Damage to the multiple demand network causes people with dementia to struggle to adapt to changes in their environment

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People with dementia struggle to adapt to changes in their environment because of damage to areas of the brain known as “multiple demand networks,” highly-evolved areas of the brain that support general intelligence, say scientists at the University of Cambridge.

There are many different types of dementia, such as Alzheimer’s disease and frontotemporal dementia (FTD), which are characterized by the build-up of different toxic proteins in different parts of the brain.

This means that the symptoms of dementia vary, and can include problems with memory, speech, behavior or vision. But one symptom seen across every type of dementia is a difficulty in responding to unexpected situations.

Dr. Thomas Cope from the MRC Cognition and Brain Science Unit and Department of Clinical Neurosciences at the University of Cambridge said, “At the heart of all dementias is one core symptom, which is that when things change or go unexpectedly, people find it very difficult.

If people are in their own environment and everything is going to plan, then they are OK. But as soon as the kettle’s broken or they go somewhere new, they can find it very hard to deal with.”

To understand why this happens, Dr. Cope and colleagues analyzed data from 75 patients, all of whom were affected by one of four types of dementia that affect different areas of the brain. The patients, together with 48 healthy controls, listened to changing sounds while their brain activity was recorded by a magnetoencephalography machine, which measures the tiny magnetic fields produced by electrical currents in the brain. Unlike traditional MRI scanners, these machines allow very precise timing of what is happening in the brain and when.

The results of their experiment are published today in the Journal of Neuroscience.

During the scan, the volunteers watched a silent film – David Attenborough’s Planet Earth, but without its soundtrack—while listening to a series of beeps. The beeps occurred at a steady pattern, but occasionally a beep would be different, for example a higher pitch or different volume.

The team found that the unusual beep triggered two responses in the brain: an immediate response followed by a second response around 200 milliseconds – a fifth of a second – a later.

The initial response came from the basic auditory system, recognizing that it had heard a beep. This response was the same in the patients and healthy volunteers.

The second response, however, recognized that the beep was unusual. This response was much smaller among the people with dementia than among the healthy volunteers. In other words, in the healthy controls, the brain was better at recognizing that something had changed.

The researchers looked at which brain areas activated during the task and how they were connected up, and combined their data with that from MRI scans, which show the structure of the brain. They showed that damage to areas of the brain known as “multiple demand networks” was associated with a reduction in the later response.

Multiple demand networks, which are found both at the front and rear of the brain, are areas of the brain that do not have a specific task, but instead are involved in general intelligence – for example, problem solving. They are highly evolved, found only in humans, primates and more intelligent animals. It is these networks that allow us to be flexible in our environment.

In the healthy volunteers, the sound is picked up by the auditory system, which relays information to the multiple demand network to be processed and interpreted. The network then “reports back” to the auditory system, instructing it whether to carry on or to attend to the sound.

“There’s a lot of controversy about what exactly multiple demand networks do and how involved they are in our basic perception of the world,” said Dr. Cope. “There’s been an assumption that these intelligence networks work ‘above’ everything else, doing their own thing and just taking in information. But what we’ve shown is no, they’re fundamental to how we perceive the world.

“That’s why we can look at a picture and immediately pick out the faces and immediately pick out the relevant information, whereas somebody with dementia will look at that scene a bit more randomly and won’t immediately pick out what’s important.”

While the research does not point to any treatments that may alleviate the symptom, it reinforces advice given to dementia patients and their families, said Dr. Cope.

“The advice I give in my clinics is that you can help people who are affected by dementia by taking a lot more time to signpost changes, flagging to them that you’re going to start talking about something different or you’re going to do something different. And then repeat yourself more when there’s a change, and understand why it’s important to be patient as the brain recognizes the new situation.”

Although their study only looked at patients with dementia, the findings may explain similar phenomena experienced by people living with conditions such as schizophrenia, where brain networks can become disrupted.


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-b-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.

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.

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).

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.

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.

reference link:: https://doi.org/10.1101/2021.11.10.468042


Original Research: Closed access.
Causal evidence for the multiple demand network in change detection: auditory mismatch magnetoencephalography across focal neurodegenerative diseases” by Thomas E. Cope et al. Journal of Neuroscience

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