Author name cluster

Ariana Anderson

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

YNICL Journal 2018 Journal Article

Hemispheric brain asymmetry differences in youths with attention-deficit/hyperactivity disorder

P.K. Douglas
Boris Gutman
Ariana Anderson
C. Larios
Katherine E. Lawrence
Katherine Narr
Biswa Sengupta
Gerald Cooray

Introduction: Attention-deficit hyperactive disorder (ADHD) is the most common neurodevelopmental disorder in children. Diagnosis is currently based on behavioral criteria, but magnetic resonance imaging (MRI) of the brain is increasingly used in ADHD research. To date however, MRI studies have provided mixed results in ADHD patients, particularly with respect to the laterality of findings. Methods: = 341) and age-matched typically developing (TD) controls with structural brain MRI. We calculated volumetric measures from 34 cortical and 14 non-cortical brain regions per hemisphere, and detailed shape morphometry of subcortical nuclei. Diffusion tensor imaging (DTI) data were collected for a subset of 104 subjects; from these, we calculated mean diffusivity and fractional anisotropy of white matter tracts. Group comparisons were made for within-hemisphere (right/left) and between hemisphere asymmetry indices (AI) for each measure. Results: < 0.0001) in ADHD individuals compared to TD, and that asymmetry differences were more significant than lateralized comparisons. Conclusions: Brain asymmetry measures allow each individual to serve as their own control, diminishing variability between individuals and when pooling data across sites. Asymmetry group differences were more significant than lateralized comparisons between ADHD and TD subjects across morphometric, volumetric, and DTI comparisons.

Details DOI

YNIMG Journal 2014 Journal Article

Cognitive and neurodevelopmental benefits of extended formula-feeding in infants: Re: Deoni et al. 2013

Ariana Anderson
Alison Burggren

The recent Deoni et al. (2013) manuscript proposed that breastfeeding was associated with increased cognitive ability and white-matter in older children (over 26months), using ms-DESPOT MRI imaging to indirectly measure white matter in children who were either breastfed, formula fed, or combined breast+formula fed. In this response, we identify limitations in drawing causal inference among white matter, cognitive ability, and breastfeeding. We propose that the observed cognitive and neurodevelopmental differences between breastfed and formula-fed infants might actually be caused by the premature introduction of cow's milk in the second year of life, among other contributing factors. The implication of a causal relationship between intelligence and white matter metrics, especially in a developmentally young population, is premature given the recency of this field. The original analyses did not control for important covariates; when comparing both white matter and test scores, mothers were not controlled for age and socio-economic status (SES) and their children were not controlled for gender. Raw test scores, instead of age-adjusted test scores, were used even though the children were of different ages. Mothers were not controlled for reason(s) not to breastfeed, even though many prenatal factors are known to predict this such as stress, parity, obesity, and smoking habits. The observed cognitive ability and white matter benefits identified primarily within the long-term breastfed children are at least partially attributable to other factors such as age, gender, and SES. We suggest methodological approaches to removing such ambiguity, and ways to dissociate cause from effect. The formula and breastfeeding groups didn't show differences until the “formula fed” children likely had been fed cow's milk for longer than they had been fed formula, at 2. 2years. The greatest cognitive differences however were observed within the high SES breastfed infants depending on breastfeeding duration; infants who were breastfed over 15months showed increased cognitive ability compared to those breastfed less than months. This implicates the source of dairy during the second year of life, and not other SES factors or infant formula, as the most likely nutritional factor responsible for the observed differences within the breastfed children. Given the known nutritional deficiencies of cow's milk, these findings imply infants who received cow's milk during the second year of life were at a disadvantage compared to those who were breastfed, independent of whether they were fed formula or breast milk during the first year of life. This evidence suggests that infants should receive formula in lieu of cow's milk when breast milk is unavailable as a dairy source, until roughly 2years of age.

Details DOI

YNIMG Journal 2014 Journal Article

Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD

Ariana Anderson
Pamela K. Douglas
Wesley T. Kerr
Virginia S. Haynes
Alan L. Yuille
Jianwen Xie
Ying Nian Wu
Jesse A. Brown

In the multimodal neuroimaging framework, data on a single subject are collected from inherently different sources such as functional MRI, structural MRI, behavioral and/or phenotypic information. The information each source provides is not independent; a subset of features from each modality maps to one or more common latent dimensions, which can be interpreted using generative models. These latent dimensions, or “topics, ” provide a sparse summary of the generative process behind the features for each individual. Topic modeling, an unsupervised generative model, has been used to map seemingly disparate features to a common domain. We use Non-Negative Matrix Factorization (NMF) to infer the latent structure of multimodal ADHD data containing fMRI, MRI, phenotypic and behavioral measurements. We compare four different NMF algorithms and find that the sparsest decomposition is also the most differentiating between ADHD and healthy patients. We identify dimensions that map to interpretable, recognizable dimensions such as motion, default mode network activity, and other such features of the input data. For example, structural and functional graph theory features related to default mode subnetworks clustered with the ADHD-Inattentive diagnosis. Structural measurements of the default mode network (DMN) regions such as the posterior cingulate, precuneus, and parahippocampal regions were all related to the ADHD-Inattentive diagnosis. Ventral DMN subnetworks may have more functional connections in ADHD-I, while dorsal DMN may have less. ADHD topics are dependent upon diagnostic site, suggesting diagnostic differences across geographic locations. We assess our findings in light of the ADHD-200 classification competition, and contrast our unsupervised, nominated topics with previously published supervised learning methods. Finally, we demonstrate the validity of these latent variables as biomarkers by using them for classification of ADHD in 730 patients. Cumulatively, this manuscript addresses how multimodal data in ADHD can be interpreted by latent dimensions.

Details DOI

YNIMG Journal 2014 Journal Article

The utility of data-driven feature selection: Re: Chu et al. 2012

Wesley T. Kerr
Pamela K. Douglas
Ariana Anderson
Mark S. Cohen

The recent Chu et al. (2012) manuscript discusses two key findings regarding feature selection (FS): (1) data driven FS was no better than using whole brain voxel data and (2) a priori biological knowledge was effective to guide FS. Use of FS is highly relevant in neuroimaging-based machine learning, as the number of attributes can greatly exceed the number of exemplars. We strongly endorse their demonstration of both of these findings, and we provide additional important practical and theoretical arguments as to why, in their case, the data-driven FS methods they implemented did not result in improved accuracy. Further, we emphasize that the data-driven FS methods they tested performed approximately as well as the all-voxel case. We discuss why a sparse model may be favored over a complex one with similar performance. We caution readers that the findings in the Chu et al. report should not be generalized to all data-driven FS methods.

Details DOI

YNIMG Journal 2011 Journal Article

Common component classification: What can we learn from machine learning?

Ariana Anderson
Jennifer S. Labus
Eduardo P. Vianna
Emeran A. Mayer
Mark S. Cohen

Machine learning methods have been applied to classifying fMRI scans by studying locations in the brain that exhibit temporal intensity variation between groups, frequently reporting classification accuracy of 90% or better. Although empirical results are quite favorable, one might doubt the ability of classification methods to withstand changes in task ordering and the reproducibility of activation patterns over runs, and question how much of the classification machines' power is due to artifactual noise versus genuine neurological signal. To examine the true strength and power of machine learning classifiers we create and then deconstruct a classifier to examine its sensitivity to physiological noise, task reordering, and across-scan classification ability. The models are trained and tested both within and across runs to assess stability and reproducibility across conditions. We demonstrate the use of independent components analysis for both feature extraction and artifact removal and show that removal of such artifacts can reduce predictive accuracy even when data has been cleaned in the preprocessing stages. We demonstrate how mistakes in the feature selection process can cause the cross-validation error seen in publication to be a biased estimate of the testing error seen in practice and measure this bias by purposefully making flawed models. We discuss other ways to introduce bias and the statistical assumptions lying behind the data and model themselves. Finally we discuss the complications in drawing inference from the smaller sample sizes typically seen in fMRI studies, the effects of small or unbalanced samples on the Type 1 and Type 2 error rates, and how publication bias can give a false confidence of the power of such methods. Collectively this work identifies challenges specific to fMRI classification and methods affecting the stability of models.

Details DOI

YNIMG Journal 2010 Journal Article

Classification of spatially unaligned fMRI scans

Ariana Anderson
Ivo D. Dinov
Jonathan E. Sherin
Javier Quintana
A.L. Yuille
Mark S. Cohen

The analysis of fMRI data is challenging because they consist generally of a relatively modest signal contained in a high-dimensional space: a single scan can contain millions of voxel recordings over space and time. We present a method for classification and discrimination among fMRI that is based on modeling the scans as distance matrices, where each matrix measures the divergence of spatial network signals that fluctuate over time. We used single-subject independent components analysis (ICA), decomposing an fMRI scan into a set of statistically independent spatial networks, to extract spatial networks and time courses from each subject that have unique relationship with the other components within that subject. Mathematical properties of these relationships reveal information about the infrastructure of the brain by measuring the interaction between and strength of the components. Our technique is unique, in that it does not require spatial alignment of the scans across subjects. Instead, the classifications are made solely on the temporal activity taken by the subject's unique ICs. Multiple scans are not required and multivariate classification is implementable, and the algorithm is effectively blind to the subject-uniform underlying task paradigm. Classification accuracy of up to 90% was realized on a resting-scanned schizophrenia/normal dataset and a tasked multivariate Alzheimer's/old/young dataset. We propose that the ICs represent a plausible set of imaging basis functions consistent with network-driven theories of neural activity in which the observed signal is an aggregate of independent spatial networks having possibly dependent temporal activity.

Details DOI