Human Verbal Memory Encoding Is Hierarchically Distributed in a Continuous Processing Stream.

Processing of memory is supported by coordinated activity in a network of sensory, association, and motor brain regions. It remains a major challenge to determine where memory is encoded for later retrieval. Here, we used direct intracranial brain recordings from epilepsy patients performing free recall tasks to determine the temporal pattern and anatomical distribution of verbal memory encoding across the entire human cortex. High γ frequency activity (65-115 Hz) showed consistent power responses during encoding of subsequently recalled and forgotten words on a subset of electrodes localized in 16 distinct cortical areas activated in the tasks. More of the high γ power during word encoding, and less power before and after the word presentation, was characteristic of successful recall and observed across multiple brain regions. Latencies of the induced power changes and this subsequent memory effect (SME) between the recalled and forgotten words followed an anatomical sequence from visual to prefrontal cortical areas. Finally, the magnitude of the memory effect was unexpectedly found to be the largest in selected brain regions both at the top and at the bottom of the processing stream. These included the language processing areas of the prefrontal cortex and the early visual areas at the junction of the occipital and temporal lobes. Our results provide evidence for distributed encoding of verbal memory organized along a hierarchical posterior-to-anterior processing stream

Closed-loop Stimulation of Temporal Cortex Rescues Functional Networks and Improves Memory

Memory failures are frustrating and often the result of ineffective encoding. One approach to improving memory outcomes is through direct modulation of brain activity with electrical stimulation. Previous efforts, however, have reported inconsistent effects when using open-loop stimulation and often target the hippocampus and medial temporal lobes. Here we use a closed-loop system to monitor and decode neural activity from direct brain recordings in humans. We apply targeted stimulation to lateral temporal cortex and report that this stimulation rescues periods of poor memory encoding. This system also improves later recall, revealing that the lateral temporal cortex is a reliable target for memory enhancement. Taken together, our results suggest that such systems may provide a therapeutic approach for treating memory dysfunction

Lateralized hippocampal oscillations underlie distinct aspects of human spatial memory and navigation

The hippocampus plays a vital role in various aspects of cognition including both memory and spatial navigation. To understand electrophysiologically how the hippocampus supports these processes, we recorded intracranial electroencephalographic activity from 46 neurosurgical patients as they performed a spatial memory task. We measure signals from multiple brain regions, including both left and right hippocampi, and we use spectral analysis to identify oscillatory patterns related to memory encoding and navigation. We show that in the left but not right hippocampus, the amplitude of oscillations in the 1–3-Hz “low theta” band increases when viewing subsequently remembered object–location pairs. In contrast, in the right but not left hippocampus, low-theta activity increases during periods of navigation. The frequencies of these hippocampal signals are slower than task-related signals in the neocortex. These results suggest that the human brain includes multiple lateralized oscillatory networks that support different aspects of cognition

Functionally distinct high and low theta oscillations in the human hippocampus.

Based on rodent models, researchers have theorized that the hippocampus supports episodic memory and navigation via the theta oscillation, a ~4-10 Hz rhythm that coordinates brain-wide neural activity. However, recordings from humans have indicated that hippocampal theta oscillations are lower in frequency and less prevalent than in rodents, suggesting interspecies differences in theta\u27s function. To characterize human hippocampal theta, we examine the properties of theta oscillations throughout the anterior-posterior length of the hippocampus as neurosurgical subjects performed a virtual spatial navigation task. During virtual movement, we observe hippocampal oscillations at multiple frequencies from 2 to 14 Hz. The posterior hippocampus prominently displays oscillations at ~8-Hz and the precise frequency of these oscillations correlates with the speed of movement, implicating these signals in spatial navigation. We also observe slower ~3 Hz oscillations, but these signals are more prevalent in the anterior hippocampus and their frequency does not vary with movement speed. Our results converge with recent findings to suggest an updated view of human hippocampal electrophysiology. Rather than one hippocampal theta oscillation with a single general role, high- and low-frequency theta oscillations, respectively, may reflect spatial and non-spatial cognitive processes

Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect

Recent evidence suggests that emotions have a distributed neural representation, which has significant implications for our understanding of the mechanisms underlying emotion regulation and dysregulation as well as the potential targets available for neuromodulation-based emotion therapeutics. This work adds to this evidence by testing the distribution of neural representations underlying the affective dimensions of valence and arousal using representational models that vary in both the degree and the nature of their distribution. We used multi-voxel pattern classification (MVPC) to identify whole-brain patterns of functional magnetic resonance imaging (fMRI)-derived neural activations that reliably predicted dimensional properties of affect (valence and arousal) for visual stimuli viewed by a normative sample (n = 32) of demographically diverse, healthy adults. Inter-subject leave-one-out cross-validation showed whole-brain MVPC significantly predicted (p < 0.001) binarized normative ratings of valence (positive vs. negative, 59% accuracy) and arousal (high vs. low, 56% accuracy). We also conducted group-level univariate general linear modeling (GLM) analyses to identify brain regions whose response significantly differed for the contrasts of positive versus negative valence or high versus low arousal. Multivoxel pattern classifiers using voxels drawn from all identified regions of interest (all-ROIs) exhibited mixed performance; arousal was predicted significantly better than chance but worse than the whole-brain classifier, whereas valence was not predicted significantly better than chance. Multivoxel classifiers derived using individual ROIs generally performed no better than chance. Although performance of the all-ROI classifier improved with larger ROIs (generated by relaxing the clustering threshold), performance was still poorer than the whole-brain classifier. These findings support a highly distributed model of neural processing for the affective dimensions of valence and arousal. Finally, joint error analyses of the MVPC hyperplanes encoding valence and arousal identified regions within the dimensional affect space where multivoxel classifiers exhibited the greatest difficulty encoding brain states – specifically, stimuli of moderate arousal and high or low valence. In conclusion, we highlight new directions for characterizing affective processing for mechanistic and therapeutic applications in affective neuroscience

Wireless Programmable Recording and Stimulation of Deep Brain Activity in Freely Moving Humans.

Uncovering the neural mechanisms underlying human natural ambulatory behavior is a major challenge for neuroscience. Current commercially available implantable devices that allow for recording and stimulation of deep brain activity in humans can provide invaluable intrinsic brain signals but are not inherently designed for research and thus lack flexible control and integration with wearable sensors. We developed a mobile deep brain recording and stimulation (Mo-DBRS) platform that enables wireless and programmable intracranial electroencephalographic recording and electrical stimulation integrated and synchronized with virtual reality/augmented reality (VR/AR) and wearables capable of external measurements (e.g., motion capture, heart rate, skin conductance, respiration, eye tracking, and scalp EEG). When used in freely moving humans with implanted neural devices, this platform is adaptable to ecologically valid environments conducive to elucidating the neural mechanisms underlying naturalistic behaviors and to the development of viable therapies for neurologic and psychiatric disorders

A wearable platform for closed-loop stimulation and recording of single-neuron and local field potential activity in freely moving humans.

Advances in technologies that can record and stimulate deep brain activity in humans have led to impactful discoveries within the field of neuroscience and contributed to the development of novel therapies for neurological and psychiatric disorders. Further progress, however, has been hindered by device limitations in that recording of single-neuron activity during freely moving behaviors in humans has not been possible. Additionally, implantable neurostimulation devices, currently approved for human use, have limited stimulation programmability and restricted full-duplex bidirectional capability. In this study, we developed a wearable bidirectional closed-loop neuromodulation system (Neuro-stack) and used it to record single-neuron and local field potential activity during stationary and ambulatory behavior in humans. Together with a highly flexible and customizable stimulation capability, the Neuro-stack provides an opportunity to investigate the neurophysiological basis of disease, develop improved responsive neuromodulation therapies, explore brain function during naturalistic behaviors in humans and, consequently, bridge decades of neuroscientific findings across species