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The thalamus consists of multiple nuclei that have been previously defined by their chemoarchitectual and cytoarchitectual properties ex vivo. These form discrete, functionally specialized, territories with topographically arranged graduated patterns of connectivity. However, previous in vivo thalamic parcellation with MRI has been hindered by substantial inter-individual variability or discrepancies between MRI derived segmentations and histological sections. Here, we use the Euclidean distance to characterize probabilistic tractography distributions derived from diffusion MRI. We generate 12 feature maps by performing voxel-wise parameterization of the distance histograms (6 feature maps) and the distribution of three-dimensional distance transition gradients generated by applying a Sobel kernel to the distance metrics. We use these 12 feature maps to delineate individual thalamic nuclei, then extract the tractography profiles for each and calculate the voxel-wise tractography gradients. Within each thalamic nucleus, the tractography gradients were topographically arranged as distinct non-overlapping cortical networks with transitory overlapping mid-zones. This work significantly advances quantitative segmentation of the thalamus in vivo using 3T MRI. At an individual subject level, the thalamic segmentations consistently achieve a close relationship with a priori histological atlas information, and resolve in vivo topographic gradients within each thalamic nucleus for the first time. Additionally, these techniques allow individual thalamic nuclei to be closely aligned across large populations and generate measures of inter-individual variability that can be used to study both basic function and pathological processes in vivo. [Display omitted] •We delineate nine thalamic nuclei at an individual subject level using tractography.•We show in vivo topographic gradients within each thalamic nucleus.•Graduated overlapping and non-overlapping projections are suggestive of spiral loops.•We show a close relationship between our results and a priori histological atlas.•These results exhibit a high degree of stability compared to existing methods.

Cerebral small vessel disease (SVD) is a common cause of vascular cognitive impairment. A number of disease features can be assessed on MRI including lacunar infarcts, T2 lesion volume, brain atrophy, and cerebral microbleeds. In addition, diffusion tensor imaging (DTI) is sensitive to disruption of white matter ultrastructure, and recently it has been suggested that additional information on the pattern of damage may be obtained from axial diffusivity, a proposed marker of axonal damage, and radial diffusivity, an indicator of demyelination. We determined the contribution of these whole brain MRI markers to cognitive impairment in SVD. Consecutive patients with lacunar stroke and confluent leukoaraiosis were recruited into the ongoing SCANS study of cognitive impairment in SVD (n = 115), and underwent neuropsychological assessment and multimodal MRI. SVD subjects displayed poor performance on tests of executive function and processing speed. In the SVD group brain volume was lower, white matter hyperintensity volume higher and all diffusion characteristics differed significantly from control subjects (n = 50). On multi-predictor analysis independent predictors of executive function in SVD were lacunar infarct count and diffusivity of normal appearing white matter on DTI. Independent predictors of processing speed were lacunar infarct count and brain atrophy. Radial diffusivity was a stronger DTI predictor than axial diffusivity, suggesting ischaemic demyelination, seen neuropathologically in SVD, may be an important predictor of cognitive impairment in SVD. Our study provides information on the mechanism of cognitive impairment in SVD.

Abstract Rationale Brain pathology is a poorly understood systemic manifestation of chronic obstructive pulmonary disease (COPD). Imaging techniques using magnetic resonance (MR) diffusion tensor imaging (DTI) and resting state functional MR imaging (rfMRI) provide measures of white matter microstructure and gray functional activation, respectively. Objectives We hypothesized that patients with COPD would have reduced white matter integrity and that functional communication between gray matter resting-state networks would be significantly different to control subjects. In addition, we tested whether observed differences related to disease severity, cerebrovascular comorbidity, and cognitive dysfunction. Methods DTI and rfMRI were acquired in stable nonhypoxemic patients with COPD (n = 25) and compared with age-matched control subjects (n = 25). Demographic, disease severity, stroke risk, and neuropsychologic assessments were made. Measurements and Main Results Patients with COPD (mean age, 68; FEV1 53 ± 21% predicted) had widespread reduction in white matter integrity (46% of white matter tracts; P < 0.01). Six of the seven resting-state networks showed increased functional gray matter activation in COPD (P < 0.01). Differences in DTI, but not rfMRI, remained significant after controlling for stroke risk and smoking (P < 0.05). White matter integrity and gray matter activation seemed to account for difference in cognitive performance between patients with COPD and control subjects. Conclusions In stable nonhypoxemic COPD there is reduced white matter integrity throughout the brain and widespread disturbance in functional activation of gray matter, which may contribute to cognitive dysfunction. White matter microstructural integrity but not gray matter functional activation is independent of smoking and cerebrovascular comorbidity. The mechanisms remain unclear, but may include cerebral small vessel disease caused by COPD.

Cognitive impairment, predominantly affecting processing speed and executive function, is an important consequence of cerebral small vessel disease (SVD). To date, few longitudinal studies of cognition in SVD have been conducted. We determined the pattern and rate of cognitive decline in SVD and used the results to determine sample size calculations for clinical trials of interventions reducing cognitive decline. 121 patients with MRI confirmed lacunar stroke and leukoaraiosis were enrolled into the prospective St George's Cognition And Neuroimaging in Stroke (SCANS) study. Patients attended one baseline and three annual cognitive assessments providing 36 month follow-up data. Neuropsychological assessment comprised a battery of tests assessing working memory, long-term (episodic) memory, processing speed and executive function. We calculated annualized change in cognition for the 98 patients who completed at least two time-points. Task performance was heterogeneous, but significant cognitive decline was found for the executive function index (p<0.007). Working memory and processing speed decreased numerically, but not significantly. The executive function composite score would require the smallest samples sizes for a treatment trial with an aim of halting decline, but this would still require over 2,000 patients per arm to detect a 30% difference with power of 0.8 over a three year follow-up. The pattern of cognitive decline seen in SVD over three years is consistent with the pattern of impairments at baseline. Rates of decline were slow and sample sizes would need to be large for clinical trials aimed at halting decline beyond initial diagnosis using cognitive scores as an outcome measure. This emphasizes the importance of more sensitive surrogate markers in this disease.

Changes in brain structure and cognitive decline occur in Chronic Obstructive Pulmonary Disease (COPD). They also occur with smoking and coronary artery disease (CAD), but it is unclear whether a common mechanism is responsible. Brain MRI markers of brain structure were tested for association with disease markers in other organs. Where possible, principal component analysis (PCA) was used to group markers within organ systems into composite markers. Univariate relationships between brain structure and the disease markers were explored using hierarchical regression and then entered into multivariable regression models. 100 participants were studied (53 COPD, 47 CAD). PCA identified two brain components: brain tissue volumes and white matter microstructure, and six components from other organ systems: respiratory function, plasma lipids, blood pressure, glucose dysregulation, retinal vessel calibre and retinal vessel tortuosity. Several markers could not be grouped into components and were analysed as single variables, these included brain white matter hyperintense lesion (WMH) volume. Multivariable regression models showed that less well organised white matter microstructure was associated with lower respiratory function (p = 0.028); WMH volume was associated with higher blood pressure (p = 0.036) and higher C-Reactive Protein (p = 0.011) and lower brain tissue volume was associated with lower cerebral blood flow (p<0.001) and higher blood pressure (p = 0.001). Smoking history was not an independent correlate of any brain marker. Measures of brain structure were associated with a range of markers of disease, some of which appeared to be common to both COPD and CAD. No single common pathway was identified, but the findings suggest that brain changes associated with smoking-related diseases may be due to vascular, respiratory, and inflammatory changes.

To enable application of non-Gaussian diffusion magnetic resonance imaging (dMRI) techniques in large-scale clinical trials and facilitate translation to clinical practice there is a requirement for fast, high contrast, techniques that are sensitive to changes in tissue structure which provide diagnostic signatures at the early stages of disease. Here we describe a new way to compress the acquisition of multi-shell b-value diffusion data, Quasi-Diffusion MRI (QDI), which provides a probe of subvoxel tissue complexity using short acquisition times (1–4 ​min). We also describe a coherent framework for multi-directional diffusion gradient acquisition and data processing that allows computation of rotationally invariant quasi-diffusion tensor imaging (QDTI) maps. QDI is a quantitative technique that is based on a special case of the Continuous Time Random Walk model of diffusion dynamics and assumes the presence of non-Gaussian diffusion properties within tissue microstructure. QDI parameterises the diffusion signal attenuation according to the rate of decay (i.e. diffusion coefficient, D in mm2 s−1) and the shape of the power law tail (i.e. the fractional exponent, α). QDI provides analogous tissue contrast to Diffusional Kurtosis Imaging (DKI) by calculation of normalised entropy of the parameterised diffusion signal decay curve, Hn, but does so without the limitations of a maximum b-value. We show that QDI generates images with superior tissue contrast to conventional diffusion imaging within clinically acceptable acquisition times of between 84 and 228 ​s. We show that QDI provides clinically meaningful images in cerebral small vessel disease and brain tumour case studies. Our initial findings suggest that QDI may be added to routine conventional dMRI acquisitions allowing simple application in clinical trials and translation to the clinical arena.

Mild cognitive impairment is a common systemic manifestation of chronic obstructive pulmonary disease (COPD). However, its pathophysiological origins are not understood. Since, cognitive function relies on efficient communication between distributed cortical and subcortical regions, we investigated whether people with COPD have disruption in white matter connectivity. Structural networks were constructed for 30 COPD patients (aged 54-84 years, 57% male, FEV1 52.5% pred.) and 23 controls (aged 51-81 years, 48% Male). Networks comprised 90 grey matter regions (nodes) interconnected by white mater fibre tracts traced using deterministic tractography (edges). Edges were weighted by the number of streamlines adjusted for a) streamline length and b) end-node volume. White matter connectivity was quantified using global and nodal graph metrics which characterised the networks connection density, connection strength, segregation, integration, nodal influence and small-worldness. Between-group differences in white matter connectivity and within-group associations with cognitive function and disease severity were tested. COPD patients' brain networks had significantly lower global connection strength (p = 0.03) and connection density (p = 0.04). There was a trend towards COPD patients having a reduction in nodal connection density and connection strength across the majority of network nodes but this only reached significance for connection density in the right superior temporal gyrus (p = 0.02) and did not survive correction for end-node volume. There were no other significant global or nodal network differences or within-group associations with disease severity or cognitive function. COPD brain networks show evidence of damage compared to controls with a reduced number and strength of connections. This loss of connectivity was not sufficient to disrupt the overall efficiency of network organisation, suggesting that it has redundant capacity that makes it resilient to damage, which may explain why cognitive dysfunction is not severe. This might also explain why no direct relationships could be found with cognitive measures. Smoking and hypertension are known to have deleterious effects on the brain. These confounding effects could not be excluded.

Brain damage and cardiovascular disease are extra-pulmonary manifestations of chronic obstructive pulmonary disease (COPD). Cardiovascular risk factors and smoking are contributors to neurodegeneration. This study investigates whether there is a specific, COPD-related deterioration in brain structure and function independent of cardiovascular risk factors and smoking. Neuroimaging and clinical markers of brain structure (micro- and macro-) and function (cognitive function and mood) were compared between 27 stable COPD patients (age: 63.0±9.1 years, 59.3% male, forced expiratory volume in 1 second [FEV ]: 58.1±18.0% pred.) and 23 non-COPD controls with >10 pack years smoking (age: 66.6±7.5 years, 52.2% male, FEV : 100.6±19.1% pred.). Clinical relationships and group interactions with brain structure were also tested. All statistical analyses included correction for cardiovascular risk factors, smoking, and aortic stiffness. COPD patients had significantly worse cognitive function ( 0.011), lower mood ( 0.046), and greater gray matter atrophy ( =0.020). In COPD patients, lower mood was associated with markers of white matter (WM) microstructural damage ( <0.001), and lower lung function (FEV /forced vital capacity and FEV ) with markers of both WM macro ( =0.047) and microstructural damage ( =0.028). COPD is associated with both structural (gray matter atrophy) and functional (worse cognitive function and mood) brain changes that cannot be explained by measures of cardiovascular risk, aortic stiffness, or smoking history alone. These results have important implications to guide the development of new interventions to prevent or delay progression of neuropsychiatric comorbidities in COPD. Relationships found between mood and microstructural abnormalities suggest that in COPD, anxiety, and depression may occur secondary to WM damage. This could be used to better understand disabling symptoms such as breathlessness, improve health status, and reduce hospital admissions.

Background Brain pathology is relatively unexplored in chronic obstructive pulmonary disease (COPD). This study is a comprehensive investigation of grey matter (GM) and white matter (WM) changes and how these relate to disease severity and cognitive function. Methods T1-weighted and fluid-attenuated inversion recovery images were acquired for 31 stable COPD patients (FEV 1 52.1% pred., PaO 2 10.1 kPa) and 24 age, gender-matched controls. T1-weighted images were segmented into GM, WM and cerebrospinal fluid (CSF) tissue classes using a semi-automated procedure optimised for use with this cohort. This procedure allows, cohort-specific anatomical features to be captured, white matter lesions (WMLs) to be identified and includes a tissue repair step to correct for misclassification caused by WMLs. Tissue volumes and cortical thickness were calculated from the resulting segmentations. Additionally, a fully-automated pipeline was used to calculate localised cortical surface and gyrification. WM and GM tissue volumes, the tissue volume ratio (indicator of atrophy), average cortical thickness, and the number, size, and volume of white matter lesions (WMLs) were analysed across the whole-brain and regionally – for each anatomical lobe and the deep-GM. The hippocampus was investigated as a region-of-interest. Localised (voxel-wise and vertex-wise) variations in cortical gyrification, GM density and cortical thickness, were also investigated. Statistical models controlling for age and gender were used to test for between-group differences and within-group correlations. Robust statistical approaches ensured the family-wise error rate was controlled in regional and local analyses. Results There were no significant differences in global, regional, or local measures of GM between patients and controls, however, patients had an increased volume ( p  = 0.02) and size ( p  = 0.04) of WMLs. In patients, greater normalised hippocampal volume positively correlated with exacerbation frequency ( p  = 0.04), and greater WML volume was associated with worse episodic memory ( p  = 0.05). A negative relationship between WML and FEV 1 % pred. approached significance ( p  = 0.06). Conclusions There was no evidence of cerebral atrophy within this cohort of stable COPD patients, with moderate airflow obstruction. However, there were indications of WM damage consistent with an ischaemic pathology. It cannot be concluded whether this represents a specific COPD, or smoking-related, effect.

Quasi-diffusion imaging (QDI) is a novel quantitative diffusion magnetic resonance imaging (dMRI) technique that enables high quality tissue microstructural imaging in a clinically feasible acquisition time. QDI is derived from a special case of the continuous time random walk (CTRW) model of diffusion dynamics and assumes water diffusion is locally Gaussian within tissue microstructure. By assuming a Gaussian scaling relationship between temporal (α) and spatial (β) fractional exponents, the dMRI signal attenuation is expressed according to a diffusion coefficient, D (in mm2 s−1), and a fractional exponent, α. Here we investigate the mathematical properties of the QDI signal and its interpretation within the quasi-diffusion model. Firstly, the QDI equation is derived and its power law behaviour described. Secondly, we derive a probability distribution of underlying Fickian diffusion coefficients via the inverse Laplace transform. We then describe the functional form of the quasi-diffusion propagator, and apply this to dMRI of the human brain to perform mean apparent propagator imaging. QDI is currently unique in tissue microstructural imaging as it provides a simple form for the inverse Laplace transform and diffusion propagator directly from its representation of the dMRI signal. This study shows the potential of QDI as a promising new model-based dMRI technique with significant scope for further development.