Improving memory performance using a wearable BCI

Human ability to encode and memorize information fluctuates from moment to moment. Several studies have reported differences in electroencephalography (EEG) signals recorded during memorization of items that were forgotten at a later point of time compared to those that were remembered. Given these observations the question then arises whether or not a wearable BCI system can be designed to identify poorly encoded items. Such a device could be used to provide feedback to the user so as to improve the memory encoding process. This paper reports on an experimental study designed to assess this possibility

A role for endogenous brain states in organizational research:moving toward a dynamic view of cognitive processes

The dominant view in neuroscience, including functional neuroimaging, is that the brain is an essentially reactive system, in which some sensory input causes some neural activity, which in turn results in some important response such as a motor activity or some hypothesized higher-level cognitive or affective process. This view has driven the rise of neuroscience methods in management and organizational research. However, the reactive view offers at best a partial understanding of how living organisms function in the real world. In fact, like any neural system, the human brain exhibits a constant ongoing activity. This intrinsic brain activity is produced internally, not in response to some environmental stimulus, and is thus termed endogenous brain activity (EBA). In the present article we introduce EBA to organizational research conceptually, explain its measurement, and go on to show that including EBA in management and organizational theory and empirical research has the potential to revolutionize how we think about human choice and behavior in organizations

Modulating episodic memory formation using non-invasive brain stimulation

Oscillatory activity in the beta frequency range accompanies the formation of long-term memories. Beta power decreases have frequently been shown to correlate with memory formation. However, the causal relationship between beta desynchronization and episodic memory encoding remains unclear. This thesis investigates the causal role beta oscillations play in memory formation and explores ways in which non-invasive brain stimulation can be used to test these causal mechanisms. More specifically, this thesis investigates whether increasing beta power impairs memory formation and whether decreasing beta power improves memory. We used two different non-invasive brain stimulation techniques: tACS was used to increase beta power and impair memory formation, while rTMS was used as a means of decreasing beta power and enhancing memory performance. Chapters 2 and 3 indicate that transient beta tACS does not modulate beta oscillations and does not impair memory formation, while slow rTMS effectively enhanced memory formation by modulating beta power in remote areas, in Chapter 4. This thesis emphasises that negative results are not only important, but necessary to advance our understanding of how non-invasive brain stimulation can help us unravel the causal role that beta oscillatory activity plays in the formation of episodic memories

Investigating the cortico-hippocampal dynamics involved in human episodic memory with neural stimulation

The human episodic memory system depends on specific interactions between the hippocampus and neocortex. The three studies performed as part of this doctoral thesis each sought to improve our understanding of the cortico-hippocampal system in the context of episodic memory. Each study used a different approach to directly manipulate neural activity with the aim of revealing causal relationships between certain patterns of neural activity and behaviour. In the first study the cortico-hippocampal network was investigated by using occipital transcranial alternating current stimulation (tACS) and auditory sensory stimulation with the aim of altering memory performance during an audio-visual association task. The electrical stimulation was hypothesized to interact with the auditory sensory stimulation after propagating from the neocortex to the hippocampus. This study was unsuccessful in modulating behaviour through stimulation. In the second study, the left Dorsolateral Prefrontal Cortex (DLPFC) was targeted using 1 Hz repetitive transcranial magnetic stimulation (rTMS) over the course of two experiments, during a set of list learning tasks. This study found a beneficial effect on memory performance when stimulation occurred over the left DLPFC compared to stimulation over the vertex (control site). This behavioural effect was further characterized by a beta-power decrease over parietal sensors as measured by electroencephalography (EEG). The third study probed the cortico-hippocampal network by directly stimulating the hippocampus and the neocortex, by applying direct electrical stimulation through implanted electrodes in human subjects. This study used measures of population activity as well as single neuron activity to monitor how the brain responds to direct stimulation. This study found that direct stimulation throughout the network produces a neural response that is characterized by short, intense excitation and prolonged follow-up inhibition which has the potential to travel throughout the brain. The ability of the response to travel between the neocortex and hippocampus was leveraged to measure a transduction delay of ~140 ms between the two regions. Together these findings have advanced our understanding on how different stimulation methods can be used to manipulate neural activity and consequently affect the episodic memory system. Through these methods we might one day be able to aid persons suffering from cognitive impairments or related pathologies

Investigation of the multiple-demand network at multiple spatial scales

This dissertation investigates the frontoparietal ‘multiple-demand’ (MD) network that is involved in the processing of diverse cognitive demands. This network is active when the task at hand is made more demanding, in a variety of different tasks including working memory, task switching, inhibition, math, language etc. While the different MD regions have partly different functions, they are highly interconnected allowing them to function together as a network. The experiment in Chapter 2 looked at the interplay between functional differences as well as co-recruitment within this multiple-demand network. Quantitative differences between regions were more prominent in simple tasks. A strong co-recruitment was seen with increased challenge or incentive. In Chapter 3, task preferences were studied at the voxel level. MD regions were equally well localised in single-subjects using any of three task demands. Voxels localised by all three tasks also captured the underlying neural representations to a similar level in a separate criterion task. Chapter 4 investigated if task representations, as measured by multi-voxel patterns, were modified due to external motivation. The effect was limited to the cue phase and did not extend to the stimulus processing phase where the stimulus is integrated with the cue to arrive at the response. Chapter 5 examined neural representations in frontal and parietal regions more directly through single unit activity and local field potentials (LFPs), during a spatial working memory task. While single neurons showed dynamic coding of target information rather than persistent coding, LFPs held this information constant through time. The impact of reference voltages on LFP data was further investigated. Together, these results explore the functional differences between and within the MD regions, and provide evidence for flexible task representations at the voxel and neuronal level.Funded by Gates Cambridg

When the Whole Is Less Than the Sum of Its Parts: Maximum Object Category Information and Behavioral Prediction in Multiscale Activation Patterns.

Neural codes are reflected in complex neural activation patterns. Conventional electroencephalography (EEG) decoding analyses summarize activations by averaging/down-sampling signals within the analysis window. This diminishes informative fine-grained patterns. While previous studies have proposed distinct statistical features capable of capturing variability-dependent neural codes, it has been suggested that the brain could use a combination of encoding protocols not reflected in any one mathematical feature alone. To check, we combined 30 features using state-of-the-art supervised and unsupervised feature selection procedures (n = 17). Across three datasets, we compared decoding of visual object category between these 17 sets of combined features, and between combined and individual features. Object category could be robustly decoded using the combined features from all of the 17 algorithms. However, the combination of features, which were equalized in dimension to the individual features, were outperformed across most of the time points by the multiscale feature of Wavelet coefficients. Moreover, the Wavelet coefficients also explained the behavioral performance more accurately than the combined features. These results suggest that a single but multiscale encoding protocol may capture the EEG neural codes better than any combination of protocols. Our findings put new constraints on the models of neural information encoding in EEG