DOI: 10.1148/radiol.2461061994
(Radiology 2008;246:20-32.)
© RSNA, 2008
Imaging Genetics of Brain Longevity and Mental Wellness: The Next Frontier?1
Jeffrey R. Petrella, MD,
Venkata S. Mattay, MD, and
P. Murali Doraiswamy, MD
1 From the Alzheimer Imaging Research Laboratory, Department of Radiology (J.R.P.) and Department of Psychiatry and Medicine (P.M.D.), Duke University Medical Center, Box 3808, Durham, NC 27710; and Genes, Cognition and Psychosis Program, National Institute of Mental Health, Bethesda, Md (V.S.M.). Received November 22, 2006; revision requested January 23, 2007; revision received February 19; accepted March 20; final version accepted May 7; final review by J.R.P. June 20. P.M.D. has received grants and consulting fees from pharmaceutical and diagnostic companies.
Address correspondence to J.R.P. (e-mail: jeffrey.petrella{at}duke.edu).
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ABSTRACT
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The advent of new "omics" technologies (genomics, proteomics, and metabolomics) has ushered in a new era of biomedical discovery that is already affecting every field of medicine. With the rapid growth of the older population worldwide, there is great interest in applying these technologies not only to diagnose and prevent disease, but also to enhance brain longevity and mental wellness. Nearly two-thirds of the approximately 30 000 genes in the human genome are related to brain function, and up to half of the variance in age-related changes in cognition, brain volume, and neuronal function appears to be genetically determined. Selected examples will be used to illustrate how neuroimaging is being employed to study the effects of genes and how neurogenetics may affect future radiology research and practice.
© RSNA, 2008
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INTRODUCTION
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Man's mind once stretched by a new idea, never regains its original dimensions.
—Oliver Wendell Holmes, 1809–1894
"Omics" is a general term referring to a broad new field of biology, engineering, and informatics focused on mapping and studying the interaction of various components of a biologic system, such as genes, proteins, and metabolites. Omics includes the study of how environmental factors interact with the overlapping fields of genomics, proteomics, and metabolomics to determine the state of health or disease in a biologic system (Fig 1). Genomics is a science that seeks to understand an organism in terms of its genetic sequence by using the tools of molecular biology and bioinformatics. This field is rapidly advancing, particularly because of high throughput techniques such as microarray technology for sequencing and characterizing the organization and expression of various gene candidates. Proteomics seeks to study all proteins in a biologic system, particularly where and when proteins are synthesized, in order to understand their role in normal physiology. Metabolomics seeks to identify small, low-molecular-weight proteins (metabolites) produced by cells under different physiologic conditions or genetic modifications, by using nuclear magnetic resonance (MR) spectroscopy and mass spectrometry to identify the chemical components of biologic pathways: substrates, enzymes, and their products. The correlation of gene-expression profiling with protein and metabolic profiling to identify candidate genes and their enzymes is revolutionizing biologic research. Such work is rapidly advancing our understanding of biologic systems in normal physiology and disease.
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Figure 1: Schematic representation of the "omics" technologies. "Omics" includes the study of how lifestyle factors interact with the overlapping fields of genomics, proteomics, and metabolomics to determine the state of health or disease in a biologic system. The various numbers are estimates and the exact number of proteins or metabolites constituting the human proteome or metabolome is still not known. See text for details.
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In this article, we review the basic principles of imaging genetics (how imaging has been used to study the effects of genes), summarize selected genetic polymorphisms that may potentially affect brain aging, and, by using three selected genetic polymorphisms as models, illustrate how genetic-imaging findings are evolving and how they may affect radiology in the near future.
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BASIC TERMINOLOGY OF GENETIC ALTERATIONS
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Of the greater than 3 billion base pairs and approximately 30 000 genes in the human genome, most of these are conserved across the species (genetic universality). However, natural interindividual variability (singularity) occurs in a substantial portion of the genome, giving rise to individual characteristics, such as hair and eye color. This genetic singularity is partly found in nucleotide variations. Variability in the sequence of a particular gene locus (location) is known as a polymorphism and occurs when two or more forms (alleles) of the gene exist. In the 1980s, much of our knowledge of genetic polymorphisms came from markers derived by breaking up DNA with restriction endonucleases into fragments of different lengths and separating them on gels by means of electrophoresis. Differences in the lengths of these fragments were known as restriction fragment length polymorphisms and formed the basis of our knowledge of many monogenetic diseases, including Huntington chorea and Duchenne muscular dystrophy.
In the 1990s, these markers were replaced by polymorphisms in microsatellite repeats, tandem repeating sequences of nucleotides found to have the advantage of uniform regularity along the length of the genome. Perhaps the most useful marker today for gene association studies is the substitution of one nucleotide, or single nucleotide polymorphism (SNP) (Fig 2). SNPs are abundant in the human genome, estimated to number over 1.4 million, and occurring with a regularity of about 1 every 2000 base pairs (1), with an estimated 15 million SNP variants in the genome. Although most SNPs are of no consequence and do not even lead to amino acid substitution, a smaller subset known as coding SNPs (cSNPs) do lead to substitutions (Fig 2). These compose only 1%–2% of all SNPs. Further, only a small percentage of cSNPs give rise to changes in phenotype and confer additional risk for disease. Ninety-nine percent of all human disease-causing mutations affect cSNPs. Therefore, in the absence of complete mapping data, a systematic screen of the 50 000–100 000 cSNPs could represent an economical way to conduct a rapid genomic screen (2). The identification of these markers has recently created much interest on the part of industry and academia to identify therapies early and tailor them to diseases with multiple, complex, interacting genetic and environmental factors.
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Figure 2: Single nucleotide polymorphisms. A change in the DNA sequence of a single nucleotide may not change the amino acid sequence or protein product produced by the genetic code (second row), or it may change the amino acid sequence and protein product (third row). The latter scenario could affect phenotype, including disease susceptibility. A = adenine, Ala = alanine, Arg = arginine, Asn = asparagine, Asp = aspartic acid, C = cytosine, Cys = cysteine, G = guanine, Lys = lysine, T = thymine. (Image courtesy of Oak Ridge National Laboratory, http://www.ornl.gov/sci/techresources/Human_Genome/graphics/slides/98-649jpg.shtml.)
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Genomics of Brain Function
Initially, it had been estimated that about one-third of all the 30 000 or so genes in the human genome were directly related to brain function; however, recently that one-third estimate has been doubled. Many of these genes play a role in neurologic disease, as well as in the normal cognitive aging process. So far, approximately 2000 genes related to brain function have been mapped. Microsoft co-founder Paul Allen has donated $100 million to launch the Allen Brain Atlas Project (http://www.alleninstitute.org), a privately funded organization that will map all genes related to the brain by using mouse data. This effort could accelerate work on identifying genes active in the brain over that already being performed for other existing databases. There are already almost 100 commercially available tests for genes related to human brain function.
Proteomics and Metabolomics of Brain Function
Our knowledge and technique at analyzing the proteome and metabolome are now proceeding at the same rapid pace that has characterized the genomics race. However, adding to the complexity of identifying proteins is that between one-third and two-thirds of all genes give rise to alternatively spliced isoforms, or different proteins coded by a single gene due to variable splice locations. Therefore, each of the approximately 30 000 human genes can give rise to multiple functionally different proteins (3,4). The proteome is estimated to have over 1 000 000 proteins (Fig 1). The Human Proteome Organization (http://www.hupo.org) is an international collaborative attempt to catalogue the human proteome.
The term metabolome is defined as the total set of small-molecule metabolites, including membrane lipids, intermediary chemicals in various pathways such as the Krebs cycle and in neurotransmitter physiology, and hormones, that exist in a human sample, such as plasma or cerebrospinal fluid (5). It is not clear how many metabolites constitute the metabolome, but one estimate is that there are approximately 3000 metabolites (6) (Fig 1). A subset of the metabolome is the "lipidome," and a technology called lipidomics, which can map global changes in 200 or more lipid components, is under study in cardio-vascular disease (7). The Human Metabolome Project (http://www.metabolomics.ca/) is one of several efforts attempting to map the entire human metabolome. Unlike the genome, both the proteome and metabolome are dynamic, that is, they change from cell to cell and from one time point to another. Initial human studies of proteomic and metabolomic patterns in brain disorders, including Alzheimer disease (AD), amyotrophic lateral sclerosis, and schizophrenia, have already been completed and findings are expected to be published soon (8). Because these latter technologies are new, few studies to date have correlated human neuroimaging findings with either proteomic or metabolomic findings. Hence, this review focuses primarily on imaging genetics, but in the future it is expected that all three omics technologies may interact with neuroimaging. In particular, we expect MR spectroscopy will have a strong relationship with metabolomics, given characteristic spectroscopy findings in a variety of pediatric metabolic diseases affecting the central nervous system, including disorders of lipid, amino acid, carbohydrate, and mitochondrial metabolism (9).
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THE PRINCIPLES OF IMAGING GENETICS
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Genes ultimately exert their influence on cognition and behavior through a complex and interactive series of steps. The first step involves regulation and transcription of genes to various proteins. These proteins eventually influence cell processes and function through enzymatic reactions. Groups of cells, mostly neurons, comprising neural systems work together in a complex pattern of stimulation and inhibition, along with other interactions, to produce a given cognitive behavior. While behavioral tests may report a single, final measurement summarizing these complex interactive processes, neuroimaging techniques, including positron emission tomography (PET), MR imaging, MR spectroscopy, and functional MR imaging, allow us to examine more closely and immediately the biologic effects of genetic alterations. Micro-PET technology is also being used to image gene expression by using gene RNA or proteomic-specific probes. These experiments have shown promise in animal models. There have also been substantial advances in molecular imaging, and PET ligands have been developed to track various receptors, neurotransmitter enzymes, and proteins, such as β-amyloid, tau, and acetylcholine (10,11). Given that cholinergic deficits, as well as amyloid and tau deposition, are characteristic of AD, these ligands offer great promise, in conjunction with genotyping, for furthering our understanding of normal and pathologic aging. For example, Pittsburgh compound B, to image β-amyloid, and 2-(1-{6-[(2-[18F] fluoroethyl) (methyl) amino]-2-naphthyl}ethylidene)malononitrile (FDDNP), to image β-amyloid and tau, are in advanced development as diagnostic markers for AD (12,13). Findings of a recent study (12) in which FDDNP imaging was used in 83 subjects with memory complaints showed that subjects with mild cognitive impairment had global binding values that were greater than those of controls and lower than those of subjects with AD. Using Pittsburgh compound B, investigators have recently shown increased uptake of the amyloid-binding agent in subjects at genetic risk for AD (14,15). Another PET ligand, which binds to cholinergic receptors, has been evaluated in a study of healthy older subjects genetically at risk for AD. Findings of that study (10) showed lower distribution volumes of the ligand in the at-risk group, compared with controls, suggesting lower synaptic concentrations of acetylcholine.
A common method for identifying genes is to compare the genetic code of groups demonstrating different cognitive phenotypes. Such population-based approaches, known as candidate gene association studies, form the basis of our knowledge of the function of various individual genes making up the human genome and their influence on the cognitive aging process. Evidence of an association between a particular gene and cognitive phenotype suggests, though does not prove, a causal relationship. Of note, many genetic variants exist in association with one another (linkage disequilibrium), which makes it difficult to determine which locus is causal (16).
Clinical or cognitive phenotype in brain research is traditionally determined by means of various mental status examinations, neuropsychologic batteries, and personality inventories; however, the advent of noninvasive in vivo imaging techniques presents the opportunity to study an endophenotype, which consists of regional neurophysiologic, neurochemical, and neuroanatomic information. Imaging endophenotype, for example, with functional or molecular imaging, may be closer to the level of genetic alterations than measuring a clinical change. Such tools potentially allow for a more powerful assessment of the influence of genes on human cognitive function and phenotype (Fig 3) than do traditional clinical assessments. In addition, use of new mapping technologies to select candidate genes from a limited region of the genome will markedly increase the efficiency of gene association studies (19). This technology is expanding at an unprecedented rate. With the development of the gene array, such as the Affymetrix GeneChip array (Affymetrix, Santa Clara, Calif), gene expression can be rapidly quantified. For example, a team recently used human-mapping 500K arrays (Affymetrix) to simultaneously compare 500 000 genetic markers between people with and people without good memory and to identify the KIBRA gene (Table 1) as a link to poor memory performance (25).
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Figure 3: Schematic of imaging genetics. The biologic effect of a variation in a gene traverses an increasingly divergent path from subtle molecular alterations at the cellular level to alterations in neural systems that eventually lead to variability in cognition and behavior. Imaging genetics allows for the estimation of genetic effects at the level of neural systems or brain information processing, which represents a more proximate biologic link to genes, as well as an obligatory intermediate of cognition and behavior. (Adapted and reprinted, with permission, from references 17 and 18.)
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Sequencing an entire mammalian-sized genome currently costs between $10 and $50 million, but the National Institutes of Health hopes that this number can be reduced by four orders of magnitude over the next 10 years, with the ultimate goal being a $1000 genome. (Applications can be found at http://grants.nih.gov/grants/guide/rfa-files/RFA-HG-05-004.html.)
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GENES AND BRAIN LONGEVITY
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Narrowing down the list of genes expressed in the human brain, from about two-thirds of the 30 000 or so genes in the human genome, to those that could influence brain longevity or age-related cognitive decline is a daunting and arduous endeavor. We have summarized some selected genes in Tables 1 and 2. Readers seeking additional information about other genes affecting cognition are referred elsewhere (2).
The use of "imaging genetics" in this endeavor is still in its nascency, and so far only a handful of genes have been explored. Using selected examples of model genes, we will illustrate how neuroimaging has been used to study the effects of brain longevity genes.
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APOLIPOPROTEIN E
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The APOE gene (apolipoprotein E) plays a role in lipoprotein transport, as well as in neuronal health, both of which may have an interactive effect on brain amyloid deposition, as well as recovery from injury (38). APOE polymorphisms are the most widely studied example of a gene-imaging interaction. Three known allelic variants (
2, 3, and 4) occur, and one copy is inherited from each parent (Fig 4). The APOE4 allele occurs in roughly 25% of the Caucasian population and is the gene that has been implicated in neuronal dysfunction (38). Polymorphisms of this gene modulate risk for developing AD; for example, patients with the 4 allele have an increased risk for AD by about three- to eightfold, the 3 allele is neutral, and people with the 2 allele may have a reduced risk for AD (38). A dose-dependent relationship is present–two copies of the 4 allele confer the greatest risk; one copy, less risk; and no copies, the least risk (39). The 4 allele lowers the age at onset of AD by about 7–15 years, depending on the gene dose. The APOE4 gene is also associated with greater deposition of amyloid plaques and is a predictor of conversion of individuals with mild cognitive impairment to those with AD (38,41).
In addition to a risk for AD, the 4 allele has been linked to a risk for strokes and lipid abnormalities, as well as a greater risk for cognitive dysfunction after bypass surgery, benzodiazepine challenge, or head injury (42). In animal models, there appears to be an interaction between dietary cholesterol intake, apolipoprotein E, and brain amyloid deposition (43). Thus, the 4 allele may have a broader role in modulating the aging brain's ability to repair itself after various types of insults. This makes it an ideal candidate gene for imaging genetic studies.
Numerous in vivo imaging studies have demonstrated alterations in brain metabolism, chemistry, and anatomy in asymptomatic patients who are carriers of the 4 allele (44–46) Initial studies showed that nondemented aging APOE4 carriers demonstrate hypometabolism on fluorodeoxyglucose (FDG) PET studies in the posterior cingulate, temporoparietal, and prefrontal regions, the very same regions showing substantial metabolic deficits in AD patients (44). Functional MR studies have demonstrated substantially different activation patterns among elderly asymptomatic 4 carriers and noncarriers during a word-association task without differences in performance, which suggests modification in the neural networks subserving episodic memory function similar to that observed in subjects with early mild cognitive impairment (47,48). Finally, 4-related morphologic differences in brain volumes have been demonstrated in many (but not all) studies, with 4 carriers having smaller hippocampal and white matter volume compared with noncarriers (49). A study examined 4 gene dose effects on hippocampal volume in subjects with mild cognitive impairment who do not yet meet criteria for dementia. Authors of that study (50) found that hippocampal volume was largest in non-4 carriers, intermediate in subjects with one 4 allele, and smallest in those with two 4 alleles (Fig 5).
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Figure 5: Bar graph demonstrates the relationship between total hippocampal volume, measured at MR imaging, and the number of copies of the APOE4 allele. Note the dose-dependent relationship, with a greater number of copies of 4 associated with declining hippocampal volume. The P value demonstrates a significant omnibus F value for the analysis of variance. (Adapted and reprinted, with permission, from reference 50.)
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Two studies specifically are of interest with regard to brain aging, particularly the study of the effects on brain areas affected by normal aging (eg, the frontal cortex). The frontal cortex is one of the earliest sites of amyloid deposition during normal aging, and many of the signs of age-associated cognitive decline are associated with frontal cortical changes. Authors of an MR spectroscopic study (51) looked at 4 effects on neuronal function by using frontal cortex N-acetylaspartate levels, an intracellular marker of neuronal viability. Approximately 165 older, nondemented subjects were recruited, stratified according to 4 status, and examined with MR spectroscopy. The age-related decline in N-acetylaspartate levels (cross-sectionally between 55 and 85 years of age) in the 4 carriers was greater than that seen in non-4 carriers, and baseline N-acetylaspartate levels correlated with memory scores (51). These data raise the possibility that the 4 allele is associated with accelerated frontal cortex age-related neuronal changes. Although the aforementioned study was conducted in older adults, a more recent study (52) suggests that FDG PET metabolic deficits may be detectable in APOE4 carriers as early as their 20s and 30s (Fig 6). In that study, FDG PET scans of 12 4 carriers aged 20–39 years were compared with those of 12 non-4 carriers matched for age, sex, education, and cognition. The 4 carriers showed greater glucose metabolic deficits than did noncarriers in the bilateral posterior cingulate, parietal, temporal, and frontal regions. Clearly the above studies were pilot studies, and the absence of longitudinal follow-up limits a definitive assessment of the clinical significance of these findings. We still do not know whether the 4 imaging phenotype is a "trait" or a "state" marker, and whether these findings are pathologic or merely physiologic compensatory adjustment. But taken in the context of the extensive evidence linking the 4 allele to risk for AD, one can speculate that the 4 allele is associated with accelerated brain aging decades before possible onset of dementia and potentially is also one of the genes mediating the interaction between dietary cholesterol and brain health. Future research may shed further light on this issue.
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GENES REGULATING NEUROTRANSMITTER ENZYMES OR RECEPTORS
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Genetic polymorphisms are now known to exist in enzymes and receptors involved in various neurotransmitter pathways. For example, the serotonin (5-hydroxytryptamine) and norepinephrine systems are critically involved in many aspects of mental wellness, including mood, anxiety, energy, depression, and attention. Current antidepressants inhibit 5-hydroxytryptamine or norepinephrine reuptake transporter proteins (referred to as 5HT-T and NE-T), and polymorphisms in these transporters have been linked to variability in treatment outcomes in mood disorders, as well as greater vulnerability to stressful life events. In this section, we will use dopamine genes as a model for illustrating the imaging genetic correlations.
As stated above, cognitive abilities, particularly those subserved by the prefrontal cortex, decline with age (53). For example, converging evidence indicates that dopamine, a critical neurotransmitter for several brain functions, improves the efficiency of information processing in the prefrontal cortex by focusing and stabilizing the prefrontal cortical (PFC) networks (54). Accumulating evidence suggests that catechol-O-methyl transferase (COMT), an enzyme that inactivates released dopamine, may play a role in regulating dopamine flux in the prefrontal cortex (55–57). In humans, a functional polymorphism in the gene for COMT (Fig 7) has been identified: An evolutionarily recent methionine (Met) for valine (Val) substitution at codon 108–158 results in a thermolabile protein with two to four times lower activity (59). Thus, the Val form of the protein is putatively more efficient at degrading dopamine than the Met form and thereby is associated with less dopamine in the synapse.
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Figure 7: The COMT gene Val(108/158)Met polymorphism. An evolutionarily recent Met for Val substitution at codon 109–158 in the COMT gene on chromosome 22q11.23 results in thermolabile protein with two to four times lower activity. A = adenine, COMT-MB = COMT membrane–bound isoform, COMT-S = COMT-soluble isoform, G = guanine. (Reprinted, with permission, from reference 58.)
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Consistent with this functional polymorphism in the COMT gene and with the evidence that COMT is important in PFC dopamine flux, Egan et al (60) demonstrated that COMT gene Met allele carriers had superior performance on an executive function task (eg, planning for a dinner party, organization of a week's schedule, and card sorting). In the same study, at functional MR imaging during a working memory task, the Val allele carriers consistently demonstrated a less efficient physiologic response in the prefrontal cortex (ie, greater prefrontal cortex activity) for a fixed level of cognitive task performance, when compared with subjects with the Met allele. This association between COMT Val-Met genotype and prefrontal cognition has since been replicated (61).
More recently, de Frias et al (62) explored the association between this polymorphism and age-related decline in prefrontal function. The authors studied healthy volunteers aged 35–85 years over a period of 5 years and reported greater rates of decline on tests of executive function in Val carriers relative to the Met homozygotes. In a more recent study, Sambataro et al (63), using blood oxygen level–dependent functional MR imaging in young and elderly healthy volunteers, explored the correlation between this gene and age-related changes at PFC information processing (Fig 8). While confirming the association between Val158Met COMT polymorphism and PFC function, not only in the young but also in the elderly subjects, the authors observed that the genotype association—that is, greater PFC activity in the Val-Val group relative to the Met-Met group—was much more exaggerated in the elderly subjects. The relative difference between the Val-Val elderly and Met-Met elderly subjects was much more pronounced when compared with the relative difference in prefrontal activity between the Val-Val young and Met-Met young subjects.
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Figure 8: Graph depicts the linear association between COMT Val(108–158)Met polymorphism and information processing in the prefrontal cortex during a working memory task. The Met polymorphism leads to substantial decrease in COMT enzyme activity, preferentially increasing prefrontal extrasynaptic dopamine. Since dopamine enhances neurophysiologic signal, this may lead to decreased, more efficient activation, which can be measured with techniques such as functional MR. Val allele carriers demonstrate a less efficient physiologic response in prefrontal cortex for a fixed level of task performance (ie, greater PFC activity) when compared with Met allele carriers. Image on right shows the focus in the prefrontal cortex demonstrating differential activation. BOLD = blood oxygen level–dependent. (Reprinted, with permission, from reference 58.)
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Together these results suggest that COMT Val158Met polymorphism may modulate age-related decline in prefrontal function. Although these results clearly need to be replicated in larger samples examined longitudinally, they raise the intriguing possibility that the Met allele in the COMT gene may confer a protective role, and individuals carrying the Met allele may show a relatively slower age-related decline in prefrontal function compared to those carrying the Val allele.
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GENES REGULATING NEURONAL GROWTH FACTORS
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Growth factors are active not only during brain development but also in the adult human brain and in aging brains. Brain-derived neurotrophic factor (BDNF) was the second growth factor identified in the brain, after nerve growth factor. BDNF regulates cortical neuron survival, proliferation, and synaptic growth in the developing central nervous system and is expressed throughout the brain, particularly in the hippocampus and prefrontal cortex. Converging evidence indicates that it is a critical element in modulating synaptic changes such as long-term potentiation in the hippocampus, which is associated with learning and memory formation (64). BDNF acts through several receptors, including a tyrosine kinase receptor. Agonists at this site are being developed to treat various brain disorders (65). Stress and cortisol suppress BDNF levels, whereas caloric restriction and exercise stimulate BDNF. The role of BDNF in normal brain aging, as well as in several neuropsychiatric disorders, for example, dementia, depression, and Parkinson disease, is under active study.
Several BDNF gene polymorphisms exist in humans and their frequencies vary according to ethnicity. A common Val66Met polymorphism in the BDNF gene (Fig 9) has been shown to affect intracellular packaging and regulated secretion of BDNF. Consistent with this finding, Egan et al (66) reported that BDNF Val66Met polymorphism affects hippocampal function and episodic memory. The Met allele is associated with relatively poorer episodic memory, a decline in N-acetylaspartate at MR spectroscopy, and a disruption of normal brain disengagement during a working memory task in young healthy volunteers at functional MR imaging (67). In another study, Hariri et al (68) similarly reported that Met-BDNF carriers displayed relatively reduced hippocampal engagement during both encoding and retrieval of a declarative memory task along with more recognition errors than did young healthy Val-Val homozygote volunteers (Fig 10). Using high-spatial-resolution structural MR imaging, Szeszko et al (70) reported relatively lower hippocampal volumes in Met carriers than in Val-Val homozygotes. Consistent with the role of BDNF in cortical development and with cellular and clinical effects of the BDNF Val66Met polymorphism, using high-spatial-resolution structural MR in concert with optimized voxel based morphometry, Pezawas et al (71) demonstrated that Met carriers, compared with Val carriers, have relatively reduced gray matter volume in the hippocampus and prefrontal cortex (Fig 11). These studies illustrate the potential utility of brain imaging measures in characterizing the biologic effects of the BDNF gene and also support the role of the gene in brain aging.
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Figure 9: BDNF gene Val66Met polymorphism. In humans, a frequent polymorphism at nucleotide 196 (adenine [A]-guanine [G]), producing a nonconservative amino acid substitution (Val to Met) at codon 66, has been identified in the BDNF gene located on the short arm of chromosome 11. This sequence variant, though located in the 5 proBDNF sequence, encodes the precursor peptide (proBDNF), which is proteolytically cleaved to form mature protein (66). Though this BDNF polymorphism does not affect the mature BDNF protein function, it has been shown to affect regulated secretion of the mature peptide by altering the intracellular trafficking and packaging of proBDNF (67).
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Figure 10a: BDNF66Met and reduced hippocampal engagement. (a, b) Functional MR images show greater hippocampal activity in Val homozygotes of BDNF gene when compared with Met carriers during (a) encoding and (b) retrieval of (c) visual stimuli. Subjects were asked to determine whether stimuli were indoor versus outdoor during the encoding phase of the experiment, and whether the stimuli were new or presented previously during (d) the retrieval phase. There is an interaction between BDNFVal66Met genotype and hippocampal blood oxygen level–dependent response during encoding, such that hippocampal activity either positively or negatively correlates with memory performance, depending on the allele. This interaction accounts for almost 25% of the variance in recognition memory performance. (Reprinted, with permission, from references 68 and 69.)
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Figure 10b: BDNF66Met and reduced hippocampal engagement. (a, b) Functional MR images show greater hippocampal activity in Val homozygotes of BDNF gene when compared with Met carriers during (a) encoding and (b) retrieval of (c) visual stimuli. Subjects were asked to determine whether stimuli were indoor versus outdoor during the encoding phase of the experiment, and whether the stimuli were new or presented previously during (d) the retrieval phase. There is an interaction between BDNFVal66Met genotype and hippocampal blood oxygen level–dependent response during encoding, such that hippocampal activity either positively or negatively correlates with memory performance, depending on the allele. This interaction accounts for almost 25% of the variance in recognition memory performance. (Reprinted, with permission, from references 68 and 69.)
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Figure 10c: BDNF66Met and reduced hippocampal engagement. (a, b) Functional MR images show greater hippocampal activity in Val homozygotes of BDNF gene when compared with Met carriers during (a) encoding and (b) retrieval of (c) visual stimuli. Subjects were asked to determine whether stimuli were indoor versus outdoor during the encoding phase of the experiment, and whether the stimuli were new or presented previously during (d) the retrieval phase. There is an interaction between BDNFVal66Met genotype and hippocampal blood oxygen level–dependent response during encoding, such that hippocampal activity either positively or negatively correlates with memory performance, depending on the allele. This interaction accounts for almost 25% of the variance in recognition memory performance. (Reprinted, with permission, from references 68 and 69.)
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Figure 10d: BDNF66Met and reduced hippocampal engagement. (a, b) Functional MR images show greater hippocampal activity in Val homozygotes of BDNF gene when compared with Met carriers during (a) encoding and (b) retrieval of (c) visual stimuli. Subjects were asked to determine whether stimuli were indoor versus outdoor during the encoding phase of the experiment, and whether the stimuli were new or presented previously during (d) the retrieval phase. There is an interaction between BDNFVal66Met genotype and hippocampal blood oxygen level–dependent response during encoding, such that hippocampal activity either positively or negatively correlates with memory performance, depending on the allele. This interaction accounts for almost 25% of the variance in recognition memory performance. (Reprinted, with permission, from references 68 and 69.)
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Figure 11a: BDNF Val66Met polymorphism and reduced gray matter volume. (a) Color overlay statistical parametric map of a group-wise comparison demonstrates areas of reduced volume (yellow) in the bilateral hippocampi. (b) Scatterplot demonstrates reduced hippocampal volume in Met carriers compared to carriers of the Val-Val polymorphism. (c) Color overlay superimposed on a three-dimensional anatomic rendering demonstrates reduced frontal lobe volume (red and yellow) in Met carriers. 1SE = 1 standard error. (Reprinted, with permission, from reference 71.)
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Figure 11b: BDNF Val66Met polymorphism and reduced gray matter volume. (a) Color overlay statistical parametric map of a group-wise comparison demonstrates areas of reduced volume (yellow) in the bilateral hippocampi. (b) Scatterplot demonstrates reduced hippocampal volume in Met carriers compared to carriers of the Val-Val polymorphism. (c) Color overlay superimposed on a three-dimensional anatomic rendering demonstrates reduced frontal lobe volume (red and yellow) in Met carriers. 1SE = 1 standard error. (Reprinted, with permission, from reference 71.)
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Figure 11c: BDNF Val66Met polymorphism and reduced gray matter volume. (a) Color overlay statistical parametric map of a group-wise comparison demonstrates areas of reduced volume (yellow) in the bilateral hippocampi. (b) Scatterplot demonstrates reduced hippocampal volume in Met carriers compared to carriers of the Val-Val polymorphism. (c) Color overlay superimposed on a three-dimensional anatomic rendering demonstrates reduced frontal lobe volume (red and yellow) in Met carriers. 1SE = 1 standard error. (Reprinted, with permission, from reference 71.)
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Given the evidence of increasing memory deficits with advancing age, Mattay et al (72) explored the effect of Val66Met on hippocampal function in a healthy elderly cohort. Though the performance was similar across the two genotype groups, Mattay et al found that BDNF-Met allele carriers showed substantially decreased hippocampal engagement during recognition when compared with Val homozygotes. Additionally, the BDNF-Met allele carriers showed greater prefrontal cortical activity than did the BDNF-Val homozygotes, which probably reflects a compensatory mechanism to maintain performance. Although these results need to be replicated in a larger, multiethnic sample studied longitudinally, they suggest that Val homozygote elderly BDNF individuals show relatively better preserved hippocampal function when compared to their BDNF-Met-allele–carrying counterparts, who had to resort to compensatory mechanisms to maintain performance on a simple declarative memory task.
Using high-spatial-resolution structural MR imaging coupled with optimized voxel-based morphometry, Nemoto et al (73) explored the effect of this polymorphism on morphologic changes associated with aging. They examined healthy controls 20–72 years of age and reported an exaggerated age-related volume reduction in the dorsolateral prefrontal cortex of Met carriers (73). Together these neuroimaging studies illustrate that the Val66Met polymorphism of BDNF may play an important role in vulnerability to age-related changes in structure and function.
Other polymorphisms of the BDNF gene have also been associated with variations in brain morphology in both healthy subjects and those with neuropsychiatric disease (74). This and other research supports an emerging model where genetics and lifestyle interact to determine brain longevity and disease in part through modulation of BDNF levels and raises the hypothesis that the combined use of neuroimaging and BDNF genotyping may offer clinical prognostic value in many brain diseases.
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IMPLICATIONS FOR THE FUTURE AND CAVEATS
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With an increased understanding of genetic polymorphisms, as well as proteomic and metabolomic profiles that confer disease risk or affect age-related brain changes, the availability of such information in routine patient care may take on increasing importance. The alliance between imaging and genetics can be used to clarify the effects of genes (eg, APOE4, BDNF, COMT) and disease mechanisms. If validated, this alliance may in the future also supplement clinical measures in determining diagnosis and patient outcomes. Several limitations still remain to be overcome, however, and they are discussed next.
Despite the rapid pace at which new findings become available, readers should understand that the "omics" field is still evolving and many of the existing genetic imaging studies are limited by (a) small convenience samples, (b) cross-sectional, in contrast to longitudinal, design, (c) their exploratory nature, without post hoc testing, and (d) lack of long-term clinical correlations. Longitudinal studies examining multiple gene markers in thousands of aging subjects stratified according to ethnicity with clinical-imaging-genotype outcome correlations will be needed to test diagnostic and prognostic value. The large overlap between genotype groups also currently limits application at an individual patient level and raises the need for specificity and sensitivity studies. With such studies, we may be able to begin to apply group-level findings to the individual, while keeping in mind that even sensitive and specific genetic-clinical associations may not necessarily represent a causative relationship. The National Institutes of Health–funded Alzheimer Disease Neuroimaging Initiative is an example of a longitudinal genetic-imaging-outcome study, and its results are expected to be available in a few years (75). Further, both the frequency and the effects of various polymorphisms vary greatly according to ethnicity. For example, findings in Caucasians may not apply to Asians. There are likely dozens of genes that affect a single-tissue structure (eg, the hippocampus), and hence studies correlating a single gene with hippocampal volume should be interpreted cautiously since they may miss the big picture in terms of interactions among various genes. For example, while both APOE4 and BDNF Val-Met polymorphisms appear to affect hippocampal volume, it is not known how these two genes interact. In addition, lifestyle and environmental issues interact with the genome, but few imaging studies to date have examined this interaction. Last, genotyping raises ethical concerns, such as the lack of insurance coverage, low predictive value, and whether subjects should be informed of their results. All these issues will need to be quickly addressed by society, including the research community.
However, technology is advancing so rapidly that it is likely that in the near future a genetic profile of the most common clinically relevant polymorphisms could be available for many patients as part of their medical record. Confidential genotyping services may possibly even become widely available commercially through Web sites. Indeed, it is already possible to obtain genotyping for ancestral tracking through several Web sites for as little as $99. Over 250 000 individuals (including two of the authors, J.R.P., P.M.D.) have already been genotyped through National Geographic's Genographic Project, in which buccal swab kits are shipped to subjects' homes, and results are posted within a month on a password-pro-tected Web site (https://www3.nationalgeographic.com/genographic/index.html). As the technology becomes increasingly widespread, physicians should be wary of rushing to adopt new technology until appropriate sensitivity and specificity studies are conducted and genomics can be shown to improve patient outcomes above and beyond currently available clinical measures. Until then, imaging-genetics correlative studies will remain a useful research tool to enhance our knowledge of the aging and diseased brain.
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ESSENTIALS
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- Advances in genomics, proteomics, and metabolomics will likely impact every field of medicine, including radiology.
- Nearly two-thirds of all genes in the genome influence brain function, and many genes that influence brain longevity and mental wellness are already under intense study.
- The combined use of neuroimaging with genotyping has revealed important clues about the phenotypic effects of various genes and could enhance our understanding of normal aging and common brain diseases.
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ACKNOWLEDGMENTS
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The authors thank Richard Youngblood, MA, for his editorial assistance.
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FOOTNOTES
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Abbreviations: AD = Alzheimer disease • BDNF = brain-derived neurotrophic factor • COMT = catechol-O-methyl transferase • FDG = fluorodeoxyglucose • PFC = prefrontal cortical • SNP = single nucleotide polymorphism
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A.J. Nemeth
Functional MR Imaging and the Future of Neuroradiology
AJNR Am. J. Neuroradiol.,
February 1, 2009;
30(2):
218 - 218.
[Full Text]
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ABSTRACT
INTRODUCTION
C transition at codon 112. The 