Algorithms that are capable of capturing subject-specific abnormalities (SSA) in neuroimaging data have long been an area of focus for diverse neuropsychiatric conditions such as multiple sclerosis, schizophrenia, and traumatic brain injury. Several algorithms have been proposed that define SSA in patients (i.e., comparison group) relative to image intensity levels derived from healthy controls (HC) (i.e., reference group) based on extreme values. However, the assumptions underlying these approaches have not always been fully validated, and may be dependent on the statistical distributions of the transformed data. The current study evaluated variations of two commonly used techniques ("pothole" method and standardization with an independent reference group) for identifying SSA using simulated data (derived from normal, t and chi-square distributions) and fractional anisotropy maps derived from 50 HC. Results indicated substantial group-wise bias in the estimation of extreme data points using the pothole method, with the degree of bias being inversely related to sample size. Statistical theory was utilized to develop a distribution-corrected z-score (DisCo-Z) threshold, with additional simulations demonstrating elimination of the bias and a more consistent estimation of extremes based on expected distributional properties. Data from previously published studies examining SSA in mild traumatic brain injury were then re-analyzed using the DisCo-Z method, with results confirming the evidence of group-wise bias. We conclude that the benefits of identifying SSA in neuropsychiatric research are substantial, but that proposed SSA approaches require careful implementation under the different distributional properties that characterize neuroimaging data.

Objective: To examine the underlying pathophysiology of mild traumatic brain injury through changes in gray matter diffusion and atrophy during the semiacute stage.Methods: Fifty patients and 50 sex-, age-, and education-matched controls were evaluated with a clinical and neuroimaging battery approximately 14 days postinjury, with 26 patients returning for follow-up 4 months postinjury. Clinical measures included tests of attention, processing speed, executive function, working memory, memory, and self-reported postconcussive symptoms. Measures of diffusion (fractional anisotropy [FA], mean diffusivity) and atrophy were obtained for cortical and subcortical structures to characterize effects of injury as a function of time.Results: Patients reported more cognitive, somatic, and emotional complaints during the semiacute injury phase, which were significantly reduced 4 months postinjury. Patients showed evidence of increased FA in the bilateral superior frontal cortex during the semiacute phase, with the left superior frontal cortex remaining elevated 4 months postinjury. There were no significant differences between patients and matched controls on neuropsychological testing or measures of gray matter atrophy/mean diffusivity at either time point.Conclusions: Increased cortical FA is largely consistent with an emerging animal literature of gray matter abnormalities after neuronal injury. Potential mechanistic explanations for increased FA include cytotoxic edema or reactive gliosis. In contrast, there was no evidence of cortical or subcortical atrophy in the current study, suggesting that frank neuronal or neuropil loss does not occur early in the chronic disease course for patients with typical mild traumatic brain injury. Although numerous diffusion tensor imaging studies have explored axonal integrity after mild traumatic brain injury (mTBI), 1,2 the effects of mTBI on gray matter are more poorly characterized. Previous studies reported nonsignificant trends 3 and reduced 4 anisotropic diffusion after mTBI. However, both studies were conducted with chronically symptomatic and/or mixed patient populations. Both variables contribute to neurobehavioral sequelae 5 and white matter integrity, 1 indicating the need for a well-powered study of gray matter diffusion metrics in patients with more typical mTBI. Additionally, recent animal models indicate increased anisotropy within the thalamus and hippocampus during acute and more chronic injury phases. 6,7 Although evidence of atrophy has been found as early as 1 to 3 weeks postinjury in moderate to severe TBI,8,9 it becomes more prevalent at 6 to 12 months, 10-13 even in the absence of macroscopically detectable lesions.14,15 Studies of patients with complicated 16 and symptomatic mild to moderate 17 TBI indicate atrophy as a function of disease progression approximately 6 months 16 or 1 year 17 postinjury. To our knowledge, no studies have directly assessed cortical thickness changes prospectively after mTBI.

Although several functional magnetic resonance imaging (fMRI) studies have been conducted in human models of mild traumatic brain injury (mTBI), to date no studies have explicitly examined how injury may differentially affect both the positive phase of the hemodynamic response function (HRF) as well as the post-stimulus undershoot (PSU). Animal models suggest that the acute and semi-acute stages of mTBI are associated with significant disruptions in metabolism and to the microvasculature, both of which could impact on the HRF. Therefore, fMRI data were collected on a cohort of 30 semi-acute patients with mTBI (16 males; 27.83 -9.97 years old; 13.00 -2.18 years of education) and 30 carefully matched healthy controls (HC; 16 males; 27.17 -10.08 years old; 13.37 -2.31 years of education) during a simple sensory-motor task. Patients reported increased cognitive, somatic, and emotional symptoms relative to controls, although no group differences were detected on traditional neuropsychological examination. There were also no differences between patients with mTBI and controls on fMRI data using standard analytic techniques, although mTBI exhibited a greater volume of activation during the task qualitatively. A significant Group · Time interaction was observed in the right supramarginal gyrus, bilateral primary and secondary visual cortex, and the right parahippocampal gyrus. The interaction was the result of an earlier time-to-peak and positive magnitude shift throughout the estimated HRF in patients with mTBI relative to HC. This difference in HRF shape combined with the greater volume of activated tissue may be indicative of a potential compensatory mechanism to injury. The current study demonstrates that direct examination and modeling of HRF characteristics beyond magnitude may provide additional information about underlying neuropathology that is not available with more standard fMRI analyses.

Based on the consideration of Boolean dynamics, it has been hypothesized that cell types may correspond to alternative attractors of a gene regulatory network. Recent stochastic Boolean network analysis, however, raised the important question concerning the stability of such attractors. In this paper a detailed numerical analysis is performed within the framework of Langevin dynamics. While the present results confirm that the noise is indeed an important dynamical element, the cell type as represented by attractors can still be a viable hypothesis. It is found that the stability of an attractor depends on the strength of noise related to the distance of the system to the bifurcation point and it can be exponentially stable depending on biological parameters.

Results suggest a prominent role for dysfunction within auditory, sensorimotor and parietal areas relative to prefrontal CCN nodes during multisensory cognitive control.

This notice describes a correction to the above-mentioned paper. In Figure 1 of the manuscript, the labels for the associated graphs (attend auditory = AA; attend visual = AV) were erroneously switched. The corrected image is shown below. The data contained within the graphs, the results section and all means and standard deviations reported therein are correct.