Functional Localizer Tasks

Collected during ses-02, ses-03, and ses-30 (final session).

BIDS task label Description Sessions Runs
task-prf Population receptive field mapping ses-02, ses-03 3/session
task-floc Functional localizer (category-selective) ses-02, ses-03, ses-04 3–6/session
task-auditory Auditory cortex localizer ses-02 or ses-03, ses-30 1/session
task-tone Tonotopy mapping ses-02, ses-03 1–2/session
task-motor Motor cortex localizer ses-30 2
task-fixation Fixation baseline ses-30 1

These labels are expected to remain unchanged.

PRF (task-prf)

Source: Experiment code adapted from the analyzePRF toolbox — https://kendrickkay.net/analyzePRF/

Citation: Kay, K. N., Winawer, J., Mezer, A., & Wandell, B. A. (2013). Compressive spatial summation in human visual cortex. Journal of Neurophysiology, 110(2), 481–494.

Model: Compressive Spatial Summation (CSS) — extends the standard 2D isotropic Gaussian pRF model (Dumoulin & Wandell, 2008) with a static power-law nonlinearity after spatial summation. Fitted parameters per voxel: center position (x, y), pRF size (sigma), compressive exponent, and gain.

Design

  • 3 runs per session, 300 s each
  • Two stimulus types:
    • Multibar (exp 93): bars sweeping in 8 directions (same as RETBAR in the HCP 7T Retinotopy Dataset)
    • Wedgering (exp 94): combination of rotating wedges and expanding/contracting rings
  • Acquisition order: [MWM] for ses-02, [WMW] for ses-03 (M = multibar, W = wedgering)
  • A total of 6 runs (3 multibar, 3 wedgering) collected across two localizer sessions
  • Stimuli filled a circular region with diameter 10.6°
  • A small semi-transparent fixation dot (0.2° × 0.2°) was present at center throughout
  • Participants maintained fixation on a central dot that switched randomly between three colors (black, white, red) every 1–5 s and pressed a button at each color change
  • Stimulus frame rate: 15 Hz; pre-generated aperture matrices stored in MATLAB workspace

Data inventory

| Subject | Session | Runs | |———|———|——| | sub-03 | ses-02 | 3 | | sub-03 | ses-03 | 3 | | sub-04 | ses-02 | 3 | | sub-04 | ses-03 | 3 | | sub-05 | ses-02 | 3 | | sub-05 | ses-03 | 3 |

fLoc (task-floc)

Source: Stanford VPNL fLoc localizer — https://vpnl.stanford.edu/fLoc/

Citation: Stigliani, A., Weiner, K. S., & Grill-Spector, K. (2015). Temporal processing capacity in high-level visual cortex is domain specific. Journal of Neuroscience, 35(36), 12412–12424.

Design

  • Miniblock format: 8 images per 4-second block (500 ms stimulus duration each)
  • 5 stimulus domains, each with 2 subcategories:
    • Characters: pseudowords, numbers
    • Bodies: whole bodies, limbs
    • Faces: adults, children
    • Places: corridors, buildings (houses)
    • Objects: vehicles (cars), instruments
  • Baseline condition: fixation
  • Behavioral task: oddball detection (scrambled image among category images)
  • 75 blocks per run (~300 s total)
  • 3–6 runs per session; sessions with 6 runs used two stimulus sets
  • Code written in MATLAB/Psychtoolbox-3

Experiment code

  • Original fLoc code: sourcedata/shared/experiment_code/localizer/floc/ (Stigliani et al. V2.0, August 2015)
  • Updated version: sourcedata/shared/experiment_code/localizer/floc_new/ (V3.0, August 2017; 12 stimuli per block)
  • Block-level timing in .par files; trial-level timing in detailed script files

Data inventory

| Subject | Session | Runs | |———|———|——| | sub-03 | ses-03 | 6 | | sub-04 | ses-04 | 6 | | sub-05 | ses-02 | 4 | | sub-05 | ses-03 | 3 |

Motor Localizer (task-motor)

Source: Adapted from the motor localizer in Tang et al. (2023) / LeBel et al. (2023). The original protocol included a sixth “speak” (covert narrative) condition used to define Broca’s area; the MMMData version omits this condition.

Citations:

Design

  • Block design with 20-second blocks
  • 5 conditions: foot movement, mouth movement, saccade (eye movement), hand movement, and rest
  • 2 runs per subject, collected during ses-30 (final session)
  • Implemented in PsychoPy (both Builder .psyexp and hand-coded .py versions exist)

Experiment code

  • PsychoPy Builder: sourcedata/shared/experiment_code/localizer/other localizers/motor/motor.psyexp
  • Hand-coded: sourcedata/shared/experiment_code/final_cued_recall/final_cued_recall_localizers/localizers/motor/

Data inventory

| Subject | Session | Runs | Events? | |———|———|——|———| | sub-03 | ses-30 | 2 | Yes | | sub-04 | ses-30 | 2 | Yes | | sub-05 | ses-30 | 2 | Yes |

Auditory Category Localizer (task-auditory)

Source: Adapted from the auditory cortex localizer in Tang et al. (2023) / LeBel et al. (2023). The original protocol used 10 repeats of a 1-minute stimulus (20 s music, 20 s speech, 20 s nature sounds) with repeatability-based ROI definition. The MMMData version uses a single continuous ~562 s auditory stimulus containing music, speech, and natural sounds.

Citations:

Design

  • Single continuous auditory stimulus (~562 s) followed by a post-stimulus fixation period (~50 s)
  • Stimulus content: music, speech, and natural sounds (single WAV file: auditory_localizer_filtered.wav)
  • 1 run per session
  • Collected during ses-02 or ses-03 (1st or 2nd localizer session) and again during ses-30 (final session)
  • Implemented in PsychoPy (both Builder .psyexp and hand-coded .py versions exist)

Experiment code

  • PsychoPy Builder: sourcedata/shared/experiment_code/localizer/other localizers/auditory/auditory.psyexp
  • Hand-coded: sourcedata/shared/experiment_code/localizer/other localizers/auditory/localizer_auditory.py

Data inventory

| Subject | Session | Runs | Events? | |———|———|——|———| | sub-03 | ses-02 | 1 | No | | sub-03 | ses-30 | 1 | Yes | | sub-04 | ses-02 | 1 | No | | sub-04 | ses-30 | 1 | Yes | | sub-05 | ses-03 | 1 | No | | sub-05 | ses-30 | 1 | Yes |

Events for ses-30 were converted via mmmdata/raw2bids_converters/localizer_events.py. Events for ses-02/ses-03 are missing (behavioral timing CSVs not collected for those sessions).

Tonotopy Mapping (task-tone)

Source: PsychoPy implementation by Futing Zou, based on the phase-encoded tonotopy paradigm from Da Costa et al. (2011, 2013).

Citations:

  • Da Costa, S., van der Zwaag, W., Marques, J. P., Frackowiak, R. S. J., Clarke, S., & Saenz, M. (2011). Human primary auditory cortex follows the shape of Heschl’s gyrus. Journal of Neuroscience, 31(40), 14067–14075. https://doi.org/10.1523/JNEUROSCI.2000-11.2011
  • Da Costa, S., van der Zwaag, W., Miller, L. M., Clarke, S., & Saenz, M. (2013). Tuning in to sound: Frequency-selective attentional filter in human primary auditory cortex. Journal of Neuroscience, 33(5), 1858–1863. https://doi.org/10.1523/JNEUROSCI.4405-12.2013

Design

  • 15 trials per run, 32 s each (28 s swept tone + 4 s ITI)
  • Run 1: low-to-high frequency sweep (pure_tones_low_to_high_filtered.wav)
  • Run 2: high-to-low frequency sweep (pure_tones_high_to_low_filtered.wav)
  • 8 s leading-in silence, 12 s leading-out silence
  • TR: 1.5 s, 320 volumes per run (~480 s total)
  • Participants instructed to close their eyes and focus on the tones
  • 1 run per session (some sessions have 2 runs with both sweep directions)

Experiment code

  • sourcedata/shared/experiment_code/localizer/other localizers/tone/localizer_tone.py
  • Stimulus WAV files in tone/ subdirectory

Data inventory

| Subject | Session | Runs | |———|———|——| | sub-03 | ses-02 | 1 | | sub-03 | ses-03 | 1 | | sub-04 | ses-02 | 1 | | sub-04 | ses-03 | 1 | | sub-05 | ses-02 | 1 | | sub-05 | ses-03 | 2 |

No events.tsv files exist. Timing is deterministic and can be reconstructed from experiment code parameters.

Fixation (task-fixation)

Fixation baseline scan collected during ses-30 (final session). Participants maintained fixation on a central dot. One run per subject.

Used as calibration data for gaze reconstruction from BOLD signal (PEER / DeepMReye).

Data inventory

| Subject | Session | Runs | |———|———|——| | sub-03 | ses-30 | 1 | | sub-04 | ses-30 | 1 | | sub-05 | ses-30 | 1 |