Cognitive reserve and network efficiency in Alzheimer’s disease.
Name: Iris Corbeek
Email: [email protected]
Student number: 3681157
Supervisor(s)
Name: J. Verwer, Msc & prof. dr. G.J. Biessels
Contact details:[email protected]
Name: prof. dr. J.L. Kenemans
1
Abstract
Background: Cognitive reserve (CR) refers to the ability to compensate for a certain amount of
brain damage and sustain cognitive functions. The extent to which Alzheimer’s disease (AD) leads
to cognitive decline is believed to be related to the concept of CR. It was expected that an
efficiently organized network structure is part of the neural basis of reserve, altering the effects of
AD pathology on cognition. The purpose of this study was to explore this hypothesis by
investigating the relationship between CR, network connectivity, cognitive functioning, and brain
atrophy.
Methods: Fifty patients, with either an AD or Mild Cognitive Impairment (MCI) diagnosis, were
included in this study. Memory performance, information processing speed, and executive
functioning were assessed by standardized neuropsychological testing. CR was operationalized as
the combination of education level and occupational attainment. Brain volumes were assessed
and processed diffusion tensor imaging (DTI) data was obtained and used to investigate properties
of the structural network that represent network efficiency. Correlation analyses were performed,
while controlling for the effects of age.
Results: The results showed higher levels of CR were significantly correlated to longer path length
(rs=.155, p=.007) and low smallworldness (rs=-.135, p=.02). Also, a relationship between total brain
atrophy and clustering coefficient (rs=-.188, p=.044) and path length (rs =-.201, p=.001) was
revealed. Furthermore, a significant correlation between CR and memory performance was found
(rs=.359, p=.013): memory performance was better in patients with higher CR. Network efficiency
was not directly related to cognitive performance.
Conclusion: Results suggest that network efficiency is more deteriorated in high CR individuals
compared to low CR patients, when clinical presentation of AD emerges. Despite the worse
network structure, memory performance is still better in patients with higher CR. This might either
be due to higher premorbid memory functioning or it could mean memory performance declines
at a slower rate as the result of higher CR. Furthermore, network organization was expected to be
a neural component of CR leading to better cognitive performance. This might be the case in
healthy adults. However, in AD and MCI patients this relationship is greatly affected by the degree
of brain damage, as significant correlations were revealed between network efficiency and brain
atrophy.
Keywords: Alzheimer’s disease, Cognitive reserve, Brain reserve, network connectivity, network
efficiency
2
Alzheimer disease (AD) is a neurodegenerative disease characterized by progressive
cognitive impairment, decreased daily life functioning, neuropsychiatric symptoms, and physical
symptoms. The key pathology is generally understood to be the accumulation of both amyloid β
(Aβ) peptide and tau (p-tau) protein resulting in amyloid plaques and neurofibrillary tangles. This
eventually leads to generalized brain atrophy (Reitz, Brayne, & Mayeux, 2011). The extent to which
AD leads to cognitive decline is believed to be related to the concept of reserve. The theory of
reserve originates from the observation that some individuals adapt better to certain disruptions
that are caused by brain pathology than others, i.e. some individuals show less cognitive decline
than others when degree of brain damage is equal (Stern, 2002). In 2005 it was estimated that
24.3 million people worldwide suffer from dementia, and the number of people affected will
double every 20 years to 81.1 million by 2040 (Ferri et al., 2005). The expected rise in AD patients
makes cognitive reserve an increasingly important topic for research, since certain lifestyle factors
related to this concept might provide a platform for early prevention. However, the neural
mechanisms underlying reserve remain to be entirely understood.
The first theory of reserve is the passive model of Satz (1993). This theory states there are
individual differences in brain reserve capacity (BRC), which are structural brain differences such
as brain volume or number of synapses. Once BRC is depleted past a critical threshold, clinical
symptoms and deficits emerge. A second and more recent theory of reserve is that of cognitive
reserve (CR). This is an active theory of reserve and refers to variability in brain networks at the
level of synaptic organization, or in relative utilization of specific brain regions. Individuals with
high CR have better ability to compensate for brain damage and process tasks in a more efficient
manner through better utilization of brain networks (Stern, 2002).
Although these two theories of reserve are often presented as separate models, they
show more overlap than previously thought (Bartes-Faz et al., 2009; Mortimer, Snowdon, &
Markesbery, 2003). This possibly makes a strict division between these models unnecessary.
Somewhat in line with this idea, Steffener & Stern (2012) proposed two neural mechanisms
underlying cognitive reserve: neural reserve and neural compensation. The concept of neural
reserve revolves around the idea that network capacity varies among individuals. Neural
compensation refers to the use of alternative brain networks in order to compensate for brain
pathology, which disrupts the brain networks normally used (Stern, 2002; Steffener & Stern, 2012).
However, since no exact neural substrate for CR has been found yet, research often uses
intellectual and lifestyle factors such as educational level, occupational attainment, cognitive
leisure activity and premorbid intelligence as proxies for CR (Harrison et al., 2015; Lojo-Seoane,
Facal, Guàrdia-Olmos, & Juncos-Rabadán, 2014). These factors have been found to relate to
cognitive performance. Additionally, the meta-analysis of Opdebeeck, Martyr, and Clare (2015)
show that a combination of measures of CR is more strongly correlated to cognitive performance
3
than certain measures alone.
Recently, studies have built on the idea that connectivity of brain networks is an important
aspect of CR and has an impact on cognitive performance in AD patients. The efficiency of networks
can be reflected by certain network measures, such as the clustering coefficient and characteristic
shortest path length. The clustering coefficient refers to the degree of clustering between
neighboring brain regions (Reid & Evans, 2013), while characteristic shortest path length reflect
the integration of networks (Rubinov & Sporns, 2010). Small-worldness (Watts & Strogatz, 1998)
is a measure that reflects overall network efficiency.
Yoo et al. (2015) studied these network properties in relation to CR proxies in both AD
patients and healthy controls. It was suggested that education and social leisure activities may help
to develop these alternative network connections and facilitate the use of functional
compensation when brain pathology emerges. In addition, higher network efficiency has been
linked to intelligence in healthy young individuals (Van den Heuvel, Stam, Kahn, & Hulshoff Pol,
2009) and to lesser decline of network efficiency in healthy elders (Fisher, Wolf, Scheurich, &
Fellgiebel, 2014), suggesting intelligence might be associated with the ability to compensate for
brain network degeneration to some extent.
Network connectivity has also been studied in relation to AD and Mild Cognitive
Impairment (MCI). In patients it was demonstrated that AD is associated with severely reduced
structural connectivity (Daianu et al., 2015; Sanz-Arigita et al., 2010), and a decreased clustering
coefficient and a shorter path length (De Haan et al., 2009). Decreased smallworld organization
was reported within the AD population as well, reflecting less efficient information processing (De
Haan et al., 2009; Stam, Jones, Nolte, Breakspear, & Scheltens, 2007). The meta-analysis of Dai &
He (2014) reported similar findings for both AD and MCI patients in the majority of studies.
Furthermore, network connectivity was found to be related to cognitive performance. Reijmer et
al. (2013) showed that less efficient network organization in AD patients is related to worse
memory and executive functioning. Likewise, the study of Shu et al. (2012) found that executive
functioning and processing speed correlated to path length, global and local clustering. In addition,
the study of Lo et al. (2010) indicated that longer path length and a decrease in global efficiency
were associated with lower verbal memory performance.
As Steffener and Stern (2012) stated, the ability to make efficient use of networks is
related to better ability to compensate for brain damage, maintaining current cognitive function.
Accordingly, it is possible that an efficiently organized network structure is a major part of the
neural basis of reserve, which alters the effects of AD pathology on cognition. The existing research
on this topic suggests that this might indeed be the case. Therefore, the purpose of the present
study is to explore this hypothesis by investigating the relationship between CR proxies, network
connectivity measures, cognitive functioning and brain atrophy. Since the relationship between
4
these variables is assumed to be complex, this study is of exploratory nature. Several hypotheses
were investigated:
1)
Since earlier research showed no direct relationship between brain pathology and
degree of cognitive decline, it was expected that no direct relationship exists between
total brain atrophy – as a proxy for total burden of pathology – and cognitive
performance.
2)
CR was hypothesized to be related to better network efficiency measures.
3)
It was expected that network efficiency is not solely influenced by CR proxies but also by
the extent of brain damage. Also, it was assumed that the rate of decline in in AD
pathology is independent of reserve. Therefore, a relation between total brain atrophy
and network efficiency was expected.
4)
CR was hypothesized to be related to better cognitive performance.
5)
Network
efficiency
measures
were
expected
to
be
related
to
cognitive
performance; higher network efficiency is likely related to better cognitive functioning.
Methods
Participants
Fifty patients were recruited via the memory clinic at the University Medical Center
Utrecht and at the Diakonessen Hospital Zeist. Patients were included if diagnosed with possible
or probable AD according to the National Institute of Neurological and Communicative Disorders
and Stroke and the Alzheimer Disease and Related Disorders Association (NINCDS-ADRDA) criteria
(McKahn et al., 1984). Patients with mild cognitive impairment (MCI) were included as well. MCI
refers to a stage where memory impairment can be objectified, but the patients’ daily life function
is still intact (American Psychiatric Association, 2013). In a substantial proportion of cases it can be
considered a pre-stage of dementia; patients with MCI have a mean annual conversion rate of 10%
to dementia (Bruscoli & Lovestone, 2004). Also, patients had to have a Clinical Dementia Rating
(CDR) 0, 0.5 or 1, and a Mini Mental State Examination (MMSE) of 20 or higher. Exclusion criteria
were: 1) absence of Diffusion Tensor Imaging (DTI) data and data on network properties described
by graph theory analysis, 2) absence of data of gray and white matter volume, 3) absence of
information on education level or occupational attainment. The study was approved by the
medical ethics committee of the University Medical Center Utrecht, and was conducted according
to the principles expressed in the Declaration of Helsinki. Written informed consent was obtained
from all participants.
5
Procedure
All data was gathered from the Pearl Neurodegenerative Diseases, one of the cohorts of
the Parelsnoer Institute. The Parelsnoer Institute is a collaboration of eight Dutch University
Medical Centers (UMCs) designed to create a shared database with clinical data and biomaterial
from patients with chronic diseases. The key focus of the Pearl Neurodegenerative Diseases is
research on etiology, progress and biomarkers of early dementia, and in particular Alzheimer’s
disease (Aalten et al., 2014). It is a longitudinal three-year follow-up study of patients referred to
a memory clinic with cognitive complaints. All patients underwent a standardized examination.
During the baseline visit, a clinical interview was conducted to provide information on
cognitive complaints, as reported by the patient and as perceived by the informant (usually the
partner or a relative). Information of this semi-structured interview was used to obtain a Clinical
Dementia Rating (CDR) score (Morris, 1993). Furthermore, MRI and DTI data, neuropsychological
test scores, and demographic information on gender, age, educational level and occupation was
gathered for the purpose of this study.
MRI data acquisition
All participants underwent a standardized MRI protocol using MR systems operating at
3.0 Tesla. For each patient, a T1-weighted gradient-echo sequence and a Diffusion weighted
imaging scan were obtained during the same scanning session. The following parameters were
used for the 3D T1: Slab thickness 180 mm, 180 partitions, 1.0 mm effective slice thickness, inplane resolution 1.0 mm. For diffusion weighted imaging the following parameters were used: slice
thickness < 5 mm, gap 10-30%, in plane resolution 2 mm b value (0/500/1000) or b value (0/900)
or b value (0/1000) s/mm2).
DTI processing
For the purpose of this study, processed DTI scans and network measures of the study of
Reijmer et al. (2013) were used. The processing of DTI data involved several steps. First, Reimer et
al. (2013) analyzed and processed the DTI scans in ExploreDTI (www.exploredti.com). The method
of preprocessing of DTI data is thoroughly described in the study of Vos, Jones, Viergever, and
Leemans (2011) and Vos, Jones, Jeurissen, Viergever, and Leemans (2012). Then, white matter
tracts were reconstructed using a deterministic streamline approach (Basser, Pajevic, Pierpaoli,
Duda, & Aldroubi, 2000). Fibers were reconstructed by starting seed samples uniformly throughout
the data at 2 mm isotropic resolution and by following the main diffusion direction (as defined by
the principle eigenvector). A fiber was considered to have reached an endpoint when it entered a
voxel with a fractional anisotropy (FA) of <0.2 or when it made a high angular turn considered to
be not anatomically plausible (angle <30). The step size was set at 1 mm. These whole-brain fiber
6
tracts were parcellated by the use of the automated anatomical labeling atlas (AAL) (TzourioMazoyer et al., 2002). Using this procedure, 90 cortical and subcortical regions were obtained (45
for each hemisphere with the cerebellum excluded). For each patient a binary 90 x 90 connectivity
matrix was obtained using this procedure, in which each of the regions of interest of the AAL
template represents a node of the network. Two nodes were considered to be connected when a
fiber bundle with two endpoints was located in these regions. Edges were determined by the total
number of fiber bundles between two nodes, resulting in a weighted connectivity matrix for each
individual.
Network measures
Properties of the structural network were investigated by the use of the Brain Connectivity
Toolbox. The clustering coefficient quantifies the degree of segregation within a network,
reflecting short-range connectivity in a network. First, the number of connections between
neighbors of a node as a proportion of the maximum number of possible connections between
these neighbors, is given. The clustering coefficient is then calculated by averaging the clustering
coefficients over all nodes in the network. Integration of brain networks reflects the long-range
connectivity of a networks, and is represented by the characteristic shortest path length. This
measure calculates the average minimum number of connections that link all pairs of nodes in the
network. Smallworld networks reflect an optimal balance between functional segregation and
integration of networks (Rubinov & Sporns, 2010). Meaning that these networks are significantly
more clustered than random networks (i.e. form clusters of brain regions connecting to one
another), yet have a small path length (i.e. most brain regions can be reached through a small
number of steps) (Watts & Strogatz, 1998). Higher Smallworldness indicated a more efficient
network. Small-world properties were calculated by: 1) dividing the clustering coefficient by the
mean value obtained from 100 matched random networks, 2) dividing the characteristic shortest
path length by the mean value obtained from 100 matched random networks, 3) dividing the
smallworld properties of the characteristic shortest path length by the smallworld properties of
the clustering coefficient, resulting in a single smallworld value.
Cognitive assessment
Cognitive performance was assessed by a standardized neuropsychological test battery.
Reliability and validity of the tests used in this study are reported in Appendix 1. The results of the
neuropsychological tests were then used to represent several cognitive domains. Memory function
was calculated by a compound score of the immediate and delayed recall of the Rey Auditory
Verbal Learning test (Brand & Jolles, 1985) and the Visual Association Test (VAT, short version)
(Lindeboom, Schmand, Tulner, Walstra, & Jonker, 2002). Speed of information processing was
7
calculated by a compound score of the Letter Digit Substitution Test (LDST, 60 seconds) (Van der
Elst, Van Boxtel, Van Breukelen, & Jolles, 2006), Trail Making Test part A (TMT-A) (Reitan, 1958)
and the Stroop Color Word Test I and II (Stroop, 1935; Van der Elst, Van Boxtel, Van Breukelen, &
Jolles, 2008). Finally, executive functioning was assessed by the Trail Making Test part B (TMT-B),
the Fluency test (animals, 60 seconds) (Lezak, 1995), and the Stroop Color Word Test III. These
compound score were calculated by averaging the z-scores of the neuropsychological tests that
formed a cognitive domain. Positive scores indicate better performance in each of the three
domains.
Other measurements
CR was operationalized as the combination of educational and occupational attainment.
Education level was determined using a 7-point likert-scale (Verhage, 1964). Occupational
attainment was categorized in 1 of 10 subscales, according to the International Standard
Classification of Occupations 2008 (ISCO-08) (International Labor Organization, 2008). ISCO scores
can originally be coded with a 4 digit number: the first digit represents the major group of an
occupation based on skill level, and each additional digit specifies the exact occupation. A metaanalysis on an earlier version of the ISCO (ISCO-88) reported that agreement rates are highest at 1
digit ratings. The exact rates vary from 75 to 87% in various studies (Elias, 1997). The information
of occupation and education was then summarized into a factor score of CR for each participant
using principle component analysis. This was done following the method of Stern et al. (2005). The
extracted CR composite score, accounted for 81.4% of the common variance of these measures.
Brain Parenchymal Fraction (BPF) was used as an estimate of total brain atrophy. This measure can
be defined as the ratio of total brain volume to the total intracranial volume (Rudick, Fisher, Lee,
Duda, & Simon, 2000). Total brain volume was calculated as the sum of gray matter and white
matter volumes, which were obtained from 3D T1-weighted images. Higher BPF scores indicate
more intact brain volumes and thus less atrophy.
Statistical analysis
CR is a continuous variable and represented by a single value. Cognitive performance was
divided into cognitive domain scores of memory performance, information processing speed, and
executive functioning. The characteristic shortest path length, the clustering coefficient and
smallworldness, represent network efficiency. Correlation analyses were performed for the
following variables: 1) BPF and cognitive performance, 2) CR and network efficiency, 3) BPF and
network efficiency, 4) CR and cognitive performance and, 5) network efficiency and cognitive
performance. Normally distributed variables were analyzed by a Pearson correlation, while
variables that were not normally distributed were analyzed by the non-parametric Spearman
8
correlation. There was controlled for the effects of age in all correlation analyses. All analyses were
performed with SPSS version 22.
Results
Subject characteristics
Demographics and clinical features of the patients are summarized in Table 1. Of this
sample, 18 patients received a MCI diagnoses, 23 patients were diagnosed with probable or
possible AD, and 9 patients had a mixed diagnosis of AD and Vascular dementia (VaD). The clinical
severity of this group ranges from a CDR score of 0 to 1. Also, disability in daily life (DAD) and
neuropsychiatric symptoms (NPI) were seen, such as depression, anxiety, sleep problems, eating
disorders, or apathy.
Table 1
Demographics and clinical features of the patients (N=50)
Median
IQR
Min.
Max.
79.3
9.2
61.5
91.4
5
1
2
7
7
2
1
9
DAD
88.6
25.2
42.9
100
CDR
0.5
0.5
0
1
Age
Education
1
Occupation2
NPI
14
15
0
60
Note. Median and interquartile range (IQR) of age, education and occupational level, Disability
Assessment of Dementia (DAD), Clinical Dementia Rating (CDR), Neuropsychiatric Inventory (NPI), 1.
Verhage scale (1964), 2. ISCO-09 (Elias, 1997).
Figure 1 represents a hypothesized model for the influence of cognitive reserve on the relationship
between brain atrophy and cognitive performance. The letters a to e each represent a correlation
analysis that was performed.
9
Figure 1. Hypothesized model for the influence of cognitive reserve on the relationship between brain
atrophy and cognitive performance.
Relationship between BPF and cognitive performance
Table 2 summarizes the cognitive performance of the group. In Table 3, mean brain
volumes and BPF scores are presented. The BPF was used as a measure of total brain atrophy, with
higher scores indicating less atrophy.
Table 2
Median, IQR, and minimum and maximum scores of the neuropsychological tests used in this study
Cognitive domain
Neuropsychological test
Median
IQR
Min.
Max.
Memory
RAVLT (immediate recall)
20.9*
7.4*
0
40
RAVLT (delayed recall)
1
3
0
5
RAVLT (recognition)
23
6
11
29
VAT
8
5.5
0
12
TMT-A
70
35.5
34
151
Stroop I
61
20
40
113
Stroop II
83,5
26
39
176
LDST
20.2*
6.2*
5
32
TMT-B
239
130
86
459
Stroop III
170
76
101
505
Fluency
13
7
3
28
Processing speed
Executive functioning
* Variables that were normally distributed, therefore the mean and standard deviation were given
instead of the median and IQR.
10
Table 3
Mean, standard deviation, minimal and maximal scores of atrophy and brain volumes
Mean
Stdev.
Min.
Max.
BPF
0.65*
0.11*
0.51
0.92
WM volume (ml3)
411.8
58.2
310
533
GM volume (ml3)
531.1
51.7
436
664
Total volume (ml3)
942.9
103.9
779
1197
ICV (ml3)
1455.8
212.4
970
1916
Note. BPF is the ratio of total brain volume divided by the intracranial volume.
* Median and IQR are reported instead of mean and standard deviation since these variables were not
normally distributed.
The relationship between BPF and cognitive performance was analyzed using a correlation analysis
(Line a. in Figure 1). The results are presented in table 5 and showed no significant correlations
between BPF and; memory performance, executive functioning or processing speed.
Table 4
Correlation coefficients and significance for the relationship between BPF and cognitive performance
BPF
rs
p-value
Memory
.187
.23
Executive functioning
.029
.867
Processing speed
.017
.919
Relationship between CR and network measures
The frequency distribution of education and occupation level can be seen in Figure 2 and 3. Since
network organization is expected to be closely related to CR, this relationship was investigated
(line b in Figure 1). Nonparametric spearman correlation analyses were used. There was no
significant relation between CR and the clustering coefficient. A positive relation was found
between CR and path length, i.e. higher CR is associated with longer path lengths. A negative
correlation was found between CR and smallworldness.
11
Frequency
25
20
15
10
5
0
1
2
3
4
5
6
7
Education level
Figure 2. The frequency distribution of education level, based on the 7-point likert-scale of Verhage
(1964).
Figure 3. The frequency distribution of occupation level, based on the 10 point scale of the ISCO-09
(Elias, 1997).
Table 5
Correlation coefficients and significance for the relationship between CR and network measures
CR
rs
p-value
Clustering coefficient
-.04
.493
Path length
.155
.007
Smallworldness
-.135
.02
12
Relationship between BPF and network measures
Nonparametric Spearman correlation analyses between BPF and network measures showed a
significant negative correlation between BPF and clustering coefficient (line c. in Figure 1). Also, a
significant negative correlation was shown between BPF and path length. No significant
correlations were found between BPF and smallworldness.
Table 6
Correlation coefficients and significance for the relationship between BPF and network measures
BPF
rs
p-value
Clustering coefficient
-.201
.001
Path length
-.118
.044
Smallworldness
.099
.092
Relationship between CR and cognitive performance
A Spearman correlation analysis was performed between CR and each of the three cognitive
domains (line d. in Figure 1). Results showed a significant positive relation between CR and
memory performance. No correlations were found between CR and executive functioning, or
between CR and processing speed.
Table 7
Correlation coefficients and significance for the relationship between CR and cognitive performance
CR
rs
p-value
Memory
.359
.013
Executive functioning
-.25
.119
Processing speed
.063
.694
Relationship between network measures and cognitive performance
Next, the relationship between cognitive performance and network measures (line e. in Figure 1)
was analyzed using nonparametric spearman correlation (unless indicated otherwise). Analysis did
not reveal a significant relationship between memory or any of the three measures of network
efficiency. Also, no significant correlations were shown between executive functioning and
clustering coefficient, between executive functioning and path length, or between executive
13
functioning and smallworldness. Finally, no significant correlations between processing speed and
clustering coefficient, processing speed and path length, or processing speed and smallworldness
were shown.
Table 8
Correlation coefficients and significance for the relationship between network measures and cognitive performance
Clustering coefficient
Path length
Smallworldness
rs
p-value
rs
p-value
rs
p-value
Memory
-.227
.12
-.213*
.156*
.264*
.076*
Executive functioning
-.006
.972
.006
.972
.14
.932
Processing speed
-.11
.489
.024*
.885*
.202*
.21*
* Pearson correlation analysis was performed.
Discussion
The goal of this study was to explore the relationship between well-established proxies of
CR, network efficiency and cognitive performance in AD and MCI patients. Several correlation
analyses were performed to investigate the extent to which these variables were related to one
another (see figure 1. for a visual representation of the hypothesized model). The hypothesis that
efficient network organization is an important biological component contributing to reserve, was
not confirmed in the current study.
Total brain atrophy, used as a proxy measure of AD pathology, revealed no correlations
with memory performance, executive functioning, or processing speed. These results are in line
with previous observations that the amount of brain damage or pathology, does not lead to the
same amount of cognitive decline in all individuals equally (Katzman et al., 1989; Ince, 2001).
Cognitive reserve is likely one of the factors that contribute to this effect.
The following correlation analysis revealed that high CR is associated with longer path
length and low smallworldness, indicating that high CR is associated with more randomness in
networks of AD and MCI patients. This finding might seem counterintuitive at first, since network
organization is found to be more efficient in healthy elderly with higher CR and intelligence (Fischer
et al., 2014; Kim, Chey, Kim, & Kim, 2015; Van den Heuvel et al., 2009). In patients however,
network organization is also affected by AD and MCI related pathology (Sanz-Arigata et al., 2010;
Daianu et al., 2015). This suggests that patients with higher CR were able to cope with more
network deterioration before symptoms of AD became apparent. This result corresponds with the
findings of Yoo et al. (2014) who measured the number of alternative paths between two brain
14
regions reflecting the robustness of their networks. This study showed that patients with higher
education levels had more deterioration in structural brain connectivity, while in healthy controls
education was associated to more robust networks. This implies that CR arises from the ability to
reorganize networks to secure information flow and has a protective factor for the onset of AD.
Since it was expected that AD related brain atrophy affected network efficiency, the third
analysis tested this relationship. Results showed more brain atrophy is related to longer path
length, suggesting that a decrease in network integration is related to – and probably affected by
– brain atrophy. Analysis also revealed that more atrophy is related to a higher clustering
coefficient. Taken together, it seems that brain atrophy alters the network towards a more regular
network. Regular networks are characterized by decreased integration and increased network
segregation (Bullmore & Sporns, 2009). Although it seems that AD is mostly associated with more
randomness in network organization (characterized by decreased segregation and decreased
integration), other studies have found a more regular organization suggesting that this might lead
to a decrease in network efficiency as well (Tijms et al., 2013).
Next, the relationship between CR and cognitive performance was explored; high CR
scores were found to correlate with better memory performance. Literature on this topic is
inconsistent. Some studies suggest that once pathology clinically manifests, cognition declines at
a faster rate in patients with higher CR as the result of more severe pathology (Wilson et al., 2004;
Helzner, Scarmeas, Cosentino, Portet, & Stern, 2007). Other studies suggest that higher CR levels
slow the process of cognitive decline, regardless of the stage of AD (Fritsch, McClendon, Smyth, &
Ogrocki, 2002). Furthermore, AD patients with higher CR scores were found to have better general
cognitive performance, memory performance, attention, visuospatial abilities, executive
functioning and processing speed (Sobral, Pestana, & Paúl, 2015), even when controlling for
disease duration. The present results could be interpreted in multiple ways. First, the positive
correlation between CR and memory performance may indicate that decline in memory occurs at
a slower rate in patients with high CR compared to low CR, at least in early stages of the disease.
Second, high CR patients are likely to have better premorbid memory functioning. This higher
premorbid memory functioning might work as an buffer, explaining why high CR is still related to
better memory performance early in the course of the disease. No relationship has been found
between CR and executive performance, or between CR and processing speed. This suggests CR,
and possible neural systems that are associated with this construct, might moderate memory
function more than others domains.
The final analysis revealed no significant correlations between cognitive functioning and
network measures, in contrast to other studies on this topic (Lo et al., 2010; Reijmer et al., 2013;
Kim et al., 2015). For example, the study of Reijmer et al. (2013) found low local efficiency to be
related to low memory function, while longer path length and low global efficiency are related to
15
worse executive performance. Likewise, the study of Shu et al. (2012) found that executive
functioning and processing speed was correlated to path length, global and local clustering. In
addition, the study of Lo et al. (2010) indicated that decrease in global efficiency and longer path
lengths were associated with lower verbal memory performance. The current study suggests that
network efficiency is not associated to cognitive performance. However, this finding does not
correspond with the results of other studies. No clear explanation can be offered for this
discrepancy, although it is possible that differences in methodology or sample size could have led
to diverging results.
The main question of this study – whether an efficiently organized network structure is a
biological contributor to reserve – proves difficult to answer. No convincing evidence for the
mediating effect of network measures on the relationship between CR and cognitive performance,
was found. However, the proposed neural substrate of CR was also affected by total brain atrophy,
and likely also by other markers of AD pathology. In addition, the networks of patients with high
CR showed worse network efficiency, suggesting their brain networks are more deteriorated than
the network organization of patients with low CR. This means that network measures in AD and
MCI patients reflect network disruptions as a consequence of AD pathology, while the
compensational properties of the networks remain unclear. More research is necessary to gain
insight in the true association between these variables. Another finding of this study indicates that
reserve is one of the factors affecting cognition. First, no direct relationship between atrophy and
cognitive performance was found, showing that factors other than brain atrophy impact cognitive
performance. Second, despite less efficient network organization in patients with high CR
compared to low CR, memory performance was better in patients with high CR. Additionally,
performance in the cognitive domains of executive functioning and processing speed did not
change with CR. This could mean that high CR patients were able to compensate for these network
disruptions to a greater extent than low CR patients, as a result of higher reserve.
A possible explanation for inconsistencies between the current study and other studies
might lie in the different construction of constructs. One important construct in this study is that
of CR, which was reflected by the combined effect of educational and occupational attainment.
However, other studies choose to include other variables – or a combination of variables – such as
IQ, leisure activity, social engagement, or literacy, since these variables have all been linked to
cognitive reserve (Scarmeas & Stern, 2004). It is not entirely clear which variables contribute to
reserve the most, but many variables that are descriptive of life experiences might influence
reserve. In fact, a combination of CR measures was found to correlate more strongly to cognitive
performance than certain measures alone (Opdebeek et al., 2015). The construct of CR in this study
did not include all variables that could influence CR. However, education level and occupational
attainment are variables that are strongly linked to reserve (Garibotto et al., 2008) and show a
16
protective effect for AD and MCI (Antilla et al., 2002; Ngandu et al., 2007; Potter, Helms, Burke,
Steffens, & Plassman, 2007). The different constructions of cognitive domains is another
explanation for inconsistencies between studies. This study included an extensive
neuropsychological assessment, making it possible to compose performance scores of multiple
cognitive domains; memory function, executive function and processing speed. However,
administered neuropsychological test batteries were different across studies. In addition,
neuropsychological tests often test more than one specific cognitive domain. These factors can
lead to a difference in construction of cognitive domain scores. A third explanation could lie in the
differences in the construction of network measures. Studies differed in the use of imaging
modalities (e.g. DTI, sMRI, EEG, MEG, fMRI) and methods of network construction (e.g. the use of
structural or functional brain atlases to define nodes, binary or weighted construction of edges). It
is unclear to what extent imaging modalities and network construction methods account for the
difference in results, since not many studies have directly compared the different methods (He &
Evans, 2010).
There were several limitations in this study. First, the analyses were performed with a
relatively small sample size. This could have led to a lack of power, making it harder to detect an
effect. Secondly, although the cross-sectional nature of this study gave insight into the complex
relationship between CR, cognitive performance, and network efficiency, it does not allow for
conclusions to be made on the directionality of these relationships. Furthermore, it made it
impossible to gain insight into premorbid network structure, as well as change of network
efficiency over time. Thirdly, the complexity of interactions between proxy measures of AD
pathology, CR, and network efficiency, made it hard to draw firm conclusions on the extent to
which network efficiency contributes to reserve. One of the assumptions was that the rate of
accumulation of AD pathology is independent of reserve. However, a control group of healthy
individuals was absent. This made it impossible to separate the influence of AD pathology on
network efficiency, from the premorbid network capacity as a neural contributor to reserve.
For future research, a different study design should be used since the current study design
was not sufficient enough to study the complex interactions between CR, brain pathology and
network efficiency. It would be useful to compare change in cognitive functioning and network
efficiency over three measurement moments. The initial phase would include participants aged
fifty without cognitive complaints, who would undergo an examination and interview. The second
phase (baseline) would then include participants in whom clinical symptoms of dementia have
started to emerge and that received a diagnosis of early AD or amnestic MCI. Also, healthy controls
would be randomly selected from the database, which allowed for the comparison of cognitive
performance, network efficiency and AD pathology between healthy controls and AD and MCI
patients. The third phase would be a follow-up visit of both patients and controls, with the purpose
17
of gaining insight into decline in cognition and network organization, and how this is impacted by
CR. Furthermore, total brain atrophy was now chosen as a proxy of AD pathologic burden, while
other indicators of AD pathology could be used as well. For example, the presence of amyloid in
the aging brain is considered one of the most important biomarkers for AD (Reitz et al., 2011) and
might be a more appropriate measure to represent AD pathology. Finally, a more carefully
constructed instrument to quantify CR – including a comprehensive set of variables such as IQ,
education level, occupational attainment, leisure activity, and social engagement – would give a
more accurate proxy of CR.
18
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24
Appendix 1
Reliability and validity of neuropsychological tests used in this study
Neuropsychological test
Authors (year)
Sample (N)
Reliability
Rey Auditory Verbal Learning Test
RAVLT)
Lemay, Bedard, Rouleau, &
Tremblay (2004)
Healthy participants aged 5280 (N=37)
Test-retest reliability
between r=.72 - .78.
Intraclass coefficient of
r=.74.
Fernaeus, Ӧstberg, Wahlund, &
Hellstrӧm, 2014
Healthy participant and
patients with MCI or dementia
diagnosis (N=183)
Lindeboom et al. (2002)
Healthy elderly, declining
population, AD patients
(N=161)
Test-retest reliability of
r=.72, P<.001
Harrison, Buxton, Husain, & Wise
(2000)
Healthy participants (N=265)
Test-retest reliability of
r=.68.
Bird, Papadopoulou, Riciardelli,
Rossor, & Cipolotti (2004)
Healthy participants (N=188)
Test-retest reliability of r=.56
(p<.001) for the main group.
For the low-IQ subgroup a
test-retest reliability of r=.36
(not significant) was found.
Visual Association Test (VAT)
Fluency (animals)
Validity
The diagnositic valididty is studied using
a factor analysis. Three main memory
factors could be extracted from the
RAVLT: Primacy, recency and RINT
(resistance of interference).
The predictive validity of the VAT for
diagnosing dementia, is greater than the
MMSE. The difference at baseline
between the declining and nondemented group was highly significant
(p<.001).
Correlation between IQ (National Adult
Reading test) and Fluency test (animals)
was non-significant.
25
Letter Digit Substitution Test (LDST)
Van Elst et al. (2008)
Healthy participants aged 2481 (N=1.858)
Test-retest reliability is r=.87,
p<.001
Semantic fluency showed a modest
correlation with IQ (r =.29, p <.01) and
with years of education (r =.22; p<.0001).
Neuropsychological test
Word-color Stroop Test
Authors (year)
Franzen, Tishelman, Sharp, &
Friedman (1987)
Sample (N)
Healthy participants aged 1826 (N=60)
Reliability
Test-retest reliability of
Stroop I: r=.831, Stroop II:
r=.738, and Stroop III:
r=.671.
Validity
Van der Elst, Van Boxtel, Van
Breukelen, & Jolles (2006)
Healthy participants aged 2481 (N=1048)
Test-retest reliability is r=.68,
p<.001
Wagner, Halmreich, Dahmen, Lieb,
& Tadic (2011)
DSV-IV diagnosed major
depressive disorder patients
(N=55)
Test-retest reliability of TMTA is r=.81, and of the TMT-B
is r=.86.
Fals-Stewart (1992)
Healthy undergraduate
students (N=39)
The inter-rater reliability to
correct errors on the TMT A
was r=.94. The inter-rater
reliability of TMT B was r=.90
Sanches-Cubillo et al. (2009)
Healthy old adults (N=41)
Trail Making Test
TMT-A correlated significantly to TMT-B
(r=.73), Digit Symbol Substitutions
(WAIS_III) (r=-.63), Digit Backward
(WAIS-III (r=-.50), and Stroop Color-word
Test (r=-.34).
TMT-B correlated significantly to Digit
Symbol Substitutions (WAIS_III) (r=-.57),
Digit Backward (WAIS-III (r=-.54), and
Stroop Color-word Test (r=-.38).
26