Androgen receptor (AR)3 is a hormone-induced transcription factor that controls male sexual development and other important physiologies. Similar to other members of the nuclear receptor family (1, 2), AR has three major functional domains: an N-terminal transactivation domain, a DNA-binding domain, and a C-terminal ligand-binding domain (3-5). Mutations found in each of these domains lead to a series of AR functional defects associated with androgen insensitivity syndrome (AIS) or partial AIS in humans (6, 7). The majority of AIS and partial AIS patients have developmental defects in the male reproductive system. Loss-of-function AR mutations in mice recapitulate many of the reproductive defects found in AIS patients. For example, the AR-deficient (androgen receptor knock-out; ARKO) mouse (8) and the tfm (testicular feminization mutant) mouse (9) both develop severe defects of testicular development and an overall lack of male sexual differentiation, including hypospadias and penile agenesis. The tfm male mouse demonstrates many female secondary structures, including vagina and teats (10).Molecular regulation of AR function can be achieved at several levels, such as spatial-temporal expression of the receptor, modulation of ligand binding, cytoplasm to nucleus translocation, and DNA binding and transcriptional activities (11,12). Prior to hormone binding, steroid receptors form large protein complexes containing the molecular chaperone heat shock protein 90 (Hsp90) as well as various co-chaperone tetratricopeptide repeat (TPR) proteins (13-15). These co-chaperones include Fkbp52 and Fkbp51 (FK506-binding protein 52 and 51, respectively), Cyp40 (cyclophilin 40), and PP5 (protein phosphatase 5). Fkbp52 and Fkbp51 are ubiquitously expressed proteins with peptidyl prolyl cis/trans-isomerase activity that is inhibited by the binding of . Each TPR protein enters into steroid receptor complexes through a direct and competitive binding at the C terminus of Hsp90 via its essential TPR domain (19 -21). Although Fkbp52 and Fkbp51 share a similar domain structure, as well as 60% sequence identity and 75% similarity, they do differ in that Fkbp51 is missing a C-terminal calmodulin-binding domain.To date, most studies on TPR control of the steroid receptor (SR) action have been done using conventional molecular and cellular approaches and using the glucocorticoid (GR) and progesterone (PR) receptors as models. It has been shown that Fkbp52 is localized to both cytoplasm and nucleus but that the cytoplasmic fraction co-localizes with microtubules in a complex containing dynein (22, 23). For these reasons, it was pro-

Lipids are fundamental building blocks of cells and their organelles, yet nanoscale resolution imaging of lipids has been largely limited to electron microscopy techniques. We introduce and validate a chemical tag that enables lipid membranes to be imaged optically at nanoscale resolution via a lipid-optimized form of expansion microscopy, which we call membrane expansion microscopy (mExM). mExM, via a novel post-expansion antibody labeling protocol, enables protein-lipid relationships to be imaged in organelles such as mitochondria, the endoplasmic reticulum, the nuclear membrane, and the Golgi apparatus. mExM may be of use in a variety of biological contexts, including the study of cell-cell interactions, intracellular transport, and neural connectomics. MainExpansion microscopy (ExM) physically magnifies biological specimens by covalently anchoring biomolecules or labels to a swellable polymer network (typically sodium polyacrylate) synthesized in situ throughout the specimen 1-4 . Following tissue softening and solvent exchange, the hydrogel-specimen composite expands isotropically, typically to a physical magnification of ~4.5x in linear dimension. The net result is that biomolecules or labels that are initially localized within the diffraction limit of a traditional optical microscope can now be separated in space to distances far enough that they can be resolved on ordinary microscopes. Expansion microscopy protocols 5 for the visualization of proteins 3,6,7 and nucleic acids 4 are in increasingly widespread use, raising the question of whether other biological molecule classes, such as lipids, can also be visualized by ExM. We here report an expansion microscopy-compatible lipid stain, as well as a

ObjectivesTo examine longitudinal changes in structural and functional connectivity post-stroke in patients with motor impairment, and define their importance for recovery and outcome at 12 months.MethodsFirst-time stroke patients (N = 31) were studied at 1–2 weeks, 3 months, and 12 months post-injury with a validated motor battery and resting-state fMRI to measure inter-hemispheric functional connectivity (FC). Fractional anisotropy (FA) of the cortico-spinal tract (CST) was derived from diffusion tensor imaging as a measure of white matter organization. ANOVAs were used to test for changes in FC, FA, and motor performance scores over time, and regression analysis related motor outcome to clinical and neuroimaging variables.ResultsFA of the ipsilesional CST improved significantly from 3 to 12 months and was strongly correlated with motor performance. FA improved even in the absence of direct damage to the CST. Inter-hemispheric FC also improved over time, but did not correlate with motor performance at 12 months. Clinical variables (early motor score, education level, and age) predicted 80.4% of the variation of motor outcome, and FA increased the predictability to 84.6%. FC did not contribute to the prediction of motor outcome.ConclusionsStroke causes changes to the CST microstructure that can account for behavioral variability even in the absence of demonstrable lesion. Ipsilesional CST undergoes remodeling post-stroke, even past the three-month window when most of the motor recovery happens. FA of the CST, but not inter-hemispheric FC, can improve to the prediction of motor outcome based on early motor scores.

BACKGROUND AND PURPOSE:Interpretation of fMRI depends on accurate functional-to-structural alignment. This study explores registration methods used by FDA-approved software for clinical fMRI and aims to answer the following question: What is the degree of misalignment when registration is not performed, and how well do current registration methods perform? MATERIALS AND METHODS:This retrospective study of presurgical fMRI for brain tumors compares nonregistered images and 5 registration cost functions: Hellinger, mutual information, normalized mutual information, correlation ratio, and local Pearson correlation. To adjudicate the accuracy of coregistration, we edge-enhanced echo-planar maps and rated them for alignment with structural anatomy. Lesion-to-activation distances were measured to evaluate the effects of different cost functions. RESULTS:Transformation parameters were congruent among Hellinger, mutual information, normalized mutual information, and the correlation ratio but divergent from the local Pearson correlation. Edge-enhanced images validated the local Pearson correlation as the most accurate. Hellinger worsened misalignment in 59% of cases, primarily exaggerating the inferior translation; no cases were worsened by the local Pearson correlation. Three hundred twenty lesion-to-activation distances from 25 patients were analyzed among nonregistered images, Hellinger, and the local Pearson correlation. ANOVA analysis revealed significant differences in the coronal (P Ͻ .001) and sagittal (P ϭ .04) planes. If registration is not performed, 8% of cases may have a Ͼ3-mm discrepancy and up to a 5.6-mm lesion-toactivation distance difference. If a poor registration method is used, 23% of cases may have a Ͼ3-mm discrepancy and up to a 6.9-mm difference. CONCLUSIONS:The local Pearson correlation is a special-purpose cost function specifically designed for T2*-T1 coregistration and should be more widely incorporated into software tools as a better method for coregistration in clinical fMRI. ABBREVIATIONS: AFNI ϭ Analysis of Functional Neuro Images; CR ϭ correlation ratio; HEL ϭ Hellinger; LAD ϭ lesion-to-activation distance; LPC ϭ local Pearson correlation; MI ϭ mutual information; NMI ϭ normalized mutual information; NR ϭ nonregistered

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