Mapping distributed brain function and networks with diffuse optical tomography

Cortical functional mechanisms in emotional cognitive tasks in first-episode, drug-naïve with major depressive disorder: A fNIRS study

BACKGROUND

Previous research has revealed that patients with major depressive disorder (MDD) have negative biases in various aspects of information processing, and these biases are mainly manifested in recognizing facial expressions. However, the link between this emotional cognitive inhibition and neural activation mechanisms in cortical brain regions remains poorly understood. Therefore, this study employed functional near-infrared spectroscopy (fNIRS) to explore the potential impaired regions and neural mechanisms associated with facial emotion cognition in MDD patients.

METHODS

37 MDD patients and 34 healthy controls (HC) were recruited to participate in three sets of cognitive tasks for emotion recognition, and the cortical activation in the brain was synchronously recorded using multi-channel fNIRS.

RESULTS

During tasks requiring the motions identification of sad versus happy emotional states, MDD patients exhibit altered activation in both the left frontopolar cortex (FPC) and the right dorsolateral prefrontal cortex (DLPFC). Notably, the FPC demonstrates a higher level of internal coherence and broader correlation with other cortical areas. Moreover, MDD patients showed lower accuracy in distinguishing emotional cues associated with sadness versus those associated with neutral and happy emotions.

LIMITATIONS

The study had a relatively small sample size, and it specifically examined only three prevalent facial expressions.

CONCLUSION

Facial expression recognition in MDD patients is characterized by negative cognitive interpretation of expressions, which are associated with various cortical altered activations. Neuroimaging further suggests that the cognitive inhibition of emotion signal recognition in everyday interpersonal interactions in MDD patients may primarily be influenced by activation in the left FPC.

Lights on music cognition: A systematic and critical review of fNIRS applications and future perspectives

Research investigating the neural processes related to music perception and production constitutes a well-established field within the cognitive neurosciences. While most neuroimaging tools have limitations in studying the complexity of musical experiences, functional Near-Infrared Spectroscopy (fNIRS) represents a promising, relatively new tool for studying music processes in both laboratory and ecological settings, which is also suitable for both typical and pathological populations across development. Here we systematically review fNIRS studies on music cognition, highlighting prospects and potentialities. We also include an overview of fNIRS basic theory, together with a brief comparison to characteristics of other neuroimaging tools. Fifty-nine studies meeting inclusion criteria (i.e., using fNIRS with music as the primary stimulus) are presented across five thematic sections. Critical discussion of methodology leads us to propose guidelines of good practices aiming for robust signal analyses and reproducibility. A continuously updated world map is proposed, including basic information from studies meeting the inclusion criteria. It provides an organized, accessible, and updatable reference database, which could serve as a catalyst for future collaborations within the community. In conclusion, fNIRS shows potential for investigating cognitive processes in music, particularly in ecological contexts and with special populations, aligning with current research priorities in music cognition.

Functional near-infrared spectroscopy: A novel tool for detecting consciousness after acute severe brain injury

Recent advancements in functional neuroimaging have demonstrated that some unresponsive patients in the intensive care unit retain a level of consciousness that is inconsistent with their behavioral diagnosis of awareness. Functional near-infrared spectroscopy (fNIRS) is a portable optical neuroimaging method that can be used to measure neural activity with good temporal and spatial resolution. However, the reliability of fNIRS for detecting the neural correlates of consciousness remains to be established. In a series of studies, we evaluated whether fNIRS can record sensory, perceptual, and command-driven neural processing in healthy participants and in behaviorally nonresponsive patients. At the individual healthy subject level, we demonstrate that fNIRS can detect commonly studied resting state networks, sensorimotor processing, speech-specific auditory processing, and volitional command-driven brain activity to a motor imagery task. We then tested fNIRS with three acutely brain injured patients and found that one could willfully modulate their brain activity when instructed to imagine playing a game of tennis-providing evidence of preserved consciousness despite no observable behavioral signs of awareness. The successful application of fNIRS for detecting preserved awareness among behaviorally nonresponsive patients highlights its potential as a valuable tool for uncovering hidden cognitive states in critical care settings.

Using mindfulness-based intervention to promote executive function in young children: a multivariable and multiscale sample entropy study

Early childhood marks a pivotal period in the maturation of executive function, the cognitive ability to consciously regulate actions and thoughts. Mindfulness-based interventions have shown promise in bolstering executive function in children. This study used the functional near-infrared spectroscopy technique to explore the impact of mindfulness-based training on young children. Brain imaging data were collected from 68 children (41 boys, aged 61.8 ± 10.7 months) who were randomly assigned to either an intervention group (N = 37, aged 60.03 ± 11.14 months) or a control group (N = 31, aged 59.99 ± 10.89 months). Multivariate and multiscale sample entropy analyses were used. The results showed that: (1) brain complexity was reduced in the intervention group after receiving the mindfulness-based intervention in all three executive function tasks (ps < 0.05), indicating a more efficient neural processing mechanism after the intervention; (2) difference comparisons between the intervention and control groups showed significant differences in relevant brain regions during cognitive shifting (left dorsolateral prefrontal cortex and medial prefrontal cortex) and working memory tasks (left dorsolateral prefrontal cortex), which corroborates with improved behavioral results in the intervention group (Z = -3.674, P < 0.001 for cognitive shifting; Z = 2.594, P < 0.01 for working memory). These findings improve our understanding of early brain development in young children and highlight the neural mechanisms by which mindfulness-based interventions affect executive function. Implications for early intervention to promote young children's brain development are also addressed.

Association between caregiver and infant visual neurocognition

Previous work has shown that caregiver attention shapes visual cognition in infants through dyadic interactions. Is this association measurable when visual cognition is objectively measured in caregivers and infants using comparable experimental paradigms? In the current study, we presented infants (N = 86) and caregivers (N = 78) with age-specific variants of the same preferential looking visual cognition task to investigate whether caregiver visual cognition was associated with their infants' visual cognition. In each trial of the task, two side-by-side flashing displays of coloured shapes were presented. On the 'unchanging' side, the colours of the shapes remained the same. On the 'changing' side, the colour of one shape changed after each flash. Load was varied by changing the number of shapes across trials (low, medium, and high loads). We extracted looking dynamics using video recordings and brain function using functional near-infrared spectroscopy as both infants and caregivers engaged with the task. Change preference (CP) score, which represented the amount of time spent looking at the changing side divided by the total looking duration, showed a load-dependent modulation for both infants and caregivers. Both groups showed the highest CP scores at the low load. Further, higher caregiver CP scores was associated with higher infant CP scores at the low load. Both infants and caregivers engaged canonical regions of the fronto-parietal network involved in visual cognition. Critically, higher caregiver CP scores were associated with greater activation in the left superior parietal lobule in younger infants, a region involved in allocating visuo-spatial attention and working memory maintenance. Further, there was spatial overlap between performance-dependent regions in the right parietal cortex in caregivers and younger infants. Our findings provide first evidence of a heritability-related visual neurocognitive association between caregivers and their children in the first year of life.

Mapping neural correlates of biological motion perception in autistic children using high-density diffuse optical tomography

BACKGROUND

Autism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits.

METHODS

We used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models.

RESULTS

We found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits.

LIMITATIONS

Optical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism.

CONCLUSIONS

This study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits.

Distinct Time-Resolved Brain-Wide Coactivations in Oxygenated and Deoxygenated Hemoglobin

OBJECTIVE

Human resting-state networks (RSNs) estimated from oxygenated (HbO) and deoxygenated hemoglobin (HbR) data exhibit strong similarities, while task-based studies show different dynamics in HbR and HbO responses. Such a discrepancy might be explained due to time-averaged estimations of RSNs. Our study investigated differences between HbO and HbR on time-resolved brain-wide coactivation patterns (CAPs).

METHODS

Diffuse optical tomography was reconstructed from resting-state whole-head functional near-infrared spectroscopy data of HbR and HbO in individual healthy participants. Time-averaged RSNs were obtained using the group-level independent component analysis. Time-resolved CAPs were estimated using a clustering approach on the time courses of all obtained RSNs. Characteristics of the RSNs and CAPs from HbR and HbO were compared.

RESULTS

Spatial patterns of HbR and HbO RSNs exhibited significant similarities. Meanwhile, HbR CAPs revealed much more organized spatial and dynamic characteristics than HbO CAPs. The entire set of HbR CAPs suggests a superstructure resulted from brain-wide neuronal dynamics, which is less evident in the set of HbO CAPs. These differences between HbO and HbR CAPs were consistently replicated in individual session data.

CONCLUSION

Our results suggest that human resting brain-wide neuronal activations are preserved better in time-resolved brain-wide patterns, i.e., CAPs, from HbR than those from HbO, while such a difference is lost between time-averaged HbR and HbO RSNs.

SIGNIFICANCE

Our results reveal, for the first time, HbR concentration fluctuations are more directly coupled with resting dynamics of brain-wide neuronal activations in human brains.

Optimizing spatial accuracy in electroencephalography reconstruction through diffuse optical tomography priors in the auditory cortex

Diffuse optical tomography (DOT) enhances the localization accuracy of neural activity measured with electroencephalography (EEG) while preserving EEG's high temporal resolution. However, the spatial resolution of reconstructed activity diminishes for deeper neural sources. In this study, we analyzed DOT-enhanced EEG localization of neural sources modeled at depths ranging from 11-25 mm in simulations. Our findings reveal systematic biases in reconstructed depth related to DOT channel length. To address this, we developed a data-informed method for selecting DOT channels to improve the spatial accuracy of DOT-enhanced EEG reconstruction. Using our method, the average absolute reconstruction depth errors of DOT reconstruction across all depths are 0.9 ± 0.6 mm, 1.2 ± 0.9 mm, and 1.2 ± 1.1 mm under noiseless, low-level noise, and high-level noise conditions, respectively. In comparison, using fixed channel lengths resulted in errors of 2.6 ± 1.5 mm, 5.0 ± 2.6 mm, and 7.3 ± 4.5 mm under the same conditions. Consequently, our method improved the depth accuracy of DOT reconstructions and facilitated the use of more accurate spatial priors for EEG reconstructions, enhancing the overall precision of the technique.

Diffuse optical tomography of the brain: effects of inaccurate baseline optical parameters and refinements using learned post-processing

Diffuse optical tomography (DOT) uses near-infrared light to image spatially varying optical parameters in biological tissues. In functional brain imaging, DOT uses a perturbation model to estimate the changes in optical parameters, corresponding to changes in measured data due to brain activity. The perturbation model typically uses approximate baseline optical parameters of the different brain compartments, since the actual baseline optical parameters are unknown. We simulated the effects of these approximate baseline optical parameters using parameter variations earlier reported in literature, and brain atlases from four adult subjects. We report the errors in estimated activation contrast, localization, and area when incorrect baseline values were used. Further, we developed a post-processing technique based on deep learning methods that can reduce the effects due to inaccurate baseline optical parameters. The method improved imaging of brain activation changes in the presence of such errors.

Reliability of brain metrics derived from a Time-Domain Functional Near-Infrared Spectroscopy System

With the growing interest in establishing brain-based biomarkers for precision medicine, there is a need for noninvasive, scalable neuroimaging devices that yield valid and reliable metrics. Kernel's second-generation Flow2 Time-Domain Functional Near-Infrared Spectroscopy (TD-fNIRS) system meets the requirements of noninvasive and scalable neuroimaging, and uses a validated modality to measure brain function. In this work, we investigate the test-retest reliability (TRR) of a set of metrics derived from the Flow2 recordings. We adopted a repeated-measures design with 49 healthy participants, and quantified TRR over multiple time points and different headsets-in different experimental conditions including a resting state, a sensory, and a cognitive task. Results demonstrated high reliability in resting state features including hemoglobin concentrations, head tissue light attenuation, amplitude of low frequency fluctuations, and functional connectivity. Additionally, passive auditory and Go/No-Go inhibitory control tasks each exhibited similar activation patterns across days. Notably, areas with the highest reliability were in auditory regions during the auditory task, and right prefrontal regions during the Go/No-Go task, consistent with prior literature. This study underscores the reliability of Flow2-derived metrics, supporting its potential to actualize the vision of using brain-based biomarkers for diagnosis, treatment selection and treatment monitoring of neuropsychiatric and neurocognitive disorders.

Quantitative Optical Imaging of Oxygen in Brain Vasculature

The intimate relationship between neuronal activity and cerebral oxygenation underpins fundamental brain functions like cognition, sensation, and motor control. Optical imaging offers a noninvasive approach to assess brain oxygenation and often serves as an indirect proxy for neuronal activity. However, deciphering neurovascular coupling─the intricate interplay between neuronal activity, blood flow, and oxygen delivery─necessitates independent, high spatial resolution, and high temporal resolution measurements of both microvasculature oxygenation and neuronal activation. This Perspective examines the established optical techniques employed for brain oxygen imaging, specifically functional near-infrared spectroscopy, photoacoustic imaging, optical coherence tomography, and two-photon phosphorescent lifetime microscopy, highlighting their fundamental principles, strengths, and limitations. Several other emerging optical techniques are also introduced. Finally, we discuss key technological challenges and future directions for quantitative optical oxygen imaging, paving the way for a deeper understanding of oxygen metabolism in the brain.

Flexible circuit-based spatially aware modular optical brain imaging system for high-density measurements in natural settings

Significance

Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic conditions remains a significant challenge.

Aim

The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide a real-time probe three-dimensional (3D) shape estimation to improve the use of fNIRS in everyday conditions.

Approach

The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3D positions in real time to enable advanced tomographic data analysis and motion tracking.

Results

Optical characterization of the MOBI detector reports a noise equivalence power of 8.9 and 7.3    pW / Hz at 735 and 850 nm, respectively, with a dynamic range of 88 dB. The 3D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared with positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided.

Conclusions

To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3D optode position acquisition, combined with lightweight modules ( 18    g / module ) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

Real-time motion-enabling positron emission tomography of the brain of upright ambulatory humans

BACKGROUND

Mobile upright PET devices have the potential to enable previously impossible neuroimaging studies. Currently available options are imagers with deep brain coverage that severely limit head/body movements or imagers with upright/motion enabling properties that are limited to only covering the brain surface.

METHODS

In this study, we test the feasibility of an upright, motion-compatible brain imager, our Ambulatory Motion-enabling Positron Emission Tomography (AMPET) helmet prototype, for use as a neuroscience tool by replicating a variant of a published PET/fMRI study of the neurocorrelates of human walking. We validate our AMPET prototype by conducting a walking movement paradigm to determine motion tolerance and assess for appropriate task related activity in motor-related brain regions. Human participants (n = 11 patients) performed a walking-in-place task with simultaneous AMPET imaging, receiving a bolus delivery of F18-Fluorodeoxyglucose.

RESULTS

Here we validate three pre-determined measure criteria, including brain alignment motion artifact of less than <2 mm and functional neuroimaging outcomes consistent with existing walking movement literature.

CONCLUSIONS

The study extends the potential and utility for use of mobile, upright, and motion-tolerant neuroimaging devices in real-world, ecologically-valid paradigms. Our approach accounts for the real-world logistics of an actual human participant study and can be used to inform experimental physicists, engineers and imaging instrumentation developers undertaking similar future studies. The technical advances described herein help set new priorities for facilitating future neuroimaging devices and research of the human brain in health and disease.

Diffuse optical tomography for mapping cerebral hemodynamics and functional connectivity in delirium

INTRODUCTION

Delirium is associated with mortality and new onset dementia, yet the underlying pathophysiology remains poorly understood. Development of imaging biomarkers has been difficult given the challenging nature of imaging delirious patients. Diffuse optical tomography (DOT) offers a promising approach for investigating delirium given its portability and three-dimensional capabilities.

METHODS

Twenty-five delirious and matched non-delirious patients (n = 50) were examined using DOT, comparing cerebral oxygenation and functional connectivity in the prefrontal cortex during and after an episode of delirium.

RESULTS

Total hemoglobin values were significantly decreased in the delirium group, even after delirium resolution. Functional connectivity between the dorsolateral prefrontal cortex and dorsomedial prefrontal cortex was strengthened post-resolution compared to during an episode; however, this relationship was still significantly weaker compared to controls.

DISCUSSION

These findings highlight DOT's potential as an imaging biomarker to measure impaired cerebral oxygenation and functional dysconnectivity during and after delirium. Future studies should focus on the role of cerebral oxygenation in delirium pathogenesis and exploring the etiological link between delirium and dementias.

HIGHLIGHTS

We developed a portable diffuse optical tomography (DOT) system for bedside three-dimensional functional neuroimaging to study delirium in the hospital. We implemented a novel DOT task-focused seed-based correlation analysis. DOT revealed decreased cerebral oxygenation and functional connectivity strength in the delirium group, even after resolution of delirium.

Flexible-circuit-based 3-D aware modular optical brain imaging system for high-density measurements in natural settings

Significance

Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic condition remains a significant challenge.

Aim

The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide real-time probe 3-D shape estimation to improve the use of fNIRS in everyday conditions.

Approach

The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3-D positions in real-time to enable advanced tomographic data analysis and motion tracking.

Results

Optical characterization of the MOBI detector reports a noise equivalence power (NEP) of 8.9 and 7.3 pW / H z at 735 nm and 850 nm, respectively, with a dynamic range of 88 dB. The 3-D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared to positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided.

Conclusions

To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3-D optode position acquisition, combined with lightweight modules (18 g/module) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

Identifying Naturalistic Movies from Human Brain Activity with High-Density Diffuse Optical Tomography

Modern neuroimaging modalities, particularly functional MRI (fMRI), can decode detailed human experiences. Thousands of viewed images can be identified or classified, and sentences can be reconstructed. Decoding paradigms often leverage encoding models that reduce the stimulus space into a smaller yet generalizable feature set. However, the neuroimaging devices used for detailed decoding are non-portable, like fMRI, or invasive, like electrocorticography, excluding application in naturalistic use. Wearable, non-invasive, but lower-resolution devices such as electroencephalography and functional near-infrared spectroscopy (fNIRS) have been limited to decoding between stimuli used during training. Herein we develop and evaluate model-based decoding with high-density diffuse optical tomography (HD-DOT), a higher-resolution expansion of fNIRS with demonstrated promise as a surrogate for fMRI. Using a motion energy model of visual content, we decoded the identities of novel movie clips outside the training set with accuracy far above chance for single-trial decoding. Decoding was robust to modulations of testing time window, different training and test imaging sessions, hemodynamic contrast, and optode array density. Our results suggest that HD-DOT can translate detailed decoding into naturalistic use.

Mapping brain function in adults and young children during naturalistic viewing with high-density diffuse optical tomography

Human studies of early brain development have been limited by extant neuroimaging methods. MRI scanners present logistical challenges for imaging young children, while alternative modalities like functional near-infrared spectroscopy have traditionally been limited by image quality due to sparse sampling. In addition, conventional tasks for brain mapping elicit low task engagement, high head motion, and considerable participant attrition in pediatric populations. As a result, typical and atypical developmental trajectories of processes such as language acquisition remain understudied during sensitive periods over the first years of life. We evaluate high-density diffuse optical tomography (HD-DOT) imaging combined with movie stimuli for high resolution optical neuroimaging in awake children ranging from 1 to 7 years of age. We built an HD-DOT system with design features geared towards enhancing both image quality and child comfort. Furthermore, we characterized a library of animated movie clips as a stimulus set for brain mapping and we optimized associated data analysis pipelines. Together, these tools could map cortical responses to movies and contained features such as speech in both adults and awake young children. This study lays the groundwork for future research to investigate response variability in larger pediatric samples and atypical trajectories of early brain development in clinical populations.

Initial non-invasive in vivo sensing of the lung using time domain diffuse optics

The in vivo diagnosis and monitoring of pulmonary disorders (caused for example by emphysema, Covid-19, immature lung tissue in infants) could be effectively supported by the non-invasive sensing of the lung through light. With this purpose, we investigated the feasibility of probing the lung by means of time-resolved diffuse optics, leveraging the increased depth (a few centimeters) attained by photons collected after prolonged propagation time (a few nanoseconds). We present an initial study that includes measurements performed on 5 healthy volunteers during a breathing protocol, using a time-resolved broadband diffuse optical spectroscopy system. Those measurements were carried out across the spectral range of 600-1100 nm at a source-detector distance of 3 cm, and at 820 nm over a longer distance (7-9 cm). The preliminary analysis of the in vivo data with a simplified homogeneous model revealed a maximum probing depth of 2.6-3.9 cm, suitable for reaching the lung. Furthermore, we observed variations in signal associated with respiration, particularly evident at long photon propagation times. However, challenges stemming from both intra- and inter-subject variability, along with inconsistencies potentially arising from conflicting scattering and absorption effects on the collected signal, hindered a clear interpretation. Aspects that require further investigation for a more comprehensive understanding are outlined.

The impact of caregiver inhibitory control on infant visual working memory

Visual working memory (VWM) emerges in the first year of life and has far-reaching implications for academic and later life outcomes. Given that caregivers play a significant role in shaping cognitive function in children, it is important to understand how they might impact VWM development as early as infancy. The current study investigated whether caregivers' efficiency of regulating inhibitory control was associated with VWM function in their infants. Eighty-eight caregivers were presented with a Go-NoGo task to assess inhibitory control. An efficiency score was calculated using their behavioural responses. Eighty-six 6-to-10-month-old infants were presented with a preferential looking task to assess VWM function. VWM load was manipulated across one (low load), two (medium load) and three (high load) items. Functional near-infrared spectroscopy was used to record brain activation from caregivers and their infants. We found no direct association between caregiver efficiency and infant VWM behaviour. However, we found an indirect association - caregiver efficiency was linked to infant VWM through left-lateralized fronto-parietal engagement. Specifically, infants with low efficiency caregivers showed decreasing left-lateralized parietal engagement with increasing VWM performance at the medium and high loads compared to infants with high efficiency caregivers, who did not show any load- or performance-dependent modulation. Our findings contribute to a growing body of literature examining the role that caregivers play in early neurocognitive development.

Subject-specific information enhances spatial accuracy of high-density diffuse optical tomography

Functional near-infrared spectroscopy (fNIRS) is a widely used imaging method for mapping brain activation based on cerebral hemodynamics. The accurate quantification of cortical activation using fNIRS data is highly dependent on the ability to correctly localize the positions of light sources and photodetectors on the scalp surface. Variations in head size and shape across participants greatly impact the precise locations of these optodes and consequently, the regions of the cortical surface being reached. Such variations can therefore influence the conclusions drawn in NIRS studies that attempt to explore specific cortical regions. In order to preserve the spatial identity of each NIRS channel, subject-specific differences in NIRS array registration must be considered. Using high-density diffuse optical tomography (HD-DOT), we have demonstrated the inter-subject variability of the same HD-DOT array applied to ten participants recorded in the resting state. We have also compared three-dimensional image reconstruction results obtained using subject-specific positioning information to those obtained using generic optode locations. To mitigate the error introduced by using generic information for all participants, photogrammetry was used to identify specific optode locations per-participant. The present work demonstrates the large variation between subjects in terms of which cortical parcels are sampled by equivalent channels in the HD-DOT array. In particular, motor cortex recordings suffered from the largest optode localization errors, with a median localization error of 27.4 mm between generic and subject-specific optodes, leading to large differences in parcel sensitivity. These results illustrate the importance of collecting subject-specific optode locations for all wearable NIRS experiments, in order to perform accurate group-level analysis using cortical parcellation.

Shedding light on ultrasound in action: Optical and optoacoustic monitoring of ultrasound brain interventions

Monitoring brain responses to ultrasonic interventions is becoming an important pillar of a growing number of applications employing acoustic waves to actuate and cure the brain. Optical interrogation of living tissues provides a unique means for retrieving functional and molecular information related to brain activity and disease-specific biomarkers. The hybrid optoacoustic imaging methods have further enabled deep-tissue imaging with optical contrast at high spatial and temporal resolution. The marriage between light and sound thus brings together the highly complementary advantages of both modalities toward high precision interrogation, stimulation, and therapy of the brain with strong impact in the fields of ultrasound neuromodulation, gene and drug delivery, or noninvasive treatments of neurological and neurodegenerative disorders. In this review, we elaborate on current advances in optical and optoacoustic monitoring of ultrasound interventions. We describe the main principles and mechanisms underlying each method before diving into the corresponding biomedical applications. We identify areas of improvement as well as promising approaches with clinical translation potential.

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

A high-density diffuse optical tomography dataset of naturalistic viewing

Traditional laboratory tasks offer tight experimental control but lack the richness of our everyday human experience. As a result many cognitive neuroscientists have been motivated to adopt experimental paradigms that are more natural, such as stories and movies. Here we describe data collected from 58 healthy adult participants (aged 18-76 years) who viewed 10 minutes of a movie (The Good, the Bad, and the Ugly, 1966). Most (36) participants viewed the clip more than once, resulting in 106 sessions of data. Cortical responses were mapped using high-density diffuse optical tomography (first- through fourth nearest neighbor separations of 1.3, 3.0, 3.9, and 4.7 cm), covering large portions of superficial occipital, temporal, parietal, and frontal lobes. Consistency of measured activity across subjects was quantified using intersubject correlation analysis. Data are provided in both channel format (SNIRF) and projected to standard space (NIfTI), using an atlas-based light model. These data are suitable for methods exploration as well as investigating a wide variety of cognitive phenomena.

Growth in early infancy drives optimal brain functional connectivity which predicts cognitive flexibility in later childhood

Functional brain network organization, measured by functional connectivity (FC), reflects key neurodevelopmental processes for healthy development. Early exposure to adversity, e.g. undernutrition, affects neurodevelopment, observable via disrupted FC, and leads to poorer outcomes from preschool age onward. We assessed longitudinally the impact of early growth trajectories on developmental FC in a rural Gambian population from age 5 to 24 months. To investigate how these early trajectories relate to later childhood outcomes, we assessed cognitive flexibility at 3-5 years. We observed that early physical growth before the fifth month of life drove optimal developmental trajectories of FC that in turn predicted cognitive flexibility at pre-school age. In contrast to previously studied developmental populations, this Gambian sample exhibited long-range interhemispheric FC that decreased with age. Our results highlight the measurable effects that poor growth in early infancy has on brain development and the subsequent impact on pre-school age cognitive development, underscoring the need for early life interventions throughout global settings of adversity.

Stunting in infancy is associated with atypical activation of working memory and attention networks

Stunting is associated with poor long-term cognitive, academic and economic outcomes, yet the mechanisms through which stunting impacts cognition in early development remain unknown. In a first-ever neuroimaging study conducted on infants from rural India, we demonstrate that stunting impacts a critical, early-developing cognitive system-visual working memory. Stunted infants showed poor visual working memory performance and were easily distractible. Poor performance was associated with reduced engagement of the left anterior intraparietal sulcus, a region involved in visual working memory maintenance and greater suppression in the right temporoparietal junction, a region involved in attentional shifting. When assessed one year later, stunted infants had lower problem-solving scores, while infants of normal height with greater left anterior intraparietal sulcus activation showed higher problem-solving scores. Finally, short-for-age infants with poor physical growth indices but good visual working memory performance showed more positive outcomes suggesting that intervention efforts should focus on improving working memory and reducing distractibility in infancy.

Effectiveness assessment of repetitive transcranial alternating current stimulation with concurrent EEG and fNIRS measurement

Transcranial alternating current stimulation (tACS) exhibits the capability to interact with endogenous brain oscillations using an external low-intensity sinusoidal current and influences cerebral function. Despite its potential benefits, the physiological mechanisms and effectiveness of tACS are currently a subject of debate and disagreement. The aims of our study are to (i) evaluate the neurological and behavioral impact of tACS by conducting repetitive sham-controlled experiments and (ii) propose criteria to evaluate effectiveness, which can serve as a benchmark to determine optimal individual-based tACS protocols. In this study, 15 healthy adults participated in the experiment over two visiting: sham and tACS (i.e., 5 Hz, 1 mA). During each visit, we used multimodal recordings of the participants' brain, including simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), along with a working memory (WM) score to quantify neurological effects and cognitive changes immediately after each repetitive sham/tACS session. Our results indicate increased WM scores, hemodynamic response strength, and EEG power in theta and delta bands both during and after the tACS period. Additionally, the observed effects do not increase with prolonged stimulation time, as the effects plateau towards the end of the experiment. In conclusion, our proposed closed-loop scheme offers a promising advance for evaluating the effectiveness of tACS during the stimulation session. Specifically, the assessment criteria use participant-specific brain-based signals along with a behavioral output. Moreover, we propose a feedback efficacy score that can aid in determining the optimal stimulation duration based on a participant-specific brain state, thereby preventing the risk of overstimulation.

Surface-based integration approach for fNIRS-fMRI reliability assessment

INTRODUCTION

Studies integrating functional near-infrared spectroscopy (fNIRS) with functional MRI (fMRI) employ heterogeneous methods in defining common regions of interest in which similarities are assessed. Therefore, spatial agreement and temporal correlation may not be reproducible across studies. In the present work, we address this issue by proposing a novel method for integration and analysis of fNIRS and fMRI over the cortical surface.

MATERIALS AND METHODS

Eighteen healthy volunteers (age mean±SD 30.55 ± 4.7, 7 males) performed a motor task during non-simultaneous fMRI and fNIRS acquisitions. First, fNIRS and fMRI data were integrated by projecting subject- and group-level source maps over the cortical surface mesh to define anatomically constrained functional ROIs (acfROI). Next, spatial agreement and temporal correlation were quantified as Dice Coefficient (DC) and Pearson's correlation coefficient between fNIRS-fMRI in the acfROIs.

RESULTS

Subject-level results revealed moderate to substantial spatial agreement (DC range 0.43 - 0.64), confirmed at the group-level only for blood oxygenation level-dependent (BOLD) signal vs. HbO2 (0.44 - 0.69), while lack of agreement was found for BOLD vs. HbR in some instances (0.05 - 0.49). Subject-level temporal correlation was moderate to strong (0.79 - 0.85 for BOLD vs. HbO2 and -0.62 to -0.72 for BOLD vs. HbR), while an overall strong correlation was found for group-level results (0.95 - 0.98 for BOLD vs. HbO2 and -0.91 to -0.94 for BOLD vs. HbR).

CONCLUSION

The proposed method directly compares fNIRS and fMRI by projecting individual source maps to the cortical surface. Our results indicate spatial and temporal correspondence between fNIRS and fMRI, and promotes the use of fNIRS when more ecological acquision settings are required, such as longitudinal monitoring of brain activity before and after rehabilitation.

Anatomical Modeling and Optimization of Speckle Contrast Optical Tomography

Traditional methods for mapping cerebral blood flow (CBF), such as positron emission tomography and magnetic resonance imaging, offer only isolated snapshots of CBF due to scanner logistics. Speckle contrast optical tomography (SCOT) is a promising optical technique for mapping CBF. However, while SCOT has been established in mice, the method has not yet been demonstrated in humans - partly due to a lack of anatomical reconstruction methods and uncertainty over the optimal design parameters. Herein we develop SCOT reconstruction methods that leverage MRI-based anatomical head models and finite-element modeling of the SCOT forward problem (NIRFASTer). We then simulate SCOT for CBF perturbations to evaluate sensitivity of imaging performance to exposure time and SD-distances. We find image resolution comparable to intensity-based diffuse optical tomography at superficial cortical tissue depth (~1.5 cm). Localization errors can be reduced by including longer SD-measurements. With longer exposure times speckle contrast decreases, however, noise decreases faster, resulting in a net increase in SNR. Specifically, extending exposure time from 10μs to 10ms increased SCOT SNR by 1000X. Overall, our modeling methods provide anatomically-based image reconstructions that can be used to evaluate a broad range of tissue conditions, measurement parameters, and noise sources and inform SCOT system design.

On-line clearing and staining method for the efficient optical imaging of large volume samples at the cellular resolution

Optical microscopy is a powerful tool for exploring the structure and function of organisms. However, the three-dimensional (3D) imaging of large volume samples is time-consuming and difficult. In this manuscript, we described an on-line clearing and staining method for efficient imaging of large volume samples at the cellular resolution. The optimized cocktail can increase staining and imaging depth to reduce the sectioning and scanning time, more than doubling the operational efficiency of the system. Using this method, we demonstrated the rapid acquisition of Aβ plaques in whole mouse brain and obtained a complete set of cytoarchitecture images of an adult porcine hemisphere at 1.625 × 1.625 × 10 µm3 voxel resolution for about 49 hours.

Achieving Broadband NIR-I to NIR-II Emission in an All-Inorganic Halide Double-Perovskite Cs2NaYCl6:Cr3+ Phosphor for Night Vision Imaging

Near-infrared phosphor-converted light-emitting diodes (NIR pc-LEDs) offer numerous advantages, including compact size, tunable emission spectra, energy efficiency, and high integration potential. These features make them highly promising for various applications, such as night vision monitoring, food safety inspection, biomedical imaging, and theragnostics. All-inorganic halide double-perovskite materials, known for their large absorption cross section, excellent defect tolerance, and long carrier diffusion radius, serve as unique matrices for constructing near-infrared fluorescent materials. In this study, we successfully prepared the all-inorganic metal halide double-perovskite Cs2NaYCl6:Cr3+ using a grinding-sintering method. A small fraction of the [YCl6] octahedra within the host material's lattice was substituted with Cr3+ ions, resulting in the creation of the Cs2NaYCl6:Cr3+ phosphor. When excited with λ = 310 nm UV light, the phosphor exhibited a broad emission range spanning from 800 to 1400 nm, covering the NIR-I and NIR-II regions. It had a broad bandwidth emission of 185 nm and achieved a fluorescence quantum yield of 20.2%. The unique broadband emission of the phosphor originates from the weak crystal field environment provided by the Cs2NaYCl6 host matrix, which enhances the luminescence properties of the Cr3+ ions. To create NIR pc-LEDs, the phosphor was encapsulated onto a commercially available UV LED chip operating at 310 nm. The potential application of these NIR pc-LEDs in night vision imaging was successfully validated.

Mapping cortical activations underlying covert and overt language production using high-density diffuse optical tomography

Gold standard neuroimaging modalities such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and more recently electrocorticography (ECoG) have provided profound insights regarding the neural mechanisms underlying the processing of language, but they are limited in applications involving naturalistic language production especially in developing brains, during face-to-face dialogues, or as a brain-computer interface. High-density diffuse optical tomography (HD-DOT) provides high-fidelity mapping of human brain function with comparable spatial resolution to that of fMRI but in a silent and open scanning environment similar to real-life social scenarios. Therefore, HD-DOT has potential to be used in naturalistic settings where other neuroimaging modalities are limited. While HD-DOT has been previously validated against fMRI for mapping the neural correlates underlying language comprehension and covert (i.e., "silent") language production, HD-DOT has not yet been established for mapping the cortical responses to overt (i.e., "out loud") language production. In this study, we assessed the brain regions supporting a simple hierarchy of language tasks: silent reading of single words, covert production of verbs, and overt production of verbs in normal hearing right-handed native English speakers (n = 33). First, we found that HD-DOT brain mapping is resilient to movement associated with overt speaking. Second, we observed that HD-DOT is sensitive to key activations and deactivations in brain function underlying the perception and naturalistic production of language. Specifically, statistically significant results were observed that show recruitment of regions in occipital, temporal, motor, and prefrontal cortices across all three tasks after performing stringent cluster-extent based thresholding. Our findings lay the foundation for future HD-DOT studies of imaging naturalistic language comprehension and production during real-life social interactions and for broader applications such as presurgical language assessment and brain-machine interfaces.

Ultra-high density imaging arrays for diffuse optical tomography of human brain improve resolution, signal-to-noise, and information decoding

Functional magnetic resonance imaging (fMRI) has dramatically advanced non-invasive human brain mapping and decoding. Functional near-infrared spectroscopy (fNIRS) and high-density diffuse optical tomography (HD-DOT) non-invasively measure blood oxygen fluctuations related to brain activity, like fMRI, at the brain surface, using more-lightweight equipment that circumvents ergonomic and logistical limitations of fMRI. HD-DOT grids have smaller inter-optode spacing (∼13 mm) than sparse fNIRS (∼30 mm) and therefore provide higher image quality, with spatial resolution ∼1/2 that of fMRI. Herein, simulations indicated reducing inter-optode spacing to 6.5 mm would further improve image quality and noise-resolution tradeoff, with diminishing returns below 6.5 mm. We then constructed an ultra-high-density DOT system (6.5-mm spacing) with 140 dB dynamic range that imaged stimulus-evoked activations with 30-50% higher spatial resolution and repeatable multi-focal activity with excellent agreement with participant-matched fMRI. Further, this system decoded visual stimulus position with 19-35% lower error than previous HD-DOT, throughout occipital cortex.

Intrinsic brain activity is increasingly complex and develops asymmetrically during childhood and early adolescence

A large body of evidence suggests that brain signal complexity (BSC) may be an important indicator of healthy brain functioning or alternately, a harbinger of disease and dysfunction. However, despite recent progress our current understanding of how BSC emerges and evolves in large-scale networks, and the factors that shape these dynamics, remains limited. Here, we utilized resting-state functional near-infrared spectroscopy (rs-fNIRS) to capture and characterize the nature and time course of BSC dynamics within large-scale functional networks in 107 healthy participants ranging from 6-13 years of age. Age-dependent increases in spontaneous BSC were observed predominantly in higher-order association areas including the default mode (DMN) and attentional (ATN) networks. Our results also revealed asymmetrical developmental patterns in BSC that were specific to the dorsal and ventral ATN networks, with the former showing a left-lateralized and the latter demonstrating a right-lateralized increase in BSC. These age-dependent laterality shifts appeared to be more pronounced in females compared to males. Lastly, using a machine-learning model, we showed that BSC is a reliable predictor of chronological age. Higher-order association networks such as the DMN and dorsal ATN demonstrated the most robust prognostic power for predicting ages of previously unseen individuals. Taken together, our findings offer new insights into the spatiotemporal patterns of BSC dynamics in large-scale intrinsic networks that evolve over the course of childhood and adolescence, suggesting that a network-based measure of BSC represents a promising approach for tracking normative brain development and may potentially aid in the early detection of atypical developmental trajectories.

Diffuse Optical Tomography Spatial Prior for EEG Source Localization in Human Visual Cortex

Electroencephalography (EEG) and diffuse optical tomography (DOT) are imaging methods which are widely used for neuroimaging. While the temporal resolution of EEG is high, the spatial resolution is typically limited. DOT, on the other hand, has high spatial resolution, but the temporal resolution is inherently limited by the slow hemodynamics it measures. In our previous work, we showed using computer simulations that when using the results of DOT reconstruction as the spatial prior for EEG source reconstruction, high spatio-temporal resolution could be achieved. In this work, we experimentally validate the algorithm by alternatingly flashing two visual stimuli at a speed that is faster than the temporal resolution of DOT. We show that the joint reconstruction using both EEG and DOT clearly resolves the two stimuli temporally, and the spatial confinement is drastically improved in comparison to reconstruction using EEG alone.

Scalable, modular continuous wave functional near-infrared spectroscopy system (Spotlight)

Significance

We present a fiberless, portable, and modular continuous wave-functional near-infrared spectroscopy system, Spotlight, consisting of multiple palm-sized modules-each containing high-density light-emitting diode and silicon photomultiplier detector arrays embedded in a flexible membrane that facilitates optode coupling to scalp curvature.

Aim

Spotlight's goal is to be a more portable, accessible, and powerful functional near-infrared spectroscopy (fNIRS) device for neuroscience and brain-computer interface (BCI) applications. We hope that the Spotlight designs we share here can spur more advances in fNIRS technology and better enable future non-invasive neuroscience and BCI research.

Approach

We report sensor characteristics in system validation on phantoms and motor cortical hemodynamic responses in a human finger-tapping experiment, where subjects wore custom 3D-printed caps with two sensor modules.

Results

The task conditions can be decoded offline with a median accuracy of 69.6%, reaching 94.7% for the best subject, and at a comparable accuracy in real time for a subset of subjects. We quantified how well the custom caps fitted to each subject and observed that better fit leads to more observed task-dependent hemodynamic response and better decoding accuracy.

Conclusions

The advances presented here should serve to make fNIRS more accessible for BCI applications.

Achieving High Quantum Efficiency Broadband NIR Mg4 Ta2 O9 :Cr3+ Phosphor Through Lithium-Ion Compensation

Ultra-efficient broadband near-infrared (NIR) phosphor-converted light-emitting diodes (pc-LEDs) are urgently needed to improve the detection sensitivity and spatial resolution of current smart NIR spectroscopy-based techniques. Nonetheless, the performance of NIR pc-LED has severely limited owing to the external quantum efficiency (EQE) bottleneck of NIR light-emitting materials. Herein, a blue LED excitable Cr³⁺ -doped tetramagnesium ditantalate (Mg₄ Ta₂ O₉ , MT) phosphor is advantageously modified through lithium ion as a key efficient broadband NIR emitter to achieve high optical output power of the NIR light source. The emission spectrum encompasses the 700-1300 nm electromagnetic spectrum of first biological window (λ_max  = 842 nm) with a full-width at half-maximum (FWHM) of ≈2280 cm^-1 (≈167 nm), and achieves a record EQE of 61.25% detected at 450 nm excitation through Li-ion compensation. A prototype NIR pc-LED is fabricated with MT:Cr³⁺ , Li⁺ to evaluate its potential practical application, which reveals an NIR output power of 53.22 mW at a driving current of 100 mA, and a photoelectric conversion efficiency of 25.09% at 10 mA. This work provides an ultra-efficient broadband NIR luminescent material, which shows great promise in practical applications and presents a novel option for the next-generation high-power compact NIR light sources.

Single photon single pixel imaging into thick scattering medium

Imaging into thick scattering medium is a long-standing challenge. Beyond the quasi-ballistic regime, multiple scattering scrambles the spatiotemporal information of incident/emitted light, making canonical imaging based on light focusing nearly impossible. Diffusion optical tomography (DOT) is one of the most popular approach to look inside scattering medium, but quantitatively inverting the diffusion equation is ill-posed, and prior information of the medium is typically necessary, which is nontrivial to obtain. Here, we show theoretically and experimentally that, by synergizing the one-way light scattering characteristic of single pixel imaging with ultrasensitive single photon detection and a metric-guided image reconstruction, single photon single pixel imaging can serve as a simple and powerful alternative to DOT for imaging into thick scattering medium without prior knowledge or inverting the diffusion equation. We demonstrated an image resolution of 12 mm inside a 60 mm thick (∼ 78 mean free paths) scattering medium.

Research Progress and Development of Near-Infrared Phosphors

Near-infrared (NIR) light has attracted considerable attention in diverse applications, such as food testing, security monitoring, and modern agriculture. Herein, the advanced applications of NIR light, as well as various devices to realize NIR light, have been described. Among the diverse NIR light source devices, the NIR phosphor-converted light-emitting diode (pc-LED), serving as a new-generation NIR light source, has obtained attention due to its wavelength-tunable behavior and low-cost. As one of the key materials of the NIR pc-LED, a series of NIR phosphors have been summarized depending on the type of luminescence center. Meanwhile, the characteristic transitions and luminescence properties of the above phosphors are illustrated in detail. In addition, the status quo of NIR pc-LEDs, as well as the potential problems and future developments of NIR phosphors and applications have also been discussed.

Fusion deep learning approach combining diffuse optical tomography and ultrasound for improving breast cancer classification

Diffuse optical tomography (DOT) is a promising technique that provides functional information related to tumor angiogenesis. However, reconstructing the DOT function map of a breast lesion is an ill-posed and underdetermined inverse process. A co-registered ultrasound (US) system that provides structural information about the breast lesion can improve the localization and accuracy of DOT reconstruction. Additionally, the well-known US characteristics of benign and malignant breast lesions can further improve cancer diagnosis based on DOT alone. Inspired by a fusion model deep learning approach, we combined US features extracted by a modified VGG-11 network with images reconstructed from a DOT deep learning auto-encoder-based model to form a new neural network for breast cancer diagnosis. The combined neural network model was trained with simulation data and fine-tuned with clinical data: it achieved an AUC of 0.931 (95% CI: 0.919-0.943), superior to those achieved using US images alone (0.860) or DOT images alone (0.842).

Single-shot photoacoustic imaging with single-element transducer through a spatiotemporal encoder

Significance

Current photoacoustic (PA) imaging modalities typically require either serial detection with a single-element transducer or parallel detections with an ultrasonic array, indicating a dilemma between system cost and imaging throughput. PA topography through ergodic relay (PATER) was recently developed to address this bottleneck. However, PATER requires object-specific calibration due to varied boundary condition and must be recalibrated through pointwise scanning for each object before measurements, which is time-consuming and severely limits practical application.

Aim

We aim to develop a new single-shot PA imaging technique that only requires a one-time calibration for imaging different objects using a single-element transducer.

Approach

We develop an imaging method, PA imaging through a spatiotemporal encoder (PAISE), to address the above issue. The spatial information is effectively coded into unique temporal features by the spatiotemporal encoder, which allows for compressive image reconstruction. An ultrasonic waveguide is proposed as a critical element to guide the PA waves from the object into the prism, which effectively accounts for the varied boundary condition of different objects. We further add irregular-shaped edges on the prism to introduce randomized internal reflections and further facilitate the scrambling of acoustic waves.

Results

The proposed technique is validated through comprehensive numerical simulations and experiments, and it is demonstrated that PAISE can successfully overcome the changed boundary condition and can image different samples given a single calibration.

Conclusions

The proposed PAISE technique is capable of single-shot widefield PA imaging with a single-element transducer and does not require sample-specific calibration, which successfully overcomes the major limitation of previous PATER technology.

Wearable, high-density fNIRS and diffuse optical tomography technologies: a perspective

Recent progress in optoelectronics has made wearable and high-density functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) technologies possible for the first time. These technologies have the potential to open new fields of real-world neuroscience by enabling functional neuroimaging of the human cortex at a resolution comparable to fMRI in almost any environment and population. In this perspective article, we provide a brief overview of the history and the current status of wearable high-density fNIRS and DOT approaches, discuss the greatest ongoing challenges, and provide our thoughts on the future of this remarkable technology.

Controlling jaw-related motion artifacts in functional near-infrared spectroscopy

BACKGROUND

Functional near-infrared spectroscopy (fNIRS) as a non-invasive optical neuroimaging technique has demonstrated great potential in monitoring cerebral activity. Due to its portability and compatibility with medical implants, fNIRS has seen increasing applications in studying the hearing, language and cognitive functions. However, fNIRS is susceptible to artifacts related to jaw movements, such as teeth clenching, swallowing and speaking, which affect recordings over the temporal, parietal and frontal/prefrontal cortices.

NEW METHOD

We investigated two new approaches to control the jaw-related motion artifacts, an individually customized bite bar apparatus and a denoising algorithm namely PCA-GLM based on multi-channel fNIRS recordings from long-separation and short-separation montage. We first recorded data while subjects performed a clenching task, then an auditory task and a resting-state task with and without the bite bar.

RESULTS

Our results have shown that jaw clenching can introduce spurious, task-evoked-like responses in fNIRS signals. A bite bar customized for each participant effectively suppressed the movement-related activities in fNIRS, at both task and resting-state conditions. Moreover, the bite bar and the PCA-GLM denoising method are shown to improve auditory responses, by significantly reducing the within-subject standard deviation, increasing the task-related contrast-to-noise ratio, and yielding stronger activations to the auditory stimuli.

COMPARISON WITH EXISTING METHOD(S)

The current study has demonstrated a novel method to control the jaw-related motion artifacts in fNIRS signals.

CONCLUSIONS

Our method will benefit the study of the hearing, language and cognitive functions in normal healthy subjects and patients.

Full-visibility 3D imaging of oxygenation and blood flow by simultaneous multispectral photoacoustic fluctuation imaging (MS-PAFI) and ultrasound Doppler

We present a method and setup that provide complementary three-dimensional (3D) images of blood oxygenation (via quantitative photoacoustic imaging) and blood flow dynamics (via ultrasound Doppler). The proposed approach is label-free and exploits blood-induced fluctuations, and is implemented on a sparse array with only 256 elements, driven with a commercially available ultrasound electronics. We first implement 3D photoacoustic fluctuation imaging (PAFI) to image chicken embryo, and obtain full-visibility images of the vascular morphology. We obtain simultaneously 3D ultrasound power Doppler with a comparable image quality. We then introduce multispectral photoacoustic fluctuation imaging (MS-PAFI), and demonstrate that it can provide quantitative measurements of the absorbed optical energy density with full visibility and enhanced contrast, as compared to conventional delay-and-sum multispectral photoacoustic imaging. We finally showcase the synergy and complementarity between MS-PAFI, which provides 3D quantitative oxygenation (SO[Formula: see text]) imaging, and 3D ultrasound Doppler, which provides quantitative information on blood flow dynamics. MS-PAFI represents a promising alternative to model-based inversions with the advantage of resolving all the visibility artefacts without prior and regularization, by use of a straightforward processing scheme.

Impact of Anatomical Variability on Sensitivity Profile in fNIRS-MRI Integration

Functional near-infrared spectroscopy (fNIRS) is an important non-invasive technique used to monitor cortical activity. However, a varying sensitivity of surface channels vs. cortical structures may suggest integrating the fNIRS with the subject-specific anatomy (SSA) obtained from routine MRI. Actual processing tools permit the computation of the SSA forward problem (i.e., cortex to channel sensitivity) and next, a regularized solution of the inverse problem to map the fNIRS signals onto the cortex. The focus of this study is on the analysis of the forward problem to quantify the effect of inter-subject variability. Thirteen young adults (six males, seven females, age 29.3 ± 4.3) underwent both an MRI scan and a motor grasping task with a continuous wave fNIRS system of 102 measurement channels with optodes placed according to a 10/5 system. The fNIRS sensitivity profile was estimated using Monte Carlo simulations on each SSA and on three major atlases (i.e., Colin27, ICBM152 and FSAverage) for comparison. In each SSA, the average sensitivity curves were obtained by aligning the 102 channels and segmenting them by depth quartiles. The first quartile (depth < 11.8 (0.7) mm, median (IQR)) covered 0.391 (0.087)% of the total sensitivity profile, while the second one (depth < 13.6 (0.7) mm) covered 0.292 (0.009)%, hence indicating that about 70% of the signal was from the gyri. The sensitivity bell-shape was broad in the source-detector direction (20.953 (5.379) mm FWHM, first depth quartile) and steeper in the transversal one (6.082 (2.086) mm). The sensitivity of channels vs. different cortical areas based on SSA were analyzed finding high dispersions among subjects and large differences with atlas-based evaluations. Moreover, the inverse cortical mapping for the grasping task showed differences between SSA and atlas based solutions. In conclusion, integration with MRI SSA can significantly improve fNIRS interpretation.

Neural and visual processing of social gaze cueing in typical and ASD adults

Atypical eye gaze in joint attention is a clinical characteristic of autism spectrum disorder (ASD). Despite this documented symptom, neural processing of joint attention tasks in real-life social interactions is not understood. To address this knowledge gap, functional-near infrared spectroscopy (fNIRS) and eye-tracking data were acquired simultaneously as ASD and typically developed (TD) individuals engaged in a gaze-directed joint attention task with a live human and robot partner. We test the hypothesis that face processing deficits in ASD are greater for interactive faces than for simulated (robot) faces. Consistent with prior findings, neural responses during human gaze cueing modulated by face visual dwell time resulted in increased activity of ventral frontal regions in ASD and dorsal parietal systems in TD participants. Hypoactivity of the right dorsal parietal area during live human gaze cueing was correlated with autism spectrum symptom severity: Brief Observations of Symptoms of Autism (BOSA) scores (r = âˆ'0.86). Contrarily, neural activity in response to robot gaze cueing modulated by visual acquisition factors activated dorsal parietal systems in ASD, and this neural activity was not related to autism symptom severity (r = 0.06). These results are consistent with the hypothesis that altered encoding of incoming facial information to the dorsal parietal cortex is specific to live human faces in ASD. These findings open new directions for understanding joint attention difficulties in ASD by providing a connection between superior parietal lobule activity and live interaction with human faces.

Lay Summary

Little is known about why it is so difficult for autistic individuals to make eye contact with other people. We find that in a live face-to-face viewing task with a robot, the brains of autistic participants were similar to typical participants but not when the partner was a live human. Findings suggest that difficulties in real-life social situations for autistic individuals may be specific to difficulties with live social interaction rather than general face gaze.

Homodyne time-of-flight acousto-optic imaging for low-gain photodetector

Acousto-optics imaging (AOI) is a hybrid imaging modality that is capable of mapping the light fluence rate in deep tissue by local ultrasound modulation of the diffused photons. Since the intensity of the modulated photons is relatively low, AOI systems often rely on high-gain photodetectors, e.g. photomultiplier tubes (PMTs), which limit scalability due to size and cost and may significantly increase the relative shot-noise in the detected signal due to low quantum yields or gain noise. In this work, we have developed a homodyne AOI scheme in which the modulated photons are amplified by interference with a reference beam, enabling their detection with a single low-gain photodetector in reflection-mode configuration. We experimentally demonstrate our approach with a silicon photodiode, achieving over a 4-fold improvement in SNR in comparison to a PMT-based setup. The increased SNR manifested in lower background noise level thus enabling deeper imaging depths. The use of a fiber-based configuration enables the integration of our scheme in a hand-held AOI probe.

The first year in formal schooling improves working memory and academic abilities

Neurocognition and academic abilities during the period of 4 and 7 years of age are impacted by both the transition from kindergarten to primary school and age-related developmental processes. Here, we used a school cut-off design to tease apart the impact of formal schooling from age, on working memory (WM) function, vocabulary, and numeracy scores. We compared two groups of children with similar age, across two years: first-graders (FG), who were enrolled into primary school the year that they became eligible and kindergarteners (KG), who were deferred school entry until the following year. All children completed a change detection task while brain activation was recorded using portable functional near-infrared spectroscopy, a vocabulary assessment, and a numeracy screener. Our results revealed that FG children showed greater improvement in WM performance and greater engagement of a left-lateralized fronto-parietal network compared to KG children. Further, they also showed higher gains in vocabulary and non-symbolic numeracy scores. This improvement in vocabulary and non-symbolic numeracy scores following a year in primary school was predicted by WM function. Our findings contribute to a growing body of literature examining neurocognitive and academic benefits conferred to children following exposure to formal schooling.

Automatic analysis of skull thickness, scalp-to-cortex distance and association with age and sex in cognitively normal elderly

Personalized neurostimulation has been a potential treatment for many brain diseases, which requires insights into brain/skull geometry. Here, we developed an open source efficient pipeline BrainCalculator for automatically computing the skull thickness map, scalp-to-cortex distance (SCD), and brain volume based on T 1 -weighted magnetic resonance imaging (MRI) data. We examined the influence of age and sex cross-sectionally in 407 cognitively normal older adults (71.9±8.0 years, 60.2% female) from the ADNI. We demonstrated the compatibility of our pipeline with commonly used preprocessing packages and found that BrainSuite Skullfinder was better suited for such automatic analysis compared to FSL Brain Extraction Tool 2 and SPM12- based unified segmentation using ground truth. We found that the sphenoid bone and temporal bone were thinnest among the skull regions in both females and males. There was no increase in regional minimum skull thickness with age except in the female sphenoid bone. No sex difference in minimum skull thickness or SCD was observed. Positive correlations between age and SCD were observed, faster in females (0.307%/y) than males (0.216%/y) in temporal SCD. A negative correlation was observed between age and whole brain volume computed based on brain surface (females -1.031%/y, males -0.998%/y). In conclusion, we developed an automatic pipeline for MR-based skull thickness map, SCD, and brain volume analysis and demonstrated the sex-dependent association between minimum regional skull thickness, SCD and brain volume with age. This pipeline might be useful for personalized neurostimulation planning.

Network organization of resting-state cerebral hemodynamics and their aliasing contributions measured by functional near-infrared spectroscopy

Objective. Spontaneous fluctuations of cerebral hemodynamics measured by functional magnetic resonance imaging (fMRI) are widely used to study the network organization of the brain. The temporal correlations among the ultra-slow, <0.1 Hz fluctuations across the brain regions are interpreted as functional connectivity maps and used for diagnostics of neurological disorders. However, despite the interest narrowed in the ultra-slow fluctuations, hemodynamic activity that exists beyond the ultra-slow frequency range could contribute to the functional connectivity, which remains unclear.Approach. In the present study, we have measured the brain-wide hemodynamics in the human participants with functional near-infrared spectroscopy (fNIRS) in a whole-head, cap-based and high-density montage at a sampling rate of 6.25 Hz. In addition, we have acquired resting state fMRI scans in the same group of participants for cross-modal evaluation of the connectivity maps. Then fNIRS data were deliberately down-sampled to a typical fMRI sampling rate of ∼0.5 Hz and the resulted differential connectivity maps were subject to a k-means clustering.Main results. Our diffuse optical topographical analysis of fNIRS data have revealed a default mode network (DMN) in the spontaneous deoxygenated and oxygenated hemoglobin changes, which remarkably resemble the same fMRI network derived from participants. Moreover, we have shown that the aliased activities in the down-sampled optical signals have altered the connectivity patterns, resulting in a network organization of aliased functional connectivity in the cerebral hemodynamics.Significance.The results have for the first time demonstrated that fNIRS as a broadly accessible modality can image the resting-state functional connectivity in the posterior midline, prefrontal and parietal structures of the DMN in the human brain, in a consistent pattern with fMRI. Further empowered by the fast sampling rate of fNIRS, our findings suggest the presence of aliased connectivity in the current understanding of the human brain organization.

Imaging Cerebral Blood Vessels Using Near-Infrared Optical Tomography: A Simulation Study

Cerebral veins have received increasing attention due to their importance in preoperational planning and the brain oxygenation measurement. There are different modalities to image those vessels, such as magnetic resonance angiography (MRA) and recently, contrast-enhanced (CE) 3D gradient-echo sequences. However, the current techniques have certain disadvantages, i.e., the long examination time, the requirement of contrast agents or inability to measure oxygenation. Near-infrared optical tomography (NIROT) is emerging as a viable new biomedical imaging modality that employs near infrared light (650-950 nm) to image biological tissue. It was proven to easily penetrate the skull and therefore enables the brain vessels to be assessed. NIROT utilizes safe non-ionizing radiation and can be applied in e.g., early detection of neonatal brain injury and ischemic strokes. The aim is to develop non-invasive label-free dynamic time domain (TD) NIROT to image the brain vessels. A simulation study was performed with the software (NIRFAST) which models light propagation in tissue with the finite element method (FEM). Both a simple shape mesh and a real head mesh including all the segmented vessels from MRI images were simulated using both FEM and a hybrid FEM-U-Net network, we were able to visualize the superficial vessels with NIROT with a Root Mean Square Error (RMSE) lower than 0.079.