:orphan: .. _ahelp_1dTrdm: ****** 1dTrdm ****** .. contents:: :local: | .. code-block:: none 1dTrdm: labeled temporal representational-dissimilarity movies OVERVIEW -------- 1dTrdm follows how the geometry of experimental conditions changes over time. At each latency or time window, it averages independently estimated observations into condition patterns and converts those patterns into a representational dissimilarity matrix (RDM). The resulting RDM movie shows when conditions become distinguishable, when a hypothesized organization emerges, and whether an earlier representational geometry later recurs. This organization is especially useful for EEG and MEG analyses. Each input observation can be an epoched trial or independently estimated trial pattern, with time along rows and sensors, source vertices, frequency features, or other measurements along columns. The same contract can also describe ECoG, intracranial recordings, time-resolved fMRI beta estimates, or any labeled feature-by-time data. 1dTrdm operates on prepared numeric matrices: artifact handling, epoching, baseline correction, and first-level trial estimation remain in the acquisition/preprocessing software best suited to the modality. There are four related questions the program can address: 1. RDM movie | What is the condition geometry at each time/window? 2. Model inference | When does that geometry match a labeled model RDM? 3. RDM dynamics | Does a representational geometry persist or recur? 4. Cross-time | Does a condition contrast generalize across times crossnobis | when estimated in independent partitions? The last two are symmetric descriptive RSA products. They are not classifier train-time x test-time decoding. Subject sign flips or synchronized condition relabeling provide corrected inference for the one-time fixed-model series; cross-temporal inference is not performed in this release. Usage: 1dTrdm -dataTable DATA.txt -time_axis TIME.txt -feature_axis FEAT.txt \ -metric corr|cosine|euclid|crossnobis -prefix OUT [options] DATA.txt has one independently estimated observation per row: Subj Observation Condition Partition InputFile s01 tr0001 face run1 s01_tr0001.1D Subj, Observation, Condition and InputFile are required. Observation IDs are unique within subject. Partition is optional for ordinary RDMs and required for crossnobis. InputFile is time rows x feature columns; every input must have the same finite shape. Relative paths resolve beside DATA.txt. TIME.txt is a strict four-column table with one row per input time sample: time_index time_value time_unit time_label 0 -0.100 s -100ms 1 0.000 s 0ms Indices are zero-based/contiguous, values finite/strictly increasing, one unit applies to the axis, and labels are unique. FEAT.txt has at least one unique feature_label column. Optional metadata are retained but never treated as implicit adjacency; -feature_neighborhoods supplies the explicit graph. NEIGH.txt is a strict two-column membership graph; overlaps are allowed: Neighborhood Feature left_temporal MEG0111 left_temporal MEG0121 posterior MEG0121 Each Feature must match FEAT.txt. Neighborhood and membership row order do not affect results. No coordinate system or column adjacency is inferred. Metrics: corr 1 - Pearson correlation between condition patterns cosine 1 - cosine similarity euclid Euclidean distance crossnobis crossvalidated squared Euclidean over independent partitions; every subject x partition must contain every condition Options: -dataTable FILE observation table (required); columns are Subj, Observation, Condition, optional Partition, and InputFile -prefix OUT output filename prefix (required) -window_width N samples per output window [1] -window_step N samples between window starts [1] -window_reduce mean|concat mean features over samples [default], or concatenate sample x feature values in time-major order -center_conditions none|subject|partition none [default]; subject is ordinary-RDM cocktail-blank removal after condition averaging; partition applies the same operation separately in every crossnobis fold -feature_neighborhoods NEIGH.txt add an explicit, possibly overlapping feature search; NEIGH.txt has exactly Neighborhood Feature columns, with one membership per row. Feature labels must occur in FEAT.txt; row order and column adjacency are ignored. -model_series_out independent|paired|loo write an ordered model series for downstream 3dRSA. independent averages all subjects and writes the existing two-column fixed-model series. paired writes a Subj x Time manifest pointing to each subject's own RDM movie. loo writes a Subj x Time manifest whose model for each subject is the mean RDM of every OTHER subject. Both require at least two subjects; paired also requires subject matrices. All three are all-feature bridges and reject neighborhoods. -rdm_dynamics pearson|spearman correlate each subject's RDM triangles across every pair of output windows (representational recurrence) -cross_time_crossnobis for -metric crossnobis, estimate each condition dyad across pairs of windows using ordered independent partition pairs; diagonal equals the primary RDM -model_mat MODEL.1D -model_conditions MODEL_CONDITIONS.1D compare each subject/window RDM with one labeled fixed condition-dissimilarity model and run temporal inference -compare spearman|pearson RDM-triangle comparison [spearman] -temporal_null subjects|conditions subjects [default] tests a population using synchronized subject sign flips and the one-sample t of Fisher-z fits; conditions applies one condition relabeling to every subject/window and tests the fixed observed sample -nperm N requested null draws [10000]; small groups are exact -seed S random-draw seed [1234567] -subject_matrices yes|no also write each subject/window square matrix [yes] -jobs N OpenMP workers when available [runtime default] -quiet suppress informational messages -help show this help OUTPUTS ------- Output file | Written when | Contents -----------------------------------------|--------------------|----------------------------- OUT.trdm.1D | always | subject x time x RDM dyads OUT.trdm.meta | always | estimator/axis provenance OUT.trdm.time.1D | always | windows and member bounds OUT.trdm.conditions.1D | always | lexical condition axis OUT.trdm.features.1D | always | input feature axis OUT.trdm.counts.1D | always | observations per estimate OUT.trdm.neighborhoods.1D | -feature_... | neighborhood RDM movie OUT.trdm.neighborhood_axis.1D | -feature_... | graph memberships/axis OUT_s####__t####.1D | matrices=yes | subject/window square RDM -----------------------------------------|--------------------|----------------------------- OUT.trdm.fits.1D | -model_mat | subject fixed-model fits OUT.trdm.inference.1D | -model_mat | time-family p/q/max-FWE OUT.model_series.1D | independent bridge | ordered RDM list for 3dRSA OUT_group_t####.1D | independent bridge | group-mean fixed model RDM OUT.paired_model_series.1D | paired bridge | Subj x Time own-RDM manifest OUT.loo_model_series.1D | LOO bridge | Subj x Time held-out manifest OUT_loo_s####__t####.1D | LOO bridge | mean RDM excluding that subject -----------------------------------------|--------------------|----------------------------- OUT.trdm.dynamics.1D | -rdm_dynamics | unique time x time recurrence OUT_s####__dynamics.1D | dynamics+matrices | mirrored recurrence matrix OUT.trdm.cross_time_crossnobis.1D | -cross_time_... | time x time x condition dyad OUT.trdm.neighborhood_{fits,inference}.1D| graph + model | joint time x graph inference OUT.trdm.neighborhood_dynamics.1D | graph + dynamics | recurrence within each graph OUT.trdm.neighborhood_cross_time_...1D | graph + cross-time | crossnobis within each graph MODEL_CONDITIONS.1D is a strict two-column table in MODEL.1D row order: ConditionIndex Condition 0 face Labels must match the observation conditions exactly; arbitrary model order is realigned by label. Inference Effect is tanh(mean subject Fisher-z fit). For the subject null Stat is its one-sample t; for the condition null Stat is mean Fisher z. P is two-sided empirical. Q and PFWE span the complete time family, or the complete time x neighborhood family when a graph is supplied; PFWE uses one synchronized maximum-statistic null over every declared cell. EXAMPLE: EEG/MEG RDM MOVIE -> fMRI RSA -------------------------------------- First, turn an independently sampled EEG/MEG observation set into an ordered fixed RDM series. The literal 'independent' assertion documents that these subjects are not the subjects in the later fMRI analysis: 1dTrdm -dataTable meg_observations.txt \ -time_axis meg_time.txt -feature_axis meg_sensors.txt \ -metric crossnobis -center_conditions partition \ -model_series_out independent -prefix meg_rdm Then use the generated ordered list as a time-resolved fixed model in 3dRSA: 3dRSA -mode RSA -dataTableFile fmri_conditions.txt \ -mask gray_mask+tlrc -model_series meg_rdm.model_series.1D \ -metric spearman -nperm 10000 -prefix meg_fmri_fusion 3dRSA then owns the joint time x ROI/searchlight inference family. If the EEG/MEG and fMRI subjects overlap, use one of these dependent workflows. Subject labels in the 1dTrdm and 3dRSA data tables must match exactly. EXAMPLE: PAIRED EEG/MEG -> fMRI RSA (SAME SUBJECTS) --------------------------------------------------- Each subject's EEG/MEG RDM is paired with that same subject's fMRI RDM: 1dTrdm -dataTable meg_observations.txt \ -time_axis meg_time.txt -feature_axis meg_sensors.txt \ -metric crossnobis -center_conditions partition \ -subject_matrices yes -model_series_out paired -prefix meg_rdm 3dRSA -mode RSA -dataTableFile fmri_conditions.txt \ -mask gray_mask+tlrc \ -model_series_subjects paired meg_rdm.paired_model_series.1D \ -metric spearman -classic_null subjects -nperm 10000 \ -prefix paired_meg_fmri_fusion EXAMPLE: LEAVE-ONE-SUBJECT-OUT EEG/MEG -> fMRI RSA ------------------------------------------------- Each fMRI subject is tested against the mean EEG/MEG RDM from every other subject, so that subject never contributes to their own model: 1dTrdm -dataTable meg_observations.txt \ -time_axis meg_time.txt -feature_axis meg_sensors.txt \ -metric crossnobis -center_conditions partition \ -model_series_out loo -prefix meg_rdm 3dRSA -mode RSA -dataTableFile fmri_conditions.txt \ -mask gray_mask+tlrc \ -model_series_subjects loo meg_rdm.loo_model_series.1D \ -metric spearman -classic_null conditions -nperm 10000 \ -prefix loo_meg_fmri_fusion The primary RDMs are subject-level. 1dTrdm does not estimate trial responses, read raw FIF/SET/vendor formats, or implement decoding train/test generalization. Cross-temporal outputs are descriptive in this release. ++ Compile date = Sep 19 2026 {AFNI_26.2.09:linux_ubuntu_24_64}