Neuroimaging at 7 Tesla: a pictorial narrative review

Neuroimaging using the 7-Tesla (7T) human magnetic resonance (MR) system is rapidly gaining popularity after being approved for clinical use in the European Union and the USA. This trend is the same for functional MR imaging (MRI). The primary advantages of 7T over lower magnetic fields are its higher signal-to-noise and contrast-to-noise ratios, which provide high-resolution acquisitions and better contrast, making it easier to detect lesions and structural changes in brain disorders. Another advantage is the capability to measure a greater number of neurochemicals by virtue of the increased spectral resolution. Many structural and functional studies using 7T have been conducted to visualize details in the white matter and layers of the cortex and hippocampus, the subnucleus or regions of the putamen, the globus pallidus, thalamus and substantia nigra, and in small structures, such as the subthalamic nucleus, habenula, perforating arteries, and the perivascular space, that are difficult to observe at lower magnetic field strengths. The target disorders for 7T neuroimaging range from tumoral diseases to vascular, neurodegenerative, and psychiatric disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, epilepsy, major depressive disorder, and schizophrenia. MR spectroscopy has also been used for research because of its increased chemical shift that separates overlapping peaks and resolves neurochemicals more effectively at 7T than a lower magnetic field. This paper presents a narrative review of these topics and an illustrative presentation of images obtained at 7T. We expect 7T neuroimaging to provide a new imaging biomarker of various brain disorders

Quantification of 18F-FDG PET kinetic parameters using an image-derived input function and multimodal integration with resting-state fMRI metrics

Metabolic demand associated with resting-state brain activity is one of the main focus of neuroscience research. Task-free brain activation has been found to exhibit coherent spatial patterns, and the associated glucose consumption is predominant if compared to task activation. However, a complete characterization of the link between energy and function in the brain is still missing. The aim of this thesis project was to explore novel strategies for the integration between metabolic measures coming from Positron Emission Tomography based on fluorodeoxyglucose ([18F]FDG PET) and functional information extracted from resting-state Functional Magnetic Resonance Imaging (rsfMRI) measures. This was done adopting two different perspectives. On one hand, it was verified how metabolic and functional networks, inferred from time-series correlation across brain regions, relate to each other. On the other hand, across-subject similarity between sets of metabolic parameters and functional features was assessed. The analysis was performed on a dataset provided by Washington University in St.Louis, consisting of non-simultaneous PET and MR acquisitions on a large cohort of subjects. A first part of the work focused on [18F]FDG data. An Image-derived input function (IDIF) was extracted from the internal carotid arteries. This was later used for microparameter estimation with Variational Bayesian approach. Across-subjects correlation matrices were obtained for subjects series of K1 and k3 values. Moreover, average metabolic connectivity matrix was extracted from [18F]FDG parcel-level TACs. Similarly, from fMRI data, average functional connectivity matrix was extracted. Regional Homogeneity (ReHo) and Global Functional Connectivity (GFC) were estimated and across-subjects connectivity matrices were obtained for both parameters. Time-series connectivity matrices coming from both PET and fMRI images were used to assess similarity between metabolic and functional networks, whereas across-subject connectivity matrices were used to compare metabolic and functional parameters. To agevolate comparison, embedding was used on both timeseries and across-subjects connectivity: this was based on application of a gaussian kernel, followed by calculation of the Laplacian Eigenmaps, a nonlinear dimensionality reduction techinque. Resulting manifolds are called gradients in neuroscience, and are commonly used to study functional architecture in the brain. From a network perspective, metabolic and functional gradients exhibited significant correlation, and the regions in which they overlapped the most belong to visual and sensorimotor networks. Similar results were found between all combinations of [18F]FDG microparameters and fMRI features gradients, implying that both local and global functional relationship in the brain may be associated with specific metabolic fingerprints.Metabolic demand associated with resting-state brain activity is one of the main focus of neuroscience research. Task-free brain activation has been found to exhibit coherent spatial patterns, and the associated glucose consumption is predominant if compared to task activation. However, a complete characterization of the link between energy and function in the brain is still missing. The aim of this thesis project was to explore novel strategies for the integration between metabolic measures coming from Positron Emission Tomography based on fluorodeoxyglucose ([18F]FDG PET) and functional information extracted from resting-state Functional Magnetic Resonance Imaging (rsfMRI) measures. This was done adopting two different perspectives. On one hand, it was verified how metabolic and functional networks, inferred from time-series correlation across brain regions, relate to each other. On the other hand, across-subject similarity between sets of metabolic parameters and functional features was assessed. The analysis was performed on a dataset provided by Washington University in St.Louis, consisting of non-simultaneous PET and MR acquisitions on a large cohort of subjects. A first part of the work focused on [18F]FDG data. An Image-derived input function (IDIF) was extracted from the internal carotid arteries. This was later used for microparameter estimation with Variational Bayesian approach. Across-subjects correlation matrices were obtained for subjects series of K1 and k3 values. Moreover, average metabolic connectivity matrix was extracted from [18F]FDG parcel-level TACs. Similarly, from fMRI data, average functional connectivity matrix was extracted. Regional Homogeneity (ReHo) and Global Functional Connectivity (GFC) were estimated and across-subjects connectivity matrices were obtained for both parameters. Time-series connectivity matrices coming from both PET and fMRI images were used to assess similarity between metabolic and functional networks, whereas across-subject connectivity matrices were used to compare metabolic and functional parameters. To agevolate comparison, embedding was used on both timeseries and across-subjects connectivity: this was based on application of a gaussian kernel, followed by calculation of the Laplacian Eigenmaps, a nonlinear dimensionality reduction techinque. Resulting manifolds are called gradients in neuroscience, and are commonly used to study functional architecture in the brain. From a network perspective, metabolic and functional gradients exhibited significant correlation, and the regions in which they overlapped the most belong to visual and sensorimotor networks. Similar results were found between all combinations of [18F]FDG microparameters and fMRI features gradients, implying that both local and global functional relationship in the brain may be associated with specific metabolic fingerprints

Relationship between white matter alteration and encoding related brain activation in connected brain regions

Background: Aging is associated with alterations of white matter and brain activation. Functional MRI studies in elderly subjects showed changes in encoding related brain activation such as hyperactivation in frontal areas and hypoactivation in occipital gyrus in comparison to a younger control group. A contributing factor could be alterations of white matter integrity, resulting from age-related small vessel disease (SVD), a pathology that effects the small vessels of the brain or Wallerian Degeneration (WD) that explains axonal degeneration distal of an injury. These processes lead to changes such as white matter hyperintensities (WMH) detected by MRI or microstructural change assessed by the diffusion tensor imaging (DTI), marker mean diffusivity (MD) or peak width (PW). It was hypothesized that the directionality of the structure-function relationship of the brain is dependent on the investigated brain region. We aim to verify this assumption. Other studies focused on frontal brain regions. Instead we implemented a whole-brain analysis of the structure-function relationship. Therefore, the goal of this study was to investigate changes in white matter as a predictor of changed encoding-related brain activation in anatomically connected brain regions in cognitively normal performing older subjects. Furthermore, there are different theories that try to explain the changed brain activation in association with white matter change, such as compensatory mechanisms, dedifferentiation theory or inefficient neuronal processes. To gain a better understanding we examined the association between decreased brain activation respectively increased white matter changes in relation to cognitive performance of the subjects. Methods: Cognitively healthy elderly subjects (N = 35) performed a face-name matching paradigm within the fMRI scanner with the encoding phase being relevant in the present study. The integrity of white matter was determined with measurement of WMH volume and DTI based markers such as MD and PW. We performed ANOVAs with DTI-markers as dependent and activation as the independent variable. Furthermore, we performed ANOVAs with white matter change or brain activation as dependent variable and cognitive performance as independent variable. Since we assumed that there are local differences of white matter change we created boxplots for the chosen MD, PW and WMH-ratio within the chosen fiber tracts and global MD, PW and WMH-ratio. Additionally, we computed a correlation matrix between tract-specific MD or PW for a comparison of these two markers. Results: We could demonstrate a significant positive association between PW in the inferior fronto-occipital fasciculus left (IFOF L) and the activation in the left frontal gyrus as well as PW in the inferior longitudinal fasciculus right (ILF R) and activation in the occipital gyrus. Furthermore, the data revealed no significant result for the relationship between white matter change and cognition or brain activation and cognition. The boxplot showed a significant difference between the white matter tracts when using MD and PW as marker. Because of its low burden we had to exclude WMH-ratio as a marker for white matter change. The correlation-matrix revealed that PW within the tracts correlated less with each other than MD. Conclusion: These results suggest that microstructural changes lead to increased brain activation due to decreased white matter connectivity and reduced fidelity of data transmission. Additionally, the subjects' cognitive performance appears not to benefit from the increased brain activation. Thus, the negative structure-function relationship seems not to be based on a compensatory mechanism or dedifferentiation theory but most likely on an inefficient neuronal response. White matter change can be considered as regionally variable as revealed by the boxplots and the correlation matrix. However, the structure-function relation seems not be dependent on brain region, because the whole brain analysis showed a consistent directionality of the structure-function relationship

On the neurobiology of apathy and depression in cerebral small vessel disease

Cerebral small vessel disease (SVD) is a cerebrovascular pathology that affects the small vessels of the brain, resulting in heterogeneous brain tissue changes. These can lead to neuropsychiatric symptoms such as apathy, a loss of motivation, and depression, which is characterised by low mood and a loss of pleasure. Apathy and depression are both prevalent symptoms in SVD, but an understanding of the relationship between underlying disease processes and the expression of these neuropsychiatric symptoms remains poor. This thesis uses magnetic resonance imaging techniques to examine the neurobiological basis of apathy and depression in SVD. We show that apathy is related to focal grey matter damage and distributed white matter microstructural change. These microstructural changes underlie large-scale white matter network disruption, which is related to apathy, but not depression. We then show that depression, as a construct, can be dissociated into distinct symptoms which are associated with overlapping and distinct areas of cortical atrophy over time. This suggests that depression as a general syndrome may be characterised by atrophy in core structures, while different symptoms are associated with atrophy in more specialised areas. Consistent with these patterns of overarching tissue damage, we find that apathy, but not depression, predicts conversion to dementia in patients with SVD. Our findings suggest that different types of SVD-related pathology lead to apathy and depression. Diffuse white matter damage may lead to widespread network disruption, resulting in apathy and cognitive impairment. In contrast, depressive symptoms are associated with focal patterns of grey matter atrophy over time. This highlights the importance of differentiating neuropsychiatric symptoms, and paves the way for targeted treatment approaches.Cambridge International Scholarship (Cambridge Trust)

Disruption of brainstem monoaminergic fibre tracts in multiple sclerosis as a putative mechanism for cognitive fatigue: a fixel-based analysis

In multiple sclerosis (MS), monoaminergic systems are altered as a result of both inflammation-dependent reduced synthesis and direct structural damage. Aberrant monoaminergic neurotransmission is increasingly considered a major contributor to fatigue pathophysiology. In this study, we aimed to compare the integrity of the monoaminergic white matter fibre tracts projecting from brainstem nuclei in a group of patients with MS (n=68) and healthy controls (n=34), and to investigate its association with fatigue. Fibre tracts integrity was assessed with the novel fixel-based analysis that simultaneously estimates axonal density, by means of ‘fibre density’, and white matter atrophy, by means of fibre ‘cross section’. We focused on ventral tegmental area, locus coeruleus, and raphe nuclei as the main source of dopaminergic, noradrenergic, and serotoninergic fibres within the brainstem, respectively. Fourteen tracts of interest projecting from these brainstem nuclei were reconstructed using diffusion tractography, and compared by means of the product of fibre-density and cross- section (FDC). Finally, correlations of monoaminergic axonal damage with the modified fatigue impact scale scores were evaluated in MS. Fixel-based analysis revealed significant axonal damage – as measured by FDC reduction – within selective monoaminergic fibre-tracts projecting from brainstem nuclei in MS patients, in comparison to healthy controls; particularly within the dopaminergic-mesolimbic pathway, the noradrenergic-projections to prefrontal cortex, and serotoninergic-projections to cerebellum. Moreover, we observed significant correlations between severity of cognitive fatigue and axonal damage within the mesocorticolimbic tracts projecting from ventral tegmental area, as well as within the locus coeruleus projections to prefrontal cortex, suggesting a potential contribution of dopaminergic and noradrenergic pathways to central fatigue in MS. Our findings support the hypothesis that axonal damage along monoaminergic pathways contributes to the reduction/dysfunction of monoamines in MS and add new information on the mechanisms by which monoaminergic systems contribute to MS pathogenesis and fatigue. This supports the need for further research into monoamines as therapeutic targets aiming to combat and alleviate fatigue in MS