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Fleshy fruits have evolved to be attractive to frugivorous seed dispersers. As a result, many fruit traits like size, color, scent and nutritional content are assumed to be the result of selective pressures exerted by frugivores. At the same time, fruit traits are also subjected to a set of other selective pressures and constraints. One such trait is fruit hardness. On one hand, haptic cues have been...
Objective
This study determined the clinical utility of an fMRI classification algorithm predicting medication‐class of response in patients with challenging mood diagnoses.
Methods
Ninety‐nine 16–27‐year‐olds underwent resting state fMRI scans in three groups—BD, MDD and healthy controls. A predictive algorithm was trained and cross‐validated on the known‐diagnosis patients using maximally spatially...
Table of contents O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents...
Functional magnetic resonance imaging (fMRI) data are acquired as a natively complex data set, however for various reasons the phase data is typically discarded. Over the past few years, interest in incorporating the phase information into the analyses has been growing and new methods for modeling and processing the data have been developed. In this paper, we provide an overview of approaches to understand...
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 and improvements when applied to multi-group...
Summary form only given. In this talk, we discuss an ICA-based framework to combine or fuse multimodal data in groups of subjects using features extracted from the single-subject data. Many studies are currently collecting multiple types of imaging data from the same participants. ICA has proven particularly useful for data fusion of multiple tasks or data modalities such as single nucleotide polymorphism...
The acquisition of multiple brain imaging types for a given study is a very common practice. However these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform joint independent component analysis across image modalities, including structural MRI data, functional MRI activation data and EEG data, and to visualize the results via...
This paper presents the technique of parallel independent component analysis (paraICA) with adaptive dynamic constraints applied to two datasets simultaneously. As a framework to investigate the integration of data from two imaging modalities, this method is dedicated to identify components of both modalities and connections between them through enhancing intrinsic interrelationships. The performance...
Schizophrenia (SZ) and bipolar disorder (BP) are currently diagnosed on the basis of a constellation of psychiatric symptoms and longitudinal course. The clinical profile of SZ and BP can sometimes look similar, especially when symptoms are overlapping. The determination of a reliable biologically-based indicator of these diseases (a biomarker) would be a significant advance and could provide the...
The acquisition of multiple brain imaging types for a given study is a very common practice. However these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform joint independent component analysis across image modalities, including structural MRI data, functional MRI activation data and EEG data, and to visualize the results via...
The acquisition of multiple brain imaging types for a given study is a very common practice. However these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform joint independent component analysis across image modalities, including structural MRI data, functional MRI activation data and EEG data, and to visualize the results via...
The acquisition of multiple brain imaging types for a given study is a very common practice. However these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform joint independent component analysis across image modalities, including structural MRI data, functional MRI activation data and EEG data, and to visualize the results via...
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