Age regression

Name: age

Category: Others

Dataset: Shirazi2024 (HBN)

Objective: Regression

Split: Leave-subjects-out

Usage

neuralbench eeg age

Show config.yaml

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

data:
  study:
    source:
      name: Shirazi2024Hbn
    filter_resting_state_with_age:
      name: QueryEvents
      query: "(type == 'Eeg') & (task == 'task-RestingState') & (duration > 180.0) & age.notnull()"
    crop_timelines:
      name: CropTimelines
      event_type: Eeg
      start_offset_s: 60.0
      max_duration_s: 120.0
    split:
      name: PredefinedSplit
      test_split_query: "release in ['R5']"
      col_name: split
      valid_split_by: release
      valid_split_ratio: 0.091  # 1/11
      valid_random_state: 33
  target:
    =replace=: true
    name: EventField
    event_types: Eeg
    event_field: age
    aggregation: single
  trigger_event_type: Eeg
  start: 0.0
  duration: 2.0
  stride: 2.0
  summary_columns: [release, age]
brain_model_output_size: &brain_model_output_size 1
trainer_config:
  monitor: val/pearsonr
  mode: max
  strategy: auto
  patience: 7
  n_epochs: 40
loss:
  name: MSELoss
metrics: !!python/object/apply:neuralbench.defaults.metrics.get_regression_metric_configs
  - *brain_model_output_size

Description

Brain age prediction is the task of estimating a person’s age from their brain signals [Engemann2022]. It is typically studied in the context of neurological and psychiatric disorders, where deviations from typical brain aging patterns can serve as biomarkers for disease diagnosis and progression monitoring.

Dataset Notes

• Shirazi2024 (HBN) contains EEG recordings from 11 cohorts (“releases”) containing different participants. Here, we leave one release out for testing.

• The dataset contains different tasks (resting-state, contrast change detection, etc.). Here, we only use the resting-state data for age prediction.

References

[Engemann2022]

Engemann, Denis A., et al. “A reusable benchmark of brain-age prediction from M/EEG resting-state signals.” Neuroimage 262 (2022): 119521.