Fast and accurate initialization of the free-water imaging model parameters from multi-shell diffusion MRI.

Cerebrospinal fluid partial volume effect is a known bias in the estimation of Diffusion Tensor Imaging (DTI) parameters from diffusion MRI data. The Free-Water Imaging model for diffusion MRI data adds a second compartment to the DTI model, which explicitly accounts for the signal contribution of extracellular free-water, such as cerebrospinal fluid. As a result the DTI parameters obtained through the free-water model are corrected for partial volume effects, and thus better represent tissue microstructure. In addition, the model estimates the fractional volume of free-water, and can be used to monitor changes in the extracellular space. Under certain assumptions, the model can be estimated from single-shell diffusion MRI data. However, by using data from multi-shell diffusion acquisitions, these assumptions can be relaxed, and the fit becomes more robust. Nevertheless, fitting the model to multi-shell data requires high computational cost, with a non-linear iterative minimization, which has to be initialized close enough to the global minimum to avoid local minima and to robustly estimate the model parameters. Here we investigate the properties of the main initialization approaches that are currently being used, and suggest new fast approaches to improve the initial estimates of the model parameters. We show that our proposed approaches provide a fast and accurate initial approximation of the model parameters, which is very close to the final solution. We demonstrate that the proposed initializations improve the final outcome of non-linear model fitting.

The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data.

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

BDNF val66met modulates the association between childhood trauma, cognitive and brain abnormalities in psychoses.

OBJECTIVE: Brain derived neurotrophic factor (BDNF) is important for brain development and plasticity, and here we tested if the functional BDNF val66met variant modulates the association between high levels of childhood abuse, cognitive function, and brain abnormalities in psychoses. METHOD: 249 patients with a broad DSM-IV schizophrenia spectrum disorder or bipolar disorder were consecutively recruited to the TOP research study (mean±age: 30.7±10.9; gender: 49% males). History of childhood trauma was obtained using the Childhood Trauma Questionnaire. Cognitive function was assessed through a standardized neuropsychological test battery. BDNF val66met was genotyped using standardized procedures. A sub-sample of n=106 Caucasians with a broad DSM-IV schizophrenia spectrum disorder or bipolar disorder (mean±age: 32.67±10.85; 49% males) had data on sMRI. RESULTS: Carriers of the Methionine (met) allele exposed to high level of childhood abuse demonstrated significantly poorer cognitive functioning compared to homozygotic Valine (val/val) carriers. Taking in consideration multiple testing, using a more conservative p value, this was still shown for physical abuse and emotional abuse, as well as a trend level for sexual abuse. Further, met carriers exposed to high level of childhood sexual abuse showed reduced right hippocampal volume (r(2)=0.43; p=0.008), and larger right and left lateral ventricles (r(2)=0.37; p=0.002, and r(2)=0.27; p=0.009, respectively). Our findings were independent of age, gender, diagnosis and intracranial volume. CONCLUSION: Our data demonstrate that in patients with psychoses, met carriers of the BDNF val66met with high level of childhood abuse have more cognitive and brain abnormalities than all other groups.

ZNF804A and cortical thickness in schizophrenia and bipolar disorder.

ZNF804A SNP rs1344706 confers genome-wide risk for schizophrenia and bipolar disorder. Both disorders affect cortical thickness. To determine if single nucleotide polymorphisms (SNPs) across ZNF804A are associated with cortical thinning, we investigated 63 SNPs (including rs1344706) in 365 psychosis patients and healthy controls. Results show no significant associations.

A 5-year follow-up study of brain cortical and subcortical abnormalities in a schizophrenia cohort.

BACKGROUND: Magnetic resonance imaging studies have demonstrated that patients with schizophrenia have thinner cortex in prefrontal and temporal brain regions, and enlarged lateral ventricles, compared to healthy subjects. Longitudinal studies have shown progressive brain tissue loss and ventricular dilatation among patients, predominantly in the early phase of the illness. Evidence for progression in more chronic phases of schizophrenia is less established. METHODS: Measurements of cortical thickness, cortical volume and subcortical volumes were obtained from 52 patients with long-term treated schizophrenia and 63 healthy subjects who were scanned twice over five years. Differences in brain measurements across time and group were investigated using general linear models. RESULTS: Compared to controls, patients had similar patterns of thinner cortex and smaller cortical volumes in prefrontal and temporal regions at both time points. In the follow-up interval regional cortical volumes decreased and lateral ventricle volumes increased in both groups. There was a trend level interaction effect of group and time for the right lateral ventricle, but not for cortical measurements. This effect was related to higher degree of negative symptoms at follow-up. CONCLUSIONS: Regional differences in cortical thickness and volume between long-term treated patients with schizophrenia and healthy subjects are stable across five years, while right lateral ventricle volumes tend to increase more in the patients. The findings indicate that brain structure abnormalities found in schizophrenia are not progressive in the chronic stage of the disease, but that some progression in subcortical structures may be present in patients with poor outcome.

Identification of common variants associated with human hippocampal and intracranial volumes.

Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer's disease and is reduced in schizophrenia, major depression and mesial temporal lobe epilepsy. Whereas many brain imaging phenotypes are highly heritable, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10(-16)) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10(-12)). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10(-7)).

Cortical volume, surface area, and thickness in schizophrenia and bipolar disorder.

BACKGROUND: Magnetic resonance imaging studies have shown that structural brain abnormalities are present in both schizophrenia and bipolar disorder. Most previous studies have focused on brain tissue volumes, but advances in neuroimaging data processing have made it possible to separate cortical area and cortical thickness. The purpose of the present study was to provide a more complete picture of cortical morphometric differences in schizophrenia and bipolar disorder, decomposing cortical volume into its constituent parts, cortical thickness and cortical area. METHODS: We analyzed magnetic resonance imaging images from a sample of 173 patients with schizophrenia, 139 patients with bipolar disorder, and 207 healthy control subjects. Maps of cortical volume, area, and thickness across the continuous cortical surface were generated within groups and compared between the groups. RESULTS: There were widespread reductions in cortical volume in schizophrenia relative to healthy control subjects and patients with bipolar disorder type I. These reductions were mainly driven by cortical thinning, but there were also cortical area reductions in more circumscribed regions, which contributed to the observed volume reductions. CONCLUSIONS: The current surface-based methodology allows for a distinction between cortical thinning and reduction in cortical area and reveals that cortical thinning is the most important factor in volume reduction in schizophrenia. Cortical area reduction was not observed in bipolar disorder type I and may be unique to schizophrenia.

Shape-adaptive DCT for denoising of 3D scalar and tensor valued images.

During the last ten years or so, diffusion tensor imaging has been used in both research and clinical medical applications. To construct the diffusion tensor images, a large set of direction sensitive magnetic resonance image (MRI) acquisitions are required. These acquisitions in general have a lower signal-to-noise ratio than conventional MRI acquisitions. In this paper, we discuss computationally effective algorithms for noise removal for diffusion tensor magnetic resonance imaging (DTI) using the framework of 3-dimensional shape-adaptive discrete cosine transform. We use local polynomial approximations for the selection of homogeneous regions in the DTI data. These regions are transformed to the frequency domain by a modified discrete cosine transform. In the frequency domain, the noise is removed by thresholding. We perform numerical experiments on 3D synthetical MRI and DTI data and real 3D DTI brain data from a healthy volunteer. The experiments indicate good performance compared to current state-of-the-art methods. The proposed method is well suited for parallelization and could thus dramatically improve the computation speed of denoising schemes for large scale 3D MRI and DTI.

Diffusion k-tensor estimation from Q-ball imaging using discretized principal axes.

A reoccurring theme in the diffusion tensor imaging literature is the per-voxel estimation of a symmetric 3 x 3 tensor describing the measured diffusion. In this work we attempt to generalize this approach by calculating 2 or 3 or up to k diffusion tensors for each voxel. We show that our procedure can more accurately describe the diffusion particularly when crossing fibers or fiber-bundles are present in the datasets.