Összefoglalás. A hálózatkutatás idegtudományi alkalmazása áttörő eredményt
hozott a humán kogníció és a neurális rendszerek közötti kapcsolat megértésében. Jelen
tanulmány célja a neurális hálózatok néhány kutatási területét mutatja be a laborunkban
végzett vizsgálatok eredményein keresztül. Bemutatjuk az agyi aktivitás mérésének és az
agyi területek közötti kommunikációs hálózatok modellezésének technikáját. Majd kiemelünk
két kutatási terület: 1) az agyi hálózatok életkori változásainak vizsgálatát, ami választ
ad arra, hogy hogyan öregszik az emberi agy; 2) az emberi agyak közötti hálózat
modelljének vizsgálatát, amely a hatékony emberi kommunikáció idegrendszeri mechanizmusait
próbálja feltárni. Tárgyaljuk a humán kommunikációra képes mesterséges intelligencia
fejlesztésének lehetőségét is. Végül kitérünk az agyi hálózatok kutatásának
biztonságpolitikai vonatkozásaira.
Summary. The human brain consists of 100 billion neurons connected by about
100 trillion synapses, which are hierarchically organized in different scales in
anatomical space and time. Thus, it sounds reasonable to assume that the brain is the most
complex network known to man. Network science applications in neuroscience are aimed to
understand how human feeling, thought and behavior could emerge from this biological
system of the brain. The present review focuses on the recent results and the future of
network neuroscience. The following topics will be discussed:
Modeling the network of communication among brain areas. Neural activity
can be recorded with high temporal precision using electroencephalography (EEG).
Communication strength between brain regions then might be estimated by calculating
mathematical synchronization indices between source localized EEG time series. Finally,
graph theoretical models can describe the relationship between system elements (i.e.
efficiency of communication or centrality of an element).
How does the brain age? While for a newborn the high plasticity of the
brain provides the foundation of cognitive development, cognition declines with advanced
age due to so far largely unknown neural mechanisms. In one of our studies, we
demonstrated that there is a correlation between the anatomical development of the brain
(at prenatal age) and its network topology. Specifically, the more developed the baby’s
brain, the more functionally specialized/modular it was. In another study we found that in
older adults, when compared to young adults, connectivity within modules of their brain
network is decreased, with an associated decline in their short-term memory capacity.
Moreover, Mild Cognitive Impairment patients (early stage of Alzheimer) were characterized
with a significantly lower level of connectivity between their brain modules than the
healthy elderly.
Human communication via shared network of brain activity. In another
study we recorded the brain activity of a speaker and multiple listeners. We investigated
the brain network similarity across listeners and between the speaker and listeners. We
found that brain activity was significantly correlated among listeners, providing evidence
for the fact that the same content is processed via similar neural computations within
different brains. The data also suggested that the more the brain activity synchronizes
the more the mental state of the individuals overlap. We also found significantly
synchronized brain activity between speaker and listeners. Specifically 1) listeners’
brain activity within the speech processing cortices was synchronized to speaker’s brain
activity with a time lag, indicating that listeners’ speech comprehension processes
replicated the speaker’s speech production processes; and 2) listeners’ frontal cortical
activity was synchronized to speaker’s later brain activity, that is, listeners preceded
the speaker, indicating that speech content is predicted by the listeners based on the
context.
Future challenges. Future research could target artificial intelligence
development that is capable of human-like communication. To achieve this, the simultaneous
recording of brain activity from listener and speaker is needed together with efficiency
of the communication. These data could be then modelled via AI to detect biomarkers of
communication efficiency. In general, neurotechnology has been rapidly developing within
and outside of research and in clinical fields thus it is time for re-conceptualizing the
corresponding human right law in order to avoid unwanted consequences of technological
applications.
Neurophysiological network dynamics of pitch change detection
