A neuromarker of sustained attention from whole-brain functional connectivity

Neural Interactions in a Spatially-Distributed Cortical Network During Perceptual Decision-Making

Behavioral experiments evidence that attention is not maintained at a constant level, but fluctuates with time. Recent studies associate such fluctuations with dynamics of attention-related cortical networks, however the exact mechanism remains unclear. To address this issue, we consider functional neuronal interactions during the accomplishment of a reaction time (RT) task which requires sustained attention. The participants are subjected to a binary classification of a large number of presented ambiguous visual stimuli with different degrees of ambiguity. Generally, high ambiguity causes high RT and vice versa. However, we demonstrate that RT fluctuates even when the stimulus ambiguity remains unchanged. The analysis of neuronal activity reveals that the subject's behavioral response is preceded by the formation of a distributed functional network in the β-frequency band. This network is characterized by high connectivity in the frontal cortex and supposed to subserve a decision-making process. We show that neither the network structure nor the duration of its formation depend on RT and stimulus ambiguity. In turn, RT is related to the moment of time when the β-band functional network emerges. We hypothesize that RT is affected by the processes preceding the decision-making stage, e.g., encoding visual sensory information and extracting decision-relevant features from raw sensory information.

Dysfunction between dorsal caudate and salience network associated with impaired cognitive flexibility in obsessive-compulsive disorder: A resting-state fMRI study

Background

Impaired cognitive flexibility has been implicated in the genetic basis of obsessive-compulsive disorder (OCD). Recent endophenotype studies of OCD showed neural inefficiency in the cognitive control network and interference by the limbic network of the cognitive control network. Exploring the relationship between the functional brain network and impaired cognitive flexibility may provide novel information about the neurobiological basis of OCD.

Methods

We obtained resting-state functional magnetic resonance imaging (rsfMRI) scans and measured the cognitive flexibility of 37 medication-free OCD patients and 40 healthy control (HC) participants using the Wisconsin Card Sorting Test (WCST). We explored the difference between OCD and HC groups in the functional brain network related to impaired cognitive flexibility from the amygdala and dorsal striatal regions of interest (ROIs) by using a seed-based approach.

Results

Significant differences between the OCD and HC groups were identified in the resting state functional network from the dorsal caudate. Increased functional connectivity from the dorsal caudate to the dorsal anterior cingulate cortex (dACC) and anterior insula (AI) was associated with poorer cognitive flexibility in the OCD group, but better cognitive flexibility in the HC group.

Conclusions

These results provide evidence that the impaired cognitive flexibility of OCD may be associated with dysfunctions of the brain network from the dorsal caudate (DC) to important nodes of the salience network. Our results extend the neuropsychological model of OCD by showing intrinsically different associations between OCD and HC in functional network and cognitive flexibility.

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Increased functional connectivity from the dorsal caudate to the dorsal anterior cingulate cortex and anterior insula was associated with poorer cognitive flexibility in the OCD group, but better cognitive flexibility in the HC group.

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Our results may suggest that the dysfunction from DC to SN is associated with impaired cognitive flexibility of OCD.

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These findings could provide additional insights into the important role of cooperative interactions between the dorsal striatum and the large-scale intrinsic brain networks in human cognitive function.

Dynamic functional connectivity states characterize NREM sleep and wakefulness

According to recent neuroimaging studies, temporal fluctuations in functional connectivity patterns can be clustered into dynamic functional connectivity (DFC) states and correspond to fluctuations in vigilance. However, whether there consistently exist DFC states associated with wakefulness and sleep stages and what are the characteristics and electrophysiological origin of these states remain unclear. The aims of the current study were to investigate the properties of DFC in different sleep stages and to explore the relationship between the characteristics of DFC and slow‐wave activity. We collected both eyes‐closed wakefulness and sleep data from 48 healthy young volunteers with simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. EEG data were employed as the gold standard of sleep stage scoring, and DFC states were estimated based on fMRI data. The results demonstrated that DFC states of the fMRI signals consistently corresponded to wakefulness and nonrapid eye movement sleep stages independent of the number of clusters. Furthermore, the mean dwell time of these states significantly correlated with slow‐wave activity. The inclusion or omission of regression of the global signal and the selection of parcellation schemes exerted minimal effects on the current findings. These results provide strong evidence that DFC states underlying fMRI signals match the fluctuations of vigilance and suggest a possible electrophysiological source of DFC states corresponding to vigilance states.

Cognitive functioning in glioma patients is related to functional connectivity measures of the non-tumoural hemisphere

Previous studies have shown that cognitive functioning in patients with brain tumour is associated with the functional network characteristics of specific resting‐state networks or with whole‐brain network characteristics. These studies, however, did not acknowledge the functional contribution of areas in the contralesional, non‐tumoural hemisphere, even though these healthy remote areas likely play a critical role in compensating for the loss of function in damaged tissue. In the current study, we examined whether there is an association between cognitive performance and functional network features of the contralesional hemisphere of patients with glioma. We found that local efficiency of the contralesional hemisphere was associated with performance on the reaction time domain, whereas contralesional assortativity was associated with complex attention and cognitive flexibility scores. Our results suggest that a less segregated organization of the contralesional hemisphere is associated with better reaction time scores, whereas a better spread of information over the contralesional hemisphere through mutually interconnected contralesional hubs is associated with better cognitive flexibility and better complex attention scores. These findings urge researchers to recognize the functional contribution of remote, undamaged regions and to focus more on the graph metrics of the contralesional hemisphere in the search for predictors of cognitive functioning in patients with brain tumour.

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.

Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors

Brain functional connectivity features can predict cognition and behavior at the level of the individual. Most studies measure univariate signals, correlating timecourses from the average of constituent voxels in each node. While straightforward, this approach overlooks the spatial patterns of voxel-wise signals within individual nodes. Given that multivariate spatial activity patterns across voxels can improve fMRI measures of mental representations, here we asked whether using voxel-wise timecourses can better characterize region-by-region interactions relative to univariate approaches. Using two fMRI datasets, the Human Connectome Project sample and a local test-retest sample, we measured multivariate functional connectivity with multivariate distance correlation and univariate connectivity with Pearson's correlation. We compared multivariate and univariate connectivity estimates, demonstrating that relative to univariate estimates, multivariate estimates exhibited higher reliability at both the edge-level and connectome-level, stronger prediction of individual differences, and greater sensitivity to brain states within individuals. Our findings suggest that multivariate estimates reliably provide more powerful information about an individual's functional brain organization and its relation to cognitive skills.

The Roles of Statistics in Human Neuroscience

Statistics plays three important roles in brain studies. They are (1) the study of differences between brains in distinctive populations; (2) the study of the variability in the structure and functioning of the brain; and (3) the study of data reduction on large-scale brain data. I discuss these concepts using examples from past and ongoing research in brain connectivity, brain information flow, information extraction from large-scale neuroimaging data, and neural predictive modeling. Having dispensed with the past, I attempt to present a few areas where statistical science facilitates brain decoding and to write prospectively, in the light of present knowledge and in the quest for artificial intelligence, about questions that statistical and neurobiological communities could work closely together to address in the future.

Accelerated Aging of Functional Brain Networks Supporting Cognitive Function in Psychotic Disorders

BACKGROUND

Across networks, connectivity within the frontoparietal network (FPN) and cingulo-opercular network (CON) exhibits reductions earliest during healthy aging, contributing to cognitive impairment. Individuals with psychotic disorders demonstrate evidence of accelerated aging across multiple biological systems. By leveraging a large sample of patients with psychosis from early to chronic illness stages, this study sought to determine whether the CON and FPN exhibit evidence of accelerated aging in psychotic disorders, confirm associations between network efficiency and cognition, and determine whether reduced network efficiency is observed in early-stage psychosis.

METHODS

Resting-state functional magnetic resonance imaging and cognitive data were obtained on 240 patients with psychotic disorder and 178 healthy control participants (HCs). Global efficiency, a measure of functional integration, was calculated for the CON, FPN, subcortical network, and visual network. Associations with age and cognition were assessed and compared between groups.

RESULTS

Consistent with accelerated aging, significant group by age interactions reflected significantly stronger relationships between efficiency and age in patients with psychosis than in HCs for both the CON (psychosis: r = -.37; HC: r = -.16) and FPN (psychosis: r = -.31; HC: r = -.05). Accelerated aging was not observed in either the subcortical or visual network, suggesting specificity for cognitive networks that decline earliest in healthy aging. Replicating prior findings, efficiency of both the CON and FPN correlated with cognitive function across all participants (rs > .11, ps < .031). Furthermore, patients with chronic psychosis (p = .004), but not patients with early psychosis (p = .553), exhibited significantly lower FPN efficiency compared with HCs.

CONCLUSIONS

Functional integration of higher-order cognitive networks is intact in early psychosis but exhibits evidence of accelerated aging, suggesting the potential for intervention targeting cognition within the early psychosis period.

Phasic alerting effects on visual processing speed are associated with intrinsic functional connectivity in the cingulo-opercular network

Phasic alertness refers to short-lived increases in the brain's "state of readiness", and thus to optimized performance following warning cues. Parametric modelling of whole report task performance based on the computational theory of visual attention (TVA) has demonstrated that visual processing speed is increased in such cue compared to no-cue conditions. Furthermore, with respect to the underlying neural mechanisms, individual visual processing speed has been related to intrinsic functional connectivity (iFC) within the cingulo-opercular network, suggesting that this network's iFC is relevant for the tonic maintenance of an appropriate readiness or alertness state. In the present study, we asked whether iFC in the cingulo-opercular network is also related to the individual ability to actively profit from warning cues, i.e. to the degree of phasic alerting. We obtained resting-state functional magnetic resonance imaging (rs-fMRI) data from 32 healthy young participants and combined an independent component analysis of rs-fMRI time courses and dual regression approach to determine iFC in the cingulo-opercular network. In a separate behavioural testing session, we parametrically assessed the effects of auditory phasic alerting cues on visual processing speed in a TVA-based whole report paradigm. A voxel-wise multiple regression revealed that higher individual phasic alerting effects on visual processing speed were significantly associated with lower iFC in the cingulo-opercular network, with a peak in the left superior orbital gyrus. As phasic alertness was neither related to iFC in other attention-relevant, auditory, or visual networks nor associated with any inter-network connectivity pattern, the results suggest that the individual profit in visual processing speed gained from phasic alerting is primarily associated with iFC in the cingulo-opercular network.

Resting-state dynamics as a neuromarker of dopamine administration in healthy female adults

BACKGROUND

Different neuromarkers of people's emotions, personality traits and behavioural performance have recently been identified. However, not much attention has been devoted to neuromarkers of neural responsiveness to drug administration.

AIMS

We investigated the predictive neuromarkers of acute dopamine (DA) administration.

METHODS

In a double-blind, within-subject study, we administrated a DA agonist (pramipexole) or placebo to 27 healthy female subjects. Using multivariate classification and prediction analyses, we examined whether dopaminergic modulations of task-free resting-state brain dynamics predict individual differences in pramipexole's modulation of facial attractiveness evaluations.

RESULTS

Our results demonstrate that pramipexole's effects on brain dynamics could be successfully discriminated from resting-state functional connectivity (accuracy: 78.9%; p < 0.0001). On the behavioural level, pramipexole increased facial attractiveness evaluations (t₍₃₉₎ = 4.44; p < 0.0001). In particular, pramipexole administration enhanced connectivity strength of the cinguloopercular network (t₍₂₃₎ = 3.29; p = 0.003) and increased brain signal variability in subcortical and prefrontal brain areas (t₍₁₃₎ = 3.05, p = 0.009). Importantly, multivariate predictive models reveal that pramipexole-dependent modulation of resting-state dynamics predicted the increase of facial attractiveness evaluations after pramipexole (connectivity strength: standardized mean squared error, smse = 0.65; p = 0.0007; brain signal variability: smse = 0.94, p = 0.015).

CONCLUSION

These results demonstrate that modulations of resting-state brain dynamics induced by a DA agonist predict drug-related effects on evaluation processes, providing a neuromarker of the neural responsiveness of specific brain networks to DA administration.

An information network flow approach for measuring functional connectivity and predicting behavior

INTRODUCTION

Connectome-based predictive modeling (CPM) is a recently developed machine-learning-based framework to predict individual differences in behavior from functional brain connectivity (FC). In these models, FC was operationalized as Pearson's correlation between brain regions' fMRI time courses. However, Pearson's correlation is limited since it only captures linear relationships. We developed a more generalized metric of FC based on information flow. This measure represents FC by abstracting the brain as a flow network of nodes that send bits of information to each other, where bits are quantified through an information theory statistic called transfer entropy.

METHODS

With a sample of individuals performing a sustained attention task and resting during functional magnetic resonance imaging (fMRI) (n = 25), we use the CPM framework to build machine-learning models that predict attention from FC patterns measured with information flow. Models trained on n - 1 participants' task-based patterns were applied to an unseen individual's resting-state pattern to predict task performance. For further validation, we applied our model to two independent datasets that included resting-state fMRI data and a measure of attention (Attention Network Task performance [n = 41] and stop-signal task performance [n = 72]).

RESULTS

Our model significantly predicted individual differences in attention task performance across three different datasets.

CONCLUSIONS

Information flow may be a useful complement to Pearson's correlation as a measure of FC because of its advantages for nonlinear analysis and network structure characterization.

Identifying inter-individual differences in pain threshold using brain connectome: a test-retest reproducible study

Individuals are unique in terms of brain and behavior. Some are very sensitive to pain, while others have a high tolerance. However, how inter-individual intrinsic differences in the brain are related to pain is unknown. Here, we performed longitudinal test-retest analyses to investigate pain threshold variability among individuals using a resting-state fMRI brain connectome. Twenty-four healthy subjects who received four MRI sessions separated by at least 7 days were included in the data analysis. Subjects' pain thresholds were measured using two modalities of experimental pain (heat and pressure) on two different locations (heat pain: leg and arm; pressure pain: leg and thumbnail). Behavioral results showed strong inter-individual variability and strong within-individual stability in pain threshold. Resting state fMRI data analyses showed that functional connectivity profiles can accurately identify subjects across four sessions, indicating that an individual's connectivity profile may be intrinsic and unique. By using multivariate pattern analyses, we found that connectivity profiles could be used to predict an individual's pain threshold at both within-session and between-session levels, with the most predictive contribution from medial-frontal and frontal-parietal networks. These results demonstrate the potential of using a resting-state fMRI brain connectome to build a 'neural trait' for characterizing an individual's pain-related behavior, and such a 'neural trait' may eventually be used to personalize clinical assessments.

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.

State-unspecific patterns of whole-brain functional connectivity from resting and multiple task states predict stable individual traits

An increasing number of functional magnetic resonance imaging (fMRI) studies have revealed potential neural substrates of individual differences in diverse types of brain function and dysfunction. Although most previous studies have inherently focused on state-specific characterizations of brain networks and their functions, several recent studies reported on the potential state-unspecific nature of functional brain networks, such as global similarities across different experimental conditions or states, including both task and resting states. However, no previous studies have carried out direct, systematic characterizations of state-unspecific brain networks, or their functional implications. Here, we quantitatively identified several modes of state-unspecific individual variations in whole-brain functional connectivity patterns, called "Common Neural Modes" (CNMs), from a large-scale fMRI database including eight task/resting states. Furthermore, we tested how CNMs accounted for variability in individual cognitive measures. The results revealed that three CNMs were robustly extracted under various dimensions of features used. Each of these CNMs was preferentially correlated with different aspects of representative cognitive measures, reflecting stable individual traits. Importantly, the association between CNMs and cognitive measures emerged from brain connectivity data alone ("unsupervised"), whereas previous related studies have explicitly used both connectivity and cognitive measures to build their prediction models ("supervised"). The three CNMs were also able to predict several life outcomes, including income and life satisfaction, and achieved the highest level of performance when combined with a conventional cognitive measure. Our findings highlight the importance of state-unspecific brain networks in characterizing fundamental individual variation.

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.

Tracking mood fluctuations with functional network patterns

Subjective mood is a psychophysiological property that depends on complex interactions among the central and peripheral nervous systems. How network interactions in the brain drive temporal fluctuations in mood is unknown. Here we investigate how functional network configuration relates to mood profiles in a single individual over the course of 1 year. Using data from the 'MyConnectome Project', we construct a comprehensive mapping between resting-state functional connectivity (FC) patterns and subjective mood scales using an associative multivariate technique (partial least squares). We report three principal findings. First, FC patterns reliably tracked daily fluctuations in mood. Second, positive mood was marked by an integrated architecture, with prominent interactions between canonical resting-state networks. Finally, one of the top-ranked nodes in mood-related network reconfiguration was the subgenual anterior cingulate cortex, an area commonly associated with mood regulation and dysregulation. Altogether, these results showcase the utility of highly sampled individual-focused data sets for affective neuroscience.

Predicting the Post-therapy Severity Level (UPDRS-III) of Patients With Parkinson's Disease After Drug Therapy by Using the Dynamic Connectivity Efficiency of fMRI

Parkinson's disease (PD) is a multi-systemic disease in the brain arising from the dysfunction of several neural networks. The diagnosis and treatment of PD have gained more attention for clinical researchers. While there have been many fMRI studies about functional topological changes of PD patients, whether the dynamic changes of functional connectivity can predict the drug therapy effect is still unclear. The primary objective of this study was to assess whether large-scale functional efficiency changes of topological network are detectable in PD patients, and to explore whether the severity level (UPDRS-III) after drug treatment can be predicted by the pre-treatment resting-state fMRI (rs-fMRI). Here, we recruited 62 Parkinson's disease patients and calculated the dynamic nodal efficiency networks based on rs-fMRI. With connectome-based predictive models using the least absolute shrinkage and selection operator, we demonstrated that the dynamic nodal efficiency properties predict drug therapy effect well. The contributed regions for the prediction include hippocampus, post-central gyrus, cingulate gyrus, and orbital gyrus. Specifically, the connections between hippocampus and cingulate gyrus, hippocampus and insular gyrus, insular gyrus, and orbital gyrus are positively related to the recovery (post-therapy severity level) after drug therapy. The analysis of these connection features may provide important information for clinical treatment of PD patients.

Neuroimaging-based biomarkers for pain: state of the field and current directions

Chronic pain is an endemic problem involving both peripheral and brain pathophysiology. Although biomarkers have revolutionized many areas of medicine, biomarkers for pain have remained controversial and relatively underdeveloped. With the realization that biomarkers can reveal pain-causing mechanisms of disease in brain circuits and in the periphery, this situation is poised to change. In particular, brain pathophysiology may be diagnosable with human brain imaging, particularly when imaging is combined with machine learning techniques designed to identify predictive measures embedded in complex data sets. In this review, we explicate the need for brain-based biomarkers for pain, some of their potential uses, and some of the most popular machine learning approaches that have been brought to bear. Then, we evaluate the current state of pain biomarkers developed with several commonly used methods, including structural magnetic resonance imaging, functional magnetic resonance imaging and electroencephalography. The field is in the early stages of biomarker development, but these complementary methodologies have already produced some encouraging predictive models that must be tested more extensively across laboratories and clinical populations.

Latent resting-state network dynamics in boys and girls with attention-deficit/hyperactivity disorder

Neuroimaging studies of subjects with ADHD typically show altered functional connectivity in prefrontal, striatal, and several temporal brain regions. While the majority of studies have focused on connectivity that is averaged over time, we investigated the temporal dynamics of brain network changes in resting-state fMRI. Using the ADHD-200 consortium, we characterized the time course of latent state changes using Hidden Markov Modeling, and compared state changes between boys and girls with ADHD along with typically developing controls. Sex differences were found in latent state switching, with boys dwelling longer in a given state than girls, and concurrently having fewer overall state transitions. These sex differences were found in children with ADHD and in typically developing controls. Children with ADHD were also found to be more variable in terms of state transitions than controls. These findings add to the growing literature on neural sex differences and may be related to the sex difference in focal versus diffuse attention.

Selective sustained attention: a developmental foundation for cognition

Higher-order cognition, particularly in real-life settings, often requires that parts of the sensory input be processed at the exclusion of others over a period of time. Consequently, this review focuses on the development of attention that is both selective (which entails processing parts of the sensory input at the exclusion of others) and sustained (which entails maintaining sensitivity to incoming stimuli for a period of time). Recent findings from four distinct areas of research reviewed here suggest that: (1) the underlying neural circuitry of selective sustained attention involves multiple cortical and subcortical brain regions; (2) selective sustained attention in infancy provides a developmental foundation for the emergence of executive function later in life; (3) suppression-based mechanisms of attentional selection that begin to emerge during the first year of life are important for memory and learning; and (4) selective sustained attention appears to be malleable through pre-natal and post-natal nutritional supplementation and interactions with mature social partners.

Distinct neural correlates of episodic memory among apolipoprotein E alleles in cognitively normal elderly

The apolipoprotein E (APOE) ε4 and ε2 alleles are acknowledged genetic factors modulating Alzheimer's disease (AD) risk and episodic memory (EM) deterioration in an opposite manner. Mounting neuroimaging studies describe EM-related brain activity differences among APOE alleles but remain limited in elucidating the underlying mechanism. Here, we hypothesized that the APOE ε2, ε3, and ε4 alleles have distinct EM neural substrates, as a manifestation of degeneracy, underlying their modulations on EM-related brain activity and AD susceptibility. To test the hypothesis, we identified neural correlates of EM function by correlating intrinsic hippocampal functional connectivity networks with neuropsychological EM performances in a voxelwise manner, with 129 cognitively normal elderly subjects (36 ε2 carriers, 44 ε3 homozygotes, and 49 ε4 carriers). We demonstrated significantly different EM neural correlates among the three APOE allele groups. Specifically, in the ε3 homozygotes, positive EM neural correlates were characterized in the Papez circuit regions; in the ε4 carriers, positive EM neural correlates involved the lateral temporal cortex, premotor cortex/sensorimotor cortex/superior parietal lobule, and cuneus; and in the ε2 carriers, negative EM neural correlates appeared in the bilateral frontopolar, posteromedial, and sensorimotor cortex. Further, in the ε4 carriers, the interaction between age and EM function occurred in the temporoparietal junction and prefrontal cortex. Our findings suggest that the underlying mechanism of APOE polymorphism modulations on EM function and AD susceptibility is genetically related to the neural degeneracy of EM function across APOE alleles.

Neural substrates of self- and external-preoccupation: A voxel-based morphometry study

INTRODUCTION

Self- and external-preoccupation have been linked to psychopathological states. The neural substrates underlying self- and external-preoccupation remain unclear. In the present study, we aim to provide insight into the information-processing mechanisms associated with self- and external-preoccupation at the structural level.

METHODS

To investigate the neural substrates of self- and external-preoccupation, we acquired high-resolution T1-weighted structural images and Preoccupation Scale scores from 1,122 young subjects. Associations between regional gray matter volume (rGMV) and Preoccupation Scale subscores for self- and external-preoccupation were estimated using voxel-based morphometry.

RESULTS

Significant positive associations between self-preoccupation and rGMV were observed in widespread brain areas such as the bilateral precuneus and posterior cingulate gyri, structures known to be associated with self-triggered self-reference during rest. Significant negative associations between external-preoccupation and rGMV were observed only in the bilateral cerebellum, regions known to be associated with behavioral addiction, sustained attention, and reward system.

CONCLUSION

Our results reveal distinct neural substrates for self- and external-preoccupation at the structural level.

Classification of schizophrenia by intersubject correlation in functional connectome

Functional connectomes have been suggested as fingerprinting for individual identification. Accordingly, we hypothesized that subjects in the same phenotypic group have similar functional connectome features, which could help to discriminate schizophrenia (SCH) patients from healthy controls (HCs) and from depression patients. To this end, we included resting-state functional magnetic resonance imaging data of SCH, depression patients, and HCs from three centers. We first investigated the characteristics of connectome similarity between individuals, and found higher similarity between subjects belonging to the same group (i.e., SCH-SCH) than different groups (i.e., HC-SCH). These findings suggest that the average connectome within group (termed as group-specific functional connectome [GFC]) may help in individual classification. Consistently, significant accuracy (75-77%) and area under curve (81-86%) were found in discriminating SCH from HC or depression patients by GFC-based leave-one-out cross-validation. Cross-center classification further suggests a good generalizability of the GFC classification. We additionally included normal aging data (255 young and 242 old subjects with different scanning sequences) to show factors could be improved for better classification performance, and the findings emphasized the importance of increasing sample size but not temporal resolution during scanning. In conclusion, our findings suggest that the average functional connectome across subjects contained group-specific biological features and may be helpful in clinical diagnosis for schizophrenia.

Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion

Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.

Trauma-related psychiatric and behavioral conditions are uniquely associated with sustained attention dysfunction

OBJECTIVE

It is increasingly recognized that trauma victims, particularly Veterans, have co-occurring psychological and physical conditions that impact cognition, especially the domains of sustained attention and executive functioning. Although previous work has generally attempted to isolate the unique cognitive effects of common combat-related comorbidities, less work has been done to examine how these conditions co-occur, and whether unique cognitive signatures accompany certain clinical combinations.

METHOD

To address this gap, we examined how several deployment-related conditions were associated with performance on a well-validated measure of sustained attention (i.e., gradual onset continuous performance task [gradCPT]) and a battery of standard neuropsychological measures in 123 Veterans from the Translational Research Center for TBI and Stress Disorders. Initially, a Principal component analysis was conducted to investigate how comorbid conditions grouped together.

RESULTS

Several sustained attention measures from the gradCPT were differentially associated with four unique combinations of trauma-related pathology. Specifically, a somatic component representing the combination of current pain, sleep disturbance, and mild traumatic brain injury was associated with a higher rate of failures of attentional engagement. On the other hand, a comorbid posttraumatic stress disorder (PTSD) and mood disorder component (moodPTSD), as well as a substance use disorder component, were associated with higher rates of inhibitory control failures. Increased attentional instability was associated with moodPTSD as well as an anxiety disorder component. In contrast, the cognitive effects of deployment-related trauma were not observed on standard neuropsychological measures.

CONCLUSION

These findings suggest that unique combinations of trauma-related pathology have dissociable effects on sustained attentional control. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology

Many medical imaging data, especially the magnetic resonance imaging (MRI) data, usually have a small sample size, but a large number of features. How to reduce effectively the data dimension and locate accurately the biomarkers from such kinds of data are quite crucial for diagnosis and further precision medicine. In this paper, we propose a hybrid feature selection method based on machine learning and traditional statistical approaches and explore the brain abnormalities of schizophrenia by using the functional and structural MRI data. The results show that the abnormal brain regions are mainly distributed in the supramarginal gyrus, cingulate gyrus, frontal gyrus, precuneus and caudate, and the abnormal functional connections are related to the caudate nucleus, insula and rolandic operculum. In addition, some complex network analyses based on graph theory are utilized on the functional connection data, and the results demonstrate that the located abnormal functional connections in brain can distinguish schizophrenia patients from healthy controls. The identified abnormalities in brain with schizophrenia by the proposed hybrid feature selection method show that there do exist some abnormal brain regions and abnormal disruption of the network segregation and network integration for schizophrenia, and these changes may lead to inaccurate and inefficient information processing and synthesis in the brain, which provide further evidence for the cognitive dysmetria of schizophrenia.

Spatiotemporal Dynamics of Attention Networks Revealed by Representational Similarity Analysis of EEG and fMRI

The fronto-parietal attention networks have been extensively studied with functional magnetic resonance imaging (fMRI), but spatiotemporal dynamics of these networks are not well understood. We measured event-related potentials (ERPs) with electroencephalography (EEG) and collected fMRI data from identical experiments where participants performed visual and auditory discrimination tasks separately or simultaneously and with or without distractors. To overcome the low temporal resolution of fMRI, we used a novel ERP-based application of multivariate representational similarity analysis (RSA) to parse time-averaged fMRI pattern activity into distinct spatial maps that each corresponded, in representational structure, to a short temporal ERP segment. Discriminant analysis of ERP-fMRI correlations revealed 8 cortical networks-2 sensory, 3 attention, and 3 other-segregated by 4 orthogonal, temporally multifaceted and spatially distributed functions. We interpret these functions as 4 spatiotemporal components of attention: modality-dependent and stimulus-driven orienting, top-down control, mode transition, and response preparation, selection and execution.

Circadian phenotype impacts the brain's resting-state functional connectivity, attentional performance, and sleepiness

INTRODUCTION

Functional connectivity (FC) of the human brain's intrinsically connected networks underpins cognitive functioning and disruptions of FC are associated with sleep and neurological disorders. However, there is limited research on the impact of circadian phenotype and time of day on FC.

STUDY OBJECTIVES

The aim of this study was to investigate resting-state FC of the default mode network (DMN) in Early and Late circadian phenotypes over a socially constrained day.

METHODS

Thirty-eight healthy individuals (14 male, 22.7 ± 4.2 years) categorized as Early (n = 16) or Late (n = 22) using the Munich ChronoType Questionnaire took part. Following a 2-week baseline of actigraphy coupled with saliva samples for melatonin and cortisol rhythms, participants underwent testing at 14:00 hours, 20:00 hours, and 08:00 hours the following morning. Testing consisted of resting-state functional magnetic resonance imaging (fMRI), a structural T1 scan, attentional cognitive performance tasks, and self-reported daytime sleepiness. Seed-based FC analysis from the medial prefrontal and posterior cingulate cortices of the DMN was performed, compared between groups and linked with behavioral data.

RESULTS

Fundamental differences in the DMN were observed between Early and Late circadian phenotypes. Resting-state FC of the DMN predicted individual differences in attention and subjective ratings of sleepiness.

CONCLUSION

Differences in FC of the DMN may underlie the compromised attentional performance and increased sleepiness commonly associated with Late types when they conform to a societally constrained day that does not match their intrinsic circadian phenotype.

Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias

When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.

Connectome-based model predicts individual differences in propensity to trust

Trust constitutes a fundamental basis of human society and plays a pivotal role in almost every aspect of human relationships. Although enormous interest exists in determining the neuropsychological underpinnings of a person's propensity to trust utilizing task-based fMRI; however, little progress has been made in predicting its variations by task-free fMRI based on whole-brain resting-state functional connectivity (RSFC). Here, we combined a one-shot trust game with a connectome-based predictive modeling approach to predict propensity to trust from whole-brain RSFC. We demonstrated that individual variations in the propensity to trust were primarily predicted by RSFC rooted in the functional integration of distributed key nodes-caudate, amygdala, lateral prefrontal cortex, temporal-parietal junction, and the temporal pole-which are part of domain-general large-scale networks essential for the motivational, affective, and cognitive aspects of trust. We showed, further, that the identified brain-behavior associations were only evident for trust but not altruistic preferences and that propensity to trust (and its underlying neural underpinnings) were modulated according to the extent to which a person emphasizes general social preferences (i.e., horizontal collectivism) rather than general risk preferences (i.e., trait impulsiveness). In conclusion, the employed data-driven approach enables to predict propensity to trust from RSFC and highlights its potential use as an objective neuromarker of trust impairment in mental disorders.

Individual differences in sustained attention are associated with cortical thickness

Several studies have examined how individual differences in sustained attention relate to functional brain measures (e.g., functional connectivity), but far fewer studies relate sustained attention ability, or cognition in general, to individual differences in cortical structure. Functional magnetic resonance imaging meta-analyses and patient work have highlighted that frontoparietal regions, lateralized to the right hemisphere, are critical for sustained attention, though recent work implicates a broader expanse of brain regions. The current study sought to determine if and where variation in cortical thickness is significantly associated with sustained attention performance. Sustained attention was measured using the gradual onset continuous performance task and the Test of Variables of Attention in 125 adult Veteran participants after acquiring two high-resolution structural MRI scans. Whole-brain vertex-wise analyses of the cortex demonstrated that better sustained attention was associated with increased thickness in visual, somatomotor, frontal, and parietal cortices, especially in the right hemisphere. Network-based analyses revealed relationships between sustained attention and cortical thickness in the dorsal attention, ventral attention, somatomotor, and visual networks. These results indicate cortical thickness in multiple regions and networks is associated with sustained attention, and add to the growing knowledge of how structural MRI can help explain individual differences in cognition.

Global signal regression strengthens association between resting-state functional connectivity and behavior

Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion. By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures. Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures. Code for the variance component model and ridge regression can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/Li2019_GSR.

A Comparison of Static and Dynamic Functional Connectivities for Identifying Subjects and Biological Sex Using Intrinsic Individual Brain Connectivity

Functional magnetic resonance imaging has revealed correlated activities in brain regions even in the absence of a task. Initial studies assumed this resting-state functional connectivity (FC) to be stationary in nature, but recent studies have modeled these activities as a dynamic network. Dynamic spatiotemporal models better model the brain activities, but are computationally more involved. A comparison of static and dynamic FCs was made to quantitatively study their efficacies in identifying intrinsic individual connectivity patterns using data from the Human Connectome Project. Results show that the intrinsic individual brain connectivity pattern can be used as a 'fingerprint' to distinguish among and identify subjects and is more accurately captured with partial correlation and assuming static FC. It was also seen that the intrinsic individual brain connectivity patterns were invariant over a few months. Additionally, biological sex identification was successfully performed using the intrinsic individual connectivity patterns, and group averages of male and female FC matrices. Edge consistency, edge variability and differential power measures were used to identify the major resting-state networks involved in identifying subjects and their sex.

Memory and the developing brain: From description to explanation with innovation in methods

Recent advances in human cognitive neuroscience show great promise in extending our understanding of the neural basis of memory development. We briefly review the current state of knowledge, highlighting that most work has focused on describing the neural correlates of memory in cross-sectional studies. We then delineate three examples of the application of innovative methods in addressing questions that go beyond description, towards a mechanistic understanding of memory development. First, structural brain imaging and the harmonization of measurements across laboratories may uncover ways in which the maturation of the brain constrains the development of specific aspects of memory. Second, longitudinal designs and sophisticated modeling of the data may identify age-driven changes and the factors that determine individual developmental trajectories. Third, recording memory-related activity directly from the developing brain presents an unprecedented opportunity to examine how distinct brain structures support memory in real time. Finally, the growing prevalence of data sharing offers additional means to tackle questions that demand large-scale datasets, ambitious designs, and access to rare samples. We propose that the use of such innovative methods will move our understanding of memory development from a focus on describing trends to explaining the causal factors that shape behavior.

Movies in the magnet: Naturalistic paradigms in developmental functional neuroimaging

The use of movie-watching as an acquisition state for functional connectivity (FC) MRI has recently enabled multiple groups to obtain rich data sets in younger children with both substantial sample sizes and scan durations. Using naturalistic paradigms such as movies has also provided analytic flexibility for these developmental studies that extends beyond conventional resting state approaches. This review highlights the advantages and challenges of using movies for developmental neuroimaging and explores some of the methodological issues involved in designing pediatric studies with movies. Emerging themes from movie-watching studies are discussed, including an emphasis on intersubject correlations, developmental changes in network interactions under complex naturalistic conditions, and dynamic age-related changes in both sensory and higher-order network FC even in narrow age ranges. Converging evidence suggests an enhanced ability to identify brain-behavior correlations in children when using movie-watching data relative to both resting state and conventional tasks. Future directions and cautionary notes highlight the potential and the limitations of using movies to study FC in pediatric populations.

Increased functional segregation of brain network associated with symptomatology and sustained attention in chronic post-traumatic stress disorder

BACKGROUND

Traditional regional or voxel-based analyses only focus on specific brain regions or connectivity rather than the whole brain's functional organization. Using resting state functional magnetic resonance imaging (rs-fMRI), we aimed to explore the altered topological metrics, clinical symptoms and cognitive function in chronic post-traumatic stress disorder (PTSD) in order to identify the brain network mechanisms underlying these clinical and cognitive symptoms.

METHODS

Forty patients with unmedicated chronic PTSD and forty-two matched trauma-exposed healthy controls (TEHCs) underwent rs-fMRI, and the topological organization of the whole-brain network was calculated using graph theory. The Rapid Visual Information Processing (RVP) task and Wechsler Memory Scale-IV (WMS-IV) were used to evaluate the subjects' sustained attention and memory capacity. All clinical and cognitive measures and topological parameters of the PTSD patients and TEHCs were compared, and the relationships between altered network metrics and symptom severity were explored.

RESULTS

Compared with the TEHCs, the patients showed increases in the normalized clustering coefficient, small-worldness, normalized local efficiency and efficiency-based small-worldness. The left middle occipital gyrus showed increases in nodal global efficiency and nodal degree that were negatively correlated with the severity of PTSD symptoms. The altered connections in PTSD only involved the default mode network (DMN) and the occipital network.

LIMITATIONS

Comorbid conditions were included, and current cross-sectional study cannot conclude on causality.

CONCLUSIONS

Patients with chronic PTSD showed increased functional brain network segregation, mainly in the occipital cortex, which could be a protective or compensatory mechanism to alleviate clinical symptoms.

Resting-state brain information flow predicts cognitive flexibility in humans

The human brain is a dynamic system, where communication between spatially distinct areas facilitates complex cognitive functions and behaviors. How information transfers between brain regions and how it gives rise to human cognition, however, are unclear. In this article, using resting-state functional magnetic resonance imaging (fMRI) data from 783 healthy adults in the Human Connectome Project (HCP) dataset, we map the brain's directed information flow architecture through a Granger-Geweke causality prism. We demonstrate that the information flow profiles in the general population primarily involve local exchanges within specialized functional systems, long-distance exchanges from the dorsal brain to the ventral brain, and top-down exchanges from the higher-order systems to the primary systems. Using an information flow map discovered from 550 subjects, the individual directed information flow profiles can significantly predict cognitive flexibility scores in 233 novel individuals. Our results provide evidence for directed information network architecture in the cerebral cortex, and suggest that features of the information flow configuration during rest underpin cognitive ability in humans.

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.

Functional connectivity changes associated with fMRI neurofeedback of right inferior frontal cortex in adolescents with ADHD

Attention Deficit Hyperactivity Disorder (ADHD) is associated with poor self-control, underpinned by inferior fronto-striatal deficits. We showed previously that 18 ADHD adolescents over 11 runs of 8.5 min of real-time functional magnetic resonance neurofeedback of the right inferior frontal cortex (rIFC) progressively increased activation in 2 regions of the rIFC which was associated with clinical symptom improvement. In this study, we used functional connectivity analyses to investigate whether fMRI-Neurofeedback of rIFC resulted in dynamic functional connectivity changes in underlying neural networks. Whole-brain seed-based functional connectivity analyses were conducted using the two clusters showing progressively increased activation in rIFC as seed regions to test for changes in functional connectivity before and after 11 fMRI-Neurofeedback runs. Furthermore, we tested whether the resulting functional connectivity changes were associated with clinical symptom improvements and whether they were specific to fMRI-Neurofeedback of rIFC when compared to a control group who had to self-regulate another region. rIFC showed increased positive functional connectivity after relative to before fMRI-Neurofeedback with dorsal caudate and anterior cingulate and increased negative functional connectivity with regions of the default mode network (DMN) such as posterior cingulate and precuneus. Furthermore, the functional connectivity changes were correlated with clinical improvements and the functional connectivity and correlation findings were specific to the rIFC-Neurofeedback group. The findings show for the first time that fMRI-Neurofeedback of a typically dysfunctional frontal region in ADHD adolescents leads to strengthening within fronto-cingulo-striatal networks and to weakening of functional connectivity with posterior DMN regions and that this may be underlying clinical improvement.

Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks

The organization of human brain networks can be measured by capturing correlated brain activity with functional MRI data. There have been a variety of studies showing that human functional connectivities undergo an age-related change over development. In the present study, we employed resting-state functional MRI data to construct functional network models. Principal component analysis was performed on the FC matrices across all the subjects to explore meaningful components especially correlated with age. Coefficients across the components, edge features after a newly proposed feature reduction method as well as temporal features based on fALFF, were extracted as predictor variables and three different regression models were learned to make prediction of brain age. We observed that individual's functional network architecture was shaped by intrinsic component, age-related component and other components and the predictive models extracted sufficient information to provide comparatively accurate predictions of brain age.