Lili 🏳️⚧️ @neurolili@qoto.org
PhD student in neuroscience
Mostly modeling of movement
Nonfiction book nerd
She / her 🏳️⚧️ 🏳️🌈
Joined Nov 2022
I'll note that I think part of the confusion is a cultural one. Having gone from a cognitive neuroscience fMRI/ECoG lab to a drosophila neuroscience lab, I feel there is a disconnect between the goals and methods between the two fields. The drosophila neuroscientists (unfairly) dismiss cognitive neuroscience as not rigorous enough, whereas the cognitive neuroscientists (unfairly) dismiss work on drosophila as they see flies as "too simple".
I think this reflects a larger gap that I've seen of neuroscientists approaching the brain from a cognitive versus biological angle and how this leads to them to pursuing different goals using different methods. Cognitive scientists are often looking at neuroscience for fingerprints that can clarify cognitive concepts, whereas biologists often look at neuroscience trying to understand natural computation and biological processes connecting to the rest of the body.
@albertcardona Thank you for this post! I do enjoy thinking about this topic. There's a lot to respond to here, so perhaps I can do it in pieces.
1) The BOLD signal (captured by fMRI) is only "somewhat correlated with neural activity"
It is correlated with neural activity, albeit there is a low-pass filter on top. It is most correlated with LFP signals in monkeys (Logothetis et al, 2001, https://www.nature.com/articles/35084005 ) and humans (Nir et al, 2007, https://www.cell.com/fulltext/S0960-9822(07)01635-1 ).
The BOLD signal is fairly slow, but you can deconvolve it and get temporal precision on the order of 0.5s or so (also subject to your sampling time). The lower temporal precision and indirect aspect of fMRI compared to ephysn is not so different from GCaMP sensors (especially earlier ones), which have been much less controversial.
For studying cognitive neuroscience, 0.5s can actually be really powerful, as you can set up much more complex tasks in humans and jitter stimulus presentation.
2) Each voxel is too large to see anything interesting
If decades of fMRI research have taught us anything, it's that brain areas do get engaged in a grouped enough fashion in order to measure it. Reward signals, for instance, are particularly salient in fMRI. Just that fact has been enough to get researchers excited about connecting economic rewards to their neural instantiation.
One of my favorite studies showing the power of fMRI, despite these bigger voxels, is a clever analysis showing how visual and language representations are right up against each other in the cortex (Popham et al, 2021, https://www.nature.com/articles/s41593-021-00921-6 ). In what other modality could you get such a result??
Besides, MRI technology is getting better and better. You can already get better spatial resolution if you image a specific brain region. There's also efforts to increase the spatial resolution overall by redesigning scanner components and improving signal processing, leading voxels that can image individual columns:
https://vcresearch.berkeley.edu/news/134-million-build-next-gen-mri-brain-scanner-uc-berkeley
3) Brain areas are plastic, so does it make sense to build a science around regions that generalizes across people?
The most general form of this argument makes neuroscience feel hopeless. Yes, if you wire visual signals to the auditory cortex of a marmoset, it might indeed process these. However, does that mean we should not study the development and fine structure of the visual cortex for processing vision? Surely not!
Plus, it's true that brain regions can be reorganized across people, but to me it amazing at how much is preserved! For instance, Wager et al (2013, https://www.nejm.org/doi/full/10.1056/nejmoa1204471 ) found a signature of pain that could predict a participant's pain even when trained on other subjects. This is not to downplay individual variability, I think all of neuroscience (across all methods and levels) needs to more fully grasp with this. That said, there are common patterns and they are already quite interesting on their own!
@ruslan
These are really cool results!! I wonder if you tried Anipose for reconstructing the 3D pose and joint angles of the mouse?
Lili 🏳️⚧️
boosted
During the pandemic I read a few wonderful "cat books" by Japanese authors. Would love some more recommendations (from Japan or any other country).
I Am a Cat (Sōseki Natsume)
The Traveling Cat Chronicles (Hiro Arikawa)
The Guest Cat (Takashi Hiraide)
#Japan #JapaneseLiterature #Cats #RecommendedReading #Bookstodon
Fem HRT
@MiaWinter
Really deciding this should be up to you and your doctor. For more information, you may want to check out this page on progesterone and breast development though:
https://transfemscience.org/articles/p4-breast-dev-puberty/
Also, it's possible that oral progesterone may only achieve low effects, if you're doing that:
https://transfemscience.org/articles/oral-p4-low-levels/
For #TransAwarnessWeek , I want to give a shout-out to @juliaserano , whose books have really helped me make sense of my identity as a trans woman and of my sexuality.
"Whipping Girl" helped me think about the social dynamics of gender in a richer way. In particular, the idea of feminity itself being undervalued in society (along with the implications) was a lightbulb moment for me.
"Sexed Up" set up and dismantled the common "predator prey" framework around sex. It is much more pervasive and harmful than I feared, and Serano helped me think through alternatives.
In both books, my favorite parts were really the anecdotes of Serano's experiences as a trans woman, many of which I identified with.
Lili 🏳️⚧️
boosted
Had trouble choosing a mastodon instance? I got GPT-3 to generate some new possibilities.
flounder.club
Members discuss all different forms of flounder, from deep-sea to land-based.
dinosaur.pocket
All messages must consist of the two letters 'D', for fear that the dinosaurs will eat people's pocketbooks.
frozen.forest
Users must provide a source of warmth in order to post.
https://www.aiweirdness.com/ai-designed-mastodon-communities/
Lili 🏳️⚧️
boosted
#Glyptodon, like mastodon, is a #megafauna named for its teeth. Ridged molars helped glyptodon-or "Carved Tooth"--process low trees and grasses after shearing sustenance with its strong snout.
The massive #armadillo weighed in at over 4,000lbs and was around 3m long. Protected from most predators by the bony plates of its shell, glyptodon may have been fed on by sabre-tooth cats, #giant bears, and humans. Its extinction coincides with changing climate and human arrival in its territory.
Lili 🏳️⚧️
boosted
🚨 BIG DATA RELEASE 🚨 We are beyond excited to announce the release of our Brain Wide Map of neural activity during decision making! It consists of 547 Neuropixel recordings of 32784 neurons across 194 regions of the mouse brain 🐭🧠
All these recordings were performed in a distributed fashion in 12 different labs, spanning Europe and the US 🌎 Rigorous standardization of methods and materials allowed us to pool the data from these labs together into a single gigantic dataset 🐙
Mice are performing our standardized perceptual decision-making task in which they have to position a stimulus in the center of a screen to receive reward. The dataset contains the stimuli and decisions, but also videos from three angles and DeepLabCut pose information. We're even releasing all the raw ephys data!
We know, it's a lot. At your own pace you can read all the details about the experimental setup, the task, processing of the data, and much more in the technical paper which accompanies this data release: https://figshare.com/articles/preprint/Data_release_-_Brainwide_map_-_Q4_2022/21400815
To explore the data at your leisure, visit our visualization website where you can scroll through different recording sessions, look at neural activity during example trials, and see trial-based activity of single neurons: https://viz.internationalbrainlab.org
Do you have itchy fingers to run your models on this humongous dataset? We totally get it! Here you can find how to download the data using our API so you can fire up those computing clusters: https://int-brain-lab.github.io/iblenv/notebooks_external/data_release_brainwidemap.html
This was a collective effort of our stellar team, who all put in so much work to make this monumental achievement possible. Our collaboration consists of 22 PIs, 37 researchers, and 11 staff members who all worked tirelessly to bring these data to you, the community 👏🍾
Lili 🏳️⚧️
boosted
Our most recent #preprint (https://www.biorxiv.org/content/10.1101/2022.10.17.512253v1.full) was submitted for peer review by eLife under 'the new model' - I'm happy to share that it is now undergoing in-depth peer review. To celebrate, here's a cell that's making a little dance, guided by light
#OptoGenetics
Lili 🏳️⚧️
boosted
Keeping my tradition of painting octopuses on the bottoms of tables in hotel rooms alive 😀
Lili 🏳️⚧️
boosted
Today's #megafauna is the giant #squid. This cephalopod can grow up to 43 ft and nearly 2000lbs--yet only lives 5 years. While its size protects it from most predators, sperm whales often feed on the squid.
For food, giant squids attack fish from below with two long feeding #tentacles. 12" diameter eyes help them spot prey. Eight arms with toothed suckers move meals to the beak and tongue, which grind food up so that it can safely pass through the ring-shaped brain on the way to the stomach.
@David_Baranger That is a compelling argument, but the issue feels more complex than that? Looking at the Chang et al paper, they do restrict the analysis to a sparser set of regions and still get good results.
It does seem counterintuitive from a statistics perspective as well. Wouldn't finding an effect in a smaller region with a simpler model provide more robust results?
I suppose for the Chang et al paper, the lasso-pcr could be a better regularizer though, to make it generalize? Probably we should all switch to such regularized techniques when finding patterns in very high dimensional neural data... But is it just the regularization then? If so, the lasso model is actually finding a simpler pattern in the data than previous contrasts.
(I hope this message doesn't come across as critical, I'm trying to understand the issues better and playing around with the ideas you have in this discussion helps.)
Lili 🏳️⚧️
boosted
Need a license for your weekend project? I found this intriguing
https://firstdonoharm.dev/adopters/
Who authored the Hippocratic License 3.0 (HL3)?
The Organization for Ethical Source worked with our partner organization, Corporate Accountability Lab (CAL), to create HL3.
What projects have adopted the Hippocratic License?
You can view a list of featured adopters here.
The Hippocratic License has been adopted by hundreds of projects including:
Animate.css
Animus
cachish
DCAN
DimeNet
FODA Card Game
format_parser
Functional Programming for Mortals with Cats in Scala (book)
ghost_adapter
Gryphon
Honeycomb Serilog Sink
Initiative Solidaires
Launchlet
leisure
libhoney-kotlin
NLPIA-bot Chatbot
Pangolin
python-install
postgres-mitm
rack-read_only
react-leaflet
suo
TidyBlocks
VCR
WeTransfer
zip_tricks
#software #license #model #opensource #data #humanrights #ethical #standards #community #hippocratic #gpl #gnu #future
@David_Baranger I do think regardless, these predictors would be so cool to apply in an fMRI neurofeedback setting! You could ask subjects with chronic pain to decrease pain or gamblers to decrease reward of gambling. I know some studies do this already, but if I recall correctly, they don't use these nicer classifiers.
@David_Baranger It would be concerning if these methods only work with a group of subjects... that would make it hard to gauge the individual differences you seek.
@David_Baranger
That's an interesting set of studies, thank you for sharing! The pain study in particular is quite promising.
I am having a hard time trying to reconcile the "abysmal reliability" you cite with the great out-of-sample predictions for pain and reward. Are pain and reward special in some way, that their neural basis is more consistent across subjects and tasks? Or is it that these methods are trained on a bunch of subjects? Or could it be that the regularization provided by LASSO-PCR in Chang et al gives better weights?
Lili 🏳️⚧️
boosted
Neuroimaging, machine learning, reward, journal club
What's the next step for #neuroimaging analyses of individual differences? This question has occupied my thoughts a lot over the past few months. A recent preprint from Luke Chang & co. offers an interesting perspective: https://www.biorxiv.org/content/10.1101/2022.08.23.504939v1
#fmri #reward #IndividualDifferences #ML #JournalClub
PhD student in neuroscience
Mostly modeling of movement
Nonfiction book nerd
She / her 🏳️⚧️ 🏳️🌈
Joined Nov 2022
