Fully Integrated PET/MR Imaging for the Assessment of the Relationship Between Functional Connectivity and Glucose Metabolic Rate

PET-based brain molecular connectivity in neurodegenerative disease

PURPOSE OF REVIEW

Molecular imaging has traditionally been used and interpreted primarily in the context of localized and relatively static neurochemical processes. New understanding of brain function and development of novel molecular imaging protocols and analysis methods highlights the relevance of molecular networks that co-exist and interact with functional and structural networks. Although the concept and evidence of disease-specific metabolic brain patterns has existed for some time, only recently has such an approach been applied in the neurotransmitter domain and in the context of multitracer and multimodal studies. This review briefly summarizes initial findings and highlights emerging applications enabled by this new approach.

RECENT FINDINGS

Connectivity based approaches applied to molecular and multimodal imaging have uncovered molecular networks with neurodegeneration-related alterations to metabolism and neurotransmission that uniquely relate to clinical findings; better disease stratification paradigms; an improved understanding of the relationships between neurochemical and functional networks and their related alterations, although the directionality of these relationships are still unresolved; and a new understanding of the molecular underpinning of disease-related alteration in resting-state brain activity.

SUMMARY

Connectivity approaches are poised to greatly enhance the information that can be extracted from molecular imaging. While currently mostly contributing to enhancing understanding of brain function, they are highly likely to contribute to the identification of specific biomarkers that will improve disease management and clinical care.

1H magnetic resonance spectroscopic imaging of deuterated glucose and of neurotransmitter metabolism at 7 T in the human brain

Impaired glucose metabolism in the brain has been linked to several neurological disorders. Positron emission tomography and carbon-13 magnetic resonance spectroscopic imaging (MRSI) can be used to quantify the metabolism of glucose, but these methods involve exposure to radiation, cannot quantify downstream metabolism, or have poor spatial resolution. Deuterium MRSI (2H-MRSI) is a non-invasive and safe alternative for the quantification of the metabolism of 2H-labelled substrates such as glucose and their downstream metabolic products, yet it can only measure a limited number of deuterated compounds and requires specialized hardware. Here we show that proton MRSI (1H-MRSI) at 7 T has higher sensitivity, chemical specificity and spatiotemporal resolution than 2H-MRSI. We used 1H-MRSI in five volunteers to differentiate glutamate, glutamine, γ-aminobutyric acid and glucose deuterated at specific molecular positions, and to simultaneously map deuterated and non-deuterated metabolites. 1H-MRSI, which is amenable to clinically available magnetic-resonance hardware, may facilitate the study of glucose metabolism in the brain and its potential roles in neurological disorders.

Combining CRISPR-Cas9 and brain imaging to study the link from genes to molecules to networks

Receptors, transporters, and ion channels are important targets for therapy development in neurological diseases, but their mechanistic role in pathogenesis is often poorly understood. Gene editing and in vivo imaging approaches will help to identify the molecular and functional role of these targets and the consequence of their regional dysfunction on the whole-brain level. We combine CRISPR-Cas9 gene editing with in vivo positron emission tomography (PET) and functional MRI (fMRI) to investigate the direct link between genes, molecules, and the brain connectome. The extensive knowledge of the Slc18a2 gene encoding the vesicular monoamine transporter (VMAT2), involved in the storage and release of dopamine, makes it an excellent target for studying the gene network relationships while structurally preserving neuronal integrity and function. We edited the Slc18a2 in the substantia nigra pars compacta of adult rats and used in vivo molecular imaging besides behavioral, histological, and biochemical assessments to characterize the CRISPR-Cas9-mediated VMAT2 knockdown. Simultaneous PET/fMRI was performed to investigate molecular and functional brain alterations. We found that stage-specific adaptations of brain functional connectivity follow the selective impairment of presynaptic dopamine storage and release. Our study reveals that recruiting different brain networks is an early response to the dopaminergic dysfunction preceding neuronal cell loss. Our combinatorial approach is a tool to investigate the impact of specific genes on brain molecular and functional dynamics, which will help to develop tailored therapies for normalizing brain function.

The principle of 'brain energy on demand' and its predictive power for stress, sleep, stroke, obesity and diabetes

Does the brain actively draw energy from the body when needed? There are different schools of thought regarding energy metabolism. In this study, the various theoretical models are classified into one of two categories: (1) conceptualizations of the brain as being purely passively supplied, which we call 'P-models,' and (2) models understanding the brain as not only passively receiving energy but also actively procuring energy for itself on demand, which we call 'A-models.' One prominent example of such theories making use of an A-model is the selfish-brain theory. The ability to make predictions was compared between the A- and P-models. A-models were able to predict and coherently explain all data examined, which included stress, sleep, caloric restriction, stroke, type-1-diabetes mellitus, obesity, and type-2-diabetes, whereas the predictions of P-models failed in most cases. The strength of the evidence supporting A-models is based on the coherence of accurate predictions across a spectrum of metabolic states. The theory test conducted here speaks to a brain that pulls its energy from the body on-demand.

Cortical excitatory and inhibitory correlates of the fronto-limbic circuit in major depression and differential effects of left frontal brain stimulation in a randomized sham-controlled trial

BACKGROUND

Major depressive disorder (MDD), particularly treatment-resistant ones, is associated with abnormal fronto-limbic glucose metabolism. 10-Hz repetitive transcranial magnetic stimulation (rTMS) over left prefrontal cortex (PFC) is believed to normalize the abnormal metabolism to treat depression. However, the exact molecular mechanisms underlying the mood circuit of depressed brains and whether brain stimulation techniques regulate the underlying molecules remain elusive.

METHODS

Whole-brain glucose metabolism and cortical excitatory and inhibitory markers including P30, N45, P60, N100, and LICI (long-interval cortical inhibition) of TMS-evoked potentials from left DLPFC were measured in 40 subjects with MDD patients. The neurophysiological markers were repeated immediately after 1st session of left PFC rTMS, intermittent theta-burst stimulation (iTBS), and sham (randomly assigned).

RESULTS

Brain glucose metabolism in the limbic structures significantly correlated with left PFC P30 (mainly GABA-A and glutamate receptor mediated) and with LICI (mainly GABA-B receptor mediated inhibition) (FWE-corrected p < 0.001). Correlations between other neurophysiological markers (left PFC N45, P60, and N100) and posterior cingulate cortex, a key region in the default mode network, were also noted. One session of rTMS significantly decreased left PFC P60 (mainly glutamate receptor mediated), while a significant group effect was found for LICI (iTBS < sham).

CONCLUSION

The first study showed that the underlying molecular mechanisms of fronto-limbic circuit of MDD brains involved glutamatergic excitation and GABAergic inhibition at specific time points. In addition, one session of rTMS mainly modulated glutamatergic neurotransmission at left PFC, while the mechanisms of iTBS might involve GABA-B receptor mediated inhibition.

CLINICAL TRIALS REGISTRY NUMBER

UMIN000044951.

Abnormal default mode network could be a potential prognostic marker in patients with disorders of consciousness

OBJECTIVES

The study aimed to investigate disorders of consciousness (DOC) mechanisms of patients with severe traumatic brain injury (sTBI) related to default mode network (DMN) and to introduce a machine learning model that predicts the prognosis of these patients for 6 months.

METHODS

The sTBI patients suffering from DOC and healthy controls underwent functional magnetic resonance imaging. We defined patients with Extended Glasgow Outcome Score ≥ 5 as good outcome group, otherwise they were poor outcome group. The differences of DMN between sTBI and healthy controls and between good and poor outcome groups were compared. Based on the brain regions with altered functional connectivity between good and poor outcome groups, they were divided into 8 regions of interests according to side. The Z values of the regions of interests were extracted by Rest 1.8. Based on Z values, the Subspace K-Nearest Neighbor (Subspace KNN) was conducted to classify prognosis of sTBI patients suffering from DOC.

RESULTS

A total of 84 DMNs derived from patients and 45 DMNs from healthy controls were finally analyzed. The connectivity of the DMN was significantly decreased in sTBI patients suffering from DOC (Alphasim corrected, P < 0.05). In addition, compared with the poor outcome group (DMN samples = 60), the brain regions of DMN with decreased functional connectivity in the good outcome group (DMN samples = 24) the following bilateral areas: brodman Area 11, anterior cingulate and paracingulate gyri, brodman Area 25, olfactory cortex (Alphasim corrected, P < 0.05). The ability of Subspace KNN machine learning to distinguish the prognosis of patients (area under curve) was 0.97.

CONCLUSIONS

The interruption of DMN may be one of the reasons for DOC in patients with sTBI. Furthermore, based on early DMN (1-4 weeks), Subspace KNN machine learning has the potential value to distinguish the prognosis (6 months after brain trauma) of sTBI patients suffering from DOC.

Hybrid PET-MRI for early detection of dopaminergic dysfunction and microstructural degradation involved in Parkinson's disease

Dopamine depletion and microstructural degradation underlie the neurodegenerative processes in Parkinson’s disease (PD). To explore early alterations and underlying associations of dopamine and microstructure in PD patients utilizing the hybrid positron emission tomography (PET)-magnetic resonance imaging (MRI). Twenty-five PD patients in early stages and twenty-four matched healthy controls underwent hybrid ¹⁸F-fluorodopa (DOPA) PET-diffusion tensor imaging (DTI) scanning. The striatal standardized uptake value ratio (SUVR), DTI maps (fractional anisotropy, FA; mean diffusivity, MD) in subcortical grey matter, and deterministic tractography of the nigrostriatal pathway were processed. Values in more affected (MA) side, less affected (LA) side and mean were analysed. Correlations and mediations among PET, DTI and clinical characteristics were further analysed. PD groups exhibited asymmetric pattern of dopaminergic dysfunction in putamen, impaired integrity in the microstructures (nigral FA, putaminal MD, and FA of nigrostriatal projection). On MA side, significant associations between DTI metrics (nigral FA, putaminal MD, and FA of nigrostriatal projection) and motor performance were significantly mediated by putaminal SUVR, respectively. Early asymmetric disruptions in putaminal dopamine concentrations and nigrostriatal pathway microstructure were detected using hybrid PET-MRI. The findings further implied that molecular degeneration mediates the modulation of microstructural disorganization on motor dysfunction in the early stages of PD.

To explore early alterations and underlying associations of dopamine levels and microstructure in Parkinson’s Disease (PD), Shang et al use a hybrid positron emission tomography (PET)-magnetic resonance imaging (MRI) approach in early stage patients and age-matched controls. Their data implies that molecular degeneration mediates the effects of microstructural disorganization on motor dysfunction in the early stages of PD.

Simultaneous BOLD-fMRI and constant infusion FDG-PET data of the resting human brain

Simultaneous [18 F]-fluorodeoxyglucose positron emission tomography and functional magnetic resonance imaging (FDG-PET/fMRI) provides the capability to image two sources of energetic dynamics in the brain - cerebral glucose uptake and the cerebrovascular haemodynamic response. Resting-state fMRI connectivity has been enormously useful for characterising interactions between distributed brain regions in humans. Metabolic connectivity has recently emerged as a complementary measure to investigate brain network dynamics. Functional PET (fPET) is a new approach for measuring FDG uptake with high temporal resolution and has recently shown promise for assessing the dynamics of neural metabolism. Simultaneous fMRI/fPET is a relatively new hybrid imaging modality, with only a few biomedical imaging research facilities able to acquire FDG PET and BOLD fMRI data simultaneously. We present data for n = 27 healthy young adults (18-20 yrs) who underwent a 95-min simultaneous fMRI/fPET scan while resting with their eyes open. This dataset provides significant re-use value to understand the neural dynamics of glucose metabolism and the haemodynamic response, the synchrony, and interaction between these measures, and the development of new single- and multi-modality image preparation and analysis procedures.

An in vivo multimodal feasibility study in a rat brain tumour model using flexible multinuclear MR and PET systems

Background

In addition to the structural information afforded by ¹H MRI, the use of X-nuclei, such as sodium-23 (²³Na) or phosphorus-31 (³¹P), offers important complementary information concerning physiological and biochemical parameters. By then combining this technique with PET, which provides valuable insight into a wide range of metabolic and molecular processes by using of a variety of radioactive tracers, the scope of medical imaging and diagnostics can be significantly increased. While the use of multimodal imaging is undoubtedly advantageous, identifying the optimal combination of these parameters to diagnose a specific dysfunction is very important and is advanced by the use of sophisticated imaging techniques in specific animal models.

Methods

In this pilot study, rats with intracerebral 9L gliosarcomas were used to explore a combination of sequential multinuclear MRI using a sophisticated switchable coil set in a small animal 9.4 T MRI scanner and, subsequently, a small animal PET with the tumour tracer O-(2-[¹⁸F]-fluoroethyl)-L-tyrosine ([¹⁸F]FET). This made it possible for in vivo multinuclear MR-PET experiments to be conducted without compromising the performance of either multinuclear MR or PET.

Results

High-quality in vivo images and spectra including high-resolution ¹H imaging, ²³Na-weighted imaging, detection of ³¹P metabolites and [¹⁸F]FET uptake were obtained, allowing the characterisation of tumour tissues in comparison to a healthy brain. It has been reported in the literature that these parameters are useful in the identification of the genetic profile of gliomas, particularly concerning the mutation of the isocitrate hydrogenase gene, which is highly relevant for treatment strategy.

Conclusions

The combination of multinuclear MR and PET in, for example, brain tumour models with specific genetic mutations will enable the physiological background of signal alterations to be explored and the identification of the optimal combination of imaging parameters for the non-invasive characterisation of the molecular profile of tumours.