Identification of selection and inhibition components in a Go/NoGo task from EEG spectra using a machine learning classifier

A modified neural circuit framework for semantic memory retrieval with implications for circuit modulation to treat verbal retrieval deficits

Word finding difficulty is a frequent complaint in older age and disease states, but treatment options are lacking for such verbal retrieval deficits. Better understanding of the neurophysiological and neuroanatomical basis of verbal retrieval function may inform effective interventions. In this article, we review the current evidence of a neural retrieval circuit central to verbal production, including words and semantic memory, that involves the pre-supplementary motor area (pre-SMA), striatum (particularly caudate nucleus), and thalamus. We aim to offer a modified neural circuit framework expanded upon a memory retrieval model proposed in 2013 by Hart et al., as evidence from electrophysiological, functional brain imaging, and noninvasive electrical brain stimulation studies have provided additional pieces of information that converge on a shared neural circuit for retrieval of memory and words. We propose that both the left inferior frontal gyrus and fronto-polar regions should be included in the expanded circuit. All these regions have their respective functional roles during verbal retrieval, such as selection and inhibition during search, initiation and termination of search, maintenance of co-activation across cortical regions, as well as final activation of the retrieved information. We will also highlight the structural connectivity from and to the pre-SMA (e.g., frontal aslant tract and fronto-striatal tract) that facilitates communication between the regions within this circuit. Finally, we will discuss how this circuit and its correlated activity may be affected by disease states and how this circuit may serve as a novel target engagement for neuromodulatory treatment of verbal retrieval deficits.

Cognitive trajectories in longitudinally trained 3xTg-AD mice

Preclinical studies in Alzheimer's disease (AD) often rely on cognitively naïve animal models in cross-sectional designs that can fail to reflect the cognitive exposures across the lifespan and heterogeneous neurobehavioral features observed in humans. To determine whether longitudinal cognitive training may affect cognitive capacities in a well-characterized AD mouse model, 3xTg and wild-type mice (n = 20) were exposed daily to a training variant of the Go-No-Go (GNG) operant task from 3 to 9 months old. At 3, 6, and 9 months, performance on a testing variant of the GNG task and anxiety-like behaviors were measured, while long-term recognition memory was also assessed at 9 months. In general, GNG training improved performance with increasing age across genotypes. At 3 months old, 3xTg mice showed slight deficits in inhibitory control that were accompanied by minor improvements in signal detection and decreased anxiety-like behavior, but these differences did not persist at 6 and 9 months old. At 9 months old, 3xTg mice displayed minor deficits in signal detection, and long-term recognition memory capacity was comparable with wild-type subjects. Our findings indicate that longitudinal cognitive training can render 3xTg mice with cognitive capacities that are on par with their wild-type counterparts, potentially reflecting functional compensation in subjects harboring AD genetic mutations.

Event-related EEG synchronization and desynchronization in patients with obsessive-compulsive disorder

Symptoms in patients with obsessive-compulsive disorder (OCD) are associated with impairment in cognitive control, attention, and action inhibition. We investigated OCD group differences relative to healthy subjects in terms of event-related alpha and beta range synchronization (ERS) and desynchronization (ERD) during a visually cued Go/NoGo task. Subjects were 62 OCD patients and 296 healthy controls (HC). The OCD group in comparison with HC, showed a changed value of alpha/beta oscillatory power over the central cortex, in particular, an increase in the alpha/beta ERD over the central-parietal cortex during the interstimulus interval (Cue condition) as well as changes in the postmovement beta synchronization topography and frequency. Over the frontal cortex, the OCD group showed an increase in magnitude of the beta ERS in NoGo condition. Within the parietal-occipital ERS/ERD modulations, the OCD group showed an increase in the alpha/beta ERD over the parietal cortex after the presentation of the visual stimuli as well as a decrease in the beta ERD over the occipital cortex after the presentation of the Cue and Go stimuli. The specific properties in the ERS/ERD patterns observed in the OCD group may reflect high involvement of the frontal and central cortex in action preparation and action inhibition processes and, possibly, in maintaining the motor program, which might be a result of the dysfunction of the cortico-striato-thalamo-cortical circuits involving prefrontal cortex. The data about enhanced involvement of the parietal cortex in the evaluation of the visual stimuli are in line with the assumption about overfocused attention in OCD.

Investigation of frequency components embedded in EEG recordings underlying neuronal mechanism of cognitive control and attentional functions

Attentional cognitive control regulates the perception to enhance human behaviour. The current study examines the atltentional mechanisms in terms of time and frequency of EEG signals. The cognitive load is higher for processing local attentional stimulus, thereby demanding higher response time (RT) with low response accuracy (RA). On the other hand, the global attentional mechanisms broadly promote the perception while demanding a low cognitive load with faster RT and high RA. Attentional mechanisms refer to perceptual systems that afford and allocate the adaptive behaviours for prioritizing the processing of relevant stimuli based on the local and global features. The early sensory component of C1, which was associated with the local attentional mechanism, showed higher amplitudes than the global attentional mechanisms in parieto-occipital regions. Further, the local attentional mechanisms were also sustained in N2 and P3 components increasing higher amplitude in the left and right hemispheric sides of temporal regions (T7 and T8). Theta band frequency had shown higher power spectrum density (PSD) values while processing local attentional mechanisms. However, the significance of other frequency bands was noticeably minute. Hence, integrating the attentional mechanisms in terms of ERP and frequency signatures, a hybrid custom weight allocation model (CWAM) was built to assess and predict the contribution of insignificant channels to significant ones. The CWAM model was formulated based on the computational linear regression derivatives. All the derivatives are computationally derived the significant score while channelizing the hierarchical performance of each channel with respect to the frequent and deviant occurrences of global-local stimulus. This model enables us to configure the neural dynamicity of cognitive allocation of resources within the different locations of the human brain while processing the attentional stimulus. CWAM is reported to be the first model to evaluate the performance of the non-significant channels for enhancing the response of significant channels. The findings of the CWAM model suggest that the brain's performance may be determined by the underlying contribution of the non-significant channels.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09888-x.

Application of Machine Learning to Diagnostics of Schizophrenia Patients Based on Event-Related Potentials

Schizophrenia is a major psychiatric disorder that significantly reduces the quality of life. Early treatment is extremely important in order to mitigate the long-term negative effects. In this paper, a machine learning based diagnostics of schizophrenia was designed. Classification models were applied to the event-related potentials (ERPs) of patients and healthy subjects performing the visual cued Go/NoGo task. The sample consisted of 200 adult individuals ranging in age from 18 to 50 years. In order to apply the machine learning models, various features were extracted from the ERPs. The process of feature extraction was parametrized through a special procedure and the parameters of this procedure were selected through a grid-search technique along with the model hyperparameters. Feature extraction was followed by sequential feature selection transformation in order to prevent overfitting and reduce the computational complexity. Various models were trained on the resulting feature set. The best model was support vector machines with a sensitivity and specificity of 91% and 90.8%, respectively.

Kinematic Studies of the Go/No-Go Task as a Dynamic Sensorimotor Inhibition Task for Assessment of Motor and Executive Function in Stroke Patients: An Exploratory Study in a Neurotypical Sample

Inhibition of reaching and grasping actions as an element of cognitive control and executive function is a vital component of sensorimotor behaviour that is often impaired in patients who have lost sensorimotor function following a stroke. To date, there are few kinematic studies detailing the fine spatial and temporal upper limb movements associated with the millisecond temporal trajectory of correct and incorrect responses to visually driven Go/No-Go reaching and grasping tasks. Therefore, we aimed to refine the behavioural measurement of correct and incorrect inhibitory motor responses in a Go/No-Go task for future quantification and personalized rehabilitation in older populations and those with acquired motor disorders, such as stroke. An exploratory study mapping the kinematic profiles of hand movements in neurotypical participants utilizing such a task was conducted using high-speed biological motion capture cameras, revealing both within and between subject differences in a sample of healthy participants. These kinematic profiles and differences are discussed in the context of better assessment of sensorimotor function impairment in stroke survivors.

High-definition transcranial direct current stimulation modulates theta response during a Go-NoGo task in traumatic brain injury

OBJECTIVE

High Definition transcranial Direct Current Stimulation (HD-tDCS) has been shown to improve cognitive performance in individuals with chronic traumatic brain injury (TBI), although electrophysiological mechanisms remain unclear.

METHODS

Veterans with TBI underwent active anodal (N = 15) vs sham (N = 10) HD-tDCS targeting the pre-supplementary motor area (pre-SMA). A Go-NoGo task was conducted simultaneously with electroencephalography (EEG) at baseline and after intervention completion.

RESULTS

We found increased theta event-related spectral perturbation (ERSP) and inter-trial phase coherence (ITPC) during Go in the frontal midline electrodes overlying the pre-SMA after active HD-tDCS intervention, but not after sham. We also found increased theta phase coherence during Go between the frontal midline and left posterior regions after active HD-tDCS. A late increase in alpha-theta ERSP was found in the left central region after active HD-tDCS. Notably, lower baseline theta ERSP/ITPC in the frontal midline region predicted more post-intervention improvement in Go performance only in the active group.

CONCLUSIONS

There are local and interregional oscillatory changes in response to HD-tDCS modulation in chronic TBI.

SIGNIFICANCE

These findings may guide future research in utilizing EEG time-frequency metrics not only to measure interventional effects, but also in selecting candidates who may optimally respond to treatment.

Sustained attention alterations in major depressive disorder: A review of fMRI studies employing Go/No-Go and CPT tasks

BACKGROUND

Major depressive disorder (MDD) is a severe psychiatric condition characterized by selective cognitive dysfunctions. In this regard, functional Magnetic Resonance Imaging (fMRI) studies showed, both at resting state and during tasks, alterations in the brain functional networks involved in cognitive processes in MDD patients compared to controls. Among those, it seems that the attention network may have a role in the disease pathophysiology. Therefore, in this review we aim at summarizing the current fMRI evidence investigating sustained attention in MDD patients.

METHODS

We conducted a search on PubMed on case-control studies on MDD employing fMRI acquisitions during Go/No-Go and continuous performance tasks. A total of 12 studies have been included in the review.

RESULTS

Overall, the majority of fMRI studies reported quantitative alterations in the response to attentive tasks in selective brain regions, including the prefrontal cortex, the cingulate cortex, the temporal and parietal lobes, the insula and the precuneus, which are key nodes of the attention, the executive, and the default mode networks.

LIMITATIONS

The heterogeneity in the study designs, fMRI acquisition techniques and processing methods have limited the generalizability of the results.

CONCLUSIONS

The results from the included studies showed the presence of alterations in the activation patterns of regions involved in sustained attention in MDD, which are in line with current evidence and seemed to explain some of the key symptoms of depression. However, given the paucity and heterogeneity of studies available, it may be worthwhile to continue investigating the attentional domain in MDD with ad-hoc study designs to retrieve more robust evidence.

EEG theta and alpha oscillations in early versus late mild cognitive impairment during a semantic Go/NoGo task

Amnestic mild cognitive impairment (aMCI) is marked by episodic memory deficits, which can be used to classify individuals into early MCI (EMCI) and late MCI (LMCI). Although mounting evidence suggests that individuals with aMCI have additional cognitive alterations including deficits in cognitive control, few have examined if EMCI and LMCI differ on processes other than episodic memory. Using a semantic Go/NoGo task, we examined differences in cognitive control between EMCI and LMCI on behavioral (accuracy and reaction time) and neural (scalp-recorded event-related oscillations in theta and alpha band) measures. Although no behavioral differences were observed between the EMCI and LMCI groups, differences in neural oscillations were observed. The LMCI group had higher theta synchronization on Go trials at central electrodes compared to the EMCI group. In addition, the EMCI group showed differences in theta power at central electrodes and alpha power at central and centro-parietal electrodes between Go and NoGo trials, while the LMCI group did not exhibit such differences. These findings suggest that while behavioral differences may not be observable, neural changes underlying cognitive control processes may differentiate EMCI and LMCI stages and may be useful to understand the trajectory of aMCI in future studies.

Identification of selection and inhibition components in a Go/NoGo task from EEG spectra using a machine learning classifier

INTRODUCTION

Prior Go/NoGo studies have localized specific regions and EEG spectra for which traditional approaches have distinguished between Go and NoGo conditions. A more detailed characterization of the spatial distribution and timing of the synchronization of frequency bands would contribute substantially to the clarification of neural mechanisms that underlie performance of the Go/NoGo task.

METHODS

The present study used a machine learning approach to learn the features that distinguish between ERSPs involved in selection and inhibition in a Go/NoGo task. A single-layer neural network classifier was used to predict task conditions for each subject to characterize ERSPs associated with Go versus NoGo trials.

RESULTS

The final classifier accurately identified individual task conditions at an overall rate of 92%, estimated by fivefold cross-validation. The detailed accounting of EEG time-frequency patterns localized to brain regions (i.e., thalamus, pre-SMA, orbitofrontal cortex, and superior parietal cortex) corroborates and also elaborates upon previous findings from fMRI and EEG studies, and expands the information about EEG power changes in multiple frequency bands (i.e., primarily theta power increase, alpha decreases, and beta increases and decreases) within these regions underlying the selection and inhibition processes engaged in the Go and NoGo trials.

CONCLUSION

This time-frequency-based classifier extends previous spatiotemporal findings and provides information about neural mechanisms underlying selection and inhibition processes engaged in Go and NoGo trials, respectively. This neural network classifier can be used to assess time-frequency patterns from an individual subject and thus may offer insight into therapeutic uses of neuromodulation in neural dysfunction.