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This editorial explores the novel The Secret of Secrets by Dan Brown as a gateway to longstanding debates in neuroscience, particularly surrounding consciousness and free will. Highlighting the novels references to real neurophotonic technologies, it examines where scientific evidence ends and speculative fiction begins.
The editorial introduces the articles in the Neurophotonics Special Issue on Imaging Brain Metabolism and Neuroenergetics.
Adapting optical imaging technology to avian models can overcome many limitations imposed by functional magnetic resonance imaging (fMRI), which currently restricts the number of species used to study functional connectivity. Developing advanced technology to expand the diversity of species that can be effectively imaged is crucial for addressing significant questions that are currently unreachable, such as understanding the evolution of cognition from a comparative perspective. We assessed the potential of optical imaging technology to measure functional connectivity in birds, utilizing pigeons as an avian model. We evaluated whether we could partition the dorsal surface of the pigeon brain into units that correspond to known anatomical regions. Finally, we compared our results with those obtained from a separate dataset acquired using fMRI. Using optical intrinsic signal imaging, a widefield optical imaging method, we imaged resting state functional connectivity in scalp-retracted anesthetized pigeons. We then used iterative parcellation and hierarchical clustering to create functional connectivity maps of correlation between parcels at two spatial scales. We recorded a second independent dataset of ten pigeons using a single-shot multi-slice gradient echo EPI sequence fMRI and applied the same parcellation method to compare functional connectivity patterns between the two methodologies. We successfully partitioned signal activity into clusters of parcels that exhibit left-right symmetry between hemispheres and which align well with known anatomical regions of the dorsal surface of the pigeon brain. Moreover, functional connectivity matrices reveal positive correlations between homotopic regions. These cluster partitions and functional connectivity maps display similar patterns across and within individuals. Finally, WOI imaging results were comparable to resting state data acquired using fMRI. Taken together, these results demonstrate the potential of optical imaging technology for the reliable and cost-effective characterization of functional connectivity in birds. In addition, they position optical imaging methods as a valuable tool for large-scale comparative and network-level studies in this taxon.
Neonatal hypoxic-ischemic encephalopathy (HIE) remains a leading global cause of mortality and morbidity. Neurovascular coupling (NVC), assessed via wavelet transform coherence (WTC) between electroencephalogram and cerebral tissue oxygenation ( SctO 2 ), has shown promise in identifying brain injury severity; however, statistical estimation of WTC was derived using Monte Carlo (MC) simulations with surrogate non-physiological signals. To assess NVC using a data-driven method without MC for distinguishing the severity of HIE on the first day of life. NVC was assessed on the first day of life with direct data in neonates diagnosed with HIE ranging from mild to severe. Neonates with moderate to severe HIE received therapeutic hypothermia (TH) at 5 h of life (TH group), whereas those with mild HIE did not based on evidence-based protocols (non-TH group). Significant time-scale ranges in WTC were identified using a cluster-based permutation test, and average NVC within these ranges was compared among groups with a linear mixed-effects model over the first 20 h of recording. A total of 57 full-term neonates with HIE (29 non-TH and 28 TH) were included. The linear mixed-effects revealed a significant interaction between group and time within the 25- to 60-min time (0.28 to 0.67 mHz) scale ( p < 0.001 ), indicating reduced NVC with increased encephalopathy severity. Specifically, NVC was significantly reduced in neonates in the TH group ( p = 0.0103 ). We demonstrate that a data-driven approach can significantly distinguish NVC patterns by HIE severity without relying on MC simulations. By enhancing robustness and bedside applicability in the early hours of life, it may support informed decisions regarding initiation of TH to improve outcomes.
Over the past decade, techniques enabling bidirectional modulation of neuronal activity with single-cell precision have rapidly advanced in the form of two-photon optogenetic stimulation. Unlike conventional electrophysiological approaches or one-photon optogenetics, which inevitably activate many neurons surrounding the target, two-photon optogenetics can drive hundreds of specifically targeted neurons simultaneously, with stimulation patterns that can be flexibly and rapidly reconfigured. In this review, we trace the development of two-photon optogenetic stimulation, focusing on its progression toward implementations in large field of view two-photon microscopes capable of targeted multi-neuron control. We highlight three principal strategies: spiral scanning, temporal focusing, and three-dimensional computer-generated holography, along with their combinations, which together provide powerful tools for causal interrogation of neural circuits and behavior. Finally, we discuss the integration of these optical technologies into brain-machine interfaces, emphasizing both their transformative potential and the technical challenges that must be addressed to realize their broader impact.
Immature neurons in the adult brain migrate into existing circuits, contributing to plasticity, learning, and complex behaviors. While prior studies have examined the molecular mechanisms and functional consequences of adult neurogenesis, few have investigated the physical interactions between migrating neurons and their surrounding microenvironment. Here, we used electron microscopy (EM)-based connectomics to examine how migrating neurons interact with mature circuit elements in the adult zebra finch striatum. Migratory neurons contacted diverse structures in their microenvironment, including the axons, dendrites, synapses, and somas of mature neurons. Surprisingly, these interactions were structurally complex, often involving pronounced deformations of mature somas and the surrounding neuropil. These deformations appeared as "tunnels" made by the migratory neurons as they displaced mature structures along their path. Together, these findings suggest that migrating neurons may physically reshape the mature circuit to reach their targets, revealing an unexpected degree of structural and functional plasticity in the adult brain. VIDEO ABSTRACT.
Grid cells in the human entorhinal cortex (EC) play a critical role in spatial navigation and memory. The EC is also one of the first regions affected by ageing and Alzheimer's disease. This pre-registered functional magnetic resonance imaging (fMRI) study aimed to detect grid-cell-like signals (GLS) in a passive virtual navigation task. Contrary to our hypotheses and previous findings, we did not observe significant GLS at a population level, even in younger participants. Further exploratory analyses investigated the impact of task-engagement, as inferred from object-location memory performance, and showed no relationship with GLS magnitude. We also examined potential influences of a confounding one-fold directional signal and various data-processing choices but observed no consistent patterns. Our findings, consistent with recent null results from similar studies, suggest that passive navigation paradigms may be insufficient for reliably eliciting grid-like signals in human fMRI.
A fundamental question in neuroscience is how memory formation shapes brain activity at the level of neuronal populations. Recent studies of hippocampal 'engram' cells-neurons identified by learning-induced immediate early gene (IEG) expression-propose that these populations form the cellular substrate for memory. Previous experimental work suggests that cells are recruited into engrams via elevated intrinsic excitability and that learning drives coactivity among these cells to support retrieval. Despite this, an understanding of how engram dynamics evolve across learning and recall remains incomplete. Here, we combined activity-dependent genetic tagging with longitudinal two-photon calcium imaging to track CA1 engram population dynamics before and after fear conditioning. Our results reveal that engram activity is modulated by intrinsic dynamics, behavioral state, and stimulus-cued reactivation. Consistent with the idea that intrinsic dynamics bias engram allocation, spontaneous activity during rest predicted future engram membership-up to two days prior to Fos expression. In parallel, we found sequential activity during locomotion recruited both engram and non-engram cells, but that engram cells were less modulated by velocity. Furthermore, after fear conditioning, within the engram population, we identified a subset of cells that increased their correlations after fear learning, specifically during quiet rest. We also used a trace fear conditioning paradigm to show that conditioned stimulus presentation drove elevated activity and stable correlations amongst engram cells, demonstrating learning-dependent reactivation. Finally, computational modeling of CA3-CA1 circuit dynamics demonstrated that a network with strong excitatory-inhibitory balance, capable of CA3-driven reactivation, is consistent with our experimental results. Together, these results show that engram population dynamics are shaped by spontaneous states, behavior, and memory.
To learn mathematics, young children require accurate interpretations of mathematics vocabulary. When school language differs from children's home language, mathematics performance often decreases. Little is known about cortical activation during mathematics vocabulary processing in different languages. Although behavioral data highlight a difference in L1 and L2 mathematics learning, neuroimaging insights will help us to better understand how and why there is a difference in children's mathematical learning in multilingual societies. We investigated behavioral and brain responses (fNIRS) of 42 isiZulu and Sesotho (L1) first graders (6.75 to 7.83 years, 22 girls) who learn mathematics in English (L2) at school when they encounter mathematics vocabulary in L2 compared with L1 and mathematics vocabulary compared with object recognition in L1. The results show that higher accuracy in the L1 mathematics vocabulary, as compared with the L2 mathematics vocabulary, comes with the costs of higher cognitive demands in the right superior and middle frontal gyri for first graders. Mathematics vocabulary required longer response time than object recognition and a higher activation in the right superior frontal gyrus. No parietal difference was observed between conditions. Neuroimaging revealed that children engaged additional frontoparietal regions when processing L1 mathematics vocabulary-patterns not detectable through behavioral measures alone. Increased frontal activation suggests that the interpretation of mathematics vocabulary in L2 is not yet automatized. This study demonstrates how educational neuroimaging refines interpretations of behavioral outcomes within multilingual contexts.
Observing the activity of large populations of neurons in vivo is critical for understanding brain function and dysfunction. The use of fluorescent genetically encoded calcium indicators (GECIs) in conjunction with miniaturized microscopes is an exciting emerging toolset for recording neural activity in unrestrained animals. Despite their potential, current miniaturized microscope designs are limited using image sensors with low frame rates, sensitivity, and resolution. Beyond GECIs, there are many neuroscience applications that would benefit from the use of other emerging neural indicators, such as fluorescent genetically encoded voltage indicators (GEVIs) that have faster temporal resolution to match neuron spiking, yet require imaging at high speeds. We integrated an advanced CMOS image sensor into a popular open-source one-photon miniaturized microscope platform. MiniFAST is a fast and sensitive miniaturized microscope capable of 1080p video ( 1920 × 1080    pixels ), 1.5    μ m resolution, frame rates up to 500 Hz (achieved with windowing: 1920 × 55    pixels height), and high gain ability (up to 70 dB) to image in extremely low-light conditions. We report results of ∼ 300 - Hz in vivo imaging of freely behaving transgenic Thy1-GCaMP6f mice, high-speed 500-Hz in vitro imaging of a GEVI, and in vivo GEVI imaging in head-fixed mice. Our results extend miniaturized microscope capabilities in high-speed imaging, high sensitivity, and increased resolution.
Goal-directed navigation requires animals to continuously evaluate their current direction and speed of travel relative to landmarks to discern whether they are approaching or deviating from their goal. Striatal dopamine release signals the reward-predictive value of cues1,2, probably contributing to motivation3,4, but it is unclear how dopamine incorporates an animal's ongoing trajectory for effective behavioural guidance. Here we demonstrate that cue-evoked striatal dopamine release in mice encodes bidirectional trajectory errors reflecting the relationship between the speed and direction of ongoing movement relative to optimal goal trajectories. Trajectory error signals could be computed from locomotion or visual flow, and were independent from simultaneous dopamine increases reflecting learned cue value. Joint trajectory error and cue-value encoding were reproduced by the reward prediction error term in a standard reinforcement learning algorithm with mixed sensorimotor inputs. However, these two signals had distinct state space requirements, suggesting that they could arise from a common reinforcement learning algorithm with distinct neural inputs. Striatum-wide multifibre array measurements resolved overlapping, yet temporally and anatomically separable, representations of trajectory error and cue value, indicating how functionally distinct dopamine signals for motivation and guidance are multiplexed across striatal regions to facilitate goal-directed behaviour.
The developmental functional near-infrared spectroscopy (fNIRS) literature relies heavily on measurements acquired during natural sleep, yet it remains unclear whether and how different sleep stages modulate infant hemodynamic responses and potentially confound interpretations of early brain activation. This study investigates the effect of quiet sleep (QS) and active sleep (AS) on fNIRS-measured hemodynamic responses to two auditory paradigms-social selectivity and habituation and novelty detection (HaND)-in 1-month-old infants from the United Kingdom ( N = 39 ) and the Gambia ( N = 51 ). The infants were tested during natural sleep with an 18-channel bilateral frontal-temporal fNIRS array. Sleep stages were coded from video using a micro-coding scheme. For the social selectivity paradigm, infants in both cohorts showed robust responses to vocal and non-vocal stimuli and non-vocal selectivity. In the HaND paradigm, UK infants in AS exhibited a higher initial response and stronger habituation in both chromophores than QS infants. In the Gambian cohort, infants in QS showed a more widespread initial response and evidence of habituation, whereas infants in AS did not. Sleep stage can modulate infant hemodynamic responses, with patterns differing across paradigms and cohorts, underscoring the importance of modeling sleep stages in neuroimaging studies with sleeping infants.
Traditional methods for assessing photothrombotic stroke rely on in vivo imaging techniques or ex vivo histological analyses. Unlike in vivo modalities such as magnetic resonance imaging (MRI), light sheet fluorescence microscopy (LSFM) provides cellular-level high-resolution imaging without motion artifacts and can capture fine-scale morphological features of infarcts. In addition, compared with conventional histology, LSFM preserves organ integrity as the entire brain is imaged without the need for serial sectioning, thereby enabling accurate volumetric reconstruction of ischemic lesions. Here, we introduce an in-depth semi-automated method for reliable quantification of stroke volume using LSFM in optically cleared whole mouse brains following photothrombotic stroke. We demonstrate that the infarct can be delineated via endogenous autofluorescence, providing a reproducible and robust method for ischemic volume assessment. Our data show that LSFM-based stroke volume measurements are strongly correlated with in vivo laser speckle contrast imaging, in vivo MRI, and cresyl violet histology measures of stroke volume. Moreover, we show that the ischemic core remains autofluorescent regardless of the tissue preparation method, supporting the applicability of LSFM for both freshly processed and long-term stored samples. Overall, our findings validate LSFM as a reliable, versatile, and powerful alternative method for stroke volume quantification, offering significant advantages for experimental stroke research.
Infant functional near-infrared spectroscopy (fNIRS) data are particularly vulnerable to noise; participant behavior can result in motion artifacts, and reduced set-up times can cause poor optode coupling. Accurate channel pruning is therefore essential, but approaches vary and often use adult-derived thresholds, risking unnecessary data loss. We systematically compared pruning approaches and parameter choices to evaluate their effects on data quality and retention in infant fNIRS. Data from 5 to 24-month-old infants were collected across two cohorts, using two paradigms. Channel pruning was performed using the coefficient of variation (CV) and the quality testing of near-infrared scans (QT-NIRS) tool, varying key thresholds. Multilevel models assessed the effects of pruning method, parameter choice, age, motion, and testing site on signal-to-noise ratio (SNR) and channels retained. QT-NIRS produced significantly higher SNR than CV pruning across nearly all age, task, and cohort combinations when matched for data retention. Higher QT-NIRS thresholds improved quality but reduced retention. Motion prevalence strongly reduced both SNR and retention; testing site and age had smaller but notable effects. QT-NIRS offers a better balance of data quality and retention than CV pruning. Lower QT-NIRS thresholds than adult defaults are recommended for infant data. These findings provide practical guidance for preprocessing pipelines in developmental fNIRS research.
Clear and consistent reporting of statistical values such as means, standard deviations, confidence intervals, p values, correlations, and test statistics is essential for reproducibility in neurophotonics. However, inconsistent rounding and unclear formatting remain common issues, particularly among new students and researchers. This technical note presents a concise and practical procedure for reporting statistical results with appropriate precision. It includes step-by-step rules, examples, and a summary table. The guide is intended as a quick reference for students, authors, and reviewers working with optical imaging data.
Excessive internet gaming has been associated with social, cognitive, and behavioral impairments; however, the neural mechanisms underlying Internet Gaming Disorder (IGD) remain insufficiently explored. This study examined neurophysiological differences among individuals with IGD, recreational game users (RGU), and healthy controls (HC). A total of 125 male undergraduate participants (54 IGD, 29 RGU, 42 HC; aged 18-23 years) completed a visual/auditory oddball task while simultaneous event-related potentials (ERP) and functional near-infrared spectroscopy (fNIRS) signals were recorded. ERP features included P300 and N200 amplitudes and latencies, whereas fNIRS features consisted of General Linear Model (GLM) beta coefficients and statistical descriptors (e.g., mean, standard deviation, maximum peak, kurtosis, skewness, slope). Following ReliefF-based feature selection, multiple machine learning algorithms were trained using feature subsets ranging from 10 to 100 features. ReliefF identified P300 amplitude and latency, N200 latency, and several fNIRS-derived statistical features (standard deviation, maximum peak, skewness, GLM beta values) as the most discriminative markers. The highest accuracy (88.17%) was achieved with the top 80 multimodal features and a Random Forest classifier, while ERP-only features reached 82.57%. To our knowledge, this is the first study to classify IGD, RGU, and HC using multimodal ERP-fNIRS recordings during an oddball task assessing divided attention. The findings demonstrate that multimodal integration substantially improves classification performance, underscoring its potential to develop objective neurobiological markers of problematic gaming behavior.
Wide-field imaging from brain slices stained with a voltage-sensitive dye (VSD) and simultaneously loaded with a Ca 2 + indicator allows investigating neuronal excitability and synaptic transmission at a multi-cellular scale. So far, achieving this combined imaging has been limited by experimental constraints. We assessed the ability of the red-IR emitting VSD ElectroFluor630 (EF-630) to be combined with blue-excitable green-emitting Ca 2 + indicators to record signals elicited by electrical stimulation in hippocampal slices. Transversal mouse hippocampal slices were stained with EF-630. Ca 2 + indicators, either Fluo-4, Fluo-8, Cal520, or Calbryte520, were loaded using their AM-ester forms. Fluorescence, during stimulation of the CA3 region, was imaged at 5 kHz from the hippocampal areas of ∼ 750 × 250    μ m 2 at 1 - μ m pixel resolution. After assessing all Ca 2 + indicators, we selected Calbryte520 for achieving > 30 - min stable recordings in combination with EF-630. Action potentials and related Ca 2 + transients were sequentially detected in the CA3 stimulated area, whereas synaptic signals were observed in the CA1 region. On these signals, we tested the pharmacological blockade of either action potentials or glutamatergic synaptic potentials. We report unprecedented optical measurements of both electrical and Ca 2 + transients in brain slices, providing unique information on neuronal excitability and network activity.
Anusha Mishra interviewed David Attwell, Jodrell Professor of Physiology at University College London, whose work has been foundational to our understanding of glial physiology, neurovascular coupling, and vascular contributions to dementia. A video of the interview is available: https://doi.org/10.1117/1.NPh.13.2.020401.
Diffuse optical tomography (DOT) enables mapping of functional near-infrared spectroscopy channel-based optical density changes to spatial images of oxy- and deoxyhemoglobin. Accurate reconstruction requires optimization for specific probe geometries. Although prior work focused on volumetric voxel reconstructions with grid arrays, here we examine high-density hexagonal arrays for surface-based reconstructions of the brain and scalp. We evaluate measurement and spatial regularization, spatial basis functions, and reconstruction strategies to reduce crosstalk and improve localization. Both single-wavelength (indirect) and dual-wavelength (direct) approaches are compared. Simulations with a white-noise model guided parameter optimization using image quality metrics. Resting-state data were augmented with synthetic hemodynamic response functions (HRFs) to incorporate real measurement variance into the parameter optimization pipeline, and results were validated with a ball-squeezing motor task. Gaussian spatial bases reduced brain-scalp crosstalk but lowered contrast-to-noise ratio and increased localization error. Indirect hemoglobin reconstruction decreased oxy-deoxy crosstalk. Validation data showed strong, lateralized motor cortex activation contralateral to the active hand. High-density hexagonal arrays enable accurate surface DOT reconstructions when optimized. Resting-state data augmented with synthetic HRFs provide an effective strategy for parameter selection, yielding localized activation with a high contrast-to-noise ratio.