Enhancing cognitive control with transcranial magnetic stimulation in subject-specific frontoparietal networks.

BACKGROUND: Cognitive control processes, including those involving frontoparietal networks, are highly variable between individuals, posing challenges to basic and clinical sciences. While distinct frontoparietal networks have been associated with specific cognitive control functions such as switching, inhibition, and working memory updating functions, there have been few basic tests of the role of these networks at the individual level. METHODS: To examine the role of cognitive control at the individual level, we conducted a within-subject excitatory transcranial magnetic stimulation (TMS) study in 19 healthy individuals that targeted intrinsic ("resting") frontoparietal networks. Person-specific intrinsic networks were identified with resting state functional magnetic resonance imaging scans to determine TMS targets. The participants performed three cognitive control tasks: an adapted Navon figure-ground task (requiring set switching), n-back (working memory), and Stroop color-word (inhibition). OBJECTIVE: Hypothesis: We predicted that stimulating a network associated with externally oriented control [the "FPCN-B" (fronto-parietal control network)] would improve performance on the set switching and working memory task relative to a network associated with attention (the Dorsal Attention Network, DAN) and cranial vertex in a full within-subjects crossover design. RESULTS: We found that set switching performance was enhanced by FPCN-B stimulation along with some evidence of enhancement in the higher-demand n-back conditions. CONCLUSION: Higher task demands or proactive control might be a distinguishing role of the FPCN-B, and personalized intrinsic network targeting is feasible in TMS designs.

Individual-level functional connectivity predicts cognitive control efficiency.

Cognitive control (CC) is essential for problem-solving in everyday life, and CC-related deficits occur alongside costly and debilitating disorders. The tri-partite model suggests that CC comprises multiple behaviors, including switching, inhibiting, and updating. Activity within the fronto-parietal control network B (FPCN-B), the dorsal attention network (DAN), the cingulo-opercular network (CON), and the lateral default-mode network (L-DMN) is related to switching and inhibiting behaviors. However, our understanding of how these brain regions interact to bring about cognitive switching and inhibiting in individuals is unclear. In the current study, subjects performed two in-scanner tasks that required switching and inhibiting. We used support vector regression (SVR) models containing individually-estimated functional connectivity between the FPCN-B, DAN, CON and L-DMN to predict switching and inhibiting behaviors. We observed that: inter-network connectivity can predict inhibiting and switching behaviors in individuals, and the L-DMN plays a role in switching and inhibiting behaviors. Therefore, individually estimated inter-network connections are markers of CC behaviors, and CC behaviors may arise due to interactions between a set of networks.

A Measure of Neural Function Provides Unique Insights into Behavioral Deficits in Acute Stroke.

BACKGROUND: Clinical and neuroimaging measures incompletely explain behavioral deficits in the acute stroke setting. We hypothesized that electroencephalography (EEG)-based measures of neural function would significantly improve prediction of acute stroke deficits. METHODS: Patients with acute stroke (n=50) seen in the emergency department of a university hospital from 2017 to 2018 underwent standard evaluation followed by a 3-minute recording of EEG at rest using a wireless, 17-electrode, dry-lead system. Artifacts in EEG recordings were removed offline and then spectral power was calculated for each lead pair. A primary EEG metric was DTABR, which is calculated as a ratio of spectral power: [(Delta*Theta)/(Alpha*Beta)]. Bivariate analyses and least absolute shrinkage and selection operator (LASSO) regression identified clinical and neuroimaging measures that best predicted initial National Institutes of Health Stroke Scale (NIHSS) score. Multivariable linear regression was then performed before versus after adding EEG findings to these measures, using initial NIHSS score as the dependent measure. RESULTS: Age, diabetes status, and infarct volume were the best predictors of initial NIHSS score in bivariate analyses, confirmed using LASSO regression. Combined in a multivariate model, these 3 explained initial NIHSS score (adjusted r2=0.47). Adding any of several different EEG measures to this clinical model significantly improved prediction; the greatest amount of additional variance was explained by adding contralesional DTABR (adjusted r2=0.60, P<0.001). CONCLUSIONS: EEG measures of neural function significantly add to clinical and neuroimaging for explaining initial NIHSS score in the acute stroke emergency department setting. A dry-lead EEG system can be rapidly and easily implemented. EEG contains information that may be useful early after stroke.

Investigating the influence of an effort-reward interaction on cognitive fatigue in individuals with multiple sclerosis.

This study examined whether an alteration in the effort-reward relationship, a theoretical framework based on cognitive neuroscience, could explain cognitive fatigue. Forty persons with MS and 40 healthy age- and education-matched cognitively healthy controls (HC) participated in a computerized switching task with orthogonal high- and low-demand (effort) and reward manipulations. We used the Visual Analog Scale of Fatigue (VAS-F) to assess subjective state fatigue before and after each condition during the task. We used mixed-effects models to estimate the association and interaction between effort and reward and their relationship to subjective fatigue and task performance. We found the high-demand condition was associated with increased VAS-F scores (p < .001), longer response times (RT) (p < .001) and lower accuracy (p < .001). The high-reward condition was associated with faster RT (p = .006) and higher accuracy (p = .03). There was no interaction effect between effort and reward on VAS-F scores or performance. Participants with MS reported higher VAS-F scores (p = .02). Across all conditions, participants with MS were slower (p < .001) and slower as a function of condition demand compared with HC (p < .001). This behavioural study did not find evidence that an effort-reward interaction is associated with cognitive fatigue. However, our findings support the role of effort in subjective cognitive fatigue and both effort and reward on task performance. In future studies, more salient reward manipulations could be necessary to identify effort-reward interactions on subjective cognitive fatigue.

The Relationship between Fatigue and a Clinically Accessible Measure of Switching in Individuals with Multiple Sclerosis.

OBJECTIVE: We examined whether fatigue in multiple sclerosis (MS) is linked to switching processes when switching is measured by the Trail Making Test (TMT). METHOD: Eighty-three participants with MS were administered a battery of standardized tests of switching, working memory, and processing speed. Ordinary least squares regression models were used to estimate the association between fatigue severity and switching above and beyond attention, working memory, and processing speed. RESULTS: We found a negative association between TMT performance and fatigue severity score. When measures of processing speed and working memory were included in the model, the switching measure continued to uniquely contribute to fatigue severity. CONCLUSIONS: There may be a unique relationship between fatigue and switching processes identifiable by clinical measures of switching. Future research should continue to investigate this relationship by using both behavioral and neural markers to test models of fatigue to eventually identify specific intervention targets.

Electroencephalography Might Improve Diagnosis of Acute Stroke and Large Vessel Occlusion.

BACKGROUND AND PURPOSE: Clinical methods have incomplete diagnostic value for early diagnosis of acute stroke and large vessel occlusion (LVO). Electroencephalography is rapidly sensitive to brain ischemia. This study examined the diagnostic utility of electroencephalography for acute stroke/transient ischemic attack (TIA) and for LVO. METHODS: Patients (n=100) with suspected acute stroke in an emergency department underwent clinical exam then electroencephalography using a dry-electrode system. Four models classified patients, first as acute stroke/TIA or not, then as acute stroke with LVO or not: (1) clinical data, (2) electroencephalography data, (3) clinical+electroencephalography data using logistic regression, and (4) clinical+electroencephalography data using a deep learning neural network. Each model used a training set of 60 randomly selected patients, then was validated in an independent cohort of 40 new patients. RESULTS: Of 100 patients, 63 had a stroke (43 ischemic/7 hemorrhagic) or TIA (13). For classifying patients as stroke/TIA or not, the clinical data model had area under the curve=62.3, whereas clinical+electroencephalography using deep learning neural network model had area under the curve=87.8. Results were comparable for classifying patients as stroke with LVO or not. CONCLUSIONS: Adding electroencephalography data to clinical measures improves diagnosis of acute stroke/TIA and of acute stroke with LVO. Rapid acquisition of dry-lead electroencephalography is feasible in the emergency department and merits prehospital evaluation.

Predicting Gains With Visuospatial Training After Stroke Using an EEG Measure of Frontoparietal Circuit Function.

The heterogeneity of stroke prompts the need for predictors of individual treatment response to rehabilitation therapies. We previously studied healthy subjects with EEG and identified a frontoparietal circuit in which activity predicted training-related gains in visuomotor tracking. Here we asked whether activity in this same frontoparietal circuit also predicts training-related gains in visuomotor tracking in patients with chronic hemiparetic stroke. Subjects (n = 12) underwent dense-array EEG recording at rest, then received 8 sessions of visuomotor tracking training delivered via home-based telehealth methods. Subjects showed significant training-related gains in the primary behavioral endpoint, Success Rate score on a standardized test of visuomotor tracking, increasing an average of 24.2 ± 21.9% (p = 0.003). Activity in the circuit of interest, measured as coherence (20-30 Hz) between leads overlying ipsilesional frontal (motor cortex) and parietal lobe, significantly predicted training-related gains in visuomotor tracking change, measured as change in Success Rate score (r = 0.61, p = 0.037), supporting the main study hypothesis. Results were specific to the hypothesized ipsilesional motor-parietal circuit, as coherence within other circuits did not predict training-related gains. Analyses were repeated after removing the four subjects with injury to motor or parietal areas; this increased the strength of the association between activity in the circuit of interest and training-related gains. The current study found that (1) Eight sessions of training can significantly improve performance on a visuomotor task in patients with chronic stroke, (2) this improvement can be realized using home-based telehealth methods, (3) an EEG-based measure of frontoparietal circuit function predicts training-related behavioral gains arising from that circuit, as hypothesized and with specificity, and (4) incorporating measures of both neural function and neural injury improves prediction of stroke rehabilitation therapy effects.