Temporal dynamics of brain anatomy

Intrinsic timescales and predictive allostatic interoception in brain health and disease

The cognitive neuroscience of brain diseases faces challenges in understanding the complex relationship between brain structure and function, the heterogeneity of brain phenotypes, and the lack of dimensional and transnosological explanations. This perspective offers a framework combining the predictive coding theory of allostatic interoceptive overload (PAIO) and the intrinsic neural timescales (INT) theory to provide a more dynamic understanding of brain health in psychiatry and neurology. PAIO integrates allostasis and interoception to assess the interaction between internal patterns and environmental stressors, while INT shows that different brain regions operate on different intrinsic timescales. The allostatic overload can be understood as a failure of INT, which involves a breakdown of proper temporal integration and segregation. This can lead to dimensional disbalances between exteroceptive/interoceptive inputs across brain and whole-body levels (cardiometabolic, cardiovascular, inflammatory, immune). This approach offers new insights, presenting novel perspectives on brain spatiotemporal hierarchies and interactions. By integrating these theories, the paper opens innovative paths for studying brain health dynamics, which can inform future research in brain health and disease.

Structural Brain Asymmetries for Language: A Comparative Approach across Primates

Humans are the only species that can speak. Nonhuman primates, however, share some ‘domain-general’ cognitive properties that are essential to language processes. Whether these shared cognitive properties between humans and nonhuman primates are the results of a continuous evolution [homologies] or of a convergent evolution [analogies] remain difficult to demonstrate. However, comparing their respective underlying structure—the brain—to determinate their similarity or their divergence across species is critical to help increase the probability of either of the two hypotheses, respectively. Key areas associated with language processes are the Planum Temporale, Broca’s Area, the Arcuate Fasciculus, Cingulate Sulcus, The Insula, Superior Temporal Sulcus, the Inferior Parietal lobe, and the Central Sulcus. These structures share a fundamental feature: They are functionally and structurally specialised to one hemisphere. Interestingly, several nonhuman primate species, such as chimpanzees and baboons, show human-like structural brain asymmetries for areas homologous to key language regions. The question then arises: for what function did these asymmetries arise in non-linguistic primates, if not for language per se? In an attempt to provide some answers, we review the literature on the lateralisation of the gestural communication system, which may represent the missing behavioural link to brain asymmetries for language area’s homologues in our common ancestor.

Variations of cingulate sulcal organization and link with cognitive performance

The sulcal morphology of the human medial frontal cortex has received marked interest because of (1) its remarkable link with the functional organization of this region, and (2) observations that deviations from 'normal' sulcal morphological variability correlate with the prevalence of some psychiatric disorders, cognitive abilities, or personality traits. Unfortunately, background studies on environmental or genetic factors influencing the ontogenesis of the sulcal organization in this region are critically lacking. We analysed the sulcal morphological organization in this region in twins and non-twin siblings, as well as in control subjects for a total of 599 subjects from the Human Connectome Project. The data first confirm significant biases in the presence of paracingulate sulci in left vs right hemispheres in the whole population (twin: p < 2.4.10-9; non-twin: p < 2.10-6) demonstrating a clear general laterality in human subjects. Second, measures of similarity between siblings and estimations of heritability suggest significant environmental factors, in particular in-womb environment, and weak additive genetic factors influencing the presence of a paracingulate sulcus. Finally, we found that relationships between sulcal organization and performance in cognitive, motor, and affective tests depend on the twin status (Twins versus Non-twins). These results provide important new insights to the issue of the significance of sulcal organization in the human medial frontal cortex.

Role of the innate immune system in the neuropathological consequences induced by adolescent binge drinking

Adolescence is a critical stage of brain maturation in which important plastic and dynamic processes take place in different brain regions, leading to development of the adult brain. Ethanol drinking in adolescence disrupts brain plasticity and causes structural and functional changes in immature brain areas (prefrontal cortex, limbic system) that result in cognitive and behavioral deficits. These changes, along with secretion of sexual and stress-related hormones in adolescence, may impact self-control, decision making, and risk-taking behaviors that contribute to anxiety and initiation of alcohol consumption. New data support the participation of the neuroimmune system in the effects of ethanol on the developing and adult brain. This article reviews the potential pathological bases that underlie the effects of alcohol on the adolescent brain, such as the contribution of genetic background, the perturbation of epigenetic programming, and the influence of the neuroimmune response. Special emphasis is given to the actions of ethanol in the innate immune receptor toll-like receptor 4 (TLR4), since recent studies have demonstrated that by activating the inflammatory TLR4/NFκB signaling response in glial cells, binge drinking of ethanol triggers the release of cytokines/chemokines and free radicals, which exacerbate the immune response that causes neuroinflammation/neural damage as well as short- and long-term neurophysiological, cognitive, and behavioral dysfunction. Finally, potential treatments that target the neuroimmune response to treat the neuropathological and behavioral consequences of adolescent alcohol abuse are discussed.

eHUGS: Enhanced Hierarchical Unbiased Graph Shrinkage for Efficient Groupwise Registration

Effective and efficient spatial normalization of a large population of brain images is critical for many clinical and research studies, but it is technically very challenging. A commonly used approach is to choose a certain image as the template and then align all other images in the population to this template by applying pairwise registration. To avoid the potential bias induced by the inappropriate template selection, groupwise registration methods have been proposed to simultaneously register all images to a latent common space. However, current groupwise registration methods do not make full use of image distribution information for more accurate registration. In this paper, we present a novel groupwise registration method that harnesses the image distribution information by capturing the image distribution manifold using a hierarchical graph with its nodes representing the individual images. More specifically, a low-level graph describes the image distribution in each subgroup, and a high-level graph encodes the relationship between representative images of subgroups. Given the graph representation, we can register all images to the common space by dynamically shrinking the graph on the image manifold. The topology of the entire image distribution is always maintained during graph shrinkage. Evaluations on two datasets, one for 80 elderly individuals and one for 285 infants, indicate that our method can yield promising results.

Abnormal asymmetry of brain connectivity in schizophrenia

Recently, a growing body of data has revealed that beyond a dysfunction of connectivity among different brain areas in schizophrenia patients (SCZ), there is also an abnormal asymmetry of functional connectivity compared with healthy subjects. The loss of the cerebral torque and the abnormalities of gyrification, with an increased or more complex cortical folding in the right hemisphere may provide an anatomical basis for such aberrant connectivity in SCZ. Furthermore, diffusion tensor imaging studies have shown a significant reduction of leftward asymmetry in some key white-matter tracts in SCZ. In this paper, we review the studies that investigated both structural brain asymmetry and asymmetry of functional connectivity in healthy subjects and SCZ. From an analysis of the existing literature on this topic, we can hypothesize an overall generally attenuated asymmetry of functional connectivity in SCZ compared to healthy controls. Such attenuated asymmetry increases with the duration of the disease and correlates with psychotic symptoms. Finally, we hypothesize that structural deficits across the corpus callosum may contribute to the abnormal asymmetry of intra-hemispheric connectivity in schizophrenia.

In vivo high-resolution diffusion tensor imaging of the mouse brain

Diffusion tensor imaging (DTI) of the laboratory mouse brain provides important macroscopic information for anatomical characterization of mouse models in basic research. Currently, in vivo DTI of the mouse brain is often limited by the available resolution. In this study, we demonstrate in vivo high-resolution DTI of the mouse brain using a cryogenic probe and a modified diffusion-weighted gradient and spin echo (GRASE) imaging sequence at 11.7 T. Three-dimensional (3D) DTI of the entire mouse brain at 0.125 mm isotropic resolution could be obtained in approximately 2 h. The high spatial resolution, which was previously only available with ex vivo imaging, enabled non-invasive examination of small structures in the adult and neonatal mouse brains. Based on data acquired from eight adult mice, a group-averaged DTI atlas of the in vivo adult mouse brain with 60 structure segmentations was developed. Comparisons between in vivo and ex vivo mouse brain DTI data showed significant differences in brain morphology and tissue contrasts, which indicate the importance of the in vivo DTI-based mouse brain atlas.

aBEAT: a toolbox for consistent analysis of longitudinal adult brain MRI

Longitudinal brain image analysis is critical for revealing subtle but complex structural and functional changes of brain during aging or in neurodevelopmental disease. However, even with the rapid increase of clinical research and trials, a software toolbox dedicated for longitudinal image analysis is still lacking publicly. To cater for this increasing need, we have developed a dedicated 4D Adult Brain Extraction and Analysis Toolbox (aBEAT) to provide robust and accurate analysis of the longitudinal adult brain MR images. Specially, a group of image processing tools were integrated into aBEAT, including 4D brain extraction, 4D tissue segmentation, and 4D brain labeling. First, a 4D deformable-surface-based brain extraction algorithm, which can deform serial brain surfaces simultaneously under temporal smoothness constraint, was developed for consistent brain extraction. Second, a level-sets-based 4D tissue segmentation algorithm that incorporates local intensity distribution, spatial cortical-thickness constraint, and temporal cortical-thickness consistency was also included in aBEAT for consistent brain tissue segmentation. Third, a longitudinal groupwise image registration framework was further integrated into aBEAT for consistent ROI labeling by simultaneously warping a pre-labeled brain atlas to the longitudinal brain images. The performance of aBEAT has been extensively evaluated on a large number of longitudinal MR T1 images which include normal and dementia subjects, achieving very promising results. A Linux-based standalone package of aBEAT is now freely available at http://www.nitrc.org/projects/abeat.

Models of the aging brain structure and individual decline

The aging brain's structural development constitutes a spatiotemporal process that is accessible by MR-based computational morphometry. Here we introduce basic concepts and analytical approaches to quantify age-related differences and changes in neuroanatomical images of the human brain. The presented models first address the estimation of age trajectories, then we consider inter-individual variations of structural decline, using a repeated measures design. We concentrate our overview on preprocessed neuroanatomical images of the human brain to facilitate practical applications to diverse voxel- and surface-based structural markers. Together these methods afford analysis of aging brain structure in relation to behavioral, health, or cognitive parameters.

Spatiotemporal mapping of brain atrophy in mouse models of Huntington's disease using longitudinal in vivo magnetic resonance imaging

Mouse models of Huntington's disease (HD) that recapitulate some of the phenotypic features of human HD, play a crucial role in investigating disease mechanisms and testing potential therapeutic approaches. Longitudinal studies of these models can yield valuable insights into the temporal course of disease progression and the effect of drug treatments on the progressive phenotypes. Atrophy of the brain, particularly the striatum, is a characteristic phenotype of human HD, is known to begin long before the onset of motor symptoms, and correlates strongly with clinical features. Elucidating the spatial and temporal patterns of atrophy in HD mouse models is important to characterize the phenotypes of these models, as well as evaluate the effects of neuroprotective treatments at specific time frames during disease progression. In this study, three dimensional in vivo magnetic resonance imaging (MRI) and automated longitudinal deformation-based morphological analysis was used to elucidate the spatial and temporal patterns of brain atrophy in the R6/2 and N171-82Q mouse models of HD. Using an established MRI-based brain atlas and mixed-effects modeling of deformation-based metrics, we report the rates of progression and region-specificity of brain atrophy in the two models. Further, the longitudinal analysis approach was used to evaluate the effects of sertraline and coenzyme Q(10) (CoQ(10)) treatments on progressive atrophy in the N171-82Q model. Sertraline treatment resulted in significant slowing of atrophy, especially in the striatum and frontal cortex regions, while no significant effects of CoQ(10) treatment were observed. Progressive cortical and striatal atrophy in the N171-82Q mice showed significant positive correlations with measured functional deficits. The findings of this report can be used for future testing and comparison of potential therapeutics in mouse models of HD.

A review of atlas-based segmentation for magnetic resonance brain images

Normal and abnormal brains can be segmented by registering the target image with an atlas. Here, an atlas is defined as the combination of an intensity image (template) and its segmented image (the atlas labels). After registering the atlas template and the target image, the atlas labels are propagated to the target image. We define this process as atlas-based segmentation. In recent years, researchers have investigated registration algorithms to match atlases to query subjects and also strategies for atlas construction. In this paper we present a review of the automated approaches for atlas-based segmentation of magnetic resonance brain images. We aim to point out the strengths and weaknesses of atlas-based methods and suggest new research directions. We use two different criteria to present the methods. First, we refer to the algorithms according to their atlas-based strategy: label propagation, multi-atlas methods, and probabilistic techniques. Subsequently, we classify the methods according to their medical target: the brain and its internal structures, tissue segmentation in healthy subjects, tissue segmentation in fetus, neonates and elderly subjects, and segmentation of damaged brains. A quantitative comparison of the results reported in the literature is also presented.

Mapping fetal brain development in utero using magnetic resonance imaging: the Big Bang of brain mapping

The development of tools to construct and investigate probabilistic maps of the adult human brain from magnetic resonance imaging (MRI) has led to advances in both basic neuroscience and clinical diagnosis. These tools are increasingly being applied to brain development in adolescence and childhood, and even to neonatal and premature neonatal imaging. Even earlier in development, parallel advances in clinical fetal MRI have led to its growing use as a tool in challenging medical conditions. This has motivated new engineering developments encompassing optimal fast MRI scans and techniques derived from computer vision, the combination of which allows full 3D imaging of the moving fetal brain in utero without sedation. These promise to provide a new and unprecedented window into early human brain growth. This article reviews the developments that have led us to this point, examines the current state of the art in the fields of fast fetal imaging and motion correction, and describes the tools to analyze dynamically changing fetal brain structure. New methods to deal with developmental tissue segmentation and the construction of spatiotemporal atlases are examined, together with techniques to map fetal brain growth patterns.

Registration of longitudinal brain image sequences with implicit template and spatial-temporal heuristics

Accurate measurement of longitudinal changes of brain structures and functions is very important but challenging in many clinical studies. Also, across-subject comparison of longitudinal changes is critical in identifying disease-related changes. In this paper, we propose a novel method to meet these two requirements by simultaneously registering sets of longitudinal image sequences of different subjects to the common space, without assuming any explicit template. Specifically, our goal is to 1) consistently measure the longitudinal changes from a longitudinal image sequence of each subject, and 2) jointly align all image sequences of different subjects to a hidden common space. To achieve these two goals, we first introduce a set of temporal fiber bundles to explore the spatial-temporal behavior of anatomical changes in each longitudinal image sequence. Then, a probabilistic model is built upon the temporal fibers to characterize both spatial smoothness and temporal continuity. Finally, the transformation fields that connect each time-point image of each subject to the common space are simultaneously estimated by the expectation maximization (EM) approach, via the maximum a posterior (MAP) estimation of the probabilistic models. Promising results have been obtained in quantitative measurement of longitudinal brain changes, i.e., hippocampus volume changes, showing better performance than those obtained by either the pairwise or the groupwise only registration methods.

Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features

Neuroimage measures from magnetic resonance (MR) imaging, such as cortical thickness, have been playing an increasingly important role in searching for biomarkers of Alzheimer's disease (AD). Recent studies show that, AD, mild cognitive impairment (MCI) and normal control (NC) can be distinguished with relatively high accuracy using the baseline cortical thickness. With the increasing availability of large longitudinal datasets, it also becomes possible to study the longitudinal changes of cortical thickness and their correlation with the development of pathology in AD. In this study, the longitudinal cortical thickness changes of 152 subjects from 4 clinical groups (AD, NC, Progressive-MCI and Stable-MCI) selected from Alzheimer's Disease Neuroimaging Initiative (ADNI) are measured by our recently developed 4 D (spatial+temporal) thickness measuring algorithm. It is found that the 4 clinical groups demonstrate very similar spatial distribution of grey matter (GM) loss on cortex. To fully utilize the longitudinal information and better discriminate the subjects from 4 groups, especially between Stable-MCI and Progressive-MCI, 3 different categories of features are extracted for each subject, i.e., (1) static cortical thickness measures computed from the baseline and endline, (2) cortex thinning dynamics, such as the thinning speed (mm/year) and the thinning ratio (endline/baseline), and (3) network features computed from the brain network constructed based on the correlation between the longitudinal thickness changes of different regions of interest (ROIs). By combining the complementary information provided by features from the 3 categories, 2 classifiers are trained to diagnose AD and to predict the conversion to AD in MCI subjects, respectively. In the leave-one-out cross-validation, the proposed method can distinguish AD patients from NC at an accuracy of 96.1%, and can detect 81.7% (AUC = 0.875) of the MCI converters 6 months ahead of their conversions to AD. Also, by analyzing the brain network built via longitudinal cortical thickness changes, a significant decrease (p < 0.02) of the network clustering coefficient (associated with the development of AD pathology) is found in the Progressive-MCI group, which indicates the degenerated wiring efficiency of the brain network due to AD. More interestingly, the decreasing of network clustering coefficient of the olfactory cortex region was also found in the AD patients, which suggests olfactory dysfunction. Although the smell identification test is not performed in ADNI, this finding is consistent with other AD-related olfactory studies.

Registration of longitudinal image sequences with implicit template and spatial-temporal heuristics

Accurate measurement of longitudinal changes of anatomical structure is important and challenging in many clinical studies. Also, for identification of disease-affected regions due to the brain disease, it is extremely necessary to register a population data to the common space simultaneously. In this paper, we propose a new method for simultaneous longitudinal and groupwise registration of a set of longitudinal data acquired from multiple subjects. Our goal is to 1) consistently measure the longitudinal changes from a sequence of longitudinal data acquired from the same subject; and 2) jointly align all image data (acquired from all time points of all subjects) to a hidden common space. To achieve these two goals, we first introduce a set of temporal fiber bundles to explore the spatial-temporal behavior of anatomical changes in each longitudinal data of the same subject. Then, a probabilistic model is built upon the hidden state of spatial smoothness and temporal continuity on the fibers. Finally, the transformation fields that connect each time-point image of each subject to the common space are simultaneously estimated by the expectation maximization (EM) approach, via the maximum a posterior (MAP) estimation of probabilistic models. Promising results are obtained to quantitatively measure the longitudinal changes of hippocampus volume, indicating better performance of our method than the conventional pairwise methods.

Effect of dynamic stiffness of the substrates on neurite outgrowth by using a DNA-crosslinked hydrogel

Central nervous system tissues, like other tissue types, undergo constant remodeling, which potentially leads to changes in their mechanical stiffness. Moreover, mechanical compliance of central nervous system tissues can also be modified under external load such as that experienced in traumatic brain or spinal cord injury, and during pathological processes. Thus, the neuronal responses to the dynamic stiffness of the microenvironment are of significance. In this study, we induced decrease in stiffness by using a DNA-crosslinked hydrogel, and subjected rat spinal cord neurons to such dynamic stiffness. The neurons respond to the dynamic cues as evidenced by the primary neurite structure, and the response from each neurite property (e.g., axonal length and primary dendrite number) is consistent with the behavior on static gels of same substrate rigidity, with one exception of mean primary dendrite length. The results on cell population distribution confirm the neuronal responses to the dynamic stiffness. Quantification on the focal adhesion kinase expression in the neuronal cell body on dynamic gels suggests that neurons also modify adhesion in coping with the dynamic stiffnesses. The results reported here extend the neuronal mechanosensing capability to dynamic stiffness of extracellular matrix, and give rise to a novel way of engineering neurite outgrowth in time dimension.

Improved detection of active multiple sclerosis lesions: 3D subtraction imaging

PURPOSE

To examine the benefits of using near-isotropic single-slab three-dimensional (3D) magnetic resonance (MR) imaging for the creation of subtraction images and to evaluate their performance in the detection of active multiple sclerosis (MS) brain lesions in comparison with two-dimensional (2D) subtraction images.

MATERIALS AND METHODS

The study protocol was approved by the local ethics review board and all subjects gave written informed consent before investigation. Three-dimensional MR sequences, including double inversion-recovery, fluid-attenuated inversion recovery, T2-weighted, and T1-weighted magnetization-prepared rapid acquisition gradient-echo (MP-RAGE), and corresponding 2D sequences were performed twice in 14 patients (eight women, six men; mean age, 37.6 years) with MS and nine age-matched healthy control subjects (three women, six men; mean age, 31.7 years). Active lesions were scored by two independent raters, followed by a consensus reading. Lesion counts were evaluated by using negative binomial regression; interrater agreement was evaluated by using intraclass correlation coefficient.

RESULTS

Three-dimensional subtraction images had less residual misregistration and flow artifacts and depicted higher numbers of active lesions with greater interobserver agreement compared with 2D subtraction images. Among the 3D sequences, MP-RAGE subtraction imaging enabled detection of a significantly higher mean number of positive active MS lesions compared with 2D subtraction imaging (2.8 versus 1.7, P = .012), particularly infratentorial lesions (0.6 vs 0.1, P < .05), and a substantially higher (nonsignificant) mean number of small (<3 mm) lesions (0.6 vs 0.1, P > .05).

CONCLUSION

Three-dimensional subtraction imaging, after image registration, produced better image quality, leading to increased detection of active MS lesions with greater interobserver agreement in comparison with 2D subtraction imaging; 3D MP-RAGE subtraction imaging represents a promising technique to increase sensitivity in ascertaining lesion dissemination in time and increase the power of MR imaging metrics for the evaluation of treatment effects in clinical trials.

Mechanisms involved in the neurotoxic, cognitive, and neurobehavioral effects of alcohol consumption during adolescence

Studies over the last decade demonstrate that adolescence is a brain maturation period from childhood to adulthood. Plastic and dynamic processes drive adolescent brain development, creating flexibility that allows the brain to refine itself, specialize, and sharpen its functions for specific demands. Maturing connections enable increased communication among brain regions, allowing greater integration and complexity. Compelling evidence has shown that the developing brain is vulnerable to the damaging effects of ethanol. It is possible to infer, therefore, that alcohol exposure during the critical adolescent developmental stages could disrupt the brain plasticity and maturation processes, resulting in behavioral and cognitive deficits. Recent neuroimaging studies have provided evidence of the impact of human adolescent drinking in brain structure and functions. Findings in experimental animals have also given new insight into the potential mechanisms of the toxic effects of ethanol on both adolescent brain maturation and the short- and long-term cognitive consequences of adolescent drinking. Adolescence is also characterized by the rapid maturation of brain systems mediating reward and by changes in the secretion of stress-related hormones, events that might participate in the increasing in anxiety and the initiation pattern of alcohol and drug consumption. Studies in human adolescents demonstrate that drinking at early ages can enhance the likelihood of developing alcohol-related problems. Experimental evidence suggests that early exposure to alcohol sensitizes the neurocircuitry of addiction and affects chromatin remodeling, events that could induce abnormal plasticity in reward-related learning processes that contribute to adolescents' vulnerability to drug addiction. In this article, we review the potential mechanisms by which ethanol impacts brain development and lead to brain impairments and cognitive and behavioral dysfunctions as well as the neurobiological and neurochemical processes underlying the adolescent-specific vulnerability to drug addiction.

High pregnancy anxiety during mid-gestation is associated with decreased gray matter density in 6-9-year-old children

Because the brain undergoes dramatic changes during fetal development it is vulnerable to environmental insults. There is evidence that maternal stress and anxiety during pregnancy influences birth outcome but there are no studies that have evaluated the influence of stress during human pregnancy on brain morphology. In the current prospective longitudinal study we included 35 women for whom serial data on pregnancy anxiety was available at 19 (+/-0.83), 25 (+/-0.9) and 31 (+/-0.9) weeks gestation. When the offspring from the target pregnancy were between 6 and 9 years of age, their neurodevelopmental stage was assessed by a structural MRI scan. With the application of voxel-based morphometry, we found regional reductions in gray matter density in association with pregnancy anxiety after controlling for total gray matter volume, age, gestational age at birth, handedness and postpartum perceived stress. Specifically, independent of postnatal stress, pregnancy anxiety at 19 weeks gestation was associated with gray matter volume reductions in the prefrontal cortex, the premotor cortex, the medial temporal lobe, the lateral temporal cortex, the postcentral gyrus as well as the cerebellum extending to the middle occipital gyrus and the fusiform gyrus. High pregnancy anxiety at 25 and 31 weeks gestation was not significantly associated with local reductions in gray matter volume.This is the first prospective study to show that a specific temporal pattern of pregnancy anxiety is related to specific changes in brain morphology. Altered gray matter volume in brain regions affected by prenatal maternal anxiety may render the developing individual more vulnerable to neurodevelopmental and psychiatric disorders as well as cognitive and intellectual impairment.

A Multilevel Developmental Contextual Approach To Substance Use and Addiction

Emerging technological advances in genetics and neuroscience have spawned innovative or elaborated conceptual models in the field of addiction science, as well as contributed to the mushrooming of new knowledge. By addictions, reference is made to chronic, often relapsing disorders typified by obsession, compulsion, or physical or psychological dependence. In this article it is proposed that a multilevel developmental contextual approach to substance use and addictions provides a useful framework for integrating existing studies across disciplines and serving as a generative guide to intriguing novel research questions. The multilevel developmental contextual approach emphasizes multiple factor influences on substance use and addiction, the conjoint influence of variables from different levels of analysis (e.g., genetic, biochemical, physiological, cognitive, social, neighborhood, societal), and dynamic, probabilistic behavior-outcome relations (i.e., the occurrence as well as the nature of expression of substance problems and addiction depend on a range of emerging, interactive factors that may vary across individuals and across time). The approach is illustrated with a long-term prospective study of predictors of binge drinking from adolescence to young adulthood and a description of the role of brain processes and mechanisms involved in the development and expression of alcohol use during adolescence.

Computational morphometry for detecting changes in brain structure due to development, aging, learning, disease and evolution

The brain, like any living tissue, is constantly changing in response to genetic and environmental cues and their interaction, leading to changes in brain function and structure, many of which are now in reach of neuroimaging techniques. Computational morphometry on the basis of Magnetic Resonance (MR) images has become the method of choice for studying macroscopic changes of brain structure across time scales. Thanks to computational advances and sophisticated study designs, both the minimal extent of change necessary for detection and, consequently, the minimal periods over which such changes can be detected have been reduced considerably during the last few years. On the other hand, the growing availability of MR images of more and more diverse brain populations also allows more detailed inferences about brain changes that occur over larger time scales, way beyond the duration of an average research project. On this basis, a whole range of issues concerning the structures and functions of the brain are now becoming addressable, thereby providing ample challenges and opportunities for further contributions from neuroinformatics to our understanding of the brain and how it changes over a lifetime and in the course of evolution.

Deformation invariant attribute vector for deformable registration of longitudinal brain MR images

This paper presents a novel approach to define deformation invariant attribute vector (DIAV) for each voxel in 3D brain image for the purpose of anatomic correspondence detection. The DIAV method is validated by using synthesized deformation in 3D brain MRI images. Both theoretic analysis and experimental studies demonstrate that the proposed DIAV is invariant to general nonlinear deformation. Moreover, our experimental results show that the DIAV is able to capture rich anatomic information around the voxels and exhibit strong discriminative ability. The DIAV has been integrated into a deformable registration algorithm for longitudinal brain MR images, and the results on both simulated and real brain images are provided to demonstrate the good performance of the proposed registration algorithm based on matching of DIAVs.

Evaluation of automated brain MR image segmentation and volumetry methods

We compare three widely used brain volumetry methods available in the software packages FSL, SPM5, and FreeSurfer and evaluate their performance using simulated and real MR brain data sets. We analyze the accuracy of gray and white matter volume measurements and their robustness against changes of image quality using the BrainWeb MRI database. These images are based on "gold-standard" reference brain templates. This allows us to assess between- (same data set, different method) and also within-segmenter (same method, variation of image quality) comparability, for both of which we find pronounced variations in segmentation results for gray and white matter volumes. The calculated volumes deviate up to >10% from the reference values for gray and white matter depending on method and image quality. Sensitivity is best for SPM5, volumetric accuracy for gray and white matter was similar in SPM5 and FSL and better than in FreeSurfer. FSL showed the highest stability for white (<5%), FreeSurfer (6.2%) for gray matter for constant image quality BrainWeb data. Between-segmenter comparisons show discrepancies of up to >20% for the simulated data and 24% on average for the real data sets, whereas within-method performance analysis uncovered volume differences of up to >15%. Since the discrepancies between results reach the same order of magnitude as volume changes observed in disease, these effects limit the usability of the segmentation methods for following volume changes in individual patients over time and should be taken into account during the planning and analysis of brain volume studies.

Transitions into underage and problem drinking: developmental processes and mechanisms between 10 and 15 years of age

Numerous developmental changes occur across levels of personal organization (eg, changes related to puberty, brain and cognitive-affective structures and functions, and family and peer relationships) in the age period of 10 to 15 years. Furthermore, the onset and escalation of alcohol use commonly occur during this period. This article uses both animal and human studies to characterize these multilevel developmental changes. The timing of and variations in developmental changes are related to individual differences in alcohol use. It is proposed that this integrated developmental perspective serve as the foundation for subsequent efforts to prevent and to treat the causes, problems, and consequences of alcohol consumption.

Surface-based and probabilistic atlases of primate cerebral cortex

Brain atlases play an increasingly important role in neuroimaging, as they are invaluable for analysis, visualization, and comparison of results across studies. For both humans and macaque monkeys, digital brain atlases of many varieties are in widespread use, each having its own strengths and limitations. For studies of cerebral cortex there is particular utility in hybrid atlases that capitalize on the complementary nature of surface and volume representations, are based on a population average rather than an individual brain, and include measures of variation as well as averages. Linking different brain atlases to one another and to online databases containing a growing body of neuroimaging data will enable powerful forms of data mining that accelerate discovery and improve research efficiency.

Direct cortical mapping via solving partial differential equations on implicit surfaces

In this paper, we propose a novel approach for cortical mapping that computes a direct map between two cortical surfaces while satisfying constraints on sulcal landmark curves. By computing the map directly, we can avoid conventional intermediate parameterizations and help simplify the cortical mapping process. The direct map in our method is formulated as the minimizer of a flexible variational energy under landmark constraints. The energy can include both a harmonic term to ensure smoothness of the map and general data terms for the matching of geometric features. Starting from a properly designed initial map, we compute the map iteratively by solving a partial differential equation (PDE) defined on the source cortical surface. For numerical implementation, a set of adaptive numerical schemes are developed to extend the technique of solving PDEs on implicit surfaces such that landmark constraints are enforced. In our experiments, we show the flexibility of the direct mapping approach by computing smooth maps following landmark constraints from two different energies. We also quantitatively compare the metric preserving property of the direct mapping method with a parametric mapping method on a group of 30 subjects. Finally, we demonstrate the direct mapping method in the brain mapping applications of atlas construction and variability analysis.

Cortical Characterization and Inter-Dipole Distance Between Unilateral Median Versus Ulnar Nerve Stimulation of Both Hands in MEG

Contralateral somatosensory evoked fields (SEF) by whole head MEG after unilateral median and ulnar nerve stimulation of both hands were studied in 10 healthy right-handed subjects. Major parameters describing cortical activity were examined to discriminate median and ulnar nerve evoked responses. Somatic sensitivity showed high similarity in the 4 study conditions for both hand and nerve. The brain SEFs consisted of 7-8 major peak stages with consistent responses in all subjects at M20, M30, M70 and M90. Comparable inter-hemispheric waveform profile but high inter-subject variability was found. Median nerve induced significantly shorter latencies in the early activities than those of the ulnar nerve. The 3D cortical maps in the post stimulus 450 ms timeframe showed for both nerves two polarity reversals, an early and a late one which is a new finding. Dipole characteristics showed differential sites for the M20 and M30 in the respective nerve. Higher dipole moments evoked by the median nerve were noticed when compared to the ulnar. Furthermore, the results of the dipole distances between both nerves for M20 were calculated to be at 11.17 mm +/- 4.93 (LH) and 16.73 mm +/- 5.66 (RH), respectively after right hand versus left hand stimulation. This study showed substantial differences in the cortical responses between median and ulnar nerve. Especially the dipole distance between median and ulnar nerve on the cortex was computed accurately for the first time in MEG. Little is known however of the cortical responses in chronic pain patients and the parameter(s) that may change in an individual patient or a group. These results provide precise basis for further evaluating cortical changes in functional disorders and disease sequelae related to median and ulnar nerves.

Mapping brain maturation

Human brain maturation is a complex, lifelong process that can now be examined in detail using neuroimaging techniques. Ongoing projects scan subjects longitudinally with structural magnetic resonance imaging (MRI), enabling the time-course and anatomical sequence of development to be reconstructed. Here, we review recent progress on imaging studies of development. We focus on cortical and subcortical changes observed in healthy children, and contrast them with abnormal developmental changes in early-onset schizophrenia, fetal alcohol syndrome, attention-deficit-hyperactivity disorder (ADHD) and Williams syndrome. We relate these structural changes to the cellular processes that underlie them, and to cognitive and behavioral changes occurring throughout childhood and adolescence.

Quantitative assessment of right and left reaching movements in infants: A longitudinal study from 6 to 36 months

This longitudinal study aimed to explore the early presence and developmental pattern of laterality in reaching kinematics and its relationship to side use. In order to do so, 3-D kinematic measurements as well as 2-D video recordings of right-left reaching movements were successively carried out for 17 infants at the ages of 6, 9, 12, and 36 months. Additional investigations of hand preference were made at 36 months. As four infants were prematurely born, their outcomes were compared to those of the fullterm participants. While most of the infants in the early ages showed a rather inconsistent preference in terms of frequency and distributions of right-left side use, the analyses of reaching kinematics revealed a more consistent pattern of fewer movements units (MUs) and straighter right-sided reaching for the majority of infants at all tested ages. However, reaching kinematics from the preterm infants were generally more variable and less side consistent. It is proposed that the development of human handedness originates from an early right arm rather than hand preference in that representations of asymmetry in bilateral projections (involved in arm movements) developmentally precede contralateral projections (involved in refined hand/finger movements).

Mapping postnatal mouse brain development with diffusion tensor microimaging

While mouse brain development has been extensively studied using histology, quantitative characterization of morphological changes is still a challenging task. This paper presents how developing brain structures can be quantitatively characterized with magnetic resonance diffusion tensor microimaging coupled with techniques of computational anatomy. High resolution diffusion tensor images of ex vivo postnatal mouse brains provide excellent contrasts to reveal the evolutions of mouse forebrain structures. Using anatomical landmarks defined on diffusion tensor images, tissue level growth patterns of mouse brains were quantified. The results demonstrate the use of these techniques to three-dimensionally and quantitatively characterize brain growth.

Magnetic resonance diffusion tensor microimaging reveals a role for Bcl-x in brain development and homeostasis

A new technique based on diffusion tensor imaging and computational neuroanatomy was developed to efficiently and quantitatively characterize the three-dimensional morphology of the developing brains. The technique was used to analyze the phenotype of conditional Bcl-x knock-out mice, in which the bcl-x gene was deleted specifically in neurons of the cerebral cortex and hippocampus beginning at embryonic day 13.5 as cells became postmitotic. Affected brain regions and associated axonal tracts showed severe atrophy in adult Bcl-x-deficient mice. Longitudinal studies revealed that these phenotypes are established by regressive processes that occur primarily during the first postnatal week, whereas neurogenesis and migration showed no obvious abnormality during embryonic stages. Specific families of white matter tracts that once formed normally during the embryonic stages underwent dramatic degeneration postnatally. Thus, this technique serves as a powerful tool to efficiently localize temporal and spatial manifestation of morphological phenotype.

Soliton dynamics in computational anatomy

Computational anatomy (CA) has introduced the idea of anatomical structures being transformed by geodesic deformations on groups of diffeomorphisms. Among these geometric structures, landmarks and image outlines in CA are shown to be singular solutions of a partial differential equation that is called the geodesic EPDiff equation. A recently discovered momentum map for singular solutions of EPDiff yields their canonical Hamiltonian formulation, which in turn provides a complete parameterization of the landmarks by their canonical positions and momenta. The momentum map provides an isomorphism between landmarks (and outlines) for images and singular soliton solutions of the EPDiff equation. This isomorphism suggests a new dynamical paradigm for CA, as well as new data representation.

Incidental neurodevelopmental episodes in the etiology of schizophrenia: an expanded model involving epigenetics and development

Epidemiological data favors genetic predisposition for schizophrenia, a common and complex mental disorder in most populations. Search for the genes involved using candidate genes, positional cloning, and chromosomal aberrations including triplet repeat expansions have established a number of susceptibility loci and genomic sites but no causal gene(s) with a proven mechanism of action. Recent genome-wide gene expression studies on brains from schizophrenia patients and their matched controls have identified a number of genes that show an alteration in expression in the diseased brains. Although it is not possible to offer a cause and effect association between altered gene expression and disease, such observations support a neurodevelopmental model in schizophrenia. Here, we offer a mechanism of this disease, which takes into account the role of developmental noise and diversions of the neural system. It suggests that the final outcome of a neural developmental process is not fixed and exact. Rather it develops with a variation around the mean. More important, the phenotypic consequence may cross the norm as a result of fortuitous and/or epigenetic events. As a result, a normal genotype may develop as abnormal with a disease phenotype. More important, susceptible genotypes may have reduced penetrance and develop as a normal phenocopy. The incidental episodes in neurodevelopment will explain the frequency of schizophrenia in most populations and high discordance of monozygotic twins.

Deformable registration of cortical structures via hybrid volumetric and surface warping

Registration of cortical structures across individuals is a very important step for quantitative analysis of the human brain cortex. This paper presents a method for deformable registration of cortical structures across individuals, using hybrid volumetric and surface warping. In the first step, a feature-based volumetric registration algorithm is used to warp a model cortical surface to the individual's space. This step greatly reduces the variation between the model and individual, thus providing a good initialization for the next step of surface warping. In the second step, a surface registration method, based on matching geometric attributes, warps the model surface to the individual. Point correspondences are also established at this step. The attribute vector, as the morphological signature of surface, was designed to be as distinctive as possible, so that each vertex on the model surface can find its correspondence on the individual surface. Experimental results on both synthesized and real brain data demonstrate the performance of the proposed method in the registration of cortical structures across individuals.

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.