Neural Activity Research Articles

Magnetoelectrics for Implantable Bioelectronics: Progress to Date.

ConspectusThe coupling of magnetic and electric properties manifested in magnetoelectric (ME) materials has unlocked numerous possibilities for advancing technologies like energy harvesting, memory devices, and medical technologies. Due to this unique coupling, the magnetic properties of these materials can be tuned by an electric field; conversely, their electric polarization can be manipulated through a magnetic field.Over the past seven years, our lab work has focused on leveraging these materials to engineer implantable bioelectronics for various neuromodulation applications. One of the main challenges for bioelectronics is to design miniaturized solutions that can be delivered with minimally invasive procedures and yet can receive sufficient power to directly stimulate tissue or power electronics to perform functions like communication and sensing.Magnetoelectric coupling in ME materials is strongest when the driving field matches a mechanical resonant mode. However, miniaturized ME transducers typically have resonance frequencies >100 kHz, which is too high for direct neuromodulation as neurons only respond to low frequencies (typically <1 kHz). We discuss two approaches that have been proposed to overcome this frequency mismatch: operating off-resonance and rectification. The off-resonance approach is most common for magnetoelectric nanoparticles (MENPs) that typically have resonance frequencies in the gigahertz range. In vivo experiments on rat models have shown that MENPs could induce changes in neural activity upon excitation with <200 Hz magnetic fields. However, the neural response has latencies of several seconds due to the weak coupling in the off-resonance regime.To stimulate neural responses with millisecond precision, we developed methods to rectify the ME response so that we could drive the materials at their resonant frequency but still produce the slowly varying voltages needed for direct neural stimulation. The first version of the stimulator combined a ME transducer and analog electronics for rectification. To create even smaller solutions, we introduced the first magnetoelectric metamaterial (MNM) that exhibits self-rectification. Both designs have effectively induced neural modulation in rat models with less than 5 ms latency.Based on our experience with in vivo testing of the rectified ME stimulators, we found it challenging to deliver the precisely controlled therapy required for clinical applications, given the ME transducer's sensitivity to the external transmitter alignment. To overcome this challenge, we developed the ME-BIT (MagnetoElectric BioImplanT), a digitally programmable stimulator that receives wireless power and data through the ME link.We further expanded the utility of this technology to neuromodulation applications that require high stimulation thresholds by introducing the DOT (Digitally programmable Overbrain Therapeutic). The DOT has voltage compliance up to 14.5 V. We have demonstrated the efficacy of these designs through various in vivo studies for applications like peripheral nerve stimulation and epidural cortical stimulation.To further improve these systems to be adaptive and enable a network of coordinated devices, we developed a bidirectional communication system to transmit data to and from the implant. To enable even greater miniaturization, we developed a way to use the same ME transducer for wireless power and data communication by developing the first ME backscatter communication protocol.

Synaptic weight dynamics underlying memory consolidation: Implications for learning rules, circuit organization, and circuit function

Systems consolidation is a common feature of learning and memory systems, in which a long-term memory initially stored in one brain region becomes persistently stored in another region. We studied the dynamics of systems consolidation in simple circuit architectures with two sites of plasticity, one in an early-learning and one in a late-learning brain area. We show that the synaptic dynamics of the circuit during consolidation of an analog memory can be understood as a temporal integration process, by which transient changes in activity driven by plasticity in the early-learning area are accumulated into persistent synaptic changes at the late-learning site. This simple principle naturally leads to a speed-accuracy tradeoff in systems consolidation and provides insight into how the circuit mitigates the stability-plasticity dilemma of storing new memories while preserving core features of older ones. Furthermore, it imposes two constraints on the circuit. First, the plasticity rule at the late-learning site must stably support a continuum of possible outputs for a given input. We show that this is readily achieved by heterosynaptic but not standard Hebbian rules. Second, to turn off the consolidation process and prevent erroneous changes at the late-learning site, neural activity in the early-learning area must be reset to its baseline activity. We provide two biologically plausible implementations for this reset that propose functional roles in stabilizing consolidation for core elements of the cerebellar circuit.

Orthographic and phonological processing in the left ventral occipitotemporal cortex during Chinese word reading.

The left ventral occipitotemporal cortex (lvOT) has been consistently identified as a crucial structure in word reading, and its function varies across subregions. Nevertheless, the specific function of the lvOT and its subregions remains controversial because the obvious grapheme-to-phoneme correspondence rules of alphabetic languages make it difficult to disentangle the contributions of orthography and phonology to neural activations. To explore information processing in lvOT subregions, the present study manipulated the orthography and phonology in a factorial design and used the fMRI rapid adaptation paradigm. The results revealed a posterior-to-anterior functional gradient in lvOT in Chinese word reading and specified that the functional transition from sublexical to lexical processing occurred in the middle subregion close to the classic VWFA. More importantly, we found that the middle and posterior subregions of lvOT are responsible for processing both orthographic and phonological information during Chinese word reading. These results elaborated the function of the lvOT in Chinese word reading.

The neural plasticity and efficacy of acupuncture for post-stroke dysphagia: protocol for a randomized controlled trial with fMRI and DTI

BackgroundDysphagia, a common complication of acute stroke, is associated with increased mortality and morbidity. Acupuncture, a widely used swallowing therapy in China, has been suggested as an effective therapy for treating Post-Stroke Dysphagia (PSD) by recent meta-analyses and guidelines. The use of resting-state functional Magnetic Resonance Imaging (rs-fMRI) and Diffusion Tensor Imaging (DTI) could explore the change of regional spontaneous neural activity, functional relationships between brain regions, and white matter connectivity patterns after acupuncture intervention for PSD. This trial aims to evaluate the efficacy of acupuncture treatment for PSD and explore its central mechanism by neuroimaging.Methods/designThis randomized controlled trial will recruit 40 PSD patients. All patients will be randomized to either the Real Acupuncture (RA) or Sham Acupuncture (SA) group by a ratio of 1:1. All patients will receive immediate acupuncture treatment in the MRI scanning room, followed by four weeks of long-term acupuncture treatment. The primary outcomes are the rs-fMRI and DTI indicators, which will be evaluated after the immediate and long-term acupuncture treatment. The secondary outcomes are the scales that assess the efficacy, including the Functional Oral Intake Scale (FOIS), Water Swallowing Test (WST), Swallowing Quality Of Life Questionnaire (SWAL-QOL), and National Institute of Health Stroke Scale (NIHSS). The modified version of the Massachusetts General Hospital Acupuncture Sensation Scale (M-MASS) and fMRI sensation record table will also be evaluated.DiscussionThis protocol presents the design of a randomized, single-blind trial that will evaluate the efficacy and explore the neural plasticity of acupuncture treatment for PSD. This trial will deepen our insight into the clinical value of acupuncture for PSD and initially probe into the time-dosage-effect mechanism of acupuncture.Trial registration numbersChinese Clinical Trial Registry (www.chictr.org.cn) ChiCTR2300067480. This study was registered on 9th January 2023.

Monolithically integrated neuromorphic electronic skin for biomimetic radiation shielding.

Melanogenesis, a natural responsive mechanism of human skin to harmful radiation, is a self-triggered defensive neural activity safeguarding the body from radiation exposure in advance. With the increasing significance of radiation shielding in diverse medical health care and wearable applications, a biomimetic neuromorphic optoelectronic system with adaptive radiation shielding capability is often needed. Here, we demonstrate a transparent and flexible metal oxide-based photovoltaic neuromorphic defensive system. By using a monolithically integrated ultraflexible optoelectronic circuitry and electrochromic device, seamless neural processing for ultraviolet (UV) radiation shielding including history-based sensing, memorizing, risk recognition, and blocking can be realized with piling the entire signal chain into the flexible devices. The UV shielding capability of the system can be evaluated as autonomous blocking up to 97% of UV radiation from 5 to 90 watts per square meter in less than 16.9 seconds, demonstrating autonomously modulated sensitivity and response time corresponding to UV environmental conditions and supplied bias.

Chunk Duration Limits the Learning of Multiword Chunks: Behavioral and Electroencephalogram Evidence from Statistical Learning.

Language comprehension involves the grouping of words into larger multiword chunks. This is required to recode information into sparser representations to mitigate memory limitations and counteract forgetting. It has been suggested that electrophysiological processing time windows constrain the formation of these units. Specifically, the period of rhythmic neural activity (i.e., slow-frequency neural oscillations) may set an upper limit of 2-3 sec. Here, we assess whether learning of new multiword chunks is also affected by this neural limit. We applied an auditory statistical learning paradigm of an artificial language while manipulating the duration of to-be-learnt chunks. Participants listened to isochronous sequences of disyllabic pseudowords from which they could learn hidden three-word chunks based on transitional probabilities. We presented chunks of 1.95, 2.55, and 3.15 sec that were created by varying the pause interval between pseudowords. In a first behavioral experiment, we tested learning using an implicit target detection task. We found better learning for chunks of 2.55 sec as compared to longer durations in line with an upper limit of the proposed time constraint. In a second experiment, we recorded participants' electroencephalogram during the exposure phase to use frequency tagging as a neural index of statistical learning. Extending the behavioral findings, results show a significant decline in neural tracking for chunks exceeding 3 sec as compared to both shorter durations. Overall, we suggest that language learning is constrained by endogenous time constraints, possibly reflecting electrophysiological processing windows.

A Bayesian Hierarchical Model of Trial-To-Trial Fluctuations in Decision Criterion.

Classical decision models assume that the parameters giving rise to choice behavior are stable, yet emerging research suggests these parameters may fluctuate over time. Such fluctuations, observed in neural activity and behavioral strategies, have significant implications for understanding decision-making processes. However, empirical studies on fluctuating human decision-making strategies have been limited due to the extensive data requirements for estimating these fluctuations. Here, we introduce hMFC (Hierarchical Model for Fluctuations in Criterion), a Bayesian framework designed to estimate slow fluctuations in the decision criterion from limited data. We first showcase the importance of considering fluctuations in decision criterion: incorrectly assuming a stable criterion gives rise to apparent history effects and underestimates perceptual sensitivity. We then present a hierarchical estimation procedure capable of reliably recovering the underlying state of the fluctuating decision criterion with as few as 500 trials per participant, offering a robust tool for researchers with typical human datasets. Critically, hMFC does not only accurately recover the state of the underlying decision criterion, it also effectively deals with the confounds caused by criterion fluctuations. Lastly, we provide code and a comprehensive demo at www.github.com/robinvloeberghs/hMFC to enable widespread application of hMFC in decision-making research.

Network statistics of the whole-brain connectome of Drosophila

Brains comprise complex networks of neurons and connections, similar to the nodes and edges of artificial networks. Network analysis applied to the wiring diagrams of brains can offer insights into how they support computations and regulate the flow of information underlying perception and behaviour. The completion of the first whole-brain connectome of an adult fly, containing over 130,000 neurons and millions of synaptic connections1–3, offers an opportunity to analyse the statistical properties and topological features of a complete brain. Here we computed the prevalence of two- and three-node motifs, examined their strengths, related this information to both neurotransmitter composition and cell type annotations4,5, and compared these metrics with wiring diagrams of other animals. We found that the network of the fly brain displays rich-club organization, with a large population (30% of the connectome) of highly connected neurons. We identified subsets of rich-club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex (https://codex.flywire.ai) and should serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

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.

Temporal and spatial analysis of event-related potentials in response to color saliency differences among various color vision types.

Human color vision exhibits significant diversity that cannot be fully explained by categorical classifications. Understanding how individuals with different color vision phenotypes perceive, recognize, and react to the same physical stimuli provides valuable insights into sensory characteristics. This study aimed to identify behavioral and neural differences between different color visions, primarily classified as typical trichromats and anomalous trichromats, in response to two chromatic stimuli, blue-green and red, during an attention-demanding oddball task. We analyzed the P3 component of event-related potentials (ERPs), associated with attention, and conducted a broad spatiotemporal exploration of neural differences. Behavioral responses were also analyzed to complement neural data. Participants included typical trichromats (n = 13) and anomalous trichromats (n = 5), and the chromatic stimuli were presented in an oddball paradigm. Typical trichromats exhibited faster potentiation from the occipital to parietal regions in response to the more salient red stimulus, particularly in the area overlapping with the P3 component. In contrast, anomalous trichromats revealed faster potentiation to the expected more salient blue-green stimulus in the occipital to parietal regions, with no other significant neural differences between stimuli. Comparisons between the color vision types showed no significant overall neural differences. The large variability in red-green sensitivity among anomalous trichromats, along with neural variability not fully explained by this sensitivity, likely contributed to the absence of clear neural distinctions based on color saliency. While reaction times were influenced by red-green sensitivity, neural signals showed ambiguity regarding saliency differences. These findings suggest that factors beyond red-green sensitivity influenced neural activity related to color perception and cognition in minority color vision phenotypes. Further research with larger sample sizes is needed to more comprehensively explore these neural dynamics and their broader implications.

Unveiling Visual Physiology and Steady-State Evoked Potentials using Low-Cost and Transferable Electroencephalography for Evaluating Neuronal Activation

Purpose: The ability to see and process images depends on the function of the eyes and the processing of visual information by neurons in the cerebral cortex, something that could be measured through electroencephalography (EEG). Although the EEG is used to evaluate visual pathways in children and demyelination diseases, the limited utilization of brain recording techniques in other applications like therapy is primarily due to budget constraints. The goal of this paper is to demonstrate results from studying brain aspects of vision, utilizing measurements based on oscillatory activity analysis, low-cost, portable equipment, and a processing pipeline relying on Python’s open-source libraries. These studies involve healthy subjects who wear glasses to assess changes in visual perception. Methods: First, electroencephalographic signals were recorded while the subjects observed a visually standardized stimulus. The signals were processed and filtered to reduce artifacts, and the power spectral density (PSD) was calculated to observe the presence of steady-state visual potentials (VEP) to confirm the capture of neuronal activation to the visual stimulus. Results: It was possible to establish a difference between subjects wearing and not wearing their glasses, allowing validation that the information acquired with the transferable equipment is adequate for the analysis of neuronal activity related to visual processing, opening the possibility to be used in future studies in therapy. Conclusion: This study contributes to the development of cost-effective and portable EEG solutions for visual system analysis. It demonstrates the potential for applying transferable EEG devices in clinical settings and highlights the importance of tailored visual stimuli for reliable neural activation.

COVID-19 related cognitive, structural and functional brain changes among Italian adolescents and young adults: a multimodal longitudinal case-control study

Coronavirus disease 2019 (COVID-19) has been associated with brain functional, structural, and cognitive changes that persist months after infection. Most studies of the neurologic outcomes related to COVID-19 focus on severe infection and aging populations. Here, we investigated the neural activities underlying COVID-19 related outcomes in a case-control study of mildly infected youth enrolled in a longitudinal study in Lombardy, Italy, a global hotspot of COVID-19. All participants (13 cases, 27 controls, mean age 24 years) completed resting-state functional (fMRI), structural MRI, cognitive assessments (CANTAB spatial working memory) at baseline (pre-COVID) and follow-up (post-COVID). Using graph theory eigenvector centrality (EC) and data-driven statistical methods, we examined differences in ECdelta (i.e., the difference in EC values pre- and post-COVID-19) and Volumetricdelta (i.e., the difference in cortical volume of cortical and subcortical areas pre- and post-COVID) between COVID-19 cases and controls. We found that ECdelta significantly between COVID-19 and healthy participants in five brain regions; right intracalcarine cortex, right lingual gyrus, left hippocampus, left amygdala, left frontal orbital cortex. The left hippocampus showed a significant decrease in Volumetricdelta between groups (p = 0.041). The reduced ECdelta in the left amygdala associated with COVID-19 status mediated the association between COVID-19 and disrupted spatial working memory. Our results show persistent structural, functional and cognitive brain changes in key brain areas associated with olfaction and cognition. These results may guide treatment efforts to assess the longevity, reversibility and impact of the observed brain and cognitive changes following COVID-19.

Brief considerations on ethics for artificial intelligence neurotechnologies

Introduction: implants and technological devices are being used to decode neural activity to move a prosthetic arm, control an avatar, and turn thoughts into text through an AI-based decoder. These situations are designed by Brain-computer Interface (BCI), one of the main AI-based neurotechnologies used to understand the brain and to improve people's welfare. In 2023, UNESCO already recognized its benefits but also revealed the potential ethical issues and problems, particularly with its use of non-invasive interventions. Objective: so, this essay aims to answer the following research question: which Ethical standards can be designed and used to balance the person’s rights with technological development to prevent vulnerability situations? Method: the methods used in this work is the bibliographic research plus the hermeneutic interpretation. Results: it proposes Ethical standards for protecting the rights of the vulnerable to ensure that these rights are respected. Conclusions: there is no need for the creation of a new neurorights. Privacy and intimacy can and will deal with all the issues of neurotechnologies. However, it is necessary to improve the protection of the owner's rights through strong ethical and governance standards.

Dorsal raphe dopaminergic neurons target CaMKII+ neurons in dorsal bed nucleus of the stria terminalis for mediating depression-related behaviors

Dopamine (DA) neurons play a crucial role in the development and manifestation of depression, as well as in response to antidepressant treatments. While the function of the predominantly distributed DA neurons in the ventral tegmental area (VTA) is well established, the contribution of a small fraction of DA neurons in the dorsal raphe nucleus (DRN) during depression remains unclear. In this study, we found that chronic unpredictable stress (CUS) induces depression-related behaviors and decreases spontaneous firing rates, excitatory and inhibitory postsynaptic currents of DA neurons in the DRN associated with reduced excitatory synaptic transmission in male and female mice. The chemogenetic inhibition of DA neurons in the DRN produces depressive phenotypes. Conversely, their activation completely reversed the anhedonic and despair behaviors induced by CUS. Furthermore, we showed that a DRN dopaminergic projecting to the dorsal bed nucleus of the stria terminalis (dBNST) selectively controls depressive behaviors by influencing the neural activity and N-methyl-D-aspartate receptor (NMDAR) mediating EPSC of calcium/calmodulin-dependent protein kinase II+ (CaMKII+) target neurons by regulating dopamine neurotransmitter and dopamine receptor 2 (DR2) in the dBNST. Overall, these findings highlight the essential role of the DRNDA → dBNSTCaMKII+ neural circuit in bi-directionally mediating stress-induced depression-related behaviors. Our findings indicate that DRN DA neurons are a key component of the neural circuitry involved in regulating depression-related behaviors, making them a potential therapeutic target for depression.

Decoding the temporal structures and interactions of multiple face dimensions using optically pumped magnetometer magnetoencephalography (OPM-MEG).

Humans possess a remarkable ability to rapidly access diverse information from others' faces with just a brief glance, which is crucial for intricate social interactions. While previous studies using event-related potentials/fields have explored various face dimensions during this process, the interplay between these dimensions remains unclear. Here, by applying multivariate decoding analysis to neural signals recorded with optically pumped magnetometer magnetoencephalography (OPM-MEG), we systematically investigated the temporal interactions between invariant and variable aspects of face stimuli, including race, gender, age and expression. First, our analysis revealed unique temporal structures for each face dimension with high test-retest reliability. Notably, expression and race exhibited a dominant and stably maintained temporal structure according to temporal generalization analysis. Further exploration into the mutual interactions among face dimensions uncovered age effects on gender and race, as well as expression effects on race, during the early stage (around 200-300 ms post face presentation). Additionally, we observed a relatively late effect of race on gender representation, peaking around 350 ms after stimulus onset. Taken together, our findings provide novel insights into the neural dynamics underlying the multi-dimensional aspects of face perception and illuminate the promising future of utilizing OPM-MEG for exploring higher-level human cognition.Significance statement In everyday social activities, people can quickly interpret a wide range of information from others' faces. Although converging evidence has shed light upon the neural substrates underpinning the perception of invariant and variable aspects of faces, such as race, gender, age and expression, it is still not fully understood how the information of one face dimension alters the perception of another. In this study, we utilized multivariate decoding analysis on neural activity captured through OPM-MEG during face perception. Our approach enabled a comprehensive exploration of the temporal interactions among different face dimensions, providing an improved understanding of the temporally structured neural dynamics that support the multi-dimensional face perception in the human brain.

Longitudinal monitoring of hypertonia through a multimodal sensing glove

As clinical evaluations of neuromuscular disorders such as hypertonia mostly rely on perception-based scales, the imprecise subjective ratings make it difficult to accurately monitor treatment progress. To promote objective evaluation, this work used a multi-modal sensing glove in a double-blind study to enable sensitive monitoring of medication effects across 19 participants. The biomechanical measurements from the sensing glove effectively distinguished patient cohorts receiving a baclofen treatment or a placebo with 95% confidence. Consistent monitoring over a two-month period was demonstrated, closely tracking variations in individual responses to treatment. The biomechanical changes were correlated to neural activities as recorded by electromyography, verifying the medication effects. The sensing glove is shown to be a reliable tool for point-of-care settings to facilitate precise evaluation of hypertonia, essential for tailoring individual treatment choices and timely management of chronic symptoms.

Magnetogenetic stimulation inside MRI induces spontaneous and evoked changes in neural circuits activity in rats.

The ability to modulate specific neural circuits and simultaneously visualize and measure brain activity with MRI would greatly impact our understanding of brain function in health and disease. The combination of neurostimulation methods and functional MRI in animal models have already shown promise in elucidating fundamental mechanisms associated with brain activity. We developed an innovative magnetogenetics neurostimulation technology that can trigger neural activity through magnetic fields. Similar to other genetic-based neuromodulation methods, magnetogenetics offers cell-, area-, and temporal-specific control of neural activity. The magnetogenetic protein-Electromagnetic Perceptive Gene (EPG)-is activated by non-invasive magnetic fields, providing a unique way to target neural circuits by the MRI static and gradient fields while simultaneously measuring their effect on brain activity. EPG was expressed in rat's visual cortex and the amplitude of low-frequency fluctuation, resting-state functional connectivity (FC), and sensory activation was measured using a 7T MRI. The results demonstrate that EPG-expressing rats had significantly higher signal fluctuations in the visual areas and stronger FC in sensory areas consistent with known anatomical visuosensory and visuomotor connections. This new technology complements the existing neurostimulation toolbox and provides a means to study brain function in a minimally-invasive way which was not possible previously.

Functional neural substrates of Parkinson’s disease and potential underpinnings of acute responses to acupuncture stimulation

Parkinson’s disease is a heterogenous neurodegenerative disorder with a wide variety of motor and non-motor symptoms. This study used resting-state fMRI to identify the neural substrates of PD and explore the acute neural response to acupuncture stimulation in 74 participants (50 patients with PD and 24 healthy controls). All participants with PD were evaluated for the severity of symptoms using the Unified Parkinson’s Disease Rating Scale and Balance Master. The z-transformed fractional amplitude of low-frequency fluctuation analysis showed significant differences between the PD and healthy controls in the cerebellar regions, which are thought to play a crucial role in PD pathology. Subsequently, seed-based functional connectivity of the cerebellum with the frontal, parietal, and limbic regions was identified as a potential diagnostic marker for PD. In addition, spontaneous neural activity in the precentral gyrus and thalamus was significantly associated with the severity of PD symptoms. Neural activity in the precentral gyrus, precuneus, and superior temporal gyrus showed a significant correlation with Balance Master indicators. Finally, acupuncture stimulation at GB34 significantly reduced the activity of the occipital regions in patients with PD, but this effect was not observed in healthy controls. The mixed-effects analysis revealed an interaction effects between group and acupuncture stimulation, suggesting that the modulatory effects of acupuncture could differ depending on disease status. Therefore, this study suggests the neural substrates of PD and potential underpinnings of acute neural response to acupuncture stimulation.

A neural mechanism for optic flow parsing in macaque visual cortex.

For the brain to compute object motion in the world during self-motion, it must discount the global patterns of image motion (optic flow) caused by self-motion. Optic flow parsing is a proposed visual mechanism for computing object motion in the world, and studies in both humans and monkeys have demonstrated perceptual biases consistent with the operation of a flow-parsing mechanism. However, the neural basis of flow parsing remains unknown. We demonstrate, at both the individual unit and population levels, that neural activity in macaque middle temporal (MT) area is biased by peripheral optic flow in a manner that can at least partially account for perceptual biases induced by flow parsing. These effects cannot be explained by conventional surround suppression mechanisms or choice-related activity and have substantial neural latency. Together, our findings establish the first neural basis for the computation of scene-relative object motion based on flow parsing.

Unveiling hidden sources of noise.

Changes in neural activity thought to reflect brain aging may be partly influenced by age-dependent signals 'leaking' from the heart.