Is there a chance we can change the colab install instructions or is there some other easier fix we can do? I am going to have 25 people using colab/brainiak for the first time next week and would prefer to minimize wrinkles
Installing BrainIAK in a fresh environment (done by pr-check.sh) gets me nibabel 3.0.1. I think the culprit is the first line of the Collab instructions:
!pip install deepdish ipython matplotlib nilearn notebook pandas seaborn watchdogThis should come after installing BrainIAK.
Although as far as I can tell from the nilearn git, it should be installing the latest nibabel actually (https://github.com/nilearn/nilearn.git). Maybe it is a mirrors issue or something?
Hello, @zacharybretton. Thanks for the feedback. Regarding the PEP 517 error, try adding the
--no-use-pep517to the install command:python3 -m pip install --no-use-pep517 brainiakSee for more details brainiak/brainiak#435
We'll look into the problem with the newest Conda version.
Has there been any progress on this? I can't seem to install it (neither with conda nor pip) with the latest anaconda version.
Hi. I'm new to this software package, but it looks like the ideal environment for fMRI experimental design. Would like to use fMRIsim functions to simulate the expected response, as a function of TR and stimulus onset times, for an event-related paradigm with three stimulus types. We have already collected pilot data without any stimuli, for construction of a noise model. Can you direct me to any additional help (online or otherwise) for constructing such simulations? Thanks.
Hi @mwagshul, glad to hear you are interested in working with fmrisim. A good place to start would be the example scripts we provide in brainiak. The examples can be translated to using a 3 parameter model somewhat easily. To generate noise with the parameters from your pilot data, this script maybe more helpful. However, please feel to reach out to me directly if you have any questions and I can help debug any problems you might have
After running the above command I tried to run the link in browser but it doesn't connect. Can anyone help please?
Hello, @kshitijd20. There are several links suggested in the message. Have you tried the one starting with "127"?
If that doesn't work, could you please confirm your Docker for Windows is properly set up by testing Nginx in your browser according to the official Docker documentation?
https://docs.docker.com/docker-for-windows/#explore-the-application
for reference to the code I'm running , here's a link https://gist.github.com/YSanchezAraujo/cad5135cd1f47d2c2eefc58a058eb3bf,
in particular im wondering about line 81 in the for loop: https://gist.github.com/YSanchezAraujo/cad5135cd1f47d2c2eefc58a058eb3bf#file-searchlight-py-L81
in particular im wondering about line 81 in the for loop: https://gist.github.com/YSanchezAraujo/cad5135cd1f47d2c2eefc58a058eb3bf#file-searchlight-py-L81
would i need to manually "clear" the space that is being referenced by sl.broadcast for each of the iterations of the for loop, or will it be overwritten by default? the concern would be, that broadcasting in a for loop like that, without some explicit flushing of the previously broadcasted data would lead to the use of all of the up-to n things broadcasted, as opposed to just only the one for the nth iteration
@LouisaSmith Hi sorry about the slow reply, are you still having issues with this? From reading your error output it seems like a problem with the LogisticRegression function. It seems the sklearn changed their error checking on solver recently. You can manually specify the as an argument when setting the LogisticRegression, e.g. LogisticRegression(penalty='l1', solver='liblinear')
@YSanchezAraujo Yes the broadcast variable is being updated every time it is set. Is this the desired behavior?
Hello, I had a question about fmrisim. When we are creating a design sequence where we have events spaced close together in time, does fmrisim generate the combined signal keeping into account the nonlinearity as discussed in Friston et.al. 1998, or does it linearly add up the signal?
Hi @soukhind2. The answer is mostly no. The event time course is convolved with the double gamma HRF, which is a linear transform, but a non-linearity is applied by default by setting
scale_function to 1. So imagine two events that occur within 1 second of each other. From what we know about the brain's response is that there is a subadditivity of those two presentations such that the evoked response will be larger than if only one event occurred but it likely won't be twice as large. If you set scaling to 0 then the height of the outputed stimulus response will be twice as high, which would be wrong. If you set the scaling to 1, then the peak of two events will be 1, just like the peak of 1 event, although the shape of the function will be the same. Hence when scaling is set to 1, there is no additivity. Note this same logic goes into GLMs using things like FEAT: they also just assume a convolution of the event boxcar. Still, building a realistic non-linearity would be valuable, although it would likely depend largely on empirical details, since different events will elicit different amounts of additivity
Here is some code to play around with this concept:
import numpy as np
from brainiak.utils import fmrisim as sim
import matplotlib.pyplot as plt
# Inputs for generate_stimfunction
onsets = [10, 12]
event_durations = [1]
tr_duration = 2
duration = 100
scale_function = 1
# Create the time course for the signal to be generated
stimfunction = sim.generate_stimfunction(onsets=onsets,
event_durations=event_durations,
total_time=duration,
)
# Create the signal function
signal_function = sim.convolve_hrf(stimfunction=stimfunction,
tr_duration=tr_duration,
scale_function=scale_function,
)
plt.plot(signal_function)
Hello gitter peeps! I adopted an inverted encoding model tutorial by Tommy Sprague from MATLAB into Python. I see there is an IEM function recently added to Brainiak, but the tutorial here is more general (can be adapted for essentially any stimulus space) and I believe is informative about the technique. Please let me know if anyone has feedback or would like me to do anything further if it seems useful for Brainiak. Thanks! https://github.com/squinto13/IEM_python/blob/master/IEM_spatial.ipynb
Not sure if it matters which is being used in the test, but switching it back to 'auto' seems to fix the test using 0.22.2
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