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      Assessing the Changes of Cortical Thickness in Alzheimer Disease With MRI Using Freesurfer Software


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          In this study, we intend to determine the correlation between the thickness of the cerebral cortex and the severity of the cognitive disorder in Alzheimer disease (AD).


          A total of 20 (14 women and 6 men) patients diagnosed with AD with a Mean age of 72.95 years, and 10 (7 women and 3 men) cognitively normal (CN) subjects with a Mean age of 70.50 years were included in the study. Of the AD patient and CN subjects, 70% were female, and 30% were male. All individuals underwent 1.5 T Magnetic resonance imaging (MRI). The MRI scanning protocol included 3D MPRAGE (3D-T1W) sequence. All images were analyzed using Freesurfer v5.3, and then the brain cortical thickness in 7 cortical areas (inferior temporal, middle temporal, superior temporal, parahippocampal, pars triangularis, rostral middle frontal, and superior frontal) was calculated.


          The analysis of covariance (ANCOVA) was conducted to compare the mean thickness of each region between the patient and the control group. There was a significant difference in the mean cortical thickness in all regions. In all cases, the mean cortical thickness in CN subjects was greater than in AD patients. However, the mean thickness of pars triangularis left hand in CN subjects was not significantly greater than that in AD patients. The receiver operating characteristic system (ROC) was designed to evaluate the predictive power of the patients and the healthy people. We have selected a thousand cut-off points from 1.5 to 3.5 mm for cortical thickness. When the cut-off points were within 2.276878–2.299680 mm in the left hemisphere, Youden’s index was maximum. The sensitivity and specificity, in this case, were 80%. Also, when the cut-off points were within the range of 2.263278–2.282278 mm in the right hemisphere, the sensitivity and specificity were 90% and 80%, respectively.


          This study demonstrates the importance of quantifying the cortical thickness changes in the early diagnosis of AD. In addition, examining the pattern of changes and quantifying the reduction in the thickness of the cortex is a crucial tool for displaying the local and global atrophy of the brain. Also, this pattern can be used as an alternative marker for the diagnosis of dementia. Finally, to the best of our knowledge, our study is the first to report finding on the cortical thickness that would help the clinician have a better differential diagnosis. Also, this study has checked the possibility of early diagnosis of the disease.

          • The correlation between the thickness of cerebral cortex and the severity of cognitive disorder in Alzheimer's disease was determined.

          • The cortical thickness change is an important factor in early diagnosis of Alzheimers disease.

          • The pattern of reduction in the thickness changes is a crucial tool for displaying the local and global atrophy of the brain.

          Plain Language Summary

          The neurodegenerative disorder Alzheimer's disease (AD) is a fast-growing epidemic in aging populations worldwide. In 2050, one new case of AD is estimated to increase up to every 33 seconds. So the diagnosis of AD in the early stage considerably decreases the progress of dementia and helps identify a correct treatment approach. The cortical thickness measured by structural neuroimaging has received a significant surrogate biomarker that could provide powerful tools for the early diagnosis of AD. Since the sensitivity and specificity of MRI are higher, it offers essential advantages for identifying brain atrophy patterns. The manual cortical thickness measurement methods are time-consuming and require experienced anatomists compared with automated methods. In this regard, Freesurfer software, which is a freely available program and provides information for quantifying the functional and structural features of the brain, is used. The current study demonstrates examining the pattern of changes and quantifying the reduction in the thickness of the cortex. This can also be used as an alternative marker for the early diagnosis of dementia using cortical thickness measurment that would help the physicians.

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          FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Measuring the thickness of the human cerebral cortex from magnetic resonance images.

            Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test-retest studies, as well as by comparison of cross-subject regional thickness measures with published values.
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              Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's Disease Neuroimaging Initiative (ADNI).

              With the increasing life expectancy in developed countries, the incidence of Alzheimer's disease (AD) and thus its socioeconomic impact are growing. Increasing knowledge over the last years about the pathomechanisms involved in AD allow for the development of specific treatment strategies aimed at slowing down or even preventing neuronal death in AD. However, this requires also that (1) AD can be diagnosed with high accuracy, because non-AD dementias would not benefit from an AD-specific treatment; (2) AD can be diagnosed in very early stages when any intervention would be most effective; and (3) treatment efficacy can be reliably and meaningfully monitored. Although there currently is no ideal biomarker that would fulfill all these requirements, there is increasing evidence that a combination of currently existing neuroimaging and cerebrospinal fluid (CSF) and blood biomarkers can provide important complementary information and thus contribute to a more accurate and earlier diagnosis of AD. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is exploring which combinations of these biomarkers are the most powerful for diagnosis of AD and monitoring of treatment effects.

                Author and article information

                Basic Clin Neurosci
                Basic Clin Neurosci
                Basic and Clinical Neuroscience
                Iranian Neuroscience Society
                Mar-Apr 2022
                01 March 2022
                : 13
                : 2
                : 185-192
                [1. ]Department of Radiology, School of Allied Medical Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
                [2. ]Department of Radiology, School of Medicine, AJA University of Medical Sciences, Tehran, Iran.
                [3. ]Department of Basic Sciences, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
                Author notes
                [* ] Corresponding Author: Faeghi Fariborz, PhD. , Address: Department of Radiology, School of Allied Medical Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Tel: +98 (912) 7171638, E-mail: f_faeghi@ 123456sbmu.ac.ir
                Author information
                Copyright© 2022 Iranian Neuroscience Society

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

                : 23 September 2019
                : 20 June 2020
                : 25 August 2020
                Research Paper

                alzheimer disease,cortical thickness,magnetic resonance imaging (mri),freesurfer software


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