A neuromarker of sustained attention from whole-brain functional connectivity

Social Responsiveness Scale Assessment of the Preterm Behavioral Phenotype in 10-Year-Olds Born Extremely Preterm

OBJECTIVE

To evaluate the correlates of a clinically significant high score on the Social Responsiveness Scale (SRS) in 10-year-old children who were born extremely preterm and who did not meet criteria for autism spectrum disorder (ASD).

METHODS

After excluding 61 participants diagnosed with ASD, we grouped children by IQ < or ≥85 and then compared the prevalence of neurocognitive and other deficits between those who had SRS total and component scores ≥65 and their peers who had lower scores.

RESULTS

Among children who had IQ ≥ 85, the prevalence of SRS total scores ≥65 was 16% (n = 103/628), and among children who had IQ < 85, it was 27% (n = 40/148), higher than the 4% prevalence expected based on normative population data. Among children who had IQ ≥ 85, those who had high SRS scores more often than their peers had deficits in attention and executive function, and language and communication, and they were more often rated by their parents and teachers as having behavioral (e.g., attention-deficit hyperactivity disorder [ADHD]) and emotional (e.g., anxiety and depression) problems.

CONCLUSION

Social Responsiveness Scale-defined social impairment was much more common in our cohort of 10-year-old children born extremely preterm than was expected based on general population norms. High SRS scores were characteristic of children who had intellectual, neurocognitive, language, and communication limitations, as well as deficits in behavior and emotion regulation.

A Supervoxel-Based Method for Groupwise Whole Brain Parcellation with Resting-State fMRI Data

Node definition is a very important issue in human brain network analysis and functional connectivity studies. Typically, the atlases generated from meta-analysis, random criteria, and structural criteria are utilized as nodes in related applications. However, these atlases are not originally designed for such purposes and may not be suitable. In this study, we combined normalized cut (Ncut) and a supervoxel method called simple linear iterative clustering (SLIC) to parcellate whole brain resting-state fMRI data in order to generate appropriate brain atlases. Specifically, Ncut was employed to extract features from connectivity matrices, and then SLIC was applied on the extracted features to generate parcellations. To obtain group level parcellations, two approaches named mean SLIC and two-level SLIC were proposed. The cluster number varied in a wide range in order to generate parcellations with multiple granularities. The two SLIC approaches were compared with three state-of-the-art approaches under different evaluation metrics, which include spatial contiguity, functional homogeneity, and reproducibility. Both the group-to-group reproducibility and the group-to-subject reproducibility were evaluated in our study. The experimental results showed that the proposed approaches obtained relatively good overall clustering performances in different conditions that included different weighting functions, different sparsifying schemes, and several confounding factors. Therefore, the generated atlases are appropriate to be utilized as nodes for network analysis. The generated atlases and major source codes of this study have been made publicly available at http://www.nitrc.org/projects/slic/.

Resting connectivity predicts task activation in pre-surgical populations

Injury and disease affect neural processing and increase individual variations in patients when compared with healthy controls. Understanding this increased variability is critical for identifying the anatomical location of eloquent brain areas for pre-surgical planning. Here we show that precise and reliable language maps can be inferred in patient populations from resting scans of idle brain activity. We trained a predictive model on pairs of resting-state and task-evoked data and tested it to predict activation of unseen patients and healthy controls based on their resting-state data alone. A well-validated language task (category fluency) was used in acquiring the task-evoked fMRI data. Although patients showed greater variation in their actual language maps, our models successfully learned variations in both patient and control responses from the individual resting-connectivity features. Importantly, we further demonstrate that a model trained exclusively on the more-homogenous control group can be used to predict task activations in patients. These results are the first to show that resting connectivity robustly predicts individual differences in neural response in cases of pathological variability.

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A method for identifying eloquent areas in the brain from resting fMRI is proposed.

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It uses supervised learning to predict task contrasts from resting connectivity.

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Good predictions were obtained in controls and in pre-surgical patient populations.

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Patient diagnoses included epilepsy, tumours, and vascular lesions.

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Language maps in patients could be predicted from models trained on controls.

Inferring Individual-Level Variations in the Functional Parcellation of the Cerebral Cortex

OBJECTIVE

Functional parcellation of the cerebral cortex is variable across different subjects or between cognitive states. Ignoring individual-or state-dependent variations in the functional parcellation may lead to inaccurate representations of individual functional connectivity, limiting the precision of interpretations of differences in individual connectivity profiles. However, it is difficult to infer the individual-level variations due to the relatively low robustness of methods for parcellation of individual subjects.

METHODS

We propose a method called "joint K-means" to robustly parcellate the cerebral cortex using functional magnetic resonance imaging (fMRI) data for contrasts between two states or subjects that intended to characterize variance in individual functional parcellations. The key idea of the proposed method is to jointly infer parcellations in contrasted datasets by iterative descent, while constraining the similarity of the two pathways in searches for local minima to reduce spurious variations.

RESULTS

Parcellations of resting-state fMRI datasets from the Human Connectome Project show that the similarity of parcellations for an individual subject studied on two sessions is greater than that between different subjects. Differences in parcellations between subjects are nonuniformly distributed across the cerebral cortex, with clusters of higher variance in the prefrontal, lateral temporal, and occipito-parietal cortices. This pattern is reproducible across sessions, between groups, and using different numbers of parcels.

CONCLUSION

The individual-level variations inferred by the proposed method are plausible and consistent with the previously reported functional connectivity variability.

SIGNIFICANCE

The proposed method is a promising tool for investigating relationships between the cerebral functional organization and behavioral differences.

Individual variation in functional brain connectivity: implications for personalized approaches to psychiatric disease

Functional brain connectivity measured with functional magnetic resonance imaging (fMRI) is a popular technique for investigating neural organization in both healthy subjects and patients with mental illness. Despite a rapidly growing body of literature, however, functional connectivity research has yet to deliver biomarkers that can aid psychiatric diagnosis or prognosis at the single-subject level. One impediment to developing such practical tools has been uncertainty regarding the ratio of intra- to interindividual variability in functional connectivity; in other words, how much variance is state- versus trait-related. Here, we review recent evidence that functional connectivity profiles are both reliable within subjects and unique across subjects, and that features of these profiles relate to behavioral phenotypes. Together, these results suggest the potential to discover reliable correlates of present and future illness and/or response to treatment in the strength of an individual's functional brain connections. Ultimately, this work could help develop personalized approaches to psychiatric illness.

Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention

Little is currently known about the coordination of neural activity over longitudinal timescales and how these changes relate to behavior. To investigate this issue, we used resting-state fMRI data from a single individual to identify the presence of two distinct temporal states that fluctuated over the course of 18 mo. These temporal states were associated with distinct patterns of time-resolved blood oxygen level dependent (BOLD) connectivity within individual scanning sessions and also related to significant alterations in global efficiency of brain connectivity as well as differences in self-reported attention. These patterns were replicated in a separate longitudinal dataset, providing additional supportive evidence for the presence of fluctuations in functional network topology over time. Together, our results underscore the importance of longitudinal phenotyping in cognitive neuroscience.

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

Fluctuations in resting-state functional connectivity occur but their behavioral significance remains unclear, largely because correlating behavioral state with dynamic functional connectivity states (DCS) engages probes that disrupt the very behavioral state we seek to observe. Observing spontaneous eyelid closures following sleep deprivation permits nonintrusive arousal monitoring. During periods of low arousal dominated by eyelid closures, sliding-window correlation analysis uncovered a DCS associated with reduced within-network functional connectivity of default mode and dorsal/ventral attention networks, as well as reduced anticorrelation between these networks. Conversely, during periods when participants' eyelids were wide open, a second DCS was associated with less decoupling between the visual network and higher-order cognitive networks that included dorsal/ventral attention and default mode networks. In subcortical structures, eyelid closures were associated with increased connectivity between the striatum and thalamus with the ventral attention network, and greater anticorrelation with the dorsal attention network. When applied to task-based fMRI data, these two DCS predicted interindividual differences in frequency of behavioral lapsing and intraindividual temporal fluctuations in response speed. These findings with participants who underwent a night of total sleep deprivation were replicated in an independent dataset involving partially sleep-deprived participants. Fluctuations in functional connectivity thus appear to be clearly associated with changes in arousal.

Supervised multimodal fusion and its application in searching joint neuromarkers of working memory deficits in schizophrenia

Multimodal fusion is an effective approach to better understand brain disease. To date, most current fusion approaches are unsupervised; there is need for a multivariate method that can adopt prior information to guide multimodal fusion. Here we proposed a novel supervised fusion model, called "MCCAR+jICA", which enables both identification of multimodal co-alterations and linking the covarying brain regions with a specific reference signal, e.g., cognitive scores. The proposed method has been validated on both simulated and real human brain data. Features from 3 modalities (fMRI, sMRI, dMRI) obtained from 147 schizophrenia patients and 147 age-matched healthy controls were included as fusion input, who participated in the Function Biomedical Informatics Research Network (FBIRN) Phase III study. Our aim was to investigate the group co-alterations seen in three types of MRI data that are also correlated with working memory performance. One joint IC was found both significantly group-discriminating (p=7.4E-06, 0.001, 7.0E-09) and highly correlated with working memory scores(r=0.296, 0.241, 0.301) and PANSS negative scores (r=-0.229, -0.276, -0.240) for fMRI, dMRI and sMRI, respectively. Given the simulation and FBIRN results, MCCAR+jICA is shown to be an effective multivariate approach to extract accurate and stable multimodal components associated with a particular measure of interest, and promises a wide application in identifying potential neuromarkers for mental disorders.

Automaticity of phasic alertness: Evidence for a three-component model of visual cueing

The automaticity of phasic alertness is investigated using the attention network test. Results show that the cueing effect from the alerting cue-double cue-is strongly enhanced by the task relevance of visual cues, as determined by the informativeness of the orienting cue-single cue-that is being mixed (80 % vs. 50 % valid in predicting where the target will appear). Counterintuitively, the cueing effect from the alerting cue can be negatively affected by its visibility, such that masking the cue from awareness can reveal a cueing effect that is otherwise absent when the cue is visible. Evidently, then, top-down influences-in the form of contextual relevance and cue awareness-can have opposite influences on the cueing effect from the alerting cue. These findings lead us to the view that a visual cue can engage three components of attention-orienting, alerting, and inhibition-to determine the behavioral cueing effect. We propose that phasic alertness, particularly in the form of specific response readiness, is regulated by both internal, top-down expectation and external, bottom-up stimulus properties. In contrast to some existing views, we advance the perspective that phasic alertness is strongly tied to temporal orienting, attentional capture, and spatial orienting. Finally, we discuss how translating attention research to clinical applications would benefit from an improved ability to measure attention. To this end, controlling the degree of intraindividual variability in the attentional components and improving the precision of the measurement tools may prove vital.

Intrinsic Functional Connectivity in Attention-Deficit/Hyperactivity Disorder: A Science in Development

Functional magnetic resonance imaging (fMRI) without an explicit task, i.e., resting state fMRI, of individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) is growing rapidly. Early studies were unaware of the vulnerability of this method to even minor degrees of head motion, a major concern in the field. Recent efforts are implementing various strategies to address this source of artifact along with a growing set of analytical tools. Availability of the ADHD-200 Consortium dataset, a large-scale multi-site repository, is facilitating increasingly sophisticated approaches. In parallel, investigators are beginning to explicitly test the replicability of published findings. In this narrative review, we sketch out broad, overarching hypotheses being entertained while noting methodological uncertainties. Current hypotheses implicate the interplay of default, cognitive control (frontoparietal) and attention (dorsal, ventral, salience) networks in ADHD; functional connectivities of reward-related and amygdala-related circuits are also supported as substrates for dimensional aspects of ADHD. Before these can be further specified and definitively tested, we assert the field must take on the challenge of mapping the "topography" of the analytical space, i.e., determining the sensitivities of results to variations in acquisition, analysis, demographic and phenotypic parameters. Doing so with openly available datasets will provide the needed foundation for delineating typical and atypical developmental trajectories of brain structure and function in neurodevelopmental disorders including ADHD when applied to large-scale multi-site prospective longitudinal studies such as the forthcoming Adolescent Brain Cognitive Development study.

Growth Charting of Brain Connectivity Networks and the Identification of Attention Impairment in Youth

IMPORTANCE

Intrinsic connectivity networks (ICNs), important units of brain functional organization, demonstrate substantial maturation during youth. In addition, interrelationships between ICNs have been reliably implicated in attention performance. It is unknown whether alterations in ICN maturational profiles can reliably detect impaired attention functioning in youth.

OBJECTIVE

To use a network growth charting approach to investigate the association between alterations in ICN maturation and attention performance.

DESIGN, SETTING, AND PARTICIPANTS

Data were obtained from the publicly available Philadelphia Neurodevelopmental Cohort, a prospective, population-based sample of 9498 youths who underwent genomic testing, neurocognitive assessment, and neuroimaging. Data collection was conducted at an academic and children's hospital health care network between November 1, 2009, and November 30, 2011, and data analysis was conducted between February 1, 2015, and January 15, 2016.

MAIN OUTCOMES AND MEASURES

Statistical associations between deviations from normative network growth were assessed as well as 2 main outcome measures: accuracy during the Penn Continuous Performance Test and diagnosis with attention-deficit/hyperactivity disorder.

RESULTS

Of the 9498 individuals identified, 1000 youths aged 8 to 22 years underwent brain imaging. A sample of 519 youths who met quality control criteria entered analysis, of whom 25 (4.8%) met criteria for attention-deficit/hyperactivity disorder. The mean (SD) age of the youth was 15.7 (3.1) years, and 223 (43.0%) were male. Participants' patterns of deviations from normative maturational trajectories were indicative of sustained attention functioning (R2 = 24%; F6,512 = 26.89; P < 2.2 × 10-16). Moreover, these patterns were found to be a reliable biomarker of severe attention impairment (peak receiver operating characteristic curve measured by area under the curve, 79.3%). In particular, a down-shifted pattern of ICN maturation (shallow maturation), rather than a right-shifted pattern (lagged maturation), was implicated in reduced attention performance (Akaike information criterion relative likelihood, 3.22 × 1026). Finally, parallel associations between ICN dysmaturation and diagnosis of attention-deficit/hyperactivity disorder were identified.

CONCLUSIONS AND RELEVANCE

Growth charting methods are widely used to assess the development of physical or other biometric characteristics, such as weight and head circumference. To date, this is the first demonstration that this method can be extended to development of functional brain networks to identify clinically relevant conditions, such as dysfunction of sustained attention.