BACKGROUND

Lactate, once considered a waste product, is now recognized as an important intermediate in brain metabolism. The astrocyte-neuron lactate shuttle (ANLS) model indicates that the ATP needed to clear glutamate from synapses is primarily derived from lactate produced by aerobic glycolysis in astrocytes. There is evidence of multiple roles for lactate: a source of energy in glutamatergic neurons, a signaling molecule modulating neuronal excitability and synaptic plasticity, and a key player in maintaining homeostasis. Furthermore, experiments in rats have shown that lactate transport is required for long-term memory formation, and that cerebral aerobic glycolysis and lactate concentration are reduced during sleep.

While there continues to be debate surrounding the degree to which neurons are fueled by astrocytic lactate, mounting evidence points to a critical role of aerobic glycolysis and lactate in brain energy metabolism. Combined PET measurements of cerebral metabolic rate of oxygen and glucose using15O-oxygen and19F-fluorodeoxyglucose, respectively, can provide an estimate of aerobic glycolysis through a measure of the oxygen to glucose index (“OGI”), which is 6.0 if glucose is fully oxidized. These PET experiments have shown evidence of non-oxidative consumption of glucose (OGI<6.0), with net brain glucose uptake exceeding the rate required to match the regional oxygen consumption. OGI is lowest in children and young adults and increases with aging, and recently, it was shown that the OGI has a distinct topography within the brain, corresponding to regions with persistent gene expression associated with childhood development (neoteny). However, these radiotracer methods that measure OGI only allow for the calculation of the non-oxidative component of glucose consumption, and cannot measure the production of lactate that ends up being used in oxidative phosphorylation in other cell compartments. Furthermore, conventional metabolite measurement methods using MR spectroscopy only enable the measurement of the steady-state metabolite concentrations, and not the rate of metabolite production and transport.

SUMMARY OF THE DISCLOSURE

The present disclosure addresses the aforementioned drawbacks by providing a method for producing a metabolite profile from magnetic resonance images. The method includes accessing magnetic resonance images acquired from a subject. The magnetic resonance images includes, a plurality of metabolite images having pixel values indicative of magnetic resonance signals associated with a metabolite and a plurality of anatomical images that depict anatomical structures in the subject. Parcellated image data are generated by parcellating the anatomical images into a plurality of regions, wherein each pixel in the parcellated image data is associated with a region from an anatomical atlas. A metabolite profile is then generated for the subject from the plurality of metabolite images and using the parcellated image data. The metabolite profile can be generated by (i) selecting a given region from the plurality of regions; (ii) retrieving pixels values from the plurality of metabolite images using the coordinates for the given region in the parcellated image data; (iii) computing a mean pixel value for the given region by computing a mean of the retrieved pixel values; and (iv) repeating steps (i) though (iii) for each of a number of the plurality of regions in order to generate a sequence of mean pixel values, wherein the metabolite profile comprises the sequence of mean pixel values.

It is another aspect of the present disclosure to provide a method for producing a metabolite profile from magnetic resonance images. The method includes accessing magnetic resonance images acquired from a subject, where the magnetic resonance images include a plurality of metabolite images, each having pixel values indicative of magnetic resonance signals associated with a metabolite, and a plurality of anatomical images that depict anatomical structures in the subject. Parcellated image data are generated by parcellating the anatomical images into a plurality of parcellated regions, where each pixel in the parcellated image data is associated with a region from an anatomical atlas. A metabolite profile is generated for the subject from the plurality of metabolite images and using the parcellated image data. This metabolite profile is generated by (i) selecting a first region from the plurality of parcellated regions; (ii) retrieving first pixel values from the plurality of metabolite images using the coordinates for the first region in the parcellated image data; (iii) selecting a second region from the plurality of parcellated regions; (iv) retrieving second pixel values from the plurality of metabolite images using the coordinates for the second region in the parcellated image data; (v) computing a ratio value between the first pixel values and the second pixel values; and (vi) repeating steps (i) though (v) for each of a number of first and second regions in the plurality of parcellated regions in order to generate a sequence of ratio values, wherein the metabolite profile comprises the sequence of ratio values.

The foregoing and other aspects and advantages of the present disclosure will appear from the following description. In the description, reference is made to the accompanying drawings that form a part hereof, and in which there is shown by way of illustration a preferred embodiment. This embodiment does not necessarily represent the full scope of the invention, however, and reference is therefore made to the claims and herein for interpreting the scope of the invention.

