We developed a general method for global conformal parameterizations based on the structure of the cohomology group of holomorphic one-forms for surfaces with or without boundaries (Gu and Yau, 2002), (Gu and Yau, 2003). For genus zero surfaces, our algorithm can find a unique mapping between any two genus zero manifolds by minimizing the harmonic energy of the map. In this paper, we apply the algorithm to the cortical surface matching problem. We use a mesh structure to represent the brain surface. Further constraints are added to ensure that the conformal map is unique. Empirical tests on magnetic resonance imaging (MRI) data show that the mappings preserve angular relationships, are stable in MRIs acquired at different times, and are robust to differences in data triangulation, and resolution. Compared with other brain surface conformal mapping algorithms, our algorithm is more stable and has good extensibility.

This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhoodand adult-onset schizophrenia, bipolar disorder, attention-deficit/ hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages. D 2004 Published by Elsevier Inc.

Background Electroconvulsive therapy (ECT) elicits a rapid and robust clinical response in patients with refractory depression. Neuroimaging measures of structural plasticity relating to and predictive of ECT response may point to the mechanisms underlying rapid antidepressant effects and establish biomarkers to inform other treatments. Here, we determine the effects of 1) diagnosis and 2) ECT on global and local variations of hippocampal and amygdalar structure in major depression and predictors of ECT-related clinical response Methods Longitudinal changes in hippocampal and amygdala structure were examined in patients with major depression (N= 43, scanned thrice; prior to ECT, after the 2nd ECT session, and within one week of completing the ECT treatment series) referred for ECT as part of their standard clinical care. Cross-sectional comparisons with demographically similar controls (N= 32, scanned twice) established effects of diagnosis. Results Patients showed smaller hippocampal volumes compared to controls at baseline (p<.04). Both hippocampal and amygdalar volumes increased with ECT (p<.001) and in relation to symptom improvement (p<.01). Hippocampal volume at baseline predicted subsequent clinical response (p<.05). Shape analysis revealed pronounced morphometric changes in the anterior hippocampus and basolateral and centromedial amygdala. All structural measures remained stable across time in controls. Conclusions ECT induced neuroplasticity in the hippocampus and amygdala relates to improved clinical response and is pronounced in regions with prominent connections to ventromedial prefrontal cortex and other limbic structures. Smaller hippocampal volumes at baseline predict a more robust clinical response. Neurotrophic processes including neurogenesis shown in preclinical studies may underlie these structural changes.

Computational anatomy methods are now widely used in clinical neuroimaging to map the profile of disease effects on the brain and its clinical correlates. In Alzheimer’s disease (AD), many research groups have modeled localized changes in hippocampal and lateral ventricular surfaces, to provide candidate biomarkers of disease progression for drug trials. We combined the power of parametric surface modeling and tensor-based morphometry to study hippocampal differences associated with AD and mild cognitive impairment (MCI) in 490 subjects (97 AD, 245 MCI, 148 controls) and ventricular differences in 804 subjects scanned as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI; 184 AD, 391 MCI, 229 controls). We aimed to show that a new multivariate surface statistic based on multivariate tensor-based morphometry (mTBM) and radial distance provides a more powerful way to detect localized anatomical differences than conventional surface-based analysis. In our experiments, we studied correlations between hippocampal atrophy and ventricular enlargement and clinical measures and cerebrospinal fluid biomarkers. The new multivariate statistics gave better effect sizes for detecting morphometric differences, relative to other statistics including radial distance, analysis of the surface tensor and the Jacobian determinant. In empirical tests using false discovery rate curves, smaller sample sizes were needed to detect associations with diagnosis. The analysis pipeline is generic and automated. It may be applied to analyze other brain subcortical structures including the caudate nucleus and putamen. This publically available software may boost power for morphometric studies of subcortical structures in the brain.

We describe an engineered family of highly antigenic molecules based on GFP-like fluorescent proteins. These molecules contain numerous copies of peptide epitopes and simultaneously bind IgG antibodies at each location. These “spaghetti monster” fluorescent proteins (smFPs) distribute well in neurons, notably into small dendrites, spines and axons. smFP immunolabeling localizes weakly expressed proteins not well resolved with traditional epitope tags. By varying epitope and scaffold, we generated a diverse family of mutually orthogonal antigens. In cultured neurons and mouse and fly brains, smFP probes allow robust, orthogonal multi-color visualization of proteins, cell populations and neuropil. smFP variants complement existing tracers, greatly increase the number of simultaneous imaging channels, and perform well in advanced preparations such as array tomography, super-resolution fluorescence imaging and electron microscopy. In living cells, the probes improve single-molecule image tracking and increase yield for RNA-Seq. These probes facilitate new experiments in connectomics, transcriptomics and protein localization.

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics -these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain.

BackgroundDepression is a heterogeneous disorder, with the exact neuronal mechanisms causing the disease yet to be discovered. Recent work suggests it is accompanied by neuro-inflammation, characterized, in particular, by microglial activation. However, microglial activation and its involvement in neuro-inflammation and stress-related depressive disorders are far from understood.MethodsWe utilized multiple detection methods to detect the neuro-inflammation in the hippocampus of rats after exposure to chronic mild stress (CMS). Male Sprague Dawley (SD) rats were subjected to chronic mild stressors for 12 weeks. Microglial activation and hippocampal neuro-inflammation were detected by using a combinatory approach of in vivo [18F] DPA-714 positron emission computed tomography (PET) imaging, ionized calcium-binding adapter molecule 1 and translocator protein (TSPO) immunohistochemistry, and detection of NOD-like receptor protein 3 (NLRP3) inflammasome and some inflammatory mediators. Then, the rats were treated with minocycline during the last 4 weeks to observe its effect on hippocampal neuro-inflammation and depressive-like behavior induced by chronic mild stress.ResultsThe results show that 12 weeks of chronic mild stress induced remarkable depressive- and anxiety-like behavior, simultaneously causing hippocampal microglial activation detected by PET, immunofluorescence staining, and western blotting. Likewise, activation of NLRP3 inflammasome and upregulation of inflammatory mediators, such as interleukin-1β (IL-1β), IL-6, and IL-18, were also observed in the hippocampus after exposure to chronic stress. Interestingly, the anti-inflammatory mediators, such as IL-4 and IL-10, were also increased in the hippocampus following chronic mild stress, which may hint that chronic stress activates different types of microglia, which produce pro-inflammatory cytokines or anti-inflammatory cytokines. Furthermore, chronic minocycline treatment alleviated the depressive-like behavior induced by chronic stress and significantly inhibited microglial activation. Similarly, the activation of NLRP3 inflammasome and the increase of inflammatory mediators were not exhibited or significantly less marked in the minocycline treatment group.ConclusionThese results together indicate that microglial activation mediates the chronic mild stress-induced depressive- and anxiety-like behavior and hippocampal neuro-inflammation.

To study the representation of olfactory information in higher brain centers, we expressed a green fluorescent protein-based Ca