Neuroscience

Oxytocin neurons signal state-dependent transitions from rest to thermogenesis and behavioral arousal in social and non-social settings

1. Morgane Vandendoren
2. Jason G Landen
3. Joseph F Rogers
4. Samantha Killmer
5. Baizar Alamiri
6. Celeste Pohlman
7. Glenn J Tattersall
8. Nicole L Bedford
9. Adam C Nelson

• Curated by eLife

  eLife Assessment

  This study presents a valuable finding regarding the role of oxytocin neurons in thermogenesis and behavioral thermoregulation. The use of numerous converging methods, including behavior, fiber photometry, optogenetics, thermal recordings, metabolic analyses, and more, produces a multi-dimensional dataset delivering findings that provide solid support for the conclusions. Conclusions would be strengthened with validation of the approaches, inclusion of a loss of function experiment, and further investigation of the social nature of the behavior. The maternal findings are, at present, somewhat disconnected from the conclusions. The findings are novel and open new doors for understanding the role of the PVT and oxytocin in thermoregulation work; the work will be of strong interest to the thermoregulation, social behavior, and oxytocin signaling communities.

Reviewed by eLife

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 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

Adaptive behavior is guided by integrated representations of controlled and non-controlled information

1. Bingfang Huang
2. Harrison Ritz
3. Jiefeng Jiang

• Curated by eLife

  eLife Assessment

  This useful study uses creative scalp EEG decoding methods to attempt to demonstrate that two forms of learned associations in a Stroop task are dissociable, despite sharing similar temporal dynamics. However, the evidence supporting the conclusions is incomplete due to concerns with the experimental design and methodology. This paper would be of interest to researchers studying cognitive control and adaptive behavior, if the concerns raised in the reviews can be addressed satisfactorily.

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

Targeting Lysosomal Dysfunction to Alleviate Plaque Deposition in an Alzheimer Disease Model

1. Leigh Ellen Fremuth
2. Diantha van de Vlekkert
3. Huimin Hu
4. Jason Andrew Weesner
5. Ida Annunziata
6. Alessandra d’Azzo

Reviewed by PREreview

Neural representation of action symbols in primate frontal cortex

1. Lucas Y. Tian
2. Kedar U. Garzón Gupta
3. Daniel J. Hanuska
4. Adam G. Rouse
5. Mark A. G. Eldridge
6. Marc H. Schieber
7. Xiao-Jing Wang
8. Joshua B. Tenenbaum
9. Winrich A. Freiwald

Reviewed by PREreview

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