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Neda Jahanshad,
Priya Rajagopalan, Xue Hua,
Derrek P Hibar,
Talia M Nir,
Arthur W Toga,
Clifford R Jack,
Andrew J Saykin,
Robert C Green,
Michael W Weiner,
Sarah E Medland,
Grant W Montgomery,
Narelle K Hansell,
Katie L McMahon,
Greig I de Zubicaray,
Nicholas G Martin,
Margaret J Wright,
Paul M Thompson
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ABSTRACT: Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, high-angular resolution diffusion MRI. We adapted GWASs to screen the brain's connectivity pattern, allowing us to discover genetic variants that affect the human brain's wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer's disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.
Proceedings of the National Academy of Sciences 03/2013; · 9.68 Impact Factor
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ABSTRACT: We propose a new method to maximize biomarker efficiency for detecting anatomical change over time in serial MRI. Drug trials using neuroimaging become prohibitively costly if vast numbers of subjects must be assessed, so it is vital to develop efficient measures of brain change. A popular measure of efficiency is the minimal sample size (n80) needed to detect 25% change in a biomarker, with 95% confidence and 80% power. For multivariate measures of brain change, we can directly optimize n80 based on a Linear Discriminant Analysis (LDA). Here we use a supervised learning framework to optimize n80, offering two alternative solutions. With a new medial surface modeling method, we track 3D dynamic changes in the lateral ventricles in 2,065 ADNI scans. We apply our LDA-based weighting to the results. Our best average n80 - in two-fold nested cross-validation - is 104 MCI subjects (95% CI: [94,139]) for a 1-year drug trial, and 75 AD subjects [64,102]. This compares favorably with other MRI analysis methods. The standard "statistical ROI" approach applied to the same ventricular surfaces requires 165 MCI or 94 AD subjects. At 2 years, the best LDA measure needs only 67 MCI and 52 AD subjects, versus 119 MCI and 80 AD subjects for the stat-ROI method. Our surface-based measures are unbiased: they give no artifactual additive atrophy over three time points. Our results suggest that statistical weighting may boost efficiency of drug trials that use brain maps.
NeuroImage 01/2013; · 5.89 Impact Factor
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ABSTRACT: Poor kidney function is associated with increased risk of cognitive decline and generalized brain atrophy. Chronic kidney disease impairs glomerular filtration rate, and this deterioration is indicated by elevated blood levels of kidney biomarkers such as creatinine and cystatin C. Here we hypothesized that impaired renal function would be associated with brain deficits in regions vulnerable to neurodegeneration. Using tensor-based morphometry, we related patterns of brain volumetric differences to creatinine, cystatin C levels, and glomerular filtration rate in a large cohort of 738 (mean age, 75.5 ± 6.8 years; 438 men, 300 women) elderly Caucasian subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative. Elevated kidney biomarkers were associated with volume deficits in the white matter region of the brain. All 3 renal parameters in our study showed significant associations consistently with a region that corresponds with the anterior limb of internal capsule, bilaterally. This is the first study to report a marked profile of structural alterations in the brain associated with elevated kidney biomarkers, helping us to explain the cognitive deficits.
Neurobiology of aging 11/2012; · 5.94 Impact Factor
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Xue Hua,
Derrek P Hibar,
Christopher R K Ching,
Christina P Boyle,
Priya Rajagopalan,
Boris A Gutman,
Alex D Leow,
Arthur W Toga,
Clifford R Jack,
Danielle Harvey,
Michael W Weiner,
Paul M Thompson
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ABSTRACT: Various neuroimaging measures are being evaluated for tracking Alzheimer's disease 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 ADNI-1 MRI dataset 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 and 95 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 vs. 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.
NeuroImage 11/2012; · 5.89 Impact Factor
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Grace J Lee,
Po H Lu,
Luis D Medina,
Yaneth Rodriguez-Agudelo,
Stephanie Melchor,
Giovanni Coppola,
Meredith N Braskie, Xue Hua,
Liana G Apostolova,
Alex D Leow,
Paul M Thompson,
John M Ringman
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ABSTRACT: BACKGROUND: Mutations in the presenilin (PSEN1, PSEN2) and amyloid precursor protein (APP) genes cause familial Alzheimer's disease (FAD) in a nearly fully penetrant, autosomal dominant manner, providing a unique opportunity to study presymptomatic individuals who can be predicted to develop Alzheimer's disease (AD) with essentially 100% certainty. Using tensor-based morphometry (TBM), we examined brain volume differences between presymptomatic and symptomatic FAD mutation carriers and non-carrier (NC) relatives. METHODS: Twenty-five mutation carriers and 10 NC relatives underwent brain MRI and clinical assessment. Four mutation carriers had dementia (MUT-Dem), 12 had amnestic mild cognitive impairment (MUT-aMCI) and nine were cognitively normal (MUT-Norm). TBM brain volume maps of MUT-Norm, MUT-aMCI and MUT-Dem subjects were compared to NC subjects. RESULTS: MUT-Norm subjects exhibited significantly smaller volumes in the thalamus, caudate and putamen. MUT-aMCI subjects had smaller volumes in the thalamus, splenium and pons, but not in the caudate or putamen. MUT-Dem subjects demonstrated smaller volumes in temporal, parietal and left frontal regions. As non-demented carriers approached the expected age of dementia diagnosis, this was associated with larger ventricular and caudate volumes and a trend towards smaller temporal lobe volume. CONCLUSIONS: Cognitively intact FAD mutation carriers had lower thalamic, caudate and putamen volumes, and we found preliminary evidence for increasing caudate size during the predementia stage. These regions may be affected earliest during prodromal stages of FAD, while cortical atrophy may occur in later stages, when carriers show cognitive deficits. Further studies of this population will help us understand the progression of neurobiological changes in AD.
Journal of neurology, neurosurgery, and psychiatry 10/2012; · 4.87 Impact Factor
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Priya Rajagopalan,
Neda Jahanshad,
Jason L Stein, Xue Hua,
Sarah K Madsen,
Omid Kohannim,
Derrek P Hibar,
Arthur W Toga,
Clifford R Jack Jr,
Andrew J Saykin,
Robert C Green,
Michael W Weiner,
Joshua C Bis,
Lewis H Kuller,
Mario Riverol,
James T Becker,
Oscar L Lopez,
Paul M Thompson
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ABSTRACT: 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 Alzheimer's 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.
NeuroImage: Clinical. 10/2012;
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ABSTRACT: We present a new method for the detection of gene pathways associated with a multivariate quantitative trait, and use it to identify causal pathways associated with an imaging endophenotype characteristic of longitudinal structural change in the brains of patients with Alzheimer's disease (AD). Our method, known as pathways sparse reduced-rank regression (PsRRR), uses group lasso penalised regression to jointly model the effects of genome-wide single nucleotide polymorphisms (SNPs), grouped into functional pathways using prior knowledge of gene-gene interactions. Pathways are ranked in order of importance using a resampling strategy that exploits finite sample variability. Our application study uses whole genome scans and MR images from 99 probable AD patients and 164 healthy elderly controls in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 66,182 SNPs are mapped to 185 gene pathways from the KEGG pathway database. Voxel-wise imaging signatures characteristic of AD are obtained by analysing 3D patterns of structural change at 6, 12 and 24months relative to baseline. High-ranking, AD endophenotype-associated pathways in our study include those describing insulin signalling, vascular smooth muscle contraction and focal adhesion. All of these have been previously implicated in AD biology. In a secondary analysis, we investigate SNPs and genes that may be driving pathway selection. High ranking genes include a number previously linked in gene expression studies to β-amyloid plaque formation in the AD brain (PIK3R3,PIK3CG,PRKCAandPRKCB), and to AD related changes in hippocampal gene expression (ADCY2, ACTN1, ACACA, and GNAI1). Other high ranking previously validated AD endophenotype-related genes include CR1, TOMM40 and APOE.
NeuroImage 08/2012; 63(3):1681-1694. · 5.89 Impact Factor
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Grace J Lee,
Po H Lu, Xue Hua,
Suh Lee,
Stephanie Wu,
Ken Nguyen,
Edmond Teng,
Alex D Leow,
Clifford R Jack,
Arthur W Toga,
Michael W Weiner,
George Bartzokis,
Paul M Thompson
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ABSTRACT: Depression has been associated with higher conversion rates from mild cognitive impairment (MCI) to Alzheimer's disease (AD) and may be a marker of prodromal AD that can be used to identify individuals with MCI who are most likely to progress to AD. Thus, we examined the neuroanatomical changes associated with depressive symptoms in MCI.
Two-hundred forty-three MCI subjects from the Alzheimer's Disease Neuroimaging Initiative who had brain magnetic resonance imaging scans at baseline and 2-year follow-up were classified into depressed (n = 44), nondepressed with other neuropsychiatric symptoms (n = 93), and no-symptom (NOSYMP; n = 106) groups based on the Neuropsychiatric Inventory Questionnaire. Tensor-based morphometry was used to create individual three-dimensional maps of 2-year brain changes that were compared between groups.
Depressed subjects had more frontal (p = .024), parietal (p = .030), and temporal (p = .038) white matter atrophy than NOSYMP subjects. Those whose depressive symptoms persisted over 2 years also had higher conversion to AD and more decline on measures of global cognition, language, and executive functioning compared with stable NOSYMP subjects. Nondepressed with other neuropsychiatric symptoms and NOSYMP groups exhibited no differences in rates of atrophy.
Depressive symptoms were associated with greater atrophy in AD-affected regions, increased cognitive decline, and higher rates of conversion to AD. Depression in individuals with MCI may be associated with underlying neuropathological changes, including prodromal AD, and may be a potentially useful clinical marker in identifying MCI patients who are most likely to progress to AD.
Biological psychiatry 02/2012; 71(9):814-21. · 8.93 Impact Factor
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Omid Kohannim,
Derrek P Hibar,
Jason L Stein,
Neda Jahanshad, Xue Hua,
Priya Rajagopalan,
Arthur W Toga,
Clifford R Jack,
Michael W Weiner,
Greig I de Zubicaray,
Katie L McMahon,
Narelle K Hansell,
Nicholas G Martin,
Margaret J Wright,
Paul M Thompson
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ABSTRACT: 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.
Frontiers in Neuroscience 01/2012; 6:115.
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ABSTRACT: Penalized or sparse regression methods are gaining increasing attention in imaging genomics, as they can select optimal regressors from a large set of predictors whose individual effects are small or mostly zero. We applied a multivariate approach, based on L(1)-L(2)-regularized regression (elastic net) to predict a magnetic resonance imaging (MRI) tensor-based morphometry-derived measure of temporal lobe volume from a genome-wide scan in 740 Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects. We tuned the elastic net model's parameters using internal crossvalidation and evaluated the model on independent test sets. Compared to 100,000 permutations performed with randomized imaging measures, the predictions were found to be statistically significant (p ~ 0.001). The rs9933137 variant in the RBFOX1 gene was a highly contributory genotype, along with rs10845840 in GRIN2B and rs2456930, discovered previously in a univariate genomewide search.
Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 01/2012;
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Xue Hua,
Paul M Thompson,
Alex D Leow,
Sarah K Madsen,
Rochelle Caplan,
Jeffry R Alger,
Joseph O'Neill,
Kishori Joshi,
Susan L Smalley,
Arthur W Toga,
Jennifer G Levitt
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ABSTRACT: Autism spectrum disorder is a heterogeneous disorder of brain development with wide ranging cognitive deficits. Typically diagnosed before age 3, autism spectrum disorder is behaviorally defined but patients are thought to have protracted alterations in brain maturation. With longitudinal magnetic resonance imaging (MRI), we mapped an anomalous developmental trajectory of the brains of autistic compared with those of typically developing children and adolescents. Using tensor-based morphometry, we created 3D maps visualizing regional tissue growth rates based on longitudinal brain MRI scans of 13 autistic and seven typically developing boys (mean age/interscan interval: autism 12.0 ± 2.3 years/2.9 ± 0.9 years; control 12.3 ± 2.4/2.8 ± 0.8). The typically developing boys demonstrated strong whole brain white matter growth during this period, but the autistic boys showed abnormally slowed white matter development (P = 0.03, corrected), especially in the parietal (P = 0.008), temporal (P = 0.03), and occipital lobes (P = 0.02). We also visualized abnormal overgrowth in autism in gray matter structures such as the putamen and anterior cingulate cortex. Our findings reveal aberrant growth rates in brain regions implicated in social impairment, communication deficits and repetitive behaviors in autism, suggesting that growth rate abnormalities persist into adolescence. Tensor-based morphometry revealed persisting growth rate anomalies long after diagnosis, which has implications for evaluation of therapeutic effects. Hum Brain Mapp, 2011. © 2011 Wiley Periodicals, Inc.
Human Brain Mapping 10/2011; · 5.88 Impact Factor
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April J Ho,
Cyrus A Raji,
James T Becker,
Oscar L Lopez,
Lewis H Kuller, Xue Hua,
Ivo D Dinov,
Jason L Stein,
Caterina Rosano,
Arthur W Toga,
Paul M Thompson
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ABSTRACT: Normal human aging is accompanied by progressive brain tissue loss and cognitive decline; however, several factors are thought to influence brain aging. We applied tensor-based morphometry to high-resolution brain MRI scans to determine whether educational level or physical activity was associated with brain tissue volumes in the elderly, particularly in regions susceptible to age-related atrophy. We mapped the 3D profile of brain volume differences in 226 healthy elderly subjects (130F/96M; 77.9 ± 3.6 SD years) from the Cardiovascular Health Study-Cognition Study. Statistical maps revealed the 3D profile of brain regions whose volumes were associated with educational level and physical activity (based on leisure-time energy expenditure). After controlling for age, sex, and physical activity, higher educational levels were associated with ~2-3% greater tissue volumes, on average, in the temporal lobe gray matter. After controlling for age, sex, and education, greater physical activity was associated with ~2-2.5% greater average tissue volumes in the white matter of the corona radiata extending into the parietal-occipital junction. Body mass index (BMI) was highly correlated with both education and physical activity, so we examined BMI as a contributing factor by including physical activity, education, and BMI in the same model; only BMI effects remained significant. This is one of the largest MRI studies of factors influencing structural brain aging, and BMI may be a key factor explaining the observed relationship between education, physical activity, and brain structure. Independent contributions to brain structure could not be teased apart as all these factors were highly correlated with one another.
Human Brain Mapping 09/2011; 32(9):1371-82. · 5.88 Impact Factor
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ABSTRACT: Elevated homocysteine levels are a known risk factor for Alzheimer's disease and vascular disorders. Here we applied tensor-based morphometry to brain magnetic resonance imaging scans of 732 elderly individuals from the Alzheimer's 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 Alzheimer's 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.
Neuroreport 06/2011; 22(8):391-5. · 1.66 Impact Factor
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Xue Hua,
Boris Gutman,
Christina P Boyle,
Priya Rajagopalan,
Alex D Leow,
Igor Yanovsky,
Anand R Kumar,
Arthur W Toga,
Clifford R Jack,
Norbert Schuff,
Gene E Alexander,
Kewei Chen,
Eric M Reiman,
Michael W Weiner,
Paul M Thompson
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ABSTRACT: 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.
NeuroImage 02/2011; 57(1):5-14. · 5.89 Impact Factor
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ABSTRACT: We set out to determine factors that influence the rate of brain atrophy in 1-year longitudinal magnetic resonance imaging (MRI) data. With tensor-based morphometry (TBM), we mapped the 3-dimensional profile of progressive atrophy in 144 subjects with probable Alzheimer's disease (AD) (age: 76.5 +/- 7.4 years), 338 with amnestic mild cognitive impairment (MCI; 76.0 +/- 7.2), and 202 healthy controls (77.0 +/- 5.1), scanned twice, 1 year apart. Statistical maps revealed significant age and sex differences in atrophic rates. Brain atrophic rates were about 1%-1.5% faster in women than men. Atrophy was faster in younger than older subjects, most prominently in mild cognitive impairment, with a 1% increase in the rates of atrophy and 2% in ventricular expansion, for every 10-year decrease in age. TBM-derived atrophic rates correlated with reduced beta-amyloid and elevated tau levels (n = 363) at baseline, baseline and progressive deterioration in clinical measures, and increasing numbers of risk alleles for the ApoE4 gene. TBM is a sensitive, high-throughput biomarker for tracking disease progression in large imaging studies; sub-analyses focusing on women or younger subjects gave improved sample size requirements for clinical trials.
Neurobiology of aging 08/2010; 31(8):1463-80. · 5.94 Impact Factor
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April J Ho,
Cyrus A Raji,
James T Becker,
Oscar L Lopez,
Lewis H Kuller, Xue Hua,
Suh Lee,
Derrek Hibar,
Ivo D Dinov,
Jason L Stein,
Clifford R Jack,
Michael W Weiner,
Arthur W Toga,
Paul M Thompson
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ABSTRACT: Obesity is associated with lower brain volumes in cognitively normal elderly subjects, but no study has yet investigated the effects of obesity on brain structure in patients with mild cognitive impairment (MCI) or Alzheimer's disease (AD). To determine if higher body mass index (BMI) is associated with brain volume deficits in cognitively impaired elderly subjects, we analyzed brain magnetic resonance imaging (MRI) scans of 700 MCI or AD patients from 2 different cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Cardiovascular Health Study-Cognition Study (CHS-CS). Tensor-based morphometry (TBM) was used to create 3-dimensional maps of regional tissue excess or deficits in subjects with MCI (ADNI, n = 399; CHS-CS, n = 77) and AD (ADNI, n = 188; CHS, n = 36). In both AD and MCI groups, higher body mass index was associated with brain volume deficits in frontal, temporal, parietal, and occipital lobes; the atrophic pattern was consistent in both ADNI and CHS populations. Cardiovascular risk factors, especially obesity, should be considered as influencing brain structure in those already afflicted by cognitive impairment and dementia.
Neurobiology of aging 08/2010; 31(8):1326-39. · 5.94 Impact Factor
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ABSTRACT: Machine learning methods pool diverse information to perform computer-assisted diagnosis and predict future clinical decline. We introduce a machine learning method to boost power in clinical trials. We created a Support Vector Machine algorithm that combines brain imaging and other biomarkers to classify 737 Alzheimer's disease Neuroimaging initiative (ADNI) subjects as having Alzheimer's disease (AD), mild cognitive impairment (MCI), or normal controls. We trained our classifiers based on example data including: MRI measures of hippocampal, ventricular, and temporal lobe volumes, a PET-FDG numerical summary, CSF biomarkers (t-tau, p-tau, and Abeta(42)), ApoE genotype, age, sex, and body mass index. MRI measures contributed most to Alzheimer's disease (AD) classification; PET-FDG and CSF biomarkers, particularly Abeta(42), contributed more to MCI classification. Using all biomarkers jointly, we used our classifier to select the one-third of the subjects most likely to decline. In this subsample, fewer than 40 AD and MCI subjects would be needed to detect a 25% slowing in temporal lobe atrophy rates with 80% power--a substantial boosting of power relative to standard imaging measures.
Neurobiology of aging 08/2010; 31(8):1429-42. · 5.94 Impact Factor
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Yi-Yu Chou,
Natasha Leporé,
Priyanka Saharan,
Sarah K Madsen, Xue Hua,
Clifford R Jack,
Leslie M Shaw,
John Q Trojanowski,
Michael W Weiner,
Arthur W Toga,
Paul M Thompson
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ABSTRACT: Ideal biomarkers of Alzheimer's disease (AD) should correlate with accepted measures of pathology in the cerebrospinal fluid (CSF); they should also correlate with, or predict, future clinical decline, and should be readily measured in hundreds to thousands of subjects. Here we explored the utility of automated 3D maps of the lateral ventricles as a possible biomarker of AD. We used our multi-atlas fluid image alignment (MAFIA) method, to compute ventricular models automatically, without user intervention, from 804 brain MRI scans with 184 AD, 391 mild cognitive impairment (MCI), and 229 healthy elderly controls (446 men, 338 women; age: 75.50 +/- 6.81 [SD] years). Radial expansion of the ventricles, computed pointwise, was strongly correlated with current cognition, depression ratings, Hachinski Ischemic scores, language scores, and with future clinical decline after controlling for any effects of age, gender, and educational level. In statistical maps ranked by effect sizes, ventricular differences were highly correlated with CSF measures of Abeta(1-42), and correlated with ApoE4 genotype. These statistical maps are highly automated, and offer a promising biomarker of AD for large-scale studies.
Neurobiology of aging 08/2010; 31(8):1386-400. · 5.94 Impact Factor
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Yi-Yu Chou,
N. Lepore,
P. Saharan,
S.K. Madsen, Xue Hua,
C.R. Jack,
L.M. Shaw,
J.Q. Trojanowski,
M.W. Weiner,
A.W. Toga,
P.M. Thompson
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ABSTRACT: There is an urgent need for neuroimaging biomarkers of Alzheimer's disease (AD) that correlate with cognitive decline, and with accepted measures of pathology detectable in cerebrospinal fluid (CSF). Ideal biomarkers should also be able to predict future decline, and should be computable automatically from hundreds to thousands of images without user intervention. Here we used our multi-atlas fluid image alignment method (MAFIA), to automatically segment parametric 3D surface models of the lateral ventricles in brain MRI scans from 184 AD, 391 MCI, and 229 healthy elderly controls. Radial expansion of the ventricles, computed pointwise, was correlated with measures of (1) clinical decline, (2) pathology from CSF, and (3) future deterioration. Surface-based correlation maps were assessed using a cumulative distribution function method to rank influential covariates according to their effect sizes. The resulting approach is highly automated, and boosts the power of fluid image registration by integrating multiple independent registrations to reduce segmentation errors.
Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on; 05/2010
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April J Ho,
Jason L Stein, Xue Hua,
Suh Lee,
Derrek P Hibar,
Alex D Leow,
Ivo D Dinov,
Arthur W Toga,
Andrew J Saykin,
Li Shen, [......],
Charles S DeCarli,
Bryan M DeChairo,
Steven G Potkin,
Clifford R Jack,
Michael W Weiner,
Cyrus A Raji,
Oscar L Lopez,
James T Becker,
Owen T Carmichael,
Paul M Thompson
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ABSTRACT: 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 approximately 1.2 kg higher weight, on average, in adults and an approximately 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 approximately 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.
Proceedings of the National Academy of Sciences 05/2010; 107(18):8404-9. · 9.68 Impact Factor
