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:We studied 849 children and adolescents (ages 6-21 y.o.) diagnosed with ADHD (n = 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:DTI mean diffusivity AI group differences were significant in cingulum, inferior and superior longitudinal fasciculus, and cortico-spinal tracts (p < 0.001) with the effect of stimulant treatment tending to reduce these patterns of asymmetry differences. Gray matter volumes were more asymmetric in medication free ADHD individuals compared to TD in twelve cortical regions and two non-cortical volumes studied (p < 0.05). Morphometric analyses revealed that caudate, hippocampus, thalamus, and amygdala were more asymmetric (p < 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.

Functional MRI (fMRI) studies typically analyze data by applying a single function - across the entire brain - to relate what is measured (blood oxygenation fluctuations) to the underlying neural activity. However, this hemodynamic response function (HRF), is known to vary considerably across brain regions in healthy individuals, and even more prominently in clinical populations (e. g., AIDS, Alzheimer's). An improved characterization of HRF variability would improve cognitive science experimentation, effective connectivity analysis, and may be crucial for early detection of certain diseases. Here, a method is suggested for altering stimulus presentation timing during task related fMRI experiments that aims to maximize characterization of HRF variability while minimizing the number of trials required to accomplish this. To do so, d-optimality constraints are applied for sparse sampling of the HRF in the temporal domain. We first demonstrate this approach using simulated data over a range of background noise fluctuations. Using simulated data, we were able to recover HRF signal estimates with < 10% sum of squared error (SSE) using 73% and 47% less stimulus events using D-optimal sampling compared to fixed or random designs respectively. We then utilized this method for designing the stimulus timing in an event-related fMRI experiment. Empirically, we were able to detect the initial dip in 53% of subjects, a part of the HRF signal that is thought to reflect oxygen usage and often obscured when using conventional experimental design paradigms.

Independent components analysis (ICA) is a popular method for the analysis of functional MRI (fMRI) signals, that is capable of revealing connected brain systems of functional significance. To be computationally tractable, estimating the independent components (ICs) inevitably requires one or more dimension reduction steps. Whereas most algorithms perform such reductions in the time domain, the input data are much more extensive in the spatial domain, and there is broad consensus that the brain obeys rules of localization of function into regions that are smaller in number than the number of voxels in a brain image. These functional units apparently reorganize dynamically into networks under different task conditions. Here we develop a new approach to ICA, producing group results by bagging and clustering over hundreds of pooled single-subject ICA results that have been projected to a lower-dimensional subspace. Averages of anatomically based regions are used to compress the single subject-ICA results prior to clustering and resampling via bagging. The computational advantages of this approach make it possible to perform group level analyses on data sets consisting of hundreds of scan sessions by combining the results of within-subject analysis, while retaining the theoretical advantage of mimicking what is known of the functional organization of the brain. The result is a compact set of spatial activity patterns that are common and stable across scan sessions and across individuals. Such representations may be used in the context of statistical pattern recognition supporting real-time state classification.