydrofolate reductase deficiency patients and healthy controls. This is the first synthetic MRI–based in vivo evaluation of treatment-associated remyelination. 1.5 T synthetic MRI and 3 T diffusion MRI were obtained from three methylenetetrahydrofolate reductase deficiency patients at baseline and 6 months after therapy initiation, as well as from age-matched healthy controls (diffusion tensor imaging: n = 14, synthetic MRI: n = 9). Global and regional synthetic MRI parameters (myelin volume fraction, proton density, and relaxation rates) were compared with diffusion metrics (fractional anisotropy, mean/radial/axial diffusivity) and related to healthy controls by calculating z-scores and z-deviation maps. Whole-brain myelin (% of intracranial volume) of the index patient was reduced to 6 versus 10% in healthy controls, which recovered to a nonetheless subnormal level of 6.6% following initiation of high-dosage betaine. Radial diffusivity was higher at baseline compared with healthy controls (1.34 versus 0.79 × 10⁻³ mm²/s), recovering at follow-up (1.19 × 10⁻³ mm²/s). The index patient’s lesion volume diminished by 58% under treatment. Regional analysis within lesion area and atlas-based regions revealed lower mean myelin volume fraction (12.7_Baseline/14.71_Follow-up%) and relaxation rates, higher proton density, as well as lower fractional anisotropy and higher radial diffusivity (1.08 × 10⁻³_Baseline/0.94 × 10⁻³_Follow-up) compared with healthy controls. The highest z-scores were observed for myelin volume fraction in the posterior thalamic radiation, with greater deviation from controls at baseline and reduced deviation at follow-up. Z-deviations of diffusion metrics were less pronounced for radial and mean diffusivity than for myelin volume fraction. Z-maps for myelin volume fraction of the index patient demonstrated high deviation within and beyond lesion areas, among others in the precentral and postcentral gyrus, as well as in the cerebellum, and partial remission of these alterations at follow-up, while radial diffusivity demonstrated more widespread deviations in supra- and infratentorial regions. Concordant changes of myelin volume fraction and radial diffusivity after treatment initiation, accompanied by dramatic clinical and paraclinical improvement, indicate the consistency of the methods, while myelin volume fraction seems to characterize remyelinated regions more specifically. Synthetic MRI–based myelin volume fraction provides myelin estimation consistent with changes of diffusion metrics to monitor short-term myelin changes on individual patient level.

Intelligenzdiagnostik der Zukunft

Zusammenfassung. Die menschliche Intelligenz ist eines der am besten erforschten und validierten Konstrukte innerhalb der Psychologie. Dennoch wird die Validität von Intelligenztests im gruppen- und insbesondere kulturvergleichenden Kontext regelmäßig und berechtigterweise kritisch hinterfragt. Obwohl verschiedene Alternativen und Weiterentwicklungen der Intelligenzdiagnostik vorgeschlagen wurden (z. B. kulturfaire Tests), sind fundamentale Probleme in der vergleichenden Intelligenzdiagnostik noch immer ungelöst und die Validitäten entsprechender Verfahren unklar. In dem vorliegenden Positionspapier wird diese Thematik aus der Perspektive der Kognitionspsychologie und der kognitiven Neurowissenschaften beleuchtet und eine prozessorientierte und biologisch inspirierte Form der Intelligenzdiagnostik als potentieller Lösungsansatz vorgeschlagen. Wir zeigen die Bedeutung elementarer kognitiver Prozesse auf (insbesondere Arbeitsgedächtniskapazität, Aufmerksamkeit, Verarbeitungsgeschwindigkeit), die individuellen Leistungsunterschieden zu Grunde liegen, und betonen, dass der Unterscheidung zwischen Inhalten und Prozessen eine zentrale, jedoch oft vernachlässigte Rolle in der Diagnostik allgemeiner kognitiver Leistungsunterschiede zukommt. Während aus kognitions- und neuropsychologischer Sicht davon ausgegangen werden kann, dass sich insbesondere Prozesse für interkulturelle Vergleiche eignen, sollten Inhalte als stärker kulturspezifisch verstanden werden. Darauf aufbauend diskutieren wir drei verschiedene Ansätze zur Verbesserung interkultureller Vergleichbarkeit der Intelligenzdiagnostik sowie deren Grenzen. Wir postulieren, dass sich die Intelligenzforschung im Austausch mit verschiedenen Disziplinen stärker auf die Identifikation von generellen kognitiven Prozessen fokussieren sollte und diskutieren das Potenzial zukünftiger Forschung hin zu einer prozessorientierten und biologisch inspirierten Intelligenzdiagnostik. Schließlich zeigen wir derzeitige Möglichkeiten auf, gehen aber auch auf etwaige Herausforderungen ein und beleuchten Implikationen für die zukünftige Intelligenzdiagnostik und -forschung.

Lifespan Volume Trajectories From Non–harmonized T1–Weighted MRI Do Not Differ After Site Correction Based on Traveling Human Phantoms

Multi–site imaging consortiums strive to increase participant numbers by pooling data across sites, but scanner related differences can bias results. This study combines data from three research MRI centers, including three different scanner models from two vendors, to examine non–harmonized T1–weighted brain imaging protocols in two cohorts. First, 23 human traveling phantoms were scanned twice each at all three sites (six scans per person; 138 scans total) to quantify within–participant variability of brain volumes (total brain, white matter, gray matter, lateral ventricles, thalamus, caudate, putamen and globus pallidus), and to calculate site–specific correction factors for each structure. Sample size calculations were used to determine the number of traveling phantoms needed to achieve effect sizes for observed differences to help guide future studies. Next, cross–sectional lifespan volume trajectories were examined in 856 healthy participants (5—91 years of age) scanned at these sites. Cross–sectional trajectories of volume versus age for each structure were then compared before and after application of traveling phantom based site–specific correction factors, as well as correction using the open–source method ComBat. Although small systematic differences between sites were observed in the traveling phantom analysis, correction for site using either method had little impact on the lifespan trajectories. Only white matter had small but significant differences in the intercept parameter after ComBat correction (but not traveling phantom based correction), while no other fits differed. This suggests that age–related changes over the lifespan outweigh systematic differences between scanners for volumetric analysis. This work will help guide pooling of multisite datasets as well as meta–analyses of data from non–harmonized protocols.

Parameters from site classification to harmonize MRI clinical studies: Application to a multi-site Parkinson's disease dataset

Multi‐site MRI datasets are crucial for big data research. However, neuroimaging studies must face the batch effect. Here, we propose an approach that uses the predictive probabilities provided by Gaussian processes (GPs) to harmonize clinical‐based studies. A multi‐site dataset of 216 Parkinson's disease (PD) patients and 87 healthy subjects (HS) was used. We performed a site GP classification using MRI data. The outcomes estimated from this classification, redefined like Weighted HARMonization PArameters (WHARMPA), were used as regressors in two different clinical studies: A PD versus HS machine learning classification using GP, and a VBM comparison (FWE‐p < .05, k = 100). Same studies were also conducted using conventional Boolean site covariates, and without information about site belonging. The results from site GP classification provided high scores, balanced accuracy (BAC) was 98.39% for grey matter images. PD versus HS classification performed better when the WHARMPA were used to harmonize (BAC = 78.60%; AUC = 0.90) than when using the Boolean site information (BAC = 56.31%; AUC = 0.71) and without it (BAC = 57.22%; AUC = 0.73). The VBM analysis harmonized using WHARMPA provided larger and more statistically robust clusters in regions previously reported in PD than when the Boolean site covariates or no corrections were added to the model. In conclusion, WHARMPA might encode global site‐effects quantitatively and allow the harmonization of data. This method is user‐friendly and provides a powerful solution, without complex implementations, to clean the analyses by removing variability associated with the differences between sites.

Same Brain, Different Look?-The Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19–54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyra^fit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.

The impact of the Siemens Tim Trio to Prisma upgrade and the addition of volumetric navigators on cortical thickness, structure volume, and 1H-MRS indices: An MRI reliability study with implications for longitudinal study designs

Many magnetic resonance imaging (MRI) measures are being studied longitudinally to explore topics such as biomarker detection and clinical staging. A pertinent concern to longitudinal work is MRI scanner upgrades. When upgrades occur during the course of a longitudinal MRI neuroimaging investigation, there may be an impact on the compatibility of pre- and post-upgrade measures. Similarly, subject motion is another issue that may be detrimental to MRI work and embedding volumetric navigators (vNavs) within acquisition sequences has emerged as a technique that allows for prospective motion correction. Our research group recently underwent an upgrade from a Siemens MAGNETOM 3T Tim Trio system to a Siemens MAGNETOM 3T Prisma Fit system. The goals of the current work were to: 1) investigate the impact of this upgrade on commonly used structural imaging measures and proton magnetic resonance spectroscopy indices ("Prisma Upgrade protocol") and 2) examine structural imaging measures in a sequence with vNavs alongside a standard acquisition sequence ("vNav protocol"). While high reliability was observed for most of the investigated MRI outputs, suboptimal reliability was observed for certain indices. Across the scanner upgrade, increases in frontal, temporal, and cingulate cortical thickness (CT) and thalamus volume, along with decreases in parietal CT and amygdala, globus pallidus, hippocampus, and striatum volumes, were observed. No significant impact of the upgrade was found in ¹H-MRS analyses. Further, CT estimates were found to be larger in MPRAGE acquisitions compared to vNav-MPRAGE acquisitions mainly within temporal areas, while the opposite was found mostly in parietal brain regions. The results from this work should be considered in longitudinal study designs and comparable prospective motion correction investigations are warranted in cases of marked head movement.

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.

A machine learning-based classification approach on Parkinson's disease diffusion tensor imaging datasets

Introduction

The presence of motor signs and symptoms in Parkinson’s disease (PD) is the result of a long-lasting prodromal phase with an advancing neurodegenerative process. The identification of PD patients in an early phase is, however, crucial for developing disease-modifying drugs. The objective of our study is to investigate whether Diffusion Tensor Imaging (DTI) of the Substantia nigra (SN) analyzed by machine learning algorithms (ML) can be used to identify PD patients.

Methods

Our study proposes the use of computer-aided algorithms and a highly reproducible approach (in contrast to manually SN segmentation) to increase the reliability and accuracy of DTI metrics used for classification.

Results

The results of our study do not confirm the feasibility of the DTI approach, neither on a whole-brain level, ROI-labelled analyses, nor when focusing on the SN only.

Conclusions

Our study did not provide any evidence to support the hypothesis that DTI-based analysis, in particular of the SN, could be used to identify PD patients correctly.

Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics

To assess the feasibility of a denoising approach with deep learning-based reconstruction (dDLR) for fast volume simultaneous multi-slice diffusion tensor imaging of the brain, noise reduction effects and the reliability of diffusion metrics were evaluated with 20 patients. Image noise was significantly decreased with dDLR. Although fractional anisotropy (FA) of deep gray matter was overestimated when the number of image acquisitions was one (NAQ1), FA in NAQ1 with dDLR became closer to that in NAQ5.

The TOTEM RRMS (Testosterone Treatment on neuroprotection and Myelin Repair in Relapsing Remitting Multiple Sclerosis) trial: study protocol for a randomized, double-blind, placebo-controlled trial

BACKGROUND

Central nervous system damage in multiple sclerosis (MS) is responsible for serious deficiencies. Current therapies are focused on the treatment of inflammation; however, there is an urgent need for innovative therapies promoting neuroregeneration, particularly myelin repair. It is demonstrated that testosterone can act through neural androgen receptors and several clinical observations stimulated an interest in the potential protective effects of testosterone treatment for MS. Here, we sought to demonstrate the effects of a testosterone supplementation in testosterone-deficient men with relapsing-remitting MS.

METHODS/DESIGN

This report presents the rationale and methodology of TOTEM RRMS, a French, phase 2, multicenter, randomized, placebo-controlled, and double-blind trial, which aims to prevent the progression of MS in men with low testosterone levels by administration of testosterone undecanoate, who were kept under natalizumab (Tysabri®) to overcome the anti-inflammatory effect of testosterone. Forty patients will be randomized into two groups receiving either a testosterone treatment (Nebido®) or a matching placebo. The intervention period for each group will last 66 weeks (treatment will be injected at baseline, week 6, and then every 12 weeks). The main objective is to determine the neuroprotective and remyelinating effects of testosterone using tensor diffusion imaging techniques and thalamic atrophy analyses. As secondary objectives, impacts of the testosterone supplementation will be studied using other conventional and unconventional MRI parameters and with clinical outcomes.

DISCUSSION

The action of testosterone is observed in different experimental autoimmune encephalomyelitis models and epidemiological studies in humans. However, despite several preclinical data and some small clinical trials in MS, clear evidence for a therapeutic effect of hormone therapy is still missing. Therefore, our goal is to demonstrate the effects of testosterone therapies in MS. As there is no effective treatment currently available on fatigue in MS, careful attention should also be paid to secondary endpoints: fatigue, cognitive functions, and other symptoms that may improve life quality. Assuming a positive outcome of the trial, this treatment could be considered as a new neuroprotective and remyelinating therapy in relapsing-remitting MS and could be applicable to other demyelinating diseases.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03910738. Registered on 10 April 2019.

Test-retest reliability and minimal detectable change of corticospinal tract integrity in chronic stroke

Diffusion tensor imaging (DTI) can be used to index white matter integrity of the corticospinal tract (CST) after stroke; however, the psychometric properties of DTI-based measures of white matter integrity are unknown. The purpose of this study was to examine test-retest reliability as determined by intraclass correlation coefficients (ICC) and calculate minimal detectable change (MDC) of DTI-based measures of CST integrity using three different approaches: a Cerebral Peduncle approach, a Probabilistic Tract approach, and a Tract Template approach. Eighteen participants with chronic stroke underwent DTI on the same magnetic resonance imaging scanner 4 days apart. For the Cerebral Peduncle approach, a researcher hand drew masks at the cerebral peduncle. For the Probabilistic Tract approach, tractography was seeded in motor areas of the cortex to the cerebral peduncle. For the Tract Template approach, a standard CST template was transformed into native space. For all approaches, the researcher performing analyses was blind to participant number and day of data collection. All three approaches had good to excellent test-retest reliability for fractional anisotropy (FA; ICCs >0.786). Mean diffusivity, axial diffusivity, and radial diffusivity were less reliable than FA. The ICC values were highest and MDC values were the smallest for the most automated approach (Tract Template), followed by the combined manual/automated approach (Probabilistic Tract) then the manual approach (Cerebral Peduncle). The results of this study may have implications for how DTI-based measures of CST integrity are used to define impairment, predict outcomes, and interpret change after stroke.

Multicenter dataset of multi-shell diffusion MRI in healthy traveling adults with identical settings

Multicenter diffusion magnetic resonance imaging (MRI) has drawn great attention recently due to the expanding need for large-scale brain imaging studies, whereas the variability in MRI scanners and data acquisition tends to confound reliable individual-based analysis of diffusion measures. In addition, a growing number of multi-shell diffusion models have been shown with the potential to generate various estimates of physio-pathological information, yet their reliability and reproducibility in multicenter studies remain to be assessed. In this article, we describe a multi-shell diffusion dataset collected from three traveling subjects with identical acquisition settings in ten imaging centers. Both the scanner type and imaging protocol for anatomical and diffusion imaging were well controlled. This dataset is expected to replenish individual reproducible studies via multicenter collaboration by providing an open resource for advanced and novel microstructural and tractography modelling and quantification.

Harmonization of Brain Diffusion MRI: Concepts and Methods

MRI diffusion data suffers from significant inter- and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and joint analysis of diffusion data across sites and over time, there is a clear need for robust data harmonization methods. This review article provides a comprehensive overview of diffusion data harmonization concepts and methods, and their limitations. Overall, the methods for the harmonization of multi-site diffusion images can be categorized in two main groups: diffusion parametric map harmonization (DPMH) and diffusion weighted image harmonization (DWIH). Whereas DPMH harmonizes the diffusion parametric maps (e.g., FA, MD, and MK), DWIH harmonizes the diffusion-weighted images. Defining a gold standard harmonization technique for dMRI data is still an ongoing challenge. Nevertheless, in this paper we provide two classification tools, namely a feature table and a flowchart, which aim to guide the readers in selecting an appropriate harmonization method for their study.

Image acquisition and quality assurance in the Boston Adolescent Neuroimaging of Depression and Anxiety study

The Connectomes Related to Human Diseases (CRHD) initiative was developed with the Human Connectome Project (HCP) to provide high-resolution, open-access, multi-modal MRI data to better understand the neural correlates of human disease. Here, we present an introduction to a CRHD project, the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) study, which is collecting multimodal neuroimaging, clinical, and neuropsychological data from 225 adolescents (ages 14-17), 150 of whom are expected to have a diagnosis of depression and/or anxiety. Our transdiagnostic recruitment approach samples the full spectrum of depressed/anxious symptoms and their comorbidity, consistent with NIMH Research Domain Criteria (RDoC). We focused on an age range that is critical for brain development and for the onset of mental illness. This project sought to harmonize imaging sequences, hardware, and functional tasks with other HCP studies, although some changes were made to canonical HCP methods to accommodate our study population and questions. We present a thorough overview of our imaging sequences, hardware, and scanning protocol. We detail similarities and differences between this study and other HCP studies. We evaluate structural-, diffusion-, and functional-image-quality measures that may be influenced by clinical factors (e.g., disorder, symptomatology). Signal-to-noise and motion estimates from the first 140 adolescents suggest minimal influence of clinical factors on image quality. We anticipate enrollment of an additional 85 participants, most of whom are expected to have a diagnosis of anxiety and/or depression. Clinical and neuropsychological data from the first 140 participants are currently freely available through the National Institute of Mental Health Data Archive (NDA).

Scan-rescan and inter-vendor reproducibility of neurite orientation dispersion and density imaging metrics

Purpose

The reproducibility of neurite orientation dispersion and density imaging (NODDI) metrics in the human brain has not been explored across different magnetic resonance (MR) scanners from different vendors. This study aimed to evaluate the scan–rescan and inter-vendor reproducibility of NODDI metrics in white and gray matter of healthy subjects using two 3-T MR scanners from two vendors.

Methods

Ten healthy subjects (7 males; mean age 30 ± 7 years, range 23–37 years) were included in the study. Whole-brain diffusion-weighted imaging was performed with b-values of 1000 and 2000 s/mm² using two 3-T MR scanners from two different vendors. Automatic extraction of the region of interest was performed to obtain NODDI metrics for whole and localized areas of white and gray matter. The coefficient of variation (CoV) and intraclass correlation coefficient (ICC) were calculated to assess the scan–rescan and inter-vendor reproducibilities of NODDI metrics.

Results

The scan–rescan and inter-vendor reproducibility of NODDI metrics (intracellular volume fraction and orientation dispersion index) were comparable with those of diffusion tensor imaging (DTI) metrics. However, the inter-vendor reproducibilities of NODDI (CoV = 2.3–14%) were lower than the scan–rescan reproducibility (CoV: scanner A = 0.8–3.8%; scanner B = 0.8–2.6%). Compared with the finding of DTI metrics, the reproducibility of NODDI metrics was lower in white matter and higher in gray matter.

Conclusion

The lower inter-vendor reproducibility of NODDI in some brain regions indicates that data acquired from different MRI scanners should be carefully interpreted.

Test-retest reproducibility of a multi-atlas automated segmentation tool on multimodality brain MRI

INTRODUCTION

The increasing use of large sample sizes for population and personalized medicine requires high-throughput tools for imaging processing that can handle large amounts of data with diverse image modalities, perform a biologically meaningful information reduction, and result in comprehensive quantification. Exploring the reproducibility of these tools reveals the specific strengths and weaknesses that heavily influence the interpretation of results, contributing to transparence in science.

METHODS

We tested-retested the reproducibility of MRICloud, a free automated method for whole-brain, multimodal MRI segmentation and quantification, on two public, independent datasets of healthy adults.

RESULTS

The reproducibility was extremely high for T1-volumetric analysis, high for diffusion tensor images (DTI) (however, regionally variable), and low for resting-state fMRI.

CONCLUSION

In general, the reproducibility of the different modalities was slightly superior to that of widely used software. This analysis serves as a normative reference for planning samples and for the interpretation of structure-based MRI studies.

Structural connectivity and risk for anhedonia after trauma: A prospective study and replication

Anhedonia emerges in some people after psychological trauma, reflected by a loss of interest, diminished affect, and detachment. Structural abnormalities in specific neural pathways at the time of trauma may influence the development of these posttraumatic anhedonia (PTA) symptoms. In this prospective study, we determined whether white matter connectivity at around one month post-trauma predicts PTA and other PTSD symptoms at six months post-trauma. Thirty men and women aged 19-62 were recruited from the emergency department of a Level I trauma center. Participants received diffusion tensor imaging at approximately one month post-trauma and clinical assessments at one and six months post-trauma. Probabilistic tractography was used to examine connectivity of select pathways. A replication sample (N = 57) in an independent, cross-sectional dataset of traumatized women was similarly analyzed. Logistic regression results indicated that, after accounting for early PTSD symptoms (at one month) and other clinical risk factors, the integrity of the uncinate fasciculus (UF) uniquely predicted the presence of PTA at six months post-trauma (Beta = -225.6, p < .05). Together, these factors contributed to 76% of the variance in PTA. Integrity of the UF also predicted re-experiencing PTSD symptoms at six months post-trauma. These results were supported in our replication analyses. Our findings indicate that the integrity of the UF around 1 month post-trauma affects vulnerability for the development of anhedonic PTSD symptoms as well as re-experiencing symptoms. Connectivity of this amygdala-ventromedial prefrontal pathway appears to be a salient predictor of anhedonia, above and beyond clinical risk factors.

Reproducibility of Structural and Diffusion Tensor Imaging in the TACERN Multi-Center Study

BACKGROUND

Multi-site MRI studies are often necessary for recruiting sufficiently sized samples when studying rare conditions. However, they require pooling data from multiple scanners into a single data set, and therefore it is critical to evaluate the variability of quantitative MRI measures within and across scanners used in multi-site studies. The aim of this study was to evaluate the reproducibility of structural and diffusion weighted (DW) MRI measurements acquired on seven scanners at five medical centers as part of the Tuberous Sclerosis Complex Autism Center of Excellence Research Network (TACERN) multisite study.

METHODS

The American College of Radiology (ACR) phantom was imaged monthly to measure reproducibility of signal intensity and uniformity within and across seven 3T scanners from General Electric, Philips, and Siemens vendors. One healthy adult male volunteer was imaged repeatedly on all seven scanners under the TACERN structural and DW protocol (5 b = 0 s/mm2 and 30 b = 1000 s/mm2) over a period of 5 years (age 22-27 years). Reproducibility of inter- and intra-scanner brain segmentation volumes and diffusion tensor imaging metrics fractional anisotropy (FA) and mean diffusivity (MD) within white matter regions was quantified with coefficient of variation.

RESULTS

The American College of Radiology Phantom signal intensity and uniformity were similar across scanners and changed little over time, with a mean intra-scanner coefficient of variation of 3.6 and 1.8%, respectively. The mean inter- and intra-scanner coefficients of variation of brain structure volumes derived from T1-weighted (T1w) images of the human phantom were 3.3 and 1.1%, respectively. The mean inter- and intra-scanner coefficients of variation of FA in white matter regions were 4.5 and 2.5%, while the mean inter- and intra-scanner coefficients of variation of MD in white matter regions were 5.4 and 1.5%.

CONCLUSION

Our results suggest that volumetric and diffusion tensor imaging (DTI) measurements are highly reproducible between and within scanners and provide typical variation amplitudes that can be used as references to interpret future findings in the TACERN network.

Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison

Neuroimaging MRI data in scientific research is increasingly pooled, but the reliability of such studies may be hampered by the use of different hardware elements. This might introduce bias, for example when cross-sectional studies pool data acquired with different head coils, or when longitudinal clinical studies change head coils halfway. In the present study, we aimed to estimate this possible bias introduced by using different head coils to create awareness and to avoid misinterpretation of results. We acquired, with both an 8 channel and 32 channel head coil, T1-weighted, diffusion tensor imaging and resting state fMRI images at 3T MRI (Philips Achieva) with stable acquisition parameters in a large group of cognitively healthy participants (n = 77). Standard analysis methods, i.e., voxel-based morphometry, tract-based spatial statistics and resting state functional network analyses, were used in a within-subject design to compare 8 and 32 channel head coil data. Signal-to-noise ratios (SNR) for both head coils showed similar ranges, although the 32 channel SNR profile was more homogeneous. Our data demonstrates specific patterns of gray and white matter volume differences between head coils (relative volume change of 6 to 9%), related to altered image contrast and therefore, altered tissue segmentation. White matter connectivity (fractional anisotropy and diffusivity measures) showed hemispherical dependent differences between head coils (relative connectivity change of 4 to 6%), and functional connectivity in resting state networks was higher using the 32 channel head coil in posterior cortical areas (relative change up to 27.5%). This study shows that, even when acquisition protocols are harmonized, the results of standardized analysis models can be severely affected by the use of different head coils. Researchers should be aware of this when combining multiple neuroimaging MRI datasets, to prevent coil-related bias and avoid misinterpretation of their findings.

Developing therapeutic strategies to promote myelin repair in multiple sclerosis

Introduction: Approved disease-modifying therapies for multiple sclerosis (MS) lessen inflammatory disease activity that causes relapses and MRI lesions. However, chronic inflammation and demyelination lead to axonal degeneration and neuronal loss, for which there currently is no effective treatment. There has been increasing interest in developing repair-promoting strategies, but there are important unanswered questions regarding the mechanisms and appropriate methods to evaluate these treatments. Areas covered: The rationale for remyelinating agents in MS is discussed, with an overview of both myelin physiology and endogenous repair mechanisms. This is followed by a discussion of the identification and development of potential remyelinating drugs. Potential biomarkers of remyelination are reviewed, including considerations regarding measuring remyelination in clinical trials. Information and data were obtained from a search of recent literature through PubMed. Peer-reviewed original articles and review articles were included. Expert opinion: There are several obstacles to the translation of potential remyelinating agents to clinical trials, particularly uncertainty regarding the most appropriate study population and method to monitor remyelination. Refinements in clinical trial design and outcome measurement, potentially via advanced imaging techniques, are needed to optimize detection of repair in patients with MS.

Attentional control abnormalities in posttraumatic stress disorder: Functional, behavioral, and structural correlates

BACKGROUND

Attentional disruptions are common in PTSD, but findings across neuropsychological and neuroimaging studies have been variable. Few PTSD studies have investigated abnormalities in attention networks using a multi-modal imaging approach and attentional tasks that include emotionally-salient images. This study combined a behavioral task that included these images (emotional Stroop) with functional and structural neuroimaging (fMRI and diffusion tensor imaging; DTI) methods to comprehensively investigate attentional control abnormalities in a highly-traumatized civilian sample.

METHODS

48 traumatized women with and without PTSD received clinical assessments, fMRI and DTI. During fMRI, the Affective Stroop (AS), an attentional control task that includes emotionally-salient distractor images (trauma-relevant, positive, neutral) and variable task demands, was administered.

RESULTS

In response to more difficult AS trials, participants with PTSD demonstrated lower activation in the dorsal and rostral anterior cingulate cortex and greater activation in the insula. This group also showed comparatively poorer performance on positive AS distractor trials, even after adjusting for trauma exposure. Performance on these trials inversely correlated with structural integrity of the cingulum bundle and uncinate fasciculus.

CONCLUSIONS

Even after adjusting for trauma exposure, participants with PTSD showed worse performance on an attentional control task in the context of emotional stimuli. They also showed relatively lower cognitive control network activation and greater salience network activation. Fronto-parietal and fronto-limbic white matter connectivity corresponded with AS performance. Our findings indicate that attentional control impairments in PTSD are most evident in the context of emotional cues, and are related to decrements in function and structure of cognitive control and salience networks.

Diagnosis and prognosis of Alzheimer's disease using brain morphometry and white matter connectomes

Accurate, reliable prediction of risk for Alzheimer's disease (AD) is essential for early, disease-modifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of the multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping—morphometry and structural connectomics—and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (National Health Insurance Service-Ilsan Hospital [NHIS-IH]; N = 211; 110 AD, 64 mild cognitive impairment [MCI], and 37 cognitively normal with subjective memory complaints [SMC]) to test the diagnostic models; and, secondly, Alzheimer's Disease Neuroimaging Initiative (ADNI)-2 to test the generalizability. Our machine learning models trained on the morphometric and connectome estimates (number of features = 34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) in NHIS-IH cohort, outperforming a benchmark model (FLAIR-based white matter hyperintensity volumes). In ADNI-2 data, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) compared with the CSF biomarker model (t-tau, p-tau, and Amyloid β, and ratios). In predicting MCI to AD progression in a smaller cohort of ADNI-2 (n = 60), the morphometry model showed similar performance with 69% accuracy compared with CSF biomarker model with 70% accuracy. Our comparisons of the classifiers trained on structural MRI, diffusion MRI, FLAIR, and CSF biomarkers showed the promising utility of the white matter structural connectomes in classifying AD and MCI in addition to the widely used structural MRI-based morphometry, when combined with machine learning.

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This study tests the utility of multimodal brain MRI and machine learning in diagnosis and prognosis of AD.

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Models trained on connectomes and morphometry beast classify elders with AD or MCI from cognitively normal elders.

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Models trained on morphometry best predict MCI to AD progression.

Imaging outcome measures of neuroprotection and repair in MS: A consensus statement from NAIMS

OBJECTIVE

To summarize current and emerging imaging techniques that can be used to assess neuroprotection and repair in multiple sclerosis (MS), and to provide a consensus opinion on the potential utility of each technique in clinical trial settings.

METHODS

Clinicians and scientists with expertise in the use of MRI in MS convened in Toronto, Canada, in November 2016 at a North American Imaging in Multiple Sclerosis (NAIMS) Cooperative workshop meeting. The discussion was compiled into a manuscript and circulated to all NAIMS members in attendance. Edits and feedback were incorporated until all authors were in agreement.

RESULTS

A wide spectrum of imaging techniques and analysis methods in the context of specific study designs were discussed, with a focus on the utility and limitations of applying each technique to assess neuroprotection and repair. Techniques were discussed under specific themes, and included conventional imaging, magnetization transfer ratio, diffusion tensor imaging, susceptibility-weighted imaging, imaging cortical lesions, magnetic resonance spectroscopy, PET, advanced diffusion imaging, sodium imaging, multimodal techniques, imaging of special regions, statistical considerations, and study design.

CONCLUSIONS

Imaging biomarkers of neuroprotection and repair are an unmet need in MS. There are a number of promising techniques with different strengths and limitations, and selection of a specific technique will depend on a number of factors, notably the question the trial seeks to answer. Ongoing collaborative efforts will enable further refinement and improved methods to image the effect of novel therapeutic agents that exert benefit in MS predominately through neuroprotective and reparative mechanisms.

BOLD signal variability and complexity in children and adolescents with and without autism spectrum disorder

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Resting-state BOLD signal variability and complexity were examined.

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No significant group differences were observed in youth with and without autism.

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A continuum of brain-behavior relationships was observed across diagnostic groups.

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Positive correlations were found between brain measures, age and global efficiency.

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Negative correlations were found between the brain measures and behavioral severity.

Variability of neural signaling is an important index of healthy brain functioning, as is signal complexity, which relates to information processing capacity. Alterations in variability and complexity may underlie certain brain dysfunctions. Here, resting-state fMRI was used to examine variability and complexity in children and adolescents with and without autism spectrum disorder (ASD). Variability was measured using the mean square successive difference (MSSD) of the time series, and complexity was assessed using sample entropy. A categorical approach was implemented to determine if the brain measures differed between diagnostic groups (ASD and controls). A dimensional approach was used to examine the continuum of relationships between each brain measure and behavioral severity, age, IQ, and the global efficiency (GE) of each participant’s structural connectome, which reflects the structural capacity for information processing. Using the categorical approach, no significant group differences were found for neither MSSD nor entropy. The dimensional approach revealed significant positive correlations between each brain measure, GE, and age. Negative correlations were observed between each brain measure and the severity of ASD behaviors across all participants. These results reveal the nature of variability and complexity of BOLD signals in children and adolescents with and without ASD.

Reproducibility of multi-shell diffusion tractography on traveling subjects: A multicenter study prospective

Reproducibility of multicenter diffusion magnetic resonance imaging has drawn more attention recently due to rapidly increasing need for large-size brain imaging studies. Advanced multi-shell diffusion models are recommended for their potentials to provide variety of physio-pathological information. While previous studies have investigated the consistency of single-shell diffusion acquisition from various hardware and protocols, a well-controlled study with multi-shell acquisition would be necessary to understand the inherent factors of reproducibility from new complexity of such acquisition protocol. In this study, three traveling subjects were scanned at eight imaging centers equipped with the same type of scanners using the same multi-shell diffusion imaging protocol. Track density imaging and structure connectomes were investigated in local-scale distribution and in distal-scale connectivity, respectively. With evaluations of the coefficient of variation and the intra-class correlation coefficient, our results indicated: 1) similar to single-shell schemes, the intra-center reproducibility of multi-shell is higher than inter-center; 2) multi-shell schemes produce higher reproducibility and precision among centers compared to the single-shell schemes; and 3) in addition to the diffusion schemes, image quality and the presence of complex fiber structure could also associated with multicenter reproducibility.

Longitudinal Progression Markers of Parkinson's Disease: Current View on Structural Imaging

PURPOSE OF REVIEW

Advances in neuroimaging techniques pave a rich avenue for in vivo progression biomarkers, which can objectively and noninvasively assess the long-term dynamic alterations in the brain of Parkinson's disease (PD) patients. This article reviews recent progress in structural magnetic resonance imaging (MRI) tools to track disease progression in PD, and discusses specific criteria a neuroimaging tool needs to meet to be a progression biomarker of PD and the potential applications of these techniques in PD based on current evidence.

RECENT FINDINGS

Recent longitudinal studies showed that quantitative structural MRI markers derived from T1-weighted, diffusion-weighted, neuromelanin-sensitive, and iron-sensitive imaging have the potential to track disease progression in PD. However, validation of these progression biomarkers is only beginning, and more work is required for multisite validation, the sample size for use in a clinical trial, and drug-responsiveness of most of these biomarkers. At present, the most clinical trial-ready biomarker is free-water diffusion imaging of the substantia nigra and seems well established to be used in disease-modifying studies in PD. A variety of structural imaging biomarkers are promising candidates to be progression biomarkers in PD. Further studies are needed to elucidate the sensitivity, reliability, sample size, and effect of confounding factors of these progression biomarkers.

Scan-rescan repeatability and cross-scanner comparability of DTI metrics in healthy subjects in the SPRINT-MS multicenter trial

PURPOSE

To assess intrascanner repeatability and cross-scanner comparability for diffusion tensor imaging (DTI) metrics in a multicenter clinical trial.

METHODS

DTI metrics (including longitudinal diffusivity [LD], fractional anisotropy [FA], mean diffusivity [MD], and transverse diffusivity [TD]) from pyramidal tracts for healthy controls were calculated from images acquired on twenty-seven 3T MR scanners (Siemens and GE) with 6 different scanner models and 7 different software versions as part of the NN102/SPRINT-MS clinical trial. Each volunteer underwent two scanning sessions on the same scanner. Signal-to-noise ratio (SNR) and signal-to-noise floor ratio (SNFR) were also assessed.

RESULTS

DTI metrics showed good scan-rescan repeatability. There were no significant differences between scans and rescans in LD, FA, MD, or TD values. Although the cross-scanner coefficient of variation (CV) values for all DTI metrics were <5.7%, significant differences were observed for LD (p < 3.3e-5) and FA (p < 0.0024) when GE scanners were compared with Siemens scanners. Significant differences were also observed for SNR when comparing GE scanners and Siemens Skyra scanners (p < 1.4e-7) and when comparing Siemens Skyra scanners and TIM Trio scanners (p < 1.0e-10). Analysis of background signal also demonstrated differences between GE and Siemens scanners in terms of signal statistics. The measured signal intensity from a background noise region of interest was significantly higher for GE scanners than for Siemens scanners (p < 1.2e-12). Significant differences were also observed for SNFR when comparing GE scanners and Siemens Skyra scanners (p < 2.5e-11), GE scanners and Siemens Trio scanners (p < 7.5e-11), and Siemens Skyra scanners and TIM Trio scanners (p < 2.5e-9).

CONCLUSIONS

The good repeatability of the DTI metrics among the 27 scanners used in this study confirms the feasibility of combining DTI data from multiple centers using high angular resolution sequences. Our observations support the feasibility of longitudinal multicenter clinical trials using DTI outcome measures. The noise floor level and SNFR are important parameters that must be assessed when comparing studies that used different scanner models.

Inter-Vendor and Inter-Session Reliability of Diffusion Tensor Imaging: Implications for Multicenter Clinical Imaging Studies

Objective

To evaluate the inter-vendor and inter-session reliability of diffusion tensor imaging (DTI) and relevant parameters.

Materials and Methods

This prospective study included 10 healthy subjects (5 women and 5 men; age range, 25-33 years). Each subject was scanned twice using 3T magnetic resonance scanners from three different vendors at two different sites. A voxel-wise statistical analysis of diffusion data was performed using Tract-Based Spatial Statistics. Fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) values were calculated for each brain voxel using FMRIB's Diffusion Toolbox.

Results

A repeated measures analysis of variance revealed that there were no significant differences in FA values across the vendors or between sessions; however, there were significant differences in MD values between the vendors (p = 0.020). Although there were no significant differences in inter-session MD and inter-session/inter-vendor RD values, a significant group × factor interaction revealed differences in MD and RD values between the 1st and 2nd sessions conducted by the vendors (p = 0.004 and 0.006, respectively).

Conclusion

Although FA values exhibited good inter-vendor and inter-session reliability, MD and RD values did not show consistent results. Researchers using DTI should be aware of these limitations, especially when implementing DTI in multicenter studies.

White-Matter Pathways for Statistical Learning of Temporal Structures

Extracting the statistics of event streams in natural environments is critical for interpreting current events and predicting future ones. The brain is known to rapidly find structure and meaning in unfamiliar streams of sensory experience, often by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the brain pathways that support this type of statistical learning. Here, we test whether changes in white-matter (WM) connectivity due to training relate to our ability to extract temporal regularities. By combining behavioral training and diffusion tensor imaging (DTI), we demonstrate that humans adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. In particular, we show that learning relates to the decision strategy that individuals adopt when extracting temporal statistics. We next test for learning-dependent changes in WM connectivity and ask whether they relate to individual variability in decision strategy. Our DTI results provide evidence for dissociable WM pathways that relate to individual strategy: extracting the exact sequence statistics (i.e., matching) relates to connectivity changes between caudate and hippocampus, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to connectivity changes between prefrontal, cingulate and basal ganglia (caudate, putamen) regions. Thus, our findings provide evidence for distinct cortico-striatal circuits that show learning-dependent changes of WM connectivity and support individual ability to learn behaviorally-relevant statistics.

The neural determinants of age-related changes in fluid intelligence: a pre-registered, longitudinal analysis in UK Biobank

Background: Fluid intelligence declines with advancing age, starting in early adulthood. Within-subject declines in fluid intelligence are highly correlated with contemporaneous declines in the ability to live and function independently. To support healthy aging, the mechanisms underlying these declines need to be better understood. Methods: In this pre-registered analysis, we applied latent growth curve modelling to investigate the neural determinants of longitudinal changes in fluid intelligence across three time points in 185,317 individuals (N=9,719 two waves, N=870 three waves) from the UK Biobank (age range: 39-73 years). Results: We found a weak but significant effect of cross-sectional age on the mean fluid intelligence score, such that older individuals scored slightly lower. However, the mean longitudinal slope was positive, rather than negative, suggesting improvement across testing occasions. Despite the considerable sample size, the slope variance was non-significant, suggesting no reliable individual differences in change over time. This null-result is likely due to the nature of the cognitive test used. In a subset of individuals, we found that white matter microstructure (N=8839, as indexed by fractional anisotropy) and grey-matter volume (N=9931) in pre-defined regions-of-interest accounted for complementary and unique variance in mean fluid intelligence scores. The strongest effects were such that higher grey matter volume in the frontal pole and greater white matter microstructure in the posterior thalamic radiations were associated with higher fluid intelligence scores. Conclusions: In a large preregistered analysis, we demonstrate a weak but significant negative association between age and fluid intelligence. However, we did not observe plausible longitudinal patterns, instead observing a weak increase across testing occasions, and no significant individual differences in rates of change, likely due to the suboptimal task design. Finally, we find support for our preregistered expectation that white- and grey matter make separate contributions to individual differences in fluid intelligence beyond age.

The neural determinants of age-related changes in fluid intelligence: a pre-registered, longitudinal analysis in UK Biobank

Background: Fluid intelligence declines with advancing age, starting in early adulthood. Within-subject declines in fluid intelligence are highly correlated with contemporaneous declines in the ability to live and function independently. To support healthy aging, the mechanisms underlying these declines need to be better understood. Methods: In this pre-registered analysis, we applied latent growth curve modelling to investigate the neural determinants of longitudinal changes in fluid intelligence across three time points in 185,317 individuals (N=9,719 two waves, N=870 three waves) from the UK Biobank (age range: 39-73 years). Results: We found a weak but significant effect of cross-sectional age on the mean fluid intelligence score, such that older individuals scored slightly lower. However, the mean longitudinal slope was positive, rather than negative, suggesting improvement across testing occasions. Despite the considerable sample size, the slope variance was non-significant, suggesting no reliable individual differences in change over time. This null-result is likely due to the nature of the cognitive test used. In a subset of individuals, we found that white matter microstructure (N=8839, as indexed by fractional anisotropy) and grey-matter volume (N=9931) in pre-defined regions-of-interest accounted for complementary and unique variance in mean fluid intelligence scores. The strongest effects were such that higher grey matter volume in the frontal pole and greater white matter microstructure in the posterior thalamic radiations were associated with higher fluid intelligence scores. Conclusions: In a large preregistered analysis, we demonstrate a weak but significant negative association between age and fluid intelligence. However, we did not observe plausible longitudinal patterns, instead observing a weak increase across testing occasions, and no significant individual differences in rates of change, likely due to the suboptimal task design. Finally, we find support for our preregistered expectation that white- and grey matter make separate contributions to individual differences in fluid intelligence beyond age.