Beneficios socioeconómicos generados por el proyecto de agua y saneamiento.

The primary cohort used to investigate the questions raised previously (Chapter 1) was the Radboud University Nijmegen Diffusion tensor and Magnetic resonance Cohort (RUN DMC). The RUN DMC study is a prospective cohort study designed to investigate the causes and consequences of cerebral small vessel disease (SVD) using magnetic resonance imaging (MRI). As the RUN DMC cohort is investigated in all subsequent experimental chapters, basic sample characteristics are summarised here.

2.2. Methods

2.2.1. Study population

In 2006, consecutive individuals referred to the Department of Neurology at Radboud University between October 2002 and November 2006 were selected for possible

participation. Inclusion criteria were: (1) age between 50 and 85 years old; and (2) evidence of cerebral SVD on neuroimaging, defined as white matter hyperintensities (WMH) or lacunar infarcts of vascular origin (Wardlaw et al., 2013). Individuals who were eligible because of a clinical lacunar stroke syndrome (Bamford et al., 1991) were included > 6 months after the event to minimise the effect of the acute infarct on outcomes.

Exclusion criteria included: (1) presence of dementia, assessed using DSM-IV-TR criteria (American Psychiatric Association, 2000); (2) presence of Parkinson's Disease or parkinsonism; (3) intracranial haemorrhage; (4) life expectancy < 6 months; (5) intracranial space-occupying lesion; (6) disease interfering with cognitive testing or follow-up, including psychiatric diseases such as bipolar disorder and schizophrenia; (7) current or recent use of acetylcholinesterase inhibitors, neuroleptic agents, levodopa or dopamine agonists or

antagonists; (8) WMH of non-vascular origin, such as multiple sclerosis; (9) prominent visual or hearing impairment; (10) language barrier; or (11) MRI contraindications or known

claustrophobia.

The study was approved by the Medical Review Ethics Committee region Arnhem- Nijmegen (van Norden et al., 2011). All participants provided written informed consent according to the Declaration of Helsinki. One thousand and four individuals were invited by

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letter. Of these, 727 were deemed eligible after contact by phone, and 525 agreed to participate. Twenty-two of these individuals were found to meet exclusion criteria upon visiting the research center. This left 503 participants for the baseline assessment.

Of the 503 participants recruited to the baseline in 2006, 398 were able to attend follow-up in 2011. Reasons for missing the assessment included death (n = 49), illness (n = 19), relocation (n = 5), lack of time (n = 30), or lost to follow-up (n = 2). Subsets of these 398 individuals at 2011 will be investigated in subsequent chapters as assessments of apathy began at this timepoint.

2.2.2. Measures

Basic demographic (age, sex, education) and information on vascular risk factors, including hypertension, hypercholesterolemia, diabetes, and body mass index (BMI), were collected.

Apathy was assessed using the clinician-rated Apathy Evaluation Scale (AES), a validated measure of apathy in stroke (Marin et al., 1991). The AES has 18 items which are graded on a Likert scale of 1-4. Items 6, 10, and 11 are negatively-worded, and must be reverse-coded prior to scoring the scale. Total scores range from 18-72, with higher scores indicating greater apathy. The AES was administered at the 2011 assessment, precluding an analysis of apathy at baseline in 2006.

Depression was assessed using the Center for Epidemiologic Studies Depression Scale (CESD), a well-validated tool for screening depressive symptoms that has previously been used in SVD (Radloff, 1977; Prins et al., 2005). The CESD is a 20-item self-report measure, with responses ranging from 0-3. Items 4, 8, 12, and 16 are reverse-coded. Total scores can range from 0-60, with higher scores indicating more depression.

Cognitive impairment was assessed using the Mini Mental State Examination

(MMSE) (Folstein et al., 1975). The MMSE is a 30-item test that assesses orientation to time and place, verbal learning, attention and mental arithmetic, verbal recall, language, and visual perception. These scores are then added to provide an aggregate measure of general cognitive function, which is sensitive to cognitive impairment (Tombaugh and McIntyre, 1992).

2.2.3. MRI acquisition protocols

MR images were acquired on a Siemens Magnetom Avanto Tim 1.5 Tesla MRI scanner (Erlangen, Germany). The protocol included a T1-weighted (T1w) three-dimensional

magnetization-prepared rapid gradient echo (MPRAGE) image (repetition time (TR) = 2250 ms, echo time (TE) = 2.95 ms, inversion time (TI) = 850 ms, flip angle = 15°, voxel size =

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1.0 mm x 1.0 mm x 1.0 mm), an axial fluid-attenuated inversion recovery (FLAIR) sequence (TR = 14240 ms, TE = 89 ms, TI = 2200 ms, voxel size = 1.2 mm x 1.0 mm x 2.5 mm, interslice gap = 0.5 mm), and a diffusion-weighted imaging (DWI) sequence (TR = 10200 ms, TE = 95 ms, voxel size = 2.5 mm x 2.5 mm x 2.5 mm; 7 scans with b = 0 s/mm², 61 scans with b = 900 s/mm²).

2.2.4. White matter hyperintensity ratings

WMH were defined as areas of hyperintense signal on FLAIR images without corresponding cerebrospinal fluid-like hypointense areas on the T1w image. Gliosis surrounding lacunar and territorial infarcts were not considered to be WMH (Hervé et al., 2005). WMH were

segmented on the FLAIR images using a semiautomatic method (Ghafoorian et al., 2016), and were visually inspected for errors.

2.2.5. Lacunar infarct rating

Lacunes were defined as hypointense areas > 2 mm and < 15 mm on FLAIR and T1w images, ruling out perivascular spaces (< 2 mm, except around the anterior commissure, where perivascular spaces can be large) and infraputaminal pseudolacunes (Hervé et al., 2005). Lacunes were counted by two trained raters blind to the clinical data, and inter-rater reliability assessed using κ (Cohen, 1960). Reliability was excellent, κ = 0.95 (van Uden et al., 2015).

2.2.6. Brain volumetry

Brain volumes were calculated using tools in FSL 6.0.1 (Smith et al., 2004). T1w images were processed using SIENAX (Smith et al., 2002). SIENAX first extracts brain and skull images from the T1w image using BET (Smith, 2002). Brain extracted images are then registered to the MNI152 standard brain using an affine transformation calculated using FLIRT (Jenkinson and Smith, 2001; Jenkinson et al., 2002). The calculated transformation is also applied to the skull image to determine a scaling factor to correct for differences in head size relative to the MNI152 brain. Scaling factors > 1 indicate a skull smaller than the

MNI152 template, factors < 1 indicate a larger skull. Finally, FIRST is used to calculate total brain volume (BV) (Zhang et al., 2001). Raw BV and WMH volumes were normalised for differences in skull size by multiplying them by the previously calculated scaling factor, creating nBV and nWMH, respectively.

43 2.2.4. Statistical analysis

Continuous variables were summarised using means and standard deviations (SD), whilst binary and ordinal variables were summarised using counts and percentages. Pearson product-moment correlation coefficients were calculated between pairs of continuous variables to determine the degree of association between both variables. Given the exploratory nature of these analyses, only raw (uncorrected) P values are presented.

All tests were two-tailed, with α = 0.05. All statistical analyses were conducted using R 3.6.2 (R Core Team, 2019).

2.3. Results

Descriptive statistics for the 398 participants in 2011 are shown in Table 1. The mean age of participants in 2011 was nearly 70, whilst 57% of the sample was male. Average MMSE scores revealed that the population suffered from minimal levels of cognitive impairment. The majority of the population was hypertensive, had under 1 lacune, and had modest levels of WMH. Apathy and depression scores were mildly elevated (Figure 1).

Figure 1. Apathy and depression in RUN DMC. a, Distribution of apathy scores. b, Distribution of depression scores. c, Scatterplot of apathy and depression scores.

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Table 1. Descriptive statistics for RUN DMC at 2011 (n = 398).

Mean (SD) or N (%) Age, years 69.9 (8.5) Sex, male 227 (57.0) Education, years 11.0 (3.4)1 MMSE, score 27.8 (2.8) AES, score 27.7 (8.2)2 CESD, score 10.5 (8.8)3 nBV, mm3 1442855.3 (87549.0)4 nWMH, mm3 _{12055.5 (17357.2)}4 Lacunes, count 0.7 (1.5)4 Microbleeds, count 0.8 (3.7)4 Hypertension, presence 322 (80.9)5 Diabetes, presence 59 (14.8)6 Hypercholesterolemia, presence 201 (50.5)6 BMI, mm/kg2 27.6 (5.4)7 Smoking, presence Never 117 (29.4) Ex 231 (58.0) Current 50 (12.6)

Note. MMSE = Mini-Mental State Examination, AES = Apathy Evaluation Scale, CESD = Center for Epidemiologic Studies Depression Scale, nBV = Brain volume, normalised, nWMH = White matter hyperintensity volume, normalised, BMI = body mass index. Superscripts denote n missing cases: 1 = 1, 2 = 34, 3 = 19, 4 = 39, 5 = 2, 6 = 18, 7 = 14.

Pearson correlations are shown in Table 2. Apathy and depression were highly correlated. Apathy also showed strong correlations with MMSE score, age, brain volumes, and radiological markers of SVD. Depression, by contrast, showed a more mixed pattern of correlations, with statistically significant relationships showing weaker coefficients compared to apathy overall. For instance, AES-MMSE r = -0.42, P < 0.001, whilst CESD-MMSE r = - 0.14, P = 0.005.

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Table 2. Pearson product-moment correlation matrix for continuous variables of interest.

AES CESD MMSE Age nBV nWMH Lacunes

AES CESD 0.50 (<0.001) MMSE -0.42 (<0.001) -0.14 (0.005) Age 0.28 (<0.001) 0.09 (0.084) -0.36 (<0.001) nBV -0.31 (<0.001) -0.13 (0.015) 0.27 (<0.001) -0.57 (<0.001) nWMH 0.29 (<0.001) 0.16 (0.002) -0.28 (<0.001) 0.41 (<0.001) -0.28 (<0.001) Lacunes 0.23 (<0.001) 0.09 (0.081) -0.18 (<0.001) 0.15 (0.005) -0.23 (<0.001) 0.53 (<0.001)

Note. Degrees of freedom for each cell are calculated on a pairwise basis due to missing data. P values are uncorrected for multiple comparisons.