Neuroscience

Deep neural networks to register and annotate cells in moving and deforming nervous systems

1. Adam A Atanas
2. Alicia Kun-Yang Lu
3. Brian Goodell
4. Jungsoo Kim
5. Saba N Baskoylu
6. Di Kang
7. Talya S Kramer
8. Eric Bueno
9. Flossie K Wan
10. Karen L Cunningham
11. Brandon Weissbourd
12. Steven W Flavell

• Curated by eLife

  eLife Assessment

  Whole-brain imaging of neuronal activity in freely behaving animals holds great promise for neuroscience, but numerous technical challenges limit its use. In this important study, the authors describe a new set of deep learning-based tools to track and identify the activity of head neurons in freely moving nematodes (C. elegans) and jellyfish (Clytia hemisphaerica). While the tools convincingly enable high tracking speed and accuracy in the settings in which the authors have evaluated them, the claim that these tools should be easily generalizable to a wide variety of datasets is incompletely supported.

Reviewed by eLife

Two time scales of adaptation in human learning rates

1. Jonas Simoens
2. Senne Braem
3. Pieter Verbeke
4. Haopeng Chen
5. Stefania Mattioni
6. Mengqiao Chai
7. Nicolas W Schuck
8. Tom Verguts

• Curated by eLife

  eLife Assessment

  This study makes a valuable contribution by separating two timescales of adaptation: rapid, within block reductions in learning rate, and slower, location specific, meta-learned adjustments. Behavioural data and computational modeling converge to support both processes. The evidence is solid with neuroimaging results suggesting that meta-learned learning rates are encoded in the orbitofrontal cortex, while prediction errors are represented in a distributed network including the ventral striatum and are modulated by expected error magnitude, though the specificity of these effects requires further contextualization. The manuscript is timely and clearly written; its main limitation is the weak linkage between neural signals and behavior, leaving uncertainty over whether the reported signals play a mechanistic role in learning.

Reviewed by eLife

Combinatorial protein barcodes enable self-correcting neuron tracing with nanoscale molecular context

1. Sung Yun Park
2. Arlo Sheridan
3. Bobae An
4. Erin Jarvis
5. Julia Lyudchik
6. William Patton
7. Jun Y. Axup
8. Stephanie W. Chan
9. Hugo G.J. Damstra
10. Daniel Leible
11. Kylie S. Leung
12. Clarence A. Magno
13. Aashir Meeran
14. Julia M. Michalska
15. Franz Rieger
16. Claire Wang
17. Michelle Wu
18. George M. Church
19. Jan Funke
20. Todd Huffman
21. Kathleen G.C. Leeper
22. Sven Truckenbrodt
23. Johan Winnubst
24. Joergen M.R. Kornfeld
25. Edward S. Boyden
26. Samuel G. Rodriques
27. Andrew C. Payne

Reviewed by Arcadia Science

Haploinsufficiency of lysosomal enzyme genes in Alzheimer’s disease

1. Bruno A. Benitez
2. Clare E. Wallace
3. Maulikkumar Patel
4. Niko-Petteri Nykanen
5. Carla M. Yuede
6. Samantha L. Eaton
7. Cyril Pottier
8. Arda Cetin
9. Matthew Johnson
10. Mia T. Bevan
11. Woodrow D. Gardiner
12. Hannah M. Edwards
13. Brookelyn M. Doherty
14. Ryan T. Harrigan
15. Dominic Kurian
16. Thomas M. Wishart
17. Colin Smith
18. John R. Cirrito
19. Mark S. Sands

Reviewed by Arcadia Science

Neural Representation of Associative Threat Learning in Pulvinar Divisions, Lateral Geniculate Nucleus, and Mediodorsal Thalamus in Humans

1. Muhammad Badarnee
2. Zhenfu Wen
3. B Isabel Moallem
4. Stephen Maren
5. Mohammed R Milad

• Curated by eLife

  eLife Assessment

  The study provides valuable insights into the role of thalamic nuclei in associative threat and extinction learning, supported by a large dataset and multipronged analyses. However, aspects of the evidence remain incomplete, particularly regarding the statistical methods, the claims of plasticity, and the network modeling framework. With this addressed, this manuscript will be of interest to those interested in learning and memory, fear, thalamic circuitry, and related mental heath conditions.

Reviewed by eLife

Asymmetric cortical projections to striatal direct and indirect pathways distinctly control actions

1. Jason R Klug
2. Xunyi Yan
3. Hilary Hoffman
4. Max D Engelhardt
5. Fumitaka Osakada
6. Edward M Callaway
7. Xin Jin

• Curated by eLife

  eLife Assessment

  This manuscript presents an important finding that D1- and D2-striatal neurons receive distinct cortical inputs, offering key insights into corticostriatal function. For instance, in the context of striatal-dependent learning, this distinction is highly informative for interpreting synaptic physiology data, particularly when inputs to one neuron subtype may change independently of the other. The strength of the evidence is solid, with anatomical and electrophysiological findings aligning well with results from optogenetic and behavioral studies. The study would be of interest to neuroscientists studying basal ganglia circuits in health and disease.

Reviewed by eLife

Enterovirus D68 2A protease causes nuclear pore complex dysfunction and motor neuron toxicity

1. Katrina M Zinn
2. Mathew W McLaren
3. Michael T Imai
4. Malavika M Jayaram
5. Jeffrey D Rothstein
6. Matthew J Elrick

• Curated by eLife

  eLife Assessment

  This valuable study examines the cleavage of motor neuron nucleoporins by proteases 2A and 3C of enterovirus D68, a pathogen associated with acute flaccid myelitis. The evidence supporting the effects of EV-D68 proteases on nuclear import and export is solid and confirms previous results on the specific targeting of nucleoporins by proteases from other enteroviruses. However, the claim that cleavage of nucleoporins by EV-D68 2A is neurotoxic, though intriguing, is incomplete, as the evidence is largely indirect.

Reviewed by eLife