Serotonergic and cholinergic modulation of functional brain connectivity: A comparison between young and older adults

ABSTRACT Aging is accompanied by changes in neurotransmission. To advance our understanding of how aging modifies specific neural circuitries, we examined serotonergic and cholinergic stimulation with resting state functional magnetic resonance imaging (RS‐fMRI) in young and older adults. The instant response to the selective serotonin reuptake inhibitor citalopram (30 mg) and the acetylcholinesterase inhibitor galantamine (8 mg) was measured in 12 young and 17 older volunteers during a randomized, double blind, placebo‐controlled, crossover study. A powerful dataset consisting of 522 RS‐fMRI scans was obtained by acquiring multiple scans per subject before and after drug administration. Group × treatment interaction effects on voxelwise connectivity with ten functional networks were investigated (p < .05, FWE‐corrected) using a non‐parametric multivariate analysis technique with cerebrospinal fluid, white matter, heart rate and baseline measurements as covariates. Both groups showed a decrease in sensorimotor network connectivity after citalopram administration. The comparable findings after citalopram intake are possibly due to relatively similar serotonergic systems in the young and older subjects. Galantamine altered connectivity between the occipital visual network and regions that are implicated in learning and memory in the young subjects. The lack of a cholinergic response in the elderly might relate to the well‐known association between cognitive and cholinergic deterioration at older age.


Introduction
During the process of aging, there is a decline in brain function (Li and Lindenberger, 2002). Reduced synaptic plasticity, transmitter release and receptor availability in the central nervous system (CNS) might affect cognitive and behavioral performance (Li et al., 2001;Mahncke et al., 2006). Impaired cholinergic transmission has been associated with age-related disruptions in attention and memory storage and retrieval, (Decker, 1987;Dumas and Newhouse, 2011;Gallagher and Colombo, 1995;Hasselmo, 1999), whereas serotonin (5-hydroxytryptamine; 5-HT) dysregulation may contribute to the increased prevalence of depressive symptoms in the elderly (Daubert and Condron, 2010;Gareri et al., 2002;Meltzer et al., 1998).
Magnetic resonance imaging (MRI) of resting state functional connectivity cannot be used to measure neurotransmission directly, but is commonly applied to improve insight into neurotransmitter function by studying brain networks after a pharmacological intervention (Borsook et al., 2006;Honey et al., 2003;Khalili-Mahani et al., 2015;Klumpers et al., 2012;Lu and Stein, 2014;Niesters et al., 2014). With regard to aging, the effects of serotonergic and cholinergic challenges on brain connectivity are especially relevant as compounds acting on these systems are used to treat depression and dementia (Carr and Lucki, 2011;Soreq and Seidman, 2001).
Acute or short-term dosing of drugs that prevent the presynaptic reuptake of serotonin seems to counteract the observed increased connectivity patterns in depression (Sundermann et al., 2014), showing reduced connectivity with several cortical and subcortical areas in healthy and depressed young subjects (Klaassens et al., 2015(Klaassens et al., , 2017aLi et al., 2013;Schaefer et al., 2014;Van de Ven et al., 2013;Van Wingen et al., 2014). Cholinesterase inhibitors (AChEIs) cause connectivity enhancement of regions that are important for learning, memory and executive control after long-term treatment in patients with Alzheimer's disease (Blautzik et al., 2016;Goveas et al., 2011;Griffanti et al., 2016;Li et al., 2012;Sole-Padull es et al., 2013) and immediately after administration in young subjects (Klaassens et al., 2017a).
Despite evidence of cholinergic and serotonergic alterations with aging, it is unknown how the corresponding connectivity pathways are altered at older age. There is little proof of differentiated effects of selective serotonin reuptake inhibitors (SSRIs) and AChEIs on subjective and cognitive measures between healthy young and older subjects (Dumont et al., 2005;Repantis et al., 2010;Vanlaar et al., 1995). However, during senescence 5-HT receptor density declines (Arranz et al., 1993) and the cholinergic system has been suggested to have diminished and more variable responsiveness (Bartus, 1979;Decker, 1987). Given a negative association of aging with neuromodulation and brain function, we anticipate that effects of the SSRI citalopram and the AChEI galantamine on resting state connectivity are more constrained in older compared to young subjects.

Subjects
Twelve healthy young volunteers (mean age 22.1 AE 2.7; age range 18-27; 6 female/6 male; body mass index range 21-28 kg/m 2 ) and 17 healthy older adults (mean age 71.2 AE 6.1; age range 61-79; 9 female/8 male; body mass index range 22-31 kg/m 2 ) were included in the study. All subjects underwent a thorough medical screening at the Centre for Human Drug Research (CHDR) to investigate whether they met the inclusion and exclusion criteria. They had a normal history of physical and mental health and were able to refrain from using nicotine and caffeine during study days. Exclusion criteria included a positive drug or alcohol test on study days, regular excessive consumption of alcohol (>4 units/day), caffeine (>6 units/day) or cigarettes (>5 cigarettes/ day), use of concomitant medication 2 weeks prior to study participation and involvement in an investigational drug trial 3 months prior to administration. The study was approved by the medical ethics committee of the Leiden University Medical Center (LUMC). Written informed consent was obtained from each subject prior to study participation.

Study design
Part of the data, showing drug effects in young adults, have been described previously (Klaassens et al., 2017a). This was a single center, double blind, placebo-controlled, crossover study with citalopram 30 mg and galantamine 8 mg. Citalopram has an average time point of maximum concentration (T max ) of 2-4 h, with a half-life (T ½ ) of 36 h. For galantamine, T max ¼ 1-2 h and T ½ ¼ 7-8 h. To correct for the different pharmacokinetic (PK) profiles and obtain all pharmacodynamic measures within an equal time frame at around the T max of both compounds, citalopram was administered earlier than galantamine. In addition, since a lower dose of SSRIs is recommended in elderly compared to young subjects (Lotrich and Pollock, 2005), it was decided to retain the opportunity of administering a lower dose of citalopram in elderly than in young subjects. Therefore, citalopram 20 mg was administered at T ¼ 0 h, followed by a second dose of 10 mg at T ¼ 1 h (only if the first dose was tolerated). Galantamine was given as a single 8 mg dose at T ¼ 2 h. Blinding was maintained by concomitant administration of double-dummy placebo's at all three time points. All subjects also received an unblinded dose of granisetron 2 mg at T ¼ À0.5 h, to prevent the most common drug-induced adverse effects of nausea and vomiting.
Several outcome measures were included in this study that were each separately analyzed as described in the next sections. Six resting state fMRI (RS-fMRI) scans were acquired during study days, two at baseline and four after administering citalopram, galantamine or placebo (at T ¼ 2.5, 3.5, 4.5 and 6 h). Each scan was followed by performance of computerized cognitive tasks (taken twice at baseline) on the NeuroCart ® test battery, developed by the CHDR for quantifying pharmacological effects on the CNS (Dumont et al., 2005;Gijsman et al., 2002;Liem-Moolenaar et al., 2011). By including multiple measurements during the T max interval, this repeated measures profile increases the statistical power of the analysis and allows for identification of time related effects, associated with changing drug concentrations related to absorption, distribution, metabolism and excretion. Nine blood samples were taken during the course of the day to define the PK profile of citalopram, citalopram's active metabolite desmethylcitalopram, galantamine and concentrations of cortisol and prolactin (Jacobs et al., 2010;Umegaki et al., 2009). Washout period between study days was at least 7 days. An overview of the study design is provided in Fig. 1.  Each subject received citalopram, galantamine and placebo on three different study days. On each study day there were three moments of administration. The second administration only took place when subjects tolerated the first dose well (did not vomit or feel too nauseous). At baseline, two RS-fMRI scans were acquired, followed by the NeuroCart ® test battery. On all study days, subjects received 2 mg of granisetron 30 min before drug administration, to prevent for possible side effects of citalopram and/or galantamine. After drug administration, four RS-fMRI scans were acquired at time points T ¼ 2.5, 3.5, 4.5 and 6 h post dosing, each time followed by the NeuroCart ® test battery. During the day, nine blood samples were taken to measure the concentrations of citalopram, desmethylcitalopram, galantamine, cortisol and prolactin.

Pharmacokinetics
Pharmacokinetic parameters for citalopram, galantamine and citalopram's active metabolite desmethylcitalopram were calculated using a non-compartmental analysis to validate the choice of time points of pharmacodynamic endpoints and, in case of equal absorption rates, increase confidence in pharmacodynamic outcomes (RS-fMRI, NeuroCart ® , neuroendocrine measures). Blood samples were collected in 4 mL EDTA plasma tubes at baseline and 1, 2, 2.5, 3, 3.5, 4.5 and 6 h post dosing, centrifuged (2000g for 10 min) and stored at À40 C until analysis with liquid chromatography-tandem mass spectrometry (LC-MS/MS).

Neuroendocrine variables
Blood samples were also obtained to determine cortisol and prolactin concentrations. Serum samples were taken in a 3.5 mL gel tube at baseline (twice) and 1, 2, 2.5, 3.5, 4.5 and 6 h post dosing, centrifuged (2000g for 10 min) and stored at À40 C until analysis. Serum concentrations were quantitatively determined with electrochemiluminescence immunoassay.

NeuroCart ® test battery
Each RS-fMRI scan was followed by functional CNS measures outside the scanner using the computerized NeuroCart ® test battery measuring alertness, mood and calmness (Visual Analogue Scales (VAS) Bond & Lader), nausea (VAS Nausea), vigilance and visual motor performance (Adaptive Tracking task), reaction time (Simple Reaction Time task), attention, short-term memory, psychomotor speed, task switching and inhibition (Symbol Digit Substitution Test and Stroop task), working memory (N-back task) and memory imprinting and retrieval (Visual Verbal Learning Test) (Bond and Lader, 1974;Borland and Nicholson, 1984;Laeng et al., 2005;Lezak, 2004;Lim et al., 2008;Norris, 1971;Rogers et al., 2004;Stroop, 1935;Wechsler, 1981). The Visual Verbal Learning Test was only performed once during each day (at 3 and 4 h post dosing) as the test itself consists of different trials (imprinting and retrieval). Duration of each series of NeuroCart ® brain function tests was approximately 20 min. To minimize learning effects, training for the NeuroCart ® tasks occurred during the screening visit within 3 weeks prior to the first study day.

MR imaging
Scanning was performed at the LUMC on a Philips 3.0 T Achieva MRI scanner (Philips Medical System, Best, The Netherlands) using a 32channel head coil. During the RS-fMRI scans, all subjects were asked to close their eyes while staying awake. Instructions were given prior to each scan on all study days. T1-weighted anatomical images were acquired once per visit. To facilitate registration to the anatomical image, each RS-fMRI scan was followed by a high-resolution T2*-weighted echoplanar scan.

Pharmacokinetics
Maximum plasma concentrations (C max ) and time of C max (T max ) were obtained directly from the plasma concentration data. The area under the plasma concentration versus time curve was calculated from time zero to the time of the last quantifiable measured plasma concentration (AUC 0last ). To investigate differences between groups, PK parameters were analyzed using a mixed effects model with group as fixed effect (SAS for Windows V9.4; SAS Institute, Inc., Cary, NC, USA).

Neuroendocrine variables
Treatment (drug versus placebo) x group (young versus older subjects) interaction effects on cortisol and prolactin concentrations were investigated using a mixed effects model with treatment, time, group, visit, treatment by time, treatment by group and treatment by group by time as fixed effects, subject, subject by treatment and subject by time as random effects and the average of the period baseline (pre-dose) values as covariate (SAS for Windows V9.4; SAS Institute, Inc., Cary, NC, USA). The data were not normally distributed and therefore log-transformed before analysis and back transformed after analysis.

NeuroCart ® test battery
All post-dose repeatedly measured NeuroCart ® measures were analyzed using the same mixed effects model as for neuroendocrine variables. As data of the Simple Reaction Time task were not normally distributed, these data were log-transformed before analysis and back transformed after analysis. The data of the Visual Verbal Learning Test were analyzed using a mixed effects model with treatment, group, visit and treatment by group as fixed effects and subject as random effect.

MR imaging
All analyses were performed using the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL, Oxford, United Kingdom) version 5.0.7 (Jenkinson et al., 2012;Smith et al., 2004;Woolrich et al., 2009).
Data preprocessing. Each individual functional EPI image was inspected, brain-extracted and corrected for geometrical displacements due to head movement with linear (affine) image registration (Jenkinson et al., 2002;Smith, 2002). Images were spatially smoothed with a 6 mm full-width half-maximum Gaussian kernel. Registration parameters for nonsmoothed data were estimated to transform fMRI scans into standard space and co-registered with the brain extracted high resolution T2*weighted EPI scans (with 6 degrees of freedom) and T1 weighted images (using the Boundary-Based-Registration method) (Greve and Fischl, 2009). The T1-weighted scans were non-linearly registered to the MNI 152 standard space (the Montreal Neurological Institute, Montreal, QC, Canada) using FMRIB's Nonlinear Image Registration Tool. Registration parameters were estimated on non-smoothed data to transform fMRI scans into standard space after Automatic Removal Of Motion Artifacts based on Independent Component Analysis (ICA-AROMA vs0.3-beta). ICA-AROMA attempts to identify and remove motion related noise components by investigating its temporal and spatial properties. As recommended, high pass temporal filtering (with a high pass filter of 150 s) was applied after denoising the fMRI data with ICA-AROMA (Pruim et al., 2015a(Pruim et al., , 2015b. Estimation of network connectivity. RS-fMRI networks were extracted from each individual denoised RS-fMRI dataset (29 subjects x 3 days Â 6 scans ¼ 522 datasets) with a dual regression analysis Filippini et al., 2009) based on 10 predefined standard network templates as used in our previous research (Klaassens et al., 2015(Klaassens et al., , 2017a. These standard templates have been identified using a datadriven approach ) and comprise the following networks: three visual networks (consisting of medial, occipital pole, and lateral visual areas), default mode network, cerebellar network, sensorimotor network, auditory network, executive control network and two frontoparietal networks (left and right). Time series of white matter (measured from the center of the corpus callosum) and cerebrospinal fluid (measured from the center of lateral ventricles) were added as confound regressors in this analysis to account for non-neuronal signal fluctuations (Birn, 2012). With the dual regression method, spatial maps representing voxel-to-network connectivity were estimated for each dataset and network separately in two stages. First, the weighted network maps were used in a spatial regression into each dataset. This stage generated 12 time series per dataset that describe the average temporal course of signal fluctuations of the 10 networks plus 2 confound regressors (cerebrospinal fluid and white matter). Next, these time series were entered in a temporal regression into the same dataset. This resulted in a whole-brain spatial map per network per dataset with regression coefficients referring to the weight of each voxel being associated with the characteristic signal change of a specific network. The higher the value of the coefficient, the stronger the connectivity of this voxel with a given network. These individual statistical maps were subsequently used for higher level analysis to examine, for each network separately, whether drug-induced alterations in voxel-to-network connectivity (within and outside networks) differed between young and older subjects.
Higher level analysis. To investigate group Â treatment interaction effects of citalopram and galantamine we used non-parametric combination (NPC) as provided by FSL's Permutation Analysis for Linear Models tool (PALM vs94-alpha) (Pesarin, 1990;Winkler et al., 2014Winkler et al., , 2016b. NPC is a multivariate method that offers the possibility to combine data of separate, possibly non-independent tests, such as our multiple time points, and investigate the presence of joint effects across time points, in a test that has fewer assumptions and is more powerful than repeatedmeasurements analysis of variance (ANOVA) or multivariate analysis of variance (MANOVA). NPC testing was used in two phases to estimate for each network whether drug versus placebo effects on connectivity were significantly different between young and older subjects.
First, tests were performed for each post-dose time point (T ¼ 2.5, 3.5, 4.5 and 6 h) separately, using 1000 synchronized permutations, followed by the fit of a generalized Pareto distribution to the tail of the  approximation distribution, thus refining the p-values at the tail further than otherwise possible with a small number of permutations (Winkler et al., 2016a). To assess group Â treatment interaction effects on voxelwise functional connectivity with each of the 10 functional networks, four two-sample t-tests (young adults: drug -placebo versus older adults: drug -placebo) were performed, one per time point, with average heart rate (beats/m) per RS-fMRI scan as confound regressor (Khalili-Mahani et al., 2013). The average of the two baseline RS-fMRI scans was used as covariate as well, by adding the coefficient spatial map as a voxel-dependent regressor in the model. This will control for the confounding influence of possibly systematic individual differences and age-related differences at baseline level as recently analyzed and described in Klaassens et al. (2017b). The same method was applied for additional investigation of treatment effects (drug versus placebo) within the group of older adults as was previously done for the group of young adults (Klaassens et al., 2017a). To that end, four one-sample t-tests (drug versus placebo) were performed for all post-dose time points (T ¼ 2.5, 3.5, 4.5 and 6 h), with average heart rate (beats/m) per RS-fMRI scan and the average of the two baseline RS-fMRI scans as covariates. Second, to analyze effects across time, the tests for the four time points were combined non-parametrically via NPC using Fisher's combining function (Fisher, 1932) and the same set of synchronized permutations as mentioned above. A liberal mask was used to investigate voxels within the MNI template, excluding voxels belonging to cerebrospinal fluid. Threshold-free cluster enhancement was applied to the tests at each time point and after the combination, and the resulting voxelwise statistical maps were corrected for the familywise error rate using the distribution of the maximum statistic (Smith and Nichols, 2009;Winkler et al., 2014). Voxels were considered significant at p-values < 0.05, corrected. Table 1 provides an overview of the PK parameters in young and older subjects, and the statistical outcomes of group analyses. In comparison to the young subjects, the T max of galantamine occurred significantly later in the older adults (p < .001). There were no further pharmacokinetic differences between young and older subjects. Fig. 2 shows individual and median PK time profiles.

Cortisol and prolactin
There was a significant group Â treatment interaction effect of citalopram and galantamine on cortisol (p < .05). In comparison to the young subjects, the increase in cortisol was significantly larger in the older adults after citalopram and galantamine, relative to placebo.
There was no significant group Â treatment interaction effect on prolactin. In both groups, there was an increase in prolactin after citalopram versus placebo (p < .005). Galantamine did not affect the level of prolactin. See Fig. 3 for cortisol and prolactin levels in young and older subjects.

NeuroCart ® test battery
Supplementary Table S-1 provides an overview of all NeuroCart ® results. There were no convincing significant group Â treatment interaction effects after galantamine or citalopram versus placebo on any NeuroCart ® measure. Functional connectivity

Serotonergic effects
There was no group Â treatment interaction effect of citalopram on functional network connectivity. We did find main treatment effects of citalopram on connectivity with the sensorimotor network in both groups. Within the young subjects, citalopram decreased connectivity between the sensorimotor network and supplementary motor area, preand postcentral gyrus, anterior and posterior cingulate cortex (ACC and PCC), precuneus, medial and orbital frontal cortex, and cerebellum. Within the older adults, citalopram decreased connectivity between the sensorimotor network and supplementary motor area, pre-and postcentral gyrus, ACC, PCC, cingulate gyrus, precuneus, superior frontal gyrus and frontal orbital cortex (Fig. 4). Specifications and extent of significant citalopram effects are summarized in Table 2.

Cholinergic effects
An interaction effect of galantamine was found for one visual network (occipital pole). An increase in connectivity after galantamine versus placebo was significantly larger in the young compared to the older adults between the occipital visual network and the precuneus, PCC, postcentral-, angular-and supramarginal gyrus (Fig. 5). Galantamine led to increased connectivity between the occipital visual network and the left and right hippocampus, precuneus, thalamus, fusiform gyrus, precentral and superior frontal gyrus, PCC and cerebellum in the young subjects, whereas no significant treatment effect of galantamine on this network was detected in the group of older adults. Specifications and extent of significant galantamine effects are summarized in Table 3.

Discussion
To study the influence of older age on neurotransmitter pathways, we investigated differences in functional network responsiveness to singledose serotonergic and cholinergic stimulation between young and older adults, independent of between-group variation in brain connectivity at baseline (Klaassens et al., 2017b). We found a significantly weaker pharmacological effect of galantamine on functional integrity of the occipital visual network in the older compared to the young subjects. Since   > 10).
B.L. Klaassens et al. NeuroImage 169 (2018) 312-322 the effects of AChEIs depend on intact cholinergic synapses, this finding might represent the general tendency towards diminished cholinergic function in the elderly (Decker, 1987;Dumas and Newhouse, 2011). In contrast, older age had no impact on the response to the SSRI citalopram, which induced a strong decrease in connectivity with the sensorimotor network within both groups.

Citalopram
Reduced connections between multiple regions after citalopram administration is consistent with previous SSRI research in healthy young individuals (Klaassens et al., 2015;Schaefer et al., 2014). Although these effects seemed less pronounced in the older adults compared to young subjects, there was no significant difference in effect between the two groups on all networks. As an altered serotonergic system in the elderly has most often been associated with a higher incidence of depression and sleep disorders (Gareri et al., 2002;Meltzer et al., 1998), one explanation for this lack of differences is that there was no evidence for any abnormality of mood, anxiety or sleep in both groups as assessed during screening. The most prominent finding in both samples was a decrease in connectivity between the sensorimotor network and sensorimotor regions, precuneus, ACC and PCC (Fig. 4). Similar findings have been detected in healthy young subjects with the SSRI sertraline, in which, amongst other networks, sensorimotor network connectivity with the ACC, PCC, precuneus, central gyri and supplementary motor cortex was decreased after single-dose administration (Klaassens et al., 2015). Aging has been shown to alter motor network connectivity, possibly representing deteriorated motor ability in the elderly (Wu et al., 2007a(Wu et al., , 2007b and leading to the somewhat smaller response in older compared to young subjects. However, in favor of a rather unaffected serotonergic system in our healthy group of elderly, we did not find significantly different changes in sensorimotor connectivity in the much younger subjects. This relative sparing of serotonergic network responsiveness in elderly subjects suggests that the reduced effects of galantamine are indicative of a selective age-related cholinergic decline. Citalopram caused a decrease in connectivity of cortical midline structures as the precuneus, ACC, PCC and prefrontal areas, related to self-referential mechanisms and emotion regulation, which is in line with opposite observations in (non-elderly) depressed patients (Sundermann et al., 2014) and indicates that SSRIs might reverse abnormalities in functional connectivity as seen in depression. The effects on sensorimotor connectivity also denote the well-known involvement of 5-HT pathways in motor behavior (Sachenko and Khorevin, 2001). For example, an (uncommon) side effect of SSRIs is the observance of movement disorders (e.g. muscle twitches), possibly due to direct adverse effects on motor neurons or enhancement of serotonergic input on dopaminergic pathways (Anderson et al., 2009;Hindmarch, 1995;Leo, 1996). In the young subjects, citalopram also reduced connectivity between the midbrain and left frontoparietal network (Klaassens et al., 2017a). This effect was restricted to a discrete region, and a comparable response could not be detected in the older adults, despite the lack of significant differences between groups on this network.
Additional measures of cognitive functioning and neuroendocrine responses were investigated to confirm the presence of neuropharmacological effects and improve the understanding of underlying changes in brain connectivity. There was no difference between young and older adults in effect of citalopram on performance on the NeuroCart ® test battery. Single dose SSRI administration does not seem to alter behavioral states in young and older subjects differently (Dumont et al., 2005;Vanlaar et al., 1995). Acute SSRI effects in healthy subjects of all ages are limited and variable and our protocol did not include more sensitive measures of SSRI modulation as EEG recordings, REM-sleep and flicker discrimination tests (Dumont et al., 2005). Since SSRIs are mainly used as a treatment for depression and anxiety disorders (Carr and Lucki, 2011;Jacobs and Azmitia, 1992), the most likely change in our study would have taken place on mood, alertness or calmness as measured with the VAS. However, such improvements are usually only noticeable after a mood-specific behavioral challenge (Browning et al., 2007;Capitao et al., 2015;Harmer et al., 2003) or after a few weeks (Frazer and Benmansour, 2002;Harmer et al., 2009), and accordingly we did not find these effects. The fact that citalopram had pharmacological effects in both age groups is demonstrated by increasing levels of cortisol and prolactin. Neuroendocrine fluctuations can be regarded as indirect measures of the 5-HT system state (Seifritz et al., 1996) and a larger increase in cortisol level in the older compared to young adults might be indicative of some serotonergic disturbances that accompany the process of normal aging. However, this interpretation is complicated by the fact that elevated cortisol has been shown to reduce amygdala-medial prefrontal cortex connectivity (Veer et al., 2012;Wu et al., 2015), which in turn seems to be related to baseline cortisol levels. As cortisol was slightly but not significantly lower at baseline in the elderly, this might lead to a stronger drug effect on cortisol and network connectivity in older compared to young adults. There are multiple factors that can cause a decrease in clearance of antidepressants in the elderly (Boyce et al., 2012;Lotrich and Pollock, 2005). But most of these affect the terminal part of the concentration-time curve and exposure during multiple dosing. The duration of our study was limited, and our elderly subjects were B.L. Klaassens et al. NeuroImage 169 (2018) 312-322 relatively healthy. Consequently, no differences in pharmacokinetic profiles were observed between groups, ensuring comparable exposure in both age groups over the observation period, with similar and limited variability. Because elderly are known to have an increased expectancy for SSRI related side effects (Lotrich and Pollock, 2005), we wanted to provide them with the opportunity to take a lower dose of 20 instead of 30 mg of citalopram, by skipping the second 10 mg if necessary. However, all young and older subjects were administered the total dose of 30 mg without vomiting or experiencing nausea as measured with the VAS. This might also be due to the 2 mg of granisetron, given 30 min prior to drug administration on all three study days. Granisetron was added to suppress nausea and vomiting, which could otherwise adversely affect study procedures or alter network effects. Yet, it cannot be excluded that the selective 5-HT 3 receptor agonist granisetron might have also altered specific functional responses (Jacobs and Azmitia, 1992).

Galantamine
Whereas galantamine increased brain connectivity in the young subjects with one visual network (Fig. 5), we could not detect any effects of galantamine on resting state connectivity in older adults. The cholinergic system is chiefly associated with an age-and dementia-related decline in memory, learning and attention with evidence pointing to cholinergic dysfunction in the hippocampus, cortex, the entorhinal area, the ventral striatum and the basal forebrain (Kasa et al., 1997;Schliebs and Arendt, 2011). In our young subjects, galantamine altered connectivity with the hippocampus, thalamus and the fusiform gyrus, areas that are involved in learning and memory (Garoff et al., 2005;Van der Werf et al., 2000;Wixted and Squire, 2011). ACh release in the primary visual cortex seems to be relevant for visual processing and learning (Dotigny et al., 2008;Kang et al., 2014). The findings in the young group are therefore consistent with studies that show an essential role for cholinergic enhancement in visual attention (Bentley et al., 2003(Bentley et al., , 2004, visual episodic memory and recall (Goekoop et al., 2004(Goekoop et al., , 2006Gron et al., 2005), processing of novel faces (Kircher et al., 2005;Rombouts et al., 2002), perceptual processing during working memory (Furey et al., 2000) and visual orientation (Murphy and Sillito, 1991). The absence of effect in the elderly might point to attenuated activity of the cholinergic system, which accompanies the process of normal aging (Muir, 1997). The sensitivity of the cholinergic system to aging is emphasized by the detection of clear and very similar serotonergic effects on connectivity in both young and older adults. An age appropriate decline in cognitive function was also observed in our older group compared to the young subjects by investigating the difference in NeuroCart ® performance at baseline level (before pharmacological stimulation), as presented in Klaassens et al. (2017b). The elderly performed worse on several tests (% correct on the Adaptive Tracking task, reaction time on the Symbol Digit Substitution Test, 0-back, 1-back and 2-back task, and number of correct responses on the 2-back task), relating to decreased attentional, memory and processing speed capacities. Our observations of reduced connectivity alterations after galantamine in the older adults seem to confirm the cholinergic hypothesis of cognitive decline during aging (Dumas and Newhouse, 2011).
Galantamine's mean T max in the elderly (mean T max : 4.52 AE 1.08) occurred significantly later than the mean T max in the young subjects (mean T max : 2.67 AE 1.11). This delay in pharmacokinetics might have caused small shifts in the time course of effects, although it is unlikely that this affected the overall response over the duration of the experiment, which was the basis for all principal analyses and comparisons. Since the T max of galantamine was within the time frame of measurements for both groups, analyzing a combination of data points would be minimally influenced by different PK timing profiles. As the level and variability of plasma concentrations, as determined by C max and AUC 0last , in the elderly was generally similar to those in the younger group, it is implausible that galantamine effects were obscured by pharmacokinetic dispersion. Moreover, the rise in plasma cortisol after galantamine was larger in elderly than in young subjects, which was particularly noticeable at T ¼ 2 and T ¼ 2.5. Because galantamine has been shown to increase cortisol (Cozanitis et al., 1980), this indicates that galantamine was absorbed well enough in the older adults to induce pharmacodynamic effects. Nevertheless, as described earlier this finding may partly influence the observed network effects (Veer et al., 2012;Wu et al., 2015). In addition, despite the administration of granisetron, an increase in nausea, a typical side effect of AChEIs (Dunbar et al., 2006), after galantamine in both groups provides further support for sufficient drug concentrations in the older adults. AChEIs are commonly used to treat cognitive symptoms of Alzheimer's disease (Tan et al., 2014) and in healthy subjects there is little evidence for neuroenhancement with this drug (Lanni et al., 2008). A few studies have been performed on AChEI efficacy in subjects without cognitive disturbances, with inconclusive and contradicting results in both young and elderly subjects (Beglinger et al., 2004(Beglinger et al., , 2005Gron et al., 2005;Lanni et al., 2008;Repantis et al., 2010). Two measures of the delayed recognition subtest of the Visual Verbal Learning Test, the number of correct responses and reaction time, showed a difference between young and older subjects with p < .05. However, the number of included NeuroCart ® tests was large and the effects were not clearly related to drug levels. Therefore, this marginal Table 3 Overview of significant galantamine effects on functional connectivity as estimated with threshold-free cluster enhancement (p < .05, corrected). Abbreviations: L ¼ left, R ¼ right, M ¼ midline, PCC ¼ posterior cingulate cortex. Voxel dimension ¼ 2 mm Â 2 mm x 2 mm (voxel volume 0.008 mL). * ¼ standardized z-value of the uncorrected peak Fisher-statistic (NPC) within regions (with # voxels > 10).
B.L. Klaassens et al. NeuroImage 169 (2018) 312-322 result might likely be due to chance, suggesting that AChEI challenges do not affect cognitive performance differently between young and older subjects.

Conclusions
The outcomes of this study illustrate the use of resting state connectivity to investigate different neurotransmitter systems, and how these selectively change with age. The SSRI citalopram affected sensorimotor network connectivity in both young and older adults, demonstrating that SSRIs consistently reduce the functional integrity of regions that are related to motor function and self-reference, regardless of age. The effect of the AChEI galantamine was restricted to the young subjects, who showed a response that indicates the contribution of acetylcholine to perceptual processing and learning mechanisms. We did not observe any network effects in the elderly, possibly reflecting a diminished cholinergic system that is associated with an age-appropriate decline in memory and attention. Combining RS-fMRI with pharmacological challenges and additional outcome measures offers a useful way to investigate agerelated functional processes, which is in line with Geerligs and Tsvetanov (2017), who recommend to implement an integrative approach in studying neurocognitive aging instead of merely using fMRI data. Compared to cognitive performance, RS-fMRI seems to serve as a relatively sensitive measure of drug-induced functional change. Our findings support the confidence in RS-fMRI as an important tool in psychopharmacological research, and its potential to measure disease specific alterations in neurotransmission.