yibeichan

Write rigorous scientific manuscripts, research papers, grant proposals, and literature reviews. Use when drafting or revising any part of a scientific document including abstracts, introductions, methods, results, and discussions. Applies IMRAD structure, citation styles (APA/AMA/Vancouver/IEEE), reporting guidelines (CONSORT/STROBE/PRISMA), and publication standards. Triggers on requests to write research papers, journal articles, scientific reports, academic manuscripts, grant applications, or improve scientific prose.

Generate scripts for the full fMRI preprocessing pipeline from raw DICOM files through BIDS conversion (heudiconv) to fMRIPrep, including HPC/SLURM execution via BABS. Use this skill whenever someone needs to preprocess fMRI data, convert DICOMs to BIDS, write heudiconv heuristics, run fMRIPrep on a cluster, set up BABS projects, fix BIDS validation errors, or generate any scripts related to the DICOM-to-fMRIPrep pipeline.

Evidence-based QC decision-making for neuroimaging data. Interpret QC metrics from any pipeline (fMRIPrep, MRIQC, FreeSurfer, MNE-Python, Homer3, custom outputs) to make justified inclusion/exclusion decisions. Covers fMRI, EEG, fNIRS, and structural MRI across populations (adults, infants, adolescents, clinical) and paradigms (resting-state, task, naturalistic, sleep). Use when filtering subjects based on QC outputs, setting exclusion thresholds, justifying QC criteria for methods sections, or parsing QC files programmatically with Python.

State-space models (SSMs) for fMRI analysis: HMM, HMM-MAR, sticky/HDP-HMM, IO-HMM, SLDS, rSLDS, SNLDS. Covers resting-state, task-based (MID, SST, N-back), and naturalistic fMRI (movie, gaming). Python code generation (hmmlearn, ssm, pyhsmm, osl-dynamics, glhmm), HRF-aware modeling, fMRIPrep/XCP-D preprocessing, CIFTI/parcellation/ICA, model selection, and single-subject + group-level inference. Trigger keywords: HMM on brain data, brain state dynamics, dynamic FC, switching dynamics, latent states from BOLD, HRF deconvolution for state models, SLDS/rSLDS on neural timeseries, choosing K for fMRI, state-space + neuroimaging, task paradigms (MID, SST, N-back, movie-watching) with dynamic/latent-state analysis, temporal dynamics beyond standard GLM.

BIDS standard for all data types — MRI, DWI, PET, EEG, MEG, iEEG, fNIRS, behavioral, annotations, motion capture, microscopy, physiology, and multi-modal datasets. Covers BIDS naming conventions (entities, suffixes, extensions), dataset creation, modality-specific conversion tools (heudiconv, dcm2bids, MNE-BIDS, pypet2bids), validation, project directory layout (sourcedata, rawdata, derivatives, code, stimuli, phenotype), derivatives organization, DataLad version control, multi-experiment projects, and sharing on OpenNeuro. Trigger keywords: BIDS dataset, BIDS format, BIDS naming, BIDS entities, BIDS convert, organize project, project structure, rawdata, derivatives, sourcedata, phenotype, participants.tsv, dataset_description.json, multi-modal BIDS, behavioral data BIDS, EEG BIDS, DWI BIDS, PET BIDS, annotation data, research data management, DataLad, OpenNeuro, data sharing.