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Contenido proporcionado por Benjamin James Kuper-Smith. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Benjamin James Kuper-Smith o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.
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87. Rick Betzel: Network neuroscience, generative modeling, and collaborations

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Manage episode 392938915 series 2800223
Contenido proporcionado por Benjamin James Kuper-Smith. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Benjamin James Kuper-Smith o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.

Rick Betzel is an Associate professor at India University Bloomington. We talk about his research on network neuroscience, how to find good collaborators, Rick's path to network neuroscience, and much more.
Support the show: https://geni.us/bjks-patreon
Timestamps
0:00:00: What's the purpose of connectomics if understanding a species' entire connectome (as in C elegans) doesn't allow us to fully understand its behaviour?
0:03:57: Rick's very very linear path to network neuroscience
0:19:41: Multi-scale brain networks
0:43:40: Collaborations (between people who collect data and people who analyse data)
0:52:33: The future of network neuroscience: generative modeling, network control, and edge-centric connectomics
1:13:15: A book or paper more people should read
1:15:55: Something Rick wishes he'd learnt sooner
1:18:01: Advice for PhD students/postdocs
Podcast links

Rick's links

Ben's links

References
Akarca ... (2021). A generative network model of neurodevelopmental diversity in structural brain organization. Nat Comm.
Barabási (2003). Linked.
Barabási & Albert (1999). Emergence of scaling in random networks. Science.
Betzel (2022). Network neuroscience and the connectomics revolution. In Connectomic deep brain stimulation.
Betzel & Bassett (2017). Multi-scale brain networks. Neuroimage.
Betzel & Bassett (2017). Generative models for network neuroscience: prospects and promise. Journal of The Royal Society Interface.
Betzel ... (2012). Synchronization dynamics and evidence for a repertoire of network states in resting EEG. Front comp neuro.
Bullmore & Sporns (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neuro.
Cook ... (2019). Whole-animal connectomes of both Caenorhabditis elegans sexes. Nature.
Feltner & Dapena (1986). Dynamics of the shoulder and elbow joints of the throwing arm during a baseball pitch. J Appl Biomech.
Lindsay (2021). Models of the mind.
Nieminen ... (2022). Multi-locus transcranial magnetic stimulation system for electronically targeted brain stimulation. Brain stimulation.
Oh ... (2014). A mesoscale connectome of the mouse brain. Nature.
Rubinov & Sporns (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage.
Scheffer ... (2020). A connectome and analysis of the adult Drosophila central brain. Elife.
Sporns (2016). Networks of the Brain.
Van Den Heuvel & Sporns (2011). Rich-club organization of the human connectome. J Neuro.
Watts & Strogatz (1998). Collective dynamics of ‘small-world’networks. Nature.
White ... (1986). The structure of the nervous system of the nematode Caenorhabditis elegans. Philos Trans R Soc Lond B.
Winding ... (2023). The connectome of an insect brain. Science.
Yan ... (2017). Network control principles predict neuron function in the Caenorhabditis elegans connectome. Nature.

  continue reading

Capíttulos

1. What's the purpose of connectomics if understanding a species' entire connectome (as in C elegans) doesn't allow us to fully understand its behaviour? (00:00:00)

2. Rick's very very linear path to network neuroscience (00:03:57)

3. Multi-scale brain networks (00:19:41)

4. Collaborations (between people who collect data and people who analyse data) (00:43:40)

5. The future of network neuroscience: generative modeling, network control, and edge-centric connectomics (00:52:33)

6. A book or paper more people should read (01:13:15)

7. Something Rick wishes he'd learnt sooner (01:15:55)

8. Advice for PhD students/postdocs (01:18:01)

104 episodios

Artwork
iconCompartir
 
Manage episode 392938915 series 2800223
Contenido proporcionado por Benjamin James Kuper-Smith. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Benjamin James Kuper-Smith o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.

Rick Betzel is an Associate professor at India University Bloomington. We talk about his research on network neuroscience, how to find good collaborators, Rick's path to network neuroscience, and much more.
Support the show: https://geni.us/bjks-patreon
Timestamps
0:00:00: What's the purpose of connectomics if understanding a species' entire connectome (as in C elegans) doesn't allow us to fully understand its behaviour?
0:03:57: Rick's very very linear path to network neuroscience
0:19:41: Multi-scale brain networks
0:43:40: Collaborations (between people who collect data and people who analyse data)
0:52:33: The future of network neuroscience: generative modeling, network control, and edge-centric connectomics
1:13:15: A book or paper more people should read
1:15:55: Something Rick wishes he'd learnt sooner
1:18:01: Advice for PhD students/postdocs
Podcast links

Rick's links

Ben's links

References
Akarca ... (2021). A generative network model of neurodevelopmental diversity in structural brain organization. Nat Comm.
Barabási (2003). Linked.
Barabási & Albert (1999). Emergence of scaling in random networks. Science.
Betzel (2022). Network neuroscience and the connectomics revolution. In Connectomic deep brain stimulation.
Betzel & Bassett (2017). Multi-scale brain networks. Neuroimage.
Betzel & Bassett (2017). Generative models for network neuroscience: prospects and promise. Journal of The Royal Society Interface.
Betzel ... (2012). Synchronization dynamics and evidence for a repertoire of network states in resting EEG. Front comp neuro.
Bullmore & Sporns (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neuro.
Cook ... (2019). Whole-animal connectomes of both Caenorhabditis elegans sexes. Nature.
Feltner & Dapena (1986). Dynamics of the shoulder and elbow joints of the throwing arm during a baseball pitch. J Appl Biomech.
Lindsay (2021). Models of the mind.
Nieminen ... (2022). Multi-locus transcranial magnetic stimulation system for electronically targeted brain stimulation. Brain stimulation.
Oh ... (2014). A mesoscale connectome of the mouse brain. Nature.
Rubinov & Sporns (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage.
Scheffer ... (2020). A connectome and analysis of the adult Drosophila central brain. Elife.
Sporns (2016). Networks of the Brain.
Van Den Heuvel & Sporns (2011). Rich-club organization of the human connectome. J Neuro.
Watts & Strogatz (1998). Collective dynamics of ‘small-world’networks. Nature.
White ... (1986). The structure of the nervous system of the nematode Caenorhabditis elegans. Philos Trans R Soc Lond B.
Winding ... (2023). The connectome of an insect brain. Science.
Yan ... (2017). Network control principles predict neuron function in the Caenorhabditis elegans connectome. Nature.

  continue reading

Capíttulos

1. What's the purpose of connectomics if understanding a species' entire connectome (as in C elegans) doesn't allow us to fully understand its behaviour? (00:00:00)

2. Rick's very very linear path to network neuroscience (00:03:57)

3. Multi-scale brain networks (00:19:41)

4. Collaborations (between people who collect data and people who analyse data) (00:43:40)

5. The future of network neuroscience: generative modeling, network control, and edge-centric connectomics (00:52:33)

6. A book or paper more people should read (01:13:15)

7. Something Rick wishes he'd learnt sooner (01:15:55)

8. Advice for PhD students/postdocs (01:18:01)

104 episodios

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