Feasibility of Reconstructing Source Functional Connectivity with Low-Density EEG.

OBJECTIVES: Functional connectivity (FC) is increasingly used as target for neuromodulation and enhancement of performance. A reliable assessment of FC with electroencephalography (EEG) currently requires a laboratory environment with high-density montages and a long preparation time. This study investigated the feasibility of reconstructing source FC with a low-density EEG montage towards a usage in real life applications. METHODS: Source FC was reconstructed with inverse solutions and quantified as node degree of absolute imaginary coherence in alpha frequencies. We used simulated coherent point sources as well as two real datasets to investigate the impact of electrode density (19 vs. 128 electrodes) and usage of template vs. individual MRI-based head models on localization accuracy. In addition, we checked whether low-density EEG is able to capture inter-individual variations in coherence strength. RESULTS: In numerical simulations as well as real data, a reduction of the number of electrodes led to less reliable reconstructions of coherent sources and of coupling strength. Yet, when comparing different approaches to reconstructing FC from 19 electrodes, source FC obtained with beamformers outperformed sensor FC, FC computed after independent component analysis, and source FC obtained with sLORETA. In particular, only source FC based on beamformers was able to capture neural correlates of motor behavior. CONCLUSION: Reconstructions of FC from low-density EEG is challenging, but may be feasible when using source reconstructions with beamformers.

Disrupted resting-state EEG alpha-band interactions as a novel marker for the severity of visual field deficits after brain lesion.

OBJECTIVE: Homonymous visual field deficits (HFVDs) are frequent following brain lesions. Current restoration treatments aim at activating areas of residual vision through numerous stimuli, but show limited effect. Recent findings suggest that spontaneous neural α-band coupling is more efficient for enabling visual perception in healthy humans than task-induced activations. Here, we evaluated whether it is also associated with the severity of HFVD. METHODS: Ten patients with HFVDs after brain damage in the subacute to chronic stage and ten matched healthy controls underwent visual stimulation with alternating checkerboards and electroencephalography recordings of stimulation-induced power changes and of spontaneous neural interactions during rest. RESULTS: Visual areas of the affected hemisphere showed reduced event-related power decrease in α and ß frequency bands, but also reduced spontaneous α-band interactions during rest, as compared to contralesional areas and healthy controls. A multivariate stepwise regression retained the degree of disruption of spontaneous interactions, but not the reduced task-induced power changes as predictor for the severity of the visual deficit. CONCLUSIONS: Spontaneous α-band interactions of visual areas appear as a better marker for the severity of HFVDs than task-induced activations. SIGNIFICANCE: Treatment attempts of HFVDs should try to enhance spontaneous α-band coupling of structurally intact ipsilesional areas.

Spontaneous Network Coupling Enables Efficient Task Performance without Local Task-Induced Activations.

Neurobehavioral studies in humans have long concentrated on changes in local activity levels during repetitive executions of a task. Spontaneous neural coupling within extended networks has latterly been found to also influence performance. Here, we intend to uncover the underlying mechanisms, the relative importance, and the interaction between spontaneous coupling and task-induced activations. To do so, we recorded two groups of healthy participants (male and female) during rest and while they performed either a visual perception or a motor sequence task. We demonstrate that, for both tasks, stronger activations during the task as well as greater network coupling through spontaneous α rhythms at rest predict performance. However, high performers present an absence of classical task-induced activations and, instead, stronger spontaneous network coupling. Activations were thus a compensation mechanism needed only in subjects with lower spontaneous network interactions. This challenges classical models of neural processing and calls for new strategies in attempts to train and enhance performance.SIGNIFICANCE STATEMENT Our findings challenge the widely accepted notion that task-induced activations are of paramount importance for behavior. This will have an important impact on interpretations of human neurobehavioral research. They further link the widely used techniques of quantifying network communication in the brain with classical neuroscience methods and demonstrate possible ways of how network communication influences human behavior. Traditional training methods attempt to enhance neural activations through task repetitions. Our findings suggest a more efficient neural target for learning: enhancing spontaneous neural interactions. This will be of major interest for a large variety of scientific fields with very broad applications in schools, work, and others.

The "implicit" serial reaction time task induces rapid and temporary adaptation rather than implicit motor learning.

The serial reaction time task (SRTT) has been widely used to induce learning of a repeated motor sequence without the participants' awareness. The task has also been of major influence for defining current concepts of offline consolidation after motor learning. The present study intended to replicate previous findings in a larger population of 53 healthy individuals. We were unable to reproduce previous results of online and offline implicit motor learning with the SRTT. Trials with a repeated sequence rapidly induced shorter reaction times compared to random trials, but this improvement was lost in a post-test obtained a few minutes after the training block. Furthermore, no offline consolidation was observed as there was no change in sequence specific reaction time gain between the post-test immediately after training and a re-test obtained 8 h after training. Online or offline learning remained absent when we modulated the number of sequence repetitions, the error levels, and the structure of random sequences. We conclude that the SRTT induces a rapid and temporary adaptation to the sequence rather than learning, since the repeated motor sequence does not seem to be encoded in memory.