Faculty Bibliography

BACKGROUND: Impulsivity is characteristic of individuals with bipolar disorder and may be a contributing factor to the high rate of suicide in patients with this disorder. Although white matter abnormalities have been implicated in the pathophysiology of bipolar disorder, their relationship to impulsivity and suicidality in this disorder has not been well-investigated. METHODS: Diffusion tensor imaging scans were acquired in 14 bipolar disorder patients with a prior suicide attempt, 15 bipolar disorder patients with no prior suicide attempt, and 15 healthy volunteers. Bipolar disorder patients received clinical assessments including measures of impulsivity, depression, mania, and anxiety. Images were processed using the Tract-Based Spatial Statistics method in the FSL software package. RESULTS: Bipolar disorder patients with a prior suicide attempt had lower fractional anisotropy (FA) within the left orbital frontal white matter (p < 0.05, corrected) and higher overall impulsivity compared to patients without a previous suicide attempt. Among patients with a prior suicide attempt, FA in the orbital frontal white matter region correlated inversely with motor impulsivity. CONCLUSIONS: Abnormal orbital frontal white matter may play a role in impulsive and suicidal behavior among patients with bipolar disorder.

OBJECTIVE: Poor insight into illness is commonly associated with schizophrenia and has implications for the clinical outcome of the disease. A better understanding of the neurobiology of these insight deficits may help the development of new treatments targeting insight. Despite the importance of this issue, the neural correlates of insight deficits in schizophrenia remain poorly understood. METHOD: Thirty-six individuals diagnosed with schizophrenia or schizoaffective disorder underwent diffusion tensor imaging (DTI). The subjects were assessed on two dimensions of insight (symptom awareness and attribution of symptoms) using the Scale to Assess Unawareness of Mental Disorder (SUMD). Level of psychosis was assessed with the Positive and Negative Syndrome Scale (PANSS). RESULTS: White matter abnormalities in the right superior frontal gyrus, left middle frontal gyrus, bilateral parahippocampal gyrus, adjacent to the right caudate head, right thalamus, left insula, left lentiform nucleus, left fusiform gyrus, bilateral posterior cingulate, left anterior cingulate, right cingulate gyrus, left lingual gyrus, and bilateral claustrum were associated with symptom unawareness. Misattribution of symptoms was related to deficits in the white matter adjacent to the right lentiform nucleus, left middle temporal gyrus, and the right precuneus. CONCLUSIONS: Impaired insight in schizophrenia implicates a complex neural circuitry: white matter deficits in fronto-temporo brain regions are linked to symptom unawareness; compromised temporal and parietal white matter regions are involved in the misattribution of symptoms. These findings suggest the multidimensional construct of insight has multiple neural determinants

This article presents two related advancements to the diffusional kurtosis imaging estimation framework to increase its robustness to noise, motion, and imaging artifacts. The first advancement substantially improves the estimation of diffusion and kurtosis tensors parameterizing the diffusional kurtosis imaging model. Rather than utilizing conventional unconstrained least squares methods, the tensor estimation problem is formulated as linearly constrained linear least squares, where the constraints ensure physically and/or biologically plausible tensor estimates. The exact solution to the constrained problem is found via convex quadratic programming methods or, alternatively, an approximate solution is determined through a fast heuristic algorithm. The computationally more demanding quadratic programming-based method is more flexible, allowing for an arbitrary number of diffusion weightings and different gradient sets for each diffusion weighting. The heuristic algorithm is suitable for real-time settings such as on clinical scanners, where run time is crucial. The advantage offered by the proposed constrained algorithms is demonstrated using in vivo human brain images. The proposed constrained methods allow for shorter scan times and/or higher spatial resolution for a given fidelity of the diffusional kurtosis imaging parametric maps. The second advancement increases the efficiency and accuracy of the estimation of mean and radial kurtoses by applying exact closed-form formulae. Magn Reson Med, 2011. (c) 2010 Wiley-Liss, Inc

The objective of this research was to determine whether fractional anisotropy (FA) and mean diffusivity (MD) maps derived from diffusion tensor imaging (DTI) of the brain are able to reliably differentiate patients with schizophrenia from healthy volunteers. DTI and high resolution structural magnetic resonance scans were acquired in 50 patients with schizophrenia and 50 age- and sex-matched healthy volunteers. FA and MD maps were estimated from the DTI data and spatially normalized to the Montreal Neurologic Institute standard stereotactic space. Individuals were divided randomly into two groups of 50, a training set, and a test set, each comprising 25 patients and 25 healthy volunteers. A pattern classifier was designed using Fisher's linear discriminant analysis (LDA) based on the training set of images to categorize individuals in the test set as either patients or healthy volunteers. Using the FA maps, the classifier correctly identified 94% of the cases in the test set (96% sensitivity and 92% specificity). The classifier achieved 98% accuracy (96% sensitivity and 100% specificity) when using the MD maps as inputs to distinguish schizophrenia patients from healthy volunteers in the test dataset. Utilizing FA and MD data in combination did not significantly alter the accuracy (96% sensitivity and specificity). Patterns of water self-diffusion in the brain as estimated by DTI can be used in conjunction with automated pattern recognition algorithms to reliably distinguish between patients with schizophrenia and normal control subjects

AIMS/HYPOTHESIS: Central nervous system abnormalities, including cognitive and brain impairments, have been documented in adults with type 2 diabetes who also have multiple co-morbid disorders that could contribute to these observations. Assessing adolescents with type 2 diabetes will allow the evaluation of whether diabetes per se may adversely affect brain function and structure years before clinically significant vascular disease develops. METHODS: Eighteen obese adolescents with type 2 diabetes and 18 obese controls without evidence of marked insulin resistance, matched on age, sex, school grade, ethnicity, socioeconomic status, body mass index and waist circumference, completed MRI and neuropsychological evaluations. RESULTS: Adolescents with type 2 diabetes performed consistently worse in all cognitive domains assessed, with the difference reaching statistical significance for estimated intellectual functioning, verbal memory and psychomotor efficiency. There were statistical trends for executive function, reading and spelling. MRI-based automated brain structural analyses revealed both reduced white matter volume and enlarged cerebrospinal fluid space in the whole brain and the frontal lobe in particular, but there was no obvious grey matter volume reduction. In addition, assessments using diffusion tensor imaging revealed reduced white and grey matter microstructural integrity. CONCLUSIONS/INTERPRETATION: This is the first report documenting possible brain abnormalities among obese adolescents with type 2 diabetes relative to obese adolescent controls. These abnormalities are not likely to result from education or socioeconomic bias and may result from a combination of subtle vascular changes, glucose and lipid metabolism abnormalities and subtle differences in adiposity in the absence of clinically significant vascular disease. Future efforts are needed to elucidate the underlying pathophysiological mechanisms

Establishing correspondences across brains for the purposes of comparison and group analysis is almost universally done by registering images to one another either directly or via a template. However, there are many registration algorithms to choose from. A recent evaluation of fully automated nonlinear deformation methods applied to brain image registration was restricted to volume-based methods. The present study is the first that directly compares some of the most accurate of these volume registration methods with surface registration methods, as well as the first study to compare registrations of whole-head and brain-only (de-skulled) images. We used permutation tests to compare the overlap or Hausdorff distance performance for more than 16,000 registrations between 80 manually labeled brain images. We compared every combination of volume-based and surface-based labels, registration, and evaluation. Our primary findings are the following: 1. de-skulling aids volume registration methods; 2. custom-made optimal average templates improve registration over direct pairwise registration; and 3. resampling volume labels on surfaces or converting surface labels to volumes introduces distortions that preclude a fair comparison between the highest ranking volume and surface registration methods using present resampling methods. From the results of this study, we recommend constructing a custom template from a limited sample drawn from the same or a similar representative population, using the same algorithm used for registering brains to the template

In vivo quantitative magnetic resonance imaging (MRI) was employed to detect brain pathology and map its distribution within control, disomic mice (2N) and in Ts65Dn and Ts1Cje trisomy mice with features of human Down syndrome (DS). In Ts65Dn, but not Ts1Cje mice, transverse proton spin-spin (T(2)) relaxation time was selectively reduced in the medial septal nucleus (MSN) and in brain regions that receive cholinergic innervation from the MSN, including the hippocampus, cingulate cortex, and retrosplenial cortex. Basal forebrain cholinergic neurons (BFCNs) in the MSN, identified by choline acetyltransferase (ChAT) and nerve growth factor receptors p75(NTR) and TrkA immunolabeling were reduced in Ts65Dn brains and in situ acetylcholinesterase (AChE) activity was depleted distally along projecting cholinergic fibers, and selectively on pre- and postsynaptic profiles in these target areas. T(2) effects were negligible in Ts1Cje mice that are diploid for App and lack BFCN neuropathology, consistent with the suspected relationship of this pathology to increased App dosage. These results establish the utility of quantitative MRI in vivo for identifying Alzheimer's disease-relevant cholinergic changes in animal models of DS and characterizing the selective vulnerability of cholinergic neuron subpopulations

PURPOSE: To determine whether frontal white matter diffusion abnormalities can help predict acute executive function impairment after mild traumatic brain injury (mTBI). MATERIALS AND METHODS: This study had institutional review board approval, included written informed consent, and complied with HIPAA. Diffusion-tensor imaging and standardized neuropsychologic assessments were performed in 20 patients with mTBI within 2 weeks of injury and 20 matched control subjects. Fractional anisotropy (FA) and mean diffusivity (MD) images (imaging parameters: 3.0 T, 25 directions, b = 1000 sec/mm(2)) were compared by using whole-brain voxelwise analysis. Spearman correlation analyses were performed to evaluate associations between diffusion measures and executive function. RESULTS: Multiple clusters of lower frontal white matter FA, including the dorsolateral prefrontal cortex (DLPFC), were present in patients (P < .005), with several clusters also demonstrating higher MD (P < .005). Patients performed worse on tests of executive function. Lower DLPFC FA was significantly correlated with worse executive function performance in patients (P < .05). CONCLUSION: Impaired executive function following mTBI is associated with axonal injury involving the DLPFC

The projections of the anterior and posterior commissures (AC/PC) on the mid-sagittal plane of the human brain are important landmarks in neuroimaging. They can be used, for example, during MRI scanning for acquiring the imaging sections in a standard orientation. In post-acquisition image processing, these landmarks serve to establish an anatomically-based frame of reference within the brain that can be extremely useful in designing automated image analysis algorithms such as image segmentation and registration methods. This paper presents a fully automatic model-based algorithm for AC/PC detection on MRI scans. The algorithm utilizes information from a number of model images on which the locations of the AC/PC and a reference point (the vertex of the superior pontine sulcus) are known. This information is then used to locate the landmarks on test scans by template matching. The algorithm is designed to be fast, robust, and accurate. The method is flexible in that it can be trained to work on different image contrasts, optimized for different populations, or scanning modes. To assess the effectiveness of this technique, we compared automatically and manually detected landmark locations on 84 T(1)-weighted and 42 T(2)-weighted test scans. Overall, the average Euclidean distance between automatically and manually detected landmarks was 1.1 mm. A software implementation of the algorithm is freely available online at www.nitrc.org/projects/art