Radiofrequency thalamotomy for tremor produces focused and predictable lesions shown on magnetic resonance images

Radiofrequency thalamotomy is a neurosurgical management option for medically-refractory tremor. In this observational study, we evaluate the MRI features of the resultant lesion, their temporal dynamics, and how they vary depending on surgical factors. We report on lesion characteristics including size and location, as well as how these vary over time and across different MRI sequences. Data from 12 patients (2 essential tremor, 10 Parkinson's disease) who underwent unilateral radiofrequency thalamotomy for tremor were analysed. Lesion characteristics were compared across five structural sequences. Volumetric analysis of lesion features was performed at early (<5 weeks) and late (>5 months) timepoints by manual segmentation. Lesion location was determined after registration of lesions to standard space. All patients showed tremor improvement (clinical global impressions scale) postoperatively. Chronic side-effects included balance disturbances (n = 4) and worsening mobility due to parkinsonism progression (n = 1). Early lesion features including a necrotic core, cytotoxic oedema and perilesional oedema were best demarcated on T2-weighted sequences. Multiple lesions were associated with greater cytotoxic oedema compared with single lesions (T2-weighted mean volume: 537 ± 112 mm³ versus 302 ± 146 mm³, P = 0.028). Total lesion volume reduced on average by 90% between the early and late scans (T2-weighted mean volume: 918 ± 517 versus 75 ± 50 mm³, t = 3.592, P = 0.023, n = 5), with comparable volumes demonstrated at ∼6 months after surgery. Lesion volumes on susceptibility-weighted images were larger than those of T2-weighted images at later timepoints. Radiofrequency thalamotomy produces focused and predictable lesion imaging characteristics over time. T2-weighted scans distinguish between the early lesion core and oedema characteristics, while lesions may remain more visible on susceptibility-weighted images in the months following surgery. Scanning patients in the immediate postoperative period and then at 6 months is clinically meaningful for understanding the anatomical basis of the transient and permanent effects of thalamotomy.

Testing the cognitive effects of tadalafil. Neuropsychological secondary outcomes from the PASTIS trial

Cerebral small vessel disease (SVD) is a major cause of cognitive impairment in older people. As secondary endpoints in a phase-2 randomised clinical trial, we tested the effects of single administration of a widely-used PDE5 inhibitor, tadalafil, on cognitive performance in older people with SVD. In a double-blinded, placebo-controlled, cross-over trial, participants received tadalafil (20 mg) and placebo on two visits ≥ 7 days apart (randomised to order of treatment). The Montreal Cognitive Assessment (MOCA) was administered at baseline, alongside a measure to estimate optimal intellectual ability (Test of Premorbid Function). Then, before and after treatment, a battery of neuropsychological tests was administered, assessing aspects of attention, information processing speed, working memory and executive function. Sixty-five participants were recruited and 55 completed the protocol (N = 55, age: 66.8 (8.6) years, range 52-87; 15/40 female/male). Median MOCA score was 26 (IQR: 23, 27], range 15-30). No significant treatment effects were seen in any of the neuropsychological tests. There was a trend towards improved performance on Digit Span Forward (treatment effect 0.37, C.I. 0.01, 0.72; P = 0.0521). We did not identify significant treatment effects of single-administration tadalafil on neuropsychological performance in older people with SVD. The trend observed on Digit Span Forward may help to inform future studies.

Clinical trial registration

http://www.clinicaltrials.gov. Unique identifier: NCT00123456, https://eudract.ema.europa.eu. Unique identifier: 2015-001,235-20NCT00123456.

Vascular Collagen Type-IV in Hypertension and Cerebral Small Vessel Disease

BACKGROUND

Cerebral small vessel disease (SVD) is common in older people and causes lacunar stroke and vascular cognitive impairment. Risk factors include old age, hypertension and variants in the genes COL4A1/COL4A2 encoding collagen alpha-1(IV) and alpha-2(IV), here termed collagen-IV, which are core components of the basement membrane. We tested the hypothesis that increased vascular collagen-IV associates with clinical hypertension and with SVD in older persons and with chronic hypertension in young and aged primates and genetically hypertensive rats.

METHODS

We quantified vascular collagen-IV immunolabeling in small arteries in a cohort of older persons with minimal Alzheimer pathology (N=52; 21F/31M, age 82.8±6.95 years). We also studied archive tissue from young (age range 6.2-8.3 years) and older (17.0-22.7 years) primates (M mulatta) and compared chronically hypertensive animals (18 months aortic stenosis) with normotensives. We also compared genetically hypertensive and normotensive rats (aged 10-12 months).

RESULTS

Collagen-IV immunolabeling in cerebral small arteries of older persons was negatively associated with radiological SVD severity (ρ: -0.427, P=0.005) but was not related to history of hypertension. General linear models confirmed the negative association of lower collagen-IV with radiological SVD (P<0.017), including age as a covariate and either clinical hypertension (P<0.030) or neuropathological SVD diagnosis (P<0.022) as fixed factors. Reduced vascular collagen-IV was accompanied by accumulation of fibrillar collagens (types I and III) as indicated by immunogold electron microscopy. In young and aged primates, brain collagen-IV was elevated in older normotensive relative to young normotensive animals (P=0.029) but was not associated with hypertension. Genetically hypertensive rats did not differ from normotensive rats in terms of arterial collagen-IV.

CONCLUSIONS

Our cross-species data provide novel insight into sporadic SVD pathogenesis, supporting insufficient (rather than excessive) arterial collagen-IV in SVD, accompanied by matrix remodeling with elevated fibrillar collagen deposition. They also indicate that hypertension, a major risk factor for SVD, does not act by causing accumulation of brain vascular collagen-IV.

The PASTIS trial: Testing tadalafil for possible use in vascular cognitive impairment

INTRODUCTION

There are few randomized clinical trials in vascular cognitive impairment (VCI). This trial tested the hypothesis that the PDE5 inhibitor tadalafil, a widely used vasodilator, increases cerebral blood flow (CBF) in older people with symptomatic small vessel disease, the main cause of VCI.

METHODS

In a double-blind, placebo-controlled, cross-over trial, participants received tadalafil (20 mg) and placebo on two visits ≥7 days apart (randomized to order of treatment). The primary endpoint, change in subcortical CBF, was measured by arterial spin labelling.

RESULTS

Tadalafil increased CBF non-significantly in all subcortical areas (N = 55, age: 66.8 (8.6) years) with greatest treatment effect within white matter hyperintensities (+9.8%, P = .0960). There were incidental treatment effects on systolic and diastolic blood pressure (-7.8, -4.9 mmHg; P < .001). No serious adverse events were observed.

DISCUSSION

This trial did not identify a significant treatment effect of single-administration tadalafil on subcortical CBF. To detect treatment effects may require different dosing regimens.

Optimization of quasi-diffusion magnetic resonance imaging for quantitative accuracy and time-efficient acquisition

PURPOSE

Quasi-diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mspace/> <mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> in mm²  s^-1 and a fractional exponent, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> , defining the non-Gaussianity of the diffusion signal decay. Here, the b-value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized.

METHODS

Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi-b-value reference (MbR) dataset comprising 29 diffusion-sensitized images arrayed between <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>b</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn></mml:mrow> <mml:annotation>$$ b=0 $$</mml:annotation></mml:semantics> </mml:math> and 5000 s mm^-2 . The effects of varying maximum b-value ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> </mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}} $$</mml:annotation></mml:semantics> </mml:math> ), number of b-value shells, and the effects of Rician noise were investigated.

RESULTS

QDTI measures showed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> </mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}} $$</mml:annotation></mml:semantics> </mml:math> dependence, most significantly for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> in white matter, which monotonically decreased with higher <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> </mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}} $$</mml:annotation></mml:semantics> </mml:math> leading to improved tissue contrast. Optimized 2 b-value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mspace/> <mml:mspace/> <mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ \kern0.50em {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> and underestimation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> in white matter, and overestimation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> <mml:mo>=</mml:mo> <mml:mn>5000</mml:mn></mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}}=5000 $$</mml:annotation></mml:semantics> </mml:math>  s mm^-2 , and 4 b-value shells at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> <mml:mo>=</mml:mo> <mml:mn>3960</mml:mn></mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}}=3960 $$</mml:annotation></mml:semantics> </mml:math>  s mm^-2 , providing minimal bias in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> compared to the MbR.

CONCLUSION

A highly detailed optimization of non-Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non-Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures.

Association of Blood Pressure Lowering Intensity With White Matter Network Integrity in Patients With Cerebral Small Vessel Disease

BACKGROUND AND OBJECTIVES

Diffusion tensor imaging (DTI) networks integrate damage from a variety of pathologic processes in cerebral small vessel disease (SVD) and may be a sensitive marker to detect treatment effects. We determined whether brain network analysis could detect treatment effects in the PRESERVE trial data set, in which intensive vs standard blood pressure (BP) lowering was compared. The primary end point of DTI had not shown treatment differences.

METHODS

Participants with lacunar stroke were randomized to standard (systolic 130-140 mm Hg) or intensive (systolic ≤ 125 mm Hg) BP lowering and followed for 2 years with MRI at baseline and at 2 years. Graph theory-based metrics were derived from DTI data to produce a measure of network integrity weighted global efficiency and compared with individual MRI markers of DTI, brain volume, and white matter hyperintensities.

RESULTS

Data were available in 82 subjects: standard n = 40 (mean age 66.3 ± 1.5 years) and intensive n = 42 (mean age 69.6 ± 1.0 years). The mean (SD) systolic BP was reduced by 13(14) and 23(23) mm Hg in the standard and intensive groups, respectively (p < 0.001 between groups). Significant differences in diffusion network metrics were found, with improved network integrity (weighted global efficiency, p = 0.002) seen with intensive BP lowering. In contrast, there were no significant differences in individual MRI markers including DTI histogram metrics, brain volume, or white matter hyperintensities.

DISCUSSION

Brain network analysis may be a sensitive surrogate marker in trials in SVD. This work suggests that measures of brain network efficiency may be more sensitive to the effects of BP control treatment than conventional DTI metrics.

TRIAL REGISTRATION INFORMATION

The trial is registered with the ISRCTN Registry (ISRCTN37694103; doi.org/10.1186/ISRCTN37694103) and the NIHR Clinical Research Network (CRN 10962; public-odp.nihr.ac.uk/QvAJAXZfc/opendoc.htm?document=crncc_users%5Cfind%20a%20clinical%20research%20study.qvw&lang=en-US&host=QVS%40crn-prod-odp-pu&anonymous=true).

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that intensive BP lowering in patients with SVD results in improved brain network function when assessed by DTI-based brain network metrics.

Test-retest reliability of arterial spin labelling for cerebral blood flow in older adults with small vessel disease

Cerebral small vessel disease (SVD) is common in older people and is associated with lacunar stroke, white matter hyperintensities (WMH) and vascular cognitive impairment. Cerebral blood flow (CBF) is reduced in SVD, particularly within white matter.Here we quantified test-retest reliability in CBF measurements using pseudo-continuous arterial spin labelling (pCASL) in older adults with clinical and radiological evidence of SVD (N=54, mean (SD): 66.9 (8.7) years, 15 females/39 males). We generated whole-brain CBF maps on two visits at least 7 days apart (mean (SD): 20 (19), range 7-117 days).Test-retest reliability for CBF was high in all tissue types, with intra-class correlation coefficient [95%CI]: 0.758 [0.616, 0.852] for whole brain, 0.842 [0.743, 0.905] for total grey matter, 0.771 [0.636, 0.861] for deep grey matter (caudate-putamen and thalamus), 0.872 [0.790, 0.923] for normal-appearing white matter (NAWM) and 0.780 [0.650, 0.866] for WMH (all p<0.001). ANCOVA models indicated significant decline in CBF in total grey matter, deep grey matter and NAWM with increasing age and diastolic blood pressure (all p<0.001). CBF was lower in males relative to females (p=0.013 for total grey matter, p=0.004 for NAWM).We conclude that pCASL has high test-retest reliability as a quantitative measure of CBF in older adults with SVD. These findings support the use of pCASL in routine clinical imaging and as a clinical trial endpoint.All data come from the PASTIS trial, prospectively registered at: https://eudract.ema.europa.eu (2015-001235-20, registered 13/05/2015), http://www.clinicaltrials.gov (NCT02450253, registered 21/05/2015).

Determining the OPTIMAL DTI analysis method for application in cerebral small vessel disease

•

We were not able to identify a single optimal diffusion-weighted imaging analysis strategy across all 6 cohorts.

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Diffusion tensor imaging measures at baseline predicted dementia conversion in cerebral small vessel disease and mild cognitive impairment.

•

Diffusion tensor imaging measures at baseline may be sensitive to differentiate between later vascular dementia vs Alzheimer’s disease dementia.

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Diffusion tensor imaging measures significantly changed over time in cohorts with cerebral small vessel disease and cohorts with mild cognitive impairment. Change in diffusion tensor imaging measures were only consistently associated with dementia conversion in severe SVD.

•

The diffusion tensor imaging measures PSMD and DSEG required the lowest minimum sample sizes for a hypothetical clinical trial in patients with sporadic cerebral small vessel disease and mild cognitive impairment.

Background

DTI is sensitive to white matter (WM) microstructural damage and has been suggested as a surrogate marker for phase 2 clinical trials in cerebral small vessel disease (SVD). The study’s objective is to establish the best way to analyse the diffusion-weighted imaging data in SVD for this purpose. The ideal method would be sensitive to change and predict dementia conversion, but also straightforward to implement and ideally automated. As part of the OPTIMAL collaboration, we evaluated five different DTI analysis strategies across six different cohorts with differing SVD severity.

Methods

Those 5 strategies were: (1) conventional mean diffusivity WM histogram measure (MD median), (2) a principal component-derived measure based on conventional WM histogram measures (PC1), (3) peak width skeletonized mean diffusivity (PSMD), (4) diffusion tensor image segmentation θ (DSEG θ) and (5) a WM measure of global network efficiency (Geff). The association between each measure and cognitive function was tested using a linear regression model adjusted by clinical markers. Changes in the imaging measures over time were determined. In three cohort studies, repeated imaging data together with data on incident dementia were available. The association between the baseline measure, change measure and incident dementia conversion was examined using Cox proportional-hazard regression or logistic regression models. Sample size estimates for a hypothetical clinical trial were furthermore computed for each DTI analysis strategy.

Results

There was a consistent cross-sectional association between the imaging measures and impaired cognitive function across all cohorts. All baseline measures predicted dementia conversion in severe SVD. In mild SVD, PC1, PSMD and Geff predicted dementia conversion. In MCI, all markers except Geff predicted dementia conversion. Baseline DTI was significantly different in patients converting to vascular dementia than to Alzheimer’ s disease. Significant change in all measures was associated with dementia conversion in severe but not in mild SVD. The automatic and semi-automatic measures PSMD and DSEG θ required the lowest minimum sample sizes for a hypothetical clinical trial in single-centre sporadic SVD cohorts.

Conclusion

DTI parameters obtained from all analysis methods predicted dementia, and there was no clear winner amongst the different analysis strategies. The fully automated analysis provided by PSMD offers advantages particularly for large datasets.

Factors affecting brain structure in smoking-related diseases: Chronic Obstructive Pulmonary Disease (COPD) and coronary artery disease

Background

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.

Methods

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.

Results

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.

Conclusions

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.

A Comparison of Connected Speech Tasks for Detecting Early Alzheimer’s Disease and Mild Cognitive Impairment Using Natural Language Processing and Machine Learning

Alzheimer’s disease (AD) has a long pre-clinical period, and so there is a crucial need for early detection, including of Mild Cognitive Impairment (MCI). Computational analysis of connected speech using Natural Language Processing and machine learning has been found to indicate disease and could be utilized as a rapid, scalable test for early diagnosis. However, there has been a focus on the Cookie Theft picture description task, which has been criticized. Fifty participants were recruited – 25 healthy controls (HC), 25 mild AD or MCI (AD+MCI) – and these completed five connected speech tasks: picture description, a conversational map reading task, recall of an overlearned narrative, procedural recall and narration of a wordless picture book. A high-dimensional set of linguistic features were automatically extracted from each transcript and used to train Support Vector Machines to classify groups. Performance varied, with accuracy for HC vs. AD+MCI classification ranging from 62% using picture book narration to 78% using overlearned narrative features. This study shows that, importantly, the conditions of the speech task have an impact on the discourse produced, which influences accuracy in detection of AD beyond the length of the sample. Further, we report the features important for classification using different tasks, showing that a focus on the Cookie Theft picture description task may narrow the understanding of how early AD pathology impacts speech.

PRESERVE: Randomized Trial of Intensive Versus Standard Blood Pressure Control in Small Vessel Disease

[Figure: see text].

Network neuroscience of apathy in cerebrovascular disease

Apathy is a reduction in motivated goal-directed behavior (GDB) that is prevalent in cerebrovascular disease, providing an important opportunity to study the mechanistic underpinnings of motivation in humans. Focal lesions, such as those seen in stroke, have been crucial in developing models of brain regions underlying motivated behavior, while studies of cerebral small vessel disease (SVD) have helped define the connections between brain regions supporting such behavior. However, current lesion-based models cannot fully explain the neurobiology of apathy in stroke and SVD. To address this, we propose a network-based model which conceptualizes apathy as the result of damage to GDB-related networks. A review of the current evidence suggests that cerebrovascular disease-related pathology can lead to network changes outside of initially damaged territories, which may propagate to regions that share structural or functional connections. The presentation and longitudinal trajectory of apathy in stroke and SVD may be the result of these network changes. Distinct subnetworks might support cognitive components of GDB, the disruption of which results in specific symptoms of apathy. This network-based model of apathy may open new approaches for investigating its underlying neurobiology, and presents novel opportunities for its diagnosis and treatment.

Disruption of white matter connectivity in chronic obstructive pulmonary disease

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Predicting Dementia in Cerebral Small Vessel Disease Using an Automatic Diffusion Tensor Image Segmentation Technique

Supplemental Digital Content is available in the text.

Cerebral small vessel disease (SVD) is the most common cause of vascular cognitive impairment, with a significant proportion of cases going on to develop dementia. We explore the extent to which diffusion tensor image segmentation technique (DSEG; which characterizes microstructural damage across the cerebrum) predicts both degree of cognitive decline and conversion to dementia, and hence may provide a useful prognostic procedure.

Contributions of cardiovascular risk and smoking to chronic obstructive pulmonary disease (COPD)-related changes in brain structure and function

Background

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.

Materials and methods

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 [FEV1]: 58.1±18.0% pred.) and 23 non-COPD controls with >10 pack years smoking (age: 66.6±7.5 years, 52.2% male, FEV1: 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.

Results

COPD patients had significantly worse cognitive function (p=0.011), lower mood (p=0.046), and greater gray matter atrophy (p=0.020). In COPD patients, lower mood was associated with markers of white matter (WM) microstructural damage (p<0.001), and lower lung function (FEV1/forced vital capacity and FEV1) with markers of both WM macro (p=0.047) and microstructural damage (p=0.028).

Conclusion

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.

Apathy is associated with large-scale white matter network disruption in small vessel disease

OBJECTIVE

To investigate whether white matter network disruption underlies the pathogenesis of apathy, but not depression, in cerebral small vessel disease (SVD).

METHODS

Three hundred thirty-one patients with SVD from the Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort (RUN DMC) study completed measures of apathy and depression and underwent structural MRI. Streamlines reflecting underlying white matter fibers were reconstructed with diffusion tensor tractography. First, path analysis was used to determine whether network measures mediated associations between apathy and radiologic markers of SVD. Next, we examined differences in whole-brain network measures between participants with only apathy, only depression, and comorbid apathy and depression and a control group free of neuropsychiatric symptoms. Finally, we examined regional network differences associated with apathy.

RESULTS

Path analysis demonstrated that network disruption mediated the relationship between apathy and SVD markers. Patients with apathy, compared to all other groups, were impaired on whole-brain measures of network density and efficiency. Regional network analyses in both the apathy subgroup and the entire sample revealed that apathy was associated with impaired connectivity in premotor and cingulate regions.

CONCLUSIONS

Our results suggest that apathy, but not depression, is associated with white matter tract disconnection in SVD. The subnetworks delineated suggest that apathy may be driven by damage to white matter networks underlying action initiation and effort-based decision making.

Identifying preclinical vascular dementia in symptomatic small vessel disease using MRI

Sporadic cerebral small vessel disease is an important cause of vascular dementia, a syndrome of cognitive impairment together with vascular brain damage. At post-mortem pure vascular dementia is rare, with evidence of co-existing Alzheimer's disease pathology in 95% of cases. This work used MRI to characterize structural abnormalities during the preclinical phase of vascular dementia in symptomatic small vessel disease. 121 subjects were recruited into the St George's Cognition and Neuroimaging in Stroke study and followed up longitudinally for five years. Over this period 22 individuals converted to dementia. Using voxel-based morphometry, we found structural abnormalities present at baseline in those with preclinical dementia, with reduced grey matter density in the left striatum and hippocampus, and more white matter hyperintensities in the frontal white-matter. The lacunar data revealed that some of these abnormalities may be due to lesions within the striatum and centrum semiovale. Using support vector machines, future dementia could be best predicted using hippocampal and striatal Jacobian determinant data, achieving a balanced classification accuracy of 73%. Using cluster ward linkage we identified four anatomical subtypes. Successful predictions were restricted to groups with lower levels of vascular damage. The subgroup that could not be predicted were younger, further from conversion, had the highest levels of vascular damage, with milder cognitive impairment at baseline but more rapid deterioration in processing speed and executive function, consistent with a primary vascular dementia. In contrast, the remaining groups had decreasing levels of vascular damage and increasing memory impairment consistent with progressively more Alzheimer's-like pathology. Voxel-wise rates of hippocampal atrophy supported these distinctions, with the vascular group closely resembling the non-dementing cohort, whereas the Alzheimer's like group demonstrated global hippocampal atrophy. This work reveals distinct anatomical endophenotypes in preclinical vascular dementia, forming a spectrum between vascular and Alzheimer's like pathology. The latter group can be identified using baseline MRI, with 73% converting within 5 years. It was not possible to predict the vascular dominant dementia subgroup, however 19% of negative predictions with high levels of vascular disease would ultimately develop dementia. It may be that techniques more sensitive to white matter damage, such as diffusion weighted imaging, may prove more useful for this vascular dominant subgroup in the future. This work provides a way to accurately stratify patients using a baseline MRI scan, and has utility in future clinical trials designed to slow or prevent the onset of dementia in these high-risk cohorts.

Texture Analysis of T1-Weighted and Fluid-Attenuated Inversion Recovery Images Detects Abnormalities That Correlate With Cognitive Decline in Small Vessel Disease

Supplemental Digital Content is available in the text.

Background and Purpose—

Magnetic resonance imaging may be useful to assess disease severity in cerebral small vessel disease (SVD), identify those individuals who are most likely to progress to dementia, monitor disease progression, and act as surrogate markers to test new therapies. Texture analysis extracts information on the relationship between signal intensities of neighboring voxels. A potential advantage over techniques, such as diffusion tensor imaging, is that it can be used on clinically obtained magnetic resonance sequences. We determined whether texture parameters (TP) were abnormal in SVD, correlated with cognitive impairment, predicted cognitive decline, or conversion to dementia.

Methods—

In the prospective SCANS study (St George’s Cognition and Neuroimaging in Stroke), we assessed TP in 121 individuals with symptomatic SVD at baseline, 99 of whom attended annual cognitive testing for 5 years. Conversion to dementia was recorded for all subjects during the 5-year period. Texture analysis was performed on fluid-attenuated inversion recovery and T1-weighted images. The TP obtained from the SVD cohort were cross-sectionally compared with 54 age-matched controls scanned on the same magnetic resonance imaging system.

Results—

There were highly significant differences in several TP between SVD cases and controls. Within the SVD population, TP were highly correlated to other magnetic resonance imaging parameters (brain volume, white matter lesion volume, lacune count). TP correlated with executive function and global function at baseline and predicted conversion to dementia, after controlling for age, sex, premorbid intelligence quotient, and magnetic resonance parameters.

Conclusions—

TP, which can be obtained from routine clinical images, are abnormal in SVD, and the degree of abnormality correlates with executive dysfunction and global cognition at baseline and decline during 5 years. TP may be useful to assess disease severity in clinically collected data. This needs testing in data clinically acquired across multiple sites.

Effect of Standard vs Intensive Blood Pressure Control on Cerebral Blood Flow in Small Vessel Disease: The PRESERVE Randomized Clinical Trial

Importance

Blood pressure (BP) lowering is considered neuroprotective in patients with cerebral small vessel disease; however, more intensive regimens may increase cerebral hypoperfusion. This study examined the effect of standard vs intensive BP treatment on cerebral perfusion in patients with severe small vessel disease.

Objective

To investigate whether standard vs intensive BP lowering over 3 months causes decreased cerebral perfusion in small vessel disease.

Design, Setting, and Participants

This randomized clinical trial took place at 2 English university medical centers. Patients were randomized via a central online system (in a 1:1 ratio). Seventy patients with hypertension and with magnetic resonance imaging-confirmed symptomatic lacunar infarct and confluent white matter hyperintensities were recruited between February 29, 2012, and October 21, 2015, and randomized (36 in the standard group and 34 in the intensive group). Analyzable data were available in 62 patients, 33 in the standard group and 29 in the intensive group, for intent-to-treat analysis. This experiment examines the 3-month follow-up period.

Interventions

Patients were randomized to standard (systolic, 130-140 mm Hg) or intensive (systolic, <125 mm Hg) BP targets, to be achieved through medication changes.

Main Outcomes and Measures

Cerebral perfusion was measured using arterial spin labeling; the primary end point was change in global perfusion between baseline and 3 months, compared between treatment groups by analysis of variance. Linear regression compared change in perfusion against change in BP. Magnetic resonance imaging scan analysis was masked to treatment group.

Results

Among 62 analyzable patients, the mean age was 69.3 years, and 60% (n = 37) were male. The mean (SD) systolic BP decreased by 8 (12) mm Hg in the standard group and by 27 (17) mm Hg in the intensive group (P < .001), with mean (SD) achieved pressures of 141 (13) and 126 (10) mm Hg, respectively. Change in global perfusion did not differ between treatment groups: the mean (SD) change was -0.5 (9.4) mL/min/100 g in the standard group vs 0.7 (8.6) mL/min/100 g in the intensive group (partial η2, 0.004; 95% CI, -3.551 to 5.818; P = .63). No differences were observed when the analysis examined gray or white matter only or was confined to those achieving target BP. The number of adverse events did not differ between treatment groups, with a mean (SD) of 0.21 (0.65) for the standard group and 0.32 (0.75) for the intensive group (P = .44).

Conclusions and Relevance

Intensive BP lowering did not reduce cerebral perfusion in severe small vessel disease.

Trial Registration

isrctn.org Identifier: ISRCTN37694103.

Involvement of the reward network is associated with apathy in cerebral small vessel disease

INTRODUCTION

Apathy is a common yet under-recognised feature of cerebral small vessel disease (SVD), but its underlying neurobiological basis is not yet understood. We hypothesized that damage to the reward network is associated with an increase of apathy in patients with SVD.

METHODS

In 114 participants with symptomatic SVD, defined as a magnetic resonance imaging confirmed lacunar stroke and confluent white matter hyperintensities, we used diffusion tensor imaging tractography to derive structural brain networks and graph theory to determine network efficiency. We determined which parts of the network correlated with apathy symptoms. We tested whether apathy was selectively associated with involvement of the reward network, compared with two "control networks" (visual and motor).

RESULTS

Apathy symptoms negatively correlated with connectivity in network clusters encompassing numerous areas of the brain. Network efficiencies within the reward network correlated negatively with apathy scores; (r = - 0.344, p < 0.001), and remained significantly correlated after co-varying for the two control networks. Of the three networks tested, only variability in the reward network independently explained variance in apathetic symptoms, whereas this was not observed for the motor or visual networks.

LIMITATIONS

The analysis refers only to cerebrum and not cerebellum. The apathy measure is derivative of depression measure.

DISCUSSION

Our results suggest that reduced neural efficiency, particularly in the reward network, is associated with increased apathy in patients with SVD. Treatments which improve connectivity in this network may improve apathy in SVD, which in turn may improve psychiatric outcome after stroke.

Longitudinal decline in structural networks predicts dementia in cerebral small vessel disease

OBJECTIVE

To determine whether longitudinal change in white matter structural network integrity predicts dementia and future cognitive decline in cerebral small vessel disease (SVD). To investigate whether network disruption has a causal role in cognitive decline and mediates the association between conventional MRI markers of SVD with both cognitive decline and dementia.

METHODS

In the prospective longitudinal SCANS (St George's Cognition and Neuroimaging in Stroke) Study, 97 dementia-free individuals with symptomatic lacunar stroke were followed with annual MRI for 3 years and annual cognitive assessment for 5 years. Conversion to dementia was recorded. Structural networks were constructed from diffusion tractography using a longitudinal registration pipeline, and network global efficiency was calculated. Linear mixed-effects regression was used to assess change over time.

RESULTS

Seventeen individuals (17.5%) converted to dementia, and significant decline in global cognition occurred (p = 0.0016). Structural network measures declined over the 3-year MRI follow-up, but the degree of change varied markedly between individuals. The degree of reductions in network global efficiency was associated with conversion to dementia (B = -2.35, odds ratio = 0.095, p = 0.00056). Change in network global efficiency mediated much of the association of conventional MRI markers of SVD with cognitive decline and progression to dementia.

CONCLUSIONS

Network disruption has a central role in the pathogenesis of cognitive decline and dementia in SVD. It may be a useful disease marker to identify that subgroup of patients with SVD who progress to dementia.

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels

BACKGROUND

Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images.

METHODS

We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue.

RESULTS

The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively.

CONCLUSION

The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

Lacunar Infarcts, but Not Perivascular Spaces, Are Predictors of Cognitive Decline in Cerebral Small-Vessel Disease

Supplemental Digital Content is available in the text.

Background and Purpose—

Cerebral small-vessel disease is a major cause of cognitive impairment. Perivascular spaces (PvS) occur in small-vessel disease, but their relationship to cognitive impairment remains uncertain. One reason may be difficulty in distinguishing between lacunes and PvS. We determined the relationship between baseline PvS score and PvS volume with change in cognition over a 5-year follow-up. We compared this to the relationship between baseline lacune count and total lacune volume with cognition. In addition, we examined change in PvS volume over time.

Methods—

Data from the prospective SCANS study (St Georges Cognition and Neuroimaging in Stroke) of patients with symptomatic lacunar stroke and confluent leukoaraiosis were used (n=121). Multimodal magnetic resonance imaging was performed annually for 3 years and neuropsychological testing annually for 5 years. Lacunes were manually identified and distinguished from PvS. PvS were rated using a validated visual rating scale, and PvS volumes calculated using T1-weighted images. Linear mixed-effect models were used to determine the impact of PvS and lacunes on cognition.

Results—

Baseline PvS scores or volumes showed no association with cognitive indices. No change was detectable in PvS volumes over the 3 years. In contrast, baseline lacunes associated with all cognitive indices and predicted cognitive decline over the 5-year follow-up.

Conclusions—

Although a feature of small-vessel disease, PvS are not a predictor of cognitive decline, in contrast to lacunes. This study highlights the importance of carefully differentiating between lacunes and PvS in studies investigating vascular cognitive impairment.

The effect of phosphodiesterase-5 inhibitors on cerebral blood flow in humans: A systematic review

Agents that augment cerebral blood flow (CBF) could be potential treatments for vascular cognitive impairment. Phosphodiesterase-5 inhibitors are vasodilating drugs established in the treatment of erectile dysfunction (ED) and pulmonary hypertension. We reviewed published data on the effects of phosphodiesterase-5 inhibitors on CBF in adult humans. A systematic review according to PRISMA guidelines was performed. Embase, Medline and Cochrane Library Trials databases were searched. Sixteen studies with 353 participants in total were retrieved. Studies included healthy volunteers and patients with migraine, ED, type 2 diabetes, stroke, pulmonary hypertension, Becker muscular dystrophy and subarachnoid haemorrhage. Most studies used middle cerebral artery flow velocity to estimate CBF. Few studies employed direct measurements of tissue perfusion. Resting CBF velocity was unaffected by phosphodiesterase-5 inhibitors, but cerebrovascular regulation was improved in ED, pulmonary hypertension, diabetes, Becker's and a group of healthy volunteers. This evidence suggests that phosphodiesterase-5 inhibitors improve responsiveness of the cerebral vasculature, particularly in disease states associated with an impaired endothelial dilatory response. This supports the potential therapeutic use of phosphodiesterase-5 inhibitors in vascular cognitive impairment where CBF is reduced. Further studies with better resolution of deep CBF are warranted. The review is registered on the PROSPERO database (registration number CRD42016029668).

Change in multimodal MRI markers predicts dementia risk in cerebral small vessel disease

Objective:

To determine whether MRI markers, including diffusion tensor imaging (DTI), can predict cognitive decline and dementia in patients with cerebral small vessel disease (SVD).

Methods:

In the prospective St George's Cognition and Neuroimaging in Stroke study, multimodal MRI was performed annually for 3 years and cognitive assessments annually for 5 years in a cohort of 99 patients with SVD, defined as symptomatic lacunar stroke and confluent white matter hyperintensities (WMH). Progression to dementia was determined in all patients. Progression of WMH, brain volume, lacunes, cerebral microbleeds, and a DTI measure (the normalized peak height of the mean diffusivity histogram distribution) as a marker of white matter microstructural damage were determined.

Results:

Over 5 years of follow-up, 18 patients (18.2%) progressed to dementia. A significant change in all MRI markers, representing deterioration, was observed. The presence of new lacunes, and rate of increase in white matter microstructural damage on DTI, correlated with both decline in executive function and global functioning. Growth of WMH and deterioration of white matter microstructure on DTI predicted progression to dementia. A model including change in MRI variables together with their baseline values correctly classified progression to dementia with a C statistic of 0.85.

Conclusions:

This longitudinal prospective study provides evidence that change in MRI measures including DTI, over time durations during which cognitive change is not detectable, predicts cognitive decline and progression to dementia. It supports the use of MRI measures, including DTI, as useful surrogate biomarkers to monitor disease and assess therapeutic interventions.

Neuropathology of White Matter Lesions, Blood-Brain Barrier Dysfunction, and Dementia

BACKGROUND AND PURPOSE

We tested whether blood-brain barrier dysfunction in subcortical white matter is associated with white matter abnormalities or risk of clinical dementia in older people (n=126; mean age 86.4, SD: 7.7 years) in the MRC CFAS (Medical Research Council Cognitive Function and Ageing Study).

METHODS

Using digital pathology, we quantified blood-brain barrier dysfunction (defined by immunohistochemical labeling for the plasma marker fibrinogen). This was assessed within subcortical white matter tissue samples harvested from postmortem T₂ magnetic resonance imaging (MRI)-detected white matter hyperintensities, from normal-appearing white matter (distant from coexistent MRI-defined hyperintensities), and from equivalent areas in MRI normal brains. Histopathologic lesions were defined using a marker for phagocytic microglia (CD68, clone PGM1).

RESULTS

Extent of fibrinogen labeling was not significantly associated with white matter abnormalities defined either by MRI (odds ratio, 0.90; 95% confidence interval, 0.79-1.03; P=0.130) or by histopathology (odds ratio, 0.93; 95% confidence interval, 0.77-1.12; P=0.452). Among participants with normal MRI (no detectable white matter hyperintensities), increased fibrinogen was significantly related to decreased risk of clinical dementia (odds ratio, 0.74; 95% confidence interval, 0.58-0.94; P=0.013). Among participants with histological lesions, increased fibrinogen was related to increased risk of dementia (odds ratio, 2.26; 95% confidence interval, 1.25-4.08; P=0.007).

CONCLUSIONS

Our data suggest that some degree of blood-brain barrier dysfunction is common in older people and that this may be related to clinical dementia risk, additional to standard MRI biomarkers.

Diffusion tensor image segmentation of the cerebrum provides a single measure of cerebral small vessel disease severity related to cognitive change

Cerebral small vessel disease (SVD) is the primary cause of vascular cognitive impairment and is associated with decline in executive function (EF) and information processing speed (IPS). Imaging biomarkers are needed that can monitor and identify individuals at risk of severe cognitive decline. Recently there has been interest in combining several magnetic resonance imaging (MRI) markers of SVD into a unitary score to describe disease severity. Here we apply a diffusion tensor image (DTI) segmentation technique (DSEG) to describe SVD related changes in a single unitary score across the whole cerebrum, to investigate its relationship with cognitive change over a three-year period. 98 patients (aged 43-89) with SVD underwent annual MRI scanning and cognitive testing for up to three years. DSEG provides a vector of 16 discrete segments describing brain microstructure of healthy and/or damaged tissue. By calculating the scalar product of each DSEG vector in reference to that of a healthy ageing control we generate an angular measure (DSEG θ) describing the patients' brain tissue microstructural similarity to a disease free model of a healthy ageing brain. Conventional MRI markers of SVD brain change were also assessed including white matter hyperintensities, cerebral atrophy, incident lacunes, cerebral-microbleeds, and white matter microstructural damage measured by DTI histogram parameters. The impact of brain change on cognition was explored using linear mixed-effects models. Post-hoc sample size analysis was used to assess the viability of DSEG θ as a tool for clinical trials. Changes in brain structure described by DSEG θ were related to change in EF and IPS (p < 0.001) and remained significant in multivariate models including other MRI markers of SVD as well as age, gender and premorbid IQ. Of the conventional markers, presence of new lacunes was the only marker to remain a significant predictor of change in EF and IPS in the multivariate models (p = 0.002). Change in DSEG θ was also related to change in all other MRI markers (p < 0.017), suggesting it may be used as a surrogate marker of SVD damage across the cerebrum. Sample size estimates indicated that fewer patients would be required to detect treatment effects using DSEG θ compared to conventional MRI and DTI markers of SVD severity. DSEG θ is a powerful tool for characterising subtle brain change in SVD that has a negative impact on cognition and remains a significant predictor of cognitive change when other MRI markers of brain change are accounted for. DSEG provides an automatic segmentation of the whole cerebrum that is sensitive to a range of SVD related structural changes and successfully predicts cognitive change. Power analysis shows DSEG θ has potential as a monitoring tool in clinical trials. As such it may provide a marker of SVD severity from a single imaging modality (i.e. DTIs).

White matter lesions characterise brain involvement in moderate to severe chronic obstructive pulmonary disease, but cerebral atrophy does not

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 (FEV₁ 52.1% pred., PaO₂ 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 FEV₁ % 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.

Electronic supplementary material

The online version of this article (doi:10.1186/s12890-017-0435-1) contains supplementary material, which is available to authorized users.

Perfusion by Arterial Spin labelling following Single dose Tadalafil In Small vessel disease (PASTIS): study protocol for a randomised controlled trial

BACKGROUND

Cerebral small vessel disease is a common cause of vascular cognitive impairment in older people, with no licensed treatment. Cerebral blood flow is reduced in small vessel disease. Tadalafil is a widely prescribed phosphodiesterase-5 inhibitor that increases blood flow in other vascular territories. The aim of this trial is to test the hypothesis that tadalafil increases cerebral blood flow in older people with small vessel disease.

METHODS/DESIGN

Perfusion by Arterial Spin labelling following Single dose Tadalafil In Small vessel disease (PASTIS) is a phase II randomised double-blind crossover trial. In two visits, 7-30 days apart, participants undergo arterial spin labelling to measure cerebral blood flow and a battery of cognitive tests, pre- and post-dosing with oral tadalafil (20 mg) or placebo.

SAMPLE SIZE

54 participants are required to detect a 15% increase in cerebral blood flow in subcortical white matter (p < 0.05, 90% power). Primary outcomes are cerebral blood flow in subcortical white matter and deep grey nuclei. Secondary outcomes are cortical grey matter cerebral blood flow and performance on cognitive tests (reaction time, information processing speed, digit span forwards and backwards, semantic fluency).

DISCUSSION

Recruitment started on 4th September 2015 and 36 participants have completed to date (19th April 2017). No serious adverse events have occurred. All participants have been recruited from one centre, St George's University Hospitals NHS Foundation Trust.

TRIAL REGISTRATION

European Union Clinical Trials Register: EudraCT number 2015-001235-20 . Registered on 13 May 2015.

Disruption of rich club organisation in cerebral small vessel disease

Cerebral small vessel disease (SVD) is an important cause of vascular cognitive impairment. Recent studies have demonstrated that structural connectivity of brain networks in SVD is disrupted. However, little is known about the extent and location of the reduced connectivity in SVD. Here they investigate the rich club organisation-a set of highly connected and interconnected regions-and investigate whether there is preferential rich club disruption in SVD. Diffusion tensor imaging (DTI) and cognitive assessment were performed in a discovery sample of SVD patients (n = 115) and healthy control subjects (n = 50). Results were replicated in an independent dataset (49 SVD with confluent WMH cases and 108 SVD controls) with SVD patients having a similar SVD phenotype to that of the discovery cases. Rich club organisation was examined in structural networks derived from DTI followed by deterministic tractography. Structural networks in SVD patients were less dense with lower network strength and efficiency. Reduced connectivity was found in SVD, which was preferentially located in the connectivity between the rich club nodes rather than in the feeder and peripheral connections, a finding confirmed in both datasets. In discovery dataset, lower rich club connectivity was associated with lower scores on psychomotor speed (β = 0.29, P < 0.001) and executive functions (β = 0.20, P = 0.009). These results suggest that SVD is characterized by abnormal connectivity between rich club hubs in SVD and provide evidence that abnormal rich club organisation might contribute to the development of cognitive impairment in SVD. Hum Brain Mapp 38:1751-1766, 2017. © 2017 Wiley Periodicals, Inc.

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI

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.

Defining thalamic nuclei and topographic connectivity gradients in vivo

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.

Longitudinal patterns of leukoaraiosis and brain atrophy in symptomatic small vessel disease

Cerebral small vessel disease is a common condition associated with lacunar stroke, cognitive impairment and significant functional morbidity. White matter hyperintensities and brain atrophy, seen on magnetic resonance imaging, are correlated with increasing disease severity. However, how the two are related remains an open question. To better define the relationship between white matter hyperintensity growth and brain atrophy, we applied a semi-automated magnetic resonance imaging segmentation analysis pipeline to a 3-year longitudinal cohort of 99 subjects with symptomatic small vessel disease, who were followed-up for ≥1 years. Using a novel two-stage warping pipeline with tissue repair step, voxel-by-voxel rate of change maps were calculated for each tissue class (grey matter, white matter, white matter hyperintensities and lacunes) for each individual. These maps capture both the distribution of disease and spatial information showing local rates of growth and atrophy. These were analysed to answer three primary questions: first, is there a relationship between whole brain atrophy and magnetic resonance imaging markers of small vessel disease (white matter hyperintensities or lacune volume)? Second, is there regional variation within the cerebral white matter in the rate of white matter hyperintensity progression? Finally, are there regionally specific relationships between the rates of white matter hyperintensity progression and cortical grey matter atrophy? We demonstrate that the rates of white matter hyperintensity expansion and grey matter atrophy are strongly correlated (Pearson's R = -0.69, P < 1 × 10(-7)), and significant grey matter loss and whole brain atrophy occurs annually (P < 0.05). Additionally, the rate of white matter hyperintensity growth was heterogeneous, occurring more rapidly within long association fasciculi. Using voxel-based quantification (family-wise error corrected P < 0.05), we show the rate of white matter hyperintensity progression is associated with increases in cortical grey matter atrophy rates, in the medial-frontal, orbito-frontal, parietal and occipital regions. Conversely, increased rates of global grey matter atrophy are significantly associated with faster white matter hyperintensity growth in the frontal and parietal regions. Together, these results link the progression of white matter hyperintensities with increasing rates of regional grey matter atrophy, and demonstrate that grey matter atrophy is the major contributor to whole brain atrophy in symptomatic cerebral small vessel disease. These measures provide novel insights into the longitudinal pathogenesis of small vessel disease, and imply that therapies aimed at reducing progression of white matter hyperintensities via end-arteriole damage may protect against secondary brain atrophy and consequent functional morbidity.

Progression of MRI markers in cerebral small vessel disease: Sample size considerations for clinical trials

Detecting treatment efficacy using cognitive change in trials of cerebral small vessel disease (SVD) has been challenging, making the use of surrogate markers such as magnetic resonance imaging (MRI) attractive. We determined the sensitivity of MRI to change in SVD and used this information to calculate sample size estimates for a clinical trial. Data from the prospective SCANS (St George’s Cognition and Neuroimaging in Stroke) study of patients with symptomatic lacunar stroke and confluent leukoaraiosis was used (n = 121). Ninety-nine subjects returned at one or more time points. Multimodal MRI and neuropsychologic testing was performed annually over 3 years. We evaluated the change in brain volume, T2 white matter hyperintensity (WMH) volume, lacunes, and white matter damage on diffusion tensor imaging (DTI). Over 3 years, change was detectable in all MRI markers but not in cognitive measures. WMH volume and DTI parameters were most sensitive to change and therefore had the smallest sample size estimates. MRI markers, particularly WMH volume and DTI parameters, are more sensitive to SVD progression over short time periods than cognition. These markers could significantly reduce the size of trials to screen treatments for efficacy in SVD, although further validation from longitudinal and intervention studies is required.

Genome-wide meta-analysis of cerebral white matter hyperintensities in patients with stroke

OBJECTIVE

For 3,670 stroke patients from the United Kingdom, United States, Australia, Belgium, and Italy, we performed a genome-wide meta-analysis of white matter hyperintensity volumes (WMHV) on data imputed to the 1000 Genomes reference dataset to provide insights into disease mechanisms.

METHODS

We first sought to identify genetic associations with white matter hyperintensities in a stroke population, and then examined whether genetic loci previously linked to WMHV in community populations are also associated in stroke patients. Having established that genetic associations are shared between the 2 populations, we performed a meta-analysis testing which associations with WMHV in stroke-free populations are associated overall when combined with stroke populations.

RESULTS

There were no associations at genome-wide significance with WMHV in stroke patients. All previously reported genome-wide significant associations with WMHV in community populations shared direction of effect in stroke patients. In a meta-analysis of the genome-wide significant and suggestive loci (p < 5 × 10(-6)) from community populations (15 single nucleotide polymorphisms in total) and from stroke patients, 6 independent loci were associated with WMHV in both populations. Four of these are novel associations at the genome-wide level (rs72934505 [NBEAL1], p = 2.2 × 10(-8); rs941898 [EVL], p = 4.0 × 10(-8); rs962888 [C1QL1], p = 1.1 × 10(-8); rs9515201 [COL4A2], p = 6.9 × 10(-9)).

CONCLUSIONS

Genetic associations with WMHV are shared in otherwise healthy individuals and patients with stroke, indicating common genetic susceptibility in cerebral small vessel disease.

Differential relationships between apathy and depression with white matter microstructural changes and functional outcomes

Small vessel disease is a stroke subtype characterized by pathology of the small perforating arteries, which supply the sub-cortical structures of the brain. Small vessel disease is associated with high rates of apathy and depression, thought to be caused by a disruption of white matter cortical-subcortical pathways important for emotion regulation. It provides an important biological model to investigate mechanisms underlying these key neuropsychiatric disorders. This study investigated whether apathy and depression can be distinguished in small vessel disease both in terms of their relative relationship with white matter microstructure, and secondly whether they can independently predict functional outcomes. Participants with small vessel disease (n = 118; mean age = 68.9 years; 65% male) defined as a clinical and magnetic resonance imaging confirmed lacunar stroke with radiological leukoaraiosis were recruited and completed cognitive testing, measures of apathy, depression, quality of life and diffusion tensor imaging. Healthy controls (n = 398; mean age = 64.3 years; 52% male) were also studied in order to interpret the degree of apathy and depression found within the small vessel disease group. Firstly, a multilevel structural equation modelling approach was used to identify: (i) the relationships between median fractional anisotropy and apathy, depression and cognitive impairment; and (ii) if apathy and depression make independent contributions to quality of life in patients with small vessel disease. Secondly, we applied a whole-brain voxel-based analysis to investigate which regions of white matter were associated with apathy and depression, controlling for age, gender and cognitive functioning. Structural equation modelling results indicated both apathy (r = -0.23, P ≤ 0.001) and depression (r = -0.41, P ≤ 0.001) were independent predictors of quality of life. A reduced median fractional anisotropy was significantly associated with apathy (r = -0.38, P ≤ 0.001), but not depression (r = -0.16, P = 0.09). On voxel-based analysis, apathy was associated with widespread reduction in white matter integrity, with the strongest effects in limbic association tracts such as the anterior cingulum, fornix and uncinate fasciculus. In contrast, when controlling for apathy, we found no significant relationship between our white matter parameters and symptoms of depression. In conclusion, white matter microstructural changes in small vessel disease are associated with apathy but not directly with depressive symptoms. These results suggest that apathy, but not depression, in small vessel disease is related to damage to cortical-subcortical networks associated with emotion regulation, reward and goal-directed behaviour.

Pattern and Rate of Cognitive Decline in Cerebral Small Vessel Disease: A Prospective Study

Objectives

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.

Methods

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.

Results

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.

Conclusions

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.

Characterising the grey matter correlates of leukoaraiosis in cerebral small vessel disease

Cerebral small vessel disease (SVD) is a heterogeneous group of pathological disorders that affect the small vessels of the brain and are an important cause of cognitive impairment. The ischaemic consequences of this disease can be detected using MRI, and include white matter hyperintensities (WMH), lacunar infarcts and microhaemorrhages. The relationship between SVD disease severity, as defined by WMH volume, in sporadic age-related SVD and cortical thickness has not been well defined. However, regional cortical thickness change would be expected due to associated phenomena such as underlying ischaemic white matter damage, and the observation that widespread cortical thinning is observed in the related genetic condition CADASIL (Righart et al., 2013). Using MRI data, we have developed a semi-automated processing pipeline for the anatomical analysis of individuals with cerebral small vessel disease and applied it cross-sectionally to 121 subjects diagnosed with this condition. Using a novel combined automated white matter lesion segmentation algorithm and lesion repair step, highly accurate warping to a group average template was achieved. The volume of white matter affected by WMH was calculated, and used as a covariate of interest in a voxel-based morphometry and voxel-based cortical thickness analysis. Additionally, Gaussian Process Regression (GPR) was used to assess if the severity of SVD, measured by WMH volume, could be predicted from the morphometry and cortical thickness measures. We found significant (Family Wise Error corrected p < 0.05) volumetric decline with increasing lesion load predominately in the parietal lobes, anterior insula and caudate nuclei bilaterally. Widespread significant cortical thinning was found bilaterally in the dorsolateral prefrontal, parietal and posterio-superior temporal cortices. These represent distinctive patterns of cortical thinning and volumetric reduction compared to ageing effects in the same cohort, which exhibited greater changes in the occipital and sensorimotor cortices. Using GPR, the absolute WMH volume could be significantly estimated from the grey matter density and cortical thickness maps (Pearson's coefficients 0.80 and 0.75 respectively). We demonstrate that SVD severity is associated with regional cortical thinning. Furthermore a quantitative measure of SVD severity (WMH volume) can be predicted from grey matter measures, supporting an association between white and grey matter damage. The pattern of cortical thinning and volumetric decline is distinctive for SVD severity compared to ageing. These results, taken together, suggest that there is a phenotypic pattern of atrophy associated with SVD severity.

Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three-dimensional shape analysis

PURPOSE

Glioblastoma multiforme (GBM) and brain metastasis (MET) are the most common intra-axial brain neoplasms in adults and often pose a diagnostic dilemma using standard clinical MRI. These tumor types require different oncological and surgical management, which subsequently influence prognosis and clinical outcome.

METHODS

Here, we hypothesize that GBM and MET possess different three-dimensional (3D) morphological attributes based on their physical characteristics. A 3D morphological analysis was applied on the tumor surface defined by our diffusion tensor imaging (DTI) segmentation technique. It segments the DTI data into clusters representing different isotropic and anisotropic water diffusion characteristics, from which a distinct surface boundary between healthy and pathological tissue was identified. Morphometric features of shape index and curvedness were then computed for each tumor surface and used to build a morphometric model of GBM and MET pathology with the goal of developing a tumor classification method based on shape characteristics.

RESULTS

Our 3D morphometric method was applied on 48 untreated brain tumor patients. Cross-validation resulted in a 95.8% accuracy classification with only two shape features needed and that can be objectively derived from quantitative imaging methods.

CONCLUSION

The proposed 3D morphometric analysis framework can be applied to distinguish GBMs from solitary METs. Magn Reson Med 75:2505-2516, 2016. © 2015 Wiley Periodicals, Inc.

Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy

Many approaches have been considered for automatic grading of brain tumors by means of pattern recognition with magnetic resonance spectroscopy (MRS). Providing an improved technique which can assist clinicians in accurately identifying brain tumor grades is our main objective. The proposed technique, which is based on the discrete wavelet transform (DWT) of whole-spectral or subspectral information of key metabolites, combined with unsupervised learning, inspects the separability of the extracted wavelet features from the MRS signal to aid the clustering. In total, we included 134 short echo time single voxel MRS spectra (SV MRS) in our study that cover normal controls, low grade and high grade tumors. The combination of DWT-based whole-spectral or subspectral analysis and unsupervised clustering achieved an overall clustering accuracy of 94.8% and a balanced error rate of 7.8%. To the best of our knowledge, it is the first study using DWT combined with unsupervised learning to cluster brain SV MRS. Instead of dimensionality reduction on SV MRS or feature selection using model fitting, our study provides an alternative method of extracting features to obtain promising clustering results.

Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain

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.

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering

PURPOSE

To investigate whether nonlinear dimensionality reduction improves unsupervised classification of (1) H MRS brain tumor data compared with a linear method.

METHODS

In vivo single-voxel (1) H magnetic resonance spectroscopy (55 patients) and (1) H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data.

RESULTS

An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With (1) H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC.

CONCLUSION

The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of (1) H MRSI data after cluster analysis.

Delineation of gliomas using radial metabolite indexing

(1) H MRSI has demonstrated the ability to characterise and delineate brain tumours, but robust data analysis methods are still needed. In this study, we present an objective analysis method for MRSI data to delineate tumour abnormality regions. The presented method is a development of the choline-to-N-acetylaspartate index (CNI), which uses perpendicular distances in a choline versus N-acetylaspartate plot as a measure of abnormality. We propose a radial CNI (rCNI) method that uses the choline to N-acetylaspartate ratio directly as an abnormality measure. To avoid problems with small or zero denominators, we perform an arctangent transformation. CNI abnormality contours were evaluated using a z-score threshold of 2 (CNI2) and 2.5 (CNI2.5) and compared with rCNI2. Simulations modelling low-grade (LGG) and high-grade (HGG) gliomas with different tissue compartments and partial volume effects suggest improved specificity of rCNI2 (LGG 92%/HGG 91%) over CNI2 (LGG 69%/HGG 69%) and CNI2.5 (LGG 74%/HGG 75%), whilst retaining a similar sensitivity to both CNI2 and CNI2.5. Our simulation results also confirm a previously reported increase in specificity of CNI2.5 over CNI2 with little penalty in sensitivity. The analysis of MRSI data acquired from 10 patients with low-grade glioma at 3 T suggests a more robust delineation of the lesions using rCNI with respect to conventional imaging compared with standard CNI. Further analysis of 29 glioma datasets acquired at 1.5 T, together with previously published estimated tumour proportions, suggests that rCNI has higher sensitivity and specificity for the identification of abnormal MRSI voxels.

Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the p:q tensor decomposition of diffusion tensor imaging

The management and treatment of high-grade glioblastoma multiforme (GBM) and solitary metastasis (MET) are very different and influence the prognosis and subsequent clinical outcomes. In the case of a solitary MET, diagnosis using conventional radiology can be equivocal. Currently, a definitive diagnosis is based on histopathological analysis on a biopsy sample. Here, we present a computerised decision support framework for discrimination between GBM and solitary MET using MRI, which includes: (i) a semi-automatic segmentation method based on diffusion tensor imaging; (ii) two-dimensional morphological feature extraction and selection; and (iii) a pattern recognition module for automated tumour classification. Ground truth was provided by histopathological analysis from pre-treatment stereotactic biopsy or at surgical resection. Our two-dimensional morphological analysis outperforms previous methods with high cross-validation accuracy of 97.9% and area under the receiver operating characteristic curve of 0.975 using a neural networks-based classifier.