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Sa-056-Broadband NIRS reconstruction with colouration maps
Colourations Maps offer a look-up table solution to inverse problems. Their reconstruction error is limited by the entries in the table in turn limited by computational demands. Crust & Crumb, a pseudo-random gradient-weighted sampling design is put forward aiming to escape the combinatorial explosion of traditional sampling designs whilst keeping the reconstruction error at bay. A synthetic exponential model alike the Beer-Lambert law is constructed to establish the validity of the new sampling design, and a finite element diffusion model exemplifies its feasibility against spectroscopic data. In experiments, Crust and Crumb exhibited superior performance over alternatives. Continue reading
Sa-057-Full model selection of the fNIRS pipeline
Most literature do not report to objectively measure the quality of pipelines. However, objectively measuring the pipeline quality is viable, and consequently, that same objective measure of quality can be exploited for optimization. Here we present what we believe to be the first attempt at full model optimization for fNIRS processing and analysis pipeline. We present both a toy synthetic example validating the approach as well as an exemplification in fNIRS using the vanilla Homer 3 functions. Our results suggest that objective optimization of the pipeline is a feasible endeavour, with implications for both quality but also automation of research. Continue reading
Sa-104-Exploring the Effect of Pregnancy Related Anxiety on Newborns’ Responses to Social Stimuli: Insights from High-Density Diffuse Optical Tomography
The Perinatal Imaging Project in partnership with families (PIPKIN) investigates the impact of family context on infant brain development. Preliminary results from a task using high-density optical tomography show that the emergence of vocal selectivity in infants is influenced by maternal pregnancy-related anxiety. These findings underscore the importance of assessing maternal mental health in understanding early brain development and emphasize the need for longitudinal studies focusing on the immediate postnatal period that might help disentangle the potential effect of pre- and postnatal contextual factors affecting functional brain development. Continue reading
Sa-006-Functional Connectivity differences among Children in the Rural Ecuadorian Amazon who Drink Chicha
Chicha is a very common fermented beverage that is used as a nutritional supplement in the Ecuadorian Amazon. Despite the prevalence, the impact of Chicha on cognitive and neural development has not been studied. A 68-channel NIRSport2 montage was used, and a resting-state connectivity analysis will be performed on all channels. Preliminary results indicated a notable lack of connectivity from the medial PFC and higher connectivity in Dorsal regions. We expect connectivity patterns to be moderated by children’s Chicha consumption. Continue reading
Sa-099-Two Human Datasets Reveal the Benefits of Event-Related Designs and Deconvolution for fNIRS Research
Choice of methods for preprocessing and analyzing fNIRS data are extremely important and can vary depending on experimental design and the type of task being investigated. Here, we contrast the more commonly used event-related averaging with deconvolution for event-related designs. Our results were consistent with the pre-established notion that deconvolution is generally the better choice for rapid tasks contaminated by order history effects. Overall, deconvolution (especially with AR-IRLS) appears to be a promising option for estimating evoked responses. Continue reading
Sa-107-fnirsPy: A Sufficient, Easy, and Flexible fNIRS Data Processing Pipeline Library
Functional near-infrared spectroscopy (fNIRS) gains prominence due to its spatial resolution and resistance to motion artifacts, making it ideal for neuroscience and brain-computer interface applications. However, simplifying and standardizing its data processing remains a challenge. This project introduced fnirsPy, a wrapper built on MNE-Python and MNE-NIRS that allows easy deployment of customizable end-to-end data processing pipelines with minimal code. It provides a standard pipeline following current recommendations and implements new preprocessing methods. fnirsPy, tested with data from 22 subjects across two systems, significantly enhances data handling, furthers processing standardization, and improves reproducibility in neuroimaging research. Continue reading
Sa-017-Neural mechanism of multisensory integration in postnatal life: from EEG to fNIRS evidence
Previous evidence showed that even at birth, multisensory integration (MSI) is spatially organized, as evidenced by enhanced electrophysiological responses when stimuli occur near the body, pinpointing the early ability to localize the bodily-self in space1. Here, to unveil the neural correlates supporting the emergence of bodily-self representation early in life, we aim to adapt the previously employed EEG task in an fNIRS version. Continue reading
Sa-020-Prefrontal cortex areas contribution to motor learning
Rotational visuomotor adaptation task was used to explore motor learning in adult subjects. One adaptation set and three retention sets of 1h, 24h and 7d were performed. The results show that the activation pattern of the prefrontal cortex during the adaptation task is maintained after 1h and 24h, but differs after 7d, indicating a possible automatization of the visuomotor map. Continue reading
Sa-106-fNIRS Short Channel Regression Improves Cortical Activation Estimates of Working Memory Load
Studies of Working Memory (WM) increasingly use functional Near-Infrared Spectroscopy (fNIRS), especially because of its tolerance to motion artifacts. However, hemodynamics measured with long-distance channels may originate from both cortex and scalp. We thus applied an fNIRS system with long and short separation channels in 20 participants during n-back tasks to separate cortical and scalp signal contributions. We used Generalized and Linear Mixed Models for analysis. Statistical effects of the n-back level on hemodynamic responses were enhanced through Short Channel Regression (SCR), hence demonstrating that SCR improves validity and accuracy of fNIRS measurements of cortical brain activation in WM tasks. Continue reading
Sa-018-A Framework for Synthetic fNIRS Data Generation
Synthetic data generation is a necessary mechanism for validation efforts requiring a compromise between the simplicity of the forward modelling and the required modelling constraints, and a large number of ad-hoc options are available to whoever needs to generate this data. We exploit the bilinear model for flexible fNIRS data emulation. Our framework provides a range of noise functions that integrate both fully synthetic and semisynthetic physiological and instrumental noise through a coherent interface. This framework facilitates synthetic data generation with implications for algorithm validation and enhancing the quality of pipelines. Continue reading