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Alberto Llera

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8 papers

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YNIMG Journal 2020 Journal Article

Single-subject Single-session Temporally-Independent Functional Modes of Brain Activity

Daniel E.P. Gomez
Alberto Llera
José P.R. F. Marques
Christian F. Beckmann
David G. Norris

Temporally independent functional modes (TFMs) are functional brain networks identified based on their temporal independence. The rationale behind identifying TFMs is that different functional networks may share a common anatomical infrastructure yet display distinct temporal dynamics. Extracting TFMs usually require a larger number of samples than acquired in standard fMRI experiments, and thus have therefore previously only been performed at the group level. Here, using an ultra-fast fMRI sequence, MESH-EPI, with a volume repetition time of 158 ms, we conducted an exploratory study with n = 6 subjects and computed TFMs at the single subject level on both task and resting-state datasets. We identified 6 common temporal modes of activity in our participants, including a temporal default mode showing patterns of anti-correlation between the default mode and the task-positive networks, a lateralised motor mode and a visual mode integrating the visual cortex and the visual streams. In alignment with other findings reported recently, we also showed that independent time-series are largely free from confound contamination. In particular for ultra-fast fMRI, TFMs can separate the cardiac signal from other fluctuations. Using a non-linear dimensionality reduction technique, UMAP, we obtained preliminary evidence that combinations of spatial networks as described by the TFM model are highly individual. Our results show that it is feasible to measure reproducible TFMs at the single-subject level, opening new possibilities for investigating functional networks and their integration. Finally, we provide a python toolbox for generating TFMs and comment on possible applications of the technique and avenues for further investigation.

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YNIMG Journal 2019 Journal Article

Disentangling common from specific processing across tasks using task potency

Roselyne J. Chauvin
Maarten Mennes
Alberto Llera
Jan K. Buitelaar
Christian F. Beckmann

When an individual engages in a task, the associated evoked activities build upon already ongoing activity, shaped by an underlying functional connectivity baseline (Fox et al. , 2009; Smith et al. , 2009; Tavor et al. , 2016). Building on the idea that rest represents the brain's full functional repertoire, we here incorporate the idea that task-induced functional connectivity modulations ought to be task-specific with respect to their underlying resting state functional connectivity. Various metrics such as clustering coefficient or average path length have been proposed to index processing efficiency, typically from single fMRI session data. We introduce a framework incorporating task potency, which provides direct access to task-specificity by enabling direct comparison between task paradigms. In particular, to study functional connectivity modulations related to cognitive involvement in a task we define task potency as the amplitude of a connectivity modulation away from its baseline functional connectivity architecture as observed during a resting state acquisition. We demonstrate the use of our framework by comparing three tasks (visuo-spatial working memory, reward processing, and stop signal task) available within a large cohort. Using task potency, we demonstrate that cognitive operations are supported by a set of common within-network interactions, supplemented by connections between large-scale networks in order to solve a specific task.

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YNICL Journal 2019 Journal Article

Linked anatomical and functional brain alterations in children with attention-deficit/hyperactivity disorder

Zhao-Min Wu
Alberto Llera
Martine Hoogman
Qing-Jiu Cao
Marcel P. Zwiers
Janita Bralten
Li An
Li Sun

OBJECTIVES: Neuroimaging studies have independently demonstrated brain anatomical and functional impairments in participants with ADHD. The aim of the current study was to explore the relationship between structural and functional brain alterations in ADHD through an integrated analysis of multimodal neuroimaging data. METHODS: We performed a multimodal analysis to integrate resting-state functional magnetic resonance imaging (MRI), structural MRI, and diffusion-weighted imaging data in a large, single-site sample of children with and without diagnosis for ADHD. The inferred subject contributions were fed into regression models to investigate the relationships between diagnosis, symptom severity, gender, and age. RESULTS: Compared with controls, children with ADHD diagnosis showed altered white matter microstructure in widespread white matter fiber tracts as well as greater gray matter volume (GMV) in bilateral frontal regions, smaller GMV in posterior regions, and altered functional connectivity (FC) in default mode and fronto-parietal networks. Age-related growth of GMV of bilateral occipital lobe, FC in frontal regions as well as age-related decline of GMV in medial regions seen in controls appeared reversed in children with ADHD. In the whole group, higher symptom severity was related to smaller GMV in widespread regions in bilateral frontal, parietal, and temporal lobes, as well as greater GMV in intracalcarine and temporal cortices. CONCLUSIONS: Through a multimodal analysis approach we show that structural and functional alterations in brain regions known to be altered in subjects with ADHD from unimodal studies are linked across modalities. The brain alterations were related to clinical features of ADHD, including disorder status, age, and symptom severity.

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YNICL Journal 2019 Journal Article

Linked MRI signatures of the brain's acute and persistent response to concussion in female varsity rugby players

Kathryn Y. Manning
Alberto Llera
Gregory A. Dekaban
Robert Bartha
Christy Barreira
Arthur Brown
Lisa Fischer
Tatiana Jevremovic

Acute brain changes are expected after concussion, yet there is growing evidence of persistent abnormalities well beyond clinical recovery and clearance to return to play. Multiparametric MRI is a powerful approach to non-invasively study structure-function relationships in the brain, however it remains challenging to interpret the complex and heterogeneous cascade of brain changes that manifest after concussion. Emerging conjunctive, data-driven analysis approaches like linked independent component analysis can integrate structural and functional imaging data to produce linked components that describe the shared inter-subject variance across images. These linked components not only offer the potential of a more comprehensive understanding of the underlying neurobiology of concussion, but can also provide reliable information at the level of an individual athlete. In this study, we analyzed resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI) within a cohort of female varsity rugby players (n = 52) through the in- and off-season, including concussed athletes (n = 21) who were studied longitudinally at three days, three months and six months after a diagnosed concussion. Linked components representing co-varying white matter microstructure and functional network connectivity characterized (a) the brain's acute response to concussion and (b) persistent alterations beyond clinical recovery. Furthermore, we demonstrate that these long-term brain changes related to specific aspects of a concussion history and allowed us to monitor individual athletes before and longitudinally after a diagnosed concussion.

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YNICL Journal 2019 Journal Article

Specific patterns of brain alterations underlie distinct clinical profiles in Huntington's disease

Clara Garcia-Gorro
Alberto Llera
Saul Martinez-Horta
Jesus Perez-Perez
Jaime Kulisevsky
Nadia Rodriguez-Dechicha
Irene Vaquer
Susana Subira

Huntington's disease (HD) is a genetic neurodegenerative disease which involves a triad of motor, cognitive and psychiatric disturbances. However, there is great variability in the prominence of each type of symptom across individuals. The neurobiological basis of such variability remains poorly understood but would be crucial for better tailored treatments. Multivariate multimodal neuroimaging approaches have been successful in disentangling these profiles in other disorders. Thus we applied for the first time such approach to HD. We studied the relationship between HD symptom domains and multimodal measures sensitive to grey and white matter structural alterations. Forty-three HD gene carriers (23 manifest and 20 premanifest individuals) were scanned and underwent behavioural assessments evaluating motor, cognitive and psychiatric domains. We conducted a multimodal analysis integrating different structural neuroimaging modalities measuring grey matter volume, cortical thickness and white matter diffusion indices - fractional anisotropy and radial diffusivity. All neuroimaging measures were entered into a linked independent component analysis in order to obtain multimodal components reflecting common inter-subject variation across imaging modalities. The relationship between multimodal neuroimaging independent components and behavioural measures was analysed using multiple linear regression. We found that cognitive and motor symptoms shared a common neurobiological basis, whereas the psychiatric domain presented a differentiated neural signature. Behavioural measures of different symptom domains correlated with different neuroimaging components, both the brain regions involved and the neuroimaging modalities most prominently associated with each type of symptom showing differences. More severe cognitive and motor signs together were associated with a multimodal component consisting in a pattern of reduced grey matter, cortical thickness and white matter integrity in cognitive and motor related networks. In contrast, depressive symptoms were associated with a component mainly characterised by reduced cortical thickness pattern in limbic and paralimbic regions. In conclusion, using a multivariate multimodal approach we were able to disentangle the neurobiological substrates of two distinct symptom profiles in HD: one characterised by cognitive and motor features dissociated from a psychiatric profile. These results open a new view on a disease classically considered as a uniform entity and initiates a new avenue for further research considering these qualitative individual differences.

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YNIMG Journal 2018 Journal Article

Thresholding functional connectomes by means of mixture modeling

Natalia Z. Bielczyk
Fabian Walocha
Patrick W. Ebel
Koen V. Haak
Alberto Llera
Jan K. Buitelaar
Jeffrey C. Glennon
Christian F. Beckmann

Functional connectivity has been shown to be a very promising tool for studying the large-scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of pair-wise interactions between the nodes of the network. These interactions are typically operationalized through the full or partial correlation between all pairs of regional time series. Estimating the structure of the latent underlying functional connectome from the set of pair-wise partial correlations remains an open research problem though. Typically, this thresholding problem is approached by proportional thresholding, or by means of parametric or non-parametric permutation testing across a cohort of subjects at each possible connection. As an alternative, we propose a data-driven thresholding approach for network matrices on the basis of mixture modeling. This approach allows for creating subject-specific sparse connectomes by modeling the full set of partial correlations as a mixture of low correlation values associated with weak or unreliable edges in the connectome and a sparse set of reliable connections. Consequently, we propose to use alternative thresholding strategy based on the model fit using pseudo-False Discovery Rates derived on the basis of the empirical null estimated as part of the mixture distribution. We evaluate the method on synthetic benchmark fMRI datasets where the underlying network structure is known, and demonstrate that it gives improved performance with respect to the alternative methods for thresholding connectomes, given the canonical thresholding levels. We also demonstrate that mixture modeling gives highly reproducible results when applied to the functional connectomes of the visual system derived from the n-back Working Memory task in the Human Connectome Project. The sparse connectomes obtained from mixture modeling are further discussed in the light of the previous knowledge of the functional architecture of the visual system in humans. We also demonstrate that with use of our method, we are able to extract similar information on the group level as can be achieved with permutation testing even though these two methods are not equivalent. We demonstrate that with both of these methods, we obtain functional decoupling between the two hemispheres in the higher order areas of the visual cortex during visual stimulation as compared to the resting state, which is in line with previous studies suggesting lateralization in the visual processing. However, as opposed to permutation testing, our approach does not require inference at the cohort level and can be used for creating sparse connectomes at the level of a single subject.

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YNICL Journal 2016 Journal Article

Integrated analysis of gray and white matter alterations in attention-deficit/hyperactivity disorder

Winke Francx
Alberto Llera
Maarten Mennes
Marcel P. Zwiers
Stephen V. Faraone
Jaap Oosterlaan
Dirk Heslenfeld
Pieter J. Hoekstra

BACKGROUND: Magnetic resonance imaging (MRI) is able to provide detailed insights into the structural organization of the brain, e.g., by means of mapping brain anatomy and white matter microstructure. Understanding interrelations between MRI modalities, rather than mapping modalities in isolation, will contribute to unraveling the complex neural mechanisms associated with neuropsychiatric disorders as deficits detected across modalities suggest common underlying mechanisms. Here, we conduct a multimodal analysis of structural MRI modalities in the context of attention-deficit/hyperactivity disorder (ADHD). METHODS: Gray matter volume, cortical thickness, surface areal expansion estimates, and white matter diffusion indices of 129 participants with ADHD and 204 participants without ADHD were entered into a linked independent component analysis. This data-driven analysis decomposes the data into multimodal independent components reflecting common inter-subject variation across imaging modalities. RESULTS: ADHD severity was related to two multimodal components. The first component revealed smaller prefrontal volumes in participants with more symptoms, co-occurring with abnormal white matter indices in prefrontal cortex. The second component demonstrated decreased orbitofrontal volume as well as abnormalities in insula, occipital, and somato-sensory areas in participants with more ADHD symptoms. CONCLUSIONS: Our results replicate and extend previous unimodal structural MRI findings by demonstrating that prefrontal, parietal, and occipital areas, as well as fronto-striatal and fronto-limbic systems are implicated in ADHD. By including multiple modalities, sensitivity for between-participant effects is increased, as shared variance across modalities is modeled. The convergence of modality-specific findings in our results suggests that different aspects of brain structure share underlying pathophysiology and brings us closer to a biological characterization of ADHD.

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YNIMG Journal 2015 Journal Article

ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data

Raimon H.R. Pruim
Maarten Mennes
Daan van Rooij
Alberto Llera
Jan K. Buitelaar
Christian F. Beckmann

Head motion during functional MRI (fMRI) scanning can induce spurious findings and/or harm detection of true effects. Solutions have been proposed, including deleting (‘scrubbing’) or regressing out (‘spike regression’) motion volumes from fMRI time-series. These strategies remove motion-induced signal variations at the cost of destroying the autocorrelation structure of the fMRI time-series and reducing temporal degrees of freedom. ICA-based fMRI denoising strategies overcome these drawbacks but typically require re-training of a classifier, needing manual labeling of derived components (e. g. ICA-FIX; Salimi-Khorshidi et al. (2014)). Here, we propose an ICA-based strategy for Automatic Removal of Motion Artifacts (ICA-AROMA) that uses a small (n=4), but robust set of theoretically motivated temporal and spatial features. Our strategy does not require classifier re-training, retains the data's autocorrelation structure and largely preserves temporal degrees of freedom. We describe ICA-AROMA, its implementation, and initial validation. ICA-AROMA identified motion components with high accuracy and robustness as illustrated by leave-N-out cross-validation. We additionally validated ICA-AROMA in resting-state (100 participants) and task-based fMRI data (118 participants). Our approach removed (motion-related) spurious noise from both rfMRI and task-based fMRI data to larger extent than regression using 24 motion parameters or spike regression. Furthermore, ICA-AROMA increased sensitivity to group-level activation. Our results show that ICA-AROMA effectively reduces motion-induced signal variations in fMRI data, is applicable across datasets without requiring classifier re-training, and preserves the temporal characteristics of the fMRI data.

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