The alignment landscape, verified

Assumes anatomy is a sufficient proxy for function; diffeomorphic warps put homologous anatomy in correspondence.

Helps / fails the strong default baseline every functional method is compared against — but residual functional misalignment survives even a perfect anatomical warp. That residual is the entire motivation for hyperalignment.

Reference Avants et al. (2011), NeuroImage. doi:10.1016/j.neuroimage.2010.09.025 · Code ANTsX/ANTs (live). (Legacy baseline: Talairach & Tournoux 1988, a monograph — real, not Crossref-verifiable.)

Assumes shared information is encoded in idiosyncratic fine-scale topographies; a common high-dimensional space is recoverable by iterative Procrustes on local response patterns.

Helps / fails between-subject classification improves severalfold over anatomical alignment — but it needs a rich shared stimulus to estimate the transforms. Validation (Haxby et al. 2011, Neuron, the Raiders of the Lost Ark movie dataset): between-subject movie-timepoint classification in the common space beat within-subject anatomical classification, and generalized to held-out subjects and a separate category-perception task.

Reference Haxby et al. (2011), Neuron. doi:10.1016/j.neuron.2011.08.026 · Code PyMVPA mvpa2.algorithms.hyperalignment (live).

Assumes / does generalizes the common model across the whole cortex via overlapping searchlights. Validation (Guntupalli et al. 2016, Cerebral Cortex): improved between-subject classification and inter-subject correlation of representational geometry across occipital, temporal, parietal, and prefrontal cortices — the shared fine structure is cortex-wide, not just ventral temporal.

Reference Guntupalli et al. (2016), Cerebral Cortex. doi:10.1093/cercor/bhw068 · Code PyMVPA (live).

Does the reference framing: it is information content, not local topography, that is preserved across brains. Not a method — the why behind all of the above.

Reference Haxby, Guntupalli, Nastase & Feilong (2020), eLife. doi:10.7554/eLife.56601.

Assumes multi-subject data factor as X_i ≈ W_i·S — a low-rank shared timecourse S plus subject-specific orthogonal bases W_i.

Helps / fails denoises via dimensionality reduction and projects held-out subjects into the shared space. Validation (Chen et al. 2015): across several naturalistic datasets and anatomical ROIs, the reduced-dimension SRM improved detection of the shared response over anatomical alignment, and removing it improved detection of group differences. Vanilla SRM discards subject-specific signal; needs an identifiable constraint.

Reference Chen et al. (2015), NeurIPS 28. (NeurIPS proceedings have no Crossref DOI — verified live at proceedings.neurips.cc.) · Code BrainIAK funcalign.srm (live).

Why they exist SSSRM (Turek et al. 2017, ICASSP) uses a labeled subset to supervise alignment. The idiosyncratic variant (Turek et al. 2018, ICASSP) models the individual component SRM normally discards — relevant when individual differences are the object of study. FastSRM (Richard et al. 2019, arXiv) projects onto an intermediate atlas so SRM scales to many subjects and limited RAM — the practical choice at our scale, and implemented in BrainIAK.

References Turek 2017 doi:10.1109/ICASSP.2017.7952326 · Turek 2018 doi:10.1109/ICASSP.2018.8462175 · Richard 2019 arXiv:1909.12537 (no version of record; live) · also Chen 2014 joint-SVD doi:10.1109/MLSP.2014.6958912. Code BrainIAK funcalign, hugorichard/FastSRM (both live).

Assumes voxels can be aligned by their whole-brain connectivity fingerprints rather than stimulus responses — so it works on resting state, no movie required.

Helps / fails Validation (Guntupalli, Feilong & Haxby 2018, PLoS Comput Biol): yielded higher inter-subject correlation of dense connectivity profiles than response-based hyperalignment, enabling alignment without a shared stimulus. (Verification caught and corrected a misremembered title here: the real title is "A computational model of shared fine-scale structure in the human connectome.")

Reference Guntupalli, Feilong & Haxby (2018), PLoS Comput Biol. doi:10.1371/journal.pcbi.1006120 · Code PyMVPA / Haxby lab (live).

Does the exact operation our pooling goal needs: folds different studies with different stimuli into one common response space by leveraging shared connectivity — the bridge from a single-stimulus cohort to a many-study pooled dataset.

Reference Nastase et al. (2020), NeuroImage. doi:10.1016/j.neuroimage.2020.116865 · preprint: bioRxiv 741975.

Why it matters evidence that connectivity-defined functional architecture is real, stable, and individual — a person's connectivity pattern identifies them from a group. That individuality is why anatomical warping alone cannot align function, and why functional alignment must sit under any discovery layer.

Reference Finn et al. (2015), Nature Neuroscience. doi:10.1038/nn.4135.

Does compares representational geometries (RDMs) across subjects and models without voxelwise correspondence — alignment happens in similarity space. For our purposes it is a downstream outcome measure after alignment, not a substitute for it.

Reference Kriegeskorte, Mur & Bandettini (2008), Front. Syst. Neurosci. doi:10.3389/neuro.06.004.2008 (Crossref lists only the first author; the 3-author list is correct per the journal page — disclosed) · toolbox: Nili et al. (2014), PLoS Comput Biol doi:10.1371/journal.pcbi.1003553. Code rsagroup/rsatoolbox (live).

BrainIAK — HPC-ready SRM, FastSRM, SSSRM, ISC/ISFC, event segmentation (HMM), and more; the go-to for functional alignment and naturalistic analysis. Kumar et al. (2022), Aperture Neuro doi:10.52294/31bb5b68-2184-411b-8c00-a1dacb61e1da (record year 2022, vol. 2021 — corrected). Module: brainiak.funcalign.

PyMVPA — the original hyperalignment implementation plus a full MVPA pipeline. Hanke et al. (2009), Neuroinformatics doi:10.1007/s12021-008-9041-y. Module: mvpa2.algorithms.hyperalignment.

nilearn — the standard scikit-learn-style entry point for decoding and connectivity; pairs with BrainIAK for the alignment step. Abraham et al. (2014), Front. Neuroinform. doi:10.3389/fninf.2014.00014. All three repos confirmed live.