Faculty Bibliography

OBJECTIVES: Schizophrenia and bipolar disorder may share common neurobiological mechanisms, but few studies have directly compared gray and white matter structure in these disorders. We used diffusion-weighted magnetic resonance imaging and a region of interest based analysis to identify overlapping and distinct gray and white matter abnormalities in 35 patients with schizophrenia and 20 patients with bipolar I disorder in comparison to 56 healthy volunteers. METHODS: We examined fractional anisotropy within the white matter and mean diffusivity within the gray matter in 42 regions of interest defined on a probabilistic atlas following non-linear registration of the images to atlas space. RESULTS: Patients with schizophrenia had significantly lower fractional anisotropy in temporal (superior temporal and parahippocampal) and occipital (superior and middle occipital) white matter compared to patients with bipolar disorder and healthy volunteers. By contrast, both patient groups demonstrated significantly higher mean diffusivity in frontal (inferior frontal and lateral orbitofrontal) and temporal (superior temporal and parahippocampal) gray matter compared to healthy volunteers, but did not differ from each other. CONCLUSIONS: Our study implicates overlapping gray matter frontal and temporal lobe structural alterations in the neurobiology of schizophrenia and bipolar I disorder, but suggests that temporal and occipital lobe white matter deficits may be an additional risk factor for schizophrenia. Our findings may have relevance for future diagnostic classification systems and the identification of susceptibility genes for these disorders.

A number of studies have reported that, "relative to brain size," the midsagittal corpus callosum cross-sectional area (CCA) in females is on average larger than in males. However, others suggest that these may be spurious differences created in the CCA-to-brain-size ratio because brain size tends to be larger in males. To help resolve this controversy, we measured the CCA on all 316 magnetic resonance imaging (MRI) scans of normal subjects (18-94 years) in the OASIS (Open Access Series of Imaging Studies) cross-sectional dataset, and used multiple regression analysis to statistically control for the confounding effects of brain size and age to test the null hypothesis that the average CCA is not different between genders. An additional analysis was performed on a subset of 74 young adults (37 males and 37 females; 18-29 years) matched closely to brain size. Our null hypothesis was rejected in both analyses. In the entire sample (n= 316), controlling for brain size and age, the average CCA was significantly (P< 0.03) larger in females. The difference favoring females was more pronounced in the young adults cohort (P< 0.0005). These results provide strong additional evidence that the CCA is larger in females after correcting for the confounding effect of brain size.

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.

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

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

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

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

BACKGROUND: There is growing evidence that adolescence is a key period for neuronal maturation. Despite the high prevalence of marijuana use among adolescents and young adults in the United States and internationally, very little is known about its impact on the developing brain. Based on neuroimaging literature on normal brain developmental during adolescence, we hypothesized that individuals with heavy cannabis use (HCU) would have brain structure abnormalities in similar brain regions that undergo development during late adolescence, particularly the fronto-temporal connection. METHOD: Fourteen young adult males in residential treatment for cannabis dependence and 14 age-matched healthy male control subjects were recruited. Patients had a history of HCU throughout adolescence; 5 had concurrent alcohol abuse. Subjects underwent structural and diffusion tensor magnetic resonance imaging. White matter integrity was compared between subject groups using voxelwise and fiber tractography analysis. RESULTS: Voxelwise and tractography analyses revealed that adolescents with HCU had reduced fractional anisotropy, increased radial diffusivity, and increased trace in the homologous areas known to be involved in ongoing development during late adolescence, particularly in the fronto-temporal connection via arcuate fasciculus. CONCLUSIONS: Our results support the hypothesis that heavy cannabis use during adolescence may affect the trajectory of normal brain maturation. Due to concurrent alcohol consumption in five HCU subjects, conclusions from this study should be considered preliminary, as the DTI findings reported here may be reflective of the combination of alcohol and marijuana use. Further research in larger samples, longitudinal in nature, and controlling for alcohol consumption is needed to better understand the pathophysiology of the effect of cannabis on the developing brain.

OBJECTIVE: To examine white matter microstructure, as assessed via diffusion tensor imaging (DTI), in adolescents with bipolar I disorder compared with control volunteers. METHOD: Twenty-six (12 male and 14 female subjects) adolescents (mean age, 16.0 years) with bipolar I disorder and 26 (14 male and 12 female subjects) control volunteers (mean age, 15.3 years) completed structural and DTI examinations. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) maps were compared between groups in the brain white matter using a voxelwise analysis after intersubject registration to Talairach space. Exploratory analyses were performed to assess structure-function correlations in a subgroup of 11 patients with available neuropsychological measures. RESULTS: Compared with the control volunteers, the patients demonstrated abnormalities in white matter regions predicted to differ a priori between groups, including lower FA in the right orbital frontal lobe and higher ADC in the right and left subgenual region (p <.005, uncorrected; cluster size >or= 100). There were no areas of higher FA or lower ADC in patients compared with control volunteers. Lower FA across regions that differed significantly between groups correlated significantly with slower visuomotor speed among patients with bipolar disorder. CONCLUSIONS: Abnormalities involving the orbital frontal and subgenual white matter in adolescents with bipolar disorder are consistent with neurobiological models that implicate dysregulation of affective systems and impulsivity in the pathophysiology of the disorder. Preliminary findings suggest that white matter abnormalities in pediatric bipolar disorder have functional correlates and may be useful in constructing neurobiological models of the disorder