Humans are a social species that engage in complex interactions during goal-oriented cooperation.¹ Social cognition is the basis for such interactions and includes three main components: simulation, empathy, and mentalization. The standard simulation notion refers to a functional process in which an observer tries to voluntarily reproduce, even with the help of imagination, the same mental states of another individual.² First, social cognition has been attributed by Gallese³ to an embodied simulation enabling an immediate understanding and associated with mirror neuron systems, i.e., neuronal systems that are activated when an intentional action is performed (such as motor action) and when the observing the same action. Studies have shown activation of the motor cortex in 6-month-old children when observing actions.^4,5 The second component is empathy, which is defined as the ability to share feelings and emotions.⁶ It is automatic, different for everyone and it is stronger depending on the type of relationship one has with the observed person.^7,8 Third, mentalization is an essential part of social cognition and the ability to read the state of mind, such as desires, beliefs, and intentions in others.^{9 – 11}

Brain activity is associated with physiological changes, which alter the optical properties of tissue. These optical changes can be detected by functional near-infrared spectroscopy (fNIRS) neuroimaging.¹ This technique is well suited for the investigation of fragile newborn infants because it enables noninvasive measurements, is portable, and delivers critical physiological information about cerebral tissue oxygenation and hemodynamics. Neuroimaging with fNIRS has the potential to provide insights into the origin of neonatal brain lesions, as well as into functional development of both the normal and the abnormal brain.^2,3 The ability of fNIRS to access neurovascular coupling associated with neuronal activity offers a useful opportunity to investigate, for example, the neural basis of visual cognitive development in infants. This is of special interest in preterm-born infants, as many of them develop visual cognitive impairment in later childhood.⁴ This visual impairment is thought to result from injury to the posterior visual pathway involving periventricular white matter lesions.⁵ The visual impairment can develop even in the absence of major neuromotor impairments.⁶ The visual function deficits seen in children born prematurely may be related to the networks involving the cortical dorsal stream and its connection to the parietal, frontal, and hippocampal areas.⁷ Even if preterm-born infants do not present with major brain injuries, higher brain dysfunctions may appear during development. There are reasons to conclude that earlier exposure to the extrauterine environment influences the developmental trajectory of the brain.^{8 – 10} From a medical point of view, higher brain dysfunction, such as aberration in the visual pathway, should be identified in the early stages of development to enable early neuroprotective intervention. The assessment of the hemodynamic response (HR) to cognitive processing could reveal some aspects of physiological traits of these higher brain functions.

The rate of parental consent for fetal and perinatal autopsy is decreasing, whereas parents are more likely to agree to virtual autopsy by non-invasive imaging methods. Fetal and perinatal virtual autopsy needs high-resolution and good soft-tissue contrast for investigation of the cause of death and underlying trauma or pathology in fetuses and stillborn infants. This is offered by micro-computed tomography (CT), as opposed to the limited resolution provided by clinical CT scanners, and this is one of the most promising tools for non-invasive perinatal postmortem imaging. We developed and optimized a micro-CT scanner with a dual-energy imaging option. It is dedicated to post-mortem CT angiography and virtual autopsy of fetuses and stillborn infants in that the chamber can be cooled down to around 5 °C; this increases tissue rigidity and slows decomposition of the native specimen. This, together with the dedicated gantry-based architecture, attempts to reduce potential motion artifacts. The developed methodology is based on prior endovascular injection of a BaSO₄-based contrast agent. We explain the design choices and considerations for this scanner prototype. We give details of the treatment of the optimization of the dual-energy and virtual mono-energetic imaging option that has been based on minimizing noise propagation and maximizing the contrast-to-noise ratio for vascular features. We demonstrate the scanner capabilities with proof-of-concept experiments on phantoms and stillborn piglets.

SignificanceIn functional near-infrared spectroscopy (fNIRS) research, ensuring signal quality is a critical preprocessing step. However, traditional index-based metrics such as the coefficient of variation (CV) and scalp coupling index (SCI) rely on arbitrary thresholds and often misclassify channels.AimWe present DL-QC-fNIRS, a deep learning framework for the automated, channel-wise assessment of signal quality.ApproachOur method involves generating continuous wavelet transform scalograms of oxyhemoglobin signals and employing subject-specific cardiac frequency extraction to improve physiological specificity. These inputs are then classified using convolutional neural networks (CNNs). We benchmarked four CNN architectures (GoogLeNet, ResNet-50, SqueezeNet, and EfficientNet-B0) on two independent datasets and one combined heterogeneous dataset.ResultsGoogLeNet achieved the highest accuracy (>93%) on the combined dataset, demonstrating strong sensitivity and specificity across test sets. Compared with CV and SCI, DL-QC-fNIRS yielded markedly higher F1-scores and a more favorable balance between sensitivity and specificity.ConclusionsDL-QC-fNIRS is provided as an open-source MATLAB-based graphical interface, enabling accessible and standardized integration into fNIRS workflows. These findings highlight DL-QC-fNIRS as a scalable, expert-level tool for improving the reliability and reproducibility of optical neuroimaging data.