Resting state · Study & Methods

Where the recordings come from

The resting recordings originate from the New York University site of ABIDE I, the first Autism Brain Imaging Data Exchange release. ABIDE I combined previously collected resting-state, anatomical and participant data across multiple sites and was released in 2012. The analysis here uses a defined subset of 96 NYU control participants; it does not include the ASD participants or the other ABIDE sites.

The imaging inputs come from the Preprocessed Connectomes Project, which distributes processed versions of ABIDE. The selected C-PAC strategy is filt_noglobal, retaining temporal filtering without global signal regression. Schaefer ROI extraction, analysis-grid resampling and input normalization were performed locally, as described below. Participant fields originate from the ABIDE phenotypic records.

• ABIDE I study and site descriptions — the original cohort, NYU site information and participant metadata.
• ABIDE preprocessed-data overview — the relationship between the original recordings and shared derivatives.
• Preprocessed Connectomes Project documentation — available processing pipelines and derivative strategies.

Study design and scientific questions

The resting-state analysis contains 96 NYU control participants from ABIDE I. There is no imposed stimulus schedule, so the principal questions concern the functional anatomy of learned networks, their consistency across people, and how their contributions vary through continuous recordings.

The current analyses use Schaefer 100 cortical ROIs. Spatial models are available at ranks 7, 12 and 20, fitted independently to the same participants and ROI order. Temporal analysis currently uses rank 7. Each rank shares one predictive basis across participants.

Resting-state activity can reflect intrinsic organization, ongoing cognition and recent experience. Without task events or thought sampling, these recordings support questions about network organization and variability rather than identifying a particular mental process.

Signal preparation and timing

The source is the ABIDE Preprocessed Connectomes Project C-PAC filt_noglobal derivative: provider motion correction, nuisance regression, MNI alignment and 0.01–0.1 Hz filtering, without global signal regression in this strategy.

Local preparation checks image timing and geometry, resamples the atlas labels to the functional grid, and calculates mask-restricted parcel means. Atlas-grid resampling relies on the existing spatial alignment; it does not perform registration. Anti-aliased temporal resampling gives a 3-second analysis interval. No additional local spatial smoothing is applied.

Each ROI is min–max scaled to 0–1 within its run. This retains continuity with the earlier research while preserving mean structure. The predictive windows use three past samples and two future samples, with ridge regularization of 1. Earlier atlas and z-score versions remain separately identified in Dataset & parameters.

How regional signals become network dynamics

Each observation is a vector of signals from 100 cortical ROIs. Short windows summarize the recent past and subsequent observations. Spectral decomposition compresses their relationships into a small predictive space, and the model maps that representation back to patterns of regional relationships.

One basis is learned across the study, with independent recordings kept separate at their boundaries. The spatial network matrices are shared; their expression changes through time and can differ across participants or runs. A common basis makes these comparisons possible, while potentially reducing effects outside the retained space.

The modeled regional second moment is M(t) = W₀ + Σₖ xₖ(t)Wₖ. Each Wₖ describes a fixed spatial pattern and xₖ(t) its evolving coefficient. Multiple networks can contribute simultaneously. The fitted operator describes predictive relationships between coordinates; its signed entries are not transition probabilities.

Reading network expression and opposing poles

A network can contain two sets of ROI-pair weights with opposite signs. Their spatial footprints may emphasize different functional systems. One coefficient scales both poles together: a positive coefficient preserves the stored signs, and a negative coefficient reverses them. The opposing anatomy can be functionally informative, but its signs alone do not identify excitation, inhibition or cognitive states.

Average network coefficient describes the prevailing orientation of the pattern during a period. Contribution magnitude measures its size regardless of orientation. Large contributions in opposite directions can cancel in the signed average, so the two measures answer different questions.

Network 1 acts as a mean/bias-like pattern and is set aside in routine figures while retained in the model. It is distinct from the explicit constant coordinate. Network numbers and signs belong to one fitted study and rank; correspondence across separate fits requires an explicit comparison.

Connecting learned networks with functional anatomy

Schaefer parcels carry assignments to seven broad cortical systems: visual, somatomotor, dorsal attention, salience/ventral attention, limbic, frontoparietal control and default mode. The finer 17-system reference is available for exploration.

For each learned network, positive and negative edge magnitudes are summarized separately by off-diagonal row sums at each ROI. Regional Pearson similarity compares that spatial footprint with each reference system. The weight-share bar shows the proportion of the pole’s regional weight assigned to that system. A high resemblance and a large weight share are complementary evidence of localization, rather than interchangeable scores.

All edges contribute to reference matching. Reducing visible edges in the atlas changes the display only. A match is a descriptive anatomical association, not a validated functional label or a significance test.

Using the main views

• Functional systems: select a system or network to connect the reference anatomy with the positive and negative poles in the relationship graph.
• Network matches: click a score to inspect its network, pole and reference in the atlas. Click a system header to sort; select a ranked ROI to inspect its connections.
• Temporal comparisons: inspect individual recordings behind the group summaries. A time-varying coefficient changes the contribution of a fixed pattern; it does not change that pattern’s anatomy.

Quality checks and interpretation limits

Eleven participants have parcels below the functional-coverage threshold. Those warnings remain attached to the recordings, and participants are retained. Visual alignment and framewise-motion review remain pending, so apparent individual differences should be considered alongside these limitations.

The atlas covers cortex rather than subcortical structures and uses adult-derived parcels across the cohort’s age range. Every parcel having a reference label does not guarantee that the label captures its function in an individual participant.

Individual-consistency analysis measures deviations within a basis learned from the cohort. Earlier networks generally agree more closely across participants, but the model cannot by itself distinguish genuine individual variation from estimation instability. Resting expressions have no experimental labels that establish a cognitive state.

Scope of the evidence

The models and anatomical associations are exploratory and in-sample. Held-out prediction, spatial-null comparisons and repeated-fit stability would be needed to assess generalization. The two studies have separate acquisition and processing histories, so similar system labels do not make their fitted coefficients directly comparable.