Voxel‐wise intermodal coupling analysis of two or more modalities using local covariance decomposition

When individual subjects are imaged with multiple modalities, biological information is present not only within each modality, but also between modalities – that is, in how modalities covary at the voxel level. Previous studies have shown that local covariance structures between modalities, or intermodal coupling (IMCo), can be summarized for two modalities, and that two‐modality IMCo reveals otherwise undiscovered patterns in neurodevelopment and certain diseases. However, previous IMCo methods are based on the slopes of local weighted linear regression lines, which are inherently asymmetric and limited to the two‐modality setting. Here, we present a generalization of IMCo estimation which uses local covariance decompositions to define a symmetric, voxel‐wise coupling coefficient that is valid for two or more modalities. We use this method to study coupling between cerebral blood flow, amplitude of low frequency fluctuations, and local connectivity in 803 subjects ages 8 through 22. We demonstrate that coupling is spatially heterogeneous, varies with respect to age and sex in neurodevelopment, and reveals patterns that are not present in individual modalities. As availability of multi‐modal data continues to increase, principal‐component‐based IMCo (pIMCo) offers a powerful approach for summarizing relationships between multiple aspects of brain structure and function. An R package for estimating pIMCo is available at: https://github.com/hufengling/pIMCo.     

Stable between‐subject statistical inference from unstable within‐subject functional connectivity estimates

Spatial or temporal aspects of neural organization are known to be important indices of how cognition is organized. However, measurements and estimations are often noisy and many of the algorithms used are probabilistic, which in combination have been argued to limit studies exploring the neural basis of specific aspects of cognition. Focusing on static and dynamic functional connectivity estimations, we propose to leverage this variability to improve statistical efficiency in relating these estimations to behavior. To achieve this goal, we use a procedure based on permutation testing that provides a way of combining the results from many individual tests that refer to the same hypothesis. This is needed when testing a measure whose value is obtained from a noisy process, which can be repeated multiple times, referred to as replications. Focusing on functional connectivity, this noisy process can be: (a) computational, for example, when using an approximate inference algorithm for which different runs can produce different results or (b) observational, if we have the capacity to acquire data multiple times, and the different acquired data sets can be considered noisy examples of some underlying truth. In both cases, we are not interested in the individual replications but on the unobserved process generating each replication. In this note, we show how results can be combined instead of choosing just one of the estimated models. Using both simulations and real data, we show the benefits of this approach in practice.     

The role of the parahippocampal cortex in landmark‐based distance estimation based on the contextual hypothesis

Parahippocampal cortex (PHC) is a vital neural bases in spatial navigation. However, its functional role is still unclear. “Contextual hypothesis,” which assumes that the PHC participates in processing the spatial association between the landmark and destination, provides a potential answer to the question. Nevertheless, the hypothesis was previously tested using the picture categorization task, which is indirectly related to spatial navigation. By now, study is still needed for testing the hypothesis with a navigation‐related paradigm. In the current study, we tested the hypothesis by an fMRI experiment in which participants performed a distance estimation task in a virtual environment under three different conditions: landmark free (LF), stable landmark (SL), and ambiguous landmark (AL). By analyzing the behavioral data, we found that the presence of an SL improved the participants'' performance in distance estimation. Comparing the brain activity in SL‐versus‐LF contrast as well as AL‐versus‐LF contrast, we found that the PHC was activated by the SL rather than by AL when encoding the distance. This indicates that the PHC is elicited by strongly associated context and encodes the landmark reference for distance perception. Furthermore, accessing the representational similarity with the activity of the PHC across conditions, we observed a high similarity within the same condition but low similarity between conditions. This result indicated that the PHC sustains the contextual information for discriminating between scenes. Our findings provided insights into the neural correlates of the landmark information processing from the perspective of contextual hypothesis.     

A simple statistical test for the vertex ratio using Monte Carlo simulation

The vertex ratio is the crucial quantity in vertex analysis, which is a method to characterize the mode of growth of neuronal tree structures (i.e. dendrites and axons). In this report we propose the use of the Monte Carlo test to calculate a level of significance for the vertex ratio. As a result the vertex ratio can be used to analyse neuronal trees with respect to a range of growth hypotheses, including terminal and segmental growth.     

Brain‐wide inferiority and equivalence tests in fMRI group analyses: Selected applications

Null hypothesis significance testing is the major statistical procedure in fMRI, but provides only a rather limited picture of the effects in a data set. When sample size and power is low relying only on strict significance testing may lead to a host of false negative findings. In contrast, with very large data sets virtually every voxel might become significant. It is thus desirable to complement significance testing with procedures like inferiority and equivalence tests that allow to formally compare effect sizes within and between data sets and offer novel approaches to obtain insight into fMRI data. The major component of these tests are estimates of standardized effect sizes and their confidence intervals. Here, we show how Hedges'' g, the bias corrected version of Cohen''s d, and its confidence interval can be obtained from SPM t maps. We then demonstrate how these values can be used to evaluate whether nonsignificant effects are really statistically smaller than significant effects to obtain “regions of undecidability” within a data set, and to test for the replicability and lateralization of effects. This method allows the analysis of fMRI data beyond point estimates enabling researchers to take measurement uncertainty into account when interpreting their findings.     

Social cognition in the blind brain: A coordinate‐based meta‐analysis

Social cognition skills are typically acquired on the basis of visual information (e.g., the observation of gaze, facial expressions, gestures). In light of this, a critical issue is whether and how the lack of visual experience affects neurocognitive mechanisms underlying social skills. This issue has been largely neglected in the literature on blindness, despite difficulties in social interactions may be particular salient in the life of blind individuals (especially children). Here we provide a meta‐analysis of neuroimaging studies reporting brain activations associated to the representation of self and others'' in early blind individuals and in sighted controls. Our results indicate that early blindness does not critically impact on the development of the “social brain,” with social tasks performed on the basis of auditory or tactile information driving consistent activations in nodes of the action observation network, typically active during actual observation of others in sighted individuals. Interestingly though, activations along this network appeared more left‐lateralized in the blind than in sighted participants. These results may have important implications for the development of specific training programs to improve social skills in blind children and young adults.     

Gray matter structures associated with neuroticism: A meta‐analysis of whole‐brain voxel‐based morphometry studies

Neuroticism is major higher‐order personality trait and has been robustly associated with mental and physical health outcomes. Although a growing body of studies have identified neurostructural markers of neuroticism, the results remained highly inconsistent. To characterize robust associations between neuroticism and variations in gray matter (GM) structures, the present meta‐analysis investigated the concurrence across voxel‐based morphometry (VBM) studies using the anisotropic effect size signed differential mapping (AES‐SDM). A total of 13 studies comprising 2,278 healthy subjects (1,275 females, 29.20 ± 14.17 years old) were included. Our analysis revealed that neuroticism was consistently associated with the GM structure of a cluster spanning the bilateral dorsal anterior cingulate cortex and extending to the adjacent medial prefrontal cortex (dACC/mPFC). Meta‐regression analyses indicated that the neuroticism‐GM associations were not confounded by age and gender. Overall, our study is the first whole‐brain meta‐analysis exploring the brain structural correlates of neuroticism, and the findings may have implications for the intervention of high‐neuroticism individuals, who are at risk of mental disorders, by targeting the dACC/mPFC.     

Cortico‐striato‐thalamo‐cerebellar networks of structural covariance underlying different epilepsy syndromes associated with generalized tonic–clonic seizures

Generalized tonic–clonic seizures (GTCS) are the severest and most remarkable clinical expressions of human epilepsy. Cortical, subcortical, and cerebellar structures, organized with different network patterns, underlying the pathophysiological substrates of genetic associated epilepsy with GTCS (GE‐GTCS) and focal epilepsy associated with focal to bilateral tonic–clonic seizure (FE‐FBTS). Structural covariance analysis can delineate the features of epilepsy network related with long‐term effects from seizure. Morphometric MRI data of 111 patients with GE‐GTCS, 111 patients with FE‐FBTS and 111 healthy controls were studied. Cortico‐striato‐thalao‐cerebellar networks of structural covariance within the gray matter were constructed using a Winner‐take‐all strategy with five cortical parcellations. Comparisons of structural covariance networks were conducted using permutation tests, and module effects of disease duration on networks were conducted using GLM model. Both patient groups showed increased connectivity of structural covariance relative to controls, mainly within the striatum and thalamus, and mostly correlated with the frontal, motor, and somatosensory cortices. Connectivity changes increased as a function of epilepsy durations. FE‐FBTS showed more intensive and extensive gray matter changes with volumetric loss and connectivity increment than GE‐GTCS. Our findings implicated cortico‐striato‐thalamo‐cerebellar network changes at a large temporal scale in GTCS, with FE‐FBTS showing more severe network disruption. The study contributed novel imaging evidence for understanding the different epilepsy syndromes associated with generalized seizures.     

A sub+cortical fMRI‐based surface parcellation

Both cortical and subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping these areas is of fundamental importance to neuroscience. A central obstacle to this task is the inaccuracy associated with bringing results from individuals into a common space. The vast individual differences in morphology pose a serious problem for volumetric registration. Surface‐based approaches fare substantially better, but have thus far been used only for cortical parcellation, leaving subcortical parcellation in volumetric space. We extend the surface‐based approach to include also the subcortical deep gray‐matter structures, thus achieving a uniform representation across both cortex and subcortex, suitable for use with surface‐based metrics that span these structures, for example, white/gray contrast. Using data from the Enhanced Nathan Klein Institute—Rockland Sample, limited to individuals between 19 and 69 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that (a) our parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels; (b) our parcels align with known cortical and subcortical architecture; and (c) our extended functional parcellation provides an improved fit to the complexity of life‐span (6–85 years) changes in white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels, supporting its use with surface‐based measures. We provide our extended functional parcellation for the use of the neuroimaging community.     

The effect of mental countermeasures on a novel brain‐based feedback concealed information test

The feedback concealed information test (fCIT) is a novel form of the CIT, providing participants with feedback regarding their memory concealment performance. The fCIT utilizes event‐related potentials (recognition‐P300 and feedback‐related event‐related potentials) and has been shown to provide high efficiency in detecting information concealment. However, it is unclear how well the fCIT performs in the presence of mental countermeasures. To address this question, participants were trained to use countermeasures during fCIT. Results showed that the recognition‐P300 efficiency decreased when participants used countermeasures. However, the efficiencies of feedback‐related negativity and feedback‐P300 were unchanged, with feedback‐P300 still showing a high detection efficiency (AUC = 0.86) during countermeasures. These findings demonstrate the potential of fCIT for subverting countermeasures.     

Common abnormality of gray matter integrity in substance use disorder and obsessive‐compulsive disorder: A comparative voxel‐based meta‐analysis

The objective of the current study is to determine robust transdiagnostic brain structural markers for compulsivity by capitalizing on the increasing number of case‐control studies examining gray matter volume (GMV) alterations in substance use disorders (SUD) and obsessive‐compulsive disorder (OCD). Voxel‐based meta‐analysis within the individual disorders and conjunction analysis were employed to reveal common GMV alterations between SUDs and OCD. Meta‐analytic coordinates and signed brain volumetric maps determining directed (reduced/increased) GMV alterations between the disorder groups and controls served as the primary outcome. The separate meta‐analysis demonstrated that SUD and OCD patients exhibited widespread GMV reductions in frontocortical regions including prefrontal, cingulate, and insular. Conjunction analysis revealed that the left inferior frontal gyrus (IFG) consistently exhibited decreased GMV across all disorders. Functional characterization suggests that the IFG represents a core hub in the cognitive control network and exhibits bidirectional (Granger) causal interactions with the striatum. Only OCD showed increased GMV in the dorsal striatum with higher changes being associated with more severe OCD symptomatology. Together the findings demonstrate robustly decreased GMV across the disorders in the left IFG, suggesting a transdiagnostic brain structural marker. The functional characterization as a key hub in the cognitive control network and casual interactions with the striatum suggest that deficits in inhibitory control mechanisms may promote compulsivity and loss of control that characterize both disorders.     

A parallel attention‐augmented bilinear network for early magnetic resonance imaging‐based diagnosis of Alzheimer's disease

Structural magnetic resonance imaging (sMRI) can capture the spatial patterns of brain atrophy in Alzheimer''s disease (AD) and incipient dementia. Recently, many sMRI‐based deep learning methods have been developed for AD diagnosis. Some of these methods utilize neural networks to extract high‐level representations on the basis of handcrafted features, while others attempt to learn useful features from brain regions proposed by a separate module. However, these methods require considerable manual engineering. Their stepwise training procedures would introduce cascading errors. Here, we propose the parallel attention‐augmented bilinear network, a novel deep learning framework for AD diagnosis. Based on a 3D convolutional neural network, the framework directly learns both global and local features from sMRI scans without any prior knowledge. The framework is lightweight and suitable for end‐to‐end training. We evaluate the framework on two public datasets (ADNI‐1 and ADNI‐2) containing 1,340 subjects. On both the AD classification and mild cognitive impairment conversion prediction tasks, our framework achieves competitive results. Furthermore, we generate heat maps that highlight discriminative areas for visual interpretation. Experiments demonstrate the effectiveness of the proposed framework when medical priors are unavailable or the computing resources are limited. The proposed framework is general for 3D medical image analysis with both efficiency and interpretability.     

A neural signature of colour and luminance correspondence in bistable apparent motion

The 'correspondence problem' refers to the ambiguity of apparent motion (AM) paths if several similar objects are displaced across successive displays. We investigated the effect of intrinsic object properties such as colour and luminance on AM paths, and used functional magnetic resonance imaging to localize neural correlates of correspondence matching in visual cortical regions. Human subjects looked at an AM display where two dots in diagonally opposite corners of an implicit rectangle were flashed in alternation with two dots in the other two corners, yielding spontaneous alternations between horizontal and vertical AM. The dots differed in colour or luminance, or were identical. Neural activity was analysed as a function of whether the perceived AM path matched the dots' colour or luminance, and was also compared to activity during bistable AM displays without correspondence cues. When AM paths matched colour and luminance cues, activity in early visual cortex was the same as during perception of uncued displays, whereas it was suppressed when perceived AM paths violated colour or luminance cues. Colour-sensitive extrastriate cortex (V4 complex) transiently activated whenever AM perception switched from a pattern violating colour correspondence to one consistent with colour. We propose that the neural correlate of correspondence in early visual cortex reflects regulatory mechanisms that flexibly gate early visual feature processing in accord with an overriding perceptual decision. Conversely, activation of feature-selective extrastriate regions depends on the type of cue used for correspondence matching and may reflect the salience of percepts that match in colour and motion.     

Stability test of canonical correlation analysis for studying brain‐behavior relationships: The effects of subject‐to‐variable ratios and correlation strengths

Canonical correlation analysis (CCA), a multivariate approach to identifying correlations between two sets of variables, is becoming increasingly popular in neuroimaging studies on brain‐behavior relationships. However, the CCA stability in neuroimaging applications has not been systematically investigated. Although it is known that the number of subjects should be greater than the number of variables due to the curse of dimensionality, it is unclear at what subject‐to‐variable ratios (SVR) and at what correlation strengths the CCA stability can be maintained. Here, we systematically assessed the CCA stability, in the context of investigating the relationship between the brain structural/functional imaging measures and the behavioral measures, by measuring the similarity of the first‐mode canonical variables across randomly sampled subgroups of subjects from a large set of 936 healthy subjects. Specifically, we tested how the CCA stability changes with SVR under two different brain‐behavior correlation strengths. The same tests were repeated using an independent data set (n = 700) for validation. The results confirmed that both SVR and correlation strength affect greatly the CCA stability—the CCA stability cannot be guaranteed if the SVR is not sufficiently high or the brain‐behavior relationship is not sufficiently strong. Based on our quantitative characterization of CCA stability, we provided a practical guideline to help correct interpretation of CCA results and proper applications of CCA in neuroimaging studies on brain‐behavior relationships.     

Brain gray matter structures associated with trait impulsivity: A systematic review and voxel‐based meta‐analysis

Trait impulsivity is a multifaceted personality characteristic that contributes to maladaptive life outcomes. Although a growing body of neuroimaging studies have investigated the structural correlates of trait impulsivity, the findings remain highly inconsistent and heterogeneous. Herein, we performed a systematic review to depict an integrated delineation of gray matter (GM) substrates of trait impulsivity and a meta‐analysis to examine concurrence across previous whole‐brain voxel‐based morphometry studies. The systematic review summarized the diverse findings in GM morphometry in the past literature, and the quantitative meta‐analysis revealed impulsivity‐related volumetric GM alterations in prefrontal, temporal, and parietal cortices. In addition, we identified the modulatory effects of age and gender in impulsivity‐GM volume associations. The present study advances understanding of brain GM morphometry features underlying trait impulsivity. The findings may have practical implications in the clinical diagnosis of and intervention for impulsivity‐related disorders.     

Brain age prediction: A comparison between machine learning models using region‐ and voxel‐based morphometric data

Brain morphology varies across the ageing trajectory and the prediction of a person''s age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such “brain age prediction” vary widely in terms of their methods and type of data, so at present the most accurate and generalisable methodological approach is unclear. Therefore, we used the UK Biobank data set (N = 10,824, age range 47–73) to compare the performance of the machine learning models support vector regression, relevance vector regression and Gaussian process regression on whole‐brain region‐based or voxel‐based structural magnetic resonance imaging data with or without dimensionality reduction through principal component analysis. Performance was assessed in the validation set through cross‐validation as well as an independent test set. The models achieved mean absolute errors between 3.7 and 4.7 years, with those trained on voxel‐level data with principal component analysis performing best. Overall, we observed little difference in performance between models trained on the same data type, indicating that the type of input data had greater impact on performance than model choice. All code is provided online in the hope that this will aid future research.     

Coordinate‐based activation likelihood estimation meta‐analysis of neuroimaging data: A random‐effects approach based on empirical estimates of spatial uncertainty

A widely used technique for coordinate‐based meta‐analyses of neuroimaging data is activation likelihood estimation (ALE). ALE assesses the overlap between foci based on modeling them as probability distributions centered at the respective coordinates. In this Human Brain Project/Neuroinformatics research, the authors present a revised ALE algorithm addressing drawbacks associated with former implementations. The first change pertains to the size of the probability distributions, which had to be specified by the used. To provide a more principled solution, the authors analyzed fMRI data of 21 subjects, each normalized into MNI space using nine different approaches. This analysis provided quantitative estimates of between‐subject and between‐template variability for 16 functionally defined regions, which were then used to explicitly model the spatial uncertainty associated with each reported coordinate. Secondly, instead of testing for an above‐chance clustering between foci, the revised algorithm assesses above‐chance clustering between experiments. The spatial relationship between foci in a given experiment is now assumed to be fixed and ALE results are assessed against a null‐distribution of random spatial association between experiments. Critically, this modification entails a change from fixed‐ to random‐effects inference in ALE analysis allowing generalization of the results to the entire population of studies analyzed. By comparative analysis of real and simulated data, the authors showed that the revised ALE‐algorithm overcomes conceptual problems of former meta‐analyses and increases the specificity of the ensuing results without loosing the sensitivity of the original approach. It may thus provide a methodologically improved tool for coordinate‐based meta‐analyses on functional imaging data. Hum Brain Mapp 2009. © 2009 Wiley‐Liss, Inc.     

Classification based hypothesis testing in neuroscience: Below‐chance level classification rates and overlooked statistical properties of linear parametric classifiers

Multivariate pattern analysis (MVPA) has recently become a popular tool for data analysis. Often, classification accuracy as quantified by correct classification rate (CCR) is used to illustrate the size of the effect under investigation. However, we show that in low sample size (LSS), low effect size (LES) data, which is typical in neuroscience, the distribution of CCRs from cross‐validation of linear MVPA is asymmetric and can show classification rates considerably below what would be expected from chance classification. Conversely, the mode of the distribution in these cases is above expected chance levels, leading to a spuriously high number of above chance CCRs. This unexpected distribution has strong implications when using MVPA for hypothesis testing. Our analyses warrant the conclusion that CCRs do not well reflect the size of the effect under investigation. Moreover, the skewness of the null‐distribution precludes the use of many standard parametric tests to assess significance of CCRs. We propose that MVPA results should be reported in terms of P values, which are estimated using randomization tests. Also, our results show that cross‐validation procedures using a low number of folds, e.g. twofold, are generally more sensitive, even though the average CCRs are often considerably lower than those obtained using a higher number of folds. Hum Brain Mapp 37:1842–1855, 2016. © 2016 Wiley Periodicals, Inc .     

MR‐PET head motion correction based on co‐registration of multicontrast MR images

Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous magnetic resonance‐positron emission tomography (MR‐PET) makes it possible to estimate head motion information from high‐resolution MR images and then correct motion artefacts in PET images. In this article, we introduce a fully automated PET motion correction method, MR‐guided MAF, based on the co‐registration of multicontrast MR images. The performance of the MR‐guided MAF method was evaluated using MR‐PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18‐F]FDG). Compared with conventional methods, MR‐guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR‐PET scanners. The fully automated motion estimation method has been implemented as a publicly available web‐service.     

Updating the dual‐mechanism model for cross‐sensory attentional spreading: The influence of space‐based visual selective attention

Selective attention to visual stimuli can spread cross‐modally to task‐irrelevant auditory stimuli through either the stimulus‐driven binding mechanism or the representation‐driven priming mechanism. The stimulus‐driven attentional spreading occurs whenever a task‐irrelevant sound is delivered simultaneously with a spatially attended visual stimulus, whereas the representation‐driven attentional spreading occurs only when the object representation of the sound is congruent with that of the to‐be‐attended visual object. The current study recorded event‐related potentials in a space‐selective visual object‐recognition task to examine the exact roles of space‐based visual selective attention in both the stimulus‐driven and representation‐driven cross‐modal attentional spreading, which remain controversial in the literature. Our results yielded that the representation‐driven auditory Nd component (200–400 ms after sound onset) did not differ according to whether the peripheral visual representations of audiovisual target objects were spatially attended or not, but was decreased when the auditory representations of target objects were presented alone. In contrast, the stimulus‐driven auditory Nd component (200–300 ms) was decreased but still prominent when the peripheral visual constituents of audiovisual nontarget objects were spatially unattended. These findings demonstrate not only that the representation‐driven attentional spreading is independent of space‐based visual selective attention and benefits in an all‐or‐nothing manner from object‐based visual selection for actually presented visual representations of target objects, but also that although the stimulus‐driven attentional spreading is modulated by space‐based visual selective attention, attending to visual modality per se is more likely to be the endogenous determinant of the stimulus‐driven attentional spreading.