A growing literature indicates that visual cortex areas viewed as primarily responsive to exogenous stimuli are susceptible to top-down modulation by selective attention. The present study examines whether brain areas involved in biological motion perception are among these areas – particularly with respect to selective attention towards human movement goals. Fifteen participants completed a point-light biological motion study following a two-by-two factorial design, with one factor representing an exogenous manipulation of human movement goals (goal-directed versus random movement), and the other an endogenous manipulation (a goal identification task versus an ancillary color-change task). Both manipulations yielded increased activation in the human homologue of motion-sensitive area MT+ (hMT+) as well as the extrastriate body area (EBA). The endogenous manipulation was associated with increased right posterior superior temporal sulcus (STS) activation, whereas the exogenous manipulation was associated with increased activation in left posterior STS. Selective attention towards goals activated portion of left hMT+/EBA only during the perception of purposeful movement consistent with emerging theories associating this area with the matching of visual motion input to known goal-directed actions. The overall pattern of results indicates that attention towards the goals of human movement activates biological motion areas. Ultimately, selective attention may explain why some studies examining biological motion show activation in hMT+ and EBA, even when using control stimuli with comparable motion properties.
biological motion; selective attention; superior temporal sulcus; extrastriate body area; hMT+
Contrary to expectations derived from preclinical studies of the effects of stress, and imaging studies of adults with PTSD, there is no evidence of hippocampus atrophy in children with PTSD. Multiple pediatric studies have reported reductions in the corpus callosum – the primary white matter tract in the brain. Consequently, in the present study, Diffusion Tensor Imaging was used to assess corpus callosum white matter integrity in 17 maltreated children with PTSD and 15 demographically matched normal controls. Children with PTSD had reduced fractional anisotropy in the medial and posterior corpus, a region which contains interhemispheric projections from brain structures involved in circuits that mediate the processing of emotional stimuli and various memory functions --- core disturbances associated with a history of trauma. Further exploration of the effects of stress on corpus callosum and white matter development appears a promising strategy to better understanding the pathophysiology of PTSD in children.
Posttraumatic Stress Disorder; Imaging; DTI; children
Brain enlargement has been observed in individuals with autism as early as two years of age. Studies using head circumference suggest that brain enlargement is a postnatal event that occurs around the latter part of the first year. To date, no brain imaging studies have systematically examined the period prior to age two. In this study we examine MRI brain volume in six month olds at high familial risk for autism.
The Infant Brain Imaging Study (IBIS) is a longitudinal imaging study of infants at high risk for autism. This cross-sectional analysis examines brain volumes at six months of age, in high risk infants (N=98) in comparison to infants without family members with autism (low risk) (N=36). MRI scans are also examined for radiologic abnormalities.
No group differences were observed for intracranial cerebrum, cerebellum, lateral ventricle volumes, or head circumference.
We did not observe significant group differences for head circumference, brain volume, or abnormalities of radiologic findings in a sample of 6 month old infants at high-risk for autism. We are unable to conclude that these changes are not present in infants who later go on to receive a diagnosis of autism, but rather that they were not detected in a large group at high familial risk. Future longitudinal studies of the IBIS sample will examine whether brain volume may differ in those infants who go onto develop autism, estimating that approximately 20% of this sample may be diagnosed with an autism spectrum disorder at age two.
autism; child psychiatry
This paper presents a method for classification of structural magnetic resonance images (MRI) of the brain. An ensemble of linear support vector machine classifiers (SVMs) is used for classifying a subject as either patient or normal control. Image voxels are first ranked based on the voxel wise t-statistics between the voxel intensity values and class labels. Then voxel subsets are selected based on the rank value using a forward feature selection scheme. Finally, an SVM classifier is trained on each subset of image voxels. The class label of a test subject is calculated by combining individual decisions of the SVM classifiers using a voting mechanism. The method is applied for classifying patients with neurological diseases such as Alzheimer’s disease (AD) and autism spectrum disorder (ASD). The results on both datasets demonstrate superior performance as compared to two state of the art methods for medical image classification.
Feature ranking; Ensemble SVM; MRI; Classification
Evidence from prospective high-risk infant studies suggests that early symptoms of autism usually emerge late in the first- or early in the second-year of life after a period of relatively typical development. This is the first neuroimaging study to prospectively examine white matter fiber tract organization during this interval in infants who develop autism spectrum disorder (ASD) by 24 months.
Participants included 92 infant siblings from an ongoing imaging study of autism. All participants had diffusion tensor imaging at 6 months and behavioral assessments at 24 months, with a majority contributing additional imaging data at either or both 12 and 24 months. At 24 months, 28 infants met criteria for ASD; 64 infants did not. Microstructural properties of white-matter fiber tracts reported to be associated with ASD or related behaviors were characterized by fractional anisotropy (FA) and radial and axial diffusivity.
FA trajectories differed significantly between infants who did versus did not develop ASD for 12 of 15 fiber tracts. Development for most fiber tracts in infants with ASD was characterized by elevated FA at 6 months followed by slower developmental change overtime relative to infants without ASD. Thus, by 24 months of age, lower FA values were evident for those with ASD.
These results suggest that the aberrant development of white matter pathways precede the manifestation of autistic symptoms in the first year of life. Longitudinal data are critical to characterizing the dynamic age-related brain and behavior changes underlying this neurodevelopmental disorder.
Half of pediatric-onset OCD cases remit by adulthood. Studies have demonstrated that initial response to pharmacotherapy, age of onset, prominent hoarding symptoms, and the presence of comorbid tic disorders are associated with long-term outcome. Our goal was to examine the association between childhood performance on neuropsychological testing and persistence of OCD into adulthood.
Twenty-four children with OCD were followed for an average of 7.5 years into early adulthood. Neuropsychological performance in childhood (<16 years) was measured. The battery included the Wechsler Intelligence Scale for Children (WISC-III), the Purdue pegboard test, the Rey-Osterreith Complex Figure Task (RCFT) and the Beery-Buktenica test of Visual Motor Integration (VMI). We hypothesized that deficits in fine-motor skills, visuospatial skills, and nonverbal memory as well as overall intelligence would be associated with adulthood outcome. We used a Cox Proportional Hazard model of survival analysis in which time to remission of OCD symptoms was the main outcome variable.
Poor childhood performance on the Purdue pegboard task and the block design subscale of WISC-III was associated with persistence of OCD symptoms into adulthood. IQ, VMI, and nonverbal memory performance did not predict significantly the persistence of OCD.
These results suggest that visuospatial and fine-motor skill deficits are predictive of poor long-term outcome in pediatric-onset OCD. Future longitudinal studies are needed to chart the course of these deficits relative to the course of symptoms in OCD and to determine whether the association of these neuropsychiatric deficits with long-term outcome is specific to pediatric-onset OCD or generalizes to other psychiatric disorders.
Obsessive-compulsive disorder; longitudinal study; neuropsychological tests; fine-motor skills and visuospatial skills
Although the extant literature on face recognition skills in Autism Spectrum Disorder (ASD) shows clear impairments compared to typically developing controls (TDC) at the group level, the distribution of scores within ASD is broad. In the present research, we take a dimensional approach and explore how differences in social attention during an eye tracking experiment correlate with face recognition skills across ASD and TDC. Emotional discrimination and person identity perception face processing skills were assessed using the Let's Face It! Skills Battery in 110 children with and without ASD. Social attention was assessed using infrared eye gaze tracking during passive viewing of movies of facial expressions and objects displayed together on a computer screen. Face processing skills were significantly correlated with measures of attention to faces and with social skills as measured by the Social Communication Questionnaire (SCQ). Consistent with prior research, children with ASD scored significantly lower on face processing skills tests but, unexpectedly, group differences in amount of attention to faces (vs. objects) were not found. We discuss possible methodological contributions to this null finding. We also highlight the importance of a dimensional approach for understanding the developmental origins of reduced face perception skills, and emphasize the need for longitudinal research to truly understand how social motivation and social attention influence the development of social perceptual skills.
autism; eye tracking; face processing; eyetracking; autism spectrum disorder; ASD
Structural connectivity models hold great promise for expanding what is known about the ways information travels throughout the brain. The physiologic interpretability of structural connectivity models depends heavily on how the connections between regions are quantified. This article presents an integrated structural connectivity framework designed around such an interpretation. The framework provides three measures to characterize the structural connectivity of a subject: (1) the structural connectivity matrix describing the proportion of connections between pairs of nodes, (2) the nodal connection distribution (nCD) characterizing the proportion of connections that terminate in each node, and (3) the connection density image, which presents the density of connections as they traverse through white matter (WM). Individually, each possesses different information concerning the structural connectivity of the individual and could potentially be useful for a variety of tasks, ranging from characterizing and localizing group differences to identifying novel parcellations of the cortex. The efficiency of the proposed framework allows the determination of large structural connectivity networks, consisting of many small nodal regions, providing a more detailed description of a subject's connectivity. The nCD provides a gray matter contrast that can potentially aid in investigating local cytoarchitecture and connectivity. Similarly, the connection density images offer insight into the WM pathways, potentially identifying focal differences that affect a number of pathways. The reliability of these measures was established through a test/retest paradigm performed on nine subjects, while the utility of the method was evaluated through its applications to 20 diffusion datasets acquired from typically developing adolescents.
HARDI; structural connectivity
Most diffusion imaging studies have used subject registration to an atlas space for enhanced quantification of anatomy. However, standard diffusion tensor atlases lack information in regions of fiber crossing and are based on adult anatomy. The degree of error associated with applying these atlases to studies of children for example has not yet been estimated but may lead to suboptimal results. This paper describes a novel technique for generating population-specific high angular resolution diffusion imaging (HARDI)-based atlases consisting of labeled regions of homogenous white matter. Our approach uses a fiber orientation distribution (FOD) diffusion model and a data driven clustering algorithm. White matter regional labeling is achieved by our automated data driven clustering algorithm that has the potential to delineate white matter regions based on fiber complexity and orientation. The advantage of such an atlas is that it is study specific and more comprehensive in describing regions of white matter homogeneity as compared to standard anatomical atlases. We have applied this state of the art technique to a dataset consisting of adolescent and preadolescent children, creating one of the first examples of a HARDI-based atlas, thereby establishing the feasibility of the atlas creation framework. The white matter regions generated by our automated clustering algorithm have lower FOD variance than when compared to the regions created from a standard anatomical atlas.
Diffusion; Atlas Generation; HARDI Template; White Matter Parcellation
The goals of the present study were 2-fold. First, we wished to investigate the neural correlates of stimulus-driven processing of stimuli strongly suppressed from awareness and in the absence of top-down influences. We accomplished this using a novel approach in which participants performed an orthogonal task atop a flash suppression noise image to prevent top-down search. Second, we wished to investigate the extent to which amygdala responses differentiate between suppressed stimuli (fearful faces and houses) based on their motivational relevance. Using continuous flash suppression (CFS) in conjunction with fMRI, we presented fearful faces, houses, and a no stimulus control to one eye while participants performed an orthogonal task that appeared atop the flashing Mondrian image presented to the opposite eye. In 29 adolescents, we show activation in subcortical regions, including the superior colliculus, amygdala, thalamus, and hippocampus for suppressed objects (fearful faces and houses) compared to a no stimulus control. Suppressed stimuli showed less activation compared to a no stimulus control in early visual cortex (EVC), indicating that object information was being suppressed from this region. Additionally, we find no activation in regions associated with conscious processing of these percepts (fusiform gyrus and/or parahippocampal cortex) as assessed by mean activations and multi-voxel patterns. A psychophysiological interaction analysis (PPI) that seeded the amygdala showed task-specific (fearful faces greater than houses) modulation of right pulvinar and left inferior parietal cortex. Taken together, our results support a role for the amygdala in stimulus-driven attentional guidance toward objects of relevance and a potential mechanism for successful suppression of rivalrous stimuli.
fMRI; continuous flash suppression; adolescents; motivated attention; vision
Structural connectivity models hold great promise for expanding what is known about the ways information travels throughout the brain. The physiologic interpretability of structural connectivity models depends heavily on how the connections between regions are quantified. This paper presents an integrated structural connectivity framework designed around such an interpretation. The framework provides three measures to characterize the structural connectivity of a subject: 1) The structural connectivity matrix describing the proportion of connections between pairs of nodes, 2) The nodal connection distribution characterizing the proportion of connections that terminate in each node and 3) the connection density image which presents the density of connections as they traverse through white matter. Individually each possess different information concerning the structural connectivity of the individual and could potentially be useful for a variety of tasks, ranging from characterizing and localizing group differences, to identifying novel parcellations of the cortex. The efficiency of the proposed framework allows the determination of large structural connectivity networks, consisting of many small nodal regions, providing a more detailed description of a subject’s connectivity. The nodal connection distribution provides a grey matter contrast that can potentially aid in investigating local cytoarchitecture and connectivity. Similarly the connectivity density images offer insight into the white matter pathways, potentially identifying focal differences that affect a number of pathways. The reliability of these measures was established through a test/retest paradigm performed on 9 subjects while the utility of the method was evaluated through its applications to 20 diffusion datasets acquired from typically developing adolescents.
Structural Connectivity; HARDI
The social motivation hypothesis posits that aberrant neural response to human faces in autism is attributable to atypical social development and consequently reduced exposure to faces. The specificity of deficits in neural specialization remains unclear, and alternative theories suggest generalized processing difficulties. The current study contrasted neural specialization for social information versus non-social information in 36 individuals with autism and 18 typically developing individuals matched for age, race, sex, handedness, and cognitive ability. Event-related potentials elicited by faces, inverted faces, houses, letters, and pseudoletters were recorded. Groups were compared on an electrophysiological marker of neural specialization (N170), as well as behavioral performance on standardized measures of face recognition and word reading/decoding. Consistent with prior results, individuals with autism displayed slowed face processing and decreased sensitivity to face inversion; however, they showed comparable brain responses to letters, which were associated with behavioral performance in both groups. Results suggest that individuals with autism display atypical neural specialization for social information but intact specialization for non-social information. They concord with the notion of specific dysfunction in social brain systems rather than non-specific information processing difficulties in autism.
Perceptual expertise; N170; event-related potential (ERP/EEG); face perception; autism spectrum disorder
Multi-voxel pattern analysis (MVPA) has been applied successfully to a variety of fMRI research questions in healthy participants. The full potential of applying MVPA to functional data from patient groups has yet to be fully explored. Our goal in this study was to investigate whether MVPA might yield a sensitive predictor of patient symptoms. We also sought to demonstrate that this benefit can be realized from existing datasets, even when they were not designed with MVPA in mind. We analyzed data from an fMRI study of the neural basis for face processing in individuals with an Autism Spectrum Disorder (ASD), who often show fusiform gyrus hypoactivation when presented with unfamiliar faces, compared to controls. We found reliable correlations between MVPA classification performance and standardized measures of symptom severity that exceeded those observed using a univariate measure; a relation that was robust across variations in ROI definition. A searchlight analysis across the ventral temporal lobes identified regions with relationships between classification performance and symptom severity that were not detected using mean activation. These analyses illustrate that MVPA has the potential to act as a sensitive functional biomarker of patient severity.
MVPA; fMRI; multivariate; pattern; patient; autism
Decades of research have documented the specialization of fusiform gyrus (FG) for facial information processes. Recent theories indicate that FG activity is shaped by input from amygdala, but effective connectivity from amygdala to FG remains undocumented. In this fMRI study, 39 participants completed a face recognition task. 11 participants underwent the same experiment approximately four months later. Robust face-selective activation of FG, amygdala, and lateral occipital cortex were observed. Dynamic causal modeling and Bayesian Model Selection (BMS) were used to test the intrinsic connections between these structures, and their modulation by face perception. BMS results strongly favored a dynamic causal model with bidirectional, face-modulated amygdala-FG connections. However, the right hemisphere connections diminished at time 2, with the face modulation parameter no longer surviving Bonferroni correction. These findings suggest that amygdala strongly influences FG function during face perception, and that this influence is shaped by experience and stimulus salience.
Functional MRI; Face processing; Amygdala; Effective connectivity; Dynamic causal modeling
The paper presents a method for creating abnormality classifiers from high angular resolution diffusion imaging (HARDI) data. We utilized the fiber orientation distribution (FOD) diffusion model to represent the local WM architecture of each subject. The FOD images are then spatially normalized to a common template using a non-linear registration technique. Regions of homogeneous white matter architecture (ROIs) are determined by applying a parcellation algorithm to the population average FOD image. Orientation invariant features of each ROI’s mean FOD are determined and concatenated into a feature vector to represent each subject. Principal component analysis (PCA) was used for dimensionality reduction and a linear support vector machine (SVM) classifier is trained on the PCA coefficients. The classifier assigns each test subject a probabilistic score indicating the likelihood of belonging to the patient group. The method was validated using a 5 fold validation scheme on a population containing autism spectrum disorder (ASD) patients and typically developing (TD) controls. A clear distinction between ASD patients and controls was obtained with a 77% accuracy.
Diffusion Imaging; HARDI; FOD; Classification; SVM
Most behavioral training regimens in autism spectrum disorders (ASD) rely on reward-based reinforcement strategies. Although proven to significantly increase both cognitive and social outcomes and successfully reduce aberrant behaviors, this approach fails to benefit a substantial number of affected individuals. Given the enormous amount of clinical and financial resources devoted to behavioral interventions, there is a surprisingly large gap in our knowledge of the basic reward mechanisms of learning in ASD. Understanding the mechanisms for reward responsiveness and reinforcement-based learning is urgently needed to better inform modifications that might improve current treatments. The fundamental goal of this review is to present a fine-grained literature analysis of reward function in ASD with reference to a validated neurobiological model of reward: the ‘wanting’/’liking’ framework. Despite some inconsistencies within the available literature, the evaluation across three converging sets of neurobiological data (neuroimaging, electrophysiological recordings, and neurochemical measures) reveals good evidence for disrupted reward-seeking tendencies in ASD, particularly in social contexts. This is most likely caused by dysfunction of the dopaminergic–oxytocinergic ‘wanting’ circuitry, including the ventral striatum, amygdala, and ventromedial prefrontal cortex. Such a conclusion is consistent with predictions derived from diagnostic criteria concerning the core social phenotype of ASD, which emphasize difficulties with spontaneous self-initiated seeking of social encounters (that is, social motivation). Existing studies suggest that social ‘wanting’ tendencies vary considerably between individuals with ASD, and that the degree of social motivation is both malleable and predictive of intervention response. Although the topic of reward responsiveness in ASD is very new, with much research still needed, the current data clearly point towards problems with incentive-based motivation and learning, with clear and important implications for treatment. Given the reliance of behavioral interventions on reinforcement-based learning principles, we believe that a systematic focus on the integrity of the reward system in ASD promises to yield many important clues, both to the underlying mechanisms causing ASD and to enhancing the efficacy of existing and new interventions.
Autism spectrum disorders; Reward; Social motivation; Ventral striatum; Ventromedial prefrontal cortex; Amygdala; Dopamine; Oxytocin; Opioids; Treatment
Neuropsychological functioning in children with Tourette Syndrome (TS) has been characterized by subtle deficits in response inhibition, visual-motor integration and fine-motor coordination. The association of these deficits with the tics of the TS versus co-occurring attention-deficit/hyperactivity disorder (ADHD) has not been well understood due to small sample sizes and lack of adequate control conditions. We examined neuropsychological functioning in relatively large and well-characterized samples of children with TS, TS-plus-ADHD, ADHD, and unaffected controls.
Fifty-six children with TS-only, 45 with TS-plus-ADHD, 64 with ADHD and 71 healthy community control subjects were assessed on a battery of neuropsychological measures including the Connors’ Continuous Performance Test (CPT), the Stroop Color-Word Interference Test (Stroop), the Beery Visual-Motor Integration Test (VMI), and the Purdue Pegboard Test.
There were no differences between children with TS-only and unaffected controls on the measures of response inhibition and visual-motor integration. Boys with TS-only but not girls with TS-only were impaired in the dominant hand Purdue performance. Children with ADHD were impaired on all study measures. Children with TS-plus-ADHD revealed no deficits on the Stroop, VMI and Purdue tests but were impaired on the sustained attention portion of the CPT.
These results indicate that co-occurring ADHD may be responsible for the neuropsychological deficits, or at least those assessed in the present study, in children with TS. Explanations in terms of neurobiological mechanisms of co-occurring TS and ADHD as well as possible compensatory mechanisms in children with TS are discussed.
Confirmatory factor analyses of the traditional 11 subtests of the Wechsler child and adult intelligence scales were accomplished for 137 children and 118 adults with high functioning autism (HFA) and for comparable age groups from the standardization samples contained in the Wechsler manuals. The objective was determining whether HFA groups produced similar best fitting models to those found in the normative samples or formed a separate “social intelligence” factor. Four-factor models incorporating a “social intelligence” factor provided the best fit in both the autism and normative, but the subtest intercorrelations were generally lower in the autism samples. Findings were interpreted in terms of underconnectivity or reduced communication among brain regions in autism.
Data sharing in autism neuroimaging presents scientific, technical, and social obstacles. We outline the desiderata for a data-sharing scheme that combines imaging with other measures of phenotype and with genetics, defines requirements for comparability of derived data and recommendations for raw data, outlines a core protocol including multispectral structural and diffusion-tensor imaging and optional extensions, provides for the collection of prospective, confound-free normative data, and extends sharing and collaborative development not only to data but to the analytical tools and methods applied to these data. A theme in these requirements is the need to preserve creative approaches and risk-taking within individual laboratories at the same time as common standards are provided for these laboratories to build on.
Imaging; MRI; PET; Morphometry; Segmentation; Data sharing
Autism spectrum disorders (ASDs) are characterized by deficits in social and communication processes. Recent data suggest that altered functional connectivity (FC), i.e. synchronous brain activity, might contribute to these deficits. Of specific interest is the FC integrity of the default mode network (DMN), a network active during passive resting states and cognitive processes related to social deficits seen in ASD, e.g. Theory of Mind. We investigated the role of altered FC of default mode sub-networks (DM-SNs) in 16 patients with high-functioning ASD compared to 16 matched healthy controls of short resting fMRI scans using independent component analysis (ICA). ICA is a multivariate data-driven approach that identifies temporally coherent networks, providing a natural measure of FC. Results show that compared to controls, patients showed decreased FC between the precuneus and medial prefrontal cortex/anterior cingulate cortex, DMN core areas, and other DM-SNs areas. FC magnitude in these regions inversely correlated with the severity of patients' social and communication deficits as measured by the Autism Diagnostic Observational Schedule and the Social Responsiveness Scale. Importantly, supplemental analyses suggest that these results were independent of treatment status. These results support the hypothesis that DM-SNs under-connectivity contributes to the core deficits seen in ASD. Moreover, these data provide further support for the use of data-driven analysis with resting-state data for illuminating neural systems that differ between groups. This approach seems especially well suited for populations where compliance with and performance of active tasks might be a challenge, as it requires minimal cooperation.
Independent component analysis; Functional MRI; Resting state; Default mode network; High-functioning autism
Autism spectrum disorders (ASDs) are childhood neurodevelopmental disorders with complex genetic origins1–4. Previous studies focusing on candidate genes or genomic regions have identified several copy number variations (CNVs) that are associated with an increased risk of ASDs5–9. Here we present the results from a whole-genome CNV study on a cohort of 859 ASD cases and 1,409 healthy children of European ancestry who were genotyped with ~550,000 single nucleotide polymorphism markers, in an attempt to comprehensively identify CNVs conferring susceptibility to ASDs. Positive findings were evaluated in an independent cohort of 1,336 ASD cases and 1,110 controls of European ancestry. Besides previously reported ASD candidate genes, such as NRXN1 (ref. 10) and CNTN4 (refs 11, 12), several new susceptibility genes encoding neuronal cell-adhesion molecules, including NLGN1 and ASTN2, were enriched with CNVs in ASD cases compared to controls (P = 9.5 × 10−3). Furthermore, CNVs within or surrounding genes involved in the ubiquitin pathways, including UBE3A, PARK2, RFWD2 and FBXO40, were affected by CNVs not observed in controls (P = 3.3 × 10−3). We also identified duplications 55 kilobases upstream of complementary DNA AK123120 (P = 3.6 × 10−6). Although these variants may be individually rare, they target genes involved in neuronal cell-adhesion or ubiquitin degradation, indicating that these two important gene networks expressed within the central nervous system may contribute to the genetic susceptibility of ASD.
We propose a new method for context-driven analysis of functional magnetic resonance images (fMRI) that incorporates spatial relationships between functional parameter clusters and anatomical structure directly for the first time. We design a parametric scheme that relates functional and structural spatially-compact regions in a single unified manner. Our method is motivated by the fact that the fMRI and anatomical MRI (aMRI) have consistent relations that provide configurations and context that aid in fMRI analysis. We develop a statistical decision-making strategy to estimate new fMRI parameter images (based on a General Linear Model-GLM) and spatially-clustered zones within these images. The analysis is based on the time-series data and contextual information related to appropriate spatial grouping of parameters in the functional data and the relationship of this grouping to relevant gray matter structure from the anatomical data. We introduce a representation for the joint prior of the functional and structural information, and define a joint probability distribution over the variations of functional clusters and the related structure contained in a set of training images. We estimate the Maximum A Posteriori (MAP) functional parameters, formulating the function-structure model in terms of level set functions. Results from 3D fMRI and aMRI show that this context-driven analysis potentially extracts more meaningful information than the standard GLM approach.
In this work, we present a method for the integration of feature and intensity information for non rigid registration. Our method is based on a free-form deformation model, and uses a normalized mutual information intensity similarity metric to match intensities and the robust point matching framework to estimate feature (point) correspondences. The intensity and feature components of the registration are posed in a single energy functional with associated weights. We compare our method to both point-based and intensity-based registrations. In particular, we evaluate registration accuracy as measured by point landmark distances and image intensity similarity on a set of seventeen normal subjects. These results suggest that the integration of intensity and point-based registration is highly effective in yielding more accurate registrations.
Autism spectrum disorders are characterised by severe deficits in socialisation, communication, and repetitive or unusual behaviours. Increases over time in the frequency of these disorders (to present rates of about 60 cases per 10 000 children) might be attributable to factors such as new administrative classifications, policy and practice changes, and increased awareness. Surveillance and screening strategies for early identification could enable early treatment and improved outcomes. Autism spectrum disorders are highly genetic and multifactorial, with many risk factors acting together. Genes that affect synaptic maturation are implicated, resulting in neurobiological theories focusing on connectivity and neural effects of gene expression. Several treatments might address core and comorbid symptoms. However, not all treatments have been adequately studied. Improved strategies for early identification with phenotypic characteristics and biological markers (eg, electrophysiological changes) might hopefully improve effectiveness of treatment. Further knowledge about early identification, neurobiology of autism, effective treatments, and the effect of this disorder on families is needed.
Spatial modeling is essential for fMRI analysis due to relatively high noise in the data. Earlier approaches have been primarily concerned with the spatial coherence of the BOLD response in local neighborhoods. In addition to a smoothness constraint, we propose to incorporate prior knowledge of brain activation patterns learned from training samples. This spatially informed prior can significantly enhance the estimation process by inducing sensitivity to task related regions of the brain. As fMRI data exhibits intersubject variability in functional anatomy, we design the prior using Independent Component Analysis (ICA). Due to the non-Gaussian assumption, ICA does not regress to the mean activation pattern and thus avoids suppressing intersubject differences. Results from a real fMRI experiment indicate that our approach provides statistically significant improvement in estimating activation compared to the standard general linear model (GLM) based methods.