Metabolic brain networks in neurodegenerative disorders: a functional imaging approach.

Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-LIJ Health System, Manhasset, NY, USA.
Trends in Neurosciences (Impact Factor: 12.9). 10/2009; 32(10):548-57. DOI: 10.1016/j.tins.2009.06.003
Source: PubMed

ABSTRACT Network analysis of functional brain imaging data is an innovative approach to study circuit abnormalities in neurodegenerative diseases. In Parkinson's disease, spatial covariance analysis of resting-state metabolic images has identified specific regional patterns associated with motor and cognitive symptoms. With functional imaging, these metabolic networks have recently been used to measure system-related progression and to evaluate novel treatment strategies. Network analysis is also being used to characterize specific functional biomarkers for Huntington's disease and Alzheimer's disease. These networks have been particularly helpful in uncovering compensatory mechanisms in genetically at-risk individuals. Ongoing developments in network applications are likely to enhance the role of functional imaging in the investigation of neurodegenerative disorders.

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    ABSTRACT: Parkinson's disease (PD) is largely attributed to disruptions in the nigrostriatal dopamine system. These neurodegenerative changes may also have a more global effect on intrinsic brain organization at the cortical level. Functional brain connectivity between neurocognitive systems related to cognitive processing is critical for effective neural communication, and is disrupted across neurological disorders. Three core neurocognitive networks have been established as playing a critical role in the pathophysiology of many neurological disorders: the default-mode network (DMN), the salience network (SN), and the central executive network (CEN). In healthy adults, DMN–CEN interactions are anti-correlated while SN–CEN interactions are strongly positively correlated even at rest, when individuals are not engaging in any task. These intrinsic between-network interactions at rest are necessary for efficient suppression of the DMN and activation of the CEN during a range of cognitive tasks. To identify whether these network interactions are disrupted in individuals with PD, we used resting state functional magnetic resonance imaging (rsfMRI) to compare between-network connectivity between 24 PD participants and 20 age-matched controls (MC). In comparison to the MC, individuals with PD showed significantly less SN–CEN coupling and greater DMN–CEN coupling during rest. Disease severity, an index of striatal dysfunction, was related to reduced functional coupling between the striatum and SN. These results demonstrate that individuals with PD have a dysfunctional pattern of interaction between core neurocognitive networks compared to what is found in healthy individuals, and that interaction between the SN and the striatum is even more profoundly disrupted in those with greater disease severity.
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    ABSTRACT: Imaging cerebral glucose metabolism with positron emission tomography (PET) has been widely used in studying Alzheimer's disease (AD) and mild cognitive impairment (MCI). In this study, we used fluoro-deoxyglucose (FDG) PET images to investigate reduced glucose metabolism in 90 AD subjects, 90 MCI subjects and 90 healthy elderly normal controls (NC). Compared to NC, the AD showed a significant hypometabolism in left and right middle temporal, left cingulate gyrus, medial frontal gyrus and left parahippocampal gyrus. Compared to NC, the MCI showed a significant hypometabolism in the right inferior temporal gyrus and right fusiform gyrus. Compared to MCIs, the AD also showed a significant hypometabolism in left and right middle temporal, left cingulate gyrus, left angular gyrus and right parahippocampal gyrus. This study demonstrates the different cerebral metabolic patterns of AD, MCI and controls. It also shows that glucose metabolism is a sensitive measure of change in cognition and functional ability in AD and MCI, and that might be valuable in predicting future cognitive decline.
    2013 6th International Conference on Biomedical Engineering and Informatics (BMEI); 12/2013
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    ABSTRACT: The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson's disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the "DMN-like" dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer's disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer's disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.
    Proceedings of the National Academy of Sciences 02/2015; DOI:10.1073/pnas.1411011112 · 9.81 Impact Factor


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