Background-Individuals with generalized social anxiety disorder (GSAD) exhibit exaggerated amygdala reactivity to aversive social stimuli. These findings could be explained by microstructural abnormalities in white matter (WM) tracts that connect the amygdala and prefrontal cortex, which is known to modulate the amygdala's response to threat. The goal of this study was to investigate brain frontal WM abnormalities by using diffusion tensor imaging (DTI) in patients with social anxiety disorder.

The development of a brain template for diffusion tensor imaging (DTI) is crucial for comparisons of neuronal structural integrity and brain connectivity across populations, as well as for the development of a white matter atlas. Previous efforts to produce a DTI brain template have been compromised by factors related to image quality, the effectiveness of the image registration approach, the appropriateness of subject inclusion criteria, the completeness and accuracy of the information summarized in the final template. The purpose of this work was to develop a DTI human brain template using techniques that address the shortcomings of previous efforts. Therefore, data containing minimal artifacts were first obtained on 67 healthy human subjects selected from an age-group with relatively similar diffusion characteristics (20–40 years of age), using an appropriate DTI acquisition protocol. Non-linear image registration based on mean diffusion-weighted and fractional anisotropy images was employed. DTI brain templates containing median and mean tensors were produced in ICBM-152 space and made publicly available. The resulting set of DTI templates is characterized by higher image sharpness, provides the ability to distinguish smaller white matter fiber structures, contains fewer image artifacts, than previously developed templates, and to our knowledge, is one of only two templates produced based on a relatively large number of subjects. Furthermore, median tensors were shown to better preserve the diffusion characteristics at the group level than mean tensors. Finally, white matter fiber tractography was applied on the template and several fiber-bundles were traced.

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<p>We present a new machine learning algorithm, Latent Similarity, and use it to predict subject (endo)phenotypes from fMRI data. fMRI can be used to predict dysfunctional mental states. In addition, endophenotypes are known to be predictive of disease status, and are of interest in developmental studies. However, fMRI studies often suffer from small cohort size and high feature dimensionality, making reproducible prediction challenging. The innovation of our algorithm is to combine a kernel similarity function with metric learning to increase the robustness of prediction. Our algorithm becomes robust by utilizing the N squared connections between the N subjects in the cohort, rather than the features of the N subjects themselves. We identify important functional connections in the default mode and uncategorized functional networks for predicting age, sex, and intelligence. We also find that only a few connections contain most of the information required for any predictive task. We believe that our algorithm can be beneficial in small sample size, high noise, high dimensionality settings.</p>

<p>In this paper we present a new decomposition and generative model for functional connectivity (FC), achieve 10x data compression, 97.3% identifiability, and modest improvent on FC-based predictive tasks. Additionally we are able to generate synthetic subjects with user-inputted clinical characteristics.</p> <p>fMRI and phenotype data came from the Neurodevelopmental Genomics: Trajectories of Complex Phenotypes database of genotypes and phenotypes repository, dbGaP Study Accession ID phs000607.v3.p2, as well as the Bipolar and Schizophrenia Network for Intermediate Phenotypes study (http://b-snip.org/). </p> <p>Additional data from OpenNeuro study ds004144 on fibromyalgia is included in the linked-to code.</p>

<p>We use the Philadelphia Neurodevelopmental Cohort (PNC) dataset to identify that intelligence prediction using fMRI data is almost entirely dependent on racial confounds. Race prediction using fMRI connectivity data (85%) is more effective that sex prediction (78%), while intelligence prediction using within-race groups reveals no advantage over the null model. This is surprising because race is not a feature that has traditionally been predicted using connectivity data. The PNC dataset is available to research groups on request from the database of genotypes of phenotypes under ascession ID phs000607.v3.p2 Neurodevelopmental Genomics: Trajectories of Complex Phenotypes. Linear models (Ridge or Logistic Regression) were used throughout on correlation-based connectivity data and SNPs. All required aprovals were obtained.</p>

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<p>We present a new machine learning algorithm, Latent Similarity, and use it to predict subject (endo)phenotypes from fMRI data. fMRI can be used to predict dysfunctional mental states. In addition, endophenotypes are known to be predictive of disease status, and are of interest in developmental studies. However, fMRI studies often suffer from small cohort size and high feature dimensionality, making reproducible prediction challenging. The innovation of our algorithm is to combine a kernel similarity function with metric learning to increase the robustness of prediction. Our algorithm becomes robust by utilizing the N squared connections between the N subjects in the cohort, rather than the features of the N subjects themselves. We identify important functional connections in the default mode and uncategorized functional networks for predicting age, sex, and intelligence. We also find that only a few connections contain most of the information required for any predictive task. We believe that our algorithm can be beneficial in small sample size, high noise, high dimensionality settings.</p>