Aberrant concordance among dynamics of spontaneous brain activity in patients with migraine without aura: A multivariate pattern analysis study

Background

Alterations in the static and dynamic characteristics of spontaneous brain activity have been extensively studied to investigate functional brain changes in migraine without aura (MwoA). However, alterations in concordance among the dynamics of spontaneous brain activity in MwoA remain largely unknown. This study aimed to determine the possibilities of diagnosis based on the concordance indices.

Methods

Resting-state functional MRI scans were performed on 32 patients with MwoA and 33 matched healthy controls (HCs) in the first cohort, as well as 36 patients with MwoA and 32 HCs in the validation cohort. The dynamic indices including fractional amplitude of low-frequency fluctuation, regional homogeneity, voxel-mirrored homotopic connectivity, degree centrality and global signal connectivity were analyzed. We calculated the concordance of grey matter volume-wise (across voxels) and voxel-wise (across time windows) to quantify the degree of integration among different functional levels represented by these dynamic indices. Subsequently, the voxel-wise concordance alterations were analyzed as features for multi-voxel pattern analysis (MVPA) utilizing the support vector machine.

Results

Compared with that of HCs, patients with MwoA had lower whole-grey matter volume-wise concordance, and the mean value of volume-wise concordance was negatively correlated with the frequency of migraine attacks. The MVPA results revealed that the most discriminative brain regions were the right thalamus, right cerebellar Crus II, left insula, left precentral gyrus, right cuneus, and left inferior occipital gyrus.

Conclusions

Concordance alterations in the dynamics of spontaneous brain activity in brain regions could be an important feature in the identification of patients with MwoA.

Altered effective connectivity from cerebellum to motor cortex in chronic low back pain: A multivariate pattern analysis and spectral dynamic causal modeling study

To explore the central processing mechanism of pain perception in chronic low back pain (cLBP) using multi-voxel pattern analysis (MVPA) based on the static and dynamic fractional amplitude of low-frequency fluctuations (fALFF) analysis, and spectral dynamic causal modeling (spDCM). Thirty-two patients with cLBP and 29 matched healthy controls (HCs) for the first cohort and 24 patients with cLBP and 22 HCs for the validation cohort underwent resting-state fMRI scan. The alterations in static and dynamic fALFF were as classification features to distinguish patients with cLBP from HCs. The brain regions gotten from the MVPA results were used for further spDCM analysis. We found that the most discriminative brain regions that contributed to the classification were the right supplementary motor area (SMA.R), left paracentral lobule (PCL.L), and bilateral cerebellar Crus II. The spDCM results displayed decreased excitatory influence from the bilateral cerebellar Crus II to PCL.L in patients with cLBP compared with HCs. Moreover, the conversion of effective connectivity from the bilateral cerebellar Crus II to SMA.R from excitatory influence to inhibitive influence, and the effective connectivity strength exhibited partially mediated effects on Chinese Short Form Oswestry Disability Index Questionnaire (C-SFODI) scores. Our findings suggest that the cerebellum and its weakened or inhibited connections to the motor cortex may be one of the underlying feedback pathways for pain perception in cLBP, and partially mediate the degree of dysfunction.

Disturbance of information in superior parietal lobe during dual-task interference in a simulated driving task

Performing a secondary task while driving causes a decline in driving performance. This phenomenon, called dual-task interference, can have lethal consequences. Previous fMRI studies have looked at the changes in the average brain activity to uncover the neural correlates of dual-task interference. From these results, it is unclear whether the overall modulations in brain activity result from general effects such as task difficulty, attentional modulations, and mental effort or whether it is caused by a change in the responses specific to each condition due to dual-task interference. To overcome this limitation, here, we used multi-voxel pattern analysis (MVPA) to interrogate the change in the information content in multiple brain regions during dual-task interference in simulated driving. Participants performed a lane-change task in a simulated driving environment, along with a tone discrimination task with either short or long onset time difference (Stimulus Onset Asynchrony, SOA) between the two tasks. Behavioral results indicated a robust dual-task effect on lane-change reaction time (RT). MVPA revealed regions that carry information about the driving lane-change direction (shift right/shift left), including the superior parietal lobe (SPL), visual, and motor regions. Comparison of decoding accuracies across SOA conditions in the SPL region revealed lower accuracy in the short compared to the long SOA condition. This change in accuracy was not observed in the visual and motor regions. These findings suggest that the dual-task interference in driving may be related to the disturbance of information processing in the SPL region.

Deciphering Functional Connectivity Differences Between Motor Imagery and Execution of Target-Oriented Grasping

This study aimed to delineate overlapping and distinctive functional connectivity in visual motor imagery, kinesthetic motor imagery, and motor execution of target-oriented grasping action of the right hand. Functional magnetic resonance imaging data were obtained from 18 right-handed healthy individuals during each condition. Seed-based connectivity and multi-voxel pattern analyses were employed after selecting seed regions with the left primary motor cortex and supplementary motor area. There was equivalent seed-based connectivity during the three conditions in the bilateral frontoparietal and temporal areas. When the seed region was the left primary motor cortex, increased connectivity was observed in the left cuneus and superior frontal area during visual and kinesthetic motor imageries, respectively, compared with that during motor execution. Multi-voxel pattern analyses revealed that each condition was differentiated by spatially distributed connectivity patterns of the left primary motor cortex within the right cerebellum VI, cerebellum crus II, and left lingual area. When the seed region was the left supplementary motor area, the connectivity patterns within the right putamen, thalamus, cerebellar areas IV-V, and left superior parietal lobule were significantly classified above chance level across the three conditions. The present findings improve our understanding of the spatial representation of functional connectivity and its specific patterns among motor imagery and motor execution. The strength and fine-grained connectivity patterns of the brain areas can discriminate between motor imagery and motor execution.

A sculpting effect of reading on later representational quality of phonology revealed by multi-voxel pattern analysis in young children

Using univariate analysis, a previous study by Wang et al. (2020) found a scaffolding effect of earlier phonological representation in superior temporal gyrus (STG) on later reading skill but failed to observe a sculpting effect of earlier reading on later phonological representation. The current study applied multi-voxel pattern analysis (MVPA) to examine if both scaffolding and sculpting effects were present in young children. We found that better initial reading skill predicted higher decoding coefficient of brain activity patterns for phonological representations in STG. This sculpting effect was present only for decoding small grain sizes (phonemes) and in younger children (6- to 7.5-year-olds), as we did not find any effects for large grain sizes (rhymes) or older children (7.5- to 9.5-year-olds). Although a scaffolding effect was not observed, the current study provides the first neural evidence of how earlier reading sculpts later phonological awareness in beginning readers.

Neural compensation in manifest neurodegeneration: systems neuroscience evidence from social cognition in frontotemporal dementia

BACKGROUND

It has been argued that symptom onset in neurodegeneration reflects the overload of compensatory mechanisms. The present study aimed to investigate whether neural functional compensation can be observed in the manifest neurodegenerative disease stage, by focusing on a core deficit in frontotemporal dementia, i.e. social cognition, and by combining psychophysical assessment, structural MRI and functional MRI with multidimensional neural markers that allow quantification of neural computations.

METHODS

Nineteen patients with clinically manifest behavioral variant frontotemporal dementia (bvFTD) and 20 controls performed facial expression recognition tasks in the MRI-scanner and offline. Group differences in grey matter volume, neural response amplitude and neural patterns were assessed via a combination of voxel-wise whole-brain, searchlight, and ROI-analyses and these measures were correlated with psychophysical measures of emotion, valence and arousal ratings.

RESULTS

Significant group effects were observed only outside task-relevant regions, converging in the caudate nucleus. This area showed a diagnostic neural pattern as well as hyperactivation and stronger neural representation of facial expressions in the bvFTD sample. Furthermore, response amplitude was associated with behavioral arousal ratings.

CONCLUSIONS

The combined findings reveal converging support for compensatory processes in clinically manifest neurodegeneration, complementing accounts that clinical onset synchronizes with the breakdown of compensatory processes. Furthermore, active compensation may proceed along nodes in intrinsically connected networks, rather than along the more task-specific networks. The findings underscore the potential of distributed multidimensional functional neural characteristics that may provide a novel class of biomarkers with both diagnostic and therapeutic implications, including biomarkers for clinical trials.

Partly recovery and compensation in anterior cingulate cortex after SSRI treatment-evidence from multi-voxel pattern analysis over resting state fMRI in depression

BACKGROUND

Anterior cingulate cortex (ACC) plays an essential role in the pathophysiology of major depressive disorder (MDD) and its treatment. However, it's still unclear whether the effects of disease and antidepressant treatment on ACC perform diversely in neural mechanisms.

METHODS

Fifty-nine MDD patients completed resting-state fMRI scanning twice at baseline and after 12-week selective serotonin reuptake inhibitor (SSRI) treatment, respectively in acute state and remission state. Fifty-nine demographically matched healthy controls were enrolled. Using fractional amplitude of low-frequency fluctuation (fALFF) in ACC as features, we performed multi-voxel pattern analysis over pretreatment MDD patients vs health control (HC), and over pretreatment MDD patients vs posttreatment MDD patients.

RESULTS

Discriminative regions in ACC for MDD impairment and changes after antidepressants were obtained. The intersection set and difference set were calculated to form ACC subregions of recovered, unrecovered and compensative, respectively. The recovered ACC subregion mainly distributed in rostral ACC (80 %) and the other two subregions had nearly equal distribution over dorsal ACC and rostral ACC. Furthermore, only the compensative subregion had significant changed functional connectivity with cingulo-opercular control network (CON) after antidepressant treatment.

LIMITATIONS

The number of subjects was relatively small. The results need to be validated with larger sample sizes and multisite data.

CONCLUSIONS

This finding suggested that the local function of ACC was partly recovered on regulating emotion after antidepressant by detecting the common subregional targets of depression impairment and antidepressive effect. Besides, changed fALFF in the compensative ACC subregion and its connectivity with CON may partly compensate for the cognition deficits.

Olfactory decoding is positively associated with ad libitum food intake in sated humans

The role of olfaction in eating behavior and body weight regulation is controversial. Here we reanalyzed data from a previous functional magnetic resonance imaging study to test whether central olfactory coding is associated with hunger/satiety state, food intake, and change in body weight over one year in healthy human adults. Since odor quality and category are coded across distributed neural patterns that are not discernible with traditional univariate analyses, we used multi-voxel pattern analyses to decode patterns of brain activation to food versus nonfood odors. We found that decoding accuracies in the piriform cortex and amygdala were greater in the sated compared to hungry state. Sated decoding accuracies in these and other regions were also associated with post-scan ad libitum food intake, but not with weight change. These findings demonstrate that the fidelity of olfactory decoding is influenced by meal consumption and is associated with immediate food intake, but not longer-term body weight regulation.

Predicting the severity of internet gaming disorder with resting-state brain features: A multi-voxel pattern analysis

BACKGROUND

Internet gaming disorder (IGD) has become a worldwide mental health concern; however, the neural mechanism underlying this disorder remains unclear. Multivoxel pattern analysis (MVPA), a newly developed data-driven approach, can be used to investigate the neural features of IGD based on massive neural data.

METHODS

Resting-state fMRI data from four hundred and two participants with varying levels of IGD severity were recruited. Regional homogeneity (ReHo) and the amplitude of low-frequency fluctuation (ALFF) were calculated and subsequently decoded by applying MVPA. The highly weighted regions in both predictive models were selected as regions of interest for further graph theory and Granger causality analysis (GCA) to explore how they affect IGD severity.

RESULTS

The results revealed that the neural patterns of ReHo and ALFF can independently and significantly predict IGD severity. The highly weighted regions that contributed to both predictive models were the right precentral gyrus and left postcentral gyrus. Moreover, topological properties of the right precentral gyrus were significantly correlated with IGD severity; further GCA revealed effective connectivity from the right precentral gyrus to left precentral gyrus and dorsal anterior cingulate cortex, both of which were significantly associated with IGD severity.

CONCLUSIONS

The present study demonstrated that IGD has distinctive neural patterns, and this pattern could be found by machine learning. In addition, the neural features in the right precentral gyrus play a key role in predicting IGD severity. The current study revealed the neural features of IGD and provided a potential target for IGD interventions using brain modulation.

Decoding of Motor Imagery Involving Whole-body Coordination

The present study investigated whether different types of motor imageries can be classified based on the location of the activation peaks or the multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) and compared the difference between visual motor imagery (VI) and kinesthetic motor imagery (KI). During fMRI scanning sessions, 25 participants imagined four movements included in the Motor Imagery Questionnaire-Revised (MIQ-R): knee lift, jump, arm movement, and waist bend. These four imagined movements were then classified based on the peak location or the patterns of fMRI signal values. We divided the participants into two groups based on whether they found it easier to generate VI (VI group, n = 10) or KI (KI group, n = 15). Our results show that the imagined movements can be classified using both the location of the activation peak and the spatial activation patterns within the sensorimotor cortex, and MVPA performs better than the activation peak classification. Furthermore, our result reveals that the KI group achieved a higher MVPA decoding accuracy within the left primary somatosensory cortex than the VI group, suggesting that the modality of motor imagery differently affects the classification performance in distinct brain regions.

Action goals and the praxis network: an fMRI study

The praxis representation network (PRN) of the left cerebral hemisphere is typically linked to the control of functional interactions with familiar tools. Surprisingly, little is known about the PRN engagement in planning and execution of tool-directed actions motivated by non-functional but purposeful action goals. Here we used functional neuroimaging to perform both univariate and multi-voxel pattern analyses (MVPA) in 20 right-handed participants who planned and later executed, with their dominant and non-dominant hands, disparate grasps of tools for different goals, including: (1) planning simple vs. demanding functional grasps of conveniently vs. inconveniently oriented tools with an intention to immediately use them, (2) planning simple—but non-functional—grasps of inconveniently oriented tools with a goal to pass them to a different person, (3) planning reaching movements directed at such tools with an intention to move/push them with the back of the hand, and (4) pantomimed execution of the earlier planned tasks. While PRN contributed to the studied interactions with tools, the engagement of its critical nodes, and/or complementary right hemisphere processing, was differently modulated by task type. E.g., planning non-functional/structural grasp-to-pass movements of inconveniently oriented tools, regardless of the hand, invoked the left parietal and prefrontal nodes significantly more than simple, non-demanding functional grasps. MVPA corroborated decoding capabilities of critical PRN areas and some of their right hemisphere counterparts. Our findings shed new lights on how performance of disparate action goals influences the extraction of object affordances, and how or to what extent it modulates the neural activity within the parieto-frontal brain networks.

Neural classification of internet gaming disorder and prediction of treatment response using a cue-reactivity fMRI task in young men

BACKGROUND

Neural mechanisms underlying internet gaming disorder (IGD) are important for diagnostic considerations and treatment development. However, neurobiological underpinnings of IGD remain relatively poorly understood.

METHODS

We employed multi-voxel pattern analysis (MVPA), a machine-learning approach, to examine the potential of neural features to statistically predict IGD status and treatment outcome (percentage change in weekly gaming time) for IGD. Cue-reactivity fMRI-task data were collected from 40 male IGD subjects and 19 male healthy control (HC) subjects. 23 IGD subjects received 6 weeks of craving behavioral intervention (CBI) treatment. MVPA was applied to classify IGD subjects from HCs and statistically predict clinical outcomes.

RESULTS

MVPA displayed a high (92.37%) accuracy (sensitivity of 90.00% and specificity of 94.74%) in the classification of IGD and HC subjects. The most discriminative brain regions that contribute to classification were the bilateral middle frontal gyrus, precuneus, and posterior lobe of the right cerebellum. MVPA statistically predicted clinical outcomes in the craving behavioral intervention (CBI) group (r = 0.48, p = 0.0032). The most strongly implicated brain regions in the prediction model were the right middle frontal gyrus, superior frontal gyrus, supramarginal gyrus, anterior/posterior lobes of the cerebellum and left postcentral gyrus.

CONCLUSIONS

The findings about cue-reactivity neural correlates could help identify IGD subjects and predict CBI-related treatment outcomes provide mechanistic insight into IGD and its treatment and may help promote treatment development efforts.

Pattern of cerebellar grey matter loss associated with ataxia severity in spinocerebellar ataxias type 3: a multi-voxel pattern analysis

Spinocerebellar ataxias type 3 (SCA3) patients are clinically characterized by progressive cerebellar ataxia combined with degeneration of the cerebellum. Previous neuroimaging studies have indicated ataxia severity associated with cerebellar atrophy using univariate methods. However, whether cerebellar atrophy patterns can be used to quantitatively predict ataxia severity in SCA3 patients at the individual level remains largely unexplored. In this study, a group of 66 SCA3 patients and 58 healthy controls were included. Disease duration and ataxia assessment, including the Scale for the Assessment and Rating of Ataxia (SARA) and the International Cooperative Ataxia Rating Scale (ICARS), were collected for SCA3 patients. The high-resolution T1-weighted MRI was obtained, and cerebellar grey matter (GM) was extracted using a spatially unbiased infratentorial template toolbox for all participants. We investigated the association between the pattern of cerebellar grey matter (GM) loss and ataxia assessment in SCA3 by using a multivariate machine learning technique. We found that the application of RVR allowed quantitative prediction of both SARA scores (leave-one-subject-out cross-validation: correlation = 0.56, p-value = 0.001; mean squared error (MSE) = 20.51, p-value = 0.001; ten-fold cross-validation: correlation = 0.52, p-value = 0.001; MSE = 21.00, p-value = 0.001) and ICARS score (leave-one-subject-out cross-validation: correlation = 0.59, p-value = 0.001; MSE = 139.69, p-value = 0.001; ten-fold cross-validation: correlation = 0.57, p-value = 0.001; MSE = 145.371, p-value = 0.001) with statistically significant accuracy. These results provide proof-of-concept that ataxia severity in SCA3 patients can be predicted by the alteration pattern of cerebellar GM using multi-voxel pattern analysis.

Representation of semantic typicality in brain activation in healthy adults and individuals with aphasia: A multi-voxel pattern analysis

This study aimed to investigate brain regions that show different activation patterns between semantically typical and atypical items in both healthy adults and individuals with aphasia (PWA). Eighteen neurologically healthy adults and twenty-one PWA participated in an fMRI semantic feature verification task that included typical and atypical stimuli from five different semantic categories. A whole-brain searchlight multi-voxel pattern analysis (MVPA) was conducted to classify brain activation patterns between typical and atypical conditions in each participant group separately. Behavioral responses were faster and more accurate for typical vs. atypical items across both groups. The searchlight MVPA identified two significant clusters in healthy adults: left middle occipital gyrus and right calcarine cortex, but no significant clusters were found in PWA. A follow-up analysis in PWA revealed a significant association between neural classification of semantic typicality in the left middle occipital gyrus and reaction times in the fMRI task. When the typicality effect was examined for each semantic category at the univariate level, significance was identified in the visual cortex for fruits in both groups of participants. These findings suggest that semantic typicality was modulated in the visual cortex in healthy individuals, but to a lesser extent in the same region in PWA.

Age differences in Neural Activation to Face Trustworthiness: Voxel Pattern and Activation Level Assessments

Judgment of trustworthiness is an important social ability. Many studies show neural activation differences to variations in face trustworthiness in brain reward regions. A previously published analysis of the present fMRI data showed that older adults' (OA) reward region activation responded significantly to trustworthiness in a set of older and younger faces, whereas younger adults' (YA) activation did not-a finding inconsistent with studies that used only younger faces. We hypothesized that voxel pattern analyses would be more sensitive to YA neural responses to trustworthiness in our set of faces, replicating YA neural discrimination in prior literature. Based on evidence for OA neural dedifferentiation, we also hypothesized that voxel pattern analyses would more accurately classify YA than OA neural responses to face trustworthiness. We reanalyzed the data with two pattern classification models and evaluated the models' performance with permutation testing. Voxel patterns discriminated face trustworthiness levels in both YA and OA reward regions, while allowing better classification of face trustworthiness for YA than OA, the reverse of previous results for neural activation levels. The moderation of age differences by analytic method shines a light on the possibility that voxel patterns uniquely index neural representations of the stimulus information content, consistent with findings of impaired representation with age.

Intrinsic functional network contributions to the relationship between trait empathy and subjective happiness

Subjective happiness (well-being) is a multi-dimensional construct indexing one's evaluations of everyday emotional experiences and life satisfaction, and has been associated with different aspects of trait empathy. Despite previous research identifying the neural substrates of subjective happiness and empathy, the mechanisms mediating the relationship between the two constructs remain largely unclear. Here, we performed a data-driven, multi-voxel pattern analysis of whole-brain intrinsic functional connectivity to reveal the neural mechanisms of subjective happiness and trait empathy in a sample of young females. Behaviorally, we found that subjective happiness was negatively associated with personal distress (i.e., self-referential experience of others' feelings). Consistent with this inverse relationship, subjective happiness was associated with the dorsolateral prefrontal cortex exhibiting decreased functional connectivity with regions important for the representation of unimodal sensorimotor information (e.g., primary sensory cortices) or multi-modal summaries of brain states (e.g., default mode network) and increased functional connectivity with regions important for the attentional modulation of these representations (e.g., frontoparietal, attention networks). Personal distress was associated with the medial prefrontal cortex exhibiting functional connectivity differences with similar networks--but in the opposite direction. Finally, intrinsic functional connectivity within and between these networks fully mediated the relationship between the two behavioral measures. These results identify an important contribution of the macroscale functional organization of the brain to human well-being, by demonstrating that lower levels of personal distress lead to higher subjective happiness through variation in intrinsic functional connectivity along a neural representation vs. modulation gradient.

Identification of internet gaming disorder individuals based on ventral tegmental area resting-state functional connectivity

Objective neuroimaging markers are imminently in need for more accurate clinical diagnosis of Internet gaming disorder (IGD). Recent neuroimaging evidence suggested that IGD is associated with abnormalities in the mesolimbic dopamine (DA) system. As the key nodes of the DA pathways, ventral tegmental area (VTA) and substantia nigra (SN) and their connected brain regions may serve as potential markers to identify IGD. Therefore, we aimed to develop optimal classifiers to identify IGD individuals by using VTA and bilateral SN resting-state functional connectivity (RSFC) patterns. A dataset including 146 adolescents (66 IGDs and 80 healthy controls (HCs)) was used to build classification models and another independent dataset including 28 subjects (14 IGDs and 14 HCs) was employed to validate the generalization ability of the models. Multi-voxel pattern analysis (MVPA) with linear support vector machine (SVM) was used to select the features. Our results demonstrated that the VTA RSFC circuits successfully identified IGD individuals (mean accuracy: 86.1%, mean sensitivity: 84.5%, mean specificity: 86.6%, the mean area under the receiver operating characteristic curve: 0.91). Furthermore, the independent generalization ability of the VTA RSFC classifier model was also satisfied (accuracy = 78.5%, sensitivity = 71.4%, specificity = 85.8%). The VTA connectivity circuits that were selected as distinguishing features were mainly included bilateral thalamus, right hippocampus, right pallidum, right temporal pole superior gyrus and bilateral temporal superior gyrus. These findings demonstrated that the potential of the resting-state neuroimaging features of VTA RSFC as objective biomarkers for the IGD clinical diagnosis in the future.

Power of mind: Attentional focus rather than palatability dominates neural responding to visual food stimuli in females with overweight

Research investigating neural responses to visual food stimuli has produced inconsistent results. Crucially, high-caloric palatable foods have a double-sided nature - they are often craved but are also considered unhealthy - which may have contributed to the inconsistency in the literature. Taking this double-sided nature into account in the current study, neural responses to individually tailored palatable and unpalatable high caloric food stimuli were measured, while participants' (females with overweight: n = 23) attentional focus was manipulated to be either hedonic or neutral. Notably, results showed that the level of neural activity was not significantly different for palatable than for unpalatable food stimuli. Instead, independent of food palatability, several brain regions (including regions in the mesocorticolimbic system) responded more strongly when attentional focus was hedonic than when neutral (p < 0.05, cluster-based FWE corrected). Multivariate analyses showed that food palatability could be decoded from multi-voxel patterns of neural activity (p < 0.05, FDR corrected), mostly with a hedonic attentional focus. These findings illustrate that the level of neural activity might not be proportionate to the palatability of foods, but that food palatability can be decoded from multi-voxel patterns of neural activity. Moreover, they underline the importance of considering attentional focus when measuring food-related neural responses.

Social network proximity predicts similar trajectories of psychological states: Evidence from multi-voxel spatiotemporal dynamics

Homophily is a prevalent characteristic of human social networks: individuals tend to associate and bond with others who are similar to themselves with respect to physical traits and demographic attributes, such as age, gender, and ethnicity. Recent research using functional magnetic resonance imaging has demonstrated a positive relationship between individuals' real-world social network proximity (i.e., whether they are friends, friends-of-friends, or farther removed in social ties) and inter-subject correlation (ISC) in their time series of neural responses when viewing audiovisual movies. However, conventional ISC methods only capture information about similarity in the temporal evolution of region-averaged neural responses, and ignore information carried in fine-grained, spatially distributed response topographies. Here, we demonstrate that temporal trajectories of multi-voxel response patterns to naturalistic stimuli are exceptionally similar among friends and predictive of social network proximity, over and above the effects of response magnitude fluctuations. Furthermore, inter-subject similarity in the temporal trajectory of multi-voxel response patterns across distant points in time was particularly positively associated with individuals' proximity in their real-world social network. The fact that exceptional similarities among friends were most pronounced in long-range temporal fluctuations of response patterns located in multimodal cortical regions (e.g., regions of posterior parietal cortex) suggests that aspects of high-level processing during naturalistic stimulation may be particularly similar among friends. Given the localization of results, we speculate that socially close individuals may be particularly similar in endogenously driven shifts in how they distribute their attention (e.g., across the environment, within internal representations) over time. These results suggest that friends may experience exceptionally similar trajectories of psychological states when exposed to a common stimulus, and, more generally, that there are meaningful individual differences in the temporal evolution of multi-voxel response patterns during naturalistic stimulation.

Intact neural representations of affective meaning of touch but lack of embodied resonance in autism: a multi-voxel pattern analysis study

Background

Humans can easily grasp the affective meaning of touch when observing social interactions. Several neural systems support this ability, including the theory of mind (ToM) network and the somatosensory system linked to embodied resonance, but it is unclear how these systems are affected in autism spectrum disorder (ASD). Individuals with ASD exhibit impairments in the use of nonverbal communication such as social and reciprocal touch. Despite the importance of touch in social communication and the reported touch aversion in ASD, surprisingly little is known about the neural systems underlying impairments in touch communication in ASD.

Methods

The present study applies a dynamic and socially meaningful stimulus set combined with functional magnetic resonance imaging (fMRI) to pinpoint atypicalities in the neural circuitry underlying socio-affective touch observation in adults with ASD. Twenty-one adults with ASD and 21 matched neurotypical adults evaluated the valence and arousal of 75 video fragments displaying touch interactions. Subsequently, they underwent fMRI while watching the same videos. Using multi-voxel pattern analysis (MVPA) and multiple regression analysis, we examined which brain regions represent the socio-affective meaning of observed touch. To further understand the brain-behavior relationship, we correlated the strength of affective representations in the somatosensory cortex with individuals' attitude towards social touch in general and with a quantitative index of autism traits as measured by the Social Responsiveness Scale.

Results

Results revealed that the affective meaning of touch was well represented in the temporoparietal junction, a core mentalizing area, in both groups. Conversely, only the neurotypical group represented affective touch in the somatosensory cortex, a region involved in self-experienced touch. Lastly, irrespective of the group, individuals with a more positive attitude towards receiving, witnessing, and providing social touch and with a higher score on social responsivity showed more differentiated representations of the affective meaning of touch in these somatosensory areas.

Conclusions

Together, our findings imply that male adults with ASD show intact cognitive understanding (i.e., "knowing") of observed socio-affective touch interactions, but lack of spontaneous embodied resonance (i.e., "feeling").

Atlas-Based Classification Algorithms for Identification of Informative Brain Regions in fMRI Data

Multi-voxel pattern analysis (MVPA) has been successfully applied to neuroimaging data due to its larger sensitivity compared to univariate traditional techniques. Searchlight is the most widely employed approach to assign functional value to different regions of the brain. However, its performance depends on the size of the sphere, which can overestimate the region of activation when a large sphere size is employed. In the current study, we examined the validity of two different alternatives to Searchlight: an atlas-based local averaging method (ABLA, Schrouff et al. Neuroinformatics 16, 117-143, 2013a) and a Multi-Kernel Learning (MKL, Rakotomamonjy et al. Journal of Machine Learning 9, 2491-2521, 2008) approach, in a scenario where the goal is to find the informative brain regions that support certain mental operations. These methods employ weights to measure the informativeness of a brain region and highly reduce the large computational cost that Searchlight entails. We evaluated their performance in two different scenarios where the differential BOLD activation between experimental conditions was large vs. small, and employed nine different atlases to assess the influence of diverse brain parcellations. Results show that both methods were able to localize informative regions when differences between conditions were large, demonstrating a large sensitivity and stability in the identification of regions across atlases. Moreover, the sign of the weights reported by these methods provided the directionality of univariate approaches. However, when differences were small, only ABLA localized informative regions. Thus, our results show that atlas-based methods are useful alternatives to Searchlight, but that the nature of the classification to perform should be taken into account when choosing the specific method to implement.

The representation of symmetry in multi-voxel response patterns and functional connectivity throughout the ventral visual stream

Several computational models explain how symmetry might be detected and represented in the human brain. However, while there is an abundance of psychophysical studies on symmetry detection and several neural studies showing where and when symmetry is detected in the brain, important questions remain about how this detection happens and how symmetric patterns are represented. We studied the representation of (vertical) symmetry in regions of the ventral visual stream, using multi-voxel pattern analyses (MVPA) and functional connectivity analyses. Our results suggest that neural representations gradually change throughout the ventral visual stream, from very similar part-based representations for symmetrical and asymmetrical stimuli in V1 and V2, over increasingly different representations for symmetrical and asymmetrical stimuli which are nevertheless still part-based in both V3 and V4, to a more holistic representation for symmetrical compared to asymmetrical stimuli in high-level LOC. This change in representations is accompanied by increased communication between left and right retinotopic areas, evidenced by higher interhemispheric functional connectivity during symmetry perception in areas V2 and V4.

Turning down the heat: Neural mechanisms of cognitive control for inhibiting task-irrelevant emotional information during adolescence

One major question in the cognitive neuroscience of cognitive control is whether prefrontal regions contribute to control by upregulating the processing of task-relevant material or by downregulating the processing of task-irrelevant material. Here we take a unique approach to addressing this question by using multi-voxel pattern analysis, which allowed us to determine the degree to which each of the task-relevant and task-irrelevant dimensions of a stimulus are being processed in posterior cortex on a trial-by-trial basis. In our study, adolescent participants performed an emotion word - emotional face Stroop task requiring them to determine the emotional valence (positive, negative) of a task-relevant word in the context of a task-irrelevant emotional face. Using mediation models, we determined whether activation of a major cognitive control region, the dorsolateral prefrontal cortex (DLPFC), influences reaction time on a trial-by-trial basis directly or if it does so indirectly by modulating processing of the task-relevant and/or task-irrelevant information in posterior brain regions. To examine the specificity of the effects observed for the DLPFC, similar analyses were performed for the amygdala, a brain region involved in processing of the salient task-irrelevant emotional information. For both congruent and incongruent trials, increased DLPFC activity on a given trial was associated with reduced perceptual processing of the task-irrelevant face, consistent with the idea that top-down cognitive control can modulate processing of task-irrelevant information. No effect of DLPFC activity was observed on processing of the task-relevant word. However, increased processing of the task-relevant word was associated with longer RT on congruent trials but not incongruent trials, which may reflect a need for greater processing of the task-relevant word to overcome any influence of the pre-potent task-irrelevant face. In a more exploratory aspect of our investigation, multi-level moderated mediation models were used to examine the influence of individual differences on the observed brain-behavior relationships. For congruent trials, the influence of task-irrelevant face processing on RT was decreased in individuals with higher self-reported Executive Control and increased in those with higher levels of self-reported Negative Affect. These results suggest that cognitive control regions in prefrontal cortex during adolescence can suppress the processing of task-irrelevant information in sensory cortex to influence performance (RT). The processing of task-relevant information may also influence performance, but such processing did not reveal evidence of being modulated by cognitive control regions. Moreover, these effects are sensitive to individual differences in the self-reported ability to exert cognitive and affective control. As such, we provide insights into the more precise mechanisms by which cognitive control influences task performance on a trial-by-trial basis during adolescence.

Stability of representational geometry across a wide range of fMRI activity levels

Fine-grained activity patterns, as measured with functional magnetic resonance imaging (fMRI), are thought to reflect underlying neural representations. Multivariate analysis techniques, such as representational similarity analysis (RSA), can be used to test models of brain representation by quantifying the representational geometry (the collection of pair-wise dissimilarities between activity patterns). One important caveat, however, is that non-linearities in the coupling between neural activity and the fMRI signal may lead to significant distortions in the representational geometry estimated from fMRI activity patterns. Here we tested the stability of representational dissimilarity measures in primary sensory-motor (S1 and M1) and early visual regions (V1/V2) across a large range of activation levels. Participants were visually cued with different letters to perform single finger presses with one of the 5 fingers at a rate of 0.3-2.6 Hz. For each stimulation frequency, we quantified the difference between the 5 activity patterns in M1, S1, and V1/V2. We found that the representational geometry remained relatively stable, even though the average activity increased over a large dynamic range. These results indicate that the representational geometry of fMRI activity patterns can be reliably assessed, largely independent of the average activity in the region. This has important methodological implications for RSA and other multivariate analysis approaches that use the representational geometry to make inferences about brain representations.

Influence of activation pattern estimates and statistical significance tests in fMRI decoding analysis

The use of Multi-Voxel Pattern Analysis (MVPA) has increased considerably in recent functional magnetic resonance imaging (fMRI) studies. A crucial step consists in the choice of a method for the estimation of responses. However, a systematic comparison of the different estimation alternatives and their adequacy to predominant experimental design is missing. In the current study we compared three pattern estimation methods: Least-Squares Unitary (LSU), based on run-wise estimation, Least-Squares All (LSA) and Least-Squares Separate (LSS), which rely on trial-wise estimation. We compared the efficiency of these methods in an experiment where sustained activity needed to be isolated from zero-duration events as well as in a block-design approach and in a event-related design. We evaluated the sensitivity of the t-test in comparison with two non-parametric methods based on permutation testing: one proposed in Stelzer et al. (2013), equivalent to performing a permutation in each voxel separately and the Threshold-Free Cluster Enhancement. LSS resulted the most accurate approach to address the large overlap of signal among close events in the event-related designs. We found a larger sensitivity of Stelzer's method in all settings, especially in the event-related designs, where voxels close to surpass the statistical threshold with the other approaches were now marked as informative regions. Our results provide evidence that LSS is the most accurate approach for unmixing events with different duration and large overlap of signal. This is consistent with previous studies showing that LSS handles large collinearity better than other methods. Moreover, Stelzer's potentiates this better estimation with its large sensitivity.

Pattern component modeling: A flexible approach for understanding the representational structure of brain activity patterns

Representational models specify how complex patterns of neural activity relate to visual stimuli, motor actions, or abstract thoughts. Here we review pattern component modeling (PCM), a practical Bayesian approach for evaluating such models. Similar to encoding models, PCM evaluates the ability of models to predict novel brain activity patterns. In contrast to encoding models, however, the activity of individual voxels across conditions (activity profiles) are not directly fitted. Rather, PCM integrates over all possible activity profiles and computes the marginal likelihood of the data under the activity profile distribution specified by the representational model. By using an analytical expression for the marginal likelihood, PCM allows the fitting of flexible representational models, in which the relative strength and form of the encoded feature spaces can be estimated from the data. We present here a number of different ways in which such flexible representational models can be specified, and how models of different complexity can be compared. We then provide a number of practical examples from our recent work in motor control, ranging from fixed models to more complex non-linear models of brain representations. The code for the fitting and cross-validation of representational models is provided in an open-source software toolbox.

Potential for false positive results from multi-voxel pattern analysis on functional imaging data

BACKGROUND

Multi-voxel pattern analysis (MVPA) provides a powerful tool to investigate neural mechanisms for various cognitive processes under functional brain imaging. However, the high sensitivity of the MVPA method could bring about false positive results, which has been overlooked by previous research.

OBJECTIVE

We investigated the potential for obtaining false positives from the MVPA method.

METHODS

We conducted MVPA on a public functional MRI dataset on the neural encoding of various object categories. Different scenarios for pattern classification were involved by varying the number of voxels for each region of interest (ROI) and the number of object categories.

RESULTS

The classification accuracy became higher with more voxels involved, and false positive results emerged for the primary auditory cortex and even a white matter ROI, where object-related neural processing was not supposed to occur.

CONCLUSIONS

Our results imply that the classification accuracy obtained from MVPA may be inflated due to the high sensitivity of the method. Therefore, we suggest involving control ROIs in future MVPA studies and comparing the classification accuracy for a target ROI with that for a control ROI, instead of comparing the obtained accuracy with the chance-level accuracy.

Different activity patterns for action and language within their shared neural areas: An fMRI study on action observation and language phonology

The neural processes for action and language activate shared brain regions including the left inferior frontal, parietal and temporal-occipital cortices. However, it still remains unclear how action and language are related and what neural activity patterns are elicited within these shared cortical regions. In this study we examined the neural activation for action observation and language phonology in their shared cortical regions by conducting three experiments in a single fMRI session: a mixed-task experiment involving both action and language phonological processing, and two independent experiments involving language phonology and action observation respectively. To control for differences in the visual processing and to enable a direct comparison between the tasks, the same visual stimuli were used for the mixed-tasks. Common neural areas for action observation and language phonology were located in the junction of the left inferior frontal/precentral gyrus, the left intraparietal sulcus and the left temporal-occipital cortex. Nevertheless, multi-voxel pattern analysis on the shared neural areas revealed that different patterns of neural activity were elicited for the action and language phonological tasks. Our results provide the first neuroimaging evidence that the common neural structures are engaged differently by action and language phonological processing.

Lingering representations of stimuli influence recall organization

Several prominent theories posit that information about recent experiences lingers in the brain and organizes memories for current experiences, by forming a temporal context that is linked to those memories at encoding. According to these theories, if the thoughts preceding an experience X resemble the thoughts preceding an experience Y, then X and Y should show an elevated probability of being recalled together. We tested this prediction by using multi-voxel pattern analysis (MVPA) of fMRI data to measure neural evidence for lingering processing of preceding stimuli. As predicted, memories encoded with similar lingering thoughts about the category of preceding stimuli were more likely to be recalled together. Our results demonstrate that the "fading embers" of previous stimuli help to organize recall, confirming a key prediction of computational models of episodic memory.

Distributed neural signatures of natural audiovisual speech and music in the human auditory cortex

During a conversation or when listening to music, auditory and visual information are combined automatically into audiovisual objects. However, it is still poorly understood how specific type of visual information shapes neural processing of sounds in lifelike stimulus environments. Here we applied multi-voxel pattern analysis to investigate how naturally matching visual input modulates supratemporal cortex activity during processing of naturalistic acoustic speech, singing and instrumental music. Bayesian logistic regression classifiers with sparsity-promoting priors were trained to predict whether the stimulus was audiovisual or auditory, and whether it contained piano playing, speech, or singing. The predictive performances of the classifiers were tested by leaving one participant at a time for testing and training the model using the remaining 15 participants. The signature patterns associated with unimodal auditory stimuli encompassed distributed locations mostly in the middle and superior temporal gyrus (STG/MTG). A pattern regression analysis, based on a continuous acoustic model, revealed that activity in some of these MTG and STG areas were associated with acoustic features present in speech and music stimuli. Concurrent visual stimulus modulated activity in bilateral MTG (speech), lateral aspect of right anterior STG (singing), and bilateral parietal opercular cortex (piano). Our results suggest that specific supratemporal brain areas are involved in processing complex natural speech, singing, and piano playing, and other brain areas located in anterior (facial speech) and posterior (music-related hand actions) supratemporal cortex are influenced by related visual information. Those anterior and posterior supratemporal areas have been linked to stimulus identification and sensory-motor integration, respectively.

Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis

The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-classifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classifiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.

Exploring stability-based voxel selection methods in MVPA using cognitive neuroimaging data: a comprehensive study

Feature selection plays a key role in multi-voxel pattern analysis because functional magnetic resonance imaging data are typically noisy, sparse, and high-dimensional. Although the conventional evaluation criterion is the classification accuracy, selecting a stable feature set that is not sensitive to the variance in dataset may provide more scientific insights. In this study, we aim to investigate the stability of feature selection methods and test the stability-based feature selection scheme on two benchmark datasets. Top-k feature selection with a ranking score of mutual information and correlation, recursive feature elimination integrated with support vector machine, and L1 and L2-norm regularizations were adapted to a bootstrapped stability selection framework, and the selected algorithms were compared based on both accuracy and stability scores. The results indicate that regularization-based methods are generally more stable in StarPlus dataset, but in Haxby dataset they failed to perform as well as others.

Neural representation of object orientation: A dissociation between MVPA and Repetition Suppression

How is object orientation represented in the brain? Behavioral error patterns reveal systematic tendencies to confuse certain orientations with one another. Using fMRI, we asked whether more confusable orientations are represented more similarly in object selective cortex (LOC). We compared two widely-used measures of neural similarity: multi-voxel pattern similarity (MVP-similarity) and Repetition Suppression. In LO, we found that multi-voxel pattern similarity was predicted by the confusability of two orientations. By contrast, Repetition Suppression effects in LO were unrelated to the confusability of orientations. To account for these differences between MVP-similarity and Repetition Suppression, we propose that MVP-similarity reflects the topographical distribution of neural populations, whereas Repetition Suppression depends on repeated activation of particular groups of neurons. This hypothesis leads to a unified interpretation of our results and may explain other dissociations between MVPA and Repetition Suppression observed in the literature.

Contour junctions underlie neural representations of scene categories in high-level human visual cortex

Humans efficiently grasp complex visual environments, making highly consistent judgments of entry-level category despite their high variability in visual appearance. How does the human brain arrive at the invariant neural representations underlying categorization of real-world environments? We here show that the neural representation of visual environments in scene-selective human visual cortex relies on statistics of contour junctions, which provide cues for the three-dimensional arrangement of surfaces in a scene. We manipulated line drawings of real-world environments such that statistics of contour orientations or junctions were disrupted. Manipulated and intact line drawings were presented to participants in an fMRI experiment. Scene categories were decoded from neural activity patterns in the parahippocampal place area (PPA), the occipital place area (OPA) and other visual brain regions. Disruption of junctions but not orientations led to a drastic decrease in decoding accuracy in the PPA and OPA, indicating the reliance of these areas on intact junction statistics. Accuracy of decoding from early visual cortex, on the other hand, was unaffected by either image manipulation. We further show that the correlation of error patterns between decoding from the scene-selective brain areas and behavioral experiments is contingent on intact contour junctions. Finally, a searchlight analysis exposes the reliance of visually active brain regions on different sets of contour properties. Statistics of contour length and curvature dominate neural representations of scene categories in early visual areas and contour junctions in high-level scene-selective brain regions.

The 'when' and 'where' of semantic coding in the anterior temporal lobe: Temporal representational similarity analysis of electrocorticogram data

Electrocorticograms (ECoG) provide a unique opportunity to monitor neural activity directly at the cortical surface. Ten patients with subdural electrodes covering ventral and lateral anterior temporal regions (ATL) performed a picture naming task. Temporal representational similarity analysis (RSA) was used, for the first time, to compare spatio-temporal neural patterns from the ATL surface with pre-defined theoretical models. The results indicate that the neural activity in the ventral subregion of the ATL codes semantic representations from 250 msec after picture onset. The observed activation similarity was not related to the visual similarity of the pictures or the phonological similarity of their names. In keeping with convergent evidence for the importance of the ATL in semantic processing, these results provide the first direct evidence of semantic coding from the surface of the ventral ATL and its time-course.

Abnormal brain activation during directed forgetting of negative memory in depressed patients

The frequent occurrence of uncontrollable negative thoughts and memories is a troubling aspect of depression. Thus, knowledge on the mechanism underlying intentional forgetting of these thoughts and memories is crucial to develop an effective emotion regulation strategy for depressed individuals. Behavioral studies have demonstrated that depressed participants cannot intentionally forget negative memories. However, the neural mechanism underlying this process remains unclear. In this study, participants completed the directed forgetting task in which they were instructed to remember or forget neutral or negative words. Standard univariate analysis based on the General Linear Model showed that the depressed participants have higher activation in the inferior frontal gyrus (IFG), superior frontal gyrus (SFG), superior parietal gyrus (SPG), and inferior temporal gyrus (ITG) than the healthy individuals. The results indicated that depressed participants recruited more frontal and parietal inhibitory control resources to inhibit the TBF items, but the attempt still failed because of negative bias. We also used the Support Vector Machine to perform multivariate pattern classification based on the brain activation during directed forgetting. The pattern of brain activity in directed forgetting of negative words allowed correct group classification with an overall accuracy of 75% (P=0.012). The brain regions which are critical for this discrimination showed abnormal activation when depressed participants were attempting to forget negative words. These results indicated that the abnormal neural circuitry when depressed individuals tried to forget the negative words might provide neurobiological markers for depression.

Reliability of dissimilarity measures for multi-voxel pattern analysis

Representational similarity analysis of activation patterns has become an increasingly important tool for studying brain representations. The dissimilarity between two patterns is commonly quantified by the correlation distance or the accuracy of a linear classifier. However, there are many different ways to measure pattern dissimilarity and little is known about their relative reliability. Here, we compare the reliability of three classes of dissimilarity measure: classification accuracy, Euclidean/Mahalanobis distance, and Pearson correlation distance. Using simulations and four real functional magnetic resonance imaging (fMRI) datasets, we demonstrate that continuous dissimilarity measures are substantially more reliable than the classification accuracy. The difference in reliability can be explained by two characteristics of classifiers: discretization and susceptibility of the discriminant function to shifts of the pattern ensemble between imaging runs. Reliability can be further improved through multivariate noise normalization for all measures. Finally, unlike conventional distance measures, crossvalidated distances provide unbiased estimates of pattern dissimilarity on a ratio scale, thus providing an interpretable zero point. Overall, our results indicate that the crossvalidated Mahalanobis distance is preferable to both the classification accuracy and the correlation distance for characterizing representational geometries.

Decoding the direction of imagined visual motion using 7T ultra-high field fMRI

There is a long-standing debate about the neurocognitive implementation of mental imagery. One form of mental imagery is the imagery of visual motion, which is of interest due to its naturalistic and dynamic character. However, so far only the mere occurrence rather than the specific content of motion imagery was shown to be detectable. In the current study, the application of multi-voxel pattern analysis to high-resolution functional data of 12 subjects acquired with ultra-high field 7T functional magnetic resonance imaging allowed us to show that imagery of visual motion can indeed activate the earliest levels of the visual hierarchy, but the extent thereof varies highly between subjects. Our approach enabled classification not only of complex imagery, but also of its actual contents, in that the direction of imagined motion out of four options was successfully identified in two thirds of the subjects and with accuracies of up to 91.3% in individual subjects. A searchlight analysis confirmed the local origin of decodable information in striate and extra-striate cortex. These high-accuracy findings not only shed new light on a central question in vision science on the constituents of mental imagery, but also show for the first time that the specific sub-categorical content of visual motion imagery is reliably decodable from brain imaging data on a single-subject level.

Representational similarity analysis offers a preview of the noradrenergic modulation of long-term fear memory at the time of encoding

Neuroimaging research on emotional memory has greatly advanced our understanding of the pathogenesis of anxiety disorders. While the behavioral expression of fear at the time of encoding does not predict whether an aversive experience will evolve into long-term fear memory, the application of multi-voxel pattern analysis (MVPA) for the analysis of BOLD-MRI data has recently provided a unique marker for memory formation. Here, we aimed to further investigate the utility of this marker by modulating the strength of fear memory with an α2-adrenoceptor antagonist (yohimbine HCl). Fifty-two healthy participants were randomly assigned to two conditions - either receiving 20mg yohimbine or a placebo pill (double-blind) - prior to differential fear conditioning and MRI-scanning. We examined the strength of fear associations during acquisition and retention of fear (48 h later) by assessing the similarity of BOLD-MRI patterns and pupil dilation responses. Additionally, participants returned for a follow-up test outside the scanner (2-4 weeks), during which we assessed fear-potentiated startle responses. Replicating our previous findings, neural pattern similarity reflected the development of fear associations over time, and unlike average activation or pupil dilation, predicted the later expression of fear memory (pupil dilation 48 h later). While no effect of yohimbine was observed on markers of autonomic arousal, including salivary α-amylase (sAA), we obtained indirect evidence for the noradrenergic enhancement of fear memory consolidation: sAA levels showed a strong increase prior to fMRI scanning, irrespective of whether participants had received yohimbine, and this increase correlated with the subsequent expression of fear (48 h later). Remarkably, this noradrenergic enhancement of fear was associated with changes in neural response patterns at the time of learning. These findings provide further evidence that representational similarity analysis is a sensitive tool for studying (enhanced) memory formation.

Predicting moment-to-moment attentional state

Although fluctuations in sustained attention are ubiquitous, most psychological experiments treat them as noise, averaging performance over many trials. The current study uses multi-voxel pattern analysis (MVPA) to decode whether, on each trial of a cognitive task, participants are in an optimal or suboptimal attentional state. During fMRI, participants performed n-back tasks, composed of central face images overlaid on distractor scenes, with low, perceptual, and working memory load. Instructions were to respond to novel faces and withhold response to rare repeats. To index attentional state, reaction time variability was calculated at each correct response. Participants' 50% least variable trials were labeled optimal, or "in the zone," and their 50% most erratic trials were labeled suboptimal, or "out of the zone." Support vector machine classifiers trained on activity in the default mode network (DMN), dorsal attention network (DAN), and task-relevant fusiform face area (FFA) distinguished in-the-zone and out-of-the-zone trials in all tasks. Consistent with evidence that distractors are processed when central task load is low, parahippocampal place area (PPA) classifiers were only successful in the low load task. Classification in anatomical regions across the brain revealed widespread coding of attentional state. In contrast to these robust pattern analyses, univariate signal in DMN, DAN, FFA, and PPA did not distinguish states, suggesting a nuanced relationship to sustained attention. In sum, MVPA can be used to decode trial-by-trial attentional state throughout much of cortex, helping to characterize how attention network fluctuations correlate with performance variability.

Brain-decoding fMRI reveals how wholes relate to the sum of parts

The human brain performs many nonlinear operations in order to extract relevant information from local inputs. How can we observe and quantify these effects within and across large patches of cortex? In this paper, we discuss the application of multi-voxel pattern analysis (MVPA) in functional magnetic resonance imaging (fMRI) to address this issue. Specifically, we show how MVPA (i) allows to compare various possibilities of part combinations into wholes, such as taking the mean, weighted mean, or the maximum of responses to the parts; (ii) can be used to quantify the parameters of these combinations; and (iii) can be applied in various experimental paradigms. Through these procedures, fMRI helps to obtain a computational understanding of how local information is integrated into larger wholes in various cortical regions.

Multi-voxel pattern analysis of noun and verb differences in ventral temporal cortex

Recent evidence suggests a probabilistic relationship exists between the phonological/orthographic form of a word and its lexical-syntactic category (specifically nouns vs. verbs) such that syntactic prediction may elicit form-based estimates in sensory cortex. We tested this hypothesis by conducting multi-voxel pattern analysis (MVPA) of fMRI data from early visual cortex (EVC), left ventral temporal (VT) cortex, and a subregion of the latter - the left mid fusiform gyrus (mid FG), sometimes called the "visual word form area." Crucially, we examined only those volumes sampled when subjects were predicting, but not viewing, nouns and verbs. This allowed us to investigate prediction effects in visual areas without any bottom-up orthographic input. We found that voxels in VT and mid FG, but not in EVC, were able to classify noun-predictive trials vs. verb-predictive trials in sentence contexts, suggesting that sentence-level predictions are sufficient to generate word form-based estimates in visual areas.

Fine-grained stimulus representations in body selective areas of human occipito-temporal cortex

Neurophysiological and functional imaging studies have investigated the representation of animate and inanimate stimulus classes in monkey inferior temporal (IT) and human occipito-temporal cortex (OTC). These studies proposed a distributed representation of stimulus categories across IT and OTC and at the same time highlighted category specific modules for the processing of bodies, faces and objects. Here, we investigated whether the stimulus representation within the extrastriate (EBA) and the fusiform (FBA) body areas differed from the representation across OTC. To address this question, we performed an event-related fMRI experiment, evaluating the pattern of activation elicited by 200 individual stimuli that had already been extensively tested in our earlier monkey imaging and single cell studies (Popivanov et al., 2012, 2014). The set contained achromatic images of headless monkey and human bodies, two sets of man-made objects, monkey and human faces, four-legged mammals, birds, fruits, and sculptures. The fMRI response patterns within EBA and FBA primarily distinguished bodies from non-body stimuli, with subtle differences between the areas. However, despite responding on average stronger to bodies than to other categories, classification performance for preferred and non-preferred categories was comparable. OTC primarily distinguished animate from inanimate stimuli. However, cluster analysis revealed a much more fine-grained representation with several homogeneous clusters consisting entirely of stimuli of individual categories. Overall, our data suggest that category representation varies with location within OTC. Nevertheless, body modules contain information to discriminate also non-preferred stimuli and show an increasing specificity in a posterior to anterior gradient.

Representation of response alternatives in human presupplementary motor area: multi-voxel pattern analysis in a go/no-go task

A debate exists as to the role of the presupplementary motor area (preSMA) in cognitive control. Recent findings suggest that preSMA plays a central role in conflict resolution and encodes response alternatives as opposed to simply the presence of conflict. Evidence of neuronal heterogeneity within preSMA of non-human primates suggests that univariate analysis of functional MRI data may not provide adequate resolution to fully characterize cognitive control-related responses. Here, multi-voxel pattern analysis (MVPA) is employed to examine the distributed patterns of activity in preSMA associated with both successful go responses and no-go inhibitions. In a go/no-go task, univariate analysis showed undifferentiated activation of preSMA in response to both go and no-go stimuli. However, when an anatomically-defined preSMA ROI was subjected to MVPA, a significant difference in the activation pattern encoded by go as compared to no-go stimuli was observed. These differences in preSMA activation are consistent with the ongoing maintenance and manipulation of stimulus-action representations.

Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA

Multi-voxel pattern analysis (MVPA) is a fruitful and increasingly popular complement to traditional univariate methods of analyzing neuroimaging data. We propose to replace the standard 'decoding' approach to searchlight-based MVPA, measuring the performance of a classifier by its accuracy, with a method based on the multivariate form of the general linear model. Following the well-established methodology of multivariate analysis of variance (MANOVA), we define a measure that directly characterizes the structure of multi-voxel data, the pattern distinctness D. Our measure is related to standard multivariate statistics, but we apply cross-validation to obtain an unbiased estimate of its population value, independent of the amount of data or its partitioning into 'training' and 'test' sets. The estimate D^ can therefore serve not only as a test statistic, but also as an interpretable measure of multivariate effect size. The pattern distinctness generalizes the Mahalanobis distance to an arbitrary number of classes, but also the case where there are no classes of trials because the design is described by parametric regressors. It is defined for arbitrary estimable contrasts, including main effects (pattern differences) and interactions (pattern changes). In this way, our approach makes the full analytical power of complex factorial designs known from univariate fMRI analyses available to MVPA studies. Moreover, we show how the results of a factorial analysis can be used to obtain a measure of pattern stability, the equivalent of 'cross-decoding'.

Format-dependent representations of symbolic and non-symbolic numbers in the human cortex as revealed by multi-voxel pattern analyses

Neuroimaging studies in the last 20 years have tried to unravel the neural correlates of number processing across formats in humans and non-human primates. Results point to the intraparietal sulcus as the core area for an abstract representation of numerical quantity. On the other hand, there exist a variety of behavioral and neuroimaging data that are difficult to reconcile with the existence of such an abstract representation. In this study, we addressed this issue by applying multi-voxel pattern analysis (MVPA) to functional Magnetic Resonance Imaging (fMRI) data to unravel the neural representations of symbolic (digits) and non-symbolic (dots) numbers and their possible overlap on three different spatial scales (entire lobules, smaller regions of interest and a searchlight analysis with 2-voxel radius). Results showed that numbers in both formats are decodable in occipital, frontal, temporal and parietal regions. However, there were no overlapping representations between dots and digits on any of the spatial scales. These data suggest that the human brain does not contain an abstract representation of numerical magnitude.

Representations of individuals in ventral temporal cortex defined by faces and biographies

The fusiform gyrus responds more strongly to faces than to other categories of objects. This response could reflect either categorical detection of faces or recognition of particular facial identities. Recent fMRI studies have attempted to address the question of what information is encoded in these regions, but have reported mixed results. We tested whether the creation of richer identity representations via training on visual and social information, and the use of an adaptation design, would reveal more robust representations of these identities in ventral temporal cortex. Examining the patterns of activation across voxels in bilateral fusiform gyri, we identified unique patterns for particular identities. Attaching distinctive biographical information to identities did not increase the strength of these representations, but did produce a grouping effect: faces associated with the same amount of biographical information were represented more similarly to each other. These results are consistent with the possibility that identity exemplars are represented in posterior visual areas best known for their role in representing categorical information, and suggest that these areas may be sensitive to some forms of non-visual information, including from the social domain.

The neural representation of face space dimensions

Functional neural imaging studies have identified a network of brain areas that are more active to faces than to other objects. However, it remains largely unclear how these areas encode individual facial identity. To investigate the neural representations of facial identity, we constructed a multidimensional face space structure, whose dimensions were derived from geometric information of faces using the Principal Component Analysis (PCA). Using fMRI, we recorded participants' neural responses when viewing blocks of faces that differed only on one dimension within a block. Although the response magnitudes to different blocks of faces did not differ in a univariate analysis, multi-voxel pattern analysis revealed distinct patterns related to different face space dimensions in brain areas that have a higher response magnitude to faces than to other objects. The results indicate that dimensions of the face space are encoded in the face-selective brain areas in a spatially distributed way.

Right fusiform response patterns reflect visual object identity rather than semantic similarity

We previously reported the neuropsychological consequences of a lesion confined to the middle and posterior part of the right fusiform gyrus (case JA) causing a partial loss of knowledge of visual attributes of concrete entities in the absence of category-selectivity (animate versus inanimate). We interpreted this in the context of a two-step model that distinguishes structural description knowledge from associative-semantic processing and implicated the lesioned area in the former process. To test this hypothesis in the intact brain, multi-voxel pattern analysis was used in a series of event-related fMRI studies in a total of 46 healthy subjects. We predicted that activity patterns in this region would be determined by the identity of rather than the conceptual similarity between concrete entities. In a prior behavioral experiment features were generated for each entity by more than 1000 subjects. Based on a hierarchical clustering analysis the entities were organised into 3 semantic clusters (musical instruments, vehicles, tools). Entities were presented as words or pictures. With foveal presentation of pictures, cosine similarity between fMRI response patterns in right fusiform cortex appeared to reflect both the identity of and the semantic similarity between the entities. No such effects were found for words in this region. The effect of object identity was invariant for location, scaling, orientation axis and color (grayscale versus color). It also persisted for different exemplars referring to a same concrete entity. The apparent semantic similarity effect however was not invariant. This study provides further support for a neurobiological distinction between structural description knowledge and processing of semantic relationships and confirms the role of right mid-posterior fusiform cortex in the former process, in accordance with previous lesion evidence.

Predicting intrinsic brain activity

Multivariate supervised learning methods exhibit a remarkable ability to decode externally driven sensory, behavioral, and cognitive states from functional neuroimaging data. Although they are typically applied to task-based analyses, supervised learning methods are equally applicable to intrinsic effective and functional connectivity analyses. The obtained models of connectivity incorporate the multivariate interactions between all brain regions simultaneously, which will result in a more accurate representation of the connectome than the ones available with standard bivariate methods. Additionally the models can be applied to decode or predict the time series of intrinsic brain activity of a region from an independent dataset. The obtained prediction accuracy provides a measure of the integration between a brain region and other regions in its network, as well as a method for evaluating acquisition and preprocessing pipelines for resting state fMRI data. This article describes a method for learning multivariate models of connectivity. The method is applied in the non-parametric prediction accuracy, influence, and reproducibility-resampling (NPAIRS) framework, to study the regional variation of prediction accuracy and reproducibility (Strother et al., 2002). The resulting spatial distribution of these metrics is consistent with the functional hierarchy proposed by Mesulam (1998). Additionally we illustrate the utility of the multivariate regression connectivity modeling method for optimizing experimental parameters and assessing the quality of functional neuroimaging data.