Cognitive neuroscience.

A self-adaptive software is developed to predict the stock market. It’s Stock
Prediction Engine functions autonomously when its skill-set suffices to achieve its goal,
and it includes human-in-the-loop when it recognizes conditions benefiting from more
complex, expert human intervention. Key to the system is a module that decides of
human participation. It works by monitoring three mental states unobtrusively and in real
time with Electroencephalography (EEG). The mental states are drawn from the
Opportunity-Willingness-Capability (OWC) model. This research demonstrates that the
three mental states are predictive of whether the Human Computer Interaction System
functions better autonomously (human with low scores on opportunity and/or
willingness, capability) or with the human-in-the-loop, with willingness carrying the
largest predictive power. This transdisciplinary software engineering research
exemplifies the next step of self-adaptive systems in which human and computer benefit from optimized autonomous and cooperative interactions, and in which neural inputs
allow for unobtrusive pre-interactions.

The endogenous, or voluntary, control of visuospatial attention relies upon
interactions within a frontoparietal dorsal attention network (DAN) and this network’s
top-down influence on visual occipital cortex (VOC). While these interactions have been
shown to occur during attention tasks, they are also known to occur to some extent at rest,
but the degree to which task-related interactions reflect either modulation or
reorganization of such ongoing intrinsic interactions is poorly understood. In addition, it
is known that in spatial neglect—a syndrome following unilateral brain lesions in which
patients fail to attend to the contralesional side of space—symptom severity covaries with
disruptions to intrinsic interhemispheric interactions between left and right homologous
regions of the DAN; however, similar covariance with disruptions to intrahemispheric
interactions within the DAN, and between the DAN and VOC, has not been demonstrated.
These issues are addressed herein via the measurement of both undirected and directed
functional connectivity (UFC, DFC) within the DAN and between the DAN and VOC. UFC and DFC were derived from correlations of, and multivariate vector autoregressive
modeling of, fMRI BOLD time-series, respectively. Time-series were recorded from
individuals performing an anticipatory visuospatial attention task and individuals at rest,
as well as from stroke patients either with or without neglect and age-matched healthy
controls. With regard to the first issue, the results show that relative to rest, top-down
DAN-to-VOC influence and within-DAN coupling are elevated during task performance,
but also that intrinsic connectivity patterns are largely preserved during the task. With
regard to the second issue, results show that interhemispheric imbalances of
intrahemispheric UFC and DFC both within the DAN and between the DAN and VOC
strongly correlate with neglect severity, and may co-occur with functional decoupling of
the hemispheres. This work thus demonstrates that the intrinsic functional integrity of the
DAN and its relationship to VOC is crucial for the endogenous control of visuospatial
attention during tasks, and that the compromise of this integrity due to stroke likely plays
a role in producing spatial neglect.

Working memory (WM) is a process that allows for the temporary and limited storage of information for an immediate goal or to be stored into a more permanent system. A large number of studies
have led to the widely accepted view that WM is mediated by the frontoparietal network (FPN), consisting
of areas in the prefrontal cortex (PFC) and posterior parietal cortex (PPC). Current evidence suggests that
task specific patterns of neuronal oscillatory activity within the FPN play a fundamental role in WM, and
yet specific spatio-temporal properties of this activity are not well characterized. This study utilized multisite
local field potential (LFP) data recorded from PFC and PPC sites in two macaque monkeys trained to
perform a rule-based, Oculomotor Delayed Match-to-Sample task. The animals were required to learn
which of two rules determined the correct match (Location matching or Identity matching). Following a
500 ms fixation period, a sample stimulus was presented for 500 ms, followed by a randomized delay
lasting 800-1200 ms in which no stimulus was present. At the end of the delay period, a match stimulus
was presented, consisting of two of three possible objects presented at two of three possible locations.
When the match stimulus appeared, the monkey made a saccadic eye movement to the target. The rule in
effect determined which object served as the target. Time-frequency plots of three spectral measures
(power, coherence, and Wiener Granger Causality (WGC) were computed from MultiVariate
AutoRegressive LFP time-series models estimated in a 100-ms window that was slid across each of three
analysis epochs (fixation, sample, and delay). Low (25- 55 Hz) and high gamma (65- 100 Hz) activity were investigated separately due to evidence that they may be functionally distinct. Within each epoch, recording sites in the PPC and PFC were classified into groups according to the similarity of their power t-f plots derived by a K-means clustering algorithm. From the power-based site groups, the corresponding coherence and WGC were analyzed. This classification procedure uncovered spatial, temporal, and frequency dynamics of FPN
involvement in WM and other co-occurring processes, such as sensory and target related processes. These processes were distinguishable by rule and performance accuracy across all three spectral measures- power,
coherence, and WGC. Location and Identity rule were distinguishable by the low and high-gamma range.