bioRxiv Subject Collection: Neuroscience's Journal

No evidence that individual alpha frequency (IAF) represents a mechanism underlying motion-position illusions
Motion-Position Illusions (MPIs) involve the position of an object being misperceived in the context of motion (i.e. when the object contains motion, is surrounded by motion, or is moving). A popular MPI is the flash-lag effect, where a static object briefly presented in spatiotemporal alignment with a moving object, is perceived in a position behind the moving object. Recently, Cottier et al. (2023) observed that there are stable individual differences in the magnitude of these illusions, and possibly even their direction. To investigate the possible neural correlates of these individual differences, the present study explored whether a trait-like component of brain activity, individual alpha frequency (IAF), could predict individual illusion magnitude. Previous reports have found some correlations between IAF and perceptual tasks. Participants (N=61) viewed the flash-lag effect (motion and luminance), Frohlich effect, flash-drag effect, flash-grab effect, motion-induced position shift, twinkle-goes effect, and the flash-jump effect. In a separate session, five minutes of eyes-open and eyes-closed resting state EEG data was recorded. Correlation analyses revealed no evidence for a correlation between IAF and the magnitude of any MPIs. Overall, these results suggest that IAF does not represent a mechanism underlying MPIs, and that no single shared mechanism underlies these effects. This suggests that discrete sampling at alpha frequency is unlikely to be responsible for any of these illusions.

A neurofunctional signature of affective arousal generalizes across valence domains and distinguishes subjective experience from autonomic reactivity
Arousal is fundamental for affective experience and, together with valence, defines the core affective space. However, a precise brain model of affective arousal is lacking, leading to continuing debates of whether the neural systems generalize across valence domains and are separable from those underlying autonomic arousal. Here, we combined naturalistic fMRI with predictive modeling to develop a brain affective arousal signature (BAAS, discovery-validation design, n = 96) and demonstrate its (1) sensitivity and generalizability across mental processes and valence domains, and (2) neural distinction from autonomic arousal (18 studies, n = 735). Affective arousal was encoded in distributed cortical (e.g., prefrontal regions, insula) and subcortical (e.g., amygdala, periaqueductal gray) systems. Given that high arousal progressively overshadows specific emotions we applied the BAAS to improve specificity of established neuroaffective signatures. Our study provides a biologically plausible affective arousal model that aligns with the affective space and has a high application potential.

Identifying a ubiquitous gene expression variation pattern in the human brain
The availability of dense sampled gene expression data from across the human brain has allowed important investigations into fundamental brain principles. Correlated expression patterns of genes tied to microstructural properties have been studied for their relationships with diverse brain features. This work looks at the specificity of these relationships based on the sets of genes targeted. We find that the same spatial pattern emerges from any set with more than 180 genes in it. Looking at the association between this pattern and cortical myelination, as represented by a T1w/T2w map, we show that correlations between myelination and theoretically guided gene sets do not differ from random ones. This observation prompts a reevaluation of current methodologies and assumptions in the study of gene-brain associations. Additionally, our research highlights covariance characteristics within specific functional networks, underscoring their significant role in shaping the spatial transcription patterns in the brain. These findings contribute insights into the complex relationships governing brain organisation and gene expression patterns. They also highlight an important methodological factor influencing the validity of inferences made from gene expression covariance patterns.

Label-free multiphoton imaging reveals volumetric shifts across development in sensory-related brain regions of a miniature transparent vertebrate
Animals integrate information from different sensory modalities as they mature and perform increasingly complex behaviors. This may parallel differential investment in specific brain regions depending on the demands of changing sensory inputs. To investigate developmental changes in the volume of canonical sensory integration brain regions, we used third harmonic generation imaging for morphometric analysis of forebrain and midbrain regions from 5 to 90 days post fertilization (dpf) in Danionella dracula, a transparent, miniature teleost fish whose brain is optically accessible throughout its lifespan. Relative to whole brain volume, increased volume or investment in telencephalon, a higher order sensory integration center, and torus longitudinalis (TL), a midbrain visuomotor integration center, is relatively consistent from 5 to 30 dpf, until it increases at 60 dpf, followed by another increase at 90 dpf, as animals reach adulthood. In contrast, investment in midbrain optic tectum (TeO), a retinal-recipient target, progressively decreases from 30-90 dpf, whereas investment is relatively consistent across all stages for the midbrain torus semicircularis (TS), a secondary auditory and mechanosensory lateral line center, and the olfactory bulb (OB), a direct target of the olfactory epithelium. In sum, increased investment in higher order integration centers (telencephalon, TL) occurs as juveniles reach adulthood and exhibit more complex cognitive tasks, whereas investment in modality-dominant regions occurs in earlier stages (TeO) or is relatively consistent across development (TS, OB). Complete optical access throughout Danionella lifespan provides a unique opportunity to investigate how changing brain structure over development correlates with changes in connectivity, microcircuitry, or behavior.

Acupuncture triggers earlier recovery from ischemic stroke than sham needling in a rat model
Acupuncture, a traditional Chinese medical treatment that has been practiced for over 2,000 years, is widely used around the world. However, its efficacy and distinction from random stimulation are still being questioned. Over the years, many studies have reported either favorable, neutral or even skeptical outcomes regarding the treatment effect of acupuncture on diverse ailments. The major question behind this controversy is whether acupuncture is different from random needle insertion and whether its efficacy can be attributed to the placebo effect. Here, we use micro-positron emission tomography (microPET) imaging in a randomized controlled animal study to show that acupuncture facilitates faster recuperation in comparison to sham acupuncture and blank control. Based on the microPET imaging of subjects undergoing daily acupuncture over two weeks' duration, we dynamically monitored the metabolic activity levels in different brain regions and found that both acupoint and non-acupoint stimulation could improve ischemic stroke recovery. This finding is consistent with previous reports that both acupuncture and sham needling show a positive effect in the treatment of diseases. More importantly, we further found that rats receiving acupuncture at Baihui (GV20) and Shuigou (GV26), two commonly used acupoints for stroke rehabilitation based on the concept of the meridian system, showed earlier recovery effects than rats receiving sham needling treatment. This difference mainly appeared in regions involved in motor control and was validated by a balance beam walking test. Our findings, in conjunction with a recent electroacupuncture study that revealed a neuroanatomical pathway to mediate the vagal-adrenal anti-inflammatory axis, provide quantitative evidence supporting the specificity of acupoints in acupuncture therapy.

Infant EEG microstate dynamics relate to fine-grained patterns of infant attention during naturalistic play with caregivers
As infants grow, they develop greater attentional control during interactions with others, shifting from patterns of attention primarily driven by caregivers (exogenous) to those that are more self-directed (endogenous). The ability to endogenously control attention during infancy is thought to reflect ongoing brain development and is influenced by patterns of joint attention between infant and caregiver. However, whether measures of infant attentional control and caregiver behavior during infant-caregiver interactions relate to patterns of infant brain activity is unknown and key for informing developmental models of attentional control. Using data from 43 infant-caregiver dyads, we quantified patterns of visual attention with dyadic, head-mounted eye tracking during infant-caregiver play and associated them with the duration of infant EEG microstate D/4 measured during rest. Importantly, microstate D/4 is a scalp potential topography thought to reflect the organization and function of attention-related brain networks. We found that microstate D/4 associated positively with infant-led joint attention rate but did not associate with caregiver-led joint attention rate, suggesting that supporting infants' initiatives in joint attention during play may be critical for the neurobiological development of attentional control. Further, we found that microstate D/4 associated negatively with infant attention shifts rate and positively with infant sustained attention duration, suggesting that increased stability of microstate D/4 may reflect maturation of attentional control and its underlying neural substrates. Together, our findings provide novel insights into how infant attentional control abilities and infant-caregiver visual behavior during play are associated with the spatial and temporal dynamics of infant brain activity.

Single cell RNA-sequencing reveals GINIP-expressing neurons as the main targets of focused ultrasound
Dorsal root ganglion (DRG) neurons have a wide range of functions, including touch, pain and itch. These neurons have emerged as promising targets for non-invasive focused ultrasound (FUS) neuromodulation. However, our knowledge of the molecular and physical mechanisms underlying FUS-evoked responses in DRG neurons is limited. Here, we investigate the neuromodulatory capabilities of FUS in cultured DRG neurons in combination with calcium imaging. We find that a 20-MHz FUS burst of 1-ms duration at an acoustic pressure of 5 MPa elicited calcium responses in 52% of DRG neurons. Single-cell RNA sequencing reveals that the majority of FUS-sensitive neurons belong to three subsets of DRG neurons; C-LTMRs, the MRGPRD-expressing C-HTMRs and A delta-LTMRs. FUS excites all these neuronal subtypes by membrane deformation, suggesting a mechanism mediated by mechanosensitive ion channels. Our results identify FUS parameters that activate distinct subsets of DRG neurons and open new avenues for using FUS stimulation to modulate DRG neuron function.

PATJ regulates cell stress responses and vascular remodeling post-stroke
PALS1-associated tight junction (PATJ) protein is linked to metabolic disease and stroke in human genetic studies. Despite the recognized role of PATJ in cell polarization, its specific functions in metabolic disease and ischemic stroke recovery remain largely unexplored. Using a mouse model of stroke, we found post-ischemic stroke duration-dependent increase of PATJ abundance in endothelial cells. PATJ knock-out (KO) HEK293 cells generated by CRISPR-Cas9 suggest roles for PATJ in cell proliferation, migration, mitochondrial stress response, and interactions with the Yes-associated protein (YAP)-1 signaling pathway. Notably, PATJ deletion altered YAP1 nuclear translocation. PATJ KO cells demonstrated extensive transcriptional reprograming based on RNA sequencing analysis. Crucially, we identified dysregulation in genes central to vascular development, stress response, and metabolism, including RUNX1, HEY1, NUPR1, and HK2. These insights offer a new understanding of PATJ's complex regulatory functions within cellular and vascular physiology and help lay the groundwork for therapeutic strategies targeting endothelial PATJ-mediated pathways for stroke rehabilitation and neurovascular repair.

An enhancer-AAV toolbox to target and manipulate distinct interneuron subtypes
In recent years, we and others have identified a number of enhancers that, when incorporated into rAAV vectors, can restrict the transgene expression to particular neuronal populations. Yet, viral tools to access and manipulate fine neuronal subtypes are still limited. Here, we performed systematic analysis of single cell genomic data to identify enhancer candidates for each of the cortical interneuron subtypes. We established a set of enhancer-AAV tools that are highly specific for distinct cortical interneuron populations and striatal cholinergic neurons. These enhancers, when used in the context of different effectors, can target (fluorescent proteins), observe activity (GCaMP) and manipulate (opto- or chemo-genetics) specific neuronal subtypes. We also validated our enhancer-AAV tools across species. Thus, we provide the field with a powerful set of tools to study neural circuits and functions and to develop precise and targeted therapy.

Adolescent alcohol exposure alters age-related progression of behavioral and neurotrophic dysfunction in the TgF344-AD model in a sex-specific manner
Alzheimer's disease (AD) and heavy alcohol use are widely prevalent and lead to brain pathology. Both alcohol-related brain damage (ARBD) and AD result in cholinergic dysfunction, impaired hippocampal neurogenesis and the emergence of hippocampal-dependent cognitive impairments. It is unknown how ARBD during a critical developmental period, such as adolescents, interacts with AD-related pathologies to accelerate disease progression later in life. The current study utilized a longitudinal design to characterize behavioral and pathological changes in a transgenic rat model of AD (TgF344-AD) following adolescent intermittent ethanol (AIE) exposure. We found that AIE accelerates cognitive decline associated with AD transgenes in female rats at 6 months of age, and male AD-rats are impaired on spatial navigation by 3-months with no additional deficits due to AIE exposure. Protein levels of various AD-pathological markers were analyzed in the dorsal and ventral hippocampus of male and female rats. The data suggests that AIE-induced alterations of the tropomyosin-related kinase A receptor / p75 neurotrophin receptor ratio creates a brain that is vulnerable to age- and AD-related pathologies, which leads to an acceleration of cognitive decline, particularly in female rats.

Spike inference from mouse spinal cord calcium imaging data
Calcium imaging is a key method to record the spiking activity of identified and genetically targeted neurons. However, the observed calcium signals are only an indirect readout of the underlying electrophysiological events (single spikes or bursts of spikes) and require dedicated algorithms to recover the spike rate. These algorithms for spike inference can be optimized using ground truth data from combined electrical and optical recordings, but it is not clear how such optimized algorithms perform on cell types and brain regions for which ground truth does not exist. Here, we use a state-of-the-art algorithm based on supervised deep learning (CASCADE) and a non-supervised algorithm based on non-negative deconvolution (OASIS) to test spike inference in spinal cord neurons. To enable these tests, we recorded specific ground truth from glutamatergic and GABAergic somatosensory neurons in the dorsal horn of spinal cord in mice of both sexes. We find that CASCADE and OASIS algorithms that were designed for cortical excitatory neurons generalize well to both spinal cord cell types. However, CASCADE models re-trained on our ground truth further improved the performance, resulting in a more accurate inference of spiking activity from spinal cord neurons. We openly provide re-trained models that can be flexibly applied to spinal cord data of variable noise levels and frame rates. Together, our ground-truth recordings and analyses provide a solid foundation for the interpretation of calcium imaging data from spinal cord and showcase how spike inference can generalize between different regions of the nervous system.

Tensor Kernel Learning for Classification of Alzheimer's Conditions using Multimodal Data
Early, timely, and accurate assessment of Alzheimer's disease (AD), particularly at its earlier stage- mild cognitive impairment (MCI) -, is central to detecting, managing, and potentially treating the disease. The biological underpinnings of AD, however, is multifaceted, from genetic variations, abnormal protein accumulation, to irregular brain functions and structure. A joint analysis of these data, therefore, may offer potentially new insights about AD-related biomarkers and AD prediction. But such explorations must confront the complexity of these data: heterogeneity, multimodality and high-dimensionality. Here, to address these challenges, we propose a new machine-learning method, namely the tensor kernel learning (TKL), leveraging tensor methods and kernel learning, to enhance AD assessment by enhancing multi-modal data integration. More specifically, TKL first uses CP/PARAFAC decomposition and graph diffusion to fuse multiple kernels learned from four complementary data modalities (MRI, PET, CSF, and SNP data). We then used a supervised kernel for a kernel SVM classifier to identify potential patients. To evaluate the effectiveness of TKL, we apply it to data from the Alzheimer's Disease Neuroimaging Initiative (ADNI; sample size n = 331 subjects), including cognitively normal individuals (CN), MCI subjects, and AD patients. TKL improves AD classification performance in both linear and nonlinear combinations, achieving accuracies of 91.31% for CN vs. AD, 81.45% for CN vs. MCI, and 78.27% for AD vs. MCI, compared to 85.48%, 70.89%, and 73.51% using the best single modality. Additionally, TKL reveals clearer, more structured patterns in the data, enhancing interpretation and understanding of the relationships among different modalities. Detailed implementation details of our method can be found at: https://github.com/thanhvd18/Tensor-Kernel-Learning-matlab.

Sensory dependencies rapidly and autonomously yield generalizable representations in recurrent cortical-like networks
How biological brains can learn so quickly to become operational and achieve complex behavior remains an unresolved issue. Here we introduce a neuromorphic learning strategy that resembles how immature biological brains learn by consisting of continual random activations of a complex mechanically coupled system with rich, dynamic intrinsic sensor dependencies, in this regard reminiscent of a biological body. Using a dynamic model of biological skin tissue with embedded sensors, we trained small, recurrent networks that emulated a primordial cortex and featured excitatory and inhibitory neurons with simultaneous independent learning in both types of synapses. Training with non-repetitive, random activations of the skin, where the recurrent network activity state was inherited between activations, autonomously led to rapid acquisition of remarkably generalizable representations of a predictive nature. The network could separate inputs and solve a kinematics task that had never been encountered, also after substantial parts of the sensor population were deleted. This strategy of focussing learning on the dominant regularities in dynamic sensory information can potentially explain complex brain operation and efficient learning.

Ex vivo functional characterization of mouse olfactory bulb projection neurons reveals a heterogenous continuum
Mitral and tufted cells in the olfactory bulb (OB) act as an input convergence hub and transmit information to higher olfactory areas. Since first characterized, they have been classed as distinct projection neurons based on size and location: laminarly-arranged mitral cells with a diameter larger than 20m in the mitral layer (ML), and smaller tufted cells spread across both the ML and external plexiform layer (EPL). Recent in vivo work has shown that these neurons encode complementary olfactory information, akin to parallel channels in other sensory systems. Yet, many ex vivo studies still collapse them into a single class, mitral/tufted, when describing their physiological properties and impact on circuit function. Using immunohistochemistry and whole-cell patch clamp electrophysiology in fixed or acute slices from adult mice, we attempted to align in vivo and ex vivo data and test a soma size-based classifier of OB projection neurons using passive and intrinsic firing properties. We found that there is no clear separation between cell types based on passive or active properties. Rather, there is a heterogeneous continuum with three loosely clustered subgroups: EPL tufted cells, putative tufted cells, and putative mitral cells in the ML. These findings illustrate the large functional heterogeneity present within the OB projection neurons and complement existing literature highlighting how heterogeneity in sensory systems is preponderant and possibly used in the OB to decode complex olfactory information.

Pre-stimulus Activity Mediates Event-Related Theta Synchronization and Alpha Desynchronization During Memory Formation in Healthy Aging
The capacity to learn is a key determinant for the quality of life but is known to decline to varying degrees with age. However, despite mounting evidence of memory deficits in older age, the neural mechanisms contributing to successful or impeded memory remain unclear. Previous research has primarily focused on memory formation through remembered versus forgotten comparisons, lacking the ability to capture the incremental nature of learning. Moreover, previous EEG studies have primarily examined oscillatory brain activity during the encoding phase, such as event-related synchronization (ERS) of mid-frontal theta and desynchronisation (ERD) of parietal alpha, while neglecting the potential influence of pre-stimulus activity. To address these limitations, we employed a sequence learning paradigm, where 113 young and 117 older participants learned a fixed sequence of visual locations through repeated observations (6423 sequence repetitions, 55 '944 stimuli). This paradigm enabled us to investigate mid-frontal theta ERS, parietal alpha ERD, and how they are affected by pre-stimulus activity during the incremental learning process. Behavioral results revealed that young subjects learned significantly faster than older subjects, in line with expected age-related cognitive decline. Successful incremental learning was directly linked to decreases of mid-frontal theta ERS and increases of parietal alpha ERD. Notably, these neurophysiological changes were less pronounced in older individuals, reflecting a slower rate of learning. Importantly, the mediation analysis revealed that in both age groups, mid-frontal pre-stimulus theta partially mediated the relationship between learning and mid-frontal theta ERS. Furthermore, the overall impact of learning on parietal alpha ERD was primarily driven by its positive influence on pre-stimulus alpha activity. Our findings offer new insights into the age-related differences in memory formation and highlight the importance of pre-stimulus activity in explaining post-stimulus responses during learning.

Normative trajectories of R1, R2* and magnetic susceptibility in basal ganglia on healthy ageing
Quantitative MRI techniques, including R1, R2*, and magnetic susceptibility mapping, have emerged as promising tools for generating surrogate imaging markers of brain tissue microstructure, enabling non-invasive in vivo measurements associated with myelination and iron deposition. Gaining insights into how these quantitative measurements evolve throughout a normal lifespan can enhance our understanding of brain maturation processes and facilitate the study of disease-related microstructural changes by distinguishing pathological alterations from normal brain development. In this study, we established the normative trajectories of R1, R2*, and magnetic susceptibility in the basal ganglia at 3T. We used a healthy ageing cohort comprising 260 subjects with an evenly distributed age range and sex ratio throughout adulthood. Utilizing the non-parametric Gaussian Process Regression model to derive the normative trajectories, we found that R1 in these structures predominantly exhibit a quadratic shape over age, while R2* and magnetic susceptibility are primarily linear. We validated the normative trajectories of R2* and magnetic susceptibility using an independent cohort. Additionally, we demonstrated that the spatial distributions of the quantitative MRI parameters also change with age in the putamen and caudate nucleus. This study not only reinforces existing findings on the association between age and qMRI but also provides valuable resources for studying cognitive ageing, in conjunction with the behavioural data available in the same data collection.

Influence of temporal information gaps on decision making: describing the dynamics of working memory
During decision making, choices are made based on assessing potential options and their expected outcomes. Traditional laboratory investigations of decision making often employ tasks involving the discrimination of perceptual evidence, where sensory information is constant and presented continuously. However, during natural behavior, this is unlikely the case. Usually, perceptual information is dynamic and presented intermittently, which requires maintaining information in memory. Thus, understanding decision making requires considering the dynamics of working memory. Here, we used a perceptual decision-making task where fifteen tokens jump from a central circle to one of two peripheral ones and disappear shortly after. Participants were required to report which target they believed would have received most tokens by the trial's end. Half of the trials included a temporal gap, during which no information was displayed. In those cases, we found that participants made choices with less available information, but their accuracy remained unchanged. Computational modeling revealed that this behavior was best explained by a model in which stored perceptual information leaks away due to the arrival of new information, rather than by the passage of time. Our results provide evidence of a decision-making process that evolves even in the absence of perceptual information, challenging the idea of a frozen state resilient to temporal gaps and shedding light on the dynamics of working memory. This study highlights the importance of considering working memory dynamics in understanding decision-making processes, particularly in environments with intermittent perceptual information.

The Three Stages of Learning to Master a Sensory Augmentation Device: Activation - Acquisition - Integration
By augmenting sensory perception through technology, researchers study how humans use and perceive the novel sensory input provided. Nevertheless, little is known about how learning unfolds over time. To this end, 27 participants trained with an augmentation device (feelSpace belt), which provides tactile feedback about the direction of the cardinal north, over the course of six weeks. During the training phase, we tracked participants' progress using two different self-report questionnaires containing Likert-scale and open questions. Experts quantified responses to open questions using a previously established category system. As human raters are known to be susceptible to biases, we later reproduced the expert categorization using ChatGPT-4, finding a high congruence between the two classification approaches. The results suggest a three-stage model that best describes the process of acquiring an augmented sense. During the early activation stage, processing the augmented signal requires effort, induces fatigue, and a heightened awareness of the environment. In the knowledge acquisition phase, participants develop a more detailed cognitive spatial representation containing information about objects and places in relation to their current location. The deep integration stage is marked by participants seamlessly integrating the augmented information into existing perceptual processes and automatic use. The results support the supervised use of large language models (LLMs) for the analysis of qualitative data.

Leveraging Grating-Based Flickers: A Leap Toward Practical, Visually Comfortable, and High-Performance Dry EEG Code-VEP BCI
Purpose: Reactive Brain-Computer Interfaces (rBCIs) typically rely on repetitive visual stimuli, which can strain the eyes and cause attentional distraction. To address these challenges, we propose a novel approach rooted in visual neuroscience to design visual Stimuli for Augmented Response (StAR). The StAR stimuli consist of small randomly-oriented Gabor or Ricker patches that optimize foveal neural response while reducing peripheral distraction. Methods: In a factorial design study, 24 participants equipped with an 8-dry electrodes EEG system focused on series of target flickers presented under three formats: traditional 'Plain' flickers, Gabor-based, or Ricker-based flickers. These flickers were part of a five-classes Code Visually Evoked Potentials (c-VEP) paradigm featuring low frequency, short, and aperiodic visual flashes. Results: Subjective ratings revealed that Gabor and Ricker gratings were visually comfortable and nearly invisible in peripheral vision compared to plain flickers. Moreover, Gabor and Ricker-based textures achieved higher accuracy (93.6% and 96.3%, respectively) with only 88 seconds of calibration data, compared to plain flickers (65.6%). A follow-up online implementation of this experiment was conducted to validate our findings within the frame of naturalistic operations. During this trial, remarkable accuracies of 97.5% in a cued task and 94.3% in an asynchronous digicode task were achieved, with a mean decoding time as low as 1.68 seconds. Conclusion: This work demonstrates the potential to expand BCI applications beyond the lab by integrating visually unobtrusive systems with gel-free, low density EEG technology, thereby making BCIs more accessible and efficient. The datasets, algorithms, and BCI implementations are shared through open-access repositories.