A new registration method based on Log-Euclidean tensor metrics and its application to genetic studies

In structural brain MRI, group differences or changes in brain structures can be detected using Tensor-Based Morphometry (TBM). This method consists of two steps: (1) a non-linear registration step, that aligns all of the images to a common template, and (2) a subsequent statistical analysis. The numerous registration methods that have recently been developed differ in their detection sensitivity when used for TBM, and detection power is paramount in epidemological studies or drug trials. We therefore developed a new fluid registration method that computes the mappings and performs statistics on them in a consistent way, providing a bridge between TBM registration and statistics. We used the Log-Euclidean framework to define a new regularizer that is a fluid extension of the Riemannian elasticity, which assures diffeomorphic transformations. This regularizer constrains the symmetrized Jacobian matrix, also called the deformation tensor. We applied our method to an MRI dataset from 40 fraternal and identical twins, to revealed voxelwise measures of average volumetric differences in brain structure for subjects with different degrees of genetic resemblance.

Mapping genetic influences on brain fiber architecture with high angular resolution diffusion imaging (HARDI)

We report the first 3D maps of genetic effects on brain fiber complexity. We analyzed HARDI brain imaging data from 90 young adult twins using an information-theoretic measure, the Jensen-Shannon divergence (JSD), to gauge the regional complexity of the white matter fiber orientation distribution functions (ODF). HARDI data were fluidly registered using Karcher means and ODF square-roots for interpol ation; each subject's JSD map was computed from the spatial coherence of the ODFs in each voxel's neighborhood. We evaluated the genetic influences on generalized fiber anisotropy (GFA) and complexity (JSD) using structural equation models (SEM). At each voxel, genetic and environmental components of data variation were estimated, and their goodness of fit tested by permutation. Color-coded maps revealed that the optimal models varied for different brain regions. Fiber complexity was predominantly under genetic control, and was higher in more highly anisotropic regions. These methods show promise for discovering factors affecting fiber connectivity in the brain.

Mapping genetic influences on ventricular structure in twins

Despite substantial progress in measuring the anatomical and functional variability of the human brain, little is known about the genetic and environmental causes of these variations. Here we developed an automated system to visualize genetic and environmental effects on brain structure in large brain MRI databases. We applied our multi-template segmentation approach termed "Multi-Atlas Fluid Image Alignment" to fluidly propagate hand-labeled parameterized surface meshes, labeling the lateral ventricles, in 3D volumetric MRI scans of 76 identical (monozygotic, MZ) twins (38 pairs; mean age = 24.6 (SD = 1.7)); and 56 same-sex fraternal (dizygotic, DZ) twins (28 pairs; mean age = 23.0 (SD = 1.8)), scanned as part of a 5-year research study that will eventually study over 1000 subjects. Mesh surfaces were averaged within subjects to minimize segmentation error. We fitted quantitative genetic models at each of 30,000 surface points to measure the proportion of shape variance attributable to (1) genetic differences among subjects, (2) environmental influences unique to each individual, and (3) shared environmental effects. Surface-based statistical maps, derived from path analysis, revealed patterns of heritability, and their significance, in 3D. Path coefficients for the 'ACE' model that best fitted the data indicated significant contributions from genetic factors (A = 7.3%), common environment (C = 38.9%) and unique environment (E = 53.8%) to lateral ventricular volume. Earlier-maturing occipital horn regions may also be more genetically influenced than later-maturing frontal regions. Maps visualized spatially-varying profiles of environmental versus genetic influences. The approach shows promise for automatically measuring gene-environment effects in large image databases.

Strong inference in functional neuroimaging

A recurring question for cognitive science is whether functional neuroimaging data can provide evidence for or against psychological theories. As posed, the question reflects an adherence to a popular scientific method known as 'strong inference'. The method entails constructing multiple hypotheses (Hs) and designing experiments so that alternative possible outcomes will refute at least one (i.e., 'falsify' it). In this article, after first delineating some well-documented limitations of strong inference, I provide examples of functional neuroimaging data being used to test Hs from rival modular information-processing models of spoken word production. 'Strong inference' for neuroimaging involves first establishing a systematic mapping of 'processes to processors' for a common modular architecture. Alternate Hs are then constructed from psychological theories that attribute the outcome of manipulating an experimental factor to two or more distinct processing stages within this architecture. Hs are then refutable by a finding of activity differentiated spatially and chronometrically by experimental condition. When employed in this manner, the data offered by functional neuroimaging may be more useful for adjudicating between accounts of processing loci than behavioural measures.

Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship

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.

Automatic clustering of white matter fibers in brain diffusion mri with an application to genetics

To understand factors that affect brain connectivity and integrity, it is beneficial to automatically cluster white matter (WM) fibers into anatomically recognizable tracts. Whole brain tractography, based on diffusion-weighted MRI, generates vast sets of fibers throughout the brain; clustering them into consistent and recognizable bundles can be difficult as there are wide individual variations in the trajectory and shape of WM pathways. Here we introduce a novel automated tract clustering algorithm based on label fusion - a concept from traditional intensity-based segmentation. Streamline tractography generates many incorrect fibers, so our top-down approach extracts tracts consistent with known anatomy, by mapping multiple hand-labeled atlases into a new dataset. We fuse clustering results from different atlases, using a mean distance fusion scheme. We reliably extracted the major tracts from 105-gradient high angular resolution diffusion images (HARDI) of 198 young normal twins. To compute population statistics, we use a pointwise correspondence method to match, compare, and average WM tracts across subjects. We illustrate our method in a genetic study of white matter tract heritability in twins.

A new combined surface and volume registration

3D registration of brain MRI data is vital for many medical imaging applications. However, purely intensitybased approaches for inter-subject matching of brain structure are generally inaccurate in cortical regions, due to the highly complex network of sulci and gyri, which vary widely across subjects. Here we combine a surfacebased cortical registration with a 3D fluid one for the first time, enabling precise matching of cortical folds, but allowing large deformations in the enclosed brain volume, which guarantee diffeomorphisms. This greatly improves the matching of anatomy in cortical areas. The cortices are segmented and registered with the software Freesurfer. The deformation field is initially extended to the full 3D brain volume using a 3D harmonic mapping that preserves the matching between cortical surfaces. Finally, these deformation fields are used to initialize a 3D Riemannian fluid registration algorithm, that improves the alignment of subcortical brain regions. We validate this method on an MRI dataset from 92 healthy adult twins. Results are compared to those based on volumetric registration without surface constraints; the resulting mean templates resolve consistent anatomical features both subcortically and at the cortex, suggesting that the approach is well-suited for cross-subject integration of functional and anatomic data.

Mapping hippocampal and ventricular change in Alzheimer disease

We developed an anatomical mapping technique to detect hippocampal and ventricular changes in Alzheimer disease (AD). The resulting maps are sensitive to longitudinal changes in brain structure as the disease progresses. An anatomical surface modeling approach was combined with surface-based statistics to visualize the region and rate of atrophy in serial MRI scans and isolate where these changes link with cognitive decline. Fifty-two high-resolution MRI scans were acquired from 12 AD patients (age: 68.4 ± 1.9 years) and 14 matched controls (age: 71.4 ± 0.9 years), each scanned twice (2.1 ± 0.4 years apart). 3D parametric mesh models of the hippocampus and temporal horns were created in sequential scans and averaged across subjects to identify systematic patterns of atrophy. As an index of radial atrophy, 3D distance fields were generated relating each anatomical surface point to a medial curve threading down the medial axis of each structure. Hippocampal atrophic rates and ventricular expansion were assessed statistically using surface-based permutation testing and were faster in AD than in controls. Using color-coded maps and video sequences, these changes were visualized as they progressed anatomically over time. Additional maps localized regions where atrophic changes linked with cognitive decline. Temporal horn expansion maps were more sensitive to AD progression than maps of hippocampal atrophy, but both maps correlated with clinical deterioration. These quantitative, dynamic visualizations of hippocampal atrophy and ventricular expansion rates in aging and AD may provide a promising measure to track AD progression in drug trials.

Corrigendum to "Mapping hippocampal and ventricular change in Alzheimer disease" [NeuroImage 22 (2004) 1754-1766] (DOI:10.1016/j.neuroimage.2004.03.040)

We recently noticed an error in the demographic data in this article. The validity of the findings and the conclusions of the paper is not affected. However, there is an error in the reported sample size and in the means and standard deviations of the subjects’ ages and MMSE scores. We would like to correct this error, which came to light when we were re-analyzing the data for a meta-analysis. The error occurred because an older version of a spreadsheet was incorrectly used when reporting the sample composition. Instead of examining 12 Alzheimer's disease patients and 14 healthy elderly controls, we in fact examined 17 Alzheimer’s disease patients and 14 healthy elderly controls. All maps and morphometric data reported in the paper are correct, except that the sample size was in fact slightly higher than that originally reported, and the maps computed in the paper were based on the larger sample (which included five more subjects in the Alzheimer’s disease group). All of the maps and figures in the paper are correct, and the conclusions of the paper are unchanged. We apologize for this error, which falls under the sole responsibility of the first author. The corrected demographic information appears below.

Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia

This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhood- and adult-onset schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages.

A functional MRI study of the relationship between naming treatment outcomes and resting state functional connectivity in post-stroke aphasia

Background: The majority of studies investigating the neural mechanisms underlying treatment in people with aphasia have examined task-based brain activity. However, the use of resting-state fMRI may provide another method of examining the brain mechanisms responsible for treatment-induced recovery, and allows for investigation into connectivity within complex functional networks Methods: Eight people with aphasia underwent 12 treatment sessions that aimed to improve object naming. Half the sessions employed a phonologically-based task, and half the sessions employed a semantic-based task, with resting-state fMRI conducted pre- and post-treatment. Brain regions in which the amplitude of low frequency fluctuations (ALFF) correlated with treatment outcomes were used as seeds for functional connectivity (FC) analysis. FC maps were compared from pre- to post-treatment, as well as with a group of 12 healthy older controls Results: Pre-treatment ALFF in the right middle temporal gyrus (MTG) correlated with greater outcomes for the phonological treatment, with a shift to the left MTG and supramarginal gyrus, as well as the right inferior frontal gyrus, post-treatment. When compared to controls, participants with aphasia showed both normalization and up-regulation of connectivity within language networks post-treatment, predominantly in the left hemisphere Conclusions: The results provide preliminary evidence that treatments for naming impairments affect the FC of language networks, and may aid in understanding the neural mechanisms underlying the rehabilitation of language post-stroke.

A reproducible method for automated extraction of brain volumes from 3D human head MR images

An automated method for extracting brain volumes from three commonly acquired three-dimensional (3D) MR images (proton density, T1 weighted, and T2-weighted) of the human head is described. The procedure is divided into four levels: preprocessing, segmentation, scalp removal, and postprocessing. A user-provided reference point is the sole operator-dependent input required. The method's parameters were first optimized and then fixed and applied to 30 repeat data sets from 15 normal older adult subjects to investigate its reproducibility. Percent differences between total brain volumes (TBVs) for the subjects' repeated data sets ranged from .5% to 2.2%. We conclude that the method is both robust and reproducible and has the potential for wide application.

Brain network efficiency and topology depend on the fiber tracking method: 11 tractography algorithms compared in 536 subjects

As connectivity analyses become more popular, claims are often made about how the brain's anatomical networks depend on age, sex, or disease. It is unclear how results depend on tractography methods used to compute fiber networks. We applied 11 tractography methods to high angular resolution diffusion images of the brain (4-Tesla 105-gradient HARDI) from 536 healthy young adults. We parcellated 70 cortical regions, yielding 70×70 connectivity matrices, encoding fiber density. We computed popular graph theory metrics, including network efficiency, and characteristic path lengths. Both metrics were robust to the number of spherical harmonics used to model diffusion (4th-8th order). Age effects were detected only for networks computed with the probabilistic Hough transform method, which excludes smaller fibers. Sex and total brain volume affected networks measured with deterministic, tensor-based fiber tracking but not with the Hough method. Each tractography method includes different fibers, which affects inferences made about the reconstructed networks.

Use of modal flexibility method to detect damage in suspended cables and the effects of cable parameters

Modal flexibility is a widely accepted technique to detect structural damage using vibration characteristics. Its application to detect damage in long span large diameter cables such as those used in suspension bridge main cables has not received much attention. This paper uses the modal flexibility method incorporating two damage indices (DIs) based on lateral and vertical modes to localize damage in such cables. The competency of those DIs in damage detection is tested by the numerically obtained vibration characteristics of a suspended cable in both intact and damaged states. Three single damage cases and one multiple damage case are considered. The impact of random measurement noise in the modal data on the damage localization capability of these two DIs is next examined. Long span large diameter cables are characterized by the two critical cable parameters named bending stiffness and sag-extensibility. The influence of these parameters in the damage localization capability of the two DIs is evaluated by a parametric study with two single damage cases. Results confirm that the damage index based on lateral vibration modes has the ability to successfully detect and locate damage in suspended cables with 5% noise in modal data for a range of cable parameters. This simple approach therefore can be extended for timely damage detection in cables of suspension bridges and thereby enhance their service during their life spans.

Polyacrylic Acid Assisted Synthesis of Cu2ZnSnS4 by Hydrothermal Method

Pure phase Cu2ZnSnS4 (CZTS) nanoparticles were successfully synthesized via polyacrylic acid (PAA) assisted one-pot hydrothermal route. The morphology, crystal structure, composition and optical properties as well as the photoactivity of the as-synthesized CZTS nanoparticles were characterized by X-ray diffraction, Raman spectroscopy, scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectrometer, UV-visible absorption spectroscopy and photoelectrochemical measurement. The influence of various synthetic conditions, such as the reaction temperature, reaction duration and the amount of PAA in the precursor solution on the formation of CZTS compound was systematically investigated. The results have shown that the crystal phase, morphology and particle size of CZTS can be tailored by controlling the reaction conditions. The formation mechanism of CZTS in the hydrothermal reaction has been proposed based on the investigation of time-dependent phase evolution of CZTS which showed that metal sulfides (e.g., Cu2S, SnS2 and ZnS) were formed firstly during the hydrothermal reaction before forming CZTS compound through nucleation. The band gap of the as-synthesized CZTS nanoparticles is 1.49 eV. The thin film electrode based on the synthesized CZTS nanoparticles in a three-electrode photoelectrochemical cell generated pronounced photocurrent under illumination provided by a red light-emitting diode (LED, 627 nm), indicating the photoactivity of the semiconductor material.