| 1. |
- van der Thiel, Mere, et al.
(author)
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Regional Cerebral Perfusion and Cerebrovascular Reactivity in Elderly Controls With Subtle Cognitive Deficits
- 2019
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In: Frontiers in Aging Neuroscience. - : FRONTIERS MEDIA SA. - 1663-4365. ; 11
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Journal article (peer-reviewed)abstract
- Background: Recent studies suggested that arterial spin labeling (ASL)-based measures of cerebral blood flow (CBF) as well as cerebral vasoreactivity to CO2 (CVR CO2) show significant alterations mainly in posterior neocortical areas both in mild cognitive impairment (MCI) and Alzheimer disease. It remains, however, unknown whether similar changes occur in at risk healthy elders without clinically overt symptoms. This longitudinal study investigated patterns of ASL perfusion and CVR CO2 as a function of the cognitive trajectories in asymptomatic elderly individuals.Methods: Seventy-nine community-dwelling subjects (mean age: 78.7 years, 34 male) underwent three neuropsychological assessments during a subsequent 3-year period. Individuals were classified as stable-stable (SS), variable (V), or progressive-progressive (PP). Between-group comparisons were conducted for ASL CBF and transit-time delay maps and beta-maps of CO2 response. Spearman's rho maps assessed the correlation between ASL (respectively, CVR CO2 measures) and Shapes test for working memory, as well as Verbal fluency test for executive functions. Three group-with-continuous-covariate-interaction designs were implemented to investigate group-based differences on the association between neuropsychological scores and ASL or CO2 measures.Results: Comparison of CBF maps demonstrates significantly lower perfusion in the V-group as to PP-cases predominantly in parietal regions, including the precuneus and, to a lesser degree, in temporal and frontal cortex. A stronger CVR CO2 response was found in the PP-group in left parietal areas compared to the V-group. V-cases showed a stronger ASL-Shape value relationship than V-group in right temporoparietal junction and superior parietal lobule. CO2-Shape value correlation was significantly higher in both SS and PP-groups compared to the V-group in right insular and superior perisylvian regions.Conclusion: Our data indicate the presence of decreased ASL and CVR CO2 values mainly in parietal and fronto-temporal areas in cases with the first signs of cognitive instability (V-group). Importantly, the PP-group, at high risk for MCI transition, displays an increase of both parameters in the same areas. Clinicoradiologic correlations also indicate a clear distinction between the V-group and both PP and SS-cases. These data imply the presence of an inverted U-shape pattern of regional blood flow and CVR in old age that might predict subsequent cognitive fate.
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| 2. |
- Badoud, Simon, et al.
(author)
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Discriminating among degenerative parkinsonisms using advanced (123)I-ioflupane SPECT analyses
- 2016
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In: NeuroImage. - : Elsevier BV. - 2213-1582. ; 12, s. 234-240
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Journal article (peer-reviewed)abstract
- (123)I-ioflupane single photon emission computed tomography (SPECT) is a sensitive and well established imaging tool in Parkinson's disease (PD) and atypical parkinsonian syndromes (APS), yet a discrimination between PD and APS has been considered inconsistent at least based on visual inspection or simple region of interest analyses. We here reappraise this issue by applying advanced image analysis techniques to separate PD from the various APS. This study included 392 consecutive patients with degenerative parkinsonism undergoing (123)I-ioflupane SPECT at our institution over the last decade: 306 PD, 24 multiple system atrophy (MSA), 32 progressive supranuclear palsy (PSP) and 30 corticobasal degeneration (CBD) patients. Data analysis included voxel-wise univariate statistical parametric mapping and multivariate pattern recognition using linear discriminant classifiers. MSA and PSP showed less ioflupane uptake in the head of caudate nucleus relative to PD and CBD, yet there was no difference between MSA and PSP. CBD had higher uptake in both putamen relative to PD, MSA and PSP. Classification was significant for PD versus APS (AUC 0.69, p < 0.05) and between APS subtypes (MSA vs CBD AUC 0.80, p < 0.05; MSA vs PSP AUC 0.69 p < 0.05; CBD vs PSP AUC 0.69 p < 0.05). Both striatal and extra-striatal regions contain classification information, yet the combination of both regions does not significantly improve classification accuracy. PD, MSA, PSP and CBD have distinct patterns of dopaminergic depletion on (123)I-ioflupane SPECT. The high specificity of 84-90% for PD versus APS indicates that the classifier is particularly useful for confirming APS cases.
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| 3. |
- Behjat, Hamid, et al.
(author)
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Anatomically-adapted Graph Wavelets for Improved Group-level fMRI Activation Mapping
- 2015
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In: NeuroImage. - : Elsevier BV. - 1095-9572 .- 1053-8119. ; 123:Online 07 June 2015, s. 185-199
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Journal article (peer-reviewed)abstract
- A graph based framework for fMRI brain activation mapping is presented. The approach exploits the spectral graph wavelet transform (SGWT) for the purpose of defining an advanced multi-resolutional spatial transformation for fMRI data. The framework extends wavelet based SPM (WSPM), which is an alternative to the conventional approach of statistical parametric mapping (SPM), and is developed specifically for group-level analysis. We present a novel procedure for constructing brain graphs, with subgraphs that separately encode the structural connectivity of the cerebral and cerebellar grey matter (GM), and address the inter-subject GM variability by the use of template GM representations. Graph wavelets tailored to the convoluted boundaries of GM are then constructed as a means to implement a GM-based spatial transformation on fMRI data. The proposed approach is evaluated using real as well as semi-synthetic multi-subject data. Compared to SPM and WSPM using classical wavelets, the proposed approach shows superior type-I error control. The results on real data suggest a higher detection sensitivity as well as the capability to capture subtle, connected patterns of brain activity.
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| 4. |
- Behjat, Hamid, et al.
(author)
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Canonical cerebellar graph wavelets and their application to fMRI activation mapping
- 2014
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In: [Host publication title missing]. - 1557-170X. ; , s. 1039-1042
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Conference paper (peer-reviewed)abstract
- Wavelet-based statistical parametric mapping (WSPM) is an extension of the classical approach in fMRI activation mapping that combines wavelet processing with voxel-wise statistical testing. We recently showed how WSPM, using graph wavelets tailored to the full gray-matter (GM) structure of each individual’s brain, can improve brain activity detection compared to using the classical wavelets that are only suited for the Euclidian grid. However, in order to perform analysis on a subject-invariant graph, canonical graph wavelets should be designed in normalized brain space. We here introduce an approach to define a fixed template graph of the cerebellum, an essential component of the brain, using the SUIT cerebellar template. We construct a corresponding set of canonical cerebellar graph wavelets, and adopt them in the analysis of both synthetic and real data. Compared to classical SPM, WSPM using cerebellar graph wavelets shows superior type-I error control, an empirical higher sensitivity on real data, as well as the potential to capture subtle patterns of cerebellar activity.
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| 5. |
- Behjat, Hamid, et al.
(author)
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Domain-Informed Spline Interpolation
- 2019
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In: IEEE Transactions on Signal Processing. - 1053-587X. ; 67:15, s. 3909-3921
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Journal article (peer-reviewed)abstract
- Standard interpolation techniques are implicitly based on the assumption that the signal lies on a single homogeneous domain. In contrast, many naturally occurring signals lie on an inhomogeneous domain, such as brain activity associated to different brain tissue. We propose an interpolation method that instead exploits prior information about domain inhomogeneity, characterized by different, potentially overlapping, subdomains. As proof of concept, the focus is put on extending conventional shift-invariant B-spline interpolation. Given a known inhomogeneous domain, B-spline interpolation of a given order is extended to a domain-informed, shift-variant interpolation. This is done by constructing a domain-informed generating basis that satisfies stability properties. We illustrate example constructions of domain-informed generating basis and show their property in increasing the coherence between the generating basis and the given inhomogeneous domain. By advantageously exploiting domain knowledge, we demonstrate the benefit of domain-informed interpolation over standard B-spline interpolation through Monte Carlo simulations across a range of B-spline orders. We also demonstrate the feasibility of domain-informed interpolation in a neuroimaging application where the domain information is available by a complementary image contrast. The results show the benefit of incorporating domain knowledge so that an interpolant consistent to the anatomy of the brain is obtained.
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| 6. |
- Behjat, Hamid, et al.
(author)
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fMRI activation mapping using wavelet-based SPM (WSPM) integrated with gray-matter graphs
- 2014
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Conference paper (peer-reviewed)abstract
- In many fMRI task-evoked studies, localized brain activity can be detected by GLM fitting and statistical hypothesis testing. Statistical parametric mapping (SPM) is the classical method that requires Gaussian pre-smoothing of the data. Instead, the wavelet transform provides a compact representation of activation patterns. Wavelet based SPM (WSPM) is an extension of SPM that combines wavelet processing with voxel-wise statistical testing. However, classical wavelets used in WSPM are designed for regular Euclidean grids and thus not adapted to the convoluted nature of the cerebral cortex. We recently showed how WSPM using graph wavelets tailored to the gray-matter structure of the cortex can improve detection of brain activity in single-subject studies. Here we extend this approach to group-level analysis by modifying the design of the brain graph.
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| 7. |
- Behjat, Hamid, et al.
(author)
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Signal-Adapted Tight Frames on Graphs
- 2016
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In: IEEE Transactions on Signal Processing. - 1053-587X. ; 64:22, s. 6017-6029
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Journal article (peer-reviewed)abstract
- The analysis of signals on complex topologies modeled by graphs is a topic of increasing importance. Decompositions play a crucial role in the representation and processing of such information. Here, we propose a new tight frame design that is adapted to a class of signals on a graph. The construction starts from a prototype Meyer-type system of kernels with uniform subbands. The ensemble energy spectral density is then defined for a given set of signals defined on the graph. The prototype design is then warped such that the resulting subbands capture the same amount of energy for the signal class. This approach accounts at the same time for graph topology and signal features. The proposed frames are constructed for three different graph signal sets and are compared with non-signal-adapted frames. Vertex localization of a set of resulting atoms is studied. The frames are then used to decompose a set of real graph signals and are also used in a setting of signal denoising. The results illustrate the superiority of the designed signal-adapted frames, over frames blind to signal characteristics, in representing data and in denoising.
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| 8. |
- Behjat, Hamid, et al.
(author)
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Spectral design of signal-adapted tight frames on graphs
- 2019
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In: Signals and Communication Technology. - Cham : Springer International Publishing. - 1860-4862 .- 1860-4870. - 9783030035747 - 9783030035730 ; , s. 177-206
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Book chapter (peer-reviewed)abstract
- Analysis of signals defined on complex topologies modeled by graphs is a topic of increasing interest. Signal decomposition plays a crucial role in the representation and processing of such information, in particular, to process graph signals based on notions of scale (e.g., coarse to fine). The graph spectrum is more irregular than for conventional domains; i.e., it is influenced by graph topology, and, therefore, assumptions about spectral representations of graph signals are not easy to make. Here, we propose a tight frame design that is adapted to the graph Laplacian spectral content of a given class of graph signals. The design exploits the ensemble energy spectral density, a notion of spectral content of the given signal set that we determine either directly using the graph Fourier transform or indirectly through approximation using a decomposition scheme. The approximation scheme has the benefit that (i) it does not require diagonalization of the Laplacian matrix, and (ii) it leads to a smooth estimate of the spectral content. A prototype system of spectral kernels each capturing an equal amount of energy is defined. The prototype design is then warped using the signal set’s ensemble energy spectral density such that the resulting subbands each capture an equal amount of ensemble energy. This approach accounts at the same time for graph topology and signal features, and it provides a meaningful interpretation of subbands in terms of coarse-to-fine representations.
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| 9. |
- Behjat, Hamid, et al.
(author)
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Statistical parametric mapping of functional MRI data using wavelets adapted to the cerebral cortex
- 2013
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In: [Host publication title missing]. - 1945-7928 .- 1945-8452. ; , s. 1070-1073
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Conference paper (peer-reviewed)abstract
- Wavelet approaches have been successfully applied to the detection of brain activity in fMRI data. Spatial activation patterns have a compact representation in the wavelet domain. However, classical wavelets designed for regular Euclidean spaces are not optimal for the topologically complicated gray-matter (GM) domain where activation is expected. We hypothesized that wavelet bases that are adapted to the structure of the GM, would be more powerful in detecting brain activity. We therefore combine (1) a GM-based graph wavelet transform as an advanced spatial transformation for fMRI data with (2) the wavelet-based statistical parametric mapping framework (WSPM). We introduce suitable design choices for the graph wavelet transform and evaluate the performance of the proposed approach both on simulated and real fMRI data. Compared to SPM and conventional WSPM, the graph-based WSPM shows improved detection of finely 3D-structured brain activity.
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| 10. |
- Behjat, Hamid, et al.
(author)
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Voxel-Wise Brain Graphs From Diffusion MRI : Intrinsic Eigenspace Dimensionality and Application to Functional MRI
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In: IEEE Open Journal of Engineering in Medicine and Biology. - 2644-1276. ; , s. 1-12
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Journal article (peer-reviewed)abstract
- Goal: Structural brain graphs are conventionally limited to defining nodes as gray matter regions from an atlas, with edges reflecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brain mask as nodes of high-resolution, subject-specific graphs. Methods: We define the strength of local voxel-to-voxel connections using diffusion tensors and orientation distribution functions derived from diffusion MRI data. We study the graphs' Laplacian spectral properties on data from the Human Connectome Project. We then assess the extent of inter-subject variability of the Laplacian eigenmodes via a procrustes validation scheme. Finally, we demonstrate the extent to which functional MRI data are shaped by the underlying anatomical structure via graph signal processing. Results: The graph Laplacian eigenmodes manifest highly resolved spatial profiles, reflecting distributed patterns that correspond to major white matter pathways. We show that the intrinsic dimensionality of the eigenspace of such high-resolution graphs is only a mere fraction of the graph dimensions. By projecting task and resting-state data on low-frequency graph Laplacian eigenmodes, we show that brain activity can be well approximated by a small subset of low-frequency components. Conclusions: The proposed graphs open new avenues in studying the brain, be it, by exploring their organisational properties via graph or spectral graph theory, or by treating them as the scaffold on which brain function is observed at the individual level.
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