Table of Contents
Overview
Definition and Scope
Molecular neuroscience is a specialized branch of neuroscience that integrates principles from molecular biology to explore the nervous systems of animals. This field covers a broad spectrum of topics, such as molecular neuroanatomy, molecular signaling mechanisms within the nervous system, and the roles of genetics and epigenetics in neuronal development. It also investigates the molecular basis of neuroplasticity, essential for understanding how the nervous system adapts to experiences and environmental factors.[4.1] The scope of molecular neuroscience is extensive, focusing on the fundamental aspects of nervous system function at the molecular and cellular levels. It examines how individual neurons operate and communicate, emphasizing the processes involved in signal transduction. This includes the reception and transmission of signals at synapses, which are vital for neuronal _communication.[3.1] Furthermore, it explores the biochemical structures and functions of nerve cell membranes, channel proteins, receptors, and other critical molecules, providing insights into the ionic and molecular basis of neuronal excitability and synaptic transmission.[5.1]Importance in Neuroscience
Molecular neuroscience is pivotal in enhancing our understanding of neurological disorders and advancing personalized medicine approaches. It plays a crucial role in the differential diagnosis of neurological disorders, particularly in their early stages, and in describing pathophysiological changes that influence disease manifestation and progression. Additionally, molecular medicine aids in evaluating and monitoring treatment effects, thereby improving patient outcomes.[6.1] Recent advancements in neurogenetics, including linkage, association, and massively parallel sequencing techniques, have significantly refined the molecular etiology of neurological disorders. These developments have improved diagnostic accuracy and challenged existing nosological classifications, establishing a foundational framework for molecular neuroscience and modeling.[7.1] Furthermore, single-cell ribonucleic acid sequencing (scRNA-seq) has emerged as a transformative technology, allowing researchers to explore cellular heterogeneity and gene expression profiles with unprecedented resolution. This technology has been particularly impactful in understanding complex neurological disorders, including brain tumors, neurodegenerative diseases, and cerebrovascular conditions.[8.1] The integration of molecular neuroscience findings into personalized medicine is essential. Personalized medicine seeks to identify individual phenotypic and genotypic characteristics, correlating these parameters with disease status, severity, and treatment responsiveness. This approach is crucial for predicting the most effective clinical protocols tailored to individual patients.[9.1] The evolution of personalized medicine is marked by significant milestones in genetics and technology, shifting healthcare from a generalized approach to one that emphasizes individualized treatment strategies based on the molecular basis of health and disease.[10.1] Moreover, understanding the interplay between genetic and environmental factors is crucial in neurological disorders. Gene-environment interactions can modify disease risk associated with genetic variations, highlighting the complexity of these conditions.[12.1] Epigenetic modifications, influenced by environmental factors, further complicate this relationship, as they can affect gene expression and neuronal development.[14.1] Understanding these interactions provides valuable insights into the mechanisms underlying neurological disorders and opens new avenues for research and clinical practice.In this section:
History
Major Milestones in Molecular Neuroscience
The development of molecular neuroscience has been marked by several significant milestones that have shaped the understanding of the nervous system. The origins of this field can be traced back to the establishment of the Unit for Molecular Biology by the Medical Research Council in Cambridge, UK, in 1947, which laid the groundwork for applying molecular biology concepts to neuroscience.[45.1] A major breakthrough occurred in the 1950s with the publication of James Watson and Francis Crick's 1953 paper, which revealed the molecular structure of DNA, profoundly impacting genetics and neurobiology.[47.1] Molecular neuroscience covers various topics, including molecular neuroanatomy, signaling mechanisms, and the molecular basis of neuroplasticity.[46.1] The late 1960s marked a pivotal advance with the advent of single-neuron recordings from awake animals, providing insights into neuronal activity and behavior.[49.1] This era also saw the shift from the reticular theory to the neuron theory, establishing neurons as the fundamental units of the nervous system.[52.1] Technological advancements have been crucial in the evolution of molecular neuroscience. New in vivo imaging techniques and molecular sensors have enabled researchers to manipulate and visualize molecular mechanisms in specific cell types, enhancing the understanding of complex neural circuits.[51.1] These innovations have allowed for unprecedented exploration of the brain's molecular landscape, further solidifying the importance of molecular neuroscience in the broader context of neuroscience research.[50.1]In this section:
Recent Advancements
Technological Innovations
Recent advancements in molecular neuroscience have been significantly influenced by technological innovations that enhance our understanding of brain disorders. The integration of big data analytics with neurotechnologies has emerged as a pivotal development, facilitating the analysis of vast amounts of multimodal data related to the human brain. This integration allows researchers to explore the complexities of brain disorders more effectively than traditional single-dataset analyses, thereby providing unprecedented opportunities to understand the etiology and progression of these conditions.[98.1] Innovative tools such as single-cell sequencing technologies and advanced imaging techniques have propelled brain science into a new era characterized by big data.[97.1] These advancements enable researchers to map neuronal connections with greater precision and monitor neural activity through novel hardware, including large-scale neural recording probes.[96.1] Furthermore, the use of in vitro organoids and stem cells has opened new avenues for probing brain development and disease mechanisms.[96.1] Artificial Intelligence (AI) has also played a transformative role in molecular neuroscience. AI technologies are being utilized to enhance diagnostics, optimize treatment plans, and analyze neuroimaging data, thereby improving patient care for various neurological disorders.[102.1] The application of machine learning algorithms allows for the identification of subtle markers of neurological conditions that may be overlooked by human observation.[102.1] Additionally, AI-driven innovations in neuroprosthetics and brain-computer interfaces (BCIs) are set to revolutionize the way we interact with and understand the nervous system.[119.1] As neurotechnologies continue to evolve, future advancements are anticipated to further enhance our ability to collect and analyze brain activity data. For instance, the integration of functional magnetic resonance imaging (fMRI) with BCIs and neurofeedback systems is expected to become more seamless and cost-effective, providing richer insights into brain function.[119.1] Moreover, the development of closed-loop neurotechnologies aims to refine the control of neural activity, potentially leading to breakthroughs in therapeutic applications for neurological disorders.[120.1]Key Findings in Molecular Mechanisms
Recent advancements in molecular neuroscience have provided significant insights into the mechanisms underlying neural function and disease. A primary focus has been synaptic plasticity, which involves the structural and functional changes that synapses undergo in response to activity. This process is essential for learning and memory, requiring an elevation in intracellular calcium in the postsynaptic neuron, often sourced from NMDA receptors activated by depolarization and glutamate release.[89.1] Synaptic adhesion proteins are crucial in synapse formation, maturation, and plasticity, bridging pre- and postsynaptic specializations and contributing to synaptic functionality.[88.1] The role of non-coding RNAs (ncRNAs) has emerged as a significant research area, revealing their central regulatory functions in cellular processes. Dysregulation of long non-coding RNAs (lncRNAs) is linked to neurological disorders such as schizophrenia and autism spectrum disorder, highlighting their potential as biomarkers and therapeutic targets.[95.1] The understanding of ncRNAs has evolved from viewing them as transcriptional noise to recognizing them as key regulators of gene expression, potentially leading to innovative therapeutic approaches for complex neurological diseases.[94.1] Recent discoveries have challenged existing theories about learning and memory mechanisms. Research indicates that memory formation relies on complex neuron structures known as multi-synaptic boutons rather than merely increasing synapse numbers, suggesting new avenues for treating memory loss.[107.1] Additionally, the presence of G4-DNA in neurons has been linked to the regulation of memory-related gene expression, further expanding our understanding of memory's molecular underpinnings.[106.1] These findings collectively underscore the dynamic and intricate nature of molecular mechanisms in neuroscience, paving the way for future research and therapeutic strategies.Molecular Mechanisms
Neuroplasticity
Neuroplasticity refers to the brain's capacity to reorganize itself by forming new neural connections throughout life, which is essential for learning, memory, and recovery from brain injuries. At the molecular level, this process is influenced by genetic and epigenetic mechanisms that regulate gene expression in response to neuronal activity. Neurons can activate numerous activity-regulated genes (ARGs) when their activity increases, indicating that different neuronal firing patterns can lead to distinct gene expression profiles crucial for neuroplastic changes.[135.1] Neurotransmitter signaling plays a significant role in neuroplasticity. Epigenetic regulation of this signaling controls the expression of genes associated with key neurotransmitters such as dopamine, GABA, glutamate, and serotonin. Targeting these mechanisms offers promising opportunities for developing treatments for neurological disorders, thereby enhancing neuroplasticity and improving the quality of life for affected individuals.[134.1] Furthermore, molecular structures within neurons, including receptors and channel proteins, are vital for facilitating communication and signaling pathways that underpin neuroplasticity. Neurotransmitter molecules released from presynaptic vesicles bind to specific receptors on the postsynaptic neuron, leading to the rapid transduction of chemical signals into electrical responses. This process is fundamental for synaptic plasticity, a key component of neuroplasticity.[139.1]Neurodegenerative Diseases
The integration of machine learning (ML) and artificial intelligence (AI) in neuroscience has significantly advanced the understanding of neurodegenerative diseases. These technologies excel at managing the complex, high-dimensional data generated by various biomedical research methods, such as functional brain imaging and omics approaches, which are crucial for exploring the molecular mechanisms underlying neurological disorders.[148.1] Machine learning techniques, particularly deep learning, have demonstrated remarkable efficacy in analyzing large datasets, such as those from clinical records and genetic information. Deep neural networks (DNNs) are employed to model disease trajectories by capturing intricate patterns from structured clinical data, enhancing predictive performance in understanding disease progression.[149.1] This capability is vital for identifying new molecular pathways involved in these disorders, allowing researchers to efficiently analyze vast datasets.[150.1] Furthermore, ML applications in neuroscience have led to breakthroughs in analyzing image-based data, such as calcium imaging, which is critical for studying neuronal activity and its implications in neurodegeneration.[151.1] The rapid growth of deep learning methodologies has enabled results comparable to human experts, accelerating discovery in the field.[152.1] Consequently, the fusion of ML and neuroscience not only enhances the exploration of neural complexities but also holds promise for uncovering novel therapeutic targets for neurodegenerative diseases.Interdisciplinary Approaches
Integration with Other Scientific Fields
Interdisciplinary approaches in molecular neuroscience are essential for advancing the understanding of the brain and nervous system. By integrating knowledge from fields such as genetics, pharmacology, computer science, and bioengineering, researchers and clinicians can collaborate more effectively.[76.1] This collaboration is crucial for combining diverse methodologies and perspectives, which is necessary for addressing complex challenges in brain science.[169.1] The integration of machine learning with traditional neuroscience methodologies has notably transformed data analysis and interpretation. Machine learning, especially deep learning, has revolutionized the processing of large datasets generated by the brain, allowing researchers to uncover previously hidden insights.[174.1] This is evident in the analysis of neuroimaging data, such as functional magnetic resonance imaging (fMRI) and calcium imaging, where advanced algorithms have enhanced the understanding of neural processes.[175.1] Additionally, systems biology is pivotal in examining the interactions between genetics, proteins, cellular processes, and environmental factors. This approach provides insights into the molecular mechanisms of neurological disorders and facilitates the integration of omics data with clinical information, promoting personalized diagnosis and treatment strategies.[177.1] The complexity of systemic diseases, including neurodegenerative disorders, requires a multifaceted approach that goes beyond traditional mono-target drug interventions, underscoring the importance of integrating pharmacology with genetics and computational biology.[179.1]Collaborative Research Efforts
The Laboratory for Drug Discovery in Neuroscience (LDDN) at Brigham and Women's Hospital and Harvard Medical School exemplifies the critical role of interdisciplinary collaboration in developing innovative treatments for central nervous system disorders. LDDN's mission focuses on identifying small molecules to treat these complex diseases, emphasizing the necessity of cooperation among scientists and clinicians throughout the drug design process.[198.1] The shift from single-target to multi-target drug design highlights the increasing reliance on diverse expertise and communication among researchers and healthcare professionals. This collaborative framework is essential for addressing the multifaceted nature of neurological disorders, where understanding the interplay of various biological pathways can lead to more effective therapies.[199.1] Recent advancements in disease-modifying therapies, such as antisense oligonucleotides, antibodies, and enzyme supplementation, underscore the potential of collaborative research to improve patient outcomes. These novel therapies have significantly enhanced prognosis and delayed relapse in various neurological conditions, demonstrating the impact of interdisciplinary approaches in drug development.[201.1] Moreover, strategic partnerships between artificial intelligence (AI) firms and pharmaceutical companies are transforming drug discovery. These collaborations enhance the efficiency of drug development by leveraging complementary capabilities and establishing effective governance mechanisms. The management of relationship-specific assets and the development of interfirm knowledge-sharing routines are critical for achieving the objectives of these partnerships, further illustrating the role of interdisciplinary collaboration in advancing drug discovery.[200.1]Future Directions
Emerging Research Areas
Emerging research areas in molecular neuroscience are increasingly focused on integrating molecular and systems-level approaches to enhance our understanding of neuronal networks. This integration is facilitated by advances in single-cell transcriptomics and RNA-based profiling, which are expected to provide deeper insights into the regulatory mechanisms involved in neurological disorders and neurodegeneration.[218.1] Over the past decade, significant developments have been driven by improvements in research techniques, enabling a more comprehensive understanding of the field. This evolution has been characterized by a shift towards systems neuroscience, supported by technologies that allow for in vivo mapping of neural circuits and real-time visualization of neuronal activity.[209.1] The emergence of molecular systems neuroscience, which examines how spatial and temporal patterns of molecular systems influence brain circuits and behavior, is anticipated to play a crucial role in future research. This area benefits from novel techniques that enable real-time tracking and manipulation of molecular systems at a cellular level, significantly impacting our understanding of brain function.[219.1] Additionally, the integration of neurotechnologies, including molecular tools and neural sensors, is transforming the landscape of neuroscience research. These advancements generate vast datasets, presenting both opportunities and challenges in data management and collaboration, highlighting the need for innovative strategies to effectively utilize big data in diagnosing and treating brain disorders.[220.1]Potential Clinical Applications
The clinical implementation of diagnostic artificial intelligence (AI) models in neuroimaging offers significant promise alongside notable challenges. These models, particularly when applied to brain images from routine hospital procedures like CT and MRI scans, can automate diagnostic processes, potentially reducing misdiagnosis rates, decreasing wait times, cutting costs, and enhancing diagnostic objectivity. They also support healthcare professionals in assessing a variety of neurological conditions.[242.1] However, integrating neurotechnologies into clinical settings presents challenges, such as the disparity in access, which may exacerbate existing healthcare inequalities. The rapid convergence of neurotechnologies with fields like AI complicates their impact, making it unpredictable and potentially disruptive.[243.1] Additionally, the dual-use nature of neurotechnology—applicable in both civilian and military contexts—raises ethical concerns about its development and deployment.[243.1] Training and education are crucial for the successful application of neurotechnologies in clinical environments. Currently, their use is limited to a few hospitals and clinics, primarily due to inadequate practitioner training. Programs like the Neuroethics, Law, and Neuroscience (NJAM) initiative aim to address this gap by integrating training across clinical practice, law, ethics, and neuroscience. This prepares the next generation of scientists to engage with communities and incorporate diverse perspectives into research priorities.[244.1] Addressing these educational needs is essential for the broader adoption and effective use of neurotechnologies in clinical practice.Challenges And Limitations
Technical and Methodological Constraints
The field of molecular neuroscience encounters several technical and methodological constraints that impede its progress. A primary challenge is integrating insights from behavioral and cognitive levels with those from molecular and cellular levels, which is essential for a comprehensive understanding of brain function and dysfunction. This integration remains complex and necessitates further technical advancements.[245.1] Another significant challenge is the development of "data ladders," which are interconnected datasets that provide a comprehensive view of specific brain areas across various organizational levels, from molecules to brain regions. These data ladders should also establish connections between homologous areas in humans and other species, facilitating comparative studies and understanding the evolutionary aspects of brain function.[247.1] Additionally, there is a need for new classification and simulation methods for brain diseases to enhance diagnosis and drug discovery. Current methodologies often lack the necessary breadth and depth of data for effective research and clinical applications.[247.1] Emerging techniques in molecular systems neuroscience, such as advanced in vivo imaging and molecular sensors, offer potential solutions to some of these challenges. These technologies enable real-time tracking and manipulation of molecular systems within specific cell types, improving our understanding of how molecular patterns influence brain circuits and behavior.[249.1] However, implementing these advanced neuroimaging techniques presents limitations, especially in longitudinal studies, where resource demands and varying inferential goals can complicate data collection and analysis.[251.1] Moreover, effectively managing the vast datasets generated by advancements in neurotechnologies and translating them into meaningful clinical applications remains a significant hurdle. This requires innovative strategies to harness big data for diagnosing and treating brain disorders.[261.1] Successfully navigating these technical and methodological constraints is crucial for the continued advancement of molecular neuroscience and its applications in understanding brain function and treating neurological disorders.In this section:
References
[3] Molecular and Cellular Neuroscience - Neuroscience for Newbies — Now we will step a little deeper into each, starting today with Molecular and Cellular Neuroscience. Molecular and Cellular Neuroscience: Key Aspects. Molecular and cellular neuroscience focuses on understanding the nervous system at its most fundamental levels—how individual neurons function, how they communicate with each other, and how
[4] Molecular Neuroscience - an overview | ScienceDirect Topics — Molecular Neuroscience refers to the branch of neuroscience that focuses on the detailed understanding of the biochemical structure and function of nerve cell membranes, channel proteins, receptors, and other molecules at the molecular level. ... A key challenge of the Human Brain Project is to fully specify the model and identify missing data.
[5] NS201A: Basic Concepts in Cellular and Molecular Neuroscience — An interdisciplinary introduction to fundamental aspects of nervous system function. The course emphasizes the ionic and molecular basis of excitability, synaptic transmission and signal transduction. Directors: Peter Sargent and Kevin Bender. Brief Introduction to the Core Course. General Reading. Monday, September 25 Time: 9AM-11AM Location
[6] Molecular Medicine Successes in Neuroscience - PMC — Molecular medicine in neuroscience and neurology has several purposes: differential diagnosis, especially in the early stage of neurological disorders; description of pathophysiological changes responsible for manifestation and disease course; and evaluation and follow-up of treatment effects. ... Such findings are detected in about a third of
[7] Neurogenetics in neurology: from molecular neuroscience to precision ... — Advances in neurogenetics, in linkage, association and massively parallel sequencing approaches have begun to define a precise molecular etiology of neurologic disorders. The findings have refined diagnoses, often challenging nosology, and have proven foundational for molecular neuroscience and modelling.
[8] The molecular landscape of neurological disorders: insights from single ... — Single-cell ribonucleic acid sequencing (scRNA-seq) has emerged as a transformative technology in neurological and neurosurgical research, revolutionising our comprehension of complex neurological disorders. Single-cell ribonucleic acid sequencing (scRNA-seq) has emerged as a particularly powerful tool, enabling the understanding of cellular heterogeneity and gene expression profiles at an unprecedented resolution . The application of scRNA-seq in neurological and neurosurgical research has grown notably, highlighting the heterogeneity of cell populations in the brain and its implications in brain tumours, neurodegenerative diseases (NDs), epileptic disorders, spinal conditions, and cerebrovascular diseases (CVDs) . The use of scRNA-seq has also facilitated investigations into the impact of chromosomal instability (CIN) on gene expression and intra-tumour heterogeneity in GBM cancer stem cells (CSCs) .
[9] Personalized Medicine in Neuroscience: Molecular to System Approach — Personalized Medicine in Neuroscience: Molecular to System Approach | Frontiers Research Topic PM approach aims to identify phenotypic and genotypic individual characteristics, observe how these parameters are correlated with the disease status, severity, and intervention responsiveness to predict the best personalized clinical protocol. This Research Topic will welcome Original Research and Review articles focusing on the recent advances, applications, and clinical challenges of PM in Neuro-oncology and Neuroscience: PM approach aims to identify phenotypic and genotypic individual characteristics, observe how these parameters are correlated with the disease status, severity, and intervention responsiveness to predict the best personalized clinical protocol. This Research Topic will welcome Original Research and Review articles focusing on the recent advances, applications, and clinical challenges of PM in Neuro-oncology and Neuroscience:
[10] Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data ... — The history of personalized medicine is punctuated by significant milestones in genetics, technology, and clinical applications, shifting healthcare from a one-size-fits-all approach to a more individualized understanding of the molecular basis of health and disease and effective treatment strategies . Advances in genomics and biotechnology in the 21st century are enabling more personalized approaches to medicine, predicting disease risks, and tailoring treatments to individual genetic profiles. Personalized medicine leverages these images in conjunction with genetic data to gain deeper insights into disease mechanisms in individual patients, enhancing precision in diagnosis and treatment strategies . Through the integration of genetic, molecular, and clinical data, personalized medicine enables more accurate diagnosis, precise treatment targeting, and effective disease management.
[12] Gene-by-environment interactions in Alzheimer's disease and Parkinson's ... — Complex neurological diseases arise from interactions between genetic (G) and environmental (E) factors (GxE interactions, (Patel, 2016)).Significant advances in genomic technologies have enabled the robust identification of genetic modulators of disease risk and susceptibility in humans; current approaches are now underway to develop standardized definitions and analyses of environmental
[14] Epigenetics of the developing and aging brain: Mechanisms that regulate ... — Environmental perturbations during embryonic development can result in epigenetic modifications that correlate with ... Only 20-30 % of the individual variation in average human life span can be attributed to genetic variation, implying that ... Lifetime requirement of the methionine cycle for neuronal development and maintenance.
[45] Molecular Neuroscience - an overview | ScienceDirect Topics — Origins Of Molecular Medicine And Molecular Neuroscience Although, as noted above, the origins of molecular biology can be traced back far into the past, it only came of age in the 1950s and, most importantly, at the Unit for Molecular Biology established by the Medical Research Council, Cambridge, UK, in 1947.
[46] Molecular neuroscience - Wikipedia — Molecular neuroscience is a branch of neuroscience that observes concepts in molecular biology applied to the nervous systems of animals. The scope of this subject covers topics such as molecular neuroanatomy, mechanisms of molecular signaling in the nervous system, the effects of genetics and epigenetics on neuronal development, and the molecular basis for neuroplasticity and
[47] History of Neuroscience - UW Faculty Web Server — 1953 - James Watson and Francis Crick publish paper revealing the molecular structure of DNA 1953 - Dilantin (phenytoin; an antiepileptic drug) is approved for use by the US Food and Drug Administration ... Tales in the History of Neuroscience, Cambridge, MIT Press, 1998. Harding, A.S., Milestones in Health and Medicine, Phoenix (AZ) Oryx Press
[49] Neuroscience: Breaking Down Scientific Barriers to the Study of ... - AAAS — The discoveries of molecular neuroscience have dramatically improved the understanding of how the brain develops its complexity. ... the history of neuroscience can be seen as a gradual ascendancy of the localizationist view. ... A pivotal advance occurred in the late 1960s when single-neuron recordings were obtained from awake,
[50] The History of Neuroscience Told Through Major Milestones — The History of Neuroscience Told Through Major Milestones | NeuLine Health The struggles of early scientists to determine which organ was responsible for cognition is one that reverberates through the millennia as it depicts the challenges of science, the limits of understanding at a given time in history, and the incredible importance of improving our understanding of the world around us a discovery at a time. Galen also advanced a crucial understanding of the association between the brain and the voice: It was Galen who discovered recurrent laryngeal nerves — through his practice of experimentation — and their role in generating voice production, which at the time, was a crucial part of a Roman culture where rhetoric reigned supreme.
[51] The emergence of molecular systems neuroscience — These new techniques allow for the unprecedented temporal, cellular and subcellular manipulation and imaging of specific molecular mechanisms in targeted cell systems, circuits and brain regions of behaving animals. We identify three major areas of novel technology development that we predict will have a significant impact in systems neuroscience: (a) new developments in in vivo imaging techniques, including imaging of molecular mechanisms in specific cell types besides neurons, (b) new molecular sensors that can report on the activity of specific molecular mechanisms in a cell specific manner, and (c) new molecular actuators that can either activate or inhibit molecular mechanisms at a cellular or even sub-cellular level.
[52] 5 Milestones in the History of Neuroscience - KnowledgeOne — This was the first step in what would become the neuron theory in which these cells represent the basic structural and functional unit of the nervous system. Before the neuron theory was recognized, the reticular theory of von Gerlach, laid out in 1871, dominated, arguing that the brain is made up of a single network of fibres and fused cells in which thought is born and circulates. This new field of exploration has prompted some neuroscientists to predict that a new understanding of the brain, less centred on neurons and making more room for their neighbouring cells, could soon be born. 3 ages of the brain under the microscope of neuroscience Brain, Learning and Neuroscience: Test Your Knowledge!
[76] Molecular and Cellular Neuroscience - Neuroscience for Newbies — Molecular and cellular neuroscience is highly interdisciplinary. Scientists in this field often work alongside experts in genetics, pharmacology, computer science, or even bioengineering.
[88] Cell adhesion molecules: signalling functions at the synapse — These molecules bridge pre- and postsynaptic specializations but do far more than simply provide a mechanical link between cells. In this review, we will discuss the roles these proteins have during development and at mature synapses. Synaptic adhesion proteins participate in the formation, maturation, function and plasticity of synaptic
[89] Molecular mechanisms underlying neuronal synaptic plasticity: systems ... — Though the molecular mechanisms leading to synaptic plasticity are quite diverse, all synaptic plasticity requires an elevation in intracellular calcium in the post-synaptic neuron 4, 5. In many cell types the source of calcium is influx through NMDA receptors in response to conjunction of depolarization and glutamate release.
[94] Non-coding RNAs and their bioengineering applications for neurological ... — More specifically, the current trend focuses on devising non-coding (nc) RNAs as therapeutic candidates for complex neurological diseases. Given the pleiotropic and regulatory role, ncRNAs such as microRNAs and long non-coding RNAs are deemed as attractive therapeutic candidates. ... Non-coding RNA, neurological disorder, brain function
[95] Long Non-coding RNA in Neuronal Development and Neurological Disorders — Emerging evidence has shown that the dysregulation of lncRNAs is related to multiple neurological disorders, such as schizophrenia (Scholz et al., 2010), autism spectrum disorder ... The role of long non-coding RNAs in neurodevelopment, brain function and neurological disease. Philos. Trans. R. Soc. Lond.
[96] The future of neurotechnology: From big data to translation — A recent meeting highlighted the resulting challenges in global collaboration, data management, and effective translation, emphasizing the need for innovative strategies to harness big data for diagnosing and treating brain disorders. These include an evolving understanding of the genetic and functional classes of cell types in the brain,1 new molecular tools that allow the monitoring and control of neural activity,2 novel hardware such as large-scale neural recording probes and innovative wide-scale microscopy,3,4 as well as the ability to harness in vitro organoids and stem cells to probe development and disease.5 One commonality across these approaches is the immense scale of the data being generated, raising important questions about how to best harness these big data to develop new and applicable knowledge. Cookies are used by this site.
[97] Big Data and the Brain: Peeking at the Future — Integrative analysis of big data is a path forward in brain science. Brain science has entered a new era of big data with the development of single-cell sequencing technologies, the development of new tools for mapping neuronal connections, the increasing resolution of imaging technologies, and the explosion of nanoscience.
[98] Integration of Multimodal Data for Deciphering Brain Disorders — The accumulation of vast amounts of multimodal data for the human brain, in both normal and disease conditions, has provided unprecedented opportunities for understanding why and how brain disorders arise. Compared with traditional analyses of single datasets, the integration of multimodal datasets …
[102] Revolutionizing Neurology: The Role of Artificial Intelligence in ... — Keywords: ai algorithms, brain signals, neuroimaging, brain-computer interfaces, precision medicine, neurological disorders, artificial intelligence Integrating AI technologies into neurology has yielded many benefits, including early diagnosis, personalized treatment plans, neuroimaging analysis, treatment optimization, and groundbreaking research endeavors . AI-powered algorithms analyze diverse patient data - medical history, imaging results, genetic profiles - to identify subtle markers of neurological disorders that might evade human observation . AI can significantly enhance the quality and effectiveness of treatment plans for neurological disorders by leveraging data-driven insights and personalizing care for individual patients. From early diagnosis and personalized treatment to BCIs and drug discovery, AI drives transformative changes that enhance patient care and our understanding of neurological disorders.
[106] Novel DNA Mechanism in Memory Discovered - Neuroscience News — This discovery not only changes our understanding of DNA's role in memory but also opens new avenues for investigating memory-related disorders. Key Facts: The study provides the first evidence of G4-DNA presence in neurons, highlighting its functional role in regulating memory-related gene expression.
[107] Rewriting the Brain's Rulebook: Scientists Uncover Memory's Hidden ... — Scripps Research scientists discovered that memory formation relies on complex neuron structures called multi-synaptic boutons, not more synapses, challenging old theories and offering new hope for treating memory loss. New structural markers of memory storage uncovered by Scripps Research may pave the way for new treatments for memory loss
[119] The Future Of Neurotechnology: Five Trends To Watch - Forbes — This article explores five key trends in neurotechnology, including neurofeedback, functional magnetic resonance imaging (fMRI), brain-computer interfaces (BCIs), transcranial magnetic stimulation (TMS) and neuroprosthetics. • Future directions: In the future, fMRI technology will likely become less costly and more integrated with other neurotechnologies, such as BCIs and neurofeedback systems. • Recent breakthroughs: Recent advancements in BCI technology include the development of non-invasive interfaces that use EEG or near-infrared spectroscopy (NIRS) to capture brain signals. • The road ahead: The future of BCIs will see improvements in signal processing, machine learning algorithms, and user interfaces. As neurofeedback, fMRI, BCIs, TMS, and neuroprosthetics continue to advance, they will offer unprecedented opportunities for understanding, enhancing, and restoring brain function.
[120] Research & White Papers | IEEE Brain — Closed-Loop Control Neural Activity Technology Roadmap White Paper IEEE Brain announces publication of Future Neural Therapeutics, a technology roadmap white paper that identifies key challenges and advances required to successfully develop next generation closed-loop neurotechnologies.
[134] Epigenetic regulation of neurotransmitter signaling in neurological ... — Epigenetic regulation of neurotransmitter signaling in neurological disorders - ScienceDirect Epigenetic regulation of neurotransmitter signaling in neurological disorders This article focuses on epigenetic regulatory mechanisms that control the expression of genes associated with four major chemical carriers in the brain: dopamine (DA), Gamma-aminobutyric acid (GABA), glutamate and serotonin. By targeting the epigenetic mechanisms that control neurotransmitter gene expression, there is a promising opportunity to advance the development of more effective treatments for neurological disorders with the potential to significantly improve the quality of life of individuals impacted by these conditions. Next article in issue No articles found. For all open access content, the Creative Commons licensing terms apply.
[135] Different Neuronal Activity Patterns Induce Different Gene Expression ... — Neurons induce hundreds of activity-regulated genes (ARGs) in response to elevations in their activity (Flavell and Greenberg, 2008), suggesting that a vast number of different neuronal firing patterns could each be coupled to a different gene expression profile.Consistent with this idea, distinct neuronal activity patterns differentially induce the expression of several individual genes
[139] Communication Networks in the Brain: Neurons, Receptors ... — Communication Networks in the Brain: Neurons, Receptors, Neurotransmitters, and Alcohol - PMC Neurons, Receptors, Neurotransmitters, and Alcohol Keywords: Alcohol and other drug effects and consequences, brain, neurons, neuronal signaling, synaptic transmission, neurotransmitter receptors, neurotrophins, steroid hormones, γ-aminobutyric acid (GABA), glutamate, dopamine, adenosine, serotonin, opioids, endocannabinoids Neurotransmitter molecules cross the synaptic cleft and bind to receptors known as ligand-gated ion channels (LGICs) and G-protein–coupled receptors (GPCRs) on the postsynaptic neuron. The neurotransmitter molecules released from the presynaptic vesicles traverse the synaptic gap and bind to proteins, termed neurotransmitter receptors, on the surface membrane of the postsynaptic neuron. LGIC receptors are proteins specialized for rapid transduction of the neurotransmitter chemical signal directly into an electrical response (Brunton et al.
[148] Machine learning and artificial intelligence in neuroscience: A primer ... — Machine learning and artificial intelligence in neuroscience: A primer for researchers - ScienceDirect Machine learning and artificial intelligence in neuroscience: A primer for researchers Machine Learning and Artificial Intelligence are important tools in exploratory neuroscience research. We discuss state-of-the-art opportunities and fallacies, and practical examples of the use of machine learning in neuroscience. Machine learning (ML) is commonly understood as a set of methods used to develop an AI. For the scientific community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, by functional brain imaging or *omics approaches. Next article in issue No data was used for the research described in the article. No articles found. For all open access content, the relevant licensing terms apply.
[149] Artificial intelligence-driven translational medicine: a machine ... — Unlike CNN-RNN systems that are best for sequential or spatial data like time-series signals or imaging, our key datasets are structured clinical records, which are best approached with the flexibility provided by DNNs. Moreover, DNNs capture complex patterns from high-dimensional inputs, which greatly boosts predictive performance in disease trajectory modeling. This paper investigates the effectiveness of a novel framework of a machine learning system that aims to predict the sequence of stages of disease on two different data sets, which are MIMIC-IV, containing information on patient population from a critical care setting, and UK Biobank with genetic, clinical and lifestyle data.
[150] Introduction to Machine Learning and Its Applications to Neuroscience — In parallel, neuroscience, a multidisciplinary pursuit, seeks to unravel the intricacies of the nervous system, spanning molecular, cellular, systems, and cognitive levels of analysis. The fusion of ML and neuroscience heralds an era where computational prowess empowers the exploration of neural complexities previously obscured by data deluge.
[151] Machine learning in neuroscience - Nature Methods — Machine learning in neuroscience | Nature Methods nature nature methods Nature Methods volume 15, page 33 (2018)Cite this article Machine-learning and, in particular, deep-learning approaches can help process and analyze large volumes of data. Image-based data, which are often analyzed manually, can benefit from advanced machine learning. Analyzing calcium-imaging data is another area that has recently seen an influx of machine-learning methods. In select examples, machine learning has proven to be a useful tool in the analysis of the growing deluge of data in neuroscientific research. Nature Communications (2023) Research articles Nature portfolio policies Author & Researcher services I agree my information will be processed in accordance with the Nature and Springer Nature Limited Privacy Policy.
[152] An Overview of Open Source Deep Learning-Based Libraries for Neuroscience — In recent years, deep learning has revolutionized machine learning and its applications, producing results comparable to human experts in several domains, including neuroscience. Each year, hundreds of scientific publications present applications of deep neural networks for biomedical data analysis. Due to the fast growth of the domain, it could be a complicated and extremely time-consuming
[169] The Potential of Interdisciplinary Research to Solve Problems in the ... — For example, the combined use of such neuroimaging techniques as positron emission tomography (PET) to look at blood flow and magnetic resonance imaging to look at structures, genetic analyses, cognitive testing, and clinical trials of pharmaceutical agents to evaluate patients with schizophrenia is allowing progress toward the development of interventions for the disease.4 Continued interdisciplinary efforts in schizophrenia research—including epidemiology, genetics, structural brain abnormalities, development, behavior, and virology—should advance the understanding and treatment of the disease.
[174] Future views on neuroscience and AI: Cell — Deep learning (DL) is revolutionizing neuroscience as it has transformed other fields. The brain generates massive multimodal datasets, combining measurements of molecular profiles like scRNA-seq and qRT-PCR with cell morphologies, connectivity maps, neural activity patterns, behaviors, and disease phenotypes.
[175] A Shared Vision for Machine Learning in Neuroscience - PMC — In another fMRI study, Chang et al., (2015) used machine learning to identify a sensitive and specific neural signature of affective responses to aversive images that was unresponsive to physical pain, thus allowing them to infer neural components differentiating negative emotion from pain, “providing a basis for new, brain-based taxonomies of affective processes.” Along these lines, independent component analysis and classification algorithms have been used to infer neural networks, decode brain states, or separate noise from signal (Jung et al., 2001; Thomas et al., 2002; Zuo et al., 2010; Lemm et al., 2011; Calhoun et al., 2014; Whitmore and Lin, 2016). While certainly powerful as a brute force approach to hypothesis generation, data-driven machine learning approaches may also yield more direct insight into brain function.
[177] Implications of systems biology in understanding the pathophysiology of ... — By examining the interplay between genetics, proteins, cellular processes, and environmental factors, systems biology can provide new insights into the molecular mechanisms of neurological disorders. System biology also enables the integration of omics data with clinical information, which may provide a more personalized approach to diagnosis
[179] Computational neurosciences and quantitative systems pharmacology: a ... — Geerts H, Spiros A et al (2018) Impact of amyloid-beta changes on cognitive outcomes in Alzheimer’s disease: analysis of clinical trials using a quantitative systems pharmacology model. Lin L, Hua F et al (2022) Quantitative systems pharmacology model for Alzheimer’s disease to predict the effect of aducanumab on brain amyloid. Geerts H, Walker M et al (2023) A combined physiologically-based pharmacokinetic and quantitative systems pharmacology model for modeling amyloid aggregation in Alzheimer’s disease. Geerts H, Spiros A et al (2018) Impact of amyloid-beta changes on cognitive outcomes in Alzheimer’s disease: analysis of clinical trials using a quantitative systems pharmacology model. Geerts H, Walker M et al (2023) A combined physiologically-based pharmacokinetic and quantitative systems pharmacology model for modeling amyloid aggregation in Alzheimer’s disease.
[198] Laboratory for Drug Discovery in Neuroscience — The Laboratory for Drug Discovery in Neuroscience (LDDN) is a collaborative academic group in the Department of Neurology at Brigham and Women's Hospital (BWH) and Harvard Medical School (HMS). Its mission is to identify small molecules that can lead to the development of innovative drugs for central nervous system (CNS) disorders.
[199] A perspective on multi-target drug discovery and design for complex ... — The success of drug design for complex diseases depends on an interdisciplinary and collaborative approach, and on scientists and clinicians who are willing to communicate and work together throughout the process. Designing multi-target drugs for complex diseases The transition from the single-target to the multi-target concept for drug design
[200] Strategic partnerships for AI-driven drug discovery: The role of ... — Strategic partnerships for AI-driven drug discovery: The role of relational dynamics - ScienceDirect Strategic partnerships for AI-driven drug discovery: The role of relational dynamics Strategic partnerships between AI firms and pharmaceutical firms enhance drug discovery and development efficiency. management of relationship-specific assets, AI models and new drug targets, is essential for achieving partnership objectives. Artificial intelligence-driven drug discovery (AIDD) companies hold significant promise for transforming pharmaceutical development, yet little is known about how they manage partnerships with established pharmaceutical firms. Through a case study approach, we focus on four key relational aspects: identifying complementary capabilities, establishing effective governance mechanisms, creating relationship-specific assets, and developing interfirm knowledge-sharing routines. For all open access content, the relevant licensing terms apply.
[201] [Recent Advances in Novel Therapies for Neurological Diseases: An ... — Disease-modifying therapies remain an important unmet medical need in many neurological diseases. However, recent advances in novel therapies, such as antisense oligonucleotides, antibodies, and enzyme supplementation have significantly improved the prognosis and delayed time until relapse of variou …
[209] Molecular Neuroscience in the 21st Century: A Personal Perspective — Within only 17 years, the 21 st century has changed neuroscience. After decades in which molecular neuroscience was pre-eminent, systems neuroscience is now in ascendance. New technologies have made it possible to map neural circuits in vivo, to visualize neuronal activity in real time, and to manipulate neural activity in behaving animals.
[218] Editorial: Rising stars in molecular neuroscience - Frontiers — Future research should focus on bridging molecular and systems-level approaches to clarify how these pathways interact within neuronal networks. Advances in single-cell transcriptomics and RNA-based profiling could provide deeper insights into these regulatory mechanisms and their roles in neurological disorders and neurodegeneration.
[219] The future of neurotechnology: From big data to translation — Advances in neurotechnologies, including molecular tools, neural sensors, and large-scale recording, are transforming neuroscience and generating vast datasets. A recent meeting highlighted the resulting challenges in global collaboration, data management, and effective translation, emphasizing the need for innovative strategies to harness big data for diagnosing and treating brain disorders.
[220] Focus on neuroscience methods - Nature — nature In this special issue, we present a series of reviews, perspectives and commentaries that highlight advances in methods and analytical approaches and provide guidelines and best practices in various areas of neuroscience. Nature Neuroscience presents a special focus issue that highlights advances in methods, analyses and practices across scales of investigation and subfields of neuroscience. We assembled this special issue to highlight techniques and analytical approaches that facilitate research across diverse areas of neuroscience, together with recommendations and guidelines for best practices. Exciting advances in genetic strategies for targeting cell populations, optical methods for recording and manipulating neuronal ensembles in behaving animals, and computational and analytical approaches for interpreting large-scale brain activity are among the many types of developments that we anticipate will facilitate new insights in the near future.
[242] Challenges of implementing computer-aided diagnostic models for ... — In this review, we analyze challenges specific to the clinical implementation of diagnostic AI models for neuroimaging data, looking at the differences between laboratory and clinical settings, the inherent limitations of diagnostic AI, and the different incentives and skill sets between research institutions, technology companies, and hospitals. The eventual, widespread clinical application of CAD models6 to brain images routinely collected in hospitals, such as CT and MRI, holds promise to automate the diagnostic process, reduce rates of misdiagnosis of brain-related disorders7–10, reduce diagnostic wait times11,12, cut costs, increase diagnostic objectivity13, and inform doctors in their assessment of patients14 for a wide range of brain disorders. In a unique report of a neuroimaging CAD model being implemented and validated clinically, Arbabshirani et al.
[243] GCSP Article | In focus: The challenges of neurotechnology — The use of neurotechnologies for enhancing purposes also raises the issue of inequality of access to these technologies. The convergence of neurotechnologies with other emerging technologies, such as artificial intelligence, is making their impact more unpredictable, disruptive and complex. Neurotechnology is a prime example of a dual-use technology, i.e. technology that has both civilian and military applications. Neurotechnology is still an emerging field, and while it is developing rapidly, most technologies discussed here remain restricted to the confines of laboratory experiments. There is an urgent need to guarantee the safe and globally beneficial development of emerging technologies and anticipate their potential misuse, malicious use, and unforeseen risks. This series of blogs provides insights into the key challenges related to three emerging technologies: artificial intelligence, synthetic biology and neurotechnology.
[244] Neurotech is Changing the Way We Treat Disease and Understand the Brain — Under a team of renowned practitioners and scholars in neurosurgery, neuroethics, and neurolaw from Massachusetts General Hospital (MGH) – a founding member of Mass General Brigham – Harvard Medical School, and the University of Minnesota, NJAM integrates training in clinical practice, law, ethics, and neuroscience – disciplines that typically operate independently. “Due in part to lack of training in a clinical setting, some neurotechnologies are applied today in just a few hospitals and clinics throughout the world,” said Theresa Williamson, M.D., MPH, assistant professor of neurosurgery and neuroethicist at MGH and Harvard Medical School, and co-director of NJAM. “It’s critical to train the next generation of scientists to engage with communities to incorporate their voice into research priorities,” said Gabriel Lázaro-Muñoz, Ph.D., J.D., assistant professor and neuroethics researcher at Harvard Medical School and the MGH Department of Neurosurgery, and co-director of NJAM.
[245] Challenges and Technical Limitations - From Molecules to Minds - NCBI ... — Challenges and Technical Limitations - From Molecules to Minds - NCBI Bookshelf from molecular to behavioral neuroscience, with extraordinary opportunities,” said Story Landis, director of the National Institute of Neurological Disorders and Stroke at the National Institutes of Health (NIH). “We need to figure out how to portray the excitement across that continuum in a way that not only the public and our funders, but, most important, the neuroscience community as a whole, can embrace.” A further challenge highlighted by some at the workshop was the need to reconcile understanding of psychological phenomenon at the behavioral and cognitive levels with understanding at the molecular and cellular levels. Despite tremendous advances in the past few years, many workshop participants highlighted the need for additional technical advances to drive the field forward.
[247] Seven challenges for neuroscience - PubMed — Seven challenges for neuroscience - PubMed Search in PubMed Search in PubMed Second, we need to create interlinked sets of data providing a complete picture of single areas of the brain at their different levels of organization with "rungs" linking the descriptions for humans and other species. On the one hand we need to develop new ways of classifying and simulating brain disease, leading to better diagnosis and more effective drug discovery. Data ladders: interlinked sets of data providing an increasingly complete picture of a single area of the brain at different levels of organization (molecules, cells, microcircuits, brain areas etc.) with “rungs” linking the descriptions for homologous areas in humans and experimental animals. Search in PubMed Search in PubMed
[249] The emergence of molecular systems neuroscience - PMC — Novel powerful techniques, that allow the tracking and manipulation of these molecular systems, in a cell specific manner and in real time, have fueled the emergence of molecular systems neuroscience, a sub-discipline of systems neuroscience that studies how the spatial and temporal patterns of molecular systems modulate circuits and brain networks, and consequently shape the properties of brain states and behavior. We identify three major areas of novel technology development that we predict will have a significant impact in systems neuroscience: (a) new developments in in vivo imaging techniques, including imaging of molecular mechanisms in specific cell types besides neurons, (b) new molecular sensors that can report on the activity of specific molecular mechanisms in a cell specific manner, and (c) new molecular actuators that can either activate or inhibit molecular mechanisms at a cellular or even sub-cellular level.
[251] PDF — limitations of study designs. Owing to high resource demands and varying inferential goals, current designs differentially emphasise sample size, measurement breadth, and longitudinal assessments.
[261] The future of neurotechnology: From big data to translation — Advances in neurotechnologies, including molecular tools, neural sensors, and large-scale recording, are transforming neuroscience and generating vast datasets. A recent meeting highlighted the resulting challenges in global collaboration, data management, and effective translation, emphasizing the need for innovative strategies to harness big data for diagnosing and treating brain disorders.