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- Dunås, Tora, et al.
(författare)
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A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries
- 2017
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Ingår i: Neuroinformatics. - : Springer Science and Business Media LLC. - 1539-2791 .- 1559-0089. ; 15:1, s. 101-110
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Tidskriftsartikel (refereegranskat)abstract
- Improved whole brain angiographic and velocity-sensitive MRI is pushing the boundaries of noninvasively obtained cerebral vascular flow information. The complexity of the information contained in such datasets calls for automated algorithms and pipelines, thus reducing the need of manual analyses by trained radiologists. The objective of this work was to lay the foundation for such automated pipelining by constructing and evaluating a probabilistic atlas describing the shape and location of the major cerebral arteries. Specifically, we investigated how the implementation of a non-linear normalization into Montreal Neurological Institute (MNI) space improved the alignment of individual arterial branches. In a population-based cohort of 167 subjects, age 64-68 years, we performed 4D flow MRI with whole brain volumetric coverage, yielding both angiographic and anatomical data. For each subject, sixteen cerebral arteries were manually labeled to construct the atlas. Angiographic data were normalized to MNI space using both rigid-body and non-linear transformations obtained from anatomical images. The alignment of arterial branches was significantly improved by the non-linear normalization (p < 0.001). Validation of the atlas was based on its applicability in automatic arterial labeling. A leave-one-out validation scheme revealed a labeling accuracy of 96 %. Arterial labeling was also performed in a separate clinical sample (n = 10) with an accuracy of 92.5 %. In conclusion, using non-linear spatial normalization we constructed an artery-specific probabilistic atlas, useful for cerebral arterial labeling.
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- Ljungquist, Bengt, et al.
(författare)
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A Bit-Encoding Based New Data Structure for Time and Memory Efficient Handling of Spike Times in an Electrophysiological Setup
- 2018
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Ingår i: Neuroinformatics. - : Springer. - 1539-2791 .- 1559-0089. ; 16:2, s. 217-229
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Tidskriftsartikel (refereegranskat)abstract
- Recent neuroscientific and technical developments of brain machine interfaces have put increasing demands on neuroinformatic databases and data handling software, especially when managing data in real time from large numbers of neurons. Extrapolating these developments we here set out to construct a scalable software architecture that would enable near-future massive parallel recording, organization and analysis of neurophysiological data on a standard computer. To this end we combined, for the first time in the present context, bit-encoding of spike data with a specific communication format for real time transfer and storage of neuronal data, synchronized by a common time base across all unit sources. We demonstrate that our architecture can simultaneously handle data from more than one million neurons and provide, in real time (< 25 ms), feedback based on analysis of previously recorded data. In addition to managing recordings from very large numbers of neurons in real time, it also has the capacity to handle the extensive periods of recording time necessary in certain scientific and clinical applications. Furthermore, the bit-encoding proposed has the additional advantage of allowing an extremely fast analysis of spatiotemporal spike patterns in a large number of neurons. Thus, we conclude that this architecture is well suited to support current and near-future Brain Machine Interface requirements.
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- Zhang, Han, et al.
(författare)
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Inter-Network High-Order Functional Connectivity (IN-HOFC) and its Alteration in Patients with Mild Cognitive Impairment
- 2019
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Ingår i: Neuroinformatics. - : Springer Science and Business Media LLC. - 1539-2791 .- 1559-0089. ; 17:4, s. 547-561
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Tidskriftsartikel (refereegranskat)abstract
- Little is known about the high-order interactions among brain regions measured by the similarity of higher-order features (other than the raw blood-oxygen-level-dependent signals) which can characterize higher-level brain functional connectivity (FC). Previously, we proposed FC topographical profile-based high-order FC (HOFC) and found that this metric could provide supplementary information to traditional FC for early Alzheimer's disease (AD) detection. However, whether such findings apply to network-level brain functional integration is unknown. In this paper, we propose an extended HOFC method, termed inter-network high-order FC (IN-HOFC), as a useful complement to the traditional inter-network FC methods, for characterizing more complex organizations among the large-scale brain networks. In the IN-HOFC, both network definition and inter-network FC are defined in a high-order manner. To test whether IN-HOFC is more sensitive to cognition decline due to brain diseases than traditional inter-network FC, 77 mild cognitive impairments (MCIs) and 89 controls are compared among the conventional methods and our IN-HOFC. The result shows that IN-HOFCs among three temporal lobe-related high-order networks are dampened in MCIs. The impairment of IN-HOFC is especially found between the anterior and posterior medial temporal lobe and could be a potential MCI biomarker at the network level. The competing network-level low-order FC methods, however, either revealing less or failing to detect any group difference. This work demonstrates the biological meaning and potential diagnostic value of the IN-HOFC in clinical neuroscience studies.
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