Tatiana Fomina

Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS

OBJECTIVE Electroencephalographic (EEG) brain-computer interfaces (BCIs) hold promise in restoring communication for patients with completely locked-in stage amyotrophic lateral sclerosis (ALS). However, these patients cannot use existing EEG-based BCIs, arguably because such systems rely on brain processes that are impaired in the late stages of ALS. In this work, we introduce a novel BCI designed for patients in late stages of ALS based on high-level cognitive processes that are less likely to be affected by ALS. APPROACH We trained two ALS patients via EEG-based neurofeedback to use self-regulation of theta or gamma oscillations in the precuneus for basic communication. Because there is a tight connection between the precuneus and consciousness, precuneus oscillations are arguably generated by high-level cognitive processes, which are less likely to be affected by ALS than processes linked to the peripheral nervous system. MAIN RESULTS Both patients learned to self-regulate their precuneus oscillations and achieved stable online decoding accuracy over the course of disease progression. One patient achieved a mean online decoding accuracy in a binary decision task of 70.55% across 26 training sessions, and the other patient achieved 59.44% across 16 training sessions. We provide empirical evidence that these oscillations were cortical in nature and originated from the intersection of the precuneus, cuneus, and posterior cingulate. SIGNIFICANCE Our results establish that ALS patients can employ self-regulation of precuneus oscillations for communication. Such a BCI is likely to be available to ALS patients as long as their consciousness supports communication.

Identification of the Default Mode Network with Electroencephalography

The Default Mode Network (DMN) is a brain resting-state network that is closely linked to consciousness and neuropsychiatric disorders. The DMN is routinely identified with functional magnetic resonance imaging (fMRI) or positron emission tomography (PET). However, both of these methods impose restrictions on the groups of patients that can be examined. We show that the DMN can also be identified by electroencephalography (EEG). Instructing subjects to alternate between self-referential memory recall and focusing on their breathing induces a spatial pattern of spectral band power modulation in the θ- and α-band (4-16 Hz) that is consistent with the DMN pattern observed with PET and fMRI. Since EEG is a portable, cheap, and safe technology, our work enables the characterization of DMN alterations in patient groups that are difficult to study with fMRI or PET.

Absence of EEG correlates of self-referential processing depth in ALS

Self-referential processing is a key cognitive process, associated with the serotonergic system and the default mode network (DMN). Decreased levels of serotonin and reduced activations of the DMN observed in amyotrophic lateral sclerosis (ALS) suggest that self-referential processing might be altered in patients with ALS. Here, we investigate the effects of ALS on the electroencephalography correlates of self-referential thinking. We find that electroencephalography (EEG) correlates of self-referential thinking are present in healthy individuals, but not in those with ALS. In particular, thinking about themselves or others significantly modulates the bandpower in the medial prefrontal cortex in healthy individuals, but not in ALS patients. This finding supports the view of ALS as a complex multisystem disorder which, as shown here, includes dysfunctional processing of the medial prefrontal cortex. It points towards possible alterations of self-consciousness in ALS patients, which might have important consequences for patients’ self-conceptions, personal relations, and decision-making.

A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

Brain-computer interfaces (BCIs) are often based on the control of sensorimotor processes, yet sensorimotor processes are impaired in patients suffering from amyotrophic lateral sclerosis (ALS). We devised a new paradigm that targets higher-level cognitive processes to transmit information from the user to the BCI. We instructed five ALS patients and twelve healthy subjects to either activate self-referential memories or to focus on a process without mnemonic content while recording a high-density electroencephalogram (EEG). Both tasks are designed to modulate activity in the default mode network (DMN) without involving sensorimotor pathways. We find that the two tasks can be distinguished after only one experimental session from the average of the combined bandpower modulations in the theta- (4-7Hz) and alpha-range (8-13Hz), with an average accuracy of 62.5% and 60.8% for healthy subjects and ALS patients, respectively. The spatial weights of the decoding algorithm show a preference for the parietal area, consistent with modulation of neural activity in primary nodes of the DMN.

Towards cognitive brain-computer interfaces for patients with amyotrophic lateral sclerosis

Brain-Computer Interfaces (BCIs) often rely on low-level cognitive processes known to be impaired in late stages of amyotrophic lateral sclerosis (ALS). We propose a BCI for ALS patients based on self-regulation of neuronal oscillations in the superior parietal lobule, which is less affected by ALS than motor and sensory cortices. We describe a case of self-regulation of band power in gamma range (55-85 Hz) based on feedback from the parietal cortex by an ALS patient, resulting in a mean offline two-class decoding accuracy of 79.2% across four sessions. Despite a good offline decoding accuracy, a source localisation analysis revealed that gamma-power modulation was not spatially localized, suggesting confounding by non-cortical artifacts. Theta-power in contrast, showed a strong localized response in the precuneus. As such, this may be an alternative possibility of using self-regulation of neuronal oscillations for cognitive BCI.

Unsupervised Clustering of EOG as a Viable Substitute for Optical Eye Tracking

Eye-movements are typically measured with video cameras and image recognition algorithms. Unfortunately, these systems are susceptible to changes in illumination during measurements. Electrooculography (EOG) is another approach for measuring eye-movements that does not suffer from the same weakness. Here, we introduce and compare two methods that allow us to extract the dwells of our participants from EOG signals under presentation conditions that are too difficult for optical eye tracking. The first method is unsupervised and utilizes density-based clustering. The second method combines the optical eye-tracker’s methods to determine fixations and saccades with unsupervised clustering. Our results show that EOG can serve as a sufficiently precise and robust substitute for optical eye tracking, especially in studies with changing lighting conditions. Moreover, EOG can be recorded alongside electroencephalography (EEG) without additional effort.

Case Series: Slowing Alpha Rhythm in Late-Stage ALS Patients

The alpha peak frequency (APF) of the human electroencephalogram (EEG) is a reliable neurophysiological marker for cognitive abilities. In these case series, we document a shift of the APF towards the lower end of the EEG spectrum in two completely locked-in ALS patients. In not completely locked-in ALS patients, the alpha rhythm lies within the common frequency range. We discuss potential implications of this shift for the largely unknown cognitive state of completely locked-in ALS patients.