Abstract
The preservation of cognitive function into old age is a public health priority. Cerebral hypoperfusion is a hallmark of dementia but its impact on maintaining cognitive ability across the lifespan is less clear. We investigated the relationship between baseline cerebral blood flow (CBF) and blood oxygenation level-dependent (BOLD) response during a fluid reasoning task in a population-based adult lifespan cohort (N=227, age 18-88 years). As age differences in baseline CBF could lead to non-neuronal contributions to the BOLD signal, we introduced commonality analysis to neuroimaging, in order to dissociate performance-related CBF effects from the physiological confounding effects of CBF on the BOLD response. Accounting for CBF, we confirmed that performance- and age-related differences in BOLD responses in the multiple-demand network (MDN) implicated in fluid reasoning. Differences in baseline CBF across the lifespan explained not only performance-related BOLD responses, but also performance-independent BOLD responses. Our results suggest that baseline CBF is important for maintaining cognitive function, while its non-neuronal contributions to BOLD signals reflect an age-related confound. Maintaining perfusion into old age may serve to support brain function with behavioural advantage, regulating brain health.
1. Introduction
The world’s population is ageing, with every sixth person expected to be over 65 by 2050 (United Nations, 2020). Cognitive decline has emerged as a major health threat in old age, including but not limited to dementia (Piguet et al., 2009; Yarchoan et al., 2012). To combat this threat, there is increasing demand to identify factors that facilitate the maintenance of cognitive function across the lifespan. Ageing causes changes to our brains in vascular, structural and functional domains (Kennedy and Raz, 2015; Cabeza et al., 2018). However, these effects are normally reported separately, and only through their integration one can better understand how these domains influence cognitive decline in old age (Tsvetanov et al., 2021).
Cerebral blood flow (CBF) changes early in experimental models of dementia, leading to neuronal dysfunction, and loss independently of amyloid-β-dependent contributions (Iadecola, 2004; Zlokovic, 2011; Kisler et al., 2017; Sweeney et al., 2018, 2019). In healthy ageing, previous reports have linked the effects of age on baseline CBF to behavioural performance measured outside of the scanner (Bangen et al., 2014; Hays et al., 2017; Leeuwis et al., 2018). However, brain perfusion measurements are highly dependent on other physiological factors such as autoregulation modulators (Lemkuil et al., 2013), medication, time of day, levels of wakefulness (Patricia et al., 2014), physical exercise, caffeine or smoking before the scan (Domino et al., 2004; Addicott et al., 2009; Merola et al., 2017). Therefore, differences in CBF signal may reflect an age-related bias in such factors, rather than a true baseline difference in CBF (Grade et al., 2015). Moreover, it remains unclear whether the observed CBF dysregulation in ageing reflects a link between somatic differences in vascular health and global cognition, or whether CBF modifies regional brain activations underlying specific cognitive processes. To understand the role of baseline CBF in cognitive ageing, one must also test whether baseline CBF is associated with performance-related brain activity during cognitive tasks.
The field of neurocognitive ageing research has often used functional magnetic resonance imaging (fMRI) to study age differences in brain activity during cognitive tasks. FMRI data are usually interpreted in terms of neuronal activity, but the blood oxygenation level-dependent (BOLD) signal measured by fMRI also reflect vascular differences and neurovascular coupling (Mishra et al., 2021), which changes with age (Tsvetanov et al., 2021). Failure to account for vascular health alterations leads to misinterpretation of fMRI BOLD signals (Hutchison et al., 2013; Liu et al., 2013; Tsvetanov et al., 2015) and their cognitive relevance (Geerligs and Tsvetanov, 2016; Tsvetanov et al., 2016; Geerligs et al., 2017). Several approaches exist to separate vascular from neural contributions to the BOLD signals, including the use of baseline CBF to normalise for age differences in cerebrovascular function (Tsvetanov et al., 2021). Normalisation with baseline CBF would improve detection of “true” neuronal changes i.e., over and above age-related differences in non-neuronal physiology. This would control for behaviourally irrelevant confounding effects, and performance-related effects where cerebral hypoperfusion reflects neuronal function and loss. Therefore, it would be better to integrate, not simply control for, baseline CBF differences in task-based BOLD studies to dissociate confounding from performance-related effects of CBF on age-related differences in the BOLD fMRI responses.
To distinguish confounding from performance effects of CBF is important to understand the neuronal substrates of multiple cognitive demands with ageing (Kaufman and Horn, 1996; Salthouse, 2012; Kievit et al., 2014). Demanding, complex or executive functions depend on a distributed network of brain regions known as the multiple-demand network (MDN), which is readily activated during tasks used to assess fluid intelligence (Crittenden et al., 2016; Tschentscher et al., 2017; Woolgar et al., 2018). The MDN parses complex tasks into subcomponents or sub-goals (Duncan, 2013; Camilleri et al., 2018). There is substantial spatial overlap between MDN and the brain regions with impaired baseline CBF in ageing (Tsvetanov et al., 2020b, 2021). Therefore, some of the age differences in MDN and cognition (Tsvetanov et al., 2016; Samu et al., 2017) may reflect confounding and/or performance-related effects of CBF dysregulation.
To characterise neurocognitive ageing, we propose the use of commonality analysis to dissociate confounding from performance-related effects of CBF on age-related differences in brain functional measures. Commonality analysis, unlike the normalisation approach, allows for adjustment of multiple variables simultaneously by identifying the variance in a dependent variable associated with each predictor uniquely, as well as the variance in common to two or more predictors (Nimon et al., 2008; Kraha et al., 2012). Here, we identify unique and common effects of age, performance, and baseline CBF on fMRI BOLD responses during a fluid reasoning task in a population-based adult lifespan cohort (age 18-88, N = 227, www.camcan.org). Reasoning was measured by the common Cattell task of fluid intelligence, which requires solving a number of problems, and is known to decline dramatically with age (Kievit et al., 2014).
We predicted that the integration of baseline CBF with task-based fMRI BOLD would improve detection of confounding and performance-related effects of CBF associated with reasoning. Performance-related effects of CBF would be indicated by variance in the BOLD response that is common to age, task performance and CBF, whereas confounding effects of CBF would be indicated by variance that is common to age and CBF, but not shared with performance.
2. Methods
2.1. Participants
Figure 1 illustrates the study design, data processing and analysis pipeline. The data were acquired from Phase 3 of the Cambridge Centre for Aging and Neuroscience (Cam-CAN), a large population-based study of the healthy adult life span (Shafto et al., 2014; Taylor et al., 2015). The ethical approval for the study was approved by the Cambridge 2 Research Ethics Committee and written informed consent was provided by all participants. Exclusion criteria included poor hearing (a sensitive threshold of 35 dB at 1000 Hz in both ears) and poor vision (below 20/50 on the Snellen test; Snellen, 1862), low Mini-Mental Status Examination (Folstein et al., 1975), self-reported substance abuse as assessed by the Drug Abuse Screening Test (Skinner, 1982), significant psychiatric disorders (e.g., schizophrenia, bipolar disorder, personality disorder), or neurological diseases (e.g. a history of stroke, epilepsy, traumatic brain injury). Demographic characteristics of the sample are described in Table 1.
2.2. Stimuli, task and procedure
Participants undertook a Fluid Intelligence task which draws on critical cognitive process of fluid reasoning, which underlies many complex cognitive operations (Duncan, 2013), and which declines with age (Horn and Cattell, 1967; Kaufman and Horn, 1996; Salthouse et al., 2003; Duncan, 2010; Salthouse, 2012; Kievit et al., 2014). We used a simplified version of the Cattell Culture Fair test (Cattell, 1971), modified to be used in the scanner (Woolgar et al., 2013; Samu et al., 2017). On each trial, participants were presented with a display of four patterns and had to select the “odd one out”. The task employed a block design, with 30-seconds blocks of trials alternating between two conditions with different difficulty level (“easy” and “hard” puzzles). There was a total of four blocks per condition. Because there was a fixed time to perform as many trials as possible, behavioural performance was measured by subtracting the number of incorrect trials from the number of correct trials (averaged over hard and easy bocks, following Samu et al., (2017), i.e. to ensure that someone responding quickly but randomly did not score highly. The suitability of this performance score was confirmed by its strong correlation (Pearson’s r[95% CI]: r(223) = 0.70 [0.63, 0.76], P < 0.001) with scores obtained from the full version of the Cattell test, administered outside the scanner at stage 2 of Cam-CAN (Shafto et al., 2014). We also excluded n = 28 participants who had disproportionately poor performance with 10 or more incorrect trials (17 females, with age range 31-88); leaving N = 223 remaining (111 females, age range 19 - 87 years).
2.3. MRI Acquisition and Preprocessing
Imaging data were acquired using a 3T Siemens TIM Trio System with a 32-channel head-coil at the MRC Cognition and Brain sciences Unit (CBU; www.mrc-cbu.cam.ac.uk). Of the initial cohort, 256 participants had valid T1, T2, arterial spinning labelling (ASL) data, and task-induced BOLD data from a fluid intelligence task.
A 3D-structural MRI was acquired on each participant using T1-weighted sequence (Generalized Auto-calibrating Partially Parallel Acquisition (GRAPPA) with the following parameters: repetition time (TR) = 2,250 ms; echo time (TE) = 2.99 ms; inversion time (TI) = 900 ms; flip angle α= 9°; field of view (FOV) = 256 × 240 × 192 mm3; resolution = 1 mm isotropic; accelerated factor = 2; acquisition time, 4 min and 32 s.
We used Release003 of the CamCAN Automatic Analysis pipelines for Phase III data (Taylor et al.,(2015), which called functions from SPM12 (Wellcome Department of Imaging Neuroscience, London, UK). The T1 image from Phase II was rigid-body coregistered to the MNI template, and the T2 image from Phase II was then rigid-body coregistered to the T1 image. The coregistered T1 and T2 images were used in a multimodal segmentation to extract probabilistic maps of six tissue classes: gray matter (GM), white matter (WM), CSF, bone, soft tissue, and residual noise. The native space GM and WM images were submitted to diffeomorphic registration to create group template images. Each template was normalized to the MNI template using a 12-parameter affine transformation.
2.4. EPI image acquisition and processing
For the Cattell-based fMRI in Phase III of CamCAN, Gradient-Echo Echo-Planar Imaging (EPI) of 150 volumes captured 32 axial slices (sequential descending order) of thickness of 3.7 mm with a slice gap of 20% for whole-brain coverage with the following parameters: TR = 1970 ms; TE = 30 ms; flip angle α = 78°; FOV = 192 × 192 mm2; resolution = 3 × 3 × 4.44 mm3, with a total duration of 5 min.
EPI data preprocessing included the following steps: (1) spatial realignment to adjust for linear head motion, (2) temporal realignment of slices to the middle slice, (3) coregistration to the T1 anatomical image from Phase II above, (4) application of the normalization parameters from the T1 stream above to warp the functional images into MNI space, and (5) smoothing by an 8mm Gaussian kernel.
For the participant-level modelling, every voxel’s time-course was regressed in a multiple linear regression on the task’s design matrix which consisted of time-courses for hard and easy conditions convolved with a canonical haemodynamic response function (HRF). Regressors of no interest included WM, CSF, 6 standard realignment parameters (accounting for in-scanner head motions), and harmonic regressors that capture low-frequency changes (1/128 Hz) in the signal typically associated with scanner drift and physiological noise. WM and CSF signals were estimated for each volume from the mean value of WM and CSF masks derived by thresholding SPM’s tissue probability maps at 0.75. The contrast of parameter estimates for hard minus easy conditions for each voxel and participant was then calculated, termed here Cattell activation.
2.5. Arterial spinning labelling (ASL) image acquisition and processing
Perfusion-weighted images of cerebral blood flow used pulsed arterial spin labelling (PASL, PICORE-Q2T-PASL with background suppression). The sequence is used with the following parameters: repetition time (TR) = 2500 ms, echo time (TE) = 13 ms, field of view (FOV) = 256 × 256 × 100 mm3, 10 slices, 8 mm slice thickness, flip angle = 90°, inversion time 1 (TI1) = 700 ms, TI2 = 1800 ms, Saturation stop time = 1600 ms, tag width = 100 mm and gap = 20.9 mm, 90 repetitions giving 45 control-tag pairs, voxel-size = 4 mm × 4 mm × 8 mm, 25% interslice gap, acquisition time of 3 minutes and 52 seconds. In addition, a single-shot EPI (M0) equilibrium magnetization scan was acquired. Pulsed arterial spin labelling time series were converted to maps of CBF using Explore ASL toolbox (https://github.com/ExploreASL/ExploreASL; Mutsaerts et al., 2018). Following rigid-body alignment, the images were coregistered with the T1 from Phase II above, normalised with normalization parameters from the T1 stream above to warp ASL images into MNI space and smoothed with a 12 mm FWHM Gaussian kernel (for more details, Tsvetanov et al., 2020b).
2.6. Analytical approach
To model random effects across participants, we performed voxel-wise analysis using multiple linear regression (MLR) with age as the main independent variable of interest, and sex and handedness as covariates of no interest. This MLR was applied to maps of both Cattell activation (BOLD) and baseline CBF.
To evaluate the confounding and performance-related effects of resting CBF on BOLD activation, we conducted commonality analysis (Nimon et al., 2008; Kraha et al., 2012). Commonality analysis partitions the variance explained by all predictors in MLR into variance unique to each predictor and variance shared between each combination of predictors. Therefore, unique effects indicate the (orthogonal) variance explained by one predictor over and above that explained by other predictors in the model, while common effects indicate the variance shared between correlated predictors. Notably, the sum of variances, also known as commonality coefficients, equals the total R2 for the regression model.
We adapted a commonality analysis algorithm (Nimon et al., 2008) for neuroimaging analysis to facilitate voxel-wise nonparametric testing in Matlab (Mathworks, https://uk.mathworks.com/). The commonality analysis was applied the Cattell activation in each voxel separately (see Figure 1). The independent variables in the model were baseline CBF for the corresponding voxel, age and task performance. Covariates of no interest included sex and handedness. The model can therefore identify unique variance explained by each of the predictors (UCBF, UAge and UP for CBF, Age and Performance, respectively). Common effects of interest were the confounding effects, defined by the shared variance between CBF and age (CCBF,Age), and performance-related effects, defined by the common variance between CBF, Age and Performance (CCBF,Age,P). Significant clusters related to effects of interest were identified with nonparametric testing using 1000 permutations and threshold-free cluster enhancement, corrected to p<.05 (Smith and Nichols, 2009). This Matlab version of commonality analysis for neuroimaging with TFCE implementation is available at https://github.com/kamentsvetanov/CommonalityAnalysis/.
2.7. Data and code availability
The dataset analysed in this study is part of the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) research project (www.cam-can.com). Raw and minimally pre-processed MRI (i.e. from automatic analysis; Taylor et al., 2015) and behavioural data are available by submitting a data request to Cam-CAN (https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/).
Task-based fMRI data was post-processed using SPM12 (http://www.fil.ion.ucl.ac.uk/spm; Friston et al., 2007). Arterial spin labelling data were post-processed using ExploreASL toolbox (https://github.com/ExploreASL/ExploreASL; Mutsaerts et al., 2018). As part of this study, MATLAB-based commonality analysis for neuroimaging with TFCE implementation was developed and made available at https://github.com/kamentsvetanov/CommonalityAnalysis/. Visualisation of all neuroimaging results was generated using MRIcroGL (https://github.com/rordenlab/MRIcroGL; Rorden and Brett, 2000). The corresponding author (K.A.T.) can provide custom-written analyses code on request.
3. Results
3.1. Main effect and effect of age on BOLD in Cattell task
Group-level analysis confirmed activations for the hard vs easy condition in the lateral prefrontal cortex, the anterior insula, the dorsal anterior cingulate cortex, the frontal eye field, the pre-supplementary motor area and areas along the intraparietal sulcus and lateral temporal lobe, recapitulating the multiple demand network (MND, Duncan, 2013; Camilleri et al., 2018), and the lateral occipital cortex and the calcarine cortex (Figure 2a). Additionally, we observed deactivations in the ventral medial prefrontal cortex (vmPFC), posterior cingulate cortex (PCC) and inferior parietal lobe (IPL), recapitulating the default network (Buckner et al., 2008; Raichle, 2015; Buckner and DiNicola, 2019). With respect to ageing, there were weaker activations in regions of the MDN, and weaker deactivations in regions of the DMN, associated with increasing age, see Figure 2b, consistent with previous studies (Samu et al., 2017).
3.2. Main effect and effect of age on baseline CBF
Group-level results revealed a pattern of relatively high cerebral blood flow in cortical and subcortical brain areas associated with high perfusion and high metabolism (Henriksen et al., 2018; Figure 2c), such as caudal middle-frontal, posterior cingulate, pericalcarine, superior temporal and thalamic regions. Moderate to low CBF values in the superior-parietal and inferior-frontal areas of the cortex (Figure 2c) may reflect the axial positioning of the partial brain coverage sequence used in the study.
We observed age-related declines in CBF in the bilateral dorsolateral prefrontal cortex, lateral parietal cortex, anterior and posterior cingulate, pericalcarine, and cerebellum (Figure 2c) in agreement with previous reports (Chen et al., 2011; Zhang et al., 2018). Also, we observed age-related CBF increase in regions susceptible to individual and group differences in arterial transit time that can bias accuracy of CBF estimation, including middle temporal gyrus and middle cingulate cortex (Mutsaerts et al., 2017).
3.3. Commonality analysis of BOLD Cattell activation
3.3.1. Unique effects
Unique effects of individual differences in performance levels on Cattell activation (BOLD) were found in regions similar to those activated by the main effect of the Cattell task (e.g. MDN), with the exception of the lateral occipital cortex and inclusion of inferior temporal gyrus, primary visual cortex, caudate and thalamus, (cf. Figure 2a and Figure 3 top panel). Unlike the case for main effects, task-negative regions (e.g., DMN) showed small to no significant associations with performance.
Unique effects of age were similar but weaker to the effect of age in the model without other predictors (cf. Figure 2b and Figure 3 middle panel). Unique positive associations between CBF and Cattell activation was observed in middle frontal gyrus and cuneus regions. Negative associations were observed in insular regions, posterior cingulate cortex, bilateral angular gyrus, precentral gyrus and superior frontal gyrus.
Unique effects of CBF were weak, but significant, showing positive associations with activation in the middle frontal gyrus, the putamen and the cuneus. Additionally, CBF was associated negatively with activation in task negative regions, namely the angular gyrus and precentral gyrus.
3.3.2. Common effects
There were many common effects between age and performance, with a positive commonality coefficient (CAge,P, Figure 4, cyan colour), i.e. a portion of the age effects on Cattell activation was related to performance effects on Cattell activation. These effects were observed in task positive (e.g. MDN) and task negative regions (e.g. DMN), in addition to thalamus, caudate, primary motor cortex.
Negative commonality coefficients between performance and age were observed in the cuneus, bilateral middle frontal gyrus, anterior and middle cingulate gyrus, and bilateral superior temporal gyrus (dark blue colour in Figure 4). Negative values of commonality coefficients indicate a suppressor relationship between predictors (Zientek and Thompson, 2006), i.e. the effects of age and/or performance are stronger with their joint consideration in the model.
Confounding effects of baseline CBF on Cattell activation were characterised by the common effect between Age and CBF (CCBF,Age). Significant confounding effects were localised within posterior cingulate cortex, fusiform gyrus and inferior occipital gyrus (orange colour in Figure 4).
Performance-related effects of baseline CBF on Cattell activation were characterised by the common effect between Age, CBF and Performance (CCBF,Age,P, black colour in Figure 4). Regions included intraparietal sulcus, posterior cingulate cortex, precuneus, thalamus and fusiform gyrus. Furthermore, consistent with previous findings (Tsvetanov et al., 2018), behaviourally-relevant effects were seen in inferior temporal and adjacent occipital regions, presumably due to attentional enhancement of visual representations in the more difficult conditions (Fedorenko et al., 2013).
4. Discussion
The study confirmed the prediction that regional cerebral blood flow (CBF) can explain both performance-related and age-dependent components of the fMRI BOLD signal in parts of the multiple-demand network (MDN) associated with more complex reasoning during a common test of fluid intelligence (Cattell task). The age-dependent differences in baseline CBF also explained variance in fMRI BOLD signal in some regions that was not related to task-performance. We propose that modelling the effects of age on baseline CBF, and in general cerebrovascular and neurovascular health (Tsvetanov et al., 2021), improves the interpretation of fMRI studies, with implications for understanding brain health with ageing and disease, and that maintaining brain perfusion as we get older may have a protective effect on brain function and cognition.
4.1. Age differences in baseline cerebral blood flow are related to behaviour-relevant Cattell BOLD activity
Age-related decreases in baseline cerebral blood flow (CBF), assessed with a non-invasive MR-perfusion technique, related to behaviourally relevant BOLD activity evoked by demanding problem-solving. Our findings are consistent with previous studies relating baseline CBF to performance on tasks carried outside the scanner (Bangen et al., 2014; Hays et al., 2017). We extend these lines of work by showing that baseline CBF is linked to BOLD activity, with behavioural correlation across individuals. Age-related decrease in CBF and decline in performance related to a lower range of activation in task-positive regions and less deactivation of task-negative regions. Of all task-positive regions, the bilateral intra-parietal sulcus, the thalamus, and the fusiform gyrus showed significant common effects between age, CBF and performance. The intraparietal sulcus and the thalamus also showed a unique association between performance and BOLD activity, suggesting a neural origin of the effects in these regions. The processes contributing to coupling between baseline CBF and neural activity are multifaceted, probably comprising neurogenic vasodilation, cardiac output and arterial remodelling (Gaballa et al., 1998; Ohanian et al., 2014; Li et al., 2015), all of which change with age and regulate baseline and stimulus-evoked CBF (Willie et al., 2014). Establishing the relative contribution and importance of these processes warrants future research.
Of all task-negative regions, only the posterior cingulate cortex showed common effects between age, CBF and performance in predicting BOLD activation in the Cattell task. In this region, age-related reduction in CBF and performance correlated with less deactivation in the posterior cingulate cortex. The posterior cingulate cortex did not show unique effects between performance and BOLD activity, suggesting a mechanism different from the one observed in task-positive regions, likely reflecting a non-neuronal origin of the effects (see also “Unique effects of performance, age and CBF in Cattell task”). While the deactivation of the default network in young adults is thought to reflect suppression of neuronal activity (Fox et al., 2018), in the present study, some of the poor performing older adults showed an over-activation, not less deactivation. This again suggests a different involvement of the posterior cingulate cortex in older adults compared to young adults, for instance, signals of non-neuronal origin caused by physiological artifacts (Birn et al., 2006; Tsvetanov et al., 2021) or ‘vascular steal’ (Shmuel et al., 2002). Taken together, these findings may reflect compromised vasodilatory reserve, resulting in an inefficient redirection of resources from task-positive regions to task-negative regions in the attempt to meet higher energy demands in task-positive regions, perhaps reflecting blood flow-dependent glycolysis and oxidative metabolism. The breath of these associations is consistent with theories of vasoactive and cardiovascular regulation of cerebral blood flow (Sobczyk et al., 2014; Digernes et al., 2017).
4.2. Vascular Confounding effects of CBF on task-related activity
Only a portion of the age differences in performance-independent BOLD activation were associated CBF decreases. Furthermore, the effects were observed in non-classical demand network task-positive and task-negative regions not showing unique associations between performance and BOLD activity, namely the fusiform gyrus and the posterior cingulate cortex (Figure 4, orange regions). This is consistent with the view that differences in baseline CBF can affect the sign and the magnitude of the evoked BOLD signal, without affecting changes in the underlying neural activity (Cohen et al., 2002; Brown et al., 2003; Stefanovic et al., 2006). We extend prior findings by showing that only a portion of the CBF effects can introduce such a behaviourally irrelevant bias; other parts of the CBF variance might be related to behaviour-relevant signal, i.e. differences in CBF could be important in their own right. Unlike the normalisation approach described in Introduction to control for CBF differences, the current commonality framework allows partition of CBF effects into effects of interest and effects of no interest. We propose that modelling the effects of age on baseline CBF, and in general cerebrovascular and neurovascular health (Tsvetanov et al., 2021), has implications for the interpretation of fMRI studies of ageing, whereby it can improve brain-behaviour relationships and provide a viable mechanistic account of maintaining and improving cognitive function in old age.
4.3. Unique effects of performance, age and CBF on task-related activity
After accounting for age and performance, higher baseline cerebral blood flow remained significantly associated with the level of BOLD activity in cortical regions modulated by demanding problem-solving processes (Figure 3). Higher baseline CBF related to higher range of activation in task positive regions under more demanding processing, including the middle frontal gyrus, the putamen, and the cuneus. The effects were spatially adjacent or overlapping with behaviour-relevant region suggesting that higher baseline CBF may provide the conditions to upregulate activity in these regions, possibly through functional hyperaemia. Additionally, higher CBF provided higher range of deactivation in task negative regions, namely the angular gyrus and precentral gyrus. These effects were spatially adjacent or overlapping with regions showing inefficient deactivation with ageing and suggest that higher baseline CBF may facilitate suppression of activity in task-negative regions. This may reflect the effect of having an intact vasodilatory reserve (Sobczyk et al., 2014; Digernes et al., 2017). Our findings have direct implications for task-based BOLD imaging whereby higher baseline CBF levels contribute to stronger changes in BOLD signal amplitude in response to demanding cognitive conditions. The myogenic response and cardiac output are two major modulators of resting CBF (Hill et al., 2006; Meng et al., 2015), which require future consideration to establish the mechanism underlying our findings.
Ageing was associated with weaker activation of the multiple demand network and less efficient suppression of the default network. These effects were over and above performance and CBF, suggesting the involvement of additional factors leading to age-related difference in BOLD activity. Some factors include genetics (Shan et al., 2016), cardiovascular and neurovascular signals not captured by baseline CBF (Abdelkarim et al., 2019; Tsvetanov et al., 2021) or effects of functional connectivity captured by regional activity (Tsvetanov et al., 2018). Age differences in the shape of the haemodynamic response function (West et al., 2019) are less likely to introduce bias in the current study given its block-related fMRI design (Liu et al., 2001). The nature of these age effects should be elucidated through further investigation. The commonality analysis framework provides a useful tool for multivariate simultaneous modelling to disentangle the multifactorial nature of age-related BOLD differences.
After accounting for age, baseline CBF and other covariates of not interest, the level of activity in the multiple-demand regions remained positively associated with performance during the Cattell task in the scanner. Our findings are in line with previous studies during diverse demanding tasks, including manipulations of working memory, target detection, response inhibition (Fedorenko et al., 2013; Tschentscher et al., 2017; Assem et al., 2020a, 2020b). Given that both age and cerebrovascular reactivity could introduce a very strong effect on the activity-behaviour associations (even with narrow age range and healthy populations), our approach to control for these factors, in combination with the population-based, large-sample, provide the strongest evidence to date that individual differences variance in executive abilities is selectively and robustly associated with the level of activity in the multiple demand network.
Our study adds evidence to the nature of suppression of the default network during externally directed task (Buckner and DiNicola, 2019). The task-induced default network deactivations were consistent with previous findings in the Cattell task (Samu et al., 2017) and in general with the extent to which task conditions are cognitive demanding (Anticevic et al., 2012; Sripada et al., 2020). The effects in the default network were related to age or baseline CBF, but not uniquely related to performance, suggesting that the level of BOLD deactivations during Cattell task do not reflect individual variability in cognitive performance. The nature of default network suppression remains to be fully defined (Fox et al., 2018), but future findings about the default network cannot be interpreted independent of age and baseline CBF, at least when aiming to understand the relevance of DMN suppression in health and disease.
5. Issues and future directions
There are issues to the study. Our findings are based on a population-based cross-sectional cohort, which cannot directly speak to individual’s progression over time (i.e, the ageing process). We only assessed the brain activations/co-activations, but do not quantify brain connectivity (Tsvetanov et al., 2016, 2020a; Geerligs et al., 2017; Samu et al., 2017; Bethlehem et al., 2020), even though both may change with in cognitive ageing (Tsvetanov et al., 2018). The relationship between baseline CBF and functional connectivity decouples with ageing (Galiano et al., 2019), but the behavioural relevance of such decoupling remains unclear, albeit motivated by prior work controlling for vascular effects from fMRI BOLD data (Tsvetanov et al., 2016; Geerligs et al., 2017). Future work should also i) evaluate the effects of CBF under different cognitive states (Campbell et al., 2015; Geerligs and Tsvetanov, 2016), ii) consider nonlinearities between CBF and BOLD signal within individuals (Chen, 2019) and across the lifespan (Tsvetanov et al., 2016; Tibon et al., 2021), and iii) the relevance of baseline CBF to stimulus-evoked CBF (Jennings et al., 2005) and other measures of cerebrovascular reactivity in ageing (Tsvetanov et al., 2020b) and neurodegenerative diseases (Chen, 2019).
6. Conclusion
We introduce a novel approach to neuroimaging that can dissociate between shared and unique signals across multiple neuroimaging modalities. Using this method, we show the effects of age on cerebral blood flow, task-related BOLD responses and performance. The results demonstrate that cerebrovascular health (i.e., baseline cerebral blood flow) explains confounding but also performance-related BOLD responses in fluid ability across the lifespan. They highlight the importance of using resting CBF data to model, rather than simply normalise for, differences in vascular health in task-based fMRI BOLD data (cf. Tsvetanov et al., 2021). Unlike the normalisation approach, our approach allows simultaneous modelling of multiple measures with independent contributions to cerebrovascular health. Here, we provide empirical evidence in support of the mechanism underlying the link between baseline CBF and neurocognitive function across the lifespan. The insights from our results may facilitate the development of new strategies to maintain cognitive ability across the life span in health and disease.
8. Competing Interests statement
J.B.R. serves as an associate editor to Brain and is a non-remunerated trustee of the Guarantors of Brain, Darwin College Cambridge, and the PSP Association (UK). He has provided consultancy to Asceneuron, Biogen, UCB and has research grants from AZ-Medimmune, Janssen, Lilly and WAVE as industry partners in the Dementias Platform UK. The other authors have no disclosures.
7. Acknowledgements
This work is supported by the Guarantors of Brain (G101149), SCNU Study Abroad Program for Elite Postgraduate Students, the Medical Research Council (MC_UU_00005/12/SUAG004/051/RG91365; SUAG04/51 R101400) and the Cambridge NIHR Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) research was supported by the Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1). We thank the Cam-CAN respondents and their primary care teams in Cambridge for their participation in this study. Further information about the Cam-CAN corporate authorship membership can be found at https://www.cam-can.org/index.php?content=corpauth#13.