9️⃣ Sensitivity analysis
• Cross-dataset replication: HCP and MSC showed highly similar individualized topographies and FOCA matrices.
• GSR stability: FOCA was consistent with/without GSR in adults and neonates.
• Scan duration: Matching adult scans to neonatal length had minimal impact.(12/12)

8️⃣ FOCA vs. conventional FC
FOCA provides a much more robust view of negative coupling.
It is far more stable to GSR, captures greater inter-individual variability, shows stronger neonate–adult developmental effects, and predicts brain age markedly better than conventional FC.(11/12)

7️⃣ Neonatal functional topography matters
The spatial alignment of functional topography encodes brain development at birth and predicts neurodevelopmental outcomes at 18 months.(10/12)

6️⃣ Why neonatal organization looks “adult-inverted”
Negative topographies show the largest developmental shifts: DAN’s strongest negative couplings move from motor cortex (neonates) to TPJ/precuneus (adults), while positive patterns stay stable. The visual network shows similar changes.(9/12)

5️⃣ Neonatal organization is "adult-inverted"
Higher-order networks show strong positive coupling, and the visual system couples positively with the DMN rather than with somatomotor regions—the reverse of the adult patterns. (8/12)

4️⃣ Hierarchical organization
FOCA revealed a clear architecture:
• Primary systems → mainly positive coupling
• Higher-order systems → mainly negative coupling
These patterns are strongly predicted by aerobic glycolysis. (7/12)

3️⃣ FOCA matrix
We quantified these relations via spatial correlations between individualized network topographies(defined as FOCA).
The resulting FOCA matrix showed high cross-subject consistency and strong within-subject stability. (6/12)

2️⃣ Positive vs. negative topographic pairs
Visual inspection revealed widespread coupling patterns:
• Positive (convergent): FP ↔ DAN
• Negative (divergent): Default-Anterolateral ↔ CO/Action-mode (5/12)

1️⃣ Individualized spatial topography
We generated whole-brain spatial topographies for 20 functional networks—including the recently proposed action-mode and somato-cognitive-action networks.(4/12)

To answer these, we introduce Functional Topography Covariance Analysis (FOCA), leveraging MSC dense-sampling, HCP adults, and dHCP neonates to map individualized network topographic interactions and their developmental origins.(3/12)

1️⃣ How is this Yin–Yang interaction organized in the brain?
2️⃣ Do newborns follow the same rules as adults?
3️⃣ From birth to adulthood, which force dominates?
4️⃣ And can negative connectivity be measured reliably, beyond global-signal artifacts? (2/12)

In Eastern philosophy, Yin-Yang describes how opposite forces interact.

Interestingly, human brain networks shows a similar balance—Yin (negative coupling) and Yang (positive coupling), such as DMN vs. Action-mode network.

But many questions remain👇(1/12)

5️⃣ Neonatal organization is "adult-inverted"
Higher-order networks show strong positive coupling, and the visual system couples positively with the DMN rather than with somatomotor regions—the reverse of the adult patterns. (8/12)

3️⃣ FOCA matrix
We quantified these relations via spatial correlations between individualized network topographies(defined as FOCA).
The resulting FOCA matrix showed high cross-subject consistency and strong within-subject stability. (6/12)

1️⃣ Individualized spatial topography
We generated whole-brain spatial topographies for 20 functional networks—including the recently proposed action-mode and somato-cognitive-action networks.(4/12)