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Imaging patterns of tissue destruction Towards a better discrimination between typesof dementiaMoller, C.

2015

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73

Chapter 3 - Patterns of gray matter loss in different forms of

dementia

74

Chapter 3.1 More atrophy of deep gray matter structures in Frontotemporal

Dementia compared to Alzheimer’s Disease

Christiane Möller MSc1, Nikki Dieleman MSc

5, Wiesje M van der Flier

PhD1,2

, Adriaan Versteeg3, Yolande Pijnenburg MD PhD

1, Philip

Scheltens MD PhD1, Frederik Barkhof MD PhD

3, Hugo Vrenken

PhD3,4

1Alzheimer center & Department of Neurology,

2Department of

Epidemiology & Biostatistics, 3Department of Radiology & Nuclear

Medicine, 4Department of Physics & Medical Technology, Neuroscience

Campus Amsterdam, VU University Medical Center, P.O. Box 7057,

1007MB Amsterdam, the Netherlands, 5Department of Radiology,

University Medical Center Utrecht, P.O. Box 85500, 3508 GA Utrecht,

the Netherlands.

Journal of Alzheimers Disease 2015 Jan 1;44(2):635-47.

Abstract

Background: The involvement of frontostriatal circuits in Frontotemporal Dementia

(FTD) suggests that deep gray matter structures (DGM) may be affected in this

disease.

Objective: We investigated whether volumes of DGM structures differed between

patients with bvFTD, Alzheimer’s Disease (AD) and subjective complaints (SC) and

explored relationships between DGM structures, cognition and neuropsychiatric

functioning.

Methods: For this cross-sectional study we included 24 patients with FTD and

matched them based on age, sex and education at a ratio of 1:3 to 72 AD patients and

72 patients with SC who served as controls. Volumes of hippocampus, amygdala,

thalamus, caudate nucleus, putamen, globus pallidus and nucleus accumbens were

estimated by automated segmentation of 3D T1-weighted MRI. MANOVA with

Bonferroni adjusted post-hoc tests was used to compare volumes between groups.

Relationships between volumes, cognition and neuropsychiatric functioning were

examined using multivariate linear regression and Spearman correlations.

Results: Nucleus accumbens and caudate nucleus discriminated all groups, with most

severe atrophy in FTD. Globus pallidus volumes were smallest in FTD and

discriminated FTD from AD and SC. Hippocampus, amygdala, thalamus and putamen

were smaller in both dementia groups compared to SC. Associations between

amygdala and memory were found to be different in AD and FTD. Globus pallidus and

nucleus accumbens were related to attention and executive functioning in FTD.

Conclusion: Nucleus accumbens, caudate nucleus, and globus pallidus were more

severely affected in FTD than in AD and SC. The associations between cognition and

DGM structures varied between the diagnostic groups. The observed difference in

volume of these DGM structures supports the idea that next to frontal cortical

atrophy, DGM structures, as parts of the frontal circuits, are damaged in FTD rather

than in AD.

75

Introduction

Alzheimer’s disease (AD) frontotemporal dementia (FTD) are the leading causes of

early-onset dementia[1,2]. Cortical atrophy of the two forms of dementia has been

extensively studied: Typically, atrophy of the medial temporal lobe (MTL) is seen in

AD patients and associated with episodic memory problems [3,4]. However, MTL

atrophy is also common in bvFTD [5,6]. Conversely, FTD is typically characterized by

atrophy of frontal and anterior temporal lobes, which is associated with changes in

behavior and executive functioning [7-9], but atrophy in these regions does not

exclude a diagnosis of AD [10,11].

In the discrimination of AD and FTD, deep gray matter (DGM) structures have

received less attention. So far, MRI-based studies have demonstrated that AD is

associated with atrophy of thalamus, putamen, and caudate nucleus [12-16].

However, results from published studies are difficult to compare as they used different

image analysis techniques or focused on different DGM structures [13,15,17,18]. In the

context of FTD, DGM structures may be even more important, as they are part of the

frontostriatal circuits, known to be affected in FTD [19,20]. Thalamus, neostriatum,

nucleus accumbens and globus pallidus connect motor- and cognitive-loops with the

prefrontal cortex. Degeneration of these DGM structures may lead to circuit failure

and eventually alterations of cognition and behavior [17,19,21].

MRI-based measures of DGM structures could provide important information on the

differential distribution of pathology between AD and FTD and may explain some of

the clinical characteristics typical of the diseases. Therefore the aim of this study was

to investigate atrophy of hippocampus, amygdala and the DGM structures in AD, FTD,

and control subjects and to explore the effect of DGM atrophy on cognitive and

neuropsychiatric functioning.

Materials and Methods

Patients

All patients visited the Alzheimer Center of the VU University Medical center between

2008 and 2011 where they underwent a standardized one-day assessment for clinical

evaluation including medical history, informant-based history, physical and

neurological examination, blood tests, neuropsychological assessment,

electroencephalography and magnetic resonance imaging (MRI) of the brain. Patients

are asked informed consent for the use of their clinical data for research purposes and

are as such included in the Amsterdam Dementia Cohort [22]. For the current study,

we retrospectively selected 24 FTD patients who met the inclusion criteria as

described below. One of the authors matched these patients through visual inspection

on a 1:3 basis by age, sex and educational level to 72 AD patients and 72 patients with

subjective complaints (SC) who were used as controls. All diagnoses were made in a

multidisciplinary consensus meeting according to the core clinical criteria of the

National Institute on Aging and the Alzheimer’s Association workgroup for probable

AD and according to the clinical diagnostic criteria of FTD based on the results of the

one-day assessment as described above [23-25]. Among the AD patients, one patient

met the criteria for posterior cortical atrophy (PCA). Six AD patients susceptible for

genetic mutations underwent a genetic screening. None of them were positive for

76

genetic mutations. Among the FTD patients there were four patients with ALS and

one patient with motor neuron disease. Eight FTD patients susceptible for genetic

mutations underwent a genetic screening. None of them were positive for known

genetic mutations. On visual inspection of the MRI scans, frontal atrophy was worse

than temporal atrophy in all FTD patients. No progressive nonfluent aphasia (PA) or

semantic dementia (SD) cases were identified. As controls, we used patients who

presented at our memory clinic with subjective complaints. They were labeled as

having subjective complaints when they presented with memory complaints, but

cognitive functioning was normal and criteria for MCI, dementia or any other

neurological or psychiatric disorder known to cause cognitive decline were not met.

The diagnosis was also made in a multidisciplinary consensus meeting taking into

account results of all examinations as described above. For inclusion in the present

study all patients had to fulfill the following criteria: (1) meet the criteria for AD, FTD

or subjective complaints based on the core clinical criteria, (2) diagnosis stayed

unchanged after 12 months clinical follow-up, (3) availability of a T1-weighted 3-

dimensional MRI scan (3DT1) at 3 tesla MRI (details see section below) and (4)

availability of neuropsychological examination. Exclusion criteria were: (1) age

younger than 40 years; (2) failure of segmentation software to analyze DGM volumes

due to abnormal tracing of structures (details see section below); and (3) lacunar

infarction in the DGM structures. Disease duration was calculated based on the time

difference between date of diagnosis and the year patients caregivers noticed the first

symptoms. Level of education was rated on a seven-point scale [26]. The local medical

ethics committee approved the study. Ethics review criteria conformed to the Helsinki

declaration. All patients gave written informed consent for their clinical data to be

used for research purposes. Demographic data can be found in table 1.

Cerebrospinal fluid (CSF)

To gain more diagnostic certainty, CSF was obtained by lumbar puncture. Amyloid-

β1−42 (Aβ42), total tau, and tau phosphorylated at threonine-181 (Ptau-181) were

measured by sandwich ELISA (Innogenetics, Gent, Belgium) [27]. CSF analyses were

performed at the VUmc Department of Clinical Chemistry. Cut-off levels in our lab are

as follows: Aβ42< 550, total tau > 375, and ptau > 52 [27]. CSF was available for 140

subjects (SC: n=59; AD: n=62; FTD: n=19).

MR image acquisition and review

Imaging was carried out on a 3 Tesla scanner (Signa HDxt, GE Healthcare, Milwaukee,

WI, USA) using an 8-channel head coil with foam padding to restrict head motion. The

scan protocol included a whole-brain isotropic 3DT1 fast spoiled gradient echo

sequence (FSPGR; TR 708 ms, TE 7 ms, flip angle 12º, 180 sagittal slices, field of view

250 mm, slice thickness 1 mm, voxel size 0.98x0.98x1 mm) which was used for

segmentation. In addition, the MRI protocol included a 3D Fluid Attenuated Inversion

Recovery (FLAIR) sequence, dual-echo T2-weighted sequence, and susceptibility

weighted imaging (SWI) which were reviewed for brain pathology other than atrophy

by an experienced radiologist.

77

Volume measurement of DGM structures

DICOM images of the FSPGR sequence were corrected for gradient nonlinearity

distortions and converted to NIfTI format. The algorithm FIRST (FMRIB’s integrated

registration and segmentation tool, FSL 4.15) [28,29] was applied to estimate left and

right volumes of seven structures: hippocampus, amygdala, thalamus, caudate

nucleus, putamen, globus pallidus, and nucleus accumbens. Left and right volumes

were summed to obtain total volume for each structure. FIRST performed both

registration and segmentation of the above mentioned anatomical structures. A two-

stage linear registration was performed to achieve a more robust and accurate pre-

alignment of the seven structures. During the first-stage registration, the 3DT1 images

were registered linearly to a common space based on the Montreal Neurological

Institute (MNI) 152 template with 1x1x1 mm resolution. After the first registration, a

second stage linear registration using a subcortical mask or weighting image, defined

in MNI space, was performed to improve registration for the seven structures. Both

stages used 12 degrees of freedom. This 2-stage registration was followed by

segmentation based on shape models and voxel intensities. Volumes of the seven

structures were extracted in native space, taking into account the transformations

matrices during registration. The final step was a boundary correction based on local

signal intensities. All registrations and segmentations were visually checked for errors.

For examples of automated segmentations see Figure 1. FIRST has been shown to

give accurate and robust results for the segmentation of subcortical structures and

that it performs comparably to or better than other automatic methods [28,30,31].

To correct the DGM structures for head size, we used the volumetric scaling factor

(VSF) derived from SIENAX (Structural Image Evaluation using Normalization of

Atrophy Cross-sectional) [32], also part of FSL. In short, first SIENAX extracted skull

and brain from the 3DT1 input whole-head image. In our study, brain extraction was

performed using optimized parameters [33]. These were then used to register the

subject’s brain and skull image to a standard space brain (derived from MNI152

template) to estimate the scaling (volumetric scaling factor (VSF)) between the

subject's image and standard space. Normalization for head size differences was done

by multiplying the raw volumes of the DGM structures by the VSF.

Neuropsychological assessment

To assess dementia severity we used the Mini-Mental State Examination (MMSE).

Cognitive functioning was assessed using a standardized neuropsychological test

battery covering five major domains: memory (immediate recall, recognition and

delayed recall of Dutch version of the Rey Auditory Verbal Learning Test and total

score of Visual Association Test A), language (Visual Association Test picture naming

and category fluency (animals: 1 min)), visuospatial functioning (subtests of Visual

Object and Space Perception Battery (VOSP): incomplete letters, dot counting, and

number location), attention (Trail Making Test part A (TMT A), Digit Span forward,

and Letter Digit Substitution Test (LDST)), and executive functioning (Digit Span

backwards, Trail Making Test part B (TMT B), letter fluency, and Stroop Color-Word

card subtask [34]). For a detailed description of neuropsychological tests see Smits et

al. [35]. For each cognitive task, z-scores were calculated from the raw test scores by

the formula z=(x-μ)/σ, where μ is the mean and σ is the standard deviation of the

78

subjective complaints group. The value z = 0 therefore reflects the average test

performance of the subjective complaints group in a given domain. Scores of TMT A,

TMT B, and Stroop color-word card were inverted by computing -1 x z-score, because

higher scores imply a worse performance. Next, composite z-scores were calculated

for each cognitive domain by averaging z-scores. Composite z-scores were calculated

when at least one neuropsychological task was available in each cognitive domain.

There was variability in the number of completed neuropsychological tests. On

average, every test was completed by 136 patients, ranging from 158 (DS forward) to

105 (LDST). When tests were not finished, this was because of cognitive impairment

or lack of time.

Neuropsychiatric assessment

To assess psychopathology we used the Neuropsychiatric Inventory (NPI)[36]. The

NPI evaluates 12 neuropsychiatric disturbances common in dementia: delusions,

hallucinations, agitation/aggression, depression/dysphoria, anxiety, elation/euphoria,

apathy/indifference, disinhibition, irritability/lability, aberrant motor behavior, sleep

and night-time behavior disturbances, and appetite/eating abnormalities. The severity

and frequency of each neuropsychiatric symptom were rated based on scripted

questions administered to the patient’s caregiver. Symptom frequency is rated on a

scale of 1 (occurs rarely) to 4 (occurs very often) and symptom severity on a scale of 1

(mild) to 3 (severe). Scores for symptom domains are calculated by multiplying the

frequency of each symptom by its severity. The total NPI score is derived by summing

up all symptom domain scores. The NPI was completed by 93 patients (31 SC, 48 AD,

14 FTD).

Statistical analysis

SPSS version 20.0 for Windows was used for statistical analysis. Differences between

groups for demographics, composite cognitive domain z-scores and NPI domain

scores were assessed using ANOVA (VSF), Kruskal-Wallis tests (age, level of eduction,

disease duration, CSF values, MMSE, composite cognitive domain z-scores and NPI

domain scores), and χ2 tests (sex) (table 1) .

Multivariate analysis of variance (MANOVA) was used to compare head size adjusted

total volumes of the MTL structures (hippocampus and amygdala) and the DGM

structures (thalamus, caudate nucleus, putamen, globus pallidus, and nucleus

accumbens) between diagnostic groups with Bonferroni adjusted post-hoc tests. Age,

sex and disease duration were used as covariates. A second MANOVA was performed

separately for left and right volumes of the seven structures. Statistical significance

was set at p<0.05.

To assess associations between the seven DGM structures (independent variables;

entered simultaneously) and cognitive domains (dependent variable) we performed

multivariate linear regression analyses. We used five models, one for each cognitive

domain. Age, sex, disease duration and diagnosis (using dummy variables) were

entered as covariates in the model. To check if associations with DGM structures

differed according to diagnosis, interaction terms (dummy-diagnosis x DGM

structure) were included in the model . If there was a significant interaction between

diagnosis and DGM structure (p<0.10), standardized β are displayed for each

79

diagnostic group separately. When no significant interaction was found, the overall β

is reported. Statistical significance was set at p<0.05.

As NPI domain scores and NPI total score were not normally distributed we used

Spearman correlations to assess associations between the seven DGM structures and

NPI domain scores and NPI total score for each diagnostic group separately.

Statistical significance was set at p<0.01.

Results

Demographics

Demographic data, composite cognitive domain z-scores and NPI domain scores are

summarized in Table 1. Groups were well matched for age, sex and level of education.

Disease duration did not differ between the groups. All CSF biomarkers in AD differed

from patients with subjective complaints and FTD, with a CSF profile in line with the

clinical diagnosis [27,37,38]. None of the FTD patients had A-beta values under the

cutoff, 10 patients with subjective complaints had lower A-beta values and 11 AD

patients had higher A-beta values. MMSE scores were different between diagnostic

groups, with AD patients having the lowest scores. The VSF did not differ between the

groups. AD patients performed worse on memory tasks, visuospatial functioning, and

executive functioning than FTD and patients with subjective complaints. For the

language and attention domains both patient groups performed worse than the

subjective complaints group. FTD patients exhibited most neuropsychiatric

symptoms: The total NPI score was higher than in patients with subjective complaints

and AD patients. They had higher scores than the two other groups on aberrant motor

behavior and appetite/eating abnormalities. Both dementia groups scored higher on

apathy/indifference than the subjective complaints group.

Deep gray matter structures

Volumes of DGM structures are summarized in Table 2 and Figure 2. MANOVA

revealed group differences in MTL and DGM structures. Post hoc tests showed that

total volumes of hippocampus and amygdala discriminated both dementia groups

from the subjective complaints group. Nucleus accumbens and caudate nucleus

volume discriminated all groups, with FTD having most severe atrophy (p<0.001).

Globus pallidus volumes were smallest in FTD and discriminated FTD from AD

(p<0.001) and from patients with subjective complaints (p<0.001). No volume

differences between patients with AD and subjective complaints for globus pallidus

were found (p=1.00). Volumes of thalamus and putamen were larger in patients with

subjective complaints than in both dementia groups.

We did not find any left-right differences for the DGM structures. The MTL structures

showed some subtle left-right differences: Whereas left hippocampus discriminated

only dementia from patients with subjective complaints, the right hippocampus also

discriminated AD from FTD (p=0.045), with smaller right hippocampal volume in FTD.

Left amygdala volumes differed only between patients with subjective complaints and

FTD, whereas right amygdale volumes also discriminated AD from patients with

subjective complaints (p=0.021). Left and right volume differences are summarized in

the supplementary material table S1.

80

Relationship between volume of DGM structures, cognitive functioning and

neuropsychiatric symptoms

As the DGM structures did not show any left-right differences and to avoid multiple

comparisons, associations between cognition, neuropsychiatric symptoms and DGM

structures were only conducted for the total DGM volumes.

Multivariate linear regression analysis showed that after adjustment for age, sex,

disease duration and diagnosis, there was a trend for an association between

hippocampus and memory (standardized β=0.19, p=0.055). In addition, the interaction

term for diagnosis and amygdala was significant, implying that for this structure,

associations with memory differed according to diagnostic group. For AD, there was a

positive association between amygdala and memory (standardized β=0.25, p=0.011),

for FTD there was a negative association (standardized β=-0.32, p=0.037), and the

association for patients with subjective complaints was not significant (standardized

β=-0.20, p=0.113). There were no associations between any of the DGM structures and

language. For visuospatial functioning, there appeared to be interactions between

diagnosis and hippocampus (p=0.042), but the associations did not reach significance

in any of the groups (hippocampus: AD: standardized β=-0.26; FTD: standardized

β=0.05; SC: standardized β=0.19; p>0.05), nor were there any associations between

any of the other DGM structures and visuospatial functioning. For attention, we

observed interactions between diagnosis and globus pallidus (p=0.005). For FTD,

there was a positive association for globus pallidus (standardized β=0.75, p=0.010). For

patients with AD and subjective complaints no associations between globus pallidus

and attention were found (globus pallidus: AD: standardized β=-0.15; SC:

standardized β=-0.13; p>0.05). There were no associations between any of the other

DGM structures and attention. Finally, for executive functions there were interactions

between diagnosis and nucleus accumbens (p=0.042) and globus pallidus (p=0.014).

For FTD, there was a positive association for globus pallidus (standardized β=0.93,

p=0.002) and a negative association for nucleus accumbens (standardized β=-0.49,

p=0.035). Associations for AD and patients with subjective complaints were not

significant.

Spearman correlations between neuropsychiatric symptoms and DGM structures

showed a significant correlation in FTD patients: Disinhibition was negatively

correlated with volume of nucleus accumbens (r=-0.69, p=0.007). Although, aberrant

motor behavior and appetite/eating abnormalities were reported very frequently in

FTD, no correlations with DGM or MTL structures were found.

Discussion

We found more prominent volume loss of DGM structures in FTD than in AD and SC.

Specifically, caudate nucleus, globus pallidus and nucleus accumbens volumes

differentiated between both types of dementia, with FTD being more severely

affected. Hippocampus, amygdala, thalamus and putaminal volumes only

discriminated dementia from patients with subjective complaints. Associations

between cognition and the DGM structures show different effects for the diagnostic

groups. In FTD, attention showed a positive association with globus pallidus and

executive functioning was positively related to globus pallidus and negatively to

81

nucleus accumbens. Memory was positively related to amygdala in AD and negatively

in FTD. Visuospatial functioning showed an overall effect with hippocampal volume.

Neuropsychiatric symptoms and DGM structures were only related in FTD patients

with volumes of nucleus accumbens correlating negatively with disinhibition.

A number of pathological studies have identified DGM atrophy in AD and FTD at

autopsy [39,40]. There is emerging evidence that compared to AD, generally regarded

as a cortical disease, FTD patients have more subcortical brain damage [5,17,41,42].

This is in line with our findings that volumes of caudate nucleus, globus pallidus and

nucleus accumbens are smaller in FTD compared to both AD patients and patients

with subjective complaints. One possible explanation for the more severe volume loss

of basal nuclei in FTD could be the specific underlying neuropathology. Whereas

amyloid deposits are mainly found in the cerebral cortex, tau inclusions are found in

subcortical regions as well [43,44]. Next to tau, fused in sarcoma protein inclusions

(FUS) is also associated with atrophy of caudate nucleus [45]. Combinations with

other clinical phenotypes like amyotrophic lateral sclerosis (ALS) could be another

explanation for more atrophy of the DGM structures in the FTD group as ALS is

associated with caudate nucleus, hippocampus and nucleus accumbens atrophy [46].

However, when we excluded the 4 patients with ALS and the 1 patient with motor

neuron disease from the analysis, as well as the matching patients with subjective

complaints and AD, results did not change essentially. Furthermore, we believe that in

our sample the number of ALS cases is too small to drive the results of our study.

Third, the involvement of basal nuclei as part of the frontostriatal circuits in FTD fits

with the signs and symptoms of this disease, that include behavioral abnormalities

and extrapyramidal symptoms [20,42]. Structural changes in components of these

circuits could lead to Wallerian degeneration of the connecting fibers and eventually

to failure of the whole circuit. Indeed, in AD patients, behavioral changes and

involvement of the extrapyramidal system tend to develop much later in the disease.

Exploration of the contribution of the DGM structures to cognition and

neuropsychiatric symptoms revealed that the associations between amygdala and

memory were different between AD and FTD patients. For attention and executive

functions we also found different effects for the diagnostic groups. As expected there

were positive associations between the MTL structures (trend for hippocampus) and

memory in the AD group. The negative correlations between amygdala volumes and

memory in the FTD group could have the following explanations: The exact

neurocognitive mechanisms underlying the episodic memory impairment in bvFTD

remain unknown, although it has been suggested that the memory deficits in FTD

reflect executive dysfunction [47], most likely due to atrophy in the prefrontal

cortices, in particular the orbitofrontal cortex [48,49] rather than to atrophy in MTL

structures. Although some studies have pointed to posterior temporal and MTL

pathology as a determinant of memory dysfunction in bvFTD [50].

Another explanation could be the type of memory test which place different demands

on prefrontal versus medial temporal lobe functioning, such as recall versus

recognition. It has been shown that memory dysfunction in bvFTD resulted from

defective cognitive (retrieval) control processes rather than true amnesia. If our

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memory test measures the relatively preserved recognition memory and not the

impaired temporal source memory (remembering whether an item was shown in list A

or list B) a negative association could occur [51,52]. Different kinds of memory have

different underlying neural correlates, therefore a negative association between

amygdala and the more frontally controlled recall test results could be the

consequence.

Another explanation could be an interaction with the underlying pathology. It has

been suggested that cases with severe memory disturbance at presentation appear to

have pathological changes associated with TDP-43 protein deposition [53]. Therefore

it could be possible that patients with TDP-43 pathology present with severe memory

disturbances but had a relatively spared amygdala, whereas a patient with tau or FUS

pathology performs relatively well on a memory test but had a lot of amygdala

atrophy. An alternative explanation could be that this finding is a type 1 error

Only in FTD, there were significant relations between attention, executive functioning

and the DGM structures. This fits our hypothesis that the DGM structures as relay

stations of the frontal circuits play a role in FTD rather than in AD. The observed

relation between globus pallidus and attention and executive functioning is in line

with symptoms observed in the dorsolateral prefrontal syndrome [20]. The syndrome

is characterized primarily by executive function deficits with the globus pallidus as an

important relay station. Contrary to our expectations, we found a negative

relationship between nucleus accumbens and executive functioning in the FTD group.

A possible explanation for the negative association, could be that FTD patients with a

small nucleus accumbens have relatively preserved executive functions or there are

some interaction effects with the underlying pathological subtypes comparable to the

effect we found with the amygdala and memory. An alternative explanation could be

that this finding is a type 1 error as the other few studies who looked at the

relationship between DGM structures and cognition did not find this association

[18,54,55]. As studies examining the relationships between DGM structures and

cognition are scarce, our results need to be replicated in larger cohorts.

Social, emotional and behavioral changes are frequent symptoms of FTD and these

symptoms might be related to volume loss of DGM structures too [56,57]. This theory

is confirmed by our results that FTD patients exhibit more neuropsychiatric symptoms

than the other groups and the negative correlation between nucleus accumbens and

disinhibition which is one of the hallmarks in FTD. These findings are supported by

other studies [58-60]. The correlation between nucleus accumbens with response

disinhibition in FTD is consistent with theories of response-inhibition networks [61,62]

like the fronto-subcortical network, including orbitofrontal cortex, left inferior frontal

cortex and bilateral dorsal and ventral striatum. That is, the stopping process is

generated by the inferior frontal cortex, leading to activation in the striatum, thereby

inhibiting thalamo-cortical output and ultimately reducing motor cortex activity.

Animal models suggest that the nucleus accumbens is crucial for response inhibition

[63]. Furthermore, the accumbens is part of the mesolimbic dopaminergic system,

which plays a key role in motivated and emotional behavior, reinforcement and

reward, as well as for goal-directed behaviors [64]. These results together with our

findings provide evidence for the hypothesis that pathology in FTD may start in parts

83

of frontostriatal circuits and eventually leads to failure of the whole circuit and may

explain that behavioral symptoms proceed cognitive deterioration in FTD. Changes in

eating behavior and aberrant motor behavior are common in FTD. Although, FTD

patients in our study scored high on these domains, we did not find any relations with

DGM or MTL structures. For aberrant motor behavior other studies failed to find an

association with DGM or MTL structures too [57,59]. However, overeating and

preference for sweet food in bvFTD have been associated with atrophy in the striatum

[65,66]. A reason that we did not find any correlations could be the relatively small

sample size in our study. Nevertheless despite a large study sample, another study

also failed to identify brain regions that were specifically involved in eating changes in

FTD [59]. Another reason could be that the causes and effects of eating disturbance in

FTD are multi-factorial. It has been shown that the autonomic nervous system plays a

role in satiety and central regulation of weight, as well as the hypothalamus [67,68].

Among the strengths of this study is the careful matching of groups enabling

comparison of DGM volumes between groups without confounding effects of age and

sex. We used FIRST, an automated and robust method for the extraction of the

subcortical volumes which has been shown to give accurate results and performs

comparable to or better than other automatic methods [28,30,31]. All subcortical

volumes segmentations were visually checked, excluding the possibility of large

segmentation errors. FIRST has clear advantages compared to voxel-based

morphometry (VBM) [5,18] when assessing basal nuclei. VBM is suitable to compare

patterns of cortical atrophy, but prone to segmentation errors in subcortical areas.

Moreover, FIRST has also advantages over manual segmentations [42,69], since

manual segmentation can take a long time and be prone to errors as well. We

investigated all DGM structures, as well as the MTL structures – hippocampus and

amygdala – and not only concentrated on a few structures.

A possible limitation of this study is that we did not have pathological data available,

so the possibility of misdiagnosis cannot be excluded. Nevertheless, we used an

extensive standardized work-up and all 72 AD patients fulfilled clinical criteria of

probable AD, all 24 patients fulfilled the criteria for FTD, i.e. frontal variant.

Furthermore, CSF biomarkers were available for the majority of patients and average

biomarker levels were congruent with the diagnosis [37,38], rendering the possibility

of misdiagnosis less likely. Availability of pathological data would also enable us to

study the effects of the different underlying pathology (tau, TDP subtypes, etc.) on

volumes of DGM structures. This will be an important next step for future

investigations. Another limitation could be the fact that we had a relatively small

group of FTD patients, which could hamper the detection of any putative volume

differences with the other diagnostic groups or associations with cognition because of

low statistical power. Nevertheless, as FTD is a quite rare diagnosis, the sample size

corresponds with that of other available studies to date. Moreover, we carefully

matched on a 1:3 basis, resulting in optimal use of the cases we had available.

Unfortunately, we did not have any cortical data available, which could have been

correlated to the DGM structures from which they receive projections from. This

would have strengthen our hypothesis about FTD as a network disorder even more.

84

However, our results are a good starting point to elaborate further on anatomical

network differences between FTD and AD,

In summary, this study yielded a comprehensive description of the differential

involvement of DGM structures in AD and FTD patients and how these structures are

related to cognitive and neuropsychiatric functioning. Volumes of nucleus accumbens,

caudate nucleus, and globus pallidus appear to be more severely impaired in FTD

compared to AD patients. These structures play an important role in frontostriatal

circuits known to be affected in bvFTD and could explain the behavioral disturbances

seen in this patient group, as illustrated by the relation between disinhibiton and

nucleus accumbens volumes. DGM atrophy could lead to Wallerian degeneration of

connecting fibers in the frontal circuits in FTD. This emphasizes the need to further

elucidate the role of these frontal networks by linking the volumes of DGM structures

to brain networks (i.e. cortical atrophy and white matter networks as measured by

DTI) in FTD patients. Although we did not have pathological data available, the

observed difference in volume of these basal nuclei supports the notion of greater

involvement of these structures in FTD in contrast to AD and could help to explain and

clarify some of the symptomatology in this multifaceted disorder.

Acknowledgements

The VUmc Alzheimer Center is supported by Alzheimer Nederland and Stichting

VUMC Fonds. The study was supported by the Netherlands Organisation for Scientific

Research (NWO, national project ‘Brain and Cognition’ “Functionele Markers voor

Cognitieve Stoornissen” (# 056-13-001)). Wiesje van der Flier is recipient of the

Alzheimer Nederland grant (Influence of age on the endophenotype of AD on MRI,

project number 2010-002).Research of the VUmc Alzheimer Center is part of the

neurodegeneration research program of the Neuroscience Campus Amsterdam. The

clinical database structure was developed with funding from Stichting Dioraphte. The

gradient non-linearity correction was kindly provided by GE medical systems,

Milwaukee.

85

Tables and Figures

Table 1. Demographics

SC AD FTD

SC vs AD SC vs FTD AD vs FTD

N N N

Test

statistic p

Test

statistic p

Test

statistic p

Test

statistic p

Sex, females 72 30 (41.7%) 72 30 (41.7%) 24 10 (41.7%) χ2=0.00 1.000

Age (years) 72 63 ± 8.1 72 63 ± 8.2 24 63 ± 8.0 K=0.00 1.000

Level of education 70 5.4 ± 1.5 71 5.5 ± 1.1 24 5.4 ± 1.3 K=0.16 0.922

Disease duration

(months) 66 40.4 ± 35.9 69 40.0 ± 24.8 23 48.5 ± 40.7 K=1.74 0.419

VSF 72 1.31 ± 0.1 72 1.29 ± 0.1 24 1.28 ± 0.2 F=0.91 0.406

NBV (cm3) 72 1459 ± 81.3 72 1387± 72.9

a 24 1369 ± 83.2

a F=20.17 <0.001 <0.001 <0.001 0.999

Aβ42 59

825 (293-

1343) 62

444.5 (268-

1365)a 19

876 (611-

1264)b K=66.87 <0.001 U=435.5 <0.001 U=494.5 0.442 U=45.0 <0.001

Tau 59

274 (69-

2088) 62

567.5 (148-

1779)a 19

335 (106-

680)b K=42.76 <0.001 U=627.0 <0.001 U=424.0 0.112 U=257.0 <0.001

Ptau 59 50 (18-190) 62

82.5 (33-

262)a 19 42 (19-112)

b K=37.75 <0.001 U=765.0 <0.001 U=507.0 0.533 U=195.5 <0.001

MMSE 72 28 ± 2.2 72 21 ± 4.9a 24 24 ± 4.5

a,b K=86.98 <0.001 U=309.0 <0.001 U=342.0 <0.001 U=562.5 0.01

Memory 70 0.0 ± 0.8 71 -3.4 ± 2.4a 23 -1.7 ± 2.2

a,b K=93.89 <0.001 U=200.0 <0.001 U=311.0 <0.001 U=424.5 0.001

Language 63 0.0 ± 0.8 65 -1.3 ± 1.6a 19 -1 ± 0.8

a K=59.05 <0.001 U=510.5 <0.001 U=173.5 <0.001 U=602.0 0.868

Visuospatial-

functioning 68 0.0 ± 0.7 65 -2.3 ± 3.2a 20 -0.9 ± 2.2

a,b K=39.97 <0.001 U=831.5 <0.001 U=430.5 0.012 U=440.5 0.03

Attention 70 0.0 ± 0.8 70 -1.8 ± 1.7a 23 -1.3 ± 1.9

a K=53.39 <0.001 U=746.0 <0.001 U=378.5 <0.001 U=603.0 0.072

Executive-

functioning 69 0.0 ± 0.8 67 -2.1 ± 1.7a 22 -1.3 ± 1.3

a,b K=72.19 <0.001 U=457.0 <0.001 U=227.0 <0.001 U=519.0 0.038

86

NPI Totalscore 31 7.2 ± 7.8 48 13.0 ± 12.1a 14 21.6 ± 14.6

a,b K=16.04 <0.001 U=465.0 0,005 U=73.5 <0.001 U=203.5 0.026

Delusions 31 0.0 ± 0.2 48 0.1 ± 0.6 14 0.3 ± 1.1 K=0.90 0.638

Hallucinations 31 0.0 ± 0.0 48 0.2 ± 0.7 14 0.0 ± 0.0 K=3.87 0.144

Agitation/

Aggression 31 0.4 ± 1.5 48 0.9 ± 2.5 14 1.4 ± 3.4 K=2.26 0.324

Depression/

Dysphoria 31 0.7 ± 1.9 48 0.6 ± 1.7 14 0.4 ± 1.6 K=0.91 0.635

Anxiety 31 0.9 ± 2.5 48 0.9 ± 2.1 14 1.7 ± 3.7 K=0.20 0.906

Elation/Euphoria 31 0.1 ± 0.7 48 0.6 ± 1.6 14 1.1 ± 2.6 K=4.47 0.107

Apathy/

Indifference 31 1.3 ± 1.8 48 4.4 ± 3.9a 14 6.6 ± 4.2

a K=24.24 <0.001 U=347.0 <0.001 U=52.5 <0.001 U=231.0 0.075

Disinhibition 31 0.5 ± 1.4 48 1.1 ± 2.9 14 1.8 ± 2.7 K=4.17 0.124

Irritability/Lability 31 1.8 ± 2.3 48 2.2 ± 3.4 14 3.7 ± 3.9 K=2.17 0.338

Aberrant Motor

Behavior 31 0.3 ± 1.1 48 0.4 ± 1.0 14 1.9 ± 3.3a,b

K=9.98 0.007 U=686.5 0.295 U=138.5 0.004 U=233.5 0.015

Sleep and Night-

time Behavior

Disturbances 31 0.8 ± 1.6 48 0.7 ± 1.8 14 0.5 ± 1.0 K=0.44 0.801

Appetite/Eating

Abnormalities 31 0.5 ± 1.2 48 0.9 ± 2.3 14 2.1 ± 2.4a,b

K=845 0.015 U=675.0 0.315 U=132.0 0.006 U=229.5 0.025

Values presented as mean ± standard deviation or n (%). Level of education is determined according to the Verhage-system. Differences between groups

for demographics and composite z-scores were assessed using ANOVA, Kruskall-Wallis tests and χ2 tests, where appropriate.

Cognitive composite z-domains were calculated of the available z-scores of each test by the MEAN function in SPSS.

Key: SC: patients with subjective complaints, AD: patients with Alzheimer’s disease, FTD: patients with frontotemporal dementia, VSF: volumetric

scaling factor derived from SIENAX, NBV: normalized brain volume, MMSE: Mini-Mental State Examination a different from SC (p<0.05),

b different from AD (p<0.05)

87

Table 2. Total volumes (cm3) of hippocampus, amygdala, and deep gray matter structures.

SC AD FTD SC > AD SC > FTD AD > FTD

p Mean difference p Mean difference p Mean difference p

Hippocampus 9.9 ± 1.1 8.7 ± 1.3 8.2 ± 1.3 <0.001 1.19 <0.001 1.81 <0.001 0.62 0.098

Amygdala 3.9 ± 0.5 3.7 ± 0.6 3.5 ± 0.6 0.001 0.28 0.01 0.48 0.002 0.19 0.46

Thalamus 19.8 ± 1.6 18.9 ± 1.6 18.5 ± 1.5 <0.001 0.94 0.001 1.39 <0.001 0.45 0.596

Caudate Nucleus 9.0 ± 0.9 8.5 ± 1.1 7.5 ± 1.2 <0.001 0.49 0.015 1.58 <0.001 1.09 <0.001

Putamen 12.4 ± 1.1 11.7 ± 1.3 11.5 ± 1.2 <0.001 0.74 0.001 0.91 0.004 0.17 1

Globus Pallidus 4.7 ± 0.4 4.7 ± 0.6 4.2 ± 0.3 <0.001 0.04 1 0.53 <0.001 0.49 <0.001

Nucleus Accumbens 1.2 ± 0.3 1.0 ± 0.2 0.7 ± 0.3 <0.001 0.17 <0.001 0.4 <0.001 0.23 <0.001

Values are presented as mean cm3± standard deviation, corrected for head size. Age, sex and disease duration were used as covariates. Comparisons are

Bonferroni corrected. Key: SC: patients with subjective complaints, AD: patients with Alzheimer’s disease, FTD: patients with frontotemporal dementia

88

Figure 1. Examples of automated segmentation of deep gray matter structures in the three

diagnostic groups using the algorithm FIRST. Segmentations are shown overlaid on the 3D T1-

weighted images in three orthogonal orientations, corresponding roughly to the axial (left

column), coronal (middle column) and sagittal (right column) planes. Top row shows results for

a subject with subjective complaints. Second row shows results for an AD patient. Third row

shows results for a FTD patients. Last row shows a sagittal view of the nucleus accumbens and

surrounding structures. Colored structures: Yellow: Hippocampus; Turquoise: Amygdala; Green:

Thalamus; Light blue: Caudate Nucleus; Pink: Putamen; Dark blue: Globus Pallidus; Orange:

Nucleus Accumbens.

SC

FTD

89

Figure 2. Boxplots of volumes (cm3) of hippocampus, amygdala, and deep gray matter

structures for each diagnostic group adjusted for head size.

**

p≤0.001, *p<0.05

SC

FTD

90

Supplementary material

Volumes (cm3) of left and right hippocampus, amygdala, and deep gray matter structures.

SC AD FTD SC > AD SC > FTD AD > FTD

p Mean difference p Mean difference p Mean difference p

L Hippocampus 4.8 ± 0.6 4.2 ± 0.8 4.0 ± 0.7 <0.001 0.65 <0.001 0.89 <0.001 0.23 0.399

R Hippocampus 5.0 ± 0.6 4.5 ± 0.7 4.2 ± 0.8 <0.001 0.54 <0.001 0.92 <0.001 0.38 0.045

L Amygdala 1.9 ± 0.3 1.8 ± 0.3 1.7 ± 0.3 0.003 0.12 0.056 0.23 0.004 0.11 0.355

R Amygdala 2.0 ± 0.3 1.8 ± 0.4 1.8 ± 0.4 0.004 0.16 0.021 0.24 0.013 0.08 1

L Thalamus 10.0 ± 0.8 9.5 ± 0.8 9.4 ± 0.8 <0.001 0.47 0.001 0.64 0.002 0.17 1

R Thalamus 9.8 ± 0.8 9.4 ± 0.8 9.1 ± 0.8 <0.001 0.47 0.001 0.75 <0.001 0.28 0.334

L Caudate N. 4.4 ± 0.4 4.2 ± 0.5 3.7 ± 0.6 <0.001 0.26 0.007 0.81 <0.001 0.55 <0.001

R Caudate N. 4.6 ± 0.5 4.4 ± 0.6 3.9 ± 0.6 <0.001 0.23 0.046 0.77 <0.001 0.54 <0.001

L Putamen 6.2 ± 0.6 5.8 ± 0.7 5.7 ± 0.6 <0.001 0.46 <0.001 0.48 0.006 0.02 1

R Putamen 6.2 ± 0.6 5.9 ± 0.7 5.8 ± 0.8 0.006 0.27 0.038 0.43 0.017 0.15 0.918

L Globus Pallidus 2.3 ± 0.2 2.3 ± 0.3 2.1 ± 0.2 <0.001 0 1 0.27 <0.001 0.26 <0.001

R Globus Pallidus 2.4 ± 0.2 2.3 ± 0.3 2.1 ± 0.2 <0.001 0.04 1 0.27 <0.001 0.23 0.001

L N. Accumbens 0.6 ± 0.2 0.5 ± 0.1 0.4 ± 0.2 <0.001 0.09 0.002 0.21 <0.001 0.13 0.001

R N. Accumbens 0.5 ± 0.1 0.4 ± 0.1 0.3 ± 0.1 <0.001 0.08 <0.001 0.19 <0.001 0.11 0.001

Values are presented as mean cm3± standard deviation, corrected for head size. Age, sex and disease duration were used as covariates. Comparisons are

Bonferroni corrected. Key: SC: patients with subjective complaints, AD: patients with Alzheimer’s disease, FTD: patients with frontotemporal dementia

91

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