A co-registered multimodal imaging system for reflectance, multiphoton, and optical coherence microscopy

Multimodal imaging is an advantageous method to increase the accuracy of disease classification. As an example, we and others have shown that optical coherence tomography images and fluorescence spectroscopy contain complementary information that can increase the sensitivity and specificity for cancer detection. A common challenge in multimodal imaging is image co-registration. The different images are often taken with separate imaging setups, making it challenging to precisely image the same tissue area or co-register the images computationally. To solve this problem, we have developed a co-registered multimodal imaging system that images the same tissue location with reflectance, multi-photon, and optical coherence microscopy. The co-registration mechanism is a dual-clad fiber that integrates with a scanning microscope or scanning endoscope, collecting all three signals using the same optical path. In the current implementation, optical coherence tomography utilizes a 1300 nm super luminescent diode, multi-photon signals are excited by a custom femtosecond 1400 nm fiber laser producing two-and three-photon signals in the 460-900 nm band, and reflectance imaging operates at 561 nm. The system separates the different signals using fiber wavelength division multiplexers, a dual-clad fiber coupler, and dichroic mirrors to deliver the different signals to the corresponding detector. This wavelength selection enables the system to work passively, meaning that there is no need for devices such as filter wheels. Using the scanning microscope configuration, we have obtained multimodal images of ex-vivo ovine ovary tissue. © 2021 SPIE.Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility

We analyzed genetic data of 47,429 multiple sclerosis (MS) and 68,374 control subjects and established a reference map of the genetic architecture of MS that includes 200 autosomal susceptibility variants outside the major histocompatibility complex (MHC), one chromosome X variant, and 32 variants within the extended MHC. We used an ensemble of methods to prioritize 551 putative susceptibility genes that implicate multiple innate and adaptive pathways distributed across the cellular components of the immune system. Using expression profiles from purified human microglia, we observed enrichment for MS genes in these brain-resident immune cells, suggesting that these may have a role in targeting an autoimmune process to the central nervous system, although MS is most likely initially triggered by perturbation of peripheral immune responses

Closed-Loop Deep Brain Stimulation for Refractory Chronic Pain

Pain is a subjective experience that alerts an individual to actual or potential tissue damage. Through mechanisms that are still unclear, normal physiological pain can lose its adaptive value and evolve into pathological chronic neuropathic pain. Chronic pain is a multifaceted experience that can be understood in terms of somatosensory, affective, and cognitive dimensions, each with associated symptoms and neural signals. While there have been many attempts to treat chronic pain, in this article we will argue that feedback-controlled ‘closed-loop’ deep brain stimulation (DBS) offers an urgent and promising route for treatment. Contemporary DBS trials for chronic pain use “open-loop” approaches in which tonic stimulation is delivered with fixed parameters to a single brain region. The impact of key variables such as the target brain region and the stimulation waveform is unclear, and long-term efficacy has mixed results. We hypothesize that chronic pain is due to abnormal synchronization between brain networks encoding the somatosensory, affective and cognitive dimensions of pain, and that multisite, closed-loop DBS provides an intuitive mechanism for disrupting that synchrony. By (1) identifying biomarkers of the subjective pain experience and (2) integrating these signals into a state-space representation of pain, we can create a predictive model of each patient's pain experience. Then, by establishing how stimulation in different brain regions influences individual neural signals, we can design real-time, closed-loop therapies tailored to each patient. While chronic pain is a complex disorder that has eluded modern therapies, rich historical data and state-of-the-art technology can now be used to develop a promising treatment