Patterns Of Neural Activity Research Articles

Empathy in schizophrenia: neural alterations during emotion recognition and affective sharing

IntroductionDeficits in emotion recognition and processing are characteristic for patients with schizophrenia [SCZ].MethodsWe targeted both emotion recognition and affective sharing, one in static and one in dynamic facial stimuli, during functional magnetic resonance imaging [fMRI] in 22 SCZ patients and 22 matched healthy controls [HC]. Current symptomatology and cognitive deficits were assessed as potential influencing factors.ResultsBehaviorally, patients only showed a prolonged response time in age-discrimination trials. For emotion-processing trials, patients showed a difference in neural response, without an observable behavioral correlate. During emotion and age recognition in static stimuli, a reduced activation of the bilateral anterior cingulate cortex [ACC] and the right anterior insula [AI] emerged. In the affective sharing task, patients showed a reduced activation in the left and right caudate nucleus, right AI and inferior frontal gyrus [IFG], right cerebellum, and left thalamus, key areas of empathy.DiscussionWe conclude that patients have deficits in complex visual information processing regardless of emotional content on a behavioral level and that these deficits coincide with aberrant neural activation patterns in emotion processing networks. The right AI as an integrator of these networks plays a key role in these aberrant neural activation patterns and, thus, is a promising candidate area for neurofeedback approaches.

Single trial Bayesian inference by population vector readout in the barn owl's sound localization system.

Bayesian models have proven effective in characterizing perception, behavior, and neural encoding across diverse species and systems. The neural implementation of Bayesian inference in the barn owl's sound localization system and behavior has been previously explained by a non-uniform population code model. This model specifies the neural population activity pattern required for a population vector readout to match the optimal Bayesian estimate. While prior analyses focused on trial-averaged comparisons of model predictions with behavior and single-neuron responses, it remains unknown whether this model can accurately approximate Bayesian inference on single trials under varying sensory reliability, a fundamental condition for natural perception and behavior. In this study, we utilized mathematical analysis and simulations to demonstrate that decoding a non-uniform population code via a population vector readout approximates the Bayesian estimate on single trials for varying sensory reliabilities. Our findings provide additional support for the non-uniform population code model as a viable explanation for the barn owl's sound localization pathway and behavior.

Elucidating the underlying components of metacognitive systematic bias in the human dorsolateral prefrontal cortex and inferior parietal cortex

Metacognitive systematic bias impairs human learning efficiency, which is characterized by the inconsistency between predicted and actual memory performance. However, the underlying mechanism of metacognitive systematic bias remains unclear in existing studies. In this study, we utilized judgments of learning task in human participants to compare the neural mechanism difference in metacognitive systematic bias. Participants encoded words in fMRI sessions that would be tested later. Immediately after encoding each item, participants predicted how likely they would remember it. Multivariate analyses on fMRI data demonstrated that working memory and uncertainty decisions are represented in patterns of neural activity in metacognitive systematic bias. The available information participants used led to overestimated bias and underestimated bias. Effective connectivity analyses further indicate that information about the metacognitive systematic bias is represented in the dorsolateral prefrontal cortex and inferior parietal cortex. Different neural patterns were found underlying overestimated bias and underestimated bias. Specifically, connectivity regions with the dorsolateral prefrontal cortex, anterior cingulate cortex, and supramarginal gyrus form overestimated bias, while less regional connectivity forms underestimated bias. These findings provide a mechanistic account for the construction of metacognitive systematic bias.

Counterfactual thinking induces different neural patterns of memory modification in anxious individuals

Episodic counterfactual thinking (eCFT) is the process of mentally simulating alternate versions of experiences, which confers new phenomenological properties to the original memory and may be a useful therapeutic target for trait anxiety. However, it remains unclear how the neural representations of a memory change during eCFT. We hypothesized that eCFT-induced memory modification is associated with changes to the neural pattern of a memory primarily within the default mode network, moderated by dispositional anxiety levels. We tested this proposal by examining the representational dynamics of eCFT for 39 participants varying in trait anxiety. During eCFT, lateral parietal regions showed progressively more distinct activity patterns, whereas medial frontal neural activity patterns became more similar to those of the original memory. Neural pattern similarity in many default mode network regions was moderated by trait anxiety, where highly anxious individuals exhibited more generalized representations for upward eCFT (better counterfactual outcomes), but more distinct representations for downward eCFT (worse counterfactual outcomes). Our findings illustrate the efficacy of examining eCFT-based memory modification via neural pattern similarity, as well as the intricate interplay between trait anxiety and eCFT generation.

Distributed and dynamical communication: a mechanism for flexible cortico-cortical interactions and its functional roles in visual attention

Perceptual and cognitive processing relies on flexible communication among cortical areas; however, the underlying neural mechanism remains unclear. Here we report a mechanism based on the realistic spatiotemporal dynamics of propagating wave patterns in neural population activity. Using a biophysically plausible, multiarea spiking neural circuit model, we demonstrate that these wave patterns, characterized by their rich and complex dynamics, can account for a wide variety of empirically observed neural processes. The coordinated interactions of these wave patterns give rise to distributed and dynamic communication (DDC) that enables flexible and rapid routing of neural activity across cortical areas. We elucidate how DDC unifies the previously proposed oscillation synchronization-based and subspace-based views of interareal communication, offering experimentally testable predictions that we validate through the analysis of Allen Institute Neuropixels data. Furthermore, we demonstrate that DDC can be effectively modulated during attention tasks through the interplay of neuromodulators and cortical feedback loops. This modulation process explains many neural effects of attention, underscoring the fundamental functional role of DDC in cognition.

Patterns of spontaneous neural activity associated with social communication abilities among infants and toddlers showing signs of autism.

Early disruptions to social communication development, including delays in joint attention and language, are among the earliest markers of autism spectrum disorder (autism, henceforth). Although social communication differences are a core feature of autism, there is marked heterogeneity in social communication-related development among infants and toddlers exhibiting autism symptoms. Neural markers of individual differences in joint attention and language abilities may provide important insight into heterogeneity in autism symptom expression during infancy and toddlerhood. This study examined patterns of spontaneous electroencephalography (EEG) activity associated with joint attention and language skills in 70 community-referred 12- to 23-month-olds with autism symptoms and elevated scores on an autism diagnostic instrument. Data-driven cluster-based permutation analyses revealed significant positive associations between relative alpha power (6-9Hz) and concurrent response to joint attention skills, receptive language, and expressive language abilities. Exploratory analyses also revealed significant negative associations between relative alpha power and measures of core autism features (i.e., social communication difficulties and restricted/repetitive behaviors). These findings shed light on the neural mechanisms underlying typical and atypical social communication development in emerging autism and provide a foundation for future work examining neural predictors of social communication growth and markers of intervention response.

Neural correlates of retrospective memory confidence during face-name associative learning.

The ability to accurately assess one's own memory performance during learning is essential for adaptive behavior, but the brain mechanisms underlying this metamemory function are not well understood. We investigated the neural correlates of memory accuracy and retrospective memory confidence in a face-name associative learning task using magnetoencephalography in healthy young adults (n = 32). We found that high retrospective confidence was associated with stronger occipital event-related fields during encoding and widespread event-related fields during retrieval compared to low confidence. On the other hand, memory accuracy was linked to medial temporal activities during both encoding and retrieval, but only in low-confidence trials. A decrease in oscillatory power at alpha/beta bands in the parietal regions during retrieval was associated with higher memory confidence. In addition, representational similarity analysis at the single-trial level revealed distributed but differentiable neural activities associated with memory accuracy and confidence during both encoding and retrieval. In summary, our study unveiled distinct neural activity patterns related to memory confidence and accuracy during associative learning and underscored the crucial role of parietal regions in metamemory.

64. Common and Distinct Patterns of Neural Activation Between Major Depressive Disorder and Generalized Anxiety Disorder: A Comparison of Meta-Analytic Findings From Functional Magnetic Resonance Imaging Studies

64. Common and Distinct Patterns of Neural Activation Between Major Depressive Disorder and Generalized Anxiety Disorder: A Comparison of Meta-Analytic Findings From Functional Magnetic Resonance Imaging Studies

60. Meta-Analysis of Functional Magnetic Resonance Imaging Studies Reveals Abnormal Patterns of Neural Activation in Panic Disorder and Agoraphobia

60. Meta-Analysis of Functional Magnetic Resonance Imaging Studies Reveals Abnormal Patterns of Neural Activation in Panic Disorder and Agoraphobia

Neural Mechanisms Determining the Duration of Task-free, Self-paced Visual Perception.

Humans spend hours each day spontaneously engaging with visual content, free from specific tasks and at their own pace. Currently, the brain mechanisms determining the duration of self-paced perceptual behavior remain largely unknown. Here, participants viewed naturalistic images under task-free settings and self-paced each image's viewing duration while undergoing EEG and pupillometry recordings. Across two independent data sets, we observed large inter- and intra-individual variability in viewing duration. However, beyond an image's presentation order and category, specific image content had no consistent effects on spontaneous viewing duration across participants. Overall, longer viewing durations were associated with sustained enhanced posterior positivity and anterior negativity in the ERPs. Individual-specific variations in the spontaneous viewing duration were consistently correlated with evoked EEG activity amplitudes and pupil size changes. By contrast, presentation order was selectively correlated with baseline alpha power and baseline pupil size. Critically, spontaneous viewing duration was strongly predicted by the temporal stability in neural activity patterns starting as early as 350msec after image onset, suggesting that early neural stability is a key predictor for sustained perceptual engagement. Interestingly, neither bottom-up nor top-down predictions about image category influenced spontaneous viewing duration. Overall, these results suggest that individual-specific factors can influence perceptual processing at a surprisingly early time point and influence the multifaceted ebb and flow of spontaneous human perceptual behavior in naturalistic settings.

Understanding the neural principles of interacting multiple internal states

In dynamic ethological environments, animals are constantly confronted with conflicting choices driven by interacting internal motivations. This intricate dance between internal states prompts the question: How do these states interact to determine the optimal innate behavior at any given moment? Specifically, when hungry animals encounter food and potential intruders, how do they navigate between feeding, attack, and mating behaviors? Despite extensive neuroscience research on instinctive behaviors, our understanding of the mechanisms governing adaptive behavioral decisions in the face of competing internal signals remains limited. This research aims to shed light on the complex interplay of internal physiological homeostasis in modulating defensive, aggressive, and sexual behaviors. Our focus lies on exploring the intricate network between the body and the brain to unravel the most adaptive responses within a concise time window. Currently, our investigations center around the hypothalamus, a crucial region known for representing emotional states upon encountering conspecifics or predators. Intriguingly, this same hypothalamic nucleus plays a pivotal role in regulating homeostatic metabolic states, including glucose homeostasis. Here, I will share the observation from innate behaviors of animals and the associated neural activity patterns in the hypothalamus. We will explore the current understanding of how these behaviors are modulated by internal physiological states and discuss the future directions of our research. Additionally, we aim to highlight the significance of combining the measurement of physiological signals to comprehensively address how multiple internal states interact with each other. By unraveling the complexities of these interactions, our research contributes to a deeper comprehension of the adaptive decision-making processes that animals employ in the face of conflicting choices. Helen Hay Whitney Foundation, HHMI. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Laying the foundations for a theory of consciousness: the significance of critical brain dynamics for the formation of conscious states.

Empirical evidence indicates that conscious states, distinguished by the presence of phenomenal qualities, are closely linked to synchronized neural activity patterns whose dynamical characteristics can be attributed to self-organized criticality and phase transitions. These findings imply that insight into the mechanism by which the brain controls phase transitions will provide a deeper understanding of the fundamental mechanism by which the brain manages to transcend the threshold of consciousness. This article aims to show that the initiation of phase transitions and the formation of synchronized activity patterns is due to the coupling of the brain to the zero-point field (ZPF), which plays a central role in quantum electrodynamics (QED). The ZPF stands for the presence of ubiquitous vacuum fluctuations of the electromagnetic field, represented by a spectrum of normal modes. With reference to QED-based model calculations, the details of the coupling mechanism are revealed, suggesting that critical brain dynamics is governed by the resonant interaction of the ZPF with the most abundant neurotransmitter glutamate. The pyramidal neurons in the cortical microcolumns turn out to be ideally suited to control this interaction. A direct consequence of resonant glutamate-ZPF coupling is the amplification of specific ZPF modes, which leads us to conclude that the ZPF is the key to the understanding of consciousness and that the distinctive feature of neurophysiological processes associated with conscious experience consists in modulating the ZPF. Postulating that the ZPF is an inherently sentient field and assuming that the spectrum of phenomenal qualities is represented by the normal modes of the ZPF, the significance of resonant glutamate-ZPF interaction for the formation of conscious states becomes apparent in that the amplification of specific ZPF modes is inextricably linked with the excitation of specific phenomenal qualities. This theory of consciousness, according to which phenomenal states arise through resonant amplification of zero-point modes, is given the acronym TRAZE. An experimental setup is specified that can be used to test a corollary of the theory, namely, the prediction that normally occurring conscious perceptions are absent under experimental conditions in which resonant glutamate-ZPF coupling is disrupted.

Body mass index–dependent shifts along large-scale gradients in human cortical organization explain dietary regulatory success

Making healthy dietary choices is essential for keeping weight within a normal range. Yet many people struggle with dietary self-control despite good intentions. What distinguishes neural processing in those who succeed or fail to implement healthy eating goals? Does this vary by weight status? To examine these questions, we utilized an analytical framework of gradients that characterize systematic spatial patterns of large-scale neural activity, which have the advantage of considering the entire suite of processes subserving self-control and potential regulatory tactics at the whole-brain level. Using an established laboratory food task capturing brain responses in natural and regulatory conditions (N = 123), we demonstrate that regulatory changes of dietary brain states in the gradient space predict individual differences in dietary success. Better regulators required smaller shifts in brain states to achieve larger goal-consistent changes in dietary behaviors, pointing toward efficient network organization. This pattern was most pronounced in individuals with lower weight status (low-BMI, body mass index) but absent in high-BMI individuals. Consistent with prior work, regulatory goals increased activity in frontoparietal brain circuits. However, this shift in brain states alone did not predict variance in dietary success. Instead, regulatory success emerged from combined changes along multiple gradients, showcasing the interplay of different large-scale brain networks subserving dietary control and possible regulatory strategies. Our results provide insights into how the brain might solve the problem of dietary control: Dietary success may be easier for people who adopt modes of large-scale brain activation that do not require significant reconfigurations across contexts and goals.

Investigating the neural basis of schematic false memories by examining schematic and lure pattern similarity

ABSTRACT Schemas allow us to make assumptions about the world based upon previous experiences and aid in memory organisation and retrieval. However, a reliance on schemas may also result in increased false memories to schematically related lures. Prior neuroimaging work has linked schematic processing in memory tasks to activity in prefrontal, visual, and temporal regions. Yet, it is unclear what type of processing in these regions underlies memory errors. The current study examined where schematic lures exhibit greater neural similarity to schematic targets, leading to this memory error, as compared to neural overlap with non-schematic lures, which, like schematic lures, are novel items at retrieval. Results showed that patterns of neural activity in ventromedial prefrontal cortex, medial frontal gyrus, middle temporal gyrus, hippocampus, and occipital cortices exhibited greater neural pattern similarity for schematic targets and schematic lures than between schematic lures and non-schematic lures. As such, results suggest that schematic membership, and not object history, may be more critical to the neural processes underlying memory retrieval in the context of a strong schema.

Inter-identity amnesia in dissociative identity disorder resolved: A behavioural and neurobiological study

IntroductionDissociative identity disorder (DID) is characterised by, among others, subjectively reported inter-identity amnesia, reflecting compromised information transfer between dissociative identity states. Studies have found conflicting results regarding memory transfer between dissociative identity states. Here, we investigated inter-identity amnesia in individuals with DID using self-relevant, subject specific stimuli, and behavioural and neural measures. MethodsData of 46 matched participants were included; 14 individuals with DID in a trauma-avoidant state, 16 trauma-avoiding DID simulators, and 16 healthy controls. Reaction times and neural activation patterns related to three types of subject specific words were acquired and statistically analysed, namely non-self-relevant trauma-related words (NSt), self-relevant trauma-related words from a trauma-avoidant identity state (St), and trauma-related words from a trauma-related identity state (XSt). ResultsWe found no differences in reaction times between XSt and St words and faster reaction times for XSt over NSt. Reaction times of the diagnosed DID group were the longest. Increased brain activation to XSt words was found in the frontal and parietal regions, while decreased brain activity was found in the anterior cingulate cortex in the diagnosed DID group. DiscussionThe current study reproduces and amalgamates previous behavioural reports as well as brain activation patterns. Our finding of increased cognitive control over self-relevant trauma-related knowledge processing has important clinical implications and calls for the redefinition of “inter-identity amnesia” to “inter-identity avoidance”.

Response stopping under conflict: The integrative role of representational dynamics associated with the insular cortex.

Coping with distracting inputs during goal-directed behavior is a common challenge, especially when stopping ongoing responses. The neural basis for this remains debated. Our study explores this using a conflict-modulation Stop Signal task, integrating group independent component analysis (group-ICA), multivariate pattern analysis (MVPA), and EEG source localization analysis. Consistent with previous findings, we show that stopping performance is better in congruent (nonconflicting) trials than in incongruent (conflicting) trials. Conflict effects in incongruent trials compromise stopping more due to the need for the reconfiguration of stimulus-response (S-R) mappings. These cognitive dynamics are reflected by four independent neural activity patterns (ICA), each coding representational content (MVPA). It is shown that each component was equally important in predicting behavioral outcomes. The data support an emerging idea that perception-action integration in action-stopping involves multiple independent neural activity patterns. One pattern relates to the precuneus (BA 7) and is involved in attention and early S-R processes. Of note, three other independent neural activity patterns were associated with the insular cortex (BA13) in distinct time windows. These patterns reflect a role in early attentional selection but also show the reiterated processing of representational content relevant for stopping in different S-R mapping contexts. Moreover, the insular cortex's role in automatic versus complex response selection in relation to stopping processes is shown. Overall, the insular cortex is depicted as a brain hub, crucial for response selection and cancellation across both straightforward (automatic) and complex (conditional) S-R mappings, providing a neural basis for general cognitive accounts on action control.

Exploring the neural correlates of fat taste perception and discrimination: Insights from electroencephalogram analysis

Understanding neural pathways and cognitive processes involved in the transformation of dietary fats into sensory experiences has profound implications for nutritional well-being. This study presents an efficient approach to comprehending the neural perception of fat taste using electroencephalogram (EEG). Through the examination of neural responses to different types of fatty acids (FAs) in 45 participants, we discerned distinct neural activation patterns associated with saturated versus unsaturated fatty acids. The spectrum analysis of averaged EEG signals revealed notable variations in δ and α-frequency bands across FA types. The topographical distribution and source localization results suggested that the brain encodes fat taste with specific activation timings in primary and secondary gustatory cortices. Saturated FAs elicited higher activation in cortical associated with emotion and reward processing. This electrophysiological evidence enhances our understanding of fundamental mechanisms behind fat perception, which is helpful for guiding strategies to manage hedonic eating and promote balanced fat consumption.

Integrated Microprism and Microelectrode Array for Simultaneous Electrophysiology and Two-Photon Imaging across All Cortical Layers.

Cerebral neural electronics play a crucial role in neuroscience research with increasing translational applications such as brain-computer interfaces for sensory input and motor output restoration. While widely utilized for decades, the understanding of the cellular mechanisms underlying this technology remains limited. Although two-photon microscopy (TPM) has shown great promise in imaging superficial neural electrodes, its application to deep-penetrating electrodes is technically difficult. Here, a novel device integrating transparent microelectrode arrays with glass microprisms, enabling electrophysiology recording and stimulation alongside TPM imaging across all cortical layers in a vertical plane, is introduced. Tested in Thy1-GCaMP6 mice for over 4 months, the integrated device demonstrates the capability for multisite electrophysiological recording/stimulationand simultaneous TPM calcium imaging. As a proof of concept, the impact of microstimulation amplitude, frequency, and depth on neural activation patterns is investigated using the setup. With future improvements in material stability and single unit yield, this multimodal tool greatly expands integrated electrophysiology and optical imaging from the superficial brain to the entire cortical column, opening new avenues for neuroscience research and neurotechnology development.

Neuroscientific Prediction Model of Mouse Brain Activity Patterns

This study presents a neuroscientific predictive model, developed using a subset of data collected by Steinmetz et al. (2019), which records neural activity in mice during a visual discrimination task. Leveraging advanced data analysis techniques, including logistic regression, Principal Component Analysis (PCA), and k-mean clustering, the model aims to predict trial outcomes- success or failure - based on the patterns of neural activity and the visual stimuli presented to the mice. This predictive model is designed to address the challenges of interpreting complex neural datasets. It emphasizes the relationship between neural responses and behavioral outcomes. The logistic regression framework was chosen for its proficiency in binary classification, while PCA and k-means clustering facilitated dimensionality reduction and data segmentation, respectively. These methodologies enhance the manageability of this neural data. Upon analysis, the model displayed moderate predictive accuracy, indicating areas for further improvement such as random forests or support vector machines, which might offer improved performance in handling the dataset’s intricacies.

Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signals.

Although cochlear implants can restore auditory inputs to deafferented auditory cortices, the quality of the sound signal transmitted to the brain is severely degraded, limiting functional outcomes in terms of speech perception and emotion perception. The latter deficit negatively impacts cochlear implant users' social integration and quality of life; however, emotion perception is not currently part of rehabilitation. Developing rehabilitation programs incorporating emotional cognition requires a deeper understanding of cochlear implant users' residual emotion perception abilities. To identify the neural underpinnings of these residual abilities, we investigated whether machine learning techniques could be used to identify emotion-specific patterns of neural activity in cochlear implant users. Using existing electroencephalography data from 22 cochlear implant users, we employed a random forest classifier to establish if we could model and subsequently predict from participants' brain responses the auditory emotions (vocal and musical) presented to them. Our findings suggest thatconsistent emotion-specific biomarkers exist in cochlear implant users, which could be used to develop effective rehabilitation programs incorporating emotion perception training. This study highlights the potential of machine learning techniques to improve outcomes for cochlear implant users, particularly in terms of emotion perception.