A Monte Carlo method for locally multivariate brain mapping

Molecular phenotyping of single pancreatic islet leader beta cells by "Flash-Seq"

AIMS

Spatially-organized increases in cytosolic Ca²⁺ within pancreatic beta cells in the pancreatic islet underlie the stimulation of insulin secretion by high glucose. Recent data have revealed the existence of subpopulations of beta cells including "leaders" which initiate Ca²⁺ waves. Whether leader cells possess unique molecular features, or localisation, is unknown.

MAIN METHODS

High speed confocal Ca²⁺ imaging was used to identify leader cells and connectivity analysis, running under MATLAB and Python, to identify highly connected "hub" cells. To explore transcriptomic differences between beta cell sub-groups, individual leaders or followers were labelled by photo-activation of the cryptic fluorescent protein PA-mCherry and subjected to single cell RNA sequencing ("Flash-Seq").

KEY FINDINGS

Distinct Ca²⁺ wave types were identified in individual islets, with leader cells present in 73 % (28 of 38 islets imaged). Scale-free, power law-adherent behaviour was also observed in 29 % of islets, though "hub" cells in these islets did not overlap with leaders. Transcripts differentially expressed (295; padj < 0.05) between leader and follower cells included genes involved in cilium biogenesis and transcriptional regulation. Providing some support for these findings, ADCY6 immunoreactivity tended to be higher in leader than follower cells, whereas cilia number and length tended to be lower in the former. Finally, leader cells were located significantly closer to delta, but not alpha, cells in Euclidian space than were follower cells.

SIGNIFICANCE

The existence of both a discrete transcriptome and unique localisation implies a role for these features in defining the specialized function of leaders. These data also raise the possibility that localised signalling between delta and leader cells contributes to the initiation and propagation of islet Ca²⁺ waves.

A heuristic information cluster search approach for precise functional brain mapping

Detection of the relevant brain regions for characterizing the distinction between cognitive conditions is one of the most sought after objectives in neuroimaging research. A popular approach for achieving this goal is the multivariate pattern analysis which is currently conducted through a number of approaches such as the popular searchlight procedure. This is due to several advantages such as being automatic and flexible with regards to size of the search region. However, these approaches suffer from a number of limitations which can lead to misidentification of truly informative regions which in turn results in imprecise information maps. These limitations mainly stem from several factors such as the fact that the information value of the search spheres are assigned to the voxel at the center of them (in case of searchlight), the requirement for manual tuning of parameters such as searchlight radius and shape, and high complexity and low interpretability in commonly used machine learning-based approaches. Other drawbacks include overlooking the structure and interactions within the regions, and the disadvantages of using certain regularization techniques in analysis of datasets with characteristics of common functional magnetic resonance imaging data. In this article, we propose a fully data-driven maximum relevance minimum redundancy search algorithm for detecting precise information value of the clusters within brain regions while alleviating the above-mentioned limitations. Moreover, in order to make the proposed method faster, we propose an efficient algorithmic implementation. We evaluate and compare the proposed algorithm with the searchlight procedure as well as least absolute shrinkage and selection operator regularization-based mapping approach using both real and synthetic datasets. The analysis results of the proposed approach demonstrate higher information detection precision and map specificity compared to the benchmark approaches.

Dissociating frequency and animacy effects in visual word processing: An fMRI study

In an fMRI investigation of the neural representation of word frequency and animacy, participants read high- and low-frequency words within living and nonliving semantic categories. Both temporal (left fusiform gyrus) and parietal (left supramarginal gyrus) activation patterns differentiated between animal and tool words after controlling for frequency. Activation patterns in a smaller ventral temporal region, a subset of the voxels identified in the animacy contrast, differentiated between high- and low-frequency words after controlling for animacy. Activation patterns in the larger temporal region distinguished between high- and low-frequency words just as well as patterns within the smaller region. However, in analyses by animacy category, frequency effects in these temporal regions were significant only for tool, not for animal, words. Thus, lexical word frequency information and semantic animacy category information are conjointly represented in left fusiform gyrus activation patterns for some, but not all, concrete nouns.

MIDAS: Regionally linear multivariate discriminative statistical mapping

Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data.

The default network and the combination of cognitive processes that mediate self-generated thought

Self-generated cognitions, such as recalling personal memories or empathizing with others, are ubiquitous and essential for our lives. Such internal mental processing is ascribed to the Default Mode Network, a large network of the human brain, though the underlying neural and cognitive mechanisms remain poorly understood. Here, we tested the hypothesis that our mental experience is mediated by a combination of activities of multiple cognitive processes. Our study included four functional MRI experiments with the same participants and a wide range of cognitive tasks, as well as an analytical approach that afforded the identification of cognitive processes during self-generated cognition. We showed that several cognitive processes functioned simultaneously during self-generated mental activity. The processes had specific and localized neural representations, suggesting that they support different aspects of internal processing. Overall, we demonstrate that internally directed experience may be achieved by pooling over multiple cognitive processes.

Distractor-resistant Short-Term Memory Is Supported by Transient Changes in Neural Stimulus Representations

Goal-directed behavior in a complex world requires the maintenance of goal-relevant information despite multiple sources of distraction. However, the brain mechanisms underlying distractor-resistant working or short-term memory (STM) are not fully understood. Although early single-unit recordings in monkeys and fMRI studies in humans pointed to an involvement of lateral prefrontal cortices, more recent studies highlighted the importance of posterior cortices for the active maintenance of visual information also in the presence of distraction. Here, we used a delayed match-to-sample task and multivariate searchlight analyses of fMRI data to investigate STM maintenance across three extended delay phases. Participants maintained two samples (either faces or houses) across an unfilled pre-distractor delay, a distractor-filled delay, and an unfilled post-distractor delay. STM contents (faces vs. houses) could be decoded above-chance in all three delay phases from occipital, temporal, and posterior parietal areas. Classifiers trained to distinguish face versus house maintenance successfully generalized from pre- to post-distractor delays and vice versa, but not to the distractor delay period. Furthermore, classifier performance in all delay phases was correlated with behavioral performance in house, but not face, trials. Our results demonstrate the involvement of distributed posterior, but not lateral prefrontal, cortices in active maintenance during and after distraction. They also show that the neural code underlying STM maintenance is transiently changed in the presence of distractors and reinstated after distraction. The correlation with behavior suggests that active STM maintenance is particularly relevant in house trials, whereas face trials might rely more strongly on contributions from long-term memory.

Semantic representations in the temporal pole predict false memories

Recent advances in neuroscience have given us unprecedented insight into the neural mechanisms of false memory, showing that artificial memories can be inserted into the memory cells of the hippocampus in a way that is indistinguishable from true memories. However, this alone is not enough to explain how false memories can arise naturally in the course of our daily lives. Cognitive psychology has demonstrated that many instances of false memory, both in the laboratory and the real world, can be attributed to semantic interference. Whereas previous studies have found that a diverse set of regions show some involvement in semantic false memory, none have revealed the nature of the semantic representations underpinning the phenomenon. Here we use fMRI with representational similarity analysis to search for a neural code consistent with semantic false memory. We find clear evidence that false memories emerge from a similarity-based neural code in the temporal pole, a region that has been called the "semantic hub" of the brain. We further show that each individual has a partially unique semantic code within the temporal pole, and this unique code can predict idiosyncratic patterns of memory errors. Finally, we show that the same neural code can also predict variation in true-memory performance, consistent with an adaptive perspective on false memory. Taken together, our findings reveal the underlying structure of neural representations of semantic knowledge, and how this semantic structure can both enhance and distort our memories.

Decoding illusory self-location from activity in the human hippocampus

Decades of research have demonstrated a role for the hippocampus in spatial navigation and episodic and spatial memory. However, empirical evidence linking hippocampal activity to the perceptual experience of being physically located at a particular place in the environment is lacking. In this study, we used a multisensory out-of-body illusion to perceptually ‘teleport’ six healthy participants between two different locations in the scanner room during high-resolution functional magnetic resonance imaging (fMRI). The participants were fitted with MRI-compatible head-mounted displays that changed their first-person visual perspective to that of a pair of cameras placed in one of two corners of the scanner room. To elicit the illusion of being physically located in this position, we delivered synchronous visuo-tactile stimulation in the form of an object moving toward the cameras coupled with touches applied to the participant’s chest. Asynchronous visuo-tactile stimulation did not induce the illusion and served as a control condition. We found that illusory self-location could be successfully decoded from patterns of activity in the hippocampus in all of the participants in the synchronous (P < 0.05) but not in the asynchronous condition (P > 0.05). At the group-level, the decoding accuracy was significantly higher in the synchronous than in the asynchronous condition (P = 0.012). These findings associate hippocampal activity with the perceived location of the bodily self in space, which suggests that the human hippocampus is involved not only in spatial navigation and memory but also in the construction of our sense of bodily self-location.

Flexible Reference Frames for Grasp Planning in Human Parietofrontal Cortex

Reaching to a location in space is supported by a cortical network that operates in a variety of reference frames. Computational models and recent fMRI evidence suggest that this diversity originates from neuronal populations dynamically shifting between reference frames as a function of task demands and sensory modality. In this human fMRI study, we extend this framework to nonmanipulative grasping movements, an action that depends on multiple properties of a target, not only its spatial location. By presenting targets visually or somaesthetically, and by manipulating gaze direction, we investigate how information about a target is encoded in gaze- and body-centered reference frames in dorsomedial and dorsolateral grasping-related circuits. Data were analyzed using a novel multivariate approach that combines classification and cross-classification measures to explicitly aggregate evidence in favor of and against the presence of gaze- and body-centered reference frames. We used this approach to determine whether reference frames are differentially recruited depending on the availability of sensory information, and where in the cortical networks there is common coding across modalities. Only in the left anterior intraparietal sulcus (aIPS) was coding of the grasping target modality dependent: predominantly gaze-centered for visual targets and body-centered for somaesthetic targets. Left superior parieto-occipital cortex consistently coded targets for grasping in a gaze-centered reference frame. Left anterior precuneus and premotor areas operated in a modality-independent, body-centered frame. These findings reveal how dorsolateral grasping area aIPS could play a role in the transition between modality-independent gaze-centered spatial maps and body-centered motor areas.

Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data

BACKGROUND

Feature selection plays an important role in improving the classification accuracy of multivariate classification techniques in the context of fMRI-based decoding due to the "few samples and large features" nature of functional magnetic resonance imaging (fMRI) data. Recently, several sparse representation methods have been applied to the voxel selection of fMRI data. Despite the low computational efficiency of the sparse representation methods, they still displayed promise for applications that select features from fMRI data.

NEW METHOD

In this study, we proposed the Laplacian smoothed L0 norm (LSL0) approach for feature selection of fMRI data. Based on the fast sparse decomposition using smoothed L0 norm (SL0) (Mohimani, 2007), the LSL0 method used the Laplacian function to approximate the L0 norm of sources.

RESULTS

Results of the simulated and real fMRI data demonstrated the feasibility and robustness of LSL0 for the sparse source estimation and feature selection.

COMPARISON WITH EXISTING METHODS

Simulated results indicated that LSL0 produced more accurate source estimation than SL0 at high noise levels. The classification accuracy using voxels that were selected by LSL0 was higher than that by SL0 in both simulated and real fMRI experiment. Moreover, both LSL0 and SL0 showed higher classification accuracy and required less time than ICA and t-test for the fMRI decoding.

CONCLUSIONS

LSL0 outperformed SL0 in sparse source estimation at high noise level and in feature selection. Moreover, LSL0 and SL0 showed better performance than ICA and t-test for feature selection.

Successful decoding of famous faces in the fusiform face area

What are the neural mechanisms of face recognition? It is believed that the network of face-selective areas, which spans the occipital, temporal, and frontal cortices, is important in face recognition. A number of previous studies indeed reported that face identity could be discriminated based on patterns of multivoxel activity in the fusiform face area and the anterior temporal lobe. However, given the difficulty in localizing the face-selective area in the anterior temporal lobe, its role in face recognition is still unknown. Furthermore, previous studies limited their analysis to occipito-temporal regions without testing identity decoding in more anterior face-selective regions, such as the amygdala and prefrontal cortex. In the current high-resolution functional Magnetic Resonance Imaging study, we systematically examined the decoding of the identity of famous faces in the temporo-frontal network of face-selective and adjacent non-face-selective regions. A special focus has been put on the face-area in the anterior temporal lobe, which was reliably localized using an optimized scanning protocol. We found that face-identity could be discriminated above chance level only in the fusiform face area. Our results corroborate the role of the fusiform face area in face recognition. Future studies are needed to further explore the role of the more recently discovered anterior face-selective areas in face recognition.

Pre-stimulus functional networks modulate task performance in time-pressured evidence gathering and decision-making

Rapid perceptual decision-making is believed to depend upon efficient allocation of neural resources to the processing of transient stimuli within task-relevant contexts. Given decision-making under severe time pressure, it is reasonable to posit that the brain configures itself, prior to processing stimulus information, in a way that depends upon prior beliefs and/or anticipation. However, relatively little is known about such configuration processes, how they might be manifested in the human brain, or ultimately how they mediate task performance. Here we show that network configuration, defined via pre-stimulus functional connectivity measures estimated from functional magnetic resonance imaging (fMRI) data, is predictive of performance in a time-pressured Go/No-Go task. Specifically, using connectivity measures to summarize network properties, we show that pre-stimulus brain state can be used to discriminate behaviorally correct and incorrect trials, as well as behaviorally correct commission and omission trial categories. More broadly, our results show that pre-stimulus functional configurations of cortical and sub-cortical networks can be a major determiner of task performance.

Patterns of neural activity in the human ventral premotor cortex reflect a whole-body multisensory percept

Previous research has shown that the integration of multisensory signals from the body in fronto-parietal association areas underlies the perception of a body part as belonging to one's physical self. What are the neural mechanisms that enable the perception of one's entire body as a unified entity? In one behavioral and one fMRI multivoxel pattern analysis experiment, we used a full-body illusion to investigate how congruent visuo-tactile signals from a single body part facilitate the emergence of the sense of ownership of the entire body. To elicit this illusion, participants viewed the body of a mannequin from the first-person perspective via head-mounted displays while synchronous touches were applied to the hand, abdomen, or leg of the bodies of the participant and the mannequin; asynchronous visuo-tactile stimuli served as controls. The psychometric data indicated that the participants perceived ownership of the entire artificial body regardless of the body segment that received the synchronous visuo-tactile stimuli. Based on multivoxel pattern analysis, we found that the neural responses in the left ventral premotor cortex displayed illusion-specific activity patterns that generalized across all tested pairs of body parts. Crucially, a tripartite generalization analysis revealed the whole-body specificity of these premotor activity patterns. Finally, we also identified multivoxel patterns in the premotor, intraparietal, and lateral occipital cortices and in the putamen that reflected multisensory responses specific to individual body parts. Based on these results, we propose that the dynamic formation of a whole-body percept may be mediated by neuronal populations in the ventral premotor cortex that contain visuo-tactile receptive fields encompassing multiple body segments.

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We examine the neural and perceptual correlates of whole-body ownership.

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Behavioral findings describe the formation of a whole-body multisensory percept.

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We use multivoxel pattern analysis of fMRI data to explore the linked neural basis.

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Activity patterns in the ventral premotor cortex reflect a whole-body percept.

Harnessing graphics processing units for improved neuroimaging statistics

Simple models and algorithms based on restrictive assumptions are often used in the field of neuroimaging for studies involving functional magnetic resonance imaging, voxel based morphometry, and diffusion tensor imaging. Nonparametric statistical methods or flexible Bayesian models can be applied rather easily to yield more trustworthy results. The spatial normalization step required for multisubject studies can also be improved by taking advantage of more robust algorithms for image registration. A common drawback of algorithms based on weaker assumptions, however, is the increase in computational complexity. In this short overview, we will therefore present some examples of how inexpensive PC graphics hardware, normally used for demanding computer games, can be used to enable practical use of more realistic models and accurate algorithms, such that the outcome of neuroimaging studies really can be trusted.

The utility of data-driven feature selection: re: Chu et al. 2012

The recent Chu et al. (2012) manuscript discusses two key findings regarding feature selection (FS): (1) data driven FS was no better than using whole brain voxel data and (2) a priori biological knowledge was effective to guide FS. Use of FS is highly relevant in neuroimaging-based machine learning, as the number of attributes can greatly exceed the number of exemplars. We strongly endorse their demonstration of both of these findings, and we provide additional important practical and theoretical arguments as to why, in their case, the data-driven FS methods they implemented did not result in improved accuracy. Further, we emphasize that the data-driven FS methods they tested performed approximately as well as the all-voxel case. We discuss why a sparse model may be favored over a complex one with similar performance. We caution readers that the findings in the Chu et al. report should not be generalized to all data-driven FS methods.

Searchlight analysis: promise, pitfalls, and potential

Multivariate pattern analysis (MVPA) is an increasingly popular approach for characterizing the information present in neural activity as measured by fMRI. For neuroimaging researchers, the searchlight technique serves as the most intuitively appealing means of implementing MVPA with fMRI data. However, searchlight approaches carry with them a number of special concerns and limitations that can lead to serious interpretation errors in practice, such as misidentifying a cluster as informative, or failing to detect truly informative voxels. Here we describe how such distorted results can occur, using both schematic illustrations and examples from actual fMRI datasets. We recommend that confirmatory and sensitivity tests, such as the ones prescribed here, should be considered a necessary stage of searchlight analysis interpretation, and that their adoption will allow the full potential of searchlight analysis to be realized.

Multivoxel pattern analysis for FMRI data: a review

Functional magnetic resonance imaging (fMRI) exploits blood-oxygen-level-dependent (BOLD) contrasts to map neural activity associated with a variety of brain functions including sensory processing, motor control, and cognitive and emotional functions. The general linear model (GLM) approach is used to reveal task-related brain areas by searching for linear correlations between the fMRI time course and a reference model. One of the limitations of the GLM approach is the assumption that the covariance across neighbouring voxels is not informative about the cognitive function under examination. Multivoxel pattern analysis (MVPA) represents a promising technique that is currently exploited to investigate the information contained in distributed patterns of neural activity to infer the functional role of brain areas and networks. MVPA is considered as a supervised classification problem where a classifier attempts to capture the relationships between spatial pattern of fMRI activity and experimental conditions. In this paper , we review MVPA and describe the mathematical basis of the classification algorithms used for decoding fMRI signals, such as support vector machines (SVMs). In addition, we describe the workflow of processing steps required for MVPA such as feature selection, dimensionality reduction, cross-validation, and classifier performance estimation based on receiver operating characteristic (ROC) curves.

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation.

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data

BACKGROUND

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming.

METHODOLOGY/PRINCIPAL FINDINGS

Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time.

CONCLUSIONS/SIGNIFICANCE

The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.