Adam R. Aron

YNIMG Journal 2022 Journal Article

Transient beta modulates decision thresholds during human action-stopping

• Vignesh Muralidharan
• Adam R. Aron
• Robert Schmidt

Action-stopping in humans involves bursts of beta oscillations in prefrontal-basal ganglia regions. To determine the functional role of these beta bursts we took advantage of the Race Model framework describing action-stopping. We incorporated beta bursts in three race model variants, each implementing a different functional contribution of beta to action-stopping. In these variants, we hypothesized that a transient increase in beta could (1) modulate decision thresholds, (2) change stop accumulation rates, or (3) promote the interaction between the Stop and the Go process. We then tested the model predictions using EEG recordings in humans performing a Stop-signal task. We found that the model variant in which beta increased decision thresholds for a brief period of time best explained the empirical data. The model parameters fitted to the empirical data indicated that beta bursts involve a stronger decision threshold modulation for the Go process than for the Stop process. This suggests that prefrontal beta influences stopping by temporarily holding the response from execution. Our study further suggests that human action-stopping could be multi-staged with the beta acting as a pause, increasing the response threshold for the Stop process to modulate behavior successfully. Overall, our approach of introducing transient oscillations into the race model and testing against human electrophysiological data provides a novel account of the puzzle of prefrontal beta in executive control.

YNIMG Journal 2021 Journal Article

Fronto-subthalamic phase synchronization and cross-frequency coupling during conflict processing

• Ke Zeng
• Neil M. Drummond
• Ayda Ghahremani
• Utpal Saha
• Suneil K. Kalia
• Mojgan Hodaie
• Andres M. Lozano
• Adam R. Aron

Growing evidence suggests that both the medial prefrontal cortex (mPFC) and the subthalamic nucleus (STN) play crucial roles in conflict processing, but how these two structures coordinate their activities remains poorly understood. We simultaneously recorded electroencephalogram from the mPFC and local field potentials from the STN using deep brain stimulation electrodes in 13 Parkinson's disease patients while they performed a Stroop task. Both mPFC and STN showed significant increases in theta activities (2-8 Hz) in incongruent trials compared to the congruent trials. The theta activity in incongruent trials also demonstrated significantly increased phase synchronization between mPFC and STN. Furthermore, the amplitude of gamma oscillation was modulated by the phase of theta activity at the STN in incongruent trials. Such theta-gamma phase-amplitude coupling (PAC) was much stronger for incongruent trials with faster reaction times than those with slower reaction times. Elevated theta-gamma PAC in the STN provides a novel mechanism by which the STN may operationalize its proposed "hold-your-horses" role. The co-occurrence of mPFC-STN theta phase synchronization and STN theta-gamma PAC reflects a neural substrate for fronto-subthalamic communication during conflict processing. More broadly, it may be a general mechanism for neuronal interactions in the cortico-basal ganglia circuits via a combination of long-range, within-frequency phase synchronization and local cross-frequency PAC.

YNICL Journal 2020 Journal Article

One-year changes in brain microstructure differentiate preclinical Huntington's disease stages

• Chris Patrick Pflanz
• Marina Charquero-Ballester
• D.S. Adnan Majid
• Anderson M. Winkler
• Emmanuel Vallée
• Adam R. Aron
• Mark Jenkinson
• Gwenaëlle Douaud

OBJECTIVE: To determine whether brain imaging markers of tissue microstructure can detect the effect of disease progression across the preclinical stages of Huntington's disease. METHODS: Longitudinal microstructural changes in diffusion imaging metrics (mean diffusivity and fractional anisotropy) were investigated in participants with presymptomatic Huntington's disease (N = 35) stratified into three preclinical subgroups according to their estimated time until onset of symptoms, compared with age- and gender-matched healthy controls (N = 19) over a 1y period. RESULTS: Significant differences were found over the four groups in change of mean diffusivity in the posterior basal ganglia and the splenium of the corpus callosum. This overall effect was driven by significant differences between the group far-from-onset (FAR) of symptoms and the groups midway- (MID) and near-the-onset (NEAR) of symptoms. In particular, an initial decrease of mean diffusivity in the FAR group was followed by a subsequent increase in groups closer to onset of symptoms. The seemingly counter-intuitive decrease of mean diffusivity in the group furthest from onset of symptoms might be an early indicator of neuroinflammatory process preceding the neurodegenerative phase. In contrast, the only clinical measure that was able to capture a difference in 1y changes between the preclinical stages was the UHDRS confidence in motor score. CONCLUSIONS: With sensitivity to longitudinal changes in brain microstructure within and between preclinical stages, and potential differential response to distinct pathophysiological mechanisms, diffusion imaging is a promising state marker for monitoring treatment response and identifying the optimal therapeutic window of opportunity in preclinical Huntington's disease.

YNIMG Journal 2020 Journal Article

Temporally-precise disruption of prefrontal cortex informed by the timing of beta bursts impairs human action-stopping

• Ricci Hannah
• Vignesh Muralidharan
• Kelsey K. Sundby
• Adam R. Aron

Human action-stopping is thought to rely on a prefronto-basal ganglia-thalamocortical network, with right inferior frontal cortex (rIFC) posited to play a critical role in the early stage of implementation. Here we sought causal evidence for this idea in experiments involving healthy human participants. We first show that action-stopping is preceded by bursts of electroencephalographic activity in the beta band over prefrontal electrodes, putatively rIFC, and that the timing of these bursts correlates with the latency of stopping at a single-trial level: earlier bursts are associated with faster stopping. From this we reasoned that the integrity of rIFC at the time of beta bursts might be critical to successful stopping. We then used fMRI-guided transcranial magnetic stimulation (TMS) to disrupt rIFC at the approximate time of beta bursting. Stimulation prolonged stopping latencies and, moreover, the prolongation was most pronounced in individuals for whom the pulse appeared closer to the presumed time of beta bursting. These results help validate a model of the neural architecture and temporal dynamics of action-stopping. They also highlight the usefulness of prefrontal beta bursts to index an apparently important sub-process of stopping, the timing of which might help explain within- and between-individual variation in impulse control.

YNIMG Journal 2014 Journal Article

Inhibitory motor control based on complex stopping goals relies on the same brain network as simple stopping

• Jan R. Wessel
• Adam R. Aron

Much research has modeled action-stopping using the stop-signal task (SST), in which an impending response has to be stopped when an explicit stop-signal occurs. A limitation of the SST is that real-world action-stopping rarely involves explicit stop-signals. Instead, the stopping-system engages when environmental features match more complex stopping goals. For example, when stepping into the street, one monitors path, velocity, size, and types of objects and only stops if there is a vehicle approaching. Here, we developed a task in which participants compared the visual features of a multidimensional go-stimulus to a complex stopping-template, and stopped their go-response if all features matched the template. We used independent component analysis of EEG data to show that the same motor inhibition brain network that explains action-stopping in the SST also implements motor inhibition in the complex-stopping task. Furthermore, we found that partial feature overlap between go-stimulus and stopping-template led to motor slowing, which also corresponded with greater stopping-network activity. This shows that the same brain system for action-stopping to explicit stop-signals is recruited to slow or stop behavior when stimuli match a complex stopping goal. The results imply a generalizability of the brain's network for simple action-stopping to more ecologically valid scenarios.

YNICL Journal 2013 Journal Article

Dopaminergic therapy in Parkinson's disease decreases cortical beta band coherence in the resting state and increases cortical beta band power during executive control

• Jobi S. George
• Jon Strunk
• Rachel Mak-McCully
• Melissa Houser
• Howard Poizner
• Adam R. Aron

It is not yet well understood how dopaminergic therapy improves cognitive and motor function in Parkinson's disease (PD). One possibility is that it reduces the pathological synchronization within and between the cortex and basal ganglia, thus improving neural communication. We tested this hypothesis by recording scalp electroencephalography (EEG) in PD patients when On and Off medication, during a brief resting state epoch (no task), and during performance of a stop signal task that is thought to engage two partially overlapping (or different) frontal-basal-ganglia circuits. For resting state EEG, we measured pair-wise coherence between scalp electrodes in several frequency bands. Consistent with previous studies, in the Off medication state, those patients with the greatest clinical impairment had the strongest coherence, especially in the beta band, indicating pathological over-synchronization. Dopaminergic medication reduced this coherence. For the stop signal task, On vs. Off medication increased beta band power over right frontal cortex for successful stopping and over bilateral sensorimotor cortex for going, especially for those patients who showed greater clinical improvement. Thus, medication reduced pathological coherence in beta band at rest and increased task related beta power for two potentially dissociable cortico-basal ganglia circuits. These results support the hypothesis that dopaminergic medication in PD improves neural communication both at rest and for executive and motor function.

YNIMG Journal 2012 Journal Article

Roles for the pre-supplementary motor area and the right inferior frontal gyrus in stopping action: Electrophysiological responses and functional and structural connectivity

• Nicole C. Swann
• Weidong Cai
• Christopher R. Conner
• Thomas A. Pieters
• Michael P. Claffey
• Jobi S. George
• Adam R. Aron
• Nitin Tandon

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 30ms in rIFG. During the task, when preparing to stop, there was increased high gamma amplitude (~70–250Hz) in both regions, with preSMA preceding rIFG by ~750ms. 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 (~16Hz) 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.

YNIMG Journal 2012 Journal Article

Stability of resting fMRI interregional correlations analyzed in subject-native space: A one-year longitudinal study in healthy adults and premanifest Huntington's disease

• Tyler M. Seibert
• D.S. Adnan Majid
• Adam R. Aron
• Jody Corey-Bloom
• James B. Brewer

The pattern of interregional functional MRI correlations at rest is being actively considered as a potential noninvasive biomarker in multiple diseases. Before such methods can be used in clinical studies it is important to establish their usefulness in three ways. First, the long-term stability of resting correlation patterns should be characterized, but there have been very few such studies. Second, analysis of resting correlations should account for the unique neuroanatomy of each subject by taking measurements in native space and avoiding transformation of functional data to a standard volume space (e. g. , Talairach–Tournox or Montreal Neurological Institute atlases). Transformation to a standard volume space has been shown to variably influence the measurement of functional correlations, and this is a particular concern in diseases which may cause structural changes in the brain. Third, comparisons within the patient population of interest and comparisons between patients and age-matched controls, should demonstrate sensitivity to any disease-related disruption of resting functional correlations. Here we examine the test–retest stability of resting fMRI correlations over a period of one year in a group of healthy adults and in a group of cognitively intact individuals who are gene-positive for Huntington's disease. A recently-developed method is used to measure functional correlations in the native space of individual subjects. The utility of resting functional correlations as a biomarker in premanifest Huntington's disease is also investigated. Results in control and premanifest Huntington's populations were both highly consistent at the group level over one year. We thus show that when resting fMRI analysis is performed in native space (to reduce confounds in registration between subjects and groups) it has good long-term stability at the group level. Individual-subject level results were less consistent between visit 1 and visit 2, suggesting further work is required before resting fMRI correlations can be useful diagnostically for individual patients. No significant effect of premanifest Huntington's disease on prespecified interregional fMRI correlations was observed relative to the control group using either baseline or longitudinal measures. Within the premanifest Huntington's group, though, there was evidence that decreased striatal functional correlations might be associated with disease severity, as gauged by estimated years to symptom onset or by striatal volume.

YNIMG Journal 2011 Journal Article

Corrigendum to “Engagement of large-scale networks is related to individual differences in inhibitory control” [NeuroImage 53/2 (2010) 653–663]

• Eliza Congdon
• Jeanette A. Mumford
• Jessica R. Cohen
• Adriana Galvan
• Adam R. Aron
• Gui Xue
• Eric Miller
• Russell A. Poldrack

YNIMG Journal 2011 Journal Article

Evaluating imaging biomarkers for neurodegeneration in pre-symptomatic Huntington's disease using machine learning techniques

• Angela Rizk-Jackson
• Diederick Stoffers
• Sarah Sheldon
• Josh Kuperman
• Anders Dale
• Jody Goldstein
• Jody Corey-Bloom
• Russell A. Poldrack

The development of MRI measures as biomarkers for neurodegenerative disease could prove extremely valuable for the assessment of neuroprotective therapies. Much current research is aimed at developing such biomarkers for use in people who are gene-positive for Huntington's disease yet exhibit few or no clinical symptoms of the disease (pre-HD). We acquired structural (T1), diffusion weighted and functional MRI (fMRI) data from 39 pre-HD volunteers and 25 age-matched controls. To determine whether it was possible to decode information about disease state from neuroimaging data, we applied multivariate pattern analysis techniques to several derived voxel-based and segmented region-based datasets. We found that different measures of structural, diffusion weighted, and functional MRI could successfully classify pre-HD and controls using support vector machines (SVM) and linear discriminant analysis (LDA) with up to 76% accuracy. The model producing the highest classification accuracy used LDA with a set of six volume measures from the basal ganglia. Furthermore, using support vector regression (SVR) and linear regression models, we were able to generate quantitative measures of disease progression that were significantly correlated with established measures of disease progression (estimated years to clinical onset, derived from age and genetic information) from several different neuroimaging measures. The best performing regression models used SVR with neuroimaging data from regions within the grey matter (caudate), white matter (corticospinal tract), and fMRI (insular cortex). These results highlight the utility of machine learning analyses in addition to conventional ones. We have shown that several neuroimaging measures contain multivariate patterns of information that are useful for the development of disease-state biomarkers for HD.

YNIMG Journal 2010 Journal Article

Engagement of large-scale networks is related to individual differences in inhibitory control

• Eliza Congdon
• Jeanette A. Mumford
• Jessica R. Cohen
• Adriana Galvan
• Adam R. Aron
• Gui Xue
• Eric Miller
• Russell A. Poldrack

Understanding which brain regions regulate the execution, and suppression, of goal-directed behavior has implications for a number of areas of research. In particular, understanding which brain regions engaged during tasks requiring the execution and inhibition of a motor response provides insight into the mechanisms underlying individual differences in response inhibition ability. However, neuroimaging studies examining the relation between activation and stopping have been inconsistent regarding the direction of the relationship, and also regarding the anatomical location of regions that correlate with behavior. These limitations likely arise from the relatively low power of voxelwise correlations with small sample sizes. Here, we pooled data over five separate fMRI studies of the Stop-signal task in order to obtain a sufficiently large sample size to robustly detect brain/behavior correlations. In addition, rather than performing mass univariate correlation analysis across all voxels, we increased statistical power by reducing the dimensionality of the data set using independent component analysis and then examined correlations between behavior and the resulting component scores. We found that components reflecting activity in regions thought to be involved in stopping were associated with better stopping ability, while activity in a default-mode network was associated with poorer stopping ability across individuals. These results clearly show a relationship between individual differences in stopping ability in specific activated networks, including regions known to be critical for the behavior. The results also highlight the usefulness of using dimensionality reduction to increase the power to detect brain/behavior correlations in individual differences research.

YNIMG Journal 2008 Journal Article

Automatic independent component labeling for artifact removal in fMRI

• Jussi Tohka
• Karin Foerde
• Adam R. Aron
• Sabrina M. Tom
• Arthur W. Toga
• Russell A. Poldrack

Blood oxygenation level dependent (BOLD) signals in functional magnetic resonance imaging (fMRI) are often small compared to the level of noise in the data. The sources of noise are numerous including different kinds of motion artifacts and physiological noise with complex patterns. This complicates the statistical analysis of the fMRI data. In this study, we propose an automatic method to reduce fMRI artifacts based on independent component analysis (ICA). We trained a supervised classifier to distinguish between independent components relating to a potentially task-related signal and independent components clearly relating to structured noise. After the components had been classified as either signal or noise, a denoised fMR time-series was reconstructed based only on the independent components classified as potentially task-related. The classifier was a novel global (fixed structure) decision tree trained in a Neyman–Pearson (NP) framework, which allowed the shape of the decision regions to be controlled effectively. Additionally, the conservativeness of the classifier could be tuned by modifying the NP threshold. The classifier was tested against the component classifications by an expert with the data from a category learning task. The test set as well as the expert were different from the data used for classifier training and the expert labeling the training set. The misclassification rate was between 0. 2 and 0. 3 for both the event-related and blocked designs and it was consistent among variety of different NP thresholds. The effects of denoising on the group-level statistical analyses were as expected: The denoising generally decreased Z-scores in the white matter, where extreme Z-values can be expected to reflect artifacts. A similar but weaker decrease in Z-scores was observed in the gray matter on average. These two observations suggest that denoising was likely to reduce artifacts from gray matter and could be useful to improve the detection of activations. We conclude that automatic ICA-based denoising offers a potentially useful approach to improve the quality of fMRI data and consequently increase the accuracy of the statistical analysis of these data.

YNIMG Journal 2006 Journal Article

Long-term test–retest reliability of functional MRI in a classification learning task

• Adam R. Aron
• Mark A. Gluck
• Russell A. Poldrack

Functional MRI is widely used for imaging the neural correlates of psychological processes and how these brain processes change with learning, development and neuropsychiatric disorder. In order to interpret changes in imaging signals over time, for example, in patient studies, the long-term reliability of fMRI must first be established. Here, eight healthy adult subjects were scanned on two sessions, 1 year apart, while performing a classification learning task known to activate frontostriatal circuitry. We show that behavioral performance and frontostriatal activation were highly concordant at a group level at both time-points. Furthermore, intra-class correlation coefficients (ICCs), which index the degree of correlation between subjects at different time-points, were high for behavior and for functional activation. ICC was significantly higher within the network recruited by learning than outside that network. We conclude that fMRI can have high long-term test–retest reliability, making it suitable as a biomarker for brain development and neurodegeneration.