Transcutaneous auricular vagus nerve stimulation for the treatment of myoarthropatic symptoms in patients with craniomandibular dysfunction - a protocol for a randomized and controlled pilot trial.

BACKGROUND: Temporomandibular disorders (TMD) are a collective term for pain and dysfunction of the masticatory muscles and the temporomandibular joints. The most common types of TMD are pain-related, which may impact the psychological behavior and quality of life. Currently, the most popular methods for the treatment of TMD patients are occlusal splint therapy, often in combination with physical- and/or pharmacotherapy. However, due to the complexity of etiology, the treatment of chronic TMD remains a challenge. Recently, CE-certified systems for non-invasive VNS (transcutaneous auricular vagus nerve stimulation, taVNS) have become available and show positive effects in the treatment of chronic pain conditions, like migraine or fibromyalgia, with which TMD shares similarities. Therefore, it is the main purpose of the study to evaluate the feasibility of daily taVNS against chronic TMD and to assess whether there is an improvement in pain severity, quality of life, and kinetic parameters. METHODS: This study is designed as a single-blinded, double-arm randomized controlled trial (RCT) in a 1:1 allocation ratio. Twenty adult patients with chronical TMD symptoms will be enrolled and randomized to stimulation or sham group. In the stimulation group, taVNS is performed on the left tragus (25 Hz, pulse width 250 µs, 28 s on/32 s off, 4 h/day). The sham group will receive no stimulation via a non-functional identical-looking electrode. Validated questionnaire data and clinical parameters will be collected at the beginning of the study and after 4 and 8 weeks. The compliance of a daily taVNS of patients with chronical TMD will be evaluated via a smartphone app recording daily stimulation time and average intensity. Additionally, the treatment impact on pain severity and quality of life will be assessed with different questionnaires, and the effect on the mandibular mobility and muscle activity will be analyzed. DISCUSSION: This is the first clinical trial to assess the feasibility of taVNS in patients with chronic TMD symptoms. If taVNS improves the symptoms of TMD, it will be a significant gain in quality of life for these chronic pain patients. The results of this pilot study will help to determine the feasibility of a large-scale RCT. TRIAL REGISTRATION: This study has been registered in the DRKS database (DRKS00029724).

The effect of task complexity on the neural network for response inhibition: An ALE meta-analysis.

Response inhibition is classically investigated using the go/no-go (GNGT) and stop-signal task (SST), which conceptually measure different subprocesses of inhibition. Further, different task versions with varying levels of additional executive control demands exist, making it difficult to identify the core neural correlates of response inhibition independent of variations in task complexity. Using neuroimaging meta-analyses, we show that a divergent pattern of regions is consistently involved in the GNGT versus SST, arguing for different mechanisms involved when performing the two tasks. Further, for the GNGT a strong effect of task complexity was found, with regions of the multiple demand network (MDN) consistently involved particularly in the complex GNGT. In contrast, both standard and complex SST recruited the MDN to a similar degree. These results complement behavioral evidence suggesting that inhibitory control becomes automatic after some practice and is performed without input of higher control regions in the classic, standard GNGT, but continues to be implemented in a top-down controlled fashion in the SST.

Success versus failure in cognitive control: Meta-analytic evidence from neuroimaging studies on error processing.

Brain mechanisms of error processing have often been investigated using response interference tasks and focusing on the posterior medial frontal cortex, which is also implicated in resolving response conflict in general. Thereby, the role other brain regions may play has remained undervalued. Here, activation likelihood estimation meta-analyses were used to synthesize the neuroimaging literature on brain activity related to committing errors versus responding successfully in interference tasks and to test for commonalities and differences. The salience network and the temporoparietal junction were commonly recruited irrespective of whether responses were correct or incorrect, pointing towards a general involvement in coping with situations that call for increased cognitive control. The dorsal posterior cingulate cortex, posterior thalamus, and left superior frontal gyrus showed error-specific convergence, which underscores their consistent involvement when performance goals are not met. In contrast, successful responding revealed stronger convergence in the dorsal attention network and lateral prefrontal regions. Underrecruiting these regions in error trials may reflect failures in activating the task-appropriate stimulus-response contingencies necessary for successful response execution.

Auditory inputs modulate intrinsic neuronal timescales during sleep.

Functional magnetic resonance imaging (fMRI) studies have demonstrated that intrinsic neuronal timescales (INT) undergo modulation by external stimulation during consciousness. It remains unclear if INT keep the ability for significant stimulus-induced modulation during primary unconscious states, such as sleep. This fMRI analysis addresses this question via a dataset that comprises an awake resting-state plus rest and stimulus states during sleep. We analyzed INT measured via temporal autocorrelation supported by median frequency (MF) in the frequency-domain. Our results were replicated using a biophysical model. There were two main findings: (1) INT prolonged while MF decreased from the awake resting-state to the N2 resting-state, and (2) INT shortened while MF increased during the auditory stimulus in sleep. The biophysical model supported these results by demonstrating prolonged INT in slowed neuronal populations that simulate the sleep resting-state compared to an awake state. Conversely, under sine wave input simulating the stimulus state during sleep, the model's regions yielded shortened INT that returned to the awake resting-state level. Our results highlight that INT preserve reactivity to stimuli in states of unconsciousness like sleep, enhancing our understanding of unconscious brain dynamics and their reactivity to stimuli.

Predicting executive functioning from brain networks: modality specificity and age effects.

Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from the gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether the differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate-to-weak brain-behavior associations (R2 < 0.07, r < 0.28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.

ALE meta-analyses of voxel-based morphometry studies: Parameter validation via large-scale simulations.

Activation likelihood estimation (ALE) meta-analysis has been applied to structural neuroimaging data since long, but up to now, any systematic assessment of the algorithm's behavior, power and sensitivity has been based on simulations using functional neuroimaging databases as their foundation. Here, we aimed to determine whether the guidelines offered by previous evaluations can be generalized to ALE meta-analyses of voxel-based morphometry (VBM) studies. We ran 365000 distinct ALE analyses filled with simulated experiments, randomly sampling parameters from BrainMap's VBM experiment database. We then examined the algorithm's sensitivity, its susceptibility to spurious convergence, and its susceptibility to excessive contributions by individual experiments. In general, the performance of the ALE algorithm was highly comparable between imaging modalities, with the algorithm's sensitivity and specificity reaching similar levels with structural data as previously observed with functional data. Because of the lower number of foci reported and the higher number of participants usually included in structural experiments, individual studies had, on average, a higher impact towards significant clusters. To prevent significant clusters from being driven by single experiments, we recommend that researchers include at least 23 experiments in a VBM ALE dataset, instead of the previously recommended minimum of n = 17. While these recommendations do not constitute hard borders, running ALE analyses on smaller datasets would require special diligence in assessing and reporting the contributions of experiments to individual clusters.

Brain functional characterization of response-code conflict in dual-tasking and its modulation by age.

Crosstalk between conflicting response codes contributes to interference in dual-tasking, an effect exacerbated in advanced age. Here, we investigated (i) brain activity correlates of such response-code conflicts, (ii) activity modulations by individual dual-task performance and related cognitive abilities, (iii) task-modulated connectivity within the task network, and (iv) age-related differences in all these aspects. Young and older adults underwent fMRI while responding to the pitch of tones through spatially mapped speeded button presses with one or two hands concurrently. Using opposing stimulus-response mappings between hands, we induced conflict between simultaneously activated response codes. These response-code conflicts elicited activation in key regions of the multiple-demand network. While thalamic and parietal areas of the conflict-related network were modulated by attentional, working-memory and task-switching abilities, efficient conflict resolution in dual-tasking mainly relied on increasing supplementary motor activity. Older adults showed non-compensatory hyperactivity in left superior frontal gyrus, and higher right premotor activity was modulated by working-memory capacity. Finally, connectivity between premotor or parietal seed regions and the conflict-sensitive network was neither conflict-specific nor age-sensitive. Overall, resolving dual-task response-code conflict recruited substantial parts of the multiple-demand network, whose activity and coupling, however, were only little affected by individual differences in task performance or age.

Preliminary findings on long-term effects of fMRI neurofeedback training on functional networks involved in sustained attention.

INTRODUCTION: Neurofeedback based on functional magnetic resonance imaging allows for learning voluntary control over one's own brain activity, aiming to enhance cognition and clinical symptoms. We previously reported improved sustained attention temporarily by training healthy participants to up-regulate the differential activity of the sustained attention network minus the default mode network (DMN). However, the long-term brain and behavioral effects of this training have not yet been studied. In general, despite their relevance, long-term learning effects of neurofeedback training remain under-explored. METHODS: Here, we complement our previously reported results by evaluating the neurofeedback training effects on functional networks involved in sustained attention and by assessing behavioral and brain measures before, after, and 2 months after training. The behavioral measures include task as well as questionnaire scores, and the brain measures include activity and connectivity during self-regulation runs without feedback (i.e., transfer runs) and during resting-state runs from 15 healthy individuals. RESULTS: Neurally, we found that participants maintained their ability to control the differential activity during follow-up sessions. Further, exploratory analyses showed that the training increased the functional connectivity between the DMN and the occipital gyrus, which was maintained during follow-up transfer runs but not during follow-up resting-state runs. Behaviorally, we found that enhanced sustained attention right after training returned to baseline level during follow-up. CONCLUSION: The discrepancy between lasting regulation-related brain changes but transient behavioral and resting-state effects raises the question of how neural changes induced by neurofeedback training translate to potential behavioral improvements. Since neurofeedback directly targets brain measures to indirectly improve behavior in the long term, a better understanding of the brain-behavior associations during and after neurofeedback training is needed to develop its full potential as a promising scientific and clinical tool.

Convergent abnormality in the subgenual anterior cingulate cortex in insomnia disorder: A revisited neuroimaging meta-analysis of 39 studies.

The neurobiological underpinnings of insomnia disorder (ID) are still poorly understood. A previous meta-analysis conducted by our research group in 2018 revealed no consistent regional alterations based on the limited number of eligible studies. Given the number of studies published during the last few years, we revisited the meta-analysis to provide an update to the field. Following the best-practice guidelines for conducting neuroimaging meta-analyses, we searched several databases (PubMed, Web of Science, and BrainMap) and identified 39 eligible structural and functional studies, reporting coordinates reflecting significant group differences between ID patients and healthy controls. A significant convergent regional alteration in the subgenual anterior cingulate cortex (sgACC) was observed using the activation likelihood estimation algorithm. Behavioural decoding using the BrainMap database indicated that this region is involved in fear-related emotional and cognitive processing. The sgACC showed robust task-based co-activation in meta-analytic connectivity modelling and task-free functional connectivity in a resting-state functional connectivity analysis with the main hubs of the salience and default mode networks, including the posterior cingulate cortex and dorsal ACC, amygdala, hippocampus, and medial prefrontal cortex. Collectively, the findings from this large-scale meta-analysis suggest a critical role of the sgACC in the pathophysiology of ID.

Predicting Executive Functioning from Brain Networks: Modality Specificity and Age Effects.

Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate to weak brain-behavior associations (R2 < .07, r < .28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.

Scale-free dynamics in the core-periphery topography and task alignment decline from conscious to unconscious states.

Scale-free physiological processes are ubiquitous in the human organism. Resting-state functional MRI studies observed the loss of scale-free dynamics under anesthesia. In contrast, the modulation of scale-free dynamics during task-related activity remains an open question. We investigate scale-free dynamics in the cerebral cortex's unimodal periphery and transmodal core topography in rest and task states during three conscious levels (awake, sedation, and anesthesia) complemented by computational modelling (Stuart-Landau model). The empirical findings demonstrate that the loss of the brain's intrinsic scale-free dynamics in the core-periphery topography during anesthesia, where pink noise transforms into white noise, disrupts the brain's neuronal alignment with the task's temporal structure. The computational model shows that the stimuli's scale-free dynamics, namely pink noise distinguishes from brown and white noise, also modulate task-related activity. Together, we provide evidence for two mechanisms of consciousness, temporo-spatial nestedness and alignment, suggested by the Temporo-Spatial Theory of Consciousness (TTC).

Success versus failure in cognitive control: meta-analytic evidence from neuroimaging studies on error processing.

Brain mechanisms of error processing have often been investigated using response interference tasks and focusing on the posterior medial frontal cortex, which is also implicated in resolving response conflict in general. Thereby, the role other brain regions may play has remained undervalued. Here, activation likelihood estimation meta-analyses were used to synthesize the neuroimaging literature on brain activity related to committing errors versus responding successfully in interference tasks and to test for commonalities and differences. The salience network and the temporoparietal junction were commonly recruited irrespective of whether responses were correct or incorrect, pointing towards a general involvement in coping with situations that call for increased cognitive control. The dorsal posterior cingulate cortex, posterior thalamus, and left superior frontal gyrus showed error-specific convergence, which underscores their consistent involvement when performance goals are not met. In contrast, successful responding revealed stronger convergence in the dorsal attention network and lateral prefrontal regions. Underrecruiting these regions in error trials may reflect failures in activating the task-appropriate stimulus-response contingencies necessary for successful response execution.

Network and State Specificity in Connectivity-Based Predictions of Individual Behavior.

Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

Predicting executive functioning from functional brain connectivity: network specificity and age effects.

Healthy aging is associated with altered executive functioning (EF). Earlier studies found age-related differences in EF performance to be partially accounted for by changes in resting-state functional connectivity (RSFC) within brain networks associated with EF. However, it remains unclear which role RSFC in EF-associated networks plays as a marker for individual differences in EF performance. Here, we investigated to what degree individual abilities across 3 different EF tasks can be predicted from RSFC within EF-related, perceptuo-motor, whole-brain, and random networks separately in young and old adults. Specifically, we were interested if (i) young and old adults differ in predictability depending on network or EF demand level (high vs. low), (ii) an EF-related network outperforms EF-unspecific networks when predicting EF abilities, and (iii) this pattern changes with demand level. Both our uni- and multivariate analysis frameworks analyzing interactions between age × demand level × networks revealed overall low prediction accuracies and a general lack of specificity regarding neurobiological networks for predicting EF abilities. This questions the idea of finding markers for individual EF performance in RSFC patterns and calls for future research replicating the current approach in different task states, brain modalities, different, larger samples, and with more comprehensive behavioral measures.

Evaluation of the reliability and validity of computerized tests of attention.

Different aspects of attention can be assessed through psychological tests to identify stable individual or group differences as well as alterations after interventions. Aiming for a wide applicability of attentional assessments, Psychology Experiment Building Language (PEBL) is an open-source software system for designing and running computerized tasks that tax various attentional functions. Here, we evaluated the reliability and validity of computerized attention tasks as provided with the PEBL package: Continuous Performance Task (CPT), Switcher task, Psychomotor Vigilance Task (PVT), Mental Rotation task, and Attentional Network Test. For all tasks, we evaluated test-retest reliability using the intraclass correlation coefficient (ICC), as well as internal consistency through within-test correlations and split-half ICC. Across tasks, response time scores showed adequate reliability, whereas scores of performance accuracy, variability, and deterioration over time did not. Stability across application sites was observed for the CPT and Switcher task, but practice effects were observed for all tasks except the PVT. We substantiate convergent and discriminant validity for several task scores using between-task correlations and provide further evidence for construct validity via associations of task scores with attentional and motivational assessments. Taken together, our results provide necessary information to help design and interpret studies involving attention assessments.

Characterizing functional modules in the human thalamus: coactivation-based parcellation and systems-level functional decoding.

The human thalamus relays sensory signals to the cortex and facilitates brain-wide communication. The thalamus is also more directly involved in sensorimotor and various cognitive functions but a full characterization of its functional repertoire, particularly in regard to its internal anatomical structure, is still outstanding. As a putative hub in the human connectome, the thalamus might reveal its functional profile only in conjunction with interconnected brain areas. We therefore developed a novel systems-level Bayesian reverse inference decoding that complements the traditional neuroinformatics approach towards a network account of thalamic function. The systems-level decoding considers the functional repertoire (i.e., the terms associated with a brain region) of all regions showing co-activations with a predefined seed region in a brain-wide fashion. Here, we used task-constrained meta-analytic connectivity-based parcellation (MACM-CBP) to identify thalamic subregions as seed regions and applied the systems-level decoding to these subregions in conjunction with functionally connected cortical regions. Our results confirm thalamic structure-function relationships known from animal and clinical studies and revealed further associations with language, memory, and locomotion that have not been detailed in the cognitive neuroscience literature before. The systems-level decoding further uncovered large systems engaged in autobiographical memory and nociception. We propose this novel decoding approach as a useful tool to detect previously unknown structure-function relationships at the brain network level, and to build viable starting points for future studies.

Scale-free dynamics of core-periphery topography.

The human brain's cerebral cortex exhibits a topographic division into higher-order transmodal core and lower-order unimodal periphery regions. While timescales between the core and periphery region diverge, features of their power spectra, especially scale-free dynamics during resting-state and their mdulation in task states, remain unclear. To answer this question, we investigated the ~1/f-like pink noise manifestation of scale-free dynamics in the core-periphery topography during rest and task states applying infra-slow inter-trial intervals up to 1 min falling inside the BOLD's infra-slow frequency band. The results demonstrate (1) higher resting-state power-law exponent (PLE) in the core compared to the periphery region; (2) significant PLE increases in task across the core and periphery regions; and (3) task-related PLE increases likely followed the task's atypically low event rates, namely the task's periodicity (inter-trial interval = 52-60 s; 0.016-0.019 Hz). A computational model and a replication dataset that used similar infra-slow inter-trial intervals provide further support for our main findings. Altogether, the results show that scale-free dynamics differentiate core and periphery regions in the resting-state and mediate task-related effects.

Response-code conflict in dual-task interference and its modulation by age.

Difficulties in performing two tasks at once can arise from several sources and usually increase in advanced age. Tasks with concurrent bimodal (e.g., manual and oculomotor) responding to single stimuli consistently revealed crosstalk between conflicting response codes as a relevant source. However, how this finding translates to unimodal (i.e., manual only) response settings and how it is affected by age remains open. To address this issue, we had young and older adults respond to high- or low-pitched tones with one (single task) or both hands concurrently (dual task). Responses were either compatible or incompatible with the pitch. When responses with the same level of compatibility were combined in dual-task conditions, their response codes were congruent to each other, whereas combining a compatible and an incompatible response created mutually incongruent (i.e., conflicting) response codes, potentially inducing detrimental crosstalk. Across age groups, dual-task costs indeed were overall highest with response-code incongruency. In these trials, compatible responses exhibited higher costs than incompatible ones, even after removing trials with strongly synchronized responses. This underadditive cost asymmetry argues against mutual crosstalk as the sole source of interference and corroborates notions of strategic prioritization of limited processing capacity based on mapping-selection difficulty. As expected, the effects of incongruent response codes were found to be especially deleterious in older adults, supporting assumptions of age-related deficits in multiple-action control at the level of task-shielding. Overall, our results suggest that aging is linked to higher response confusability and less efficient flexibility for capacity sharing in dual-task settings.