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In magnetic resonance imaging (MRI), the perception of substandard image quality may prompt repetition of the respective image acquisition protocol. Subsequently selecting the preferred high-quality image data from a series of acquisitions can be challenging. An automated workflow may facilitate and improve this selection. We therefore aimed to investigate the applicability of an automated image quality assessment for the prediction of the subjectively preferred image acquisition. Our analysis included data from 11,347 participants with whole-body MRI examinations performed as part of the ongoing prospective multi-center German National Cohort (NAKO) study. Trained radiologic technologists repeated any of the twelve examination protocols due to induced setup errors and/or subjectively unsatisfactory image quality and chose a preferred acquisition from the resultant series. Up to 11 quantitative image quality parameters were automatically derived from all acquisitions. Regularized regression and standard estimates of diagnostic accuracy were calculated. Controlling for setup variations in 2342 series of two or more acquisitions, technologists preferred the repetition over the initial acquisition in 1116 of 1396 series in which the initial setup was retained (79.9%, range across protocols: 73–100%). Image quality parameters then commonly showed statistically significant differences between chosen and discarded acquisitions. In regularized regression across all protocols, ‘structured noise maximum’ was the strongest predictor for the technologists’ choice, followed by ‘N/2 ghosting average’. Combinations of the automatically derived parameters provided an area under the ROC curve between 0.51 and 0.74 for the prediction of the technologists’ choice. It is concluded that automated image quality assessment can, despite considerable performance differences between protocols and anatomical regions, contribute substantially to identifying the subjective preference in a series of MRI acquisitions and thus provide effective decision support to readers. [ABSTRACT FROM AUTHOR]

Abstract: To date, little data is available on the reproducibility of functional connectivity MRI (fcMRI) studies. Here, we tested the variability and reproducibility of both the functional connectivity itself and different statistical methods to analyze this phenomenon. In the main part of our study, we repeatedly examined two healthy subjects in 10 sessions over 6 months with fcMRI. Cortical areas involved in motor function were examined under two different cognitive states: during continuous performance (CP) of a flexion/extension task of the fingers of the right hand and while subjects were at rest. Connectivity to left primary motor cortex (lSM1) was calculated by correlation analysis. The resulting correlation coefficients were transformed to z-scores of the standard normal distribution. For each subject, multisession statistical analyses were carried out with the z-score maps of the resting state (RS) and the CP experiments. First, voxel based t tests between the two groups of fcMRI experiments were performed. Second, ROI analyses were carried out for contralateral right SM1 and for supplementary motor area (SMA). For both ROI, mean and maximum z-score were calculated for each experiment. Also, the fraction of significantly (P<.05) correlated voxels (FCV) in each ROI was calculated. To evaluate the differences between the RS and the CP condition, paired t tests were performed for the mean and maximum z-scores, and Wilcoxon signed ranks tests for matched pairs were carried out for the FCV. All statistical methods and connectivity measures under investigation yielded a distinct loss in left–right SM1 connectivity under the CP condition. For SMA, interindividual differences were apparent. We therefore repeated the fcMRI experiments and the ROI analyses in a group of seven healthy subjects (including the two subjects of the main study). In this substudy, we were able to verify the reduction of left–right SM1 connectivity during unilateral performance. Still, the direction of SMA to lSM1 connectivity change during the CP condition remained undefined as four subjects showed a connectivity increase and three showed a decrease. In summary, we were able to demonstrate a distinct reduction in left–right SM1 synchrony in the CP condition compared to the RS both in the longitudinal and in the multisubject study. This effect was reproducible with all statistical methods and all measures of connectivity under investigation. We conclude that despite intra- and interindividual variability, serial and cross-sectional assessment of functional connectivity reveals stable and reliable results. [Copyright &y& Elsevier]

Subcortical age-related white matter changes (ARWMC) are a frequent finding in healthy elderly people suggested to cause secondary tissue changes and possibly affecting cognitive processes. We aimed to determine the influence of the extent of ARWMC load on attention and working memory processes in healthy elderly individuals. Fourteen healthy elderly subjects (MMSE >26; age 55–80 years) performed three fMRI tasks with increasing difficulty assessing alertness, attention (0-back), and working memory (2-back). We compared activation patterns in those with only minimal ARWMC (Fazekas 0–1) to those with moderate to severe ARWMC (Fazekas 2–3). During the fMRI experiments, the study population showed activation in brain areas typically involved in attention and working memory with a recruitment of cortical areas with increasing task difficulty. Subjects with higher lesion load showed a higher activation at all task levels with only sparse increase of signal with increasing complexity. In the lower lesion load group, rising task difficulty lead to a significant and widely distributed increase of activation. Although the number of patients included in the study is small, these findings suggest that even clinically silent ARWMC may affect cognitive processing and lead to compensatory activation during cognitive tasks. This can be interpreted as a reduction of functional reserve and may pose a risk for cognitive decline in these patients. [ABSTRACT FROM AUTHOR]

The article presents research that examines the influence of various magnetic resonance imaging (MRI) and clinical characteristics on cortical perfusion in multiple sclerosis (MS). The study shows the correlation between the perfusion and duration of the disease. It suggests that white matter lesions serve as a reduced perfusion and reduced metabolic demand at a cortical level.

Abstract: The diffusion properties of water are sensitive to microscopic changes in the white matter of multiple sclerosis (MS) patients. Typical MRI measures of disease burden in MS demonstrate modest to poor correlation with disability. Functional MRI and DTI-based fiber tracking were used to define the interhemispheric white matter pathway connecting bilateral supplementary motor areas (SMA) in 16 MS patients sand 16 control subjects. Fractional anisotropy (FA), mean diffusivity (MD), longitudinal (λ 1) and transverse diffusivity (λ 2) were measured along this pathway in all subjects. Mean FA was 0.587 ± 0.032 for patients and 0.608 ± 0.020 for controls (P < 0.02). Mean MD was (0.821 ± 0.055) × 10−3 mm2 s−1 for patients and (0.770 ± 0.020) × 10−3 mm2 s−1 for controls (P < 0.004). Mean λ 1 values were (1.462 ± 0.099) × 10−3 mm2 s−1 for patients and (1.400 ± 0.034) × 10−3 mm2 s−1 for controls (P < 0.02). Mean λ 2 values were (0.500 ± 0.047) × 10−3 mm2 s−1 for patients and (0.454 ± 0.027) × 10−3 mm2 s−1 for controls (P < 0.001). In addition, the correlation between the Multiple Sclerosis Functional Composite (MSFC) and transverse diffusivity was −0.341 (P < 0.05). The component test of the MSFC most related to the SMA pathway studied with our MRI method (Nine-hole Peg Test) showed significant correlation with transverse diffusivity (r = 0.392, P < 0.02), indicating that probing functional pathways with MRI measures can lead to a better reflection of disease status. [Copyright &y& Elsevier]

The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk marker of cross-disorder brain changes, growing into a cornerstone of biological age research. However, machine learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared because of data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte Carlo dropout composite quantile regression (MCCQR) Neural Network trained on N = 10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared with existing models. In two examples, we demonstrate that it prevents spurious associations and increases power to detect deviant brain aging. We make the pretrained model and code publicly available. [ABSTRACT FROM AUTHOR]

Different pathological processes like demyelination and axonal loss can alter the magnetisation transfer ratio (MTR) in brain tissue. The standard method to measure this effect is to scan the respective tissue twice, one with and one without a specific saturation pulse. A major drawback of this technique based on spoiled gradient echo (GRE) sequences relates to its long acquisition time due to the saturation pulses. Recently, an alternative concept for MT imaging based on balanced steady state free precession (bSSFP) has been proposed. Modification of the duration of the radiofrequency pulses for imaging allows scanning MT sensitive and non-sensitive images. The steady-state character of bSSFP with high intrinsic signal-to-noise ratio (SNR) allows three-dimensional (3D) whole brain MTR at high spatial resolution within short and thus clinically feasible acquisition times. In the present study, both bSSFP-MT and 2D GRE-MT imaging were used in a cohort of 31 patients with multiple sclerosis (MS) to characterize different normal appearing (NA) and pathological brain structures. Under the constraint of identical SNR and scan time, a 3.4 times higher voxel size could be achieved with bSSFP. This increased resolution allowed a more accurate delineation of the different brain structures, especially of cortex, hippocampus and MS lesions. In a multiple linear regression model, we found an association between MTR of cortical lesions and a clinical measure of disability (r = − 0.407, p = 0.035) in the bSSFP dataset only. The different relaxation weighting of the base images (T2/T1 in bSSFP, proton density in GRE) had no effects besides a larger spreading of the MTR values of the different NA structures. This was demonstrated by the nearly perfect linearity between the NA matter MTR of both techniques as well as in the absolute MTR differences between NA matter and the respective lesions. [ABSTRACT FROM AUTHOR]

Background: Mild cognitive impairment (MCI) is an intermediary state on the way to Alzheimer's disease (AD). Little is known about whole brain concentration of the neuronal marker, N-acetylaspartate (NAA) in MCI patients. Objective: To test the hypothesis that since MCI and AD are both neurodegenerative, quantification of the NAA in their whole brain (WBNAA) could differentiate them from cognitively-intact matched controls. Methods: Proton MR spectroscopy to quantify the WBNAA was applied to 197 subjects (86 females) 72.6 ± 8.4 years old (mean ± standard deviation). Of these, 102 were cognitively intact, 42 diagnosed as MCI, and 53 as probable AD. Their WBNAA amounts were converted into absolute concentration by dividing with the brain volume segmented from the MRI that also yielded the fractional brain volume (fBPV), an atrophy metric. Results: WBNAA concentration of MCI and AD patients (10.5 ± 3.0 and 10.1 ± 2.9 mM) were not significantly different (p = 0.85). They were, however, highly significantly 25-29% lower than the 14.1 ± 2.4 mM of normal matched controls (p < 10-4). The fBPV of MCI and AD patients (72.9 ± 4.9 and 69.9 ± 4.7%) differed significantly from each other (4%, p = 0.02) and both were significantly lower than the 74.6 ± 4.4% of normal elderly (2%, p = 0.003 for MCI; 6%, p < 10-4 for AD). ROC curve analysis has shown WBNAA to have 70.5% sensitivity and 84.3% specificity to differentiate MCI or AD patients from normal elderly versus just 68.4 and 65.7% for fBPV. Conclusion: Low WBNAA in MCI patients compared with cognitively normal contemporaries may indicate early neuronal damage accumulation and supports the notion of MCI as an early stage of AD. It also suggests WBNAA as a potential marker of early AD pathology. [ABSTRACT FROM AUTHOR]

Background: Mild cognitive impairment (MCI) is an intermediary state on the way to Alzheimer's disease (AD). Little is known about whole brain concentration of the neuronal marker, N-acetylaspartate (NAA) in MCI patients.Objective: To test the hypothesis that since MCI and AD are both neurodegenerative, quantification of the NAA in their whole brain (WBNAA) could differentiate them from cognitively-intact matched controls.Methods: Proton MR spectroscopy to quantify the WBNAA was applied to 197 subjects (86 females) 72.6 ± 8.4 years old (mean ± standard deviation). Of these, 102 were cognitively intact, 42 diagnosed as MCI, and 53 as probable AD. Their WBNAA amounts were converted into absolute concentration by dividing with the brain volume segmented from the MRI that also yielded the fractional brain volume (fBPV), an atrophy metric.Results: WBNAA concentration of MCI and AD patients (10.5 ± 3.0 and 10.1 ± 2.9 mM) were not significantly different (p = 0.85). They were, however, highly significantly 25-29% lower than the 14.1 ± 2.4 mM of normal matched controls (p < 10-4). The fBPV of MCI and AD patients (72.9 ± 4.9 and 69.9 ± 4.7%) differed significantly from each other (4%, p = 0.02) and both were significantly lower than the 74.6 ± 4.4% of normal elderly (2%, p = 0.003 for MCI; 6%, p < 10-4 for AD). ROC curve analysis has shown WBNAA to have 70.5% sensitivity and 84.3% specificity to differentiate MCI or AD patients from normal elderly versus just 68.4 and 65.7% for fBPV.Conclusion: Low WBNAA in MCI patients compared with cognitively normal contemporaries may indicate early neuronal damage accumulation and supports the notion of MCI as an early stage of AD. It also suggests WBNAA as a potential marker of early AD pathology. [ABSTRACT FROM AUTHOR]

The article presents research that evaluates the spinal cord (SC) of patients with multiple sclerosis (MS) using biplanar magnetic resonance imaging (MRI). The study shows a moderate correlation in the signs of atrophy, diffuse abnormalities, and focal lesions. It concludes that biplanar MRI helps facilitate the determination, localization, and grading of pathological SC results in MS patients, which improves SC imaging utility and confidence.

Background: Brain imaging studies have revealed anatomical anomalies in the brains of individuals with Tourette syndrome (TS). Prefrontal regions have been found to be larger and the corpus callosum (CC) area smaller in children and young adults with TS compared with healthy control subjects, and these anatomical features have been understood to reflect neural plasticity that helps to attenuate the severity of tics. Method: CC white matter connectivity, as measured by the Fractional Anisotropy (FA) index from diffusion tensor images, was assessed in 20 clinically well-defined boys with Tourette syndrome and 20 age- and gender-matched controls. Results: The hypothesis that children with TS would show reduced measures of connectivity in CC fibers was confirmed for all subregions of the CC. There was no significant interaction of TS and region. Reductions in FA in CC regions may reflect either fewer interhemispheric fibers or reduced axonal myelination. FA values did not correlate significantly with the severity of tic symptoms. Group differences in measures of connectivity did not seem to be attributable to the presence of comorbid ADHD or OCD, to medication exposure, or group differences in IQ. Conclusion: Our findings of a reduced interhemispheral white matter connectivity add to the understanding of neural connectivity and plasticity in the brains of children who have TS. [ABSTRACT FROM AUTHOR]