The lab's research focuses on discovering the dynamic network architecture of the human brain, especially as it relates to the cognitive and neural mechanisms that make human behavior flexible and intelligent.
Brain network mechanisms of flexible cognitive control
Flexible control – a capacity supporting adaptive, goal-directed behavior important in daily life – is affected in a variety of mental illnesses, markedly reducing quality of life. See Cole & Schneider (2007) for evidence that flexible control is implemented by a set of integrated brain regions sometimes referred to as the cognitive control network. See Cole, Pathak, et al. (2010) and Cole, Yarkoni, et al. (2012) for evidence that this network implements control via its high connectivity throughout the brain, as indexed by global brain connectivity (GBC). See Cole, Anticevic, et al. (2011) for a recent demonstration of how a breakdown in the GBC of a core node of this network may contribute to the profound cognitive control deficits associated with schizophrenia. See Cole, Reynolds, et al. (2013) for evidence that this network implements cognitive control via dynamic updating of long-range functional connectivity with other functional networks throughout the brain.
Rapid instructed task learning (RITL; "rittle")
A key aspect of flexible control is our ability to rapidly reconfigure our minds to perform a nearly infinite variety of possible tasks. For instance, you utilized RITL the first time you used a smartphone – transfering what you knew about cell phones and computers while expanding what was possible with such a device. Comprehensive understanding of this ability would have important implications for research in education, aging, and a variety of mental illnesses. See Cole, Bagic, et al. (2010) for a novel cognitive paradigm for investigating RITL, as well as evidence that RITL involves a specific shift in dynamics within prefrontal cortex. See Cole, Etzel, et al. (2011) for evidence that RITL is possible due to rapid transfer of practiced task rule representations within prefrontal cortex to novel contexts. See Cole, Laurent, & Stocco (2013) for a review of RITL findings and an integrative theory of how prefrontal cortex may implement RITL abilities and cognitive flexibility generally.
Our manuscript characterizing the functional network architecture of the human brain was accepted for publication at Neuron! Take a look.
The lab is seeking graduate students in human neuroimaging and/or computational modeling focusing on brain connectivity dynamics and cognitive control (human intelligence). If interested please email Dr. Cole with a cover letter and CV. See more info on the BNS program at Rutgers.
Etzel J.A., Cole M.W., Zacks J.M., Kay K.N., Braver T.S. (In Press). "Reward motivation enhances task coding in frontoparietal cortex". Cerebral Cortex.
Anticevic A., Hu X., Xiao Y., Hu J., Li F., Bi F., Cole M.W., Savic A., Yang G., Repovs G., Murray J., Wang X., Huang X., Lui S., Krystal J.H., and Gong Q. (In Press). "Early-Course Unmedicated Schizophrenia Patients Exhibit Elevated Prefrontal Connectivity Associated with Longitudinal Change". Journal of Neuroscience.
Meiran N., Pereg M., Kessler Y., Cole M.W., Braver T.S. (In Press). "Reflexive Activation of Newly Instructed Stimulus-Response Rules: Evidence from Lateralized Readiness Potentials in NO-GO Trials". Cognitive, Affective, & Behavioral Neuroscience.
Meiran N., Pereg M., Kessler Y., Cole M.W., Braver T.S. (In Press). "The Power of Instructions: Proactive Configuration of Stimulus-Response Translation". Journal of Experimental Psychology: Learning, Memory, and Cognition.
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.
Yang G.J., Murray J.D., Repovs G., Cole M.W., Savic A., Glasser M.F., Pittenger C., Krystal J.H., Wang X., Pearlson G.D., Glahn D.C., Anticevic A. (In Press). "Altered global brain signal in schizophrenia". Proceedings of the National Academy of Sciences. doi:10.1073/pnas.1405289111
Neuropsychiatric conditions like schizophrenia display a complex neurobiology, which has long been associated with distributed brain dysfunction. However, no investigation has tested whether schizophrenia shows alterations in global brain signal (GS), a signal derived from functional MRI and often discarded as a meaningless baseline in many studies. To evaluate GS alterations associated with schizophrenia, we studied two large chronic patient samples (n = 90, n = 71), comparing them to healthy subjects (n = 220) and patients diagnosed with bipolar disorder (n = 73). We identified and replicated increased cortical power and variance in schizophrenia, an effect predictive of symptoms yet obscured by GS removal. Voxel-wise signal variance was also increased in schizophrenia, independent of GS effects. Both findings were absent in bipolar patients, confirming diagnostic specificity. Biologically informed computational modeling of shared and nonshared signal propagation through the brain suggests that these findings may be explained by altered net strength of overall brain connectivity in schizophrenia.
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.
Anticevic A., Hu S., Zhang S., Savic A., Billingslea E., Wasylink S., Repovs G., Cole M.W., Bednarski S., Krystal J.H., Bloch M.H., Li C.R., Pittenger C. (2014). "Global resting-state functional magnetic resonance imaging analysis identifies frontal cortex, striatal, and cerebellar dysconnectivity in obsessive-compulsive disorder." Biological Psychiatry, 75(8), 595–605. doi:10.1016/j.biopsych.2013.10.021
Obsessive-compulsive disorder (OCD) is associated with regional hyperactivity in cortico-striatal circuits. However, the large-scale patterns of abnormal neural connectivity remain uncharacterized. Resting-state functional connectivity studies have shown altered connectivity within the implicated circuitry, but they have used seed-driven approaches wherein a circuit of interest is defined a priori. This limits their ability to identify network abnormalities beyond the prevailing framework. This limitation is particularly problematic within the prefrontal cortex (PFC), which is large and heterogeneous and where a priori specification of seeds is therefore difficult. A hypothesis-neutral, data-driven approach to the analysis of connectivity is vital.
We analyzed resting-state functional connectivity data collected at 3T in 27 OCD patients and 66 matched control subjects with a recently developed data-driven global brain connectivity (GBC) method, both within the PFC and across the whole brain.
We found clusters of decreased connectivity in the left lateral PFC in both whole-brain and PFC-restricted analyses. Increased GBC was found in the right putamen and left cerebellar cortex. Within regions of interest in the basal ganglia and thalamus, we identified increased GBC in dorsal striatum and anterior thalamus, which was reduced in patients on medication. The ventral striatum/nucleus accumbens exhibited decreased global connectivity but increased connectivity specifically with the ventral anterior cingulate cortex in subjects with OCD.
These findings identify previously uncharacterized PFC and basal ganglia dysconnectivity in OCD and reveal differentially altered GBC in dorsal and ventral striatum. Results highlight complex disturbances in PFC networks, which could contribute to disrupted cortical-striatal-cerebellar circuits in OCD.
Anticevic A., Tang Y., Cho Y. T., Repovs G., Cole M. W., Savic A., Wang F., Krystal J.H., and Xu K. (2014). "Amygdala Connectivity Differs Among Chronic, Early Course, and Individuals at Risk for Developing Schizophrenia". Schizophrenia Bulletin 40 (5): 1105-1116 doi:10.1093/schbul/sbt165
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.
Anticevic A., Cole M.W., Repovš G., Savic A., Driesen N.R., Yang G., Cho Y.T., Murray J.D., Glahn D.C., Wang X., and Krystal J.H. (2013). "Connectivity, Pharmacology and Computation: Towards a Mechanistic Understanding of Neural System Dysfunction in Schizophrenia". Front. Psychiatry 4:169. doi:10.3389/fpsyt.2013.00169
Neuropsychiatric diseases such as schizophrenia and bipolar illness alter the structure and function of distributed neural networks. Functional neuroimaging tools have evolved sufficiently to reliably detect system-level disturbances in neural networks. This review focuses on recent findings in schizophrenia and bipolar illness using resting-state neuroimaging, an advantageous approach for biomarker development given its ease of data collection and lack of task-based confounds. These benefits notwithstanding, neuroimaging does not yet allow the evaluation of individual neurons within local circuits, where pharmacological treatments ultimately exert their effects. This limitation constitutes an important obstacle in translating findings from animal research to humans and from healthy humans to patient populations. Integrating new neuroscientific tools may help to bridge some of these gaps. We specifically discuss two complementary approaches. The first is pharmacological manipulations in healthy volunteers, which transiently mimic some cardinal features of psychiatric conditions. We specifically focus on recent neuroimaging studies using the NMDA receptor antagonist, ketamine, to probe glutamate synaptic dysfunction associated with schizophrenia. Second, we discuss the combination of human pharmacological imaging with biophysically informed computational models developed to guide the interpretation of functional imaging studies and to inform the development of pathophysiologic hypotheses. To illustrate this approach, we review clinical investigations in addition to recent findings of how computational modeling has guided inferences drawn from our studies involving ketamine administration to healthy subjects. Thus, this review asserts that linking experimental studies in humans with computational models will advance to effort to bridge cellular, systems, and clinical neuroscience approaches to psychiatric disorders.
Anticevic, A., Cole, M.W., Repovs, G., Murray J.D., Brumbaugh, M.S., Winkler, A.M., Savic, A., Krystal, J.H., Pearlson, G.D., & Glahn, D.C. (In Press). "Characterizing Thalamo-Cortical Disturbances in Schizophrenia and Bipolar Illness". Cerebral Cortex. doi:10.1093/cercor/bht165
Schizophrenia is a devastating neuropsychiatric syndrome associated with distributed brain connectivity disturbances that may involve large-scale thalamo-cortical systems. Incomplete characterization of thalamic connectivity in schizophrenia limits our understanding of its relationship to symptoms and to diagnoses with shared clinical presentation, such as bipolar illness, which may exist on a spectrum. Using resting-state fMRI, we characterized thalamic connectivity in 90 schizophrenia patients versus 90 matched controls via: i) subject-specific anatomically-defined thalamic seeds; ii) anatomical and data-driven clustering to assay within-thalamus dysconnectivity; iii) machine learning to classify diagnostic membership via thalamic connectivity for schizophrenia and for 47 bipolar patients and 47 matched controls. Schizophrenia analyses revealed functionally related disturbances: thalamic over-connectivity with bilateral sensory-motor cortices, which predicted symptoms, but thalamic under-connectivity with prefrontal-striatal-cerebellar regions relative to controls, possibly reflective of sensory gating and top-down control disturbances. Clustering revealed this dysconnectivity was prominent for thalamic nuclei densely connected with prefrontal cortex. Classification and cross-diagnostic results suggest thalamic dysconnectivity may be a neural marker for disturbances across diagnoses. Present findings, using one of the largest schizophrenia and bipolar neuroimaging samples to date, inform basic understanding of large-scale thalamo-cortical systems and provide vital clues about the complex nature of its disturbances in severe mental illness.
Meiran N., Cole M.W., and Braver T.S. (2013). "When Planning Results in Loss of Control: Intention-Based Reflexivity and Proactive Control". Book chapter in: Seebass, G., Schmitz, M., & Gollwitzer, P. M. Acting intentionally and its limits: Individuals, groups, institutions. Berlin: De Gruyter.
Anticevic A., Brumbaugh M.S., Winkler A.M., Lombardo L.E., Barrett J., Corlett P.R., Kober H., Gruber J., Repovs G., Cole M.W., Krystal J.H., Pearlson G.D., & Glahn D.C. (2013). "Global prefrontal and fronto-amygdala dysconnectivity in bipolar I disorder with psychosis history." Biological Psychiatry. 73(6): 565-573; doi:10.1016/j.biopsych.2012.07.031
Pathophysiological models of bipolar disorder postulate that mood dysregulation arises from fronto-limbic dysfunction, marked by reduced prefrontal cortex (PFC) inhibitory control. This might occur due to both disruptions within PFC networks and abnormal inhibition over subcortical structures involved in emotional processing. However, no study has examined global PFC dysconnectivity in bipolar disorder and tested whether regions with within-PFC dysconnectivity also exhibit fronto-limbic connectivity deficits. Furthermore, no study has investigated whether such connectivity disruptions differ for bipolar patients with psychosis history, who might exhibit a more severe clinical course.
We collected resting-state functional magnetic resonance imaging at 3 T in 68 remitted bipolar I patients (34 with psychosis history) and 51 demographically matched healthy participants. We employed a recently developed global brain connectivity method, restricted to PFC (rGBC). We also independently tested connectivity between anatomically defined amygdala and PFC.
Bipolar patients exhibited reduced medial prefrontal cortex (mPFC) rGBC, increased amygdala–mPFC connectivity, and reduced connectivity between amygdala and dorsolateral PFC. All effects were driven by psychosis history. Moreover, the magnitude of observed effects was significantly associated with lifetime psychotic symptom severity.
This convergence between rGBC, seed-based amygdala findings, and symptom severity analyses highlights that mPFC, a core emotion regulation region, exhibits both within-PFC dysconnectivity and connectivity abnormalities with limbic structures in bipolar illness. Furthermore, lateral PFC dysconnectivity in patients with psychosis history converges with published work in schizophrenia, indicating possible shared risk factors. Observed dysconnectivity in remitted patients suggests a bipolar trait characteristic and might constitute a risk factor for phasic features of the disorder.
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.
Etzel, J.A., Cole M.W., Braver T.S. (2012). "Looking Outside the Searchlight". In G. Langs, I. Rish, M. Grosse-Wentrup, & B. Murphy (Eds.), Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science. (vol. 7263, pp. 26–33). Springer Berlin / Heidelberg. doi:10.1007/978-3-642-34713-9_4
Anticevic A., Cole M.W., Murray J., Corlett P.R., Wang X., & Krystal J.H. (2012). "The role of default network deactivation in cognition and disease". Trends in Cognitive Sciences. 16(12): 584–592; doi: 10.1016/j.tics.2012.10.008.
A considerable body of evidence has accumulated over recent years on the functions of the default-mode network (DMN) – a set of brain regions whose activity is high when the mind is not engaged in specific behavioral tasks and low during focused attention on the external environment. In this review, we focus on DMN suppression and its functional role in health and disease, summarizing evidence that spans several disciplines, including cognitive neuroscience, pharmacological neuroimaging, clinical neuroscience, and theoretical neuroscience. Collectively, this research highlights the functional relevance of DMN suppression for goal-directed cognition, possibly by reducing goal-irrelevant functions supported by the DMN (e.g., mind-wandering), and illustrates the functional significance of DMN suppression deficits in severe mental illness.
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.
In this review, the authors discuss the seemingly paradoxical loss of control associated with states of high readiness to execute a plan, termed “intention-based reflexivity.” The review suggests that the neuro-cognitive systems involved in the preparation of novel plans are different than those involved in preparation of practiced plans (i.e., those that have been executed beforehand). When the plans are practiced, intention-based reflexivity depends on the prior availability of response codes in long-term memory (LTM). When the plans are novel, reflexivity is observed when the plan is pending and the goal has not yet been achieved. Intention-based reflexivity also depends on the availability of working-memory (WM) limited resources and the motivation to prepare. Reflexivity is probably related to the fact that, unlike reactive control (once a plan is prepared), proactive control tends to be relatively rigid.
Flexible, adaptive behavior is thought to rely on abstract rule representations within lateral prefrontal cortex (LPFC), yet it remains unclear how these representations provide such flexibility. We recently demonstrated that humans can learn complex novel tasks in seconds. Here we hypothesized that this impressive mental flexibility may be possible due to rapid transfer of practiced rule representations within LPFC to novel task contexts. We tested this hypothesis using functional MRI and multivariate pattern analysis, classifying LPFC activity patterns across 64 tasks. Classifiers trained to identify abstract rules based on practiced task activity patterns successfully generalized to novel tasks. This suggests humans can transfer practiced rule representations within LPFC to rapidly learn new tasks, facilitating cognitive performance in novel circumstances.
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.
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.
Investigations of individual differences have become increasingly important in the cognitive neuroscience of executive control. For instance, individual variation in lateral prefrontal cortex function (and that of associated regions) has recently been used to identify contributions of executive control processes to a number of domains, including working memory capacity, anxiety, reward/motivation, and emotion regulation. However, the origins of such individual differences remain poorly understood. Recent progress toward identifying the genetic and environmental sources of variation in neural traits, in combination with progress in identifying the causal relationships between neural and cognitive processes, will be essential for developing a mechanistic understanding of executive control.
Recent advances in brain connectivity methods have made it possible to identify hubs – the brain’s most globally connected regions. Such regions are essential for coordinating brain functions due to their connectivity with numerous regions with a variety of specializations. Current structural and functional connectivity methods generally agree that default mode network (DMN) regions have among the highest global brain connectivity (GBC). We developed two novel statistical approaches using resting state functional connectivity MRI – weighted and unweighted GBC (wGBC and uGBC) – to test the hypothesis that the highest global connectivity also occurs in the cognitive control network (CCN), a network anti-correlated with the DMN across a variety of tasks. High global connectivity was found in both CCN and DMN. The newly developed wGBC approach improves upon existing methods by quantifying inter-subject consistency, quantifying the highest GBC values by percentage, and avoiding arbitrary connection strength thresholding. The uGBC approach is based on graph theory and includes many of these improvements, but still requires an arbitrary connection threshold. We found high GBC in several subcortical regions (e.g., hippocampus, basal ganglia) only with wGBC despite the regions’ extensive anatomical connectivity. These results demonstrate the complementary utility of wGBC and uGBC analyses for the characterization of the most highly connected, and thus most functionally important, regions of the brain. Additionally, the high connectivity of both the CCN and the DMN demonstrates that brain regions outside primary sensory-motor networks are highly involved in coordinating information throughout the brain.
Cognitive neuroscience research relies, in part, on homologies between the brains of human and non-human primates. A quandary therefore arises when presumed anatomical homologues exhibit different functional properties. Such a situation has recently arisen in the case of the anterior cingulate cortex (ACC). In humans, numerous studies suggest a role for ACC in detecting conflicts in information processing. Studies of macaque monkey ACC, in contrast, have failed to find conflict-related responses. We consider several interpretations of this discrepancy, including differences in research methodology and cross-species differences in functional neuroanatomy. New directions for future research are outlined, emphasizing the importance of distinguishing illusory cross-species differences from the true evolutionary differences that make our species unique.
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.
The ability to select an appropriate response among competing alternatives is a fundamental requirement for successful performance of a variety of everyday tasks. Recent research suggests that a frontal–parietal network of brain regions (including dorsal prefrontal, dorsal premotor and superior parietal cortices) mediate response selection for spatial material. Most of this research has used blocked experimental designs. Thus, the frontal–parietal activity reported may be due either to tonic activity across a block or to processing occurring at the trial level. Our current event-related fMRI study investigated response selection at the level of the trial in order to identify possible response selection sub-processes. In the study, participants responded to a visually presented stimulus with either a spatially compatible or incompatible manual response. On some trials, several seconds prior to stimulus onset, a cue indicated which task was to be performed. In this way we could identify separate brain regions for task preparation and task performance, if they exist. Our results showed that the frontal–parietal network for spatial response selection activated both during task preparation as well as during task performance. We found no evidence for preparation specific brain mechanisms in this task. These data suggest that spatial response selection and response preparation processes rely on the same neurocognitive mechanisms.
Recent functional imaging studies of working memory (WM) have suggested a relationship between the requirement for response selection and activity in dorsolateral prefrontal (DLPFC) and parietal regions. Although a number of WM operations are likely to occur during response selection, the current study was particularly interested in the contribution of this neural network to WM-based response selection when compared to the selection of an item from a list being maintained in memory, during a verbal learning task. The design manipulated stimulus–response mappings so that selecting an item from memory was not always accompanied with selecting a motor response. Functional activation during selection supported previous findings of fronto-parietal involvement, although in contrast to previous findings left, rather than right, DLPFC activity was significantly more active for selecting a memory-guided motor response, when compared to selecting an item currently maintained in memory or executing a memory-guided response. Our results contribute to the debate over the role of fronto-parietal activity during WM tasks, suggesting that this activity appears particularly related to response selection, potentially supporting the hypothesized role of prefrontal activity in biasing attention toward task-relevant material in more posterior regions.
We investigated the voluntary control of motor behavior by studying the process of deciding whether or not to execute a movement. We imaged the human dorsal cortex while subjects performed a countermanding task that allowed us to manipulate the probability that subjects would be able to cancel a planned saccade in response to an imperative stop signal. We modeled the behavioral data as a race between gaze-shifting mechanisms and gaze-holding mechanisms towards a finish line where a saccade is generated or canceled, and estimated that saccade cancelation took ∼160 ms. The frontal eye fields showed greater activation on stop signal trials regardless of successful cancelation, suggesting coactivation of saccade and fixation mechanisms. The supplementary eye fields, however, distinguished between successful and unsuccessful cancelation, suggesting a role in monitoring performance. These oculomotor regions play distinct roles in the decision processes mediating saccadic choice.
Recently Published Abstracts and Presentations
Cole M.W. (November, 2014). Reconceptualizing brain network change as shared signal dynamics. Talk presented at Society for Neuroscience, Washington, DC.
Cole M.W. (November, 2014). Functional connectivity differences in brain networks: contributions of shared and unshared variance. Invited talk presented at the Asilomar conference, Pacific Grove, CA.
Cole M.W. (August, 2014). Multi-task functional connectivity and flexible hubs. Invited talk presented at the International Conference on Cognitive Neuroscience (ICON), Brisbane, Australia.
Cole M.W. (June, 2014). Intrinsic and dynamic brain network architectures underlying adaptive behavior in humans. Invited talk presented at the Princeton Neuroscience Institute, Princeton, NJ.
Cole M.W. (May, 2014). Intrinsic and task-evoked network architectures of the human brain. Invited talk presented at the Nathan S. Kline Institute (NKI), Orangeburg, NY.
Cole M.W. (May, 2014). Flexible Thinking: Understanding Cognitive Control and Intelligence in the Brain. Invited talk presented at the Learning and the Brain conference, New York, NY.
Cole M.W., Bassett D., Power J.D., Petersen S. (November, 2013). Multi-task functional connectivity reveals the human brain’s dynamic network architecture and stable functional backbone. Poster presented at Society for Neuroscience, San Diego, CA.
Cole M.W. (November, 2013). Intrinsic and Dynamic Network Architectures of the Human Brain. Invited talk presented at the Psychology Department, UC Berkeley, Berkeley, CA.
Cole M.W. (April, 2013). Brain Network Mechanisms of Flexible Cognitive Control in Health and Disease. Invited talk presented at the Department of Psychiatry, Yale, New Haven, CT.
Cole M.W. (January, 2013). Brain Network Mechanisms of Flexible Cognitive Control. Invited talk presented at the McGovern Institute for Brain Research, MIT, Cambridge, MA.
Cole M.W. (December, 2012). A role for the brain network mechanisms of flexible cognitive control in human intelligence. Invited talk presented at the International Society for Intelligence Research, San Antonio, TX.
Cole M.W. (November, 2012). Global Brain Connectivity and Other Graph Theoretical Approaches: Methods and Findings. Invited talk presented at The Ohio State University, Columbus, OH.
Cole M.W. (November, 2012). Brain network mechanisms of flexible cognitive control. Invited talk presented at the Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ.
Cole M.W., Reynolds J.R., Power J.D., and Braver T.S. (October, 2012). Flexible Hubs:
A Novel Mechanism for Flexible Cognitive Control. Poster presented at Society for Neuroscience, New Orleans, LA.
Cole M.W., Etzel J., and Braver T.S. (April, 2012). Identifying Flexible Hubs:
A Novel Mechanism for Flexible Cognitive Control. Talk presented at Cognitive Neuroscience Society, Chicago, IL.
Brain computational modeling tools: The Brian spiking neural network simulator – a simulator for spiking neural networks available on almost all platforms Emergent – a comprehensive, full-featured neural network simulator that allows for the creation and analysis of complex, sophisticated models of the brain
Statistical tools: R - A free software environment for statistical computing and graphics MATLAB - A commercial software environment for statistical computing and graphics
Functional neuroimaging analysis tools: NITRC - A comprehensive list of neuroimaging tools AFNI - Analysis of fMRI and PET (free) MRIcron - Simple tool for visualizing structural and functional MRI data MNE - Analysis of EEG and MEG FieldTrip - Analysis of EEG and MEG in MATLAB