Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

Reduced hippocampal volume in healthy young ApoE4 carriers: an MRI study.

The E4 allele of the ApoE gene has consistently been shown to be related to an increased risk of Alzheimer's disease (AD). The E4 allele is also associated with functional and structural grey matter (GM) changes in healthy young, middle-aged and older subjects. Here, we assess volumes of deep grey matter structures of 22 healthy younger ApoE4 carriers and 22 non-carriers (20-38 years). Volumes of the nucleus accumbens, amygdala, caudate nucleus, hippocampus, pallidum, putamen, thalamus and brain stem were calculated by FMRIB's Integrated Registration and Segmentation Tool (FIRST) algorithm. A significant drop in volume was found in the right hippocampus of ApoE4 carriers (ApoE4+) relative to non-carriers (ApoE4-), while there was a borderline significant decrease in the volume of the left hippocampus of ApoE4 carriers. The volumes of no other structures were found to be significantly affected by genotype. Atrophy has been found to be a sensitive marker of neurodegenerative changes, and our results show that within a healthy young population, the presence of the ApoE4+ carrier gene leads to volume reduction in a structure that is vitally important for memory formation. Our results suggest that the hippocampus may be particularly vulnerable to further degeneration in ApoE4 carriers as they enter middle and old age. Although volume reductions were noted bilaterally in the hippocampus, atrophy was more pronounced in the right hippocampus. This finding relates to previous work which has noted a compensatory increase in right hemisphere activity in ApoE4 carriers in response to preclinical declines in memory function. Possession of the ApoE4 allele may lead to greater predilection for right hemisphere atrophy even in healthy young subjects in their twenties

A Short Window Granger Causality Approach to Identify Brain Functional Pattern Associated with Changes of Performance Induced by Sleep Deprivation

The comprehensive effect of sleep deprivation on biological and behavioral functions largely remains unknown. There is evidence to support that human sleep must be of sufficient duration and physiological continuity to ensure neurocognitive performance while we are waking. Insufficient sleep would lead to high risk of human-error related to accidents, injuries or even fatal outcomes. However, in modern society, more and more people suffer from sleep deprivation because of the increasing social, academic or occupational demand. It is important to study the effect of sleep deprivation, not only on task performance, but also on neurocognitive functions. Recent research that has explored brain effective connectivity has demonstrated the directed inference interaction among pairs of brain areas, which may bring important insight to understand how brain works to support neurocognitive function. This research aimed to identify the brain effective connectivity pattern associated with changes of a task performance, response time, following sleep deprivation. Experiments were conducted by colleagues at Neuroergonomics Department at Jagiellonian University, Krakow, Poland. Ten healthy young women, with an average age of 23-year-old, performed visual spatial sustained-attention tasks under two conditions: (1) the rest-wakeful (RW) condition, where participants had their usual sleep and (2) the sleep-deprived (SD) condition, where participants had 3 hours less sleep than their usual sleep, for 7 nights (amounting to 21 h of sleep debt). Measures included eye tracking performance and functional magnetic resonance imaging (fMRI). In each condition, each subject*s eye-position was monitored through 13 sessions, each with 46 trials, while fMRI data was recorded. There were two task performance measures, accuracy and response time. Accuracy measured the proportion of correct responses of all trials in each session. Response time measured the average amount of milliseconds until participants gazed at the target stimuli in each session. An experimental session could be treated as a short window. By splitting long trials of fMRI data into consecutive windows, Granger causality was applied based on short trials of fMRI data. This procedure helped to calculate pairwise causal influences with respect to time-varying property in brain causal interaction. Causal influence results were then averaged across sessions to create one matrix for each participant. This matrix was averaged within each condition to formulate a model of brain effective connectivity, which also served as a basis of comparison. In conclusion, significant effect of sleep deprivation was found on response time and brain effective connectivity. In addition, the change of brain effective connectivity after sleep deprivation was linked to the change of response time. First, an analysis of variance (ANOVA) showed significant difference for response time between the RW condition and the SD condition. No significant changes for accuracy were found. A paired t-test showed that response time was significantly shorter in sleep deprivation for the visual spatial sustained-attention task. Second, Granger causality analysis demonstrated a reduction of bidirectional connectivity and an increase of directed influences from low-level brain areas to high-level brain areas after sleep deprivation. This observation suggested that sleep deprivation provoked the effective connectivity engaged in salient stimuli processing, but inhibited the effective connectivity in biasing selection of attention on task and in maintaining self-awareness in day time. Furthermore, in the SD condition, attention at the visual spatial task seemed to be driven by a bottom-up modulation mechanism. Third, a relationship was found between brain effective connectivity with response time. Decreases of Granger causal influences in two directions, from medial frontal lobe to sub cortical gray nuclei and from medial parietal lobe to sub cortical gray nuclei, were associated with shorter response time in the SD condition. Additionally, an increase of Granger causal influence from medial parietal lobe to cerebellum was associated with longer response time in the SD condition

Joint analysis of eye blinks and brain activity to investigate attentional demand during a visual search task

In several fields, the need for a joint analysis of brain activity and eye activity to investigate the association between brain mechanisms and manifest behavior has been felt. In this work, two levels of attentional demand, elicited through a conjunction search task, have been modelled in terms of eye blinks, brain activity, and brain network features. Moreover, the association between endogenous neural mechanisms underlying attentional demand and eye blinks, without imposing a time-locked structure to the analysis, has been investigated. The analysis revealed statistically significant spatial and spectral modulations of the recorded brain activity according to the different levels of attentional demand, and a significant reduction in the number of eye blinks when a higher amount of attentional investment was required. Besides, the integration of information coming from high-density electroencephalography (EEG), brain source localization, and connectivity estimation allowed us to merge spectral and causal information between brain areas, characterizing a comprehensive model of neurophysiological processes behind attentional demand. The analysis of the association between eye and brain-related parameters revealed a statistically significant high correlation (R > 0.7) of eye blink rate with anterofrontal brain activity at 8 Hz, centroparietal brain activity at 12 Hz, and a significant moderate correlation with the participation of right Intra Parietal Sulcus in alpha band (R = -0.62). Due to these findings, this work suggests the possibility of using eye blinks measured from one sensor placed on the forehead as an unobtrusive measure correlating with neural mechanisms underpinning attentional demand

Multivariate Granger causality unveils directed parietal to prefrontal cortex connectivity during task-free MRI.

While a large body of research has focused on the study of functional brain "connectivity", few investigators have focused on directionality of brain-brain interactions which, in spite of the mostly bidirectional anatomical substrates, cannot be assumed to be symmetrical. We employ a multivariate Granger Causality-based approach to estimating directed in-network interactions and quantify its advantages using extensive realistic synthetic BOLD data simulations to match Human Connectome Project (HCP) data specification. We then apply our framework to resting state functional MRI (rs-fMRI) data provided by the HCP to estimate the directed connectome of the human brain. We show that the functional interactions between parietal and prefrontal cortices commonly observed in rs-fMRI studies are not symmetrical, but consists of directional connectivity from parietal areas to prefrontal cortices rather than vice versa. These effects are localized within the same hemisphere and do not generalize to cross-hemispheric functional interactions. Our data are consistent with neurophysiological evidence that posterior parietal cortices involved in processing and integration of multi-sensory information modulate the function of more anterior prefrontal regions implicated in action control and goal-directed behaviour. The directionality of functional connectivity can provide an additional layer of information in interpreting rs-fMRI studies both in health and disease

Model-based analyses: Promises, pitfalls, and example applications to the study of cognitive control

We discuss a recent approach to investigating cognitive control, which has the potential to deal with some of the challenges inherent in this endeavour. In a model-based approach, the researcher defines a formal, computational model that performs the task at hand and whose performance matches that of a research participant. The internal variables in such a model might then be taken as proxies for latent variables computed in the brain. We discuss the potential advantages of such an approach for the study of the neural underpinnings of cognitive control and its pitfalls, and we make explicit the assumptions underlying the interpretation of data obtained using this approach

True Spatio-Temporal Detection and Estimation for Functional Magnetic Resonance Imaging.

The development of fast imaging in magnetic resonance imaging (MRI) makes it possible for researchers in various fields to investigate functional activities of the human brain with a unique combination of high spatial and temporal resolution. A significant task in functional MRI data analysis is to develop a detection statistic for activation, showing subject’s localized brain responses to pre-specified stimuli. With rare exceptions in FMRI, these detection statistics have been derived from a measurement model under two main assumptions: spatial independence and space-time separability of background noise. One of the main goals of this thesis is to remove these assumptions which have been widely used in existing approaches. This thesis makes three main contributions:(1) a development of a detection statistic based on a spatiotemporally correlated noise model without space-time separability, (2) signal and noise modeling to implement the proposed detection statistic, (3) a development of a detection statistic that is robust to signal-to-noise ratio (SNR), Rician activation detection. For the first time in FMRI, we develop a properly formulated spatiotemporal detection statistic for activation, based on a spatiotemporally correlated noise model without space-time separability. The implementation of the developed detection statistic requires joint signal and noise modeling in three or four dimensions, which is non-trivial statistical model estimation. We complete the implementation with the parametric cepstrum, allowing dramatic reduction of computations in model fitting. These two are totally new contributions to FMRI data analysis. As byproducts, a novel test procedure for space-time separability is proposed and its asymptotic power is analyzed. The developed detection statistic and conventional statistics involving spatial smoothing by Gaussian kernel are compared through a model comparison technique and asymptotic relative efficiency. Most methods in FMRI data analysis are based on magnitude voxel time courses and their approximation by a Gaussian distribution. Since the magnitude images, in fact, obey Rician distribution and the Gaussian approximation is valid under a high SNR assumption, Gaussian modeling may perform poorly when SNR is low. In this thesis, we develop a detection statistic from a Rician distributed model, allowing a robust activation detection regardless of SNR.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57634/2/nohjoonk_1.pd