Task-induced brain state manipulation improves prediction of individual traits

There is no single functional atlas even for a single individual: Functional parcel definitions change with task

The goal of human brain mapping has long been to delineate the functional subunits in the brain and elucidate the functional role of each of these brain regions. Recent work has focused on whole-brain parcellation of functional Magnetic Resonance Imaging (fMRI) data to identify these subunits and create a functional atlas. Functional connectivity approaches to understand the brain at the network level require such an atlas to assess connections between parcels and extract network properties. While no single functional atlas has emerged as the dominant atlas to date, there remains an underlying assumption that such an atlas exists. Using fMRI data from a highly sampled subject as well as two independent replication data sets, we demonstrate that functional parcellations based on fMRI connectivity data reconfigure substantially and in a meaningful manner, according to brain state.

Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals

Individual differences in working memory relate to performance differences in general cognitive ability. The neural bases of such individual differences, however, remain poorly understood. Here, using a data-driven technique known as connectome-based predictive modeling, we built models to predict individual working memory performance from whole-brain functional connectivity patterns. Using n-back or rest data from the Human Connectome Project, connectome-based predictive models significantly predicted novel individuals' 2-back accuracy. Model predictions also correlated with measures of fluid intelligence and, with less strength, sustained attention. Separate fluid intelligence models predicted working memory score, as did sustained attention models, again with less strength. Anatomical feature analysis revealed significant overlap between working memory and fluid intelligence models, particularly in utilization of prefrontal and parietal regions, and less overlap in predictive features between working memory and sustained attention models. Furthermore, showing the generality of these models, the working memory model developed from Human Connectome Project data generalized to predict memory in an independent data set of 157 older adults (mean age = 69 years; 48 healthy, 54 amnestic mild cognitive impairment, 55 Alzheimer disease). The present results demonstrate that distributed functional connectivity patterns predict individual variation in working memory capability across the adult life span, correlating with constructs including fluid intelligence and sustained attention.

The Functional Brain Organization of an Individual Allows Prediction of Measures of Social Abilities Transdiagnostically in Autism and Attention-Deficit/Hyperactivity Disorder

BACKGROUND

Autism spectrum disorder and attention-deficit/hyperactivity disorder (ADHD) are associated with complex changes as revealed by functional magnetic resonance imaging. To date, neuroimaging-based models are not able to characterize individuals with sufficient sensitivity and specificity. Further, although evidence shows that ADHD traits occur in individuals with autism spectrum disorder, and autism spectrum disorder traits in individuals with ADHD, the neurofunctional basis of the overlap is undefined.

METHODS

Using individuals from the Autism Brain Imaging Data Exchange and ADHD-200, we apply a data-driven, subject-level approach, connectome-based predictive modeling, to resting-state functional magnetic resonance imaging data to identify brain-behavior associations that are predictive of symptom severity. We examine cross-diagnostic commonalities and differences.

RESULTS

Using leave-one-subject-out and split-half analyses, we define networks that predict Social Responsiveness Scale, Autism Diagnostic Observation Schedule, and ADHD Rating Scale scores and confirm that these networks generalize to novel subjects. Networks share minimal overlap of edges (<2%) but some common regions of high hubness (Brodmann areas 10, 11, and 21, cerebellum, and thalamus). Further, predicted Social Responsiveness Scale scores for individuals with ADHD are linked to ADHD symptoms, supporting the hypothesis that brain organization relevant to autism spectrum disorder severity shares a component associated with attention in ADHD. Predictive connections and high-hubness regions are found within a wide range of brain areas and across conventional networks.

CONCLUSIONS

An individual's functional connectivity profile contains information that supports dimensional, nonbinary classification in autism spectrum disorder and ADHD. Furthermore, we can determine disorder-specific and shared neurofunctional pathology using our method.

Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction

OBJECTIVE

To explain individual differences in development, behavior, and cognition, most previous studies focused on projecting resting-state functional MRI (fMRI) based functional connectivity (FC) data into a low-dimensional space via linear dimensionality reduction techniques, followed by executing analysis operations. However, linear dimensionality analysis techniques may fail to capture the nonlinearity of brain neuroactivity. Moreover, besides resting-state FC, the FC based on task fMRI can be expected to provide complementary information. Motivated by these considerations, we nonlinearly fuse resting-state and task-based FC networks (FCNs) to seek a better representation in this paper.

METHODS

We propose a framework based on alternating diffusion map (ADM), which extracts geometry-preserving low-dimensional embeddings that successfully parameterize the intrinsic variables driving the phenomenon of interest. Specifically, we first separately build resting-state and task-based FCNs by symmetric positive definite matrices using sparse inverse covariance estimation for each subject, and then utilize the ADM to fuse them in order to extract significant low-dimensional embeddings, which are used as fingerprints to identify individuals.

RESULTS

The proposed framework is validated on the Philadelphia Neurodevelopmental Cohort data, where we conduct extensive experimental study on resting-state and fractal n-back task fMRI for the classification of intelligence quotient (IQ). The fusion of resting-state and n-back task fMRI by the proposed framework achieves better classification accuracy than any single fMRI, and the proposed framework is shown to outperform several other data fusion methods.

CONCLUSION AND SIGNIFICANCE

To our knowledge, this paper is the first to demonstrate a successful extension of the ADM to fuse resting-state and task-based fMRI data for accurate prediction of IQ.

Combining multiple connectomes improves predictive modeling of phenotypic measures

Resting-state and task-based functional connectivity matrices, or connectomes, are powerful predictors of individual differences in phenotypic measures. However, most of the current state-of-the-art algorithms only build predictive models based on a single connectome for each individual. This approach neglects the complementary information contained in connectomes from different sources and reduces prediction performance. In order to combine different task connectomes into a single predictive model in a principled way, we propose a novel prediction framework, termed multidimensional connectome-based predictive modeling. Two specific algorithms are developed and implemented under this framework. Using two large open-source datasets with multiple tasks-the Human Connectome Project and the Philadelphia Neurodevelopmental Cohort, we validate and compare our framework against performing connectome-based predictive modeling (CPM) on each task connectome independently, CPM on a general functional connectivity matrix created by averaging together all task connectomes for an individual, and CPM with a naïve extension to multiple connectomes where each edge for each task is selected independently. Our framework exhibits superior performance in prediction compared with the other competing methods. We found that different tasks contribute differentially to the final predictive model, suggesting that the battery of tasks used in prediction is an important consideration. This work makes two major contributions: First, two methods for combining multiple connectomes from different task conditions in one predictive model are demonstrated; Second, we show that these models outperform a previously validated single connectome-based predictive model approach.

Cortical and subcortical alterations associated with precision visuomotor behavior in individuals with autism spectrum disorder

In addition to core deficits in social-communication abilities and repetitive behaviors and interests, many patients with autism spectrum disorder (ASD) experience developmental comorbidities, including sensorimotor issues. Sensorimotor issues are common in ASD and associated with more severe clinical symptoms. Importantly, sensorimotor behaviors are precisely quantifiable and highly translational, offering promising targets for neurophysiological studies of ASD. We used functional MRI to identify brain regions associated with sensorimotor behavior using a visually guided precision gripping task in individuals with ASD (n = 20) and age-, IQ-, and handedness-matched controls (n = 18). During visuomotor behavior, individuals with ASD showed greater force variability than controls. The blood oxygen level-dependent signal for multiple cortical and subcortical regions was associated with force variability, including motor and premotor cortex, posterior parietal cortex, extrastriate cortex, putamen, and cerebellum. Activation in the right premotor cortex scaled with sensorimotor variability in controls but not in ASD. Individuals with ASD showed greater activation than controls in left putamen and left cerebellar lobule VIIb, and activation in these regions was associated with more severe clinically rated symptoms of ASD. Together, these results suggest that greater sensorimotor variability in ASD is associated with altered cortical-striatal processes supporting action selection and cortical-cerebellar circuits involved in feedback-guided reactive adjustments of motor output. Our findings also indicate that atypical organization of visuomotor cortical circuits may result in heightened reliance on subcortical circuits typically dedicated to motor skill acquisition. Overall, these results provide new evidence that sensorimotor alterations in ASD involve aberrant cortical and subcortical organization that may contribute to key clinical issues in patients.NEW & NOTEWORTHY This is the first known study to examine functional brain activation during precision visuomotor behavior in autism spectrum disorder (ASD). We replicate previous findings of elevated force variability in ASD and find these deficits are associated with atypical function of ventral premotor cortex, putamen, and posterolateral cerebellum, indicating cortical-striatal processes supporting action selection and cortical-cerebellar circuits involved in feedback-guided reactive adjustments of motor output may be key targets for understanding the neurobiology of ASD.

Regions and Connections: Complementary Approaches to Characterize Brain Organization and Function

Functional magnetic resonance imaging has proved to be a powerful tool to characterize spatiotemporal patterns of human brain activity. Analysis methods broadly fall into two camps: those summarizing properties of a region and those measuring interactions among regions. Here we pose an unappreciated question in the field: What are the strengths and limitations of each approach to study fundamental neural processes? We explore the relative utility of region- and connection-based measures in the context of three topics of interest: neurobiological relevance, brain-behavior relationships, and individual differences in brain organization. In each section, we offer illustrative examples. We hope that this discussion offers a novel and useful framework to support efforts to better understand the macroscale functional organization of the brain and how it relates to behavior.

Reconstruction of behavior-relevant individual brain activity: an individualized fMRI study

Different patterns of brain activity are observed in various subjects across a wide functional domain. However, these individual differences, which are often neglected through the group average, are not yet completely understood. Based on the fundamental assumption that human behavior is rooted in the underlying brain function, we speculated that the individual differences in brain activity are reflected in the individual differences in behavior. Adopting 98 behavioral measures and assessing the brain activity induced at seven task functional magnetic resonance imaging states, we demonstrated that the individual differences in brain activity can be used to predict behavioral measures of individual subjects with high accuracy using the partial least square regression model. In addition, we revealed that behavior-relevant individual differences in brain activity transferred between different task states and can be used to reconstruct individual brain activity. Reconstructed individual brain activity retained certain individual differences which were lost in the group average and could serve as an individual functional localizer. Therefore, our results suggest that the individual differences in brain activity contain behavior-relevant information and should be included in group averaging. Moreover, reconstructed individual brain activity shows a potential use in precise and personalized medicine.

A Manifold Regularized Multi-Task Learning Model for IQ Prediction From Two fMRI Paradigms

OBJECTIVE

Multi-modal brain functional connectivity (FC) data have shown great potential for providing insights into individual variations in behavioral and cognitive traits. The joint learning of multi-modal data can utilize intrinsic association, and thus can boost learning performance. Although several multi-task based learning models have already been proposed by viewing feature learning on each modality as one task, most of them ignore the structural information inherent across the modalities, which may play an important role in extracting discriminative features.

METHODS

In this paper, we propose a new manifold regularized multi-task learning model by simultaneously considering between-subject and between-modality relationships. Specifically, the l_2,1-norm (i.e., group-sparsity) regularizer is enforced to jointly select a few common features across different modalities. A novelly designed manifold regularizer is further imposed as a crucial underpinning to preserve the structural information both within and between modalities. Such designed regularizers will make our model more adaptive to realistic neuroimaging data, which are usually of small sample size but high dimensional features.

RESULTS

Our model is validated on the Philadelphia Neurodevelopmental Cohort dataset, where our modalities are regarded as two types of functional MRI (fMRI) data collected under two paradigms. We conduct experimental studies on fMRI-based FC network data in two task conditions for intelligence quotient (IQ) prediction. The results show that our proposed model can not only achieve improved prediction performance, but also yield a set of IQ-relevant biomarkers.

CONCLUSION AND SIGNIFICANCE

This paper develops a new multi-task learning model, enabling the discovery of significant biomarkers that may account for a proportion of the variance in human intelligence.

Application of Convolutional Recurrent Neural Network for Individual Recognition Based on Resting State fMRI Data

In most task and resting state fMRI studies, a group consensus is often sought, where individual variability is considered a nuisance. None the less, biological variability is an important factor that cannot be ignored and is gaining more attention in the field. One recent development is the individual identification based on static functional connectome. While the original work was based on the static connectome, subsequent efforts using recurrent neural networks (RNN) demonstrated that the inclusion of temporal features greatly improved identification accuracy. Given that convolutional RNN (ConvRNN) seamlessly integrates spatial and temporal features, the present work applied ConvRNN for individual identification with resting state fMRI data. Our result demonstrates ConvRNN achieving a higher identification accuracy than conventional RNN, likely due to better extraction of local features between neighboring ROIs. Furthermore, given that each convolutional output assembles in-place features, they provide a natural way for us to visualize the informative spatial pattern and temporal information, opening up a promising new avenue for analyzing fMRI data.

General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks

Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

Deep Collaborative Learning With Application to the Study of Multimodal Brain Development

OBJECTIVE

Multi-modal functional magnetic resonance imaging has been widely used for brain research. Conventional data-fusion methods cannot capture complex relationship (e.g., nonlinear predictive relationship) between multiple data. This paper aims to develop a neural network framework to extract phenotype related cross-data relationships and use it to study the brain development.

METHODS

We propose a novel method, deep collaborative learning (DCL), to address the limitation of existing methods. DCL first uses a deep network to represent original data and then seeks their correlations, while also linking the data representation with phenotypical information.

RESULTS

We studied the difference of functional connectivity (FCs) between different age groups and also use FCs as a fingerprint to predict cognitive abilities. Our experiments demonstrated higher accuracy of using DCL over other conventional models when classifying populations of different ages and cognitive scores. Moreover, DCL revealed that brain connections became stronger at adolescence stage. Furthermore, DCL detected strong correlations between default mode network and other networks which were overlooked by linear canonical correlation analysis, demonstrating DCL's ability of detecting nonlinear correlations.

CONCLUSION

The results verified the superiority of DCL over conventional data-fusion methods. In addition, the stronger brain connection demonstrated the importance of adolescence stage for brain development.

SIGNIFICANCE

DCL can better combine complex correlations between multiple data sets in addition to their fitting to phenotypes, with the potential to overcome the limitations of several current data-fusion models.

Resting Frontal Eeg Asymmetry and Personality Traits: A Meta–Analysis

Frontal asymmetry has been widely used as a marker of emotion, motivation, and psychopathology. When assessed during the resting state, it is regarded as an index of trait approach and withdrawal motivation. However, the replicability of these associations with personality is currently unclear. The present meta–analysis seeks to provide a comprehensive quantitative review of the relationship between personality traits and resting electroencephalographic (EEG) frontal asymmetry. We distinguished five personality clusters: extraversion, neuroticism, impulsivity, anger, and defensiveness. Data from 79 independent samples with overall 5700 participants were included in the meta–analysis. The results revealed that less than 0.4% of the variance in extraversion and neuroticism could be explained by resting frontal asymmetry. Similarly, a small effect was observed for trait anger, and a small–sized to medium–sized effect was observed for defensiveness, although the number of studies was very low. No significant effect emerged for impulsivity. The effects were further reduced after adjustment for publication bias. Given some evidence for heterogeneity, sub–traits were analysed, and methodological moderators were investigated. Based on the results, we conclude that the validity of resting frontal asymmetry as a marker for personality is not supported. Finally, recommendations are given to increase the replicability of frontal asymmetry research. © 2019 European Association of Personality Psychology

Ten simple rules for predictive modeling of individual differences in neuroimaging

Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches. We hope these ten rules will increase the use of predictive models with neuroimaging data.

The individual functional connectome is unique and stable over months to years

Functional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions of the entire brain. Using four resting-state fMRI datasets with a wide range of ages, we show that individual differences of the functional connectome are stable across 3 months to 1-2 years (and even detectable at above-chance levels across 3 years). Medial frontal and frontoparietal networks appear to be both unique and stable, resulting in high ID rates, as did a combination of these two networks. We conduct analyses demonstrating that these results are not driven by head motion. We also show that edges contributing the most to a successful ID tend to connect nodes in the frontal and parietal cortices, while edges contributing the least tend to connect cross-hemispheric homologs. Our results demonstrate that the functional connectome is stable across years and that high ID rates are not an idiosyncratic aspect of a specific dataset, but rather reflect stable individual differences in the functional connectivity of the brain.

Connectome-Based Prediction of Cocaine Abstinence

OBJECTIVE

The authors sought to identify a brain-based predictor of cocaine abstinence by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. CPM is a predictive tool and a method of identifying networks that underlie specific behaviors ("neural fingerprints").

METHODS

Fifty-three individuals participated in neuroimaging protocols at the start of treatment for cocaine use disorder, and again at the end of 12 weeks of treatment. CPM with leave-one-out cross-validation was conducted to identify pretreatment networks that predicted abstinence (percent cocaine-negative urine samples during treatment). Networks were applied to posttreatment functional MRI data to assess changes over time and ability to predict abstinence during follow-up. The predictive ability of identified networks was then tested in a separate, heterogeneous sample of individuals who underwent scanning before treatment for cocaine use disorder (N=45).

RESULTS

CPM predicted abstinence during treatment, as indicated by a significant correspondence between predicted and actual abstinence values (r=0.42, df=52). Identified networks included connections within and between canonical networks implicated in cognitive/executive control (frontoparietal, medial frontal) and in reward responsiveness (subcortical, salience, motor/sensory). Connectivity strength did not change with treatment, and strength at posttreatment assessment also significantly predicted abstinence during follow-up (r=0.34, df=39). Network strength in the independent sample predicted treatment response with 64% accuracy by itself and 71% accuracy when combined with baseline cocaine use.

CONCLUSIONS

These data demonstrate that individual differences in large-scale neural networks contribute to variability in treatment outcomes for cocaine use disorder, and they identify specific abstinence networks that may be targeted in novel interventions.

Goal-Directed Processing of Naturalistic Stimuli Modulates Large-Scale Functional Connectivity

Humans selectively process external information according to their internal goals. Previous studies have found that cortical activity and interactions between specific cortical areas such as frontal-parietal regions are modulated by behavioral goals. However, these results are largely based on simple stimuli and task rules in laboratory settings. Here, we investigated how top-down goals modulate whole-brain functional connectivity (FC) under naturalistic conditions. Analyses were conducted on a publicly available functional magnetic resonance imaging (fMRI) dataset (OpenfMRI database, accession number: ds000233) collected on twelve participants who made either behavioral or taxonomic judgments of behaving animals containing in naturalistic video clips. The task-evoked FC patterns of the participants were extracted using a novel inter-subject functional correlation (ISFC) method that increases the signal-to-noise ratio for detecting task-induced inter-regional correlation compared with standard FC analysis. Using multivariate pattern analysis (MVPA) methods, we successfully predicted the task goals of the participants with ISFC patterns but not with standard FC patterns, suggests that the ISFC method may be an efficient tool for exploring subtle network differences between brain states. We further examined the predictive power of several canonical brain networks and found that many within-network and across-network ISFC measures supported task goals classification. Our findings suggest that goal-directed processing of naturalistic stimuli systematically modulates large-scale brain networks but is not limited to the local neural activity or connectivity of specific regions.

Common Functional Brain States Encode both Perceived Emotion and the Psychophysiological Response to Affective Stimuli

Multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data has critically advanced the neuroanatomical understanding of affect processing in the human brain. Central to these advancements is the brain state, a temporally-succinct fMRI-derived pattern of neural activation, which serves as a processing unit. Establishing the brain state's central role in affect processing, however, requires that it predicts multiple independent measures of affect. We employed MVPA-based regression to predict the valence and arousal properties of visual stimuli sampled from the International Affective Picture System (IAPS) along with the corollary skin conductance response (SCR) for demographically diverse healthy human participants (n = 19). We found that brain states significantly predicted the normative valence and arousal scores of the stimuli as well as the attendant individual SCRs. In contrast, SCRs significantly predicted arousal only. The prediction effect size of the brain state was more than three times greater than that of SCR. Moreover, neuroanatomical analysis of the regression parameters found remarkable agreement with regions long-established by fMRI univariate analyses in the emotion processing literature. Finally, geometric analysis of these parameters also found that the neuroanatomical encodings of valence and arousal are orthogonal as originally posited by the circumplex model of dimensional emotion.