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Expanding discriminative dimensions for analysis and imaging.

ABSTRACT: Eliminating the contribution of interfering compounds is a key step in chemical analysis. In complex media, one possible approach is to perform a preliminary separation. However purification is often demanding, long, and costly; it may also considerably alter the properties of interacting components of the mixture (e.g. in a living cell). Hence there is a strong interest for developing separation-free non-invasive analytical protocols. Using photoswitchable probes as labelling and titration contrast agents, we demonstrate that the association of a modulated monochromatic light excitation with a kinetic filtering of the overall observable is much more attractive than constant excitation to read-out the contribution from a target probe under adverse conditions. An extensive theoretical framework enabled us to optimize the out-of-phase concentration first-order response of a photoswitchable probe to modulated illumination by appropriately matching the average light intensity and the radial frequency of the light modulation to the probe dynamics. Thus, we can selectively and quantitatively extract from an overall signal the contribution from a target photoswitchable probe within a mixture of species, photoswitchable or not. This simple titration strategy is more specifically developed in the context of fluorescence imaging, which offers promising perspectives.

SUBMITTER: Querard J

PROVIDER: S-EPMC5490003 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Expanding discriminative dimensions for analysis and imaging.

Querard Jérôme J Gautier Arnaud A Le Saux Thomas T Jullien Ludovic L

Chemical science 20150318 5

Eliminating the contribution of interfering compounds is a key step in chemical analysis. In complex media, one possible approach is to perform a preliminary separation. However purification is often demanding, long, and costly; it may also considerably alter the properties of interacting components of the mixture (<i>e.g.</i> in a living cell). Hence there is a strong interest for developing separation-free non-invasive analytical protocols. Using photoswitchable probes as labelling and titrati ...[more]

PMID: 28706678

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Project description:Multivariate pattern analysis techniques have been increasingly used over the past decade to derive highly sensitive and specific biomarkers of diseases on an individual basis. The driving assumption behind the vast majority of the existing methodologies is that a single imaging pattern can distinguish between healthy and diseased populations, or between two subgroups of patients (e.g., progressors vs. non-progressors). This assumption effectively ignores the ample evidence for the heterogeneous nature of brain diseases. Neurodegenerative, neuropsychiatric and neurodevelopmental disorders are largely characterized by high clinical heterogeneity, which likely stems in part from underlying neuroanatomical heterogeneity of various pathologies. Detecting and characterizing heterogeneity may deepen our understanding of disease mechanisms and lead to patient-specific treatments. However, few approaches tackle disease subtype discovery in a principled machine learning framework. To address this challenge, we present a novel non-linear learning algorithm for simultaneous binary classification and subtype identification, termed HYDRA (Heterogeneity through Discriminative Analysis). Neuroanatomical subtypes are effectively captured by multiple linear hyperplanes, which form a convex polytope that separates two groups (e.g., healthy controls from pathologic samples); each face of this polytope effectively defines a disease subtype. We validated HYDRA on simulated and clinical data. In the latter case, we applied the proposed method independently to the imaging and genetic datasets of the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) study. The imaging dataset consisted of T1-weighted volumetric magnetic resonance images of 123 AD patients and 177 controls. The genetic dataset consisted of single nucleotide polymorphism information of 103 AD patients and 139 controls. We identified 3 reproducible subtypes of atrophy in AD relative to controls: (1) diffuse and extensive atrophy, (2) precuneus and extensive temporal lobe atrophy, as well some prefrontal atrophy, (3) atrophy pattern very much confined to the hippocampus and the medial temporal lobe. The genetics dataset yielded two subtypes of AD characterized mainly by the presence/absence of the apolipoprotein E (APOE) ε4 genotype, but also involving differential presence of risk alleles of CD2AP, SPON1 and LOC39095 SNPs that were associated with differences in the respective patterns of brain atrophy, especially in the precuneus. The results demonstrate the potential of the proposed approach to map disease heterogeneity in neuroimaging and genetic studies.

Project description:Being able to map accurately placental blood flow in clinics could have major implications in the diagnosis and follow-up of pregnancy complications such as intrauterine growth restriction (IUGR). Moreover, the impact of such an imaging modality for a better diagnosis of placental dysfunction would require to solve the unsolved problem of discriminating the strongly intricated maternal and fetal vascular networks. However, no current imaging modality allows both to achieve sufficient sensitivity and selectivity to tell these entangled flows apart. Although ultrasound imaging would be the clinical modality of choice for such a problem, conventional Doppler echography both lacks of sensibility to detect and map the placenta microvascularization and a concept to discriminate both entangled flows. In this work, we propose to use an ultrafast Doppler imaging approach both to map with an enhanced sensitivity the small vessels of the placenta (~100 μm) and to assess the variation of the Doppler frequency simultaneously in all pixels of the image within a cardiac cycle. This approach is evaluated in vivo in the placenta of pregnant rabbits: By studying the local flow pulsatility pixel per pixel, it becomes possible to separate maternal and fetal blood in 2D from their pulsatile behavior. Significance Statement: The in vivo ability to image and discriminate maternal and fetal blood flow within the placenta is an unsolved problem which could improve the diagnosis of pregnancy complications such as intrauterine growth restriction or preeclampsia. To date, no imaging modality has both sufficient sensitivity and selectivity to discriminate these intimately entangled flows. We demonstrate that Ultrafast Doppler ultrasound method with a frame rate 100x faster than conventional imaging solves this issue. It permits the mapping of small vessels of the placenta (~100 μm) in 2D with an enhanced sensitivity. By assessing pixel-per-pixel pulsatility within single cardiac cycles, it achieves maternal and fetal blood flow discrimination.

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Expanding discriminative dimensions for analysis and imaging.

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Expanding discriminative dimensions for analysis and imaging.

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