Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0033—Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room
  • A61B5/004—Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part
  • A61B5/0042—Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part for the brain
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
  • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
  • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
  • A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
  • A61N1/00—Electrotherapy; Circuits therefor
  • A61N1/02—Details
  • A61N1/04—Electrodes
  • A61N1/05—Electrodes for implantation or insertion into the body, e.g. heart electrode
  • A61N1/0526—Head electrodes
  • A61N1/0529—Electrodes for brain stimulation
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
  • A61P25/00—Drugs for disorders of the nervous system
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
  • A61P25/00—Drugs for disorders of the nervous system
  • A61P25/08—Antiepileptics; Anticonvulsants
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
  • A61P35/00—Antineoplastic agents
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
  • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
  • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
  • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
  • A61B2034/101—Computer-aided simulation of surgical operations
  • A61B2034/102—Modelling of surgical devices, implants or prosthesis
  • A61B2034/104—Modelling the effect of the tool, e.g. the effect of an implanted prosthesis or for predicting the effect of ablation or burring
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B2576/00—Medical imaging apparatus involving image processing or analysis
  • A61B2576/02—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part
  • A61B2576/026—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part for the brain

Definitions

• the present invention generally relates to a system and method for treating a tumor of the brain or an epileptic seizure condition. Certain embodiments of the method include determining an optimum position for the implantation of a depth electrode and/or a nano-transponder within the brain of a patient. When implanted, the electrode or transponder may be used to deliver an electrical charge of a magnitude effective in treating the tumor or epileptic seizure condition.
• Epilepsy is one of the world's oldest recognized conditions.
• the World Health Organization (WHO) estimates that about 50 million people worldwide suffer from epilepsy. See WHO Fact Sheet No. 999 (January 2009).
• Epilepsy is caused by sudden, usually brief, electrical discharges in the brain. The symptoms can range from a brief loss of attention to prolonged and severe convulsions.
• An EEG electroencephalogram
• An EEG is a test that detects brain waves, or electrical activity in the brain. Sensitive electrodes are placed on the scalp to pick up electrical charges propagating through the brain. These charges are then mapped on a recording or a computer screen. The mapped electrical activity is interpreted to assist in the identification of the type and location of the seizure.
• the root cause of epileptic seizures is difficult to find. When routine EEGs using electrodes on the scalp surface cannot locate where a patient's seizures are originating, neurosurgeons may need to do more invasive monitoring.
• Intracranial electrode placement is a surgical technique that puts electrodes directly on the surface of the brain, allowing for very precise and effective EEG monitoring.
• Intracranial EEG monitoring is used to precisely map epileptic areas of the brain. Not only does this technique allow for very precise mapping of areas causing the onset of seizures, but it also helps physicians identify and map critical areas of the brain, such as those controlling speech and motor control, that will need to be avoided during surgery.
• one alternative treatment method involves the use of a neuro-stimulator electrode implanted within the brain to deliver pulses of electrical stimulation when activity that could lead to a seizure is detected. The stimulation can help to prevent or reduce the effects of the seizure.
• Such devices require the implantation of depth electrodes within the brain to allow for EEG monitoring of electrical activity inside the brain and for the delivery of the electrical pulses.
• Depth electrodes are probes that are inserted into specified areas of the brain via small holes made in the skull and covering of the brain. The insertion is typically stereotactically guided using, for example, magnetic resonance imaging (MRI) techniques, targeting a specific area within the brain. The entry point, trajectory and depth are calculated by a computer to allow for precise placement of the electrode.
• MRI magnetic resonance imaging
• a multiplicity of bulky electrode may result in brain tissue damage.
• Epileptic networks can be complex and may extend well beyond implanted bulky intracranial electrodes.
• a multiplicity of nano-devices can be utilized, either alone or in combination with conventional in depth electrodes, to target specific brain cells while minimizing potential brain damage.
• carbon nanotube transponders that can be fabricated from a plurality of carbon nanotubes optionally attached to at least one biomolecule ligand and optionally connected to a nano-capacitor.
• An energy- releasing carbon nanotube transponder that can be placed in cellular tissue to treat multiple afflictions is disclosed in co-pending patent publication number PCT/US2010/048956, the contents of which are incorporation by reference.
• chemotherapeutic and radiation therapies are widely used for treating cancers.
• Chemotherapy and radiation administered both inside the CNS and outside the blood brain barrier often cannot reach densely packed malignant cells even following resective
• chemotherapeutic agents can have limited efficacy due to factors ranging from systemic toxicity, to impaired drug transport secondary to decreased vascularization of the neoplasm core and P
• the present invention provides a method for treating an epileptic seizure in the brain of a patient.
• the method includes acquiring an inter-ictal imaging profile and a post-ictal imaging profile from the brain of the patient. These profiles are compared and an ictal propagation pathway determined on the basis of this comparison.
• a plurality of virtual electrode placement positions for an electrode is determined based on the ictal propagation pathway and a volume of cortical activation determined for each virtual placement position based on the ictal propagation pathway and the virtual electrode placement position.
• An implantation position for the electrode is selected from the plurality of virtual electrode placement positions, based on the volume of cortical activation at the implantation position and the electrode implanted at this position.
• An electrical pulse, of a magnitude effective to at least reduce the epileptic seizure, is delivered from the electrode to cellular tissue within the volume of cortical activation.
• the method also includes delivering a plurality of energy-releasing carbon nanotube transponders to a region of the brain of the patient and releasing an electric change from the plurality of energy-releasing carbon nanotube transponders.
• the energy-releasing carbon nanotube transponders are delivered to the cellular tissue at a position dependent upon the ictal propagation pathway.
• the energy-releasing carbon nanotube transponder includes at least one carbon nanotube having a nanocapacitor connecting to its first end.
• the nanocapacitor is capable of storing a predetermined amount of electric energy and releasing the electrical energy in the form of a mean charge density in the range of between about 1.2* 10 " 5 and about 2.4* 10 " 5 Coulombs/cm 2 .
• the at least one carbon nanotube connects to the nanocapacitor and acts as a nanoswitch for releasing the predetermined amount of electrical energy to the cellular tissue in response to a change in the environment of the nanotube transponder.
• the plurality of the nanotube transponders is capable of releasing a biologically non-destructive electric charge in the range of between about 4 and about 20 microCoulombs/cm 2 to the cellular tissue.
• acquiring the inter-ictal imaging profile includes acquiring an inter-ictal diffusion tensor imaging MRI dataset and acquiring a post-ictal imaging profile includes acquiring a post-ictal diffusion tensor imaging MRI dataset.
• determining the ictal propagation pathway includes determining fractional anisotropy in the inter-ictal diffusion tensor imaging MRI dataset and the post-ictal diffusion tensor imaging MRI dataset.
• Acquiring the inter-ictal and post-ictal imaging profiles may also include acquiring an inter-ictal and a post-ictal single-photon emission computed tomography dataset respectively.
• determining a volume of cortical activation at each of the plurality of virtual electrode placement positions may include determining an activation function based on the electric potential produced by stimulation of the electrode in a
• determining a volume of cortical activation at each of the plurality of virtual electrode placement positions may include determining an activation function based on the electric potential produced by stimulation of the electrode in an anisotropic medium.
• the method also includes acquiring a stimulation activated single-photon emission computed tomography dataset after delivery of the electrical pulse from the electrode, comparing this dataset with the volume of cortical activation at the implantation position, and validating the volume of cortical activation at the implantation position based on the comparison.
• Another aspect of the invention provides a method for treating a tumor in the brain of a patient.
• One embodiment of the method includes acquiring a baseline diffusion tensor imaging MRI dataset from the brain of the patient and determining a tumor position based on this dataset.
• a plurality of virtual electrode placement positions for an electrode is determined based on the tumor position and a volume of cortical activation determined at each of the placement positions.
• An implantation position for the electrode is selected from the plurality of virtual electrode placement positions based on the volume of cortical activation at the implantation position and the electrode implanted at this position.
• An electrical pulse of a magnitude to effectively treat the tumor tissue is delivered from the electrode to tumor tissue within the volume of cortical activation.
• the method may also include delivering a plurality of energy- releasing carbon nanotube transponders as disclosed herein to a region of the brain of the patient and releasing an electric change from the transponders.
• the electric change from the plurality of energy-releasing carbon nanotube transponders pulse is of a magnitude to effectively treat the tumor tissue.
• Figure 1 is a flow diagram illustrating one embodiment of the steps in a depth electrode placement planning system.
• FIG. 2 is a schematic illustration of a lumped model for an axon oriented in the x direction is presented. Each compartment is considered to be composed by a membrane capacitance (c M ), connected in parallel with the membrane resistance (r M ). Compartments are connected to others through resistors in the extracellular (r E ) and intracellular space ( ).
• c M membrane capacitance
• r E membrane resistance
• r E extracellular
• V M transmembrane voltage
• Ax is the length of the compartment.
• Figure 3 are illustrations of a MRI 3D computational model.
• A The structural MRI was used to create a 3D computational model from the patients.
• Figure 4 are illustrations of a CAD electrode model.
• A The CAD electrode model consists of four conductive cylinders (1.27 mm diameter X 0.2mm height) separated by insulators (10 mm between cylinder midpoints) The numbers in the figure indicate the depth electrode conductors from anterior to posterior.
• B and C Simpleware +CAD module is used to place the electrode in temporal lobe white matter.
• Figure 5 is an illustration demonstrating the surrounding E-field magnitude amplification using single-walled carbon nanotubes (metallic and semi-conducting) between 50-200 nm in length. Cancer cells are shown destroyed in an E-field compared to E-field without carbon nanotube-based nano-electrodes and no E-field.
• FIG. 6 is a diagram of one embodiment of an energy-releasing carbon nanotube transponder
• Figure 7 (A) is a schematic illustration of a raw metallic-type carbon nanotube is depicted in side (left) and axial (center) views. Carbon nanotubes cross-linked to fluorescein isothiocyanate (FITC) are shown (right) via a zero- length amide bridge during the functionalization process.
• Figures 7(B)(i)-(iii) illustrates the characterization of various purities of carbon nanotubes via Fourier Transform Infrared (FTIR) spectroscopy using attenuated total reflectance (ATR).
• FTIR Fourier Transform Infrared
• Figure 8 is a graph illustrating the results of in vitro cytotoxicity testing of carbon nanotubes.
• Figures 9(A) and 9(B) show immunofluorescent staining to observe the influence of 99% pure FITC-functionalized CNTs injected stereotactically into the left hippocampal formation of a Rowett rat.
• Figure 9(A) the right hippocampal formation was injected with distilled PBS.
• Figure 9(B) the left hippocampal formation at the dentate gyrus was injected with 1 ⁇ _ of 25ng/ml_ 99% pure FITC-functionalized CNTs.
• Figures 9(C) and 9(D) depict a lOOx magnification of part of the views in Figures 9(A) and 9(B) respectively.
• Figures 10(A)-(D) illustrates a 100Hz, asymmetric, charge-balanced +10V, -2.5V signal waveform Figure 10(A) delivered to an agarose gel brain phantom Figure 10(B).
• Figure 10(C) A schematic of the five 1 ⁇ _ injections of 0.25ng/ml_ of 95% pure FITC-functionalized metallic-type carbon nanotubes is depicted in Figure 10(C).
• Figure 10(D) shows a recording electrode composed of 0.005" 316L stainless steel.
• Figures 11 (A)-(B) illustrate the construction of a Finite Element Method (FEM) mesh.
• FEM Finite Element Method
• Figure 12(B) is a graph illustrating a FEM simulation of the voltage drop within an agarose gel phantom. Baseline - lower graph; 3S/m CNT - upper graph; 0.2S/m CNT - middle graph.
• Figures 13(A)-(C) illustrates the effect of carbon nanotubes on electric potential.
• Figure 13(A) shows an agarose gel phantom imaged with a UV transilluminator.
• Figures 13(B) and (C) illustrate a FEM simulation comparing a baseline profile (left) to a CNT-M modified conductivity model (right).
• Figures 14(A)-(E) illustrate a 3D computational brain model constructed from structural MR-lmages.
• Figure 14(A) shows virtual electrodes were positioned within white matter that is adjacent to the hippocampal formation.
• Figure 14(B) shows a spherical volume of modified conductivity following a Gaussian profile.
• Figure 14(C) shows a FEM simulation comparing the baseline profile (14(C)(i)) to the CNT-M modified conductivity profile
• Figure 15(A)-(C) illustrates a Parametric Analysis of the Electric Potential.
• Figure 15(A) shows the analysis of the electric potential in a transept line is drawn through the volume of CNT-modified conductivity compared to a baseline profile (left peak) without CNT-Ms.
• Figure 15 (B) shows parametric analysis of the E-Field in a transept line is shown through the volume of modified conductivity compared to a baseline profile without CNTs.
• Figure 15(C) shows an expanded surface area provided by the volume of conductive CNT-Ms diminished the charge density to 1 .63 C/phase*crr ⁇ '2 when applying 5.12 ⁇ / ⁇ , which is below the damaging threshold.
• Various aspects of the present invention provide an electrode placement planning method and system and also methods of using the method or system to treat a disease or condition of the brain of a human or a veterinary patient.
• the system and method with be described with reference to a single depth electrode. However, if will be understood that the system and method are applicable to the placement of multiple depth electrodes within the brain of the patient. In general, at least two depth electrodes will be placed within the brain of the patient.
• the system and method provide for the propagation of an electrical current from the electrode to distant epileptic tissue during a direct neurostimulation therapy.
• the system provides for the treatment of a pathological condition of the brain, for example, a cancer of the brain, by providing for the propagation of an electrical current from the electrode.
• the methods of the present invention may provide for the optimal placement of an electrode in the course of the treatment involving the delivery of an electrical current to the electrode to a region of the brain.
• the electrical current may be sufficient to treat the epileptic condition or to kill a cancerous cell.
• the method may also include the delivery and optimal placement of nano-electrodes, in the presence or absence of conjugated nano-capacitors.
• nano-electrodes are disclosed in co-pending patent application publication number 2012-0209344, entitled "ENERGY-RELEASING CARBON NANOTUBE TRANSPONDER AND
• the method may allow for the nano-eletrodes to be delivered and optimally placed, for example by injection, to best implant sites, or one or more critical brain regions targeting pathological tissue, for example, brain tumor cells.
• pathological tissue for example, brain tumor cells.
• targeted brain tissue can be a critical node in an epileptic circuit.
• the delivery of the nano-electrodes can coat the brain surface and/or the nano-electrodes can be delivered into the brain parenchyma itself.
• the strategy can include bridging pathological tissue similar to
• the nano- electrodes can be guided or steered through brain tissue by an electric field supplied from the implanted electrodes.
• an electric field supplied from the implanted electrodes Such an method as disclosed in copending PCT patent application number PCT/US2010/048956, the contents of which are incorporated by reference.
• Iron nanoparticles can be inserted into the carbon nanotubes to provide an ability to guide the complexes using an external magnetic field guidance system. The accuracy of such steering may be optimized by the use of the placement planning system disclosed herein.
• Injecting the carbon nanotubes containing iron nanoparticles can provide the capability of guiding the complexes through a porous medium and offers the potential of guiding such complexes in living brain using an external magnetic field. Electrode placement may be modelled in vitro using agarose gel and hybrid microfluidic depth microelectrode arrays, for example those available from NeuroNexus, Ann Arbor, Mi, used to apply an optimal magnetic field strength to steer magnetic nanoparticle-carbon nanotube complexes in vivo.
• Such an ability augments the versatility of the nanocapacitor-carbon nanotube complexes for shaping the volume of cortical activation post- implantation of conventional depth electrodes housing the reservoir from which the nanocapacitor-carbon nanotube complexes can be injected into the medium.
• These complexes can also be conjugated or linked with fluorescent labels (e.g., FITC tagging), and therapeutic drug molecules such as growth factors.
• fluorescent labels e.g., FITC tagging
• therapeutic drug molecules such as growth factors.
• BDNF brain derived neurotrophic growth factor
• the nano-electrodes may enhance the effectiveness of the treatment by increasing the effective range of the electric field generated by the depth electrodes during the treatment.
• the effective range of the electric field around the depth electrode, and the nano-electrodes then these are present, is quantified by determining a volume of cortical activation
• VOCA VOCA
• One embodiment of the placement method utilizes intracranial depth electrodes for generating electric fields and for both steering the nano-devices strategically through brain, as well as providing therapy to pathological tissue (e.g., tumor cells, and/or epileptic networks).
• pathological tissue e.g., tumor cells, and/or epileptic networks.
• Such therapy may provide for the abatement the tissue of interest or for modulation of the epileptic circuit, respectively. That is, in the case of a hyper-excitable epileptic circuit, reverting the circuit to a non-pathological stable firing state.
• one goal of the placement method is the pre-implantation prediction of optimal electrode placement in cortical white matter for influencing the maximal extent of the epileptic circuit in the presence or absence of nano-electrodes.
• the workflow of one embodiment of the placement method is illustrated in Figure 1. This method may include three fundamental techniques to determine responsive neurostimulation electrode placement in a patient having a tumor or epileptogenic regions, for example, bilaterally independent temporal lobe epileptogenic regions.
• the method may include pre-implantation finite element modeling to predict the VOCA around an electrode using an 'activation function'.
• the VOCA is an estimate of the extent of the electric field influencing neural tissue surrounding adjacent active electrodes prior to implantation.
• the calculations may include anticipated stimulation parameters for the electrode, such as the charge delivered to the electrode.
• the VOCA will expend
• the extent of the VOCA will vary dependent upon the local environment surrounding the depth electrode, for example, the extent and geometry of white matter, grey matter and cerebrospinal fluid.
• the extent of the VOCA may be extended when nano-electrodes implanted within the brain.
• DTI diffusion tensor imaging
• MRI magnetic resonance imaging
• ratio ictal single-photon emission computed tomography (“SPECT”) co-registered to the patient's MRI (“RISCOM”) may also be used to facilitate visualizing the active epileptic circuit to which depth electrodes must interface.
• This technique allows for the visualization of blood flow within the brain.
• This image is made by computing the ratio of the ictal to the baseline image at every point. Essentially, each voxel in the ictal image is divided by the corresponding voxel in the baseline blood flow image, when the baseline activity in that voxel is significant.
• the RISCOM voxel value may also include a weighting factor.
• SISCOM Subtracted Ictal SPECT CO-registered to MRI
• the baseline is subtracted from the ictal at every point.
• SAS stimulation activated SPECT
• RNS cortical responsive neurostimulator
• Such depth electrode implantation is guided by the pre-implant neural circuit model for maximizing modulation of the maximal extent of an epileptic circuit.
• White matter pathways are targeted such that myelinated axons are utilized to propagate electrical current distant from the source of stimulation. This hypothesis is tested post-RNS implant with actual activation of associated white matter depth electrodes.
• RNS current is 'injected' while capturing transient blood flow changes using stimulation activated SPECT (SAS).
• SAS acquisition and processing may be performed at 4-18 months post-implantation of RNS depth electrodes. Bipolar stimulation of depth contacts is performed simultaneously with peripheral intraveous administration of 99Tc-HMPAO or 99mTC-ECD.
• neuroanatomically distant focal regions of hypoperfusion are associated with repetitive stimulation of consecutive bipolar depth contacts in white matter.
• Focal hypoperfusion throughout the epileptic circuit may indicate, among other possibilities, inhibition of cortical excitability downstream from stimulation.
• This information represents the maximal extent of cortical activation for a given set of stimulation parameters passed through a specific electrode contact geometry and orientation placed in human cortex.
• SAS can be used as a technique for validating presurgical mapping of the ictal onset zone.
• Presurgical planning using axonal pathways to direct the spread of current to an independent distant epileptic source can simplify the surgical approach with the available limited intracranial electrode set.
• the modeling system may be used to predict white matter connectivity and side-effects to stimulation therapy.
• SAS may validate focal blood flow changes in specific brain regions predicted pre-implant.
• the workflow demonstrates the feasibility of planning white matter-electrode placement with individual specificity to predict propagation of electrical current throughout a human epileptic circuit.
• One application of the depth electrode planning system and method is to predict preoperatively the extent to which direct stimulation therapy applied, for example, using the RNS ® Neurostimulator can propagate through pathological white matter during direct neurostimulation (RNS), including when amplified by nano-electodes, for example carbon nanotube-based nano- electrodes.
• RNS direct neurostimulation
• a pre-surgical model can be generated to calculate the VOCA in an electrode implant candidate. This model may include an iterative
• Such a model can include a method to calculate potential regions of hyperpolarization and depolarization with a VOCA and extent of neural activation amplified by nano-electrodes using an 'activation function.
• an SPGR MRI sequence, interictal diffusion tensor imaging (DTI) dataset, and post-ictal DTI may be acquired for a specific patient.
• DTI sequences are obtained on a 1.5T- higher magnet strength MRI scanner using thin slices and diffusion measurements performed in at least 60 non-collinear directions.
• Six or more non-diffusion weights (b- values) may be used with a given repetition time.
• Ictal propagation pathways are determined using subtracted postictal diffusion tensor imaging (spiDTI) (See Figure 1 ). This technique is performed by computing fractional anisotropy ("FA") in interictal and post-ictal sequences and subtracting the results.
• a given threshold is used to differentiate substantial changes in FA.
• An iterative process for virtual electrode placement, and in some cases nano-electrode placement, may be modeled as follows: First, in a virtual model, depth electrodes are strategically positioned near the spiDTI signal, in a 3D segmented mesh generated according to the MRI scan. Second, the extracellular electric potential (EP) predicted by electric stimulation is calculated using the finite element method in a homogeneous isotropic medium or an anisotropic medium. In the isotropic model, the separate conductivity values were assigned to white matter, grey matter and cerebrospinal fluid. However, the conductivity was considered to be constant within each of the three categories.
• the anisotropic model a separate conductivity is assigned for each voxel in the DTI dataset.
• the conductivity values may vary within each of the three categories.
• the conductively value is determined by calculating a conductivity matrix from the diffusion tensor matrix for each individual voxel in the DTI dataset.
• the voxel is positioned at its proper spatial coordinate and the conductivity value may be revised to consider the influence of any virtual nano-electrode positioned within the voxel.
• the anisotropic model may allow for account to be taken for the presence of lesions and the influence of micro-and nanodevices, for example, the nano- electrodes disclosed herein.
• the EP model is used to estimate an activation function for the cable equation of axons (E-field in axons - Figure 1).
• this includes computing the second directional derivative in the direction of white matter tracts. This directionality is obtained from tensor fitting acquired from the post-ictal DTI. Fourth, the magnitude of the activation function is used to determine areas of hyperpolarization and depolarization adjacent to the electrodes. Fifth, generated depolarization/hyperpolarization regions of interest are used to identify influenced tracts in the patient-specific tractography model. In those embodiments including the use of nano- electrodes, aliquots of such particles, for example carbon nanotubes, may be steered through brain tissue and used to amplify conductivity of the native tissue conductivities.
• the application of the activation function in the presurgical model may produce a VOCA defined as non-spherical overlapping regions of interest around the adjacent electrode where hyper-polarization and depolarization are determined.
• This data set demonstrates the ability to generate a preoperative electrode planning map for predicting 'best-implant' sites for both depth electrodes and nanoparticles along with steering and localization of carbon nano-electrode conductivity boosters (see Figure 5 demonstrating nano- electrode proof-of-concept with high graded glioma cells).
• the application of the activation function is an improvement to previously reported activation regions produced by the magnitude of electric fields ((Rossi, et al.
• the activation function is derived by analyzing a lumped element model of an axon compartment.
• each compartment is composed by a membrane capacitance c M , connected in parallel with the membrane resistance r M .
• Compartments are connected to others through resistors in the extracellular r E and intracellular space r t (see Figure 2).
• a non-homogenous partial differential equation may be derived by applying Kirchoff s Current Law. This PDE is known as the cable equation (Equations 1 , 2 and 3). The solution of this equation describes the
• V M transmembrane voltage
• V E extracellular voltage
• ⁇ is the length constant, which is directly proportional to the square root of the axon radius r and ⁇ is the time constant for the membrane.
• equation (1 ) can be rewritten as equation (4):
• the axon is considered to be oriented in the x direction, thus the second difference A 2 V E of extracellular potential is computed with respect to the x direction, Ax 2 .
• axons can be in any spatial direction, therefore the second difference ⁇ 2 ⁇ should be computed with respect of the axon direction which can be described with normal vector n.
• the activation function is proportional to the second directional derivative of the extracellular potential V E in the axon direction within a Cartesian coordinate system, which can be calculated with the equations (5) and (6).
• first(x, y, z) is the first directional derivative of the electric potential on the direction of axons
• second(x, y, z) is the second derivative of the electric potential on the direction of axons
• W E (x, y, z) is the gradient of the extracellular electric potential
• n(x, y, z) is a normal vector field that represents axon directionality
• W E (x, y, z) ⁇ n(x, y, z) is the scalar product between W E (x, y, z) and n(x, y, z).
• the negative gradient of the electric potential is the electric field E, thus the second directional derivative depends on the magnitude and direction of E, and the direction of the axon n. Equations (7) and (8) respectively.
• the first step consisted in creating a patient specific Finite Element Model (FEM) where a CAD model of the depth electrode was placed.
• the second step consisted in solving a system of partial differential equations to obtain V E using the FEM model that was obtained in the first step.
• FEM Finite Element Model
• the electrode placement method disclosed above may be modified for the treatment of a tumor of the brain.
• a tomography dataset is obtained from the brain of the patient and the position of the tumor determined from such a scan.
• a plurality of virtual electrode placement positions for an electrode are determined based on the tumor position, a VOCA calculated at each of a plurality of virtual electrode placement positions using the method disclosed above and based on the tumor position and the virtual electrode placement position.
• An implantation position for the electrode is selected from the plurality of virtual electrode placement positions based on the VOCA at the implantation position.
• the implantation position is selected to give a required (therapeutically effective) electrical pulse to the tumor.
• the electrode is implanted at the implantation position and an electrical current delivered from the electrode to the tumor tissue within the VOCA.
• the method of treating a tumor may also include the delivery of a plurality of nano-electrodes to the region of the brain containing the tumor.
• Such nano-electrodes may extend the VOCA beyond the region of coverage by the electrode in the absence of the nano-electrodes.
• the nano-electrodes may be steered to the required position by an electrical field generated by the electrode.
• an electrical charge contained in the nano-electrode may be released by, for example, an electrical field generated by the electrode.
• any suitable nanoparticle that can be guided to a required position within the brain and/or that can extend the VOCA of an electrode to allow for the treatment of a condition such as a tumor or an epileptic condition.
• nano-electrodes include the energy-releasing carbon nanotube transponders disclosed in co-pending patent application publication number 2012-0209344, entitled “ENERGY-RELEASING CARBON NANOTUBE TRANSPONDER AND METHOD OF USING SAME" published August 16, 2012, the contents of which are incorporated by reference. [081]
• these transponders may include at least one carbon nanotube connected to a nanocapacitor.
• a nanosensor is formed by at least one biomolecule ligand covalently attached to an end of at least one carbon nanotube.
• the nanocapacitor is connected to the opposite end of at least one of the carbon nanotube.
• the transponder is optionally coated with at least one biocompatible molecule to form a biocompatible coating.
• the transponder is optionally labeled with at least one molecular label.
• the transponder may release a biologically non-destructive electric charge to target cells or, alternatively, may release a biologically destructive electric charge to target cells.
• a biologically non-destructive electric charge is used in the treatment of epilepsy while a biologically destructive electric charge is used in the treatment of a tumor.
• FIG. 6 This figure illustrates one embodiment of the energy-releasing carbon nanotube transponder.
• the energy-releasing carbon nanotube transponder 10 integrates at least one carbon nanotube 12 attached to a nanocapacitor 18.
• the energy-releasing carbon nanotube transponder 10 also integrates at least one carbon nanotube 12 attached to a biomolecule ligand 14 in order to form a nanosensor 16
• the energy-releasing carbon nanotube transponder 10 also integrates at least one carbon nanotube 12 attached to a molecular label 22.
• a coiled nanowire located inside (24a) the nanocapacitor 18, or alternatively by design, outside (24b) the nanocapacitor 18, allows for the energy-releasing carbon nanotube transponder 10 to be recharged in energy once it has been discharged.
• the response of a nanosensor 16 involves three characteristics; 1 ) the electrostatic interaction between the net charge of a biomolecule, a carbon nanotube and counter ions in buffer; 2) movement or transport of the
• a nanosensor 16 formed by the attachment of a biomolecule ligand 14 to a carbon nanotube 12 can be introduced into one or more epileptic circuits to detect changes during seizure activity (e.g., femtomolar increases in the neurotransmitter glutamate correlates to an increased conductivity of the carbon nanotube 12 of the nanosensor 16.
• a detection threshold of the energy-releasing carbon nanotube transponder 10 can be used to discharge a nanocapacitor 18 to deliver direct stimulation therapy to potentially stabilize the epileptic circuit.
• a capacitor functions much like a battery but charges and discharges much more efficiently.
• a nanocapacitor 18 is a nanostructure large enough to connect to at least one carbon nanotube 12.
• the nanocapacitor 18 has a capacity for storing an electric charge appropriate to its application.
• the method of charging the nanocapacitor 18 is according to the requirements of the particular nanocapacitor 18.
• An example of a suitable nanocapacitor can be provided by a nanotechnology company such as SolRayo, Inc, Madison, Wl.
• a coiled nanowire located inside (24a) the nanocapacitor 18, or alternatively by design, outside (24b) the nanocapacitor 18, allows for the energy-releasing carbon nanotube transponder 10 to be recharged in energy once it has been discharged.
• the nanocapacitor 18 and the energy-releasing carbon nanotube transponder 10 release biologically destructive electric charge densities in the range of between about 21 and about 30 microCoulombs/cm 2 and preferably about 23 microCoulombs/cm 2 .
• the nanocapacitor 18 and the energy- releasing carbon nanotube transponder 10 release biologically non-destructive electric charge densities in the range of between about 4 and about 20 microCoulombs/cm 2 .
• a biocompatible coating 20 surrounds the energy- releasing carbon nanotube transponder 10, for example, to improve bio tolerance and/or adherence of the energy-releasing carbon nanotube
• the nanotransponder presently disclosed can be coated with an amphilic copolymer that can enhance biocompatibility of the nanodevice.
• the biocompatible coating is a material selected from PEG, polylactic acid (PLA), polyglycolic acid (PGA), poly lactide co-glycolide (PLGA) or chitosan or combinations of at least two of these materials.
• FIG. 10(A) A 100Hz, asymmetric, charge-balanced +10V, -2.5V signal waveform Figure 10(A) was delivered via an AM-Systems 3800 stimulator through the 1.1 mm diameter AD-Tech depth lead. The lead was cast in 0.6% Type VII agarose gel brain phantom Figure 10(B). A schematic of the five 1 ⁇ _ injections of 0.25ng/ml_ of 95% pure FITC-functionalized metallic-type carbon nanotubes is depicted in Figure 10(C). The recording electrode was composed of 0.005" 316L stainless steel Figure 10(D).
• Example 6 Carbon nanotubes cause a baseline voltage drop within an agarose gel
• Example 8 A 3D computational brain model was constructed from structural MR- 1 mages

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