Datasets:

xenificity
/

CognitionMRI

DOI:

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Split (1)

train

The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
Name: string
BIDSVersion: string
DatasetType: string
License: string
Authors: list<item: string>
  child 0, item: string
Curators: list<item: string>
  child 0, item: string
Acknowledgements: string
Funding: list<item: string>
  child 0, item: string
EthicsApprovals: list<item: string>
  child 0, item: string
DatasetDOI: string
FrequencyEncodingSteps: int64
SliceThickness: int64
Manufacturer: string
AcquisitionMatrixPE: int64
MagneticFieldStrength: int64
BodyPart: string
to
{'AcquisitionMatrixPE': Value('int64'), 'FrequencyEncodingSteps': Value('int64'), 'Manufacturer': Value('string'), 'MagneticFieldStrength': Value('int64'), 'BodyPart': Value('string'), 'SliceThickness': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 289, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 124, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Name: string
              BIDSVersion: string
              DatasetType: string
              License: string
              Authors: list<item: string>
                child 0, item: string
              Curators: list<item: string>
                child 0, item: string
              Acknowledgements: string
              Funding: list<item: string>
                child 0, item: string
              EthicsApprovals: list<item: string>
                child 0, item: string
              DatasetDOI: string
              FrequencyEncodingSteps: int64
              SliceThickness: int64
              Manufacturer: string
              AcquisitionMatrixPE: int64
              MagneticFieldStrength: int64
              BodyPart: string
              to
              {'AcquisitionMatrixPE': Value('int64'), 'FrequencyEncodingSteps': Value('int64'), 'Manufacturer': Value('string'), 'MagneticFieldStrength': Value('int64'), 'BodyPart': Value('string'), 'SliceThickness': Value('int64')}
              because column names don't match

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CognitionMRI: Tracking Ongoing Cognition with Multi-Task fMRI

Dataset Card for Hugging Face

📋 Dataset Description

This BIDS-compliant dataset contains 7 Tesla functional MRI data from 18 healthy volunteers performing a continuous multi-task paradigm. The data were originally published in the landmark paper:

"Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns"
(Gonzalez-Castillo et al., PNAS, 2015).

Participants completed one run consisting of pseudo-randomly interleaved 3-minute blocks of four conditions:

Math — evaluate short arithmetic problems
Memory — 2-back task with shapes
Video — watch a fish-tank video and respond when an “x” appears on a clown fish
Rest — passively fixate on a cross

Each block was preceded by 12 seconds of instructions. The block order was randomized but identical across all participants. This design enables the study of dynamic, whole-brain functional connectivity and real-time tracking of ongoing cognition.

Note: The original study included 20 participants; only the 18 who consented to data sharing are included here.

Key Features

Subjects: 18 healthy adults
Modalities: T1-weighted anatomical (de-faced/masked) + task-based fMRI (one run per subject)
Field Strength: 7T
Total size: 4.2 GB
Format: Fully BIDS-compliant (JSON sidecars, events files, participants.tsv)

📊 Dataset Statistics (DS-V)

Dataset	Subjects (#)	# of Scans (sMRI, fMRI)	Size (GB)	sMRI Min Size (MB)	sMRI Max Size (MB)	fMRI Min Size (MB)	fMRI Max Size (MB)
Cognition Dataset	18	36 (18, 18)	4.2	3.4	4.4	193	410

🔬 Acquisition Summary

Scanner: 7T Siemens with 32-channel head coil
fMRI (GRE-EPI): TR = 1.5 s, TE = 25 ms, FA = 50°, mostly 2 mm isotropic voxels (one subject has slightly different parameters: 1.4 mm² in-plane, 0.3 mm gap)
Anatomical: T1-weighted MPRAGE + Proton Density (PD) sequences (all de-faced / brain-extracted)

📁 File Structure (BIDS)

cognition-mri/
├── README.md
├── dataset_description.json
├── participants.tsv
├── CHANGES
├── sub-001/
│   └── ses-01/
│       ├── anat/
│       │   ├── sub-001_ses-01_T1w.json
│       │   └── sub-001_ses-01_T1w.nii.gz
│       └── func/
│           ├── sub-001_ses-01_task-ctask_run-01_bold.json
│           ├── sub-001_ses-01_task-ctask_run-01_bold.nii.gz
│           └── sub-001_ses-01_task-ctask_run-01_events.tsv
├── sub-002/
└── ... (up to sub-018)

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Total file size:

4.45 GB