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Data Repository


This page shows all 271 data sets currently available in our Data repository

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Distinct but cooperating brain networks supporting semantic cognition
Authors:
JeYoung, J., LAMBON RALPH, M.A.
Reference:
Cerebral Cortex
Year of publication:
In Press
CBU number:
8816
Abstract:
Semantic cognition is a complex multifaceted brain function involving multiple processes including sensory, semantic, and domain-general cognitive systems. However, it remains unclear how these systems cooperate with each other to achieve effective semantic cognition. Here, we used independent component analysis (ICA) to investigate the functional brain networks that support semantic cognition. We used a semantic judgement task and a pattern-matching control task, each with two levels of difficulty, to disentangle task-specific networks from domain-general networks. ICA revealed two task-specific networks (the left-lateralized semantic network [SN] and a bilateral, extended semantic network [ESN]) and domain general networks including the frontoparietal network (FPN) and default mode network (DMN). SN was coupled with the ESN and FPN but decoupled from the DMN, whereas the ESN was synchronised with the FPN alone and did not show a decoupling with the DMN. The degree of decoupling between the SN and DMN was associated with semantic task performance, with the strongest decoupling for the poorest performing participants. Our findings suggest that human higher cognition is achieved by the multiple brain networks, serving distinct and shared cognitive functions depending on task demands, and that the neural dynamics between these networks may be crucial for efficient semantic cognition.
Data for this project is held by an external institution. Please contact the authors to request a copy.
The risk of early versus later rebleeding from dural arteriovenous fistulas with cortical venous drainage
Authors:
Durnford, A.J., AKARCA, D., Culliford, D., Millar, J., Guniganti, R., Giordan , E., Brinjikji, W., Chen, C.J., Abecassis, I.J., Levitt, M., Polifka, A.J., Derdeyn, C.P., Samaniego, E.A,, Kwasnicki, A., Alaraj, A., Potgieser, A.R.E., Chen, S., Tada, Y., Phelps, R., Abla, A., Satomi, J., Starke, R.M, van Dijk, J.M.C., Amin-Hanjani, S., Hayakawa, M., Gross, B., Fox, W.C., Kim, L., Sheehan, J., Lanzino, G., Kansagra, A.P., Du, R., Lai, R., Zipfel, G.J., Bulters, D.O., CONDOR Investigators
Reference:
Stroke, 14 Apr 2022, :101161STROKEAHA121036450
Year of publication:
In Press
CBU number:
8813
Abstract:
Abstract BACKGROUND: Cranial dural arteriovenous fistulas with cortical venous drainage are rare lesions that can present with hemorrhage. A high rate of rebleeding in the early period following hemorrhage has been reported, but published long-term rates are much lower. No study has examined how risk of rebleeding changes over time. Our objective was to quantify the relative incidence of rebleeding in the early and later periods following hemorrhage. METHODS: Patients with dural arteriovenous fistula and cortical venous drainage presenting with hemorrhage were identified from the multinational CONDOR (Consortium for Dural Fistula Outcomes Research) database. Natural history follow-up was defined as time from hemorrhage to first treatment, rebleed, or last follow-up. Rebleeding in the first 2 weeks and first year were compared using incidence rate ratio and difference. RESULTS: Of 1077 patients, 250 met the inclusion criteria and had 95 cumulative person-years natural history follow-up. The overall annualized rebleed rate was 7.3% (95% CI, 3.2–14.5). The incidence rate of rebleeding in the first 2 weeks was 0.0011 per person-day; an early rebleed risk of 1.6% in the first 14 days (95% CI, 0.3–5.1). For the remainder of the first year, the incidence rate was 0.00015 per person-day; a rebleed rate of 5.3% (CI, 1.7–12.4) over 1 year. The incidence rate ratio was 7.3 (95% CI, 1.4–37.7; P, 0.026). CONCLUSIONS: The risk of rebleeding of a dural arteriovenous fistula with cortical venous drainage presenting with hemorrhage is increased in the first 2 weeks justifying early treatment. However, the magnitude of this increase may be considerably lower than previously thought. Treatment within 5 days was associated with a low rate of rebleeding and appears an appropriate timeframe.
URL:
Data available, click to request
Imagine how good that feels: The impact of anticipated positive emotions on motivation for reward activities
Authors:
Heise, H., Werthmann, J., MURPHY, F., Tuschen-Caffier, B., Renner, F.
Reference:
Cognitive Therapy and Research
Year of publication:
In Press
CBU number:
8811
Abstract:
Background: Disease burden and unsatisfactory treatment outcomes call for innovation in treatments of depression. Prospective mental imagery, i.e. future-directed voluntary imagery-based thought, about potentially-rewarding activities may offer a mechanistically-informed intervention that targets deficits in reward processing, a core clinical feature of depression. We propose that the previously described impact of prospective mental imagery on motivation for everyday activities is facilitated by affective forecasting, i.e. predictions about an individual’s emotional response to the imagined activities. Methods: Participants (N = 120) self-nominated six activities to engage in over the following week and were randomized to either: a) an affective forecasting imagery condition (n = 40); b) a neutral process imagery condition (n = 40); or c) a no-imagery control condition (n = 40). Results: As predicted, increases in motivation ratings from pre to post experimental manipulation were significantly higher following affective forecasting imagery compared to both neutral process imagery (d = 0.62) and no-imagery (d = 0.91). Contrary to predictions, the number of activities participants engaged in did not differ between conditions. Conclusions: Results provide initial evidence for a potentially important role of affective forecasting in prospective mental imagery. We discuss how these findings can inform future research aiming to harness prospective mental imagery’s potential for clinical applications.
Data for this project is available at: https://osf.io/nwx3z/
Human brain activity is based on electrochemical processes, which can only be measured invasively. Thus, quantities such as magnetic flux density (MEG) or electric potential differences (EEG) are measured non-invasively in medicine and research. The reconstruction of the neuronal current from the measurements is a severely ill-posed problem though the visualization of the cerebral activity is one of the main research tools in cognitive neuroscience. Here, using an isotropic multiple-shell model for the human head and a quasi-static approach for the electro-magnetic processes, we derive a novel vector-valued spline method based on reproducing kernel Hilbert spaces in order to reconstruct the current from the measurements. The presented method follows the path of former spline approaches and provides classical minimum norm properties. Besides, it minimizes the (infinite-dimensional) Tikhonov-Philips functional which handles the instability of the inverse problem. This optimization problem reduces to solving a finite-dimensional system of linear equations without loss of information, due to its construction. It results in a unique solution which takes into account that only the harmonic and solenoidal component of the neuronal current affects the measurements. Furthermore, we prove a convergence result: the solution achieved by the novel method converges to the generator of the data as the number of measurements increases. The vector splines are applied to the inversion of three synthetic test cases, where the irregularly distributed data situation could be handled very well. Combined with five parameter choice methods, numerical results are shown for synthetic test cases with and without additional Gaussian white noise. Former approaches based on scalar splines are outperformed by the novel vector splines results with respect to the normalized root mean square error. Finally, results for real data acquired during a visual stimulation task are demonstrated. They can be computed quickly and are reasonable with respect to physiological expectations.
Authors:
Leweke, S., HAUK, O., Michel, V.
Reference:
Inverse Problems
Year of publication:
In Press
CBU number:
8807
Abstract:
Human brain activity is based on electrochemical processes, which can only be measured invasively. Thus, quantities such as magnetic flux density (MEG) or electric potential differences (EEG) are measured non-invasively in medicine and research. The reconstruction of the neuronal current from the measurements is a severely ill-posed problem though the visualization of the cerebral activity is one of the main research tools in cognitive neuroscience. Here, using an isotropic multiple-shell model for the human head and a quasi-static approach for the electro-magnetic processes, we derive a novel vector-valued spline method based on reproducing kernel Hilbert spaces in order to reconstruct the current from the measurements. The presented method follows the path of former spline approaches and provides classical minimum norm properties. Besides, it minimizes the (infinite-dimensional) Tikhonov-Philips functional which handles the instability of the inverse problem. This optimization problem reduces to solving a finite-dimensional system of linear equations without loss of information, due to its construction. It results in a unique solution which takes into account that only the harmonic and solenoidal component of the neuronal current affects the measurements. Furthermore, we prove a convergence result: the solution achieved by the novel method converges to the generator of the data as the number of measurements increases. The vector splines are applied to the inversion of three synthetic test cases, where the irregularly distributed data situation could be handled very well. Combined with five parameter choice methods, numerical results are shown for synthetic test cases with and without additional Gaussian white noise. Former approaches based on scalar splines are outperformed by the novel vector splines results with respect to the normalized root mean square error. Finally, results for real data acquired during a visual stimulation task are demonstrated. They can be computed quickly and are reasonable with respect to physiological expectations.
Data for this project is available at: https://github.com/SarahLeweke/rkhs-splines
Towards an Objective Evaluation of EEG/MEG Source Estimation Methods – The Linear Approach
Authors:
HAUK, O., Stenroos, M,. Treder, M.S.
Reference:
NeuroImage, 255, 15 July 2022, 119177
Year of publication:
2022
CBU number:
8806
Abstract:
The spatial resolution of EEG/MEG source estimates, often described in terms of source leakage in the context of the inverse problem, poses constraints on the inferences that can be drawn from EEG/MEG source estimation results. Software packages for EEG/MEG data analysis offer a large choice of source estimation methods but few tools to experimental researchers for methods evaluation and comparison. Here, we describe a framework and tools for objective and intuitive resolution analysis of EEG/MEG source estimation based on linear systems analysis, and apply those to the most widely used distributed source estimation methods such as L2-minimum-norm estimation (L2-MNE) and linearly constrained minimum variance (LCMV) beamformers. Within this framework it is possible to define resolution metrics that define meaningful aspects of source estimation results (such as localization accuracy in terms of peak localization error, PLE, and spatial extent in terms of spatial deviation, SD) that are relevant to the task at hand and can easily be visualized. At the core of this framework is the resolution matrix, which describes the potential leakage from and into point sources (point-spread and cross-talk functions, or PSFs and CTFs, respectively). Importantly, for linear methods these functions allow generalizations to multiple sources or complex source distributions. This paper provides a tutorial-style introduction into linear EEG/MEG source estimation and resolution analysis aimed at experimental (rather than methods-oriented) researchers. We used this framework to demonstrate how L2-MNE-type as well as LCMV beamforming methods can be evaluated in practice using software tools that have only recently become available for routine use. Our novel methods comparison includes PLE and SD for a larger number of methods than in similar previous studies, such as unweighted, depth-weighted and normalized L2-MNE methods (including dSPM, sLORETA, eLORETA) and two LCMV beamformers. The results demonstrate that some methods can achieve low and even zero PLE for PSFs. However, their SD as well as both PLE and SD for CTFs are far less optimal for all methods, in particular for deep cortical areas. We hope that our paper will encourage EEG/MEG researchers to apply this approach to their own tasks at hand. Data are openly available for group-level analysis from the Wakeman & Henson multimodal dataset (https://www.nature.com/articles/sdata20151) and for individual examples from the MNE-Python sample dataset (https://mne.tools/stable/overview/datasets_index.html) Instructions for Reproduction: See scripts on GitHub repository: https://github.com/olafhauk/EEGMEG_ResolutionAtlas
Data available, click to request
The time-course of feature-based attention effects dissociated from temporal expectation and target-related processes
Authors:
Moerel, D., Grootswagers, T., Robinson, A.K., Shatek, S.M., WOOLGAR, A., Carlson, T.A., & Rich, A.N.
Reference:
Nature Scientific Reports
Year of publication:
In Press
CBU number:
8805
Abstract:
Selective attention prioritises relevant information amongst competing sensory input. Time resolved electrophysiological studies have shown stronger representation of attended compared to unattended stimuli, which has been interpreted as an effect of attention on information coding. However, because attention is often manipulated by making only the attended stimulus a target to be remembered and/or responded to, many reported attention effects have been confounded with target-related processes such as visual short-term memory or decision-making. In addition, attention effects could be influenced by temporal expectation about when something is likely to happen. The aim of this study was to investigate the dynamic effect of attention on visual processing using multivariate pattern analysis of 30 electroencephalography (EEG) data, while 1) controlling for target-related confounds, and 2)directly investigating the influence of temporal expectation. Participants viewed rapid sequences of overlaid oriented grating pairs while detecting a “target” grating of a particular orientation. We manipulated attention, one grating was attended and the other ignored (cued by colour), and temporal expectation, with stimulus onset timing either predictable or not. We controlled for target-related processing confounds by only analysing non-target trials. Both attended and ignored gratings were initially coded equally in the pattern of responses across EEG sensors. An effect of attention, with preferential coding of the attended stimulus, emerged approximately 230ms after stimulus onset. This attention effect occurred even when controlling for target-related processing confounds, and regardless of stimulus onset expectation. These results provide insight into the effect of feature-based attention on the dynamic processing of competing visual information.
Data for this project is available at: https://openneuro.org/datasets/ds004043
Windows of developmental sensitivity to social media
Authors:
ORBEN,. A., Przybylski, A.K., Blakemore, S-J., KIEVIT, R. A.
Reference:
Nature Communications
Year of publication:
In Press
CBU number:
8791
Abstract:
The relationship between social media use and life satisfaction changes across adolescent development. Our analyses of two UK datasets comprising 84,011 participants (10-80 years old) find that the cross-sectional relationship between self-reported estimates of social media use and life satisfaction ratings is most negative in younger adolescents. Furthermore, sex differences in this relationship are only present during this time. Longitudinal analyses of 17,409 participants (10-21 years old) suggest distinct developmental windows of sensitivity to social media in adolescence, when higher estimated social media use predicts a decrease in life satisfaction ratings one year later (and vice-versa: lower estimated social media use predicts an increase in life satisfaction ratings). These windows occur at different ages for males (14-15 and 19 years old) and females (11-13 and 19 years old). Decreases in life satisfaction ratings also predicted subsequent increases in estimated social media use, however these were not associated with age or sex.

Data is all open access and downloadable via the UK Data Service. See links in the paper

Late Combination shows that MEG adds to MRI in classifying MCI versus Controls
Authors:
VAGHARI, D., Kabir, E., HENSON, R.
Reference:
Neuroimage, 03 Mar 2022, 252:119054
Year of publication:
2022
CBU number:
8790
Abstract:
Early detection of Alzheimer’s Disease (AD) is essential for developing effective treatments. Neuroimaging techniques like Magnetic Resonance Imaging (MRI) have the potential to detect brain changes before symptoms emerge. Structural MRI can detect atrophy related to AD, but it is possible that functional changes are observed even earlier. We therefore examined the potential of Magnetoencephalography (MEG) to detect differences in functional brain activity in people with Mild Cognitive Impairment (MCI) – a state at risk of early AD. We introduce a framework for multimodal combination to ask whether MEG data from a resting-state provides complementary information beyond structural MRI data in the classification of MCI versus controls. More specifically, we used multi-kernel learning of support vector machines to classify 163 MCI cases versus 144 healthy elderly controls from the BioFIND dataset. When using the covariance of planar gradiometer data in the low Gamma range (30-48Hz), we found that adding a MEG kernel improved classification accuracy above kernels that captured several potential confounds (e.g., age, education, time-of-day, head motion). However, accuracy using MEG alone (68%) was worse than MRI alone (71%). When simply concatenating (normalized) features from MEG and MRI into one kernel (Early combination), there was no advantage of combining MEG with MRI versus MRI alone. When combining kernels of modality-specific features (Intermediate combination), there was an improvement in multimodal classification to 74%. The biggest multimodal improvement however occurred when we combined kernels from the predictions of modality-specific classifiers (Late combination), which achieved 77% accuracy (a reliable improvement in terms of permutation testing). We also explored other MEG features, such as the variance versus covariance of magnetometer versus planar gradiometer data within each of 6 frequency bands (delta, theta, alpha, beta, low gamma, or high gamma), and found that they generally provided complementary information for classification above MRI. We conclude that MEG can improve on the MRI-based classification of MCI.
URL:
Data for this project is available at: https://github.com/delshadv/MRI_MEG_Combination
Assessing executive functions in post stroke aphasia – utility of verbally based tests
Authors:
Schumacher, R., HALAI, A.D., LAMBON RALPH, M.A.
Reference:
Brain Communications, fcac107
Year of publication:
In Press
CBU number:
8788
Abstract:
It is increasingly acknowledged that, often, patients with post stroke aphasia not only have language impairments but also deficits in other cognitive domains (e.g., executive functions), that influence recovery and response to therapy. Many assessments of executive functions are verbally based and therefore usually not administered in this patient group. However, the performance of patients with aphasia in such tests might provide valuable insights both from a theoretical and clinical perspective. We aimed to elucidate (i) if verbal executive tests measure anything beyond the language impairment in patients with chronic post-stroke aphasia, (ii) how performance in such tests relates to performance in language tests and nonverbal cognitive functions, and (iii) the neural correlates associated with performance in verbal executive tests. In this observational study, three commonly used verbal executive tests were administered to a sample of patients with varying aphasia severity. Their performance in these tests was explored by means of principal component analyses, and the relationships with a broad range of background tests regarding their language and nonverbal cognitive functions was elucidated with correlation analyses. Furthermore, lesion analyses were performed to explore brain-behaviour relationships. In a sample of 32 participants we found that: 1) a substantial number of patients with aphasia were able to perform the verbal executive tests; 2) variance in performance was not explained by the severity of an individual’s overall language impairment alone but was related to two independent behavioural principal components per test; 3) not all aspects of performance were related to the patient’s language abilities; 4) all components were associated with separate neural correlates, some overlapping partly in frontal and parietal regions. Our findings extend our clinical and theoretical understanding of dysfunctions beyond language in patients with aphasia.
URL:
Data available, click to request
Brain charts for the human lifespan
Authors:
Bethlehem, R., HENSON, R., Seidlitz J., White, S.R., Vogel, J.W., Anderson, K.M, Adamson C., Adler S., Alexopoulos, G.S., Anagnostou E., Areces-Gonzalez A., Astle, D.E, Auyeung B., Ayub M., Bae J., Ball G., Baron-Cohen S., Beare R., Bedford, S.A., Benegal V., Beyer F., Blangero J., Blesa Cábez M., Boardman, J.P., Borzage M., Bosch-Bayard, J.F.,, Bourke N., Calhoun, V.D., Chakravarty, M.M., Chen C., Chertavian C., Chetelat G., Chong, Y.S., Cole, J.H., Corvin A., Costantino M., Courchesne E., Crivello F., Cropley, V.L., Crosbie J., Crossley N., Delarue M., Delorme R., Desrivieres S., Devenyi G., Di Biase, M.A., Dolan R., Donald, K.A., Donohoe G., Dunlop K.⁶³, Edwards, A.D., Elison, J.T., Ellis, C.T, Elman, J.A., Eyler L., Fair, D.A., Feczko E., Fletcher, P.C., Fonagy P., Franz, C.E., Galan-Garcia L., Gholipour A., Giedd J., Gilmore, J.H., Glahn, D.C., Goodyer, I.M. Grant, P.E., Groenewold, N.A., Gunning, F.M., Gur, R.E., Gur, R.C., Hammill, C.F., Hansson O., Hedden T., Heinz A., Henson, R.N., Heuer K., Hoare J., Holla B., Holmes, A.J., Holt R., Huang H., Im K., Ipser J., Jack Jr, C.R., Jackowski, A.P., Jia T., Johnson, K.A., Jones, P.B., Jones, D.T., Kahn, R.S., Karlsson H., Karlsson L., Kawashima R., Kelley, E.A., Kern S., Kim K., Kitzbichler, M.G., Kremen, W.S., Lalonde F., Landeau B., Lee S., Lerch J., Lewis, J.D., Li J., Liao W., Liston C., Lombardo, M.V., Lv J., Lynch C., Mallard, T.T., Marcelis M., Markello, R.D., Mathias, S.R., Mazoyer B., McGuire P., Meaney, M.J., Mechelli A., Medic N., Misic B., Morgan, S.E., Mothersill D., Nigg J., Ong, M.Q.W., Ortinau C., Ossenkoppele R., Ouyang M., Palaniyappan L. Paly L., Pan, P.M., Pantelis C., Park, M.M., Paus T., Pausova Z., Paz-Linares D., Pichet Binette A., Pierce K., Qian X., Qiu J., Qiu A., Raznahan A., Rittman T., Rodrigue A., Rollins, C.K. Romero-Garcia R, Ronan L., Rosenberg, M.D., Rowitch, D.H., Salum, G.A, Satterthwaite, T.D, Schaare H., Schachar, R.J., Schultz, A.P., Schumann G., Schöll M., Sharp D., Shinohara, R.T., Skoog I., Smyser, C.D., Sperling, R.A., Stein, D.J., Stolicyn A., Suckling J., Sullivan G., Taki Y., Thyreau B., Toro R., Traut N., Tsvetanov, K.A., Turk-Browne, N.B, Tuulari, J.J., Tzourio C., Vachon-Presseau É., Valdes-Sosa, M.J., Valdes-Sosa, P.A., Valk, S.L., van Amelsvoort T., Vandekar, S.N., Vasung L., Victoria, L.W., Villeneuve S., Villringer A., Vértes, P.E., Wagstyl K., Wang, Y.S., Warfield, S.K., Warrier V., Westman E., Westwater, M.L., Whalley, H.C., Witte A., Yang N., Yeo B., Yun H., Zalesky A., Zar, H.J., Zettergren A., Zhou, J.H., Ziauddeen H., Zugman A., Zuo, X.N., 3R-BRAIN, AIBL, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Repository Without Borders Investigators, CALM Team, Cam-CAN, CCNP, COBBRE, cVEDA, ENIGMA Developmental Brain Age working group, Developing Human Connectome Project, FinnBrain, Harvard Aging Brain Study, IMAGEN, KNE96, The Mayo Clinic Study of Aging, NSPN, POND, The PREVENT-AD Research Group, VETSA, Bullmore, E.T, Alexander-Bloch, A.F.
Reference:
Nature
Year of publication:
2022
CBU number:
8787
Abstract:
Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight. Here, we built an interactive open resource to benchmark brain morphology, www.brainchart.io, derived from any current or future sample of magnetic resonance imaging (MRI) data. With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across 100+ primary studies, from 101,457 participants aged from 115 days post-conception through 100 postnatal years. MRI metrics were quantified by centile scores, relative to non-linear trajectories of brain structural changes, and rates-of-change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones; showed high stability of individuals across longitudinal assessments; and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared to non-centiled MRI phenotypes, and provided a standardised measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In sum, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly-used neuroimaging phenotypes.
URL:
Data for this project is available at: x


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