Low intensity and low frequency ultrasound has been shown to modulate ion channel currents, membrane capacitive currents, and as a result, neuronal activity. Ultrasound has been used as a non-invasive way to modulate neuronal activity in vivo using mice as well as human subjects. Ultrasound with acoustic frequency as low as 0.35 MHz can be focussed on a region as small as 2 mm with reversible effects and no increase in temperature. In this study, two ultrasound transducers with different resonant frequency have been used to excite neuronal cultures. The resulting changes in the network properties such as synchronised network burst frequency, density, clustering and path length have been analysed. The study shows that ultrasound stimulation at acoustic frequency 450 kHz (ISPPA =11.3 mW/cm2) significantly modulates the above mentioned parameters and causes deviations from small world network properties of the control network.

<p>Disertacija se se bavi analizom mogućnosti brze, efikasne<br />i pouzdane klasterizacije masivnog skupa neuralnih<br />snimaka na osnovu probabilističkih parametara procenjenih<br />iz obrazaca generisanja akcionih potencijala, tzv.<br />&quot;spajkova&quot;, na izlazu pojedinih neurona. Neuralna<br />aktivnost se grubo može podeliti na periode intezivne,<br />umerene i niske aktivnosti. Shodno tome, predložena je<br />gruba dekompozicija neuralne aktivnosti na tri moda koja<br />odgovaraju navedenim obrascima neuralne aktivnosti, na<br />osnovu dobro poznatog Gilbert-Eliot modela. Modovi su<br />dodatno ra&scaron;članjeni na sopstvena stanja na osnovu osobina sukcesivnih spajkova, omogućujući finiji, kompozitni<br />opis neuralne aktivnosti. Za svaki neuron empirijski se<br />procenjuju probabilistički parametri grube dekompozicije<br />- na osnovu Gilbert-Eliotovog modela i finije dekompozicije<br />- na osnovu sopstvenih stanja modova, obezbeđujući<br />željeni skup deskriptora. Dobijeni deskriptori<br />koriste se kao obeležja nekoliko algoritama klasterizacije<br />nad simuliranim i eksperimentalnim podacima. Za generisanje<br />simuliranih podataka primenjen je jednostavan<br />model za generisanje akcionih potencijala različitih<br />oscilatornih pona&scaron;anja pobuđujućih i blokirajućih kortikalnih<br />neurona. Validacija primene probabilističkih parametara<br />za klasterizaciju rada neurona izvr&scaron;ena je na<br />osnovu estimacije parametera nad generisanim neuralnim<br />odzivima. Eksperimentalni podaci su dobijeni<br />snimanjem kortikografskih signala iz dorzalnog anteriornog<br />cingularanog korteksa i lateralnog prefrontalnog<br />korteksa korteksa budnih rezus majmuna. U okviru predloženog<br />protokola evaluacije različitih pristupa<br />klasterizacije testirano je nekoliko metoda. Klasterizacija<br />zasnovana na akumulaciji dokaza iz ansambla particija<br />dobijenih k-means klasterovanjem dala je najstabilnije<br />grupisanje neuralnih jedinica uz brzu i efikasnu implementaciju.<br />Predložena empirijska karakterizacija može da<br />posluži za identifikaciju korelacije sa spolja&scaron;njim stimulusima,<br />akcijama i pona&scaron;anjem životinja u okviru<br />eksperimentalne procedure. Prednosti ovog postupka za<br />opis neuralne aktivnosti su brza estimacija i mali skup<br />deskriptora. Računarska efikasnost omogućuje primenu<br />nad obimnim, paralelno snimanim neuralnim podacima u<br />toku snimanja ili u periodima od interesa za identifikaciju<br />aktiviranih i povezanih zona pri određenim aktivnostima.</p> / <p>The advances in extracellular neural recording techniques<br />result in big data volumes that necessitate fast,<br />reliable, and automatic identification of statistically<br />similar units. This study proposes a single framework<br />yielding a compact set of probabilistic descriptors that<br />characterise the firing patterns of a single unit. Probabilistic<br />features are estimated from an inter-spikeinterval<br />time series, without assumptions about the firing distribution or the stationarity. The first level of proposed<br />firing patterns decomposition divides the inter-spike<br />intervals into bursting, moderate and idle firing modes,<br />yielding a coarse feature set. The second level identifies<br />the successive bursting spikes, or the spiking acceleration/<br />deceleration in the moderate firing mode, yielding<br />a refined feature set. The features are estimated from<br />simulated data and from experimental recordings from<br />the lateral prefrontal cortex in awake, behaving rhesus<br />monkeys. An effcient and stable partitioning of neural<br />units is provided by the ensemble evidence accumulation<br />clustering. The possibility of selecting the number of<br />clusters and choosing among coarse and refined feature<br />sets provides an opportunity to explore and compare<br />different data partitions. The estimation of features, if<br />applied to a single unit, can serve as a tool for the firing<br />analysis, observing either overall spiking activity or the<br />periods of interest in trial-to-trial recordings. If applied to<br />massively parallel recordings, it additionally serves as an<br />input to the clustering procedure, with the potential to<br />compare the functional properties of various brain<br />structures and to link the types of neural cells to the<br />particular behavioural states.</p>