Priya Rajagopalan

Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials

Various neuroimaging measures are being evaluated for tracking Alzheimers disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimers Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimers Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI.

Accurate Measurement of Brain Changes in Longitudinal MRI Scans using Tensor-Based Morphometry

This paper responds to Thompson and Holland (2011), who challenged our tensor-based morphometry (TBM) method for estimating rates of brain changes in serial MRI from 431 subjects scanned every 6 months, for 2 years. Thompson and Holland noted an unexplained jump in our atrophy rate estimates: an offset between 0 and 6 months that may bias clinical trial power calculations. We identified why this jump occurs and propose a solution. By enforcing inverse-consistency in our TBM method, the offset dropped from 1.4% to 0.28%, giving plausible anatomical trajectories. Transitivity error accounted for the minimal remaining offset. Drug trial sample size estimates with the revised TBM-derived metrics are highly competitive with other methods, though higher than previously reported sample size estimates by a factor of 1.6 to 2.4. Importantly, estimates are far below those given in the critique. To demonstrate a 25% slowing of atrophic rates with 80% power, 62 AD and 129 MCI subjects would be required for a 2-year trial, and 91 AD and 192 MCI subjects for a 1-year trial.

Surface-based TBM boosts power to detect disease effects on the brain: An N = 804 ADNI study

Computational anatomy methods are now widely used in clinical neuroimaging to map the profile of disease effects on the brain and its clinical correlates. In Alzheimers disease (AD), many research groups have modeled localized changes in hippocampal and lateral ventricular surfaces, to provide candidate biomarkers of disease progression for drug trials. We combined the power of parametric surface modeling and tensor-based morphometry to study hippocampal differences associated with AD and mild cognitive impairment (MCI) in 490 subjects (97 AD, 245 MCI, 148 controls) and ventricular differences in 804 subjects scanned as part of the Alzheimers Disease Neuroimaging Initiative (ADNI; 184 AD, 391 MCI, 229 controls). We aimed to show that a new multivariate surface statistic based on multivariate tensor-based morphometry (mTBM) and radial distance provides a more powerful way to detect localized anatomical differences than conventional surface-based analysis. In our experiments, we studied correlations between hippocampal atrophy and ventricular enlargement and clinical measures and cerebrospinal fluid biomarkers. The new multivariate statistics gave better effect sizes for detecting morphometric differences, relative to other statistics including radial distance, analysis of the surface tensor and the Jacobian determinant. In empirical tests using false discovery rate curves, smaller sample sizes were needed to detect associations with diagnosis. The analysis pipeline is generic and automated. It may be applied to analyze other brain subcortical structures including the caudate nucleus and putamen. This publically available software may boost power for morphometric studies of subcortical structures in the brain.

Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression

We implemented least absolute shrinkage and selection operator (LASSO) regression to evaluate gene effects in genome-wide association studies (GWAS) of brain images, using an MRI-derived temporal lobe volume measure from 729 subjects scanned as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Sparse groups of SNPs in individual genes were selected by LASSO, which identifies efficient sets of variants influencing the data. These SNPs were considered jointly when assessing their association with neuroimaging measures. We discovered 22 genes that passed genome-wide significance for influencing temporal lobe volume. This was a substantially greater number of significant genes compared to those found with standard, univariate GWAS. These top genes are all expressed in the brain and include genes previously related to brain function or neuropsychiatric disorders such as MACROD2, SORCS2, GRIN2B, MAGI2, NPAS3, CLSTN2, GABRG3, NRXN3, PRKAG2, GAS7, RBFOX1, ADARB2, CHD4, and CDH13. The top genes we identified with this method also displayed significant and widespread post hoc effects on voxelwise, tensor-based morphometry (TBM) maps of the temporal lobes. The most significantly associated gene was an autism susceptibility gene known as MACROD2. We were able to successfully replicate the effect of the MACROD2 gene in an independent cohort of 564 young, Australian healthy adult twins and siblings scanned with MRI (mean age: 23.8 ± 2.2 SD years). Our approach powerfully complements univariate techniques in detecting influences of genes on the living brain.

Homocysteine effects on brain volumes mapped in 732 elderly individuals

Elevated homocysteine levels are a known risk factor for Alzheimers disease and vascular disorders. Here we applied tensor-based morphometry to brain magnetic resonance imaging scans of 732 elderly individuals from the Alzheimers Disease Neuroimaging Initiative study, to determine associations between homocysteine and brain atrophy. Those with higher homocysteine levels showed greater frontal, parietal, and occipital white matter atrophy in the entire cohort, irrespective of diagnosis, age, or sex. This association was also found when considering mild cognitive impairment individuals separately. Vitamin B supplements, such as folate, may help prevent homocysteine-related atrophy in Alzheimers disease by possibly reducing homocysteine levels. These atrophy profiles may, in the future, offer a potential biomarker to gauge the efficacy of interventions using dietary folate supplementation.

Fat-mass-related hormone, plasma leptin, predicts brain volumes in the elderly.

Leptin, a hormone produced by body fat tissue, acts on hypothalamic receptors in the brain to regulate appetite and energy expenditure, and on neurons in the arcuate nucleus to signal that an individual has had enough to eat. Leptin enters the central nervous system at levels that depend on an individual’s body fat. Obese people, on average, show greater brain atrophy in old age, so it is valuable to know whether brain atrophy relates to leptin levels, which can be targeted by interventions. We therefore determined how plasma leptin levels, and BMI, relate to brain structure, and whether leptin levels might account for BMI’s effect on the brain. We measured regional brain volumes using tensor-based morphometry, in MRI scans of 517 elderly individuals with plasma leptin measured (mean: 13.3±0.6 ng/ml; mean age: 75.2±7.3 years; 321 men/196 women). We related plasma leptin levels to brain volumes at every location in the brain after adjusting for age, sex, and diagnosis and, later, also BMI. Plasma leptin levels were significantly higher (a) in women than men, and (b) in obese versus overweight, normal or underweight individuals. People with higher leptin levels showed deficits in frontal, parietal, temporal and occipital lobes, brainstem, and the cerebellum, irrespective of age, sex, or diagnosis. These associations persisted after controlling for BMI. Greater brain atrophy may occur in people with central leptin insufficiency, a marker of obesity. Therapeutic manipulation of leptin may be a promising direction for slowing brain decline.

Higher homocysteine associated with thinner cortical gray matter in 803 participants from the Alzheimer's Disease Neuroimaging Initiative

A significant portion of our risk for dementia in old age is associated with lifestyle factors (diet, exercise, and cardiovascular health) that are modifiable, at least in principle. One such risk factor, high-homocysteine levels in the blood, is known to increase risk for Alzheimers disease and vascular disorders. Here, we set out to understand how homocysteine levels relate to 3D surface-based maps of cortical gray matter distribution (thickness, volume, and surface area) computed from brain magnetic resonance imaging in 803 elderly subjects from the Alzheimers Disease Neuroimaging Initiative data set. Individuals with higher plasma levels of homocysteine had lower gray matter thickness in bilateral frontal, parietal, occipital, and right temporal regions and lower gray matter volumes in left frontal, parietal, temporal, and occipital regions, after controlling for diagnosis, age, and sex and after correcting for multiple comparisons. No significant within-group associations were found in cognitively healthy people, patients with mild cognitive impairment, or patients with Alzheimers disease. These regional differences in gray matter structure may be useful biomarkers to assess the effectiveness of interventions, such as vitamin B supplements, that aim to prevent homocysteine-related brain atrophy by normalizing homocysteine levels.

Multilocus genetic profiling to empower drug trials and predict brain atrophy

Designers of clinical trials for Alzheimers disease (AD) and mild cognitive impairment (MCI) are actively considering structural and functional neuroimaging, cerebrospinal fluid and genetic biomarkers to reduce the sample sizes needed to detect therapeutic effects. Genetic pre-selection, however, has been limited to Apolipoprotein E (ApoE). Recently discovered polymorphisms in the CLU, CR1 and PICALM genes are also moderate risk factors for AD; each affects lifetime AD risk by ~ 10–20%. Here, we tested the hypothesis that pre-selecting subjects based on these variants along with ApoE genotype would further boost clinical trial power, relative to considering ApoE alone, using an MRI-derived 2-year atrophy rate as our outcome measure. We ranked subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI) based on their cumulative risk from these four genes. We obtained sample size estimates in cohorts enriched in subjects with greater aggregate genetic risk. Enriching for additional genetic biomarkers reduced the required sample sizes by up to 50%, for MCI trials. Thus, AD drug trial enrichment with multiple genotypes may have potential implications for the timeliness, cost, and power of trials.

Common folate gene variant, MTHFR C677T, is associated with brain structure in two independent cohorts of people with mild cognitive impairment☆

A commonly carried C677T polymorphism in a folate-related gene, MTHFR, is associated with higher plasma homocysteine, a well-known mediator of neuronal damage and brain atrophy. As homocysteine promotes brain atrophy, we set out to discover whether people carrying the C677T MTHFR polymorphism which increases homocysteine, might also show systematic differences in brain structure. Using tensor-based morphometry, we tested this association in 359 elderly Caucasian subjects with mild cognitive impairment (MCI) (mean age: 75 ± 7.1 years) scanned with brain MRI and genotyped as part of Alzheimers Disease Neuroimaging Initiative. We carried out a replication study in an independent, non-overlapping sample of 51 elderly Caucasian subjects with MCI (mean age: 76 ± 5.5 years), scanned with brain MRI and genotyped for MTHFR, as part of the Cardiovascular Health Study. At each voxel in the brain, we tested to see where regional volume differences were associated with carrying one or more MTHFR ‘T’ alleles. In ADNI subjects, carriers of the MTHFR risk allele had detectable brain volume deficits, in the white matter, of up to 2–8% per risk T allele locally at baseline and showed accelerated brain atrophy of 0.5–1.5% per T allele at 1 year follow-up, after adjusting for age and sex. We replicated these brain volume deficits of up to 5–12% per MTHFR T allele in the independent cohort of CHS subjects. As expected, the associations weakened after controlling for homocysteine levels, which the risk gene affects. The MTHFR risk variant may thus promote brain atrophy by elevating homocysteine levels. This study aims to investigate the spatially detailed effects of this MTHFR polymorphism on brain structure in 3D, pointing to a causal pathway that may promote homocysteine-mediated brain atrophy in elderly people with MCI.

Physical activity, inflammation, and volume of the aging brain

Physical activity influences inflammation, and both affect brain structure and Alzheimers disease (AD) risk. We hypothesized that older adults with greater reported physical activity intensity and lower serum levels of the inflammatory marker tumor necrosis factor α (TNFα) would have larger regional brain volumes on subsequent magnetic resonance imaging (MRI) scans. In 43 cognitively intact older adults (79.3±4.8 years) and 39 patients with AD (81.9±5.1 years at the time of MRI) participating in the Cardiovascular Health Study, we examined year-1 reported physical activity intensity, year-5 blood serum TNFα measures, and year-9 volumetric brain MRI scans. We examined how prior physical activity intensity and TNFα related to subsequent total and regional brain volumes. Physical activity intensity was measured using the modified Minnesota Leisure Time Physical Activities questionnaire at year 1 of the study, when all subjects included here were cognitively intact. Stability of measures was established for exercise intensity over 9 years and TNFα over 3 years in a subset of subjects who had these measurements at multiple time points. When considered together, more intense physical activity intensity and lower serum TNFα were both associated with greater total brain volume on follow-up MRI scans. TNFα, but not physical activity, was associated with regional volumes of the inferior parietal lobule, a region previously associated with inflammation in AD patients. Physical activity and TNFα may independently influence brain structure in older adults.