Brain Structural Patterns Research Articles

Division of labor and brain evolution in insect societies: Neurobiology of extreme specialization in the turtle ant Cephalotes varians.

Strongly polyphenic social insects provide excellent models to examine the neurobiological basis of division of labor. Turtle ants, Cephalotes varians, have distinct minor worker, soldier, and reproductive (gyne/queen) morphologies associated with their behavioral profiles: small-bodied task-generalist minors lack the phragmotic shield-shaped heads of soldiers, which are specialized to block and guard the nest entrance. Gynes found new colonies and during early stages of colony growth overlap behaviorally with soldiers. Here we describe patterns of brain structure and synaptic organization associated with division of labor in C. varians minor workers, soldiers, and gynes. We quantified brain volumes, determined scaling relationships among brain regions, and quantified the density and size of microglomeruli, synaptic complexes in the mushroom body calyxes important to higher-order processing abilities that may underpin behavioral performance. We found that brain volume was significantly larger in gynes; minor workers and soldiers had similar brain sizes. Consistent with their larger behavioral repertoire, minors had disproportionately larger mushroom bodies than soldiers and gynes. Soldiers and gynes had larger optic lobes, which may be important for flight and navigation in gynes, but serve different functions in soldiers. Microglomeruli were larger and less dense in minor workers; soldiers and gynes did not differ. Correspondence in brain structure despite differences in soldiers and gyne behavior may reflect developmental integration, suggesting that neurobiological metrics not only advance our understanding of brain evolution in social insects, but may also help resolve questions of the origin of novel castes.

Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder

Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.

Distributed cortical structural properties contribute to motor cortical excitability and inhibition.

The link between the local structure of the primary motor cortex and motor function has been well documented. However, motor function relies on a network of interconnected brain regions and the link between the structural properties characterizing these distributed brain networks and motor function remains poorly understood. Here, we examined whether distributed patterns of brain structure, extending beyond the primary motor cortex can help classify two forms of motor function: corticospinal excitability and intracortical inhibition. To this effect, we recorded high-resolution structural magnetic resonance imaging scans in 25 healthy volunteers. To measure corticospinal excitability and inhibition in the same volunteers, we recorded motor evoked potentials (MEPs) elicited by single-pulse transcranial magnetic stimulation and short-interval intracortical inhibition (SICI) in a separate session. Support vector machine (SVM) pattern classification was used to identify distributed multi-voxel gray-matter areas, which distinguished subjects who had lower and higher MEPs and SICIs. We found that MEP and SICI classification could be predicted based on a widely distributed, largely non-overlapping pattern of voxels in frontal, parietal, temporal, occipital, and cerebellar regions. Thus, structural properties distributed over the brain beyond the primary motor cortex relate to motor function.

198. Identifying Structural Brain Alterations in Adolescent Depression Based on Individual Deviations From Normative Age-Related Patterns of Brain Structure

198. Identifying Structural Brain Alterations in Adolescent Depression Based on Individual Deviations From Normative Age-Related Patterns of Brain Structure

Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach

In this study, we employed the Maximum Uncertainty Linear Discriminant Analysis (MLDA) to investigate whether the structural brain patterns in first episode psychosis (FEP) patients would be more similar to patients with chronic schizophrenia (SCZ) or healthy controls (HC), from a schizophrenia model perspective. Brain regions volumetric data were estimated by using MRI images of SCZ and FEP patients and HC. First, we evaluated the MLDA performance in discriminating SCZ from controls, which provided a score based on a model for changes in brain structure in SCZ. In the following, we compared the volumetric patterns of FEP patients with patterns of SCZ and healthy controls using these scores. The FEP group had a score distribution more similar to patients with schizophrenia (p-value = .461; Cohen's d=−.15) in comparison with healthy subjects (p-value=.003; Cohen's d = .62). Structures related to the limbic system and the circuitry involved in goal-directed behaviours were the most discriminant regions. There is a distinct pattern of volumetric changes in patients with schizophrenia in contrast to healthy controls, and this pattern seem to be detectable already in FEP.

Registration.

The goal of image registration is to find a 1-1 point-wise correspondence between two images, a subject image and a target image. Knowing the pointwise correspondence between two brain images allows comparison of structural and functional imaging data such as regions of interest, functional data (e.g., fMRI, EEG, MEG, DTI), and geometric shapes. The image registration process also allows creation of probabilistic anatomical atlases (Mazziotta et al., Neuroimage 2(2):89-101, 1995; Thompson, J Comput Assist Tomogr 21(4):567-581, 1997; Thompson et al., Detecting disease-specific patterns of brain structure using cortical pattern matching and a population-based probabilistic brain atlas. In: IPMI2001. Lecture notes in computer science, pp 488-501, 2001), automatic segmentation by label transfer, modality fusion, morphological analysis (Hua, Neuroimage 43(3):458-469, 2008), and many other applications. Image registration techniques strive to find a one-to-one correspondence between subject and target images to perform this task. This correspondence is defined by a smooth deformation field. This deformation field captures the geometric variations in the two images. In this chapter, we will review various techniques for image registration that are specifically designed for human brain.

Clinical and neurobiological factors in the management of treatment refractory attention-deficit hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent mental disorder of childhood, which often continues into adolescence and adulthood. Stimulants such as methylphenidate (MPH) and non-stimulants such as atomoxetine are effective medications for the treatment of ADHD. However, about 30% of patients do not respond to these medications. Pharmacological treatment for ADHD, although highly effective, is associated with marked variabilities in clinical response, optimal dosage needed and tolerability. This article provides an overview of up-to-date knowledge regarding the clinical and neurobiological factors which contribute to and help predict treatment-refractory ADHD. Pharmacogenetic, pharmacogenomics and neuroimaging studies are still controversial with respect to determining the associations between response to medication and genetic factors, thereby resulting in hypotheses that differences in the genetic factors and neuroimaging findings contribute to treatment outcome. Much research on the potential role of genotype in pharmacological effects has focused on the catecholaminergic gene related to executive functions. Many neuroimaging studies have also reported a relationship between treatment response and common patterns of brain structure or activity according to various genetic polymorphisms. When children, adolescents and adults with ADHD do not respond to MPH, we should consider additional pharmacological options, including other classes of psychostimulants, the nonstimulant atomoxetine, bupropion, tricyclic antidepressant, clonidine, guanfacine and lisdexamphetamine. Prudent choice of an appropriate medication and active engagement of children, parents, and teachers in daily management may help to ensure long-term adherence. Therefore, additional research might help to optimize the treatment of children, adolescents and adults with ADHD and to find new options for the treatment of patients who do not respond to stimulants and the other medications. Because these findings should be interpreted cautiously, further studies are needed to elucidate these issues more clearly.

Anatomically related gray and white matter alterations in the brains of functional dyspepsia patients.

Previous studies summarized altered brain functional patterns in functional dyspepsia (FD) patients, but how the brain structural patterns are related to FD remains largely unclear. The objective of this study was to determine the brain structural characteristics in FD patients. Optimized voxel-based morphometry and tract-based spatial statistics were employed to investigate the changes in gray matter (GM) and white matter (WM) respectively in 34 FD patients with postprandial distress syndrome and 33 healthy controls based on T1-weighted and diffusion-weighted imaging. The Pearson's correlation evaluated the link among GM alterations, WM abnormalities, and clinical variables in FD patients. The optimal brain structural parameters for identifying FD were explored using the receiver operating characteristic curve. Compared to controls, FD patients exhibited a decrease in GM density (GMD) in the right posterior insula/temporal superior cortex (marked as pINS), right inferior frontal cortex (IFC), and left middle cingulate cortex, and an increase in fractional anisotropy (FA) in the posterior limb of the internal capsule, posterior thalamic radiation, and external capsule (EC). Interestingly, the GMD in the pINS was significantly associated with GMD in the IFC and FA in the EC. Moreover, the EC adjacent to the pINS provided the best performance for distinguishing FD patients from controls. Our results showed pINS-related structural abnormalities in FD patients, indicating that GM and WM parameters were not affected independently. These findings would lay the foundation for probing an efficient target in the brain for treating FD.

Functional Nonlinear Mixed Effects Models for Longitudinal Image Data.

Motivated by studying large-scale longitudinal image data, we propose a novel functional nonlinear mixed effects modeling (FNMEM) framework to model the nonlinear spatial-temporal growth patterns of brain structure and function and their association with covariates of interest (e.g., time or diagnostic status). Our FNMEM explicitly quantifies a random nonlinear association map of individual trajectories. We develop an efficient estimation method to estimate the nonlinear growth function and the covariance operator of the spatial-temporal process. We propose a global test and a simultaneous confidence band for some specific growth patterns. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply FNMEM to investigate the spatial-temporal dynamics of white-matter fiber skeletons in a national database for autism research. Our FNMEM may provide a valuable tool for charting the developmental trajectories of various neuropsychiatric and neurodegenerative disorders.

Tensor-product kernel-based representation encoding joint MRI view similarity.

To support 3D magnetic resonance image (MRI) analysis, a marginal image similarity (MIS) matrix holding MR inter-slice relationship along every axis view (Axial, Coronal, and Sagittal) can be estimated. However, mutual inference from MIS view information poses a difficult task since relationships between axes are nonlinear. To overcome this issue, we introduce a Tensor-Product Kernel-based Representation (TKR) that allows encoding brain structure patterns due to patient differences, gathering all MIS matrices into a single joint image similarity framework. The TKR training strategy is carried out into a low dimensional projected space to get less influence of voxel-derived noise. Obtained results for classifying the considered patient categories (gender and age) on real MRI database shows that the proposed TKR training approach outperforms the conventional voxel-wise sum of squared differences. The proposed approach may be useful to support MRI clustering and similarity inference tasks, which are required on template-based image segmentation and atlas construction.

Brain morphometry of Dravet Syndrome

The aim of this study was to identify differential global and local brain structural patterns in Dravet Syndrome (DS) patients as compared with a control subject group, using brain morphometry techniques which provide a quantitative whole-brain structural analysis that allows for specific patterns to be generalized across series of individuals. Nine patients with the diagnosis of DS that tested positive for mutation in the SCN1A gene and nine well-matched healthy controls were investigated using voxel brain morphometry (VBM), cortical thickness and cortical gyrification measurements. Global volume reductions of gray matter (GM) and white matter (WM) were related to DS. Local volume reductions corresponding to several white matter regions in brainstem, cerebellum, corpus callosum, corticospinal tracts and association fibers (left inferior fronto-occipital fasciculus and left uncinate fasciculus) were also found. Furthermore, DS showed a reduced cortical folding in the right precentral gyrus. The present findings describe DS-related brain structure abnormalities probably linked to the expression of the SCN1A mutation.

Disorder-Specific Predictive Classification of Adolescents with Attention Deficit Hyperactivity Disorder (ADHD) Relative to Autism Using Structural Magnetic Resonance Imaging

ObjectiveAttention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, but diagnosed by subjective clinical and rating measures. The study’s aim was to apply Gaussian process classification (GPC) to grey matter (GM) volumetric data, to assess whether individual ADHD adolescents can be accurately differentiated from healthy controls based on objective, brain structure measures and whether this is disorder-specific relative to autism spectrum disorder (ASD).MethodTwenty-nine adolescent ADHD boys and 29 age-matched healthy and 19 boys with ASD were scanned. GPC was applied to make disorder-specific predictions of ADHD diagnostic status based on individual brain structure patterns. In addition, voxel-based morphometry (VBM) analysis tested for traditional univariate group level differences in GM.ResultsThe pattern of GM correctly classified 75.9% of patients and 82.8% of controls, achieving an overall classification accuracy of 79.3%. Furthermore, classification was disorder-specific relative to ASD. The discriminating GM patterns showed higher classification weights for ADHD in earlier developing ventrolateral/premotor fronto-temporo-limbic and stronger classification weights for healthy controls in later developing dorsolateral fronto-striato-parieto-cerebellar networks. Several regions were also decreased in GM in ADHD relative to healthy controls in the univariate VBM analysis, suggesting they are GM deficit areas.ConclusionsThe study provides evidence that pattern recognition analysis can provide significant individual diagnostic classification of ADHD patients and healthy controls based on distributed GM patterns with 79.3% accuracy and that this is disorder-specific relative to ASD. Findings are a promising first step towards finding an objective differential diagnostic tool based on brain imaging measures to aid with the subjective clinical diagnosis of ADHD.

Spatial patterns of brain amyloid- burden and atrophy rate associations in mild cognitive impairment

Amyloid-β accumulation in the brain is thought to be one of the earliest events in Alzheimer's disease, possibly leading to synaptic dysfunction, neurodegeneration and cognitive/functional decline. The earliest detectable changes seen with neuroimaging appear to be amyloid-β accumulation detected by (11)C-labelled Pittsburgh compound B positron emission tomography imaging. However, some individuals tolerate high brain amyloid-β loads without developing symptoms, while others progressively decline, suggesting that events in the brain downstream from amyloid-β deposition, such as regional brain atrophy rates, play an important role. The main purpose of this study was to understand the relationship between the regional distributions of increased amyloid-β and the regional distribution of increased brain atrophy rates in patients with mild cognitive impairment. To simultaneously capture the spatial distributions of amyloid-β and brain atrophy rates, we employed the statistical concept of parallel independent component analysis, an effective method for joint analysis of multimodal imaging data. Parallel independent component analysis identified significant relationships between two patterns of amyloid-β deposition and atrophy rates: (i) increased amyloid-β burden in the left precuneus/cuneus and medial-temporal regions was associated with increased brain atrophy rates in the left medial-temporal and parietal regions; and (ii) in contrast, increased amyloid-β burden in bilateral precuneus/cuneus and parietal regions was associated with increased brain atrophy rates in the right medial temporal regions. The spatial distribution of increased amyloid-β and the associated spatial distribution of increased brain atrophy rates embrace a characteristic pattern of brain structures known for a high vulnerability to Alzheimer's disease pathology, encouraging for the use of (11)C-labelled Pittsburgh compound B positron emission tomography measures as early indicators of Alzheimer's disease. These results may begin to shed light on the mechanisms by which amyloid-β deposition leads to neurodegeneration and cognitive decline and the development of a more specific Alzheimer's disease-specific imaging signature for diagnosis and use of this knowledge in the development of new anti-therapies for Alzheimer's disease.

Deformation-based morphometry of prospective neurodevelopmental changes in new onset paediatric epilepsy

Epilepsy is a prevalent childhood neurological disorder, but there are few prospective quantitative magnetic resonance imaging studies examining patterns of brain development compared to healthy controls. Controlled prospective investigations initiated at or near epilepsy onset would best characterize the nature, timing and course of neuroimaging abnormalities in paediatric epilepsy. In this study, we report the results of a deformation-based morphometry technique to examine baseline and 2-year prospective neurodevelopmental brain changes in children with new and recent onset localization-related epilepsies (n = 24) and idiopathic generalized epilepsies (n = 20) compared to healthy controls (n = 36). Children with epilepsy demonstrated differences from controls in baseline grey and white matter volumes suggesting antecedent anomalies in brain development, as well as abnormal patterns of prospective brain development that involved not only slowed white matter expansion, but also abnormalities of cortical grey matter development involving both greater and lesser volume changes compared to controls. Furthermore, abnormal neurodevelopmental changes extended outside the cortex affecting several subcortical structures including thalamus, cerebellum, brainstem and pons. Finally, there were significant differences between the epilepsy syndromes (localization-related epilepsies and idiopathic generalized epilepsies) with the idiopathic generalized epilepsies group showing a more disrupted pattern of brain structure both at baseline and over the 2-year interval.

Neuroanatomical Differences in Toddler Boys With Fragile X Syndrome and Idiopathic Autism

Autism is an etiologically heterogeneous neurodevelopmental disorder for which there is no known unifying etiology or pathogenesis. Many conditions of atypical development can lead to autism, including fragile X syndrome (FXS), which is presently the most common known single-gene cause of autism. To examine whole-brain morphometric patterns that discriminate young boys with FXS from those with idiopathic autism (iAUT) as well as control participants. Cross-sectional, in vivo neuroimaging study. Academic medical centers. Young boys (n = 165; aged 1.57-4.15 years) diagnosed as having FXS or iAUT as well as typically developing and idiopathic developmentally delayed controls. Univariate voxel-based morphometric analyses, voxel-based morphometric multivariate pattern classification (linear support vector machine), and clustering analyses (self-organizing map). We found that frontal and temporal gray and white matter regions often implicated in social cognition, including the medial prefrontal cortex, orbitofrontal cortex, superior temporal region, temporal pole, amygdala, insula, and dorsal cingulum, were aberrant in FXS and iAUT as compared with controls. However, these differences were in opposite directions for FXS and iAUT relative to controls; in general, greater volume was seen in iAUT compared with controls, who in turn had greater volume than FXS. Multivariate analysis showed that the overall pattern of brain structure in iAUT generally resembled that of the controls more than FXS, both with and without AUT. Our findings demonstrate that FXS and iAUT are associated with distinct neuroanatomical patterns, further underscoring the neurobiological heterogeneity of iAUT.

Behavior, Brain, and Genome in Genomic Disorders: Finding the Correspondences

Within the last decade or so, there has been an acceleration of research attempting to connect specific genetic lesions to the patterns of brain structure and activation. This article comments on observations that have been made based on these recent data and discusses their importance for the field of investigations into developmental disorders. In making these observations, the authors focus on one specific genomic lesion, the well-studied, yet still incompletely understood, 22q11.2 deletion syndrome. The authors demonstrate the degree of variability in the phenotype that occurs at both the brain and behavioral levels of genomic disorders and describe how this variability is, on close inspection, represented at the genomic level. The authors emphasize the importance of combining genetic/genomic analyses and neuroimaging for research and for future clinical diagnostic purposes and for the purposes of developing individualized, patient-tailored treatment and remediation approaches.

Invited Article: An MRI-based approach to the diagnosis of white matter disorders

There are many different white matter disorders, both inherited and acquired, and consequently the diagnostic process is difficult. Establishing a specific diagnosis is often delayed at great emotional and financial costs. The pattern of brain structures involved, as visualized by MRI, has proven to often have a high diagnostic specificity. We developed a comprehensive practical algorithm that relies mainly on the characteristics of brain MRI. The initial decision point defines a hypomyelination pattern, in which the cerebral white matter is hyperintense (normal), isointense, or slightly hypointense relative to the cortex on T1-weighted images, vs other pathologies with more prominent hypointensity of the cerebral white matter on T1-weighted images. In all types of pathology, the affected white matter is hyperintense on T2-weighted images, but, as a rule, the T2 hyperintensity is less marked in hypomyelination than in other pathologies. Some hypomyelinating disorders are typically associated with peripheral nerve involvement, while others are not. Lesions in patients with pathologies other than hypomyelination can be either confluent or isolated and multifocal. Among the diseases with confluent lesions, the distribution of the abnormalities is of high diagnostic value. Additional MRI features, such as white matter rarefaction, the presence of cysts, contrast enhancement, and the presence of calcifications, further narrow the diagnostic possibilities. Application of a systematic decision tree in MRI of white matter disorders facilitates the diagnosis of specific etiologic entities.

Unaffected Family Members and Schizophrenia Patients Share Brain Structure Patterns: A High-Dimensional Pattern Classification Study

A number of studies have provided evidence for genetic modulation of brain structure in unaffected family members (FM) of schizophrenia patients using conventional volumetric analysis. High-dimensional pattern classification methods have been reported to have the capacity to determine subtle and spatially complex structural patterns that distinguish schizophrenia patients from healthy control subjects using standard magnetic resonance imaging. This study investigates whether such endophenotypic patterns are found in FM via similar image analysis approaches. A high-dimensional pattern classifier was constructed from a group of 69 patients and 79 control subjects, via an analysis that identified a subtle and spatially complex pattern of reduced brain volumes. The constructed classifier was applied to examine brain structure of 30 FM. The classifier indicated that FM had highly overlapping structural profiles with those of patients. Moreover, an orbitofrontal region of relatively increased white matter was found to contribute significantly to the classification, indicating that white matter alterations, along with reductions of gray matter volumes, might be present in patients and unaffected FM. These findings provide evidence that high-dimensional pattern classification can identify complex and subtle structural endophenotypes that are shared by probands and their unaffected FM.

Abnormal brain structure in adults with Van der Woude syndrome

Van der Woude syndrome (VWS) is an autosomal dominant disorder manifested in cleft lip and/or palate and lip pits. Isolated clefts of the lip and/or palate (ICLP) have both genotype and phenotype overlap with VWS. Subjects with ICLP have abnormalities in brain structure and function. Given the similarities between VWS and ICLP, the current study was designed to evaluate the pattern of brain structure of adults with VWS. Fourteen adults with VWS were compared to age- and gender-matched healthy controls. Brain structure was evaluated using magnetic resonance imaging. All subjects with VWS had enlarged volumes of the anterior regions of the cerebrum. Men with VWS had reduced volumes of the posterior cerebrum. Anterior cerebrum volume was negatively correlated with intelligent quotient in the subjects with VWS indicating that the enlargement of this brain region was 'pathologic.' The pattern of brain structure in VWS is nearly identical to those seen in ICLP. In addition, men are affected more severely. Pathologic enlargement of the tissue and a gender effect with men affected more severely are common features of neurodevelopmental disorders supporting the notion that the brain structure of VWS and ICLP may be because of abnormal brain development.

Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

We report evidence that computer-based high-dimensional pattern classification of magnetic resonance imaging (MRI) detects patterns of brain structure characterizing mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's disease (AD). Ninety percent diagnostic accuracy was achieved, using cross-validation, for 30 participants in the Baltimore Longitudinal Study of Aging. Retrospective evaluation of serial scans obtained during prior years revealed gradual increases in structural abnormality for the MCI group, often before clinical symptoms, but slower increase for individuals remaining cognitively normal. Detecting complex patterns of brain abnormality in very early stages of cognitive impairment has pivotal importance for the detection and management of AD.