Neural Representation Research Articles

Molecular logic for cellular specializations that initiate the auditory parallel processing pathways.

The cochlear nuclear complex (CN), the starting point for all central auditory processing, comprises a suite of neuronal cell types that are highly specialized for neural coding of acoustic signals, yet molecular logic governing cellular specializations remains unknown. By combining single-nucleus RNA sequencing and Patch-seq analysis, we reveal a set of transcriptionally distinct cell populations encompassing all previously observed types and discover multiple new subtypes with anatomical and physiological identity. The resulting comprehensive cell-type taxonomy reconciles anatomical position, morphological, physiological, and molecular criteria, enabling the determination of the molecular basis of the remarkable cellular phenotypes in the CN. In particular, CN cell-type identity is encoded in a transcriptional architecture that orchestrates functionally congruent expression across a small set of gene families to customize projection patterns, input-output synaptic communication, and biophysical features required for encoding distinct aspects of acoustic signals. This high-resolution account of cellular heterogeneity from the molecular to the circuit level illustrates molecular logic for cellular specializations and enables genetic dissection of auditory processing and hearing disorders with unprecedented specificity.

Interference of mid-level sound statistics underlie human speech recognition sensitivity in natural noise.

Recognizing speech in noise, such as in a busy restaurant, is an essential cognitive skill where the task difficulty varies across environments and noise levels. Although there is growing evidence that the auditory system relies on statistical representations for perceiving 1-5 and coding4,6-9 natural sounds, it's less clear how statistical cues and neural representations contribute to segregating speech in natural auditory scenes. We demonstrate that human listeners rely on mid-level statistics to segregate and recognize speech in environmental noise. Using natural backgrounds and variants with perturbed spectro-temporal statistics, we show that speech recognition accuracy at a fixed noise level varies extensively across natural backgrounds (0% to 100%). Furthermore, for each background the unique interference created by summary statistics can mask or unmask speech, thus hindering or improving speech recognition. To identify the neural coding strategy and statistical cues that influence accuracy, we developed generalized perceptual regression, a framework that links summary statistics from a neural model to word recognition accuracy. Whereas a peripheral cochlear model accounts for only 60% of perceptual variance, summary statistics from a mid-level auditory midbrain model accurately predicts single trial sensory judgments, accounting for more than 90% of the perceptual variance. Furthermore, perceptual weights from the regression framework identify which statistics and tuned neural filters are influential and how they impact recognition. Thus, perception of speech in natural backgrounds relies on a mid-level auditory representation involving interference of multiple summary statistics that impact recognition beneficially or detrimentally across natural background sounds.

The role of loss aversion in social conformity: psychological and neural representations.

The impact of others' choices on decision-making is influenced by individual preferences. However, the specific roles of individual preferences in social decision-making remain unclear. In this study, we examine the contributions of risk and loss preferences as well as social influence in decision-making under uncertainty using a gambling task. Our findings indicate that while both individual preferences and social influence affect decision-making in social contexts, loss aversion plays a dominant role, especially in individuals with high loss aversion. This phenomenon is accompanied by increased functional connectivity between the anterior insular cortex and the temporoparietal junction. These results highlight the critical involvement of loss aversion and the anterior insular cortex-temporoparietal junction neural pathway in social decision-making under uncertainty. Our findings provide a computational account of how individual preferences and social information collectively shape our social decision-making behaviors.

Neural representations of beat and rhythm in motor and association regions.

Humans perceive a pulse, or beat, underlying musical rhythm. Beat strength correlates with activity in the basal ganglia and supplementary motor area, suggesting these regions support beat perception. However, the basal ganglia and supplementary motor area are part of a general rhythm and timing network (regardless of the beat) and may also represent basic rhythmic features (e.g. tempo, number of onsets). To characterize the encoding of beat-related and other basic rhythmic features, we used representational similarity analysis. During functional magnetic resonance imaging, participants heard 12 rhythms-4 strong-beat, 4 weak-beat, and 4 nonbeat. Multi-voxel activity patterns for each rhythm were tested to determine which brain areas were beat-sensitive: those in which activity patterns showed greater dissimilarities between rhythms of different beat strength than between rhythms of similar beat strength. Indeed, putamen and supplementary motor area activity patterns were significantly dissimilar for strong-beat and nonbeat conditions. Next, we tested whether basic rhythmic features or models of beat strength (counterevidence scores) predicted activity patterns. We found again that activity pattern dissimilarity in supplementary motor area and putamen correlated with beat strength models, not basic features. Beat strength models also correlated with activity pattern dissimilarities in the inferior frontal gyrus and inferior parietal lobe, though these regions encoded beat and rhythm simultaneously and were not driven by beat alone.

Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons.

Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, integrating and interpreting this data remains challenging. Concurrently, advances in supercomputing and sophisticated modeling tools now enable the development of highly detailed, large-scale biophysical circuit models. These mechanistic multiscale models offer a method to systematically integrate experimental data, facilitating investigations into brain structure, function, and disease. This review, based on a Society for Neuroscience 2024 MiniSymposium, aims to disseminate recent advances in large-scale mechanistic modeling to the broader community. It highlights (1) examples of current models for various brain regions developed through experimental data integration; (2) their predictive capabilities regarding cellular and circuit mechanisms underlying experimental recordings (e.g., membrane voltage, spikes, local-field potential, electroencephalography/magnetoencephalography) and brain function; and (3) their use in simulating biomarkers for brain diseases like epilepsy, depression, schizophrenia, and Parkinson's, aiding in understanding their biophysical underpinnings and developing novel treatments. The review showcases state-of-the-art models covering hippocampus, somatosensory, visual, motor, auditory cortical, and thalamic circuits across species. These models predict neural activity at multiple scales and provide insights into the biophysical mechanisms underlying sensation, motor behavior, brain signals, neural coding, disease, pharmacological interventions, and neural stimulation. Collaboration with experimental neuroscientists and clinicians is essential for the development and validation of these models, particularly as datasets grow. Hence, this review aims to foster interest in detailed brain circuit models, leading to cross-disciplinary collaborations that accelerate brain research.

Drawbacks to Strengthening Neural Salience Encoding: A Link Between Cortisol and Risky Drinking.

Emotionally salient experiences are encoded and remembered more strongly, an effect that can be amplified by hormones like cortisol. Such memories can in turn profoundly influence later behavior. However, little is known about the link between amplified salience encoding and subsequent behavior. This pathway may be particularly important for risky alcohol drinking, which has been linked to sensitized salience responses, memory, and cortisol. To test this possibility, we integrated pharmacology using a double-blind cross-over design with fMRI, cognitive, and motivation assays across a range of healthy male and female social drinkers. As anticipated, cortisol enhanced memory for salient alcohol-related events; critically, this bias was in turn associated with later alcohol motivation. Increased alcohol motivation was particularly pronounced in more susceptible risky drinkers, for whom cortisol enhanced brain salience responses to alcohol. These sensitized salience responses predicted both memory biases and alcohol motivation. Together, these findings reveal maladaptive consequences of enhanced salience encoding.

Microstate Analyses to Study face Processing in Healthy Individuals and Psychiatric Disorders: A Review of ERP Findings.

Microstates represent brief periods of quasi-stable electroencephalography (EEG) scalp topography, offering insights into dynamic fluctuations in event-related potential (ERP) topographies. Despite this, there is a lack of a comprehensive systematic overview of microstate findings concerning cognitive face processing. This review aims to summarize ERP findings on face processing using microstate analyses and assess their effectiveness in characterizing face-related neural representations. A literature search was conducted for microstate ERP studies involving healthy individuals and psychiatric populations, utilizing PubMed, Google Scholar, Web of Science, PsychInfo, and Scopus databases. Twenty-two studies were identified, primarily focusing on healthy individuals (n = 16), with a smaller subset examining psychiatric populations (n = 6). The evidence reviewed in this study suggests that various microstates are consistently associated with distinct ERP stages involved in face processing, encompassing the processing of basic visual facial features to more complex functions such as analytical processing, facial recognition, and semantic representations. Furthermore, these studies shed light on atypical attentional neural mechanisms in Autism Spectrum Disorder (ASD), facial recognition deficits among emotional dysregulation disorders, and encoding and semantic dysfunctions in Post-Traumatic Stress Disorder (PTSD). In conclusion, this review underscores the practical utility of ERP microstate analyses in investigating face processing. Methodologies have evolved towards greater automation and data-driven approaches over time. Future research should aim to forecast clinical outcomes and conduct validation studies to directly demonstrate the efficacy of such analyses in inverse space.

A neural code supporting prospective probabilistic reasoning for instrumental information demand in humans

When making adaptive decisions, we actively demand information, but relatively little is known about the mechanisms of active information gathering. An open question is how the brain prospectively estimates the information gains that are expected to accrue from various sources by integrating simpler quantities of prior certainty and the reliability (diagnosticity) of a source. We examine this question using fMRI in a task in which people placed bids to obtain information in conditions that varied independently in the rewards, decision uncertainty, and information diagnosticity. We show that, consistent with value of information theory, the participants’ bids are sensitive to prior certainty (the certainty about the correct choice before gathering information) and expected posterior certainty (the certainty expected after gathering information). Expected posterior certainty is decoded from multivoxel activation patterns in the posterior parietal and extrastriate cortices. This representation is independent of instrumental rewards and spatially overlaps with distinct representations of prior certainty and expected information gains. The findings suggest that the posterior parietal and extrastriate cortices are candidates for mediating the prospection of posterior probabilities as a key step to anticipating information gains during active gathering of instrumental information.

Neural Encoding of Bodies for Primate Social Perception.

Primates, as social beings, have evolved complex brain mechanisms to navigate intricate social environments. This review explores the neural bases of body perception in both human and nonhuman primates, emphasizing the processing of social signals conveyed by body postures, movements, and interactions. Early studies identified selective neural responses to body stimuli in macaques, particularly within and ventral to the superior temporal sulcus (STS). These regions, known as body patches, represent visual features that are present in bodies but do not appear to be semantic body detectors. They provide information about posture and viewpoint of the body. Recent research using dynamic stimuli has expanded the understanding of the body-selective network, highlighting its complexity and the interplay between static and dynamic processing. In humans, body-selective areas such as the extrastriate body area (EBA) and fusiform body area (FBA) have been implicated in the perception of bodies and their interactions. Moreover, studies on social interactions reveal that regions in the human STS are also tuned to the perception of dyadic interactions, suggesting a specialized social lateral pathway. Computational work developed models of body recognition and social interaction, providing insights into the underlying neural mechanisms. Despite advances, significant gaps remain in understanding the neural mechanisms of body perception and social interaction. Overall, this review underscores the importance of integrating findings across species to comprehensively understand the neural foundations of body perception and the interaction between computational modeling and neural recording.

Suppressing the Maintenance of Information in Working Memory Alters Long-term Memory Traces.

The sensory recruitment hypothesis conceptualizes information in working memory as being activated representations of information in long-term memory. Accordingly, changes made to an item in working memory would be expected to influence its subsequent retention. Here, we tested the hypothesis that suppressing information from working memory, which can reduce short-term access to that information, may also alter its long-term neural representation. We obtained fMRI data (n = 25; 13 female / 12 male participants) while participants completed a working memory removal task with scene images as stimuli, followed by a final surprise recognition test of the examined items. We applied a multivariate pattern analysis to the data to quantify the engagement of suppression on each trial, to track the contents of working memory during suppression, and to assess representational changes afterward. Our analysis confirms previous reports that suppression of information in working memory involves focused attention to target and remove unwanted information. Furthermore, our findings provide new evidence that even a single dose of suppression of an item in working memory can (if engaged with sufficient strength) produce lasting changes in its neural representation, particularly weakening the unique, item-specific features, which leads to forgetting. Our study sheds light on the underlying mechanisms that contribute to the suppression of unwanted thoughts and highlights the dynamic interplay between working memory and long-term memory.

Understanding mechanotransduction in the distal colon and rectum via multiscale and multimodal computational modeling

Visceral pain in the large bowel is a defining symptom of irritable bowel syndrome (IBS) and the primary reason that patients visit gastroenterologists. This pain is reliably triggered by mechanical distension of the distal colon and rectum (colorectum). Consequently, the process of mechanotransduction by sensory afferents, responsible for translating mechanical colorectal stimuli into neural action potentials, plays a central role in IBS-related bowel pain. In this study, we aim to enhance our understanding of colorectal mechanotransduction by combining experimental findings in colorectal biomechanics and afferent neural encoding within a comprehensive computational simulation framework. To achieve this, we implemented a three-layered, fiber-reinforced finite element model that accurately replicates the nonlinear, heterogeneous, and anisotropic mechanical characteristics of the mouse colorectum. This model facilitates the computation of local mechanical stresses and strains around individual afferent endings, which have diameters on the micron-scale. We then integrated a neural membrane model to simulate the encoding of action potentials by afferent nerves in response to microscopic stresses and strains along the afferent endings. Our multiscale simulation framework enables the assessment of three hypotheses regarding the mechanical gating of action potential generation: (1) axial stress dominates mechanical gating of mechanosensitive channels, (2) both axial and circumferential stresses contribute, and (3) membrane shear stress dominates. Additionally, we explore how the orientation of afferent endings impacts neural encoding properties. This computational framework not only allows for the virtual investigation of colorectal mechanotransduction in the context of prolonged visceral hypersensitivity but can also guide the development of new experimental studies aimed at uncovering the neural and biomechanical mechanisms underlying IBS-related bowel pain.

Neural encoding for spatial release from informational masking and its correlation with behavioral metrics.

The central auditory system encompasses two primary functions: identification and localization. Spatial release from masking (SRM) highlights speech recognition in competing noise and improves the listening experience when a spatial cue is introduced between noise and target speech. This assessment focuses on the integrity of auditory function and holds clinical significance. However, infants or pre-lingual subjects sometimes provide less reliable results. This study investigates the value of cortical auditory evoked potentials (CAEPs) onset and acoustic change complex (ACC) as an objective measurement of SRM. Thirty normal-hearing young adults (11 males) were recruited. We found the spatial separation of signals and noise (±90° symmetrically) resulted in a signal-to-noise ratio (SNR) improvement of 9.00 ± 1.71 dB behaviorally. It significantly enhanced cortical processing at all SNR levels, shortened CAEP latencies, and increased amplitudes, resulting in a greater number of measurable peaks for ACC. SRM showed mild to moderate correlations with the differences between two conditions in CAEP measures. The regression model combining N1'-P2' amplitude at 5 dB SNR (R2 = 0.26), P1 amplitude at 0 dB SNR (R2 = 0.14), and P1 latency at -5 dB SNR (R2 = 0.15), explained 45.3% of the variance in SRM. Our study demonstrates that introducing spatial cues can improve speech perception and enhance central auditory processing in normal-hearing young adults. CAEPs may contribute to predictions about SRM and hold potential for practical application.NEW & NOTEWORTHY The neural encoding of spatial release from masking (SRM) can be observed in normal-hearing young adults. Spatial separation between target and masker improves speech perception in noise and enhances central auditory processing. The behavioral results showed mild-to-moderate correlations with electrophysiological measures, with acoustic change complex (ACC) amplitude being a better indicator than onset components. Cortical auditory evoked potentials (CAEPs) may contribute to predictions about spatial release from masking, especially when behavioral tests are less reliable.

Follow-Up Attention: An Empirical Study of Developer and Neural Model Code Exploration

Follow-Up Attention: An Empirical Study of Developer and Neural Model Code Exploration

Improved Urdu-English Neural Machine Translation with a fully Convolutional Neural Network Encoder

Neural machine translation (NMT) approaches driven by artificial intelligence (AI) has gained more and more attention in recent years, mainly due to their simplicity yet state-of-the-art performance. Despite NMT models with attention mechanism relying heavily on the accessibility of substantial parallel corpora, they have demonstrated efficacy even for languages with limited linguistic resources. The convolutional neural network (CNN) is frequently employed in tasks involving visual and speech recognition. Implementing CNN for MT is still challenging compared to the predominant approaches. Recent research has shown that the CNN-based NMT model cannot capture long-term dependencies present in the source sentence. The CNN-based model can only capture the word dependencies within the width of its filters. This unnatural character often causes a worse performance for CNN-based NMT than the RNN-based NMT models. This study introduces a simple method to improve neural translation of a low-resource language, specifically Urdu-English (UR-EN). In this paper, we use a Fully Convolutional Neural Network (FConv-NN) based NMT architecture to create a powerful MT encoder for UR-EN translation that can capture the long dependency of words in a sentence. Although the model is quite simple, it yields strong empirical results. Experimental results show that the FConv-NN model consistently outperforms the traditional CNN-based model with filters. On the Urdu-English Dataset, the FConv-NN model produces translation with a gain of 18.42 BLEU points. Moreover, the quantitative and comparative analysis shows that in a low-resource setting, FConv-NN-based NMT outperforms conventional CNN-based NMT models.

Global motor dynamics - Invariant neural representations of motor behavior in distributed brain-wide recordings

Objective.Motor-related neural activity is more widespread than previously thought, as pervasive brain-wide neural correlates of motor behavior have been reported in various animal species. Brain-wide movement-related neural activity have been observed in individual brain areas in humans as well, but it is unknown to what extent global patterns exist.Approach.Here, we use a decoding approach to capture and characterize brain-wide neural correlates of movement. We recorded invasive electrophysiological data from stereotactic electroencephalographic electrodes implanted in eight epilepsy patients who performed both an executed and imagined grasping task. Combined, these electrodes cover the whole brain, including deeper structures such as the hippocampus, insula and basal ganglia. We extract a low-dimensional representation and classify movement from rest trials using a Riemannian decoder.Main results.We reveal global neural dynamics that are predictive across tasks and participants. Using an ablation analysis, we demonstrate that these dynamics remain remarkably stable under loss of information. Similarly, the dynamics remain stable across participants, as we were able to predict movement across participants using transfer learning.Significance.Our results show that decodable global motor-related neural dynamics exist within a low-dimensional space. The dynamics are predictive of movement, nearly brain-wide and present in all our participants. The results broaden the scope to brain-wide investigations, and may allow combining datasets of multiple participants with varying electrode locations or calibrationless neural decoder.

Phoneme-related potentials recorded from normal hearing listeners and cochlear implant users in a selective attention paradigm to continuous speech

Cochlear implants can restore the ability to understand speech in patients with profound sensorineural hearing loss. At present, it is not fully understood how cochlear implant users perceive speech and how electric hearing provided by a cochlear implant differs from acoustic hearing. Phoneme-related potentials characterize neural responses to individual instances of phonemes extracted from continuous speech.This retrospective study investigated phoneme-related potentials in cochlear implant users in a selective attention paradigm. Responses were compared between normal hearing listeners and cochlear implant users, and between attended and unattended conditions. Differences between phoneme categories were compared and a classifier was trained to predict the phoneme category from the neural representation.The phoneme-related potentials of cochlear implant users showed similar responses to the ones obtained in normal hearing listeners for early responses (< 100 ms) but not for later responses (> 100 ms) where peaks were smaller or absent. Attention led to an enhancement of the response, whereas latency was mostly not affected by attention. The temporal morphology of the response was influenced by the phonetic features of the stimulus, allowing a classification of the phoneme category based on the phoneme-related potentials.There is a clinical need for methods that can rapidly and objectively assess the speech understanding performance of cochlear implant users. Phoneme-related potentials may provide such a link between the acoustic and the neural representations of phonemes. They may also reveal the challenges of individual subjects and thus provide indications for patient-specific auditory training, rehabilitation programs or the fitting of cochlear implant parameters.

Neural Representations of Emotions in Visual, Auditory, and Modality-Independent Regions Reflect Idiosyncratic Conceptual Knowledge.

Growing evidence suggests that conceptual knowledge influences emotion perception, yet the neural mechanisms underlying this effect are not fully understood. Recent studies have shown that brain representations of facial emotion categories in visual-perceptual areas are predicted by conceptual knowledge, but it remains to be seen if auditory regions are similarly affected. Moreover, it is not fully clear whether these conceptual influences operate at a modality-independent level. To address these questions, we conducted a functional magnetic resonance imaging study presenting participants with both facial and vocal emotional stimuli. This dual-modality approach allowed us to investigate effects on both modality-specific and modality-independent brain regions. Using univariate and representational similarity analyses, we found that brain representations in both visual (middle and lateral occipital cortices) and auditory (superior temporal gyrus) regions were predicted by conceptual understanding of emotions for faces and voices, respectively. Additionally, we discovered that conceptual knowledge also influenced supra-modal representations in the superior temporal sulcus. Dynamic causal modeling revealed a brain network showing both bottom-up and top-down flows, suggesting a complex interplay of modality-specific and modality-independent regions in emotional processing. These findings collectively indicate that the neural representations of emotions in both sensory-perceptual and modality-independent regions are likely shaped by each individual's conceptual knowledge.

Scan-Specific Unsupervised Highly Accelerated Non-Cartesian CEST Imaging Using Implicit Neural Representation and Explicit Sparse Prior.

Chemical exchange saturation transfer (CEST) is a promising magnetic resonance imaging (MRI) technique. CEST imaging usually requires a long scan time, and reducing acquisition time is highly desirable for clinical applications. A novel scan-specific unsupervised deep learning algorithm is proposed to accelerate steady-state pulsed CEST imaging with golden-angle stack-of-stars trajectory using hybrid-feature hash encoding implicit neural representation. Additionally, imaging quality is further improved by using the explicit prior knowledge of low rank and weighted joint sparsity in the spatial and Z-spectral domain of CEST data. In the retrospective acceleration experiment, the proposed method outperforms other state-of-the-art algorithms (TDDIP, LRTES, kt-SLR, NeRP, CRNN, and PBCS) for the in vivo human brain dataset under various acceleration rates. In the prospective acceleration experiment, the proposed algorithm can still obtain results close to the fully-sampled images. The hybrid-feature hash encoding implicit neural representation combined with explicit sparse prior (INRESP) can efficiently accelerate CEST imaging. The proposed algorithm achieves reduced error and improved image quality compared to several state-of-the-art algorithms at relatively high acceleration factors. The superior performance and the training database-free characteristic make the proposed algorithm promising for accelerating CEST imaging in various applications.

Global neural encoding of behavioral strategies in mice during perceptual decision-making task with two different sensory patterns

Global neural encoding of behavioral strategies in mice during perceptual decision-making task with two different sensory patterns

Orbitofrontal Cortex Mediates Sustained Basolateral Amygdala Encoding of Cued Reward-Seeking States.

Basolateral amygdala (BLA) neurons are engaged by emotionally salient stimuli. An area of increasing interest is how BLA dynamics relate to evolving reward-seeking behavior, especially under situations of uncertainty or ambiguity. Here, we recorded the activity of individual BLA neurons in male rats across the acquisition and extinction of conditioned reward seeking. We assessed ongoing neural dynamics in a task where long reward cue presentations preceded an unpredictable, variably time reward delivery. We found that, with training, BLA neurons discriminated the CS+ and CS- cues with sustained cue-evoked activity that correlated with behavior and terminated only after reward receipt. BLA neurons were bidirectionally modulated, with a majority showing prolonged inhibition during cued reward seeking. Strikingly, population-level analyses revealed that neurons showing cue-evoked inhibitions and those showing excitations similarly represented the CS+ and behavioral state. This sustained population code rapidly extinguished in parallel with conditioned behavior. We next assessed the contribution of the orbitofrontal cortex (OFC), a major reciprocal partner to the BLA. Inactivation of the OFC while simultaneously recording in the BLA revealed a blunting of sustained cue-evoked activity in the BLA that accompanied reduced reward seeking. Optogenetic disruption of BLA activity and OFC terminals in the BLA also reduced reward seeking. Our data indicate that the BLA represents reward-seeking states via sustained, bidirectional cue-driven neural encoding. This code is regulated by cortical input and is important for the maintenance of vigilant reward-seeking behavior.