# US 2014/0155730 A1 · Apparatus, method and computer-accessible medium for diagnosing and subtyping psychiatric diseases > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0155730 A1 - Document ID: US20140155730A1 - Status: Published application - Kind: A1 - Application number: 13/994,925 - Filed: 2011-12-16 - Published: 2014-06-05 - Earliest claimed: 2010-12-17 - Assignees: The Trustees of Columbia University in the City of New York - Inventors: Ravi Bansal; Bradley S. Peterson - Web page: https://patentgrep.com/patent/US20140155730A1 ## CPC - A61B 5/4064: {Evaluating the brain (for intracranial pressure A61B5/031; for cerebral blood gases A61B5/14553; using EEG A61B5/369)} - A61B 5/0042: {for the brain} - A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging - A61B 5/165: {Evaluating the state of mind, e.g. depression, anxiety} - A61B 5/168: {Evaluating attention deficit, hyperactivity} - A61B 5/245: specially adapted for magnetoencephalographic [MEG] signals - A61B 5/372: Analysis of electroencephalograms - A61B 5/4082: {Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette} - A61B 5/726: {using Wavelet transforms} - A61B 5/7282: {Event detection, e.g. detecting unique waveforms indicative of a medical condition (cough events A61B5/0823; seizures A61B5/4094; sleep apnoea A61B5/4818)} - A61B 5/742: {using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339)} - A61B 6/032: {Transmission computed tomography [CT]} - A61B 6/501: {for diagnosis of the head, e.g. neuroimaging or craniography} - A61B 6/5217: {extracting a diagnostic or physiological parameter from medical diagnostic data} - A61B 8/0808: {for diagnosis of the brain} - G01V 3/14: operating with electron or nuclear magnetic resonance - G06V 2201/03: Recognition of patterns in medical or anatomical images - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G01V: GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS (means for indicating the location of accidentally buried, e.g. snow-buried, persons A63B29/02) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Exemplary method, system and computer-accessible medium can be provided for diagnosing at least one disease and/or a subtype within a disease. For example, it is possible to determine at least one region of interest, and obtain a plurality of data points associated with each of such region(s) of interest. It is also possible to identify a particular pattern of the data points across each of such region(s) of interest. Further, it is possible, e.g., using a computer arrangement, to determine a likelihood of the disease(s) and/or the subtype by comparing the particular pattern to at least one known pattern. ## Claims ### Claim 1 A method for diagnosing at least of at least one disease or a subtype within a disease, comprising: determining at least one region of interest; obtaining a plurality of data points associated with each of the at least one region of interest; identifying a particular pattern of the plurality of data points across each of the at least one region of interest; and using a computer arrangement, determining a likelihood of at least one of the at least one disease or the subtype by comparing the particular pattern to at least one known pattern. ### Claim 2 (depends on 1) The method of claim 1, wherein the at least one region of interest includes at least one of a cortical mantle of a brain, a cortex of the brain, or a subcortical region of the brain. ### Claim 3 (depends on 1) The method of claim 1, wherein the data points includes at least one of a magnetic resonance imaging (MRI) image, a computed tomography (CT) image, an ultrasound image, a nuclear imaging image, an electroencephalogram (EEG) image, or a magnetoencephalogram (MEG) image. ### Claim 4 (depends on 1) The method of claim 1, wherein identifying procedure is based solely on the particular pattern from the at least one region of interest. ### Claim 5 (depends on 1) The method of claim 1, wherein the identifying procedure includes mapping the plurality of data points onto a sphere. ### Claim 6 (depends on 1) The method of claim 1, wherein the identifying procedure includes applying a spherical wavelet transformation procedure. ### Claim 7 (depends on 1) The method of claim 1, further comprising encoding spatial patterns of the particular pattern as scaling coefficients, and wherein the likelihood is determined using the scaling coefficients. ### Claim 8 (depends on 1) The method of claim 1, wherein the identifying procedure includes obtaining naturalistic groupings within the data points. ### Claim 9 (depends on 1) The method of claim 1, wherein the data points includes raw image data. ### Claim 10 (depends on 1) The method of claim 1, wherein the data points represent information of at least one portion of a surface of a brain. ### Claim 11 (depends on 1) The method of claim 1, wherein the at least one disease includes autism, Tourette's syndrome, schizophrenia, bipolar disorder, attention deficit hyper-activity disorder, a major depressive disorder, stuttering, Parkinson's disease, premature birth, or toxic exposures. ### Claim 12 A non-transitory computer readable medium including instructions thereon that are accessible by a hardware processing arrangement, wherein, when the processing arrangement executes the instructions, the processing arrangement is configured to: determine at least one region of interest; obtain a plurality of data points associated with each of the at least one region of interest; identify a particular pattern of the plurality of data points across each of the at least one region of interest; and determine a likelihood of at least one of the at least one disease or a subtype by comparing the particular pattern to at least one known pattern. ### Claim 13 (depends on 12) The non-transitory computer readable medium of claim 12, wherein the at least one region of interest includes at least one of a cortical mantle of a brain, a cortex of the brain, or a subcortical region of the brain. ### Claim 14 (depends on 12) The non-transitory computer readable medium of claim 12, wherein the data points includes at least one of a magnetic resonance imaging (MRI) image, a computed tomography (CT) image, an ultrasound image, a nuclear imaging image, an electroencephalogram (EEG) image, or a magnetoencephalogram (MEG) image. ### Claim 15 (depends on 12) The non-transitory computer readable medium of claim 12, wherein identifying instruction is based solely on the particular pattern from the at least one region of interest. ### Claim 16 (depends on 12) The non-transitory computer readable medium of claim 12, wherein the identifying instruction includes mapping the plurality of data points onto a sphere. ### Claim 17 (depends on 12) The non-transitory computer readable medium of claim 12, wherein the identifying instruction includes applying a spherical wavelet transformation procedure. ### Claim 18 (depends on 12) The non-transitory computer readable medium of claim 12, wherein the arrangement is further configured to encode spatial patterns of the particular pattern as scaling coefficients, and wherein the determining procedure is performed using the scaling coefficients. ### Claim 19 (depends on 12) The non-transitory computer readable medium of claim 12, wherein the identifying instruction includes obtaining naturalistic groupings within the data points. ### Claim 20 (depends on 12) The non-transitory computer readable medium of claim 12, wherein the data points includes raw image data. ### Claim 21 (depends on 12) The non-transitory computer readable medium of claim 12, wherein the data points represent information of at least one portion of a surface of a brain. ### Claim 22 (depends on 12) The non-transitory computer readable medium of claim 12, wherein the at least one disease includes autism, Tourette's syndrome, schizophrenia, bipolar disorder, attention deficit hyper-activity disorder, a major depressive disorder, stuttering, Parkinson's disease, premature birth, or toxic exposures. ### Claim 23 An apparatus for diagnosing at least one of at least one disease or a subtype within a disease, comprising: an apparatus configured to obtain a plurality of data points associated with a region of interest; and a non-transitory computer readable medium including instructions thereon that are accessible by a hardware processing arrangement, wherein, when the processing arrangement executes the instructions, the processing arrangement is configured to obtain a plurality of data points associated with each of the at least one region of interest; identify a particular pattern of the plurality of data points across each of the at least one region of interest; and determine a likelihood of at least one of the at least one disease or the subtype by comparing the particular pattern to at least one known pattern. ### Claim 24 (depends on 23) The apparatus of claim 23, wherein the at least one region of interest includes at least one of a cortical mantle of a brain, a cortex of the brain, or a subcortical region of the brain. ### Claim 25 (depends on 23) The apparatus of claim 23, wherein the data points includes at least one of a magnetic resonance imaging (MRI) image, a computed tomography (CT) image, an ultrasound image, a nuclear imaging image, an electroencephalogram (EEG) image, or a magnetoencephalogram (MEG) image. ### Claim 26 (depends on 23) The apparatus of claim 23, wherein identifying procedure is based solely on the particular pattern from the at least one region of interest. ### Claim 27 (depends on 23) The apparatus of claim 23, wherein the identifying instruction includes mapping the plurality of data points onto a sphere. ### Claim 28 (depends on 23) The apparatus of claim 23, wherein the identifying instruction includes applying a spherical wavelet transformation procedure. ### Claim 29 (depends on 23) The apparatus of claim 23, wherein the arrangement is further configured to encode spatial patterns of the particular pattern as scaling coefficients, and wherein the determining procedure is performed using the scaling coefficients. ### Claim 30 (depends on 23) The apparatus of claim 23, wherein the identifying instruction includes obtaining naturalistic groupings within the data points. ### Claim 31 (depends on 23) The apparatus of claim 23, wherein the data points includes raw image data. ### Claim 32 (depends on 23) The apparatus of claim 23, wherein the data points represent information of at least one portion of a surface of a brain. ### Claim 33 (depends on 23) The apparatus of claim 23, wherein the at least one disease includes autism, Tourette's syndrome, schizophrenia, bipolar disorder, attention deficit hyper-activity disorder, a major depressive disorder, stuttering, Parkinson's disease, premature birth, or toxic exposures.