WO2013158709A1 - Système et appareil pour le dépistage précoce, la prévention, le confinement ou la réduction d'une activité anormale du cerveau qui se propage - Google Patents

Système et appareil pour le dépistage précoce, la prévention, le confinement ou la réduction d'une activité anormale du cerveau qui se propage Download PDF

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WO2013158709A1
WO2013158709A1 PCT/US2013/036885 US2013036885W WO2013158709A1 WO 2013158709 A1 WO2013158709 A1 WO 2013158709A1 US 2013036885 W US2013036885 W US 2013036885W WO 2013158709 A1 WO2013158709 A1 WO 2013158709A1
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node
spread
epileptic event
index
therapy
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Ivan Osorio
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Flint Hills Scientific LLC
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Flint Hills Scientific LLC
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/37—Intracranial electroencephalography [IC-EEG], e.g. electrocorticography [ECoG]
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    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
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    • 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
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    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
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    • A61N1/3606—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for a particular treatment
    • A61N1/36064—Epilepsy
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    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
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    • A61N1/36128—Control systems
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    • A61B5/02405—Determining heart rate variability
    • A—HUMAN NECESSITIES
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    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
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    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/02—Details
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    • A61N1/0526—Head electrodes
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    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/362—Heart stimulators
    • A61N1/3621—Heart stimulators for treating or preventing abnormally high heart rate

Definitions

  • This disclosure addresses the complex and demanding task of optimization of interventional brain therapies for control of undesirable changes of state, such as epileptic seizures.
  • therapies for other neurological (e.g., pain, movement), psychiatric (e.g., mood; obsessive compulsive), and cardiac (e.g., arrhythmias) disorders may be optimized using the approaches described herein.
  • a therapy cannot be optimized (in terms of increasing its beneficial effects), optimization may be effected by decreasing the number, intensity, or duration of the therapy's adverse events.
  • Adverse effects include but are not limited to increase in seizure frequency or severity, cognitive impairment in functions such as memory, language, or changes in mood (depression or mania), or in thought (psychosis). These adverse effects may be quantified using cognitive, electrical, thermal, optical and other signals and logged to computer memory. In the case of signals that lack easily detectable or recognizable electrical or other correlates, they may be characterized using a semi-quantitative approach such as psychiatric scales, care-giver observations or patient diaries.
  • the term "therapy” may be interchangeably used with the term control for which a theory exists (Control Theory) in the field of engineering. Since therapy and control share the same aim, it is appropriate to adopt certain concepts form this theory as well as from the fields of dynamics to generate a rational approach and strategy for the management of pharmaco-resistant seizures.
  • the epileptic brain may be conceptualized as a non-stationary, non-linear, "noisy” system that undergoes sudden unexplained reversible transitions from the non-seizure state.
  • the manner in which this transition occurs may be “gradual” (through a process of "attractor deformation") or sudden (through a "leap” from one state to another) as observed in bi-stable or multi-stable systems.
  • Dynamical theory teaches that a system may be defined by its dimension (which corresponds to the minimum number of variables required to specify it).
  • the identification of a system's dimension greatly benefits from the identification of a spatio-temporal scale of observation that corresponds to a representative sample of the system (so-called mesoscopic scale), thus obviating the need to study the whole system at all scales, a daunting and impracticable task in the case of the mammalian brain.
  • the epileptic brain's dimensionality and its mesoscopic scale have not been effectively specified to date. This knowledge void forces the treatment of the brain as a "black-box".
  • the present disclosure provides a method, comprising detecting an epileptic event in a neural network within a brain of a patient, wherein said epileptic event in a first node of said neural network; identifying a second node of said neural network based at least in part on at least one coupling characteristic between said first node and said second node; and applying a therapy to said second node or any connection to said second node of said neural network, in response to said detecting.
  • the present disclosure also provides a method, comprising determining a first body index indicative of an epileptic activity in a patient; monitoring a second body index different from the first body index, in response to said determining; detecting an indication of epileptic activity spread in a brain of the patient, based upon at least said second body index; and taking a responsive action in response to said detecting, wherein said responsive action is selected from delivering a therapy to at least one neural structure of said patient, modifying a therapy to at least one neural structure of said patient, logging said indication of spread, or warning said patient, a caregiver, or a medical professional of said indication of spread.
  • the present disclosure also provides a method, comprising detecting an epileptic event in a first node of a neural network in the brain of a patient; applying a first therapy to a first neural structure of said patient for treating said epileptic event; and applying a second therapy to a second neural structure of said patient based on a proclivity of a spread of said epileptic event to a third neural structure of said patient.
  • the present disclosure also provides a non-transitive, computer-readable storage device for storing data that when executed by a processor, perform a method disclosed herein.
  • Figure 1 depicts a medical device system, comprising an extra-cranial, cranial, or intracranial electrode implanted in a patient, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 2 presents a block diagram of a medical device system, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 3 presents a block diagram of a medical device system, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 4 shows a block diagram depiction of an electrical activity spread determination module of Figure 3, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 5 shows a stylized depiction of a two -dimensional reference spread mapping and of a real-time mapping of electrical activities in a patient's brain, in accordance with one illustrative embodiment of the present disclosure.
  • Figures 6 A and 6B show a stylized depiction of a sensor/electrode array mesh, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 7 A illustrates a stylized depiction of a site of emergence of abnormal electrical activity in a patient's brain, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 7B illustrates a stylized depiction of a stored reference spread mapping of abnormal electrical activity, in accordance with one illustrative embodiment of the present disclosure.
  • Figures 7C and 7D illustrate a stylized depiction of a real-time detection of abnormal electrical activity, determination of the likelihood of spread to different node or hub within the same network or to a different network based among others on estimation of a gradient, identification of the most likely spread target(s) and a comparison to a reference spread mapping, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 8 shows a flowchart depiction of a method, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 9 shows a flowchart depiction of a method, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 10 shows a flowchart depiction of a method, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 11 shows an exemplary depiction of how epileptic event spread may be blocked, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 12 shows an exemplary depiction of how epileptic event spread may be blocked, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 13 shows changes in functional connectivity under three different task conditions (13A, 13B, and 13C) among various frontal or temporal lobe brain regions, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 14 shows structural connectivity among various brain regions, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 15 shows structural connectivity among various brain regions, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 16 shows structural connectivity among various brain regions, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 17 shows structural connectivity among various brain regions, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 18 shows structural connectivity among various brain regions forming one neural network (all depicted structures) comprising a subnetwork (stippled depicted structures), in accordance with one illustrative embodiment of the present disclosure.
  • Figure 19 shows structural connectivity among various brain regions, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 20 shows structural connectivity among various brain regions, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 21 shows structural connectivity among various brain regions, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 22 shows structural connectivity among various brain regions, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 23 shows structural connectivity among various brain regions, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 24 shows structural connectivity among various brain regions, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 25 shows structural connectivity among various brain regions, in accordance with one illustrative embodiment of the present disclosure. DETAILED DESCRIPTION OF THE DISCLOSURE
  • ictiogenesis Historically, the study of seizure generation (referred herein to as ictiogenesis) has been anatomically and functionally restricted to the so-called “focus” or epileptogenic location in the brain where seizures originate. This concept connotes that ictiogenesis depends only on mechanisms inherent to the neuronal assembly(es) that make up the "focus", which in this model are largely if not entirely autonomous from and not susceptible to larger scale (intra- or interregional) interactions.
  • the "seizure focus” theory (which may be referred to herein as the "ictio- centric” or “ictiocentric” theory) ignores the anatomo -functional connectivity, between the site where a seizure first emerges and regions connected to itthat partake in its elicitation, progression and cessation.
  • the "focus” concept disregards the brain as being an assembly of networks organized on at least three spatial scales: (i) individual neurons and synapses (microscale); (ii) neuronal groups and populations (e.g., macro-columns) (mesoscale); and (iii) anatomically distinct cyto-architectonic areas and their corresponding inter-areal pathways (macroscale).
  • the term "focus” is used herein solely to refer to the "focus theory” which this disclosure does not espouse.
  • Seizure blockage triggered by automated detections is the state of the art in clinical epileptology. This line of attack is valuable only if a seizure is suppressed before it reaches a critical mass and spreads by continuity/contiguity to neighboring tissue, or via pathways/connections to distant structures. For the probability of the spread to remain low once a seizure occurs: a) therapy delivery must take place shortly (e.g. not to exceed 5 seconds) after detection of a manifestation; b) the therapy onset of action must be immediate, and (c) its therapeutic ratio must be high.
  • This disclosure provides a method and a system/apparatus for early, rapid seizure detection, prevention of spread, blockage if indicated (e.g., the seizure starts in a critical (for adaptive behavior) node or hub) and also modeling/prediction of their mode and preferred route of spread and its targets.
  • a seizure if at onset, a seizure has low probability of spread and/or if its severity is low and/or the site of emergence is not critical for preservation of highly valued functions such as wakefulness with awareness, postural tone, or cardiorespiratory integrity, it may be more efficient, safer, and more tolerable (as no adverse affects would occur) to withhold treatment.
  • This approach may also be more physiological than indiscriminate treatment, since being "relaxation phenomena", seizure blockage or suppression may lead to further accumulation of energy that may result, as observed in a clinical trial, in an increase in the severity of subsequent seizures, a shortening of the time to the next seizure or both.
  • the human brain may be regarded as a "super-" or "mega- network” composed of a large number of networks spanning several scales from microscopic (e.g. neuronal mini-column), through mesoscopic (e.g. hippocampal formation), to macroscopic (e.g. limbic system) to megascopic (e.g., the brain).
  • microscopic e.g. neuronal mini-column
  • mesoscopic e.g. hippocampal formation
  • macroscopic e.g. limbic system
  • megascopic e.g., the brain.
  • Graph theory furnishes insight into the structure/topology, distribution degree, clustering, and path length, among other properties, that allow for characterization and modeling of: a) Normal and abnormal network behavior; b) Transitions between states and how to prevent them or spatio-temporally contain them.
  • Graph theory also provides information about the vulnerability of a network to "attack", in this case by a seizure or other path
  • the behavior of variably coupled oscillators may be determined through measurements of frequency, amplitude and phase, from which coherence, synchronization or information (e.g., entropy) measurements may be derived.
  • coherence, synchronization or information e.g., entropy
  • f MRI Functional magnetic resonance imaging
  • the mutation of a network from non-epileptogenic into epileptogenic may be dependent on the network size and architecture.
  • Epileptogenic networks may be characterized by a low cluster coefficient and high path length.
  • reversible inhibition or destruction (lesioning) of a connector hub in a network may be therapeutic as it increases the small world index, and with it the flow and rate of information within a network or between networks.
  • reversible or irreversible blockage, deceleration or perturbation of impulse/information conduction along edges (pathways) connecting nodes, hubs or nodes and hubs (an action that modifies anatomical or functional/effective path length) using electrical, chemical, thermal, mechanical or endosomatic means may be to prevent seizure emergence or spread.
  • concepts of parallel distributed processing are applied to prevent seizure spread and whenever needed to abate the seizure using physical (e.g. electrical, thermal, mechanical) chemical (e.g., drugs, amino acids, ions) mechanical (e.g., negative or positive pressure) or endosomatic (e.g. "biofeedback", cognitive or sensory activation) means.
  • physical e.g. electrical, thermal, mechanical
  • chemical e.g., drugs, amino acids, ions
  • mechanical e.g., negative or positive pressure
  • endosomatic e.g. "biofeedback", cognitive or sensory activation
  • the properties of those putative to the brain, especially the brain of man have: a) much higher dimensionality and complexity emerging from, among other factors, redundancy/overlap of connections, recurrent axonal collaterals (fibers that originate from and terminate in the same neuron), and the existence of fibers "en passage” (fibers originating in a structure passing through another without forming synapses with it on their way to yet another structure where synaptic connections do occur); b) plasticity/remodeling capabilities structurally and functionally/dynamically; c) functional state- dependency; d) emergent properties, which resist proper and thorough characterization by reductionist approaches.
  • the present disclosure conceptualizes seizures as emergent network behavior that is causally irreducible to a singular part (e.g., the "focus") of a network. It further acknowledges that while brain states such as seizures may arise out of a multiplicity of relatively simple interactions, what "emerges” (e.g., a seizure) cannot be reduced to the system's disassembled/isolated constituent parts.
  • this disclosure takes into account that even in-depth, thorough, and accurate characterization of the behavior of each and every component of an epileptogenic network in isolation from the others, will not inform on the mechanisms of ictiogenesis and thus furnish little, if any, insight into how to predict seizures, prevent them from occurring, or prevent them from spreading within or between networks if prevention fails.
  • neural intervention e.g., modulation
  • an appropriate network spatio-temporal scale e.g., micro-, meso-, or macroscopic
  • neural intervention e.g., modulation
  • an appropriate network spatio-temporal scale e.g., micro-, meso-, or macroscopic
  • the present disclosure also adopts a "complex systems theory" approach (e.g., the whole is greater than the sum of its parts) as befits the high complexity-dimensionality of the human brain and its ability to readily interact with the environment (inner and outer).
  • a “complex systems theory” approach e.g., the whole is greater than the sum of its parts
  • These characteristics are borne, from among others, out of the: a) redundancy/overlap of connections, recurrent axonal collaterals and of fibers en passage (fibers originating in a structure passing through another (without forming synapses with it) on their way to another structure where synaptic connections do occur); b) plasticity/remodeling capabilities structurally and functionally/dynamically; c) functional state-dependency and optimized network topology (e.g., small world).
  • a clinical case studied by this inventor illustrates seizures as an emergent network property.
  • a patient with pharmaco-resistant seizures was evaluated for surgery with bi-temporal depth electrodes and subdural strips placed over both orbito-frontal regions. Seizures emerged independently in this case from both mesial temporal regions and while those on left outnumbered those on the right, resective surgery was not performed since it would not have been curative.
  • This patient agreed to participate in a clinical trial (approved by the inventor's institution's human subjects committee) to demonstrate feasibility and safety of high frequency electrical stimulation triggered by automated detections of seizure onset.
  • the left mesial temporal region was chosen as the stimulation target since the majority of seizures emerged from this region.
  • High frequency stimulation abated seizures emerging from this site likely prevented seizure occurrence on the right mesial temporal region but led to the emergence of seizures on the left orbito-frontal region which, while not part of the limbic network, is connected to it.
  • suppression of seizures on the left mesial frontal may have disinhibited (or excited) the ipsilateral orbito-frontal region leading to seizure emergence in this region.
  • seizures As a network property, and more specifically, of the efficacy of therapeutic intervention outside the epileptogenic network (a trans-network effect), is found in the audiogenic seizure rodent model.
  • the epileptogenic network in this rat model is constituted by the inferior (IC) and superior (SC) colliculi, pontine reticular formation (PRF), and periaqueductal gray (PAG). Seizures may be blocked with drugs that suppress neuronal activity in the IC or other sites of the network.
  • the NMDA receptor channel blocker, MK- 801 blocks audiogenic seizures even though it does not depress neuronal firing in any of this network's constituents.
  • MK-801 likely suppresses seizures through excitation of neurons in the substantia nigra reticulata, but only in the intact animal and not in vitro. That this compound not only acts on the non-epileptogenic network, but notably has no effect on the disassembled network (IC, SC and PRF) brings to the fore the relevance and clinical utility of the network theory concepts adopted in this disclosure.
  • the focus theory would not predict the observed trans-network effect, and would have erroneously predicted an in vitro ("focal”) effect, which was not observed.
  • the "trans-network” phenomenon is likely not limited to beneficial therapeutic effects as observed in rats with audiogenic epilepsy.
  • This "trans-network” phenomenon which may be an example of "hodological resonance”, may be also used in one embodiment in this disclosure for early seizure detection, that is, before a first manifestation occurs in the network of emergence (customary network).
  • the occurrence of a seizure may be signaled before the appearance (in the customary network) of the universally accepted hallmark of seizure onset: ictal electrical activity.
  • the signal properties or certain information measures may include, but are not limited to, (i) power at certain frequencies, wave morphology, etc., (ii) degree of synchronization, (iii) spatial extent of synchronization, and/or (iv) an entropy, and the analyses may be performed: (a) within the customary network, (b) between it and other network(s), and/or (c) within or between networks connected to the customary network (but excluding the customary network).
  • probes are paired pulse stimulation, collision tests, transcranial magnetic or electrical stimulation, xenon enhanced computed tomography, dynamic perfusion computed tomography, MR dynamic susceptibility contrast, arterial spin labeling, or Doppler ultrasound.
  • mapping methods such as anatomical (e.g., Marchi technique) and electrophysiological (e.g., evoked responses) about the connectivity between component elements of neuro-anatomical networks and the pathways mediating visual, auditory, or somato-sensory responses recorded from the amygdaloid complex are unknown.
  • This disclosure's central teachings rely (albeit not exclusively) on network theory as adapted herein to the study of brain behavior; its validity for certain applications is independent of certain details about its micro-, meso-, macro- or megascopic architecture or behavior and state -of the art pitfalls do not vitiate the usefulness and validity of this disclosure.
  • the different embodiments of this disclosure provide for prediction, early detection, prevention of spread or of emergence or blockage of seizures to be performed at one or more of the network(s)' structure(s) or their connection(s), either simultaneously or sequentially so as to bias or modulate said component(s) and/or alter functional or anatomical connectivity to ultimately decrease the probability of seizure emergence or spread.
  • Neural tissue has fractal or multi-fractal properties; changes in these measures may be effected via physical (e.g., electrical, thermal) or chemical means (e.g., delivery of osmotically- active agents to a certain region) to fractionate/limit electrical currents or enhance their passage, depending on the site, state of the tissue of interest and the task at hand (e.g., inhibit, disinhibit, facilitate, disfacilitate said tissue).
  • this approach modifies diffusion limited aggregation growth probabilities of electrical currents to either increase or decrease the number of fractal heterogeneity of neural tissue to alter its conductivity or resistivity properties.
  • Inhibition, disinhibition, facilitation, or dis-facilitation may be exerted on structures that are inherently excitatory or inhibitory and that are part of the network where a seizure emerges or of a network that interacts with the network from where a seizure emerges. Inhibition, disinhibition, facilitation, or disfacilitation may be implemented in any temporal order to one or more structures in a network(s).
  • this disclosure applies theories and concepts such as inter- hemispheric rivalry and competitive feedback inhibition, neuronal oscillations that coordinate/bind cross-neuronal interactions, the universal control system theory, and neuronal network stabilization based on synaptic homeostasis.
  • These sensors may collect data from: a) cerebral sites that correspond to known networks such as the hippocampus proper, hippocampal formation, limbic system, central executive network, default mode network, cortico-spinal/pyramidal network, reticular activating network; b) pre-specified extra-cerebral networks such as the cardiac, respiratory, metabolic, musculo-skeletal; c) pathways such as the corona radiata, internal capsule, commissures, fasciculae, or tracts, or d) Non-network sites cerebrally or extra-cerebrally (referring to sites that are either not connected or remotely/weakly connected to a network(s) of interest).
  • known networks such as the hippocampus proper, hippocampal formation, limbic system, central executive network, default mode network, cortico-spinal/pyramidal network, reticular activating network
  • pre-specified extra-cerebral networks such as the cardiac, respiratory, metabolic, musculo-skeletal
  • pathways such as the cor
  • reversal in the direction of flow of impulses/information between nodes, hubs, or networks has two meanings: 1. In the case of reciprocal connections between nodes, if the information flow from node A to B has been historically greater than from B to A, a reversal occurs when the flow from B to A becomes greater than from A to B; 2. In the case of unidirectional connections, if the impulse flow is normally from node A to B (orthodromic), if the direction of flow is from B to A, (antidromic), this also constitutes a direction reversal).
  • Other measures that may be performed on-line or off-line to characterize brain networks include but are not limited to degree distributions, characteristic path lengths, modular structure and local clustering properties, degree centrality (an important node is involved in a large number of interactions), closeness centrality (an important node is typically "close” to, and can communicate quickly with, the other nodes in the network), betweenness centrality (an important node will lie on a high proportion of paths between other nodes in the network, or eigenvector centrality (an important node is connected to important neighbors).
  • Other concepts of centrality that have been proposed and may be used in this disclosure are betweenness centrality and information centrality. Nodes or hubs may be ranked according to the effect their facilitation or inhibition has on the capacity or efficiency of a network in propagating information and the centrality measure based on game theoretic concepts as known to those skilled in the art.
  • Brain network topology may belong to a class known as "small world". In this class, the clustering coefficient is much greater than that of equivalent random controls ⁇ » yrandom while their path lengths are comparable ( ⁇ ⁇ random).
  • the topology of small world network endows them ith high efficiency as information transfer is fast, occurring at relatively low energy costs.
  • There may be other classes of brain networks such as power law distributed wherein the connectivity among the large majority of nodes is sparsely with a "handful" of hubs having exceedingly dense connectivity.
  • Embodiments of the present disclosure provide for determining a potential site of spread of epileptic electrical in the brain (e.g., an epileptic seizure) and providing a treatment.
  • an epileptic event may be detected in a first node of a patient's brain network.
  • a determination may be made as to whether a second node of the patient's brain network may be affected by the activity of the first node.
  • a treatment may be applied to the second node or its connections to prevent spread of the epileptic activity from the first to the second node.
  • the medical device system comprises a medical device 200 and at least one sensor 212.
  • the medical device 200 may be implantable, while in other embodiments, such as that shown in Figure 1, the medical device 200 may be completely external to the body of the patient.
  • Figure 1 depicts the medical device 200 being in wireless communication 211 with the at least one sensor 212.
  • the medical device 200 may be in communication with the at least one sensor 212 via a lead or other wired communication channel.
  • the medical device system shown in Figure 1 also includes at least one electrode 282.
  • the electrode 282 may be implanted in the patient's brain 105 such that the terminus of the electrode 282 may be in proximity to brain region 110.
  • the terms "sensor,” “electrode,” and “probe” may be used interchangeably. Sensors/electrodes/probes may be used to record physical (e.g., electrical, thermal, force/unit area or pressure), chemical (e.g., ions, neurotransmitters, 02), or cognitive (e.g., attention, memory, language, comprehension) signals, among others.
  • Two brain regions 109, 110 are depicted in Figure 1.
  • Brain region 109 may be considered a first node of a neural network
  • brain region 110 may be considered a second node of the neural network.
  • a nodal connection 111 between the brain regions 109, 110 is represented with an arrow in Figure 1.
  • neural network is used herein to refer to natural, non-artificial networks comprising a plurality of neurons assembled into nuclei or structures (nodes or hubs) connected via fibers, pathways, or comissures (edges).
  • the difference between nuclei, structures, or regions referred herein as nodes or hubs is one of connectivity, centrality, functional hierarchy being lower in the former compared to the latter.
  • a structure may be classified as a node in a network of a certain size and hub in a sub-network of said network. For example the hippocampal formation is a sub-network of the limbic network.
  • Exemplary neural networks of the brain include, but are not limited to, sensory networks, motor networks, or cognitive networks.
  • the neural network of interest in the present disclosure is a limbic system
  • said first node and said second node are selected from an amygdala, a hippocampus, a dentate gyrus, a subiculum, an entorhinal cortex, an anterior thalamic nucleus, a mammillary body, a cingulate gyrus, an anterior commissure, a fornix, an arcuate fasciculus, a temporal stem, an orbito-frontal cortex, a locus coeruleus, a reticular thalamic nucleus, a caudate nucleus, a striate nucleus, or a ventral tegmental nucleus.
  • connections between neurons, and between regions of the brain may have different weightings and that these weightings are not only determined by anatomical properties (e.g., number and type of fibers connecting two regions) but also by the functional state of the network and of the patient. That is, a neuron or a brain region may weight signals received from a first upstream/downstream brain region more heavily or more lightly than signals received from a second upstream/downstream brain region.
  • connections between neurons/brain regions may be asymmetrical. That is, a downstream brain region may weight signals received from an upstream brain region more heavily than the upstream neuron/brain region weights signals received from the downstream neuron/brain region.
  • Brain region 109 may be a node, hub or network from where a seizure emerges.
  • Brain region 110 may be a node, hub or network of that patient from where epileptic events typically do not emerge. However, upon emergence of an epileptic event in brain region 109, which is connected to region 111 may spread/propagate to brain region 110, lengthening its duration and severity.
  • Two or more electrodes 282 may be implanted in the patient's brain (not shown). If multiple electrodes 282 are implanted in the patient's brain, they may be implanted such that their termini are in proximity to one or more brain regions 110 (not shown). For example, in such an embodiment, the electrode(s) 282 may be implanted such that two or more electrode termini are in proximity to a single brain region. Alternatively, electrode(s) 282 may be implanted such that each brain region of interest is in proximity to the terminus of a single electrode.
  • Figure 1 depicts the medical device 200 being in communication with the electrode 282 via a lead 281.
  • the medical device 200 may be in wireless communication 281 with the electrode 282, or in communication using a wired communication channel other than a lead.
  • the senor(s) 212 are separate structures that may be placed on the patient's skin, such as over the patient's heart or elsewhere on the patient's torso.
  • the sensor(s) 212 and accompanying leads may be considered an interface for the medical device 200 to receive at least one of autonomic data, neurologic data, or other data.
  • the medical device 200 may also comprise a therapy module 275 configured to deliver at least one therapy, such as electrical signals generated by module 275 and delivered to one or more electrodes 282 via one or more leads 281. Electrical therapy may be delivered to the electrode(s) 282 by the therapy module 275 based upon instructions from the controller 210.
  • the therapy module 275 may comprise various components.
  • the therapy module 275 delivers electrical therapy, and comprises circuitry, such as electrical signal generators, impedance control circuitry to control the impedance "seen” by the leads, and other circuitry that receives instructions relating to the delivery of the electrical signal to tissue.
  • the therapy module 275 may be capable of delivering electrical signals over the leads 281 to the electrode(s) 282.
  • the therapy module 275 may be configured to apply a therapy to at least one neural network/structure of the patient.
  • that neural structure may be a second hub or node in the neural network, based on an indication of an epileptic event in the first hub or node in the neural network. Applying the therapy may be performed prior to the spread of the epileptic event to the second node, and/or upon detecting an indication of the epileptic event spreading or having spread to the second node.
  • a therapy may be applied to a neural structure outside said neural network, in response to detecting said epileptic event.
  • a therapy may be directly applied to a network connected to it, or indirectly via cranial nerve (e.g., a vagus nerve), a region/node of a spinal cord, or a peripheral nerve.
  • a therapy may be applied to multiple neural structures of the patient, multiple sites within a single neural structure/network.
  • Applying a therapy to a neural structure may be direct or indirect.
  • applying the therapy to the second node may be accomplished by applying the therapy to the first hub or node, in view of the relatively high and/or important connectivity presumed to exist between the first hub or node and any hub or node to which it may spread, e.g. the second hub or node.
  • the first node may be considered a connection to the second node.
  • applying a therapy to a neural structure may not be limited (due to connectivity) to only a portion of that structure.
  • the limbic system is considered a network and the amygdala a node within it, an electrode or probe implanted within and configured to deliver a therapy electrical stimulation to the amygdala would be applying a therapy to the network, in this example, the limbic system.
  • applying a therapy to the vagus nerve at a location in the neck is an application of therapy to a neural structure comprising the vagus nerve; the dorsal motor nucleus of the vagus, the nucleus ambiguus, and the solitary nucleus of the brain; and neural networks connected to one or more of those nuclei.
  • the electrical stimulation pulse may comprise a plurality of parameters, such as waveform, pulse width, amplitude, phase, or frequency, among others.
  • the medical device 200 may also comprise an activity spread determination module 285.
  • the activity spread determination module 285 may be configured to do one or more of the following: identify a second node or hub of said neural network, susceptible to invasion by said activity, based at least in part on at least one "information" (e.g., coupling or synchronization) measure between said first node and said second node and based on the behavior history and present functional state, estimate a probability of spread of said epileptic event to said second node and provide an action based on said probability value.
  • information e.g., coupling or synchronization
  • the activity spread determination module 285 may be configured to identify, detect, estimate, or determine the likely of spread (or of emergence of abnormal activity) based on one or more local (e.g., an epileptogenic network) or global (e.g., brain or body) factors.
  • local e.g., an epileptogenic network
  • global e.g., brain or body
  • the coupling characteristic, indication of epileptic activity spread, estimated proclivity, or determined probability may be selected or determined from at least one of: an autonomic index indicative of a probability of epileptic event spread from the first node, a neurologic index indicative of a probability of epileptic event spread from the first node, a metabolic index indicative of a probability of epileptic event spread from the first node, an endocrine index indicative of a probability of epileptic event spread from the first node, a tissue stress marker index indicative of a probability of epileptic event spread from the first node, a physical fitness index indicative of a probability of epileptic event spread from the first node, a body integrity index indicative of a probability of epileptic event spread from the first node, a quality of life index indicative of a probability of epileptic event spread from the first node, a seizure burden index indicative of a probability of epileptic event spread from the first node, or a number of indices indicative of a
  • the monitoring unit 270 may comprise a local database unit 255.
  • the monitoring unit 270 may also be coupled to a database unit 250, which may be separate from monitoring unit 270 (e.g., a centralized database wirelessly linked to a handheld monitoring unit 270).
  • the database unit 250 and/or the local database unit 255 are capable of storing various patient data. These data may comprise patient data acquired from a patient's body or brain, therapy parameter data, seizure severity data, and/or therapeutic efficacy data.
  • the database unit 250 and/or the local database unit 255 may comprise data for a plurality of patients, and may be organized and stored in a variety of manners, such as in date format, severity of disease format, etc.
  • the database unit 250 and/or the local database unit 255 may be relational databases in one embodiment.
  • a physician may perform various patient management functions (e.g., programming parameters for a responsive therapy and/or setting references for one or more detection parameters) using the monitoring unit 270, which may include obtaining and/or analyzing data from the medical device 200 and/or data from the database unit 250 and/or the local database unit 255.
  • the database unit 250 and/or the local database unit 255 may store various patient data.
  • One or more of the blocks illustrated in the block diagram of the medical device 200 in Figure 2 may comprise hardware units, software units, firmware units, or any combination thereof. Additionally, one or more blocks illustrated in Figure 2 may be combined with other blocks, which may represent circuit hardware units, software algorithms, etc. Additionally, any number of the circuitry or software units associated with the various blocks illustrated in Figure 2 may be combined into a programmable device, such as a field programmable gate array, an ASIC device, etc.
  • Figure 3 presents a block diagram of a medical device system, in accordance with one illustrative embodiment of the present disclosure.
  • Figure 3 contains numerous elements in common with or substantially similar to those of Figure 2, and such common/similar elements need not be discussed further.
  • the medical device 200 of Figure 3 may comprise a body index determination module 267.
  • the body index determination module 267 may be configured to determine at least a first body index indicative of an epileptic activity from at least one body signal collected by the sensor(s) 212.
  • the first body index may be a cardiac index, such as HR, HRV, or EKG complex morphology, among others.
  • An indication of epileptic activity may be an increase in HR or a change in the shape of a PQRST complex among others.
  • the medical device 200 may comprise a body index monitoring module 269, which may be configured to monitor a second body index different from the first body index.
  • the second body index may be a motor activity index, such as an amplitude, direction, or force of a body movement, among others.
  • the medical device 200 may also comprise an epileptic event spread indication module 286, which may be configured to detect an indication of epileptic activity spread, based upon at least one of the first body index or the second body index.
  • the first body index is a cardiac index indicating an epileptic activity (such as by showing an increase in HR)
  • the second body index may be a motor activity index. If monitoring the motor activity reveals an indication of a fall, an abnormal body movement, or the like, then it may be known from prior patient data that the epileptic activity has spread to a second node of a neural network.
  • the epileptic event spread indication module 286 may be configured to quantify the probability, extent and/or direction of epileptic event spread.
  • the epileptic event spread indication module 286 may be configured to determine the duration and/or severity of epileptic event spread.
  • the epileptic event spread indication module 286 may issue an indication which may be a basis for further action by one or more responsive modules, or may be stored, such as in memory 217, local database unit 255, database unit 250, remote device 292, or two or more thereof.
  • the therapy module 276 may be configured to apply a first therapy to a first neural structure/network of the patient for preventing spread of the epileptic event and/or configured to apply a second therapy to a second neural structure of the patient for preventing spread of the epileptic event.
  • the logging module 278 may be configured to log an indication of epileptic event spread.
  • the warning module 279 may be configured to warn the patient, a caregiver, or a medical professional of the indication. For example, the warning module 279 may allow the patient to cease an activity, such as driving a car, bathing, swimming, or the like, that may be contraindicated by spread of the epileptic event to the second brain region.
  • the activity spread determination module 285 may comprise an activity mapping unit 410, an activity location unit 420, a zone gradient unit 430, an activity reference comparison unit 440, and an activity classification unit 450.
  • the activity that may be mapped, located, subjected to reference comparison, and/or classified by the activity spread determination module 285 may be one or more of an electrical activity, a magnetic activity, an extracerebral activity, a hemodynamic activity, a chemical activity, or a metabolic activity.
  • the determination of an activity spread performed by the module 285 may encompass analysis of various components relating to, e.g., electrical activity in a patient's brain.
  • a quotient that may provide information regarding the probability of extent of spread of epileptic activity (and /or severity) in the brain, i.e., a "spread quotient," may be determined.
  • the spread quotient may provide information related to at least one of a probability of a spread of said epileptic activity, a speed of said spread of said epileptic activity, a direction of said spread of said epileptic event, an intensity of said epileptic activity, a duration of said epileptic and an extent, or a degree of network recruitment.
  • therapy may be strategically applied to diminish the probability of spread and/or the extent and severity of any spreading epileptic activity.
  • applying a therapy such as to the second node or hub of the neural network where the seizure emerged or to a node or hub in structure/network coupled to the network of seizure emergence, may be based at least in part upon the spread quotient.
  • the spread quotient may be quantified as the number of nodes/hubs to which an epileptic event has spread, divided by the product of the number of nodes/hubs to which the epileptic event could have spread and the number of nodes/hubs of emergence of the epileptic event. More complex quantification of the spread quotient may involve a weighting of each node based on the proclivity of spread from a node to others, with higher weighting for spread to other nodes/hubs in other networks than for spread within the network of emergence, the hierarchy of the node if in the same network, or the hierarchy of networks in the case of internetwork spread.
  • One of the terms (number of nodes/hubs where the seizure emerged) of the spread quotient's denominator may be determined as the numerator, and the other (number of nodes/hubs to which epileptic activity could have spread) from the anatomy of the network.
  • the activity spread determination module 285 may retrieve data and calculate or otherwise determine a gradient related to the detected activity; the gradient may correspond to power in a frequency band, phase of oscillations, ionic concentrations and/or flow rate, neurotransmitter concentrations and or release, flow or uptake rate, energy substrate concentrations, or temperature or blood flow (volume, rate), among others.
  • the module 285 may also perform a classification of activity and/or classification of the reference comparison results of an activity. For example, based upon the activity mapping unit data, the activity location unit 420 may determine the probable location of another activity that may be coupled to the originally detected activity, and/or may also determine the location of the potential spread of activity. Data from the zone gradient unit 430 may be also utilized by the activity location unit 420 to determine in which direction a potential spread of activity may take place and/or determine the likely location of another activity that is related and/or caused by the originally detected activity.
  • the seizure classification obtainable through this disclosure may expand the known classes (e.g., generalized vs. partial and for partial, simple vs. complex), to include other observable in a quantitative manner.
  • a patient with seizures characterized by: a) an unprovoked expression of fear without increase motor activity but with tachycardia (peak heart rate: 135 beats/min with reference/non-seizure mean heart rate of 82); with reversible 2 mm.
  • S-T depression reference EKG: normal
  • BP blood pressure
  • the likelihood of spread of abnormal activity, past history of patterns of spread relating to the activity detected, and/or other factors may be utilized to categorize a detected activity for which potential spread activities may be predicted based upon previously known data.
  • the activity classification unit 450 classifies the detected activity in order to estimate the conditional probability and extent (spread quotient) of spread and express it quantitatively as between 0-1 or as a percentage (e.g., 60%) or semi-quantitatively (low, medium or high).
  • data from the activity mapping unit 410 may be utilized by the activity reference comparison unit 440 to perform a comparison function of the detected activity to previously stored patterns to determine the likelihood of spread.
  • the medical device 200 may utilize the electrical activity mapping information depicted in Figure 5 to selectively treat nodes, hubs, or networks susceptible to invasion by abnormal electrical activity and/or preventively target uninvolved but potentially recruitable/entrainable (over weeks, months or years) nodes, to decrease the development of more epileptogenic networks. Specifically, in one embodiment, this disclosure will reduce or prevent secondary epileptogenesis.
  • the sensor/electrode array mesh 610 is depicted as a mesh-type unit for illustrative purposes only and those skilled in the art would be able to implement a variety of types of sensor/electrode arrays and remain within the spirit and scope of the present disclosure.
  • the array mesh 610 may comprise a plurality of sensors and/or electrodes positioned in any number of configurations, such as a row-column array.
  • Figure 6B illustrates a top view of the array mesh 610, in accordance with one embodiment of the present disclosure.
  • the array mesh 610 may be formed such that a predetermined arrangement of sensors and electrodes are configured in a manner such that various portions of the brain may be targeted for treatment (e.g., electrical stimulation.
  • Figure 7A illustrates a stylized depiction of an exemplary abnormal activity detected in a site 710 of a patient's brain. Based upon this detection, the activity reference comparison unit 440 (Fig. 4) may perform a look-up comparison, and may identify a stored abnormal activity mapping that is similar to that of the activity occurring at site 710 in Figure 7A.
  • Figure 7D illustrates a stylized depiction of a "real-time" mapping, similar to the depiction shown in Figure 7C.
  • the "real-time" mapping fails to match the reference spread mapping shown in Figure 7B.
  • the patient may be performing a different mental task, may be in a different body state, etc.
  • gradient 730A is much greater in magnitude than gradient 730.
  • it may be concluded that spread from 710 to 720 is unlikely, and an intervention to prevent spread from 710 to 720 is not required.
  • spread from 710 to 720A is highly likely, and an intervention to prevent spread from 710 to 720A may be implemented.
  • Applying the therapy may be based upon detecting an indication of said epileptic event, about to spread, spreading or having spread to said second node.
  • the method depicted in Figure 8 may further comprise determining at 840 at least one seizure spread characteristic indicative of a spread of the epileptic event from the first node, wherein the characteristic comprises at least one of a result of at least one of a cognitive test, an awareness test, a responsiveness test, and a chemical assay administered to said patient; or a change in at least one of an autonomic index, a neurologic index, a metabolic index, an endocrine index, a tissue stress marker index, a physical fitness index, a body integrity index, a quality of life index, or a seizure burden index. More information regarding these tests and indices can be found in U.S. Pat. Appl. No.
  • identifying at 820 the second node may be based at least in part on the at least one seizure spread characteristic determined at 840.
  • the method may further comprise identifying at 850 at least one pathway from the first node to the second node, based at least in part on the determining at 840.
  • the method may optionally further comprise applying at 860 a therapy to the pathway identified at 850.
  • Figure 9 shows a flowchart depiction of a method, according to one illustrative embodiment of the present disclosure.
  • a first body index indicative of an epileptic activity in a patient may be determined at 910.
  • the first body index may be at least one of an autonomic index, a neurologic index, a metabolic index, an endocrine index, a tissue stress marker index, a physical fitness index, a body integrity index, a quality of life index, or a seizure burden index.
  • the first body index may be a non-electrocortical neurologic index.
  • the second body index is different from the first body index
  • the second body index may be derived from the same body signal as the first body index.
  • the first body index may be heart rate (HR) and the second body index may be heart rate variability (HRV), which are different indices both derivable from one signal of cardiac activity (e.g., EKG, etc).
  • HR heart rate
  • HRV heart rate variability
  • an indication of epileptic activity spread in a brain of the patient may be detected at 930.
  • a body index indicative of an abnormal motor activity of the patient may be indicative of a spread of epileptic activity to a region of the brain controlling motor activity.
  • a responsive action may be taken in response to the detecting at 930.
  • the responsive action may be selected from delivering at 940 a therapy to at least one neural structure of the patient, modifying at 950 a therapy to at least one neural structure of the patient, logging at 960 the indication of spread, or warning at 970 said patient, a caregiver, or a medical professional of the indication of spread.
  • FIG 10 a flowchart depiction of a method, according to one illustrative embodiment of the present disclosure, is presented. Detecting an epileptic event at 1010 may be performed substantially as described above.
  • a first therapy may then be applied at 1020 to a first neural structure of the patient for treating said epileptic event.
  • the therapy may be an electrical therapy, a thermal therapy, a chemical therapy, or a mechanical (e.g., pressure) therapy, among others, as described above.
  • the first neural structure may be any neural structure encompassing one or more neural networks.
  • the first neural structure comprises the first node of the first neural network in which the epileptic event is detected at 1010.
  • a second therapy may be applied at 1130 to a second neural structure, based on a proclivity of a spread of said epileptic event to a third neural structure of said patient. For example, if the proclivity of spread is deemed sufficiently high, the second therapy may be applied at 1130.
  • the proclivity of spread may be determined roughly contemporaneously with the time of performance of the detecting at 1010 and/or applying the first therapy at 1020, or it may be previously determined through retrospective analysis of epileptic events of the patient and/or the spread database thereof.
  • One or more previously determined proclivity(ies) of spread may be stored in a look up table, such as a look up table in memory 217 of the medical device 200.
  • the second therapy may be the same as or different from the first therapy, and may be as described above.
  • the second neural structure (to which the second therapy is applied) and the third neural structure (to which the epileptic event may have a proclivity of spread) may be the same or different neural structures.
  • Any method depicted in Figures 8-10 may be performed by a non-transitive, computer- readable storage device for storing instructions that, when executed by a processor, perform the method.
  • Figures 11-12 depict exemplary mechanisms by which epileptic event spread in an exemplary neural network in the brain of a patient may be prevented, delayed, or reduced.
  • the model neural network contains three nodes, A, B, and C, with connections between each pair of nodes shown by lines.
  • the thickness of the line reflects the strength of the connection between nodes (thicker lines representing stronger anatomo-functional connections), and arrows represent the direction of electrical impulse propagation.
  • a pro- epileptogenic influence e.g., trauma to the head
  • Node A sends excitatory impulses to nodes B and C, which in turn send back inhibitory or disfacilitatory (not shown for simplicity's sakes) to node A.
  • the plastic changes caused by trauma have weakened nodes B and C, leaving node A disinhibited.
  • one or more of three depicted interventions may be performed.
  • node A may be inhibited or disfacilitated, thus lowering the intensity impulses and their rate of propagation toward B and C.
  • signals traveling from A to B or C may be shunted out of the network or fragmented.
  • nodes B and/or C may be excited facilitated or disinhibited, thus restoring their inhibitory feedback to node A.
  • Conduction block electrical, thermal, chemical or mechanical
  • of the paths connecting node A to B and C may be undertaken to interfere with the arrival of excitatory impulses to these 2 nodes or the flow of inhibitory input from them to A may be increased.
  • Figure 12 shows a non-epileptogenic network that was transformed into epileptogenic through plastic changes through weakening (not shown in the figure) of inhibitory activity flowing from this node to nodes B and C.
  • the disinhibition of nodes B and C results in a marked increase and apparent reversal of flow of information into A. Seizure spread may be prevented by exciting node A, disfacilitating or inhibiting nodes B and C or impeding/shunting/fragmenting the flow of impulses through the paths that connect them.
  • Figures 11 and 12 should be viewed as simplified (even oversimplified) depictions of brain networks, as they lack details about connections (e.g., reciprocal, collateral, etc) and their topology/structure at certain scales.
  • the depicted networks may or may not support seizure emergence.
  • the depicted networks may be influenced by other networks in a manner that supports seizure emergence in the depicted (simplified) networks.
  • FIG. 13-25 review various neural networks known in the brain.
  • the weight of the line reflects the strength of the functional connectivity between nodes (thicker lines representing more active connections), and arrows represent the direction of action potential propagation.
  • These figures represent various neural networks to which the teachings of this disclosure may be applied.
  • rFIC right hemisphere frontoinsular cortex, containing the ventrolateral prefrontal cortex and anterior insula
  • PCC posterior cingulate cortex
  • rPPC right hemisphere posterior parietal cortex
  • rDLPFC right hemisphere dorsolateral prefrontal cortex
  • VMPFC ventromedial prefrontal cortex
  • ACC anterior cingulate cortex
  • Figure 13 shows functional connectivity under three different conditions.
  • Figure 13A shows typical functional connectivity when the subject performs an auditory event segmentation task (e.g., listening to two different sound sources).
  • Figure 13B shows typical functional connectivity when the subject performs a visual "oddball" attention task.
  • Figure 13C shows typical functional connectivity when the subject is in a task-free state.
  • FIG. 14 various brain regions are shown. Four particular pathways between nodes are shown: 1, mammillo-thalamic tract; 2, fornix; 3, stria terminalis; 4, ansa peduncularis. Together, the depicted brain regions and the pathways between them may be considered a neural network.
  • FIG. 15 various brain regions are shown. Each of the depicted pathways is a monosynaptic reciprocal connection, and arrowheads are omitted. Together, the depicted brain regions and the pathways between them may be considered a neural network.
  • FIG. 16 various brain regions are shown, with afferences (signals from the body to the brain) also depicted. Five particular pathways between nodes are shown: 1, post- commissural fornix; 2, mammillo-thalamic tract; 3, cingulate gyrus; 4, pre-commissural fornix; 5, medial forebrain bundle. Together, the depicted brain regions and the pathways between them may be considered a neural network.
  • FIG. 17 various brain regions are shown. Together, the depicted brain regions and the pathways between them may be considered a neural network.
  • FIG. 18 various brain regions are shown. Together, the depicted brain regions and the pathways between them may be considered a neural network. The stippled structures and the pathways between them may together be considered a subnetwork.
  • FIG 19 various brain regions are shown.
  • the depicted bidirectional connections generally comprise afferent projections to, and efferent projections from, the amygdaloid complex. Together, the depicted brain regions and the pathways between them may be considered a neural network.
  • the amygdaloid complex may be considered the hub of the depicted neural network.
  • the depicted bidirectional connections generally comprise afferent projections to, and efferent projections from, the septal region. Together, the depicted brain regions and the pathways between them may be considered a neural network.
  • the septal region may be considered the hub of the depicted neural network.
  • the depicted bidirectional connections generally comprise afferent projections to, and efferent projections from, the amygdaloid complex.
  • the unidirectional connections include the following: afferent projections from the mammillary bodies to the anterior thalamic nucleus, from the anterior thalamic nucleus to the presubiculum, from the subiculum to the hippocampus, from the parasubiculum to the hippocampus, and from the temporal neocortex to the cingulate gyrus; and efferent projections from the cingulate gyrus to each of the subiculum, parasubiculum, presubiculum, hippocampus, the superior colliculi, pretectal area, periaqueductal gray matter, midbrain tegmentum, nucleus locus coeruleus, pontine gray, and dorsomedial thalamaic nucleus, from the mammillary bodies to the
  • FIG. 22 the Papez circuit is depicted. Together, the depicted brain regions and the pathways between them may be considered a neural network.
  • FIG. 23 various brain regions are shown. All unidirectional connections are afferent connections, except for the connection from the habenula to the mesencephalic reticular formation, which is efferent. Together, the depicted brain regions and the pathways between them may be considered a neural network.
  • the unidirectional connections include the following: afferent projections from the Raphe nucleus, nucleus locus coeruleus, and medial entorhinal area to the hippocampus and subiculum; from the olfactory bulb, prepiriform cortex, periamygdaloid cortex, septum, (dorsal) Raphe nucleus, and nucleus locus coeruleus to the lateral entorhinal area; and from the (dorsal) Raphe nucleus, nucleus locus coeruleus, thalamus, and septum to the medial entorhinal area; and efferent projections from the hippocampus and subiculum to the cingulate gyrus, anterior commissure, mammillary bodies, anterior thalamic nucleus, periaqueductal gray, and pontine nucleus; and from CA3 and the presubiculum to the medial entor
  • FIG. 25 various brain regions are shown. Together, the depicted brain regions and the pathways between them may be considered a neural network.

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Abstract

Cette invention concerne une méthode comprenant le dépistage d'un événement épileptique dans un réseau neuronal, l'événement survenant dans un premier nœud ; l'identification d'un second nœud ; et l'application d'un traitement thérapeutique au second nœud ou à toute connexion. Cette invention concerne également une méthode comprenant la détermination d'un premier indice corporel indiquant une activité épileptique ; la surveillance d'un second indice corporel ; le dépistage d'une preuve de propagation de ladite activité, basé sur au moins le second indice corporel ; et la mise en œuvre d'une action réactive, telle que l'administration d'un traitement thérapeutique, la modification d'un traitement thérapeutique, la consignation de l'indication, ou un avertissement ; ou encore une méthode comprenant le dépistage d'un événement épileptique dans le premier nœud d'un réseau neuronal, l'application d'un premier traitement thérapeutique à une première structure neuronale pour traiter l'événement ; et l'application d'un second traitement thérapeutique à une seconde structure neuronale du patient, basée sur l'inclination à la propagation de l'événement à une troisième structure neuronale.
PCT/US2013/036885 2012-04-17 2013-04-17 Système et appareil pour le dépistage précoce, la prévention, le confinement ou la réduction d'une activité anormale du cerveau qui se propage Ceased WO2013158709A1 (fr)

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CN111436929A (zh) * 2019-01-17 2020-07-24 复旦大学 一种神经生理信号的生成和识别方法
CN118749998A (zh) * 2024-06-19 2024-10-11 首都医科大学附属北京天坛医院 一种微创手术神经监测系统
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CN111436929A (zh) * 2019-01-17 2020-07-24 复旦大学 一种神经生理信号的生成和识别方法
CN111436929B (zh) * 2019-01-17 2021-06-01 复旦大学 一种神经生理信号的生成和识别方法
CN118749998A (zh) * 2024-06-19 2024-10-11 首都医科大学附属北京天坛医院 一种微创手术神经监测系统
CN121370193A (zh) * 2025-12-23 2026-01-23 首都医科大学宣武医院 脑电信号检测装置、控制方法、信号处理系统及存储介质
CN121370193B (zh) * 2025-12-23 2026-05-05 首都医科大学宣武医院 脑电信号检测装置、控制方法、信号处理系统及存储介质

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