Clinical Neuroscience and Computational Anatomy unit

Jun Soo Kwon, MD, PhD, Dept. of Psychiatry, SNU HP

2010

* White matter neuroplastic changes in long-term trained players of the game of "Baduk" (GO): A voxel-based diffusion-tensor imaging study.
[Article (PDF)]
Currently, one of the most challenging issues in modern neuroscience is learning-induced neural plasticity. Many researchers have identified activation-dependent structural brain plasticity in gray and white matter. The game of Baduk is known to require many cognitive processes, and long-term training in such processes would be expected to cause structural changes in related brain areas. We conducted voxel-based analyses of diffusion-tensor imaging (DTI) data and found that, compared to inexperienced controls, long-term trained Baduk players developed larger regions of white matter with increased fractional anisotropy (FA) values in the frontal, cingulum, and striato-thalamic areas that are related to attentional control, working memory, executive regulation, and problem-solving. In addition, inferior temporal regions with increased FA indicate that Baduk experts tend to develop a task-specific template for the game, as compared to controls. In contrast, decreased FA found in dorsolateral premotor and parietal areas indicate that Baduk experts were less likely than were controls to use structures related to load-dependent memory capacity. Right-side dominance in Baduk experts suggests that the tasks involved are mainly spatial processes. Altogether, long-term Baduk training appears to cause structural brain changes associated with many of the cognitive aspects necessary for game play, and investigation of the mechanism underpinning such changes might be helpful for improving higher-order cognitive capacities, such as learning, abstract reasoning, and self-control, which can facilitate education and cognitive therapies

* Functional connectivity in fronto-subcortical circuitry during the resting state in obsessive-compulsive disorder.
[Article (PDF)]
Obsessions and compulsions mediated by cognitive inflexibility might be associated with abnormal resting state functional connectivity in the default mode network (DMN) that represents intrinsically generated neuronal activity. It was hypothesized that decreased functional connectivity in the DMN would occur in components of fronto-subcortical circuits in patients with obsessive-compulsive disorder (OCD). Twenty-two unmedicated OCD patients and 22 age- and sex-matched healthy controls received resting state functional scanning runs. The posterior cingulate cortex (PCC) region was chosen as the seed region for the connectivity analysis. Correlations between temporal connectivity with the seed region and scores on clinical measures and obsessive-compulsive symptom dimensions were also assessed. OCD patients demonstrated less functional connectivity within the DMN in the anterior cingulate cortex, middle frontal gyrus, and putamen compared to controls. The functional connectivity to the PCC seed region in OCD patients was in the direction opposite to that in the prefrontal areas with regard to scores on cleaning and obsessions/checking dimensions of OCD. These data provide evidence for fronto-subcortical dysfunction in OCD. Results from this study also support the notion that OCD is a heterogeneous disorder mediated by distinct circuits

2009

* Association of the glutamate transporter gene SLC1A1 with atypical antipsychotics-induced obsessive-compulsive symptoms.
[Article (PDF)]
CONTEXT: Several studies have indicated that atypical antipsychotics (AAP) induce obsessive-compulsive (OC) symptoms. Research exploring the mechanism of this phenomenon, however, has been extremely limited. Considering the indirect evidence of genetic control and difficulties in developing animal models and performing gene expression studies, genetic association studies could be an important approach to understanding the molecular mechanism of AAP-induced OC symptoms. The glutamate transporter gene SLC1A1, which was recently reported to be associated with obsessive-compulsive disorder (OCD), is a promising candidate gene for susceptibility to AAP-induced OC symptoms. OBJECTIVE: To determine whether polymorphisms in SLC1A1 are associated with AAP-induced OC symptoms in patients with schizophrenia. DESIGN: A pharmacogenetic case-control association study. SETTING: Outpatient schizophrenia clinics. PATIENTS: Clinically stable patients with schizophrenia who were receiving AAP treatment (n = 94; OC group). The OC group consisted of 40 patients with AAP-induced OC symptoms, and the non-OC group consisted of 54 patients who had received AAP for more than 24 months without developing OC symptoms. MAIN OUTCOME MEASURES: Allele, genotype, and haplotype frequencies. The association was tested with a logistic regression model using age, sex, and medication type as covariates. RESULTS: Trends of association were observed in rs2228622 and rs3780412 (nominal P = .01; adjusted permutation P = .07) for the dominant model that was the inheritance model that best fit our data. In the haplotype -based analysis, the A/C/G haplotype at rs2228622-rs3780413-rs3780412 showed a significant association with AAP-induced OC symptoms; this association withstood multiple test correction (nominal P = .01; adjusted permutation P = .04; odds ratio, 3.955; 95% confidence interval, 1.366-11.452, for dominant model). CONCLUSIONS: These results suggest that sequence variations in SLC1A1 are associated with susceptibility to AAP-induced OC symptoms. This is the first published pharmacogenetic study on this phenomenon and provides preliminary evidence of the involvement of glutamatergic neurotransmission in the pathogenesis of AAP-induced OC symptoms

* A 12-week, naturalistic switch study of the efficacy and tolerability of aripiprazole in stable outpatients with schizophrenia or schizoaffective disorder.
[Article (PDF)]
The objectives of this 12-week multicenter open-label switching study were to evaluate the overall clinical efficacy, safety, and tolerability of aripiprazole in stable patients with schizophrenia or schizoaffective disorder, and to assess, in a naturalistic setting, whether such patients experience symptom worsening when switched from D2 receptor antagonists to aripiprazole (a D2 receptor partial agonist). Patients with schizophrenia or schizoaffective disorder in a symptomatically stable state were randomized to aripiprazole or standard-of-care antipsychotics. The Clinical Global Impression (CGI), Positive and Negative Syndrome Scale, and Investigator's Assessment Questionnaire were used monthly. The Udvalg for Kliniske Undersogelser side-effect rating scale scores and treatment emergent adverse events were recorded to assess the safety and tolerability of switching to aripiprazole from other antipsychotics. A total of 292 patients were randomly assigned to receive aripiprazole (N = 245) or non-aripiprazole antipsychotics (N = 47). Mean CGI-Improvement score at 12 weeks was 3.56+/-1.29 (95% confidence interval: 3.39-3.73) in the aripiprazole group, indicating that aripiprazole was effective in treating schizophrenic patients. Aripiprazole treatment resulted in improvement from baseline on all efficacy outcome measures, including Positive and Negative Syndrome Scale total, positive, negative, and general subscale, and CGI-Severity scores. In addition, after aripiprazole treatment, the remission rate was increased from 43.9% at baseline to 51.7% at 12 weeks. The proportion of patients with symptom worsening at 12 weeks was low (12.4%). Both Investigator's Assessment Questionnaire and Udvalg for Kliniske Undersogelser scores showed that there were fewer prolactin-related adverse events in the aripiprazole group than in the standard-of-care antipsychotics group (P<0.05). There were no significant between-group differences in time to failure to maintain remission and time to dropout. In the naturalistic setting, symptomatically stable outpatients with schizophrenia who were switched to aripiprazole showed clinically meaningful treatment benefits. The majority of patients was successfully switched from other antipsychotics without serious symptom exacerbation or adverse events over a course of 12 weeks.

* Proton magnetic resonance spectroscopy in subjects with high genetic risk of schizophrenia: investigation of anterior cingulate, dorsolateral prefrontal cortex and thalamus.
[Article (PDF)]
OBJECTIVE: Reduced N-acetylaspartate levels in regions of the frontal cortex, including the anterior cingulate cortex, dorsolateral prefrontal cortex, and thalamus, involved in the pathophysiology of schizophrenia suggest that brain metabolite abnormalities may be a marker of genetic vulnerability to schizophrenia. We used proton magnetic resonance spectroscopy (H-MRS) to acquire absolute concentrations of brain metabolites in subjects with a high genetic risk of schizophrenia to investigate the potential relationship between unexpressed genetic liability to schizophrenia and neuronal dysfunction. METHOD: Included in the study were 22 subjects who had at least two relatives with schizophrenia (high genetic risk group) and 22 controls with no second-degree relatives with schizophrenia. Absolute concentrations of N-acetylaspartate, creatine, choline, glutamate/glutamine, and myo-inositol and the ratios of metabolites in the anterior cingulate cortex, left dorsolateral prefrontal cortex, and left thalamus were measured using H-MRS at 1.5 Tesla. RESULTS: Relative to the controls, the high genetic risk group showed significant differences in absolute metabolite levels in the spectra of the regions of the left thalamus, including significant decreases in N-acetylaspartate, creatine, and choline concentrations. CONCLUSIONS: The study points to neuronal dysfunction, and in particular thalamic dysfunction, as a key region of the vulnerability marker of schizophrenia. Further studies should examine the nature of the thalamus more intensively to further our understanding of thalamic dysfunction as a vulnerability marker.

* White matter alterations in male patients with obsessive- compulsive disorder.
[Article (PDF)]
To investigate white matter abnormalities in patients with obsessive-compulsive disorder and to clarify the relationship between discrete white matter alterations and obsessive-compulsive symptom dimensions, the fractional anisotropy obtained from 25 male patients and 25 matched normal controls were analyzed. The patients had a significantly lower fractional anisotropy in the left anterior cingulate white matter than the controls. When stratified by clinical symptom dimensions, patients with a predominant aggressive/checking symptom dimension exhibited a significantly lower fractional anisotropy in the left anterior cingulate white matter, whereas patients with a predominant contamination/cleaning symptom dimension showed a significantly higher fractional anisotropy in the bilateral prefrontal white matter. Our findings provide evidence that obsessive-compulsive disorder may be a heterogeneous disease with distinct white matter changes.

Moo K. Chung, PhD, Dept. of Biostatistics & Medical Informatics, U of Wisconsin-Madison HP

2010

* The Encyclopedia of Research Design -R square
[Article (PDF)]
it easy to program new statistical methods. The graphics of the language allow easy production of advanced, publication-quality graphics. Since a wide variety of experts use the program, R includes a comprehensive library of statistical functions, including many cutting-edge statistical methods. In addition to this, many third-party specialized methods are publicly available. And most important, R is free and open source. A common concern of beginning users of R is the steep learning curve involved in using it. Such concern stems from the fact that R is a commanddriven environment. Consequently, the statistical analysis is performed in a series of steps, in which commands are typed out and the results from each step are stored in objects that can be used by further inquiries. This is contrary to other programs, such as SPSS and SAS, which require users to determine all characteristics of the analysis up front and provide extensive output, thus relying on the users to identify what is relevant to their initial question. Another source of complaints relates to the difficulty of writing new functions. The more complex the function, the more difficult it becomes to identify errors in syntax or logic. R will prompt the user with an error message, but no indication is given of the nature of the problem or its location within the new code. Consequently, despite the advantage afforded by being able to add new functions to R, many users may find it frustrating to write new routines. In addition, complex analyses and simulations in R tend to be very demanding on the computer memory and processor; thus, the more complex the analysis, the longer the time necessary to complete the task, sometimes days. Large data sets or complex tasks place heavy demands on computer RAM, resulting in slow output.

* Characterization of structural connectivity in autism using graph networks with DTI
[Article (PDF)]
Diffusion tensor imaging (DTI) may be used to characterize the structural connectivity of the human brain non-invasively by tracing white matter fiber tracts. Whole brain tractography studies routinely generate up to half million tracts per brain, which serves as edges in an extremely large 3D graph. Currently there is no agreed-upon method for constructing the brain structural network graphs out of large number of white matter tracts. We present the first scalable iterative framework for building a large brain network graph and apply it to testing the over-connectivity hypothesis in autism. We clearly show that autism is characterized by over-connectivity of low degree nodes indicating the connectivity difference in the brain network.

* Structural Connectivity Mapping via the Tensor-Based Morphometry
[Article (PDF)]
The tensor-based morphometry (TBM) has been widely used in characterizing tissue volume difference between populations at voxel level. So far most TBM studies have performed massive univariate tests in every voxels mainly using the Jacobian determinant. We present a novel structural connectivity analysis framework that can address various brain network hypotheses that massive univariate tests are not able to handle. The main innovation is that the proposed framework does not utilize diffusion tensor images (DTI) but still able to construct the population specific connectivity maps only using T1- weighted magnetic resonance images (MRI). The method is applied in detecting the regions of abnormal corpus callosum connectivity in neglected children (NC) who have been post-institutionalized in Wisconsin.

* Cosine series representation of 3D curves and its application to white matter fiber bundles in diffusion tensor imaging.
[Article (PDF) ]
We present a novel cosine series representation for encoding fiber bundles consisting of multiple 3D curves. The coordinates of curves are parameterized as coefficients of cosine series expansion. We address the issue of registration, averaging and statistical inference on curves in a unified Hilbert space framework. Unlike traditional splines, the proposed method does not have internal knots and explicitly represents curves as a linear combination of cosine basis. This simplicity in the representation enables us to design statistical models, register curves and perform subsequent analysis in a more unified statistical framework than splines. The proposed representation is applied in characterizing abnormal shape of white matter fiber tracts passing through the splenium of the corpus callosum in autistic subjects. For an arbitrary tract, a 19 degree expansion is usually found to be sufficient to reconstruct the tract with 60 parameters.

2009

* Classification in DTI using shapes of white matter tracts.
[Article (PDF)]
Diffusion Tensor Imaging (DTI) provides unique information about the underlying tissue structure of brain white matter in vivo, including both the geometry of fiber bundles as well as quantitative information about tissue properties as characterized by measures such as tensor orientation, anisotropy, and size. Our objective in this paper is to evaluate the utility of shape representations of white matter tracts extracted from DTI data for classification of clinically different population groups (here autistic vs control). As a first step, our algorithm extracts fiber bundles passing through approximately marked regions of interest on affinely aligned brain volumes. The subsequent analysis is entirely based on the geometric modeling of the extracted tracts. A key advantage of using such an abstraction is that it allows us to capture invariant features of brains allowing for efficient large sample size studies. We demonstrate that with the use of an appropriate representation of the tract shapes, classifiers can be built with reasonable prediction accuracies without making heavy use of the spatial normalization machinery needed when using voxel based features.

* Efficient parametric encoding scheme for white matter fiber bundles.
[Article (PDF)]
We present a novel parametric encoding scheme for efficiently recording white matter fiber bundle information obtained from diffusion tensor imaging. The coordinates of fiber tracts are parameterized using a cosine series expansion. For an arbitrary tract, a 19 degree expansion is found to be sufficient to reconstruct the tract with an average error of about 0.26 mm. Then each tract is fully parameterized with 60 parameters, which results in a substantial data reduction. Unlike traditional splines, the proposed method does not have internal knots and explicitly represents the tract as a linear combination of basis functions. This simplicity in the representation enables us to design statistical models, register tracts and perform subsequent analysis in a more streamlined mathematical framework. As an illustration, we apply the proposed method in characterizing abnormal tracts that pass through the splenium of the corpus callosum in autistic subjects.

* Robust atlas-based brain segmentation using multi-structure confidence-weighted registration.
[Article (PDF)]
We present a robust and accurate atlas-based brain segmen- tation method which uses multiple initial structure segmentations to si- multaneously drive the image registration and achieve anatomically con- strained correspondence. We also derive segmentation con�dence maps (SCMs) from a given manually segmented training set; these characterize the accuracy of a given set of segmentations as compared to manual seg- mentations.We incorporate these in our cost term to weight the in uence of initial segmentations in the multi-structure registration, such that low con�dence regions are given lower weight in the registration. To account for correspondence errors in the underlying registration, we use a super- vised atlas correction technique and present a method for correcting the atlas segmentation to account for possible errors in the underlying reg- istration. We applied our multi-structure atlas-based segmentation and supervised atlas correction to segment the amygdala in a set of 23 autis- tic patients and controls using leave-one-out cross validation, achieving a Dice overlap score of 0.84. We also applied our method to eight subcorti- cal structures in MRI from the Internet Brain Segmentation Repository, with results better or comparable to competing methods.

* Persistence diagrams of cortical surface data.
[Article (PDF)]
We present a novel framework for characterizing signals in images using techniques from computational algebraic topology. This technique is general enough for dealing with noisy multivariate data including geometric noise. The main tool is persistent homology which can be encoded in persistence diagrams. These diagrams visually show how the number of connected components of the sublevel sets of the signal changes. The use of local critical values of a function differs from the usual statistical parametric mapping framework, which mainly uses the mean signal in quantifying imaging data. Our proposed method uses all the local critical values in characterizing the signal and by doing so offers a completely new data reduction and analysis framework for quantifying the signal. As an illustration, we apply this method to a 1D simulated signal and 2D cortical thickness data. In case of the latter, extra homological structures are evident in an control group over the autistic group.

* 3D functional representation of spasely sampled 2D cortical data.
[Article (PDF)]
Various cortical measures such as cortical thickness are routinely computed along the vertices of cortical surface meshes. These metrics are used in surface-based morphometric studies. If one wishes to compare the surface-based morphometric studies to 3D volume-based studies at a voxel level, 3D interpolation of the sparsely sampled 2D cortical data is needed. In this paper, we have developed a new computational framework for explicitly representing sparsely sampled cortical data as a linear combination of eigenfunctions of the 3D Laplacian. The eigenfunctions are expressed as the product of spherical Bessel functions and spherical harmonics. The coefficients of the expansion are estimated in the least squares fashion iteratively by breaking the problem into smaller subproblems to reduce a computational bottleneck.