How spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, ...known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods to Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.
The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here ...we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of stage N1 sleep and wakefulness before and after sleep.
•Time-varying FC models sometimes fail to detect temporal changes in fMRI data.•Between-subject and within-session FC variability affect model stasis.•The choice of parcellation affects model stasis ...in real fMRI data.•The number of observations and free parameters per state critically affect model stasis.
Functional connectivity (FC) in the brain has been shown to exhibit subtle but reliable modulations within a session. One way of estimating time-varying FC is by using state-based models that describe fMRI time series as temporal sequences of states, each with an associated, characteristic pattern of FC. However, the estimation of these models from data sometimes fails to capture changes in a meaningful way, such that the model estimation assigns entire sessions (or the largest part of them) to a single state, therefore failing to capture within-session state modulations effectively; we refer to this phenomenon as the model becoming static, or model stasis. Here, we aim to quantify how the nature of the data and the choice of model parameters affect the model's ability to detect temporal changes in FC using both simulated fMRI time courses and resting state fMRI data. We show that large between-subject FC differences can overwhelm subtler within-session modulations, causing the model to become static. Further, the choice of parcellation can also affect the model's ability to detect temporal changes. We finally show that the model often becomes static when the number of free parameters per state that need to be estimated is high and the number of observations available for this estimation is low in comparison. Based on these findings, we derive a set of practical recommendations for time-varying FC studies, in terms of preprocessing, parcellation and complexity of the model.
Even in response to simple tasks such as hand movement, human brain activity shows remarkable inter-subject variability. Recently, it has been shown that individual spatial variability in fMRI task ...responses can be predicted from measurements collected at rest; suggesting that the spatial variability is a stable feature, inherent to the individual’s brain. However, it is not clear if this is also true for individual variability in the spatio-spectral content of oscillatory brain activity. Here, we show using MEG (N = 89) that we can predict the spatial and spectral content of an individual’s task response using features estimated from the individual’s resting MEG data. This works by learning when transient spectral ‘bursts’ or events in the resting state tend to reoccur in the task responses. We applied our method to motor, working memory and language comprehension tasks. All task conditions were predicted significantly above chance. Finally, we found a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. Our approach can predict individual differences in brain activity and suggests a link between transient spectral events in task and rest that can be captured at the level of individuals.
•Using AR-Hidden-Markov-Modeling, we predict diverse task responses from MEG resting state only.•On a group-level, events identified from resting state only can reconstruct group task responses.•Learning the mapping of these states onto task data enables prediction of single unseen subjects.•Genetically closer subjects show better predictability to each other than unrelated subjects.
Resting state brain activity has become a significant area of investigation in human neuroimaging. An important approach for understanding the dynamics of neuronal activity in the resting state is to ...use complementary imaging modalities. Electrophysiological recordings can access fast temporal dynamics, while functional magnetic resonance imaging (fMRI) studies reveal detailed spatial patterns. However, the relationship between these two measures is not fully established. In this study, we used simultaneously recorded electroencephalography (EEG) and fMRI, along with Hidden Markov Modelling, to investigate how network dynamics at fast sub-second time-scales, accessible with EEG, link to the slower time-scales and higher spatial detail of fMRI. We found that the fMRI correlates of fast transient EEG dynamic networks show highly reproducible spatial patterns, and that their spatial organization exhibits strong similarity with traditional fMRI resting state networks maps. This further demonstrates the potential of electrophysiology as a tool for understanding the fast network dynamics that underlie fMRI resting state networks.
•Hidden Markov Modelling of resting-state EEG and fMRI.•Model conveys connectivity information within and transition rules between networks.•fMRI correlates of HMM-EEG networks resemble resting state network (RSN) maps.•Subsecond EEG network activity underlies slow fMRI RSN fluctuations.•Similar dynamic structure of HMM-EEG and HMM-fMRI networks suggests scale-free behaviour.
Transcription factors of the auxin response factor (ARF) family have been implicated in auxin-dependent gene regulation, but little is known about the functions of individual ARFs in plants. Here, ...interaction assays, expression studies and combinations of multiple loss- and gain-of-function mutants were used to assess the roles of two ARFs, NONPHOTOTROPIC HYPOCOTYL 4 (NPH4/ARF7) and MONOPTEROS (MP/ARF5), in Arabidopsis development. Both MP and NPH4 interact strongly and selectively with themselves and with each other, and are expressed in vastly overlapping domains. We show that the regulatory properties of both genes are far more related than suggested by their single mutant phenotypes. NPH4 and MP are capable of controlling both axis formation in the embryo and auxin-dependent cell expansion. Interaction of MP and NPH4 in Arabidopsis plants is indicated by their joint requirement in a number of auxin responses and by synergistic effects associated with the co-overexpression of both genes. Finally, we demonstrate antagonistic interaction between ARF and Aux/IAA gene functions in Arabidopsis development. Overexpression of MP suppresses numerous defects associated with a gain-of-function mutation in BODENLOS ( BDL ) /IAA12 . Together these results provide evidence for the biological relevance of ARF-ARF and ARF-Aux/IAA interaction in Arabidopsis plants and demonstrate that an individual ARF can act in both invariantly programmed pattern formation as well as in conditional responses to external signals.
It is of increasing interest to study “brain age” - the apparent age of a subject, as inferred from brain imaging data. The difference between brain age and actual age (the “delta”) is typically ...computed, reflecting deviation from the population norm. This therefore may reflect accelerated aging (positive delta) or resilience (negative delta) and has been found to be a useful correlate with factors such as disease and cognitive decline. However, although there has been a range of methods proposed for estimating brain age, there has been little study of the optimal ways of computing the delta. In this technical note we describe problems with the most common current approach, and present potential improvements. We evaluate different estimation methods on simulated and real data. We also find the strongest correlations of corrected brain age delta with 5,792 non-imaging variables (non-brain physical measures, life-factor measures, cognitive test scores, etc.), and also with 2,641 multimodal brain imaging-derived phenotypes, with data from 19,000 participants in UK Biobank.
•It is of interest to study "brain age'', as inferred from brain imaging data.•The delta between brain age and actual age is typically computed.•We describe problems with the most common current approach.•We present potential improvements.•We evaluate methods on simulated data and data from UK Biobank.
The brain is a complex, multiscale dynamical system composed of many interacting
regions. Knowledge of the spatiotemporal organization of these interactions is
critical for establishing a solid ...understanding of the brain’s functional
architecture and the relationship between neural dynamics and cognition in
health and disease. The possibility of studying these dynamics through careful
analysis of neuroimaging data has catalyzed substantial interest in methods that
estimate time-resolved fluctuations in functional connectivity (often referred
to as “dynamic” or time-varying functional connectivity; TVFC). At
the same time, debates have emerged regarding the application of TVFC analyses
to resting fMRI data, and about the statistical validity, physiological origins,
and cognitive and behavioral relevance of resting TVFC. These and other
unresolved issues complicate interpretation of resting TVFC findings and limit
the insights that can be gained from this promising new research area. This
article brings together scientists with a variety of perspectives on resting
TVFC to review the current literature in light of these issues. We introduce
core concepts, define key terms, summarize controversies and open questions, and
present a forward-looking perspective on how resting TVFC analyses can be
rigorously and productively applied to investigate a wide range of questions in
cognitive and systems neuroscience.
In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous ...work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.
We report the draft genome sequences of Leptolyngbya sp. strain 7M and Leptolyngbya sp. strain 15MV, isolated from Miravalles Thermal Spring, Costa Rica. The thermophilic cyanobacteria exhibit unique ...diversity features that provide insight into the adaptation and evolution of phototrophic microorganisms in geothermal habitats.
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