Aydin, U. | Search bioRxiv

bioRxiv2021

Diffuse optical reconstructions of NIRS data using Maximum Entropy on the Mean

Functional near-infrared spectroscopy (fNIRS) measures the hemoglobin concentration changes associated with neuronal activity. Diffuse optical tomography (DOT) consists of reconstructing the optical density changes measured from scalp channels to the oxy-/deoxy-hemoglobin (i.e., HbO/HbR) concentration changes within the cortical regions. In the present study, we adapted a nonlinear source localization method developed and validated in the context of Electro- and Magneto-Encephalography (EEG/MEG): the Maximum Entropy on the Mean (MEM), to solve the inverse problem of DOT reconstruction. We first introduced depth weighting strategy within the MEM framework for DOT reconstruction to avoid biasing the reconstruction results of DOT towards superficial regions. We also proposed a new initialization of the MEM model improving the temporal accuracy of the original MEM framework. To evaluate MEM performance and compare with widely used depth weighted Minimum Norm Estimate (MNE) inverse solution, we applied a realistic simulation scheme which contained 4000 simulations generated by 250 different seeds at different locations and 4 spatial extents ranging from 3 to 40cm2 along the cortical surface. Our results showed that overall MEM provided more accurate DOT reconstructions than MNE. Moreover, we found that MEM was remained particularly robust in low signal-to-noise ratio (SNR) conditions. The proposed method was further illustrated by comparing to functional Magnetic Resonance Imaging (fMRI) activation maps, on real data involving finger tapping tasks with two different montages. The results showed that MEM provided more accurate HbO and HbR reconstructions in spatial agreement with the main fMRI cluster, when compared to MNE. HighlightsO_LIWe introduced a new fNIRS reconstruction method - Maximum Entropy on the Mean. C_LIO_LIWe implemented depth weighting strategy within the MEM framework. C_LIO_LIWe improved the temporal accuracy of the original MEM reconstruction. C_LIO_LIPerformances of MEM and MNE were evaluated with realistic simulations and real data. C_LIO_LIMEM provided more accurate and robust reconstructions than MNE. C_LI

neuroscience↗

bioRxiv2020

Altered functional integration of brain activity is a marker of impaired cognitive performance following sleep deprivation

Sleep deprivation (SD) leads to impairments in cognitive function. Here, we tested the hypothesis that cognitive changes in the sleep-deprived brain can be explained by information processing within and between large-scale cortical networks. We acquired functional magnetic resonance imaging (fMRI) scans of 20 healthy volunteers during attention and executive tasks following a regular night of sleep, a night of sleep deprivation, and a recovery nap containing non-rapid eye movement (NREM) sleep. Overall, sleep deprivation was associated with increased cortex-wide functional integration, driven by a rise of integration within cortical networks. The ratio of within vs between network integration in the cortex increased further in the recovery nap, suggesting that prolonged wakefulness drives the cortex toward a state resembling sleep. This balance of integration and segregation in the sleep-deprived state was tightly associated with deficits in cognitive performance. This was a distinct and better marker of cognitive impairment than conventional indicators of homeostatic sleep pressure, as well as the pronounced thalamo-cortical connectivity changes that occurs towards falling asleep. Importantly, restoration of the balance between segregation and integration of cortical activity was also related to performance recovery after the nap, demonstrating a bi-directional effect. These results demonstrate that intra- and inter-individual differences in cortical network integration and segregation during task performance may play a critical role in vulnerability to cognitive impairment in the sleep deprived state. Significance StatementSleep deprivation has significant negative consequences for cognitive function. Understanding how changes in brain activity underpin changes in cognition is important not only to discover why performance declines following extended periods of wakefulness, but also for answering the fundamental question of why we require regular and recurrent sleep for optimal performance. Finding neural correlates that predict performance following sleep deprivation also has the potential to understand which individuals are particularly vulnerable to sleep deprivation, and what aspects of brain function may protect them from these negative consequences on performance. Finally, understanding how perturbations to regular (well-rested) brain functioning affect cognitive performance, will provide important insight into how underlying principles of information processing in the brain may support cognition generally.

neuroscience↗