TY - JOUR
T1 - Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia
AU - Thompson, Paul M.
AU - Hayashi, Kiralee M.
AU - Sowell, Elizabeth R.
AU - Gogtay, Nitin
AU - Giedd, Jay N.
AU - Rapoport, Judith L.
AU - De Zubicaray, Greig I.
AU - Janke, Andrew L.
AU - Rose, Stephen E.
AU - Semple, James
AU - Doddrell, David M.
AU - Wang, Yalin
AU - Van Erp, Theo G.M.
AU - Cannon, Tyrone D.
AU - Toga, Arthur W.
N1 - Funding Information:
This work was funded by grants from the National Institute for Biomedical Imaging and Bioengineering, the National Center for Research Resources, and the National Institute on Aging (to P.T.: R21 EB01651, R21 RR019771, P50 AG016570), by the National Institute of Mental Health, the National Institute of Drug Abuse, and the March of Dimes, (to E.R.S.: K01 MH01733, R21 DA15878, MOD 5FY03-12), by GlaxoSmithKline Pharmaceuticals UK, and by a Human Brain Project grant to the International Consortium for Brain Mapping, funded jointly by NIMH and NIDA (P20 MH/DA52176; P41 RR13642, and P20 MH65166 to A.W.T.). Additional support was provided by NIMH intramural funding (J.L.R.), by NIMH grant R01 MH52857 (T.D.C.), and by a grant to the FIRST Biomedical Informatics Research Network (BIRN, http://www.nbirn.net ), which is funded by the NCRR and NIH (RR00827).
PY - 2004
Y1 - 2004
N2 - This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhood- and adult-onset schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages.
AB - This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhood- and adult-onset schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages.
KW - Alzheimer's disease
KW - Brain development
KW - Schizophrenia
UR - http://www.scopus.com/inward/record.url?scp=7044253600&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2004.07.071
DO - 10.1016/j.neuroimage.2004.07.071
M3 - Article
C2 - 15501091
AN - SCOPUS:7044253600
SN - 1053-8119
VL - 23
SP - S2-S18
JO - NeuroImage
JF - NeuroImage
IS - SUPPL. 1
ER -