Critical aspects of motor learning and memory happen offline, during both wake and sleep. When healthy young people learn a motor sequence task, most of their performance improvement happens not while typing, but offline, during interleaved rest breaks. In contrast, the performance of patients with dense amnesia due to hippocampal damage actually gets worse over the rest breaks and improves while typing. These findings indicate that an intact hippocampus is necessary for offline motor learning during wake, but do not specify its mechanism. Here, we studied epilepsy patients (n = 17) undergoing direct intracranial electroencephalographic monitoring of the hippocampus as they learned the same motor sequence task. Like healthy young people, they show greater speed gains across rest breaks than while typing. They also show higher hippocampal ripple rates during these rest breaks that predict offline gains in speed. This suggests that motor learning during brief rest breaks during wake is mediated by hippocampal ripples. These results expand our understanding of the role of hippocampal ripples beyond declarative memory to include enhancing motor procedural memory.

In severe epileptic encephalopathies, epileptic activity contributes to progressive cognitive dysfunction. Epileptic encephalopathies share the trait of spike-wave activation during non-REM sleep (EE-SWAS), a sleep stage dominated by sleep spindles, which are brain oscillations known to coordinate offline memory consolidation. Epileptic activity has been proposed to hijack the circuits driving these thalamocortical oscillations, thereby contributing to cognitive impairment. Using a unique dataset of simultaneous human thalamic and cortical recordings in subjects with and without EE-SWAS, we provide evidence for epileptic spike interference of thalamic sleep spindle production in patients with EE-SWAS. First, we show that epileptic spikes and sleep spindles are both predicted by slow oscillations during stage two sleep (N2), but at different phases of the slow oscillation. Next, we demonstrate that sleep-activated cortical epileptic spikes propagate to the thalamus (thalamic spike rate increases after a cortical spike, P approximate to 0). We then show that epileptic spikes in the thalamus increase the thalamic spindle refractory period (P approximate to 0). Finally, we show that in three patients with EE-SWAS, there is a downregulation of sleep spindles for 30 s after each thalamic spike (P < 0.01). These direct human thalamocortical observations support a proposed mechanism for epileptiform activity to impact cognitive function, wherein epileptic spikes inhibit thalamic sleep spindles in epileptic encephalopathy with spike and wave activation during sleep.

Research on the role of the hippocampus in memory acquisition has generally focused on active learning. But to understand memory, it is at least as important to understand processes that happen offline, during both wake and sleep. In a study of patients with amnesia, we previously demonstrated that although a functional hippocampus is not necessary for the acquisition of procedural motor memory during training session, it is required for its offline consolidation during sleep. Here, we investigated whether an intact hippocampus is also required for the offline consolidation of procedural motor memory while awake. Patients with amnesia due to hippocampal damage (n = 4, all male) and demographically matched controls (n = 10, 8 males) trained on the finger tapping motor sequence task. Learning was measured as gains in typing speed and was divided into online (during task execution) and offline (during interleaved 30 s breaks) components. Amnesic patients and controls showed comparable total learning, but differed in the pattern of performance improvement. Unlike younger adults, who gain speed across breaks, both groups gained speed only while typing. Only controls retained these gains over the breaks; amnesic patients slowed down and compensated for these losses during subsequent typing. In summary, unlike their peers, whose motor performance remained stable across brief breaks in typing, amnesic patients showed evidence of impaired access to motor procedural memory. We conclude that in addition to being necessary for the offline consolidation of motor memories during sleep, the hippocampus maintains access to motor memory across brief offline periods during wake.

Study Objectives:Sleep spindles, defining electroencephalographic oscillations of nonrapid eye movement (NREM) stage 2 sleep (N2), mediate sleep-dependent memory consolidation (SDMC). Spindles are also thought to protect sleep continuity by suppressing thalamocortical sensory relay. Schizophrenia is characterized by spindle deficits and a correlated reduction of SDMC. We investigated whether this relationship is mediated by sleep fragmentation. Methods:We detected spindles (12-15 Hz) during N2 at central electrodes in overnight polysomnography records from 56 participants with chronic schizophrenia and 59 healthy controls. Our primary measures of sleep continuity were the sleep fragmentation index and, in a subset of the data, visually scored arousals. SDMC was measured as overnight improvement on the finger-tapping motor sequence task. Results:Participants with schizophrenia showed reductions of both spindle density (#/min) and SDMC in the context of normal sleep continuity and architecture. Spindle density predicted SDMC in both groups. In contrast, neither increased sleep fragmentation nor arousals predicted lower spindle density or worse SDMC in either group. Conclusions:Our findings fail to support the hypothesis that sleep fragmentation accounts for spindle deficits, impaired SDMC, or their relationship in individuals with chronic schizophrenia. Instead, our findings are consistent with the hypothesis that spindle deficits directly impair memory consolidation in schizophrenia. Since sleep continuity and architecture are intact in this population, research aimed at developing interventions should instead focus on understanding dysfunction within the thalamocortical-hippocampal circuitry that both generates spindles and synchronizes them with other NREM oscillations to mediate SDMC.

In severe epileptic encephalopathies, epileptic activity contributes to progressive cognitive dysfunction. Several epileptic encephalopathies share the trait of spike-wave activation during non-rapid eye movement sleep (EE-SWAS), a state dominated by sleep oscillations known to coordinate offline memory consolidation. How epileptic activity impacts these thalamocortical sleep oscillations has not been directly observed in humans. Using a unique dataset of simultaneous human thalamic and cortical recordings in subjects with and without EE-SWAS, we reconcile prior conflicting observations about how epileptic spikes coordinate with sleep oscillations and provide direct evidence for epileptic spike interference of sleep spindle production. We find that slow oscillations facilitate both epileptic spikes and sleep spindles during stage 2 sleep (N2) at different phases of the slow oscillation. We show that sleep activated cortical epileptic spikes propagate to the thalamus (thalamic spike rate is increased after a cortical spike, p∼0). Thalamic spikes increase the spindle refractory period (p<1.5e-21). In patients with EE-SWAS, the abundance of thalamic spikes result in downregulation of spindles for 30 seconds after each thalamic spike (p=3.4e-11) and decreased overall spindle rate across N2 (p=2e-7). These direct human thalamocortical observations identify a novel mechanism through which epileptiform spikes could impact cognitive function, wherein sleep-activated epileptic spikes inhibit thalamic sleep spindles in epileptic encephalopathy.

STUDY OBJECTIVES:Healthy aging and many disorders show reduced sleep-dependent memory consolidation and corresponding alterations in non-rapid eye movement sleep oscillations. Yet sleep physiology remains a relatively neglected target for improving memory. We evaluated the effects of closed-loop auditory stimulation during sleep (CLASS) on slow oscillations (SOs), sleep spindles, and their coupling, all in relation to motor procedural memory consolidation.METHODS:Twenty healthy young adults had two afternoon naps: one with auditory stimulation during SO upstates and another with no stimulation. Twelve returned for a third nap with stimulation at variable times in relation to SO upstates. In all sessions, participants trained on the motor sequence task prior to napping and were tested afterward.RESULTS:Relative to epochs with no stimulation, upstate stimuli disrupted sleep and evoked SOs, spindles, and SO-coupled spindles. Stimuli that successfully evoked oscillations were delivered closer to the peak of the SO upstate and when spindle power was lower than stimuli that failed to evoke oscillations. Across conditions, participants showed similar significant post-nap performance improvement that correlated with the density of SO-coupled spindles.CONCLUSIONS:Despite its strong effects on sleep physiology, CLASS failed to enhance motor procedural memory. Our findings suggest methods to overcome this failure, including better sound calibration to preserve sleep continuity and the use of real-time predictive algorithms to more precisely target SO upstates and to avoid disrupting endogenous SO-coupled spindles and their mnemonic function. They motivate continued development of CLASS as an intervention to manipulate sleep oscillatory dynamics and improve memory.

Sleep spindles, defining oscillations of stage II non-rapid eye movement sleep (N2), mediate sleep-dependent memory consolidation. Spindles are disrupted in several neurodevelopmental, neuropsychiatric, and neurodegenerative disorders characterized by cognitive impairment. Increasing spindles can improve memory suggesting spindles as a promising physiological target for the development of cognitive enhancing therapies. This effort would benefit from more comprehensive and spatially precise methods to characterize spindles. Spindles, as detected with electroencephalography (EEG), are often widespread across electrodes. Available evidence, however, suggests that they act locally to enhance cortical plasticity in the service of memory consolidation. Here, we present a novel method to enhance the spatial specificity of cortical source estimates of spindles using combined EEG and magnetoencephalography (MEG) data constrained to the cortex based on structural MRI. To illustrate this method, we used simultaneous EEG and MEG recordings from 25 healthy adults during a daytime nap. We first validated source space spindle detection using only EEG data by demonstrating strong temporal correspondence with sensor space EEG spindle detection (gold standard). We then demonstrated that spindle source estimates using EEG alone, MEG alone and combined EEG/MEG are stable across nap sessions. EEG detected more source space spindles than MEG and each modality detected non-overlapping spindles that had distinct cortical source distributions. Source space EEG was more sensitive to spindles in medial frontal and lateral prefrontal cortex, while MEG was more sensitive to spindles in somatosensory and motor cortices. By combining EEG and MEG data this method leverages the differential spatial sensitivities of the two modalities to obtain a more comprehensive and spatially specific source estimation of spindles than possible with either modality alone.

Abstract Introduction Converging evidence supports the hypothesis that reduced sleep spindles and spindle-slow oscillation (SO) coordination contribute to cognitive deficits in schizophrenia. Closed-loop auditory stimulation in healthy adults increases sleep spindles and improves declarative memory consolidation. Here we investigated whether closed-loop auditory stimulation also improves sleep-dependent procedural memory consolidation as a first step towards an intervention in schizophrenia. Methods Thirteen healthy adults participated in two nap sessions (stimulation or detection only) with polysomnography in a counterbalanced order. Participants were trained on the finger tapping Motor Sequence Task (MST), which measures sleep-dependent motor procedural memory consolidation, prior to napping and were tested after awakening. We detected the negative peak of SOs during non-REM sleep and, in the stimulation condition, delivered 50ms of pink noise during the SO up-state. Results Auditory stimulation increased SOs and spindles during the SO up-state in a frontocentral cluster of electrodes 800-1200ms after stimulation compared to detection only (p<0.05). Stimulation also showed promise for improving memory consolidation (33% increase in MST overnap improvement from detection-only) but this did not reach significance in this small sample and data collection is ongoing. Conclusion Auditory stimulation evoked coordinated spindle-SO events that mediate memory consolidation, but more subjects are needed to evaluate whether it also improves memory. If it does, we will test the effects of stimulation on sleep-dependent memory deficits in patients with schizophrenia. Closed-loop auditory stimulation shows promise as a safe, scalable intervention for cognitive deficits that can be implemented at home with commercially available devices. Support R01 MH67720 (DSM & RS), NIH-NHLBI 5T32HL007901-17 (BB), K24MH099421 (DSM), and Simons Foundation (DSM).

Brain-computer interface (BCI) technologies aim to provide a bridge between the human brain and external devices. Prior research using non-invasive BCI to control virtual objects, such as computer cursors and virtual helicopters, and real-world objects, such as wheelchairs and quadcopters, has demonstrated the promise of BCI technologies. However, controlling a robotic arm to complete reach-and-grasp tasks efficiently using non-invasive BCI has yet to be shown. In this study, we found that a group of 13 human subjects could willingly modulate brain activity to control a robotic arm with high accuracy for performing tasks requiring multiple degrees of freedom by combination of two sequential low dimensional controls. Subjects were able to effectively control reaching of the robotic arm through modulation of their brain rhythms within the span of only a few training sessions and maintained the ability to control the robotic arm over multiple months. Our results demonstrate the viability of human operation of prosthetic limbs using non-invasive BCI technology.

Although schizophrenia is defined by waking phenomena, a growing literature documents a deficit in sleep spindles, a defining oscillation of stage 2 non-rapid eye movement sleep. Compelling evidence supports an important role for spindles in cognition, and particularly memory. In schizophrenia, although the spindle deficit correlates with impaired sleep-dependent memory consolidation, recent clinical trials find that increasing spindles does not improve memory. This may reflect that sleep-dependent memory consolidation relies not on spindles alone, but also on their precise temporal coordination with cortical slow oscillations and hippocampal sharp-wave ripples. Consequently, interventions to improve memory in schizophrenia must not only increase spindles, but also preserve or enhance slow oscillations, hippocampal ripples and their temporal relations. Because hippocampal ripples and the activity of the thalamic spindle generator are difficult to measure noninvasively, screening potential interventions requires complementary animal and human studies. In this review we (i) propose that sleep oscillations are novel pathophysiological targets for therapy to improve cognition in schizophrenia; (ii) summarize our understanding of how these oscillations interact to consolidate memory; (iii) suggest that a systems neuroscience strategy is essential to selecting and evaluating effective treatments, and illustrate this with findings from clinical trials; and (iv) selectively review the interventional literature relevant to sleep and cognition, covering both pharmacological and noninvasive brain stimulation approaches. We conclude that coordinated sleep oscillations are promising targets for improving cognition in schizophrenia and that effective therapies will need to preserve or enhance sleep oscillatory dynamics and restore function at the network level.

Brain-computer interfaces (BCIs) using signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems greatly limits their use beyond a few clinical cases. A noninvasive counterpart requiring less intervention that can provide high-quality control would profoundly improve the integration of BCIs into the clinical and home setting. Here, we present and validate a noninvasive framework using electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking. This framework addresses and improves upon both the "brain" and "computer" components by increasing, respectively, user engagement through a continuous pursuit task and associated training paradigm and the spatial resolution of noninvasive neural data through EEG source imaging. In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by more than 500% in the more realistic continuous pursuit task. We further demonstrated an additional enhancement in BCI control of almost 10% by using online noninvasive neuroimaging. Last, this framework was deployed in a physical task, demonstrating a near-seamless transition from the control of an unconstrained virtual cursor to the real-time control of a robotic arm. Such combined advances in the quality of neural decoding and the practical utility of noninvasive robotic arm control will have major implications for the eventual development and implementation of neurorobotics by means of noninvasive BCI.

University of Minnesota Ph.D. dissertation. July 2017. Major: Biomedical Engineering. Advisor: Bin He. 1 computer file (PDF); vii, 123 pages.

Transcranial direct current stimulation (tDCS) has been shown to affect motor and cognitive task performance and learning when applied to brain areas involved in the task. Targeted stimulation has also been found to alter connectivity within the stimulated hemisphere during rest. However, the connectivity effect of the interaction of endogenous task specific activity and targeted stimulation is unclear. This study examined the aftereffects of concurrent anodal high-definition tDCS over the left sensorimotor cortex with motor network connectivity during a one-dimensional EEG based sensorimotor rhythm brain-computer interface (SMR-BCI) task. Directed connectivity following anodal tDCS illustrates altered connections bilaterally between frontal and parietal regions, and these alterations occur in a task specific manner; connections between similar cortical regions are altered differentially during left and right imagination trials. During right-hand imagination following anodal tDCS, there was an increase in outflow from the left premotor cortex (PMC) to multiple regions bilaterally in the motor network and increased inflow to the stimulated sensorimotor cortex from the ipsilateral PMC and contralateral sensorimotor cortex. During left-hand imagination following anodal tDCS, there was increased outflow from the stimulated sensorimotor cortex to regions across the motor network. Significant correlations between connectivity and the behavioral measures of total correct trials and time-to-hit (TTH) correct trials were also found, specifically that the input to the left PMC correlated with decreased right hand imagination performance and that flow from the ipsilateral posterior parietal cortex (PPC) to midline sensorimotor cortex correlated with improved performance for both right and left hand imagination. These results indicate that tDCS interacts with task-specific endogenous activity to alter directed connectivity during SMR-BCI. In order to predict and maximize the targeted effect of tDCS, the interaction of stimulation with the dynamics of endogenous activity needs to be examined comprehensively and understood.

Transcranial direct current stimulation (tDCS), a noninvasive neuromodulation technology, has been found to improve motor learning. Motor-imagery based Brain-Computer Interfaces (BCIs) utilize a subject's imagined movement to control virtual and physical objects. A similar sensorimotor network active during motor movement is active during motor imagery. With this parallel between motor movement and imagery, we hypothesize that anodal tDCS can be used to improve the learning of motor imagery based BCIs. Two sets of experiments were performed: anodal/cathodal/sham stimulation using a 4 × 1 HD-tDCS combination over the left sensorimotor cortex during motor imagery performance without feedback and anodal vs. sham stimulation with feedback during BCI performance. We found a significant interaction effect on electrode C3, left sensorimotor cortex, event-related activity for stimulation type and the time block (prestim, during stim, immediately post-stim, and 30 minutes post-stim) within a single session for the motor imagery experiment. We also found a significant interaction effect of event-related activity between anode and sham stimulation during BCI learning within a single session in electrode C3, but not C4. In addition, over the course of 3 sessions of BCI learning, subjects who underwent anodal stimulation tended to have higher accuracy compared to those who had the sham stimulation (71% vs. 56% valid correct). These results illustrate that acutely, within a single session, the type of tDCS stimulation alters the neural activity underlying motor imagery and may improve motor imagery learning.

Background: Transcranial direct current stimulation (tDCS) has been used to alter the excitability of neurons within the cerebral cortex. Improvements in motor learning have been found in multiple studies when tDCS was applied to the motor cortex before or during task learning. The motor cortex is also active during the performance of motor imagination, a cognitive task during which a person imagines, but does not execute, a movement. Motor imagery can be used with noninvasive brain computer interfaces (BCIs) to control virtual objects in up to three dimensions, but to master control of such devices requires long training times. Objective: To evaluate the effect of high-definition tDCS on the performance and underlying electrophysiology of motor imagery based BCI. Methods: We utilize high-definition tDCS to investigate the effect of stimulation on motor imagery-based BCI performance across and within sessions over multiple training days. Results: We report a decreased time-to-hit with anodal stimulation both within and across sessions. We also found differing electrophysiological changes of the stimulated sensorimotor cortex during online BCI task performance for left vs. right trials. Cathodal stimulation led to a decrease in alpha and beta band power during task performance compared to sham stimulation for right hand imagination trials. Conclusion: These results suggest that unilateral tDCS over the sensorimotor motor cortex differentially affects cortical areas based on task specific neural activation.

GOAL:Sensorimotor-based brain-computer interfaces (BCIs) have achieved successful control of real and virtual devices in up to three dimensions; however, the traditional sensor-based paradigm limits the intuitive use of these systems. Many control signals for state-of-the-art BCIs involve imagining the movement of body parts that have little to do with the output command, revealing a cognitive disconnection between the user's intent and the action of the end effector. Therefore, there is a need to develop techniques that can identify with high spatial resolution the self-modulated neural activity reflective of the actions of a helpful output device.METHODS:We extend previous EEG source imaging (ESI) work to decoding natural hand/wrist manipulations by applying a novel technique to classifying four complex motor imaginations of the right hand: flexion, extension, supination, and pronation.RESULTS:We report an increase of up to 18.6% for individual task classification and 12.7% for overall classification using the proposed ESI approach over the traditional sensor-based method.CONCLUSION:ESI is able to enhance BCI performance of decoding complex right-hand motor imagery tasks.SIGNIFICANCE:This study may lead to the development of BCI systems with naturalistic and intuitive motor imaginations, thus facilitating broad use of noninvasive BCIs.

Brain-computer interfaces (BCIs) have been explored in the field of neuroengineering to investigate how the brain can use these systems to control external devices. We review the principles and approaches we have taken to develop a sensorimotor rhythm EEG based brain-computer interface (BCI). The methods include developing BCI systems incorporating the control of physical devices to increase user engagement, improving BCI systems by inversely mapping scalp-recorded EEG signals to the cortical source domain, integrating BCI with noninvasive neuromodulation strategies to improve learning, and incorporating mind-body awareness training to enhance BCI learning and performance. The challenges and merits of these strategies are discussed, together with recent findings. Our work indicates that the sensorimotor-rhythm-based noninvasive BCI has the potential to provide communication and control capabilities as an alternative to physiological motor pathways.

Current EEG based brain computer interface (BCI) systems have achieved successful control in up to 3 dimensions; however, the current sensor-based paradigm is not well suited for many rehabilitative and recreational applications that require motor imagination (MI) tasks of fine motor movements to be recognized. Therefore there is a great need to find complex MI tasks that are intuitive for BCI users to perform and that can be classified with high accuracy. In this paper we present our results on classifying four MI tasks of the right hand, flexion, extension, supination and pronation using a novel EEG source imaging approach. Using this approach we were able to improve the four-class classification of the four tasks by nearly 10% as compared to traditional sensor-based techniques.