The Cole Neurocognition Lab

Neurocognitive Basis of Cognitive Control

Network Mechanisms of Cognitive Control

• Ito T, Yang GB, Laurent P, Schultz DH, Cole MW (2022). "Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior". Nature Communications. 13, 673. doi:10.1038/s41467-022-28323-7 Read abstract Code On Twitter
  The human ability to adaptively implement a wide variety of tasks is thought to emerge from the dynamic transformation of cognitive information. We hypothesized that these transformations are implemented via conjunctive activations in “conjunction hubs”—brain regions that selectively integrate sensory, cognitive, and motor activations. We used recent advances in using functional connectivity to map the flow of activity between brain regions to construct a task-performing neural network model from fMRI data during a cognitive control task. We verified the importance of conjunction hubs in cognitive computations by simulating neural activity flow over this empirically-estimated functional connectivity model. These empirically-specified simulations produced above-chance task performance (motor responses) by integrating sensory and task rule activations in conjunction hubs. These findings reveal the role of conjunction hubs in supporting flexible cognitive computations, while demonstrating the feasibility of using empirically-estimated neural network models to gain insight into cognitive computations in the human brain.
• Cocuzza CV, Ito T, Schultz D, Bassett DS, Cole MW (2020). "Flexible coordinator and switcher hubs for adaptive task control". Journal of Neuroscience. 40(36):6949–6968. doi:10.1523/JNEUROSCI.2559-19.2020 Read abstract On Twitter On Facebook Blog Post Code
  Functional connectivity studies have identified at least two large-scale neural systems that constitute cognitive control networks – the frontoparietal network (FPN) and cingulo-opercular network (CON). Control networks are thought to support goal-directed cognition and behavior. It was previously shown that the FPN flexibly shifts its global connectivity pattern according to task goal, consistent with a “flexible hub” mechanism for cognitive control. Our aim was to build on this finding to develop a functional cartography (a multi-metric profile) of control networks in terms of dynamic network properties. We quantified network properties in (male and female) humans using a high-control-demand cognitive paradigm involving switching among 64 task sets. We hypothesized that cognitive control is enacted by the FPN and CON via distinct but complementary roles reflected in network dynamics. Consistent with a flexible “coordinator” mechanism, FPN connections were globally diverse across tasks, while maintaining within-network connectivity to aid cross-region coordination. Consistent with a flexible “switcher” mechanism, CON regions switched to other networks in a task-dependent manner, driven primarily by reduced within-network connections to other CON regions. This pattern of results suggests FPN acts as a dynamic, global coordinator of goal-relevant information, while CON transiently disbands to lend processing resources to other goal-relevant networks. This cartography of network dynamics reveals a dissociation between two prominent cognitive control networks, suggesting complementary mechanisms underlying goal-directed cognition.
• Cole M.W., Reynolds J.R., Power J.D., Repovs G., Anticevic A., Braver T.S. (2013). "Multi-task connectivity reveals flexible hubs for adaptive task control". Nature Neuroscience; 2013 Sep;16(9):1348-55. doi:10.1038/nn.3470. Read abstract More info
  Extensive evidence suggests the human ability to adaptively implement a wide variety of tasks is preferentially due to the operation of a fronto-parietal brain network. We hypothesized that this network’s adaptability is made possible by ‘flexible hubs’ – brain regions that rapidly update their pattern of global functional connectivity according to task demands. We utilized recent advances in characterizing brain network organization and dynamics to identify mechanisms consistent with the flexible hub theory. We found that the fronto-parietal network’s brain-wide functional connectivity pattern shifted more than other networks’ across a variety of task states, and that these connectivity patterns could be used to identify the current task. Further, these patterns were consistent across practiced and novel tasks, suggesting reuse of flexible hub connectivity patterns facilitates adaptive (novel) task performance. Together, these findings support a central role for fronto-parietal flexible hubs in cognitive control and adaptive implementation of task demands generally.
• Cole M.W., Yarkoni T., Repovs G., Anticevic A., Braver T.S. (2012). "Global Connectivity of Prefrontal Cortex Predicts Cognitive Control and Intelligence". Journal of Neuroscience. 32(26): 8988-8999; doi: 10.1523/JNEUROSCI.0536-12.2012 Read abstract
  Control of thought and behavior is fundamental to human intelligence. Evidence suggests a frontoparietal brain network implements such cognitive control across diverse contexts. We identify a mechanism — global connectivity — by which components of this network might coordinate control of other networks. A lateral prefrontal cortex (LPFC) region’s activity was found to predict performance in a high control demand working memory task and also to exhibit high global connectivity. Critically, global connectivity in this LPFC region, involving connections both within and outside the frontoparietal network, showed a highly selective relationship with individual differences in fluid intelligence. These findings suggest LPFC is a global hub with a brainwide influence that facilitates the ability to implement control processes central to human intelligence.
• Cole M.W., Schneider W. (2007). “The cognitive control network: Integrated cortical regions with dissociable functions”, NeuroImage 37(1): 343-360. doi: 10.1016/j.neuroimage.2007.03.071 Read abstract
  Consensus across hundreds of published studies indicates that the same regions are involved in many forms of cognitive control. Using functional magnetic resonance imaging (fMRI), we found that these coactive regions form a functionally connected cognitive control network (CCN). Network status was identified by convergent methods, including: high interregional correlations during rest and task performance, consistently higher correlations within the CCN than the rest of cortex, co-activation in a visual search task, and mutual sensitivity to decision difficulty. Regions within the CCN include anterior cingulate cortex / pre-supplementary motor area (ACC/pSMA), dorsolateral prefrontal cortex (DLPFC), inferior frontal junction (IFJ), anterior insular cortex (AIC), dorsal pre-motor cortex (dPMC), and posterior parietal cortex (PPC). We used a novel visual line search task which included periods when the probe stimuli were occluded but subjects had to maintain and update working memory in preparation for the sudden appearance of a probe stimulus. The six CCN regions operated as a tightly coupled network during the ‘non-occluded’ portions of this task, with all regions responding to probe events. In contrast, the network was differentiated during occluded search. DLPFC, not ACC/pSMA, was involved in target memory maintenance when probes were absent, while both regions became active in preparation for difficult probes at the end of each occluded period. This approach illustrates one way in which a neuronal network can be identified, its high functional connectivity established, and its components dissociated in order to better understand the interactive and specialized internal mechanisms of that network.

Rapid Instructed Task Learning (RITL; "rittle")

• Cole M.W., Ito T., Braver T.S. (2016). "The behavioral relevance of task information in human prefrontal cortex". Cerebral Cortex. doi: 10.1093/cercor/bhv072 Read abstract
  Human lateral prefrontal cortex (LPFC) is thought to play a critical role in enabling cognitive flexibility, particularly when performing novel tasks. However, it remains to be established whether LPFC representation of task-relevant information in such situations actually contributes to successful performance. We utilized pattern classification analyses of functional MRI activity to identify novelty-sensitive brain regions as participants rapidly switched between performance of 64 complex tasks, 60 of which were novel. In three of these novelty-sensitive regions – located within distinct areas of left anterior LPFC – trial-evoked activity patterns discriminated correct from error trials. Further, these regions also contained information regarding the task-relevant decision rule, but only for successfully performed trials. This suggests that left anterior LPFC may be particularly important for representing task information that contributes to the cognitive flexibility needed to perform successfully in novel task situations.
• Cole M.W., Laurent P., Stocco A. (2013). "Rapid instructed task learning: A new window into the human brain’s unique capacity for flexible cognitive control". Cognitive, Affective, & Behavioral Neuroscience. 13(1): 1-22; doi:10.3758/s13415-012-0125-7 Read abstract
  The human ability to flexibly adapt to novel circumstances is extraordinary. Perhaps the most illustrative, yet underappreciated, form of this cognitive flexibility is rapid instructed task learning (RITL) – the ability to rapidly reconfigure our minds to perform new tasks from instructions. This ability is important for everyday life (e.g., learning to use new technologies) and is used to instruct participants in nearly every study of human cognition. We review the development of RITL as a circumscribed domain of cognitive neuroscience investigation, culminating in recent demonstrations that RITL is implemented via brain circuits centered on lateral prefrontal cortex. We then build on this and the recent discovery of compositional representations within lateral prefrontal cortex to develop an integrative theory of cognitive flexibility and cognitive control that identifies mechanisms that may enable RITL within the human brain. The insights gained from this new theoretical account have important implications for further developments and applications of RITL research.
• Cole M.W., Bagic A., Kass R., Schneider W. (2010). "Prefrontal Dynamics Underlying Rapid Instructed Task Learning Reverse With Practice". Journal of Neuroscience 30(42):14245-14254. doi: 10.1523/JNEUROSCI.1662-10.2010. Read abstract
  The ability to rapidly reconfigure our minds to perform novel tasks is important for adapting to an ever-changing world, yet little is understood about its basis in the brain. Further, it is unclear how this kind of task preparation changes with practice. Previous research suggests that prefrontal cortex (PFC) is essential when preparing to perform either novel or practiced tasks. Building upon recent evidence that PFC is organized in an anterior-to-posterior hierarchy, we postulated that novel and practiced task preparation would differentiate hierarchically distinct regions within PFC across time. Specifically, we hypothesized and confirmed using functional MRI and magnetoencephalography with humans that novel task preparation is a bottom-up process that involves lower-level rule representations in dorsolateral PFC (DLPFC) prior to a higher-level rule-integrating task representation in anterior PFC (aPFC). In contrast, we identified a complete reversal of this activity pattern during practiced task preparation. Specifically, we found that practiced task preparation is a top-down process that involves a higher-level rule-integrating task representation (recalled from long-term memory) in aPFC prior to lower-level rule representations in DLPFC. These findings reveal two distinct yet highly inter-related mechanisms for task preparation, one involving task set formation from instructions during rapid instructed task learning and the other involving task set retrieval from long-term memory to facilitate familiar task performance. These two mechanisms demonstrate the exceptional flexibility of human PFC as it rapidly reconfigures cognitive brain networks to implement a wide variety of possible tasks.

Computational Network Neuroscience

Select Publications:

• Sanchez-Romero R, Ito T, Mill RD, Hanson SJ, Cole MW (2023). "Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations". NeuroImage. 278:120300. doi:10.1016/j.neuroimage.2023.120300 Read abstract On Twitter
  Brain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions. However, these models have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. We progress from simple to complex FC measures, with each adding algorithmic details reflecting causal principles. This reflects many neuroscientists’ preference for reduced FC measure complexity (to minimize assumptions, minimize compute time, and fully comprehend and easily communicate methodological details), which potentially trades off with causal validity. We start with Pearson correlation (the current field standard) to remain maximally relevant to the field, estimating causal validity across a range of FC measures using simulations and empirical fMRI data. Finally, we apply causal-FC-based activity flow modeling to a dorsolateral prefrontal cortex region (DLPFC), demonstrating distributed causal network mechanisms contributing to its strong activation during a working memory task. Notably, this fully distributed model is able to account for DLPFC working memory effects traditionally thought to rely primarily on within-region (i.e., not distributed) recurrent processes. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.
• Mill RD, Hamilton JL, Winfield EC, Lalta N, Chen RH, Cole MW (2022). "Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior". PLOS Biology. doi:10.1101/2021.01.26.428276 Read abstract Commentary on paper Code On Twitter
  How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the “where and when”) and then allow for empirical testing of alternative network models of brain function that link information to behavior (the “how”). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach—dynamic activity flow modeling—then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory–motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.
• Cole MW, Ito T, Bassett DS, & Schultz DH (2016). "Activity flow over resting-state networks shapes cognitive task activations". Nature Neuroscience. doi:10.1038/nn.4406 Read abstract More info
  Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allowed prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

  More information on Cole, Ito, et al. (2016):

  • Blog post with frequently asked questions about Cole et al., 2016
  • Code for implementing activity flow mapping
• Schultz DH & Cole MW (2016). "Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration". Journal of Neuroscience. doi:10.1523/JNEUROSCI.0358-16.2016 Read abstract
  The human brain is able to exceed modern computers on multiple computational demands (e.g., language, planning) using a small fraction of the energy. The mystery of how the brain can be so efficient is compounded by recent evidence that all brain regions are constantly active as they interact in so-called resting-state networks (RSNs). To investigate the brain's ability to process complex cognitive demands efficiently, we compared functional connectivity (FC) during rest and multiple highly distinct tasks. We found previously that RSNs are present during a wide variety of tasks and that tasks only minimally modify FC patterns throughout the brain. Here, we tested the hypothesis that, although subtle, these task-evoked FC updates from rest nonetheless contribute strongly to behavioral performance. One might expect that larger changes in FC reflect optimization of networks for the task at hand, improving behavioral performance. Alternatively, smaller changes in FC could reflect optimization for efficient (i.e., small) network updates, reducing processing demands to improve behavioral performance. We found across three task domains that high-performing individuals exhibited more efficient brain connectivity updates in the form of smaller changes in functional network architecture between rest and task. These smaller changes suggest that individuals with an optimized intrinsic network configuration for domain-general task performance experience more efficient network updates generally. Confirming this, network update efficiency correlated with general intelligence. The brain's reconfiguration efficiency therefore appears to be a key feature contributing to both its network dynamics and general cognitive ability.
• Cole M.W., Bassett D.S., Power J.D., Braver T.S., Petersen S.E. (2014). "Intrinsic and task-evoked network architectures of the human brain". Neuron. 83(1), 238 – 251. doi:10.1016/j.neuron.2014.05.014 Read abstract
  Many functional network properties of the human brain have been identified during rest and task states, yet it remains unclear how the two relate. We identified a whole-brain network architecture present across dozens of task states that was highly similar to the resting-state network architecture. The most frequent functional connectivity strengths across tasks closely matched the strengths observed at rest, suggesting this is an “intrinsic”, standard architecture of functional brain organization. Further, a set of small but consistent changes common across tasks suggests the existence of a task-general network architecture distinguishing task states from rest. These results indicate the brain’s functional network architecture during task performance is shaped primarily by an intrinsic network architecture that is also present during rest, and secondarily by evoked task-general and task-specific network changes. This establishes a strong relationship between resting-state functional connectivity and task-evoked functional connectivity – areas of neuroscientific inquiry typically considered separately.

Network Disruptions Underlying Brain Disorders and Aging

Select Publications:

• Hearne LJ, Mill RD, Keane BP, Repovs G, Anticevic A, Cole MW (2021). "Activity flow underlying abnormalities in brain activations and cognition in schizophrenia". Science Advances. 7(9) doi:10.1126/sciadv.abf2513 Read abstract Code On Twitter
  Cognitive dysfunction is a core feature of many brain disorders such as schizophrenia (SZ), and has been linked to both aberrant brain functional connectivity (FC) and aberrant cognitive brain activations. We propose that aberrant network activity flow over FC pathways leads to altered cognitive activations that produce cognitive dysfunction in SZ. We tested this hypothesis using activity flow mapping – an approach that models the movement of task-related activity between brain regions as a function of FC. Using fMRI data from SZ individuals and healthy controls during a working memory task, we found that activity flow models accurately predict aberrant cognitive activations across multiple brain networks. Within the same framework, we simulated a connectivity-based clinical intervention, predicting specific treatments that normalized brain activations and behavior in independent patients. Our results suggest that dysfunctional task-evoked activity flow is a large-scale network mechanism contributing to the emergence of cognitive dysfunction in SZ.
• Mill RD, Gordon BA, Balota DA, Cole MW (2020). "Predicting dysfunctional age-related task activations from resting-state network alterations". NeuroImage. doi:10.1016/j.neuroimage.2020.117167 Read abstract On Twitter Code
  Alzheimer’s disease (AD) is linked to changes in fMRI task activations and fMRI resting-state functional connectivity (restFC), which can emerge early in the timecourse of illness. Study of these fMRI correlates of unhealthy aging has been conducted in largely separate subfields. Taking inspiration from neural network simulations, we propose a unifying mechanism wherein restFC network alterations associated with Alzheimer’s disease disrupt the ability for activations to flow between brain regions, leading to aberrant task activations. We apply this activity flow modeling framework in a large sample of clinically unimpaired older adults, which was segregated into healthy (low-risk) and at-risk subgroups based on established imaging (positron emission tomography amyloid) and genetic (apolipoprotein) risk factors for AD. We identified healthy task activations in individuals at low risk for AD, and then by estimating activity flow using at-risk AD restFC data we were able to predict the altered at-risk AD task activations. Thus, modeling the flow of healthy activations over at-risk AD connectivity effectively transformed the healthy aged activations into unhealthy aged activations. These results provide evidence that activity flow over altered intrinsic functional connections may act as a mechanism underlying Alzheimer’s-related dysfunction, even in very early stages of the illness. Beyond these mechanistic insights linking restFC with cognitive task activations, this approach has potential clinical utility as it enables prediction of task activations and associated cognitive dysfunction in individuals without requiring them to perform in-scanner cognitive tasks.
• Schultz DH, Ito T, Solomyak L, Chen RH, Mill RD, Anticevic A, Cole MW (2019). "Global connectivity of the frontoparietal cognitive control network is related to depression symptoms in the general population". Network Neuroscience. 3:1:107-123. doi:10.1162/NETN_a_00056 Read abstract On Twitter On Facebook Blog Post
  We all vary in our mental health, even among people not meeting diagnostic criteria for mental illness. Understanding this individual variability may reveal factors driving the risk for mental illness, as well as factors driving sub-clinical problems that still adversely affect quality of life. To better understand the large-scale brain network mechanisms underlying this variability we examined the relationship between mental health symptoms and resting-state functional connectivity patterns in cognitive control systems. One such system is the frontoparietal cognitive control network (FPN). Changes in FPN connectivity may impact mental health by disrupting the ability to regulate symptoms in a goal-directed manner. Here we test the hypothesis that FPN dysconnectivity relates to mental health symptoms even among individuals who do not meet formal diagnostic criteria but may exhibit meaningful symptom variation. We found that depression symptoms severity negatively correlated with between-network global connectivity (BGC) of the FPN. This suggests that decreased connectivity between the FPN and the rest of the brain is related to increased depression symptoms in the general population. These findings complement previous clinical studies to support the hypothesis that global FPN connectivity contributes to the regulation of mental health symptoms across both health and disease.
• Spronk M, Keane BP, Ito T, Kulkarni K, Ji JL, Anticevic A, Cole MW (2021). "A whole-brain and cross-diagnostic perspective on functional brain network dysfunction". Cerebral Cortex. doi:10.1093/cercor/bhaa242 Read abstract On Twitter
  A wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations have seemed to support various theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in broad, whole-brain perspective. Using a graph distance measure - connectome-wide correlation - we found that whole-brain resting-state functional network organization in humans is highly similar across a variety of mental diseases and healthy controls. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease those differences are informative. Such small network alterations may reflect the fact that most psychiatric patients maintain overall cognitive abilities similar to those of healthy individuals (relative to, e.g., the most severe schizophrenia cases), such that whole-brain functional network organization is expected to differ only subtly even for mental diseases with devastating effects on everyday life. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases.
• Cole M.W., Repovs G., Anticevic A. (2014). "The frontoparietal control system: A central role in mental health". The Neuroscientist, 20(6), 652–664. doi:10.1177/1073858414525995 Read abstract
  Recent findings suggest the existence of a fronto-parietal control system consisting of flexible hubs that regulate distributed systems (e.g., visual, limbic, motor) according to current task goals. A growing number of studies are reporting alterations of this control system across a striking range of mental diseases. We suggest this may reflect a critical role for the control system in promoting and maintaining mental health. Specifically, we propose that this system implements feedback control to regulate symptoms as they arise (e.g., excessive anxiety reduced via regulation of amygdala), such that an intact control system is protective against a variety of mental illnesses. Consistent with this possibility, recent results indicate that several major mental illnesses involve altered brain-wide connectivity of the control system, likely altering its ability to regulate symptoms. These results suggest that this ‘immune system of the mind’ may be an especially important target for future basic and clinical research.
• Cole M.W., Anticevic A., Repovs G., Barch D. (2011). "Variable global dysconnectivity and individual differences in schizophrenia". Biological Psychiatry 70(1):43-50. doi:10.1016/j.biopsych.2011.02.010 Read abstract
  Background
  A fundamental challenge for understanding neuropsychiatric disease is identifying sources of individual differences in psychopathology, especially when there is substantial heterogeneity of symptom expression such as is found in schizophrenia. We hypothesized that such heterogeneity may arise in part from consistently widespread yet variably patterned alterations in the connectivity of focal brain regions.
  Methods
  We used resting state functional MRI to identify variable global dysconnectivity in 23 patients with DSM-IV schizophrenia relative to 22 age, gender, and parental socioeconomic status matched controls using a novel global brain connectivity (GBC) functional MRI method that is robust to high variability across individuals. We examined cognitive functioning using a modified Sternberg task and subtests from the Wechsler Adult Intelligence Scale - Third Edition. We measured symptom severity using the Scale for Assessment of Positive and Negative Symptoms.
  Results
  We identified a dorsolateral prefrontal cortex (DLPFC) region with global and highly variable dysconnectivity involving within-PFC under-connectivity and non-PFC over-connectivity in patients. Variability in this ‘under/over’ pattern of dysconnectivity strongly predicted the severity of cognitive deficits (matrix reasoning IQ, verbal IQ, and working memory performance) as well as individual differences in every cardinal symptom domain of schizophrenia (poverty, reality distortion, and disorganization).
  Conclusion
  These results suggest that global dysconnectivity underlies DLPFC involvement in the neuropathology of schizophrenia. Further, these results demonstrate the possibility that specific patterns of dysconnectivity with a given network hub region may explain individual differences in symptom presentation in schizophrenia. Critically, such findings may extend to other neuropathologies with diverse presentation.