1. 1
Supervisors – Dr. Gina Caetano and Dr. Miguel Castelo-Branco from IBILI, Coimbra.
Automated MRI measurements to identify individuals with mild
cognitive impairment, Alzheimer’s disease and frontotemporal
dementia
Paulo Emanuel Luzio de Melo
1
plmelo@mit.edu
Abstract
The number of individuals suffering from neurodegenerative diseases has been increasing in the last decades. For
this reason, non-invasive diagnostic methods are needed to identify these diseases for early therapeutic interventions.
In this study, the FreeSurfer software was used as an automated, quicker and reliable method for brain segmentation,
with the objective to identify diagnostic markers for neurodegenerative diseases, such as Alzheimer’s, mild cognitive
impairment and frontotemporal dementia. Volumes of subcortical and thicknesses of cortical regions were obtained for
healthy subjects and patients suffering from the previous diseases. However, a correlation between the brain
structures and the diseases was not clearly identified, since the type of disease that each patient had was confidential.
Keywords: MRI, freesurfer, automated volumetry, thickness, Alzheimer’s disease, mild cognitive impairment,
frontotemporal dementia.
I. Introduction ............................................................................................................................................................1
II. Methods .................................................................................................................................................................3
III. Results and Discussion..........................................................................................................................................3
IV. Conclusion .............................................................................................................................................................7
V. Acknowledgment....................................................................................................................................................8
VI. References.............................................................................................................................................................8
I. Introduction
The number of individuals suffering from neurodegenerative diseases has been increasing in the last decades. Some
of the most known neurodegenerative diseases, that mostly affect elderly individuals, include Huntington’s, Parkinson,
Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI) and Frontotemporal Dementia (FTD). The focus of the
present study will be in such individuals that have the last three diseases. From the neurodegenerative disorders,
Alzheimer’s is the most common form of dementia and cannot be cured, so prevention is the best measure to be

2. Automated MRI measurements to identify individuals with neurodegenerative diseases Page | 2
taken. The cause of AD is yet to be clarified, but there is an increasing sense and evidences that indicate a major role
of the senile plaques and neurofibrillary tangles in the progression of the disease (Tiraboschi, et al., 2004; Jellinger &
Bancher, 1998; Hyman & Tanzi, 1992). Regarding MCI pathology, it diagnosed to individuals who have cognitive
impairments beyond the normal threshold, but still are able to perform mostly all normal daily tasks (Petersen, et al.,
1999). For this reason, it is regarded as the intermediate stage between normal aging and dementia. When memory
loss is the predominant symptom associated to a MCI patient, the term amnestic MCI is used, since it is frequently
seen as a risk factor for AD (Grundman, et al., 2004). FTD is a dementia caused by degeneration of the brain’s frontal
lobe. The commons symptoms of this pathology are associated with the functions of the frontal lobe, including
behavioral symptoms and symptoms related to cognitive thinking (Kramer, et al., 2003). Again there is no known cure
for this disease, which means that for now the solution lies on the prevention (Seeley, et al., 2006).
A common trace in neurodegenerative diseases is the fact that they do not appear suddenly, the processes
that alter the brain structures and that will eventually lead to a pathological state, start prior to the first visible
symptoms appearance (ranging from months to years). Therefore, the early detection of the changing brain structures
that lead to these pathologies is a key factor for the prevention and therapy to be applied. Nowadays, one of the most
important tools that provides structural and functional information of the brain is the MRI, and for that it is a powerful
mean to aid this early detection of neurodegenerative disorders.
Non-invasive diagnostic methods are needed to identify these diseases for early therapeutic interventions.
The use of structural MRI imaging as a diagnostic marker is being increasingly used as a non-invasive diagnostic tool
for neurodegenerative pathologies. To be used as a diagnostic marker, structural MRI has to enable specific detection
and quantification of the pathology fundamental features, has to demonstrate excellent discrimination accuracy
between normal elderly controls and individuals with a specific pathology, has also to exhibit a high degree of
consistency and test-retest reproducibility across multiple, independent groups and it has to correlate strongly with
clinical measures of decline and invasive measures of cellular pathology (Desikan, et al., 2009). Traditional MRI
structural studies make use of manual region of interest (ROI) based techniques to recognize the unique features of
the pathologies (Killiany, et al., 2000; Xu, et al., 2000). The recent developments in image algorithms, particularly for
MRI imaging, has given a new direction to the strucutral MRI studies, providing an automated approach to
quantification of the brain structures (Buckner, et al., 2005; Dickerson, et al., 2009; Scahill, et al., 2002). Automated
MRI imaging software tools, such as FreeSurfer (FS), are an excellent example of novel imaging algorithms that
automatically parcellate the brain into anatomic regions and quantify each brain tissue/structure (volume and
thickness for example). FreeSurfer is a freely available software developed at the Athinoula A. Martinos Center for
Biomedical Imaging (Fischl, et al., 2008; Han & Fischl, 2007).
In this study, the quantification of brain structures associated with AD, MCI and FTD pathologies was
investigated. Using structural MRI scans from 45 participants, the volume of subcortical structures and thickness of
the cortex were quantified individually, to find a correlation between the selected anatomic brain regions and the
referred pathologies. This was accomplished through an automated approach, using the FS software. Some selected
subcortical structures were the object of study in this research work as well as some cortical regions, since they can
be potential diagnostic markers for early detection of these diseases (Jack, et al., 1999; Mikko, et al., 2000).

3. Automated MRI measurements to identify individuals with neurodegenerative diseases Page | 3
II. Methods
The study was performed with the structural MRI data from 45 participants. In this group of participants, individuals
with AD, MCI and FTD (patients subgroup) were present as well as healthy subjects (controls subgroup). The only
information available on this group was regarding the controls subjects, that were known, but as for the patients the
number of AD, MCI and FTD within the patients group was unknown, due to confidentiality constraints. Concerning the
how the MRI data was acquired, scans were performed in a 3T equipment, with a 16-channels head coil and using a
3D T1-weighted MPRAGE sequence of the whole brain.
The processing of the acquired MRI data was performed through the software FreeSurfer. This processing
allowed the cortex parcellation and subcortical segmentation to be obtained, which provided the cortical thickness and
subcortical volumetry data for each subject. The workflow used to process the data was based in information provided
by the FS support. Firstly, a pre-processing was performed on each subject data. This pre-processing included a
series of routines that would lead to a primary segmentation and parcellation of the brain. Since the FS algorithms are
based in brain atlas, not all the subjects’ brains processed were well segmented and parcellated. Therefore,
corrections were performed to the structural images, using the tools FS provided. After these corrections done, the
main processing routines had to be rerun, only for the corrected data. A detailed error description and corrections is
available at the FS website and wiki. The most common corrections performed during this study are depicted in Figure
1. The corrections that can be performed in FS, range from modifications to the talairach coordinates transform, skull
stripping (when more than just the skull is removed, as well as when very few is removed, check Figure 1a ), intensity
normalization of white matter (vide Figure 1b), defects in the white matter (vide Figure 1c), topological defects (in the
boundaries of white matter and dura) and errors in the pial surfaces.
The workflow followed, as well as the corrections procedures, are provided in appendix.
III. Results and Discussion
From the FS processing, subcortical volumes and cortical thicknesses were obtained. As it will be seen, only some of
Figure 1. Common errors after the FS pre-processing. a) Skull strip failure; b) Intensity normalization error; c) White matter defect.
a) b) c)

4. Automated MRI measurements to identify individuals with neurodegenerative diseases Page | 4
these anatomical regions were selected for the study, since they were the ones that showed more differences
considering AD, MCI, FTD patients and controls.
Subcortical Volumetry
Considering that the group of patients studied had three different neurodegenerative diseases (AD, MCI and FTD) that
affected mainly the subcortical regions of the brain, the analysis was performed based in selected anatomical regions:
the Thalamus, the Caudate Nucleus, Putamen, Pallidum, Hippocampus, Amygdala and Accumbens area. Some of
these selected regions are depicted in Figure 2, which shows a FS segmentation of a specific participant. Volumetry
measurements were obtained for each subject and for each subject’s referred anatomical regions.
As it was explained in the previous section, the participants were divided in two groups, the controls (healthy subjects)
and the patients (subjects with AD or MCI or FTD). Each subject’s volume from the several selected subcortical areas
was considered in the arithmetic average of the two groups. These averages, along with the standard deviation, are
presented in Table 1 and Table 2 (left and right hemisphere, respectively). Observing the results, it can be seen that in
all subcortical regions the average volume of a patient is lower than the one of a control subject, as it was expected
(brain structures atrophy). For both hemispheres, it is seen that the most atrophied structure is the hippocampus, with
a decreased in the average volume up to approximately 25%. This is indicator that these diseases (or possible some)
are potentially responsible for this decrease. (Jack CR, et al., 1999) stated that in older patients with MCI,
hippocampal atrophy is predictive of subsequent conversion to AD. Other structures that were also greatly affected in
volume were the Amygdala an Accumbens area, with a relative deviation up to approximately 20%. Figure 3 shows
these differences more clearly. The computed standard deviations show how big the difference between each subject
volume and the averaged value is, and can be an indicator of the differences between the diseases, only if the aging
degeneration of structures is not predominant (the participants ages was also confidential). However, due to the fact
that the information of which patients had a particular disease was confidential, it is not possible to establish a reliable
correlation between the changes in subcortical volume of patients and controls.
Putamen
Pallidum
Thalamus
Amygdala
Caudate Nucleus
Figure 2. Segmentation of subcortical structures of the brain, using FS. Some of the selected structures are highlighted.

5. Automated MRI measurements to identify individuals with neurodegenerative diseases Page | 5
Table 1. Arithmetic averaged volume (and standard deviation) of the selected subcortical structures, in mm
3
, for the
left hemisphere.
volume
Left Thalamus
Proper
Left
Caudate
Left
Putamen
Left
Pallidum
Left
Hippocampus
Left
Amygdala
Left Accumbens
area
Control 6383 ± 1081 3510 ± 246 5259 ± 471 1388 ± 284 3919 ± 411 1599 ± 161 570 ± 84
Patients 5725 ± 738 3205 ± 615 4661 ± 611 1312 ± 312 2927 ± 588 1231 ± 270 498 ± 120
Table 2. Arithmetic averaged volume (and standard deviation) of the selected subcortical structures, in mm
3
, for the
right hemisphere.
volume
Right Thalamus
Proper
Right
Caudate
Right
Putamen
Right
Pallidum
Right
Hippocampus
Right
Amygdala
Right Accumbens
area
Control 6362 ± 885 3509 ± 270 4945 ± 542 1225 ± 316 4059 ± 420 1731 ± 289 638 ± 91
Patients 5713 ± 683 3417 ± 999 4396 ± 580 1172 ± 254 3045 ± 599 1375 ± 295 536 ± 100
Figure 3. Comparison between the Control and Patients groups averaged volume of the selected subcortical
structures, for both hemispheres.

6. Automated MRI measurements to identify individuals with neurodegenerative diseases Page | 6
Cortical Thickness
To study the cortical thickness dependence of the three diseases, AD, MCI and FTD, some cortex regions were
selected, namely the frontal, the temporal and the parietal cortical regions. Thickness measurements were obtained
for each subject and for each subject’s referred anatomical regions. Each subject’s volume from the several selected
cortical areas was considered in the arithmetic average of the two groups. These averages, along with the standard
deviation, are presented in Table 3 and Table 4 (left and right hemisphere, respectively). Observing the results, it can
be seen that in all cortical regions the average thickness of a patient is lower than the one of a control subject, as it
was expected (cortex atrophy). For both hemispheres, it is seen that the most atrophied region is the Entorhinal
cortex, with a decreased in the average thickness up to approximately 20%. This is indicator that these diseases (or
possible some) are potentially responsible for this decrease. Along the remaining cortical regions, the thickness
decreased approximately the same, with differences ranging up to 7%. Figure 4 shows these differences more clearly.
As in the volumetry analysis, an equal thought between the standard deviation and the diseases can be considered.
However, for the same previously stated reasons, it is not possible to establish a reliable correlation between the
changes in the cortical thickness of patients and controls.
Table 3. Arithmetic averaged thickness (and standard deviation) of the selected cortical structures, in mm, for the left
hemisphere.
Caudal
Middle
Frontal
Entorhinal
Inferior
Temporal
Middle
Temporal
Superior
Parietal
Superior
Temporal
Temporal
Pole
Control 2,521 ± 0,136 3,648 ± 0,231 2,761 ± 0,175 2,781 ± 0,118 2,270 ± 0,150
2,640 ±
0,086
3,710 ±
0,249
Patients 2,356 ± 0,161 2,977 ± 0,484 2,563 ± 0,198 2,565 ± 0,204 2,089 ± 0,163
2,452 ±
0,188
3,437 ±
0,317
Table 4. Arithmetic averaged thickness (and standard deviation) of the selected cortical structures, in mm, for the right
hemisphere.
Caudal
Middle
Frontal
Entorhinal
Inferior
Temporal
Middle
Temporal
Superior
Parietal
Superior
Temporal
Temporal
Pole
Control 2,506 ± 0,122 3,588 ± 0,312 2,839 ± 0,126 2,808 ± 0,127 2,259 ± 0,169
2,649 ±
0,130
3,708 ±
0,287
Patients 2,35 ± 0,169 3,18 ± 0,490 2,647 ± 0,235 2,626 ± 0,243 2,067 ± 0,177
2,462 ±
0,202
3,581 ±
0,296

7. Automated MRI measurements to identify individuals with neurodegenerative diseases Page | 7
Figure 4. Comparison between the Control and Patients groups averaged thickness of the selected cortical
structures, for both hemispheres.
IV. Conclusion
In this work, automated volumetry of subcortical brain structures, as well as thickness measurements were performed,
through the MRI imaging software FreeSurfer. FS software presented itself as a good, quicker and automated
alternative for segmentation, but still presents some errors in certain situations, and their correction will ultimately
depend only on the user’s knowledge of the brain structures. In both hemispheres, the average volume of every
subcortical brain structure from the patients group was always lower than the one for the controls group, as it was
expected. The same happened to the cortical thickness values. This is an indicator that AD, MCI and FTD are
potentially responsible for these decreases. The most affected subcortical structure volume occurred in the
hippocampus, with a decrease in the average volume up to 25%, and the most affected cortical region was the
Entorhinal cortex, with a decrease up to 20%. Additionally, it was seen the FS has difficulty in a well defined detection
of some regions, such as the Entorhinal cortex, and in this case and also considering the MRI sequence used, the
segmentation for this structure should probably be done manually, despite the fact that FS detected clear differences
between patients and controls. However, due to the fact that the information of which patients had a particular disease
was confidential, it is not possible to establish a reliable correlation between the changes in subcortical volume of
patients and controls. Moreover, additional information would also be adequate since depending on the subject state,

8. Automated MRI measurements to identify individuals with neurodegenerative diseases Page | 8
preclinical or symptomatic, different approaches can be considered. Therefore, to perform a more detailed analysis
that will allow the identification of potential diagnostic markers for neurodegenerative diseases larger number of
participants will be required, as well the clear classification of their pathological state. Furthermore, the optimization of
the MRI sequences for a better discrimination of subcortical and cortical structures will also improve reliability and
reproducibility. This optimization should pass by finding other MRI sequences with better signal-to-noise ratio,
sequences with information both in T1 and T2 parameters, such as multi-echo flash that will probably enable a
superior subcortical segmentation.
V. Acknowledgment
The author gratefully thanks Gina Caetano, Gil Cunha and Miguel Castelo-Branco from IBILI, for their technical
support and discussions.
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