RESUMO
BACKGROUND: Spectral features of human electroencephalographic (EEG) recordings during learning predict subsequent recall variability. METHODS: Capitalizing on these fluctuating neural features, we develop a non-invasive closed-loop (NICL) system for real-time optimization of human learning. Participants play a virtual navigation and memory game; recording multi-session data across days allowed us to build participant-specific classification models of recall success. In subsequent closed-loop sessions, our platform manipulated the timing of memory encoding, selectively presenting items during periods of predicted good or poor memory function based on EEG features decoded in real time. RESULTS: We observed greater memory modulation (difference between recall rates when presenting items during predicted good vs. poor learning periods) for participants with higher out-of-sample classification accuracy. COMPARISON WITH EXISTING METHODS: This study demonstrates greater-than-chance memory decoding from EEG recordings in a naturalistic virtual navigation task with greater real-world validity than basic word-list recall paradigms. Here we modulate memory by timing stimulus presentation based on noninvasive scalp EEG recordings, whereas prior closed-loop studies for memory improvement involved intracranial recordings and direct electrical stimulation. Other noninvasive studies have investigated the use of neurofeedback or remedial study for memory improvement. CONCLUSION: These findings present a proof-of-concept for using non-invasive closed-loop technology to optimize human learning and memory through principled stimulus timing, but only in those participants for whom classifiers reliably predict out-of-sample memory function.
RESUMO
The default mode network (DMN) has been identified reliably during rest, as well as during the performance of tasks such as episodic retrieval and future imagining. It remains unclear why this network is engaged across these seemingly distinct conditions, though many hypotheses have been proposed to account for these effects. Prior to generating hypotheses explaining common DMN involvement, the degree of commonality in the DMN across these conditions, within individuals, must be statistically determined to test whether or not the DMN is truly a unitary network, equally engaged across rest, retrieval and future imagining. To provide such a test, we used comparable paradigms (self-directed, uninterrupted thought of equal duration) across the three conditions (rest, retrieval, and future imagining) in a within-participant design. We found lower than expected pattern similarity in DMN functional connectivity across the three conditions. Similarity in connectivity accounted for only 40-50% of the total variance. Partial Least Squares (PLS) analyses revealed the medial temporal regions of the DMN were preferentially coupled with one another during episodic retrieval and future imagining, whereas the non-medial temporal regions of the DMN (e.g., medial prefrontal cortex, lateral temporal cortex, and temporal pole) were preferentially coupled during rest. These results suggest that DMN connectivity may be more flexible than previously considered. Our findings are in line with emerging evidence that the DMN is not a static network engaged commonly across distinct cognitive processes, but is instead a dynamic system, topographically changing in relation to ongoing cognitive demands. Hum Brain Mapp 38:1155-1171, 2017. © 2016 Wiley Periodicals, Inc.
