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1. Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

   https://doi.org/10.1371/journal.pone.0314268  

   Chang, Yin-Jui; Chen, Yuan-I; Stealey, Hannah M; Zhao, Yi; Lu, Hung-Yun; Contreras-Hernandez, Enrique; Baker, Megan N; Castillo, Edward; Yeh, Hsin-Chih; Santacruz, Samantha R (December 2024, PLOS ONE)

   Čanađija, Marko (Ed.)

   Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termedmultiscale neuraldynamicsneuralordinarydifferentialequation (msDyNODE), to describe multiscale brain communications governing cognition and behavior. We demonstrate that msDyNODE successfully captures multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived causal interactions between recording locations and scales not only aligned well with the abstraction of the hierarchical neuroanatomy of the mammalian central nervous system but also exhibited behavioral dependences. This work offers a new approach for mechanistic multiscale studies of neural processes. 

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2. Brain image data processing using collaborative data workflows on Texera

   https://doi.org/10.3389/fncir.2024.1398884  

   Ding, Yunyan; Huang, Yicong; Gao, Pan; Thai, Andy; Chilaparasetti, Atchuth Naveen; Gopi, M; Xu, Xiangmin; Li, Chen (July 2024, Frontiers in Neural Circuits)

   In the realm of neuroscience, mapping the three-dimensional (3D) neural circuitry and architecture of the brain is important for advancing our understanding of neural circuit organization and function. This study presents a novel pipeline that transforms mouse brain samples into detailed 3D brain models using a collaborative data analytics platform called “Texera.” The user-friendly Texera platform allows for effective interdisciplinary collaboration between team members in neuroscience, computer vision, and data processing. Our pipeline utilizes the tile images from a serial two-photon tomography/TissueCyte system, then stitches tile images into brain section images, and constructs 3D whole-brain image datasets. The resulting 3D data supports downstream analyses, including 3D whole-brain registration, atlas-based segmentation, cell counting, and high-resolution volumetric visualization. Using this platform, we implemented specialized optimization methods and obtained significant performance enhancement in workflow operations. We expect the neuroscience community can adopt our approach for large-scale image-based data processing and analysis. 

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3. Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer’s Disease Using Biophysical Modeling and Deep Learning

   https://doi.org/10.1007/s11538-024-01273-5  

   Saghafi, Soheil; Rumbell, Timothy; Gurev, Viatcheslav; Kozloski, James; Tamagnini, Francesco; Wedgwood, Kyle C. A.; Diekman, Casey O. (March 2024, Bulletin of Mathematical Biology)

   Abstract Alzheimer’s disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We used deep hybrid modeling (DeepHM), a recently developed parameter inference technique that combines deep learning with biophysical modeling, to map experimental data recorded from hippocampal CA1 neurons in transgenic AD mice and age-matched wildtype littermate controls to the parameter space of a conductance-based CA1 model. Although mechanistic modeling and machine learning methods are by themselves powerful tools for approximating biological systems and making accurate predictions from data, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. DeepHM addresses these shortcomings by using conditional generative adversarial networks to provide an inverse mapping of data to mechanistic models that identifies the distributions of mechanistic modeling parameters coherent to the data. Here, we demonstrated that DeepHM accurately infers parameter distributions of the conductance-based model on several test cases using synthetic data generated with complex underlying parameter structures. We then used DeepHM to estimate parameter distributions corresponding to the experimental data and infer which ion channels are altered in the Alzheimer’s mouse models compared to their wildtype controls at 12 and 24 months. We found that the conductances most disrupted by tauopathy, amyloidopathy, and aging are delayed rectifier potassium, transient sodium, and hyperpolarization-activated potassium, respectively. 

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4. Music and Brain Circuitry: Strategies for Strengthening Evidence-Based Research for Music-Based Interventions

   https://doi.org/10.1523/JNEUROSCI.1135-22.2022  

   Chen, Wen Grace; Iversen, John Rehner; Kao, Mimi H.; Loui, Psyche; Patel, Aniruddh Dhiren; Zatorre, Robert J.; Edwards, Emmeline (November 2022, The Journal of Neuroscience)

   The neuroscience of music and music-based interventions (MBIs) is a fascinating but challenging research field. While music is a ubiquitous component of every human society, MBIs may encompass listening to music, performing music, music-based movement, undergoing music education and training, or receiving treatment from music therapists. Unraveling the brain circuits activated and influenced by MBIs may help us gain better understanding of the therapeutic and educational values of MBIs by gathering strong research evidence. However, the complexity and variety of MBIs impose unique research challenges. This article reviews the recent endeavor led by the National Institutes of Health to support evidence-based research of MBIs and their impact on health and diseases. It also highlights fundamental challenges and strategies of MBI research with emphases on the utilization of animal models, human brain imaging and stimulation technologies, behavior and motion capturing tools, and computational approaches. It concludes with suggestions of basic requirements when studying MBIs and promising future directions to further strengthen evidence-based research on MBIs in connections with brain circuitry. SIGNIFICANCE STATEMENT Music and music-based interventions (MBI) engage a wide range of brain circuits and hold promising therapeutic potentials for a variety of health conditions. Comparative studies using animal models have helped in uncovering brain circuit activities involved in rhythm perception, while human imaging, brain stimulation, and motion capture technologies have enabled neural circuit analysis underlying the effects of MBIs on motor, affective/reward, and cognitive function. Combining computational analysis, such as prediction method, with mechanistic studies in animal models and humans may unravel the complexity of MBIs and their effects on health and disease. 

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5. From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction

   Tanaka, H; Nayebi, A; Maheswaranathan, N; McIntosh, L; Baccus, S; Ganguli, S. (December 2019, Advances in neural information processing systems)

   null (Ed.)

   Recently, deep feedforward neural networks have achieved considerable success in modeling biological sensory processing, in terms of reproducing the input-output map of sensory neurons. However, such models raise profound questions about the very nature of explanation in neuroscience. Are we simply replacing one complex system (a biological circuit) with another (a deep network), without understanding either? Moreover, beyond neural representations, are the deep network's computational mechanisms for generating neural responses the same as those in the brain? Without a systematic approach to extracting and understanding computational mechanisms from deep neural network models, it can be difficult both to assess the degree of utility of deep learning approaches in neuroscience, and to extract experimentally testable hypotheses from deep networks. We develop such a systematic approach by combining dimensionality reduction and modern attribution methods for determining the relative importance of interneurons for specific visual computations. We apply this approach to deep network models of the retina, revealing a conceptual understanding of how the retina acts as a predictive feature extractor that signals deviations from expectations for diverse spatiotemporal stimuli. For each stimulus, our extracted computational mechanisms are consistent with prior scientific literature, and in one case yields a new mechanistic hypothesis. Thus overall, this work not only yields insights into the computational mechanisms underlying the striking predictive capabilities of the retina, but also places the framework of deep networks as neuroscientific models on firmer theoretical foundations, by providing a new roadmap to go beyond comparing neural representations to extracting and understand computational mechanisms. 

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