Inter-individual Differences in fMRI Entropy Measurements in Old Age

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkWe investigated the association between individual differences in cognitive performance in old age and the approximate entropy (ApEn) measured from functional magnetic resonance imaging (fMRI) data acquired from 40 participants of the Aberdeen Birth Cohort 1936 (ABC1936), while undergoing a visual information processing task: inspection time (IT). Participants took a version of the Moray House Test (MHT) No. 12 at age 11, a valid measure of childhood intelligence. The same individuals completed a test of non-verbal reasoning (Raven’s Standard Progressive Matrices [RPM]) aged about 68 years. The IT, MHT and RPM scores were used as indicators of cognitive performance. Our results show that higher regional signal entropy is associated with better cognitive performance. This finding was independent of ability in childhood but not independent of current cognitive ability. ApEn is used for the first time to identify a potential source of individual differences in cognitive ability using fMRI data

Disruption of transfer entropy and inter-hemispheric brain functional connectivity in patients with disorder of consciousness

Severe traumatic brain injury can lead to disorders of consciousness (DOC) characterized by deficit in conscious awareness and cognitive impairment including coma, vegetative state, minimally consciousness, and lock-in syndrome. Of crucial importance is to find objective markers that can account for the large-scale disturbances of brain function to help the diagnosis and prognosis of DOC patients and eventually the prediction of the coma outcome. Following recent studies suggesting that the functional organization of brain networks can be altered in comatose patients, this work analyzes brain functional connectivity (FC) networks obtained from resting-state functional magnetic resonance imaging (rs-fMRI). Two approaches are used to estimate the FC: the Partial Correlation (PC) and the Transfer Entropy (TE). Both the PC and the TE show significant statistical differences between the group of patients and control subjects; in brief, the inter-hemispheric PC and the intra-hemispheric TE account for such differences. Overall, these results suggest two possible rs-fMRI markers useful to design new strategies for the management and neuropsychological rehabilitation of DOC patients.Comment: 25 pages; 4 figures; 3 tables; 1 supplementary figure; 4 supplementary tables; accepted for publication in Frontiers in Neuroinformatic

A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes this task more difficult, unless relying on some specific prior physiological hypothesis. In order to overcome these issues and to allow a more general approach, here we present a simple and novel blind-deconvolution technique for BOLD-fMRI signal. Coming to the second limitation, a fully multivariate conditioning with short and noisy data leads to computational problems due to overfitting. Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions

Nonlinear complexity analysis of brain fMRI signals in schizophrenia

Peer reviewedPublisher PD

Fuzzy approximate entropy analysis of resting state fMRI signal complexity across the adult life span

Acknowledgment The authors would like to acknowledge the work of the International Consortium for Brain Mapping (ICBM) fMRI community in creating the resting state database and making it publicly available within the framework of the 1000 Functional Connectomes project (https://www.nitrc.org/projects/fcon_1000/). M.O. Sokunbi was supported by an MRC grant G1100629.Peer reviewedPreprin

Willingness towards cognitive engagement: a preliminary study based on a behavioural entropy approach

Faced with a novel task some people enthusiastically embark in it and work with determination, while others soon lose interest and progressively reduce their efforts. Although cognitive neuroscience has explored the behavioural and neural features of apathy, the why’s and how’s of positive engagement are only starting to be understood. Stemming from the observation that the left hemisphere is commonly associated to a proactive (‘do something’) disposition, we run a preliminary study exploring the possibility that individual variability in eagerness to engage in cognitive tasks could reflect a preferred left- or right-hemisphere functioning mode. We adapted a task based on response-independent reinforcement and used entropy to characterize the degree of involvement, diversification, and predictability of responses. Entropy was higher in women, who were overall more active, less dependent on instructions, and never reduced their engagement during the task. Conversely, men showed lower entropy, took longer pauses, and became significantly less active by the end of the allotted time, renewing their efforts mainly in response to negative incentives. These findings are discussed in the light of neurobiological data on gender differences in behaviour

Willingness towards cognitive engagement: a preliminary study based on a behavioural entropy approach

Faced with a novel task some people enthusiastically embark in it and work with determination, while others soon lose interest and progressively reduce their efforts. Although cognitive neuroscience has explored the behavioural and neural features of apathy, the why’s and how’s of positive engagement are only starting to be understood. Stemming from the observation that the left hemisphere is commonly associated to a proactive (‘do something’) disposition, we run a preliminary study exploring the possibility that individual variability in eagerness to engage in cognitive tasks could reflect a preferred left- or right-hemisphere functioning mode. We adapted a task based on response-independent reinforcement and used entropy to characterize the degree of involvement, diversification, and predictability of responses. Entropy was higher in women, who were overall more active, less dependent on instructions, and never reduced their engagement during the task. Conversely, men showed lower entropy, took longer pauses, and became significantly less active by the end of the allotted time, renewing their efforts mainly in response to negative incentives. These findings are discussed in the light of neurobiological data on gender differences in behaviour

Brain information processing capacity modeling

Neurophysiological measurements suggest that human information processing is evinced by neuronal activity. However, the quantitative relationship between the activity of a brain region and its information processing capacity remains unclear. We introduce and validate a mathematical model of the information processing capacity of a brain region in terms of neuronal activity, input storage capacity, and the arrival rate of afferent information. We applied the model to fMRI data obtained from a flanker paradigm in young and old subjects. Our analysis showed that-for a given cognitive task and subject-higher information processing capacity leads to lower neuronal activity and faster responses. Crucially, processing capacity-as estimated from fMRI data-predicted task and age-related differences in reaction times, speaking to the model's predictive validity. This model offers a framework for modelling of brain dynamics in terms of information processing capacity, and may be exploited for studies of predictive coding and Bayes-optimal decision-making