Decoding unattended fearful faces with whole-brain correlations: an approach to identify condition-dependent large-scale functional connectivity

Perception and memory retrieval states are reflected in distributed patterns of background functional connectivity

The same visual input can serve as the target of perception or as a trigger for memory retrieval depending on whether cognitive processing is externally oriented (perception) or internally oriented (memory retrieval). While numerous human neuroimaging studies have characterized how visual stimuli are differentially processed during perception versus memory retrieval, perception and memory retrieval may also be associated with distinct neural states that are independent of stimulus-evoked neural activity. Here, we combined human fMRI with full correlation matrix analysis (FCMA) to reveal potential differences in "background" functional connectivity across perception and memory retrieval states. We found that perception and retrieval states could be discriminated with high accuracy based on patterns of connectivity across (1) the control network, (2) the default mode network (DMN), and (3) retrosplenial cortex (RSC). In particular, clusters in the control network increased connectivity with each other during the perception state, whereas clusters in the DMN were more strongly coupled during the retrieval state. Interestingly, RSC switched its coupling between networks as the cognitive state shifted from retrieval to perception. Finally, we show that background connectivity (1) was fully independent from stimulus-related variance in the signal and, further, (2) captured distinct aspects of cognitive states compared to traditional classification of stimulus-evoked responses. Together, our results reveal that perception and memory retrieval are associated with sustained cognitive states that manifest as distinct patterns of connectivity among large-scale brain networks.

Decoding six basic emotions from brain functional connectivity patterns

Although distinctive neural and physiological states are suggested to underlie the six basic emotions, basic emotions are often indistinguishable from functional magnetic resonance imaging (fMRI) voxelwise activation (VA) patterns. Here, we hypothesize that functional connectivity (FC) patterns across brain regions may contain emotion-representation information beyond VA patterns. We collected whole-brain fMRI data while human participants viewed pictures of faces expressing one of the six basic emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise) or showing neutral expressions. We obtained FC patterns for each emotion across brain regions over the whole brain and applied multivariate pattern decoding to decode emotions in the FC pattern representation space. Our results showed that the whole-brain FC patterns successfully classified not only the six basic emotions from neutral expressions but also each basic emotion from other emotions. An emotion-representation network for each basic emotion that spanned beyond the classical brain regions for emotion processing was identified. Finally, we demonstrated that within the same brain regions, FC-based decoding consistently performed better than VA-based decoding. Taken together, our findings revealed that FC patterns contained emotional information and advocated for paying further attention to the contribution of FCs to emotion processing.

Rapidly Decoding Image Categories From MEG Data Using a Multivariate Short-Time FC Pattern Analysis Approach

Recent advances in the development of multivariate analysis methods have led to the application of multivariate pattern analysis (MVPA) to investigate the interactions between brain regions using graph theory (functional connectivity, FC) and decode visual categories from functional magnetic resonance imaging (fMRI) data from a continuous multicategory paradigm. To estimate stable FC patterns from fMRI data, previous studies required long periods in the order of several minutes, in comparison to the human brain that categories visual stimuli within hundreds of milliseconds. Constructing short-time dynamic FC patterns in the order of milliseconds and decoding visual categories is a relatively novel concept. In this study, we developed a multivariate decoding algorithm based on FC patterns and applied it to magnetoencephalography (MEG) data. MEG data were recorded from participants presented with image stimuli in four categories (faces, scenes, animals and tools). MEG data from 17 participants demonstrate that short-time dynamic FC patterns yield brain activity patterns that can be used to decode visual categories with high accuracy. Our results show that FC patterns change over the time window, and FC patterns extracted in the time window of 0∼200 ms after the stimulus onset were most stable. Further, the categorizing accuracy peaked (the mean binary accuracy is above 78.6% at individual level) in the FC patterns estimated within the 0∼200 ms interval. These findings elucidate the underlying connectivity information during visual category processing on a relatively smaller time scale and demonstrate that the contribution of FC patterns to categorization fluctuates over time.

Bilingualism and domain-general cognitive functions from a neural perspective: A systematic review

A large body of research has indicated that bilingualism - through continual practice in language control - may impact cognitive functions, as well as relevant aspects of brain function and structure. The present review aimed to bring together findings on the relationship between bilingualism and domain-general cognitive functions from a neural perspective. The final sample included 210 studies, covering findings regarding neural responses to bilingual language control and/or domain-general cognitive tasks, as well as findings regarding effects of bilingualism on non-task-related brain function and brain structure. The evidence indicates that a) bilingual language control likely entails neural mechanisms responsible for domain-general cognitive functions; b) bilingual experiences impact neural responses to domain-general cognitive functions; and c) bilingual experiences impact non-task-related brain function (both resting-state and metabolic function) as well as aspects of brain structure (both macrostructure and microstructure), each of which may in turn impact mental processes, including domain-general cognitive functions. Such functional and structural neuroplasticity associated with bilingualism may contribute to both cognitive and neural reserves, producing benefits across the lifespan.

Network Representations of Facial and Bodily Expressions: Evidence From Multivariate Connectivity Pattern Classification

Emotions can be perceived from both facial and bodily expressions. Our previous study has found the successful decoding of facial expressions based on the functional connectivity (FC) patterns. However, the role of the FC patterns in the recognition of bodily expressions remained unclear, and no neuroimaging studies have adequately addressed the question of whether emotions perceiving from facial and bodily expressions are processed rely upon common or different neural networks. To address this, the present study collected functional magnetic resonance imaging (fMRI) data from a block design experiment with facial and bodily expression videos as stimuli (three emotions: anger, fear, and joy), and conducted multivariate pattern classification analysis based on the estimated FC patterns. We found that in addition to the facial expressions, bodily expressions could also be successfully decoded based on the large-scale FC patterns. The emotion classification accuracies for the facial expressions were higher than that for the bodily expressions. Further contributive FC analysis showed that emotion-discriminative networks were widely distributed in both hemispheres, containing regions that ranged from primary visual areas to higher-level cognitive areas. Moreover, for a particular emotion, discriminative FCs for facial and bodily expressions were distinct. Together, our findings highlight the key role of the FC patterns in the emotion processing, indicating how large-scale FC patterns reconfigure in processing of facial and bodily expressions, and suggest the distributed neural representation for the emotion recognition. Furthermore, our results also suggest that the human brain employs separate network representations for facial and bodily expressions of the same emotions. This study provides new evidence for the network representations for emotion perception and may further our understanding of the potential mechanisms underlying body language emotion recognition.

Decoding disparity categories in 3-dimensional images from fMRI data using functional connectivity patterns

Humans use binocular disparity to extract depth information from two-dimensional retinal images in a process called stereopsis. Previous studies usually introduce the standard univariate analysis to describe the correlation between disparity level and brain activity within a given brain region based on functional magnetic resonance imaging (fMRI) data. Recently, multivariate pattern analysis has been developed to extract activity patterns across multiple voxels for deciphering categories of binocular disparity. However, the functional connectivity (FC) of patterns based on regions of interest or voxels and their mapping onto disparity category perception remain unknown. The present study extracted functional connectivity patterns for three disparity conditions (crossed disparity, uncrossed disparity, and zero disparity) at distinct spatial scales to decode the binocular disparity. Results of 27 subjects' fMRI data demonstrate that FC features are more discriminatory than traditional voxel activity features in binocular disparity classification. The average binary classification of the whole brain and visual areas are respectively 87% and 79% at single subject level, and thus above the chance level (50%). Our research highlights the importance of exploring functional connectivity patterns to achieve a novel understanding of 3D image processing.

Fear Network Model in Panic Disorder: The Past and the Future

The core concept for pathophysiology in panic disorder (PD) is the fear network model (FNM). The alterations in FNM might be linked with disturbances in the autonomic nervous system (ANS), which is a common phenomenon in PD. The traditional FNM included the frontal and limbic regions, which were dysregulated in the feedback mechanism for cognitive control of frontal lobe over the primitive response of limbic system. The exaggerated responses of limbic system are also associated with dysregulation in the neurotransmitter system. The neuroimaging studies also corresponded to FNM concept. However, more extended areas of FNM have been discovered in recent imaging studies, such as sensory regions of occipital, parietal cortex and temporal cortex and insula. The insula might integrate the filtered sensory information via thalamus from the visuospatial and other sensory modalities related to occipital, parietal and temporal lobes. In this review article, the traditional and advanced FNM would be discussed. I would also focus on the current evidences of insula, temporal, parietal and occipital lobes in the pathophysiology. In addition, the white matter and functional connectome studies would be reviewed to support the concept of advanced FNM. An emerging dysregulation model of fronto-limbic-insula and temporooccipito-parietal areas might be revealed according to the combined results of recent neuroimaging studies. The future delineation of advanced FNM model can be beneficial from more extensive and advanced studies focusing on the additional sensory regions of occipital, parietal and temporal cortex to confirm the role of advanced FNM in the pathophysiology of PD.

Image categorization from functional magnetic resonance imaging using functional connectivity

BACKGROUND

Previous studies have attempted to infer the category of objects in a stimulus image from functional magnetic resonance imaging (fMRI) data recoded during image-viewing. Most studies focus on extracting activity patterns within a given region or across multiple voxels, and utilize the relationships among voxels to decipher the category of a stimulus image. Yet, the functional connectivity (FC) patterns across regions of interest in response to image categories, and their potential contributions to category classification are largely unknown.

NEW METHOD

We investigated whole-brain FC patterns in response to 4 image category stimuli (cats, faces, houses, and vehicles) using fMRI in healthy adult volunteers, and classified FC patterns using machine learning framework (Support Vector Machine [SVM] and Random Forest). We further examined the FC robustness and the influence of the window length on FC patterns for neural decoding.

RESULTS

The average one-vs.-one classification accuracy of the two classification models were 74% within subjects and 80% between subjects, which are higher than the chance level (50%). The Random Forest results were better than SVM results, and the 48-s FC results were better than the 24-s FC results.

COMPARISON WITH EXISTING METHOD(S)

We compared the classification performance of our FC patterns with two other existing methods, inter-block and intra-block, without overlapping temporal information.

CONCLUSIONS

Whole-brain FC patterns for different window lengths (24 and 48 s) can predict images categories with high accuracy. These results reveal novel mechanisms underlying the representation of categorical information in large-scale FC patterns in the human brain.

Higher 5-HT1A autoreceptor binding as an endophenotype for major depressive disorder identified in high risk offspring - A pilot study

Higher serotonin-1A (5-HT1A) receptor binding potential (BPF) has been found in major depressive disorder (MDD) during and between major depressive episodes. We investigated whether higher 5-HT1A binding is a biologic trait transmitted to healthy high risk (HR) offspring of MDD probands. Data were collected contemporaneously from: nine HR, 30 depressed not-recently medicated (NRM) MDD, 18 remitted NRM MDD, 51 healthy volunteer (HV) subjects. Subjects underwent positron emission tomography (PET) using [11C]WAY100635 to quantify 5-HT1A BPF, estimated using metabolite, free fraction-corrected arterial input function and cerebellar white matter as reference region. Multivoxel pattern analyses (MVPA) of PET data evaluated group status classification of individuals. When tested across 13 regions of interest, an effect of diagnosis is found on BPF which remains significant after correction for sex, age, injected mass and dose: HR have higher BPF than HV (84.3% higher in midbrain raphe, 40.8% higher in hippocampus, mean BPF across all 13 brain regions is 49.9% ± 11.8% higher). Voxel-level BPF maps distinguish HR vs. HV. Elevated 5-HT1A BPF appears to be a familially transmitted trait abnormality. Future studies are needed to replicate this finding in a larger cohort and demonstrate the link to the familial transmission of mood disorders.

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity

It is an important question how human beings achieve efficient recognition of others’ facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition.

Altered task-dependent functional connectivity patterns during subjective recollection experiences of episodic retrieval in postpartum women

Numerous studies have suggested that postpartum women show a decline in cognitive abilities. However, to date, no study has investigated the presence of qualitative alterations in recognition memory processes in postpartum women that may lead to a decline in cognitive ability. To address this issue, we employed the Remember/Know procedure and functional magnetic resonance imaging (fMRI). Behavioral results demonstrated that compared with the matched control (CTRL) group, the postpartum (PP) group endorsed "Remember" less and "Know" more to old items. A univariate analysis of fMRI data indicated lower neural activity of the subjective recollection network in the PP group than in the CTRL group. We also performed a large-scale functional connectivity multivariate pattern analysis (fcMVPA) using task-dependent time-series to detect differences in functional connectivity patterns and neural interactivity between the PP and CTRL groups. The fcMVPA results revealed that the PP group exhibited altered functional connectivity patterns from which machine learning algorithms could discriminate group membership with 94% accuracy. Collectively, these findings demonstrated that altered subjective recollection processes in the PP group during episodic memory decisions are associated with diminished neural activity and abnormal interactivity across the subjective recollection network. We believe that this is one of the first studies demonstrating qualitative alterations in recognition memory processes in postpartum women.

Representing object categories by connections: Evidence from a mutivariate connectivity pattern classification approach

The representation of object categories is a classical question in cognitive neuroscience and compelling evidence has identified specific brain regions showing preferential activation to categories of evolutionary significance. However, the potential contributions to category processing by tuning the connectivity patterns are largely unknown. Adopting a continuous multicategory paradigm, we obtained whole-brain functional connectivity (FC) patterns of each of four categories (faces, scenes, animals and tools) in healthy human adults and applied multivariate connectivity pattern classification analyses. We found that the whole-brain FC patterns made high-accuracy predictions of which category was being viewed. The decoding was successful even after the contributions of regions showing classical category-selective activations were excluded. We further identified the discriminative network for each category, which span way beyond the classical category-selective regions. Together, these results reveal novel mechanisms about how categorical information is represented in large-scale FC patterns, with general implications for the interactive nature of distributed brain areas underlying high-level cognition. Hum Brain Mapp 37:3685-3697, 2016. © 2016 Wiley Periodicals, Inc.

Functional Mesh Model with Temporal Measurements for brain decoding

We propose a method called Functional Mesh Model with Temporal Measurements (FMM-TM) to estimate a functional relationship among voxels using temporal data, and employ these relationships for brain decoding. For each sample, we measure Blood Oxygenation Level Dependent (BOLD) responses from each voxel, and construct a functional mesh around each voxel (called seed voxel) with its nearest neighbors selected using distance metrics namely Pearson correlation, cosine similarity and Euclidean distance. Then, we represent the BOLD response of a seed voxel in terms of linear combination of BOLD responses of its p-nearest neighbors. The relationship between the seed voxel and its neighbors is represented using a set of weights which are estimated by employing linear regression. We train Support Vector Machine and k-Nearest Neighbor classifiers using the estimated weights as features. We test our model in an event-related design experiment, namely object recognition, and observe that our features perform better than raw voxel intensity values, features obtained using various pairwise distance metrics, and local mesh model features extracted using stationary and temporal measurements.

Beyond emotions: A meta-analysis of neural response within face processing system in social anxiety

Patients with social anxiety disorder (SAD) experience anxiety and avoidance in face-to-face interactions. We performed a meta-analysis of functional magnetic resonance imaging (fMRI) studies in SAD to provide a comprehensive understanding of the neural underpinnings of face perception in this disorder. To this purpose, we adopted an innovative approach, asking authors for unpublished data. This is a common procedure for behavioral meta-analyses, which, however has never been used in neuroimaging studies. We searched Pubmed with the key words "Social Anxiety AND faces" and "Social Phobia AND faces." Then, we selected those fMRI studies for which we were able to obtain data for the comparison between SAD and healthy controls (HC) in a face perception task, either from the published papers or from the authors themselves. In this way, we obtained 23 studies (totaling 449 SAD and 424 HC individuals). We identified significant clusters in which faces evoked a higher response in SAD in bilateral amygdala, globus pallidus, superior temporal sulcus, visual cortex, and prefrontal cortex. We also found a higher activity for HC in the lingual gyrus and in the posterior cingulate. Our findings show that altered neural response to face in SAD is not limited to emotional structures but involves a complex network. These results may have implications for the understanding of SAD pathophysiology, as they suggest that a dysfunctional face perception process may bias patient person-to-person interactions.

Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns

Functional connectivity (FC) patterns in functional MRI exhibit dynamic behavior on the scale of seconds, with rich spatiotemporal structure and limited sets of whole-brain, quasi-stable FC configurations (FC states) recurring across time and subjects. Based on previous evidence linking various aspects of cognition to group-level, minute-to-minute FC changes in localized connections, we hypothesized that whole-brain FC states may reflect the global, orchestrated dynamics of cognitive processing on the scale of seconds. To test this hypothesis, subjects were continuously scanned as they engaged in and transitioned between mental states dictated by tasks. FC states computed within windows as short as 22.5 s permitted robust tracking of cognition in single subjects with near perfect accuracy. Accuracy dropped markedly for subjects with the lowest task performance. Spatially restricting FC information decreased accuracy at short time scales, emphasizing the distributed nature of whole-brain FC dynamics, beyond univariate magnitude changes, as valuable markers of cognition.

Full correlation matrix analysis (FCMA): An unbiased method for task-related functional connectivity

BACKGROUND

The analysis of brain imaging data often requires simplifying assumptions because exhaustive analyses are computationally intractable. Standard univariate and multivariate analyses of brain activity ignore interactions between regions and analyses of interactions (functional connectivity) reduce the computational challenge by using seed regions of interest or brain parcellations.

NEW METHOD

To meet this challenge, we developed full correlation matrix analysis (FCMA), which leverages and optimizes algorithms from parallel computing and machine learning to efficiently analyze the pairwise correlations of all voxels in the brain during different cognitive tasks, with the goal of identifying task-related interactions in an unbiased manner.

RESULTS

When applied to a localizer dataset on a small compute cluster, FCMA accelerated a naive, serial approach by four orders of magnitude, reducing running time from two years to one hour. In addition to this performance gain, FCMA emphasized different brain areas than existing methods. In particular, beyond replicating known category selectivity in visual cortex, FCMA also revealed a region of medial prefrontal cortex whose selectivity derived from differential patterns of functional connectivity across categories.

COMPARISON WITH EXISTING METHOD(S)

For benchmarking, we started with a naive approach and progressively built up to the complete FCMA procedure by adding optimized classifier algorithms, multi-threaded parallelism, and multi-node parallelism. To evaluate what can be learned with FCMA, we compared it against multivariate pattern analysis of activity and seed-based analysis of functional connectivity.

CONCLUSIONS

FCMA demonstrates how advances in computer science can alleviate computational bottlenecks in neuroscience. We have released a software toolbox to help others evaluate FCMA.

Decoding the processing of lying using functional connectivity MRI

Background

Previous functional MRI (fMRI) studies have demonstrated group differences in brain activity between deceptive and honest responses. The functional connectivity network related to lie-telling remains largely uncharacterized.

Methods

In this study, we designed a lie-telling experiment that emphasized strategy devising. Thirty-two subjects underwent fMRI while responding to questions in a truthful, inverse, or deceitful manner. For each subject, whole-brain functional connectivity networks were constructed from correlations among brain regions for the lie-telling and truth-telling conditions. Then, a multivariate pattern analysis approach was used to distinguish lie-telling from truth-telling based on the functional connectivity networks.

Results

The classification results demonstrated that lie-telling could be differentiated from truth-telling with an accuracy of 82.81% (85.94% for lie-telling, 79.69% for truth-telling). The connectivities related to the fronto-parietal networks, cerebellum and cingulo-opercular networks are most discriminating, implying crucial roles for these three networks in the processing of deception.

Conclusions

The current study may shed new light on the neural pattern of deception from a functional integration viewpoint.

Electronic supplementary material

The online version of this article (doi:10.1186/s12993-014-0046-4) contains supplementary material, which is available to authorized users.

Survey of encoding and decoding of visual stimulus via FMRI: an image analysis perspective

A variety of exciting scientific achievements have been made in the last few decades in brain encoding and decoding via functional magnetic resonance imaging (fMRI). This trend continues to rise in recent years, as evidenced by the increasing number of published papers in this topic and several published survey papers addressing different aspects of research issues. Essentially, these survey articles were mainly from cognitive neuroscience and neuroimaging perspectives, although computational challenges were briefly discussed. To complement existing survey articles, this paper focuses on the survey of the variety of image analysis methodologies, such as neuroimage registration, fMRI signal analysis, ROI (regions of interest) selection, machine learning algorithms, reproducibility analysis, structural and functional connectivity, and natural image analysis, which were employed in previous brain encoding/decoding research works. This paper also provides discussions of potential limitations of those image analysis methodologies and possible future improvements. It is hoped that extensive discussions of image analysis issues could contribute to the advancements of the increasingly important brain encoding/decoding field.

Approaching the biology of human parental attachment: brain imaging, oxytocin and coordinated assessments of mothers and fathers

Brain networks that govern parental response to infant signals have been studied with imaging techniques over the last 15 years. The complex interaction of thoughts and behaviors required for sensitive parenting enables the formation of each individual's first social bonds and critically shapes development. This review concentrates on magnetic resonance imaging experiments which directly examine the brain systems involved in parental responses to infant cues. First, we introduce themes in the literature on parental brain circuits studied to date. Next, we present a thorough chronological review of state-of-the-art fMRI studies that probe the parental brain with a range of baby audio and visual stimuli. We also highlight the putative role of oxytocin and effects of psychopathology, as well as the most recent work on the paternal brain. Taken together, a new model emerges in which we propose that cortico-limbic networks interact to support parental brain responses to infants. These include circuitry for arousal/salience/motivation/reward, reflexive/instrumental caring, emotion response/regulation and integrative/complex cognitive processing. Maternal sensitivity and the quality of caregiving behavior are likely determined by the responsiveness of these circuits during early parent-infant experiences. The function of these circuits is modifiable by current and early-life experiences, hormonal and other factors. Severe deviation from the range of normal function in these systems is particularly associated with (maternal) mental illnesses - commonly, depression and anxiety, but also schizophrenia and bipolar disorder. Finally, we discuss the limits and extent to which brain imaging may broaden our understanding of the parental brain given our current model. Developments in the understanding of the parental brain may have profound implications for long-term outcomes in families across risk, resilience and possible interventions. This article is part of a Special Issue entitled Oxytocin and Social Behav.

Individual differences in attentional bias associated with cocaine dependence are related to varying engagement of neural processing networks

Cocaine and other drug dependencies are associated with significant attentional bias for drug use stimuli that represents a candidate cognitive marker of drug dependence and treatment outcomes. We explored, using fMRI, the role of discrete neural processing networks in the representation of individual differences in the drug attentional bias effect associated with cocaine dependence (AB-coc) using a word counting Stroop task with personalized cocaine use stimuli (cocStroop). The cocStroop behavioral and neural responses were further compared with those associated with a negative emotional word Stroop task (eStroop) and a neutral word counting Stroop task (cStroop). Brain-behavior correlations were explored using both network-level correlation analysis following independent component analysis (ICA) and voxel-level, brain-wide univariate correlation analysis. Variation in the attentional bias effect for cocaine use stimuli among cocaine-dependent men and women was related to the recruitment of two separate neural processing networks related to stimulus attention and salience attribution (inferior frontal-parietal-ventral insula), and the processing of the negative affective properties of cocaine stimuli (frontal-temporal-cingulate). Recruitment of a sensory-motor-dorsal insula network was negatively correlated with AB-coc and suggested a regulatory role related to the sensorimotor processing of cocaine stimuli. The attentional bias effect for cocaine stimuli and for negative affective word stimuli were significantly correlated across individuals, and both were correlated with the activity of the frontal-temporal-cingulate network. Functional connectivity for a single prefrontal-striatal-occipital network correlated with variation in general cognitive control (cStroop) that was unrelated to behavioral or neural network correlates of cocStroop- or eStroop-related attentional bias. A brain-wide mass univariate analysis demonstrated the significant correlation of individual attentional bias effect for cocaine stimuli with distributed activations in the frontal, occipitotemporal, parietal, cingulate, and premotor cortex. These findings support the involvement of multiple processes and brain networks in mediating individual differences in risk for relapse associated with drug dependence.

Reduced anterior temporal and hippocampal functional connectivity during face processing discriminates individuals with social anxiety disorder from healthy controls and panic disorder, and increases following treatment

Group functional magnetic resonance imaging (fMRI) studies suggest that anxiety disorders are associated with anomalous brain activation and functional connectivity (FC). However, brain-based features sensitive enough to discriminate individual subjects with a specific anxiety disorder and that track symptom severity longitudinally, desirable qualities for putative disorder-specific biomarkers, remain to be identified. Blood oxygen level-dependent (BOLD) fMRI during emotional face perceptual tasks and a new, large-scale and condition-dependent FC and machine learning approach were used to identify features (pair-wise correlations) that discriminated patients with social anxiety disorder (SAD, N=16) from controls (N=19). We assessed whether these features discriminated SAD from panic disorder (PD, N=16), and SAD from controls in an independent replication sample that performed a similar task at baseline (N: SAD=15, controls=17) and following 8-weeks paroxetine treatment (N: SAD=12, untreated controls=7). High SAD vs HCs discrimination (area under the ROC curve, AUC, arithmetic mean of sensitivity and specificity) was achieved with two FC features during unattended neutral face perception (AUC=0.88, P<0.05 corrected). These features also discriminated SAD vs PD (AUC=0.82, P=0.0001) and SAD vs HCs in the independent replication sample (FC during unattended angry face perception, AUC=0.71, P=0.01). The most informative FC was left hippocampus-left temporal pole, which was reduced in both SAD samples (replication sample P=0.027), and this FC increased following the treatment (post>pre, t(11)=2.9, P=0.007). In conclusion, SAD is associated with reduced FC between left temporal pole and left hippocampus during face perception, and results suggest promise for emerging FC-based biomarkers for SAD diagnosis and treatment effects.

Functional interactions as big data in the human brain

Noninvasive studies of human brain function hold great potential to unlock mysteries of the human mind. The complexity of data generated by such studies, however, has prompted various simplifying assumptions during analysis. Although this has enabled considerable progress, our current understanding is partly contingent upon these assumptions. An emerging approach embraces the complexity, accounting for the fact that neural representations are widely distributed, neural processes involve interactions between regions, interactions vary by cognitive state, and the space of interactions is massive. Because what you see depends on how you look, such unbiased approaches provide the greatest flexibility for discovery.

Changes in regional homogeneity of parieto-temporal regions in panic disorder patients who achieved remission with antidepressant treatment

OBJECTIVE

This study was aimed to study the treatment effects of antidepressant for regional homogeneity (ReHo), an indicator of synchronization of brain function, in panic disorder (PD) patients.

METHOD

Twenty-one remitted PD patients with escitalopram treatment and 21 healthy controls all received 3-T magnetic resonance imaging scanning at baseline and sixth week. We utilized REST (Resting State FMRI Data Analysis Toolkit, version 1.4) to calculate regional homogeneity (ReHo) of patients and controls at baseline and sixth week. We compared the ReHo at baseline with the ReHo at sixth week to estimate the treatment effects for the ReHo of remitted patients. Besides, inter-scan effects were evaluated in the control group. The group-related differences between remitted patients and controls were also estimated.

RESULTS

Remitted PD patients had increases in ReHo of right Heschl gyrus (superior temporal lobe) and decreases in ReHo of right angular gyrus (parietal lobe). The improvements in severity of panic symptoms were negatively correlated with the changes of ReHo in right superior parietal lobe. However, remitted patients still had lower ReHo than controls in right Heschl gyrus and left thalamus.

CONCLUSION

The changes in ReHo of temporo-parietal regions might represent treatment-related ReHo changes for remission of PD. The residual alterations in ReHo of temporo-thalamic regions might represent group-related ReHo differences for patients with PD.

Multivariate decoding of brain images using ordinal regression

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations — whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds — lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection.

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Often in neuroimaging the independent variables are ranked or ordered.

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Classification and regression models cannot explicitly model an ordinal target.

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We present a novel multivariate ordinal regression approach for neuroimaging data.

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Our results show that ordinal regression is a powerful method for ranking data.

Cortical functional connectivity decodes subconscious, task-irrelevant threat-related emotion processing

It is currently unclear to what extent cortical structures are required for and engaged during subconscious processing of biologically salient affective stimuli (i.e. the 'low-road' vs. 'many-roads' hypotheses). Here we show that cortical-cortical and cortical-subcortical functional connectivity (FC) contain substantially more information, relative to subcortical-subcortical FC (i.e. 'subcortical alarm' and other limbic regions), that predicts subliminal fearful face processing within individuals using training data from separate subjects. A plot of classification accuracy vs. number of selected whole-brain FC features revealed 92% accuracy when learning was based on the top 8 features from each training set. The most informative FC was between right amygdala and precuneus, which increased during subliminal fear conditions, while left and right amygdala FC decreased, suggesting a bilateral decoupling of this key limbic region during processing of subliminal fear-related stimuli. Other informative FC included angular gyrus, middle temporal gyrus and cerebellum. These findings identify FC that decodes subliminally perceived, task-irrelevant affective stimuli, and suggest that cortical structures are actively engaged by and appear to be essential for subliminal fear processing.