Axonal diameter and density estimated with 7-Tesla hybrid diffusion imaging in transgenic Alzheimer rats

Diffusion-weighted MR imaging (DWI) is a powerful tool to study brain tissue microstructure. DWI is sensitive to subtle changes in the white matter (WM), and can provide insight into abnormal brain changes in diseases such as Alzheimer’s disease (AD). In this study, we used 7-Tesla hybrid diffusion imaging (HYDI) to scan 3 transgenic rats (line TgF344-AD; that model the full clinico-pathological spectrum of the human disease) ex vivo at 10, 15 and 24 months. We acquired 300 DWI volumes across 5 q-sampling shells (b=1000, 3000, 4000, 8000, 12000 s/mm^2). From the top three b-value shells with highest signal-to-noise ratios, we reconstructed markers of WM disease, including indices of axon density and diameter in the corpus callosum (CC) – directly quantifying processes that occur in AD. As expected, apparent anisotropy progressively decreased with age; there were also decreases in the intra- and extra-axonal MR signal along axons. Axonal diameters were larger in segments of the CC (splenium and body, but not genu), possibly indicating neuritic dystrophy – characterized by enlarged axons and dendrites as previously observed at the ultrastructural level (see Cohen et al., J. Neurosci. 2013). This was further supported by increases in MR signals trapped in glial cells, CSF and possibly other small compartments in WM structures. Finally, tractography detected fewer fibers in the CC at 10 versus 24 months of age. These novel findings offer great potential to provide technical and scientific insight into the biology of brain disease

Functional connectome of arousal and motor brainstem nuclei in living humans by 7 Tesla resting-state fMRI

Brainstem nuclei play a pivotal role in many functions, such as arousal and motor control. Nevertheless, the connectivity of arousal and motor brainstem nuclei is understudied in living humans due to the limited sensitivity and spatial resolution of conventional imaging, and to the lack of atlases of these deep tiny regions of the brain. For a holistic comprehension of sleep, arousal and associated motor processes, we investigated in 20 healthy subjects the resting-state functional connectivity of 18 arousal and motor brainstem nuclei in living humans. To do so, we used high spatial-resolution 7 Tesla resting-state fMRI, as well as a recently developed in-vivo probabilistic atlas of these nuclei in stereotactic space. Further, we verified the translatability of our brainstem connectome approach to conventional (e.g. 3 Tesla) fMRI. Arousal brainstem nuclei displayed high interconnectivity, as well as connectivity to the thalamus, hypothalamus, basal forebrain and frontal cortex, in line with animal studies and as expected for arousal regions. Motor brainstem nuclei showed expected connectivity to the cerebellum, basal ganglia and motor cortex, as well as high interconnectivity. Comparison of 3 Tesla to 7 Tesla connectivity results indicated good translatability of our brainstem connectome approach to conventional fMRI, especially for cortical and subcortical (non-brainstem) targets and to a lesser extent for brainstem targets. The functional connectome of 18 arousal and motor brainstem nuclei with the rest of the brain might provide a better understanding of arousal, sleep and accompanying motor functions in living humans in health and disease

Reconstruction of major fibers using 7T multi-shell Hybrid Diffusion Imaging in mice

Diffusion weighted imaging (DWI) can reveal the orientation of the underlying fiber populations in the brain. High angular resolution diffusion imaging (HARDI) is increasingly used to better resolve the orientation and mixing of fibers. Here, we assessed the added value of multi-shell q-space sampling on the reconstruction of major fibers using mathematical frameworks from q-ball imaging (QBI) and generalized q-sampling imaging (GQI), as compared to diffusion tensor imaging (DTI). We scanned a healthy mouse brain using 7-Tesla 5-shell HARDI (b=1000, 3000, 4000, 8000, 12000 s/mm2), also known as hybrid diffusion imaging (HYDI). We found that QBI may provide greater reconstruction accuracy for major fibers, which improves with the addition of higher b-value shells, unlike GQI or DTI (as expected). Although QBI is a special case of GQI, the major fiber orientation in QBI was more closely related to the orientation in DTI, rather than GQI. HYDI can aid the clinical outcomes of research and especially – more advanced human and animal connectomics projects to map the brain’s neural pathways and networks

Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition

abstract: Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.View the article as published at http://journal.frontiersin.org/article/10.3389/fnins.2015.00257/ful

Axonal diameter and density estimated with 7-Tesla hybrid diffusion imaging in transgenic Alzheimer rats

Diffusion-weighted MR imaging (DWI) is a powerful tool to study brain tissue microstructure. DWI is sensitive to subtle changes in the white matter (WM), and can provide insight into abnormal brain changes in diseases such as Alzheimer’s disease (AD). In this study, we used 7-Tesla hybrid diffusion imaging (HYDI) to scan 3 transgenic rats (line TgF344-AD; that model the full clinico-pathological spectrum of the human disease) ex vivo at 10, 15 and 24 months. We acquired 300 DWI volumes across 5 q-sampling shells (b=1000, 3000, 4000, 8000, 12000 s/mm^2). From the top three b-value shells with highest signal-to-noise ratios, we reconstructed markers of WM disease, including indices of axon density and diameter in the corpus callosum (CC) – directly quantifying processes that occur in AD. As expected, apparent anisotropy progressively decreased with age; there were also decreases in the intra- and extra-axonal MR signal along axons. Axonal diameters were larger in segments of the CC (splenium and body, but not genu), possibly indicating neuritic dystrophy – characterized by enlarged axons and dendrites as previously observed at the ultrastructural level (see Cohen et al., J. Neurosci. 2013). This was further supported by increases in MR signals trapped in glial cells, CSF and possibly other small compartments in WM structures. Finally, tractography detected fewer fibers in the CC at 10 versus 24 months of age. These novel findings offer great potential to provide technical and scientific insight into the biology of brain disease

A Network Neuroscience Approach to Typical and Atypical Brain Development.

Human brain networks based on neuroimaging data have already proven useful in characterizing both normal and abnormal brain structure and function. However, many brain disorders are neurodevelopmental in origin, highlighting the need to go beyond characterizing brain organization in terms of static networks. Here, we review the fast-growing literature shedding light on developmental changes in network phenotypes. We begin with an overview of recent large-scale efforts to map healthy brain development, and we describe the key role played by longitudinal data including repeated measurements over a long period of follow-up. We also discuss the subtle ways in which healthy brain network development can inform our understanding of disorders, including work bridging the gap between macroscopic neuroimaging results and the microscopic level. Finally, we turn to studies of three specific neurodevelopmental disorders that first manifest primarily in childhood and adolescence/early adulthood, namely psychotic disorders, attention-deficit/hyperactivity disorder, and autism spectrum disorder. In each case we discuss recent progress in understanding the atypical features of brain network development associated with the disorder, and we conclude the review with some suggestions for future directions

Neural processes underpinning pain perception : genetic, temporal, and behavioral factors

Pain is an alarm system – warning us of dangers in the environment – yet becomes problematic when it transitions into chronic pain. It is defined, according to the International Association of Pain as “An unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage”. In advancing our knowledge of the underlying mechanisms of acute pain, it is relevant to understand sources of variability in pain perception. One such source is the genetic influence on brain function. This can be studied using a classic twin design to infer the proportion of variance in brain activation attributed to genetics. Another source of variation pertains to the temporal fluctuations in brain activity that could track pain processing. This was studied here using time-varying functional connectivity. Furthermore, since pain arises through large-scale interactions in the brain – the purpose here is to study pain and related processes through network neuroscience. Specifically, how functionally specialized – or segregated – neural structures of the brain integrate to shape pain

Multi-Shell Hybrid Diffusion Imaging (HYDI) at 7 Tesla in TgF344-AD Transgenic Alzheimer Rats

Diffusion weighted imaging (DWI) is widely used to study microstructural characteristics of the brain. Diffusion tensor imaging (DTI) and high-angular resolution imaging (HARDI) are frequently used in radiology and neuroscience research but can be limited in describing the signal behavior in composite nerve fiber structures. Here, we developed and assessed the benefit of a comprehensive diffusion encoding scheme, known as hybrid diffusion imaging (HYDI), composed of 300 DWI volumes acquired at 7-Tesla with diffusion weightings at b = 1000, 3000, 4000, 8000 and 12000 s/mm^2 and applied it in transgenic Alzheimer rats (line TgF344-AD) that model the full clinico-pathological spectrum of the human disease. We studied and visualized the effects of the multiple concentric “shells” when computing three distinct anisotropy maps–fractional anisotropy (FA), generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA). We tested the added value of the multi-shell q-space sampling scheme, when reconstructing neural pathways using mathematical frameworks from DTI and q-ball imaging (QBI). We show a range of properties of HYDI, including lower apparent anisotropy when using high b-value shells in DTI-based reconstructions, and increases in apparent anisotropy in QBI-based reconstructions. Regardless of the reconstruction scheme, HYDI improves FA-, GFA- and NQA-aided tractography. HYDI may be valuable in human connectome projects and clinical research, as well as magnetic resonance research in experimental animals