A computational model of shared fine-scale structure in the human connectome

Guntupalli, J. Swaroop; Haxby, James V.

doi:10.1101/108738

Cited by 19 publications

(28 citation statements)

References 51 publications

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“…However, there are a variety of ways to construct connectivity targets (cf. Guntupalli et al, 2018); for example, in the context of naturalistic stimuli, vertices with temporal ISCs exceeding some threshold could serve as potentially finer-grained connectivity targets. The second, related concept is that constructing a shared space based on functional connectivity yields subject-specific topographic transformations suitable for aligning response time series.…”

Section: Discussionmentioning

confidence: 99%

Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space

Nastase

Liu²,

Hillman

et al. 2019

Preprint

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AbstractConnectivity hyperalignment can be used to estimate a single shared response space across disjoint datasets. We develop a connectivity-based shared response model that factorizes aggregated fMRI datasets into a single reduced-dimension shared connectivity space and subject-specific topographic transformations. These transformations resolve idiosyncratic functional topographies and can be used to project response time series into shared space. We evaluate this algorithm on a large collection of heterogeneous, naturalistic fMRI datasets acquired while subjects listened to spoken stories. Projecting subject data into shared space dramatically improves between-subject story time-segment classification and increases the dimensionality of shared information across subjects. This improvement generalizes to subjects and stories excluded when estimating the shared space. We demonstrate that estimating a simple semantic encoding model in shared space improves between-subject forward encoding and inverted encoding model performance. The shared space estimated across all datasets is distinct from the shared space derived from any particular constituent dataset; the algorithm leverages shared connectivity to yield a consensus shared space conjoining diverse story stimuli.HighlightsConnectivity SRM estimates a single shared space across subjects and stimuliTopographic transformations resolve idiosyncrasies across individualsShared connectivity space enhances spatiotemporal intersubject correlationsSemantic model-based encoding and decoding improves across subjectsTransformations project into a consensus space conjoining diverse stimuli

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Section: Discussionmentioning

confidence: 99%

Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space

Nastase

Liu²,

Hillman

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Furthermore, the characteristic size of misalignments probably represents a limit in terms of the size of functional features we can project from the group back to the subject-level; while the native resolution of the subject-level data may well be higher, methods that work on the functional data alone like ICA-DR or PROFUMO will always struggle in the absence of additional constraints if the misalignments are large enough to mean some regions do not overlap with their group-level homologues at all. Fortunately, recent work has suggested that there is scope to further reduce the size of the residual misalignments [Guntupalli and Haxby 2017], and use multimodal data to help identify regions at the subject-level [Glasser et al 2016a], both of which will be essential parts of the push towards finer spatial scales.…”

Section: Group-level Representa Ons and Inherent Spa Al Scalesmentioning

confidence: 99%

“…Notably however, the multiple recent observations that single functional regions can be manifested as multiple disjoint regions in some subjects, is something that not even advanced functional registration algorithms reliant on diffeomorphic warps can correct for. The minimum requirement for this approach is therefore the use of advanced registration techniques that can non-homotopically reorganise the spatial layout of functional regions, as, for example, introduced by Conroy et al [2013], Guntupalli et al [2016] and Guntupalli and Haxby [2017], or Langs et al [2010].…”

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confidence: 99%

Modelling Subject Variability in the Spatial and Temporal Characteristics of Functional Modes

Harrison

Bijsterbosch

Segerdahl

et al. 2019

Preprint

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Recent work has highlighted the scale and ubiquity of subject variability in observa ons from func onal MRI data (fMRI). Furthermore, it is highly likely that errors in the es ma on of either the spa al presenta on of, or the coupling between, func onal regions can confound cross-subject analyses, making accurate and unbiased representa ons of func onal data essen al for interpre ng any downstream analyses.Here, we extend the framework of probabilis c func onal modes (PFMs) [Harrison et al. ] to capture cross-subject variability not only in the mode spa al maps, but also in the func onal coupling between modes and in mode amplitudes. A new implementa on of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. Using res ng-state data from , subjects collected as part of the Human Connectome Project [Van Essen et al. ], and an analysis of subjects in a variety of con nuous task-states [Kieliba et al.], we demonstrate how PFMs are able to capture, within a single model, a rich descrip on of how the spa o-temporal structure of res ng-state fMRI ac vity varies across subjects.We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assump ons, much more of the (behaviorally relevant) cross-subject variability in fMRI ac vity should be a ributed to the variability in spa al maps. This has fundamental implica ons for the interpreta on of cross-sec onal studies of func onal connec vity that do not capture cross-subject variability to the same extent as PFMs.

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“…In addition, cross-subject alignment of anatomical features could still be an issue at a "high" resolution. Though a sophisticated multimodal surface normalization procedure (MSM-All) was used by the HCP for this dataset (Glasser et al, 2013), cross-subject alignment issues, however minimal, are still likely to be present (Guntupalli, Feilong, & Haxby, 2018). These factors make for a complicated interpretation of wholebrain activation pattern comparisons at a "fine-grained" resolution near to the vertex or voxel-level, which would not be present a coarser ROI parcellation level.…”

Section: Differences Across Levels Of Resolutionmentioning

confidence: 99%

The situation or the person? Individual and task‐evoked differences in BOLD activity

Bolt¹,

Nomi

Bainter

et al. 2019

Human Brain Mapping

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Investigations of between‐person variability are enjoying a recent resurgence in functional magnetic resonance imaging (fMRI) research. Several recent studies have found persistent between‐person differences in blood‐oxygenated‐level dependent (BOLD) activation patterns and resting‐state functional connectivity. Conflicting findings have been reported regarding the extent to which (a) between‐person or (b) within‐person cognitive state differences explain differences in BOLD activation patterns. These discrepancies may arise due to statistical analysis choices, parcellation resolution, and limited sampling of task‐fMRI datasets. We attempt to address these issues in a large‐scale analysis of several task‐fMRI paradigms. Using a novel application of multivariate distance matrix regression, we examine between‐person and task‐condition variability estimates across varying levels of “resolution”, from a coarse region‐of‐interest level to the vertex‐level, and across different distance metrics. These analyses revealed that under most circumstances, differences in task conditions explained a greater amount of variance in activation map differences than between‐person differences. However, this finding was reversed when comparing activation maps at a “high‐resolution” vertex level. More generally, we observed that when moving from “low” to “high” resolutions, the variance explained by between‐person differences increased while variance explained by task conditions decreased. We further analyzed the relationships among subject‐level activation maps across all task‐conditions using an unsupervised clustering approach and identified a superordinate task structure. This structure went beyond conventional task labels and highlighted those experimental manipulations across task conditions that produce contrasting versus similar whole‐brain activation patterns. Overall, these analyses suggest that the question of the subject‐ versus task‐effects on BOLD activation patterns is nontrivial, and depends on the comparison “resolution,” choice of distance metric, and the coding of task‐conditions.

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A computational model of shared fine-scale structure in the human connectome

Cited by 19 publications

References 51 publications

Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space

Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space

Modelling Subject Variability in the Spatial and Temporal Characteristics of Functional Modes

The situation or the person? Individual and task‐evoked differences in BOLD activity

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