SUMMARY - The Future of the Brain - Marcus, Gary, Freeman, Jeremy



Here are the key points summarized:

  • Project MindScope aims to comprehensively map the circuitry and neural activity in the mouse visual cortico-thalamic system using genetic, molecular, anatomical, and physiological techniques.

  • Over 40 Cre lines have been developed to target different cell types, and tools like GCaMP allow visualization and manipulation of neural activity.

  • The large-scale Allen Mouse Brain Connectivity Atlas is mapping whole-brain axonal projections at single cell resolution using genetic tracing and microscopy.

  • The project will characterize physiological, anatomical, transcriptional and synaptic properties of cell types in the visual system at single cell resolution to develop a comprehensive cell type taxonomy.

  • Higher-order connectivity rules between neuron groups will be studied using electron microscopy and rabies tracing.

  • The goal is to understand visual computations by recording large ensembles of neurons in visual cortex areas during visuomotor behaviors, integrating results across disciplines.

  • Insights from comprehensively mapping the mouse visual circuit could provide understanding of visual processing and computation in cortex.

    Here is a summary of the key points about using silicon probes and two-photon microscopy to study motor behaviors in mice:

  • The goal is to construct a detailed "projectome" map linking physiological signals in different visual areas of the mouse brain to identified cell types during motor behaviors.

  • Silicon probes will be used to record neural activity from multiple brain areas simultaneously as mice perform tasks. This will provide functional connectivity data across areas.

  • Two-photon microscopy will allow imaging of genetically-encoded calcium indicators in identified neuronal populations. This will reveal activity patterns of specific cell types.

  • Combining the electrophysiology from silicon probes with calcium imaging will enable linking physiological signals between brain areas to activation of particular neuronal populations.

  • Ultimately, this integrated approach aims to delineate a functional "projectome" map showing how signals are transmitted between defined cell types in different visual areas related to motor behaviors under observation. This will provide insights into visual-motor processing circuits in the mouse brain.

    Here is a summary of the key points:

  • Large-scale neuroscience experiments are generating terabytes of data per experiment, comparable to what social media platforms collect daily.

  • Analyzing such massive neural datasets poses major computational challenges and can take hours or days even for simple analyses using traditional methods.

  • New distributed computing frameworks like Apache Spark enable analyses across computer clusters, making analyses possible that were previously computationally infeasible.

  • However, analysis alone is not sufficient - targeted experiment design informed by analyses is also needed. Insights often come not just from large datasets but from hypothesis testing using customized experimental stimuli.

  • Going forward, a strategy of simply collecting massive datasets first before analysis may not be realistic, as the desired data and optimal conditions are unclear at the outset. Continuous interplay between analysis and experiment design is crucial for understanding brain function from large-scale neural data.

    Here is a summary of the key points:

  • Current approaches to linking language and neurobiology, like brain imaging, provide correlations but not explanatory mechanisms. They don't fully explain how the brain implements linguistic functions.

  • A more mechanistic approach is needed that decomposes language tasks into more basic computational/cognitive operations that can then be linked to specific brain architectures and functions.

  • The challenges involve finding the right level of granularity to meaningfully connect linguistic categories to neural descriptions. Simply localizing functions to brain regions is insufficient.

  • Progress will come from decomposing complex functions into hypothesized primitive operations, then studying these at the neural level using techniques like high-resolution recording.

  • This could provide satisfying accounts of how specific brain regions underpin language by explicitly linking linguistic concepts to neural mechanisms, bridging between the different levels of analysis in linguistics and neuroscience.

  • Frameworks like Marr's computational, algorithmic, and implementational levels provide a way to link high-level linguistic concepts to low-level neural mechanisms.

    Here is a summary:

  • There is a debate in consciousness research around the relationship between conscious perception and cognitive access/reportability. Some key issues are discussed.

  • The "measurement problem" refers to the fact that we can only determine if a perception is conscious or unconscious based on a subject's report. But reporting relies on cognitive processes, circularly conflating consciousness with cognition.

  • Some proposed methods to get around this, like looking for brain activity in perception areas without reports, still have challenges. There are also debates around whether reporting is constitutive of consciousness.

  • Evidence from Lamme's lab suggests conscious representation can be maintained without full cognitive access, challenging cognitive theories of consciousness. But the issues remain heavily debated.

  • Mouse studies using optogenetics to disrupt top-down feedback aim to study consciousness, but have concerns like how to interpret results given the measurement problem.

  • Burge's model differentiates between nonconceptual perception and conceptual judgments, which is important for understanding this debate and thinking about studying consciousness in non-verbal subjects.

In summary, it outlines some of the key ongoing discussions and challenges regarding the relationship between conscious perception and cognitive access/reporting in the study of consciousness. The measurement problem remains a significant issue.

Here is a summary of the key points:

  • Large-scale brain mapping projects like the BRAIN Initiative and Human Brain Project aim to advance neuroscience through technologies to map the brain at high resolution.

  • They face challenges of defining mapping goals and translating findings, as well as managing expectations, as was seen with the early promises of the Human Genome Project.

  • Issues around data sharing, commercialization, proprietary interests, and representation need oversight to prevent problems that emerged with genomics.

  • There are debates around what constitutes "mapping" the brain just as there were for the genome. Standards and definitions will be important.

  • Sources of funding, whether public or private, can impact research goals, independence, and data sharing transparency. Conflicts may arise.

  • Practical applications and purported benefits need reasonable timelines to avoid disappointing results. Promises must be achievable.

  • Ethical issues like informed consent and protecting privacy will be important as was seen with selecting genomes to map initially.

Overall, lessons from the experience of the Human Genome Project in these areas can help guide large-scale brain mapping to avoid similar challenges.

Here is a summary of the key points:

  • Current brain-machine interfaces have limitations such as wires, rigidity of probes, inability to scale up number of recording sites, and lack of lifetime operation.

  • "Neural dust" is a new approach using tiny wireless sensor devices that could help address these challenges. It uses piezoelectric crystals and minuscule CMOS chips.

  • The crystals harvest energy from ultrasound to power the chips. The chips record neural signals with surface electrodes and transmit the data wirelessly back using the crystals.

  • This novel technique could provide highly scalable recordings across the brain without wires or need for replacement over a lifetime.

  • Neural dust aims to realize the goal of fully implantable brain-machine interfaces that enable natural, lifelong prosthetic control by overcoming existing constraints of electrode arrays.

  • More research is still needed but neural dust shows promise as a disruptive technology for next-generation brain-computer interfaces if the technical hurdles can be overcome.

    Here is a summary of the key points:

  • In situ sequencing refers to determining the DNA sequence directly within tissue samples, without having to first extract and purify DNA. This allows sequencing of DNA from specific cell types or brain regions.

  • It provides important spatial information about the DNA that is lost with traditional extraction and purification methods before sequencing.

  • For the Rosetta Brain project, in situ sequencing could be used to determine the DNA sequences within specific brain regions mapped during electrical activity recordings.

  • This would help link the activity histories to the underlying gene sequences and expression profiles in those regions, helping unravel how genetics influence neural dynamics.

  • Technological challenges include developing methods for high-quality sequencing of DNA directly within intact tissue samples while preserving the spatial information about where the DNA originated.

In summary, in situ sequencing has the potential to provide spatially-resolved genetic information from brain regions to help correlate activity patterns with underlying molecular properties, but faces technical hurdles around sequencing DNA within intact tissue structures.

Here is a summary of the key points:

The Rosetta Brain project aims to map connectivity across the entire brain at single-cell resolution. Currently, techniques like Project MindScope using two-photon microscopy and microendoscopes allow monitoring neurons in specific regions of the brain in awake, behaving mice. Ultrasound imaging is also discussed as a promising approach. Advances in ultrasound now enable whole brain imaging of small animals in real-time. Further development of ultrasonic techniques could potentially provide a way to monitor activity across the entire mouse brain as part of the Rosetta Brain project's goal of mapping connectivity at the whole-brain level at single-cell resolution. While current technologies only image specific brain areas or regions, ultrasound shows promise for the future to allow non-invasive imaging of the entire mouse brain, which could help achieve the goals of the Rosetta Brain project.

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