Misophonia Project

Overview

What is misophonia?

Also, known as selective sound sensitivity syndrome or sound-rage, misophonia is a disorder in which certain sounds trigger emotional or physiological responses that some might perceive as unreasonable given the circumstance. Those who have misophonia might describe it as when a sound “drives you crazy.” Their reactions can range from anger and annoyance to panic and the need to flee.

• Misophonia on Wikipedia

• Ward, R. T., Gilbert, F. E., Pouliot, J., Chiasson, P., McIlvanie, S., Traiser, C., … & Keil, A. (2022). The Relationship Between Self-Reported Misophonia Symptoms and Auditory Aversive Generalization Leaning: A Preliminary Report. Frontiers in Neuroscience, 16, 899476. https://doi.org/10.3389/fnins.2022.899476

• Farkas, A. H., Ward, R. T., Gilbert, F. E., Pouliot, J., Chiasson, P., McIlvanie, S., … & Keil, A. (2023). Auditory Aversive Generalization Learning Prompts Threat-Specific Changes in Alpha-Band Activity. bioRxiv, 2023-12. https://doi.org/10.1101/2023.12.04.569971

• Accepted Pre-registration: Ward, R., Keil, A., Pouliot, J., Mears, R. P., Chiasson, P., & McIlvanie, S. (2021, September 27). Psychophysiological mechanisms underlying aversive conditioning in Misophonia.https://doi.org/10.17605/OSF.IO/E26AD

• Misophonia: Zotero Refs

Related concept (but not the same phenomenon)

ASMR (Autonomous sensory meridian response) is a tingling sensation that typically begins on the scalp and moves down the back of the neck and upper spine. A pleasant form of paresthesia, it has been compared with auditory-tactile synesthesia and may overlap with frisson.

Auditory Steady-State Response

• Brugge, J. F., Nourski, K. V., Oya, H., Reale, R. A., Kawasaki, H., Steinschneider, M., & Howard III, M. A. (2009). Coding of repetitive transients by auditory cortex on Heschl’s gyrus. Journal of neurophysiology, 102(4), 2358-2374. https://doi.org/10.1152/jn.91346.2008

• Popov, T., Oostenveld, R., & Schoffelen, J. M. (2018). FieldTrip made easy: an analysis protocol for group analysis of the auditory steady state brain response in time, frequency, and space. Frontiers in neuroscience, 12, 711.https://doi.org/10.3389/fnins.2018.00711

• Pantev, C., Roberts, L. E., Elbert, T., Roβ, B., & Wienbruch, C. (1996). Tonotopic organization of the sources of human auditory steady-state responses. Hearing research, 101(1-2), 62-74.

• Parker, D. A., Hamm, J. P., McDowell, J. E., Keedy, S. K., Gershon, E. S., Ivleva, E. I., … & Clementz, B. A. (2019). Auditory steady-state EEG response across the schizo-bipolar spectrum. Schizophrenia research, 209, 218-226. https://doi.org/10.1016/j.schres.2019.04.014

MNE-Python Workflow (Schematic)

%%{init: {'theme':'forest'}}%%

graph LR;
        subgraph Sensor-level-analysis

       A[Decode time-by-time </br>  using a 'sliding' estimator] --> B[Time-frequency </br> decomposition] 
       B --> C[Decoding </br> based on </br> common spatial patterns] 
       C --> D[Noise covariance </br> estimation] 
       D --> E[Group average </br> at the </br> sensor level] 
   end
  
    subgraph Preprocessing

       l1[Assess </br> channel-wise </br> data quality] --> l2[Estimate </br> head positions] 
       l2 --> l3[Apply low- and </br> high-pass filters] 
       l3 --> l4[Temporal regression </br> for artifact removal]
       l4 --> l5a[Fit ICA] 
       l5a --> l6a[Find ICA </br> artifacts] 
       l4 --> l5b[Extract epochs] 
       l5b --> l7[Apply ICA] 
       l6a --> l7 --> l8[Remove epochs </br> based on  </br> PTP amplitudes]  
       l5b --> l6b[Apply SSP] 
       l4 --> l5c[Compute SSP] 
       l5c --> l6b[Apply SSP] 
       l6b --> l8[Remove epochs </br> based on </br> PTP amplitudes] 
       l8 --> l9[Extract </br> evoked data for </br> each condition] 
       l9 --> l10[Decode pairs of </br>  conditions based on </br> entire epochs] 
    end
    

   
   %%l0[Raw Data] --> l1
   %%E --> F[Output </br> Statistical </br> Analysis Pipeline]    
   
   classDef green fill:#9f6,stroke:#333,stroke-width:0.5px;
   classDef orange fill:#f96,stroke:#333,stroke-width:1px;
   classDef white fill:#fff,stroke:#333,stroke-width:1px;
   classDef sq stroke:#f66,stroke-width:1px;
   classDef blue fill:#6699cc,stroke:#333,stroke-width:1px;
   classDef red fill:#D32737,stroke:#FFF,stroke-width:1.5px;
     
   class Preprocessing,Sensor-level-analysis white

https://github.com/mne-tools

https://mne.tools/stable/index.html

MNE-Python

• MNE-Python is a Python package for MEG and EEG data analysis.

MNE-BIDS

• MNE-BIDS is a Python package for converting data from the Brain Imaging Data Structure (BIDS) format to MNE-Python format.

• BIDS (Brain Imaging Data Structure) A simple and intuitive way to organize and describe your neuroimaging and behavioral data.

MNE-BIDS-Pipeline

• MNE-BIDS-Pipeline is a full-flegded processing pipeline Python package for preprocessing MEG and EEG data.

Prepare your dataset

MNE-BIDS-Pipeline only works with BIDS-formatted raw data.

Create a configuration file

All parameters of the pipeline are controlled via a configuration file. Create a template:

mne_bids_pipeline --create-config=/path/to/custom_config.py

import numpy as np

study_name = "ds000247"
bids_root = f"~/mne_data/{study_name}"
deriv_root = f"~/mne_data/derivatives/mne-bids-pipeline/{study_name}"

subjects = ["0002"]
sessions = ["01"]
task = "rest"
task_is_rest = True

crop_runs = (0, 100)  # to speed up computations

ch_types = ["meg"]
spatial_filter = "ssp"

l_freq = 1.0
h_freq = 40.0

rest_epochs_duration = 10
rest_epochs_overlap = 0

epochs_tmin = 0
baseline = None

• from example ds000247

Run the pipeline

A config file controls main pipeline parameters. CLI runs all (or part with an override).

• Re-running a specific stage of the pipeline for additional data
  • mne_bids_pipeline --config=custom_config.py --steps=preprocessing --subjects=0051,0052,0053
• Running the pipeline with different parameters for a specific stage (e.g., changing filter cutoffs, interpolating bad channels, etc.)
  • mne_bids_pipeline --config=custom_config.py --steps=preprocessing/ica
• Running the pipeline with different parameters for a specific subject or session
  • mne_bids_pipeline --config=custom_config.py --steps=preprocessing/ica --session=cond --subjects=0051,0052,0053

Visualize the results (HTML reports)

BIDS projects are located two places:

1. Miso Project /blue/psb4934/share/misophonia/bids_eeg

   • The derivatives folder contains the results of the pipeline, including the HTML reports. The overall structure of the derivatives folder is as follows:

   • Original BIDS-format data are in a different location than reports.

   • The original BIDS data for subject sub-M603 is located in a subfolder named /sub-M603/eeg/ in the main BIDS folder.

     • the report for subject sub-M603 is located in a subfolder named /derivatives/mne-bids-pipeline/sub-M603/eeg/ in the derivatives folder.

   • the report for the group-level analysis (i.e., including grand averages) located in a subfolder named /derivatives/mne-bids-pipeline/sub-average/eeg/ in the derivatives folder.

   • 
     .
     ├── dataset_description.json
     ├── derivatives
     │   └── mne-bids-pipeline
     │       ├── sub-average
     │       │   └── eeg
     │       │       └──     sub-average_task-genaudi3_report.html
     │       ├── sub-M603
     │       │   └── eeg
     │       ├── sub-M604
     │       │   └── eeg
     │       .
     │       :
     │
     ├── sub-M603
     │   ├── eeg
     │   └── sub-M603_scans.tsv
     ├── sub-M604
     │   ├── eeg
     │   └── sub-M604_scans.tsv
     │
     .
     :

     • So, to find the grand aveage report, you need to go to /blue/psb4934/share/misophonia/bids_eeg/derivatives/mne-bids-pipeline/sub-average/eeg and look for the sub-average_task-genaudi3_report.html file. (For convenience, the grand average report is copied to /blue/psb4934/share/misophonia/MNE-BIDS_reports/subaverage)
2. SZ/BP BIDS data: /blue/psb4934/share/mears/assr_data/bids_assr
   • The BIDS paths are similar for this dataset as well. The reports are located in the derivatives folder. The grand average report is located in the sub-average folder.

Finding things on HiPerGator

• UF Research Computing Documentation

Correct address for python env :

exec /blue/psb4934/share/conda/envs/mne_1_6_0/mne/bin/python -m ipykernel "$@"

Shared Conda environments on HiPerGator