Obesity is associated with increased risk for cardiovascular health problems including diabetes, hypertension, and stroke. These cardiovascular afflictions increase risk for cognitive decline and dementia, but it is unknown whether these factors, specifically obesity and type II diabetes, are associated with specific patterns of brain atrophy. We used tensor-based morphometry (TBM) to examine gray matter (GM) and white matter (WM) volume differences in 94 elderly subjects who remained cognitively normal for at least 5 years after their scan. Bivariate analyses with corrections for multiple comparisons strongly linked body mass index (BMI), fasting plasma insulin (FPI) levels, and Type II Diabetes Mellitus (DM2) with atrophy in frontal, temporal, and subcortical brain regions. A multiple regression model, also correcting for multiple comparisons, revealed that BMI was still negatively correlated with brain atrophy (FDR < 5%), while DM2 and FPI were no longer associated with any volume differences. In an Analysis of Covariance (ANCOVA) model controlling for age, gender, and race, obese subjects with a high BMI (BMI > 30) showed atrophy in the frontal lobes, anterior cingulate gyrus, hippocampus, and thalamus compared to individuals with a normal BMI (18.5–25). Overweight subjects (BMI: 25–30) had atrophy in the basal ganglia and corona radiata of the WM. Overall brain volume did not differ between overweight and obese persons. Higher body mass index was associated with lower brain volumes in overweight and obese elderly subjects. Obesity is therefore associated with detectable brain volume deficits in cognitively normal elderly subjects.

In one of the largest brain MRI studies to date, we used tensor-based morphometry (TBM) to create 3D maps of structural atrophy in 676 subjects with Alzheimer’s disease (AD), mild cognitive impairment (MCI), and healthy elderly controls, scanned as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Using inverse-consistent 3D non-linear elastic image registration, we warped 676 individual brain MRI volumes to a population mean geometric template. Jacobian determinant maps were created, revealing the 3D profile of local volumetric expansion and compression. We compared the anatomical distribution of atrophy in 165 AD patients (age: 75.6 ± 7.6 years), 330 MCI subjects (74.8 ± 7.5), and 181 controls (75.9 ± 5.1). Brain atrophy in selected regions-of-interest was correlated with clinical measurements - the sum-of-boxes clinical dementia rating (CDR-SB), mini-mental state examination (MMSE), and the logical memory test scores - at voxel level followed by correction for multiple comparisons. Baseline temporal lobe atrophy correlated with current cognitive performance, future cognitive decline, and conversion from MCI to AD over the following year; it predicted future decline even in healthy subjects. Over half of the AD and MCI subjects carried the ApoE4 (apolipoprotein E4) gene, which increases risk for AD; they showed greater hippocampal and temporal lobe deficits than non-carriers. ApoE2 gene carriers - 1/6 of the normal group - showed reduced ventricular expansion, suggesting a protective effect. As an automated image analysis technique, TBM reveals 3D correlations between neuroimaging markers, genes, and future clinical changes, and is highly efficient for large-scale MRI studies.

The structure of the human brain is highly heritable, and is thought to be influenced by many common genetic variants, many of which are currently unknown. Recent advances in neuroimaging and genetics have allowed collection of both highly detailed structural brain scans and genome-wide genotype information. This wealth of information presents a new opportunity to find the genes influencing brain structure. Here we explore the relation between 448,293 single nucleotide polymorphisms in each of 31,622 voxels of the entire brain across 740 elderly subjects (mean age±s.d.: 75.52±6.82 years; 438 male) including subjects with Alzheimer's disease, Mild Cognitive Impairment, and healthy elderly controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used tensor-based morphometry to measure individual differences in brain structure at the voxel level relative to a study-specific template based on healthy elderly subjects. We then conducted a genome-wide association at each voxel to identify genetic variants of interest. By studying only the most associated variant at each voxel, we developed a novel method to address the multiple comparisons problem and computational burden associated with the unprecedented amount of data. No variant survived the strict significance criterion, but several genes worthy of further exploration were identified, including CSMD2 and CADPS2. These genes have high relevance to brain structure. This is the first voxelwise genome wide association study to our knowledge, and offers a novel method to discover genetic influences on brain structure.

A recently identified variant within the fat mass and obesity-associated ( FTO ) gene is carried by 46% of Western Europeans and is associated with an ~1.2 kg higher weight, on average, in adults and an ~1 cm greater waist circumference. With >1 billion overweight and 300 million obese persons worldwide, it is crucial to understand the implications of carrying this very common allele for the health of our aging population. FTO is highly expressed in the brain and elevated body mass index (BMI) is associated with brain atrophy, but it is unknown how the obesity-associated risk allele affects human brain structure. We therefore generated 3D maps of regional brain volume differences in 206 healthy elderly subjects scanned with MRI and genotyped as part of the Alzheimer's Disease Neuroimaging Initiative. We found a pattern of systematic brain volume deficits in carriers of the obesity-associated risk allele versus noncarriers. Relative to structure volumes in the mean template, FTO risk allele carriers versus noncarriers had an average brain volume difference of ~8% in the frontal lobes and 12% in the occipital lobes—these regions also showed significant volume deficits in subjects with higher BMI. These brain differences were not attributable to differences in cholesterol levels, hypertension, or the volume of white matter hyperintensities; which were not detectably higher in FTO risk allele carriers versus noncarriers. These brain maps reveal that a commonly carried susceptibility allele for obesity is associated with structural brain atrophy, with implications for the health of the elderly.

Population-based brain mapping provides great insight into the trajectory of aging and dementia, as well as brain changes that normally occur over the human life span. We describe three novel brain mapping techniques, cortical thickness mapping, tensor-based morphometry (TBM), and hippocampal surface modeling, which offer enormous power for measuring disease progression in drug trials, and shed light on the neuroscience of brain degeneration in Alzheimer's disease (AD) and mild cognitive impairment (MCI).We report the first time-lapse maps of cortical atrophy spreading dynamically in the living brain, based on averaging data from populations of subjects with Alzheimer's disease and normal subjects imaged longitudinally with MRI. These dynamic sequences show a rapidly advancing wave of cortical atrophy sweeping from limbic and temporal cortices into higher-order association and ultimately primary sensorimotor areas, in a pattern that correlates with cognitive decline. A complementary technique, TBM, reveals the 3D profile of atrophic rates, at each point in the brain. A third technique, hippocampal surface modeling, plots the profile of shape alterations across the hippocampal surface. The three techniques provide moderate to highly automated analyses of images, have been validated on hundreds of scans, and are sensitive to clinically relevant changes in individual patients and groups undergoing different drug treatments. We compare time-lapse maps of AD, MCI, and other dementias, correlate these changes with cognition, and relate them to similar time-lapse maps of childhood development, schizophrenia, and HIV-associated brain degeneration. Strengths and weaknesses of these different imaging measures for basic neuroscience and drug trials are discussed.