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A tartalmat a Karen Toffler Charitable Trust biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Karen Toffler Charitable Trust vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
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Decoding Alzheimer’s: Breakthroughs in Neural Recording using and Biostatistics & Machine Learning

49:57
 
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Manage episode 491964374 series 3557210
A tartalmat a Karen Toffler Charitable Trust biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Karen Toffler Charitable Trust vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.

In this exciting episode of Research Renaissance, host Deborah Westphal speaks with Dr. Ted Zwang, Assistant Professor of neurology at Massachusetts General Hospital and Harvard Medical School, Dr. Andrew Holbrook, Assistant Professor at UCLA and Jasen Zhang, PhD student in biostatistics in Holbrook’s lab. Together, they share how novel neural recording devices and advanced machine learning techniques are transforming the study of Alzheimer’s disease.

Dr. Ted and Jasen discuss their collaborative project—funded by the Kavli Foundation, Cure Alzheimer’s Fund, and the Karen Toffler Charitable Trust—which captures how neurons change over time in Alzheimer’s mouse models. They reveal surprising discoveries about how some neurons “go quiet” and later recover—challenging long-held assumptions about neurodegeneration.

The conversation also explores how these insights could lead to earlier diagnostics, predictive models of cognitive decline, and more personalized treatments for patients.

🔬 Topics include:
- How flexible neural recording devices track neuron activity over months
- Why biostatistics and machine learning is key to decoding massive neural datasets
- New findings about reversible neuronal dysfunction in Alzheimer’s
- How predictive models could inform personalized medicine
- The challenges of translating animal research to human diagnostics
- The importance of interdisciplinary collaboration in brain science

Whether you’re a researcher, student, clinician, or curious learner, this episode offers a glimpse into the future of Alzheimer’s research—and why there’s new reason for hope.

🧠 Guests: Dr. Ted Zwang https://zwanglab.com/, Dr. Andrew Holbrook https://andrewjholbrook.github.io/ and Jasen Zhang https://www.linkedin.com/in/jasen-zhang/
🌐 For more episodes and updates, visit: tofflertrust.org

To learn more about the breakthroughs discussed in this episode and to support ongoing research, visit our website at tofflertrust.org.
Technical Podcast Support by Jon Keur at Wayfare Recording Co.

  continue reading

59 epizódok

Artwork
iconMegosztás
 
Manage episode 491964374 series 3557210
A tartalmat a Karen Toffler Charitable Trust biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Karen Toffler Charitable Trust vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.

In this exciting episode of Research Renaissance, host Deborah Westphal speaks with Dr. Ted Zwang, Assistant Professor of neurology at Massachusetts General Hospital and Harvard Medical School, Dr. Andrew Holbrook, Assistant Professor at UCLA and Jasen Zhang, PhD student in biostatistics in Holbrook’s lab. Together, they share how novel neural recording devices and advanced machine learning techniques are transforming the study of Alzheimer’s disease.

Dr. Ted and Jasen discuss their collaborative project—funded by the Kavli Foundation, Cure Alzheimer’s Fund, and the Karen Toffler Charitable Trust—which captures how neurons change over time in Alzheimer’s mouse models. They reveal surprising discoveries about how some neurons “go quiet” and later recover—challenging long-held assumptions about neurodegeneration.

The conversation also explores how these insights could lead to earlier diagnostics, predictive models of cognitive decline, and more personalized treatments for patients.

🔬 Topics include:
- How flexible neural recording devices track neuron activity over months
- Why biostatistics and machine learning is key to decoding massive neural datasets
- New findings about reversible neuronal dysfunction in Alzheimer’s
- How predictive models could inform personalized medicine
- The challenges of translating animal research to human diagnostics
- The importance of interdisciplinary collaboration in brain science

Whether you’re a researcher, student, clinician, or curious learner, this episode offers a glimpse into the future of Alzheimer’s research—and why there’s new reason for hope.

🧠 Guests: Dr. Ted Zwang https://zwanglab.com/, Dr. Andrew Holbrook https://andrewjholbrook.github.io/ and Jasen Zhang https://www.linkedin.com/in/jasen-zhang/
🌐 For more episodes and updates, visit: tofflertrust.org

To learn more about the breakthroughs discussed in this episode and to support ongoing research, visit our website at tofflertrust.org.
Technical Podcast Support by Jon Keur at Wayfare Recording Co.

  continue reading

59 epizódok

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