| 1. |
- Abramian, David, 1992-, et al.
(författare)
-
Diffusion-Informed Spatial Smoothing of fMRI Data in White Matter Using Spectral Graph Filters
- 2021
-
Ingår i: NeuroImage. - : Elsevier. - 1053-8119 .- 1095-9572. ; 237
-
Tidskriftsartikel (refereegranskat)abstract
- Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detachability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject’s unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project’s 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.
|
|
| 2. |
- Behjat, Hamid, et al.
(författare)
-
Anatomically-adapted Graph Wavelets for Improved Group-level fMRI Activation Mapping
- 2015
-
Ingår i: NeuroImage. - : Elsevier BV. - 1095-9572 .- 1053-8119. ; 123:Online 07 June 2015, s. 185-199
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 3. |
- Bellander, Martin, et al.
(författare)
-
Behavioral correlates of changes in hippocampal gray matter structure during acquisition of foreign vocabulary
- 2016
-
Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 131, s. 205-213
-
Tidskriftsartikel (refereegranskat)abstract
- Experience can affect human gray matter volume. The behavioral correlates of individual differences in such brain changes are not well understood. In a group of Swedish individuals studying Italian as a foreign language, we investigated associations among time spent studying, acquired vocabulary, baseline performance on memory tasks, and gray matter changes. As a way of studying episodic memory training, the language learning focused on acquiring foreign vocabulary and lasted for 10 weeks. T-1-weighted structural magnetic resonance imaging and cognitive testing were performed before and after the studies. Learning behavior was monitored via participants' use of a smartphone application dedicated to the study of vocabulary. A whole-brain analysis showed larger changes in gray matter structure of the right hippocampus in the experimental group (N = 33) compared to an active control group (N = 23). A first path analyses revealed that time spent studying rather than acquired knowledge significantly predicted change in gray matter structure. However, this association was not significant when adding performance on baseline memory measures into the model, instead only the participants' performance on a short-term memory task with highly similar distractors predicted the change. This measure may tap similar individual difference factors as those involved in gray matter plasticity of the hippocampus.
|
|
| 4. |
- De Luca, Alberto, et al.
(författare)
-
On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types : Chronicles of the MEMENTO challenge
- 2021
-
Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 240
-
Tidskriftsartikel (refereegranskat)abstract
- Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
|
|
| 5. |
- Degerman, Alexander, et al.
(författare)
-
Human brain activity associated with audiovisual perception and attention
- 2007
-
Ingår i: NeuroImage. - : Elsevier BV. - 1095-9572 .- 1053-8119. ; 34:4, s. 1683-1691
-
Tidskriftsartikel (refereegranskat)abstract
- Coherent perception of objects in our environment often requires perceptual integration of auditory and visual information. Recent behavioral data suggest that audiovisual integration depends on attention. The current study investigated the neural basis of audiovisual integration using 3-Tesla functional magnetic resonance imaging (fMRI) in 12 healthy volunteers during attention to auditory or visual features, or audiovisual feature combinations of abstract stimuli (simultaneous harmonic sounds and colored circles). Audiovisual attention was found to modulate activity in the same frontal, temporal, parietal and occipital cortical regions as auditory and visual attention. In addition, attention to audiovisual feature combinations produced stronger activity in the superior temporal cortices than attention to only auditory or visual features. These modality-specific areas might be involved in attention-dependent perceptual binding of synchronous auditory and visual events into coherent audiovisual objects. Furthermore, the modality-specific temporal auditory and occipital visual cortical areas showed attention-related modulations during both auditory and visual attention tasks. This result supports the proposal that attention to stimuli in one modality can spread to encompass synchronously presented stimuli in another modality.
|
|
| 6. |
|
|
| 7. |
- Dyrby, Tim B., et al.
(författare)
-
Validation strategies for the interpretation of microstructure imaging using diffusion MRI
- 2018
-
Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 182, s. 62-79
-
Tidskriftsartikel (refereegranskat)abstract
- Extracting microanatomical information beyond the image resolution of MRI would provide valuable tools for diagnostics and neuroscientific research. A number of mathematical models already suggest microstructural interpretations of diffusion MRI (dMRI) data. Examples of such microstructural features could be cell bodies and neurites, e.g. the axon's diameter or their orientational distribution for global connectivity analysis using tractography, and have previously only been possible to access through conventional histology of post mortem tissue or invasive biopsies. The prospect of gaining the same knowledge non-invasively from the whole living human brain could push the frontiers for the diagnosis of neurological and psychiatric diseases. It could also provide a general understanding of the development and natural variability in the healthy brain across a population. However, due to a limited image resolution, most of the dMRI measures are indirect estimations and may depend on the whole chain from experimental parameter settings to model assumptions and implementation. Here, we review current literature in this field and highlight the integrative work across anatomical length scales that is needed to validate and trust a new dMRI method. We encourage interdisciplinary collaborations and data sharing in regards to applying and developing new validation techniques to improve the specificity of future dMRI methods.
|
|
| 8. |
- Elfgren, Christina, et al.
(författare)
-
fMRI activity in the medial temporal lobe during famous face processing
- 2006
-
Ingår i: NeuroImage. - : Elsevier BV. - 1095-9572 .- 1053-8119. ; 30:2, s. 609-616
-
Tidskriftsartikel (refereegranskat)abstract
- The current event-related fMRI study examined the relative involvement of different parts of the medial temporal lobe (MTL), particularly the contribution of hippocampus and perirhinal cortex, in either intentional or incidental recognition of famous faces in contrast to unfamiliar faces. Our intention was to further explore the controversial contribution of MTL in the processing of semantic memory tasks. Subjects viewed a sequence of famous and unfamiliar faces. Two tasks were used encouraging attention to either fame or gender. In the fame task, the subjects were requested to identify the person when seeing his/her face and also to try to generate the name of this person. In the gender task, the subjects were asked to conduct a judgement of a person's gender when seeing his/her face. The visual processing was hence directed to gender and thereby expected to diminish attention to semantic information leading only to a “passive” registration of famous and non-familiar faces. Recognition of famous faces, in both contrasts, produced significant activations in the MTL. First, during the intentional recognition (the person identification task) increased activity was observed in the anterolateral part of left hippocampus, in proximity to amygdala. Second, during the incidental recognition of famous faces (the gender classification task), there was increased activity in the left posterior MTL with focus in the perirhinal cortex. Our results suggest that the hippocampus may be centrally involved in the intentional retrieval of semantic memories while the perirhinal cortex is associated with the incidental recognition of semantic information.
|
|
| 9. |
|
|
| 10. |
- Helms, Gunther, et al.
(författare)
-
Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps
- 2009
-
Ingår i: NeuroImage. - : Elsevier BV. - 1095-9572 .- 1053-8119. ; 47:1, s. 194-198
-
Tidskriftsartikel (refereegranskat)abstract
- Basal ganglia and brain stem nuclei are involved in the pathophysiology of various neurological and neuropsychiatric disorders. Currently available structural T1-weighted (T1w) magnetic resonance images do not provide sufficient contrast for reliable automated segmentation of various subcortical grey matter structures. We use a novel, semi-quantitative magnetization transfer (MT) imaging protocol that overcomes limitations in T1w images, which are mainly due to their sensitivity to the high iron content in subcortical grey matter. We demonstrate improved automated segmentation of putamen, pallidum, pulvinar and substantia nigra using MT images. A comparison with segmentation of high-quality T1w images was performed in 49 healthy subjects. Our results show that MT maps are highly suitable for automated segmentation, and so for multi-subject morphometric studies with a focus on subcortical structures.
|
|
