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- Abramian, David, 1992-, et al.
(författare)
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Diffusion-Informed Spatial Smoothing of fMRI Data in White Matter Using Spectral Graph Filters
- 2021
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Ingår i: NeuroImage. - : Elsevier. - 1053-8119 .- 1095-9572. ; 237
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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.
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| 5. |
- Andersen, L. M., et al.
(författare)
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On-scalp MEG SQUIDs are sensitive to early somatosensory activity unseen by conventional MEG
- 2020
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 221
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Tidskriftsartikel (refereegranskat)abstract
- Magnetoencephalography (MEG) has a unique capacity to resolve the spatio-temporal development of brain activity from non-invasive measurements. Conventional MEG, however, relies on sensors that sample from a distance (20–40 mm) to the head due to thermal insulation requirements (the MEG sensors function at 4 K in a helmet). A gain in signal strength and spatial resolution may be achieved if sensors are moved closer to the head. Here, we report a study comparing measurements from a seven-channel on-scalp SQUID MEG system to those from a conventional (in-helmet) SQUID MEG system. We compared the spatio-temporal resolution between on-scalp and conventional MEG by comparing the discrimination accuracy for neural activity patterns resulting from stimulating five different phalanges of the right hand. Because of proximity and sensor density differences between on-scalp and conventional MEG, we hypothesized that on-scalp MEG would allow for a more high-resolved assessment of these activity patterns, and therefore also a better classification performance in discriminating between neural activations from the different phalanges. We observed that on-scalp MEG provided better classification performance during an early post-stimulus period (10–20 ms). This corresponded to the electroencephalographic (EEG) component P16/N16 and was an unexpected observation as this component is usually not observed in conventional MEG. This finding shows that on-scalp MEG enables a richer registration of the cortical signal, indicating a sensitivity to what are potentially sources in the thalamo-cortical radiation. We had originally expected that on-scalp MEG would provide better classification accuracy based on activity in proximity to the P60m component compared to conventional MEG. This component indeed allowed for the best classification performance for both MEG systems (60–75%, chance 50%). However, we did not find that on-scalp MEG allowed for better classification than conventional MEG at this latency. We suggest that this absence of differences is due to the limited sensor coverage in the recording, in combination with our strategy for positioning the on-scalp MEG sensors. We show how the current sensor coverage may have limited our chances to register the necessary between-phalange source field dissimilarities for fair hypothesis testing, an approach we otherwise believe to be useful for future benchmarking measurements. © 2020 The Authors
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| 7. |
- Awad, Amar, et al.
(författare)
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Deep brain stimulation in the caudal zona incerta modulates the sensorimotor cerebello-cerebral circuit in essential tremor
- 2020
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Ingår i: NeuroImage. - : Elsevier. - 1053-8119 .- 1095-9572. ; 209
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Tidskriftsartikel (refereegranskat)abstract
- Essential tremor is effectively treated with deep brain stimulation (DBS), but the neural mechanisms underlying the treatment effect are poorly understood. Essential tremor is driven by a dysfunctional cerebello-thalamo-cerebral circuit resulting in pathological tremor oscillations. DBS is hypothesised to interfere with these oscillations at the stimulated target level, but it is unknown whether the stimulation modulates the activity of the cerebello-thalamo-cerebral circuit during different task states (with and without tremor) in awake essential tremor patients. To address this issue, we used functional MRI in 16 essential tremor patients chronically implanted with DBS in the caudal zona incerta. During scanning, the patients performed unilateral tremor-inducing postural holding and pointing tasks as well as rest, with contralateral stimulation turned On and Off.We show that DBS exerts both task-dependent as well as task-independent modulation of the sensorimotor cerebello-cerebral regions (p ≤ 0.05, FWE cluster-corrected for multiple comparisons). Task-dependent modulation (DBS × task interaction) resulted in two patterns of stimulation effects. Firstly, activity decreases (blood oxygen level-dependent signal) during tremor-inducing postural holding in the primary sensorimotor cortex and cerebellar lobule VIII, and activity increases in the supplementary motor area and cerebellar lobule V during rest (p ≤ 0.05, post hoc two-tailed t-test). These effects represent differences at the effector level and may reflect DBS-induced tremor reduction since the primary sensorimotor cortex, cerebellum and supplementary motor area exhibit less motor task-activity as compared to the resting condition during On stimulation. Secondly, task-independent modulation (main effect of DBS) was observed as activity increase in the lateral premotor cortex during all motor tasks, and also during rest (p ≤ 0.05, post hoc two-tailed t-test). This task-independent effect may mediate the therapeutic effects of DBS through the facilitation of the premotor control over the sensorimotor circuit, making it less susceptible to tremor entrainment.Our findings support the notion that DBS in essential tremor is modulating the sensorimotor cerebello-cerebral circuit, distant to the stimulated target, and illustrate the complexity of stimulation mechanisms by demonstrating task-dependent as well as task-independent actions in cerebello-cerebral regions.
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- Brehmer, Yvonne, et al.
(författare)
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The importance of the ventromedial prefrontal cortex for associative memory in older adults : A latent structural equation analysis
- 2020
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 209
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Tidskriftsartikel (refereegranskat)abstract
- Older adults show relatively minor age-related decline in memory for single items, while their memory for associations is markedly reduced. Inter-individual differences in memory function in older adults are substantial but the neurobiological underpinnings of such differences are not well understood. In particular, the relative importance of inter-individual differences in the medio-temporal lobe (MTL) and the lateral prefrontal cortex (PFC) for associative and item recognition in older adults is still ambiguous. We therefore aimed to first establish the distinction between inter-individual differences in associative memory (recollection-based) performance and item memory (familiarity-based) performance in older adults and subsequently link these two constructs to differences in cortical thickness in the MTL and lateral PFC regions, in a latent structural equation modelling framework. To this end, a sample of 160 older adults (65-75 years old) performed three intentional itemassociative memory tasks, of which a subsample (n = 72) additionally had cortical thickness measures in MTL and PFC regions of interest available. The results provided support for a distinction between familiarity-based item memory and recollection-based associative memory performance in older adults. Cortical thickness in the ventro-medial prefrontal cortex was positively correlated with associative recognition performance, above and beyond any relationship between item recognition performance and cortical thickness in the same region and between associative recognition performance and brain structure in the MTL (parahippocampus). The findings highlight the relative importance of the ventromedial prefrontal cortex in allowing for intentional recollection-based associative memory functioning in older adults.
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| 10. |
- De Luca, Alberto, et al.
(författare)
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On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types : Chronicles of the MEMENTO challenge
- 2021
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 240
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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.
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