The quest to map brain connectivity is being pursued worldwide using diffusion imaging, among other techniques. Even so, we know little about how brain connectivity measures depend on the magnetic field strength of the scanner. To investigate this, we scanned 10 healthy subjects at 7 and 3 tesla—using 128-gradient high-angular resolution diffusion imaging. For each subject and scan, whole-brain tractography was used to estimate connectivity between 113 cortical and subcortical regions. We examined how scanner field strength affects (i) the signal-to-noise ratio (SNR) of the non-diffusion-sensitized reference images (b0); (ii) diffusion tensor imaging (DTI)-derived fractional anisotropy (FA), mean, radial, and axial diffusivity (MD/RD/AD), in atlas-defined regions; (iii) whole-brain tractography; (iv) the 113×113 brain connectivity maps; and (v) five commonly used network topology measures. We also assessed effects of the multi-channel reconstruction methods (sum-of-squares, SOS, at 7T; adaptive recombine, AC, at 3T). At 7T with SOS, the b0 images had 18.3% higher SNR than with 3T-AC. FA was similar for most regions of interest (ROIs) derived from an online DTI atlas (ICBM81), but higher at 7T in the cerebral peduncle and internal capsule. MD, AD, and RD were lower at 7T for most ROIs. The apparent fiber density between some subcortical regions was greater at 7T-SOS than 3T-AC, with a consistent connection pattern overall. Suggesting the need for caution, the recovered brain network was apparently more efficient at 7T, which cannot be biologically true as the same subjects were assessed. Care is needed when comparing network measures across studies, and when interpreting apparently discrepant findings.
brain network analysis; DTI; fractional anisotropy; graph theory; high-field MRI; high angular resolution diffusion imaging (HARDI); signal-to-noise ratio; tractography
Understanding how the brain matures in healthy individuals is critical for evaluating deviations from normal development in psychiatric and neurodevelopmental disorders. The brain’s anatomical networks are profoundly re-modeled between childhood and adulthood, and diffusion tractography offers unprecedented power to reconstruct these networks and neural pathways in vivo. Here we tracked changes in structural connectivity and network efficiency in 439 right-handed individuals aged 12 to 30 (211 female/126 male adults, mean age=23.6, SD=2.19; 31 female/24 male 12 year olds, mean age=12.3, SD=0.18; and 25 female/22 male 16 year olds, mean age=16.2, SD=0.37). All participants were scanned with high angular resolution diffusion imaging (HARDI) at 4 Tesla. After we performed whole brain tractography, 70 cortical gyral-based regions of interest were extracted from each participant’s co-registered anatomical scans. The degree of fiber connections between all pairs of cortical regions, or nodes, were found to create symmetric fiber density matrices, reflecting the structural brain network. From those 70×70 matrices we computed graph theory metrics characterizing structural connectivity. Several key global and nodal metrics changed across development, showing increased network integration, with some connections pruned and others strengthened. The increases and decreases in fiber density, however, were not distributed proportionally across the brain. The frontal cortex had a disproportionate number of decreases in fiber density while the temporal cortex had a disproportionate number of increases in fiber density. This large-scale analysis of the developing structural connectome offers a foundation to develop statistical criteria for aberrant brain connectivity as the human brain matures.
HARDI; structural connectivity; graph theory; development
Antiretroviral therapies have become widely available, and as a result, individuals infected with the human immunodeficiency virus (HIV) are living longer, and becoming integrated into the geriatric population. Around half of the HIV+ population shows some degree of cognitive impairment, but it is unknown how their neural networks and brain connectivity compare to those of noninfected people. Here we combined magnetic resonance imaging-based cortical parcellations with high angular resolution diffusion tensor imaging tractography in 55 HIV-seropositive patients and 30 age-matched controls, to map white matter connections between cortical regions. We set out to determine selective virus-associated disruptions in the brain's structural network. All individuals in this study were aged 60–80, with full access to antiretroviral therapy. Frontal and motor connections were compromised in HIV+ individuals. HIV+ people who carried the apolipoprotein E4 allele (ApoE4) genotype—which puts them at even greater risk for neurodegeneration—showed additional network structure deficits in temporal and parietal connections. The ApoE4 genotype interacted with duration of illness. Carriers showed greater brain network inefficiencies the longer they were infected. Neural network deficiencies in HIV+ populations exceed those typical of normal aging, and are worse in those genetically predisposed to brain degeneration. This work isolates neuropathological alterations in HIV+ elders, even when treated with antiretroviral therapy. Network impairments may contribute to the neuropsychological abnormalities in elderly HIV patients, who will soon account for around half of all HIV+ adults.
ApoE4; diffusion tensor imaging (DTI); fractional anisotropy (FA); geriatrics; high angular resolution diffusion imaging; imaging genetics; structural brain networks
The development of late-onset Alzheimer’s disease (LOAD) is under strong genetic control and there is great interest in the genetic variants that confer increased risk. The Alzheimer’s disease risk gene, growth factor receptor bound protein 2-associated protein (GAB2), has been shown to provide a 1.27–1.51 increased odds of developing LOAD for rs7101429 major allele carriers, in case-control analysis. GAB2 is expressed across the brain throughout life, and its role in LOAD pathology is well understood. Recent studies have begun to examine the effect of genetic variation in the GAB2 gene on differences in the brain. However, the effect of GAB2 on the young-adult brain has yet to be considered. Here we found a significant association between the GAB2 gene and morphological brain differences in 755 young-adult twins (469 females) (M = 23.1, SD = 3.1 years), using a gene-based test with principal components regression (PCReg). Detectable differences in brain morphology are therefore associated with variation in the GAB2 gene, even in young adults, long before the typical age of onset of Alzheimer’s disease.
GAB2; imaging genetics; tensor-based morphometry; Alzheimer’s disease
Several common genetic variants have recently been discovered that appear to influence white matter microstructure, as measured by diffusion tensor imaging (DTI). Each genetic variant explains only a small proportion of the variance in brain microstructure, so we set out to explore their combined effect on the white matter integrity of the corpus callosum. We measured six common candidate single-nucleotide polymorphisms (SNPs) in the COMT, NTRK1, BDNF, ErbB4, CLU, and HFE genes, and investigated their individual and aggregate effects on white matter structure in 395 healthy adult twins and siblings (age: 20–30 years). All subjects were scanned with 4-tesla 94-direction high angular resolution diffusion imaging. When combined using mixed-effects linear regression, a joint model based on five of the candidate SNPs (COMT, NTRK1, ErbB4, CLU, and HFE) explained ∼6% of the variance in the average fractional anisotropy (FA) of the corpus callosum. This predictive model had detectable effects on FA at 82% of the corpus callosum voxels, including the genu, body, and splenium. Predicting the brain's fiber microstructure from genotypes may ultimately help in early risk assessment, and eventually, in personalized treatment for neuropsychiatric disorders in which brain integrity and connectivity are affected.
neuroimaging; brain structure; DTI; genetics; genetic profiles; prediction; imaging; clinical or preclinical; neuroanatomy; neurogenetics; pharmacogenetics / pharmacogenomics; neuroimaging; brain structure; DTI; genetics; genetic profiles
HIV-associated neurocognitive disorder remains prevalent in HIV-infected individuals despite effective antiretroviral therapy. As these individuals age, comorbid cerebrovascular disease will likely impact cognitive function. Effective tools to study this impact are needed. This study used diffusion tensor imaging (DTI) to characterize brain microstructural changes in HIV-infected individuals with and without cerebrovascular risk factors. Diffusion-weighted MRIs were obtained in 22 HIV-infected subjects aged 50 years or older (mean age = 58 years, standard deviation = 6 years; 19 males, three females). Tensors were calculated to obtain fractional anisotropy (FA) and mean diffusivity (MD) maps. Statistical comparisons accounting for multiple comparisons were made between groups with and without cerebrovascular risk factors. Abnormal glucose metabolism (i.e., impaired fasting glucose, impaired glucose tolerance, or diabetes mellitus) was associated with significantly higher MD (false discovery rate (FDR) critical p value = 0.008) and lower FA FDR critical p value = 0.002) in the caudate and lower FA in the hippocampus (FDR critical p value = 0.004). Pearson correlations were performed between DTI measures in the caudate and hippocampus and age- and education-adjusted composite scores of global cognitive function, memory, and psychomotor speed. There were no detectable correlations between the neuroimaging measures and measures of cognition. In summary, we demonstrate that brain microstructural abnormalities are associated with abnormal glucose metabolism in the caudate and hippocampus of HIV-infected individuals. Deep gray matter structures and the hippocampus may be vulnerable in subjects with comorbid abnormal glucose metabolism, but our results should be confirmed in further studies.
HIV; Cerebrovascular disease; Diffusion tensor imaging
The Alzheimer's Disease Neuroimaging Initiative (ADNI)
recently added diffusion tensor imaging (DTI), among several other new imaging
modalities, in an effort to identify sensitive biomarkers of Alzheimer's disease
(AD). While anatomical MRI is the main structural neuroimaging method used in
most AD studies and clinical trials, DTI is sensitive to microscopic white
matter (WM) changes not detectable with standard MRI, offering additional
markers of neurodegeneration. Prior DTI studies of AD report lower fractional
anisotropy (FA), and increased mean, axial, and radial diffusivity (MD, AxD, RD)
throughout WM. Here we assessed which DTI measures may best identify differences
among AD, mild cognitive impairment (MCI), and cognitively healthy elderly
control (NC) groups, in region of interest (ROI) and voxel-based analyses of 155
ADNI participants (mean age: 73.5 ± 7.4; 90
M/65 F; 44 NC, 88 MCI, 23 AD). Both VBA and ROI analyses
revealed widespread group differences in FA and all diffusivity measures. DTI
maps were strongly correlated with widely-used clinical ratings (MMSE, CDR-sob,
and ADAS-cog). When effect sizes were ranked, FA analyses were least sensitive
for picking up group differences. Diffusivity measures could detect more subtle
MCI differences, where FA could not. ROIs showing strongest group
differentiation (lowest p-values) included tracts that
pass through the temporal lobe, and posterior brain regions. The left
hippocampal component of the cingulum showed consistently high effect sizes for
distinguishing groups, across all diffusivity and anisotropy measures, and in
correlations with cognitive scores.
•DTI scans in ADNI2 provide numerous biomarkers of
Alzheimer's disease.•FA, MD, AxD, and RD measures all detect MCI and AD
white matter deficits.•DTI FA and diffusivity measures are correlated with
clinical cognitive scores.•FA is the least sensitive DTI measure for detecting
AD related differences.•WM in the temporal lobe, corpus callosum and
cingulum is repeatedly implicated.
NC, normal control; RD, radial diffusivity; AxD, axial diffusivity; ADNI, Alzheimer's Disease Neuroimaging Initiative; DTI; Alzheimer's disease; MCI; White matter; Clinical scores; Biomarkers
Several dietary factors and their genetic modifiers play a role in neurological disease and affect the human brain. The structural and functional integrity of the living brain can be assessed using neuroimaging, enabling large-scale epidemiological studies to identify factors that help or harm the brain. Iron is one nutritional factor that comes entirely from our diet, and its storage and transport in the body are under strong genetic control. In this review, we discuss how neuroimaging can help to identify associations between brain integrity, genetic variations, and dietary factors such as iron. We also review iron’s essential role in cognition, and we note some challenges and confounds involved in interpreting links between diet and brain health. Finally, we outline some recent discoveries regarding the genetics of iron and its effects on the brain, suggesting the promise of neuroimaging in revealing how dietary factors affect the brain.
Neuroimaging; Genetics; Iron; Diet; Transferrin; HFE; Nutrition; Brain development
Designers of clinical trials for Alzheimer's 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 Alzheimer's 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.
•ApoE genotype status helps enrich MCI trials, using a structural MRI outcome measure.•CLU, PICALM and CR1 risk genes boost potential MCI trial power beyond ApoE alone.•CLU, PICALM and CR1 show significant, aggregate effects on TBM maps of brain atrophy.
Alzheimer's disease; Neuroimaging; Brain atrophy; Genetics; Genetic risk score; Clinical trial enrichment
Bipolar disorder (BD) is a chronic mental illness characterized by severe disruptions in mood and cognition. Diffusion tensor imaging (DTI) studies suggest that white matter (WM) tract abnormalities may contribute to the clinical hallmarks of the disorder. Using DTI and whole brain voxel-based analysis, we mapped the profile of WM anomalies in BD. All patients in our sample were euthymic and lithium free when scanned.
Diffusion-weighted and T1-weighted structural brain images were acquired from 23 lithium-free euthymic subjects with bipolar I disorder and 19 age- and sex-matched healthy control subjects on a 1.5 T MRI scanner. Scans were processed to provide measures of fractional anisotropy (FA) and mean and radial diffusivity (MD and RD) at each WM voxel, and processed scans were nonlinearly aligned to a customized brain imaging template for statistical group comparisons.
Relative to controls, the bipolar group showed widespread regions of lower FA, including the corpus callosum, cortical and thalamic association fibers. MD and RD were abnormally elevated in patients in many of these same regions.
Our findings agree with prior reports of WM abnormalities in the corpus callosum and further link a bipolar diagnosis with structural abnormalities of the tapetum, fornix and stria terminalis. Future studies assessing the diagnostic specificity and prognostic implications of these abnormalities would be of interest.
•Using DTI and whole brain voxel-based analysis, we mapped WM anomalies in BD.•Relative to controls, the bipolar group showed widespread regions of lower FA.•MD and RD were abnormally elevated in patients in many of these same regions.
Bipolar disorder; White matter; Neuroimaging; DTI; Brain mapping; Fractional anisotropy
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.
Anatomical brain connectivity; Complex networks; Diffusion weighted MRI; Topological analysis; Hierarchical analysis; False discovery rate; Sex and kinship brain network differences
A recent genome-wide association meta-analysis showed a suggestive association between alcohol intake in humans and a common single nucleotide polymorphism in the ras-specific guanine nucleotide releasing factor 2 gene. Here, we tested whether this variant – associated with lower alcohol consumption – showed associations with brain structure and longitudinal ventricular expansion over time, across two independent elderly cohorts, totaling 1,032 subjects. We first examined a large sample of 738 elderly participants with neuroimaging and genetic data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI1). Then, we assessed the generalizability of the findings by testing this polymorphism in a replication sample of 294 elderly subjects from a continuation of the first ADNI project (ADNI2) to minimize the risk of reporting false positive results. The minor allele – previously linked with lower alcohol intake – was associated with larger volumes in various cortical regions, notably the medial prefrontal cortex and cingulate gyrus in both cohorts. Intriguingly, the same allele also predicted faster ventricular expansion rates in the ADNI1 cohort at 1- and 2-year follow up. Despite a lack of alcohol consumption data in this study cohort, these findings, combined with earlier functional imaging investigations of the same gene, suggest the existence of reciprocal interactions between genes, brain, and drinking behavior.
neuroimaging genetics; ventricular expansion; aging neuroscience; rasgrf2; brain volume; structural MRI
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.
Connectivity; diffusion imaging; tractography; networks; MRI; brain
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.
Neuroimaging; MRI; Prediction; Elastic net; Imaging Genetics
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.
diffusion imaging; graph theory; connectivity; predictive models; Alzheimer’s disease
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.
imaging genetics; DTI protocol stability; corpus callosum; genome-wide association study; multi-site analysis
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.
graph theory; high angular resolution diffusion imaging (HARDI); tractography; network analyses; development; structural connectivity
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.
genetics; high angular resolution diffusion imaging (HARDI); cortical surfaces; twin modeling; human connectome
Recently, carriers of a common variant in the autism risk gene, CNTNAP2, were found to have altered functional brain connectivity using functional MRI. Here, we scanned 328 young adults with high-field (4-Tesla) diffusion imaging, to test the hypothesis that carriers of this gene variant would have altered structural brain connectivity. All participants (209 women, 119 men, age: 23.4±2.17 SD years) were scanned with 105-gradient high-angular-resolution diffusion imaging (HARDI) at 4 Tesla. After performing a whole-brain fiber tractography using the full angular resolution of the diffusion scans, 70 cortical surface-based regions of interest were created from each individual's co-registered anatomical data to compute graph metrics for all pairs of cortical regions. In graph theory analyses, subjects homozygous for the risk allele (CC) had lower characteristic path length, greater small-worldness and global efficiency in whole-brain analyses, and lower eccentricity (maximum path length) in 60 of the 70 nodes in regional analyses. These results were not reducible to differences in more commonly studied traits such as fiber density or fractional anisotropy. This is the first study that links graph theory metrics of brain structural connectivity to a common genetic variant linked with autism and will help us understand the neurobiology of the circuits implicated in the risk for autism.
autism; CNTNAP2; graph theory; HARDI; structural connectivity; twins
The NTRK1 gene (also known as TRKA) encodes a high affinity receptor for NGF, a neurotrophin involved in nervous system development and myelination. NTRK1 has been implicated in neurological function via links between the T allele at rs6336 (NTRK1-T) and schizophrenia risk. A variant in the neurotrophin gene, BDNF, was previously associated with white matter integrity in young adults, highlighting the importance of neurotrophins to white matter development. We hypothesized that NTRK1-T would relate to lower FA in healthy adults.
We scanned 391 healthy adult human twins and their siblings (mean age: 23.6 ± 2.2 years; 31 NTRK1-T carriers, 360 non-carriers) using 105-gradient diffusion tensor imaging at 4 Tesla. We evaluated in brain white matter how NTRK1-T and NTRK1 rs4661063 allele A (rs4661063-A, which is in moderate linkage disequilibrium with rs6336) related to voxelwise fractional anisotropy – a common diffusion tensor imaging measure of white matter microstructure. We used mixed-model regression to control for family relatedness, age, and sex. The sample was split in half to test results reproducibility. The false discovery rate method corrected for voxelwise multiple comparisons.
NTRK1-T and rs4661063-A correlated with lower white matter fractional anisotropy, independent of age and sex (multiple comparisons corrected: false discovery rate critical p = 0.038 for NTRK1-T and 0.013 for rs4661063-A). In each half-sample, the NTRK1-T effect was replicated in the cingulum, corpus callosum, superior and inferior longitudinal fasciculi, inferior fronto-occipital fasciculus, superior corona radiata, and uncinate fasciculus. Our results suggest that NTRK1-T is important for developing white matter microstructure.
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.
► Commonly carried MTHFR gene polymorphism affects plasma homocysteine levels. ► It also accelerates brain tissue loss in the elderly with mild cognitive impairment. ► We have mapped MTHFR brain structure associations in 3D in two independent cohorts. ► We suggest a causal pathway for homocysteine-mediated brain atrophy in the elderly.
Growth factors and their receptors are important for cellular migration as well as axonal guidance and myelination in the brain. They also play a key role in programmed cell death, and are implicated in a number of mental illnesses. Recently, we reported that healthy young adults who carry the T allele variant in the growth factor gene, NTRK1 (at location rs6336), had lower white matter integrity than non-carriers on diffusion images of the brain. Diffusion tensor imaging (DTI) revealed how this single nucleotide polymorphism affects white matter microstructure in human populations; DTI is also used to identify characteristic features of brain connectivity in typically developing children and in patients. Newly discovered links between neuroimaging measures and growth factors whose molecular neuroscience is well known offer an important step in understanding mechanisms that contribute to brain connectivity. Altered fiber connectivity may mediate the relationship between some genetic risk factors and a variety of mental illnesses.
neurotrophin; growth factor; tropomyosin-related kinase receptor A; neurotrophic tyrosine kinase receptor 1; myelin; development; fractional anisotropy; radial diffusivity; diffusion tensor imaging; schizophrenia
Recently, carriers of a common variant in the autism risk gene, CNTNAP2, were found to have altered functional brain connectivity using functional MRI. Here we scanned 328 young adults with high-field (4-Tesla) diffusion imaging, to test the hypothesis that carriers of this gene variant would have altered structural brain connectivity. All participants (209 females, 119 males, age: 23.4 +/−2.17 SD years) were scanned with 105-gradient high angular diffusion imaging (HARDI) at 4 Tesla. After performing a whole-brain fiber tractography using the full angular resolution of the diffusion scans, 70 cortical surface-based regions of interest were created from each individual’s co-registered anatomical data to compute graph metrics for all pairs of cortical regions. In graph theory analyses, subjects homozygous for the risk allele (CC) had lower characteristic path length, greater small-worldness and global efficiency in whole brain analyses, as well as greater eccentricity (maximum path length) in 60 of 70 nodes in regional analyses. These results were not reducible to differences in more commonly studied traits such as fiber density or fractional anisotropy. This is the first study to link graph theory metrics of brain structural connectivity to a common genetic variant linked with autism and will help us understand the neurobiology of circuits implicated in risk for autism.
structural connectivity; HARDI; autism; CNTNAP2; graph theory; twins
A global probabilistic fiber tracking approach based on the voting process provided by the Hough transform is introduced in this work. The proposed framework tests candidate 3D curves in the volume, assigning to each one a score computed from the diffusion images, and then selects the curves with the highest scores as the potential anatomical connections. The algorithm avoids local minima by performing an exhaustive search at the desired resolution. The technique is easily extended to multiple subjects, considering a single representative volume where the registered high-angular resolution diffusion images (HARDI) from all the subjects are non-linearly combined, thereby obtaining population-representative tracts. The tractography algorithm is run only once for the multiple subjects, and no tract alignment is necessary. We present experimental results on HARDI volumes, ranging from simulated and 1.5T physical phantoms to 7T and 4T human brain and 7T monkey brain datasets.
Tractography; diffusion-weighted magnetic resonance imaging (DWI); Hough transform; orientation distribution function (ODF); population studies
Imaging traits provide a powerful and biologically relevant substrate to examine the influence of genetics on the brain. Interest in genome-wide, brain-wide search for influential genetic variants is growing, but has mainly focused on univariate, SNP-based association tests. Moving to gene-based multivariate statistics, we can test the combined effect of multiple genetic variants in a single test statistic. Multivariate models can reduce the number of statistical tests in gene-wide or genome-wide scans and may discover gene effects undetectable with SNP-based methods. Here we present a gene-based method for associating the joint effect of single nucleotide polymorphisms (SNPs) in 18,044 genes across 31,662 voxels of the whole brain in 731 elderly subjects (mean age: 75.56 ± 6.82SD years; 430 males) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. Using the voxel-level volume difference values as the phenotype, we selected the most significantly associated gene (out of 18,044) at each voxel across the brain. No genes identified were significant after correction for multiple comparisons, but several known candidates were re-identified, as were other genes highly relevant to brain function. GAB2, which has been previously associated with late-onset AD, was identified as the top gene in this study, suggesting the validity of the approach. This multivariate, gene-based voxelwise association study offers a novel framework to detect genetic influences on the brain.
principal components regression; voxelwise; multivariate; gene-based; GWAS; GAB2 (max. 6 keywords)