Nanoparticles for use for enhancing brain performances or for treating stress

Abstract

The present invention relates to the medical field, in particular to the enhancement of brain performances and to the treatment of pathological stress. More specifically the present invention relates to a nanoparticle or nanoparticles' aggregate for use in enhancing brain performances or in prevention or treatment of pathological stress in a subject when the nanoparticle and/or nanoparticles' aggregate is exposed to an electric field, wherein the nanoparticle's or nanoparticles' aggregate's material is selected from a conductor material, a semiconductor material, an insulator material with a dielectric constant ε.sub.ijk equal to or above 200, and an insulator material with a dielectric constant ε.sub.ijk equal to or below 100. It further relates to compositions and kits comprising such nanoparticles and/or nanoparticles' aggregates as well as to uses thereof.

Claims

1. A method for enhancing learning, memorizing, sense perception, attention and/or decision making or treating chronic stress in a subject, wherein the method comprises i) administering a nanoparticle or nanoparticle aggregate to the subject, the nanoparticle or nanoparticle aggregate material being selected from a metal having a standard reduction potential E° above 0.2 that is Ir, Pd, Pt, Au, or a mixture thereof, an insulator material with a dielectric constant ε.sub.ijk equal to or above 200, and an insulator material with a dielectric constant ε.sub.ijk equal to or above 100, and wherein the nanoparticle and/or nanoparticle aggregate are coated with a biocompatible agent conferring a neutral surface charge, or with a biocompatible agent conferring a negative surface charge, and ii) exposing the nanoparticle or nanoparticle aggregate to an electric field applied through transcranial electric stimulation or transcranial magnetic stimulation.

2. The method according to claim 1, wherein the material is an insulator material with a band gap Eg equal to or above 3.0 eV and the relative dielectric constant ε.sub.ijk, is measured between 20° C. and 30° C. and between 10.sup.2 Hz up to the infrared frequency.

3. The method according to claim 2, wherein the material is an insulator material with a band gap Eg equal to or above 3.0 eV and the relative dielectric constant ε.sub.ijk is equal to or above 200 and the material of the nanoparticle or nanoparticle aggregate is a dielectric material which is a mixed-metal oxide selected from BaTiO.sub.3, KTaNbO.sub.3, KTaO.sub.3, SrTiO.sub.3 and BaSrTiO.sub.3.

4. The method according to claim 2, wherein the material is an insulator material with a band gap Eg equal to or above 3.0 eV and the relative dielectric constant ε.sub.ijk is equal to or above 100 and the material of the nanoparticle or nanoparticle aggregate is a dielectric material which is selected from a metal oxide, a mixed metal oxide, the metallic element of which is from period 3, 5 or 6 of the Mendeleev periodic table or a lanthanide, and a carbon material.

5. The method according to claim 1, wherein the nanoparticle or nanoparticle aggregate material is an insulator material with a dielectric constant ε.sub.ijk equal to or below 100 and is selected from Al.sub.2O.sub.3, LaAlO.sub.3, La.sub.2O.sub.3, CeO.sub.2, SiO.sub.2, SnO.sub.2, Ta.sub.2O.sub.5, ZrO.sub.2, HfO.sub.2 and Y.sub.2O.sub.3.

6. The method according to claim 5, wherein the nanoparticle or nanoparticle aggregate material is the insulator material with a dielectric constant ε.sub.ijk equal to or below 100 is selected from ZrO.sub.2 and HfO.sub.2.

7. The method according to claim 1, wherein the biocompatible agent conferring a neutral surface charge is a hydrophilic agent displaying a functional group selected from an alcohol (R—OH), an aldehyde (RCOH), a ketone (R—CO—R), an ester (R—COOR), an acid (R—COOH), a thiol (R—SH), a saccharide, glucose, fructose, ribose, an anhydride (RCOOOC—R), and a pyrrole.

8. The method according to claim 7, wherein R is selected from a thiol, a silane, a carboxylic and a phosphate group.

9. The method according to claim 1, wherein the biocompatible agent conferring a neutral surface charge to the nanoparticle or nanoparticle aggregate is hydrophilic agent that is a monomer, a dimer, an oligomer, a polymer or a copolymer.

10. The method according to claim 9, wherein the oligomer is an oligosaccharide.

11. The method according to claim 10, wherein the oligosaccharide is cyclodextrin.

12. The method according to claim 9, wherein the polymer is selected from a polyester, a polyether, a polyethylene oxide, a polyethylene glycol, a polyvinylalcohol, a polycaprolactone, a polyvinylpyrrolidone, a polysaccharide and a polypyrrole.

13. The method according to claim 12, wherein the polyester is a poly(lactic acid) or a polyhydroxyalkanoic acid.

14. The method according to claim 12, wherein the polysaccharide is cellulose.

15. The method according to claim 1, wherein the biocompatible agent conferring a negative surface charge is a phosphate or a sulphate.

16. The method according to claim 15, wherein the biocompatible agent conferring a negative surface charge is selected from a polyphosphate, a metaphosphate and a pyrophosphate.

17. A method for enhancing learning, memorizing, sense perception, attention and/or decision making or treating chronic stress in a subject, wherein the method comprises i) administering a composition to the subject, the composition comprising nanoparticles and/or nanoparticle aggregates and a pharmaceutically acceptable support, and the nanoparticle or nanoparticle aggregate material being selected from a metal having a standard reduction potential E° above 0.2 that is Ir, Pd, Pt, Au, or a mixture thereof, an insulator material with a dielectric constant ε.sub.ijk equal to or above 200, and an insulator material with a dielectric constant ε.sub.ijk equal to or below 100, and wherein the nanoparticle and/or nanoparticle aggregate are coated with a biocompatible agent conferring a neutral surface charge, or with a biocompatible agent conferring a negative surface charge, and ii) exposing the subject to an electric field applied through transcranial electric stimulation or transcranial magnetic stimulation.

18. The method according to claim 17, wherein the composition comprises at least two distinct nanoparticles and/or nanoparticle aggregates.

19. The method according to claim 17, wherein the nanoparticle or nanoparticle aggregate material is an insulator material with a dielectric constant ε.sub.ijk equal to or below 100 and is selected from Al.sub.2O.sub.3, LaAlO.sub.3, La.sub.2O.sub.3, CeO.sub.2, SiO.sub.2, SnO.sub.2, Ta.sub.2O.sub.5, ZrO.sub.2, HfO.sub.2 and Y.sub.2O.sub.3.

20. The method according to claim 19, wherein the nanoparticle or nanoparticle aggregate material is the insulator material with a dielectric constant ε.sub.ijk equal to or below 100 is ZrO.sub.2 or HfO.sub.2.

21. A kit comprising at least two distinct nanoparticles and/or nanoparticle aggregates, each nanoparticle or nanoparticle aggregate comprising a distinct material selected from a metal having a standard reduction potential E° above 0.2 that is Ir, Pd, Pt, Au, or a mixture thereof, an insulator material with a dielectric constant ε.sub.ijk equal to or above 200 and an insulator material with a dielectric constant ε.sub.ijk equal to or below 100, and wherein the nanoparticle and/or nanoparticle aggregate are coated with a biocompatible agent conferring a neutral surface charge, or with a biocompatible agent conferring a negative surface charge.

Description

FIGURES

(1) FIG. 1. Modulation of the cortical excitability by tDCS: a) schema of a pyramidal cortical neuron; b) anodal stimulation; c) cathodal stimulation.

(2) FIG. 2. Different electrodes montages for transcranial Direct Current Stimulation (tDCS).

(3) FIG. 3. Inhibitory and excitatory effects of electric stimulation (tDCS).

(4) FIG. 4. a): local increase of inhibitory effect with nanoparticles “NP1”, b) local increase of excitatory effect with nanoparticles “NP2”, c): local increase of inhibitory effect with nanoparticles “NP1” and local increase of excitatory effect with nanoparticles “NP2”; where NP2 is a conductor or a semi-conductor and NP1 is an insulator.

(5) FIG. 5. Experimental scheme of the cultures of neurons exposed to low frequency stimulation (LFS) at step i) and step ii), with or without an intermediary step i′) of high frequency stimulation (HFS). The mouse frontal cortex cultures were prepared from embryonic day 15/16 NMRI mice and cultured on 48 well MEAs for 26 days (culture period; native phase). The cultures were treated for 2 days with the suspensions of nanoparticles (“Nanoparticles” groups) or with water (“Control” group). After 2 days of incubation, the activity was recorded for 2 hours (term “Pre-Stim” recording). The recording was followed by two distinct steps (steps i) and ii)) or three distinct steps (steps i), i′) and ii)) in the following order: a low frequency stimulation (LFS-1) phase for 30 minutes (step i)), optionally, an intermediary tetanic stimulation (high frequency, HFS) phase for 5 minutes (step i′)), and a low frequency stimulation (LFS-2) phase for 90 minutes (step ii)). After the native phase, two active electrodes were identified per well and selected for stimulation. One of them was stimulated with LFS in steps i) and ii), and both electrodes were stimulated with HFS in step i′) when carried out. Recording was performed during step i) (values were derived from 60 seconds bin data taken from a 30 minutes span) and step ii) (values were derived from 60 seconds bin data taken from a 30 minutes span after 60 minutes of LFS).

(6) FIG. 6. Scheme of two simplified bursts outlining some of the parameters that can be extracted from the electrical activity recording. Parameters describing general activity (spike, burst, inter burst interval (IBI) and burst period) and burst structure (burst duration, burst plateau, burst amplitude, burst inter spike interval (ISI) and burst area) are indicated. Standard deviations (SD) of these parameters are measures for regularity of general activity and burst structure respectively. Coefficient of variation in time (CVtime) reflects the temporal regularity of the activity pattern of each unit. CVtime is calculated by the ratio of parameter's standard deviation and mean. Coefficient of variation among the network (CVnet) reflects synchronization among neurons within the network. CVnet is calculated by the ratio of parameter's standard deviation by mean over the network. Large CVnet values imply a wide range of variation in the activity across the network, meaning less synchronization.

(7) FIG. 7. Functional effects of “Nanoparticles” group (nanoparticles from example 3) when exposed to high frequency stimulation (HFS) compared to “Control” groups (no nanoparticles/with or without high frequency stimulation) on frontal cortex network activity. The results indicate HFS-specific potentiation at the cellular level in presence of nanoparticles when compared to “Control” groups.

(8) FIG. 8. Functional effects of “Nanoparticles” group (nanoparticles from example 1) when exposed to high frequency stimulation (HFS) compared to “Control” groups (with or without high frequency stimulation) on frontal cortex network activity. The results indicate HFS-specific potentiation at the cellular level in presence of nanoparticles when compared to “Control” groups.

EXAMPLES

(9) Simulation

(10) Simulation can be used to assess the effect on neuronal network(s) of nanoparticles exposed to an electrical stimulus (electric field).

(11) In Vitro Studies of Neurons

(12) At the neuron level, Patch clamp technique is very useful for detecting action potentials, as it allows simultaneous direct measurement and control of membrane potential of a neuron.

(13) This technique is used to assess the effects of nanoparticles on a single neuron.

(14) In Vitro Studies of a Network of Neurons

(15) Multi-electrode arrays (MEAs) permit stimulation and recording of a large number of neurons (neuronal network). Dissociated neuronal cultures on MEAs provide a simplified model in which network activity can be manipulated with electrical stimulation sequences through the array's multiple electrodes. This technique is very useful to assess physiologically relevant questions at the network and cellular levels leading to a better understanding of brain function and dysfunction.

(16) Dissociated neuronal cultures coupled to MEAs are indeed widely used to better understand the complexity of brain networks. In addition, the use of dissociated neuronal assemblies allows the manipulation and control of the network's connectivity. The use of dissociated neuronal cultures coupled to MEA allows the design of experiments where neurons can be extracellularly stimulated by mean of electrical pulses delivered through the same electrodes of the device. In this way, it becomes reasonable to investigate how the emerging neuronal dynamics can be modulated by the electrical stimulation, and, consequently, whether the underlying functional connectivity is modified or not (Poli D. et al, Frontiers in Neural Circuits, 2015, 9 (article 57), 1-14: Functional connectivity in in vitro neuronal assemblies).

(17) The MEA system enables non-invasive, long-lasting, simultaneous extracellular recordings from multiple sites in the neuronal network in real time, increasing spatial resolution and thereby providing a robust measure of network activity. The simultaneous gathering of action potential and field potential data over long periods of time allows the monitoring of network functions that arise from the interaction of all cellular mechanisms responsible for spatio-temporal pattern generation (Johnstone A. F. M. et al., Neurotoxicology (2010), 31: 331-350, Microelectrode arrays: a physiologically based neurotoxicity testing platform for the 21.sup.st century). Compared to patch-clamp and other single electrode recording techniques, MEA measures responses of a whole network, integrating global information on the interaction of all receptors, synapses and neuronal types which are present in the network (Novellino A. et al., Frontiers in Neuroengineering. (2011), 4(4), 1-14, Development of micro-electrode array based tests for neurotoxicity: assessment of interlaboratory reproducibility with neuroactive chemicals). As such, MEA recordings have been employed to understand neuronal communication, information encoding, propagation, and processing in neuronal cultures (Taketani, M., et al., (2006). Advances in Network Electrophysiology. New York, N.Y.: Springer; Obien et al., Frontiers in Neurosciences, 2015, 8(423): Revealing neuronal functions through microelectrode array recordings). The MEA technology is a sophisticated phenotypic high-content screening method to characterize functional changes in network activity in electrically active cell cultures which is very sensitive to neurogenesis, as well as to neurogenerative and neurodegenerative aspects. Moreover, neuronal networks grown on MEAs are known as being capable of responding to neuroactive or neurotoxic compounds in approximately the same concentration ranges that alter functions of an intact mammalian nervous system (Xia et al., Alcohol, 2003, 30, 167-174: Histiotypic electrophysiological responses of cultured neuronal networks to ethanol; Gramowski et al., European Journal of Neuroscience, 2006, 24, 455-465: Functional screening of traditional antidepressants with primary cortical neuronal networks grown on multielectrode neurochips; Gramowski et al., Frontiers in Neurology, 2015, 6(158): Enhancement of cortical network activity in vitro and promotion of GABAergic neurogenesis by stimulation with an electromagnetic field with 150 MHz carrier wave pulsed with an alternating 10 and 16 Hz modulation).

(18) This technique is used to assess the effect of nanoparticles on neuronal network(s).

(19) In Vivo Studies of a Network of Neurons

(20) An appropriate animal model is considered to assess the effect on the neuronal networks of animals of nanoparticles of the invention when exposed to an electrical stimulus.

(21) For instance, mazes are used to study spatial learning and memory in rats. Studies using a maze helps uncover general principles about learning that can be applied to many species, including humans. Today, mazes are typically used to determine whether different treatments or conditions affect learning and memory in rats.

Example 1. Nanoparticles Prepared with a Conductor Material: Synthesis of Gold Nanoparticles Coated with a Biocompatible Coating Having a Neutral Surface Charge

(22) Gold nanoparticles were synthesized by reducing a gold chloride salt (HAuCl.sub.4) with a capping agent (sodium citrate) (protocol was adapted from G. Frens Nature Physical Science 241 (1973) 21). In a typical experiment, HAuCl.sub.4 solution was heated to boiling. Subsequently, sodium citrate solution was added. The resulting solution was maintained under boiling for an additional period of 5 minutes.

(23) A 0.22 μm filtration (filter membrane: poly(ether sulfone) (PES)) of the nanoparticles' suspension was performed and gold concentration in suspension was determined by a UV-visible spectroscopy assay at 530 nm.

(24) A surface coating was performed using α-methoxy-ω-mercaptopoly(ethylene glycol) 20 kDa (“thiol-PEG20 kDa”). A sufficient amount of “thiol-PEG 20 kDa” was added to the nanoparticles' suspension to reach at least half a monolayer coverage (2.5 molecules/nm.sup.2) on the gold nanoparticle surface. pH was adjusted between 7 and 7.2, and the nanoparticles' suspension was stirred overnight.

(25) The hydrodynamic diameter (measure in intensity) was determined by Dynamic Light Scattering (DLS) with a Nano-Zetasizer (Malvern) at a scattering angle of 173° with a laser emitting at 633 nm, by diluting the nanoparticles' suspension in water (final concentration: 0.1 g/L). The hydrodynamic diameter of the so obtained biocompatible gold nanoparticles in suspension was found equal to 118 nm, with a polydispersity index (dispersion of the nanoparticles' population in size) of 0.13.

(26) The zeta potential was determined by measuring the electrophoretic mobility of the nanoparticles (Nano-Zetasizer, Malvern) by diluting the nanoparticles' suspension in a NaCl solution at 1 mM at pH 7 (final concentration: 0.1 g/L). The zeta potential at pH 7 was found equal to −1 mV.

Example 2. Nanoparticles Prepared with a Conductor Material: Synthesis of Gold Nanoparticles Coated with a Biocompatible Coating Having a Negative Surface Charge

(27) Gold nanoparticles were prepared as described in example 1 (same gold inorganic core).

(28) A 0.22 μm filtration on PES membrane filter was performed and gold concentration in suspension was determined by a UV-visible spectroscopy assay at 530 nm.

(29) A biocompatible surface coating was performed using meso-2, 3-dimercaptosuccinic acid (DMSA). A sufficient amount of DMSA was added to the nanoparticles' suspension to reach at least half a monolayer coverage (2.5 molecules/nm.sup.2) on the surface. pH was adjusted between 7 and 7.2, and the nanoparticles' suspension was stirred overnight.

(30) The hydrodynamic diameter (measure in intensity) was determined by Dynamic Light Scattering (DLS) with a Nano-Zetasizer (Malvern) at a scattering angle of 173° with a laser emitting at 633 nm, by diluting the nanoparticles' suspension in water (final concentration: 0.1 g/L). The hydrodynamic diameter of the so obtained nanoparticles in suspension was equal to 76 nm, with a polydispersity index (dispersion of the nanoparticles' population in size) of 0.46.

(31) The zeta potential was determined by measuring the electrophoretic mobility of the nanoparticles (Nano-Zetasizer, Malvern) by diluting the nanoparticles' suspension in a NaCl solution at 1 mM at pH 7 (final concentration: 0.1 g/L). The zeta potential at pH 7 was found equal to −23 mV.

Example 3. Nanoparticles Prepared with an Insulator Material Having a Low Relative Dielectric Constant Equal to or Below 100: Synthesis of Zirconium Oxide Nanoparticles Coated with a Biocompatible Coating Having a Neutral Surface Charge

(32) Zirconium oxide (ZrO.sub.2) nanoparticles were synthesized by precipitation of zirconium chloride (ZrCl.sub.4) with tetramethyl ammonium hydroxide (TMAOH) at a basic pH. The resulting suspension was transferred in an autoclave and heated at a temperature above 110° C. After cooling, the suspension was washed with deionized water and acidified.

(33) A 0.22 μm filtration on PES membrane filter was performed and (ZrO.sub.2) nanoparticles' concentration was determined by drying the aqueous solution into a powder and weighing the as-obtained mass.

(34) A biocompatible coating was prepared using silane-poly(ethylene) glycol 2 kDa (“Si-PEG 2 kDa”). A sufficient amount of “Si-PEG 2 kDa” was added to the nanoparticles' suspension to reach at least half a monolayer coverage (2.5 molecules/nm.sup.2) on the surface. The nanoparticles' suspension was stirred overnight and subsequently the pH was adjusted to 7.

(35) The hydrodynamic diameter (measure in intensity) was determined by Dynamic Light Scattering (DLS) with a Nano-Zetasizer (Malvern) at a scattering angle of 173° with a laser emitting at 633 nm, by diluting the nanoparticles' suspension in water (final concentration: 0.1 g/L). The nanoparticles' hydrodynamic diameter was found equal to 55 nm, with a polydispersity index (dispersion of the nanoparticles' population in size) of 0.1.

(36) The zeta potential was determined by measuring the electrophoretic mobility of the nanoparticles (Nano-Zetasizer, Malvern) by diluting the nanoparticles' suspension in a NaCl solution at 1 mM at pH 7 (final concentration: 0.1 g/L). The zeta potential at pH7 was found equal to −1 mV.

Example 4. Nanoparticles Prepared with an Insulator Material Having a Low Relative Dielectric Constant Equal to or Below 100: Synthesis of Zirconium Oxide Nanoparticles Coated with a Biocompatible Coating Having a Negative Surface Charge

(37) Zirconium oxide nanoparticles were prepared as described in example 3 (same inorganic core).

(38) A 0.22 μm filtration on PES membrane filter was performed and the (ZrO.sub.2) nanoparticles' concentration was determined by drying the aqueous suspension to a powder and weighing the as-obtained mass.

(39) Surface functionalization was performed using sodium hexametaphosphate. A sufficient mass of sodium hexametaphosphate was added to the nanoparticles' suspension to reach at least half a monolayer coverage (2.5 molecules/nm.sup.2) on the surface. The nanoparticles' suspension was stirred overnight and pH was subsequently adjusted to 7.

(40) The hydrodynamic diameter (measure in intensity) was determined by Dynamic Light Scattering (DLS) with a Nano-Zetasizer (Malvern) at a scattering angle of 173° with a laser emitting at 633 nm, by diluting the nanoparticles' suspension in water (final concentration: 0.1 g/L). The nanoparticles' hydrodynamic diameter was found equal to 70 nm, with a polydispersity index (dispersion of the nanoparticles population in size) of 0.11.

(41) The zeta potential was determined by measuring the electrophoretic mobility of the nanoparticles (Nano-Zetasizer, Malvern) by diluting the nanoparticles' suspension in a NaCl solution at 1 mM at pH 7 (final concentration: 0.1 g/L). The zeta potential at pH 7 was found equal to −33 mV.

Example 5. Nanoparticles Prepared with a Semiconductor Material: Silicon Nanoparticles Coated with a Biocompatible Coating Having a Negative Surface Charge

(42) Silicon (Si) nanoparticles (powder) were obtained from US Research Nanomaterials Inc. They were dispersed in water at 30 g/L under sonication (with a probe).

(43) A 0.22 μm filtration on PES membrane filter was performed and the (Si) nanoparticles' concentration was determined by drying the suspension to a powder and weighing the as-obtained mass.

(44) The hydrodynamic diameter (measure in intensity) was determined by Dynamic Light Scattering (DLS) with a Nano-Zetasizer (Malvern) at a scattering angle of 173° with a laser emitting at 633 nm, by diluting the nanoparticles' suspension in water (final concentration: 0.1 g/L). The nanoparticles' hydrodynamic diameter was found equal to 164 nm, with a polydispersity index (dispersion of the nanoparticles' population in size) of 0.16.

(45) The zeta potential was determined by measuring the electrophoretic mobility of the nanoparticles (Nano-Zetasizer, Malvern) by diluting the nanoparticles' suspension in a NaCl solution at 1 mM at pH 7 (final concentration: 0.1 g/L). The zeta potential at pH7 was found equal to −19 mV.

Example 6. Nanoparticles Prepared with an Insulator Material Having a High Relative Dielectric Constant Equal to or Above 200: Barium Titanate Nanoparticles Coated with a Biocompatible Coating Having a Negative Surface Charge

(46) Barium titanate (BaTiO.sub.3) nanoparticles' suspension (20% wt in water) was obtained from US Research Materials Inc. (US3835).

(47) Surface functionalization was performed using Silane-poly(ethylene) glycol 10 kDa (“Si-PEG 10 kDa”). Briefly, “Si-PEG 10 kDa” was first dissolved in an ethanol/water solution (1/3 v/v) and added to the BaTiO.sub.3 suspension (20% wt in water) to achieve a full monolayer coverage on the surface of the nanoparticles. The suspension was sonicated and subsequently stirred overnight. After a 0.22 μm filtration (filter membrane: poly(ether sulfone)), a washing step was performed in order to eliminate unreacted “Si-PEG 10 kDa” polymers.

(48) The hydrodynamic diameter (measure in intensity) was determined by Dynamic Light Scattering (DLS) with a Nano-Zetasizer (Malvern) at a scattering angle of 173° with a laser emitting at 633 nm, by diluting the nanoparticles' suspension in water (final concentration: 0.1 g/L). The nanoparticles' hydrodynamic diameter was found equal to 164 nm, with a polydispersity index (dispersion of the nanoparticles' population in size) of 0.16.

(49) The zeta potential was determined by measuring the electrophoretic mobility of the nanoparticles (Nano-Zetasizer, Malvern) by diluting the nanoparticles' suspension in a NaCl solution at 1 mM at pH 7 (final concentration: 0.1 g/L). The zeta potential at pH7 was found at −11 mV.

Example 7. Long Term Plasticity Study Using Electrical Stimulation of Frontal Cortex Neurons with MEAs and Functional Evaluation of the Nanoparticles of the Invention

(50) Material and Methods

(51) Microelectrode Array Neurochips

(52) The 48 well microelectrode array neurochips were purchased from Axion Biosystems Inc. These chips have 16 passive electrodes per well. The surface was coated for 1 hour with Polyethyleneimine (PEI, 50% in Borate buffer), washed and air-dried.

(53) Primary Cell Culture, Treatment Conditions and Electrical Stimulation

(54) Frontal cortex tissue was harvested from embryonic day 15/16 chr:NMRI mice (Charles River). Mice were sacrificed by cervical dislocation. Tissue was dissociated by enzymatic digestion (133.3 Kunitz units/ml DNase; 10 Units/ml Papain) and mechanical trituration, counted, vitality controlled, and plated in a 20 μl drop of DMEM containing laminin (10 μg/ml), 10% fetal bovine serum and 10% horse serum on MEAs. Cultures on MEAs were incubated at 37° C. in a 10% CO.sub.2 atmosphere until ready for use. The developing co-cultures were treated with the mitosis inhibitors 5-fluoro-2′-deoxyuridine (25 μM) and uridine (63 μM) on day 5 after seeding to prevent further glial proliferation. Culture media were replenished two times a week with DMEM containing 10% horse serum.

(55) The frontal cortex was cultured for 26 days (culture period, also identified as “native phase”). The number of active wells was quantified and the nanoparticles' suspensions (800 μM) (“Nanoparticles” groups) or water (“Control” group) were added to the active wells. After 2 days (48 hours) of incubation, the activity was recorded for 2 hours (“Pre-Stim” recording), followed by 30 minutes of low frequency stimulation (LFS-1) (step i)), and 90 minutes of low frequency stimulation (LFS-2) (step ii)), with or without an intermediary step i′) (after step i) and before step ii)) of tetanic stimulation (high frequency, HFS) for 5 minutes. After the native phase, two active electrodes were identified per well and selected for stimulation. One of them was stimulated with LFS in steps i) and ii), and both electrodes were stimulated with HFS in step i′). Recording was performed in step i) (values were derived from 60 seconds bin data taken from a 30 minutes span) and ii) (values were derived from 60 seconds bin data taken from a 30 minutes span after 60 minutes of LFS) (cf. FIG. 5).

(56) Electrical Stimulation Parameters

(57) Low Frequency Stimulation (steps i) and ii)): 30 minutes or 90 minutes Stimulation of one electrode per well in 48 well MEA Minimum stimulation duration: 100 μs Artefact elimination of 2 ms after pulse 1 pulse (biphasic) at +/−500 mV (frequency 0.2 Hz)

(58) High Frequency Stimulation (step i′)): 5 minutes Stimulation of one electrode per well in 48 well MEA Minimum stimulation duration: 100 μs Artefact elimination of 2 ms after pulse 11 pulses (biphasic) at +/−500 mV (frequency 20 Hz) and a pulse trains period (frequency 0.2 Hz)

(59) Multichannel Recording and Multiparametric Data Analysis

(60) For the recording, the multichannel MAESTRO recording system by Axion Biosystems (USA) was used. For extracellular recording, 48-well MEAs were placed into the MAESTRO recording station and maintained at 37° C. Recordings were made in DMEM/10% heat inactivated horse serum. The pH was maintained at 7.4 with a continuous stream of filtered, humidified airflow with 10% CO.sub.2.

(61) Each unit represents the activity originating from one neuron recorded at one electrode. Units are separated at the beginning of the recording. For each unit, action potentials (i.e. spikes), were recorded as spike trains, which are clustered in so-called “bursts”. Bursts were quantitatively described via direct spike train analysis using the programs Spike Wrangler and NPWaveX (both NeuroProof GmbH, Rostock, Germany). Bursts were defined by the beginning and end of short spike events (cf. FIG. 6).

(62) With a multiparametric high-content analysis of the network activity patterns, 204 activity-describing spike train parameters were extracted. These parameters allow obtaining a precise description of activity changes in the following four categories: general activity, burst structure, oscillatory behavior and synchronicity. Changes in “general activity parameters” describe the effects on action potential firing rate (spike rate), burst rate, and burst period as the time between the bursts. “Burst structure parameters” define not only the internal structure of spikes within a high-frequency spiking phase (“burst”), e.g., spike frequency in bursts, spike rate in bursts, and burst spike density, but also the overall structure of the burst, such as duration, area, and plateau. “Oscillatory parameters” quantify the regularity of occurrence or structure of bursts, which is calculated by coefficients of variation of primary activity parameters describing the variability of parameters (general activity, burst structure) within experimental episodes (Gramowski A. et al., Eur. J. Neurosci., 2004, 19, 2815-2825: Substance identification by quantitative characterization of oscillator activity in murine spinal cord networks on microelectrode arrays). Higher values indicate less regular burst structure or less regular general activity (e.g., spiking, bursting). As a measure of synchronicity in the spike trains, “CVnet parameters” reflect “synchronization” among neurons within the network (Gramowski A. et al., Eur. J. Neurosci., 2004, 19, 2815-2825: Substance identification by quantitative characterization of oscillator activity in murine spinal cord networks on microelectrode arrays). CVnet is the coefficient of variation over the network. Large CVnet values imply a wide range of variation in the activity across the network, meaning less synchronization. (Gramowski A. et al., Frontiers in Neurology, 2015, 6(158): Enhancement of cortical network activity in vitro and promotion of GABAergic neurogenesis by stimulation with an electromagnetic field with 150 MHz carrier wave pulsed with an alternating 10 and 16 Hz modulation).

(63) Functional effects induced by high frequency stimulation (HFS) on neuronal network, in the presence or in the absence of the nanoparticles of the invention, were evaluated through the above described parameters (also recapitulated for some of them in Table 1 below).

(64) TABLE-US-00001 TABLE 1 Activity-describing parameters from the multiparametric data analysis in the two following categories: general activity and oscillatory behavior. General Spike Number of spikes per second, averaged over activity rate all spike trains recorded Burst Number of bursts per minute, averaged over rate all units recorded Event Number of events per minute. Event is rate defined as synchronous burst activity of at least 50% of all units in a network within a time frame of 300 ms Oscillatory Burst Standard deviation of burst duration, behavior duration reflecting the variability of burst SD duration within experimental episodes Burst Standard deviation of area under the area curve after integrating the burst, SD defined by burst duration, number of spikes in bursts, spike frequency in bursts. The parameter describes the variability of burst area within experimental episodes. Higher values indicate less regular burst structure. Burst Standard deviation of spike number in bursts spike describes the variation of single unit spike number SD number in burst within experimental episodes. Lower values are a measure for lower degree of variation in burst spike number, therewith a more regular burst structure. Burst Standard deviation of number of bursts per rate minute, indicating the variability of SD burstiness of units within experimental episodes.

(65) Functional effects on network activity during LFS-2 (step ii), therefore after the HFS step i′), in the presence of the tested nanoparticles or in the absence thereof, were normalized to the “LFS-1” activity, i.e. the activity measured during low frequency stimulation step i). Values were derived from 60 seconds bin data taken from a 30 minutes span. Results (parameter values) were expressed as mean±SEM of independent networks. For each “Nanoparticles” group or “Control” group, at least 8 active wells (“active” meaning wells with a sufficient number of electrodes measuring electrical activity) were included in the analysis. The absolute parameters' distributions were tested for normality and the statistical significance between groups was assessed via one-way ANOVA.

(66) FIGS. 7 and 8 present some representative parameters (general activity and oscillatory behavior) characterizing functional effects induced by HFS for the “Control” groups and for the “Nanoparticles” groups (nanoparticles from example 1 and from example 3). An increase of these effects beyond the “Control” groups' effects in the presence of nanoparticles at the cellular level, indicates a potentiating effect due to these nanoparticles.

(67) FIG. 7 shows that pretreatment of the neuronal network with nanoparticles from example 3 and exposition to high frequency electrical stimulation (HSF) increases functional effects when compared to the “Control” groups. Interestingly, enhanced functional effects are observed for parameters belonging to the general activity category (typically “burst rate” and “event rate”), and they reach levels beyond that observed in the HFS-stimulated “Control” group. This indicates a nanoparticle-specific HFS-mediated potentiation which can be correlated to an enhancement of effective connections in the network and thus to an enhancement of the neuronal network's memory capacity. FIG. 8 shows that pretreatment of the neuronal network with nanoparticles from example 1 and exposition to high frequency electrical stimulation (HSF) increase functional effects when compared to the “Control” groups. Interestingly, enhanced functional effects are observed for parameters belonging to the oscillatory behavior category (typically “burst duration SD”, “burst area SD” and “burst spike number SD”), and they reach more favorable levels than that observed in HFS-stimulated “Control” group. This indicates a nanoparticle-specific HFS-mediated potentiation which can be correlated to restructured bursts facilitating information storage within the network and thus enhancing the neuronal network's memory capacity.

(68) These results highlight the advantageous performances of the nanoparticles described in the present application in enhancing functional effects (neurons connection and information storage within the neuronal network) induced by an electrical stimulation in the neuronal network.