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Analysing linear multivariate pattern transformations in neuroimaging data.

ABSTRACT: Most connectivity metrics in neuroimaging research reduce multivariate activity patterns in regions-of-interests (ROIs) to one dimension, which leads to a loss of information. Importantly, it prevents us from investigating the transformations between patterns in different ROIs. Here, we applied linear estimation theory in order to robustly estimate the linear transformations between multivariate fMRI patterns with a cross-validated ridge regression approach. We used three functional connectivity metrics that describe different features of these voxel-by-voxel mappings: goodness-of-fit, sparsity and pattern deformation. The goodness-of-fit describes the degree to which the patterns in an input region can be described as a linear transformation of patterns in an output region. The sparsity metric, which relies on a Monte Carlo procedure, was introduced in order to test whether the transformation mostly consists of one-to-one mappings between voxels in different regions. Furthermore, we defined a metric for pattern deformation, i.e. the degree to which the transformation rotates or rescales the input patterns. As a proof of concept, we applied these metrics to an event-related fMRI data set consisting of four subjects that has been used in previous studies. We focused on the transformations from early visual cortex (EVC) to inferior temporal cortex (ITC), fusiform face area (FFA) and parahippocampal place area (PPA). Our results suggest that the estimated linear mappings explain a significant amount of response variance in the three output ROIs. The transformation from EVC to ITC shows the highest goodness-of-fit, and those from EVC to FFA and PPA show the expected preference for faces and places as well as animate and inanimate objects, respectively. The pattern transformations are sparse, but sparsity is lower than would have been expected for one-to-one mappings, thus suggesting the presence of one-to-few voxel mappings. The mappings are also characterised by different levels of pattern deformations, thus indicating that the transformations differentially amplify or dampen certain dimensions of the input patterns. While our results are only based on a small number of subjects, they show that our pattern transformation metrics can describe novel aspects of multivariate functional connectivity in neuroimaging data.

SUBMITTER: Basti A

PROVIDER: S-EPMC6793861 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Project description:Multivariate pattern recognition approaches have recently facilitated the search for reliable neuroimaging-based biomarkers in psychiatric disorders such as schizophrenia. By taking into account the multivariate nature of brain functional and structural changes as well as their distributed localization across the whole brain, they overcome drawbacks of traditional univariate approaches. To evaluate the overall reliability of neuroimaging-based biomarkers, we conducted a comprehensive literature search to identify all studies that used multivariate pattern recognition to identify patterns of brain alterations that differentiate patients with schizophrenia from healthy controls. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across studies as well as to assess the robustness to potentially confounding variables. In the total sample of n=38 studies (1602 patients and 1637 healthy controls), patients were differentiated from controls with a sensitivity of 80.3% (95% CI: 76.7-83.5%) and a specificity of 80.3% (95% CI: 76.9-83.3%). Analysis of neuroimaging modality indicated higher sensitivity (84.46%, 95% CI: 79.9-88.2%) and similar specificity (76.9%, 95% CI: 71.3-81.6%) of rsfMRI studies as compared with structural MRI studies (sensitivity: 76.4%, 95% CI: 71.9-80.4%, specificity of 79.0%, 95% CI: 74.6-82.8%). Moderator analysis identified significant effects of age (p=0.029), imaging modality (p=0.019), and disease stage (p=0.025) on sensitivity as well as of positive-to-negative symptom ratio (p=0.022) and antipsychotic medication (p=0.016) on specificity. Our results underline the utility of multivariate pattern recognition approaches for the identification of reliable neuroimaging-based biomarkers. Despite the clinical heterogeneity of the schizophrenia phenotype, brain functional and structural alterations differentiate schizophrenic patients from healthy controls with 80% sensitivity and specificity.

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Analysing linear multivariate pattern transformations in neuroimaging data.

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