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Abstract

Background

A structural magnetic resonance imaging measure of combined neurovascular and neurodegenerative burden may be useful as these features often coexist in older people, stroke and dementia.

Aim

We aimed to develop a new automated approach for quantifying visible brain injury from small vessel disease and brain atrophy in a single measure, the brain health index.

Materials and methods

We computed brain health index in N = 288 participants using voxel-based Gaussian mixture model cluster analysis of T1, T2, T2*, and FLAIR magnetic resonance imaging. We tested brain health index against a validated total small vessel disease visual score and white matter hyperintensity volumes in two patient groups (minor stroke, N = 157; lupus, N = 51) and against measures of brain atrophy in healthy participants (N = 80) using multiple regression. We evaluated associations with Addenbrooke’s Cognitive Exam Revised in patients and with reaction time in healthy participants.

Results

The brain health index (standard beta = 0.20–0.59, P < 0.05) was significantly and more strongly associated with Addenbrooke’s Cognitive Exam Revised, including at one year follow-up, than white matter hyperintensity volume (standard beta = 0.04–0.08, P > 0.05) and small vessel disease score (standard beta = 0.02–0.27, P > 0.05) alone in both patient groups. Further, the brain health index (standard beta = 0.57–0.59, P < 0.05) was more strongly associated with reaction time than measures of brain atrophy alone (standard beta = 0.04–0.13, P > 0.05) in healthy participants.

Conclusions

The brain health index is a new image analysis approach that may usefully capture combined visible brain damage in large-scale studies of ageing, neurovascular and neurodegenerative disease.

Keywords

Atrophy cerebral small vessel diseases cognition image processing computer-assisted magnetic resonance imaging stroke

Introduction

Cerebral small vessel disease (SVD) features and brain tissue atrophy both increase with age, are often present together, and are risk factors for stroke and dementia.^1–3 The importance of vascular disease on accelerating neurodegenerative pathologies and cognitive decline has recently been recognized,^3,4 emphasizing the need for an inclusive approach to assessing common brain disorders of ageing.

A clinical total SVD score has recently been proposed and correlated with vascular risk factors and cognition in mild stroke and community-dwelling older adults >70 years.^5,6 This score has a maximum level of four and does not include tissue atrophy, as atrophy did not improve the score’s performance in some studies.^5,6 Yet brain atrophy is a major risk factor for, and correlate of, functional and cognitive decline.^2,7

Many automated tissue quantification tools have been developed to assess differences and changes in brain volumes across age and disease, e.g. FMRIB software library (FSL), statistical parametric mapping (SPM), and atropos.^8–10 However, these tools often misclassify ischaemic stroke lesions as normal appearing grey matter (GM), white matter hyperintensities (WMH), or cerebrospinal fluid (CSF).¹¹ This means that existing tissue quantification tools may not be suitable for older participants or stroke patients who may have ischaemic stroke lesions, or require manual editing to correct GM, WMH, and CSF volumes.^12,13

Aims and hypothesis

In the present work, we aimed to extend the promise shown by a holistic clinical measure of vascular brain damage⁵ and develop a holistic image processing measure of atrophy and SVD that can be applied without manual identification of ischaemic lesions. We thus describe the development of an automated composite measure of whole brain tissue atrophy, ischaemia, and SVD burden: the “Brain Health Index” (BHI). We hypothesize that BHI will perform well compared with existing clinical and quantitative measures of atrophy and SVD.

Material and methods

Approvals and patient consents

The contributing studies were approved by Lothian Research Ethics (09/81101/54 and 05/S1104/45) and South-East Scotland Research Ethics (14/SS/0003) and were conducted according to the Declaration of Helsinki. All participants provided written informed consent.

Mild stroke patients

One hundred and fifty-seven patients were recruited consecutively as part of a wider study of N = 264 patients who presented to hospital with mild (National Institutes of Neurological Disorders and Stroke (NIHSS) scale <7) or non-disabling lacunar/cortical ischemic stroke as diagnosed by an expert stroke physician.¹¹ This subset of N = 157 patients was used because it provided full brain MRI, vascular risk factors, and cognitive test data at baseline and one year, i.e. those who returned for follow-up. Full recruitment and assessment procedures for these subjects were described previously.¹¹

Systemic lupus erythematosus patients

Fifty-one patients with SLE, representing a range of disease severity and duration, were prospectively recruited as consecutively as possible from regional specialist clinics between April 2014 and December 2014. SLE was diagnosed by expert rheumatologists according to the American College of Rheumatology 1997; this diagnosis and full patient recruitment and assessment procedures were described previously.^14,15

Healthy participants

Eighty clinically normal, healthy working age (25–65 years) volunteers were recruited (without financial or other incentive) from staff at The University of Edinburgh, the Western General Hospital and Royal Infirmary, Edinburgh, Scotland by advertisements. Health status was assessed using medical questionnaires and all structural MRI scans were reported by a consultant neuroradiologist as described previously.¹⁶

Brain MRI acquisition

Brain MRI acquisition parameters were broadly the same across all participant groups and have been described in detail previously.^11,14,16,17

The brain health index

The BHI is computed automatically in a relatively simple manner using Gaussian mixture model clustering of four co-registered, routinely collected structural brain MRI sequences: T1, T2, T2*, and FLAIR. Affine 12 point registration was used to align within-subject images to their native T2 space.¹⁸ Intracranial volume (ICV) masks were estimated with T2* using the brain extraction tool (BET).¹⁹ The contrast between CSF (bright) and skull (dark) on T2* provides optimal BET results¹³; however, this can leave a dark rim of skull around the brain. We addressed this by converting the T2* image to z-scores²⁰ and thresholding at −1 standard deviation (SD) to remove the skull rim. ICV masks were visually inspected and manually edited if necessary.

BHI uses Gaussian mixture model cluster analysis to group intracranial voxels into two classes based on their intensities in each of the four sequences: (1) probably normal brain tissue; and (2) probably abnormal tissue or cerebrospinal fluid (Figure 1). The four sequences are overlaid within each subject to give each point within the intracranial space (voxel) four values (one from each sequence) that contribute to the cluster analysis. BHI – using the expectation-maximization (EM) algorithm – computes posterior probabilities of belonging to a particular cluster (“healthy” versus “abnormal”/CSF) based on the four intensities (one from each sequence) present in each voxel. Using the initially calculated posterior probabilities as weights, the algorithm estimates the cluster means, covariance matrices, and mixing proportions by applying maximum likelihood until convergence. Convergence is reached when the distance between each voxel and the multi-variate cluster mean is minimized. For example, hyperintense (e.g. ventricles, sulci, WMH, focal infarcts, lacunes, PVS) and hypointense (e.g. microbleed) voxels on T2- and T2*-based sequences, and hypointense voxels on T1-based sequences (e.g. ventricles, sulci, lacunes) are classed into a single cluster of probable “pathological” or cerebrospinal fluid voxels within each subject. The remaining voxels constitute probable “healthy” brain tissue within each subject, i.e. isointense voxels that likely do not represent features of SVD or cerebrospinal fluid (Figure 1).

Figure 1.

Flow diagram of the brain health index (BHI) method. Blue areas in the “healthy” tissue masks (middle bottom panel) indicate probable normal/ “healthy” appearing tissue, areas without overlay colour are more likely “pathological” tissue or cerebrospinal fluid. BHI masks are from three separate subjects showing exclusion of cortical stroke (Cort Strk – left mask), white matter hyperintensities (WMH) and atrophy (WMH+atr – middle mask), and a lacune/ WMH (Lac/WMH – right mask) from “healthy tissue”. FLAIR/ FL: fluid attenuated inversion recovery; sbj: subject; V: voxel in BHI mask; n: number of voxels in BHI mask; i: voxel at a given location x,y,z in BHI mask.

The initial result of BHI is a 3D mask image of the intracranial contents where each voxel contains a probability of containing “healthy” brain tissue; the final BHI measure for each subject is the mean value of these probabilities where values closest to one (100%) are “healthiest.”

SVD scores and brain tissue volumes

Brain MRI visual rating was performed in stroke by trained neuroradiologists and by image analysts supervised by a consultant neuroradiologist in SLE patients according to previously published methods.^7,21–25 Inter-rater differences were limited.¹¹ We calculated the total SVD score using a recently validated and published method.^5,6 WMH were quantified semi-automatically and normalized by ICV in stroke and SLE patients using a previously described and validated method consistent with STRIVE guidelines.^17,26 Normal appearing grey, white, and whole brain tissue volumes were quantified in healthy participants using an optimized automated segmentation method.²⁷

Vascular risk factors

Vascular risk factors were assessed in stroke and SLE patients, including hypertension, hyperlipidaemia, diabetes, smoking, and systolic and diastolic blood pressure measured at the same time point as brain MRI. Healthy participants did not have any history of vascular disorders and did not provide measurements.

Cognitive testing

We administered the Addenbrooke’s Cognitive Examination Revised (ACER),²⁸ a test of cognitive impairment often used in stroke patients,²⁹ at the same time point as brain MRI in SLE patients and at one month and one year post-stroke in stroke patients. We measured four choice reaction time, a sensitive measure of cognitive ageing,³⁰ in healthy participants.

Statistical analyses

All statistical analyses were performed using Matrix Laboratory (MATLAB) Statistics and Machine Learning Toolbox R2014a (© 1994–2014 The MathWorks, Inc.). We used multiple regression with standardized beta (a commonly used technique where data with varying units, i.e. clinical scores versus tissue volumes, are converted to z-scores²⁰) to determine the imaging metric with the strongest association with cognitive performance, identifying the metric with the largest standardized beta and the lowest P-value as having the highest relative importance among competing metrics.³¹

We specified ACER as the dependent variable and included WMH volume, total SVD score, and BHI as predictor variables in patient groups. We specified reaction time as the dependent variable and included GM, WM, and BHI as predictor variables in healthy participants. In a separate model, we again specified reaction time as the dependent variable and included whole brain volume (i.e. roughly gray + white matter volume) and BHI as predictor variables in healthy participants. Finally, we repeated all models while adjusting for age.

Results

Participants

Full characteristics of all patients and healthy participants are provided in Table 1. BHI took approximately 2 min to run per participant on a 2.60 GHz Intel Core i5-4310U CPU with 16 GB RAM. There were no total failures albeit some subjects had slight tissue misclassification around the grey/CSF boundary on close inspection (Figure 1).

Table 1.

Patient and healthy participant characteristics

Parameter	Mild stroke (N = 157)	SLE (N = 51)	Healthy participants (N = 80)
Age	65 ± 11 years	49 ± 14 years	43 ± 11 years
Gender	M = 93 F = 64	M = 4 F = 47	M = 40 F = 40
Hypertension	N = 127 (73%)	N = 11 (22%)	–
Hyperlipidaemia	N = 104 (60%)	N = 3 (6%)	–
Diabetes	N = 18 (10%)	N = 0 (0%)	–
Current smoking	N = 58 (34%)	N = 6 (12%)	–
Systolic BP	145 ± 25 mmHg	126 ± 20 mmHg	–
Diastolic BP	82 ± 13 mmHg	77 ± 17 mmHg	–
ACER	89 ± 8	90 ± 7	–
Reaction time	–	–	0.51 ± 0.06 s
Total SVD score	1, IQR = 2	1, IQR = 1	–
WMH volume	1.67 ± 1.70% ICV	0.28 ± 0.56% ICV	–
Grey matter volume	–	–	39.5 ± 1.5% ICV
White matter volume	–	–	33.3 ± 0.9% ICV
Whole brain volume	–	–	0.73 ± 1.5% ICV
BHI	0.62 ± 0.07	0.72 ± 0.05	0.71 ± 0.03

Note: – data unavailable (subjects with medical conditions were excluded).

SLE: systemic lupus erythematosus; BP: blood pressure; ACER: Addenbrooke’s Cognitive Exam Revised; SVD: small vessel disease; IQR: interquartile range; ICV: intracranial volume; WMH: white matter hyperintensity; BHI: brain health index.

Correlation between BHI and total SVD clinical score

There was a moderate negative correlation between the BHI and total SVD score (Spearman rho = −0.38, P < 0.001). A one unit increase in total SVD score was associated with a −0.02 (2%) reduction in BHI.

BHI in mild stroke and SLE patients

The BHI was significantly and more strongly associated with ACER than total SVD score and WMH volume in mild stroke patients (at both one month and one year post stroke) and in SLE patients (Table 2).

Table 2.

Multiple regression models comparing BHI, SVD score, and WMH volume as independent variables with ACER as dependent variable

Patient group	Time point^a	Imaging metric	Standard beta	SE	P
Mild stroke patients	One month	BHI	0.272	0.094	0.005*
		Total SVD score	−0.020	0.093	0.830
		WMH volume	0.064	0.088	0.466
Mild stroke patients	One year	BHI	0.200	0.092	0.030*
		Total SVD score	−0.075	0.091	0.411
		WMH volume	0.080	0.084	0.344
SLE patients	Same as MRI	BHI	0.356	0.172	0.045*
		Total SVD score	0.268	0.198	0.182
		WMH volume	−0.037	0.187	0.844

Time point for mild stroke patients is time after stroke; *P < 0.05.

SE: standard error; BHI: brain health index; SVD: small vessel disease; WMH: white matter hyperintensities; SLE: systemic lupus erythematosus.

BHI in healthy participants

The BHI was significantly and more strongly associated with reaction time than was GM, WM, and whole brain tissue volumes in healthy participants (Table 3).

Table 3.

Multiple regression models comparing BHI, grey matter, white matter, and whole brain volumes as independent variables with reaction time as dependent variable

Model	Imaging metric	Standard beta	SE	P
Individual tissues versus BHI	BHI	−0.572	0.192	0.004*
	Grey matter volume	−0.041	0.166	0.805
	White matter volume	−0.126	0.143	0.380
Whole brain tissue versus BHI	BHI	−0.589	0.191	0.003*
	Whole brain volume	−0.062	0.166	0.711

P < 0.05.

SE: standard error; BHI: brain health index.

Age adjusted analyses

Associations between BHI and ACER remained statistically significant in stroke patients (beta = 0.27, P = 0.003) but not in SLE patients (beta = 0.37, P = 0.09) after adjusting for age. The statistically significant association between reaction time and BHI remained after adjusting for age in healthy participants (beta = −0.32, P = 0.04).

Discussion

The BHI is a new automated approach for determining the combined effects of whole brain tissue atrophy and vascular disease in a single measure. It mirrored a recently validated combined SVD clinical rating score but was more strongly correlated with cognitive performance in three independent samples representative of a wide range of brain damage and clinical presentations.

The BHI compares favourably with existing qualitative and quantitative image analysis methods because: (1) it requires very limited labour time and manual intervention compared to WMH volumes which can take up to 1 h per subject¹¹; (2) the BHI is on a continuous scale versus clinical scales which often lack granularity; (3) the BHI assesses whole brain rather than tissue- or lesion-specific damage³¹; and (4) the BHI was significantly and more strongly associated with cognitive function compared with the commonly used imaging metrics assessed here. The stronger associations with cognition may reflect the inclusion of brain atrophy, a marker of tissue loss secondary to vascular disease and to neurodegeneration, as well as the vascular lesions. For example, a participant with a large burden of WMH but little atrophy would limit correlations in atrophy only assessments, whereas a participant with little WMH and much atrophy would limit correlations in WMH only assessments. WMH and atrophy have been shown to correlate with cognition individually,³² and combing them into a single measure may thus strengthen associations with cognition. Given the potential use of BHI, we will make the MATLAB executable code publicly available via our website (https://datashare.is.ed.ac.uk/) following wider compatibility testing on other operating systems and hardware.

Despite these strengths, there are limitations in this work. The BHI requires T1, T2, T2*, and FLAIR MRI to capture the full range of SVD and atrophy features and, in its current implementation, would likely provide unstable results if any of these four sequences were missing. However, all four of these sequences are commonly collected in SVD, ageing, and neurodegenerative studies and indeed are considered as standard in STRIVE guidelines.^11,26,31,33 We used two Gaussians to cluster voxels as these provided the most stable results across subjects with a wide range of SVD features. There were some misclassifications of normal tissue as “unhealthy” at the grey/CSF boundary in BHI (Figure 1). Further work is required to determine dependency on image acquisition factors such as slice thickness or gap, or whether additional clusters, e.g. grades of isointense and hyperintense voxels, can be stabilized to improve classifications at tissue boundaries. Although we had a rich diversity of acquired data in three independent cohorts, the sample sizes (total N = 288) are relatively small compared to others.^33,34 Stroke patients included here were those who returned for one-year follow-up. These patients are likely healthier than those who did not return and this limits the generalization of our results to all ischaemic strokes. However, this is a common problem in observational cohort studies³⁵ and there are limited solutions to address this problem. BHI was not significantly associated with ACER in SLE patients when adjusting for age; however, statistically significant associations remained in stroke patients and healthy participants. The small sample size in SLE patients may explain this attenuation (P was close to significance at 0.09). The assessment of BHI was mainly cross-sectional and further work is required in longitudinal imaging studies to determine the predictive utility of BHI.

Notwithstanding these limitations, we present the first results of a new, automated measure of whole brain damage in cerebrovascular disease. The BHI correlated more strongly with cognitive deficits commonly seen in stroke and SVD than a validated clinical scoring method and validated tissue volume processing methods. The BHI may therefore be a useful marker of brain damage in large-scale studies of cerebrovascular disease and should be tested more widely.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding from the Stroke Association (DAD), Technology Strategy Board/ Innovate UK (46917-348146), Wellcome Trust (WT088134/Z/09/A), Row Fogo Charitable Trust, Lupus UK, National Institutes of Health (R01 EB004155-03), European Union Horizon 2020 PHC-03-15, project No 666881, SVDs@Target, and Fondation Leducq (Transatlantic Network of Excellence for the Study of Perivascular Spaces in Small Vessel Disease, ref no. 16 CVD 05) is gratefully acknowledged.

References

Debette

Markus

. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ 2010; 341: c3666.

Fox

Schott

. Imaging cerebral atrophy: normal ageing to Alzheimer’s disease. Lancet 2004; 363: 392–394.

Shi

Wardlaw

. Update on cerebral small vessel disease: a dynamic whole-brain disease. Stroke Vasc Neurol 2016; 1: 83–92.

Pantoni

. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet Neurol 2010; 9: 689–701.

Staals

Booth

Morris

et al.

Total MRI load of cerebral small vessel disease and cognitive ability in older people. Neurobiol Aging 2015; 36: 2806–2811.

Staals

Makin

SDJ

Doubal

Dennis

Wardlaw

. Stroke subtype, vascular risk factors, and total MRI brain small-vessel disease burden. Neurology 2014; 83: 1228–1234.

The IST-3 collaborative group. Association between brain imaging signs, early and late outcomes, and response to intravenous alteplase after acute ischaemic stroke in the third International Stroke Trial (IST-3): secondary analysis of a randomised controlled trial. Lancet Neurol 2015; 14: 485–496.

Ashburner

Friston

. Voxel-Based morphometry – the methods. Neuroimage 2000; 11: 805–821.

Avants

Tustison

Cook

Gee

. An open source multivariate framework for n-tissue segmentation with evaluation on public data. Neuroinformatics 2011; 9: 381–400.

10.

Zhang

Brady

Smith

. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 2001; 20: 45–57.

11.

Valdés Hernández

del

Armitage

Thrippleton

et al.

Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke. Brain Behav 2015; 5(: e00415.

12.

Wang

Hernández

Doubal

Chappell

Wardlaw

. How much do focal infarcts distort white matter lesions and global cerebral atrophy measures? Cerebrovasc Dis 2012; 34: 336–342.

13.

Miller

Alfaro-Almagro

Bangerter

et al.

Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 2016; 19: 1523–1536.

14.

Wiseman

Bastin

Jardine

et al.

Cerebral small vessel disease burden is increased in systemic lupus erythematosus. Stroke 2016; 47: 2722–2728.

15.

Hochberg

. Updating the American college of rheumatology revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum 1997; 40: 1725.

16.

Dickie

Mikhael

Job

Wardlaw

Laidlaw

Bastin

. Permutation and parametric tests for effect sizes in voxel-based morphometry of gray matter volume in brain structural MRI. Magn Reson Imaging 2015; 33: 1299–305.

17.

Valdés Hernández

Ferguson

Chappell

Wardlaw

. New multispectral MRI data fusion technique for white matter lesion segmentation: method and comparison with thresholding in FLAIR images. Eur Radiol 2010; 20: 1684–1691.

18.

Jenkinson

Smith

. A global optimisation method for robust affine registration of brain images. Med Image Anal 2001; 5: 143–156.

19.

Smith

. Fast robust automated brain extraction. Hum Brain Mapp 2002; 17: 143–155.

20.

Freedman

Pisani

Purves

. Statistics, New York: WW Norton, 2007.

21.

Farrell

Chappell

Armitage

et al.

Development and initial testing of normal reference MR images for the brain at ages 65–70 and 75–80 years. Eur Radiol 2009; 19: 177–183.

22.

Wardlaw

Sellar

. A simple practical classification of cerebral infarcts on CT and its interobserver reliability. Am J Neuroradiol 1994; 15: 1933–1939.

23.

Potter

Chappell

Morris

Wardlaw

. Cerebral perivascular spaces visible on magnetic resonance imaging: development of a qualitative rating scale and its observer reliability. Cerebrovasc Dis 2015; 39: 224–231.

24.

Cordonnier

Potter

Jackson

et al.

Improving Interrater agreement about brain microbleeds: development of the brain observer microbleed scale (BOMBS). Stroke 2009; 40: 94–99.

25.

Fazekas

Niederkorn

Schmidt

et al.

White matter signal abnormalities in normal individuals: correlation with carotid ultrasonography, cerebral blood flow measurements, and cerebrovascular risk factors. Stroke 1988; 19: 1285–1288.

26.

Wardlaw

Smith

Biessels

et al.

Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013; 12: 822–838.

27.

Dickie DA, Job DE, Rodriguez D, et al. Brain Imaging of Normal Subjects (BRAINS) age-specific MRI atlases from young adults to the very elderly (v1.0), [dataset]. University of Edinburgh, Edinburgh Imaging, Centre for Clinical Brain Sciences, Edinburgh. http://dx.doi.org/10.7488/ds/1369.

28.

Mioshi

Dawson

Mitchell

Arnold

Hodges

. The Addenbrooke’s cognitive examination revised (ACE-R): a brief cognitive test battery for dementia screening. Int J Geriatr Psychiatry 2006; 21: 1078–1085.

29.

Pendlebury

Mariz

Bull

Mehta

Rothwell

. MoCA, ACE-R, and MMSE versus the National Institute of Neurological Disorders and Stroke–Canadian Stroke Network Vascular Cognitive Impairment Harmonization Standards Neuropsychological Battery After TIA and stroke. Stroke 2012; 43: 464–469.

30.

Deary

Gow

Taylor

et al.

The Lothian Birth Cohort 1936: a study to examine influences on cognitive ageing from age 11 to age 70 and beyond. BMC Geriatr 2007; 7: 28.

31.

Baykara

Gesierich

Adam

et al.

A novel imaging marker for small vessel disease based on skeletonization of white matter tracts and diffusion histograms. Ann Neurol 2016; 80: 581–592.

32.

Ritchie

Dickie

Cox

et al.

Brain volumetric changes and cognitive ageing during the eighth decade of life. Hum Brain Mapp 2015; 36: 4910–4925.

33.

Wardlaw

Bastin

Valdés Hernández

et al.

Brain aging, cognition in youth and old age and vascular disease in the Lothian Birth Cohort 1936: rationale, design and methodology of the imaging protocol. Int J Stroke 2011; 6: 547–559.

34.

Cees De Groot

De Leeuw

F-E

et al.

Cerebral white matter lesions and cognitive function: the Rotterdam scan study. Ann Neurol 2000; 47: 145–151.

35.

Dickie

Ritchie

Cox

et al.

Vascular risk factors and progression of white matter hyperintensities in the Lothian Birth Cohort 1936. Neurobiol Aging 2016; 42: 116–123. Available from: http://www.sciencedirect.com/science/article/pii/S0197458016002098.

The brain health index: Towards a combined measure of neurovascular and neurodegenerative structural brain injury

Abstract

Background

Aim

Materials and methods

Results

Conclusions

Keywords

Introduction

Aims and hypothesis

Material and methods

Approvals and patient consents

Mild stroke patients

Systemic lupus erythematosus patients

Healthy participants

Brain MRI acquisition

The brain health index

SVD scores and brain tissue volumes

Vascular risk factors

Cognitive testing

Statistical analyses

Results

Participants

Correlation between BHI and total SVD clinical score

BHI in mild stroke and SLE patients

BHI in healthy participants

Age adjusted analyses

Discussion

Footnotes

Declaration of conflicting interests

Funding

References