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Single subject task-based fMRI analyses generally suffer from low detection sensitivity with parameter estimates from the general linear model (GLM) lying below the significance threshold especially for similar contrasts or conditions. In this paper, we present a shape-based approach for alignment of condition-specific time course activity for single subject task-based fMRI. Our approach extracts signals for each condition from the entire time course, constructs an unbiased average of those signals, and warps each signal to the mean. As the warping is diffeomorphic, non-linear and allows large deformations of time series if required, we term this approach as elastic functional registration. On a single subject level, our method significantly detects more clusters and more activated voxels in relevant subcortical regions in healthy controls.

By analyzing multitissue gene expression and genome-wide genetic variation data in samples from a vervet monkey pedigree, we generated a transcriptome resource and produced the first catalog of expression quantitative trait loci (eQTLs) in a nonhuman primate model. This catalog contains more genome-wide significant eQTLs per sample than comparable human resources and identifies sex- and age-related expression patterns. Findings include a master regulatory locus that likely has a role in immune function and a locus regulating hippocampal long noncoding RNAs (lncRNAs), whose expression correlates with hippocampal volume. This resource will facilitate genetic investigation of quantitative traits, including brain and behavioral phenotypes relevant to neuropsychiatric disorders.

We present a shape matching approach for functional magnetic resonance imaging (fMRI) time course and spectral alignment. We use ideas from differential geometry and functional data analysis to define a functional representation for fMRI signals. The space of fMRI functions is then equipped with a reparameterization invariant Riemannian metric that enables elastic alignment of both amplitude and phase of the fMRI time courses as well as their power spectral densities. Experimental results show significant increases in pairwise node to node correlations and coherences following alignment. We apply this method for finding group differences in connectivity between patients with major depression and healthy controls.

Patients with major depressive disorder (MDD) who do not achieve full symptomatic recovery after antidepressant treatment have a higher risk of relapse. Compared to pharmacotherapies, electroconvulsive therapy (ECT) more rapidly produces a greater extent of response in severely depressed patients. However, prediction of which candidates are most likely to improve after ECT remains challenging. Using structural MRI data from 42 ECT patients scanned prior to ECT treatment, we developed a random forest classifier based on data-driven shape cluster selection and cortical thickness features to predict remission. Right hemisphere hippocampal shape and right inferior temporal cortical thickness was most predictive of remission, with the predicted probability of recovery decreasing when these regions were thicker prior to treatment. Remission was predicted with an average 73% balanced accuracy. Classification of MRI data may help identify treatment-responsive patients and aid in clinical decision-making. Our results show promise for the development of personalized treatment strategies.

We present a Riemannian framework for linear and quadratic discriminant classification on the tangent plane of the shape space of curves. The shape space is infinite dimensional and is constructed out of square root velocity functions of curves. We introduce the notion of mean and covariance of shape-valued random variables and samples from a tangent space to the pre-shapes (invariant to translation and scaling) and then extend it to the full shape space (rotational invariance). The shape observations from the population are approximated by coefficients of a Fourier basis of the tangent space. The algorithms for linear and quadratic discriminant analysis are then defined using reduced dimensional features obtained by projecting the original shape observations on to the truncated Fourier basis. We show classification results on synthetic data and shapes of cortical sulci, corpus callosum curves, as well as facial midline curve profiles from patients with fetal alcohol syndrome (FAS).

Major depressive disorder (MDD) is associated with dysfunctional corticolimbic networks, making functional connectivity studies integral for understanding the mechanisms underlying MDD pathophysiology and treatment. Resting-state functional connectivity (RSFC) studies analyze patterns of temporally coherent intrinsic brain activity in "resting-state networks" (RSNs). The default-mode network (DMN) has been of particular interest to depression research; however, a single RSN is unlikely to capture MDD pathophysiology in its entirety, and the DMN itself can be characterized by multiple RSNs. This, coupled with conflicting previous results, underscores the need for further research. Here, we measured RSFC in MDD by targeting RSNs overlapping with corticolimbic regions and further determined whether altered patterns of RSFC were restored with electroconvulsive therapy (ECT). MDD patients exhibited hyperconnectivity between ventral striatum (VS) and the ventral default-mode network (vDMN), while simultaneously demonstrating hypoconnectivity with the anterior DMN (aDMN). ECT influenced this pattern: VS-vDMN hyperconnectivity was significantly reduced while VS-aDMN hypoconnectivity only modestly improved. RSFC between the salience RSN and dorsomedial prefrontal cortex was also reduced in MDD, but was not affected by ECT. Taken together, our results support a model of ventral/dorsal imbalance in MDD and further suggest that the VS is a key structure contributing to this desynchronization., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Neuroimaging studies have indicated that prenatal alcohol exposure is associated with alterations in the structure of specific brain regions. However, the temporal specificity of such changes and their behavioral consequences are less known. Here we explore the brain structure of infants with in utero exposure to alcohol shortly after birth. T2 structural MRI images were acquired from 28 alcohol-exposed infants and 45 demographically matched healthy controls at 2-4 weeks of age on a 3T Siemens Allegra system as part of large birth cohort study, the Drakenstein Child Health Study (DCHS). Neonatal neurobehavior was assessed at this visit; early developmental outcome assessed on the Bayley Scales of Infant Development III at 6 months of age. Volumes of gray matter regions were estimated based on the segmentations of the University of North Carolina neonatal atlas. Significantly decreased total gray matter volume was demonstrated for the alcohol-exposed cohort compared to healthy control infants (p < 0.001). Subcortical gray matter regions that were significantly different between groups after correcting for overall gray matter volume included left hippocampus, bilateral amygdala and left thalamus (p < 0.01). These findings persisted even when correcting for infant age, gender, ethnicity and maternal smoking status. Both early neurobehavioral and developmental adverse outcomes at 6 months across multiple domains were significantly associated with regional volumes primarily in the temporal and frontal lobes in infants with prenatal alcohol exposure. Alcohol exposure during the prenatal period has potentially enduring neurobiological consequences for exposed children. These findings suggest the effects of prenatal alcohol exposure on brain growth is present very early in the first year of life, a period during which the most rapid growth and maturation occurs.

Disorders of the central nervous system are often accompanied by brain abnormalities detectable with MRI. Advances in biomedical imaging and pattern detection algorithms have led to classification methods that may help diagnose and track the progression of a brain disorder and/or predict successful response to treatment. These classification systems often use high-dimensional signals or images, and must handle the computational challenges of high dimensionality as well as complex data types such as shape descriptors. Here, we used shape information from subcortical structures to test a recently developed feature-selection method based on regularized random forests to 1) classify depressed subjects versus controls, and 2) patients before and after treatment with electroconvulsive therapy. We subsequently compared the classification performance of high-dimensional shape features with traditional volumetric measures. Shape-based models outperformed simple volumetric predictors in several cases, highlighting their utility as potential automated alternatives for establishing diagnosis and predicting treatment response.

We present an intrinsic framework for constructing sulcal shape atlases on the human cortex. We propose the analysis of sulcal and gyral patterns by representing them by continuous open curves in ℝ(3). The space of such curves, also termed as the shape manifold is equipped with a Riemannian L(2) metric on the tangent space, and shows desirable properties while matching shapes of sulci. On account of the spherical nature of the shape space, geodesics between shapes can be computed analytically. Additionally, we also present an optimization approach that computes geodesics in the quotient space of shapes modulo rigid rotations and reparameterizations. We also integrate the elastic shape model into a surface registration framework for a population of 176 subjects, and show a considerable improvement in the constructed surface atlases.

Given deformations for mapping images or surfaces into an atlas configuration, methods are described for characterizing the mean deformation and deviations from this mean. Jacobian matrices are used to characterize the deformations locally, and the method can be applied to any image warping method for which Jacobian matrices can be computed. The method makes use of the fact that each matrix descriptor of the local deformation required to match an image to the atlas corresponds to a point on a semi-Riemannian manifold. By assuring that the mean matrix lies within this manifold, fundamental geometric properties common to all of the images can be preserved. Local deviations from the mean can be characterized in a euclidean space tangent to the semi-Riemannian manifold at the mean and can be accumulated globally across multiple sampling locations within the atlas to generate a global multivariate characterization of how each image deviates from the mean.

Imitation is a basic form of motor learning during development. We have a preference to imitate the actions of others as if looking in a mirror (specular imitation: i.e., when the actor moves the left hand, the imitator moves the right hand) rather than with the anatomically congruent hand (anatomic imitation: i.e., actor and imitator both moving the right hand). We hypothesized that this preference reflects changes in activity in previously described frontoparietal cortical areas involved in directly matching observed and executed actions (mirror neuron areas). We used functional magnetic resonance imaging to study brain activity in normal volunteers imitating left and right hand movements with their right hand. Bilateral inferior frontal and right posterior parietal cortex were more active during specular imitation compared with anatomic imitation and control motor tasks. Furthermore this same pattern of activity was also observed in the rostral part of the supplementary motor area (SMA-proper) of the right hemisphere. These findings suggest that the degree of involvement of frontoparietal mirror areas in imitation depends on the nature of the imitative behavior, ruling out a linguistic mediation of these areas in imitation. Moreover, activity in the SMA appears to be tightly coupled to frontoparietal mirror areas when subjects copy the actions of others.

Our goal was to establish whether altered hippocampal morphology represents a trait marker for genetic vulnerability in schizophrenia. We outlined the hippocampi on high-resolution MR images obtained from matched samples of control and discordant monozygotic and dizygotic co-twins (N = 40 pairs). Hippocampal measures were used in statistical tests specifically designed to identify disease-associated genetic and nongenetic influences on morphology. 3D surface average maps of the hippocampus were additionally compared in biological risk groups. Smaller hippocampal volumes were confirmed in schizophrenia. Dizygotic affected co-twins showed smaller left hippocampi compared to their healthy siblings. Disease-associated effects were not present between monozygotic discordant co-twins. Monozygotic, but not dizygotic, unaffected co-twins exhibited smaller left hippocampi compared to control twins, supporting genetic influences. Surface areas and posterior volumes similarly revealed schizophrenia and genetic liability effects. Results suggest that hippocampal volume reduction may be a trait marker for identifying individuals possessing a genetic predisposition for schizophrenia.

Behavioral studies reveal that imitation performance and the motor system are strongly influenced by the goal of the action to be performed. We used functional magnetic resonance imaging (fMRI) to assess the effect of explicit action goals on neural activity during imitation. Subjects imitated index finger movements in the absence and presence of visible goals (red dots that were reached for by the finger movement). Finger movements were either ipsilateral or contralateral. The pars opercularis of the inferior frontal gyrus showed increased blood oxygen level-dependent fMRI signal bilaterally for imitation of goal-oriented actions, compared with imitation of actions with no explicit goal. In addition, bilateral dorsal premotor areas demonstrated greater activity for goal-oriented actions, for contralateral movements and an interaction effect such that goal-oriented contralateral movements yielded the greatest activity. These results support the hypothesis that areas relevant to motor preparation and motor execution are tuned to coding goal-oriented actions and are in keeping with single-cell recordings revealing that neurons in area F5 of the monkey brain represent goal-directed aspects of actions.

Patients with schizophrenia exhibit abnormalities in midsagittal corpus callosum area, shape, and/or displacement. Our goal was to confirm these findings and to establish the genetic and nongenetic contributions to altered callosal morphology in schizophrenia. Relationships between ventricular enlargements potentially contributing to callosal displacements were assessed as a secondary goal. High-resolution magnetic resonance images were obtained from co-twins of monozygotic and dizygotic pairs discordant for schizophrenia and healthy control twins (N = 40 pairs). Investigators blind to group status segmented the corpus callosum and ventricles in native brain volumes aligned using a rigid-body transformation with no scaling. Total and parcellated midsagittal callosal areas and measures indexing vertical displacements of the corpus callosum were used in statistical tests to identify schizophrenia and sex effects and to dissociate genetic and nongenetic influences on morphology. Anatomical mesh modeling methods provided group average and surface variability maps of the callosum. Callosal areas did not differ between groups defined by sex or biological risk. Vertical displacements of the callosum, pronounced in male patients, were confirmed in schizophrenia and observed between dizygotic, but not monozygotic co-twins discordant for schizophrenia. Like their affected twins, however, unaffected monozygotic co-twins of the schizophrenia probands exhibited significant callosal displacements. Lateral and third ventricle enlargements were related to callosal displacements. Results clearly support that genetic rather than disease-specific or shared environmental influences contribute to altered callosal morphology in schizophrenia. An upward bowing of the callosum may thus provide an easily identifiable neuroanatomic marker to screen individuals possessing a biological vulnerability for schizophrenia.

In the field of template-based medical image analysis, image registration and normalization are frequently used to evaluate and interpret data in a standard template or reference atlas space. Despite the large number of image-registration (warping) techniques developed recently in the literature, only a few studies have been undertaken to numerically characterize and compare various alignment methods. In this paper, we introduce a new approach for analyzing image registration based on a selective-wavelet reconstruction technique using a frequency-adaptive wavelet shrinkage. We study four polynomial-based and two higher complexity nonaffine warping methods applied to groups of stereotaxic human brain structural (magnetic resonance imaging) and functional (positron emission tomography) data. Depending upon the aim of the image registration, we present several warp classification schemes. Our method uses a concise representation of the native and resliced (pre- and post-warp) data in compressed wavelet space to assess quality of registration. This technique is computationally inexpensive and utilizes the image compression, image enhancement, and denoising characteristics of the wavelet-based function representation, as well as the optimality properties of frequency-dependent wavelet shrinkage.