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Individuality, the uniqueness that distinguishes one person from another, may manifest as diverse rearrangements of functional connectivity during heterogeneous cognitive demands; yet, the neurobiological substrates of individuality, reflected in inter-individual variations of large-scale functional connectivity, have not been fully evidenced. Accordingly, we explored inter-individual variations of functional connectivity dynamics, subnetwork patterns and modular architecture while subjects watched identical video clips designed to induce different arousal levels. How inter-individual variations are manifested in the functional brain networks was examined with respect to four contrasting divisions: edges within the anterior versus posterior part of the brain, edges with versus without corresponding anatomically-defined structural pathways, inter- versus intra-module connections, and rich club edge types. Inter-subject variation in dynamic functional connectivity occurred to a greater degree within edges localized to anterior rather than posterior brain regions, without adhering to structural connectivity, between modules as opposed to within modules, and in weak-tie local edges rather than strong-tie rich-club edges. Arousal level significantly modulates inter-subject variability in functional connectivity, edge patterns, and modularity, and particularly enhances the synchrony of rich-club edges. These results imply that individuality resides in the dynamic reconfiguration of large-scale brain networks in response to a stream of cognitive demands.

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Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber O8 Analytic solution of cable energy function for cortical axons and dendrites Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network Jimin Kim, Will Leahy, Eli Shlizerman O10 Is the model any good? Objective criteria for computational neuroscience model selection Justas Birgiolas, Richard C. Gerkin, Sharon M. 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Background: Prestimulus EEG alpha activity in humans has been considered to reflect ongoing top-down preparation for the performance of subsequent tasks. Since theta oscillations may be related to poststimulus top-down processing, we investigated whether prestimulus EEG theta activity also reflects top-down cognitive preparation for a stimulus. Results: We recorded EEG data from 15 healthy controls performing a color and shape discrimination task, and used the wavelet transformation to investigate the time course and power of oscillatory activity in the signals. We observed a relationship between both anterior theta and posterior alpha power in the prestimulus period and the type of subsequent task. Conclusions: Since task-differences were reflected in both theta and alpha activities prior to stimulus onset, both prestimulus theta (particularly around the anterior region) and prestimulus alpha (particularly around the posterior region) activities may reflect prestimulus top-down preparation for the performance of subsequent tasks.

Background: The hippocampus has been shown to be abnormal in schizophrenia. The fornix is one of the main fiber tracts connecting the hippocampus with other brain regions. Few studies have evaluated the fornix in schizophrenia, however. A focus on fornix abnormalities and their association with hippocampal abnormalities might figure importantly in our understanding of the pathophysiology of schizophrenia. Methods: Line-scan diffusion tensor imaging (DTI) was used to evaluate diffusion in the fornix in 24 male patients with chronic schizophrenia and 31 male control subjects. Maps of fractional anisotropy (FA) and mean diffusivity (Dm), which are indices sensitive to white-matter integrity, were generated to quantify diffusion within the fornix. We used high spatial resolution magnetic resonance imaging (MRI) to measure hippocampal volume. Results: FA and cross-sectional area of the fornix were significantly reduced in patients compared with control subjects. Dm was significantly increased, whereas hippocampal volume was bilaterally reduced in patients. Reduced hippocampal volume was correlated with increased mean Dm and reduced cross-sectional area of the fornix for patients. Patients also showed a significant correlation between reduced scores on neuropsychologic measures of declarative-episodic memory and reduced hippocampal volumes. Conclusions: These findings demonstrate a disruption in fornix integrity in patients with schizophrenia.

Context: Structural brain abnormalities, including larger cerebrospinal fluid (CSF) volumes, have been observed in men diagnosed as having schizotypal personality disorder (SPD). Objectives: To determine whether women with SPD have abnormalities similar to those of men with SPD and to elucidate specific SPD regional volume deficits and symptom correlations. Design: Naturalistic study. Setting and Participants: Thirty neuroleptic-naive women with SPD and 29 female control subjects, both recruited from the community. Participants were group matched for age, parental socioeconomic status, handedness, and IQ. Interventions: A new segmentation method was applied to magnetic resonance images to automatically parcel the images into CSF, gray matter, and white matter. The neocortex was manually separated from subcortical and other nonneocortical structures. Voxel-based morphometry was applied to determine global and regional volume deficits. Main Outcome Measures: Left and right neocortical gray matter, white matter, and CSF relative volumes as well as clinical symptoms from the Structured Interview for Schizotypy and the Schizotypal Personality Questionnaire–Brief Version. Results: Smaller left (3.84%) and right (3.83%) neocortical gray matter relative volumes associated with larger left (9.66%) and right (9.61%) sulcal CSF relative volumes were found in women with SPD compared with controls. Voxel-based morphometry showed that the neocortical deficits in SPD were especially prominent in the left superior and middle temporal gyri, left inferior parietal region with postcentral gyrus, and right superior frontal and inferior parietal gyri. In the SPD group, larger lateral ventricle volumes correlated with more severe symptoms on the Structured Interview for Schizotypy and the Schizotypal Personality Questionnaire–Brief Version. Conclusions: The smaller neocortical gray matter volume and larger sulcal CSF volume provide evidence of the brain basis of this personality disorder and emphasize the communality of brain abnormalities in the schizophrenia spectrum.

We created a spatial probability atlas of schizophrenia to provide information about the neuroanatomic variability of brain regions of patients with the disorder. Probability maps of 16 regions of interest (ROIs) were constructed by taking manually parcellated ROIs from subjects’ magnetic resonance images (MRIs) and linearly transforming them into Talairach space using the Montreal Neurological Institute (MNI) template. ROIs included temporal, parietal, and prefrontal cortex subregions, with a principal focus on temporal lobe structures. Subject Ns ranged from 11 to 28 for the different ROIs. Our global measure of the spatial distribution of the transformed ROI was the sum of voxels with 50% overlap among subjects. The superior temporal gyrus (STG) and fusiform gyrus (FG) had lower values for schizophrenic subjects than for normal controls, suggestive of greater spatial variability for these ROIs in schizophrenic subjects. For the computation of statistical significance of group differences in portions of the ROI, we used voxel-wise comparisons and Fisher's exact test. First-episode schizophrenic patients compared with controls showed lower probability (P < 0.05) at dorso-posterior areas of planum temporale and Heschl's gyrus, lateral and anterior regions in the left hippocampus (HIPP), and dorsolateral regions of fusiform gyrus. Importantly, most ROIs of schizophrenic subjects showed a significantly lower spatial overlap than controls, even after nonlinear spatial normalization, suggesting a greater heterogeneity in the spatial distribution of ROIs. There is consequently a need for caution in neuroimaging studies where data from schizophrenic subjects are normalized to a particular stereotaxic coordinate system based on healthy controls. Apparent group differences in activation may simply reflect a greater heterogeneity of spatial distribution in schizophrenia.

Hemisphere asymmetry was explored in normal healthy subjects and in patients with schizophrenia using a novel voxel-based tensor analysis applied to fractional anisotropy (FA) of the diffusion tensor. Our voxel-based approach, which requires precise spatial normalization to remove the misalignment of fiber tracts, includes generating a symmetrical group average template of the diffusion tensor by applying nonlinear elastic warping of the demons algorithm. We then normalized all 32 diffusion tensor MRIs from healthy subjects and 23 from schizophrenic subjects to the symmetrical average template. For each brain, six channels of tensor component images and one T2-weighted image were used for registration to match tensor orientation and shape between images. A statistical evaluation of white matter asymmetry was then conducted on the normalized FA images and their flipped images. In controls, we found left-higher-than-right anisotropic asymmetry in the anterior part of the corpus callosum, cingulum bundle, the optic radiation, and the superior cerebellar peduncle, and right-higher-than-left anisotropic asymmetry in the anterior limb of the internal capsule and the anterior limb’s prefrontal regions, in the uncinate fasciculus, and in the superior longitudinal fasciculus. In patients, the asymmetry was lower, although still present, in the cingulum bundle and the anterior corpus callosum, and not found in the anterior limb of the internal capsule, the uncinate fasciculus, and the superior cerebellar peduncle compared to healthy subjects. These findings of anisotropic asymmetry pattern differences between healthy controls and patients with schizophrenia are likely related to neuro-developmental abnormalities in schizophrenia.

In this paper we present a framework for unsupervised segmentation of white matter fiber traces obtained from diffusion weighted MRI data. Fiber traces are compared pairwise to create a weighted undirected graph which is partitioned into coherent sets using the normalized cut (N cut) criterion. A simple and yet effective method for pairwise comparison of fiber traces is presented which in combination with the N cut criterion is shown to produce plausible segmentations of both synthetic and real fiber trace data. Segmentations are visualized as colored stream-tubes or transformed to a segmentation of voxel space, revealing structures in a way that looks promising for future explorative studies of diffusion weighted MRI data.

We introduce a method for combining fiber tracking from diffusion-tensor (DT) imaging with cortical gray matter parcellation from structural high-spatial-resolution 3D spoiled gradient-recalled acquisition in the steady state images. We applied this method to a tumor case to determine the impact of the tumor on white matter architecture. We conclude that this new method for combining structural and DT imaging data is useful for understanding cortical connectivity and the localization of fiber tracts and their relationship with cortical anatomy and brain abnormalities.

Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components. To evaluate the registration of tensor data, we defined two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the fiber bundles of target images and registered images at the same seed points in white matter segmentation. We also evaluated the tensor registration by examining the voxel-by-voxel alignment of tensors in a sample of 15 normalized DT-MRIs. In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance in effectively normalizing the tract morphology and tensor orientation. We also present a nonlinear method for creating a group diffusion tensor atlas using the average tensor field and the average deformation field, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population.