Clinical Assistant Professor, Psychiatry and Behavioral Sciences
Ph.D., State University of New York at Stony Brook
M.S., Peking University
B.S., Shanghai Jiao Tong University
Attention-deficit hyperactivity disorder (ADHD) is associated with pervasive impairments in attention and cognitive control. Although brain circuits underlying these impairments have been extensively investigated with resting-state fMRI, little is known about task-evoked functional brain circuits and their relation to cognitive control deficits and inattention symptoms in children with ADHD. Children with ADHD and age, gender and head motion matched typically developing (TD) children completed a Go/NoGo fMRI task. We used multivariate and dimensional analyses to investigate impairments in two core cognitive control systems: (i) cingulo-opercular "salience" network (SN) anchored in the right anterior insula, dorsal anterior cingulate cortex (rdACC), and ventrolateral prefrontal cortex (rVLPFC) and (ii) dorsal frontoparietal "central executive" (FPN) network anchored in right dorsolateral prefrontal cortex (rDLPFC) and posterior parietal cortex (rPPC). We found that multivariate patterns of task-evoked effective connectivity between brain regions in SN and FPN distinguished the ADHD and TD groups, with rDLPFC-rPPC connectivity emerging as the most distinguishing link. Task-evoked rdACC-rVLPFC connectivity was positively correlated with NoGo accuracy, and negatively correlated with severity of inattention symptoms. Brain-behavior relationships were robust against potential age, gender, and head motion confounds. Our findings highlight aberrancies in task-evoked modulation of SN and FPN connectivity in children with ADHD. Crucially, cingulo-frontal connectivity was a common locus of deficits in cognitive control and clinical measures of inattention symptoms. Our study provides insights into a parsimonious systems neuroscience model of cognitive control deficits in ADHD, and suggests specific circuit biomarkers for predicting treatment outcomes in childhood ADHD.
View details for DOI 10.1038/s41380-019-0564-4
View details for PubMedID 31664176
Inhibitory control is fundamental to children's self-regulation and cognitive development. Here we investigate cortical-basal ganglia pathways underlying inhibitory control in children and their adult-like maturity. We first conduct a comprehensive meta-analysis of extant neurodevelopmental studies of inhibitory control and highlight important gaps in the literature. Second, we examine cortical-basal ganglia activation during inhibitory control in children ages 9-12 and demonstrate the formation of an adult-like inhibitory control network by late childhood. Third, we develop a neural maturation index (NMI), which assesses the similarity of brain activation patterns between children and adults, and demonstrate that higher NMI in children predicts better inhibitory control. Fourth, we show that activity in the subthalamic nucleus and its effective connectivity with the right anterior insula predicts children's inhibitory control. Fifth, we replicate our findings across multiple cohorts. Our findings provide insights into cortical-basal ganglia circuits and global brain organization underlying the development of inhibitory control.
View details for DOI 10.1038/s41467-019-12756-8
View details for PubMedID 31641118
BACKGROUND: Schizophrenia is a highly disabling psychiatric disorder characterized by a range of positive "psychosis" symptoms. However, the neurobiology of psychosis and associated systems-level disruptions in the brain remain poorly understood. Here, we test an aberrant saliency model of psychosis, which posits that dysregulated dynamic cross-network interactions among the salience network (SN), central executive network, and default mode network contribute to positive symptoms in patients with schizophrenia.METHODS: Using task-free functional magnetic resonance imaging data from two independent cohorts, we examined 1) dynamic time-varying cross-network interactions among the SN, central executive network, and default mode network in 130 patients with schizophrenia versus well-matched control subjects; 2) accuracy of a saliency model-based classifier for distinguishing dynamic brain network interactions in patients versus control subjects; and 3) the relation between SN-centered network dynamics and clinical symptoms.RESULTS: In both cohorts, we found that dynamic SN-centered cross-network interactions were significantly reduced, less persistent, and more variable in patients with schizophrenia compared with control subjects. Multivariate classification analysis identified dynamic SN-centered cross-network interaction patterns as factors that distinguish patients from control subjects, with accuracies of 78% and 80% in the two cohorts, respectively. Crucially, in both cohorts, dynamic time-varying measures of SN-centered cross-network interactions were correlated with positive, but not negative, symptoms.CONCLUSIONS: Aberrations in time-varying engagement of the SN with the central executive network and default mode network is a clinically relevant neurobiological signature of psychosis in schizophrenia. Our findings provide strong evidence for dysregulated brain dynamics in a triple-network saliency model of schizophrenia and inform theoretically motivated systems neuroscience approaches for characterizing aberrant brain dynamics associated with psychosis.
View details for PubMedID 30177256
BACKGROUND: Despite dopaminergic depletion that is severe enough to cause the motor symptoms of Parkinson's disease (PD), many patients remain cognitively unimpaired. Little is known about brain mechanisms underlying such preserved cognitive abilities and their alteration by dopaminergic medications.OBJECTIVES: We investigated brain activations underlying dopamine-related differences in cognitive function using a unique experimental design with PD patients off and on dopaminergic medications. We tested the dopamine overdose hypothesis, which posits that the excess of exogenous dopamine in the frontal cortical regions can impair cognition.METHODS: We used a two-choice forced response Choice Reaction Time (CRT) task to probe cognitive processes underlying response selection and execution. Functional magnetic resonance imaging data were acquired from 16 cognitively unimpaired (Level-II) PD participants and 15 well-matched healthy controls (HC). We compared task performance (i.e. reaction time and accuracy) and brain activation of PD participants off dopaminergic medications (PD_OFF) in comparison with HC, and PD_OFF participants with those on dopaminergic medications (PD_ON).RESULTS: PD_OFF and PD_ON groups did not differ from each other, or from the HC group, in reaction time or accuracy. Compared to HC, PD_OFF activated the bilateral putamen less, and this was compensated by higher activation of the anterior insula. No such differences were observed in the PD_ON group, compared to HC. Compared to both HC and PD_OFF, PD_ON participants showed dopamine-related hyperactivation in the frontal cortical regions and hypoactivation in the amygdala.CONCLUSION: Our data provide further evidence that PD_OFF and PD_ON participants engage different cortical and subcortical systems to achieve similar levels of cognitive performance as HC. Crucially, our findings demonstrate dopamine-related dissociation in brain activation between cortical and subcortical regions, and provide novel support for the dopamine overdose hypothesis.
View details for PubMedID 30040957
Human cognition is influenced not only by external task demands but also latent mental processes and brain states that change over time. Here, we use novel Bayesian switching dynamical systems algorithm to identify hidden brain states and determine that these states are only weakly aligned with external task conditions. We compute state transition probabilities and demonstrate how dynamic transitions between hidden states allow flexible reconfiguration of functional brain circuits. Crucially, we identify latent transient brain states and dynamic functional circuits that are optimal for cognition and show that failure to engage these states in a timely manner is associated with poorer task performance and weaker decision-making dynamics. We replicate findings in a large sample (N=122) and reveal a robust link between cognition and flexible latent brain state dynamics. Our study demonstrates the power of switching dynamical systems models for investigating hidden dynamic brain states and functional interactions underlying human cognition.
View details for PubMedID 29950686
BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is thought to stem from aberrancies in large-scale cognitive control networks. However, the exact nature of aberrant brain circuit dynamics involving these control networks is poorly understood. Using a saliency-based triple-network model of cognitive control, we tested the hypothesis that dynamic cross-network interactions among the salience, central executive, and default mode networks are dysregulated in children with ADHD, and we investigated how these dysregulations contribute to inattention.METHODS: Using functional magnetic resonance imaging data from 140 children with ADHD and typically developing children from two cohorts (primary cohort= 80 children, replication cohort= 60 children) in a case-control design, we examined both time-averaged and dynamic time-varying cross-network interactions in each cohort separately.RESULTS: Time-averaged measures of salience network-centered cross-network interactions were significantly lower in children with ADHD compared with typically developing children and were correlated with severity of inattention symptoms. Children with ADHD displayed more variable dynamic cross-network interaction patterns, including less persistent brain states, significantly shorter mean lifetimes of brain states, and intermittently weaker cross-network interactions. Importantly, dynamic time-varying measures of cross-network interactions were more strongly correlated with inattention symptoms than with time-averaged measures of functional connectivity. Crucially, we replicated these findings in the two independent cohorts of children with ADHD and typically developing children.CONCLUSIONS: Aberrancies in time-varying engagement of the salience network with the central executive network and default mode network are a robust and clinically relevant neurobiological signature of childhood ADHD symptoms. The triple-network neurocognitive model provides a novel, replicable, and parsimonious dynamical systems neuroscience framework for characterizing childhood ADHD and inattention.
View details for PubMedID 29486868
There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks-three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal flexibility among the three networks. Our proposed methods provide a novel and powerful generative model for investigating dynamic brain connectivity.
View details for DOI 10.1016/j.neuroimage.2017.02.083
View details for PubMedID 28267626
Little is currently known about dynamic brain networks involved in high-level cognition and their ontological basis. Here we develop a novel Variational Bayesian Hidden Markov Model (VB-HMM) to investigate dynamic temporal properties of interactions between salience (SN), default mode (DMN), and central executive (CEN) networks-three brain systems that play a critical role in human cognition. In contrast to conventional models, VB-HMM revealed multiple short-lived states characterized by rapid switching and transient connectivity between SN, CEN, and DMN. Furthermore, the three "static" networks occurred in a segregated state only intermittently. Findings were replicated in two adult cohorts from the Human Connectome Project. VB-HMM further revealed immature dynamic interactions between SN, CEN, and DMN in children, characterized by higher mean lifetimes in individual states, reduced switching probability between states and less differentiated connectivity across states. Our computational techniques provide new insights into human brain network dynamics and its maturation with development.
View details for DOI 10.1371/journal.pcbi.1005138
View details for Web of Science ID 000392126000005
View details for PubMedID 27959921
View details for PubMedCentralID PMC5154470
Causal estimation methods are increasingly being used to investigate functional brain networks in fMRI, but there are continuing concerns about the validity of these methods.Multivariate Dynamical Systems (MDS) is a state-space method for estimating dynamic causal interactions in fMRI data. Here we validate MDS using benchmark simulations as well as simulations from a more realistic stochastic neurophysiological model. Finally, we applied MDS to investigate dynamic casual interactions in a fronto-cingulate-parietal control network using Human Connectome Project (HCP) data acquired during performance of a working memory task. Crucially, since the ground truth in experimental data is unknown, we conducted novel stability analysis to determine robust causal interactions within this network.MDS accurately recovered dynamic causal interactions with an area under receiver operating characteristic (AUC) above 0.7 for benchmark datasets and AUC above 0.9 for datasets generated using the neurophysiological model. In experimental fMRI data, bootstrap procedures revealed a stable pattern of causal influences from the anterior insula to other nodes of the fronto-cingulate-parietal network.MDS is effective in estimating dynamic causal interactions in both the benchmark and neurophysiological model based datasets in terms of AUC, sensitivity and false positive rates.Our findings demonstrate that MDS can accurately estimate causal interactions in fMRI data. Neurophysiological models and stability analysis provide a general framework for validating computational methods designed to estimate causal interactions in fMRI. The right anterior insula functions as a causal hub during working memory.
View details for DOI 10.1016/j.jneumeth.2016.03.010
View details for Web of Science ID 000379104400017
View details for PubMedID 27015792
The ability to anticipate and detect behaviorally salient stimuli is important for virtually all adaptive behaviors, including inhibitory control that requires the withholding of prepotent responses when instructed by external cues. Although right fronto-operculum-insula (FOI), encompassing the anterior insular cortex (rAI) and inferior frontal cortex (rIFC), involvement in inhibitory control is well established, little is known about signaling mechanisms underlying their differential roles in detection and anticipation of salient inhibitory cues. Here we use 2 independent functional magnetic resonance imaging data sets to investigate dynamic causal interactions of the rAI and rIFC, with sensory cortex during detection and anticipation of inhibitory cues. Across 2 different experiments involving auditory and visual inhibitory cues, we demonstrate that primary sensory cortex has a stronger causal influence on rAI than on rIFC, suggesting a greater role for the rAI in detection of salient inhibitory cues. Crucially, a Bayesian prediction model of subjective trial-by-trial changes in inhibitory cue anticipation revealed that the strength of causal influences from rIFC to rAI increased significantly on trials in which participants had higher anticipation of inhibitory cues. Together, these results demonstrate the dissociable bottom-up and top-down roles of distinct FOI regions in detection and anticipation of behaviorally salient cues across multiple sensory modalities.
View details for PubMedID 27473319
One of the most fundamental features of the human brain is its ability to detect and attend to salient goal-relevant events in a flexible manner. The salience network (SN), anchored in the anterior insula and the dorsal anterior cingulate cortex, plays a crucial role in this process through rapid detection of goal-relevant events and facilitation of access to appropriate cognitive resources. Here, we leverage the subsecond resolution of large multisession fMRI datasets from the Human Connectome Project and apply novel graph-theoretical techniques to investigate the dynamic spatiotemporal organization of the SN. We show that the large-scale brain dynamics of the SN are characterized by several distinctive and robust properties. First, the SN demonstrated the highest levels of flexibility in time-varying connectivity with other brain networks, including the frontoparietal network (FPN), the cingulate-opercular network (CON), and the ventral and dorsal attention networks (VAN and DAN). Second, dynamic functional interactions of the SN were among the most spatially varied in the brain. Third, SN nodes maintained a consistently high level of network centrality over time, indicating that this network is a hub for facilitating flexible cross-network interactions. Fourth, time-varying connectivity profiles of the SN were distinct from all other prefrontal control systems. Fifth, temporal flexibility of the SN uniquely predicted individual differences in cognitive flexibility. Importantly, each of these results was also observed in a second retest dataset, demonstrating the robustness of our findings. Our study provides fundamental new insights into the distinct dynamic functional architecture of the SN and demonstrates how this network is uniquely positioned to facilitate interactions with multiple functional systems and thereby support a wide range of cognitive processes in the human brain.
View details for DOI 10.1371/journal.pbio.1002469
View details for Web of Science ID 000378611200001
View details for PubMedID 27270215
View details for PubMedCentralID PMC4896426
Cognitive control plays an important role in goal-directed behavior, but dynamic brain mechanisms underlying it are poorly understood. Here, using multisite fMRI data from over 100 participants, we investigate causal interactions in three cognitive control tasks within a core Frontal-Cingulate-Parietal network. We found significant causal influences from anterior insula (AI) to dorsal anterior cingulate cortex (dACC) in all three tasks. The AI exhibited greater net causal outflow than any other node in the network. Importantly, a similar pattern of causal interactions was uncovered by two different computational methods for causal analysis. Furthermore, the strength of causal interaction from AI to dACC was greater on high, compared with low, cognitive control trials and was significantly correlated with individual differences in cognitive control abilities. These results emphasize the importance of the AI in cognitive control and highlight its role as a causal hub in the Frontal-Cingulate-Parietal network. Our results further suggest that causal signaling between the AI and dACC plays a fundamental role in implementing cognitive control and are consistent with a two-stage cognitive control model in which the AI first detects events requiring greater access to cognitive control resources and then signals the dACC to execute load-specific cognitive control processes.
View details for DOI 10.1093/cercor/bhv046
View details for PubMedID 25778346
Cognitive impairments in Parkinson's disease (PD) are thought to be caused in part by dopamine dysregulation. However, even when nigrostriatal dopamine neuron loss is severe enough to cause motor symptoms, many patients remain cognitively unimpaired. It is unclear what brain mechanisms allow these patients to remain cognitively unimpaired despite substantial dopamine dysregulation.31 cognitively unimpaired PD participants OFF dopaminergic-medications were scanned using fMRI while they performed a working memory task, along with 23 controls. We first compared the PD_OFF medication group with controls to determine whether PD participants engage compensatory frontostriatal mechanisms during working memory. We then studied the same PD participants ON dopaminergic-medications to determine whether these compensatory brain changes are altered with dopamine.Controls and PD showed working memory load-dependent activation in the bilateral putamen, anterior-dorsal insula, supplementary motor area, and anterior cingulate cortex. Compared to controls, PD_OFF showed compensatory hyper-activation of bilateral putamen and posterior insula, and machine learning algorithms identified robust differences in putamen activation patterns. Compared to PD_OFF, PD_ON showed reduced compensatory activation in the putamen. Loss of compensatory hyper-activation ON dopaminergic-medication correlated with slower performance on the working memory task and slower cognitive speed on the Symbol Digit Modality Test.Our results provide novel evidence that PD patients maintain normal cognitive performance through compensatory hyper-activation of the putamen. Dopaminergic-medication down-regulates this hyper-activation and the degree of down-regulation predicts behavior. Identifying cognitive compensatory mechanisms in PD is important for understanding how some patients maintain intact cognitive performance, despite nigrostriatal dopamine loss. This article is protected by copyright. All rights reserved.
View details for DOI 10.1002/ana.24585
View details for PubMedID 26696272
Clustering methods are increasingly employed to segment brain regions into functional subdivisions using resting-state functional magnetic resonance imaging (rs-fMRI). However, these methods are highly sensitive to the (i) precise algorithms employed, (ii) their initializations, and (iii) metrics used for uncovering the optimal number of clusters from the data.To address these issues, we develop a novel consensus clustering evidence accumulation (CC-EAC) framework, which effectively combines multiple clustering methods for segmenting brain regions using rs-fMRI data. Using extensive computer simulations, we examine the performance of widely used clustering algorithms including K-means, hierarchical, and spectral clustering as well as their combinations. We also examine the accuracy and validity of five objective criteria for determining the optimal number of clusters: mutual information, variation of information, modified silhouette, Rand index, and probabilistic Rand index.A CC-EAC framework with a combination of base K-means clustering (KC) and hierarchical clustering (HC) with probabilistic Rand index as the criterion for choosing the optimal number of clusters, accurately uncovered the correct number of clusters from simulated datasets. In experimental rs-fMRI data, these methods reliably detected functional subdivisions of the supplementary motor area, insula, intraparietal sulcus, angular gyrus, and striatum.Unlike conventional approaches, CC-EAC can accurately determine the optimal number of stable clusters in rs-fMRI data, and is robust to initialization and choice of free parameters.A novel CC-EAC framework is proposed for segmenting brain regions, by effectively combining multiple clustering methods and identifying optimal stable functional clusters in rs-fMRI data.
View details for DOI 10.1016/j.jneumeth.2014.11.014
View details for PubMedID 25450335
View details for PubMedID 26482801
A network of brain regions involving the ventral inferior frontal gyrus/anterior insula (vIFG/AI), presupplementary motor area (pre-SMA) and basal ganglia has been implicated in stopping impulsive, unwanted responses. However, whether this network plays an equal role in response inhibition under different sensorimotor contexts has not been tested systematically. Here, we conducted an fMRI experiment using the stop signal task, a sensorimotor task requiring occasional withholding of the planned response upon the presentation of a stop signal. We manipulated both the sensory modality of the stop signal (visual versus auditory) and the motor response modality (hand versus eye). Results showed that the vIFG/AI and the preSMA along with the right middle frontal gyrus were commonly activated in response inhibition across the various sensorimotor conditions. Our findings provide direct evidence for a common role of these frontal areas, but not striatal areas in response inhibition independent of the sensorimotor contexts. Nevertheless, these three frontal regions exhibited different activation patterns during successful and unsuccessful stopping. Together with the existing evidence, we suggest that the vIFG/AI is involved in the early stages of stopping such as triggering the stop process while the preSMA may play a role in regulating other cortical and subcortical regions involved in stopping.
View details for DOI 10.1002/hbm.22315
View details for Web of Science ID 000334012100023
View details for PubMedID 23798325
In the welter of everyday life, people can stop particular response tendencies without affecting others. A key requirement for such selective suppression is that subjects know in advance which responses need stopping. We hypothesized that proactively setting up and implementing selective suppression relies on the basal ganglia and, specifically, regions consistent with the inhibitory indirect pathway for which there is scant functional evidence in humans. Consistent with this hypothesis, we show, first, that the degree of proactive motor suppression when preparing to stop selectively (indexed by transcranial magnetic stimulation) corresponds to striatal, pallidal, and frontal activation (indexed by functional MRI). Second, we demonstrate that greater striatal activation at the time of selective stopping correlates with greater behavioral selectivity. Third, we show that people with striatal and pallidal volume reductions (those with premanifest Huntington's disease) have both absent proactive motor suppression and impaired behavioral selectivity when stopping. Thus, stopping goals are used to proactively set up specific basal ganglia channels that may then be triggered to implement selective suppression. By linking this suppression to the striatum and pallidum, these results provide compelling functional evidence in humans of the basal ganglia's inhibitory indirect pathway.
View details for DOI 10.1523/JNEUROSCI.5651-12.2013
View details for Web of Science ID 000323155700002
View details for PubMedID 23946385
Rapidly stopping action engages a network in the brain including the right presupplementary motor area (preSMA), the right inferior frontal gyrus, and the basal ganglia. Yet the functional role of these different regions within the overall network still remains unclear. Here we focused on the role of the right preSMA in behavioral stopping. We hypothesized that the underlying neurocognitive function of this region is one or more of setting up a stopping rule in advance, modulating response tendencies (e.g., slowing down in anticipation of stopping), and implementing stopping when the stop signal occurs. We performed two experiments with magnetic resonance imaging (MRI)-guided, event-related, transcranial magnetic stimulation(TMS), during the performance of variants of the stop signal task. In experiment 1 we show that stimulation of the right preSMA versus vertex (control site) slowed the implementation of stopping (measured via stop signal reaction time) but had no influence on modulation of response tendencies. In experiment 2, we showed that stimulation of the right preSMA slowed implementation of stopping in a mechanistically selective form of stopping but had no influence on setting up stopping rules. The results go beyond the replication of prior findings by showing that TMS of the right preSMA impairs stopping behavior (including a behaviorally selective form of stopping) through a specific disruption of the implementation of stopping. Future studies are required to establish whether this was due to stimulation of the right preSMA itself or because of remote effects on the wider stopping network.
View details for DOI 10.1152/jn.00132.2012
View details for Web of Science ID 000306416400002
View details for PubMedID 22514296
Transcranial magnetic stimulation (TMS) is increasingly used in cognitive neuroscience to probe non-motor cortical regions. A key question for such studies is the choice of stimulation intensity. Early studies used a simple metric such as 115% of motor threshold (MT) for non-motor regions; where MT is the stimulation intensity required to elicit a particular amplitude of motor evoked potential or visible muscle twitch when the coil is placed over primary motor cortex. Recently, however, it was demonstrated that this simple metric for stimulation of non-motor regions is inadequate - it could lead to over or under-stimulation depending on the distance between the coil and the cortex. Instead, a method was developed to scale the motor threshold based on coil-cortex distance, at least for standard figure-of-eight stimulating coils. Here we validate the same method for a 'batwing coil', which is designed to stimulate deeper cortical structures such as the medial frontal cortex. We modulated coil-cortex distance within-participant by inserting spacers of different thickness between coil and scalp. We then measured MT at each spacer. We show that for every millimeter between coil and scalp an additional 1.4% of TMS output is required to induce an equivalent level of brain stimulation at the motor cortex. Using this parameter we describe a linear function to adjust MT for future studies of non-motor regions-of-interest using the batwing coil. This is the first study to demonstrate the effects of coil-cortical distance on stimulation efficiency via a monophasic system using a batwing coil.
View details for DOI 10.1016/j.jneumeth.2011.11.020
View details for Web of Science ID 000301612700005
View details for PubMedID 22138632
Some situations require one to quickly stop an initiated response. Recent evidence suggests that rapid stopping engages a mechanism that has diffuse effects on the motor system. For example, stopping the hand dampens the excitability of the task-irrelevant leg. However, it is unclear whether this 'global suppression' could apply across wider motor modalities. Here we tested whether stopping speech leads to suppression of the task-irrelevant hand. We used Transcranial Magnetic Stimulation over the primary motor cortex with concurrent electromyography from the hand. We found that when speech was successfully stopped the motor evoked potential from the task-irrelevant hand was significantly reduced compared to when the participant failed to stop speaking, or responded on non stop signal trials, or compared to baseline. This shows that when speech is quickly stopped, there is a broad suppression across the motor system. This has implications for the neural basis of speech control and stuttering.
View details for DOI 10.1016/j.bandl.2011.11.006
View details for Web of Science ID 000301759100022
View details for PubMedID 22206872
Both the pre-supplementary motor area (preSMA) and the right inferior frontal gyrus (rIFG) are important for stopping action outright. These regions are also engaged when preparing to stop. We aimed to elucidate the roles of these regions by harnessing the high spatio-temporal resolution of electrocorticography (ECoG), and by using a task that engages both preparing to stop and stopping outright. First, we validated the task using fMRI in 16 healthy control participants to confirm that both the preSMA and the rIFG were active. Next, we studied a rare patient with intracranial grid coverage of both these regions, using macrostimulation, diffusion tractography, cortico-cortical evoked potentials (CCEPs) and task-based ECoG. Macrostimulation of the preSMA induced behavioral motor arrest. Diffusion tractography revealed a structural connection between the preSMA and rIFG. CCEP analysis showed that stimulation of the preSMA evoked strong local field potentials within 30 ms in rIFG. During the task, when preparing to stop, there was increased high gamma amplitude (~70-250 Hz) in both regions, with preSMA preceding rIFG by ~750 ms. For outright stopping there was also a high gamma amplitude increase in both regions, again with preSMA preceding rIFG. Further, at the time of stopping, there was an increase in beta band activity (~16 Hz) in both regions, with significantly stronger inter-regional coherence for successful vs. unsuccessful stop trials. The results complement earlier reports of a structural/functional action control network between the preSMA and rIFG. They go further by revealing between-region timing differences in the high gamma band when preparing to stop and stopping outright. They also reveal strong between-region coherence in the beta band when stopping is successful. Implications for theories of action control are discussed.
View details for DOI 10.1016/j.neuroimage.2011.09.049
View details for Web of Science ID 000299494000085
View details for PubMedID 21979383
Stopping an initiated response is an essential function, investigated in many studies with go/no-go and stop-signal paradigms. These standard tests require rapid action cancellation. This appears to be achieved by a suppression mechanism that has "global" effects on corticomotor excitability (i.e., affecting task-irrelevant muscles). By contrast, stopping action in everyday life may require selectivity (i.e., targeting a specific response tendency without affecting concurrent action). We hypothesized that while standard stopping engages global suppression, behaviorally selective stopping engages a selective suppression mechanism. Accordingly, we measured corticomotor excitability of the task-irrelevant leg using transcranial magnetic stimulation while subjects stopped the hand. Experiment 1 showed that for standard (i.e., nonselective) stopping, the task-irrelevant leg was suppressed. Experiment 2 showed that for behaviorally selective stopping, there was no mean leg suppression. Experiment 3 directly compared behaviorally nonselective and selective stopping. Leg suppression occurred only in the behaviorally nonselective condition. These results argue that global and selective suppression mechanisms are dissociable. Participants may use a global suppression mechanism when speed is stressed; however, they may recruit a more selective suppression mechanism when selective stopping is behaviorally necessary and preparatory information is available. We predict that different fronto-basal-ganglia pathways underpin these different suppression mechanisms.
View details for DOI 10.1093/cercor/bhr112
View details for Web of Science ID 000299124400011
View details for PubMedID 21666129
The human inferior frontal cortex (IFC) is a large heterogeneous structure with distinct cytoarchitectonic subdivisions and fiber connections. It has been found involved in a wide range of executive control processes from target detection, rule retrieval to response control. Since these processes are often being studied separately, the functional organization of executive control processes within the IFC remains unclear.We conducted an fMRI study to examine the activities of the subdivisions of IFC during the presentation of a task cue (rule retrieval) and during the performance of a stop-signal task (requiring response generation and inhibition) in comparison to a not-stop task (requiring response generation but not inhibition). We utilized a mixed event-related and block design to separate brain activity in correspondence to transient control processes from rule-related and sustained control processes. We found differentiation in control processes within the IFC. Our findings reveal that the bilateral ventral-posterior IFC/anterior insula are more active on both successful and unsuccessful stop trials relative to not-stop trials, suggesting their potential role in the early stage of stopping such as triggering the stop process. Direct countermanding seems to be outside of the IFC. In contrast, the dorsal-posterior IFC/inferior frontal junction (IFJ) showed transient activity in correspondence to the infrequent presentation of the stop signal in both tasks and the left anterior IFC showed differential activity in response to the task cues. The IFC subdivisions also exhibited similar but distinct patterns of functional connectivity during response control.Our findings suggest that executive control processes are distributed across the IFC and that the different subdivisions of IFC may support different control operations through parallel cortico-cortical and cortico-striatal circuits.
View details for DOI 10.1371/journal.pone.0020840
View details for Web of Science ID 000291356400032
View details for PubMedID 21673969
View details for PubMedCentralID PMC3108978
While most research on stopping action examines how an initiated response is stopped when a signal occurs (i.e., reactively), everyday life also calls for a mechanism to prepare to stop a particular response tendency (i.e., proactively and selectively). We hypothesized that human subjects can prepare to stop a particular response by proactively suppressing that response representation in the brain. We tested this by using single-pulse transcranial magnetic stimulation and concurrent electromyography. This allowed us to interrogate the corticomotor excitability of specific response representations even before action ensued. We found that the motor evoked potential of the effector that might need to be stopped in the future was significantly reduced compared with when that effector was at rest. Further, this neural index of proactive and selective suppression predicted the subsequent selectivity with which the behavioral response was stopped. These results go further than earlier reports of reduced motor excitability when responses are stopped. They show that the control can be applied in advance (proactively) and also targeted at a particular response channel (selectively). This provides novel evidence for an active mechanism of suppression in the brain that is setup according to the subject's goals and even before action ensues.
View details for DOI 10.1523/JNEUROSCI.6292-10.2011
View details for Web of Science ID 000289769400010
View details for PubMedID 21508221
Stopping an initiated response could be implemented by a fronto-basal-ganglia circuit, including the right inferior frontal cortex (rIFC) and the subthalamic nucleus (STN). Intracranial recording studies in humans reveal an increase in beta-band power (approximately 16-20 Hz) within the rIFC and STN when a response is stopped. This suggests that the beta-band could be important for communication in this network. If this is the case, then altering one region should affect the electrophysiological response at the other. We addressed this hypothesis by recording scalp EEG during a stop task while modulating STN activity with deep brain stimulation. We studied 15 human patients with Parkinson's disease and 15 matched healthy control subjects. Behaviorally, patients OFF stimulation were slower than controls to stop their response. Moreover, stopping speed was improved for ON compared to OFF stimulation. For scalp EEG, there was greater beta power, around the time of stopping, for patients ON compared to OFF stimulation. This effect was stronger over the right compared to left frontal cortex, consistent with the putative right lateralization of the stopping network. Thus, deep brain stimulation of the STN improved behavioral stopping performance and increased the beta-band response over the right frontal cortex. These results complement other evidence for a structurally connected functional circuit between right frontal cortex and the basal ganglia. The results also suggest that deep brain stimulation of the STN may improve task performance by increasing the fidelity of information transfer within a fronto-basal-ganglia circuit.
View details for DOI 10.1523/JNEUROSCI.6135-10.2011
View details for Web of Science ID 000289472400021
View details for PubMedID 21490213
It has been suggested that the right inferior frontal gyrus (IFG) plays a critical role in manual response inhibition, although neuroimaging studies of healthy adults have also reported widespread activations in other cortical regions during a variety of response inhibition tasks. We conducted a functional magnetic resonance imaging (fMRI) experiment to examine whether the activation of the IFG is dependent on the type of visuo-motor associations during response inhibition by varying the feature of the stop signal (color vs. orientation) in the stop-signal task. Results from 12 subjects showed that the bilateral ventral posterior IFG, anterior insula, inferior frontal junction (IFJ), middle temporal gyrus (MTG) and fusiform gyrus (FG) are active during response inhibition cued by both color and orientation stop signals. While only the MTG showed differential activity to the two stop signals, both MTG and FG showed significantly stronger activity during successful than unsuccessful stopping of unwanted responses cued by orientation and color, respectively. Our findings suggest that the right ventral posterior IFG may play a more general role in response inhibition regardless of the feature of the visual signal, while successful inhibition may depend on efficient processing of the signal.
View details for DOI 10.1016/j.brainres.2008.12.073
View details for Web of Science ID 000264693300003
View details for PubMedID 19401178
View details for PubMedCentralID PMC2783273
The inferior frontal cortex, particularly the ventrolateral prefrontal cortex (VLPFC) in the right hemisphere, has been implicated to serve as a general inhibitory mechanism in the cognitive control of behavior. Because this notion was primarily based on studies of response inhibition in manual tasks, it has yet to be validated in other response modalities. We conducted a functional magnetic resonance imaging study to examine whether the VLPFC is commonly activated during inhibition of responses by hand and by eye within the same subjects. We used the stop-signal task, a relatively pure measure of response inhibition, as the behavioral paradigm. Results from 12 subjects showed that both the right and the left caudal VLPFC and anterior insula, rostral to the premotor area, are activated during inhibition of both manual and saccadic responses. Within the posterior VLPFC, activations overlapped to a significant extent across the two response modalities, although a weaker functionally differentiation was also found along the dorsoventral axis. Other areas such as medial superior frontal gyrus (pre-supplementary motor area/supplementary eye field), dorsolateral prefrontal cortex, and inferior parietal cortex were also activated during canceling both hand and eye movements. Our findings suggest that a common VLPFC network is involved in response inhibition, although the specific control of the different response modalities may be partially segregated within the lateral prefrontal cortex.
View details for DOI 10.1523/JNEUROSCI.2837-07.2007
View details for Web of Science ID 000249415000010
View details for PubMedID 17855604