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Journal Article•DOI•

The Structure of the Cerebral Cortex Across Adult Life: Age-Related Patterns of Surface Area, Thickness, and Gyrification

Larson J. Hogstrom1, Lars T. Westlye1, Kristine B. Walhovd1, Anders M. Fjell1•
University of Oslo1
01 Nov 2013-Cerebral Cortex (Oxford University Press)-Vol. 23, Iss: 11, pp 2521-2530
TL;DR: It is suggested that this pattern of regional "cortical stretching" reflects the well-established phylogenetic principle of maximizing surface area and gyrification rather than increase thickness to facilitate brain connectivity and functional development.
Abstract: Older adults exhibit global reductions in cortical surface area, but little is known about the regional patterns of reductions or how these relate to other measures of brain structure. This knowledge is critical to understanding the dynamic relationship between different macrostructural properties of the cortex throughout adult life. Here, cortical arealization, local gyrification index (LGI), and cortical thickness were measured vertex wise across the brain surface in 322 healthy adults (20-85 years), with the aims of 1) characterizing age patterns of the three separate cortical measures and 2) testing the age-independent relationships among cortical surface area, gyrification, and thickness. Surface area showed strong age-related decreases, particularly pronounced in dorsomedial prefrontal, lateral temporal, and fusiform cortices, independently of total white matter volume. LGI decreased with age independently of regional surface area, with strongest effects laterally, extending from the angular gyrus in all directions. As expected, regional surface area and LGI were positively related. However, both measures correlated negatively with thickness, indicating increasing local arealization and gyrification with decreasing cortical thickness. We suggest that this pattern of regional "cortical stretching" reflects the well-established phylogenetic principle of maximizing surface area and gyrification rather than increase thickness to facilitate brain connectivity and functional development.

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Citations
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Mini Mental State

[...]

Hana Vaňková
21 Jun 2010

1,966 citations

Journal Article•DOI•
What is normal in normal aging? Effects of aging, amyloid and Alzheimer's disease on the cerebral cortex and the hippocampus

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Anders M. Fjell1, Linda K. McEvoy2, Dominic Holland2, Anders M. Dale2, Kristine B. Walhovd1 •
University of Oslo1, University of California, San Diego2
01 Jun 2014-Progress in Neurobiology
TL;DR: It will be difficult to understand AD without understanding why it preferably affects older brains, and that a model that accounts for age-related changes in AD-vulnerable regions independently of AD-pathology is needed.

581 citations

Cites background from "The Structure of the Cerebral Corte..."

  • ...2010) or even negative (Hogstrom et al., 2012) correlations between thickness and area have been observed....

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  • ...Few studies have systematically compared differences in agevulnerability between cortical thickness, area and volume, but there are indications that thickness is more affected than area (Hogstrom et al., 2012) (see Fig....

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  • ...In human adults, weak (Winkler et al., 2010) or even negative (Hogstrom et al., 2012) correlations between thickness and area have been observed....

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Journal Article•DOI•
Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements.

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Nicholas J. Tustison1, Philip A. Cook2, Arno Klein3, Gang Song2, Sandhitsu R. Das2, Jeffrey T. Duda2, Benjamin M. Kandel2, Niels M. van Strien4, James R. Stone1, James C. Gee2, Brian B. Avants2 •
University of Virginia1, University of Pennsylvania2, Sage Bionetworks3, Norwegian University of Science and Technology4
01 Oct 2014-NeuroImage
TL;DR: The largest evaluation of automated cortical thickness measures in publicly available data is conducted, comparing FreeSurfer and ANTs measures computed on 1205 images from four open data sets, with parcellation based on the recently proposed Desikan-Killiany-Tourville cortical labeling protocol.

571 citations

Cites methods from "The Structure of the Cerebral Corte..."

  • ...For example, in Hogstrom et al. (2013), 322 T1-weighted MRI of healthy adults with an age range of [20,85] were used, in part, to characterize the relationship between age and cortical thickness using FreeSurfer and a similar linear modeling approach....

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Journal Article•DOI•
Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

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Pim Moeskops1, Max A. Viergever1, Adriënne M. Mendrik1, Linda S. de Vries1, Manon J. N. L. Benders1, Ivana Išgum1 •
Utrecht University1
30 Mar 2016-IEEE Transactions on Medical Imaging
TL;DR: This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network, and demonstrates its robustness to differences in age and acquisition protocol.
Abstract: Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal $ {\rm T}_{2}$ -weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial $ {\rm T}_{2}$ -weighted images of preterm infants acquired at 40 weeks PMA, axial $ {\rm T}_{1}$ -weighted images of ageing adults acquired at an average age of 70 years, and $ {\rm T}_{1}$ -weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86, and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.

560 citations

Journal Article•DOI•
Automatic segmentation of MR brain images with a convolutional neural network

[...]

Pim Moeskops1, Max A. Viergever1, Adriënne M. Mendrik1, Linda S. de Vries1, Manon J. N. L. Benders1, Ivana Išgum1 •
Utrecht University1
11 Apr 2017-arXiv: Computer Vision and Pattern Recognition
TL;DR: In this article, a convolutional neural network (CNN) was used for segmentation of MR brain images into a number of tissue classes using a single anatomical MR image only.
Abstract: Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86 and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.

470 citations

References
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Journal Article•DOI•
“Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician

[...]

Marshal F. Folstein1, Marshal F. Folstein2, Susan E B Folstein3, Susan E B Folstein4, Paul R. McHugh1, Paul R. McHugh2 •
Oregon Health & Science University1, Cornell University2, University of Oregon3, NewYork–Presbyterian Hospital4
01 Nov 1975-Journal of Psychiatric Research
TL;DR: A simplified, scored form of the cognitive mental status examination, the “Mini-Mental State” (MMS) which includes eleven questions, requires only 5-10 min to administer, and is therefore practical to use serially and routinely.

76,181 citations

"The Structure of the Cerebral Corte..." refers background in this paper

  • ...…have prompted authors to evaluate cortical thickness and surface area as separate morphometric features of neurodevelopment, aging, and disease (Im et al. 2008; Dickerson et al. 2009; Ostby et al. 2009; Panizzon et al. 2009; Lemaitre et al. 2010; Winkler et al. 2010; Eyler et al. 2011)....

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  • ...All included participants scored <16 on the Beck Depression Inventory (Beck and Steer 1987) and participants over the age of 40 scored >25 on the mini-mental state examination (Folstein et al. 1975; Bravo and Hebert 1997)....

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A practical method for grading the cognitive state of patients for the clinician

[...]

Marshal Folstein, Susan E B Folstein, F. Folstein, Paul R. McHugh
01 Jan 2002
TL;DR: The Mini-Mental State (MMS) as mentioned in this paper is a simplified version of the standard WAIS with eleven questions and requires only 5-10 min to administer, and is therefore practical to use serially and routinely.
Abstract: EXAMINATION of the mental state is essential in evaluating psychiatric patients.1 Many investigators have added quantitative assessment of cognitive performance to the standard examination, and have documented reliability and validity of the several “clinical tests of the sensorium”.2*3 The available batteries are lengthy. For example, WITHERS and HINTON’S test includes 33 questions and requires about 30 min to administer and score. The standard WAIS requires even more time. However, elderly patients, particularly those with delirium or dementia syndromes, cooperate well only for short periods.4 Therefore, we devised a simplified, scored form of the cognitive mental status examination, the “Mini-Mental State” (MMS) which includes eleven questions, requires only 5-10 min to administer, and is therefore practical to use serially and routinely. It is “mini” because it concentrates only on the cognitive aspects of mental functions, and excludes questions concerning mood, abnormal mental experiences and the form of thinking. But within the cognitive realm it is thorough. We have documented the validity and reliability of the MMS when given to 206 patients with dementia syndromes, affective disorder, affective disorder with cognitive impairment “pseudodementia”5T6), mania, schizophrenia, personality disorders, and in 63 normal subjects.

70,887 citations

Journal Article•DOI•
An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

[...]

Rahul S. Desikan1, Florent Ségonne2, Bruce Fischl3, Bruce Fischl2, Brian T. Quinn3, Bradford C. Dickerson3, Deborah Blacker3, Randy L. Buckner4, Randy L. Buckner3, Anders M. Dale5, R. Paul Maguire6, Bradley T. Hyman3, Marilyn S. Albert7, Ronald J. Killiany1 •
Boston University1, Massachusetts Institute of Technology2, Harvard University3, Howard Hughes Medical Institute4, University of California, San Diego5, Pfizer6, Johns Hopkins University School of Medicine7
01 Jul 2006-NeuroImage
TL;DR: An automated labeling system for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable and may be useful for both morphometric and functional studies of the cerebral cortex.

9,940 citations

"The Structure of the Cerebral Corte..." refers background in this paper

  • ...Cortical surfaces then undergo inflation, registration to a spherical atlas, and identification of gyral and sulcal regions (Desikan et al. 2006)....

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Journal Article•DOI•
Cortical surface-based analysis. I. Segmentation and surface reconstruction

[...]

Anders M. Dale1, Bruce Fischl1, Martin I. Sereno2•
Harvard University1, University of California, San Diego2
01 Feb 1999-NeuroImage
TL;DR: A set of automated procedures for obtaining accurate reconstructions of the cortical surface are described, which have been applied to data from more than 100 subjects, requiring little or no manual intervention.

9,599 citations

"The Structure of the Cerebral Corte..." refers background or methods in this paper

  • ...Detailed descriptions of the surface-based methods are given elsewhere (Dale et al. 1999; Fischl et al. 1999, 2001; Fischl and Dale 2000)....

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  • ...Keywords: aging, cortical thickness, gray matter, magnetic resonance imaging morphometry, neurodevelopment...

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  • ...All subjects were right-handed, native Norwegian speakers....

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  • ...Intensity and continuity information from the 3D volume are used in segmentation and deformation procedures to reconstruct a gray/white matter boundary throughout the brain (Dale et al. 1999)....

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  • ...By recording the local distortions needed to fit a subject’s brain to a standardized atlas, a point-by-point map of areal expansion or reduction can be created (Dale et al. 1999; Fischl and Dale 2000; Joyner et al. 2009; Rimol et al. 2010)....

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Journal Article•DOI•
Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.

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Bruce Fischl1, David H. Salat1, Evelina Busa1, Marilyn S. Albert1, Megan E. Dieterich, Christian Haselgrove, Andre van der Kouwe1, Ronald J. Killiany2, David N. Kennedy, Shuna Klaveness, Albert Montillo3, Nikos Makris, Bruce R. Rosen1, Anders M. Dale1 •
Harvard University1, Boston University2, University of Pennsylvania3
31 Jan 2002-Neuron
TL;DR: In this paper, a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set is presented.

7,120 citations

Related Papers (5)
Cortical surface-based analysis. I. Segmentation and surface reconstruction

[...]

01 Feb 1999-NeuroImage
Anders M. Dale, Bruce Fischl, Martin I. Sereno
Measuring the thickness of the human cerebral cortex from magnetic resonance images

[...]

26 Sep 2000-Proceedings of the National Academy of Sciences of the United States of America
Bruce Fischl, Anders M. Dale
An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

[...]

01 Jul 2006-NeuroImage
Rahul S. Desikan, Florent Ségonne, Bruce Fischl, Bruce Fischl, Brian T. Quinn, Bradford C. Dickerson, Deborah Blacker, Randy L. Buckner, Randy L. Buckner, Anders M. Dale, R. Paul Maguire, Bradley T. Hyman, Marilyn S. Albert, Ronald J. Killiany 
Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System

[...]

01 Feb 1999-NeuroImage
Bruce Fischl, Martin I. Sereno, Anders M. Dale
Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.

[...]

31 Jan 2002-Neuron
Bruce Fischl, David H. Salat, Evelina Busa, Marilyn S. Albert, Megan E. Dieterich, Christian Haselgrove, Andre van der Kouwe, Ronald J. Killiany, David N. Kennedy, Shuna Klaveness, Albert Montillo, Nikos Makris, Bruce R. Rosen, Anders M. Dale