Journal
NEUROIMAGE
Volume 23, Issue -, Pages S2-S18Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2004.07.071
Keywords
Alzheimer's disease; brain development; schizophrenia
Funding
- NCRR NIH HHS [R21 RR019771, RR00827, P41 RR13642] Funding Source: Medline
- NIA NIH HHS [P50 AG016570] Funding Source: Medline
- NIBIB NIH HHS [R21 EB01651] Funding Source: Medline
- NIDA NIH HHS [R21 DA15878] Funding Source: Medline
- NIMH NIH HHS [P20 MH/DA52176, P20 MH65166, R01 MH52857, K01 MH01733] Funding Source: Medline
- NATIONAL CENTER FOR RESEARCH RESOURCES [M01RR000827, P41RR013642, R21RR019771] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF MENTAL HEALTH [K01MH001733, P20MH052176, Z01MH000178, Z01MH002581, P20MH065166, Z01MH002794, R01MH052857] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON AGING [P50AG016570] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON DRUG ABUSE [R21DA015878] Funding Source: NIH RePORTER
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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. (C) 2004 Published by Elsevier Inc.
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