From Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder

We propose a novel approach to identify the foci of a neurological disorder based on anatomical and functional connectivity information. Specifically, we formulate a generative model that characterizes the network of abnormal functional connectivity emanating from the affected foci. This allows us to aggregate pairwise connectivity changes into a region-based representation of the disease. We employ the variational expectation-maximization algorithm to fit the model and subsequently identify both the afflicted regions and the differences in connectivity induced by the disorder. We demonstrate our method on a population study of schizophrenia.National Alliance for Medical Image Computing (U.S.) (Grant NIH NIBIB NAMIC U54-EB005149)Neuroimaging Analysis Center (U.S.) (Grant NIH NCRR NAC P41-RR13218)Neuroimaging Analysis Center (U.S.) (Grant NIH NCRR NAC P41-EB015902)National Science Foundation (U.S.) (CAREER Grant 0642971)National Institutes of Health (U.S.) (R01MH074794)National Institutes of Health (U.S.). Advanced Multimodal Neuroimaging Training Progra

Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI

Presented at the 2nd MICCAI Workshop on Mathematical Foundations of Computational Anatomy: Geometrical and Statistical Methods for Biological Shape Variability Modeling, September 6th, 2008, Kimmel Center, New York, USA.This work provides a framework for modeling and extracting the Cingulum Bundle (CB) from Diffusion-Weighted Imagery (DW-MRI) of the brain. The CB is a tube-like structure in the brain that is of potentially of tremendous importance to clinicians since it may be helpful in diagnosing Schizophrenia. This structure consists of a collection of fibers in the brain that have locally similar diffusion patterns, but vary globally. Standard region-based segmentation techniques adapted to DW-MRI are not suitable here because the diffusion pattern of the CB cannot be described by a global set of simple statistics. Active surface models extended to DW-MRI are not suitable since they allow for arbitrary deformations that give rise to unlikely shapes, which do not respect the tubular geometry of the CB. In this work, we explicitly model the CB as a tube-like surface and construct a general class of energies defined on tube-like surfaces. An example energy of our framework is optimized by a tube that encloses a region that has locally similar diffusion patterns, which differ from the diffusion patterns immediately outside. Modeling the CB as a tube-like surface is a natural shape prior. Since a tube is characterized by a center-line and a radius function, the method is reduced to a 4D (center-line plus radius) curve evolution that is computationally much less costly than an arbitrary surface evolution. The method also provides the center-line of CB, which is potentially of clinical significance

Diffusion Tensor Imaging, Structural Connectivity, and Schizophrenia

A fundamental tenet of the “disconnectivity” theories of schizophrenia is that the disorder is ultimately caused by abnormal communication between spatially disparate brain structures. Given that the white matter fasciculi represent the primary infrastructure for long distance communication in the brain, abnormalities in these fiber bundles have been implicated in the etiology of schizophrenia. Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that enables the visualization of white matter macrostructure in vivo, and which has provided unprecedented insight into the existence and nature of white matter abnormalities in schizophrenia. The paper begins with an overview of DTI and more commonly used diffusion metrics and moves on to a brief review of the schizophrenia literature. The functional implications of white matter abnormalities are considered, particularly with respect to myelin's role in modulating the transmission velocity of neural discharges. The paper concludes with a speculative hypothesis about the relationship between gray and white matter abnormalities associated with schizophrenia

4,4-Bis(4-methyl­phenyl­sulfan­yl)-1,1-diphenyl-2-aza­buta-1,3-diene

In the title compound, C29H25NS2, both the Cl atoms of the aza­diene precursor 4,4-dichloro-1,1-diphenyl-2-aza­buta-1,3-diene are replaced by two vicinal S-p-tolyl substituents attached to the terminal C atom of a π-conjugated 2-aza­butadiene array. The aza­diene chain is planar to within 0.01 Å. One of the phenyl rings seems to be slightly π-conjugated with the aza­diene core [dihedral angle 5.1 (2)°]

Whole brain resting state functional connectivity abnormalities in schizophrenia

Background Schizophrenia has been associated with disturbances in brain connectivity; however the exact nature of these disturbances is not fully understood. Measuring temporal correlations between the functional MRI time courses of spatially disparate brain regions obtained during rest has recently emerged as a popular paradigm for estimating brain connectivity. Previous resting state studies in schizophrenia explored connections related to particular clinical or cognitive symptoms (connectivity within a-priori selected networks), or connections restricted to functional networks obtained from resting state analysis. Relatively little has been done to understand global brain connectivity in schizophrenia. Methods Eighteen patients with chronic schizophrenia and 18 healthy volunteers underwent a resting state fMRI scan on a 3 T magnet. Whole brain temporal correlations have been estimated using resting-state fMRI data and free surfer cortical parcellations. A multivariate classification method was then used to indentify brain connections that distinguish schizophrenia patients from healthy controls. Results The classification procedure achieved a prediction accuracy of 75% in differentiating between groups on the basis of their functional connectivity. Relative to controls, schizophrenia patients exhibited co-existing patterns of increased connectivity between parietal and frontal regions, and decreased connectivity between parietal and temporal regions, and between the temporal cortices bilaterally. The decreased parieto-temporal connectivity was associated with the severity of patients' positive symptoms, while increased fronto-parietal connectivity was associated with patients' negative and general symptoms. Discussion Our analysis revealed two co-existing patterns of functional connectivity abnormalities in schizophrenia, each related to different clinical profiles. Such results provide further evidence that abnormalities in brain connectivity, characteristic of schizophrenia, are directly related to the clinical features of the disorder.National Alliance for Medical Image Computing (U.S.) (Grant U54 EB005149)National Institutes of Health (U.S.) (R01 M074794)Medical Research Council of Australia (Overseas-Based Biomedical Traning Fellowship 520627

Medial Frontal White and Gray Matter Contributions to General Intelligence

The medial orbitofrontal cortex (mOFC) and rostral anterior cingulate cortex (rACC) are part of a wider neural network that plays an important role in general intelligence and executive function. We used structural brain imaging to quantify magnetic resonance gray matter volume and diffusion tensor white matter integrity of the mOFC-rACC network in 26 healthy participants who also completed neuropsychological tests of intellectual abilities and executive function. Stochastic tractography, the most effective Diffusion Tensor Imaging method for examining white matter connections between adjacent gray matter regions, was employed to assess the integrity of mOFC-rACC pathways. Fractional anisotropy (FA), which reflects the integrity of white matter connections, was calculated. Results indicated that higher intelligence correlated with greater gray matter volumes for both mOFC and rACC, as well as with increased FA for left posterior mOFC-rACC connectivity. Hierarchical regression analyses revealed that DTI-derived FA of left posterior mOFC-rACC uniquely accounted for 29%–34% of the variance in IQ, in comparison to 11%–16% uniquely explained by gray matter volume of the left rACC. Together, left rACC gray matter volume and white matter connectivity between left posterior mOFC and rACC accounted for up to 50% of the variance in general intelligence. This study is to our knowledge the first to examine white matter connectivity between OFC and ACC, two gray matter regions of interests that are very close in physical proximity, and underscores the important independent contributions of variations in rACC gray matter volume and mOFC-rACC white matter connectivity to individual differences in general intelligence

Structural diversity in the borohydrido lanthanides series: first isolation and X Ray crystal structure of ionic [Sm(BH4)2(THF)5]+[Cp*'Sm(BH4)3]-

International audienceTwo new borohydrido complexes of samarium were prepared: [Sm(BH4)2(THF)5]+[Cp*'Sm(BH4)3]- (1) and Cp*'2Sm(BH4)(THF) (3) (Cp*' = C5Me4nPr). X Ray studies revealed that 1 displays an unprecedented ionic structure comprising a half samarocene moiety, whereas 3 is monomeric and bears a terminal BH4 ligand