Double-blind disruption of right inferior frontal cortex with TMS reduces right frontal beta power for action stopping

Add, subtract and multiply: Meta-analyses of brain correlates of arithmetic operations in children and adults

Mathematical operations are cognitive actions we take to calculate relations among numbers. Arithmetic operations, addition, subtraction, multiplication, and division are elemental in education. Addition is the first one taught in school and is most popular in functional magnetic resonance imaging (fMRI) studies. Division, typically taught last is least studied with fMRI. fMRI meta-analyses show that arithmetic operations activate brain areas in parietal, cingulate and insular cortices for children and adults. Critically, no meta-analysis examines concordance across brain correlates of separate arithmetic operations in children and adults. We review and examine using quantitative meta-analyses data from fMRI articles that report brain coordinates separately for addition, subtraction, multiplication, and division in children and adults. Results show that arithmetic operations elicit common areas of concordance in fronto-parietal and cingulo-opercular networks in adults and children. Between operations differences are observed primarily for adults. Interestingly, higher within-group concordance, expressed in activation likelihood estimates, is found in brain areas associated with the cingulo-opercular network rather than the fronto-parietal network in children, areas also common between adults and children. Findings are discussed in relation to constructivist cognitive theory and practical directions for future research.

Improving social cognition following theta burst stimulation over the right inferior frontal gyrus in autism spectrum: an 8-week double-blind sham-controlled trial

BACKGROUND

The right inferior frontal gyrus (RIFG) is a potential beneficial brain stimulation target for autism. This randomized, double-blind, two-arm, parallel-group, sham-controlled clinical trial assessed the efficacy of intermittent theta burst stimulation (iTBS) over the RIFG in reducing autistic symptoms (NCT04987749).

METHODS

Conducted at a single medical center, the trial enrolled 60 intellectually able autistic individuals (aged 8-30 years; 30 active iTBS). The intervention comprised 16 sessions (two stimulations per week for eight weeks) of neuro-navigated iTBS or sham over the RIFG. Fifty-seven participants (28 active) completed the intervention and assessments at Week 8 (the primary endpoint) and follow-up at Week 12.

RESULTS

Autistic symptoms (primary outcome) based on the Social Responsiveness Scale decreased in both groups (significant time effect), but there was no significant difference between groups (null time-by-treatment interaction). Likewise, there was no significant between-group difference in changes in repetitive behaviors and exploratory outcomes of adaptive function and emotion dysregulation. Changes in social cognition (secondary outcome) differed between groups in feeling scores on the Frith-Happe Animations (Week 8, p = 0.026; Week 12, p = 0.025). Post-hoc analysis showed that the active group improved better on this social cognition than the sham group. Dropout rates did not vary between groups; the most common adverse event in both groups was local pain. Notably, our findings would not survive stringent multiple comparison corrections.

CONCLUSIONS

Our findings suggest that iTBS over the RIFG is not different from sham in reducing autistic symptoms and emotion dysregulation. Nonetheless, RIFG iTBS may improve social cognition of mentalizing others' feelings in autistic individuals.

Neural activity during inhibitory control predicts suicidal ideation with machine learning

Suicide is a leading cause of death in the US and worldwide. Current strategies for preventing suicide are often focused on the identification and treatment of risk factors, especially suicidal ideation (SI). Hence, developing data-driven biomarkers of SI may be key for suicide prevention and intervention. Prior attempts at biomarker-based prediction models for SI have primarily used expensive neuroimaging technologies, yet clinically scalable and affordable biomarkers remain elusive. Here, we investigated the classification of SI using machine learning (ML) on a dataset of 76 subjects with and without SI(+/-) (n = 38 each), who completed a neuro-cognitive assessment session synchronized with electroencephalography (EEG). SI+/- groups were matched for age, sex, and mental health symptoms of depression and anxiety. EEG was recorded at rest and while subjects engaged in four cognitive tasks of inhibitory control, interference processing, working memory, and emotion bias. We parsed EEG signals in physiologically relevant theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequencies and performed cortical source imaging on the neural signals. These data served as SI predictors in ML models. The best ML model was obtained for beta band power during the inhibitory control (IC) task, demonstrating high sensitivity (89%), specificity (98%). Shapley explainer plots further showed top neural predictors as feedback-related power in the visual and posterior default mode networks and response-related power in the ventral attention, fronto-parietal, and sensory-motor networks. We further tested the external validity of the model in an independent clinically depressed sample (n = 35, 12 SI+) that engaged in an adaptive test version of the IC task, demonstrating 50% sensitivity and 61% specificity in this sample. Overall, the study suggests a promising, scalable EEG-based biomarker approach to predict SI that may serve as a target for risk identification and intervention.

Impulsivity and neural correlates of response inhibition in bipolar disorder and their unaffected relatives: A MEG study

BACKGROUND

Response inhibition is a core cognitive impairment in bipolar disorder (BD), leading to increased impulsivity in BD. However, the relationship between the neural mechanisms underlying impaired response inhibition and impulsivity in BD is not yet clear. Individuals who are genetically predisposed to BD give a way of identifying potential endophenotypes.

METHODS

A total of 97 participants, including 39 patients with BD, 22 unaffected relatives (UR) of patients with BD, and 36 healthy controls performed a Go/No-Go task during magnetoencephalography. We carried out time-frequency and connectivity analysis on MEG data.

RESULTS

Decreased beta power, prolonged latency and increased peak frequency in rIFG, decreased beta power in pre-SMA and reduced rIFG-to-pre-SMA connectivity were found in BD relative to healthy controls. In the UR group, we found a decrease in the beta power of pre-SMA and prolonged latency of rIFG. Furthermore, increased motor impulsiveness in BD was related to abnormal alterations in beta oscillatory activity of rIFG and functional connectivity between rIFG and pre-SMA.

CONCLUSIONS

Hypoactivity activity in rIFG and impaired dominant role of rIFG in the prefrontal control network may underlie the neuropathology of response inhibition dysfunction, resulting increased motor impulsivity in BD. Our findings point to measuring rIFG dysfunction as a potential means of identifying individuals at genetic high risk for transition to BD disease expression.

Effects of transcranial magnetic stimulation on reactive response inhibition

Reactive response inhibition cancels impending actions to enable adaptive behavior in ever-changing environments and has wide neuropsychiatric implications. A canonical paradigm to measure the covert inhibition latency is the stop-signal task (SST). To probe the cortico-subcortical network underlying motor inhibition, transcranial magnetic stimulation (TMS) has been applied over central nodes to modulate SST performance, especially to the right inferior frontal cortex and the presupplementary motor area. Since the vast parameter spaces of SST and TMS enabled diverse implementations, the insights delivered by emerging TMS-SST studies remain inconclusive. Therefore, a systematic review was conducted to account for variability and synthesize converging evidence. Results indicate certain protocol specificity through the consistent perturbations induced by online TMS, whereas offline protocols show paradoxical effects on different target regions besides numerous null effects. Ancillary neuroimaging findings have verified and dissociated the underpinning network dynamics. Sources of heterogeneity in designs and risk of bias are highlighted. Finally, we outline best-practice recommendations to bridge methodological gaps and subserve the validity as well as replicability of future work.

Transcranial focused ultrasound to human rIFG improves response inhibition through modulation of the P300 onset latency

Response inhibition in humans is important to avoid undesirable behavioral action consequences. Neuroimaging and lesion studies point to a locus of inhibitory control in the right inferior frontal gyrus (rIFG). Electrophysiology studies have implicated a downstream event-related potential from rIFG, the fronto-central P300, as a putative neural marker of the success and timing of inhibition over behavioral responses. However, it remains to be established whether rIFG effectively drives inhibition and which aspect of P300 activity uniquely indexes inhibitory control-ERP timing or amplitude. Here, we dissect the connection between rIFG and P300 for inhibition by using transcranial-focused ultrasound (tFUS) to target rIFG of human subjects while they performed a Stop-Signal task. By applying tFUS simultaneously with different task events, we found behavioral inhibition was improved, but only when applied to rIFG simultaneously with a 'stop' signal. Improved inhibition through tFUS to rIFG was indexed by faster stopping times that aligned with significantly shorter N200/P300 onset latencies. In contrast, P300 amplitude was modulated during tFUS across all groups without a paired change in behavior. Using tFUS, we provide evidence for a causal connection between anatomy, behavior, and electrophysiology underlying response inhibition.

Fronto-central resting-state 15-29 Hz transient beta events change with therapeutic transcranial magnetic stimulation for posttraumatic stress disorder and major depressive disorder

Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD) and shows promise for posttraumatic stress disorder (PTSD), yet effectiveness varies. Electroencephalography (EEG) can identify rTMS-associated brain changes. EEG oscillations are often examined using averaging approaches that mask finer time-scale dynamics. Recent advances show some brain oscillations emerge as transient increases in power, a phenomenon termed "Spectral Events," and that event characteristics correspond with cognitive functions. We applied Spectral Event analyses to identify potential EEG biomarkers of effective rTMS treatment. Resting 8-electrode EEG was collected from 23 patients with MDD and PTSD before and after 5 Hz rTMS targeting the left dorsolateral prefrontal cortex. Using an open-source toolbox ( https://github.com/jonescompneurolab/SpectralEvents ), we quantified event features and tested for treatment associated changes. Spectral Events in delta/theta (1-6 Hz), alpha (7-14 Hz), and beta (15-29 Hz) bands occurred in all patients. rTMS-induced improvement in comorbid MDD PTSD were associated with pre- to post-treatment changes in fronto-central electrode beta event features, including frontal beta event frequency spans and durations, and central beta event maxima power. Furthermore, frontal pre-treatment beta event duration correlated negatively with MDD symptom improvement. Beta events may provide new biomarkers of clinical response and advance the understanding of rTMS.

Fronto-central resting-state 15-29Hz transient beta events change with therapeutic transcranial magnetic stimulation for posttraumatic stress disorder and major depressive disorder

Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD) and shows promise for posttraumatic stress disorder (PTSD), yet effectiveness varies. Electroencephalography (EEG) can identify rTMS-associated brain changes. EEG oscillations are often examined using averaging approaches that mask finer time-scale dynamics. Recent advances show some brain oscillations emerge as transient increases in power, a phenomenon termed "Spectral Events," and that event characteristics correspond with cognitive functions. We applied Spectral Event analyses to identify potential EEG biomarkers of effective rTMS treatment. Resting 8-electrode EEG was collected from 23 patients with MDD and PTSD before and after 5Hz rTMS targeting the left dorsolateral prefrontal cortex. Using an open-source toolbox ( https://github.com/jonescompneurolab/SpectralEvents ), we quantified event features and tested for treatment associated changes. Spectral Events in delta/theta (1-6 Hz), alpha (7-14 Hz), and beta (15-29 Hz) bands occurred in all patients. rTMS-induced improvement in comorbid MDD PTSD were associated with pre-to post-treatment changes in fronto-central electrode beta event features, including frontal beta event frequency spans and durations, and central beta event maxima power. Furthermore, frontal pre-treatment beta event duration correlated negatively with MDD symptom improvement. Beta events may provide new biomarkers of clinical response and advance the understanding of rTMS.

Self-regulation of the brain's right frontal Beta rhythm using a brain-computer interface

Neural oscillations, or brain rhythms, fluctuate in a manner reflecting ongoing behavior. Whether these fluctuations are instrumental or epiphenomenal to the behavior remains elusive. Attempts to experimentally manipulate neural oscillations exogenously using noninvasive brain stimulation have shown some promise, but difficulty with tailoring stimulation parameters to individuals has hindered progress in this field. We demonstrate here using electroencephalography (EEG) neurofeedback in a brain-computer interface that human participants (n = 44) learned over multiple sessions across a 6-day period to self-regulate their Beta rhythm (13-20 Hz), either up or down, over the right inferior frontal cortex. Training to downregulate Beta was more effective than training to upregulate Beta. The modulation was evident only during neurofeedback task performance but did not lead to offline alteration of Beta rhythm characteristics at rest, nor to changes in subsequent cognitive behavior. Likewise, a control group (n = 38) who underwent training to up or downregulate the Alpha rhythm (8-12 Hz) did not exhibit behavioral changes. Although the right frontal Beta rhythm has been repeatedly implicated as a key component of the brain's inhibitory control system, the present data suggest that its manipulation offline prior to cognitive task performance does not result in behavioral change in healthy individuals. Whether this form of neurofeedback training could serve as a useful therapeutic target for disorders with dysfunctional inhibitory control as their basis remains to be tested in a context where performance is abnormally poor and neural dynamics are different.

Partial response electromyography as a marker of action stopping

Response inhibition is among the core constructs of cognitive control. It is notoriously difficult to quantify from overt behavior, since the outcome of successful inhibition is the lack of a behavioral response. Currently, the most common measure of action stopping, and by proxy response inhibition, is the model-based stop signal reaction time (SSRT) derived from the stop signal task. Recently, partial response electromyography (prEMG) has been introduced as a complementary physiological measure to capture individual stopping latencies. PrEMG refers to muscle activity initiated by the go signal that plummets after the stop signal before its accumulation to a full response. Whereas neither the SSRT nor the prEMG is an unambiguous marker for neural processes underlying response inhibition, our analysis indicates that the prEMG peak latency is better suited to investigate brain mechanisms of action stopping. This study is a methodological resource with a comprehensive overview of the psychometric properties of the prEMG in a stop signal task, and further provides practical tips for data collection and analysis.

Effects of beta-band and gamma-band rhythmic stimulation on motor inhibition

To investigate whether beta oscillations are causally related to motor inhibition, thirty-six participants underwent two concurrent transcranial alternating current stimulation (tACS) and electroencephalography (EEG) sessions during which either beta (20 Hz) or gamma (70 Hz) stimulation was applied while participants performed a stop-signal task. In addition, we acquired magnetic resonance images to simulate the electric field during tACS. 20 Hz stimulation targeted at the pre-supplementary motor area enhanced inhibition and increased beta oscillatory power around the time of the stop-signal in trials directly following stimulation. The increase in inhibition on stop trials followed a dose-response relationship with the strength of the individually simulated electric field. Computational modeling revealed that 20 and 70 Hz stimulation had opposite effects on the braking process. These results highlight that the effects of tACS are state-dependent and demonstrate that fronto-central beta activity is causally related to successful motor inhibition, supporting its use as a functional biomarker.

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Beta tACS over preSMA improved motor inhibition

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Gamma tACS slowed down the stop process but primarily affected movement execution

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Beta tACS resulted in higher beta spectral power around the time of the stop-signal

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Effects of tACS showed a dose-response relationship with electric field strength

Decoupling countermands nonselective response inhibition during selective stopping

Response inhibition is essential for goal-directed behavior within dynamic environments. Selective stopping is a complex form of response inhibition where only part of a multi-effector response must be cancelled. A substantial response delay emerges on unstopped effectors when a cued effector is successfully stopped. This stopping-interference effect is indicative of nonselective response inhibition during selective stopping which may, in-part, be a consequence of functional coupling. The present study examined selective stopping of (de)coupled bimanual responses in healthy human participants of either sex. Participants performed synchronous and asynchronous versions of an anticipatory stop-signal paradigm across two sessions while mu (µ) and beta (β) rhythm were measured with electroencephalography. Results showed that responses were behaviorally decoupled during asynchronous go trials and the extent of response asynchrony was associated with lateralized sensorimotor µ and β desynchronization during response preparation. Selective stopping produced a stopping-interference effect and was marked by a nonselective increase and subsequent rebound in prefrontal and sensorimotor β. In support of the coupling account, stopping-interference was smaller during selective stopping of asynchronous responses, and negatively associated with the magnitude of decoupling. However, the increase in sensorimotor β during selective stopping was equivalent between the stopped and unstopped hand irrespective of response synchrony. Overall, the findings demonstrate that decoupling facilitates selective stopping after a global pause process and emphasizes the importance of considering the influence of both the go and stop context when investigating response inhibition.

Towards real-world generalizability of a circuit for action-stopping

Two decades of cross-species neuroscience research on rapid action-stopping in the laboratory has provided motivation for an underlying prefrontal-basal ganglia circuit. Here we provide an update of key studies from the past few years. We conclude that this basic neural circuit is on increasingly firm ground, and we move on to consider whether the action-stopping function implemented by this circuit applies beyond the simple laboratory stop signal task. We advance through a series of studies of increasing 'real-worldness', starting with laboratory tests of stopping of speech, gait and bodily functions, and then going beyond the laboratory to consider neural recordings and stimulation during moments of control presumably required in everyday activities such as walking and driving. We end by asking whether stopping research has clinical relevance, focusing on movement disorders such as stuttering, tics and freezing of gait. Overall, we conclude there are hints that the prefrontal-basal ganglia action-stopping circuit that is engaged by the basic stop signal task is recruited in myriad scenarios; however, truly proving this for real-world scenarios requires a new generation of studies that will need to overcome substantial technical and inferential challenges.

Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition

In humans, impaired response inhibition is characteristic of a wide range of psychiatric diseases and of normal aging. It is hypothesized that the right inferior frontal cortex (rIFC) plays a key role by inhibiting the motor cortex via the basal ganglia. The electroencephalography (EEG)-derived β-rhythm (15-29 Hz) is thought to reflect communication within this network, with increased right frontal β-power often observed before successful response inhibition. Recent literature suggests that averaging spectral power obscures the transient, burst-like nature of β-activity. There is evidence that the rate of β-bursts following a Stop signal is higher when a motor response is successfully inhibited. However, other characteristics of β-burst events, and their topographical properties, have not yet been examined. Here, we used a large human (male and female) EEG Stop Signal task (SST) dataset (n = 218) to examine averaged normalized β-power, β-burst rate, and β-burst "volume" (which we defined as burst duration × frequency span × amplitude). We first sought to optimize the β-burst detection method. In order to find predictors across the whole scalp, and with high temporal precision, we then used machine learning to (1) classify successful versus failed stopping and to (2) predict individual stop signal reaction time (SSRT). β-burst volume was significantly more predictive of successful and fast stopping than β-burst rate and normalized β-power. The classification model generalized to an external dataset (n = 201). We suggest β-burst volume is a sensitive and reliable measure for investigation of human response inhibition.SIGNIFICANCE STATEMENT The electroencephalography (EEG)-derived β-rhythm (15-29 Hz) is associated with the ability to inhibit ongoing actions. In this study, we sought to identify the specific characteristics of β-activity that contribute to successful and fast inhibition. In order to search for the most relevant features of β-activity, across the whole scalp and with high temporal precision, we employed machine learning on two large datasets. Spatial and temporal features of β-burst "volume" (duration × frequency span × amplitude) predicted response inhibition outcomes in our data significantly better than β-burst rate and normalized β-power. These findings suggest that multidimensional measures of β-bursts, such as burst volume, can add to our understanding of human response inhibition.

Right inferior frontal gyrus implements motor inhibitory control via beta-band oscillations in humans

Motor inhibitory control implemented as response inhibition is an essential cognitive function required to dynamically adapt to rapidly changing environments. Despite over a decade of research on the neural mechanisms of response inhibition, it remains unclear, how exactly response inhibition is initiated and implemented. Using a multimodal MEG/fMRI approach in 59 subjects, our results reliably reveal that response inhibition is initiated by the right inferior frontal gyrus (rIFG) as a form of attention-independent top-down control that involves the modulation of beta-band activity. Furthermore, stopping performance was predicted by beta-band power, and beta-band connectivity was directed from rIFG to pre-supplementary motor area (pre-SMA), indicating rIFG's dominance over pre-SMA. Thus, these results strongly support the hypothesis that rIFG initiates stopping, implemented by beta-band oscillations with potential to open up new ways of spatially localized oscillation-based interventions.