3^rd International conference on Neuroscience, Neuroradiology & Imaging October 03-04, 2018 Osaka, Japan

Mario Forcione is a Medical Graduate in La Sapienza, University of Rome in 2015. Currently, he is a PhD student of the University of Birmingham and is working in the project “Brain Injury and Trauma Monitoring Using Advanced Photonics”.

Statement of the Problem: Currently, the diagnosis and follow-up of acute Sport-Related Concussion (SRC) is based mainly on symptom score. Th is may lead to underreported or underestimated episodes of concussion among contact-sport players or pre-emptive return to play. Studies using task-related functional Magnetic Resonance Imaging (fMRI) reported abnormal brain activation patterns during neurocognitive tasks in SRC. Th is can be used as an objective parameter to assess players with suspected concussion and to track their recovery. However, the results are not consistent between fMRI studies. Functional Near-Infrared Spectroscopy (fNIRS) can be a valid alternative to assess the cerebral activation in concussed patients illuminating their brain with NIR light. No study has been conducted on SRC within 72 hours of injury using fNIRS. Th e objective of this study is to identify a pathological brain activation pattern that can be used as a biomarker for concussion using fNIRS. Methodology & Theoretical Orientation: An observational study on concussed athletes within 72 hours from injury and non-concussed athletes is conducted using fNIRS. Results are compared with clinical assessment, validated neurocognitive tests (e.g. WAIS-IV) and neuroimaging techniques (e.g. Magnetic Resonance Spectroscopy). Finding: Preliminary results show the capacity to detect brain activations using fNIRS. Further measurements are needed to detect a constant activation pattern in SRC and establish its relationship with neurocognitive tasks and imaging techniques. Conclusion & Significance: fNIRS is a valid tool to detect brain activation. Further measurements are needed to define the type of fNIRS signal that can be used as a biomarker of SRC.

Joshua Deepak Veesa has his expertise in theoretical and computational optics, developing models and algorithms to understand the tissue optical properties by studying the propagation of light in the tissue.

Near-Infrared (NIR) light can propagate deep with biological tissue as compared to other wavelengths of light due to low absorption of tissue at 650-100 nm. It is now commonly used for non-invasive monitoring of brain health, specifically looking at functional response by observing cerebral hemodynamics i.e. the changes in hemoglobin concentrations and level of tissue oxygenation which is defined as the ratio of oxygenated to total hemoglobin. NIR is transmitted through the tissue and the measured transmitted/reflected light depends on the optical properties of the tissue i.e. the spectrally varying absorption and scattering. A spectroscopic analysis of its absorption related properties can then retrieve the concentrations of the light absorbing tissue constituents such as oxygenated and deoxygenated hemoglobin. Among different available NIR Spectroscopy (NIRS) instrumentations, continuous wave NIRS which measure only the attenuation of light intensity is the most widely used due to its low cost, high signal quality and robustness. However, the existing methods utilized for CW NIRS based hemodynamics parameter recovery is based on several factors with the most prominent being the often-inaccurate assumption of the underlying scattering properties of tissue (how far the light has travelled, the so-called path-length). This assumption is known and shown to lead to uncertainty in the recovered hemoglobin concentration levels and tissue oxygenation, as the scattering properties show a significant inter-subject variability. Here, we present a new modified method that uses the spatially resolved measurement of measured data at multiple wavelengths, to recover not only the normalized hemoglobin concentrations and absolute tissue oxygenation, but also the often-ignored scattering related parameters. Th is method is shown to overcome many limitations of the parameter recovery algorithms incorporated into commercial systems and will be demonstrated to be a much more reliable and accurate methodology for absolute parameter recovery, using only 3 CW NIR wavelengths.

Xing Tian is an Assistant Professor of Neural and Cognitive Sciences at NYU Shanghai. Using electrophysiological (MEG/EEG), neuroimaging (fMRI) techniques and behavioral, computational approaches, he investigates sensorimotor integration and transformation, speech and language, memory, mental imagery and other human higher-level cognitive functions. He has published 22 peer-reviewed journal papers, such as in Nature Human Behavior, Nature Neuroscience, Psychological Science, PLOS Biology, Current Biology, Journal of Cognitive Neuroscience, Cortex, Cognitive Psychology and Brain Topography.

Sensory cortices can be activated without any external stimuli. Yet, it is still unclear how this perceptual reactivation occurs, and which neural structures mediate this reconstruction process. In this study, we employed fMRI with mental imagery paradigms to investigate the neural networks involved in perceptual reactivation. Participants performed two speech imagery tasks: Articulation Imagery (AI) and Hearing Imagery (HI). We found that AI induced greater activity in frontal-parietal sensorimotor systems, including sensorimotor cortex, subcentral (BA 43), middle frontal cortex (BA 46) and Parietal Operculum (PO), whereas HI showed stronger activation in regions that have been implicated in memory retrieval: Middle frontal (BA 8), inferior parietal cortex and intraparietal sulcus. Moreover, posterior Superior Temporal Sulcus (pSTS) and anterior Superior Temporal Gyrus (aSTG) was activated more in AI compared with HI, suggesting that covert motor processes induced stronger perceptual reactivation in the auditory cortices. These results suggest that motor-to-perceptual transformation and memory retrieval act as two complementary mechanisms to internally reconstruct corresponding perceptual outcomes. These two mechanisms can serve as a neurocomputational foundation for predicting perceptual changes, either via a previously learned relationship between actions and their perceptual consequences or via stored perceptual experiences of stimulus and episodic or contextual regularity.