Gabriel Capitán Ramírez

Translation Research Lab. Stroke models Neuroscience & MRI. @UNAM_MX, @Queensu

[SMMCE @SMMCE_MX]

Delirium 🤪

Revisión narrativa: desde Epidemiologia, diagnóstico, fisiopatología hasta tratamiento.

Gratis 🆓

https://t.co/aYLFOHS8dT

[Gabriel Capitán Ramírez @GabboRam77]

@Elrromanov @ISSSTE_mx En Canadá si, tienes que esperar de 4 a 8 horas.

[layerfMRI @layerfMRI]

New layer-fMRI paper proposes how to reduce vascular biases by means of using respiratory signals.
By Yuhui Chai (@mripku) et al.
https://t.co/3fJz9PNuIW

[Joshua Siegel @realJoshSiegel]

After a high dose of psilocybin, the brain desynchronizes at a massive scale, causing loss of our sense of self, time, and space. This may drive the burst of plasticity caused by psychedelics. The next day, brain activity has largely returned to normal, but an echo remains – a reset of circuits critical to the sense of self.

Our study is out today in @Nature https://t.co/PjACA6nkAy

[Jayson Jeganathan @jaysonjeg]

Do you use surface fMRI? We found spurious correlations in surface fMRI, with potentially serious implications for test-retest reliability, fingerprinting, functional parcellations and brain-behaviour associations (1/n)
https://t.co/Sv98S53P7x

[LANIREM INB UNAM @lanirem_unam]

@PJ_Villasenor and team @UNAMINB use #diffusion MRI to characterize the microstructure of the cerebral cortex, and explain how it can be used to detect abnormalities that occur in neurological disorders, such as #epilepsy. https://t.co/YP7uYcxe60

[layerfMRI @layerfMRI]

New layer-fMRI preprint investigating the venous vascular bias across subfields of Hippocampus.
By @VPf4ffenrot et al.,
https://t.co/sjfhNKGLM9

[Slava Bobrov @slava__bobrov]

What happens to your brain during a concussion

#neuroscience

[Gabriel Capitán Ramírez @GabboRam77]

@ImDrChimichanga Arizona Robins @GreysABC

[Juan Eugenio Iglesias @JuanEugenioIgl1]

We have labeled the right hemisphere of the 100-micron MRI scan released by Edlow et al and will be making it publicly available soon. Stay tuned!
Here's a video flying over axial slices:
https://t.co/SRvofeH8xz

[Facultad de Ciencias UNAM @fciencias]

#UnDíaComoHoy es el Día Mundial del Chimpancé 🐵🙉

¡Nuestros parientes más cercanos! 🙈

#ViveTuMuseo

[Matt Dancho (Business Science) @mdancho84]

Linear Regression is one of the most important tools in a Data Scientist's toolbox. Here's everything you need to know in 3 minutes.

1. OLS regression aims to find the best-fitting linear equation that describes the relationship between the dependent variable (often denoted as Y) and independent variables (denoted as X1, X2, ..., Xn).

2. OLS does this by minimizing the sum of the squares of the differences between the observed dependent variable values and those predicted by the linear model. These differences are called "residuals."

3. "Best fit" in the context of OLS means that the sum of the squares of the residuals is as small as possible. Mathematically, it's about finding the values of β0, β1, ..., βn that minimize this sum.

4. Slopes (β1, β2, ..., βn): These coefficients represent the change in the dependent variable for a one-unit change in the corresponding independent variable, holding other variables constant.

5. R-squared (R²): This statistic measures the proportion of variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, with higher values indicating a better fit of the model to the data.

6. t-Statistics and p-Values: For each coefficient, the t-statistic and its associated p-value test the null hypothesis that the coefficient is equal to zero (no effect). A small p-value (< 0.05) suggests that you can reject the null hypothesis.

7. Confidence Intervals: These intervals provide a range of plausible values for each coefficient (usually at the 95% confidence level).

There you have it- my top 7 concepts on Linear Regression. The next problem you'll face is how to apply data science to business.

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[Cristina Macía @reumacia]

“Qué va, si la inmunología es sencilla”

La inmunología:

[Imaging Neuroscience @ImagingNeurosci]

New paper in Imaging Neuroscience by Frederik J. Lange, Jesper L. R. Andersson, et al:

MMORF—FSL’s MultiMOdal Registration Framework

https://t.co/q2Z0AwSnvB

[Nicholas Fabiano, MD @NTFabiano]

These findings are from a study in @ScienceAdvances which used data from 4,800 children to evaluate associations between functional brain networks, binary assigned sex at birth, and gender along a continuum. https://t.co/epVNLEmZyh 2/11

[Neuroskeptic 🇺🇦 @Neuro_Skeptic]

The future of data analysis is now: data science and generative AI in neuroimaging methods development https://t.co/T8UWFTqYVv From @emdupre_ and @russpoldrack "Most researchers lack necessary data science training"

[Moritz Zaiss @altustro]

Nice MR images are actually easier to simulate than not-so-nice ones. MR-zero with Phase Distribution Graphs simulates typical MRI artifacts quite well, which helps to study and mitigate them.
https://t.co/K00md0jHJf

PDG: https://t.co/t2X79yFRXE

[OpenBehavior @OpenBehavior]

Interested in using miniscopes for imaging brain activity in awake, freely moving animals? Check out the #opensource large-field-of-view miniscope from the Aharoni lab in this week's post on OpenBehavior. @DanielBAharoni

https://t.co/fIW5X6XMkA

[@parkvillegeek @ParkvilleGeek]

Architecture of the Human Default Mode Network: cytoarchitecture, wiring and signal flow | bioRxiv https://t.co/DNZwr8f3Cq

[Xiaoxiao Wang @xiaoxiao_mri]

Tired of the low SNR of fMRI in white matter？
Here comes another choice, the ADC-fMRI.
https://t.co/FaymGiejI2