Stefi A. Baum

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We present a novel integrated wavelet-domain based framework (w-ICA) for 3-D denoising functional magnetic resonance imaging (fMRI) data followed by source separation analysis using independent component analysis (ICA) in the wavelet domain. We propose the idea of a 3-D wavelet-based multi-directional denoising scheme where each volume in a 4-D fMRI data(More)
When both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) data are collected they are typically analyzed separately and the joint information is not examined. Techniques that examine joint information can help to find hidden traits in complex disorders such as schizophrenia. The brain is vastly interconnected, and local brain(More)
A common pre-processing challenge associated with group level fMRI analysis is spatial registration of multiple subjects to a standard space. Spatial normalization, using a reference image such as the Montreal Neurological Institute brain template, is the most common technique currently in use to achieve spatial congruence across multiple subjects. This(More)
Denoising is amongst the most challenging steps involved in analyzing fMRI data. The conventionally used Gaussian smoothing improves the SNR at the cost of spatial sensitivity and specificity. We briefly describe a 3-D framework for wavelet based fMRI analysis that includes denoising and signal separation followed by a detailed illustration of the benefits(More)
The clinical heterogeneity of schizophrenia (scz) and the overlap of self reported and observed symptoms with other mental disorders makes its diagnosis a difficult task. At present no laboratory-based or image-based diagnostic tool for scz exists and such tools are desired to support existing methods for more precise diagnosis. Functional magnetic(More)
Diagnosis of Autism Spectrum Disorder (ASD) using structural magnetic resonance imaging (sMRI) of the brain has been a topic of significant research interest. Previous studies using small datasets with well-matched Typically Developing Controls (TDC) report high classification accuracies (80-96%) but studies using the large heterogeneous ABIDE dataset(More)
We present the first results from a major Hubble Space Telescope programme designed to investigate the cosmological evolution of quasar host galaxies from z ≃ 2 to the present day. Here we describe J and H-band NICMOS imaging of two quasar samples at redshifts of 0.9 and 1.9 respectively. Each sample contains equal numbers of radioloud and radio-quiet(More)
Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector(More)
Abnormalities in white matter (WM) brain regions are attributed as a possible biomarker for schizophrenia (SZ). Diffusion tensor imaging (DTI) is used to capture WM tracts. Psychometric tests that evaluate the severity of symptoms of SZ are clinically used in the diagnosis process. In this study we investigate the correlates of scalar DTI measures, such as(More)
We present the results of a deep HST/WFPC2 imaging study of 17 quasars at z ≃ 0.4, designed to determine the properties of their host galaxies. The sample consists of quasars with absolute magnitudes in the range −24 ≥ MV ≥ −28, allowing us to investigate host galaxy properties across a decade in quasar luminosity, but at a single redshift. Our previous(More)