Task-based Data Research Articles

Sex differences in the effects of trait anxiety and age on resting-state functional connectivities of the amygdala

BackgroundNumerous studies characterized how resting-state functional connectivities (rsFCs) of the amygdala were disrupted in emotional disorders and varied with emotional traits, including anxiety. With trait anxiety known to diminish with age, a critical issue concerns disambiguating the effects of age and anxiety on amygdala rsFCs in studying the neural bases of individual differences in anxiety. MethodsTwo-hundred adults (83 women) 19–85 years of age underwent fMRI and assessment for trait anxiety. Amygdala rsFC correlates were identified using multiple regression with age and anxiety in the same model for all and separately in men and women. The rsFC correlates were examined for age-anxiety interaction. ResultsAnxiety was negatively correlated with amygdala-temporooccipital gyri rsFC in all and in men alone. In women, amgydala rsFC with the thalamus/pallidum, angular/supramarginal gyri, inferior temporal gyrus, and posterior insula correlated positively and rsFC with calcarine cortex and caudate correlated negatively with anxiety. We also observed sex differences in age correlation of amgydala-posterior cingulate cortex/precuneus and -insula/temporoparietal rsFCs, with stronger associations in women. In women alone, anxiety and age interacted to determine amygdala rsFC with the thalamus/pallidum, calcarine cortex, and caudate, with older age associated with stronger correlation between anxiety and the rsFCs. LimitationsThe findings need to be validated in an independent sample and further explored using task-based data. ConclusionHighlighting anxiety- and age- specific as well as interacting correlates of amygdala rsFCs and sex differences in the correlates, the findings may shed light on the neural markers of anxiety.

Engineering Rehabilitation: Blending Two Tool-supported Approaches to Close the Loop from Tasks-based Rehabilitation to Exercises and Back Again

Post-stroke or post-brain injuries rehabilitation is a long-term process defined by therapists and tuned to the specific damages and their consequences for each patient. This process requires the definition of well-defined multiple exercises to be performed multiple times by each patient over a long period of time. One of the key findings in the rehabilitation domain is that exercises should be tasks and goals oriented to increase functional independence, enhance development and prevent disability [24]. Patients performing the exercises must be carefully monitored to identify progress, possible regression and prevent damages due to the inadequate performance of the exercises. The SIVIRE tool [20] provides interactive graphical feedback to patients about the performance of their exercises and deviations from nominal, planed practice but fails at providing high-level descriptions of exercises and at connecting them to high-level rehabilitation goals (e.g. gain autonomy in cleaning a room). The HAMSTERS-XLE tool [16] provides a tuneable editor and a simulator to describe goals and task models including low-level tasks covering perception of the various senses, cognition and motoric actions. This paper presents the integration of these two tools and the benefits it brings both to the patients and to the therapists. The resulting tool and its engineering are presented in detail highlighting some generic integration mechanisms. On a case study, we show how exercises can be defined in HAMSTERS-XLE, transferred to SIVRE for fine tuning, monitored by SIVRE during the multiple practices of the patient and transferred back again to HAMSTERS-XLE to monitor progress, identify issues with the exercises and provide therapists with goal and task-based data about patients' evolutions

Comparing resting state and task-based EEG using machine learning to predict vulnerability to depression in a non-clinical population

Major Depressive Disorder (MDD) affects a large portion of the population and levies a huge societal burden. It has serious consequences like decreased productivity and reduced quality of life, hence there is considerable interest in understanding and predicting it. As it is a mental disorder, neural measures like EEG are used to study and understand its underlying mechanisms. However most of these studies have either explored resting state EEG (rs-EEG) data or task-based EEG data but not both, we seek to compare their respective efficacy. We work with data from non-clinically depressed individuals who score higher and lower on the depression scale and hence are more and less vulnerable to depression, respectively. Forty participants volunteered for the study. Questionnaires and EEG data were collected from participants. We found that people who are more vulnerable to depression had on average increased EEG amplitude in the left frontal channel, and decreased amplitude in the right frontal and occipital channels for raw data (rs-EEG). Task-based EEG data from a sustained attention to response task used to measure spontaneous thinking, an increased EEG amplitude in the central part of the brain for individuals with low vulnerability and an increased EEG amplitude in right temporal, occipital and parietal regions in individuals more vulnerable to depression were found. In an attempt to predict vulnerability (high/low) to depression, we found that a Long Short Term Memory model gave the maximum accuracy of 91.42% in delta wave for task-based data whereas 1D-Convolution neural network gave the maximum accuracy of 98.06% corresponding to raw rs-EEG data. Hence if one has to look at the primary question of which data will be good for predicting vulnerability to depression, rs-EEG seems to be better than task-based EEG data. However, if mechanisms driving depression like rumination or stickiness are to be understood, task-based data may be more effective. Furthermore, as there is no consensus as to which biomarker of rs-EEG is more effective in the detection of MDD, we also experimented with evolutionary algorithms to find the most informative subset of these biomarkers. Higuchi fractal dimension, phase lag index, correlation and coherence features were also found to be the most important features for predicting vulnerability to depression using rs-EEG. These findings bring up new possibilities for EEG-based machine/deep learning diagnostics in the future.

Quality control practices in FMRI analysis: Philosophy, methods and examples using AFNI.

Quality control (QC) is a necessary, but often an under-appreciated, part of FMRI processing. Here we describe procedures for performing QC on acquired or publicly available FMRI datasets using the widely used AFNI software package. This work is part of the Research Topic, "Demonstrating Quality Control (QC) Procedures in fMRI." We used a sequential, hierarchical approach that contained the following major stages: (1) GTKYD (getting to know your data, esp. its basic acquisition properties), (2) APQUANT (examining quantifiable measures, with thresholds), (3) APQUAL (viewing qualitative images, graphs, and other information in systematic HTML reports) and (4) GUI (checking features interactively with a graphical user interface); and for task data, and (5) STIM (checking stimulus event timing statistics). We describe how these are complementary and reinforce each other to help researchers stay close to their data. We processed and evaluated the provided, publicly available resting state data collections (7 groups, 139 total subjects) and task-based data collection (1 group, 30 subjects). As specified within the Topic guidelines, each subject's dataset was placed into one of three categories: Include, exclude or uncertain. The main focus of this paper, however, is the detailed description of QC procedures: How to understand the contents of an FMRI dataset, to check its contents for appropriateness, to verify processing steps, and to examine potential quality issues. Scripts for the processing and analysis are freely available.

Few-shot incremental learning with continual prototype calibration for remote sensing image fine-grained classification

With the rapid acquisition of remote sensing (RS) data, new categories of objects continue to emerge, and some categories can only obtain a few training samples. Thus, few-shot class-incremental learning (FSCIL) has received intense attention in recent years. Nevertheless, the existing FSCIL methods are difficult to adapt to RS characteristics with high inter-class similarity, large intra-class differences, and complex backgrounds. In this paper, we propose the FSCIL method called continual prototype calibration (CPC), which applies the decoupled learning strategy to cope with the demand to retrain the model when new few-shot categories appear. It can also address the tedious problems in fine-grained RS images by calibrating inter-class and intra-class prototypes. Concretely, considering the severe issue of inter-class confusion, we introduce a prototype separability module (PSM) to update the distribution of inter-class prototypes. In this way, we can distinguish different categories more clearly by weakening their similarities, and the phenomenon of catastrophic forgetting can also be effectively mitigated. Furthermore, to generate more representative and accurate intra-class prototypes, we design a Meta-Network based on foreground enhancement mechanism (FEM). The Meta-Network can boost the learning ability of the model for few-shot data and suppress the occurrence of overfitting by training on task-based data. And the FEM enhances the object features by filtering out redundant background information, facilitating fine-grained classification. Our CPC method is the first FSCIL method applied to optical RS images, and we verify its effectiveness on three datasets. Extensive experiments illustrate that our method achieves state-of-the-art performance in FSCIL of fine-grained RS images. The code is available at https://github.com/ningerhhh/CPC.

The topology, stability, and instability of learning-induced brain network repertoires in schizophrenia.

There is a paucity of graph theoretic methods applied to task-based data in schizophrenia (SCZ). Tasks are useful for modulating brain network dynamics, and topology. Understanding how changes in task conditions impact inter-group differences in topology can elucidate unstable network characteristics in SCZ. Here, in a group of patients and healthy controls (n = 59 total, 32 SCZ), we used an associative learning task with four distinct conditions (Memory Formation, Post-Encoding Consolidation, Memory Retrieval, and Post-Retrieval Consolidation) to induce network dynamics. From the acquired fMRI time series data, betweenness centrality (BC), a metric of a node's integrative value was used to summarize network topology in each condition. Patients showed (a) differences in BC across multiple nodes and conditions; (b) decreased BC in more integrative nodes, but increased BC in less integrative nodes; (c) discordant node ranks in each of the conditions; and (d) complex patterns of stability and instability of node ranks across conditions. These analyses reveal that task conditions induce highly variegated patterns of network dys-organization in SCZ. We suggest that the dys-connection syndrome that is schizophrenia, is a contextually evoked process, and that the tools of network neuroscience should be oriented toward elucidating the limits of this dys-connection.

A multimodal imaging approach to foreign accent syndrome. A case report

ABSTRACT This article describes a case of Foreign accent syndrome (FAS) in an Italian woman who developed a Canadian-like foreign accent without brain damage (functional FAS). The patient underwent an in-depth neuroimaging and (neuro)psychological evaluation. Language networks in the frontotemporal-parietal areas were typically activated bilaterally through fMRI and MEG assessments based on task-based data. Resting-state fMRI showed preserved connectivity between language areas. An obsessive-compulsive personality profile and mild anxiety were found, suggesting psychological and psychiatric factors may be relevant. Accordingly with our findings, multimodal imaging is beneficial to understand FAS neurological and functional etiologies.

Exploratory Study of the Brain Response in Facial Synkinesis after Bell Palsy with Systematic Review and Meta-analysis of the Literature.

Facial synkinesis, characterized by unintentional facial movements paired with intentional movements, is a debilitating sequela of Bell palsy. Our aim was to determine whether persistent peripheral nerve changes arising from Bell palsy result in persistent altered brain function in motor pathways in synkinesis. A literature search using terms related to facial paralysis, Bell palsy, synkinesis, and fMRI through May 2021 was conducted in MEDLINE and EMBASE. Additionally, an fMRI study examined lip and eyeblink movements in 2 groups: individuals who fully recovered following Bell palsy and individuals who developed synkinesis. Task-based data of the whole brain that required lip movements in healthy controls were extracted from 7 publications. Three studies contributed similar whole-brain analyses in acute Bell palsy. The meta-analysis of fMRI in healthy control and Bell palsy groups determined common clusters of activation within each group using activation likelihood estimates. A separate fMRI study used multivariate general linear modeling to identify changes associated with synkinesis in smiling and blinking tasks. A region of the precentral gyrus contralateral to the paretic side of the face was hypoactive in synkinesis during lip movements compared with controls. This region was centered in a cluster of activation identified in the meta-analysis of the healthy controls but absent from individuals with Bell palsy. The meta-analysis relied on a small set of studies. The small sample of subjects with synkinesis limited the power of the fMRI analysis. Premotor pathways show persistent functional changes in synkinesis first identifiable in acute Bell palsy.

+microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG

+microstate is a MATLAB toolbox for brain functional microstate analysis. It builds upon previous EEG microstate literature and toolboxes by including algorithms for source-space microstate analysis. +microstate includes codes for performing individual- and group-level brain microstate analysis in resting-state and task-based data including event-related potentials/fields. Functions are included to visualise and perform statistical analysis of microstate sequences, including novel advanced statistical approaches such as statistical testing for associated functional connectivity patterns, cluster-permutation topographic ANOVAs, and χ2 analysis of microstate probabilities in response to stimuli. Additionally, codes for simulating microstate sequences and their associated M/EEG data are included in the toolbox, which can be used to generate artificial data with ground truth microstates and to validate the methodology. +microstate integrates with widely used toolboxes for M/EEG processing including Fieldtrip, SPM, LORETA/sLORETA, EEGLAB, and Brainstorm to aid with accessibility, and includes wrappers for pre-existing toolboxes for brain-state estimation such as Hidden Markov modelling (HMM-MAR) and independent component analysis (FastICA) to aid with direct comparison with these techniques. In this paper, we first introduce +microstate before subsequently performing example analyses using open access datasets to demonstrate and validate the methodology. MATLAB live scripts for each of these analyses are included in +microstate, to act as a tutorial and to aid with reproduction of the results presented in this manuscript.

Task-Based LSTM Kinematic Modeling for a Tendon-Driven Flexible Surgical Robot

Tendon-driven flexible surgical robots are normally suffering from the inaccurate modeling and imprecise motion control problems due to the nonlinearities of tendon transmission. Learning-based approaches are experimental data-driven with uncertainties modeled empirically, which can be adopted to improve the inevitable issues. This work proposes a LSTM-based kinematic modeling approach with task-based data for a flexible tendon-driven surgical robot to improve the control accuracy. Real experiments demonstrated the effectiveness and superiority of the proposed learned model when completing path following tasks, especially compared to the traditional modeling.

An open-access accelerated adult equivalent of the ABCD Study neuroimaging dataset (a-ABCD)

As public access to longitudinal developmental datasets like the Adolescent Brain Cognitive Development StudySM (ABCD Study®) increases, so too does the need for resources to benchmark time-dependent effects. Scan-to-scan changes observed with repeated imaging may reflect development but may also reflect practice effects, day-to-day variability in psychological states, and/or measurement noise. Resources that allow disentangling these time-dependent effects will be useful in quantifying actual developmental change. We present an accelerated adult equivalent of the ABCD Study dataset (a-ABCD) using an identical imaging protocol to acquire magnetic resonance imaging (MRI) structural, diffusion-weighted, resting-state and task-based data from eight adults scanned five times over five weeks. We report on the task-based imaging data (n = 7). In-scanner stop-signal (SST), monetary incentive delay (MID), and emotional n-back (EN-back) task behavioral performance did not change across sessions. Post-scan recognition memory for emotional n-back stimuli, however, did improve as participants became more familiar with the stimuli. Functional MRI analyses revealed that patterns of task-based activation reflecting inhibitory control in the SST, reward success in the MID task, and working memory in the EN-back task were more similar within individuals across repeated scan sessions than between individuals. Within-subject, activity was more consistent across sessions during the EN-back task than in the SST and MID task, demonstrating differences in fMRI data reliability as a function of task. The a-ABCD dataset provides a unique testbed for characterizing the reliability of brain function, structure, and behavior across imaging modalities in adulthood and benchmarking neurodevelopmental change observed in the open-access ABCD Study.

EEG microstate analysis of emotion regulation reveals no sequential processing of valence and emotional arousal

In electroencephalography (EEG), microstates are distributions of activity across the scalp that persist for several tens of milliseconds before changing into a different pattern. Microstate analysis is a way of utilizing EEG as both temporal and spatial imaging tool, but has rarely been applied to task-based data. This study aimed to conceptually replicate microstate findings of valence and emotional arousal processing and investigate the effects of emotion regulation on microstates, using data of an EEG paradigm with 107 healthy adults who actively viewed emotional pictures, cognitively detached from them, or suppressed facial reactions. Within the first 600 ms after stimulus onset only the comparison of viewing positive and negative pictures yielded significant results, caused by different electrodes depending on the microstate. Since the microstates associated with more and less emotionally arousing pictures did not differ, sequential processing could not be replicated. When extending the analysis to 2000 ms after stimulus onset, differences were exclusive to the comparison of viewing and detaching from negative pictures. Intriguingly, we observed the novel phenomenon of a microstate difference that could not be attributed to single electrodes. This suggests that microstate analysis can detect differences beyond those detected by event-related potential analysis.

What drives employment–unemployment transitions? Evidence from Italian task-based data

Relying on a unique longitudinal integrated database supplying micro-level information on labor market transitions (concerning the 2011–2017 period) and occupation task characteristics (e.g. routine-task intensity), this paper provides fresh evidence of the determinants of unemployment risk in Italy. We find that workers employed in routine-intensive occupations (measured with the RTI proposed by Acemoglu and Autor in Handb Labor Econ 4B:1043–1171, 2011) display—on average—higher unemployment risks than the rest of the workforce. This result is driven by workers employed in occupations entailing a large proportion of routine cognitive tasks and it is concentrated in high and medium–low skill occupations. In addition, the distribution of unemployment risk and its relation with routine-task intensity varies significantly across sectors—with higher risk in manufacturing and construction—confirming the importance of industry-level economic, technological and institutional heterogeneities. Finally, by exploring the gender dimension, we find that that being in a routine-intensive occupation increases unemployment risk for male workers only.

Task-Specific Trajectory Optimisation for Twin-Robotic X-Ray Tomography

With the advent of robotic C-arm computed tomography (CT) systems in medicine and twin-robotic CT systems in industry, new possibilities for the realisation of complex trajectories for CT scans are emerging. These trajectories will increase the range of CT applications, enable optimisation of image quality for many applications and open up new possibilities to reduce scan time and radiation dose. In this work, trajectory optimisation methods for optimising both, task-based data quality and data completeness, are presented by combining two different metrics. On the one hand, task-based data quality is optimised with a proven observer model. On the other hand, a Tuy-based metric is utilised to optimise data completeness. Both metrics capture mutually exclusive properties of the trajectory which are necessary, but alone are not sufficient for trajectory optimisation. Hence, existing task-driven trajectory optimisation approaches require additional input to decide on an overall optimal trajectory, e.g. in most cases constraints on the trajectory. Advantages and disadvantages of the presented methods are investigated. It is shown that by combining both metrics, trajectory optimisation for arbitrary geometries becomes possible. In application examples it is shown that this can be used for trajectory optimisation of challenging scanning tasks involving metal parts as well as for trajectory optimisation to reduce the number of projections while ensuring task-dependently high image quality. In total, the results of this work enable new applications for X-ray CT, especially for twin-robotic CT systems which are able to benefit from a high number of degrees of freedom.

Identifiable Patterns of Trait, State, and Experience in Chronic Stroke Recovery

Background Considerable evidence indicates that the functional connectome of the healthy human brain is highly stable, analogous to a fingerprint. Objective We investigated the stability of functional connectivity across tasks and sessions in a cohort of individuals with chronic stroke using a supervised machine learning approach. Methods Twelve individuals with chronic stroke underwent functional magnetic resonance imaging (fMRI) seven times over 18 weeks. The middle 6 weeks consisted of intensive aphasia therapy. We collected fMRI data during rest and performance of 2 tasks. We calculated functional connectivity metrics for each imaging run, then applied a support vector machine to classify data on the basis of participant, task, and time point (pre- or posttherapy). Permutation testing established statistical significance. Results Whole brain functional connectivity matrices could be classified at levels significantly greater than chance on the basis of participant (87.1% accuracy; P < .0001), task (68.1% accuracy; P = .002), and time point (72.1% accuracy; P = .015). All significant effects were reproduced using only the contralesional right hemisphere; the left hemisphere revealed significant effects for participant and task, but not time point. Resting state data could also be used to classify task-based data according to subject (66.0%; P < .0001). While the strongest posttherapy changes occurred among regions outside putative language networks, connections with traditional language-associated regions were significantly more positively correlated with behavioral outcome measures, and other regions had more negative correlations and intrahemispheric connections. Conclusions Findings suggest the profound importance of considering interindividual variability when interpreting mechanisms of recovery in studies of functional connectivity in stroke.

Interhemispheric Functional Reorganization and its Structural Base After BCI-Guided Upper-Limb Training in Chronic Stroke.

Brain-computer interface (BCI)-guided robot-assisted upper-limb training has been increasingly applied to stroke rehabilitation. However, the induced long-term neuroplasticity modulation still needs to be further characterized. This study investigated the functional reorganization and its structural base after BCI-guided robot-assisted training using resting-state fMRI, task-based fMRI, and diffusion tensor imaging (DTI) data. The clinical improvement and the neurological changes before, immediately after, and six months after 20-session BCI-guided robot hand training were explored in 14 chronic stroke subjects. The structural base of the induced functional reorganization and motor improvement were also investigated using DTI. Repeated measure ANOVA indicated long-term motor improvement was found (F[2, 26] = 6.367, p = 0.006). Significantly modulated functional connectivity (FC) was observed between ipsilesional motor regions (M1 and SMA) and some contralesional areas (SMA, PMd, SPL) in the seed-based analysis. Modulated FC with ipsilesional M1 was significantly correlated with motor function improvement (r = 0.6455, p = 0.0276). Besides, increased interhemispheric FC among the sensorimotor area from resting-state data and increased laterality index from task-based data together indicated the re-balance of the two hemispheres during the recovery. Multiple linear regression models suggested that both motor function improvement and the functional change between ipsilesional M1 and contralesional premotor area were significantly associated with the ipsilesional corticospinal tract integrity. The results in the current study provided solid support for stroke recovery mechanism in terms of interhemispheric interaction and its structural substrates, which could further enhance the understanding of BCI training in stroke rehabilitation. This study was registered at https://clinicaltrials.gov (NCT02323061).

Digitalization, routineness and employment: An exploration on Italian task-based data

This paper explores the relation between the digitalization of labour processes, the level of routineness of labour tasks and changes in employment in the case of Italy in the period 2011-16. The levels of digitalization and routineness of occupations in more than 500 4-digit ISCO professional groups are measured using data from a unique Italian profession-level survey on skill, tasks and work contents – the INAPP-ISTAT Survey on Italian Occupations (ICP), an O*NET-type dataset. Two digitalization indices are used: a digital use index, measuring the use of computers and e-mail in the workplace, and a digital tasks index, capturing the presence of a set of key digital tasks, such as those related to programming or activities concerning the use of specialized hardware. The same dataset is used to compute a composite routine task intensity index. The descriptive evidence presented in the paper shows strong differences across occupations in the level of digitalization and routineness, and the presence of a negative relation between the two in most professional groups. The econometric estimates show that digital-intensive occupations tend to grow more than the rest of the workforce, particularly when digitalization is measured relying on the digital use indicator. The level of routineness, in turn, is negatively or, in some specifications, not significantly associated to employment change. However, occupations that are both digital and routine-intensive turn out to be penalized in terms of employment growth, providing further support to (and further qualifying) the the routine biased technological change (RBTC) hypothesis. In other words, our results show that the impact of digitalization on employment is mediated by the level of routineness characterizing the tasks bundled in each occupation.

A multimodal neuroimaging classifier for alcohol dependence

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality – grey-matter density – by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence.

Sensitive Period for Cognitive Repurposing of Human Visual Cortex.

Studies of sensory loss are a model for understanding the functional flexibility of human cortex. In congenital blindness, subsets of visual cortex are recruited during higher-cognitive tasks, such as language and math tasks. Is such dramatic functional repurposing possible throughout the lifespan or restricted to sensitive periods in development? We compared visual cortex function in individuals who lost their vision as adults (after age 17) to congenitally blind and sighted blindfolded adults. Participants took part in resting-state and task-based fMRI scans during which they solved math equations of varying difficulty and judged the meanings of sentences. Blindness at any age caused "visual" cortices to synchronize with specific frontoparietal networks at rest. However, in task-based data, visual cortices showed regional specialization for math and language and load-dependent activity only in congenital blindness. Thus, despite the presence of long-range functional connectivity, cognitive repurposing of human cortex is limited by sensitive periods.

Connectome-based Models Predict Separable Components of Attention in Novel Individuals.

Although we typically talk about attention as a single process, it comprises multiple independent components. But what are these components, and how are they represented in the functional organization of the brain? To investigate whether long-studied components of attention are reflected in the brain's intrinsic functional organization, here we apply connectome-based predictive modeling (CPM) to predict the components of Posner and Petersen's influential model of attention: alerting (preparing and maintaining alertness and vigilance), orienting (directing attention to a stimulus), and executive control (detecting and resolving cognitive conflict) [Posner, M. I., & Petersen, S. E. The attention system of the human brain. Annual Review of Neuroscience, 13, 25-42, 1990]. Participants performed the Attention Network Task (ANT), which measures these three factors, and rested during fMRI scanning. CPMs tested with leave-one-subject-out cross-validation successfully predicted novel individual's overall ANT accuracy, RT variability, and executive control scores from functional connectivity observed during ANT performance. CPMs also generalized to predict participants' alerting scores from their resting-state functional connectivity alone, demonstrating that connectivity patterns observed in the absence of an explicit task contain a signature of the ability to prepare for an upcoming stimulus. Suggesting that significant variance in ANT performance is also explained by an overall sustained attention factor, the sustained attention CPM, a model defined in prior work to predict sustained attentional abilities, predicted accuracy, RT variability, and executive control from task-based data and predicted RT variability from resting-state data. Our results suggest that, whereas executive control may be closely related to sustained attention, the infrastructure that supports alerting is distinct and can be measured at rest. In the future, CPM may be applied to elucidate additional independent components of attention and relationships between the functional brain networks that predict them.