Methods and systems for analyzing metabolite levels

Abstract

Methods, systems, and apparatuses are described for analyzing metabolite levels from regions of interest (ROI) of a human brain. An MRI scan of an individual's brain may be received, and may be normalized to produce a Montreal Neurological Institute (MNI)-space MRI scan. A region of interest (ROI) of the individual's brain may be identified on the MNI-space MRI scan. The identified ROI of the individual's brain may- be converted back to the native space and then mapped to a spectroscopic grid. A weighted spectral average method may be applied to determine a spectrum value associated with the ROI of the individual's brain. A metabolite map of the metabolite levels of the ROI of the individual's brain may generated based on the spectrum value.

Description

ATTORNEY DOCKET NO.: 21101.0491P1 METHODS AND SYSTEMS FOR ANALYZING METABOLITE LEVELS CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 707,036, filed October 14, 2024, which is incorporated by reference herein in its entirety. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0002] This invention was made with government support under I01CX000812, and I01CX001611 awarded by the U. S. Department of Veterans and R01 DA043248 awarded by the National Institutes of Health. The government has certain rights in the invention. BACKGROUND

[0003] Recent research has shown that the pathophysiology of psychiatric mood disorders may be closely associated with mitochondrial dysfunction. This impaired brain energy metabolism is characterized by alteration in the levels of high energy phosphate- bearing neuro metabolites. How the brain regulates energy metabolism may be studied by measuring the changes of high energy phosphate concentration alterations in the brain in healthy subjects. Phosphorus-31 magnetic resonance spectroscopy (31P MRS) is a key method for non-invasive in vivo measurement of these neurochemicals, in research with human subjects. Given that most phosphorus metabolites have relatively longer T1 relaxation times, shorter T2 relaxation times, low sensitivity nuclei, and extremely low concentration in humans, these factors hinder the clinical usage of 31P MRS and limit detection technique options. Spectroscopic data can only be obtained from a limited number of voxels in the spectroscopic grid to achieve a sufficiently high signal-to-noise ratio. Furthermore, defining a region-of-interest with arbitrary boundaries is not feasible in 31P MRS data acquisition. SUMMARY

[0004] It is to be understood that both the following general description and the following detailed description are exemplary and explanatory only and are not restrictive.ATTORNEY DOCKET NO.: 21101.0491P1

[0005] Methods, apparatuses, and systems for analyzing metabolite levels from regions of interest (ROI) of a human brain, are described. An MRI scan of an individual’s brain may be received and may be normalized to produce a Montreal Neurological Institute (MNI)-space MRI scan. A region of interest (ROI) of the individual’s brain may be identified on the MNI-space MRI scan. The identified ROI of the individual’s brain on the MNI-space MRI scan may be converted back to the native space and then mapped to a spectroscopic grid. A weighted spectral average method may be applied to the mapped MRI scan of the ROI of the individual’s brain to determine a spectrum value associated with the ROI of the individual’s brain. A metabolite map of metabolite levels of the ROI of the individual’s brain may be generated based on the spectrum value.

[0006] In an embodiment, are methods for analyzing metabolite levels from regions of interest (ROI) of a human brain comprising receiving, by a computing device, from a magnetic resonance imaging (MRI) scanner, a MRI scan of a brain of an individual, generating, based on normalizing the MRI scan into a standardized space, a standardized MRI scan, determining a ROI of the brain of the individual on the standardized MRI scan, generating, based on the ROI of the brain of the individual, a ROI-identified magnetic resonance spectroscopy (MRS) scan, determining, based on the ROI-identified MRS scan, a spectrum value associated with the ROI of the brain of the individual, and generating, based on the spectrum value associated with the ROI of the brain of the individual, a metabolite map associated with the ROI of the brain of the individual.

[0007] In an embodiment, are apparatuses for analyzing metabolite levels from regions of interest (ROI) of a human brain comprising one or more processors a memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to receive, from a magnetic resonance imaging (MRI) scanner, a MRI scan of a brain of an individual, generate, based on normalizing the MRI scan into a standardized space, a standardized MRI scan, determine a ROI of the brain of the individual on the standardized MRI scan, generate, based on the ROI of the brain of the individual, a ROI-identified magnetic resonance spectroscopy (MRS) scan, determine, based on the ROI-identified MRS scan, a spectrum value associated with the ROI of the brain of the individual, and generate, based on the spectrum value associated with the ROI of the brain of the individual, a metabolite map associated with the ROI of the brain of the individual.ATTORNEY DOCKET NO.: 21101.0491P1

[0008] This summary is not intended to identify critical or essential features of the disclosure, but merely to summarize certain features and variations thereof. Other details and features will be described in the sections that follow. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to provide understanding techniques described, the figures provide non- limiting examples in accordance with one or more implementations of the present disclosure, in which:

[0010] FIG. 1 shows an example system for analyzing metabolite levels;

[0011] FIG. 2 shows an example system environment for analyzing metabolite levels;

[0012] FIG. 3 shows an example MRI scan illustrating regions of interest of a human brain;

[0013] FIGS. 4A-4D show example metabolite maps of a human brain;

[0014] FIGS. 5A-5B show example data determined from metabolite maps of a human brain;

[0015] FIGS. 6A-6D show example data determined from metabolite maps of a human brain;

[0016] FIGS. 7A-7D show example metabolite maps of a human brain; and

[0017] FIG. 8 shows a flowchart of an example method. DETAILED DESCRIPTION

[0018] As used in the specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from “about” one particular value, and / or to “about” another particular value. When such a range is expressed, another configuration includes from the one particular value and / or to the other particular value. When values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another configuration. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint.

[0019] It is understood that when combinations, subsets, interactions, groups, etc. of components are described that, while specific reference of each various individual and collective combinations and permutations of these may not be explicitly described, eachATTORNEY DOCKET NO.: 21101.0491P1 is specifically contemplated and described herein. This applies to all parts of this application including, but not limited to, steps in described methods. Thus, if there are a variety of additional steps that may be performed it is understood that each of these additional steps may be performed with any specific configuration or combination of configurations of the described methods.

[0020] As will be appreciated by one skilled in the art, hardware, software, or a combination of software and hardware may be implemented. Furthermore, the methods and systems may take the form of a computer program product on a computer-readable storage medium (non-transitory) having processor-executable instructions (e.g., computer software) embodied in the storage medium. Any suitable computer-readable storage medium may be utilized including hard disks, CD-ROMs, optical storage devices, magnetic storage devices, memristors, Non-Volatile Random Access Memory (NVRAM), flash memory, or a combination thereof.

[0021] Throughout this application reference is made to block diagrams and flowcharts. It will be understood that each block of the block diagrams and flowcharts, and combinations of blocks in the block diagrams and flowcharts, respectively, may be implemented by processor-executable instructions. These processor-executable instructions may be loaded onto a computer (e.g., a special purpose computer), or other programmable data processing apparatus to produce a machine, such that the processor- executable instructions which execute on the computer or other programmable data processing apparatus create a device for implementing the functions specified in the flowchart block or blocks.

[0022] This detailed description may refer to a given entity performing some action. It should be understood that this language may in some cases mean that a system (e.g., a computer) owned and / or controlled by the given entity is actually performing the action.

[0023] Blocks of the block diagrams and flowcharts support combinations of devices for performing the specified functions, combinations of steps for performing the specified functions and program instruction means for performing the specified functions. It will also be understood that each block of the block diagrams and flowcharts, and combinations of blocks in the block diagrams and flowcharts, may be implemented by special purpose hardware-based computer systems that perform the specified functions or steps, or combinations of special purpose hardware and computer instructions.ATTORNEY DOCKET NO.: 21101.0491P1

[0024] The method steps recited throughout this disclosure may be combined, omitted, rearranged, or otherwise reorganized with any of the figures presented herein and are not intend to be limited to the four corners of each sheet presented.

[0025] FIG. 1 shows an example system 100 for analyzing metabolite levels from regions of interest (ROI) of a human brain. For example, a computing device may be configured to process a magnetic resonance imaging (MRI) scan of an individual’s brain to generate a metabolite map associated with the ROI of the individual’s brain. The system 100 may include a computing device 101, an imaging device 102, one or more electronic devices 104, and / or one or more servers 106. The computing device 101 may be in communication with the imaging device 102, the one or more electronic devices 104, and / or the one or more servers 106 via a network (e.g., network 162).

[0026] The computing device 101 may include a bus 110, one or more processors 120, a memory 140, an input / output interface 160, a display 170, and a communication interface 180. In certain examples, the computing device 101 may omit at least one of the aforementioned elements or may additionally include other elements. The computing device 101 may comprise one or more of a tablet computer, a mobile phone, a smartphone, a laptop computer, a desktop computer, and the like.

[0027] The bus 110 may comprise a circuit for connecting the bus 110, the one or more processors 120, the memory 140, the input / output interface 160, the display 170, and / or the communication interface 180 to each other and for delivering communication (e.g., a control message and / or data) between the bus 110, the one or more processors 120, the memory 140, the input / output interface 160, the display 170, and / or the communication interface 180.

[0028] The one or more processors 120 may include one or more of a Central Processing Unit (CPU), an Application Processor (AP), or a Communication Processor (CP). The one or more processors 120 may control, for example, at least one of the bus 110, the memory 140, the input / output interface 160, the display 170, and / or the communication interface 180 of the computing device 101 and / or may execute an arithmetic operation or data processing for communication. The processing (or controlling) operation of the one or more processors 120 according to various embodiments is described in detail with reference to the following drawings.

[0029] The processor-executable instructions executed by the one or more processors 120 may be stored and / or maintained by the memory 140. The memory 140 may includeATTORNEY DOCKET NO.: 21101.0491P1 a volatile and / or non-volatile memory. The memory 140 may include random-access memory (RAM), flash memory, solid state or inertial disks, or any combination thereof. As an example, the memory 140 may include an Embedded MultiMedia Card (eMMC). The memory 140 may store, for example, a command or data related to at least one of the bus 110, the one or more processors 120, the signal input interface 130, the memory 140, the input / output interface 160, the display 170, and / or the communication interface 180 of the computing device 101. According to various examples, the memory 140 may store software and / or a program 150 or may comprise firmware. For example, the program 150 may include a kernel 151, a middleware 153, one or more Application Programming Interfaces (APIs) 155, an application program 157, and / or the like, configured for controlling one or more functions of the computing device 101 and / or an external device (e.g., the one or more electrical devices 102). At least one part of the kernel 151, middleware 153, or the APIs 155 may be referred to as an Operating System (OS). The memory 140 may include a computer-readable recording medium (e.g., a non-transitory computer-readable medium) having a program recorded therein to perform the methods according to various embodiments by the one or more processors 120. In an example, the computing device 101 may receive patient data which may be stored in the memory 140.

[0030] The kernel 151 may control or manage, for example, system resources (e.g., the bus 110, the one or more processors 120, the memory 140, etc.) used to execute an operation or function implemented in other programs (e.g., the middleware 153, the API 155, and / or the application program 157). Further, the kernel 151 may provide an interface capable of controlling or managing the system resources by accessing individual elements of the controller 101 in the middleware 153, the API 155, or the application program 157.

[0031] The middleware 153 may perform, for example, a mediation role, so that the API 155, and / or the application program 157 can communicate with the kernel 151 to exchange data. Further, the middleware 153 may handle one or more task requests received from the application program 157 according to a priority. For example, the middleware 153 may assign a priority of using the system resources (e.g., the bus 110, the one or more processors 120, or the memory 140) of the computing device 101 to the application program 157. For example, the middleware 153 may process the one or more task requests according to the priority assigned to at least one of the applicationATTORNEY DOCKET NO.: 21101.0491P1 programs, and thus, may perform scheduling or load balancing on the one or more task requests.

[0032] The one or more APIs 155 may include one or more interfaces or functions (e.g., instructions), for example, for file control, window control, video processing, and / or character control, as an interface capable of controlling a function provided by the application program 157 in the kernel 151 or the middleware 153. As an example, the application program 157 may be independent of each other or integrally combined, in whole or in part, into a single processing program.

[0033] The application program 157 may include logic (e.g., hardware, software, firmware, etc.) that may be implemented to cause the computing device 101 to generate a metabolite map of an individual’s brain based on analyzing an MRI scan of the individual’s brain. For example, the computing device 101 may receive an MRI scan of the individual’s brain from the imaging device 102. The imaging device 102 may comprise an MRI scanner. As an example, the MRI scan may comprise one of a structural MRI scan or a functional MRI scan. The application program 157 may cause the computing device 101 to generate a standardized MRI scan based on normalizing the MRI scan into a standardized space. The standardized space may comprise a Montreal Neurological Institute (MNI) space. As an example, the standardized MRI scan may comprise the MRI scan in MNI space. For example, the computing device 101 may use a non-linear registration algorithm to calculate the deformation field needed to warp the MRI scan to the MNI space.

[0034] The application program 157 may cause the computing device 101 to determine a ROI of the brain of the individual on the standardized MRI scan. The ROI may be associated with a default mode network (DMN), a dorsal attention network, a frontal parietal network, a visual network, a sensorimotor network, a language network, or a salience network. For example, the DMN brain regions may comprise one or more of a medial prefrontal cortex (MPFC), a posterior cingulate cortex (PCC) and precuneus, an inferior parietal lobule (IPL), an inferior temporal gyrus, or a hippocampal formation. The dorsal attention network brain regions may comprise one or more of frontal eye fields (FEF), intraparietal sulci (IPS), a superior parietal lobule (SPL), a supplementary eye field (SEF), a middle temporal (MT+) region, or a cerebellum. The frontal parietal network brain regions may comprise one or more of a lateral prefrontal cortex (PFC) or a posterior parietal cortex (PPC). The visual network brain regions may comprise one orATTORNEY DOCKET NO.: 21101.0491P1 more of an occipital lobe or a visual cortex. The sensorimotor network brain regions may comprise one or more of a primary motor cortex (M1), a primary somatosensory cortex (S1, premotor cortex and supplementary motor area (SMA), a posterior associative sensory cortex, a cerebellum, a basal ganglia, or a thalamus. The language network brain regions may comprise one or more of Broca’s area or Wernicke’s area. The salience network brain regions may comprise one or more of an anterior insula (AI), a dorsal anterior cingulate cortex (dACC), amygdala, temporal poles, or ventral striatum.

[0035] The application program 157 may cause the computing device 101 to generate a ROI-identified magnetic resonance spectroscopy (MRS) scan based on the ROI of the brain of the individual. As an example, the computing device 101 may generate a forward transformation matrix (e.g., forward deformation fields) and an inverse transformation matrix (e.g., inverse deformation fields) in order to normalize the MRI scan into the standardized space. The computing device 101 may use the inverse transformation matrix to map the MNI coordinates of the standardized MRI scan back to the individual’s brain space. For example, the application program 157 may cause the computing device 101 to generate a ROI image aligned to the brain of the individual based on applying the inverse transformation matrix to the determined ROI on the standardized MRI scan. The application program 157 may cause the computing device 101 to generate the ROI-identified MRS scan based on mapping the ROI image aligned to the brain of the individual to an MRS scan (e.g., a spectroscopic grid) of the brain of the individual. As an example, the ROI-identified MRS scan may comprise an ROI image aligned to the brain of the individual that is mapped on an MRS scan (e.g., a spectroscopic grid). As an example, the computing device 101 may use the inverse transformation matrix to warp the ROI from the MNI space into correct anatomical locations on the individual’s structural scan. As an example, a nearest-neighbor interpolation may be performed to preserve integer values associated with the ROI. As an example, the spectroscopic grid may comprise a set of defined volumes, or voxels, from which magnetic resonance spectroscopy (MRS) data is acquired. The spectroscopic grid may comprise a single voxel or a multi-voxel grid, wherein the grid may be prescribed in three dimensions and placed over the ROI. The computing device 101 may receive (e.g., from the imaging device 102) spectroscopic data from each voxel within the spectroscopic grid (e.g., the MRS produces a spectrum for each voxel, whereas the MRI creates an image). The spectrum from the MRS plots the chemical shifts ofATTORNEY DOCKET NO.: 21101.0491P1 different metabolites. The computing device 101 may analyze the spectrum from each voxel to determine chemical profiles within the ROI that can be compared against neighboring tissue or a healthy brain region.

[0036] The application program 157 may cause the computing device 101 to determine a spectrum value associated with the ROI of the brain of the individual based on the ROI-identified MRS scan. For example, the application program 157 may cause the computing device 101 to determine the spectrum value associated with the ROI of the brain of the individual based on applying a weighted spectral average method to the ROI-identified MRS scan. In an example, the weighting factors of the weighted spectral average may be determined by a squared distance between each labeled voxel center and the ROI’s center of mass. As an example, the computing device 101 may determine a center of mass for the ROI of the brain of the individual on the ROI-identified MRS scan and determine a value associated with each voxel of a plurality of voxels. The computing device 101 may then determine a plurality of voxel spectrum values based on multiplying each value of the plurality of values by a distance of the corresponding voxel of the plurality of voxels to the center of mass for the ROI. The computing device 101 may determine the spectrum value based on summing the plurality of voxel spectrum values.

[0037] The application program 157 may cause the computing device 101 to generate a metabolite map associated with the ROI of the brain of the individual based on the spectrum value associated with the ROI of the brain of the individual. The metabolite map may comprise one or more metabolite levels associated with one or more of N- acetylaspartate (NAA), choline (Cho), creatine (Cr), myo-inositol (ml), lactate (Lac), glutamate (Glu), or glutamine (Glx). In an example, the application program 157 may cause the computing device 101 to generate an image comprising an overlay of the metabolite map on the MRI scan. As an example, the computing device 101 may overlay the MRI scan with a color or gray scale indicating the metabolite levels associated with the different ROIs of the brain of the individual. In an example, prior to computing the weighted spectral average, the computing device 101 may apply optimal zero-order and first-order phase corrections to the ROI-identified MRS scan in order to achieve a desired appearance of the real part of the spectra for the generated image comprising the overall of the metabolite map on the MRI scan. In an example, the application program 157 may cause the computing device 101 to cause a treatment of the individual based onATTORNEY DOCKET NO.: 21101.0491P1 the determined metabolite levels. For example, the computing device 101 may cause a medical treatment device to administer a treatment to the individual. In an example, the computing device 101 may utilize the metabolite map to monitor and track changes in metabolism during disease progression or in response to therapeutic interventions or changes in metabolism over time as the individual ages.

[0038] As an example, the computing device 101 may analyze the metabolite map of the different ROIs of the brain of the individual under one or more medical conditions (e.g., hypoxia, hyperoxia, etc.). For example, the computing device 101 may analyze the metabolite map to determine that the MPFC region shows a decreased trend under a hypoxic condition. However, the computing device 101 may analyze the metabolite map to determine that, for most of the other regions, inorganic phosphate levels are significantly elevated under hypoxia when compared with that in hyperoxia. In another example, the computing device 101 may analyze the metabolite map to determine that mild hypoxia does not sufficiently stress the brain to cause a significant reduction in phosphocreatine levels or that neuronal mitochondria is able to operate at full capacity to meet the brain energy demands in the resting hypoxic state. In another example, the computing device 101 may analyze the metabolite map of the MPFC region to evaluate the relative changes in phosphocreatine (PCr) and inorganic phosphate (Pi) levels in individuals. Under hypoxia, most individuals may exhibit lower PCr levels and higher Pi levels compared to those under hyperoxia. Therefore, in the cerebral tissue, the depletion of PCr and the decline in the PCr to Pi ratio may indicate the degree of brain fatigue. Significant group differences may be observed in the ratio of PCr to Pi across the MPFC, PCC, left lateral parietal and right SMG regions. In another example, the computing device 101 may analyze the metabolite map to determine an increase in pH values across all assessed brain regions under hypoxia. In another example, the computing device 101 may generate PCr and adenosine triphosphate beta-phosphate group (βATP) maps, illustrating the relative magnitudes across various brain regions, despite no significant group differences between hypoxia and hyperoxia. As an example, higher PCr levels may be observed in the left-side brain regions compared to the contralateral side of the brain (e.g., left LPFC versus right LPFC). In contrast, a βATP level profile may appear more homogeneous.

[0039] The input / output interface 160 may include an interface for delivering an instruction or data input from a user (e.g., an operator of the computing device 101) orATTORNEY DOCKET NO.: 21101.0491P1 from a different external device (e.g., the one or more electronic devices 102) to the different elements of the computing device 101. Further, the input / output interface 160 may output an instruction or data received from one or more elements of the computing device 101 to one or more external devices (e.g., the one or more electronic devices 102). For example, the input / output interface 160 may receive user input for activating the computing device 101 and / or the imaging device 104 in order to initiate the imaging (e.g., MRI) scanning process. In an example, the input / output interface 160 may receive user input identifying one or more ROIs of the individual’s brain. The input / output interface 160 may comprise one or more of a touch screen interface, a keyboard, a mouse, a microphone, and the like for receiving the user input. In an example, the user input may comprise text input or audio input associated with monitoring the individual undergoing the MRI scanning process.

[0040] The communication interface 180 may establish, for example, communication between the computing device 101 and one or more external devices (e.g., the imaging device 102, the one or more electronic devices 104, and / or the one or more servers 106). In an example, the communication interface 180 may communicate with one or more of the external devices (e.g., the imaging device 102, the one or more electronic devices 104, and / or the one or more servers 106) by being connected to a network 162 through wireless communication or wired communication. For example, as a cellular communication protocol, the wireless communication may use at least one of Long-Term Evolution (LTE), LTE Advance (LTE-A), Code Division Multiple Access (CDMA), Wideband CDMA (WCDMA), Universal Mobile Telecommunications System (UMTS), Wireless Broadband (WiBro), Global System for Mobile Communications (GSM), and the like. In an example, the network 162 may include at least one of a telecommunications network, a computer network (e.g., LAN or WAN), the internet, and a telephone network. In an example, the communication interface 180 may include or be communicably coupled to a transmitter, receiver and / or transceiver for communication with one or more of the external devices (e.g., the imaging device 102, the one or more electronic devices 104, and / or the one or more servers 106).

[0041] In addition, the communication interface 180 may communicate with an external device (e.g., the imaging device 102 and / or the one or more electronic devices 104) via a communication connection 164, 165 such as wireless communications and / or wired communications. The wireless communications may include, for example, near-distanceATTORNEY DOCKET NO.: 21101.0491P1 communications 164, 165. The near-distance communications 164, 165 may include, for example, at least one of Wireless Fidelity (WiFi), Bluetooth, Near Field Communication (NFC), Global Navigation Satellite System (GNSS), and the like. According to a usage region or a bandwidth or the like, the GNSS may include, for example, at least one of Global Positioning System (GPS), Global Navigation Satellite System (Glonass), Beidou Navigation Satellite System (hereinafter, “Beidou”), Galileo, the European global satellite-based navigation system, and the like. Hereinafter, the “GPS” and the “GNSS” may be used interchangeably in the present document. The wired communication may include, for example, at least one of Universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), Recommended Standard-232 (RS-232), power-line communication, Plain Old Telephone Service (POTS), and the like.

[0042] The display 170 may comprise a display device such as one or more of a monitor, a television, an audio / video monitor, a streaming device, and the like. The display 170 may include various types of displays, for example, a Liquid Crystal Display (LCD) display, a Light Emitting Diode (LED) display, an Organic Light-Emitting Diode (OLED) display, a MicroElectroMechanical Systems (MEMS) display, or an electronic paper display. In an example, the display 170 may be configured as a part of the computing device 101 or as a separate external device in communication with the computing device 101 via the communication interface 180. The display 170 may be configured to display a user interface for displaying MRI scans of human brains. In addition, the display 170 may display the image comprising the overlay of the metabolite maps of the human brains on the MRI scans. For example, the display 170 may be configured to display the MRI scans overlaid with a color or gray scale indicating the metabolite levels associated with the different ROIs of the human brains.

[0043] The one or more servers 106 may include one or more groups of servers. For example, all or some of the operations executed by the computing device 101 may be executed in a different one or a plurality of electronic devices (e.g., one or more of the electronic devices 102 and / or one or more of the servers 106). In an example, if the computing device 101 needs to perform a certain function or service either automatically or based on a request, the computing device 101 may request at least some parts of functions related thereto alternatively or additionally to a different electronic device (e.g., one or more of the electronic devices 102 and / or one or more of the servers 106) instead of executing the function or the service autonomously. The different electronicATTORNEY DOCKET NO.: 21101.0491P1 devices (e.g., the one or more electronic devices 102 and / or the one or more servers 106) may execute the requested function or additional function, and may deliver a result thereof to the computing device 101. The computing device 101 may provide the requested function or service either directly or by additionally processing the received result. For example, a cloud computing, distributed computing, or client-server computing technique may be used. In an example, one or more of the electronic devices 102 may be configured to process the MRI scans to generate the metabolite map associated with the ROI of the brain of the individual and transmit the metabolite map to the computing device 101. For example, the one or more electronic devices 104 may comprise a user device such as a mobile device, a smart phone, a tablet computer, a desktop computer, and the like. The computing device 101 may send the MRI scan to one or more of the electronic devices 102. One or more of the electronic devices 102 may be configured to generate the image comprising the overlay of the metabolite map on the MRI scan and transmit the image to the computing device 101, wherein the computing device 101 may display the image. In another example, one or more of the electronic devices 102 may be configured to display the image. In an example, one or more of the servers 106 may be configured to process the MRI scans to generate the metabolite map associated with the ROI of the brain of the individual and transmit the metabolite map to the computing device 101. The computing device 101 may send the MRI scan to one or more of the servers 106. One or more of the servers 106 may be configured to generate the image comprising the overlay of the metabolite map on the MRI scan and transmit the image to the computing device 101, wherein the computing device 101 may display the image.

[0044] FIG. 2 shows an example system environment 200. The system environment may comprise a computing device 101, an imaging device 102, one or more electronic devices, and / or a server 106. As example, the one or more electronic devices may comprise at least a first electronic device 206, such as a mobile phone or a smart phone, and a second electronic device 208, such as a laptop computer, a desktop computer, a tablet device, and the like. The imaging device 102 may comprise a magnetic resonance imaging (MRI) scanner. The computing device 101, the imaging device 102, the one or more electronic devices 206, 208, and / or the server 106 may be in communication with each other via a communication interface 202. For example, the communication interface 202 may comprise a wired communication interface and / or a wireless interface.ATTORNEY DOCKET NO.: 21101.0491P1 In one example, the wired interface may include at least one of Universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), Recommended Standard-232 (RS-232), power-line communication, Plain Old Telephone Service (POTS), and the like. In another example, the wireless interface may utilize a cellular communication protocol such as one of Long-Term Evolution (LTE), LTE Advance (LTE-A), Code Division Multiple Access (CDMA), Wideband CDMA (WCDMA), Universal Mobile Telecommunications System (UMTS), Wireless Broadband (WiBro), Global System for Mobile Communications (GSM), and the like. In another example, the wireless interface may utilize one of a computer (e.g., LAN or WAN), the internet, and a telephone network. In another example, the wireless interface may utilize near-distance communications such as one of Wireless Fidelity (WiFi), Bluetooth, Near Field Communication (NFC), Global Navigation Satellite System (GNSS), and the like.

[0045] The computing device 101 may process a magnetic resonance imaging (MRI) scan, received from the imaging device 102, of an individual’s 204 brain to generate a metabolite map associated with ROIs of the individual’s 204 brain. For example, the computing device 101 may generate a standardized MRI scan (e.g., MRI scan in MNI space) from the MRI scan of the individual’s 204 brain. ROIs may be identified on the standardized MRI scan. The computing device 101 may generate ROI-identified magnetic resonance spectroscopy (MRS) scans of the individual’s 204 brain based on the ROIs of the individual’s 204 brain. For example, the computing device 101 may generate the ROI-identified MRS scans based on mapping the ROI images to aligned to the individual’s 204 brain to MRS scans (e.g., spectroscopic grids) of the individual’s 204 brain. As an example, the ROS-identified MRS scans may comprise ROI images aligned to the individual’s 204 brain that is mapped on the MRS scans. The computing device 101 may determine a spectrum value associated with each of the ROIs of the individual’s 204 brain based on the ROI-identified MRS scans. Based on the spectrum values, the computing device 101 may generate metabolite maps associated with each of the ROIs of the individual’s 204 brain. As an example, the computing device 101 may generate an image comprising an overlay of the metabolite maps on the MRI scan and display the image. In one example, the computing device 101 may transmit the MRI scans, received from the imaging device 102, to one or more of the first electronic device 206, the second electronic device 208, and / or the server 106, wherein one or more of the first electronic device 206, the second electronic device 208, and / or the server 106 mayATTORNEY DOCKET NO.: 21101.0491P1 be configured to process the MRI scans to generate the metabolite maps associated with the ROIs of the individual’s 204 brain and transmit the metabolite maps to the computing device 101 for the computing device 101 to generate an image comprising the overlay of the metabolite maps on the MRI scan. In another example, one or more of the first electronic device 206, the second electronic device 208, and / or the server 106 may generate the image comprising the overlay of the metabolite maps on the MRI scan and transmit the image to the computing device 101, wherein the computing device 101 may display the image.

[0046] FIG. 3 shows an example MRI scan 300 illustrating regions of interest (ROIs) of a human brain. As an example, a computing device (e.g., computing device 101) may process a magnetic resonance imaging (MRI) scan of a human brain to generate a metabolite map associated with ROIs of the human brain. For example, the computing device may generate a standardized MRI scan (e.g., MRI scan in MNI space) from an MRI scan of an individual’s brain. ROIs may be identified on the standardized MRI scan. For example, as shown in FIG. 3, the ROIs may be associated with a default mode network (DMN), a dorsal attention network, a frontal parietal network, a visual network, a sensorimotor network, a language network, or a salience network. For example, the DMN brain regions may comprise one or more of a medial prefrontal cortex (MPFC), a posterior cingulate cortex (PCC) and precuneus, an inferior parietal lobule (IPL), an inferior temporal gyrus, or a hippocampal formation. The dorsal attention network brain regions may comprise one or more of frontal eye fields (FEF), intraparietal sulci (IPS), a superior parietal lobule (SPL), a supplementary eye field (SEF), a middle temporal (MT+) region, or a cerebellum. The frontal parietal network brain regions may comprise one or more of a lateral prefrontal cortex (PFC) or a posterior parietal cortex (PPC). The visual network brain regions may comprise one or more of an occipital lobe or a visual cortex. The sensorimotor network brain regions may comprise one or more of a primary motor cortex (M1), a primary somatosensory cortex (S1, premotor cortex and supplementary motor area (SMA), a posterior associative sensory cortex, a cerebellum, a basal ganglia, or a thalamus. The language network brain regions may comprise one or more of Broca’s area or Wernicke’s area. The salience network brain regions may comprise one or more of an anterior insula (AI), a dorsal anterior cingulate cortex (dACC), amygdala, temporal poles, or ventral striatum.ATTORNEY DOCKET NO.: 21101.0491P1

[0047] FIGS. 4A-4D show example metabolite maps of a human brain. As an example, a computing device (e.g., computing device 101) may process a magnetic resonance imaging (MRI) scan of a human brain to generate a metabolite map associated with ROIs of the human brain. For example, the computing device may generate a standardized MRI scan (e.g., MRI scan in MNI space) from an MRI scan of an individual’s brain. ROIs may be identified on the standardized MRI scan. The computing device may generate ROI-identified magnetic resonance spectroscopy (MRS) scans of the individual’s brain based on the ROIs of the individual’s brain. The computing device may determine a spectrum value associated with each of the ROIs of the individual’s brain based on the ROI-identified MRS scans. Based on the spectrum values, the computing device may generate metabolite maps associated with each of the ROIs of the individual’s brain, as shown in FIGS. 4A-4D. In one example, as shown in FIG. 4A, the computing device may generate a metabolite map of phosphocreatine (PCr) / inorganic phosphate (Pi) ratios when an individual is experiencing hypoxia. In another example, as shown in FIG. 4B, the computing device may generate a metabolite map of PCr / Pi ratios when an individual is experiencing hyperoxia. In another example, as shown in FIG. 4C, the computing device may generate a pH map when an individual is experiencing hypoxia. In another example, as shown in FIG. 4D, the computing device may generate a pH map when an individual is experiencing hyperoxia. As an example, regions with a value < 0.05 between hypoxic and hyperoxic groups may be displayed.

[0048] FIGS. 5A-5B show example data determined from metabolite maps of a human brain. As an example, a computing device (e.g., computing device 101) may process a magnetic resonance imaging (MRI) scan of a human brain to generate a metabolite map associated with ROIs of the human brain. For example, the computing device may generate a standardized MRI scan (e.g., MRI scan in MNI space) from an MRI scan of an individual’s brain. ROIs may be identified on the standardized MRI scan. The computing device may generate ROI-identified magnetic resonance spectroscopy (MRS) scans of the individual’s brain based on the ROIs of the individual’s brain. The computing device may determine a spectrum value associated with each of the ROIs of the individual’s brain based on the ROI-identified MRS scans. Based on the spectrum values, the computing device may generate metabolite maps associated with the ROIs of the individual’s brain. In one example, as shown in FIG. 5A, mean pH values associated with an individual experiencing hypoxia and hyperoxia may be determined acrossATTORNEY DOCKET NO.: 21101.0491P1 different ROIs associated with one or more of a medial prefrontal cortex (MPFC), a posterior cingulate cortex (PCC) and precuneus, an inferior parietal lobule (IPL), an inferior temporal gyrus, or a hippocampal formation. In another example, as shown in FIG. 5B, mean respiration rates over time during periods of hypoxia and hyperoxia may be determined across the different ROIs.

[0049] FIGS. 6A-6D show example data associated with a metabolite maps of a human brain. As an example, a computing device (e.g., computing device 101) may process a magnetic resonance imaging (MRI) scan of a human brain to generate a metabolite map associated with ROIs of the human brain. For example, the computing device may generate a standardized MRI scan (e.g., MRI scan in MNI space) from an MRI scan of an individual’s brain. ROIs may be identified on the standardized MRI scan. The computing device may generate ROI-identified magnetic resonance spectroscopy (MRS) scans of the individual’s brain based on the ROIs of the individual’s brain. The computing device may determine a spectrum value associated with each of the ROIs of the individual’s brain based on the ROI-identified MRS scans. Based on the spectrum values, the computing device may generate metabolite maps associated with the ROIs of the individual’s brain. In one example, as shown in FIGS. 6A-6B, PCr / total protein (TP) levels may be compared between hypoxia and hyperoxia across the different ROIs in healthy individuals. As shown in FIGS. 6A-6B group mean values may be determined 602 and 604. As an example, it is noted that the level of inorganic phosphate in human calf muscles may increase during physical exercise due to the breakdown of phosphocreatine. As observed in the data, as shown in FIGS. 6A-6B, this may be due to mild hypoxia not sufficiently stressing the brain to cause a significant reduction in phosphocreatine levels or due to neuronal mitochondria being able to operate at full capacity to meet the brain energy demands in the resting hypoxic state. Any instantaneous energy shortage can be compensated by the breakdown of PCr. In another example, as shown in FIGS. 6C-6D, PCr / TP and Pi / TP may be compared between hypoxia and hyperoxia in individuals. As an example, the MPFC region may be examined to analyze the relative changes in PCR and Pi levels in each individual, as shown in FIGS. 6C-6D. Under hypoxia, most individuals exhibit lower PCr levels and higher Pi levels compared to those under hyperoxia. Thus, in the cerebral tissue, the depletion of PCr and the decline in the PCr to Pi ratio may indicate the degree of brain fatigue. As shown in FIGS. 4A-4B, significant group differences may be observed inATTORNEY DOCKET NO.: 21101.0491P1 the ratio of PCr to Pi across the MPFC, PCC, left lateral parietal and right SMG regions. In an example, functional MRI studies show increased brain activity in default mode network regions during rest. As such, the higher energy demand from these regions can trigger the most significant changes under hypoxic condition. In an example, the supramarginal gyrus may help to downregulate egocentricity bias. Under hypoxia, increased energy consumption in the right SMG region may indicate heightened brain activity. In an example, as shown in FIGS. 4C, 4D, and 5A, an increase in pH values may be observed across all assessed brain regions under hypoxia. As an example, as shown in FIG. 5B, pH elevation may result from an increased respiration rate. For example, as individuals breathe more deeply and rapidly, they exhale more carbon dioxide, leading to a rise in blood pH, which subsequently increases brain tissue pH.

[0050] FIGS. 7A-7D show example metabolite maps of a human brain. As an example, a computing device (e.g., computing device 101) may process a magnetic resonance imaging (MRI) scan of a human brain to generate a metabolite map associated with ROIs of the human brain. For example, the computing device may generate a standardized MRI scan (e.g., MRI scan in MNI space) from an MRI scan of an individual’s brain. ROIs may be identified on the standardized MRI scan. The computing device may generate ROI-identified magnetic resonance spectroscopy (MRS) scans of the individual’s brain based on the ROIs of the individual’s brain. The computing device may determine a spectrum value associated with each of the ROIs of the individual’s brain based on the ROI-identified MRS scans. Based on the spectrum values, the computing device may generate metabolite maps associated with the ROIs of the individual’s brain. As shown in FIGS. 7A-7D, PCr and adenosine triphosphate beta- phosphate group (βATP) maps may be displayed, showing relative magnitudes across various brain regions, despite no significant group differences between hypoxia and hyperoxia. As an example, higher PCr levels may be observed in the left-side brain regions compared to the contralateral side of the brain (e.g., left LPFC versus right LPFC). In contrast, βATP level profile may appear more homogeneous. In one example, as shown in FIG. 7A, the computing device may generate a PCr / TP map across the different ROIs when an individual is experiencing hypoxia. In another example, as shown in FIG. 7B, the computing device may generate a PCr / TP map across the different ROIs when an individual is experiencing hyperoxia. In another example, as shown in FIG. 7C, the computing device may generate a βATP / TP map when anATTORNEY DOCKET NO.: 21101.0491P1 individual is experiencing hypoxia. In another example, as shown in FIG. 7D, the computing device may generate a βATP / TP map when an individual is experiencing hyperoxia. As an example, lower metabolite levels and higher metabolite levels may be indicated by different color markings overlaid onto the MRI scan. As an example, the metabolite map may be analyzed to determine: (1) an increased utilization of PCr under hypoxia; (2) there is a significant increase in Pi deposition; (3) a significant rise in pH may be noted across all cerebral tissues; and / or (4) the pH value in the MPFC may be lower than in other regions under hypoxia, suggesting that metabolic shifts towards glycolysis may occur earlier in the MPFC under hypoxia.

[0051] FIG. 8 shows a flowchart of an example method 800 for analyzing metabolite levels from regions of interest (ROIs) of a human brain. Method 800 may be implemented, for example, by a computing device (e.g., the computing device 101, the imaging device 102, the one or more electronic devices 104, and / or the one or more servers 106, or any combinations thereof). At step 802, a magnetic resonance imaging (MRI) scan of a brain of an individual may be received. For example, a computing device (e.g., computing device 101, one of the electronic devices 104, one of the servers 106, etc.) may receive the MRI scan of the brain of the individual from a MRI scanner (e.g., imaging device 102). As an example, the MRI scan may comprise one of a structural MRI scan or a functional MRI scan.

[0052] At step 804, a standardized MRI scan may be generated. For example, the computing device (e.g., computing device 101, one of the electronic devices 104, one of the servers 106, etc.) may generate the standardized MRI scan based on normalizing the MRI scan into a standardized space. The standardized space may comprise a Montreal Neurological Institute (MNI) space. As an example, the standardized MRI scan may comprise the MRI scan in MNI space. For example, a non-linear registration algorithm may be utilized to calculate the deformation field needed to warp the MRI scan to the MNI space.

[0053] At step 806, a ROI of the brain of the individual may be determined. For example, the computing device (e.g., computing device 101, one of the electronic devices 104, one of the servers 106, etc.) may identify the ROI of the brain of the individual on the standardized MRI scan. In an example, a user of the computing device may provide input via the computing device identifying the ROI of the brain of the individual. The ROI may be associated with a default mode network (DMN), a dorsalATTORNEY DOCKET NO.: 21101.0491P1 attention network, a frontal parietal network, a visual network, a sensorimotor network, a language network, or a salience network. For example, the DMN brain regions may comprise one or more of a medial prefrontal cortex (MPFC), a posterior cingulate cortex (PCC) and precuneus, an inferior parietal lobule (IPL), an inferior temporal gyrus, or a hippocampal formation. The dorsal attention network brain regions may comprise one or more of frontal eye fields (FEF), intraparietal sulci (IPS), a superior parietal lobule (SPL), a supplementary eye field (SEF), a middle temporal (MT+) region, or a cerebellum. The frontal parietal network brain regions may comprise one or more of a lateral prefrontal cortex (PFC) or a posterior parietal cortex (PPC). The visual network brain regions may comprise one or more of a occipital lobe or a visual cortex. The sensorimotor network brain regions may comprise one or more of a primary motor cortex (M1), a primary somatosensory cortex (S1, premotor cortex and supplementary motor area (SMA), a posterior associative sensory cortex, a cerebellum, a basal ganglia, or a thalamus. The language network brain regions may comprise one or more of Broca’s area or Wernicke’s area. The salience network brain regions may comprise one or more of an anterior insula (AI), a dorsal anterior cingulate cortex (dACC), amygdala, temporal poles, or ventral striatum.

[0054] At step 808, a ROI-identified magnetic resonance spectroscopy (MRS) scan may be generated based on the ROI of the brain of the individual. For example, the computing device (e.g., computing device 101, one of the electronic devices 104, one of the servers 106, etc.) may generate the ROI-identified MRS scan based on the ROI of the brain of the individual. As an example, a forward transformation matrix (e.g., forward deformation fields) and an inverse transformation matrix (e.g., inverse deformation fields) may be generated in order to normalize the MRI scan into the standardized space. The inverse transformation matrix may be utilized to map the MNI coordinates of the standardized MRI scan back to the individual’s brain space. For example, a ROI image aligned to the brain of the individual may be generated based on applying the inverse transformation matrix to the determined ROI on the standardized MRI scan. The ROI- identified MRS scan may be generated based on mapping the ROI image aligned to the brain of the individual to an MRS scan (e.g., spectroscopic grid) of the brain of the individual. As an example, the ROI-identified MRS scan may comprise an ROI image aligned to the brain of the individual that is mapped on an MRS scan (e.g., spectroscopic grid) of the brain of the individual. As an example, the inverse transformation matrixATTORNEY DOCKET NO.: 21101.0491P1 may be utilized to warp the ROI from the MNI space into correct anatomical locations on the individual’s structural scan. As an example, a nearest-neighbor interpolation may be performed to preserve integer values associated with the ROI. As an example, the spectroscopic grid may comprise a set of defined volumes, or voxels, from which magnetic resonance spectroscopy (MRS) data is acquired. The spectroscopic grid may comprise a single voxel or a multi-voxel grid, wherein the grid may be prescribed in three dimensions and placed over the ROI. The computing device may receive (e.g., from the MRI scanner) spectroscopic data from each voxel within the spectroscopic grid (e.g., the MRS produces a spectrum for each voxel, whereas the MRI creates an image). The spectrum from the MRS plots the chemical shifts of different metabolites. The computing device may analyze the spectrum from each voxel to determine chemical profiles within the ROI that can be compared against neighboring tissue or a healthy brain region.

[0055] At step 810, a spectrum value associated with the ROI of the of the brain of the individual may be determined. For example, the computing device (e.g., computing device 101, one of the electronic devices 104, one of the servers 106, etc.) may determine the spectrum value associated with the ROI of the brain of the individual based on the ROI-identified MRs scan. For example, the spectrum value associated with the ROI of the brain of the individual may be determined based on applying a weighted spectral average method to the ROI-identified MRs scan. In an example, the weighting factors of the weighted spectral average may be determined by a squared distance between each labeled voxel center and the ROI’s center of mass. As an example, a center of mass for the ROI of the brain of the individual may be determined on the ROI- identified MRs scan. In addition, a value associated with each voxel of a plurality of voxels may be determined. A plurality of voxel spectrum values may then be determined based on multiplying each value of the plurality of values by a distance of the corresponding voxel of the plurality of voxels to the center of mass for the ROI. The spectrum value may be determined based on summing the plurality of voxel spectrum values.

[0056] At step 812, a metabolite map associated with the ROI of the brain of the individual may be generated. For example, the computing device (e.g., computing device 101, one of the electronic devices 104, one of the servers 106, etc.) may generate the metabolite map associated with the ROI of the brain of the individual based on theATTORNEY DOCKET NO.: 21101.0491P1 spectrum value associated with the ROI of the brain of the individual. The metabolite map may comprise one or more metabolite levels associated with one or more of N- acetylaspartate (NAA), choline (Cho), creatine (Cr), myo-inositol (ml), lactate (Lac), glutamate (Glu), or glutamine (Glx). In an example, an image may be generated comprising an overlay of the metabolite map on the MRI scan. As an example, the MRI scan may be overlaid with a color or gray scale indicating the metabolite levels associated with the different ROIs of the brain of the individual. In an example, prior to computing the weighted spectral average, the computing device may apply optimal zero- order and first-order phase corrections to the ROI-identified MRS scan in order to achieve a desired appearance of the real part of the spectra for the generated image comprising the overall of the metabolite map on the MRI scan. In an example, a treatment of the individual may be caused to be administered based on the determined metabolite levels. For example, the computing device may cause a medical treatment device to administer a treatment to the individual based on the determined metabolite levels.

[0057] As an example, the metabolite map of the different ROIs of the brain of the individual may be analyzed under one or more medical conditions (e.g., hypoxia, hyperoxia, etc.). For example, the metabolite map may be analyzed to determine that the MPFC region shows a decreased trend under a hypoxic condition. It may be determined that, for most of the other regions, inorganic phosphate levels are significantly elevated under hypoxia when compared with that in hyperoxia. In another example, the metabolite map may be analyzed to determine that mild hypoxia does not sufficiently stress the brain to cause a significant reduction in phosphocreatine levels or that neuronal mitochondria is able to operate at full capacity to meet the brain energy demands in the resting hypoxic state. In another example, the metabolite map of the MPFC region may be analyzed to evaluate the relative changes in phosphocreatine (PCr) and inorganic phosphate (Pi) levels in individuals. Under hypoxia, most individuals may exhibit lower PCr levels and higher Pi levels compared to those under hyperoxia. Therefore, in the cerebral tissue, the depletion of PCr and the decline in the PCr to Pi ratio may indicate the degree of brain fatigue. Significant group differences may be observed in the ratio of PCr to Pi across the MPFC, PCC, left lateral parietal and right SMG regions. In another example, the metabolite map may be analyzed to determine an increase in pH values across all assessed brain regions under hypoxia. In another example, PCr and adenosineATTORNEY DOCKET NO.: 21101.0491P1 triphosphate beta-phosphate group (βATP) maps may be generated that illustrate the relative magnitudes across various brain regions, despite no significant group differences between hypoxia and hyperoxia. As an example, higher PCr levels may be observed in the left-side brain regions compared to the contralateral side of the brain (e.g., left LPFC versus right LPFC). In contrast, βATP level profile may appear more homogeneous.

[0058] While the methods and systems have been described in connection with specific examples, it is not intended that the scope be limited to the particular embodiments set forth, as the embodiments herein are intended in all respects to be illustrative rather than restrictive.

[0059] Unless otherwise expressly stated, it is in no way intended that any method set forth herein be construed as requiring that its steps be performed in a specific order. Accordingly, where a method claim does not actually recite an order to be followed by its steps or it is not otherwise specifically stated in the claims or descriptions that the steps are to be limited to a specific order, it is in no way intended that an order be inferred, in any respect. This holds for any possible non-express basis for interpretation, including: matters of logic with respect to arrangement of steps or operational flow; plain meaning derived from grammatical organization or punctuation; the number or type of embodiments described in the specification.

[0060] It will be apparent to those skilled in the art that various modifications and variations can be made without departing from the scope or spirit. Other embodiments will be apparent to those skilled in the art from consideration of the specification and practice disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit being indicated by the following claims.

Claims

ATTORNEY DOCKET NO.: 21101.0491P1 CLAIMS What is claimed is:

1. A method for analyzing metabolite levels from regions of interest (ROI) of a human brain comprising: receiving, by a computing device, from a magnetic resonance imaging (MRI) scanner, an MRI scan of a brain of an individual; generating, based on normalizing the MRI scan into a standardized space, a standardized MRI scan; determining a ROI of the brain of the individual on the standardized MRI scan; generating, based on the ROI of the brain of the individual, a ROI-identified magnetic resonance spectroscopy (MRS) scan; determining, based on the ROI-identified MRS scan, a spectrum value associated with the ROI of the brain of the individual; and generating, based on the spectrum value associated with the ROI of the brain of the individual, a metabolite map associated with the ROI of the brain of the individual.

2. The method of claim 1, wherein the standardized space comprises a Montreal Neurological Institute (MNI) space.

3. The method of claim 1, wherein the ROI is associated with one or more of a default mode network (DMN), a dorsal attention network, a frontal parietal network, a visual network, a sensorimotor network, a language network, or a salience network.

4. The method of claim 1, further comprising generating, based on normalizing the MRI scan into the standardized space, an inverse transformation matrix.

5. The method of claim 1, wherein generating, based on the ROI of the brain of the individual, the ROI-identified MRS scan comprises: generating, based on applying an inverse transformation matrix to the determined ROI on the standardized MRI scan, a ROI image aligned to the brain of the individual; andATTORNEY DOCKET NO.: 21101.0491P1 generating, based on mapping the ROI image aligned to the brain of the individual to an MRS scan of the brain of the individual, the ROI-identified MRS scan.

6. The method of claim 1, wherein the ROI-identified MRS scan comprises an ROI image aligned to the brain of the individual that is mapped on an MRS scan of the brain of the individual.

7. The method of claim 1, wherein determining, based on the ROI-identified MRS scan, the spectrum value associated with the ROI of the brain of the individual comprises determining, based on applying a weighted spectral average method to the ROI-identified MRS scan, the spectrum value associated with the ROI of the brain of the individual.

8. The method of claim 1, wherein determining, based on the ROI-identified MRS scan, the spectrum value associated with the ROI of the brain of the individual comprises: determining a center of mass for the ROI of the brain of the individual on the ROI- identified MRS scan; determining a value associated with each voxel of a plurality of voxels; determining, based on multiplying each value of the plurality of values by a distance of the corresponding voxel of the plurality of voxels to the center of mass for the ROI, a plurality of voxel spectrum values; and generating, based on summing the plurality of voxel spectrum values, the spectrum value.

9. The method of claim 1, wherein the metabolite map comprises one or more metabolite levels associated with one or more of N-acetylaspartate (NAA), choline (Cho), creatine (Cr), myo-inositol (ml), lactate (Lac), glutamate (Glu), or glutamine (Glx).

10. The method of claim 1, further comprising generating an image comprising an overlay of the metabolite map on the MRI scan.

11. An apparatus for analyzing metabolite levels from regions of interest (ROI) of a human brain comprising: one or more processors;ATTORNEY DOCKET NO.: 21101.0491P1 a memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to: receive, from a magnetic resonance imaging (MRI) scanner, an MRI scan of a brain of an individual; generate, based on normalizing the MRI scan into a standardized space, a standardized MRI scan; determine a ROI of the brain of the individual on the standardized MRI scan; generate, based on the ROI of the brain of the individual, a ROI-identified magnetic resonance spectroscopy (MRS) scan; determine, based on the ROI-identified MRS scan, a spectrum value associated with the ROI of the brain of the individual; and generate, based on the spectrum value associated with the ROI of the brain of the individual, a metabolite map associated with the ROI of the brain of the individual.

12. The apparatus of claim 11, wherein the standardized space comprises the Montreal Neurological Institute (MNI) space.

13. The apparatus of claim 11, wherein the ROI is associated with one or more of a default mode network (DMN), a dorsal attention network, a frontal parietal network, a visual network, a sensorimotor network, a language network, or a salience network.

14. The apparatus of claim 11, wherein the processor-executable instructions, when executed by the one or more processors, further cause the apparatus to generate, based on normalizing the MRI scan into the standardized space, an inverse transformation matrix.

15. The apparatus of claim 11, wherein the processor-executable instructions that, when executed by the one or more processors, cause the apparatus to generate, based on the ROI of the brain of the individual, the ROI-identified MRS scan, further cause the apparatus to generate, based on applying an inverse transformation matrix to the determined ROI on the standardized MRI scan, a ROI image aligned to the brain of the individual; and generate, based on mapping the ROI image aligned to the brain of the individual to an MRS scan of the brain of the individual, the ROI-identified MRS scan.ATTORNEY DOCKET NO.: 21101.0491P1 16. The apparatus of claim 11, wherein the ROI-identified MRS scan comprises an ROI image aligned to the brain of the individual that is mapped on an MRS scan of the brain of the individual.

17. The apparatus of claim 11, wherein the processor-executable instructions that, when executed by the one or more processors, cause the apparatus to determine, based on the ROI- identified MRS scan, the spectrum value associated with the ROI of the brain of the individual, further cause the apparatus to determined, based on applying a weighted spectral average method to the ROI-identified MRS scan, the spectrum value associated with the ROI of the brain of the individual.

18. The apparatus of claim 11, wherein the processor-executable instructions that, when executed by the one or more processors, cause the apparatus to determine, based on the ROI- identified MRS scan, the spectrum value associated with the ROI of the brain of the individual, further cause the apparatus to: determine a center of mass for the ROI of the brain of the individual on the ROI- identified MRS scan; determine a value associated with each voxel of a plurality of voxels; determine, based on multiplying each value of the plurality of values by a distance of the corresponding voxel of the plurality of voxels to the center of mass for the ROI, a plurality of voxel spectrum values; and generate, based on summing the plurality of voxel spectrum values, the spectrum value.

19. The apparatus of claim 11, wherein the metabolite map comprises one or more metabolite levels associated with one or more of N-acetylaspartate (NAA), choline (Cho), creatine (Cr), myo-inositol (ml), lactate (Lac), glutamate (Glu), or glutamine (Glx).

20. The apparatus of claim 11, wherein the processor-executable instructions, when executed by the one or more processors, further cause the apparatus to generate an image comprising an overlay of the metabolite map on the MRI scan.

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