Enhancing the synergy of in-vivo multimodal brain imaging

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Background. Brain illness is considered the major health challenge in the 21st century; knowledge on brain function and disease mechanisms is still limited. Non-invasive brain imaging has fundamentally contributed to the current understanding of brain function, which depicts the brain as a series of networks. Neurodegenerative diseases are hypothesized to induce pathological alterations within specific networks, considered to be mediated by molecular pathology. Multi-modality (hybrid) imaging comprising simultaneous Positron Emission Tomography (PET) and magnetic resonance imaging (MRI), is particularly well suited to explore this hypothesis by investigating neurochemical correlates (PET) of the disease-associated structural and connectivity alterations (MRI). While the hardware compatibility of PET and MRI is fairly mature, data acquisition protocols and analysis methods to maximize the synergy of hybrid imaging still need to be developed. Goal. To develop algorithms aimed at enhancing the synergy of hybrid PET/MRI for brain imaging with three inter-related aims: (i) improving PET image quality; (ii) developing accurate measurements of novel multi-modality metrics related to fundamental aspects of brain function, such as energetics and interactions between neurotransmitter activity and brain functional/structural networks (iii) developing methods to quantify interactions between metabolic, functional and structural networks and disease specific multi-parameter features that could serve as disease biomarkers. Methods. The following will be developed: (i) Data denoising to improve the PET temporal resolution to make it more commensurate with MRI; (ii.a) Simultaneous calibrated BOLD (MRI) and dynamic glucose uptake (PET) measurements to obtain simultaneous separate estimates of cerebral blood flow, oxygen extraction fraction and cerebral oxygen and glucose metabolic rates. These measurements will be combined with algorithms developed under aim 3 to yield estimates of the novel metrics listed above; (ii.b) Estimation of stimuli-induced transient neurotransmitter release (PET) coupled with its effect on brain connectivity and signaling (MRI); (iii) Canonical correlation analysis, orthogonal signal correction, consensus clustering, hypergraph learning and multilayer graph analyses to extract common and unique patterns from the multimodal data and advanced statistical or machine-learning based clustering algorithm to extract relevant features. Impact. This program reflects technological and conceptual advancements in instrumentation and brain research. While some proposed methods are not intrinsically original, the novelty and significance result from selectively adapting them to hybrid imaging; they will (i) enable exploration and quantification of fundamental aspects of brain function such as brain energetics, thus opening new research avenues and (ii) maximize the investigative power of hybrid imaging also applicable to other research fields.