Background

The neurobiological underpinnings of bipolar disorder are not well understood. Previous neuroimaging findings have been inconsistent; however, new methods for three-dimensional (3-D) computational image analysis may better characterize neuroanatomic changes than standard volumetric measures.

Methods

We used high-resolution magnetic resonance imaging and cortical pattern matching methods to map gray matter differences in 28 adults with bipolar disorder, 70% of whom were lithium-treated (mean age = 36.1 ± 10.5; 13 female subject), and 28 healthy control subjects (mean age = 35.9 ± 8.5; 11 female subjects). Detailed spatial analyses of gray matter density (GMD) were conducted by measuring local proportions of gray matter at thousands of homologous cortical locations.

Results

Gray matter density was significantly greater in bipolar patients relative to control subjects in diffuse cortical regions. Greatest differences were found in bilateral cingulate and paralimbic cortices, brain regions critical for attentional, motivational, and emotional modulation. Secondary region of interest (ROI) analyses indicated significantly greater GMD in the right anterior cingulate among lithium-treated bipolar patients (n = 20) relative to those not taking lithium (n = 8).

Conclusions

These brain maps are consistent with previous voxel-based morphometry reports of greater GMD in portions of the anterior limbic network in bipolar patients and suggest neurotrophic effects of lithium as a possible etiology of these neuroanatomic differences.

The Center for Computational Biology (CCB) is a multidisciplinary program where biomedical scientists, engineers, and clinicians work jointly to combine modern mathematical and computational techniques, to perform phenotypic and genotypic studies of biological structure, function, and physiology in health and disease. CCB has developed a computational framework built around the Manifold Atlas, an integrated biomedical computing environment that enables statistical inference on biological manifolds. These manifolds model biological structures, features, shapes, and flows, and support sophisticated morphometric and statistical analyses. The Manifold Atlas includes tools, workflows, and services for multimodal population-based modeling and analysis of biological manifolds. The broad spectrum of biomedical topics explored by CCB investigators include the study of normal and pathological brain development, maturation and aging, discovery of associations between neuroimaging and genetic biomarkers, and the modeling, analysis, and visualization of biological shape, form, and size. CCB supports a wide range of short-term and long-term collaborations with outside investigators, which drive the center's computational developments and focus the validation and dissemination of CCB resources to new areas and scientific domains.

Scanning the entire genome in search of variants related to imaging phenotypes holds great promise in elucidating the genetic etiology of neurodegenerative disorders. Here we discuss the application of a penalized multivariate model, sparse reduced-rank regression (sRRR), for the genome-wide detection of markers associated with voxel-wise longitudinal changes in the brain caused by Alzheimer’s disease (AD). Using a sample from the Alzheimer’s Disease Neuroimaging Initiative database, we performed three separate studies that each compared two groups of individuals to identify genes associated with disease development and progression. For each comparison we took a two-step approach: initially, using penalized linear discriminant analysis, we identified voxels that provide an imaging signature of the disease with high classification accuracy; then we used this multivariate biomarker as a phenotype in a genome-wide association study, carried out using sRRR. The genetic markers were ranked in order of importance of association to the phenotypes using a data re-sampling approach. Our findings confirmed the key role of the APOE and TOMM40 genes but also highlighted some novel potential associations with AD.

Protein arginine methyltransferases (PRMTs) catalyze the transfer of methyl groups from S-adenosylmethionine (SAM) to the guanidinium group of arginine residues in a number of important cell signaling proteins. PRMT1 is the founding member of this family and its activity appears to be dysregulated in heart disease and cancer. To begin to characterize the catalytic mechanism of this isozyme, we assessed the effects of mutating a number of highly conserved active site residues (i.e., Y39, R54, E100, E144, E153, M155, and H293), which are believed to play key roles in SAM recognition, substrate binding, and catalysis. The results of these studies, as well as pH rate studies, and the determination of solvent isotope effects (SIEs), indicate that M155 plays a critical role in both SAM binding and the processivity of the reaction, but is not responsible for the regiospecific formation of asymmetrically dimethylated arginine (ADMA). Additionally, mutagenesis studies on H293, combined with pH studies and the lack of a normal SIE, do not support a role for this residue as a general base. Furthermore, the lack of a normal SIE with either the WT or catalytically impaired mutants suggests that general acid/base catalysis is not important for promoting methyl transfer. This result, combined with the fact that the E144A/E153A double mutant retains considerably more activity then the single mutants alone, suggests that the PRMT1 catalyzed reaction is primarily driven by bringing the substrate guanidinium into close proximity to the S-methyl group of SAM and that the prior deprotonation of the substrate guanidinium is not required for methyl transfer.

HALT-PKD consists of two randomized trials comparing treatment with an angiotensin converting inhibitor (ACEI)-angiotensin receptor blocker (ARB) combination vs ACEI alone and standard vs low blood pressure target in Study A (eGFR >60 ml/min/1.73 m2) and ACEI-ARB vs ACEI alone in Study B (eGFR 25-60 ml/min/1.73 m2). It includes the largest cohort of systematically studied ADPKD patients (558 A and 486 B) to date. We used correlation and multiple regression cross-sectional analyses to ascertain associations of baseline parameters with total kidney (TKV) and liver (TLV) or liver cyst (LCV) volumes measured by MRI in Study A and with eGFR in both studies. Lower eGFR and higher natural log transformed urine albumin excretion are independently associated with larger natural log transformed TKV adjusted for height (HtTKV). Higher BSA is independently associated with higher ln(HtTKV) and lower eGFR. Men have larger HtTKV and smaller LCV than women. A weak correlation was found between ln(HtTKV) and ln(HtTLV) or ln(LCV) in women only. Women have higher urine aldosterone excretions and lower plasma potassium levels. In summary, this analysis 1) confirms a strong association between renal volume and functional parameters, 2) shows that gender and other factors differentially affect the development of polycystic disease in the kidney and liver, and 3) suggests an association between anthropomorphic measures reflecting preand/or post-natal growth and the severity of the disease.

Diffusion imaging tractography is a valuable tool for neuroscience researchers because it allows the generation of individualized virtual dissections of major white matter tracts in the human brain. It facilitates between-subject statistical analyses tailored to the specific anatomy of each participant. There is prominent variation in diffusion imaging metrics (e.g., fractional anisotropy, FA) within tracts, but most tractography studies use a “tract-averaged” approach to analysis by averaging the scalar values from the many streamline vertices in a tract dissection into a single point-spread estimate for each tract. Here we describe a complete workflow needed to conduct an along-tract analysis of white matter streamline tract groups. This consists of 1) A flexible MATLAB toolkit for generating along-tract data based on B-spline resampling and compilation of scalar data at different collections of vertices along the curving tract spines, and 2) Statistical analysis and rich data visualization by leveraging tools available through the R platform for statistical computing. We demonstrate the effectiveness of such an along-tract approach over the tract-averaged approach in an example analysis of 10 major white matter tracts in a single subject. We also show that these techniques easily extend to between-group analyses typically used in neuroscience applications, by conducting an along-tract analysis of differences in FA between 9 individuals with fetal alcohol spectrum disorders (FASDs) and 11 typically-developing controls. This analysis reveals localized differences between FASD and control groups that were not apparent using a tract-averaged method. Finally, to validate our approach and highlight the strength of this extensible software framework, we implement 2 other methods from the literature and leverage the existing workflow tools to conduct a comparison study.

Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88.5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.

In medical imaging, parameterized 3D surface models are of great interest for anatomical modeling and visualization, statistical comparisons of anatomy, and surface-based registration and signal processing. By solving the Yamabe equation with the Ricci flow method, we can conformally parameterize a brain surface via a mapping to a multi-hole disk. The resulting parameterizations do not have any singularities and are intrinsic and stable. To illustrate the technique, we computed parameterizations of cortical surfaces in MRI scans of the brain. We also show the parameterization results are consistent with constraints imposed on the mappings of selected landmark curves, and the resulting surfaces can be matched to each other using constrained harmonic maps. Unlike previous planar conformal parameterization methods, our algorithm does not introduce any singularity points.

Protein arginine deiminase activity (PAD) is increased in cancer, rheumatoid arthritis, and ulcerative colitis. Although the link between abnormal PAD activity and disease is clear, the relative contribution of the individual PADs to human disease is not known; there are 5 PAD isozymes in humans. Building on our previous development of F- and Cl-amidine as potent pan-PAD irreversible inhibitors, we describe herein a library approach that was used to identify PAD-selective inhibitors. Specifically, we describe the identification of Thr-Asp-F-amidine (TDFA) as a highly potent PAD4 inactivator that displays ≥15-fold selectivity for PAD4 versus PAD1 and ≥50-fold versus PADs 2 and 3. This compound is active in cells and can be used to inhibit PAD4 activity in cellulo. The structure of the PAD4•TDFA complex has also been solved and the structure and mutagenesis data indicate that the enhanced potency is due to interactions between the side chains of Q346, R374, and R639. Finally, we converted TDFA into a PAD4-selective ABPP and demonstrate that this compound, biotin-TDFA, can be used to selectively isolate purified PAD4 in vitro. In total, TDFA and biotin-TDFA represent PAD4-selective chemical probes that can be used to study the physiological roles of this enzyme.

Background

Negation occurs frequently in scientific literature, especially in biomedical literature. It has previously been reported that around 13% of sentences found in biomedical research articles contain negation. Historically, the main motivation for identifying negated events has been to ensure their exclusion from lists of extracted interactions. However, recently, there has been a growing interest in negative results, which has resulted in negation detection being identified as a key challenge in biomedical relation extraction. In this article, we focus on the problem of identifying negated bio-events, given gold standard event annotations.

Results

We have conducted a detailed analysis of three open access bio-event corpora containing negation information (i.e., GENIA Event, BioInfer and BioNLP’09 ST), and have identified the main types of negated bio-events. We have analysed the key aspects of a machine learning solution to the problem of detecting negated events, including selection of negation cues, feature engineering and the choice of learning algorithm. Combining the best solutions for each aspect of the problem, we propose a novel framework for the identification of negated bio-events. We have evaluated our system on each of the three open access corpora mentioned above. The performance of the system significantly surpasses the best results previously reported on the BioNLP’09 ST corpus, and achieves even better results on the GENIA Event and BioInfer corpora, both of which contain more varied and complex events.

Conclusions

Recently, in the field of biomedical text mining, the development and enhancement of event-based systems has received significant interest. The ability to identify negated events is a key performance element for these systems. We have conducted the first detailed study on the analysis and identification of negated bio-events. Our proposed framework can be integrated with state-of-the-art event extraction systems. The resulting systems will be able to extract bio-events with attached polarities from textual documents, which can serve as the foundation for more elaborate systems that are able to detect mutually contradicting bio-events.

Protein Arginine Deiminases (PADs) catalyze the post-translational conversion of peptidyl-Arginine to peptidyl-Citrulline in a calcium-dependent, irreversible reaction. Evidence is emerging that PADs play a role in carcinogenesis. To determine the cancer-associated functional implications of PADs, we designed a small molecule PAD inhibitor (called Chor-amidine or Cl-amidine), and tested the impact of this drug on the cell cycle. Data derived from experiments in colon cancer cells indicate that Cl-amidine causes a G1 arrest, and that this was p53-dependent. In a separate set of experiments, we found that Cl-amidine caused a significant increase in microRNA-16 (miRNA-16), and that this increase was also p53-dependent. Because miRNA-16 is a putative tumor suppressor miRNA, and others have found that miRNA-16 suppresses proliferation, we hypothesized that the p53-dependent G1 arrest associated with PAD inhibition was, in turn, dependent on miRNA-16 expression. Results are consistent with this hypothesis. As well, we found the G1 arrest is at least in part due to the ability of Cl-amidine-mediated expression of miRNA-16 to suppress its' G1-associated targets: cyclins D1, D2, D3, E1, and cdk6. Our study sheds light into the mechanisms by which PAD inhibition can protect against or treat colon cancer.

Functional neuroimaging studies have implicated the involvement of the amygdala and ventrolateral prefrontal cortex (vlPFC) in the pathophysiology of bipolar disorder. Hyperactivity in the amygdala and hypoactivity in the vlPFC have been reported in manic bipolar patients scanned during the performance of an affective faces task. Whether this pattern of dysfunction persists during euthymia is unclear. Using functional magnetic resonance imaging (fMRI), 24 euthymic bipolar and 26 demographically matched healthy control subjects were scanned while performing an affective task paradigm involving the matching and labeling of emotional facial expressions. Neuroimaging results showed that, while amygdala activation did not differ significantly between groups, euthymic patients showed a significant decrease in activation of the right vlPFC (BA47) compared to healthy controls during emotion labeling. Additionally, significant decreases in activation of the right insula, putamen, thalamus and lingual gyrus were observed in euthymic bipolar relative to healthy control subjects during the emotion labeling condition. These data, taken in context with prior studies of bipolar mania using the same emotion recognition task, could suggest that amygdala dysfunction may be a state-related abnormality in bipolar disorder, whereas vlPFC dysfunction may represent a trait-related abnormality of the illness. Characterizing these patterns of activation is likely to help in understanding the neural changes related to the different mood states in bipolar disorder, as well as changes that represent more sustained abnormalities. Future studies that assess mood-state related changes in brain activation in longitudinal bipolar samples would be of interest.

Alzheimer's disease (AD) is the most common type of dementia worldwide. Hippocampal atrophy and ventricular enlargement have been associated with AD but also with normal aging. We analyzed 1.5T brain MRI data from 46 cognitively normal elderly (NC), 33 mild cognitive impairment (MCI) and 43 AD subjects. Hippocampal and ventricular analyses were conducted with two novel semi-automated segmentation approaches followed by the radial distance mapping technique. Multiple linear regression was used to assess effects of age and diagnosis on hippocampal and ventricular volumes and radial distance. Additional 3D map correction for multiple comparisons was conducted with permutation testing. As expected, most significant hippocampal atrophy and ventricular enlargement were seen in the AD vs. NC comparison. MCI subjects showed intermediate levels of hippocampal atrophy and ventricular enlargement. Significant effects of age on hippocampal volume and radial distance were seen in the pooled sample as well as in the NC and AD groups considered separately. Age-associated differences were detected in all hippocampal subfields and the frontal and body/occipital horn portions of the lateral ventricles. Aging affects both the hippocampus and lateral ventricles independent of AD pathology and should be included as covariate in all structural hippocampal and ventricular analyses when possible.

Diffusion tensor imaging (DTI) is sensitive to the directionally- constrained flow of water, which diffuses preferentially along axons. Tractography programs may be used to infer matrices of connectivity (anatomical networks) between pairs of brain regions. Little is known about how these computed connectivity measures depend on the scans’ spatial and angular resolutions. To determine this, we scanned 8 young adults with DTI at 2.5 and 3 mm resolutions, and an additional subject at 4 resolutions between 2–4 mm. We computed 70×70 connectivity matrices, using whole-brain tractography to measure fiber density between all pairs of 70 cortical and subcortical regions. Spatial and angular resolution affected the computed connectivity for narrower tracts (internal capsule and cerebellum), but also for the corticospinal tract. Data resolution affected the apparent role of some key structures in cortical anatomic networks. Care is needed when comparing network data across studies, and interpreting apparent disagreements among findings.

We consider the problem of processing high angular resolution diffusion images described by orientation distribution functions (ODFs). Prior work showed that several processing operations, e.g., averaging, interpolation and filtering, can be reduced to averaging in the space of ODFs. However, this approach leads to anatomically erroneous results when the ODFs to be processed have very different orientations. To address this issue, we propose a group action induced distance for averaging ODFs, which leads to a novel processing framework on the spaces of orientation (the space of 3D rotations) and shape (the space of ODFs with the same orientation). Experiments demonstrate that our framework produces anatomically meaningful results.

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 L1-L2-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.

Alzheimer’s Disease (AD) has long been considered a cortical degenerative disease, but impaired brain connectivity, due to white matter injury, may exacerbate cognitive problems. Predicting brain changes is critically important for early treatment. In a longitudinal diffusion tensor imaging study, we investigated white matter fiber integrity in 19 patients (mean age: 74.7 +/− 8.4 yrs at baseline) displaying early signs of mild cognitive impairment (eMCI). We first examined whether baseline average fractional anisotropy (FA) measures in the corpus callosum (CC) predicted changes in white matter integrity over the following 6 months. We then examined whether “small world” architecture measures - calculated from baseline connectivity maps - predicted white matter changes over the next 6 months. While average CC FA measures at baseline were not associated with future changes in FA, network measures were a sensitive biomarker for predicting white matter changes during this critical time before AD strikes.

Large multi-site image-analysis studies have successfully discovered genetic variants that affect brain structure in tens of thousands of subjects scanned worldwide. Candidate genes have also associated with brain integrity, measured using fractional anisotropy in diffusion tensor images (DTI). To evaluate the heritability and robustness of DTI measures as a target for genetic analysis, we compared 417 twins and siblings scanned on the same day on the same high field scanner (4-Tesla) with two protocols: (1) 94-directions; 2mm-thick slices, (2) 27-directions; 5mm-thickness. Using mean FA in white matter ROIs and FA ‘skeletons’ derived using FSL, we (1) examined differences in voxelwise means, variances, and correlations among the measures; and (2) assessed heritability with structural equation models, using the classical twin design. FA measures from the genu of the corpus callosum were highly heritable, regardless of protocol. Genome-wide analysis of the genu mean FA revealed differences across protocols in the top associations.

Graph theory can be applied to matrices that represent the brain’s anatomical connections, to better understand global properties of anatomical networks, such as their clustering, efficiency and “small-world” topology. Network analysis is popular in adult studies of connectivity, but only one study – in just 30 subjects – has examined how network measures change as the brain develops over this period. Here we assessed the developmental trajectory of graph theory metrics of structural brain connectivity in a cross-sectional study of 467 subjects, aged 12 to 30. We computed network measures from 70×70 connectivity matrices of fiber density generated using whole-brain tractography in 4-Tesla 105-gradient high angular resolution diffusion images (HARDI). We assessed global efficiency and modularity, and both age and age2 effects were identified. HARDI-based connectivity maps are sensitive to the remodeling and refinement of structural brain connections as the human brain develops.

Human brain connectivity is disrupted in a wide range of disorders – from Alzheimer’s disease to autism – but little is known about which specific genes affect it. Here we conducted a genome-wide association for connectivity matrices that capture information on the density of fiber connections between 70 brain regions. We scanned a large twin cohort (N=366) with 4-Tesla high angular resolution diffusion imaging (105-gradient HARDI). Using whole brain HARDI tractography, we extracted a relatively sparse 70×70 matrix representing fiber density between all pairs of cortical regions automatically labeled in co-registered anatomical scans. Additive genetic factors accounted for 1–58% of the variance in connectivity between 90 (of 122) tested nodes. We discovered genome-wide significant associations between variants and connectivity. GWAS permutations at various levels of heritability, and split-sample replication, validated our genetic findings. The resulting genes may offer new leads for mechanisms influencing aberrant connectivity and neurodegeneration.

A major challenge in neuroscience is finding which genes affect brain integrity, connectivity, and intellectual function. Discovering influential genes holds vast promise for neuroscience, but typical genome-wide searches assess around one million genetic variants one-by-one, leading to intractable false positive rates, even with vast samples of subjects. Even more intractable is the question of which genes interact and how they work together to affect brain connectivity. Here we report a novel approach that discovers which genes contribute to brain wiring and fiber integrity at all pairs of points in a brain scan. We studied genetic correlations between thousands of points in human brain images from 472 twins and their non-twin siblings (mean age: 23.7±2.1 SD years; 193 M/279 F). We combined clustering with genome-wide scanning to find brain systems with common genetic determination. We then filtered the image in a new way to boost power to find causal genes. Using network analysis, we found a network of genes that affect brain wiring in healthy young adults. Our new strategy makes it more computationally tractable to discover genes that affect brain integrity. The gene network showed small-world and scale-free topologies, suggesting efficiency in genetic interactions, and resilience to network disruption. Genetic variants at hubs of the network influence intellectual performance by modulating associations between performance intelligence quotient (IQ) and the integrity of major white matter tracts, such as the callosal genu and splenium, cingulum, optic radiations, and the superior longitudinal fasciculus.

Rapid developments in medical neuroimaging have made it possible to reconstruct the trajectory of Alzheimer’s disease (AD) as it spreads through the living brain. The current review focuses on the progressive signature of brain changes throughout the different stages of AD. We integrate recent findings on changes in cortical gray matter volume, white matter fiber tracts, neuropathological alterations, and brain metabolism assessed with molecular positron emission tomography (PET). Neurofibrillary tangles accumulate first in transentorhinal and cholinergic brain areas, and 4-D maps of cortical volume changes show early progressive temporo-parietal cortical thinning. Findings from diffusion tensor imaging (DTI) for assessment fiber tract integrity show cortical disconnection in corresponding brain networks. Importantly, the developmental trajectory of brain changes is not uniform and may be modulated by several factors such as onset of disease mechanisms, risk-associated and protective genes, converging comorbidity, and individual brain reserve. There is a general agreement between in vivo brain maps of cortical atrophy and amyloid pathology assessed through PET, reminiscent of post mortem histopathology studies that paved the way in the staging of AD. The association between in vivo and post mortem findings will clarify the temporal dynamics of pathophysiological alterations in the development of preclinical AD. This will be important in designing effective treatments that target specific underlying disease AD mechanisms.

OBJECTIVES

We sought to examine the effect of atorvastatin therapy on exercise leg blood flow in healthy middle-aged and older, men and women.

BACKGROUND

The vasodilatory response to exercise decreases in humans with aging and disease and this reduction may contribute to reduced exercise capacity.

METHODS

We used a double-blind, randomly assigned, placebo-controlled protocol to assess the effect of atorvastatin treatment on exercising leg hemodynamics. We measured femoral artery blood flow (FBF) using Doppler ultrasound and calculated femoral vascular conductance (FVC) from brachial mean arterial pressure (MAP) before and during single knee-extensor exercise in healthy adults (ages 40–71) before (PRE) and after (POST) 6 months of 80 mg atorvastatin (A: 14 men, 16 women) or placebo (P: 14 men, 22 women) treatment. FBF and FVC were normalized to exercise power output and estimated quadriceps muscle mass.

RESULTS

Atorvastatin reduced LDL cholesterol by approximately 50%, but not in the placebo group (p < 0.01). Atorvastatin also increased exercise FBF from 44.2 ± 19.0 to 51.4 ± 22.0 mL/min/W/kg muscle whereas FBF in the placebo group was unchanged (40.1 ± 16.0 vs 39.5 ± 16.1) (p <0.01). FVC also increased with atorvastatin from 0.5 ± 0.2 to. 0.6 ± 0.2 mL/min/mmHg/W/kg muscle, but not in the placebo subjects (P: 0.4 ± 0.2 vs 0.4 ± 0.2) ( p < 0.01).

CONCLUSIONS

High-dose atorvastatin augments exercising leg hyperemia. Statins may mitigate reductions in the exercise vasodilatory response in humans that are associated with aging and disease.

Performance on measures of cognitive processing speed (CPS) slows with age, but the biological basis associated with this cognitive phenomenon remains incompletely understood. We assessed the hypothesis that the age-related slowing in CPS is associated with myelin breakdown in late-myelinating regions in a very healthy elderly population. An in vivo MRI biomarker of myelin integrity was obtained from the prefrontal lobe white matter and the genu of the corpus callosum for 152 healthy elderly adults. These regions myelinate later in brain development and are more vulnerable to breakdown due to the effects of normal aging. To evaluate regional specificity, we also assessed the splenium of the corpus callosum as a comparison region, which myelinates early in development and primarily contains axons involved in visual processing. The measure of myelin integrity was significantly correlated with CPS in highly vulnerable late-myelinating regions but not in the splenium. These results have implications for the neurobiology of the cognitive changes associated with brain aging.

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 24 months 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, PRKCA and PRKCB), 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.

Highlights

► New sparse regression method to find pathways associated with a multivariate trait ► Imaging phenotypes describe longitudinal structural change in Alzheimer's disease. ► Insulin signalling and vascular smooth muscle contraction pathways highly ranked ► Top genes include PIK3R3 PIK3CG PRKCA/B ADCY2 ACTN1 ACACA GNAI1 CR1 TOMM40 and APOE. ► All these genes previously implicated in different aspects of AD biology.