29 research outputs found
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Inverse transformed encoding models - A solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding
Techniques of multivariate pattern analysis (MVPA) can be used to decode the discrete experimental condition or a continuous modulator variable from measured brain activity during a particular trial. In functional magnetic resonance imaging (fMRI), trial-wise response amplitudes are sometimes estimated from the measured signal using a general linear model (GLM) with one onset regressor for each trial. When using rapid event-related designs with trials closely spaced in time, those estimates are highly variable and serially correlated due to the temporally extended shape of the hemodynamic response function (HRF). Here, we describe inverse transformed encoding modelling (ITEM), a principled approach of accounting for those serial correlations and decoding from the resulting estimates, at low computational cost and with no loss in statistical power. We use simulated data to show that ITEM outperforms the current standard approach in terms of decoding accuracy and analyze empirical data to demonstrate that ITEM is capable of visual reconstruction from fMRI signals
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MACS - a new SPM toolbox for model assessment, comparison and selection
Background: In cognitive neuroscience, functional magnetic resonance imaging (fMRI) data are widely analyzed using general linear models (GLMs). However, model quality of GLMs for fMRI is rarely assessed, in part due to the lack of formal measures for
statistical model inference.
New Method: We introduce a new SPM toolbox for model assessment, comparison and selection (MACS) of GLMs applied to fMRI data. MACS includes classical, information-theoretic and Bayesian methods of model assessment previously applied to GLMs for fMRI as well as recent methodological developments of model selection and model averaging in fMRI data analysis.
Results: The toolbox - which is freely available from GitHub - directly builds on the Statistical Parametric Mapping (SPM) software package and is easy-to-use, general-purpose, modular, readable and extendable. We validate the toolbox by reproducing model selection and model averaging results from earlier publications. Comparison with Existing Methods: A previous toolbox for model diagnosis in fMRI
has been discontinued and other approaches to model comparison between GLMs have not been translated into reusable computational resources in the past.
Conclusions: Increased attention on model quality will lead to lower false-positive rates in cognitive neuroscience and increased application of the MACS toolbox will increase the reproducibility of GLM analyses and is likely to increase the replicability of fMRI
studies
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Kullback-Leibler Divergence for the Normal-Gamma Distribution
We derive the Kullback-Leibler divergence for the normal-gamma distribution and show that it is identical to the Bayesian complexity penalty for the univariate general linear model with conjugate priors. Based on this finding, we provide two applications of the KL divergence, one in simulated and one in empirical data
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How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection
Voxel-wise general linear models (GLMs) are a standard approach for analyzing functional magnetic resonance imaging (fMRI) data. An advantage of GLMs is that they are flexible and can be adapted to the requirements of many different data sets. However, the specification of first-level GLMs leaves the researcher with many degrees of freedom which is problematic given recent efforts to ensure robust and reproducible fMRI data analysis. Formal model comparisons that allow a systematic assessment of GLMs are only rarely performed. On the one hand, too simple models may underfit data and leave real effects undiscovered. On the other hand, too complex models might overfit data and also reduce statistical power. Here we present a systematic approach termed cross-validated Bayesian model selection (cvBMS) that allows to decide which GLM best describes a given fMRI data set. Importantly, our approach allows for non-nested model comparison, i.e. comparing more than two models that do not just differ by adding one or more regressors. It also allows for spatially heterogeneous modelling, i.e. using different models for different parts of the brain. We validate our method using simulated data and demonstrate potential applications to empirical data. The increased use of model comparison and model selection should increase the reliability of GLM results and reproducibility of fMRI studies
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Exceedance Probabilities for the Dirichlet Distribution
We derive an efficient method to calculate exceedance probabilities (EP) for the Dirichlet distribution when the number of event types is larger than two. Also, we present an intuitive application of Dirichlet EPs and compare our method to a sampling approach which is the current practice in neuroimaging model selection
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How to improve parameter estimates in GLM-based fMRI data analysis: cross-validated Bayesian model averaging
In functional magnetic resonance imaging (fMRI), model quality of general linear models (GLMs) for first-level analysis is rarely assessed. In recent work (Soch et al., 2016: âHow to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selectionâ, NeuroImage, vol. 141, pp. 469â489; http://dx.doi.org/10.1016/j.neuroimage.2016.07.047), we have introduced cross-validated Bayesian model selection (cvBMS) to infer the best model for a group of subjects and use it to guide second-level analysis. While this is the optimal approach given that the same GLM has to be used for all subjects, there is a much more efficient procedure when model selection only addresses nuisance variables and regressors of interest are included in all candidate models. In this work, we propose cross-validated Bayesian model averaging (cvBMA) to improve parameter estimates for these regressors of interest by combining information from all models using their posterior probabilities. This is particularly useful as different models can lead to different conclusions regarding experimental effects and the most complex model is not necessarily the best choice. We find that cvBMS can prevent not detecting established effects and that cvBMA can be more sensitive to experimental effects than just using even the best model in each subject or the model which is best in a group of subjects
Neurocan genome-wide psychiatric risk variant affects explicit memory performance and hippocampal function in healthy humans
Alterations of the brain extracellular matrix (ECM) can perturb the structure and function of brain networks like the hippocampus, a key region in human memory that is commonly affected in psychiatric disorders. Here, we investigated the potential effects of a genomeâwide psychiatric risk variant in the NCAN gene encoding the ECM proteoglycan neurocan (rs1064395) on memory performance, hippocampal function and cortical morphology in young, healthy volunteers. We assessed verbal memory performance in two cohorts (N = 572, 302) and found reduced recall performance in risk allele (A) carriers across both cohorts. In 117 participants, we performed functional magnetic resonance imaging using a noveltyâencoding task with visual scenes. Risk allele carriers showed higher false alarm rates during recognition, accompanied by inefficiently increased left hippocampal activation. To assess effects of rs1064395 on brain morphology, we performed voxelâbased morphometry in 420 participants from four independent cohorts and found lower grey matter density in the ventrolateral and rostral prefrontal cortex of risk allele carriers. In silico eQTL analysis revealed that rs1064395 SNP is linked not only to increased prefrontal expression of the NCAN gene itself, but also of the neighbouring HAPLN4 gene, suggesting a more complex effect of the SNP on ECM composition. Our results suggest that the NCAN rs1064395 A allele is associated with lower hippocampusâdependent memory function, variation of prefrontal cortex structure and ECM composition. Considering the wellâdocumented hippocampal and prefrontal dysfunction in bipolar disorder and schizophrenia, our results may reflect an intermediate phenotype by which NCAN rs1064395 contributes to disease risk
A comprehensive score reflecting memory-related fMRI activations and deactivations as potential biomarker for neurocognitive aging
Older adults and particularly those at risk for developing dementia typically show a
decline in episodic memory performance, which has been associated with altered
memory network activity detectable via functional magnetic resonance imaging
(fMRI). To quantify the degree of these alterations, a score has been developed as a
putative imaging biomarker for successful aging in memory for older adults (Functional Activity Deviations during Encoding, FADE; DĂŒzel et al., Hippocampus, 2011; 21:
803â814). Here, we introduce and validate a more comprehensive version of the
FADE score, termed FADE-SAME (Similarity of Activations during Memory Encoding),
which differs from the original FADE score by considering not only activations but
also deactivations in fMRI contrasts of stimulus novelty and successful encoding, and
by taking into account the variance of young adults' activations. We computed both
scores for novelty and subsequent memory contrasts in a cohort of 217 healthy
adults, including 106 young and 111 older participants, as well as a replication cohort
of 117 young subjects. We further tested the stability and generalizability of both
scores by controlling for different MR scanners and gender, as well as by using different data sets of young adults as reference samples. Both scores showed robust agegroup-related differences for the subsequent memory contrast, and the FADE-SAME
score additionally exhibited age-group-related differences for the novelty contrast.
Furthermore, both scores correlate with behavioral measures of cognitive aging,
namely memory performance. Taken together, our results suggest that single-value
scores of memory-related fMRI responses may constitute promising biomarkers for
quantifying neurocognitive aging
Machine learningâbased classification of Alzheimer's disease and its atârisk states using personality traits, anxiety, and depression
Background
Alzheimer's disease (AD) is often preceded by stages of cognitive impairment, namely subjective cognitive decline (SCD) and mild cognitive impairment (MCI). While cerebrospinal fluid (CSF) biomarkers are established predictors of AD, other non-invasive candidate predictors include personality traits, anxiety, and depression, among others. These predictors offer non-invasive assessment and exhibit changes during AD development and preclinical stages.
Methods
In a cross-sectional design, we comparatively evaluated the predictive value of personality traits (Big Five), geriatric anxiety and depression scores, resting-state functional magnetic resonance imaging activity of the default mode network, apoliprotein E (ApoE) genotype, and CSF biomarkers (tTau, pTau181, AÎČ42/40 ratio) in a multi-class support vector machine classification. Participants included 189 healthy controls (HC), 338 individuals with SCD, 132 with amnestic MCI, and 74 with mild AD from the multicenter DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE).
Results
Mean predictive accuracy across all participant groups was highest when utilizing a combination of personality, depression, and anxiety scores. HC were best predicted by a feature set comprised of depression and anxiety scores and participants with AD were best predicted by a feature set containing CSF biomarkers. Classification of participants with SCD or aMCI was near chance level for all assessed feature sets.
Conclusion
Our results demonstrate predictive value of personality trait and state scores for AD. Importantly, CSF biomarkers, personality, depression, anxiety, and ApoE genotype show complementary value for classification of AD and its at-risk stages
Phase Behavior of Aqueous Na-K-Mg-Ca-CI-NO3 Mixtures: Isopiestic Measurements and Thermodynamic Modeling
A comprehensive model has been established for calculating thermodynamic properties of multicomponent aqueous systems containing the Na{sup +}, K{sup +}, Mg{sup 2+}, Ca{sup 2+}, Cl{sup -}, and NO{sub 3}{sup -} ions. The thermodynamic framework is based on a previously developed model for mixed-solvent electrolyte solutions. The framework has been designed to reproduce the properties of salt solutions at temperatures ranging from the freezing point to 300 C and concentrations ranging from infinite dilution to the fused salt limit. The model has been parameterized using a combination of an extensive literature database and new isopiestic measurements for thirteen salt mixtures at 140 C. The measurements have been performed using Oak Ridge National Laboratory's (ORNL) previously designed gravimetric isopiestic apparatus, which makes it possible to detect solid phase precipitation. Water activities are reported for mixtures with a fixed ratio of salts as a function of the total apparent salt mole fraction. The isopiestic measurements reported here simultaneously reflect two fundamental properties of the system, i.e., the activity of water as a function of solution concentration and the occurrence of solid-liquid transitions. The thermodynamic model accurately reproduces the new isopiestic data as well as literature data for binary, ternary and higher-order subsystems. Because of its high accuracy in calculating vapor-liquid and solid-liquid equilibria, the model is suitable for studying deliquescence behavior of multicomponent salt systems