Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients

Computational whole-brain models describe the resting activity of each brain region based on a local model, inter-regional functional interactions, and a structural connectome that specifies the strength of inter-regional connections. Strokes damage the healthy structural connectome that forms the backbone of these models and produce large alterations in inter-regional functional interactions. These interactions are typically measured by correlating the time series of the activity between two brain regions in a process, called resting functional connectivity. We show that adding information about the structural disconnections produced by a patient’s lesion to a whole-brain model previously trained on structural and functional data from a large cohort of healthy subjects enables the prediction of the resting functional connectivity of the patient and fits the model directly to the patient’s data (Pearson correlation = 0.37; mean square error = 0.005). Furthermore, the model dynamics reproduce functional connectivity-based measures that are typically abnormal in stroke patients and measures that specifically isolate these abnormalities. Therefore, although whole-brain models typically involve a large number of free parameters, the results show that, even after fixing those parameters, the model reproduces results from a population very different than that on which the model was trained. In addition to validating the model, these results show that the model mechanistically captures the relationships between the anatomical structure and the functional activity of the human brain

Correction: Protocol of the Healthy Brain Study:An accessible resource for understanding the human brain and how it dynamically and individually operates in its bio-social context

[This corrects the article DOI: 10.1371/journal.pone.0260952.]

Predicting effects of stroke lesions and recovery through whole-brain modeling and brain dynamics

Stroke is the second leading cause of death worldwide and a major contributor to disability. However, our understanding of the consequences of stroke lesions remains limited, relying mainly on behavioral reports and descriptive correlations from neuroimaging techniques. Functional magnetic resonance imaging (fMRI), one of the most commonly used methods, offers various possibilities that have not been extensively explored in stroke patients. In this thesis, we introduce a novel analysis approach that shifts the focus to the connections between brain regions, aiming to identify biomarkers for severity and recovery. Moreover, by employing whole-brain models, we demonstrate how the integration of structural and functional information can enhance the accuracy of existing analyses. Additionally, we present a model capable of predicting the functional information based only on the structural damage of the patients. Lastly, given the high-dimensional nature of the data, we utilize a deep learning autoencoder to uncover the embedded information and nonlinear dynamics of the brain following a stroke event. All of the findings presented in this thesis contribute to the improvement of diagnostics, classification, and prediction of recovery for this significant disorder.Los accidentes cerebrovasculares son la segunda causa de muerte a nivel mundial y una de las principales causas de discapacidad. Sin embargo, nuestra comprensión de las consecuencias de las lesiones por accidentes cerebrovasculares sigue siendo limitada y se basa principalmente en reportes de comportamiento y correlaciones descriptivas de técnicas de neuroimagen. La resonancia magnética funcional (fMRI), el método más utilizado, ofrece varias posibilidades que no se han explorado ampliamente en pacientes con accidente cerebrovascular. En nuestro estudio, presentamos un enfoque novedoso centrado en las conexiones entre las regiones del cerebro, con el objetivo de identificar biomarcadores de severidad y recuperación. Al emplear modelos de cerebro completo, demostramos cómo la integración de información estructural y funcional puede mejorar la precisión de los análisis existentes. Adicionalmente, presentamos un modelo capaz de predecir la información funcional basándose únicamente en el daño estructural de los pacientes. Por último, dada la naturaleza de alta dimensionalidad de los datos, utilizamos un codificador automático para investigar la información latente y la dinámica no lineal del cerebro después de un accidente cerebrovascular. Todos los hallazgos presentados en este estudio contribuyen a mejorar el diagnóstico, la clasificación y la predicción de la recuperación de este importante trastorno.Programa de Doctorat en Biomedicin

Respuesta Electrodérmica y Conductual Frente a Palabras Emocionales en Español en Hablantes Nativos

The present study aimed to validate a Spanish-word database that elicits different levels of emotional response. To control subjects’ engagement to the stimuli, an emotional Stroop task was administered to Spanish-speaking population. In order to assess to which extent valence and emotionality are automatically processed when reading a word, participants’ reaction time was recorded as a complement of their electrodermal response. These measurements were used to rank the words into two different lists, conforming the set of Spanish-words. The reaction time to negative words were only significantly slower to reaction times of positive ones (and not to the neutral ones). We found that words with a negative emotional content elicited higher skin conductance responses (SCR) and longer reaction time than those with neutral and positive emotional content. These findings are consistent with previous literature and therefore supports word’s emotionality of the implemented database. l; Emoción, Stroop emocional; Conductancia de la piel; Población hispano-hablante.El presente trabajo tuvo como objetivo validar una base de palabras en español que elicitaran diferentes niveles de respuesta emocional. Para controlar el compromiso de los sujetos con los estímulos, se administró una tarea de Stroop emocional a una población de hispanohablantes nativos. Se midió el tiempo de reacción y la respuesta electrodérmica de los sujetos. Posterior al experimento se interrogó a los participantes por la valencia emocional de cada estímulo evaluado. Finalmente, estas medidas se utilizaron para jerarquizar las palabras en dos listas, elaborando una base final de 30 palabras en español. Se encontró que las palabras con valencia negativa elicitaron mayores respuestas de conductancia de la piel y tiempos de reacción más lentos en comparación con las palabras de valencia neutra y positiva. Estos datos son consistentes con los reportados por la literatura y, por lo tanto, respaldan la emocionalidad elicitada por las palabras de la base implementada

Edge-centric analysis of stroke patients: an alternative approach for biomarkers of lesion recovery

Most neuroimaging studies of post-stroke recovery rely on analyses derived from standard node-centric functional connectivity to map the distributed effects in stroke patients. Here, given the importance of nonlocal and diffuse damage, we use an edge-centric approach to functional connectivity in order to provide an alternative description of the effects of this disorder. These techniques allow for the rendering of metrics such as normalized entropy, which describes the diversity of edge communities at each node. Moreover, the approach enables the identification of high amplitude co-fluctuations in fMRI time series. We found that normalized entropy is associated with stroke lesion severity and continually increases across the time of patients’ recovery. Furthermore, high amplitude co-fluctuations not only relate to the lesion severity but are also associated with patients’ level of recovery. The current study is the first edge-centric application for a clinical population in a longitudinal dataset and demonstrates how a different perspective for functional data analysis can further characterize topographic modulations of brain dynamics.S.I is supported by the EU-project euSNN (MSCA-ITN-ETN. H2020-860563). G.D. is supported by the Spanish national research project (ref. PID2019-105772GB-I00/AEI/https://doi.org/10.13039/ 501100011033) funded by the Spanish Ministry of Science, Innovation and Universities (MCIU). This material is based upon work supported by the National Science Foundation under Grant No. 076059-00003C (RFB, JF, OS). MC was supported by FLAG-ERA JTC 2017 (grant ANR-17- HBPR-0001); MIUR – Departments of Excellence Italian Ministry of Research (MART_ECCELLENZA18_01); Fondazione Cassa di Risparmio di Padova e Rovigo (CARIPARO) – Ricerca Scientifica di Eccellenza 2018 – (Grant Agreement number 55403); Ministry of Health Italy Brain connectivity measured with high-density electroencephalography: a novel neurodiagnostic tool for stroke- NEUROCONN (RF-2008- 12366899); Celeghin Foundation Padova (CUP C94I20000420007); BIAL foundation grant (No. 361/18); H2020 European School of Network Neuroscience- euSNN, H2020-SC5-2019-2, (Grant Agreement number 869505); H2020 Visionary Nature Based Actions For Heath, Wellbeing and Resilience in Cities (VARCITIES), H2020-SC5-2019-2 (Grant Agreement number 869505); Ministry of Health Italy: Eyemovement dynamics during free viewing as biomarker for assessment of visuospatial functions and for closed-loop rehabilitation in stroke – EYEMOVINSTROKE (RF-2019-12369300)

A low dimensional embedding of brain dynamics enhances diagnostic accuracy and behavioral prediction in stroke

Abstract Large-scale brain networks reveal structural connections as well as functional synchronization between distinct regions of the brain. The latter, referred to as functional connectivity (FC), can be derived from neuroimaging techniques such as functional magnetic resonance imaging (fMRI). FC studies have shown that brain networks are severely disrupted by stroke. However, since FC data are usually large and high-dimensional, extracting clinically useful information from this vast amount of data is still a great challenge, and our understanding of the functional consequences of stroke remains limited. Here, we propose a dimensionality reduction approach to simplify the analysis of this complex neural data. By using autoencoders, we find a low-dimensional representation encoding the fMRI data which preserves the typical FC anomalies known to be present in stroke patients. By employing the latent representations emerging from the autoencoders, we enhanced patients’ diagnostics and severity classification. Furthermore, we showed how low-dimensional representation increased the accuracy of recovery prediction

Inferring the dynamical effects of stroke lesions through whole-brain modeling

Understanding the effect of focal lesions (stroke) on brain structure-function traditionally relies on behavioral analyses and correlation with neuroimaging data. Here we use structural disconnection maps from individual lesions to derive a causal mechanistic generative whole-brain model able to explain both functional connectivity alterations and behavioral deficits induced by stroke. As compared to other models that use only the local lesion information, the similarity to the empirical fMRI connectivity increases when the widespread structural disconnection information is considered. The presented model classifies behavioral impairment severity with higher accuracy than other types of information (e.g.: functional connectivity). We assessed topological measures that characterize the functional effects of damage. With the obtained results, we were able to understand how network dynamics change emerge, in a nontrivial way, after a stroke injury of the underlying complex brain system. This type of modeling, including structural disconnection information, helps to deepen our understanding of the underlying mechanisms of stroke lesions.S.I is supported by the EU-project euSNN (MSCA-ITN-ETNH2020-860563). G.D. is supported by the Spanish national research project (ref. PID2019-105772GB-I00/AEI/10.13039/501100011033) funded by the Spanish Ministry of Science, Innovation, and Universities (MCIU). MC was supported by FLAG-ERA JTC 2017 (grant ANR-17-HBPR-0001); MIUR – Departments of Excellence Italian Ministry of Research (MART_ECCELLENZA18_01); Fondazione Cassa di Risparmio di Padova e Rovigo (CARIPARO) – Ricerca Scientifica di Eccellenza 2018 – (Grant Agreement number 55403); Ministry of Health Italy Brain connectivity measured with high-density electroencephalography: a novel neurodiagnostic tool for stroke- NEUROCONN (RF-2008 -12366899); Celeghin Foundation Padova (CUP C94I20000420007); BIAL foundation grant (No. 361/18); Ministry of Health Italy: Eye-movement dynamics during free viewing as biomarker for assessment of visuospatial functions and for closed-loop rehabilitation in stroke – EYEMOVINSTROKE (RF-2019-12369300)

Fig 3 -

HMM analysis: (A) Principal component analysis was calculated for each functional system and the three first components (one from each system) were used as the input signal for the Hidden Markov Model Analysis (HMM). (B) At each time point, the probability of occurrence of each network-state is displayed, next to the predominant one for the corresponding moment. (C) The average life-time (time spent at each network state) was calculated for each behavioral state and displayed together to observe the similarities between them (D) Entropy production was calculated by observing how asymmetrical the transitions between the network-states were. (E) The results, grouped by behavioral state indicated a higher level of transfer entropy in the AA/REM state (F) The joint transition probability between the network states is displayed for each behavioral state. Furthermore, the difference matrix (upper triangle minus lower triangle) is displayed to enhance the visualization of the effect (G) The level of determinism was calculated showing the highest value for the SWS state (H) The level of degeneracy was calculated showing the highest value for the SWS state (I) The level of mutual information was calculated showing the highest value for the SWS state.</p

Fig 2 -

Irreversibility level across time and regions: (A) The irreversibility level was assessed at each time point, displayed next to the behavioral state at the corresponding moment. Over time, a significant increase in irreversibility can be observed during the Active-Awake(AA)/REM stages. (B) Signal irreversibility was calculated for the three different behavioral states. AA/REM (left), Quiet-Awake (QA) (center) and Slow-wave-sleep (SWS) (right). For each state, it is displayed the level of irreversibility for each functional system (Visual, auditory, and parietal) and the level corresponding to the within-system relations and the between-system relations. Across sleep stages, the parietal cortex and within-system relations were revealed to be the drivers of the irreversibility in the system. At the bottom, the irreversibility level was measured at bins of 1 Hz between 0 and 50 Hz. The analysis was also performed for all the different behavioral states. Bar figures indicated the level of irreversibility when grouping the frequencies by previously reported frequency bands being the theta range the one with the highest irreversibility value.</p