scholarly journals Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Lana Kambeitz-Ilankovic ◽  
Sophia Vinogradov ◽  
Julian Wenzel ◽  
Melissa Fisher ◽  
Shalaila S. Haas ◽  
...  
Keyword(s):  
Cognitive Training ◽  
Cross Validation ◽  
Pattern Analysis ◽  
Anterior Cingulate ◽  
Support Vector ◽  
Single Subject ◽  
Multivariate Pattern Analysis ◽  
Whole Brain ◽  
Training Interventions ◽  
Multivariate Pattern

AbstractCognitive gains following cognitive training interventions are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single-subject level. We employed whole-brain multivariate pattern analysis with support vector machine (SVM) modeling to identify gray matter (GM) patterns that predicted higher vs. lower functioning after 40 h of ABCT at the single-subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an out-of-sample cross-validation analysis to unseen SCZ patients from an independent validation sample who underwent 50 h of ABCT. The whole-brain GM volume-based pattern classification predicted higher vs. lower functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%). In particular, greater baseline GM volumes in regions within superior temporal gyrus, thalamus, anterior cingulate, and cerebellum predicted improved functioning at the single-subject level following ABCT in SCZ participants. The present findings provide a structural MRI fingerprint associated with preserved GM volumes at a single baseline timepoint, which predicted improved functioning following an ABCT intervention, and serve as a model for how to facilitate precision clinical therapies for SCZ based on imaging data, operating at the single-subject level.

scholarly journals Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

2020 ◽  
Author(s):  
Lana Kambeitz-Ilankovic ◽  
Sophia Vinogradov ◽  
Julian Wenzel ◽  
Melissa Fisher ◽  
Shalaila S. Haas ◽  
...  
Keyword(s):  
Cognitive Training ◽  
Cross Validation ◽  
Pattern Analysis ◽  
Anterior Cingulate ◽  
Support Vector ◽  
Single Subject ◽  
Multivariate Pattern Analysis ◽  
Whole Brain ◽  
Training Interventions ◽  
Multivariate Pattern

AbstractBackgroundCognitive gains following cognitive training interventions (CT) are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single subject level.MethodsWe employed whole-brain multivariate pattern analysis (MVPA) with support vector machine (SVM) modeling to identify grey matter (GM) patterns that predicted ‘higher’ vs. ‘lower’ functioning after 40 hours of ABCT at the single subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an Out-Of-Sample Cross Validation analysis (OOCV) to unseen SCZ patients from an independent sample that underwent 50 hours of ABCT.ResultsThe whole-brain GM volume-based pattern classification predicted ‘higher’ vs. ‘lower’ functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%).ConclusionsIn particular, greater baseline GM volume in regions within superior temporal gyrus, thalamus, anterior cingulate and cerebellum -- predicted improved functioning at the single-subject level following ABCT in SCZ participants.

scholarly journals The Decision Decoding ToolBOX (DDTBOX) – A multivariate pattern analysis toolbox for event-related potentials

2017 ◽  
Author(s):  
Stefan Bode ◽  
Daniel Feuerriegel ◽  
Daniel Bennett ◽  
Phillip M. Alday
Keyword(s):  
Open Source ◽  
Pattern Analysis ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
Statistical Testing ◽  
Support Vector ◽  
Single Subject ◽  
Multivariate Pattern Analysis ◽  
Related Potentials ◽  
Multivariate Pattern

AbstractIn recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG) data. DDTBOX runs under MATLAB and is well integrated with the EEGLAB/ERPLAB and Fieldtrip toolboxes (Delorme and Makeig, 2004; Lopez-Calderon and Luck, 2014; Oostenveld et al. 2011). It trains support vector machines (SVMs) on patterns of event-related potential (ERP) amplitude data, following or preceding an event of interest, for classification or regression of experimental variables. These amplitude patterns can be extracted across space/electrodes (spatial decoding), time (temporal decoding), or both (spatiotemporal decoding). DDTBOX can also extract SVM feature weights, generate empirical chance distributions based on shuffled-labels decoding for group-level statistical testing, provide estimates of the prevalence of decodable information in the population, and perform a variety of corrections for multiple comparisons. It also includes plotting functions for single subject and group results. DDTBOX complements conventional analyses of ERP components, as subtle multivariate patterns can be detected that would be overlooked in standard analyses. It further allows for a more explorative search for information when no ERP component is known to be specifically linked to a cognitive process of interest. In summary, DDTBOX is an easy-to-use and open-source toolbox that allows for characterising the time-course of information related to various perceptual and cognitive processes. It can be applied to data from a large number of experimental paradigms and could therefore be a valuable tool for the neuroimaging community.

scholarly journals Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine

NeuroImage ◽  
2016 ◽  
Vol 132 ◽  
pp. 157-166 ◽  
Author(s):  
Kristin A. Linn ◽  
Bilwaj Gaonkar ◽  
Theodore D. Satterthwaite ◽  
Jimit Doshi ◽  
Christos Davatzikos ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Pattern Analysis ◽  
Structural Mri ◽  
Support Vector ◽  
Control Group ◽  
Multivariate Pattern Analysis ◽  
Feature Normalization ◽  
Multivariate Pattern

scholarly journals Functional connectivity differences in early infancy precede autism symptoms: a multivariate pattern analysis

2019 ◽  
Author(s):  
Abigail Dickinson ◽  
Manjari Daniel ◽  
Andrew Marin ◽  
Bilwaj Goanker ◽  
Mirella Dapretto ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Abilities ◽  
Pattern Analysis ◽  
Brain Connectivity ◽  
Familial Risk ◽  
Autism Spectrum ◽  
Support Vector ◽  
Multivariate Pattern Analysis ◽  
Autism Symptoms ◽  
Multivariate Pattern

AbstractFunctional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Mapping pre-symptomatic functional disruptions in ASD could identify infants based on neural risk, providing a crucial opportunity to mediate outcomes before behavioral symptoms emerge.Here we quantify functional connectivity using scalable EEG measures of oscillatory phase coherence (6-12Hz). Infants at high and low familial risk for ASD (N=65) underwent an EEG recording at 3 months of age and were assessed for ASD symptoms at 18 months using the Autism Diagnostic Observation Schedule-Toddler Module. Multivariate pattern analysis was used to examine early functional patterns that are associated with later ASD symptoms.Support vector regression (SVR) algorithms accurately predicted observed ASD symptoms at 18 months from EEG data at 3 months (r=0.76, p=0.02). Specifically, lower frontal connectivity and higher right temporo-parietal connectivity predicted higher ASD symptoms. The SVR model did not predict non-verbal cognitive abilities at 18 months (r=0.15, p=0.36), suggesting specificity of these brain alterations to ASD.These data suggest that frontal and temporo-parietal dysconnectivity play important roles in the early pathophysiology of ASD. Early functional differences in ASD can be captured using EEG during infancy and may inform much-needed advancements in the early detection of ASD.

scholarly journals Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis

Brain ◽  
2012 ◽  
Vol 135 (5) ◽  
pp. 1498-1507 ◽  
Author(s):  
Ling-Li Zeng ◽  
Hui Shen ◽  
Li Liu ◽  
Lubin Wang ◽  
Baojuan Li ◽  
...  
Keyword(s):  
Major Depression ◽  
Functional Connectivity ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Whole Brain ◽  
Multivariate Pattern ◽  
Brain Functional Connectivity

scholarly journals Diagnostic Prediction for Social Anxiety Disorder via Multivariate Pattern Analysis of the Regional Homogeneity

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wenjing Zhang ◽  
Xun Yang ◽  
Su Lui ◽  
Yajing Meng ◽  
Li Yao ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Social Anxiety ◽  
Social Anxiety Disorder ◽  
Pattern Analysis ◽  
Initial Step ◽  
Regional Homogeneity ◽  
Support Vector ◽  
Multivariate Pattern Analysis ◽  
Diagnostic Potential ◽  
Multivariate Pattern

Although decades of efforts have been spent studying the pathogenesis of social anxiety disorder (SAD), there are still no objective biological markers that could be reliably used to identify individuals with SAD. Studies using multivariate pattern analysis have shown the potential value in clinically diagnosing psychiatric disorders with neuroimaging data. We therefore examined the diagnostic potential of regional homogeneity (ReHo) underlying neural correlates of SAD using support vector machine (SVM), which has never been studied. Forty SAD patients and pairwise matched healthy controls were recruited and scanned by resting-state fMRI. The ReHo was calculated as synchronization of fMRI signals of nearest neighboring 27 voxels. A linear SVM was then adopted and allowed the classification of the two groups with diagnostic accuracy of ReHo that was 76.25% (sensitivity = 70%, and specificity = 82.5%,P≤0.001). Regions showing different discriminating values between diagnostic groups were mainly located in default mode network, dorsal attention network, self-referential network, and sensory networks, while the left medial prefrontal cortex was identified with the highest weight. These results implicate that ReHo has good diagnostic potential in SAD, and thus may provide an initial step towards the possible use of whole brain local connectivity to inform the clinical evaluation.

scholarly journals Temporal Multivariate Pattern Analysis (tMVPA): a single trial approach exploring the temporal dynamics of the BOLD signal

2018 ◽  
Author(s):  
Luca Vizioli ◽  
Alexander Bratch ◽  
Junpeng Lao ◽  
Kamil Ugurbil ◽  
Lars Muckli ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Temporal Dynamics ◽  
Temporal Information ◽  
Single Subject ◽  
Bold Signal ◽  
Multivariate Pattern Analysis ◽  
Single Trial ◽  
Neural Representations ◽  
Multivariate Pattern ◽  
Over Time

AbstractBackgroundfMRI provides spatial resolution that is unmatched by any non-invasive neuroimaging technique. Its temporal dynamics however are typically neglected due to the sluggishness of the hemodynamic based fMRI signal.New MethodsWe present temporal multivariate pattern analysis (tMVPA), a method for investigating the temporal evolution of neural representations in fMRI data, computed using pairs of single-trial BOLD time-courses, leveraging both spatial and temporal components of the fMRI signal. We implemented an expanding sliding window approach that allows identifying the time-window of an effect.ResultsWe demonstrate that tMVPA can successfully detect condition-specific multivariate modulations over time, in the absence of univariate differences. Using Monte Carlo simulations and synthetic data, we quantified family-wise error rate (FWER) and statistical power. Both at the group and at the single subject level, FWER was either at or significantly below 5%. For the group level, we reached the desired power with 18 subjects and 12 trials; for the single subject scenario, 14 trials were required to achieve comparable power.Comparison with existing methodstMVPA adds a temporal multivariate dimension to the tools available for fMRI analysis, enabling investigations of the evolution of neural representations over time. Moreover, tMVPA permits performing single subject inferential statistics by considering single-trial distribution.ConclusionThe growing interest in fMRI temporal dynamics, motivated by recent evidence suggesting that the BOLD signal carries temporal information at a finer scale than previously thought, advocates the need for analytical tools, such as the tMVPA approach proposed here, tailored to investigating BOLD temporal information.

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