Detection of normal pressure hydrocephalus

Abstract

The goal is to detect normal pressure hydrocephalus. The system includes a plurality of electrodes configured to record respective signals generated by the brain of a patient in response to magnetic stimulation of the brain, and a processor configured to receive the signals, perform an assessment for normal pressure hydrocephalus (NPH) by analyzing the signals, and output an output indicative of the assessment. Other embodiments are also described.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims priority to U.S. Provisional Patent Application No. 63 / 431,712 to Fogel et al., entitled "Detection of Normal Pressure Hydrocephalus," filed December 11, 2022, the disclosure of which is incorporated herein by reference.

[0002] The present invention relates to methods and devices for use in medical procedures, and in particular to devices and methods for detecting normal pressure hydrocephalus (NPH). [Background technology]

[0003] Electrophysiology is a well-established and important system for assessing the functionality of brain networks. The use of electrophysiological measurements to characterize and monitor brain network activity has been widely used over the past 70 years. Electrophysiological measurements can generally be categorized into two groups of parameters: network integrity (meaning its connectivity and coherence) and network plasticity. Functional network connectivity depends on the synchronous activation of neurons and is used to determine the integrity of a functional network. Network coherence refers to the level of synchrony between two or more brain regions and is used to determine the strength of connectivity between specific brain regions. Neuroplasticity, or brain plasticity, is the brain's ability to continuously adapt its functional and structural organization to changing requirements. Neuroplasticity enables the brain to reorganize neuronal networks in response to environmental stimuli, store information, and recover from brain and spinal cord injuries. Neuroplasticity is essential for the establishment and maintenance of brain circuits.

[0004] Magnetic stimulation is a noninvasive brain stimulation technique that allows for the study of human cortical function in vivo. The use of magnetic stimulation to examine human cortical function is enhanced by combining such stimulation with simultaneous recording of electrical evoked responses, such as electroencephalography (EEG). EEG provides the opportunity to directly measure brain responses to magnetic stimulation, measuring cortical evoked potentials (EPPs). A key feature of EPP topography is that stimulation of only one cortical hemisphere elicits bilateral EEG responses with distinct characteristics. Magnetic stimulation-evoked activity propagates from the stimulation site ipsilaterally via association fibers, contralaterally via transcallosal fibers, and to subcortical structures via projection fibers. A single stimulation pulse (e.g., delivered over the primary motor cortex (M1)) produces a series of positive and negative EEG peaks at specific characteristic latencies, typically including negative peaks at 45 ms (N45) and 100 ms (N100) post-stimulus, and positive peaks at 60 ms (P60) and 180 ms (P180) post-stimulus. This response pattern indicates synaptic activity: these evoked cortical potentials last up to 300 ms, both in the vicinity of the stimulus as well as in distant, interconnected brain regions.

[0005] Normal pressure hydrocephalus (NPH) is a condition in which excess cerebrospinal fluid builds up in the ventricles of the brain. When the ventricles enlarge with excess cerebrospinal fluid, they can destroy and damage nearby brain tissue, resulting in difficulty walking, problems thinking and reasoning, and loss of bladder control. In many cases, normal pressure hydrocephalus can be treated with the surgical insertion of a shunt. Summary of the Invention

[0006] For patients with suspected or confirmed symptoms of NPH, it is typically important to identify or rule out NPH in order to determine the course of treatment. For example, NPH can be treated with a ventriculoperitoneal shunt, while other conditions with symptoms similar to those of NPH cannot. Furthermore, even in confirmed cases of NPH, it can be important to evaluate the potential of shunt treatment to alleviate the patient's symptoms. However, conventional techniques for performing these evaluations, such as the use of medical imaging or cerebrospinal fluid (CSF) tap tests, have limited accuracy and / or limited practicality (e.g., due to high cost or inaccessibility).

[0007] To address this problem, embodiments of the present invention provide a system including a plurality of electrodes configured to record respective signals generated by a patient's brain in response to magnetic stimulation of the brain. The system further includes a processor configured to receive the signals, perform an assessment for normal pressure hydrocephalus (NPH) by analyzing the signals, and output an output indicative of the assessment. In some embodiments, the processor is configured to perform the assessment by determining whether the patient is likely to have NPH. In other embodiments, the processor is configured to perform the assessment by determining whether shunt surgery is likely to improve the patient's NPH symptoms.

[0008] Typically, the system further includes a magnetic stimulator including a coil configured to apply magnetic stimuli to the patient's brain. The stimuli may be applied to any suitable portion of the patient's brain, such as the primary motor cortex and / or the dorsolateral prefrontal cortex. After each stimulation, the electrodes record evoked potential signals generated by the brain in response to the stimulation, and the processor analyzes this neurophysiological response as described above.

[0009] In some embodiments, the processor is configured to determine, by analyzing the evoked potential signals, whether the patient is more likely to be suffering from NPH or a different degenerative disorder, such as Parkinson's disease, whose symptoms are similar to those of NPH.

[0010] Thus, according to some embodiments of the present invention, there is provided a system including a plurality of electrodes configured to record respective signals generated by a patient's brain in response to magnetic stimulation of the brain, the system further including a processor configured to receive the signals, perform an assessment for normal pressure hydrocephalus (NPH) by analyzing the signals, and output an output indicative of the assessment.

[0011] In some embodiments, the magnetic stimulation comprises magnetic stimulation of the primary motor cortex of the brain.

[0012] In some embodiments, the magnetic stimulation comprises magnetic stimulation of the dorsolateral prefrontal cortex of the brain.

[0013] In some embodiments, the processor is configured to perform the assessment by identifying that the patient is likely to have NPH.

[0014] In some embodiments, the processor is configured to perform the assessment by determining the likelihood that the patient has NPH.

[0015] In some embodiments, the processor is configured to identify that the patient is likely to have NPH by identifying that the patient is likely to respond to shunt surgery.

[0016] In some embodiments, the patient has NPH, and the processor is configured to perform the assessment by ascertaining whether shunt surgery is likely to improve the patient's symptoms of NPH.

[0017] In some embodiments, the processor is configured to perform the assessment by determining the likelihood that shunt surgery will improve the condition.

[0018] In some embodiments, the processor is configured to perform the assessment by determining the severity of the patient's condition.

[0019] In some embodiments, the processor is configured to perform the assessment by determining whether the patient is more likely to be suffering from NPH or a different degenerative disorder.

[0020] In some embodiments, the system further includes a magnetic stimulation device including a coil configured to apply magnetic stimulation to the patient's brain.

[0021] In some embodiments, the processor is further configured to drive the coil to apply magnetic stimulation to the patient's brain.

[0022] In some embodiments, the system further comprises an electrical signal detector comprising an electrode.

[0023] In some embodiments, the processor: Measuring the motor threshold of the magnetic stimulation; and performing an evaluation in response to a motor threshold.

[0024] In some embodiments, the processor: calculating one or more measures of neurophysiological activity indicated in the signal; and performing an evaluation based on the measurements.

[0025] In some embodiments, the processor is configured to perform the evaluation by inputting measurements of neurophysiological activity into a model.

[0026] In some embodiments, the model comprises a logistic regression model.

[0027] In some embodiments, the model comprises a neural network.

[0028] In some embodiments, the measure of neurophysiological activity comprises adherence of the waveform of the signal to a predetermined waveform indicative of a healthy response to the magnetic stimulation.

[0029] In some embodiments, the measure of neurophysiological activity comprises the amplitude of a portion of the signal.

[0030] In some embodiments, at least a portion of the period is between 45 ms and 90 ms after the magnetic stimulation.

[0031] In some embodiments, at least a portion of the period is between 100 ms and 180 ms after the magnetic stimulation.

[0032] In some embodiments, the measure of neurophysiological activity comprises the slope of a line passing through the two main peaks of the signal.

[0033] In some embodiments, the main peak is achieved at approximately 100 ms and 180 ms after magnetic stimulation in young, healthy subjects.

[0034] In some embodiments, the measure of neurophysiological activity comprises a principal peak latency measure that quantifies the latency of a principal peak in the signal.

[0035] In some embodiments, the main peak is the positive peak achieved approximately 60 ms after magnetic stimulation in young, healthy subjects.

[0036] In some embodiments, the main peak is the negative peak achieved approximately 100 ms after magnetic stimulation in young, healthy subjects.

[0037] In some embodiments, the main peak is the positive peak achieved at approximately 180 ms after magnetic stimulation in young, healthy subjects.

[0038] In some embodiments, the measure of neurophysiological activity comprises a latency difference measure that measures the difference between a first latency of a first principal peak in the signal and a second latency of a second principal peak in the signal.

[0039] In some embodiments, The first main peak is a positive peak achieved approximately 180 ms after magnetic stimulation in young healthy subjects. The second main peak is a positive peak that is reached approximately 60 ms after magnetic stimulation in young healthy subjects.

[0040] According to some embodiments of the present invention, there is further provided a method comprising receiving, by a processor, from a plurality of electrodes, respective signals generated by a patient's brain in response to magnetic stimulation of the brain, the method further comprising performing an assessment for normal pressure hydrocephalus (NPH) by analyzing the signals, and outputting an output indicative of the assessment.

[0041] There is further provided, according to some embodiments of the present invention, a computer software product including a tangible, non-transitory computer-readable medium having stored thereon program instructions that, when read by a processor, cause the processor to receive, from a plurality of electrodes, respective signals generated by a patient's brain in response to magnetic stimulation of the brain, analyze the signals to perform an assessment for normal pressure hydrocephalus (NPH), and output an output indicative of the assessment.

[0042] According to some embodiments of the present invention, there is further provided a system including a plurality of electrodes configured to record respective signals produced by a patient's brain in response to magnetic stimulation of the brain, the system further including a processor configured to receive the signals, analyze the signals to determine whether the patient is more likely to have normal pressure hydrocephalus or a different degenerative disorder, and output an output indicative of whether the patient is more likely to have normal pressure hydrocephalus or a different degenerative disorder.

[0043] In some embodiments, the system further includes a magnetic stimulation device including a coil configured to apply magnetic stimulation to the patient's brain.

[0044] In some embodiments, the processor is further configured to drive the coil to apply magnetic stimulation to the patient's brain.

[0045] In some embodiments, the system further comprises an electrical signal detector comprising an electrode.

[0046] In some embodiments, the processor: determining a score for the signal, the score comprising: Adherence of the signal waveform to a predetermined waveform indicative of a healthy response to the magnetic stimulation; The characteristics of signals that indicate plasticity in the brain and The amplitude of a portion of the signal, the slope of a line passing through two main peaks of the signal; and and determining, based on the score, whether the patient is more likely to be suffering from NPH or a different degenerative disorder.

[0047] In some embodiments, the different degenerative disorders comprise a disorder selected from the group of disorders consisting of vascular dementia, Alzheimer's disease, Parkinson's disease, and frontotemporal dementia.

[0048] In some embodiments, the different degenerative disorder comprises Parkinson's disease.

[0049] In some embodiments, the processor: calculating adherence of the waveform of the signal to a predetermined waveform indicative of a healthy response to the magnetic stimulation; and determining, in response to adherence being above a predetermined threshold, that the patient is more likely to have NPH.

[0050] In some embodiments, the threshold is a first threshold, and the processor is further configured to determine that the patient is more likely to suffer from Parkinson's disease in response to adherence being below the first threshold and above a second predetermined threshold.

[0051] In some embodiments, the processor: determining characteristics of signals indicative of plasticity in the brain; and determining that the patient is more likely to be suffering from NPH in response to the plasticity being below a predetermined threshold.

[0052] In some embodiments, the processor: determining an amplitude of a portion of the signal; and determining that the patient is more likely to be suffering from NPH in response to the amplitude being below a predetermined threshold.

[0053] In some embodiments, at least a portion of the period is between 45 ms and 90 ms after the magnetic stimulation.

[0054] In some embodiments, the processor: Determining the slope of a line passing through the two main peaks of the signal; and determining that the patient is more likely to be suffering from NPH in response to the slope being above a predetermined threshold.

[0055] In some embodiments, the main peak is achieved at approximately 100 ms and 180 ms after magnetic stimulation in young, healthy subjects.

[0056] According to some embodiments of the present invention there is further provided a method comprising receiving, by a processor, from a plurality of electrodes, respective signals produced by a patient's brain in response to magnetic stimulation of the brain, the method further comprising analyzing the signals to determine whether the patient is more likely to have normal pressure hydrocephalus or more likely to have a different degenerative disorder, and outputting an output indicative of whether the patient is more likely to have normal pressure hydrocephalus or more likely to have a different degenerative disorder.

[0057] There is also provided, according to some embodiments of the present invention, a computer software product including a tangible, non-transitory computer-readable medium having stored thereon program instructions that, when read by a processor, cause the processor to receive, from a plurality of electrodes, respective signals produced by the patient's brain in response to magnetic stimulation of the brain, analyze the signals to determine whether the patient is more likely to have normal pressure hydrocephalus or a different degenerative disorder, and output an output indicative of whether the patient is more likely to have normal pressure hydrocephalus or a different degenerative disorder.

[0058] A more complete understanding of the present invention will be obtained from the following detailed description of the embodiments of the present disclosure when read in conjunction with the drawings. [Brief explanation of the drawings]

[0059] [Figure 1] 1 is a schematic diagram of a clinician performing a diagnostic procedure on a patient, in accordance with some applications of the present invention. [Figure 2] FIG. 1 is a flow diagram of a method for identifying whether a patient is likely to have NPH, according to some embodiments of the present invention. [Figure 3] FIG. 1 is a flow diagram of measurement calculation steps according to some embodiments of the present invention. [Figure 4A] Experimental results demonstrating the utility of embodiments of the present invention are presented. [Figure 4B] Experimental results demonstrating the utility of embodiments of the present invention are presented. [Figure 5A] Experimental results demonstrating the utility of embodiments of the present invention are presented. [Figure 5B] Experimental results demonstrating the utility of embodiments of the present invention are presented. [Figure 5C] Experimental results demonstrating the utility of embodiments of the present invention are presented. [Figure 6] 1 illustrates a receiver operating characteristic (ROC) curve obtained in accordance with some embodiments of the present invention. [Figure 7A] 1 is a bar graph showing data obtained for NPH patients and healthy control subjects. [Figure 7B] 1 is a bar graph showing data obtained for NPH patients and healthy control subjects. [Figure 7C] 1 is a bar graph showing data obtained for NPH patients and healthy control subjects. [Figure 7D] 1 is a bar graph showing data obtained for NPH patients and healthy control subjects. [Figure 8A] Data obtained for NPH patients treated with shunt surgery are shown. [Figure 8B] Data obtained for NPH patients treated with shunt surgery are shown. [Figure 9A] 1 is a bar graph showing data obtained for NPH patients, healthy control subjects, and Parkinson's disease patients. [Figure 9B] 1 is a bar graph showing data obtained for NPH patients, healthy control subjects, and Parkinson's disease patients. [Figure 9C]1 is a bar graph showing data obtained for NPH patients, healthy control subjects, and Parkinson's disease patients. [Figure 9D] 1 is a bar graph showing data obtained for NPH patients, healthy control subjects, and Parkinson's disease patients. [Figure 9E] 1 is a bar graph showing data obtained for NPH patients, healthy control subjects, and Parkinson's disease patients. DETAILED DESCRIPTION OF THE INVENTION

[0060] System Description Referring initially to Figure 1, Figure 1 is a schematic diagram of a clinician 10 performing a diagnostic procedure on a patient 12 using a diagnostic system 28, according to some embodiments of the present invention. The diagnostic procedure may be performed, for example, when the patient 12 has suspected or confirmed symptoms of normal pressure hydrocephalus (NPH).

[0061] System 28 includes a magnetic stimulation device 20 configured to be positioned near the head of patient 12, for example, by being placed on a cap 30 worn on the head. Device 20 includes a coil 21 configured to generate a magnetic field that stimulates activity in the brain of patient 12. In general, device 20 may be positioned over any portion of the brain associated with NPH symptoms to stimulate that portion. Exemplary portions include the frontal cortex (e.g., the primary motor cortex, any other portion of the motor cortex, or the dorsolateral prefrontal cortex), the occipital cortex (e.g., the visual cortex), the parietal cortex (e.g., the posterior parietal cortex), the temporal cortex, and any portion of the brain connected to any of the above. In particular, the inventors have found that for the analyses described herein, stimulating the left and / or right primary motor cortex and / or the left and / or right dorsolateral prefrontal cortex of the brain can be effective. Accordingly, device 20 may be positioned over the left and / or right primary motor cortex and / or the left and / or right dorsolateral prefrontal cortex of the patient's brain.

[0062] System 28 further comprises a plurality of electrodes 22 configured to record respective signals generated by the brain in response to stimuli. Electrodes 22 may be coupled to the patient's head via a low-impedance adhesive material. Alternatively, electrodes 22 may be coupled to cap 30 (optionally via an impedance-reducing gel) such that the electrodes contact the head when cap 30 is snugly placed on the patient's head. Typically, electrodes 22 belong to an electrical signal detector (e.g., an electroencephalograph (EEG) detector) that, in addition to electrodes 22, may include leads 38 and / or other elements to facilitate detection and communication of electrical signals. (For ease of illustration, only one lead 38 is shown in FIG. 1.)

[0063] System 28 further includes a control unit 24 that includes a signal generator 34 and other circuits, such as analog-to-digital (A / D) conversion circuitry and / or noise reduction circuitry. Typically, control unit 24 is connected to stimulator 20 via cable 36. System 28 further includes a computer processor 32, which can reside within control unit 24 or in an external device, such as a laptop, that communicates with control unit 24 (e.g., via a universal serial bus (USB) cable). Typically, processor 32 is configured to drive coil 21 to apply magnetic stimulation to the patient's brain. In particular, processor 32 drives signal generator 34 to generate electrical signals that flow through coil 21 via cable 36, thus generating a magnetic field in the device, which in turn induces signals (or "potentials") in the patient's brain. These signals are recorded by electrodes 22 and passed from the electrodes via respective leads 38 or via wireless transmission to the control unit. After optional noise reduction and digitization within control unit 24, processor 32 receives the signals.

[0064] The processor 32 is further configured to analyze the signal. The processor performs an assessment regarding NPH based on the analysis. The processor then outputs an output indicative of the assessment, for example, by displaying the output on the display 26.

[0065] In some embodiments, performing the assessment includes determining whether the patient is likely to have NPH, and the output indicates this diagnosis. For example, the processor can determine whether the patient is more likely to have NPH or a different degenerative disorder (the symptoms of which are typically similar to those of NPH). In some embodiments, the processor further determines the likelihood that the patient has NPH (or another degenerative disorder), and the output indicates the likelihood. For example, the processor may output a 90% likelihood that the patient has NPH. In other embodiments, the processor does not explicitly calculate the likelihood.

[0066] Alternatively or additionally, performing the assessment for a patient with NPH includes determining whether shunt surgery is likely to improve the patient's NPH symptoms. In some embodiments, the processor further determines the likelihood that shunt surgery will improve the symptoms. Alternatively or additionally, performing the assessment for a patient with NPH includes determining the severity of the patient's condition.

[0067] Typically, when analyzing the signals, the processor calculates one or more measures of neurophysiological activity indicated in the signals and performs an evaluation based on the measures. In some embodiments, to calculate the measure of neurophysiological activity, the processor calculates a characteristic of multiple evoked potential signals (e.g., the latency of the main peak, as described below) as a characteristic of a representative signal based on the multiple signals, such as an average of the multiple signals. Alternatively or additionally, the processor calculates a statistic (e.g., an average) of the characteristic across the signals, i.e., the processor calculates the characteristic separately for each of the signals and then calculates the statistic. Alternatively or additionally, as shown in FIG. 3 described below, the processor calculates a statistic of the characteristic across multiple representative signals, each of which is based on a different respective subset of the evoked potential signals. In the context of this specification and claims, each reference to a characteristic of multiple signals (or a characteristic of a portion of multiple signals) encompasses all of these possibilities within its scope.

[0068] In some embodiments, the calculated measures of neurophysiological activity include a waveform adherence measure that quantifies the similarity between the waveform of a portion of the signal and the waveform of a corresponding portion of a benchmark signal, which may be obtained, for example, from relevant literature. Typically, the benchmark signal represents the response of a healthy subject, such as a young, healthy subject. In other words, waveform adherence measures the degree to which the brain's evoked electrical response to the magnetic stimulation adheres to a predetermined healthy response in terms of the overall shape of the evoked response curve (i.e., waveform).

[0069] Alternatively or additionally, the calculated measures of neurophysiological activity include cortical excitability measures based on the amplitude of portions of the signal, for example, the cortical excitability measures may be based on statistics of the amplitude, such as the integral (which is amplitude dependent) or mean amplitude of the signal.

[0070] Alternatively or additionally, the calculated measure of neurophysiological activity includes a waveform excitability measure of a portion of the signal based on both the amplitude of the portion of the signal and the similarity of the waveform to a benchmark waveform.

[0071] Alternatively or additionally, the computed measures of neurophysiological activity include interhemispheric connectivity measures that quantify the similarity between the response of the right side of the patient's brain to the stimuli and the response of the left side of the patient's brain to the stimuli.

[0072] Alternatively or additionally, the calculated measures of neurophysiological activity include a principal peak latency measure that quantifies the latency of a principal peak in the signal.

[0073] For example, measurements can include N100 (or "medial phase") latency, which is the latency of the negative peak achieved approximately 100 ms after magnetic stimulation in young, healthy subjects (i.e., healthy subjects whose latency has not yet been affected by age). For example, with respect to stimulation of the primary motor cortex (M1), the M1 N100 latency is the latency of the negative peak achieved approximately 100 ms after magnetic stimulation of the primary motor cortex in young, healthy subjects. In some embodiments, the N100 latency is determined as the latency of the maximum negative peak between 60 ms and 190 ms after stimulation.

[0074] As another example, measurements may include P60 (or "early phase") latency, which is the latency of the positive peak achieved approximately 60 ms after magnetic stimulation in young, healthy subjects. For example, for stimulation of the primary motor cortex, M1 P60 latency is the latency of the positive peak achieved approximately 60 ms after magnetic stimulation of the primary motor cortex in young, healthy subjects. In some embodiments, P60 latency is determined as the latency of the largest positive peak between 35 or 40 ms after stimulation and 10 ms before the N100 peak.

[0075] As another example, measurements can include P180 (or "late phase") latency, which is the latency of the positive peak achieved at approximately 180 ms after magnetic stimulation in young, healthy subjects. For example, in the case of stimulation of the dorsolateral prefrontal cortex (DLPFC), DLPFC P180 latency is the latency of the positive peak achieved at approximately 180 ms after magnetic stimulation of the DLPFC in young, healthy subjects. In some embodiments, P180 latency is determined as the latency of the largest positive peak at least 10 ms from the N100 peak.

[0076] Alternatively or additionally, the measure of neurophysiological activity includes a latency difference measure that measures the difference in latency between two major peaks in the signal, for example, a P180-P60 difference measure is the duration between the P60 peak and the P180 peak.

[0077] Alternatively or additionally, the measure of neurophysiological activity includes the slope of a line passing through the two main peaks of the signal, e.g., the P60-N100 slope is the slope of a line passing through the P60 and N100 peaks, and the N100-P180 slope is the slope of a line passing through the N100 and P180 peaks.

[0078] Alternatively or additionally, in analyzing the signals, the processor measures the motor threshold, which is the strength of the weakest magnetic signal that elicits a motor response. This strength may be expressed in absolute terms or relatively, for example, as a percentage of the maximum strength of magnetic stimulation device 20 (as shown in FIG. 7A, described below).

[0079] In some embodiments, the processor performs the assessment by comparing one or more of the above parameters to respective thresholds, each of which may be based on at least one characteristic of the patient, such as the patient's age or gender. Alternatively or additionally, the processor inputs one or more of the above parameters into any type of nonlinear or linear model, such as a neural network or logistic regression model, which is calibrated to output the assessment. As a specific example, the processor may input measurements of M1 P60, M1 P180-P60 difference, DLPFC P180, and DLPFC P180-P60 difference into a logistic regression model, as described below with reference to FIG. 6.

[0080] In some embodiments, the processor determines whether a patient is likely to have NPH by determining whether the patient is likely to respond to shunt surgery (e.g., ventriculoperitoneal shunt therapy). For example, the processor may make a diagnosis and predict a response to such therapy based on one or more parameters empirically found to predict response to such therapy, e.g., as described below with reference to Figures 4A-4B, 5A-5B, and 8A-8B. Alternatively or additionally, for a patient known to have NPH (through the techniques described herein or through another technique), the processor determines whether shunt surgery is likely to improve the patient's NPH symptoms based on such parameters, and the output further indicates whether shunt surgery is likely to improve the symptoms.

[0081] As described above, in some embodiments, for a patient with NPH, the processor determines the severity of the patient's condition by analyzing the signals. For example, the processor may determine the severity based on one or more measures of neurophysiological activity that have been experimentally found to correlate with severity, e.g., as described below with reference to Figures 5A-5B.

[0082] In some embodiments, the processor performs a differential diagnosis based on one or more parameters of the signal. In other words, by analyzing the signal, the processor determines whether the patient is more likely to suffer from NPH or a different degenerative disorder, such as vascular dementia, Alzheimer's disease, Parkinson's disease, or frontotemporal dementia, each of which typically shares one or more symptoms with NPH. (For example, gait disturbance is a symptom of both NPH and Parkinson's disease.) Examples of parameters that can be used to perform such a differential diagnosis are described below with reference to experimental results.

[0083] In general, processor 32 may be embodied as a single processor or as a set of cooperatively networked or clustered processors. The functionality of processor 32 may be implemented solely in hardware, e.g., using one or more fixed-function or general-purpose integrated circuits, application-specific integrated circuits (ASICs), and / or field-programmable gate arrays (FPGAs). Alternatively, the functionality may be implemented at least partially in software. For example, processor 32 may be embodied as a programmed processor, e.g., including a central processing unit (CPU) and / or a graphics processing unit (GPU). Program code and / or data, including software programs, may be loaded for execution and processing by the CPU and / or GPU. The program code and / or data may be downloaded to the processor in electronic form, e.g., over a network. Alternatively or additionally, the program code and / or data may be provided and / or stored on a non-transitory, tangible medium, such as magnetic, optical, or electronic memory. Such program code and / or data, when provided to the processor, generates a machine or special-purpose computer configured to perform the tasks described herein.

[0084] Signal Analysis Reference is now made to FIG. 2, which is a flow diagram of a method 39 for determining whether a patient 12 (FIG. 1) is likely to have NPH, according to some embodiments of the present invention.

[0085] Method 39 begins with a signal receiving step 40 in which processor 32 receives respective signals recorded by electrodes 22 (FIG. 1). As described above with reference to FIG. 1, these signals are generated by the patient's brain in response to magnetic stimulation of the brain.

[0086] After receiving the signal, the processor checks whether a further stimulus should be delivered in a check step 42. For example, the processor may perform the check step 42 by processing input from the clinician. If a further stimulus is to be delivered, the processor returns to the receive signal step 40 to receive evoked potentials from the next stimulus.

[0087] Generally, any cortex of the patient's brain may be stimulated any number of times, and the processor may perform signal receiving step 40 after each of these stimulations. For example, a clinician may perform several stimuli (e.g., 8-12) of the right primary motor cortex, several stimuli (e.g., 8-12) of the left primary motor cortex, several stimuli (e.g., 8-12) of the right dorsolateral prefrontal cortex, and several stimuli (e.g., 8-12) of the left dorsolateral prefrontal cortex. Typically, the stimulation protocol (including the number of repetitions and the strength and frequency of the stimulation signal at each stimulation site) is predetermined. Alternatively, the clinician sets up a simulation protocol.

[0088] Upon determining that no further stimulation should be performed, the processor performs a measurement calculation step 43, in which the processor calculates one or more measurements of the neurophysiological activity indicated in the signal. (Alternatively, or additionally, the processor calculates another parameter, such as a motor threshold, based on the signal.) Then, in an evaluation step 50, the processor performs an evaluation based on the calculated measurements, for example, by comparing the measurements to respective threshold measurements and / or by inputting the measurements into a model. Finally, in an output step 52, the processor outputs the evaluation.

[0089] Reference is now made to FIG. 3, which is a flow diagram of measurement calculation step 43, according to some embodiments of the present invention.

[0090] In some embodiments, measurement calculation step 43 begins with signal averaging substep 44. In signal averaging substep 44, the processor calculates an average signal for each stimulated cortex by averaging the signals received across electrodes and stimuli. Typically, the processor first calculates an average for each electrode by averaging across stimuli, and then averages these per-electrode averages.

[0091] The processor then calculates one or more measures of neurophysiological activity from each of the averaged signals in a measure calculation substep 46. Finally, in a measure averaging substep 48, the processor calculates the average of each of these measures.

[0092] For example, assuming that the left and right primary motor cortices (M1) and the left and right dorsolateral prefrontal cortices (DLPFC) are stimulated, the processor may calculate four average signals, i.e., right M1, left M1, right DLPFC, and left DLPFC, in signal averaging substep 44. Next, in measurement calculation substep 46, the processor may calculate one or more measures for each of these average signals. For example, the processor may calculate the M1 P60 and the M1 P180-P60 difference for each of the left and right M1 signals, and the DLPFC P180 and DLPFC P180-P60 difference for each of the left and right DLPFC signals. Finally, in measurement averaging substep 48, the processor may calculate an average of the two M1 P60 measures, another average of the two M1 P180-P60 difference measures, another average of the two DLPFC P180 measures, and another average of the two DLPFC P180-P60 difference measures.

[0093] Instead of averaging each of the measurements in measurement averaging substep 48, the processor calculates a weighted average, maximum, minimum, or any other statistical value for each of the measurements.

[0094] Alternatively, the processor may combine the left and right cortices in signal averaging substep 44 and omit measurement averaging substep 48. For example, the processor may calculate a single M1 signal and a single DLPFC signal, and then calculate the difference between M1 P60 and M1 P180-P60 for the M1 signal, and the difference between DLPFC P180 and DLPFC P180-P60 for the DLPFC signal.

[0095] Experimental results Reference is now made to Figures 4A-4B, which show experimental results demonstrating the utility of embodiments of the present invention.

[0096] Seventeen patients, all over 65 years of age and with symptoms strongly suggestive of NPH, underwent magnetic stimulation of the primary motor cortex, and P60 and N100 latencies were measured. After stimulation, each patient underwent ventriculoperitoneal shunt therapy. Each patient was assessed on the modified Rankin Scale (MRS) both before and 3 months after treatment. Patients were assessed for response to treatment based on changes in the MRS. In total, there were 11 responders and 6 non-responders.

[0097] 4A shows M1 P60 latency statistics for responders 54 and non-responders 56. For both responders and non-responders, box 58 indicates the range of latency measurements between the 5th and 95th percentiles, the mean is indicated by the position of vertical line 60 relative to the horizontal axis, and horizontal line 62 indicates the full range of latency values.

[0098] The mean M1 P60 latency for responders was 56.5 ms, with a standard deviation of 13.2 ms. In contrast, the mean for non-responders was 72.8 ms, with a standard deviation of 9.7 ms. Thus, these results demonstrate that P60 latency (e.g., M1 P60 latency) can be used in at least two ways. First, P60 latency can be used to assess whether an NPH patient is likely to respond to shunt therapy. Second, given that successful shunt therapy usually indicates that the patient has NPH (rather than another condition with similar symptoms), P60 latency can be used to perform a differential diagnosis of NPH. In particular, a lower P60 latency may indicate a higher likelihood of response to NPH and / or shunt therapy, while a higher P60 latency may indicate a lower likelihood of response to another condition and / or shunt therapy.

[0099] Similarly, Figure 4B shows M1 N100 latency statistics 64 for responders and non-responders 66. The mean M1 N100 latency for responders was 104.1 ms with a standard deviation of 23.3 ms. In contrast, the mean for non-responders was 124.3 ms with a standard deviation of 22.9 ms. Thus, these results demonstrate that N100 latency (e.g., M1 N100 latency) can be used in at least two ways, as described above for P60 latency. In particular, a lower N100 latency may indicate a higher likelihood of response to NPH and / or shunt treatment, while a higher N100 latency may indicate a lower likelihood of response to another condition and / or shunt treatment.

[0100] In addition to MRS evaluation, each patient underwent a computed tomography (CT) or magnetic resonance imaging (MRI) scan before shunt treatment. Scans of these patients with smaller M1 P60 and M1 N100 latencies showed wider temporal horns, indicating a higher likelihood of developing NPH and / or responding to shunt treatment.

[0101] Reference is now made to Figures 5A-5C, which show further experimental results demonstrating the utility of embodiments of the present invention.

[0102] In addition to M1 P60 and M1 N100 latencies, the M1 P180-P60 difference and DLPFC P180 latency were measured for each of the 17 patients. Additionally, each patient was assessed for their Clinical Global Improvement Scale (CGIC) at 3 months after shunt treatment. (A CGIC score of 1 corresponds to very much improved, CGIC score of 2 corresponds to very much improved, CGIC score of 3 corresponds to minimally improved, CGIC score of 4 corresponds to no change, and higher CGIC scores correspond to increasing degrees of deterioration.) Fourteen patients had a CGIC score of less than 4, indicating that each of these patients responded to treatment, while the other three patients had a CGIC score of 4 or greater, indicating that each of these patients did not respond. (Because the CGIC is more subjective than the MRS, it was expected that the number of responders per CGIC may differ from the number of responders per MRS.)

[0103] Figure 5A shows the mean M1 P180-P60 difference for each group of responders (CGIC 1, CGIC 2, and CGIC 3) and non-responders (CGIC ≥ 4). As seen in Figure 5A, the M1 P180-P60 difference was negatively correlated with CGIC score. Therefore, the results in Figure 5A demonstrate that the P180-P60 difference (e.g., M1 P180-P60 difference) can be used to differentially diagnose NPH and / or assess the likelihood of successful shunt therapy.

[0104] Figure 5B shows the mean DLPFC (referred to as "frontal" in Figure 5B) P180 latency difference for each group of responders and non-responders. As seen in Figure 5B, DLPFC P180 latency also negatively correlated with CGIC score. Thus, the results in Figure 5B demonstrate that P180 latency (e.g., DLPFC P180 latency) can be used to differentially diagnose NPH and / or assess the likelihood of successful shunt treatment. Scans of these patients with lower DLPFC P180 latency also showed larger sylvian fissures, indicating a higher likelihood of NPH.

[0105] Before treatment, each patient also underwent a cerebrospinal fluid (CSF) tap test, a conventional method for assessing whether a patient could benefit from shunt treatment. The results of the CSF tap test, shown in Figure 5C, were less negatively correlated with CGIC scores than the M1 P180-P60 difference and DLPFC P180 latency.

[0106] Reference is now made to FIG. 6, which illustrates a receiver operating characteristic (ROC) curve obtained in accordance with some embodiments of the present invention.

[0107] Measurements of M1 P60, M1 P180-P60 difference, DLPFC P180, and DLPFC P180-P60 difference for 17 patients were entered into a logistic regression model. The ROC curves shown in Figure 6 demonstrate the performance of the model with different sets of model parameters. The area under the curve (AUC) of the ROC curve was relatively high, indicating good performance of the model.

[0108] Reference is now made to Figures 7A-7D, which are bar graphs showing data obtained for patients with normal pressure hydrocephalus and healthy control (HC) subjects. The bar graphs are based on experiments performed on 20 patients with normal pressure hydrocephalus, with a mean age of 74, and 20 healthy control subjects of a similar mean age. Of the 20 patients with normal pressure hydrocephalus, eight were identified as candidates for shunt surgery. Of these, seven patients underwent shunt surgery, with one showing significant improvement, three showing improvement, one showing no change, and two showing worsening of symptoms.

[0109] Referring first to FIG. 7A , this bar graph shows the mean motor threshold (MT) of stimulation of the left primary motor cortex (M1L) for patients with normal pressure hydrocephalus and healthy subjects, demonstrating a correlation between a lower motor threshold and the likelihood of having normal pressure hydrocephalus. Thus, according to some applications of the present invention, a processor measures the motor threshold of magnetic stimulation and identifies and / or determines the likelihood of a patient having NPH based at least in part on the motor threshold. For example, the processor may identify and / or determine the likelihood of a patient having NPH in response to the motor threshold being below a given (predetermined) threshold. (A motor threshold below a given threshold may be used as an indicator in combination with one or more additional indicators described herein.) Alternatively, for example, the processor may input the motor threshold into a model such as a logistic regression model or a neural network.

[0110] Referring now to FIG. 7B, the bar graph shows the mean waveform adherence (WFA) after stimulation of several brain regions determined for patients with normal pressure hydrocephalus and healthy subjects of similar average age. The bar graph demonstrates a correlation between greater waveform adherence and the likelihood of having normal pressure hydrocephalus. As described above, waveform adherence typically measures the degree to which the waveform of a magnetic stimulation-evoked signal detected by an electrode closely resembles a predetermined waveform that indicates a healthy response to magnetic stimulation observed in relatively young subjects. Typically, adherence is measured by generating a waveform score or similar measure that indicates the overall shape of the evoked response waveform, with higher adherence indicating that the overall shape of the evoked response waveform is more similar to that of a healthy subject than the overall shape of a waveform with lower adherence. Therefore, it is somewhat counterintuitive that patients with normal pressure hydrocephalus had greater waveform adherence than healthy patients of similar age. This is hypothesized to be because, compared with healthy elderly subjects whose overall responses are typically lower amplitude than those of healthy young subjects, in elderly NPH patients the waveforms differ from those of healthy young subjects, but changes in the later part of the waveform compensate for changes in the earlier part of the waveform, or vice versa.

[0111] Thus, according to some applications of the present invention, a processor may identify and / or determine the likelihood that a patient has NPH based at least in part on waveform adherence of the evoked electrical response to the magnetic stimulation. For example, the processor may identify and / or determine the likelihood that a patient has NPH in response to waveform adherence exceeding a given (predetermined) threshold. (Waveform adherence exceeding a given threshold may be used as an indicator in combination with one or more additional indicators described herein.) Alternatively, for example, the processor may input waveform adherence into a model such as a logistic regression model or a neural network.

[0112] Referring now to Figure 7C, this bar graph shows the mean amplitude of the early portion of the response signal, as determined for normal pressure hydrocephalus patients and healthy subjects. The early portion begins 45 ms and ends 90 ms after stimulation. The bar graph demonstrates a correlation between this "early amplitude," a form of local cortical excitability, and the likelihood of having normal pressure hydrocephalus.

[0113] Thus, according to some applications of the present invention, a processor may identify and / or determine the likelihood that a patient has NPH based at least in part on the amplitude of a portion of the signal (e.g., average, minimum, or maximum amplitude). In some embodiments, the portion is an early portion in that at least a portion of the portion (e.g., the entire portion) is between 45 ms and 90 ms after stimulation. For example, the processor may identify and / or determine the likelihood that a patient has NPH in response to the amplitude falling below a given (predetermined) threshold. An amplitude below a given threshold may be used as an indicator in combination with one or more additional indicators described herein. Alternatively, for example, the processor may input the amplitude into a model such as a logistic regression model or a neural network.

[0114] Referring now to FIG. 7D, this bar graph shows the average slope of the line passing through the major peaks depicting the late portion of the response signal, determined for patients with normal pressure hydrocephalus and healthy subjects. These major peaks, called N100 and P180, are reached in healthy subjects at approximately 100 ms and 180 ms after magnetic stimulation. The bar graph demonstrates a correlation between this slope and the likelihood of having normal pressure hydrocephalus.

[0115] Thus, according to some applications of the present invention, a processor may identify and / or determine the likelihood that a patient has NPH based at least in part on the slope of a line passing through two major peaks of the patient's response signal, such as the N100 and P180 peaks. For example, the processor may identify and / or determine the likelihood that a patient has NPH in response to the slope exceeding a given (predetermined) threshold. (A slope exceeding a given threshold may be used as an indicator in combination with one or more additional indicators described herein.) Alternatively, for example, the processor may input the slope into a model such as a logistic regression model or a neural network.

[0116] 7A-7D, in some embodiments, one or more of the parameters described herein are combined, for example, using a model, into a score, and based on the score, the likelihood that the patient has normal pressure hydrocephalus is determined.

[0117] It should be noted that the results of additional experiments performed on NPH patients indicate a correlation between the parameters described herein and the severity of the patient's symptoms. Thus, in some applications, the values ​​of one or more of the parameters described herein are used to determine the severity of a patient's condition in a patient suffering from normal pressure hydrocephalus.

[0118] Reference is now made to Figures 8A-8B, which show data acquired on patients with normal pressure hydrocephalus treated with shunt surgery. The data demonstrate a correlation between parameters detected by a system such as that shown in Figure 1 and the likelihood of successful surgical shunt treatment in patients with normal pressure hydrocephalus. Typically, shunt surgery effectiveness is scored by comparing measurements such as the Timed Up-and-Go Test (TUG), which scores a patient's gait before and after surgery. (For Figure 8B, several brain regions were stimulated.)

[0119] Referring to FIG. 8A , as shown, for patients with normal pressure hydrocephalus, a higher M1L motor threshold correlates with the likelihood that shunt surgery will improve symptoms in NPH patients. Thus, according to some applications of the present invention, a processor measures the motor threshold of magnetic stimulation (which may be applied to the M1L cortex or a different cortex) and identifies and / or determines the likelihood that shunt surgery will improve symptoms based at least in part on the motor threshold. For example, the processor may identify and / or determine the likelihood that shunt surgery will improve symptoms in response to the motor threshold exceeding a given (predetermined) threshold. (A motor threshold exceeding a threshold may be used as an indicator in combination with one or more additional indicators described herein.) Alternatively, for example, the processor may input the motor threshold into a model such as a logistic regression model or a neural network.

[0120] Referring to FIG. 8B, as shown, for patients with normal pressure hydrocephalus, a higher amplitude of the late portion of the magnetic stimulation-induced signal, which begins 100 ms after stimulation and ends 180 ms after stimulation, correlates with the likelihood of shunt surgery improving the patient's symptoms. Thus, according to some applications of the present invention, a processor measures the amplitude of a portion of the signal (e.g., minimum, maximum, or average amplitude) and identifies and / or determines the likelihood of shunt surgery improving the symptoms based at least in part on the amplitude. For example, the processor may identify and / or determine the likelihood of shunt surgery improving the symptoms in response to the amplitude exceeding a given (predetermined) threshold. (Amplitude above a threshold may be used as an indicator in combination with one or more additional indicators described herein.) Alternatively, for example, the processor may input the amplitude into a model such as a logistic regression model or a neural network. In some embodiments, at least a portion (e.g., the entire portion) is between 100 ms and 180 ms after magnetic stimulation.

[0121] 8A-8B, in some embodiments, one or more of the parameters described herein are combined (e.g., using a model) into a score based on which the likelihood of the patient's condition improving as a result of surgical shunt treatment is determined.

[0122] Reference is now made to Figures 9A-9E, which are bar graphs showing data obtained for patients with normal pressure hydrocephalus, healthy control subjects, and patients with Parkinson's disease (PD). (PD has symptoms similar to those of NPH.) The bar graphs shown in Figures 9A-9E demonstrate that parameters detected by the system shown in Figure 1 can be used to distinguish between NPH patients, healthy subjects, and patients with Parkinson's disease.

[0123] Referring to FIG. 9A, it can be observed that both normal pressure hydrocephalus and Parkinson's disease patients have relatively low motor thresholds compared to healthy subjects.

[0124] 9B shows that normal pressure hydrocephalus patients typically exhibit lower scores on plasticity measures compared to both healthy control subjects and Parkinson's disease patients. Typically, plasticity is measured by measuring how evoked electrical signals change in response to magnetic stimulation as the stimulation continues to be applied. Thus, in some embodiments, the processor determines characteristics of the evoked signals indicative of plasticity in the patient's brain, and in response to plasticity falling below a predetermined threshold, determines that the patient is more likely to have NPH rather than PD.

[0125] 9C demonstrates that the waveform adherence of both normal pressure hydrocephalus patients and Parkinson's disease patients is greater than that of healthy subjects of similar age, but that the waveform adherence of normal pressure hydrocephalus patients is typically even greater than that of Parkinson's disease patients. Thus, in some embodiments, the processor calculates the waveform adherence of the evoked signal and determines that the patient is more likely to have NPH rather than PD in response to the waveform adherence being above a predetermined threshold. Alternatively, the processor determines that the patient is more likely to have PD rather than NPH in response to the waveform adherence being below the aforementioned threshold and above another predetermined threshold.

[0126] 9D demonstrates that the amplitude of the early portion of the magnetic stimulation-evoked signal in both patients with normal pressure hydrocephalus and patients with Parkinson's disease is lower than that of healthy subjects of similar age, but that in patients with normal pressure hydrocephalus is typically even lower than that in patients with Parkinson's disease. Thus, in some embodiments, the processor determines the amplitude of a portion of the signal (e.g., maximum, minimum, or average amplitude) and, in response to the amplitude being below a predetermined threshold, determines that the patient is more likely to have NPH rather than PD. In some embodiments, at least a portion of the portion (e.g., the entire portion) is between 45 ms and 90 ms after magnetic stimulation.

[0127] 9E demonstrates that the slope of the line between the two late peaks for both the normal pressure hydrocephalus patient and the Parkinson's disease patient is greater than the slope for healthy subjects of similar age, but the slope for the normal pressure hydrocephalus patient is typically even greater than the slope for the Parkinson's disease patient. Thus, in some embodiments, the processor determines the slope of a line passing through the two main peaks of the signal (e.g., the N100 and P180 peaks) and, in response to the slope being above a predetermined threshold, determines that the patient is more likely to be suffering from NPH rather than PD.

[0128] More generally, in some embodiments, the processor (e.g., using a model) determines a score for the signal based on a combination of two or more of the four parameters shown in Figures 9B-9E and / or any other parameters described herein. Based on the score, the processor further determines whether the patient is (a) healthy, (b) suffering from normal pressure hydrocephalus, or (c) more likely to suffer from a different condition (such as vascular dementia, Alzheimer's disease, frontotemporal dementia, or Parkinson's disease).

[0129] Applications of the invention described herein may take the form of a computer program product accessible from a computer-usable or computer-readable medium (e.g., a non-transitory computer-readable medium) that provides program code for use by or in connection with a computer, such as computer processor 32, or any instruction execution system. As used herein, a computer-usable or computer-readable medium may be any apparatus that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The medium may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus, or device) or propagation medium. Typically, the computer-usable or computer-readable medium is a non-transitory computer-usable or computer-readable medium.

[0130] Examples of a computer-readable medium include a semiconductor or solid-state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk, and an optical disk, current examples of which include compact disk-read-only memory (CD-ROM), compact disk-read / write (CD-R / W), and DVD.

[0131] A data processing system suitable for storing and / or executing program code includes at least one processor (e.g., computer processor 32) coupled directly or indirectly to memory elements via a system bus. The memory elements may include local memory used during the actual execution of the program code, mass storage devices, and cache memory that provides temporary storage of at least some of the program code to reduce the number of times the code must be retrieved from mass storage devices during execution. The system is capable of reading instructions of the present invention on the program storage devices and performing the methods of embodiments of the present invention in accordance with these instructions.

[0132] A network adapter may be coupled to a processor to enable the processor to be coupled to other processors or remote printers or storage devices through intervening private or public networks. Modems, cable modems, and Ethernet cards are just a few of the currently available types of network adapters.

[0133] Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages, such as the C programming language or similar programming languages.

[0134] Computer processor 32 is typically a hardware device programmed with computer program instructions to create a special-purpose computer. For example, when programmed to execute the algorithms described with reference to the drawings, computer processor 32 typically functions as a special-purpose diagnostic computer processor. Typically, the operations described herein performed by computer processor 32 transform the physical state of memory, which is an actual physical item, to have different magnetic polarities, charges, etc., depending on the memory technology used. In some applications, the operations described as being performed by computer processor 32 are performed by multiple computer processors in combination with one another.

[0135] It will be appreciated by those skilled in the art that the present invention is not limited to what has been particularly shown and described above, but rather the scope of the present invention includes both combinations and subcombinations of the various features described above, as well as variations and modifications thereof which would occur to those skilled in the art upon reading the foregoing description and which are not in the prior art.

Claims

1. a plurality of electrodes configured to record respective signals generated by the patient's brain in response to magnetic stimulation of the brain; 1. A processor, comprising: receiving the signal; performing an assessment for normal pressure hydrocephalus (NPH) by analyzing the signal; a processor configured to output an output indicative of said evaluation; A system comprising:

2. The system of claim 1 , wherein the magnetic stimulation comprises magnetic stimulation of the primary motor cortex of the brain.

3. The system of claim 1 , wherein the magnetic stimulation comprises magnetic stimulation of the dorsolateral prefrontal cortex of the brain.

4. The system of claim 1 , wherein the processor is configured to perform the assessment by identifying the patient as likely to have NPH.

5. The system of claim 4 , wherein the processor is configured to perform the assessment by determining a likelihood that the patient has NPH.

6. 5. The system of claim 4, wherein the processor is configured to identify the patient as likely to have NPH by identifying the patient as likely to respond to shunt surgery.

7. 10. The system of claim 1, wherein the patient has NPH, and the processor is configured to perform the assessment by determining whether shunt surgery is likely to improve the patient's symptoms of NPH.

8. The system of claim 7 , wherein the processor is configured to perform the assessment by determining the likelihood that the shunt procedure will improve the condition.

9. The system of claim 1 , wherein the processor is configured to perform the assessment by determining a severity of the patient's condition.

10. 10. The system of claim 1, wherein the processor is configured to perform the assessment by determining whether the patient is more likely to be suffering from NPH or a different degenerative disorder.

11. 10. The system of claim 1, further comprising a magnetic stimulation device comprising a coil configured to apply the magnetic stimuli to the brain of the patient.

12. 12. The system of claim 11, wherein the processor is further configured to drive the coil to apply the magnetic stimulation to the brain of the patient.

13. The system of claim 1 further comprising an electrical signal detector comprising the electrode.

14. The processor: measuring a motor threshold of the magnetic stimulation; and performing the evaluation in response to the motor threshold.

15. The processor: calculating one or more measures of neurophysiological activity indicated in the signal; and performing said evaluation based on said measurements.

16. The system of claim 15 , wherein the processor is configured to perform the assessment by inputting the measurements of neurophysiological activity into a model.

17. The system of claim 16 , wherein the model comprises a logistic regression model.

18. The system of claim 16 , wherein the model comprises a neural network.

19. 16. The system of claim 15, wherein the measure of neurophysiological activity comprises adherence of the waveform of the signal to a predetermined waveform indicative of a healthy response to magnetic stimulation.

20. The system of claim 15 , wherein the measure of neurophysiological activity comprises an amplitude of a portion of the signal.

21. 21. The system of claim 20, wherein at least a portion of the period is between 45 ms and 90 ms after the magnetic stimulation.

22. 21. The system of claim 20, wherein at least a portion of the period is between 100 ms and 180 ms after the magnetic stimulation.

23. 16. The system of claim 15, wherein the measure of neurophysiological activity comprises the slope of a line passing through two major peaks of the signal.

24. 24. The system of claim 23, wherein the main peak is achieved at approximately 100 ms and 180 ms after magnetic stimulation in young, healthy subjects.

25. 16. The system of claim 15, wherein the measures of neurophysiological activity include a principal peak latency measure that quantifies the latency of a principal peak in the signal.

26. 26. The system of claim 25, wherein the main peak is a positive peak achieved approximately 60 ms after magnetic stimulation in young, healthy subjects.

27. 26. The system of claim 25, wherein the main peak is a negative peak achieved approximately 100 ms after magnetic stimulation in young, healthy subjects.

28. 26. The system of claim 25, wherein the main peak is a positive peak achieved approximately 180 ms after magnetic stimulation in young, healthy subjects.

29. 26. The system of claim 25, wherein the measure of neurophysiological activity comprises a latency difference measure that measures the difference between a first latency of a first principal peak in the signal and a second latency of a second principal peak in the signal.

30. the first major peak is a positive peak achieved at approximately 180 ms after magnetic stimulation in young, healthy subjects; The second main peak is a positive peak achieved approximately 60 ms after magnetic stimulation in young, healthy subjects.

30. The system of claim 29.

31. 1. A method comprising: receiving, by a processor, from a plurality of electrodes, respective signals generated by the patient's brain in response to magnetic stimulation of the brain; performing an assessment for normal pressure hydrocephalus (NPH) by analyzing said signal; and outputting an output indicative of the evaluation.

32. 1. A computer software product comprising a tangible, non-transitory computer-readable medium having stored thereon program instructions that, when read by a processor, cause the processor to: receiving from a plurality of electrodes respective signals generated by the patient's brain in response to magnetic stimulation of the brain; analyzing the signal to perform an assessment for normal pressure hydrocephalus (NPH); and outputting an output indicative of said evaluation.

33. a plurality of electrodes configured to record respective signals generated by the patient's brain in response to magnetic stimulation of the brain; 1. A processor, comprising: receiving the signal; analyzing the signal to determine whether the patient is more likely to have normal pressure hydrocephalus or a different degenerative disorder; a processor configured to output an output indicating whether the patient is more likely to have normal pressure hydrocephalus or more likely to have the different degenerative disorder; A system comprising:

34. 34. The system of claim 33, further comprising a magnetic stimulation device comprising a coil configured to apply the magnetic stimuli to the patient's brain.

35. 35. The system of claim 34, wherein the processor is further configured to drive the coil to apply the magnetic stimulation to the brain of the patient.

36. 34. The system of claim 33, further comprising an electrical signal detector comprising the electrode.

37. The processor: determining a score for the signal, the score comprising: - adherence of the waveform of the signal to a predetermined waveform indicative of a healthy response to magnetic stimulation; a characteristic of the signal that indicates plasticity in the brain; the amplitude of a portion of the signal; a slope of a line passing through two main peaks of the signal; and determining a score for the signal based on a combination of two or more of the slope of a line passing through two main peaks of the signal. determining whether the patient is more likely to suffer from NPH or the different degenerative disorder based on the score; 34. The system of claim 33, configured to analyze the signal by:

38. 38. The system of any one of claims 33 to 37, wherein the different degenerative disorders comprise disorders selected from the group of disorders consisting of vascular dementia, Alzheimer's disease, Parkinson's disease, and frontotemporal dementia.

39. 39. The system of claim 38, wherein the different degenerative disorders include Parkinson's disease.

40. The processor: calculating an adherence of the waveform of the signal to a predetermined waveform indicative of a healthy response to magnetic stimulation; determining that the patient is more likely to have NPH in response to the adherence being above a predetermined threshold; 40. The system of claim 39, configured to analyze the signal by:

41. 41. The system of claim 40, wherein the threshold is a first threshold, and the processor is further configured to determine that the patient is more likely to suffer from Parkinson's disease in response to the adherence being below the first threshold and above a second predetermined threshold.

42. The processor: determining a characteristic of the signal indicative of plasticity in the brain; determining that the patient is more likely to be afflicted with NPH in response to the plasticity being below a predetermined threshold; 40. The system of claim 39, configured to analyze the signal by:

43. The processor: determining an amplitude of a portion of the signal; determining that the patient is more likely to be afflicted with NPH in response to the amplitude being below a predetermined threshold; 40. The system of claim 39, configured to analyze the signal by:

44. 44. The system of claim 43, wherein at least a portion of the portion is between 45 ms and 90 ms after the magnetic stimulation.

45. The processor: determining the slope of a line passing through two major peaks of said signal; determining that the patient is more likely to be afflicted with NPH in response to the slope exceeding a predetermined threshold; 40. The system of claim 39, configured to analyze the signal by:

46. 46. ​​The system of claim 45, wherein the main peak is reached at approximately 100 ms and 180 ms after magnetic stimulation in young, healthy subjects.

47. 1. A method comprising: receiving, by a processor, from a plurality of electrodes, respective signals generated by the patient's brain in response to magnetic stimulation of the brain; analyzing the signal to determine whether the patient is more likely to suffer from normal pressure hydrocephalus or a different degenerative disorder; and outputting an output indicating whether the patient is more likely to have normal pressure hydrocephalus or more likely to have the different degenerative disorder.

48. 1. A computer software product comprising a tangible, non-transitory computer-readable medium having stored thereon program instructions, said instructions, when read by a processor, causing said processor to: receiving from a plurality of electrodes respective signals generated by the patient's brain in response to magnetic stimulation of the brain; analyzing the signal to determine whether the patient is more likely to be suffering from normal pressure hydrocephalus or a different degenerative disorder; and outputting an output indicating whether the patient is more likely to have normal pressure hydrocephalus or more likely to have the different degenerative disorder.

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