Javeria Hashmi
@netphys.bsky.social
1.4K followers 2.6K following 110 posts
Neuroscientist: pain perception, human neuroimaging, networks and graphs, cautious Neuro AI, clinical translation
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netphys.bsky.social
***New paper from our lab*** By Jennika Veinot

Low working memory underpins the association between aberrant functional properties of pain modulation circuitry and chronic back pain severity.

Journal of PAIN, 2025

www.sciencedirect.com/science/arti...

#pain #neuroscience #cognition
netphys.bsky.social
@Ohbm Talairach lecture. All about AI and how consortiums are fueling biomarker development.

#OHBM2025 @ohbmtrainees.bsky.social
netphys.bsky.social
Amazing story. Awesome guy Mr.Rogers
Reposted by Javeria Hashmi
efrenpolipsy.bsky.social
As the quarter ends at UCLA 🇲🇽🇺🇸
Reposted by Javeria Hashmi
Reposted by Javeria Hashmi
danclab.bsky.social
It's been a while since our last laminar MEG paper, but we're back! This time we push beyond deep versus superficial distinctions and go whole hog. Check it out- lots more exciting stuff to come! 🧠📈
maciekszul.bsky.social
🚨🚨🚨PREPRINT ALERT🚨🚨🚨
Neural dynamics across cortical layers are key to brain computations - but non-invasively, we’ve been limited to rough "deep vs. superficial" distinctions. What if we told you that it is possible to achieve full (TRUE!) laminar (I, II, III, IV, V, VI) precision with MEG!
Overview of the simulation strategy and analysis. a) Pial and white matter boundaries
surfaces are extracted from anatomical MRI volumes. b) Intermediate equidistant surfaces are
generated between the pial and white matter surfaces (labeled as superficial (S) and deep (D)
respectively). c) Surfaces are downsampled together, maintaining vertex correspondence across
layers. Dipole orientations are constrained using vectors linking corresponding vertices (link vectors).
d) The thickness of cortical laminae varies across the cortical depth (70–72), which is evenly sampled
by the equidistant source surface layers. e) Each colored line represents the model evidence (relative
to the worst model, ΔF) over source layer models, for a signal simulated at a particular layer (the
simulated layer is indicated by the line color). The source layer model with the maximal ΔF is
indicated by “˄”. f) Result matrix summarizing ΔF across simulated source locations, with peak
relative model evidence marked with “˄”. g) Error is calculated from the result matrix as the absolute
distance in mm or layers from the simulated source (*) to the peak ΔF (˄). h) Bias is calculated as the
relative position of a peak ΔF(˄) to a simulated source (*) in layers or mm.
Reposted by Javeria Hashmi
vincentbp.bsky.social
My laboratory at Université Laval in Québec City, Canada, is looking for a postdoc to study how neuromodulatory systems impact cortical processing during foraging behavior. This fully-funded position offers a collaborative environment in a great city. More info👇

can-acn.org/postdoctoral...
Postdoctoral position available in the Breton-Provencher Lab at Université Laval to study neuromodulatory systems and foraging behavior – Canadian Association for Neuroscience
can-acn.org
netphys.bsky.social
Lab photo @canacn.bsky.social

Come see posters from these awesome people presenting their latest research!

#CANACN2025
#PsychSciSky
#neuroscience
#PhD
netphys.bsky.social
We are having an amazing time learning about the brain circuitry at the Canadian Association for Neuroscience meeting in Toronto. Not a lot of human brain neuroimaging or pain mechanisms, but hey, that can change!

#CANACN2025
#neuroscience

@canacn.bsky.social
netphys.bsky.social
Sheena Josselyn speaking about history of memory research in Toronto. A CAN ACN symposium. #canacn2025
netphys.bsky.social
"These findings challenge the traditional brain-centric view of reward processing, supporting a more unified and integrated model in which gut-derived and vagus-mediated interoceptive signals are pivotal in intrinsically shaping motivation and reinforcement."
biorxiv-neursci.bsky.social
The gut-brain vagal axis governs mesolimbic dopamine dynamics and reward events https://www.biorxiv.org/content/10.1101/2025.05.12.653303v1
Reposted by Javeria Hashmi
peterkok.bsky.social
Looking forward to #VSS2025! On the first day, @dotproduct.bsky.social sky.social will be presenting a poster on how predictions embedded in alpha oscillations modulate perception of noisy stimuli. Come one, come all! Poster 16.322, Friday 3-5pm, Banyan Breezeway. #neuroskyence #visionscience
Reposted by Javeria Hashmi
mariamaly.bsky.social
When you successfully anticipate future events, what happens to your ability to encode the present? 🤔

Successful prediction increases the likelihood of successful encoding. We speculate about how switching between distinct encoding & prediction states can produce this effect.
osf.io/preprints/ps...
OSF
osf.io
Reposted by Javeria Hashmi
johnholbein1.bsky.social
a little pick me up lol for the academics in the room
Reposted by Javeria Hashmi
wonderofscience.bsky.social
These boxes are not moving. A mind-bending optical illusion by Japanese artist Jagarikin.
netphys.bsky.social
Amazing talks by Katie Birnie and Yves Deconninck @canadianpain.bsky.social annual meeting. #canadianpain2025
netphys.bsky.social
Spontaneous pain dynamics characterized by stochasticity in neural recordings of awake humans with chronic pain.

"during periods of higher pain states, we observed enhanced functional connectivity between the examined hub structures and the prefrontal cortex."
journals.lww.com/pain/abstrac...
Spontaneous pain dynamics characterized by stochasticity in ... : PAIN
ocal field potentials (LFPs) from pain-processing hub structures, including the ventral posteromedial nucleus of the thalamus, subgenual cingulate cortex, and periventricular and periaqueductal gray, ...
journals.lww.com