Dissociable Processes of Cognitive Control during Error and Non-Error Conflicts: A Study of the Stop Signal Task

Conflict detection and subsequent behavioral adjustment are critical to daily life, and how this process is controlled has been increasingly of interest. A medial cortical region which includes the anterior cingulate cortex (ACC) has been theorized to act as a conflict detector that can direct prefrontal activity for behavioral adjustments. This conflict monitoring hypothesis was supported by many imaging studies of the Stroop task, with a focus on non-error processes. Here we sought to examine whether this circuit could be generalized to the stop signal task (SST), another behavioral paradigm widely used to study cognitive control. In particular, with a procedure to elicit errors in the SST, we examined whether error and non-error control were mediated by the same pathways.In functional magnetic resonance imaging of 60 healthy adults, we demonstrated that the medial cortical activity during stop success (SS) as compared to go success (G) trials is correlated with increased prefrontal activity in post-stop SS as compared to post-go SS trials, though this correlation was not specific to the medial cortical region. Furthermore, thalamic and insular rather than medial cortical activation during stop error (SE) as compared to G trials correlated with increased prefrontal activity in post-stop SS as compared to post-go SS trials.Taken together, these new findings challenge a specific role of the ACC and support distinct pathways for error and non-error conflict processing in cognitive control

Using network dynamic fMRI for detection of epileptogenic foci

Abstract\ud \ud Background\ud Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable for about one-third of patients with focal epilepsy, for whom precise localization of the epileptogenic zone responsible for seizure initiation may be critical for successful surgery. Existing fMRI literature points to widespread network disturbances in functional connectivity. Per previous scalp and intracranial EEG studies and consistent with excessive local synchronization during interictal discharges, we hypothesized that, relative to same regions in healthy controls, epileptogenic foci would exhibit less chaotic dynamics, identifiable via entropic analyses of resting state fMRI time series.\ud \ud \ud Methods\ud In order to first validate this hypothesis on a cohort of patients with known ground truth, here we test individuals with well-defined epileptogenic foci (left mesial temporal lobe epilepsy). We analyzed voxel-wise resting-state fMRI time-series using the autocorrelation function (ACF), an entropic measure of regulation and feedback, and performed follow-up seed-to-voxel functional connectivity analysis. Disruptions in connectivity of the region exhibiting abnormal dynamics were examined in relation to duration of epilepsy and patients’ cognitive performance using a delayed verbal memory recall task.\ud \ud \ud Results\ud ACF analysis revealed constrained (less chaotic) functional dynamics in left temporal lobe epilepsy patients, primarily localized to ipsilateral temporal pole, proximal to presumed focal points. Autocorrelation decay rates differentiated, with 100 % accuracy, between patients and healthy controls on a subject-by-subject basis within a leave-one-subject out classification framework. Regions identified via ACF analysis formed a less efficient network in patients, as compared to controls. Constrained dynamics were linked with locally increased and long-range decreased connectivity that, in turn, correlated significantly with impaired memory (local left temporal connectivity) and epilepsy duration (left temporal – posterior cingulate cortex connectivity).\ud \ud \ud Conclusions\ud Our current results suggest that data driven functional MRI methods that target network dynamics hold promise in providing clinically valuable tools for identification of epileptic regions.This research was supported by the National Science Foundation (CBET\ud 1264440; LRMP) and the Sao Paulo Research Foundation (Young\ud Investigators Awards FAPESP 11/08573-4; JSI)

Spatiotemporal progression of ubiquitin-proteasome system inhibition after status epilepticus suggests protective adaptation against hippocampal injury

BACKGROUND: The ubiquitin-proteasome-system (UPS) is the major intracellular pathway leading to the degradation of unwanted and/or misfolded soluble proteins. This includes proteins regulating cellular survival, synaptic plasticity and neurotransmitter signaling; processes controlling excitability thresholds that are altered by epileptogenic insults. Dysfunction of the UPS has been reported to occur in a brain region- and cell-specific manner and contribute to disease progression in acute and chronic brain diseases. Prolonged seizures, status epilepticus, may alter UPS function but there has been no systematic attempt to map when and where this occurs in vivo or to determine the consequences of proteasome inhibition on seizure-induced brain injury. METHOD: To determine whether seizures lead to an impairment of the UPS, we used a mouse model of status epilepticus whereby seizures are triggered by an intra-amygdala injection of kainic acid. Status epilepticus in this model causes cell death in selected brain areas, in particular the ipsilateral CA3 subfield of the hippocampus, and the development of epilepsy after a short latent period. To monitor seizure-induced dysfunction of the UPS we used a UPS inhibition reporter mouse expressing the ubiquitin fusion degradation substrate ubiquitin(G76V)-green fluorescent protein. Treatment with the specific proteasome inhibitor epoxomicin was used to establish the impact of proteasome inhibition on seizure-induced pathology. RESULTS AND CONCLUSIONS: Our studies show that status epilepticus induced by intra-amygdala kainic acid causes select spatio-temporal UPS inhibition which is most evident in damage-resistant regions of the hippocampus, including CA1 pyramidal and dentate granule neurons then appears later in astrocytes. In support of this exerting a beneficial effect, injection of mice with the proteasome inhibitor epoxomicin protected the normally vulnerable hippocampal CA3 subfield from seizure-induced neuronal death in the model. These studies reveal brain region- and cell-specific UPS impairment occurs after seizures and suggest UPS inhibition can protect against seizure-induced brain damage. Identifying networks or pathways regulated through the proteasome after seizures may yield novel target genes for the treatment of seizure-induced cell death and possibly epilepsy

Error-Related Functional Connectivity of the Habenula in Humans

Error detection is critical to the shaping of goal-oriented behavior. Recent studies in non-human primates delineated a circuit involving the lateral habenula (LH) and ventral tegmental area (VTA) in error detection. Neurons in the LH increased activity, preceding decreased activity in the VTA, to a missing reward, indicating a feedforward signal from the LH to VTA. In the current study we used connectivity analyses to reveal this pathway in humans. In 59 adults performing a stop signal task during functional magnetic resonance imaging, we identified brain regions showing greater psychophysiological interaction with the habenula during stop error as compared to stop success trials. These regions included a cluster in the VTA/substantia nigra (SN), internal segment of globus pallidus, bilateral amygdala, and insula. Furthermore, using Granger causality and mediation analyses, we showed that the habenula Granger caused the VTA/SN, establishing the direction of this interaction, and that the habenula mediated the functional connectivity between the amygdala and VTA/SN during error processing. To our knowledge, these findings are the first to demonstrate a feedforward influence of the habenula on the VTA/SN during error detection in humans

Bayesian network models in brain functional connectivity analysis

Much effort has been made to better understand the complex integration of distinct parts of the human brain using functional magnetic resonance imaging (fMRI). Altered functional connectivity between brain regions is associated with many neurological and mental illnesses, such as Alzheimer and Parkinson diseases, addiction, and depression. in computational science, Bayesian networks (BN) have been used in a broad range of studies to model complex data set in the presence of uncertainty and when expert prior knowledge is needed. However, little is done to explore the use of BN in connectivity analysis of fMRI data. in this paper, we present an up-to-date literature review and methodological details of connectivity analyses using BN, while highlighting caveats in a real-world application. We present a BN model of fMRI dataset obtained from sixty healthy subjects performing the stop-signal task (SST), a paradigm widely used to investigate response inhibition. Connectivity results are validated with the extant literature including our previous studies. By exploring the link strength of the learned BNs and correlating them to behavioral performance measures, this novel use of BN in connectivity analysis provides new insights to the functional neural pathways underlying response inhibition. (C) 2013 Elsevier Inc. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)NIHUniversidade Federal de São Paulo, Dept Sci & Technol, BR-12231 Sao Jose Dos Campos, SP, BrazilYale Univ, Sch Med, Dept Psychiat, New Haven, CT 06519 USAYale Univ, Sch Med, Interdept Neurosci Program, New Haven, CT 06520 USAYale Univ, Sch Med, Dept Neurobiol, New Haven, CT 06520 USAUniversidade Federal de São Paulo, Dept Sci & Technol, BR-12231 Sao Jose Dos Campos, SP, BrazilFAPESP: 2011/08573-4NIH: R01DA023248NIH: K02DA026990NIH: R21AA018004Web of Scienc