Background: Damage to white matter tracts, resulting in “cerebral disconnection,” may underlie age-related cognitive decline. Methods: Using diffusion tensor MRI (DTI) to investigate white matter damage, and magnetic resonance spectroscopy (MRS) to look at its underlying pathologic basis, the authors investigated the relationship between white matter structure and cognition in 106 healthy middle-aged and elderly adults. Fractional anisotropy (FA) and mean diffusivity (MD) values, whole brain white matter histograms, and regions of interest placed in the white matter of the centrum semiovale were analyzed. Correlations with executive function, working memory, and information-processing speed were performed. Results: There was a progressive reduction in FA and increase in diffusivity with age in both region of interest (r = 0.551, p < 0.001), and whole brain histograms (r = 0.625, p < 0.001). DTI values correlated with performance in all three cognitive domains. After controlling for age, DTI parameters correlated with working memory but not with the other two cognitive domains. MRS studies found a correlation of N-acetyl aspartate, a neuronal marker, with DTI parameters (r = 0.253, p < 0.05). Conclusion: The results are consistent with white matter damage due to axonal loss, causing age- related cognitive decline. Working memory may be particularly dependent on complex networks dependent on white matter connections.

Objective: To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. Methods: A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested. Results: Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed. Conclusions: Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies.

Purpose We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). Methods The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. Results The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. Conclusions This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

Background and aim The pathogenesis of cerebral small-vessel disease (SVD) is incompletely understood. Endothelial dysfunction has been implicated and may result in increased blood–brain barrier (BBB) permeability with leakage of blood constituents into the vessel wall and white matter. We used contrast-enhanced MRI to determine whether there was any evidence for BBB permeability in the white matter of patients with SVD, and whether this was present not only in areas of leucoaraiosis (white-matter lesions) but also in normal-appearing white matter (NAWM). Methods Subjects underwent T1 volumetric MRI before and after bolus injection of contrast. Scanning was continued for 30 min postinjection to determine the contrast-enhancement time course. The mean signal intensity change was plotted against time to calculate the area under the curve values, a parameter related to BBB permeability. Automated brain segmentation and regions of interest analysis were performed to determine ‘permeability’ in different brain compartments. Results Compared with controls (n=15), the SVD patient group (n=24) had signal changes consistent with increased BBB permeability in NAWM (p=0.033). Multivariate regression analyses identified leucoaraiosis grade as an independent predictor of these permeability related signal changes in NAWM after adjustment for age, gender, weight, brain volume, area under the curve in the internal carotid arteries and cardiovascular risk factors. Conclusion This study provides evidence for increased BBB permeability in SVD, and this is particularly seen in SVD with leucoaraiosis. Its presence in NAWM would be consistent with it playing a causal role in disease pathophysiology.

The purpose of this work was to assess the reproducibility of diffusion imaging, and in particular the apparent diffusion coefficient (ADC), intra-voxel incoherent motion (IVIM) parameters and diffusion tensor imaging (DTI) parameters, across multiple centres using clinically available protocols with limited harmonization between sequences. An ice-water phantom and nine healthy volunteers were scanned across fives centres on eight scanners (four Siemens 1.5T, four Philips 3T). The mean ADC, IVIM parameters (diffusion coefficient D and perfusion fraction f) and DTI parameters (mean diffusivity MD and fractional anisotropy FA), were measured in grey matter, white matter and specific brain sub-regions. A mixed effect model was used to measure the intra- and inter-scanner coefficient of variation (CV) for each of the five parameters. ADC, D, MD and FA had a good intra- and inter-scanner reproducibility in both grey and white matter, with a CV ranging between 1% and 7.4%; mean 2.6%. Other brain regions also showed high levels of reproducibility except for small structures such as the choroid plexus. The IVIM parameter f had a higher intra-scanner CV of 8.4% and inter-scanner CV of 24.8%. No major difference in the inter-scanner CV for ADC, D, MD and FA was observed when analysing the 1.5T and 3T scanners separately. ADC, D, MD and FA all showed good intra-scanner reproducibility, with the inter-scanner reproducibility being comparable or faring slightly worse, suggesting that using data from multiple scanners does not have an adverse effect compared with using data from the same scanner. The IVIM parameter f had a poorer inter-scanner CV when scanners of different field strengths were combined, and the parameter was also affected by the scan acquisition resolution. This study shows that the majority of diffusion MRI derived parameters are robust across 1.5T and 3T scanners and suitable for use in multi-centre clinical studies and trials.