Imaging genetics of structural brain connectivity and neural integrity markers

Ridge Penalization in High-Dimensional Testing With Applications to Imaging Genetics

High-dimensionality is ubiquitous in various scientific fields such as imaging genetics, where a deluge of functional and structural data on brain-relevant genetic polymorphisms are investigated. It is crucial to identify which genetic variations are consequential in identifying neurological features of brain connectivity compared to merely random noise. Statistical inference in high-dimensional settings poses multiple challenges involving analytical and computational complexity. A widely implemented strategy in addressing inference goals is penalized inference. In particular, the role of the ridge penalty in high-dimensional prediction and estimation has been actively studied in the past several years. This study focuses on ridge-penalized tests in high-dimensional hypothesis testing problems by proposing and examining a class of methods for choosing the optimal ridge penalty. We present our findings on strategies to improve the statistical power of ridge-penalized tests and what determines the optimal ridge penalty for hypothesis testing. The application of our work to an imaging genetics study and biological research will be presented.

A Review of Statistical Methods in Imaging Genetics

With the rapid growth of modern technology, many biomedical studies are being conducted to collect massive datasets with volumes of multi-modality imaging, genetic, neurocognitive, and clinical information from increasingly large cohorts. Simultaneously extracting and integrating rich and diverse heterogeneous information in neuroimaging and/or genomics from these big datasets could transform our understanding of how genetic variants impact brain structure and function, cognitive function, and brain-related disease risk across the lifespan. Such understanding is critical for diagnosis, prevention, and treatment of numerous complex brain-related disorders (e.g., schizophrenia and Alzheimer's disease). However, the development of analytical methods for the joint analysis of both high-dimensional imaging phenotypes and high-dimensional genetic data, a big data squared (BD2) problem, presents major computational and theoretical challenges for existing analytical methods. Besides the high-dimensional nature of BD2, various neuroimaging measures often exhibit strong spatial smoothness and dependence and genetic markers may have a natural dependence structure arising from linkage disequilibrium. We review some recent developments of various statistical techniques for imaging genetics, including massive univariate and voxel-wise approaches, reduced rank regression, mixture models, and group sparse multi-task regression. By doing so, we hope that this review may encourage others in the statistical community to enter into this new and exciting field of research.

Common Genetic Variant in VIT Is Associated with Human Brain Asymmetry

Brain asymmetry varies across individuals. However, genetic factors contributing to this normal variation are largely unknown. Here we studied variation of cortical surface area asymmetry in a large sample of subjects. We performed principal component analysis (PCA) to capture correlated asymmetry variation across cortical regions. We found that caudal and rostral anterior cingulate together account for a substantial part of asymmetry variation among individuals. To find SNPs associated with this subset of brain asymmetry variation we performed a genome-wide association study followed by replication in an independent cohort. We identified one SNP (rs11691187) that had genome-wide significant association (P_Combined = 2.40e-08). The rs11691187 is in the first intron of VIT. In a follow-up analysis, we found that VIT gene expression is associated with brain asymmetry in six donors of the Allen Human Brain Atlas. Based on these findings we suggest that VIT contributes to normal brain asymmetry variation. Our results can shed light on disorders associated with altered brain asymmetry.

Cognitive control and the COMT Val¹⁵⁸Met polymorphism: genetic modulation of videogame training and transfer to task-switching efficiency

The study investigated whether successful transfer of game-based cognitive improvements to untrained tasks might be modulated by preexisting neuro-developmental factors, such as genetic variability related to the catechol-O-methyltransferase (COMT)-an enzyme responsible for the degradation of dopamine. The COMT Val(158)Met genotype may differentially affect cognitive stability and flexibility, and we hypothesized that Val/Val homozygous individuals (who possess low prefrontal dopamine levels) show more pronounced cognitive flexibility than Met/-carriers (who possess high prefrontal dopamine levels). We trained participants, genotyped for the COMT Val(158)Met polymorphism on playing "Half-Life 2", a first-person shooter game which has been shown to improve cognitive flexibility. Pre-training (baseline) and post-training measures of cognitive flexibility were acquired by means of a task-switching paradigm. As expected, Val/Val homozygous individuals showed larger beneficial transfer effects than Met/-carriers. Our findings support the idea that genetic predisposition modulates transfer effects and that playing first-person shooter games promotes cognitive flexibility in individuals with a suitable genetic predisposition.

Microstructural white matter alterations in psychotic disorder: a family-based diffusion tensor imaging study

BACKGROUND

There is evidence for microstructural white matter alterations in patients with psychotic disorder, suggesting altered interregional connectivity. Less is known about the presence and role of white matter alterations in well individuals at higher than average genetic risk for psychotic disorder.

METHODS

85 patients with psychotic disorder, 93 non-psychotic siblings of patients with psychotic disorder and 80 healthy controls underwent a diffusion tensor imaging (DTI) scanning protocol. In a whole brain voxel-based analysis using Tract Based Spatial Statistics (TBSS), fractional anisotropy (FA) values were compared between the three groups. Effects of antipsychotic medication and drug use were examined.

RESULTS

The patients displayed significantly lower mean FA than the controls in the following regions: corpus callosum (genu, body, splenium), forceps major and minor, external capsule bilaterally, corona radiata (anterior, posterior) bilaterally, left superior corona radiata and posterior thalamic radiation bilaterally. Similar FA differences existed between the patients and siblings; the siblings did not differ from the controls.

CONCLUSION

Profound microstructural white matter alterations were found in the corpus callosum and other tracti and fasciculi in the patients with psychotic disorder, but not in siblings and the controls. These alterations may reflect brain pathology associated with the illness, illness-related environmental risk factors, or its treatment, rather than genetic risk.

Predicting white matter integrity from multiple common genetic variants

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.

White matter integrity, language, and childhood onset schizophrenia

BACKGROUND

The heterogeneity of symptoms and cognitive deficits in schizophrenia can be explained by abnormal connectivity between brain regions. Childhood-onset schizophrenia (COS) is a particularly severe form of schizophrenia, with an onset during a key time period for both cerebral pruning and myelination.

METHODS

Diffusion tensor images were acquired from 18 children and adolescents with COS and 25 controls. The COS group was divided into two sub-groups-one with linguistic impairment (LI) and the other without (NLI). The fractional anisotropy (FA), axial (AD), and radial diffusivity (RD) data from the two COS sub-groups were compared to each other and to the controls using tract-based spatial statistics (TBSS) analyses, which is a voxel-based method used to identify regions of white matter abnormalities.

RESULTS

TBSS identified several regions in the left hemisphere where the LI group had increased AD and RD relative to the NLI and the control groups. These areas primarily localized to linguistic tracts: left superior longitudinal fasciculus and left inferior longitudinal fasciculus/inferior fronto-occipital fasciculus. Regions of increased RD overlapped regions of increased AD, with the former showing more pronounced effects.

CONCLUSIONS

Studies of adult-onset schizophrenia typically identify areas of higher RD but unchanged AD; however, normal development studies have shown that while RD decreases are pronounced over this age range, smaller decreases in AD can also be detected. The observed increases in both RD and AD suggest that developmental disturbances affecting the structural connectivity of these pathways are more severe in COS accompanied by severe linguistic impairments.

Association of the brain-derived neurotrophic factor val66met polymorphism with magnetic resonance spectroscopic markers in the human hippocampus: in vivo evidence for effects on the glutamate system

The brain-derived neurotrophic factor (BDNF) is a key regulator of synaptic plasticity and has been suggested to be involved in the pathophysiology and pathogenesis of psychotic disorders, with particular emphasis on dysfunctions of the hippocampus. The aim of the present study was to replicate and to extend prior findings of BDNF val66met genotype effects on hippocampal volume and N-acetyl aspartate (NAA) levels. Hundred and fifty-eight caucasians (66 schizophrenic, 45 bipolar, and 47 healthy subjects; 105 subjects underwent MRI and 103 MRS scanning) participated in the study and were genotyped with regard to the val66met polymorphism (rs6265) of the BDNF gene. Hippocampal volumes were determined using structural magnetic resonance imaging (MRI), and measures of biochemical markers were taken using proton magnetic resonance spectroscopy ((1)H-MRS) in the hippocampus and other brain regions. Verbal memory was assessed as a behavioral index of hippocampal function. BDNF genotype did not impact hippocampal volumes. Significant genotype effects were found on metabolic markers specifically in the left hippocampus. In particular, homozygous carriers of the met-allele exhibited significantly lower NAA/Cre and (Glu + Gln)/Cre metabolic ratios compared with val/val homozygotes, independently of psychiatric diagnoses. BDNF genotype had a numerical, but nonsignificant effect on verbal memory performance. These findings provide first in vivo evidence for an effect of the functional BDNF val66met polymorphism on the glutamate system in human hippocampus.

Brain connectivity in psychiatric imaging genetics

In the past decade, imaging genetics has evolved into a highly successful neuroimaging discipline with a variety of sophisticated research tools. To date, several neural systems mechanisms have been identified that mediate genetic risk for mental disorders linked to common candidate and genome-wide-supported variants. In particular, the examination of intermediate connectivity phenotypes has recently gained increasing popularity. This paper gives an overview of the scientific methods and evidence that link indices of neural network organization to the genetic susceptibility for mental illness with a focus on the effects of candidate genes and genome-wide supported risk variants on brain structure and function.

Mapping corticocortical structural integrity in schizophrenia and effects of genetic liability

BACKGROUND

Structural and diffusion tensor imaging studies implicate gray and white matter (WM) abnormalities and disruptions of neural circuitry in schizophrenia. However, the structural integrity of the superficial WM, comprising short-range association (U-fibers) and intracortical axons, has not been investigated in schizophrenia.

METHODS

High-resolution structural and diffusion tensor images and sophisticated cortical pattern matching methods were used to measure and compare global and local variations in superficial WM fractional anisotropy between schizophrenia patients and their relatives and community comparison subjects and their relatives (n = 150).

RESULTS

Compared with control subjects, patients showed reduced superficial WM fractional anisotropy distributed across each hemisphere, particularly in left temporal and bilateral occipital regions (all p < .05, corrected). Furthermore, by modeling biological risk for schizophrenia in patients, patient relatives, and control subjects, fractional anisotropy was shown to vary in accordance with relatedness to a patient in both hemispheres and in the temporal and occipital lobes (p < .05, corrected). However, effects did not survive correction procedures for two-group comparisons between patient relatives and control subjects.

CONCLUSIONS

Results extend previous findings restricted to deep WM pathways to demonstrate that disturbances in corticocortical connectivity are associated with schizophrenia and might indicate a genetic predisposition for the disorder. Because the structural integrity of WM plays a crucial role in the functionality of networks linking gray matter regions, disturbances in the coherence and organization of fibers at the juncture of the neuropil might relate to features of schizophrenia at least partially attributable to disease-related genetic factors.

Mean diffusivity and fractional anisotropy as indicators of disease and genetic liability to schizophrenia

The goals of this study were to first determine whether the fractional anisotropy (FA) and mean diffusivity (MD) of major white matter pathways associate with schizophrenia, and secondly to characterize the extent to which differences in these metrics might reflect a genetic predisposition to schizophrenia. Differences in FA and MD were identified using a comprehensive atlas-based tract mapping approach using diffusion tensor imaging and high-resolution structural data from 35 patients, 28 unaffected first-degree relatives of patients, 29 community controls, and 14 first-degree relatives of controls. Schizophrenia patients had significantly higher MD in the following tracts compared to controls: the right anterior thalamic radiations, the forceps minor, the bilateral inferior fronto-occipital fasciculus (IFO), the temporal component of the left superior longitudinal fasciculus (tSLF), and the bilateral uncinate. FA showed schizophrenia effects and a linear relationship to genetic liability (represented by schizophrenia patients, first-degree relatives, and controls) for the bilateral IFO, the left inferior longitudinal fasciculus (ILF), and the left tSLF. Diffusion tensor imaging studies have previously identified white matter abnormalities in all three of these tracts in schizophrenia; however, this study is the first to identify a significant genetic liability. Thus, FA of these three tracts may serve as biomarkers for studies seeking to identify how genes influence brain structure predisposing to schizophrenia. However, differences in FA and MD in frontal and temporal white matter pathways may be additionally driven by state variables that involve processes associated with the disease.

MAO A VNTR polymorphism and amygdala volume in healthy subjects

The X-linked Monoamine Oxidase A (MAO A) gene presents a well known functional polymorphism consisting of a variable number of tandem repeats (VNTR) (long and short variants) previously associated with altered neural function of the amygdala. Using automatic subcortical segmentation (Freesurfer), we investigated whether amygdala volume could be influenced by this genotype. We studied 109 healthy subjects (age range 18-80 years; 59 male and 50 female), 74 carrying the MAO A High-activity allele and 35 the MAO A Low-activity allele. No significant effect of the MAO A polymorphism or interaction effect between polymorphism × gender was found on amygdalar volume. Thus, our findings suggest that the reported impact of the MAO A polymorphism on amygdala function is not coupled with consistent volumetric changes in healthy subjects. Future studies are needed to investigate whether the association between volume of the amygdala and the MAO A VNTR polymorphism is influenced by social/psychological variables, such as impulsivity, trauma history and cigarette smoking behaviour, not taken into account in this work.

Imaging genomics

PURPOSE OF REVIEW

Imaging genomics is an emerging field that is rapidly identifying genes that influence the brain, cognition, and risk for disease. Worldwide, thousands of individuals are being scanned with high-throughput genotyping (genome-wide scans), and new imaging techniques [high angular resolution diffusion imaging and resting state functional magnetic resonance imaging (MRI)] that provide fine-grained measures of the brain's structural and functional connectivity. Along with clinical diagnosis and cognitive testing, brain imaging offers highly reproducible measures that can be subjected to genetic analysis.

RECENT FINDINGS

Recent studies of twin, pedigree, and population-based datasets have discovered several candidate genes that consistently show small to moderate effects on brain measures. Many studies measure single phenotypes from the images, such as hippocampal volume, but voxel-wise genomic methods can plot the profile of genetic association at each 3D point in the brain. This exploits the full arsenal of imaging statistics to discover and replicate gene effects.

SUMMARY

Imaging genomics efforts worldwide are now working together to discover and replicate many promising leads. By studying brain phenotypes closer to causative gene action, larger gene effects are detectable with realistic sample sizes obtainable from meta-analysis of smaller studies. Imaging genomics has broad applications to dementia, mental illness, and public health.