Lagged and instantaneous dynamical influences related to brain structural connectivity

Contemporary neuroimaging methods can shed light on the basis of human neural and cognitive specializations, with important implications for neuroscience and medicine. Different MRI acquisitions provide different brain networks at the macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural connectivity (SC) coincident with the bundles of parallel fibers between brain areas, functional MRI (fMRI) accounts for the variations in the blood-oxygenation-level-dependent T2* signal, providing functional connectivity (FC).Understanding the precise relation between FC and SC, that is, between brain dynamics and structure, is still a challenge for neuroscience. To investigate this problem, we acquired data at rest and built the corresponding SC (with matrix elements corresponding to the fiber number between brain areas) to be compared with FC connectivity matrices obtained by 3 different methods: directed dependencies by an exploratory version of structural equation modeling (eSEM), linear correlations (C) and partial correlations (PC). We also considered the possibility of using lagged correlations in time series; so, we compared a lagged version of eSEM and Granger causality (GC). Our results were two-fold: firstly, eSEM performance in correlating with SC was comparable to those obtained from C and PC, but eSEM (not C nor PC) provides information about directionality of the functional interactions. Second, interactions on a time scale much smaller than the sampling time, captured by instantaneous connectivity methods, are much more related to SC than slow directed influences captured by the lagged analysis. Indeed the performance in correlating with SC was much worse for GC and for the lagged version of eSEM. We expect these results to supply further insights to the interplay between SC and functional patterns, an important issue in the study of brain physiology and function.Comment: Accepted and published in Frontiers in Psychology in its current form. 27 pages, 1 table, 5 figures, 2 suppl. figure

Cómo impartir una clase magistral según la neurociencia

Las clases magistrales han sido una de las técnicas fundamentales de enseñanza desde tiempos de la civilización griega, y probablemente incluso desde mucho antes. Una clase magistral es una actividad centrada en el profesor, en la que el estudiante es un agente pasivo que “recibe” las explicaciones del agente transmisor, que en teoría es un amplio conocedor del tema estudiado. Como agente pasivo que es, el estudiante aprende en una clase magistral en función del interés que tenga en el tema que se trata. En este artículo se aborda el aprendizaje desde la perspectiva de estudiar sus tres fases principales (motivación, atención, memorización), y cómo cada una de estas fases debe abordarse en una clase magistral. Para realizar este análisis se usan los conocimientos actuales desde la perspectiva de la neurociencia, de forma que en el artículo se proporcionan un conjunto de recomendaciones sobre cómo debe hacerse una clase magistral teniendo en cuenta el funcionamiento del cerebro humano.Master classes have been one of the fundamental teaching techniques since Greek civilization times, and probably even from much earlier. A master class is an activity focused on the teacher, in which the student is a passive agent who "receives" explanations from the transmitting agent, who in theory is a broad expert on the studied subject. As a passive agent, the student learns in a master class according to the interest he/she has in the subject. In this paper, learning is approached from the perspective of studying its three main phases (motivation, attention, and memorization) and how each of these phases should be addressed in a master class form neuroscience’s perspective. The knowledge of current neuroscience is used to perform this analysis, so that the paper provides a set of recommendations on how a master class should be given considering the functioning of the human brain.Peer ReviewedPostprint (published version

Comparison of patient-specific and normative connectivity profiles in deep brain stimulation

Objective: Brain connectivity profiles seeding from deep brain stimulation (DBS) electrodes have emerged as informative tools to estimate outcome variability across DBS patients. Given the limitations of acquiring and processing patient-specific diffusion-weighted imaging data, a number of studies have employed normative atlases of the human connectome. To date, it remains unclear whether patient-specific connectivity information would strengthen the accuracy of such analyses. Here, we compared similarities and differences between patient-specific, disease-matched and normative structural connectivity data and estimated the clinical improvement that they may generate. Methods: Data from 33 patients suffering from Parkinson’s disease who underwent surgery at three different centers were retrospectively collected. Stimulation-dependent connectivity profiles seeding from active contacts were estimated using three modalities, namely, either patient-specific diffusion-MRI data, disease-matched or normative group connectome data (acquired in healthy young subjects). Based on these profiles, models of optimal connectivity were constructed and used to estimate the clinical improvement in out-of-sample data. Results: All three modalities resulted in highly similar optimal connectivity profiles that could largely reproduce findings from prior research based on a novel multicenter cohort. In a data-driven approach that estimated optimal whole-brain connectivity profiles, out-of-sample predictions of clinical improvements were calculated. Using either patient-specific connectivity (R = 0.43 at p = 0.001), an age- and disease-matched group connectome (R = 0.25, p = 0.048) or a normative connectome based on healthy/young subjects (R = 0.31 at p = 0.028), significant predictions could be made, and the underlying optimal connectivity profiles were highly similar. Conclusion: Our results of patient-specific connectivity and normative connectomes lead to similar main conclusions about which brain areas are associated with clinical improvement. Nevertheless, although the results were not significantly different, they hint at the fact that patient-specific connectivity has potential for estimating slightly more variance when compared to group connectomes. Furthermore, the use of normative connectomes involves datasets with high signal-to-noise acquired on specialized MRI hardware, while clinical datasets such as the ones used here may not exactly match their quality. Our findings support the role of DBS electrode connectivity profiles as a promising method to investigate DBS effects and to potentially guide DBS programming.Zielsetzung: Konnektivitätsprofile des Gehirns, die von Elektroden zur Tiefenhirnstimulation (THS) ausgehen, haben sich als informativ für die Schätzung von Variabilität im Behandlungserfolg bei THS-PatientInnen erwiesen. Angesichts von Einschränkungen bei der Erhebung und Verarbeitung patientenspezifischer, diffusionsgewichteter Bilddaten wurden in einer Reihe von Studien normative Atlanten des menschlichen Konnektivitätsprofils verwendet. Bis heute ist unklar, ob patientenspezifische Konnektivitätsinformation die Genauigkeit solcher Analysen verbessern würde. Ziel dieser Studie war der Vergleich zwischen Ähnlichkeiten und Unterschieden patientenspezifischer, krankheits-gematchter und normativer, struktureller Konnektivitätsdaten, sowie der Fähigkeit dieser Methoden zur Vorhersage eines etwaigen klinischen Behandlungserfolges. Methoden: Die Analysen basierten auf retrospektiven Daten von 33 Parkinson-PatientInnen, welche an drei verschiedenen Zentren operiert worden waren. Stimulationsabhängige Konnektivitätsprofile mit Ursprung in aktiven DBS-Kontakten wurden mittels der drei Modalitäten geschätzt, also entweder basierend auf patientenspezifischen, diffusionsgewichteten MRT-Daten, oder auf krankheits-gematchten sowie auf normativen Gruppenkonnektivitätsdaten (erhoben an gesunden, jungen ProbandInnen). Auf Grundlage dieser Profile wurden Modelle optimaler Konnektivität konstruiert und zur Schätzung des klinischen Behandlungserfolgs in unabhängigen Daten herangezogen. Ergebnisse: Alle drei Modalitäten führten zu sehr ähnlichen optimalen Konnektivitätsprofilen, mit Hilfe derer sich auf Grundlage einer neuartigen multizentrischen Kohorte vorherige Forschungsbefunde weitgehend reproduzieren ließen. In einem datengesteuerten Ansatz, bei dem optimale Konnektivitätsprofile über das gesamte Gehirn hinweg geschätzt wurden, wurden Vorhersagen über den klinischen Behandlungserfolg in unabhängigen Daten berechnet. Unter Verwendung entweder der patientenspezifischen Konnektivität (R = 0,43 bei p = 0,001), eines alters- und krankheits-gematchten Gruppenkonnektivitätsprofils (R = 0,25, p = 0,048) oder eines normativen Konnektivitätsprofils basierend auf Daten gesunder/junger ProbandInnen (R = 0,31 bei p = 0,028) konnten signifikante Vorhersagen getroffen werden, wobei die zugrunde liegenden optimalen Konnektivitätsprofile große Ähnlichkeit aufwiesen. Schlussfolgerung: Unsere Ergebnisse, welche patientenspezifische sowie normative Konnektivitätsprofile einbeziehen, führen zu ähnlichen Hauptschlussfolgerungen darüber, welche Hirnareale mit klinischem Behandlungserfolg assoziiert sind. Obwohl sich die Ergebnisse nicht signifikant unterschieden, deuten sie dennoch darauf hin, dass patientenspezifische Konnektivität über Potenzial zur Schätzung geringfügig höherer Varianz im Vergleich zu gruppenbasierten Konnektivitäsprofilen verfügt. Darüber hinaus stützen sich Analysen, welche auf normativen Konnektivitätsprofile basieren, auf Datensätze mit hohem Signal-Rausch-Verhältnis, welche durch spezialisierte MRT-Technologie erfasst wurden, während klinische Datensätze, wie sie auch in dieser Studie herangezogen wurden, diesen an Qualität möglicherweise nicht gleichkommen. Unsere Befunde stützen die Rolle von Konnektivitätsprofilen, welche von THS-Elektroden ausgehen, als eine vielversprechende Methode zur Untersuchung von THS-Effekten und möglicherweise zur Verbesserung der THS-Programmierung

Default Mode Contributions to Automated Information Processing

Concurrent with mental processes that require rigorous computation and control, a series of automated decisions and actions govern our daily lives, providing efficient and adaptive responses to environmental demands. Using a cognitive flexibility task, we show that a set of brain regions collectively known as the default mode network play a crucial role in such “autopilot” behavior, i.e. when rapidly selecting appropriate responses under predictable behavioral contexts. While applying learned rules, the default mode network shows both greater activity and connectivity. Furthermore, functional interactions between this network and hippocampal, parahippocampal areas as well as primary visual cortex correlate with the speed of accurate responses. These findings indicate a memory-based “autopilot role” for the default mode network, which may have important implications for our current understanding of healthy and adaptive brain processing

Learning Common Harmonic Waves on Stiefel Manifold - A New Mathematical Approach for Brain Network Analyses

Converging evidence shows that disease-relevant brain alterations do not appear in random brain locations, instead, their spatial patterns follow large-scale brain networks. In this context, a powerful network analysis approach with a mathematical foundation is indispensable to understand the mechanisms of neuropathological events as they spread through the brain. Indeed, the topology of each brain network is governed by its native harmonic waves, which are a set of orthogonal bases derived from the Eigen-system of the underlying Laplacian matrix. To that end, we propose a novel connectome harmonic analysis framework that provides enhanced mathematical insights by detecting frequency-based alterations relevant to brain disorders. The backbone of our framework is a novel manifold algebra appropriate for inference across harmonic waves. This algebra overcomes the limitations of using classic Euclidean operations on irregular data structures. The individual harmonic differences are measured by a set of common harmonic waves learned from a population of individual Eigen-systems, where each native Eigen-system is regarded as a sample drawn from the Stiefel manifold. Specifically, a manifold optimization scheme is tailored to find the common harmonic waves, which reside at the center of the Stiefel manifold. To that end, the common harmonic waves constitute a new set of neurobiological bases to understand disease progression. Each harmonic wave exhibits a unique propagation pattern of neuropathological burden spreading across brain networks. The statistical power of our novel connectome harmonic analysis approach is evaluated by identifying frequency-based alterations relevant to Alzheimer's disease, where our learning-based manifold approach discovers more significant and reproducible network dysfunction patterns than Euclidean methods

Differential effects of Down's syndrome and Alzheimer's neuropathology on default mode connectivity.

Down's syndrome is a chromosomal disorder that invariably results in both intellectual disability and Alzheimer's disease neuropathology. However, only a limited number of studies to date have investigated intrinsic brain network organisation in people with Down's syndrome, none of which addressed the links between functional connectivity and Alzheimer's disease. In this cross-sectional study, we employed 11 C-Pittsburgh Compound-B (PiB) positron emission tomography in order to group participants with Down's syndrome based on the presence of fibrillar beta-amyloid neuropathology. We also acquired resting state functional magnetic resonance imaging data to interrogate the connectivity of the default mode network; a large-scale system with demonstrated links to Alzheimer's disease. The results revealed widespread positive connectivity of the default mode network in people with Down's syndrome (n = 34, ages 30-55, median age = 43.5) and a stark lack of anti-correlation. However, in contrast to typically developing controls (n = 20, ages 30-55, median age = 43.5), the Down's syndrome group also showed significantly weaker connections in localised frontal and posterior brain regions. Notably, while a comparison of the PiB-negative Down's syndrome group (n = 19, ages 30-48, median age = 41.0) to controls suggested that alterations in default mode connectivity to frontal brain regions are related to atypical development, a comparison of the PiB-positive (n = 15, ages 39-55, median age = 48.0) and PiB-negative Down's syndrome groups indicated that aberrant connectivity in posterior cortices is associated with the presence of Alzheimer's disease neuropathology. Such distinct profiles of altered connectivity not only further our understanding of the brain physiology that underlies these two inherently linked conditions but may also potentially provide a biomarker for future studies of neurodegeneration in people with Down's syndrome