A conscious rabbit preparation developed for fMRI, and the results from visual stimulation studies at a 4.7T magnetic field are described. The rabbit is ideal for these experiments because of its natural tolerance for restraint. High spatial and temporal resolution magnetic resonance images, without movement artifacts, were obtained during long periods of restraint. Functional activation in primary visual cortex and lateral geniculate nucleus (LGN) were reproducibly observed in response to light stimulus. In comparison to existing anesthetized animal models, a functional response free of the anesthetic modulation can be recorded with the new approach. The conscious animal model can be applied to functional studies of sensory systems, learning and memory, and drug-induced cerebral activation.
We conducted functional magnetic resonance imaging (fMRI) with a visual search paradigm to test the hypothesis that aging is associated with increased frontoparietal involvement in both target detection and bottom-up attentional guidance (featural salience). Participants were 68 healthy adults, distributed continuously across 19 to 78 years of age. Frontoparietal regions of interest (ROIs) were defined from resting-state scans obtained prior to task-related fMRI. The search target was defined by a conjunction of color and orientation. Each display contained one item that was larger than the others (i.e., a size singleton) but was not informative regarding target identity. Analyses of search reaction time (RT) indicated that bottom-up attentional guidance from the size singleton (when coincident with the target) was relatively constant as a function of age. Frontoparietal fMRI activation related to target detection was constant as a function of age, as was the reduction in activation associated with salient targets. However, for individuals 35 years of age and older, engagement of the left frontal eye field (FEF) in bottom-up guidance was more prominent than for younger individuals. Further, the age-related differences in left FEF activation were a consequence of decreasing resting-state functional connectivity in visual sensory regions. These findings indicate that age-related compensatory effects may be expressed in the relation between activation and behavior, rather than in the magnitude of activation, and that relevant changes in the activation-RT relation may begin at a relatively early point in adulthood. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.
Aberrant accumulation of transition metals in the brain may have an early and important role in the pathogenesis of several neurodegenerative disorders, including Huntington disease (HD).
Functional magnetic resonance imaging (fMRI) technique with blood oxygenation level dependent (BOLD) contrast is a powerful tool for noninvasive mapping of brain function under task and resting states. The removal of cardiac- and respiration-induced physiological noise in fMRI data has been a significant challenge as fMRI studies seek to achieve higher spatial resolutions and characterize more subtle neuronal changes. The low temporal sampling rate of most multi-slice fMRI experiments often causes aliasing of physiological noise into the frequency range of BOLD activation signal. In addition, changes of heartbeat and respiration patterns also generate physiological fluctuations that have similar frequencies with BOLD activation. Most existing physiological noise-removal methods either place restrictive limitations on image acquisition or utilize filtering or regression based post-processing algorithms, which cannot distinguish the frequency-overlapping BOLD activation and the physiological noise. In this work, we address the challenge of physiological noise removal via the kernel machine technique, where a nonlinear kernel machine technique, kernel principal component analysis, is used with a specifically identified kernel function to differentiate BOLD signal from the physiological noise of the frequency. The proposed method was evaluated in human fMRI data acquired from multiple task-related and resting state fMRI experiments. A comparison study was also performed with an existing adaptive filtering method. The results indicate that the proposed method can effectively identify and reduce the physiological noise in fMRI data. The comparison study shows that the proposed method can provide comparable or better noise removal performance than the adaptive filtering approach.
Advances in molecular and structural and functional neuroimaging are rapidly expanding the complexity of neurobiological understanding of Parkinson's disease (PD). This review article begins with an introduction to PD neurobiology as a foundation for interpreting neuroimaging findings that may further lead to more integrated and comprehensive understanding of PD. Diverse areas of PD neuroimaging are then reviewed and summarized, including positron emission tomography, single photon emission computed tomography, magnetic resonance spectroscopy and imaging, transcranial sonography, magnetoencephalography, and multimodal imaging, with focus on human studies published over the last five years. These included studies on differential diagnosis, co-morbidity, genetic and prodromal PD, and treatments from L-DOPA to brain stimulation approaches, transplantation and gene therapies. Overall, neuroimaging has shown that PD is a neurodegenerative disorder involving many neurotransmitters, brain regions, structural and functional connections, and neurocognitive systems. A broad neurobiological understanding of PD will be essential for translational efforts to develop better treatments and preventive strategies. Many questions remain and we conclude with some suggestions for future directions of neuroimaging of PD.
