ANALYTIC PROGRAMMING WITH fMRI DATA: A QUICK-START GUIDE FOR STATISTICIANS USING R

Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project along with the relevant R code. The goal is to give tatisticians who would like to pursue research in this area a quick start for programming with fMRI data along with the available data visualization tools

POPULATION FUNCTIONAL DATA ANALYSIS OF GROUP ICA-BASED CONNECTIVITY MEASURES FROM fMRI

In this manuscript, we use a two-stage decomposition for the analysis of func- tional magnetic resonance imaging (fMRI). In the first stage, spatial independent component analysis is applied to the group fMRI data to obtain common brain networks (spatial maps) and subject-specific mixing matrices (time courses). In the second stage, functional principal component analysis is utilized to decompose the mixing matrices into population- level eigenvectors and subject-specific loadings. Inference is performed using permutation-based exact conditional logistic regression for matched pairs data. Simulation studies suggest the ability of the decomposition methods to recover population brain networks and the major direction of variation in the mixing matrices. The method is applied to a novel fMRI study of Alzheimer\u27s disease risk under a verbal paired associates task. We found empirical evidence of alternative ICA-based metrics of connectivity in clinically asymptomatic at risk subjects when compared to controls

Two-stage Decompositions for the Analysis of Functional Connectivity for fMRI With Application to Alzheimer\u27s Disease Risk

Functional connectivity is the study of correlations in measured neurophysiological signals. Altered functional connectivity has been shown to be associated with numerous diseases including Alzheimer\u27s disease and mild cognitive impairment. In this manuscript we use a two-stage application of the singular value decomposition to obtain data driven population-level measures of functional connectivity in functional magnetic resonance imaging (fMRI). The method is computationally simple and amenable to high dimensional fMRI data with large numbers of subjects. Simulation studies suggest the ability of the decomposition methods to recover population brain networks and their associated loadings. We further demonstrate the utility of these decompositions in a case-control functional logistic regression model. The method is applied to a novel fMRI study of Alzheimer\u27s disease risk under a verbal paired associates task. We found empirical evidence of alternative connectivity in clinically asymptomatic at-risk subjects when compared to controls. The relevant brain network loads primarily on the temporal lobe and overlaps significantly with the olfactory areas and temporal poles

Overcoming status quo bias in the human brain

Humans often accept the status quo when faced with conflicting choice alternatives. However, it is unknown how neural pathways connecting cognition with action modulate this status quo acceptance. Here we developed a visual detection task in which subjects tended to favor the default when making difficult, but not easy, decisions. This bias was suboptimal in that more errors were made when the default was accepted. A selective increase in subthalamic nucleus (STN) activity was found when the status quo was rejected in the face of heightened decision difficulty. Analysis of effective connectivity showed that inferior frontal cortex, a region more active for difficult decisions, exerted an enhanced modulatory influence on the STN during switches away from the status quo. These data suggest that the neural circuits required to initiate controlled, nondefault actions are similar to those previously shown to mediate outright response suppression. We conclude that specific prefrontal-basal ganglia dynamics are involved in rejecting the default, a mechanism that may be important in a range of difficult choice scenarios

Covariate Assisted Principal Regression for Covariance Matrix Outcomes

In this study, we consider the problem of regressing covariance matrices on associated covariates. Our goal is to use covariates to explain variation in covariance matrices across units. As such, we introduce Covariate Assisted Principal (CAP) regression, an optimization-based method for identifying components associated with the covariates using a generalized linear model approach. We develop computationally efficient algorithms to jointly search for common linear projections of the covariance matrices, as well as the regression coefficients. Under the assumption that all the covariance matrices share identical eigencomponents, we establish the asymptotic properties. In simulation studies, our CAP method shows higher accuracy and robustness in coefficient estimation over competing methods. In an example resting-state functional magnetic resonance imaging study of healthy adults, CAP identifies human brain network changes associated with subject demographics

Developmental trajectory of subtle motor signs in attention-deficit/hyperactivity disorder: a longitudinal study from childhood to adolescence

This study examined the developmental trajectory of neurodevelopmental motor signs among boys and girls with attention-deficit/hyperactivity disorder (ADHD) and typically-developing (TD) children. Seventy children with ADHD and 48 TD children, aged 8–17 years, were evaluated on at least two time-points using the Physical and Neurological Assessment of Subtle Signs (PANESS). Age-related changes in subtle motor signs (overflow, dysrhythmia, speed) were modeled using linear mixed-effects models to compare the developmental trajectories among four subgroups (ADHD girls and boys and TD girls and boys). Across visits, both boys and girls with ADHD showed greater overflow, dysrhythmia, and slower speed on repetitive motor tasks compared to TD peers; whereas, only girls with ADHD were slower on sequential motor tasks than TD girls. Developmental trajectory analyses revealed a greater reduction in overflow with age among boys with ADHD than TD boys; whereas, trajectories did not differ among girls with and without ADHD, or among boys and girls with ADHD. For dysrhythmia and speed, there were no trajectory differences between the subgroups, with all groups showing similar reductions with age. Children with ADHD show developmental trajectories of subtle motor signs that are consistent with those of TD children, with one clear exception: Boys with ADHD show more significant reductions in overflow from childhood to adolescence than do their TD peers. Our findings affirm the presence of subtle motor signs in children with ADHD and suggest that some of these signs, particularly motor overflow in boys, resolve through adolescence while dysrhythmia and slow speed, may persist

Neuropsychiatric Disease Classification Using Functional Connectomics - Results of the Connectomics in NeuroImaging Transfer Learning Challenge

Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI (rsfMRI) time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided Challenge participants with demographic information of age, sex, IQ, and handedness. The second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Classification methodologies were submitted in a standardized format as containerized Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 metrics: accuracy, area under the curve, F1-score, false discovery rate, false negative rate, false omission rate, false positive rate, geometric mean, informedness, markedness, Matthew’s correlation coefficient, negative predictive value, optimized precision, precision, sensitivity, and specificity. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each methodology. Five participants submitted their method for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are still needed to reach the clinical translation of functional connectomics. We have kept the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of connectomics