Kilometer-long ordered nanophotonic devices by preform-to-fiber fabrication

Cataloged from PDF version of article.A preform-fo-fiber approach to the fabrication of functional fiber-based devices by thermal drawing in the viscous state is presented. A macroscopic preform rod containing metallic, semiconducting, and insulating constituents in a variety of geometries and close contact produces kilometer-long novel nanostructured fibers and fiber devices. We first review the material selection criteria and then describe metal-semiconductor-metal photosensitive and thermally sensitive fibers. These flexible, lightweight, and low-cost functional fibers may pave the way for new types of fiber sensors, such as thermal sensing fabrics, artificial skin, and large-area optoelectronic screens. Next, the preform-to-fiber approach is used to fabricate spectrally tunable photodetectors that integrate a photosensitive core and a nanostructured photonic crystal structure containing a resonant cavity. An integrated, self-monitoring optical-transmission waveguide is then described that incorporates optical transport and thermal monitoring. This fiber allows one to predict power-transmission failure, which is of paramount importance if high-power optical transmission lines are to be operated safely and reliably in medical, industrial and defense applications. A hybrid electron-photon fiber consisting of a hollow core (for optical transport by means of a photonic bandgap) and metallic wires (for electron transport) is described that may be used for transporting atoms and molecules by radiation pressure. Finally, a solid microstructured fiber fabricated with a highly nonlinear chalcogenide glass enables the generation of supercontinumn light at near-infrared wavelengths

Label-free immunodetection of \u3b1-synuclein by using a microfluidics coplanar electrolyte-gated organic field-effect transistor

The aggregation of \u3b1-synuclein is a critical event in the pathogenesis of neurological diseases, such as Parkinson or Alzheimer. Here, we present a label-free sensor based on an Electrolyte-Gated Organic Field-Effect Transistor (EGOFET) integrated with microfluidics that allows for the detection of amounts of \u3b1-synuclein in the range from 0.25 pM to 25 nM. The lower limit of detection (LOD) measures the potential of our integrated device as a tool for prognostics and diagnostics. In our device, the gate electrode is the effective sensing element as it is functionalised with anti-(\u3b1-synuclein) antibodies using a dual strategy: i) an amino-terminated self-assembled monolayer activated by glutaraldehyde, and ii) the His-tagged recombinant protein G. In both approaches, comparable sensitivity values were achieved, featuring very low LOD values at the sub-pM level. The microfluidics engineering is central to achieve a controlled functionalisation of the gate electrode and avoid contamination or physisorption on the organic semiconductor. The demonstrated sensing architecture, being a disposable stand-alone chip, can be operated as a point-of-care test, but also it might represent a promising label-free tool to explore in-vitro protein aggregation that takes place during the progression of neurodegenerative illnesses

Altered Resting-State Functional Connectivity of the Frontal-Striatal Reward System in Social Anxiety Disorder

We investigated differences in the intrinsic functional brain organization (functional connectivity) of the human reward system between healthy control participants and patients with social anxiety disorder. Functional connectivity was measured in the resting-state via functional magnetic resonance imaging (fMRI). 53 patients with social anxiety disorder and 33 healthy control participants underwent a 6-minute resting-state fMRI scan. Functional connectivity of the reward system was analyzed by calculating whole-brain temporal correlations with a bilateral nucleus accumbens seed and a ventromedial prefrontal cortex seed. Patients with social anxiety disorder, relative to the control group, had (1) decreased functional connectivity between the nucleus accumbens seed and other regions associated with reward, including ventromedial prefrontal cortex; (2) decreased functional connectivity between the ventromedial prefrontal cortex seed and lateral prefrontal regions, including the anterior and dorsolateral prefrontal cortices; and (3) increased functional connectivity between both the nucleus accumbens seed and the ventromedial prefrontal cortex seed with more posterior brain regions, including anterior cingulate cortex. Social anxiety disorder appears to be associated with widespread differences in the functional connectivity of the reward system, including markedly decreased functional connectivity between reward regions and between reward regions and lateral prefrontal cortices, and markedly increased functional connectivity between reward regions and posterior brain regions.Massachusetts Institute of Technology (Janet and Sheldon Razin Fellowship

Principal Component Regression predicts functional responses across individuals

International audienceInter-subject variability is a major hurdle for neuroimaging group-level inference, as it creates complex image patterns that are not captured by standard analysis models and jeopardizes the sensitivity of statistical procedures. A solution to this problem is to model random subjects effects by using the redundant information conveyed by multiple imaging contrasts. In this paper, we introduce a novel analysis framework, where we estimate the amount of variance that is fit by a random effects subspace learned on other images; we show that a principal component regression estimator outperforms other regression models and that it fits a significant proportion (10% to 25%) of the between-subject variability. This proves for the first time that the accumulation of contrasts in each individual can provide the basis for more sensitive neuroimaging group analyzes

Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging

Context: Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD. Objective: To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT). Design: Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli. Setting: Patients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology. Patients: Thirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD. Interventions: Brain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT. Main Outcome Measures: Whole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure. Results: Pretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline. Conclusions: The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient

Dynamic studies of antibody-antigen interactions with an electrolyte-gated organic transistor

Affinity-based biosensors employing surface-bound biomolecules for analyte detection are important tools in clinical diagnostics and drug development. In this context, electrolyte-gated organic transistors (EGOTs) are emerging as ultrasensitive label-free biosensors. In this study, we present an EGOT sensor integrated within a microfluidic system. The sensor utilizes the cytomegalovirus (CMV) phosphoprotein 65 as a biorecognition element to detect the pathological biomarker human anti-cytomegalovirus antibody in solution. The biorecognition element is grafted onto the gate electrode by exploiting the polyhistidine-tag technology. Real-time monitoring of the EGOT response, coupled with a twocompartment kinetic model analysis, enables the determination of analyte concentration, binding kinetics, and thermodynamics of the interaction. The analysis of the relevant kinetic parameters of the binding process yields a reliable value for the thermodynamic equilibrium constant and suggests that the measured deviations from the Langmuir binding model arise from the co-existence of binding sites with different affinities toward the antibodies

High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas

Available online 4 May 2017The amygdala is composed of multiple nuclei with unique functions and connections in the limbic system and to the rest of the brain. However, standard in vivo neuroimaging tools to automatically delineate the amygdala into its multiple nuclei are still rare. By scanning postmortem specimens at high resolution (100–150 µm) at 7 T field strength (n = 10), we were able to visualize and label nine amygdala nuclei (anterior amygdaloid, cortico-amygdaloid transition area; basal, lateral, accessory basal, central, cortical medial, paralaminar nuclei). We created an atlas from these labels using a recently developed atlas building algorithm based on Bayesian inference. This atlas, which will be released as part of FreeSurfer, can be used to automatically segment nine amygdala nuclei from a standard resolution structural MR image. We applied this atlas to two publicly available datasets (ADNI and ABIDE) with standard resolution T1 data, used individual volumetric data of the amygdala nuclei as the measure and found that our atlas i) discriminates between Alzheimer's disease participants and age-matched control participants with 84% accuracy (AUC=0.915), and ii) discriminates between individuals with autism and age-, sex- and IQ-matched neurotypically developed control participants with 59.5% accuracy (AUC=0.59). For both datasets, the new ex vivo atlas significantly outperformed (all p < .05) estimations of the whole amygdala derived from the segmentation in FreeSurfer 5.1 (ADNI: 75%, ABIDE: 54% accuracy), as well as classification based on whole amygdala volume (using the sum of all amygdala nuclei volumes; ADNI: 81%, ABIDE: 55% accuracy). This new atlas and the segmentation tools that utilize it will provide neuroimaging researchers with the ability to explore the function and connectivity of the human amygdala nuclei with unprecedented detail in healthy adults as well as those with neurodevelopmental and neurodegenerative disorders.This work was supported by the PHS grant DA023427 and NICHD/ NIH grant F32HD079169 (Z.M.S); Feodor Lynen Postdoctoral Fellowship of the Alexander von Humboldt Foundation (D.K.); R21(MH106796), R21 (AG046657) and K01AG28521 (J.C.A.), the National Cancer Institute (1K25CA181632-01) as well as the Genentech Foundation (M.R.); the European Union's Horizon 2020 Marie Sklodowska-Curie grant agreement No 654911 (project ”THALAMODEL”) and ERC Starting Grant agreement No 677697 (project “BUNGEE-TOOLS”); and the Spanish Ministry of Economy and Competitiveness (MINECO) reference TEC2014-51882-P (J.E.I.); and the NVIDIA hardware award (M.R. and J.E.I.). Further support for this research was provided in part by the National Institute for Biomedical Imaging and Bioengineering (P41EB015896, R01EB006758, R21EB018907, R01EB019956, R01- EB013565), the National Institute on Aging (5R01AG008122, R01AG016495), the National Institute of Diabetes and Digestive and Kidney Diseases (1-R21-DK-108277-01), the National Institute for Neurological Disorders and Stroke (R01NS0525851, R21NS072652, R01NS070963, R01NS083534, 5U01NS086625), the Massachusetts ADRC (P50AG005134) and was made possible by the resources provided by Shared Instrumentation Grants 1S10RR023401, 1S10RR019307, and 1S10RR023043. Additional support was provided by the NIH Blueprint for Neuroscience Research (5U01-MH093765), part of the multi-institutional Human Connectome Project. In addition, BF has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies. BF's interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies. The collection and sharing of the ADNI MRI data used in the evaluation was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2- 0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www. fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California

Impaired perception of facial motion in autism spectrum disorder

Copyright: © 2014 O’Brien et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This article has been made available through the Brunel Open Access Publishing Fund.Facial motion is a special type of biological motion that transmits cues for socio-emotional communication and enables the discrimination of properties such as gender and identity. We used animated average faces to examine the ability of adults with autism spectrum disorders (ASD) to perceive facial motion. Participants completed increasingly difficult tasks involving the discrimination of (1) sequences of facial motion, (2) the identity of individuals based on their facial motion and (3) the gender of individuals. Stimuli were presented in both upright and upside-down orientations to test for the difference in inversion effects often found when comparing ASD with controls in face perception. The ASD group’s performance was impaired relative to the control group in all three tasks and unlike the control group, the individuals with ASD failed to show an inversion effect. These results point to a deficit in facial biological motion processing in people with autism, which we suggest is linked to deficits in lower level motion processing we have previously reported

Kilometer-long ordered nanophotonic devices by preform-to-fiber fabrication

A preform-to-flber approach to the fabrication of functional fiber-based devices by thermal drawing in the viscous state is presented. A macroscopic preform rod containing metallic, semiconducting, and insulating constituents in a variety of geometries and close contact produces kilometer-long novel nanostructured fibers and fiber devices. We first review the material selection criteria and then describe metal-semiconductor-metal photosensitive and thermally sensitive fibers. These flexible, lightweight, and low-cost functional fibers may pave the way for new types of fiber sensors, such as thermal sensing fabrics, artificial skin, and large-area optoelectronic screens. Next, the preform-to-fiber approach is used to fabricate spectrally tunable photodetectors that integrate a photosensitive core and a nanostructured photonic crystal structure containing a resonant cavity. An integrated, self-monitoring optical-transmission waveguide is then described that incorporates optical transport and thermal monitoring. This fiber allows one to predict power-transmission failure, which is of paramount importance if high-power optical transmission fines are to be operated safely and reliably in medical, industrial and defense applications. A hybrid electron-photon fiber consisting of a hollow core (for optical transport by means of a photonic bandgap) and metallic wires (for electron transport) is described that may be used for transporting atoms and molecules by radiation pressure. Finally, a solid microstructured fiber fabricated with a highly nonlinear chalcogenide glass enables the generation of supercontinuum light at near-infrared wavelengths. © 2006 IEEE