Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia

Paul M. Thompson, Kiralee M. Hayashi, Elizabeth R. Sowell, Nitin Gogtay, Jay N. Giedd, Judith L. Rapoport, Greig I. De Zubicaray, Andrew L. Janke, Stephen E. Rose, James Semple, David M. Doddrell, Yalin Wang, Theo G.M. Van Erp, Tyrone D. Cannon, Arthur W. Toga

Research output: Contribution to journalArticlepeer-review

303 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)S2-S18
JournalNeuroImage
Volume23
Issue numberSUPPL. 1
DOIs
StatePublished - 2004

Keywords

  • Alzheimer's disease
  • Brain development
  • Schizophrenia

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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