Effects of age on tissues and regions of the cerebrum and cerebellum

Jernigan, Terry L.; Archibald, Sarah; Fennema-Notestine, Christine; Gamst, Anthony; Stout, Julie C.; Bonner, Julie; Hesselink, John R.

doi:10.1016/s0197-4580(01)00217-2

Cited by 822 publications

(737 citation statements)

References 37 publications

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“…It is also well known that the brain volume decreases with age in our sample by À113 ml/50 years (8.2%), which is similar to studies based on autopsy (Debakan and Sadowsky, 1978;Miller et al, 1980;Svennerholm et al, 1997) and MRI data (Blatter et al, 1995;Jernigan et al, 2001). This loss is more pronounced in the males than females and in the gray than the white matter compartment.…”

Section: Resultssupporting

confidence: 91%

“…Following some initial studies using cranial computed tomography (CCT) (Abbott et al, 2000;Hahn et al, 1984;Schwartz et al, 1985), MRI quickly became the method of choice for data collection because MRI allows discriminating between several tissue types (Caviness et al, 1995(Caviness et al, , 1999Kennedy et al, 2003). Several studies focused on the determination of brain compartments and their gender differences (Allen et al, 2002(Allen et al, , 2003Blatter et al, 1995;Filipek et al, 1989Filipek et al, , 1994Sato et al, 2003;Schlaepfer et al, 1995) and changes of compartment volumes with age (Blatter et al, 1995;Courchesne et al, 2000;Ge et al, 2002;Giedd et al, 1996;Harris et al, 1994;Jernigan et al, 2001;Pfefferbaum et al, 1994;Resnick et al, 2003) and tried to find MRI-detectable discriminators of healthy and pathological aging in neurodegenerative diseases (Edland et al, 2002;Jenkins et al, 2000;Wolf et al, 2003Wolf et al, , 2004. Often these studies include a small sample size or a few parameters only.…”

Section: Introductionmentioning

confidence: 99%

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MRI-based volumetry of head compartments: Normative values of healthy adults

Kruggel

2006

NeuroImage

121

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The size of head compartments (head and brain volume, intracranial volume, gray and white matter volume, cerebrospinal fluid volume) and their ratios were determined on the basis of magnetic resonance images of the head acquired in a reference population of 502 healthy subjects. Age-matched subgroups were selected to reveal genderrelated differences and changes with age. Normative data are provided in the form of simple equations that allow transforming measured compartment volumes into z scores, offering the possibility to relate individual data to a larger population. D

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Section: Resultssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

MRI-based volumetry of head compartments: Normative values of healthy adults

Kruggel

2006

NeuroImage

121

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“…The simplest and perhaps most intuitive type of analysis then requires the parcellation of the gray and white matter volumes into lobes, or sometimes into finer subdivisions, for regional quantification of tissue volumes (Fig. 2, panels 2a and 2b; see e.g., Giedd et al, 1999;Jernigan et al, 2001;Kennedy et al, 1998, for this type of approach). These brain volume measures can then be compared using standard statistical techniques, such as analysis of variance or multiple regression.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2004

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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, childhoodand 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. D 2004 Published by Elsevier Inc.

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“…Studies with healthy adult controls generally reveal a linear decline of neocortical GM volume with increasing age (e.g., Courchesne et al, 2000;Jernigan et al, 2001). We used multivariate analysis of covariance (MANCOVA) with age as a covariate to control for age-volume relationships in all group comparisons.…”

Section: Statistical Analysesmentioning

confidence: 99%

Non-treatment-seeking heavy drinkers: Effects of chronic cigarette smoking on brain structure

Durazzo

Cardenas

Studholme

et al. 2007

Drug and Alcohol Dependence

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We previously reported [Cardenas, V.A., Studholme, C., Meyerhoff, D.J., Song, E., Weiner, M.W., 2005. Chronic active heavy drinking and family history of problem drinking modulate regional brain tissue volumes. Psychiatry Res. 138, 115−130] that non-treatment-seeking, active heavy drinkers (HD) demonstrated smaller regional neocortical gray matter volumes compared to light drinking controls; however, the potential effects of chronic cigarette smoking on regional brain volumes were not addressed. The goal of this retrospective analysis was to determine if chronic smoking affected brain structure in the non-treatment-seeking heavy drinking sample from our earlier report (i.e., Cardenas et al., 2005). Regional volumetric comparisons were made among age-matched smoking HD (n = 17), non-smoking HD (n = 16), and non-smoking light drinkers (nsLD; n = 20) from our original sample. Quantitative volumetric measures of neocortical gray matter (GM), white matter (WM), subcortical structures, and cerebral spinal fluid (CSF) were derived from high-resolution magnetic resonance imaging. Smoking HD demonstrated smaller volumes than nsLD in the frontal, parietal, temporal GM, and for total neocortical GM. Smoking HD also demonstrated smaller temporal and total GM volumes than non-smoking HD. Non-smoking HD and nsLD did not differ significantly on GM volumes. Further, the three groups did not differ on lobar WM, subcortical structures or regional CSF volumes. These retrospective analyses indicate neocortical GM volume reductions in non-treatment-seeking smoking HD, but not in non-smoking HD, which are consistent with our studies in recently detoxified treatment-seeking alcohol-dependent samples.

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Effects of age on tissues and regions of the cerebrum and cerebellum

Cited by 822 publications

References 37 publications

MRI-based volumetry of head compartments: Normative values of healthy adults

MRI-based volumetry of head compartments: Normative values of healthy adults

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

Non-treatment-seeking heavy drinkers: Effects of chronic cigarette smoking on brain structure

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