Explanation of the procudure & code files

• 1 Use the model to get the hypo_ch_res(trials, ch) for each encoding trial
  Direct problem : angle --> Model --> channel_activity
  For each trial of the encoding task --> hypo_ch_res(trials, ch)

• 2 Raw data encoding --> Preprocessed SPM --> Apply ROI mask --> High-pass filter & z-score per voxel

• 3 Get the TRs of interest (2 consecutive TRs) and average them--> Enc_TRs: (trials, vx)

• 4 Estimation the weights of each Voxel --> Loop of Liniar model with Lasso Regularization for each Vx
  for each Vx in Enc_TRs:
  Enc_TRs(trials,1) = hypo_ch_res(trials, ch) x weights(ch, 1)
  Append the weights(ch, 1) of each voxel --> Weight_matrix(vx, ch) (WM )

• 1 Raw data WM --> Preprocessed SPM --> Apply ROI mask --> High-pass filter & z-score per voxel

• 2 Get the TRs of every trial--> WM_TRs: (trials, TR, vx)

• 3 Get the subset matrix of interest--> WM_TRs: (trials, TR, vx) smaller

• 4 INVERTED ENCODING MODEL: Transform voxel activity into Channel activity
  for all the TRs:
  ch_activity(ch,1) = inv( WMt(ch, vx) x WM(vx, ch) ) x WMt(ch, vx) x WM_TR1 (vx, 1)
  

• 5 Solve the inverse problem : angle <-- Model <-- channel_activity
  Using a smoother model (instead of a model of 36 ch, a model of 720 ch2)
  Kind of population vector: ch_activity(ch,1) --> sum (ch(x) x "Model(ch2(x))) --> Angle repr(ch2, 1)
  Angle representation --> Roll to preferred location
  Angle_reo_all(trial, TR, ch2)
  Average trials in each TR : Angle representation matrix(angles, TR)
  

• 5 Visual Respresentation (heatmap for each TR)

Inside "scripts" you have the two main scripts. They are the files described below put together

• Takes the paths for the files and the mask depending on the subject and method of analysis
• specification depending on close, far or mix distances
• All the functions that you need

• Trains the encoding model (always making the average of 2TR)
• Tests in individual TRs of WM

• Trains the encoding model (always making the average of 2TR)
• Tests in the average of 2 consecutive TRs of WM

• Skips the training
• Tests in individual TRs of WM

• Skips the training
• Tests in the average of 2 consecutive TRs of WM

• Average the individual results into a population result
• by condition

• Plot one line per subject
• by condition

• Plot the 4 conditions per subject
• by condition

• Example of the T NT dist distribution when rolling
• one subject example

• Example bold signal
• not important

for each subject For each ROI For each condition of WM task (4)

Depending on:

• Method of analysis + together --> train in all the encoding sessions at the same time + bysess --> train and test by session

• 1.1 STEP 1 : Crete the model of the channels (36 channels)

• 1.2 STEP 2 : From the encoding (beh & images) --> Extract the portion of data (images) & Generate the hypothetical channel coefficients (beh)

• 1.3 STEP 3 : Estimate the channel weights in each voxel

• 2.1 STEP 4 : Extract the encoded channel response from the WM task trails

• 2.2 STEP 5 : Visualization of Heatmap and preferred

After running the most convineint loop, you can average the results of each individual by running the "combine_subjects.py". It will return the decoding value by condition and ROI. It generates the heatmap average population, the preferred and the whole ROI (360 degrees)