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The Default Mode Network (DMN) is a network of brain areas that is engaged when people are at rest. The medial prefrontal cortex, posterior cingulate cortex and the inferior parietal lobe are thought to be the core of the DMN. Different functional processes have been linked to the DMN. However it remains difficult to define the cognitions that are at work when one is at rest. Recently more studies have focussed on the changes of the DMN in childhood and old age. To understand the development of the DMN will expand our understanding of its function. In this review an overview will be given from the literature about the development of the DMN from infancy to old age and about the DMN in Autism Spectrum Disorders
(ASD) and Alzheimer’s disease (AD). The findings of the development of the DMN are supported by results from other areas of research such as underlying brain anatomy. Finally, implications of the development of the DMN on the functionality will be discussed, along with the criticism directed at the concept of a default mode of brain function.
In the last decades a large number of studies have used imaging techniques like Positron
Emission Tomography (PET) and functional Magnetic Resonance Imaging (fMRI) to investigate what happens in the brain during different tasks. Performing a task induces activity in the brain. To know what activity is specific for the performed task a control condition is used and the activity of this control condition is subtracted from the task induced activity. Generally, in the control condition participants are asked either to lie down with their eyes closed or to visually fixate at a set point. A small activity increase, or activation, of certain brain areas in the experimental condition compared with the control condition is expected. Interestingly, specific brain regions seemed to show consistent deactivations in goal directed tasks compared with the control state (Shulman 1997 and Mazoyer 2001 and
Shannon 2006). It was this persistent observation of deactivations that has led to an increasing interest in the brain at resting state, often referred to as the default state of the brain.
The
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question what happens in the brain when one is at rest has been the basis of a fast growing research area (Gusnard and Raichle, 2001; Miall and Robertson 2006; Raichle and Snyder,
2007; Damoiseaux et al 2006; Van den Heuvel et al. 2008a, Van den Heuvel et al. 2008b). A network of brain regions that is engaged when people are at rest was proposed, the so called default network or default mode of brain function (Raichle et al. 2001; Greicius et al. 2003).
This Default Mode Network (DMN) is a brain system that encapsulates a set of interacting brain regions that are functionally connected and distinct from other networks in the brain
(Buckner et al. 2008). Recently studies are published that examine the DMN in very young and old age. However the full development of the DMN from early childhood until old age is unknown. Here we report an overview of how this DMN develops during ageing. In the first chapter, we will give a short overview of the background of the DMN and discuss different approaches that are used to examine the default network. Furthermore we discuss which brain areas are involved in the DMN and the chapter ends with a discussion about suggested cognitive functions of the DMN. In the second chapter we will examine the development of the DMN by discussing the studies that have investigated the relations between the DMN and childhood, autism, normal ageing and Alzheimer’s disease. The overview will end with a discussion.
Different methods showing the Default Mode Network
A brain in rest consumes much more energy (20% of body’s energy) than would be expected form its size (i.e. 2% of the body mass). The increase in metabolism that arise from task related activity is mostly small (<5%) relative to the resting condition (Raichle Mintun, 2006).
It is of high interest to investigate what happens in the brain in this resting state for a more complete understanding of the brain. Two lines of methodology have been used to examine resting state activity. The first is to study negative activation and the second to study synchronization between brain areas during rest. Both lines will be discussed here.
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The first line of methodology is to examine negative activation by comparing the resting state to a state of activity. Both PET and fMRI studies have shown that during resting state a consistent set of areas in the brain are more active compared with goal directed task conditions. (Shulman et al. 1997; Mazoyer et al 2001; Shannon 2006). These findings have inspired the idea of the DMN. The idea behind it is the same as a ‘traditional’ fMRI experiment in which activity during a task is the focus of research. In a ‘traditional’ fMRI experiment one would subtract the activity during the control condition from task related activity. However in order to look at the negative activation one should subtract the task related activity from the activity during control state. The activity that remains is called deactivations and may reflect the default state of the brain i.e. the state during rest. Although the control state is often carefully designed to represent baseline as much as possible it basically still is another task state with its own areas of activation. With this reason in mind
Raichle et al (2001) designed a PET study to investigate whether these deactivations really represent a default state of the brain or whether they are the result of activations in the control state. They conclude that these decreases indeed are decreases from baseline and represent a default state of the brain.
A second line of methodology to investigate the DMN is to examine coherency between different brain areas during rest. Several brain areas with similar function show a high level of coherency between their activation patterns over time when one is ‘at rest’. This indicates a high level of functional connectivity between these anatomically separate regions (Fox et al
2005; Greicius et al 2003; Fransson et al 2005). Around 8 of these so-called resting-state networks have been consistently found, including the DMN (Beckman et al. 2005;
Damoiseaux et al. 2006; Van den heuvel et al. 2008a; De Luca et al. 2006).
Within this second line of methodology, different techniques have been developed to study functional connectivity. In this section we will discuss the ones that have been used most. The first method is the seed method which is the most frequently used technique. (Biswal et al
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1995; Fox et al. 2005; Greicius et al. 2003; Fransson 2005; Cordes et al 2001). With this method spontaneous changes in BOLD signal during rest is measured over time and correlated to the time course of BOLD changes from all other brain voxels (see fig 1).
Advantages of this method are its simplicity, sensitivity and easy interpretation but the results are dependent on an a priori definition of a seed region. (Fox & Raichle, 2007). A Second technique is PICA which decomposes a data matrix into a set of time courses and associated spatial maps (De Luca et al. 2006; Beckmann et al 2005 Kiviniemi et al. 2004; Van de Ven et al. 2004 Damoiseaux et al. 2006) (see fig 1). Each of the resulting components can reflect noise or neuro-anatomical dependent systems. The user has to determine which of the fitted components reflects noise and which neuro-anatomical systems; this may make the method more vulnerable for differences in interpretation (Fox & Raichle 2007). Advantages of this method are independence of a priori seed regions, a (model-free) group based analysis and the method is voxel-based. A third technique is clustering, extracting the time courses of many seed regions instead of just one by forming a correlation matrix. A topological map is made which shows which regions are most closely related. (Cordes et al. 2002; Salvador et al 2005).
Unlike the other methods it is not voxel-based and the brain is divided in several areas which are used for comparison which makes it harder to interpret the analysis into detail. Also it is not possible to do group based analysis, only individual analysis that are afterwards put together. However, recently some improvements have been made by using the Normalization
Cut Group Clustering as proposed by Van den Heuvel et al. (van den Heuvel et al. 2008).
With this method inter-voxel correlations are calculated and are put in a functional connectivity graph which is clustered for each individual. Afterwards the consistency across the individual clustermaps is computed and clustered, defining the group resting state networks. This approach is voxel-based and the computation of the number of is less vulnerable for manual interpretation.
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In sum there are two different lines of methodology that examine the brain at its resting state;
The first looks at the deactivations from resting activity compared with task related activity, the second studies the functional connectivity between the different brain regions. Both can give us valuable information about the DMN.
Anatomy of the Default Mode Network
The definition of the anatomical parts of the DMN is not straight forward. Different studies use different techniques to define the DMN and sometimes point out different regions to be part of the DMN. An important review by Buckner et al. (2008) gives an overview of the different strategies of defining the DMN. A convergence is found that comprises the medial prefrontal cortex (approximately brodmann area’s: 24, 10, 32, 9), posterior cingulate cortex/restrosplenial cortex (BA: 29/30, 23/31) and the inferior parietal lobe (BA: 39/40).
Also the hippocampal formation is shown to be involved however it is less prominent using the approach of task induced deactivations. The lateral temporal cortex is consistently observed across approaches but less robust as is the hippocampal formation. A good definition of the anatomy of the DMN is important and can give us important insights about its functionality.
Function of the Default Mode Network
The question about the function of the default network has led to many suggestions. It is not an easy question to answer because the deactivations with goal directed tasks suggest that something is happening in the brain during rest but not what is happening. Basically two methods are used to investigate the function of the DMN. The first is task induced activation in the DMN. There are certain tasks that seem to activate the DMN or large parts of the DMN.
The nature of the tasks is likely to give us information about the possible workings of the
DMN (Kennedy & Courchesne, 2008a; Iacoboni et al. 2004; Greene et al. 2001). The second
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method is to investigate the anatomy of the DMN. These two methods seem very similar but there is a difference. In the first method the tasks are examined that activate the whole of the
DMN or at least a big part of the DMN. With the second method, the individual parts of the
DMN are studied and compared for their function. Studying the separate functions of the brain areas involved in the DMN can provide us with clues about the functionality of the network as a whole. (Gusnard et al. 2001; Cavanna & Trimble, 2006). This has led researchers to propose different functions for the DMN: self referential mental activity (Beer,
2007; Greicius et al 2003; Gusnard & Raichle, 2001; Goldberg et al. 2007; Gusnard et al.
2001; Wicker et al. 2003), stimulus independent thought (Mason et al 2007), consolidate and stabilize information processing (Raichle & Snyder, 2007; Buckner & Vincent 2007), theory of mind (Amodio & Frith, 2006), envisioning the future (Addis et al 2007; Buckner &
Vincent 2007) and monitoring the environment around us (Gilbert et al. 2007; Gusnard &
Raichle, 2001).
A short description will be given of the different theories. Self referential mental activity is evoked by tasks which make you think about yourself. An example of this is when people are asked to monitor their feelings when pictures are presented to them (Gusnard et al. 2001) or when people are asked to make judgement about themselves (Ochsner et al. 2005). Stimulus independent thought refers to mind wandering. When you are to left to yourself without any specific task to do, often spontaneous thoughts, images and feelings will come up. Another function of the DMN that has been suggested is consolidation and stabilization of information processing (Buckner et al. 2008). Furthermore the theory of mind is implied to activate areas of the DMN (Amodio & Frith, 2006). The theory of mind is used to refer to the ability of people to put themselves in other people’s mind. This means that they can understand that other people have thoughts and feelings that are different from their own. Therefore you need the ability to attribute thoughts and feelings to oneself and to others. Envisioning the future refers to thinking about the future in a way that you imagine hypothetical scenarios. The last
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suggested DMN function, monitoring the environment, stands out from the rest in the way that it is more extrospective. With monitoring the environment, there is an enhanced watchfulness towards the external environment like one would have when waiting for an upcoming event. This is in contrast to the functions mentioned before where attention is focussed on the internal environment. People have to go within themselves to know what they feel or to think about themselves, to think about the feelings of others and to think about the future.
With all these different theories it is difficult to draw any conclusions about the function of the DMN. However some researchers have attempted to integrate the different theories.
Buckner et al (2008) reviewed the different functions and suggested an ‘internal mentation hypothesis’ which encapsulates most of these introspectrive theories and places it against the
‘monitoring external environment theory’ which consist of the monitoring the environment theory. The internal mentation hypothesis states that the function of the DMN is to make imaginative constructions out of hypothetical events or scenarios. These scenarios can be about the past or future, about themselves or others, possibly this happens spontaneously without effort and its function might be to consolidate and stabilize information processing.
Therefore the function of the DMN might encapsulate or overlap with functions like autobiographical memory (and some self referential mental activity), theory of mind, envisioning future and possibly with thinking about moral dilemma’s (Buckner et al 2008).
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:
It would be of great value to gain more insight about the development of the DMN from infancy to old age. When we have more information about how the network develops it can give valuable information about the function it is related to. This can be done by looking at the modulation of the DMN and to link its development with the differences and similarities of cognitive functioning at a certain age. The last few years there has been an increasing amount of studies about the DMN either in young aged children or older aged people. The increasing interest in the DMN does not confine itself only to healthy development; also developmental disorders and dementia are the subject of DMN studies. To our knowledge there has been no overview about the development of the default network from young aged children to elderly people yet. To understand how the DMN changes and develops with age will broaden our knowledge about the function of the DMN and could provide more insight about pathological disorders that are linked to alterations of the default network.
Default Mode Network in children
Studying the DMN in infants and children has gained growing interest from the neuroscience community. Three recent studies investigated the DMN in the brains of children (Fransson et al. 2007; Fair et al. 2008; Thomason et al. 2008). Their results show remarkable overlap and differences between the DMN in children and adults. Fransson et al. (2007) investigated resting state networks in the brains of infants (mean gestational age of 25 weeks). They found five resting state networks but they did not find a DMN. However there was a network that included the bilateral parietal cortex and the precuneus (Fransson et al. 2007). With regard to resting state networks in general, the resting state networks of infants showed strong functional correlations across hemispheres and little lateralization compared with adult resting state networks. Furthermore the functional correlations between regions separated along the
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anterior-posterior direction are often found in resting state networks in adults but this component seems to lack in the brains of infants. In older children (>7 years) both similarities and differences with the DMN of adults have been reported. Results show similarities such as task related decreases in DMN regions and functional connectivity within the DMN (Marsh et al. 2006; Thomason et al. 2008; Fair et al. 2008). The differences that are found in this age group are task induced deactivations in the posterieur insula (BA13), postcentral gyrus (BA3) inferior occipital region (BA18) and the lack of deactivations mostly in the prefrontal cortex
(BA10,11,45,46,47) (Thomason et al. 2008). Concerning functional connectivity, reduced connectivity within the network is found in children. It seems that with increasing age the connectivity within the DMN increases and at the same time more dissociation between the
DMN and other networks evolves. Results suggest that interhemispheric correlations change little between children and adults but especially long distance connectivity and connection strength between the medial prefrontal cortex and the posterieur cingulate cortex develops with age (Fair et al. 2008). When the results of these studies are taken together there seems to be a continuum in the development of the DMN in childhood that evolves with age. In the infant brain the DMN was not found. This could be a result of the immaturity of the infant brain. However Fransson et al. (2007) speculate that the network, which includes the bilateral parietal cortex and the precuneus, might be an early stage network from which the actual
DMN develops. As children grow older, the connectivity within the network seems to grow stronger and more dissociation between the DMN and other networks evolves. The three areas that Thomasen et al. (2008) found to deactivate in children as a result of increased task difficulty or demands were all involved in sensory processing. This may indicate a greater integration between DMN and sensory processing regions in children than adults. Additional results point out that certain aspects change little with age like the interhemispheric connectivity and the short distance connections whereas other aspects change more like the long distance connections between regions separated posterior and anterior. In short we can
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say that differences between adults and children concerning the DMN are evident. In infancy the DMN is still absent but it seems to gain more of its future characteristics during the course of the development into adulthood.
These findings are consistent with findings in other areas of imaging research. Dubois et al.
(2008) looked at the development of white matter bundles in infants and found that the cingulum was one of the most immature tracts. The cingulum tract is a bundle of white matter fibers that connect the precuneus/posterior cingulated cortex and medial frontal cortex which are key areas in the DMN (Greicius et al. 2009; Van den Heuvel et al. 2008a). Moreover a positive correlation was found between the microstructural organization of the cingulum tract and the level of default mode functional connectivity (van den Heuvel et al. 2008a). This suggests that the cingulum plays an important role in relation to the DMN and the immaturity of this tract in the infant brain is in accordance with the absence of a DMN in this age group.
The idea that the DMN in the infant brain is not yet present but the possibility of a proto-
DMN which develops over time, could indicate that the cognitive processes linked to the
DMN will have a similar maturation. As discussed above the function of the DMN is often related to self referential and social processing, processes which are closely related to language. It is therefore not surprising to see an absence of the DMN of infants (Fransson et al. 2007). The finding that some connections within the DMN are intact and function already with children of older age might be the reason that most functions associated with the DMN are already present in 7-9 year olds. In this age group the foundation of the DMN seems to be present and with increasing age the connectivity within the network increases as well as the dissociation with other networks. In the same way the foundation of the functions associated with the DMN are present in 7-9 year olds and together with increasing age and further development of the DMN, these functions also expand. With age children will develop a growing number of alternative strategies or higher order strategies which make them become more efficient and expand their possibilities with these functions (Fair et al. 2008).
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Default Mode Network and a developmental disorder: Autism Spectrum Disorders
Autism and related disorders, Autism Spectrum Disorders (ASD), are disorders that are characterised by impairments in social interaction and the ability to communicate. The disorders can vary in severity. Several suggested functions of the DMN overlap with the impaired functions observed in ASD, such as processing information about self and others and the theory of mind . Furthermore there seems to be an anatomical overlap in the regions that show functional and structural abnormalities in autism and the regions of the DMN
(Pierce et al. 2004).
Different studies indicate that individuals with ASD fail to show the task related deactivation effect in the regions of the DMN with little evidence of difference in task performance
(Kennedy et al 2006; Kennedy et al 2008a). A correlation was found between higher social impairments scores and less task related deactivation in the Medial Prefrontal Cortex (MPFC)
(Kennedy et al. 2006). This lack of deactivation might be due to low midline activation during rest. In addition functional connectivity during rest was studied in ASD (Cherkassky et al.
2006). Consistent with the previous studies they found less connectivity between key regions of the DMN: the anterior cingulate and the posterior cingulate/precuneus region during rest, which suggests an absence of self referential thoughts in autism. However in contrast to
Kennedy et al (2006), Cherkassky et al. (2006) did not find any difference in resting state network activation between individuals with autism and healthy controls. In a different study
(Kennedy & Courchesne 2008b) results show a reduction in connectivity within the DMN due to abnormalities of the MPFC and left angular gyrus. However there was no difference in connectivity within a task activated network between individuals with ASD and healthy controls. These results together suggest abnormalities in the functioning of the DMN in ASD.
However the extend of these impairments are not entirely clear.
A remaining question is whether the abnormalities found in regions of the DMN in autism, in particular abnormalities in the MPFC, are task independent or task dependent. Task
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independent dysfunction would mean that no matter which task is being used these regions are unable to function properly in the brains of individual with autism. A task dependent dysfunction on the other side might reflect the disability of individuals with autism to engage automatically in socio-emotional and introspective processes when no explicit instruction is given. Kennedy & Courchesne (2008a) tested both these hypotheses and came to the conclusion that abnormalities found in the ventral MPFC (vMPFC) and the ventral Anterior
Cingulate Cortex (vACC) are likely to be task independent.
Abnormalities found in other
DMN regions such as the dorsal MPFC (dMPFC) and Retrosplenial Cortices (RSC)/PCC seem task dependent.
Therefore it is probable that some DMN dysfunctions are task dependent and other task independent.
How do the differences in brain functionality within the DMN relate to cognitive abnormalities observed in autism? In a study by Hurlburt et al. (1994) two out of three individuals with ASD had difficulty to understand what it meant to describe their inner experience which illustrates a reduced ability to introspect. Furthermore individuals with ASD report very different internal thoughts as controls do. The reports of the inner experience of individuals with ASD were characterized by the absence of any features of inner thought or feelings but only images. (Hurlburt et al 1994; Frith & Happé 1999). It is difficult to say how reliable these reports are, and whether it is a lack of inner experience or lack of understanding the concept of inner experience. More research is needed in this area. However it seems plausible that with altered DMN function in ASD also the cognitive functions associated with the DMN such as ‘mind wandering’ and ‘self referential mental activity’ are impaired in
ASD.
Another interesting point of discussion is the interaction between the deficits seen in the
DMN and the development of ADS. Raichle et al. (2001) pointed out the high metabolic rate of parts of the DMN, the precuneus and PCC. They hypothesize that these areas therefore could be more vulnerable to damage. This idea is partly supported by animal models of
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schizophrenia (Olney et al. 1999). It is based on pharmacologically induced, excitatory amino acid toxicity that preferentially targets this area. In that context one could argue that a developmental disturbance underlying ASD goes together with the disturbance of the DMN.
Another possibility is that the deficits in the DMN reflect a developmental endpoint in ASD rather than being linked to the originating developmental events that cause ASD (Buckner et al. 2008). However at this point the link between the disturbance seen in DMN and the development of ADS is still unknown.
Taken together these results indicate abnormalities in the DMN in ASD. It is still unclear however if these abnormalities are caused by deficits in the overall deactivation of the DMN or by deficits in deactivation in more specific areas of the DMN. The result of Kennedy et al.
(2006) would suggest a more general deficit in the DMN which might be due to less or no self referential processing during rest (Iacoboni 2006). On the other hand the results of
Cherkassky et al. (2006) might indicate a dysfunction in control system of the DMN where the DMN itself is still largely intact (Buckner et al. 2008). More research is needed in order to know more about the relationship between ASD and the DMN.
Default Mode Network and normal ageing
A growing body of research suggests that the default network is affected in old age. Both the functional connectivity of the DMN (Damoiseaux et al. 2007; Andrews-Hanna et al. 2007;
Sambatoro et al. 2008) and the decrease of task related deactivations (Sambatoro et al. 2008;
Grady et al 2006) are affected. Grady et al (2006) suggested that the decreases found in task related deactivation in DMN regions with age are more robust than the decreases in task related activation. Specifically the anterior part of the DMN seems vulnerable in ageing. One study shows an inverse relationship between age and deactivation decreases for the anterior part of the DMN (Damoiseaux et al. 2007). Other studies (Andrews-Hanna et al 2007;
Sambataro et al. 2008) show that the functional correlation between the anterior and posterior components of the DMN is affected. These results could support the frontal lobe theory of
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ageing (West 1996; Grady et al. 2006) which states that that altered changes in the brain with age would be primarily found in the frontal lobes. On the other hand some studies also find
DMN abnormalities in other parts of the brain (Persson et al 2007; Grady et al 2006). In other words, DMN abnormalities with age are mostly found in the anterior part of the brain.
Nonetheless research showing DMN abnormalities in other parts of the brain indicates it is not exclusively the anterior part of the DMN that is affected in ageing.
We know that certain cognitive functions decline with age, such as attention, concentration, processing speed, memory function and executive functioning. Interestingly, several studies show an association between cognitive decline and decreased DMN functioning.
(Damoiseaux et al. 2007; Sambataro et al. 2008; Andrew-Hanna et al 2007). More specifically, one study shows a correlation between deactivation in DMN and cognitive control in ageing subjects (Persson et al. 2007). A decrease of cognitive control could affect the ability to constrain attention in a relevant task. When there was a high demand for cognitive control they found significant differences in task related deactivations between young and older adults (Persson et al. 2007).
However not all studies found a correlation between DMN activity and cognitive functioning. Grady et al (2006) did find differences in task related deactivation with age but found no age effect on cognitive performance. This could very well be due to a difference in sensitivity, where the brain measures are more sensitive for age than the behavioural measures (Grady et al. 2006). Overall it seems to be the case that decline in DMN function with age is associated with a decrease in cognitive functioning.
The results from studies focussing on white matter bundle differences in ageing correspond to the impaired DMN function found with increasing age. Advanced ageing goes together with a reduced integrity of white matter (WM) (Sullivan and Pfefferbaum 2006; O’Sullivan et al.
2001; Salat et al. 2005). Interestingly a reduction in WM integrity with advanced age is found primarily in the more anterior brain regions (Pfefferbaum et al. 2005; Sullivan and
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Pfefferbaum 2006; O’Sullivan et al. 2001). Pfefferbaum et al. (2005) found reduced WM integrity in the anterior parts in healthy older individuals compared with young adults. Salat et al. (2005) examined these prefrontal changes more closely and found them to be regionally selective. Reductions were found in deep frontal and medial orbitofrontal WM but not in inferior frontal WM in older adults relative to young adults. Moreover Andrew-Hanna et al
(2007) found a direct relationship between the reduction in functional correlation between the anterior and posterior parts of the DMN and reduced fractional anisotropy measured by diffusion tensor imaging (DTI). This leads to the suggestion that both the decline in DMN function as the reduced integrity of white matter are parts of the same process that characterise changes in the brain found in normal ageing. It would be interesting to further investigate this association for causal links. Does decreased white matter integrity lead to functional differences in DMN or the other way around?
We have seen that a correlation exists between the decline in DMN functioning with old age and certain areas of cognitive functioning. What about the functions that are directly related to the DMN? Is there any relationship between the compromised cognitive functions with old age and the functions related to the DMN? Research that has been done about the effect of age on mind wandering suggests an inverse relationship between age and mind wandering
(Giambra 1989; Giambra 1993). Thus in accordance with the found decreased function of the
DMN, a decrease in mind wandering is found with ageing which is one of the functions associated with the DMN. In addition, an association between executive functioning, one of the cognitive functions found to decline with age, and mind wandering has been suggested
(Teasdale et al. 1995). A different idea comes from Grady et al (2006) who propose that the differences in deactivation in old age reflect the reduced ability to ignore distracting or irrelevant internal or external information. This is supported by the found correlation between age and declined cognitive control. Cognitive control is used to divert attention away from task irrelevant default mode processing. These theories seem incompatible at first because one
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would expect more mind wandering when one is less able to divert attention towards the task.
However this does not have to be the case, it could be that both the frequency of task unrelated thoughts (mind wandering) and the flexibility to switch attention between task and these unrelated thoughts decreases. Buckner and Vincent (2007) suggest that the decline in
DMN function in old age does represent a more intrinsic fundamental property of the brains functional organisation. This function would mainly serve to stabilize brain ensembles, consolidate the past and prepare for the future. Which of these theories explain the function linked to the decline in DMN observed in older age the best remains uncertain. Nevertheless they give some indication about the possible function of the DMN in general.
Default Mode Network and dementia: Alzheimer’s Disease
Alzheimer Disease (AD) is the most common form of dementia (Brookmeyer et al. 2007). It is a progressive neurodegenerative disease which leads to a decline in cognitive functioning, especially memory impairments. In the brain of patients so-called “senile plagues”
(accumulation of amyloid in gray matter) and “neurofibrillary tangles” (accumulation of protein in neuron) are found which are thought to be the residue of a process that begins with toxic forms of amyloid ß protein (A ß) and ends with cell death and synaptic dysfunction
(Walsh & Selkoe 2004). A syndrome that is thought to be a prodromal fase of AD is called
Mild Cognitive Impairment (MCI). Patients with MCI show a faster cognitive decline than would be expected on the basis of age but it does not interfere significantly with their daily functioning. It is thought that half of the patients with MCI will progress into AD within five years. With regard to the research in AD, differences found in the MCI group are often used to make prediction about changes before the actual onset of AD (Gautier et al. 2006).
Several findings in the research concerning Alzheimer have led to a link with possible default network deficits. First, patterns of reduced metabolism seen in AD compared to healthy controls show striking overlap with DMN regions. Specifically the posterior cingulate shows very consistent reduced activity in AD (Alexander et al. 2002;Matsuda 2001; Small et al
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2000). Furthermore the medial temporal lobe (MTL) is one of the first regions targeted by AD pathology (Matsuda 2001). The hippocampus, a structure in the MTL, shows activation patterns correlating with the activation of the DMN in healthy subjects (Greicius et al 2004;
Sorg et al 2007). Greicius et al. (2004) pointed out that the found coactivity between the
DMN and the hippocampus support the idea that the DMN is involved in episodic memory processing. These findings show a compelling link between AD and the DMN and therefore have been the foundation of most research in this area.
To examine functional disruptions in the DMN both functional connectivity and task induced deactivations are used. Several studies found a decreased functional connectivity of the DMN in AD compared to healthy controls (Wang et al 2006; Wang et al 2007; Greicius et al. 2004).
Studies that looked at the functional connectivity between hippocampus (HC) and other brain regions found a decreased connectivity between the hippocampus and areas of the DMN in
AD (Wang et al. 2006; Allen et al. 2007; Greicius et al. 2004). Furthermore a decreased positive correlation between prefrontal and parietal areas of the DMN has been found (Wang et al. 2007). In addition they showed decreased anticorrelations between the DMN and task positive network in AD compared with controls (Wang et al. 2007). This may be associated with the impairments in attention seen in AD patients. The anticorrelations between task negative and task positive networks are thought to be involved in attention (Fox et al 2005;
Fransson 2005). A study with MCI subjects (Sorg et al. 2007) show an absent functional connectivity between HC and left PCC suggesting that functional connectivity deficits are present in the early stages even before the development of AD. Also differences in deactivation patterns are found between AD subjects and healthy controls. Several studies find less deactivation in anterior and posterior DMN brain regions in AD (Lustig et al 2003;
Rombouts et al. 2005). Again, individuals with high risk of developing AD show similar deficits in task related deactivation patterns as found in AD but to a lesser extent (Persson et al. 2008; Rombouts et al. 2005).
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More recently an imaging method has developed that shows amyloid deposition. Results
(Klunk et al. 2004) show that maps of A ß plagues in the early stages of Alzheimer have a remarkable overlap with the DMN regions. This finding taken together with the previous discussed results have let to the concept of “metabolism hypothesis” as put forward by
Buckner et al (2005). This hypothesis states that metabolism patterns during the lifespan might contribute to the amyloid disposition in AD. Differences in default activity and associated metabolic patterns might be partly responsible for the variation in AD risk. They also underline the relation between the MTL and DMN.
We have seen that there is a strong link between AD and functional abnormalities in the
DMN. Both functional connectivity and task induced deactivations show abnormal pattern in patients. These patterns are also found in MCI patients although to a lesser extend. The brain
. regions most affected in early stages of AD (by metabolism changes and amyloid disposition) show remarkable overlap with the DMN brain regions. This gives further strength to the idea of an association between disruptions in the DMN and Alzheimer’s disease. In addition, this area of research suggests strong links between the hippocampus and the DMN which would support the idea that the function of the DMN is related to memory processing.
Recent neuroimaging studies have shown a network in the brain that is activated during rest: the DMN. This network develops and changes with age. The reviewed findings show the course of the development from infancy to old age. Additionally, Autism Spectrum Disorders
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and Alzheimer’s disease are discussed. All these findings together may provide us with more insight about the DMN.
Results show that there is not yet a DMN present in infancy. However Fransson et al. (2007) argued that one of the resting state networks they found which included the bilateral parietal cortex and the precuneus might be the DMN in a very early stage of development. When children grow older, a DMN develops. At the age of 7-9 the DMN can be found. Like the
DMN in adults this network shows deactivations during tasks and functional connectivity between the different regions of the DMN. However the functional connectivity within the network is not as high as it is in adults. Furthermore there is more connectivity between the
DMN and regions of other (sensory processing) networks in children. When children grow to adulthood the DMN gets more dissociated from other networks and gains a higher functional connectivity within the network. In old age we see a decrease of functional connectivity and task related deactivations within the DMN. Especially the anterior part of the DMN and the functional correlation between the anterior part and the posterior part is affected.
These findings are supported by the results from other areas of research. In children we have seen that the development of the cingulum corresponds with the development of the DMN.
The cingulum is thought to play an import role in the functioning of the DMN. In ageing individuals we have seen that white matter integrity decreases are mostly found in the anterior part of the brain which is in accordance with the DMN findings. Furthermore the functions associated with the DMN such as ‘self referential mental activity’, ‘mind wandering’ and
‘envisioning the future’ seem to develop from infancy to old age in a parallel manner as the
DMN. These findings give the concept of the DMN as it exist today more legitimacy.
One of the reasons to examine and review the findings about the development of the DMN was to provide us with more information about the possible functions of the network. The
DMN gets activated when one is at rest and left to himself. The functions linked to this brain system suggest that it is devoted to internal modes of cognition. This poses further challenges
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to the field of cognitive neuroscience which traditionally has been more concerned with the processing of sensory input. We already discussed the methods used to examine the function of the DMN. Buckner et al. 2008 proposed two different opposing theories: The ‘internal mentation hypothesis’ that incapsules most of the introspective functions and the ‘monitoring external environment theory’ that argues the function of the DMN is to enhance watchfulness towards the external environment. The internal mentation theory suggests the function of the
DMN is to make imaginative constructions out of hypothetical events or scenarios. These scenarios can be about the past or future and about themselves or others. This is a function absent in infancy and develops through childhood. Furthermore in both ASD and Alzheimer’s disease, where DMN functioning is defected, these functions are clearly impaired. More specifically in ASD there is a correlation between higher social impairment scores and less task related deactivation in the medial prefrontal cortex. We can speculate that particularly improvement of the functions as proposed by the internal mentation theory will correlate with a higher social impairment score. In contrast, the monitoring external environment theory does not seem to have such an apparent connection with the development of the DMN. It is questionable if this function is strongly impaired in both ASD and Alzheimer’s disease.
Moreover we would expect this function to be partly present in infancy, whereas the DMN does not seem to be. In sum the reviewed findings seem to support the internal mentation theory over the monitoring external environment theory.
Not all scientists in the field of neuro-imaging have been convinced of the value of the concept of the DMN. Recently Morcom & Fletcher (2007) have criticised several assumptions that form the foundation of the research done about the DMN. Even though a detailed description of this controversy goes beyond the scope of this manuscript, it seems important to discuss the main arguments on both sides. In their article, Morcom and Flether question the utility of the concept of a default mode for understanding brain function. They do not argue with the finding of increased activity in specific parts of the brain during rest compared to
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task condition. However they strongly doubt that this ‘intrinsic activity’ has any special significance. They see no reason to believe that in terms of processing, ‘rest’ has any particular qualities. To understand cognitive processing underlying observed physiological changes we need to use constrained task, they argue, in which specific experimental manipulations can be made. Therefore the studies done with resting state are not constructive in drawing conclusions about cognition. In a reaction, Buckner and Vincent (2007) state that the exclusive use of active tasks is too limited to study all functional processes. They discuss the restriction of the assumption that only task evoked behaviour can be examined for its underlying cognitive processing. Furthermore they point out the robustness of the finding of default network patterns, the importance it can have for understanding mental disorders and the potential resting state research has for finding functional homologies between humans, apes and monkeys. However both sides do agree on the issue that the resting sate should not be exclusively used as baseline condition in neuro-imaging studies.
Whether or not one agrees with the criticisms directed at the DMN area, it does show the the difficulty of this type of research. How can we draw conclusions about the cognitive processes in the brain during a state of ‘rest’? And how valuable and validated is it to define a baseline of brain activity? To answer these questions further research is needed. To define which functional processes underlie the default state of the brain has appeared very complex.
There are no simple correlations to be found between stimuli and neurofunctional differences as there are in task induced activations. Therefore it is my opinion that the only way to investigate these processes is to adopt a holistic approach. This means an approach in which different methods are used to examine the DMN and its functioning. It is important to make comparisons between different lines of methodology, between functions of the brain areas involved in the DMN and between differences in DMN functioning in varied groups of subjects. In this review we have looked at the development of the DMN from infancy to old age. Yet there are more ways of examining differences in DMN functioning between people.
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For example, the more we learn about the differences in the DMN functioning between individuals and about DMN functioning in mental illnesses the closer we will come to an integrative perspective that can provide more insight in the DMN. Moreover, I would like to emphasize the importance of research in other fields that link to the DMN such as research concerned with white matter integrity. In addition a further understanding of the underlying physiological processes in imaging research will be crucial for the exploration of the DMN. It will be a challenge to get a better understanding of the DMN but the large amount of research that has been published recently gives confidence that it is an attainable goal.
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