Neuroscience Approach Research Articles

Learning and memory using Drosophila melanogaster: a focus on advances made in the fifth decade of research.

In the last decade, researchers using Drosophila melanogaster have made extraordinary progress in uncovering the mysteries underlying learning and memory. This progress has been propelled by the amazing toolkit available that affords combined behavioral, molecular, electrophysiological, and systems neuroscience approaches. The arduous reconstruction of electron microscopic images resulted in a first-generation connectome of the adult and larval brain, revealing complex structural interconnections between memory-related neurons. This serves as substrate for future investigations on these connections and for building complete circuits from sensory cue detection to changes in motor behavior. Mushroom body output neurons (MBOn) were discovered, which individually forward information from discrete and non-overlapping compartments of the axons of mushroom body neurons (MBn). These neurons mirror the previously discovered tiling of mushroom body axons by inputs from dopamine neurons and have led to a model that ascribes the valence of the learning event, either appetitive or aversive, to the activity of different populations of dopamine neurons and the balance of MBOn activity in promoting avoidance or approach behavior. Studies of the calyx, which houses the MBn dendrites, have revealed a beautiful microglomeruluar organization and structural changes of synapses that occur with long-term memory (LTM) formation. Larval learning has advanced, positioning it to possibly lead in producing new conceptual insights due to its markedly simpler structure over the adult brain. Advances were made in how cAMP response element-binding protein interacts with protein kinases and other transcription factors to promote the formation of LTM. New insights were made on Orb2, a prion-like protein that forms oligomers to enhance synaptic protein synthesis required for LTM formation. Finally, Drosophila research has pioneered our understanding of the mechanisms that mediate permanent and transient active forgetting, an important function of the brain along with acquisition, consolidation, and retrieval. This was catalyzed partly by the identification of memory suppressor genes-genes whose normal function is to limit memory formation.

Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses

Structural covariance network (SCN) studies on first-episode antipsychotic-naïve psychosis (FEAP) have examined less granular parcellations on one morphometric feature reporting lower network resilience among other findings. We examined SCNs of volume, cortical thickness, and surface area using the Human Connectome Project atlas-based parcellation (n = 358 regions) from 79 FEAP and 68 controls to comprehensively characterize the networks using a descriptive and perturbational network neuroscience approach. Using graph theoretical methods, we examined network integration, segregation, centrality, community structure, and hub distribution across the small-worldness threshold range and correlated them with psychopathology severity. We used simulated nodal “attacks” (removal of nodes and all their edges) to investigate network resilience, calculated DeltaCon similarity scores, and contrasted the removed nodes to characterize the impact of simulated attacks. Compared to controls, FEAP SCN showed higher betweenness centrality (BC) and lower degree in all three morphometric features and disintegrated with fewer attacks with no change in global efficiency. SCNs showed higher similarity score at the first point of disintegration with ≈ 54% top-ranked BC nodes attacked. FEAP communities consisted of fewer prefrontal, auditory and visual regions. Lower BC, and higher clustering and degree, were associated with greater positive and negative symptom severity. Negative symptoms required twice the changes in these metrics. Globally sparse but locally dense network with more nodes of higher centrality in FEAP could result in higher communication cost compared to controls. FEAP network disintegration with fewer attacks suggests lower resilience without impacting efficiency. Greater network disarray underlying negative symptom severity possibly explains the therapeutic challenge.

NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing indirect immunofluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is very different from the conventional neural network approaches but has an equivalent quantitative and qualitative performance, and it is also robust against adversative noise. The method is robust, based on formally correct functions, and does not suffer from having to be tuned on specific data sets. Results: This work demonstrates the robustness of the method against variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on three datasets (Neuroblastoma, NucleusSegData, and ISBI 2009 Dataset) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional and structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) in segmenting cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches.

Composite Measures of Brain Activation Predict Individual Differences in Behavioral Stroop Interference.

The goal of the current study was to interrogate aspects of the cascade-of-control model [Banich, M. T. The Stroop effect occurs at multiple points along a cascade of control: Evidence from cognitive neuroscience approaches. Frontiers in Psychology, 10, 2164, 2019] (Banich, 2009), a neurocognitive model that posits how portions of pFC interact in a cascade-like manner to overcome interference from task-irrelevant information, and to test whether it could be used to predict individual differences in cognitive control outside the scanner. Participants (n = 62) completed two fMRI Word-Picture Stroop tasks, one containing emotional stimuli and one containing non-emotional stimuli, as well as a behavioral out-of-scanner Color-Word Stroop task at each of two time points. In a departure from the traditional approach of using a single task contrast to index neural activation across all ROIs, the current study utilized specific ROI by contrast pairings selected based on the specific level of control hypothesized by the cascade-of-control model to occur within that region. In addition, data across both tasks and both time points were combined to create composite measures of neural activation and of behavior. Consistent with the cascade-of-control model, individual differences in brain activation for specific contrasts within each of the three ROIs were associated with behavioral interference on the standard Color-Word Stroop task. Testing of alternative models revealed that these brain-behavior relationships were specific to the theoretically driven ROI by contrast pairings. Furthermore, such relationships were not observed across single-task and single-time point measures, but instead emerged from the composite measures. These findings provide evidence that brain activation observed across multiple regions of frontal cortex, each of which likely exerts cognitive control in a differential manner, is capable of predicting individual differences in behavioral performance.

Regulation of arousal and performance of a healthy non-human primate using closed-loop central thalamic deep brain stimulation.

Application of closed-loop approaches in systems neuroscience and brain-computer interfaces holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation strategies to restore lost function. The anterior forebrain mesocircuit (AFM) of the mammalian brain is hypothesized to underlie arousal regulation of the cortex and striatum, and support cognitive functions during wakefulness. Dysfunction of arousal regulation is hypothesized to contribute to cognitive dysfunctions in various neurological disorders, and most prominently in patients following traumatic brain injury (TBI). Several clinical studies have explored the use of daily central thalamic deep brain stimulation (CT-DBS) within the AFM to restore consciousness and executive attention in TBI patients. In this study, we explored the use of closed-loop CT-DBS in order to episodically regulate arousal of the AFM of a healthy non-human primate (NHP) with the goal of restoring behavioral performance. We used pupillometry and near real-time analysis of ECoG signals to episodically initiate closed-loop CT-DBS and here we report on our ability to enhance arousal and restore the animal's performance. The initial computer based approach was then experimentally validated using a customized clinical-grade DBS device, the DyNeuMo-X, a bi-directional research platform used for rapidly testing closed-loop DBS. The successful implementation of the DyNeuMo-X in a healthy NHP supports ongoing clinical trials employing the internal DyNeuMo system (NCT05437393, NCT05197816) and our goal of developing and accelerating the deployment of novel neuromodulation approaches to treat cognitive dysfunction in patients with structural brain injuries and other etiologies.

Infographic: Neuroscientific approaches to increase motor automaticity

Infographic: Neuroscientific approaches to increase motor automaticity

DEVELOPING A NEUROSAINS-BASED ARABIC CURRICULUM

<p style="text-align: justify;"><em>The purpose of this article is to discuss the role of Neurosains in the modernization of the Arabic curriculum, including its strategic and practical reconstruction. One is the emergence of international integration, brain-based learning, and character development based on neuroscience or neuroscience approaches. The basic principle of this study is the correct brain functional statement as the foundation of Arabic curriculum development. This paper aims to demonstrate how the Arabic learning process can be integrated with neuroscience approaches by optimizing right and left brain functions. This type of research is a literature study with Content Analysis (Analysis content) in the form of descriptive analysis. The research method uses primary sources from previous articles and books. The results showed that the application of neurosains can be implemented into the formulation of Arabic curriculum development with Circuit; 1) optimization of the right and left brain nervous system; and 2) optimization of the nervous system of the right and left brain. 2) incorporate the role of the brain into the Educational Foundation and the psychological aspects of cognitive (the substance of Arabic), psychomotor (mastery of Arabic skills), and affective (attitudes, values, and character) into Arabic learning design; and 3) link these three aspects into the nature of Arabic learning objectives, methods, strategies, and techniques. On that basis, neuroscience can be incorporated into modern-era Arabic learning curriculum development standards.</em></p>

Why a dynamic multicomponential model of decision making: some milestones and a preliminary tool

Why is it necessary today to propose a new tool to evaluate decision-making skills? The neuroscientific approach proposed in this paper constitutes a new frontier for grasping the multi-component nature of the decision-maker’s thought and action skills, using an innovative tool, through a modular methodology (5 domains that characterize the decision-making process: style, strategy, efficacy, awareness and metacognition), a multimethodological approach (through tasks, tests and self-report measures, in addition to the neurophysiological level) and a digitized format, thus restoring a 360-degree perspective on the potential of making decisions in real-life contexts. DassDec - Decisional Assessment for Decision-making- sets out to do all of this.

Translational Neuroscience Approaches to Understanding Autism

While autism spectrum disorder affects nearly 2% of children in the United States, little is known with certainty concerning the etiologies and brain systems involved. This is due, in part, to the substantial heterogeneity in the presentation of the core symptoms of autism as well as the great number of co-occurring conditions that are common in autistic individuals. Understanding the neurobiology of autism is further hampered by the limited availability of postmortem brain tissue to determine the cellular and molecular alterations that take place in the autistic brain. Animal models therefore provide great translational value in helping to define the neural systems that constitute the social brain and mediate repetitive behaviors or interests. If they are based on genetic or environmental factors that contribute to autism, organisms from flies to nonhuman primates may serve as models of the neural structure or function of the autistic brain. Ultimately, successful models can also be employed to test the safety and effectiveness of potential therapeutics. This is an overview of the major animal species that are currently used as models of autism, including an appraisal of the advantages and limitations of each.

Equity, diversity, and inclusion in developmental neuroscience: Practical lessons from community-based participatory research.

Exclusion of racialized minorities in neuroscience directly harms communities and potentially leads to biased prevention and intervention approaches. As magnetic resonance imaging (MRI) and other neuroscientific techniques offer progressive insights into the neurobiological underpinnings of mental health research agendas, it is incumbent on us as researchers to pay careful attention to issues of diversity and representation as they apply in neuroscience research. Discussions around these issues are based largely on scholarly expert opinion without actually involving the community under study. In contrast, community-engaged approaches, specifically Community-Based Participatory Research (CBPR), actively involve the population of interest in the research process and require collaboration and trust between community partners and researchers. This paper outlines a community-engaged neuroscience approach for the development of our developmental neuroscience study on mental health outcomes in preadolescent Latina youth. We focus on "positionality" (the multiple social positions researchers and the community members hold) and "reflexivity" (the ways these positions affect the research process) as conceptual tools from social sciences and humanities. We propose that integrating two unique tools: a positionality map and Community Advisory Board (CAB) into a CBPR framework can counter the biases in human neuroscience research by making often invisible-or taken-for-granted power dynamics visible and bolstering equitable participation of diverse communities in scientific research. We discuss the benefits and challenges of incorporating a CBPR method in neuroscience research with an illustrative example of a CAB from our lab, and highlight key generalizable considerations in research design, implementation, and dissemination that we hope are useful for scholars wishing to take similar approaches.

Not Every Thing Must Go.

In The Entangled Brain, Pessoa criticizes standard approaches in cognitive neuroscience in which the brain is seen as a functionally decomposable, modular system with causal operations built up hierarchically. Instead, he advocates for an emergentist perspective whereby dynamic brain networks are associated, not with traditional psychological categories, but with behavioral functions characterized in evolutionary terms. Here, we raise a number of concerns with such a radical approach. We ultimately believe that although much revision to cognitive neuroscience is welcome and needed, Pessoa's more radical proposals may be counterproductive.

Rapid progress in neuroimaging technologies fuels central nervous system translational medicine.

Central nervous system (CNS) drug discovery suffers from high attrition rates; translational neuroscience approaches aiming to reduce these high rates include the use of brain imaging technologies. However, there is a need to better understand what methods are being used and for what diseases and purposes. Our analysis of the literature found that magnetic resonance imaging (MRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT) were the neuroimaging techniques used most often in clinical trials for the most prevalent CNS diseases: Alzheimer's disease (AD), Parkinson's disease (PD), depression, and schizophrenia. Moreover, the number of initiated clinical trials using MRI, PET, and SPECT increased over the period 1981-2021. Such insights indicate that the significant increase in the use of neuroimaging studies could decrease the attrition of novel drug candidates in late clinical development.

Brain Wave Patterns in Patients With Chronic Low Back Pain: A Case-control Study.

Research evidence indicates that maladaptive reorganization of the brain plays a critical role in amplifying pain experiences and pain chronification; however, no clear evidence of change exists in brain wave activity among patients with chronic low back pain (CLBP). The objective of this study was to assess brain wave activity in patients with CLBP, compared to healthy controls. Twenty-five patients with CLBP and twenty-four healthy controls participated in the study. A quantitative electroencephalography device was used to assess brain wave activity in eyes-open and eyes-closed (EO and EC) conditions. The regional absolute and relative power of brain waves were compared between the groups. Our results showed a significant increase in the absolute power of theta (F=5.905, P=0.019), alpha (F=5.404, P=0.024) waves in patients with CLBP compared to healthy subjects in both EC and EO conditions. Patients with CLBP showed a reduced delta absolute power in the frontal region (F=5.852, P=0.019) and augmented delta absolute power in the central region (F=5.597, P=0.022) in the EO condition. An increased delta absolute power was observed in the frontal (F=7.563 P=0.008), central (F=10.430, P=0.002), and parietal (F=4.596, P=0.037) regions in patients with CLBP compared to the healthy subjects in the EC condition. In the EC condition, significant increases in theta relative power (F=4.680, P=0.036) in the parietal region were also found in patients with CLBP. The increased absolute power of brain waves in people with CLBP may indicate cortical overactivity and changes in the pain processing mechanisms in these patients. Chronic low back pain (CLBP) increases the alpha, theta, and delta power in the brain.CLBP is associated with increased brain wave activity in the frontal, central, and parietal regions.Our findings suggest altered central pain processing in CLBP. Traditional diagnosis and treatment of CLBP are mainly focused on peripheral pathology. But, the modern neuroscience approach to pain highlights the role of cortical plasticity in chronic musculoskeletal pain. In this regard, several studies found structural and functional changes in the brain in patients with chronic pain. Detailed knowledge about cortical changes in CLBP can improve our understanding of mechanisms involved in CLBP, opening a new window to better treatment of LBP (Low back pain). This study investigated brain wave activity in patients with CLBP compared to healthy individuals. Our findings suggest increased brain activity in various parts of the brain in patients with chronic LBP. This finding indicates that CLBP treatment should focus on both peripheral and cortical factors rather than local tissue damage.

The Impact of Neuroscience-Based Modern Islamic Education on the Aspect of Creative Imagination and Development of Religious Moderation on Madura Island

<p class="abstrak">The fading of Islamic education in Pamekasan is explicitly in remote villages. The decline in Islamic education on Pamekasan Island in the Modern Era, especially in urban areas and remote villages. This is due to the fact that Islamic education in Madura still adopts Islamic boarding school-based education. The need for the development of Islamic education such as religious moderation and the use of information technology, strengthening knowledge management for educators, <em>ustadz</em>, and teachers in Pamekasan Madura. The purpose of this study was to analyse the development of Islamic education on the island of Madura. This study used a field research approach, with the informants being the Pamekasan community, <em>santri</em>, students, teachers, lecturers, and community leaders. Data were collected by observation, in-depth interviews, and documentation. The results of the study proved that the Madurese follow the concepts and alternative approaches to knowledge based on modern Islam. Knowledge and approaches in the technological era are quite accepted by young people, the younger generation, and adults. The neuroscience approach that is packaged with modern Islamic education is able to provide awareness and technology literacy in the Madurese community. One of them is more sensitive and aware of the importance of Islamic education.</p>

Rationalization of Character Education in Islamic Religious Education Through Anti-Corruption Education Strategies

It is a new phenomenon in Indonesia regarding cases involving several Muslim officials and politicians. In order to anticipate and eradicate corruption, there is an alternative that can be developed in Islamic Religious Education that is a neuroscience approach using the Anti-Corruption Education strategy at the Early Childhood Education level. The research used is a literature study by reviewing journals that are relevant to the author's research, using secondary data, namely data obtained indirectly. The data needed in this study were taken from documents related to research. The data collection technique was carried out by examining and analyzing books and journals with rules related to the implementation of Anti-Corruption Education through fun learning activities for children carried out by participating in the National Movement for Learning I'm an Honest Child while the implementation of Islamic Religious Education is carried out scientifically and holistically integratively but both not yet integrated. Integration is carried out between Anti-Corruption Education and Islamic Religious Education by using the expansion of various forms of corruption related to the themes conveyed during learning activities. Islamic Religious Education is based on neuroscience learning theories. The data analysis technique used is qualitative analysis. The results of the study show that an educator can apply a neuroscience approach by integrating Anti-Corruption Education and Islamic Religious Education through learning activities that are used as habituation programs, carried out repeatedly and continuously with various learning methods so that honest character recognition can be implemented properly. The educator also stimulates and facilitates introducing this honest character according to the stages of early childhood development. The author's argument states that there is a neuroscience correlation, Islamic Religious Education and Anti-Corruption Education in implementing honesty and the development of Moral and Religious Values in early childhood can develop more optimally. Keywords: Islamic education, brains, goals, character, anti-corruption, and early childhood

Home alone: A population neuroscience investigation of brain morphology substrates

As a social species, ready exchange with peers is a pivotal asset - our “social capital”. Yet, single-person households have come to pervade metropolitan cities worldwide, with unknown consequences in the long run. Here, we systematically explore the morphological manifestations associated with singular living in ∼40,000 UK Biobank participants. The uncovered population-level signature spotlights the highly associative default mode network, in addition to findings such as in the amygdala central, cortical and corticoamygdaloid nuclei groups, as well as the hippocampal fimbria and dentate gyrus. Both positive effects, equating to greater gray matter volume associated with living alone, and negative effects, which can be interpreted as greater gray matter associations with not living alone, were found across the cortex and subcortical structures Sex-stratified analyses revealed male-specific neural substrates, including somatomotor, saliency and visual systems, while female-specific neural substrates centered on the dorsomedial prefrontal cortex. In line with our demographic profiling results, the discovered neural pattern of living alone is potentially linked to alcohol and tobacco consumption, anxiety, sleep quality as well as daily TV watching. The persistent trend for solitary living will require new answers from public-health decision makers. Significance statementLiving alone has profound consequences for mental and physical health. Despite this, there has been a rapid increase in single-person households worldwide, with the long-term consequences yet unknown. In the largest study of its kind, we investigate how the objective lack of everyday social interaction, through living alone, manifests in the brain. Our population neuroscience approach uncovered a gray matter signature that converged on the 'default network', alongside targeted subcortical, sex and demographic profiling analyses. The human urge for social relationships is highlighted by the evolving COVID-19 pandemic. Better understanding of how social isolation relates to the brain will influence health and social policy decision-making of pandemic planning, as well as social interventions in light of global shifts in houseful structures.

Nine insights from internet engineering that help us understand brain network communication

Philosophers have long recognized the value of metaphor as a tool that opens new avenues of investigation. By seeing brains as having the goal of representation, the computer metaphor in its various guises has helped systems neuroscience approach a wide array of neuronal behaviors at small and large scales. Here I advocate a complementary metaphor, the internet. Adopting this metaphor shifts our focus from computing to communication, and from seeing neuronal signals as localized representational elements to seeing neuronal signals as traveling messages. In doing so, we can take advantage of a comparison with the internet's robust and efficient routing strategies to understand how the brain might meet the challenges of network communication. I lay out nine engineering strategies that help the internet solve routing challenges similar to those faced by brain networks. The internet metaphor helps us by reframing neuronal activity across the brain as, in part, a manifestation of routing, which may, in different parts of the system, resemble the internet more, less, or not at all. I describe suggestive evidence consistent with the brain's use of internet-like routing strategies and conclude that, even if empirical data do not directly implicate internet-like routing, the metaphor is valuable as a reference point for those investigating the difficult problem of network communication in the brain and in particular the problem of routing.

Social-interactive reward elicits similar neural response in autism and typical development and predicts future social experiences

Challenges in initiating and responding to social-interactive exchanges are a key diagnostic feature of autism spectrum disorder, yet investigations into the underlying neural mechanisms of social interaction have been hampered by reliance on non-interactive approaches. Using an innovative social-interactive neuroscience approach, we investigated differences between youth with autism and youth with typical development in neural response to a chat-based social-interactive reward, as well as factors such as age and self-reported social enjoyment that may account for heterogeneity in that response. We found minimal group differences in neural and behavioral response to social-interactive reward, and variation within both groups was related to self-reported social enjoyment during the task. Furthermore, neural sensitivity to social-interactive reward predicted future enjoyment of a face-to-face social interaction with a novel peer. These findings have important implications for understanding the nature of social reward and peer interactions in typical development as well as for future research informing social interactions in individuals on the autism spectrum.

EEG-based drowsiness analysis using OpenBCI and deep learning approaches to establish driving safety

Drowsiness is the inactivated state of the brain and observed during the transition from awaken state to sleepy state. This inactive state diminishes an individual’s attention and leads to accidents during professional or personal activities. The prediction of this inactive (drowsiness) state using AI plays a substantial role in the avoidance of accidents. The advancements in the field of Artificial Intelligence and Neuroscience approaches are used for the prediction of this inactive drowsy state. In order to prevent these devastating accidents, the state of drowsiness of the driver has to be be monitored. Electroencephalogram (EEG) is a predominant tool adopted to examine various states of the brain effectually. It is generally known as Brain-Computer Interface System. The EEG channels are used for predicting the inactive state while implementing the real-time applications. However, the researchers face various challenges during execution based on the classification and channel selection process. This research concentrates on modelling and efficient drowsiness prediction methods and intends to bridge the gap encountered in the existing approaches. A novel stacked Long Short-Term Memory(s - LSTM) with Deep Fully Connected- Convolutional Neural Network (DFC - CNN) is used to learn and memorize the long-term feature dependencies and attains essential information based on time-series prediction. Single and multi-channel EEG data is considered to measure the statistical characteristics of available EEG signals. The online available OpenBCI sleep analysis data is used for performing the experimentation, and run in GoogleColab environment. The proposed s - LSTM model provides a better trade-off compared to existing approaches. The model generalization is improved with the validation of combined feature subjects. Here, metrics like prediction accuracy, RMSE, false positives, scaling coefficients related to false positives are measured to show the significance of the model.

How does the human brain process noisy speech in real life? Insights from the second-person neuroscience perspective.

Comprehending speech with the existence of background noise is of great importance for human life. In the past decades, a large number of psychological, cognitive and neuroscientific research has explored the neurocognitive mechanisms of speech-in-noise comprehension. However, as limited by the low ecological validity of the speech stimuli and the experimental paradigm, as well as the inadequate attention on the high-order linguistic and extralinguistic processes, there remains much unknown about how the brain processes noisy speech in real-life scenarios. A recently emerging approach, i.e., the second-person neuroscience approach, provides a novel conceptual framework. It measures both of the speaker's and the listener's neural activities, and estimates the speaker-listener neural coupling with regarding of the speaker's production-related neural activity as a standardized reference. The second-person approach not only promotes the use of naturalistic speech but also allows for free communication between speaker and listener as in a close-to-life context. In this review, we first briefly review the previous discoveries about how the brain processes speech in noise; then, we introduce the principles and advantages of the second-person neuroscience approach and discuss its implications to unravel the linguistic and extralinguistic processes during speech-in-noise comprehension; finally, we conclude by proposing some critical issues and calls for more research interests in the second-person approach, which would further extend the present knowledge about how people comprehend speech in noise.