Cognitive function in patients with irritable bowel syndrome: impairment is common and only weakly correlated with depression/anxiety and severity of gastrointestinal symptoms.

OBJECTIVE: To investigate cognitive function in patients with irritable bowel syndrome (IBS) and its relation to anxiety/depression and severity of gastrointestinal (GI) symptoms. METHODS: Patients with IBS (n = 65) and healthy controls (HCs, n = 37) performed the ten subtests of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Age-normed index scores of five cognitive domains (Immediate memory, Visuospatial function, Language function, Attention, Recall) and a total (Fullscale) score were derived from the performance. Emotional function was assessed using the Hospital Anxiety and Depression Scale (HADS), and the IBS Symptom Scoring System (IBS-SSS) was used to define the severity of GI symptoms. RESULTS: Patients with IBS reported significantly higher scores than the HC group on symptom measures of anxiety and depression, and significantly lower scores on the Immediate memory, Recall, and Fullscale RBANS indexes. Approximately 30% of the IBS patients obtained index scores at least one standard deviation below the population mean, and more than 50% scored above the screening threshold for an anxiety disorder. The severity of GI symptoms was significantly correlated with the severity level of anxiety symptoms (p=.006), but neither the severity level of emotional nor GI symptoms was significantly correlated with the RBANS index scores in the IBS group. CONCLUSION: Cognitive and emotional function were more severely affected in patients with IBS than in HCs. The weak correlation between the two functional areas suggests that both should be assessed as part of a clinical examination of patients with IBS.

Cognitive and emotional function should be assessed in patients with IBS.Cognitive impairment was less closely related to symptoms of anxiety/depression and severity of GI symptoms than expected.An independent contribution of both emotional symptoms and cognitive function should be considered when developing treatment programs for patients with IBS.

Brain-wide associations between white matter and age highlight the role of fornix microstructure in brain ageing.

Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6-82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.

Brain scans from 21,297 individuals reveal the genetic architecture of hippocampal subfield volumes.

The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer's disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields' genetic architecture. T1-weighted brain scans (n = 21,297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, co-varying for whole hippocampal volume. We further calculated the single-nucleotide polymorphism (SNP)-based heritability of 12 subfields, as well as their genetic correlation with each other, with other structural brain features and with AD and schizophrenia. All outcome measures were corrected for age, sex and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from 0.14 to 0.27, all p < 1 × 10-16) and clustered together based on their genetic correlations compared with other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.

Healthy Kidney Segmentation in the Dce-Mr Images Using a Convolutional Neural Network and Temporal Signal Characteristics.

Quantification of renal perfusion based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) requires determination of signal intensity time courses in the region of renal parenchyma. Thus, selection of voxels representing the kidney must be accomplished with special care and constitutes one of the major technical limitations which hampers wider usage of this technique as a standard clinical routine. Manual segmentation of renal compartments-even if performed by experts-is a common source of decreased repeatability and reproducibility. In this paper, we present a processing framework for the automatic kidney segmentation in DCE-MR images. The framework consists of two stages. Firstly, kidney masks are generated using a convolutional neural network. Then, mask voxels are classified to one of three regions-cortex, medulla, and pelvis-based on DCE-MRI signal intensity time courses. The proposed approach was evaluated on a cohort of 10 healthy volunteers who underwent the DCE-MRI examination. MRI scanning was repeated on two time events within a 10-day interval. For semantic segmentation task we employed a classic U-Net architecture, whereas experiments on voxel classification were performed using three alternative algorithms-support vector machines, logistic regression and extreme gradient boosting trees, among which SVM produced the most accurate results. Both segmentation and classification steps were accomplished by a series of models, each trained separately for a given subject using the data from other participants only. The mean achieved accuracy of the whole kidney segmentation was 94% in terms of IoU coefficient. Cortex, medulla and pelvis were segmented with IoU ranging from 90 to 93% depending on the tissue and body side. The results were also validated by comparing image-derived perfusion parameters with ground truth measurements of glomerular filtration rate (GFR). The repeatability of GFR calculation, as assessed by the coefficient of variation was determined at the level of 14.5 and 17.5% for the left and right kidney, respectively and it improved relative to manual segmentation. Reproduciblity, in turn, was evaluated by measuring agreement between image-derived and iohexol-based GFR values. The estimated absolute mean differences were equal to 9.4 and 12.9 mL/min/1.73 m2 for scanning sessions 1 and 2 and the proposed automated segmentation method. The result for session 2 was comparable with manual segmentation, whereas for session 1 reproducibility in the automatic pipeline was weaker.

Longitudinal stability of the brain functional connectome is associated with episodic memory performance in aging.

The brain functional connectome forms a relatively stable and idiosyncratic backbone that can be used for identification or "fingerprinting" of individuals with a high level of accuracy. While previous cross-sectional evidence has demonstrated increased stability and distinctiveness of the brain connectome during the course of childhood and adolescence, less is known regarding the longitudinal stability in middle and older age. Here, we collected structural and resting-state functional MRI data at two time points separated by 2-3 years in 75 middle-aged and older adults (age 49-80, SD = 6.91 years) which allowed us to assess the long-term stability of the functional connectome. We show that the connectome backbone generally remains stable over a 2-3 years period in middle and older age. Independent of age, cortical volume was associated with the connectome stability of several canonical resting-state networks, suggesting that the connectome backbone relates to structural properties of the cortex. Moreover, the individual longitudinal stability of subcortical and default mode networks was associated with individual differences in cross-sectional and longitudinal measures of episodic memory performance, providing new evidence for the importance of these networks in maintaining mnemonic processing in middle and old age. Together, the findings encourage the use of within-subject connectome stability analyses for understanding individual differences in brain function and cognition in aging.

A new framework for assessing subject-specific whole brain circulation and perfusion using MRI-based measurements and a multi-scale continuous flow model.

A large variety of severe medical conditions involve alterations in microvascular circulation. Hence, measurements or simulation of circulation and perfusion has considerable clinical value and can be used for diagnostics, evaluation of treatment efficacy, and for surgical planning. However, the accuracy of traditional tracer kinetic one-compartment models is limited due to scale dependency. As a remedy, we propose a scale invariant mathematical framework for simulating whole brain perfusion. The suggested framework is based on a segmentation of anatomical geometry down to imaging voxel resolution. Large vessels in the arterial and venous network are identified from time-of-flight (ToF) and quantitative susceptibility mapping (QSM). Macro-scale flow in the large-vessel-network is accurately modelled using the Hagen-Poiseuille equation, whereas capillary flow is treated as two-compartment porous media flow. Macro-scale flow is coupled with micro-scale flow by a spatially distributing support function in the terminal endings. Perfusion is defined as the transition of fluid from the arterial to the venous compartment. We demonstrate a whole brain simulation of tracer propagation on a realistic geometric model of the human brain, where the model comprises distinct areas of grey and white matter, as well as large vessels in the arterial and venous vascular network. Our proposed framework is an accurate and viable alternative to traditional compartment models, with high relevance for simulation of brain perfusion and also for restoration of field parameters in clinical brain perfusion applications.

Magnetic resonance radiomics for prediction of extraprostatic extension in non-favorable intermediate- and high-risk prostate cancer patients.

BACKGROUND: To investigate whether magnetic resonance (MR) radiomic features combined with machine learning may aid in predicting extraprostatic extension (EPE) in high- and non-favorable intermediate-risk patients with prostate cancer. PURPOSE: To investigate the diagnostic performance of radiomics to detect EPE. MATERIAL AND METHODS: MR radiomic features were extracted from 228 patients, of whom 86 were diagnosed with EPE, using prostate and lesion segmentations. Prediction models were built using Random Forest. Further, EPE was also predicted using a clinical nomogram and routine radiological interpretation and diagnostic performance was assessed for individual and combined models. RESULTS: The MR radiomic model with features extracted from the manually delineated lesions performed best among the radiomic models with an area under the curve (AUC) of 0.74. Radiology interpretation yielded an AUC of 0.75 and the clinical nomogram (MSKCC) an AUC of 0.67. A combination of the three prediction models gave the highest AUC of 0.79. CONCLUSION: Radiomic analysis combined with radiology interpretation aid the MSKCC nomogram in predicting EPE in high- and non-favorable intermediate-risk patients.

Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer.

BACKGROUND: Improved methods for preoperative risk stratification in endometrial cancer are highly requested by gynecologists. Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in various cancer types, but largely unexplored in endometrial cancer. PURPOSE: To explore whether tumor texture parameters from preoperative MRI are related to known prognostic features (deep myometrial invasion, cervical stroma invasion, lymph node metastases, and high-risk histological subtype) and to outcome in endometrial cancer patients. STUDY TYPE: Prospective cohort study. POPULATION/SUBJECTS: In all, 180 patients with endometrial carcinoma were included from April 2009 to November 2013 and studied until January 2017. FIELD STRENGTH/SEQUENCES: Preoperative pelvic MRI including contrast-enhanced T1 -weighted (T1 c), T2 -weighted, and diffusion-weighted imaging at 1.5T. ASSESSMENT: Tumor regions of interest (ROIs) were manually drawn on the slice displaying the largest cross-sectional tumor area, using the proprietary research software TexRAD for analysis. With a filtration-histogram technique, the texture parameters standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis were calculated. STATISTICAL TESTS: Associations between texture parameters and histological features were assessed by uni- and multivariable logistic regression, including models adjusting for preoperative biopsy status and conventional MRI findings. Multivariable Cox regression analysis was used for survival analysis. RESULTS: High tumor entropy in apparent diffusion coefficient (ADC) maps independently predicted deep myometrial invasion (odds ratio [OR] 3.2, P lt 0.001), and high MPP in T1 c images independently predicted high-risk histological subtype (OR 1.01, P = 0.004). High kurtosis in T1 c images predicted reduced recurrence- and progression-free survival (hazard ratio [HR] 1.5, P lt 0.001) after adjusting for MRI-measured tumor volume and histological risk at biopsy. DATA CONCLUSION: MRI-derived tumor texture parameters independently predicted deep myometrial invasion, high-risk histological subtype, and reduced survival in endometrial carcinomas, and thus, represent promising imaging biomarkers providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies in endometrial cancer. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1637-1647.

Dynamic contrast-enhanced MRI measurement of renal function in healthy participants.

Background High repeatability, accuracy, and precision for renal function measurements need to be achieved to establish renal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as a clinically useful diagnostic tool. Purpose To investigate the repeatability, accuracy, and precision of DCE-MRI measured renal perfusion and glomerular filtration rate (GFR) using iohexol-GFR as the reference method. Material and Methods Twenty healthy non-smoking volunteers underwent repeated DCE-MRI and an iohexol-GFR within a period of 10 days. Single-kidney (SK) MRI measurements of perfusion (blood flow, Fb) and filtration (GFR) were derived from parenchymal intensity time curves fitted to a two-compartment filtration model. The repeatability of the SK-MRI measurements was assessed using coefficient of variation (CV). Using iohexol-GFR as reference method, the accuracy of total MR-GFR was determined by mean difference (MD) and precision by limits of agreement (LoA). Results SK-Fb (MR1, 345 ± 84; MR2, 371 ± 103 mL/100 mL/min) and SK-GFR (MR1, 52 ± 14; MR2, 54 ± 10 mL/min/1.73 m2) measurements achieved a repeatability (CV) in the range of 15-22%. With reference to iohexol-GFR, MR-GFR was determined with a low mean difference but high LoA (MR1, MD 1.5 mL/min/1.73 m2, LoA [-42, 45]; MR2, MD 6.1 mL/min/1.73 m2, LoA [-26, 38]). Eighty percent and 90% of MR-GFR measurements were determined within ± 30% of the iohexol-GFR for MR1 and MR2, respectively. Conclusion Good repeatability of SK-MRI measurements and good agreement between MR-GFR and iohexol-GFR provide a high clinical potential of DCE-MRI for renal function assessment. A moderate precision in MR-derived estimates indicates that the method cannot yet be used in clinical routine.

Quantification of Single-Kidney Function and Volume in Living Kidney Donors Using Dynamic Contrast-Enhanced MRI.

OBJECTIVE: The objective of our study was to investigate whether dynamic contrast-enhanced MRI (DCE-MRI) can detect differences and potential adaption in single-kidney parenchymal volume, blood flow, glomerular filtration rate (GFR), and filtration fraction in the remaining kidney of healthy donors compared with nondonors. Further, we evaluated the agreement in donor GFRs measured using DCE-MRI versus serum clearance of iohexol. SUBJECTS AND METHODS: Twenty living kidney donors and 20 healthy control subjects underwent DCE-MRI and iohexol GFR. Renal parenchymal volume was assessed from maximum-signal-intensity maps. Single-kidney MRI measurements of blood flow and GFR were derived from parenchymal signal intensity-time curves fitted to a two-compartment filtration model. The Student t test, Pearson correlation coefficient, mean differences, and limits of agreement were applied to analyze MRI measurements between groups and agreement with iohexol GFR. RESULTS: MRI findings showed significantly higher blood flow (difference in mean values of donors vs control subjects, 54%; p = 0.001), GFR (78%, p < 0.0001), and renal parenchymal volume (65%, p < 0.0001) in the single kidney of donors compared with the single kidney of healthy control subjects. In the donors, a proportional increase in blood flow and GFR resulted in a comparable filtration fraction, as was observed in the control subjects. Significant correlations were found between MRI-derived GFR and parenchymal volume (p < 0.0016) as well as with iohexol GFR (p < 0.0001). The mean difference between MRI-derived GFR and iohexol GFR was 14.0 mL/min, and the limits of agreement between MRI-derived GFR and iohexol GFR were -24.1 and 52.1 mL/min. CONCLUSION: DCE-MRI-derived values for single-kidney function and volume in kidney donors were significantly higher than those in control subjects and suggest a future potential benefit of DCE-MRI for diagnostic and prognostic structural and functional assessments in living kidney donors.

High-expanding cortical regions in human development and evolution are related to higher intellectual abilities.

Cortical surface area has tremendously expanded during human evolution, and similar patterns of cortical expansion have been observed during childhood development. An intriguing hypothesis is that the high-expanding cortical regions also show the strongest correlations with intellectual function in humans. However, we do not know how the regional distribution of correlations between intellectual function and cortical area maps onto expansion in development and evolution. Here, in a sample of 1048 participants, we show that regions in which cortical area correlates with visuospatial reasoning abilities are generally high expanding in both development and evolution. Several regions in the frontal cortex, especially the anterior cingulate, showed high expansion in both development and evolution. The area of these regions was related to intellectual functions in humans. Low-expanding areas were not related to cognitive scores. These findings suggest that cortical regions involved in higher intellectual functions have expanded the most during development and evolution. The radial unit hypothesis provides a common framework for interpretation of the findings in the context of evolution and prenatal development, while additional cellular mechanisms, such as synaptogenesis, gliogenesis, dendritic arborization, and intracortical myelination, likely impact area expansion in later childhood.

Use of 3D DCE-MRI for the estimation of renal perfusion and glomerular filtration rate: an intrasubject comparison of FLASH and KWIC with a comprehensive framework for evaluation.

OBJECTIVE. The purpose of this article is to compare two 3D dynamic contrast-enhanced (DCE) MRI measurement techniques for MR renography, a radial k-space weighted image contrast (KWIC) sequence and a cartesian FLASH sequence, in terms of intrasubject differences in estimates of renal functional parameters and image quality characteristics. SUBJECTS AND METHODS. Ten healthy volunteers underwent repeated breath-hold KWIC and FLASH sequence examinations with temporal resolutions of 2.5 and 2.8 seconds, respectively. A two-compartment model was used to estimate MRI-derived perfusion parameters and glomerular filtration rate (GFR). The latter was compared with the iohexol GFR and the estimated GFR. Image quality was assessed using a visual grading characteristic analysis of relevant image quality criteria and signal-to-noise ratio calculations. RESULTS. Perfusion estimates from FLASH were closer to literature reference values than were the KWIC sequences. In relation to the iohexol GFR (mean [± SD], 103 ± 11 mL/min/1.73 m(2)), KWIC produced significant underestimations and larger bias in GFR values (mean, 70 ± 30 mL/min/1.73 m(2); bias = -33.2 mL/min/1.73 m(2)) compared with the FLASH GFR (110 ± 29 mL/min/1.73 m(2); bias = 6.4 mL/min/1.73 m(2)). KWIC was statistically significantly (p < 0.005) more impaired by artifacts than was FLASH (AUC = 0.18). The average signal-enhancement ratio (delta ratio) in the cortex was significantly lower for KWIC (delta ratio = 0.99) than for FLASH (delta ratio = 1.40). Other visually graded image quality characteristics and signal-to-noise ratio measurements were not statistically significantly different. CONCLUSION. Using the same postprocessing scheme and pharmacokinetic model, FLASH produced more accurate perfusion and filtration parameters than did KWIC compared with clinical reference methods. Our data suggest an apparent relationship between image quality characteristics and the degree of stability in the numeric model-based renal function estimates.

Post-adolescent developmental changes in cortical complexity.

BACKGROUND: Post-adolescence is known to be a period of general maturation and development in the human brain. In brain imaging, volumetric and morphologic cortical grey-matter changes can easily be assessed, but the analysis of cortical complexity seems to have been broadly neglected for this age interval. METHODS: Magnetic resonance imaging (MRI) was used to acquire structural brain images. The study involved 17 adolescents (mean age 14.1 ± 0.27, 11 girls) who were compared with 14 young adults (mean age 24.24 ± 2.76, 7 women) for measures of brain complexity (fractal dimension--FD), grey matter (GM) volume and surface-area of cortical ribbon. FD was calculated using box-counting and Minkowski-Bouligand methods; FD and GM volume were measured for the whole brain, each hemisphere and lobes: frontal, occipital, parietal and temporal. RESULTS: The results show that the adults have a lower cortical complexity than the adolescents, which was significant for whole brain, left and right hemisphere, frontal and parietal lobes for both genders; and only for males in left temporal lobe. The GM volume was smaller in men than in boys for almost all measurements, and smaller in women than in girls just for right parietal lobe. A significant Pearson correlation was found between FD and GM volume for whole brain and each hemisphere in both genders. The decrease of the GM surface-area was significant in post-adolescence for males, not for females. CONCLUSIONS: During post-adolescence there are common changes in cortical complexity in the same regions for both genders, but there are also gender specific changes in some cortical areas. The sex differences from different cortical measurements (FD, GM volume and surface-area of cortical ribbon) could suggest a maturation delay in specific brain regions for each gender in relation to the other and might be explained through the functional role of the corresponding regions reflected in gender difference of developed abilities.

Fecal bacteria and short-chain fatty acids in irritable bowel syndrome: Relations to subtype.

BACKGROUND: The relationship between gut microbiota and irritable bowel syndrome (IBS) subtype is unclear. We aimed to explore whether differences in fecal bacteria composition and short-chain fatty acid (SCFA) levels were associated with subtypes and symptoms of IBS. METHODS: All participants delivered fecal samples and self-reports on IBS Symptom Severity Score (IBS-SSS), Bristol Stool Scale (BSS), and Gastrointestinal Symptom Rating Scale (GSRS). Fecal bacteria composition was assessed by the GA-map® Dysbiosis Test based on 16S rRNA sequences of bacterial species/groups. SCFAs were analyzed by vacuum distillation followed by gas chromatography. KEY RESULTS: Sixty patients with IBS were included (mean age 38 years, 46 [77%] females): Twenty-one patients were classified as IBS-D (diarrhea), 31 IBS-M (mixed diarrhea and constipation), and eight IBS-C (constipation). Forty-two healthy controls (HCs) (mean age 35 years, 27 [64%] females) were included. Patients had a significantly higher relative frequency of dysbiosis, lower levels of Actinobacteria, and higher levels of Bacilli than HCs. Eight bacterial markers were significantly different across IBS subgroups and HCs, and 13 bacterial markers were weakly correlated with IBS symptoms. Clostridia and Veillonella spp. had a weak negative correlation with constipation scores (GSRS) and a weak positive correlation with loose stools (BSS). Diarrhea scores (GSRS) and looser stool (BSS) were weakly correlated with levels of total SCFAs, acetic and butyric acid. Levels of total SCFAs and acetic acid were weakly correlated with symptom severity (IBS-SSS). CONCLUSIONS & INFERENCES: Patients with IBS had a different fecal bacteria composition compared to HCs, and alterations of SCFAs may contribute to the subtype.

Nutritional safety and status following a 12-week strict low FODMAP diet in patients with irritable bowel syndrome.

BACKGROUND: A low FODMAP diet (LFD) is an established dietary treatment for patients with irritable bowel syndrome (IBS). However, knowledge on the extended effects of the restriction phase regarding nutrient intake, symptom severity, and quality of life (QoL) is sparse. Therefore, our objectives were to evaluate the safety of a dietitian-led 12-week strict LFD on measures of blood biochemistry, nutritional status, symptom severity, and QoL. METHODS: In this open-label dietitian-led 12-week strict LFD intervention for IBS patients with predominantly diarrhea or mixed stool pattern (IBS-D/-M), we collected data on diet intake (3-day dietary record), overnight fasting routine blood samples, body weight, IBS symptoms (IBS Severity Scoring System (IBS-SSS)), and IBS-related QoL (IBS-QoL) at baseline and after 12 weeks. KEY RESULTS: Thirty-six participants completed the 12-week follow-up (mean age: 37 years, 67% women, IBS-SSS: 242 (101)). All blood parameters measured were within established reference values at both time points. We found no change in intake of macro- or micronutrients, but several micronutrients were below the recommendations both before and after 12 weeks. BMI slightly decreased, primarily driven by participants with BMI >25 (p < 0.005). QoL improved among most subdomains (p ≤ 0.002), except food avoidance and social reaction. CONCLUSION: An extended dietitian-guided LFD (12 weeks) is not inferior to the participants' baseline diet, since no clinically meaningful changes in nutritionally related blood samples and no changes in macro- or micronutrient intake were observed. However, the intake of several nutrients was below the recommendations at both time points indicating low diet quality.

Brain asymmetries from mid- to late life and hemispheric brain age.

The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N=48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.

General fluid-type intelligence is related to indices of white matter structure in middle-aged and old adults.

General fluid-type intelligence (gF) reflects abstract reasoning and problem solving abilities, and is an important predictor for lifetime trajectories of cognition, and physical and mental health. Structural and functional neuroimaging studies have demonstrated the role of parieto-frontal gray matter, but the white matter (WM) underpinnings of gF and the contribution of individual gF components to gF-WM relationship still need to be explored. The aim of this study was to characterize, in a sample of 100 healthy middle-aged and old subjects (mean=63.8 years), the relationship between gF and indices of WM structure obtained from diffusion tensor magnetic resonance imaging (DT-MRI) (fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD)). gF was estimated by principal component analysis including measures of episodic memory, reasoning, and processing speed. Tract-based spatial statistics and permutation-based inference statistics were used to test the association between gF and WM indices, while controlling for the effect of age and sex. We hypothesized a positive relationship between gF and WM structure. Based on previous studies, we further hypothesized that this relationship was heavily influenced by the processing speed component of gF. We found a robust relationship between gF and DT-MRI measures of FA, RD and MD in all major WM tracts. Higher gF score was related to higher degree of WM integrity, in middle-aged as well as old individuals. Thus, the distributed relationship between gF and indices of WM microstructure is consistent with the notion that gF reflects efficient signaling between cortical areas. Furthermore, analysis of relationships between WM measures and gF components revealed an association with information processing speed and reasoning ability, but not with episodic memory. Thus, although all subcomponents loaded high on gF factor, the speed-related components were most strongly associated with DT-MRI-derived measures. These results suggest that DT-MRI can be used to parse gF.

Registration of FA and T1-weighted MRI data of healthy human brain based on template matching and normalized cross-correlation.

In this work, we propose a new approach for three-dimensional registration of MR fractional anisotropy images with T1-weighted anatomy images of human brain. From the clinical point of view, this accurate coregistration allows precise detection of nerve fibers that is essential in neuroscience. A template matching algorithm combined with normalized cross-correlation was used for this registration task. To show the suitability of the proposed method, it was compared with the normalized mutual information-based B-spline registration provided by the Elastix software library, considered a reference method. We also propose a general framework for the evaluation of robustness and reliability of both registration methods. Both registration methods were tested by four evaluation criteria on a dataset consisting of 74 healthy subjects. The template matching algorithm has shown more reliable results than the reference method in registration of the MR fractional anisotropy and T1 anatomy image data. Significant differences were observed in the regions splenium of corpus callosum and genu of corpus callosum, considered very important areas of brain connectivity. We demonstrate that, in this registration task, the currently used mutual information-based parametric registration can be replaced by more accurate local template matching utilizing the normalized cross-correlation similarity measure.

Feasibility of deep learning-based tumor segmentation for target delineation and response assessment in grade-4 glioma using multi-parametric MRI.

Background: Tumor burden assessment is essential for radiation therapy (RT), treatment response evaluation, and clinical decision-making. However, manual tumor delineation remains laborious and challenging due to radiological complexity. The objective of this study was to investigate the feasibility of the HD-GLIO tool, an ensemble of pre-trained deep learning models based on the nnUNet-algorithm, for tumor segmentation, response prediction, and its potential for clinical deployment. Methods: We analyzed the predicted contrast-enhanced (CE) and non-enhancing (NE) HD-GLIO output in 49 multi-parametric MRI examinations from 23 grade-4 glioma patients. The volumes were retrospectively compared to corresponding manual delineations by 2 independent operators, before prospectively testing the feasibility of clinical deployment of HD-GLIO-output to a RT setting. Results: For CE, median Dice scores were 0.81 (95% CI 0.71-0.83) and 0.82 (95% CI 0.74-0.84) for operator-1 and operator-2, respectively. For NE, median Dice scores were 0.65 (95% CI 0.56-0,69) and 0.63 (95% CI 0.57-0.67), respectively. Comparing volume sizes, we found excellent intra-class correlation coefficients of 0.90 (P < .001) and 0.95 (P < .001), for CE, respectively, and 0.97 (P < .001) and 0.90 (P < .001), for NE, respectively. Moreover, there was a strong correlation between response assessment in Neuro-Oncology volumes and HD-GLIO-volumes (P < .001, Spearman's R2 = 0.83). Longitudinal growth relations between CE- and NE-volumes distinguished patients by clinical response: Pearson correlations of CE- and NE-volumes were 0.55 (P = .04) for responders, 0.91 (P > .01) for non-responders, and 0.80 (P = .05) for intermediate/mixed responders. Conclusions: HD-GLIO was feasible for RT target delineation and MRI tumor volume assessment. CE/NE tumor-compartment growth correlation showed potential to predict clinical response to treatment.

Assessment of Self-Reported Executive Function in Patients with Irritable Bowel Syndrome Using a Machine-Learning Framework.

Introduction: Irritable bowel syndrome (IBS) is characterized as a disorder of the gut-brain interaction (DGBI). Here, we explored the presence of problems related to executive function (EF) in patients with IBS and tested the relative importance of cognitive features involved in EF. Methods: A total of 44 patients with IBS and 22 healthy controls (HCs) completed the Behavior Rating Inventory of Executive Function (BRIEF-A), used to identify nine EF features. The PyCaret 3.0 machine-learning library in Python was used to explore the data, generate a robust model to classify patients with IBS versus HCs and identify the relative importance of the EF features in this model. The robustness of the model was evaluated by training the model on a subset of data and testing it on the unseen, hold-out dataset. Results: The explorative analysis showed that patients with IBS reported significantly more severe EF problems than the HC group on measures of working memory function, initiation, cognitive flexibility and emotional control. Impairment at a level in need of clinical attention was found in up to 40% on some of these scales. When the nine EF features were used as input to a collection of different binary classifiers, the Extreme Gradient Boosting algorithm (XGBoost) showed superior performance. The working memory subscale was consistently selected with the strongest importance in this model, followed by planning and emotional control. The goodness of the machine-learning model was confirmed in an unseen dataset by correctly classifying 85% of the IBS patients. Conclusions: The results showed the presence of EF-related problems in patients with IBS, with a substantial impact of problems related to working memory function. These results suggest that EF should be part of an assessment procedure when a patient presents other symptoms of IBS and that working memory function should be considered a target when treating patients with the disorder. Further studies should include measures of EF as part of the symptom cluster characterizing patients with IBS and other DGBIs.