A Bayesian approach to modeling dynamic effective connectivity with fMRI data

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.

Optimal stimulus scheduling for active estimation of evoked brain networks

OBJECTIVE

We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation.

APPROACH

We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling.

MAIN RESULTS

By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples.

SIGNIFICANCE

The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.

Bayesian Models for fMRI Data Analysis

Functional magnetic resonance imaging (fMRI), a noninvasive neuroimaging method that provides an indirect measure of neuronal activity by detecting blood flow changes, has experienced an explosive growth in the past years. Statistical methods play a crucial role in understanding and analyzing fMRI data. Bayesian approaches, in particular, have shown great promise in applications. A remarkable feature of fully Bayesian approaches is that they allow a flexible modeling of spatial and temporal correlations in the data. This paper provides a review of the most relevant models developed in recent years. We divide methods according to the objective of the analysis. We start from spatio-temporal models for fMRI data that detect task-related activation patterns. We then address the very important problem of estimating brain connectivity. We also touch upon methods that focus on making predictions of an individual's brain activity or a clinical or behavioral response. We conclude with a discussion of recent integrative models that aim at combining fMRI data with other imaging modalities, such as EEG/MEG and DTI data, measured on the same subjects. We also briefly discuss the emerging field of imaging genetics.

Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis

Recent work on both task-induced and resting-state functional magnetic resonance imaging (fMRI) data suggests that functional connectivity may fluctuate, rather than being stationary during an entire scan. Most dynamic studies are based on second-order statistics between fMRI time series or time courses derived from blind source separation, e.g., independent component analysis (ICA), to investigate changes of temporal interactions among brain regions. However, fluctuations related to spatial components over time are of interest as well. In this paper, we examine higher-order statistical dependence between pairs of spatial components, which we define as spatial functional network connectivity (sFNC), and changes of sFNC across a resting-state scan. We extract time-varying components from healthy controls and patients with schizophrenia to represent brain networks using independent vector analysis (IVA), which is an extension of ICA to multiple data sets and enables one to capture spatial variations. Based on mutual information among IVA components, we perform statistical analysis and Markov modeling to quantify the changes in spatial connectivity. Our experimental results suggest significantly more fluctuations in patient group and show that patients with schizophrenia have more variable patterns of spatial concordance primarily between the frontoparietal, cerebellar and temporal lobe regions. This study extends upon earlier studies showing temporal connectivity differences in similar areas on average by providing evidence that the dynamic spatial interplay between these regions is also impacted by schizophrenia.

A review of functional magnetic resonance imaging for Brainnetome

The functional brain network using blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) has revealed the potentials for probing brain architecture, as well as for identifying clinical biomarkers for brain diseases. In the general context of Brainnetome, this review focuses on the development of approaches for modeling and analyzing functional brain networks with BOLD fMRI. The prospects for these approaches are also discussed.

Neural correlates of cognitive ability

The challenge to neuroscientists working on intelligence is to discover what neural structures and mechanisms are at the basis of such a complex and variegated capability. Several psychologists agree on the view that behavioral flexibility is a good measure of intelligence, resulting in the appearance of novel solutions that are not part of the animal's normal behavior. This article tries to indicate how the supposed differences in intelligence between species can be related to brain properties and suggests that the best neural indicators may be the ones that convey more information processing capacity to the brain, i.e., high conduction velocity of fibers and small distances between neurons, associated with a high number of neurons and an adequate level of connectivity. The neural bases of human intelligence have been investigated by means of anatomical, neurophysiological, and neuropsychological methods. These investigations have led to two important findings that are briefly discussed: the parietofrontal integration theory of intelligence, which assumes that a distributed network of cortical areas having its main nodes in the frontal and parietal lobes constitutes a probable substrate for smart behavior, and the neural efficiency hypothesis, according to which intelligent people process information more efficiently, showing weaker neural activations in a smaller number of areas than less intelligent people.

Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model

Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one to tens of milliseconds. In addition, neurons produce bursts of spikes spanning multiple time bins. To address these issues, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance, we applied these extensions of TE to a spiking cortical network model (Izhikevich, 2006) with known connectivity and a range of synaptic delays. For comparison, we also investigated single-delay TE, at a message length of one bin (D1TE), and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE, this dramatically improved to 73% of true connections. In addition, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE, when used on currently available desktop computers, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons.

A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia

OBJECTIVE

In this paper, we develop a dynamic functional network connectivity (FNC) analysis approach using correlations between windowed time-courses of different brain networks (components) estimated via spatial independent component analysis (sICA). We apply the developed method to fMRI data to evaluate it and to study task-modulation of functional connections.

MATERIALS AND METHODS

We study the theoretical basis of the approach, perform a simulation analysis and apply it to fMRI data from schizophrenia patients (SP) and healthy controls (HC). Analyses on the fMRI data include: (a) group sICA to determine regions of significant task-related activity, (b) static and dynamic FNC analysis among these networks by using maximal lagged-correlation and time-frequency analysis, and (c) HC-SP group differences in functional network connections and in task-modulation of these connections.

RESULTS

This new approach enables an assessment of task-modulation of connectivity and identifies meaningful inter-component linkages and differences between the two study groups during performance of an auditory oddball task (AOT). The static FNC results revealed that connectivities involving medial visual-frontal, medial temporal-medial visual, parietal-medial temporal, parietal-medial visual and medial temporal-anterior temporal were significantly greater in HC, whereas only the right lateral fronto-parietal (RLFP)-orbitofrontal connection was significantly greater in SP. The dynamic FNC revealed that task-modulation of motor-frontal, RLFP-medial temporal and posterior default mode (pDM)-parietal connections were significantly greater in SP, and task modulation of orbitofrontal-pDM and medial temporal-frontal connections were significantly greater in HC (all P < 0.05).

CONCLUSION

The task-modulation of dynamic FNC provided findings and differences between the two groups that are consistent with the existing hypothesis that schizophrenia patients show less segregated motor, sensory, cognitive functions and less segregated default mode network activity when engaged with a task. Dynamic FNC, based on sICA, provided additional results which are different than, but complementary to, those of static FNC. For example, it revealed dynamic changes in default mode network connectivities with other regions which were significantly different in schizophrenia in terms of task-modulation, findings which were not possible to discover by static FNC.

A stimulus-locked vector autoregressive model for slow event-related fMRI designs

Neuroscientists have become increasingly interested in exploring dynamic relationships among brain regions. Such a relationship, when directed from one region toward another, is denoted by "effective connectivity." An fMRI experimental paradigm which is well-suited for examination of effective connectivity is the slow event-related design. This design presents stimuli at sufficient temporal spacing for determining within-trial trajectories of BOLD activation, allowing for the analysis of stimulus-locked temporal covariation of brain responses in multiple regions. This may be especially important for emotional stimuli processing, which can evolve over the course of several seconds, if not longer. However, while several methods have been devised for determining fMRI effective connectivity, few are adapted to event-related designs, which include nonstationary BOLD responses and multiple levels of nesting. We propose a model tailored for exploring effective connectivity of multiple brain regions in event-related fMRI designs--a semi-parametric adaptation of vector autoregressive (VAR) models, termed "stimulus-locked VAR" (SloVAR). Connectivity coefficients vary as a function of time relative to stimulus onset, are regularized via basis expansions, and vary randomly across subjects. SloVAR obtains flexible, data-driven estimates of effective connectivity and hence is useful for building connectivity models when prior information on dynamic regional relationships is sparse. Indices derived from the coefficient estimates can also be used to relate effective connectivity estimates to behavioral or clinical measures. We demonstrate the SloVAR model on a sample of clinically depressed and normal controls, showing that early but not late cortico-amygdala connectivity appears crucial to emotional control and early but not late cortico-cortico connectivity predicts depression severity in the depressed group, relationships that would have been missed in a more traditional VAR analysis.

Discrete dynamic Bayesian network analysis of fMRI data

We examine the efficacy of using discrete Dynamic Bayesian Networks (dDBNs), a data-driven modeling technique employed in machine learning, to identify functional correlations among neuroanatomical regions of interest. Unlike many neuroimaging analysis techniques, this method is not limited by linear and/or Gaussian noise assumptions. It achieves this by modeling the time series of neuroanatomical regions as discrete, as opposed to continuous, random variables with multinomial distributions. We demonstrated this method using an fMRI dataset collected from healthy and demented elderly subjects (Buckner, et al., 2000: J Cogn Neurosci 12:24-34) and identify correlates based on a diagnosis of dementia. The results are validated in three ways. First, the elicited correlates are shown to be robust over leave-one-out cross-validation and, via a Fourier bootstrapping method, that they were not likely due to random chance. Second, the dDBNs identified correlates that would be expected given the experimental paradigm. Third, the dDBN's ability to predict dementia is competitive with two commonly employed machine-learning classifiers: the support vector machine and the Gaussian naive Bayesian network. We also verify that the dDBN selects correlates based on non-linear criteria. Finally, we provide a brief analysis of the correlates elicited from Buckner et al.'s data that suggests that demented elderly subjects have reduced involvement of entorhinal and occipital cortex and greater involvement of the parietal lobe and amygdala in brain activity compared with healthy elderly (as measured via functional correlations among BOLD measurements). Limitations and extensions to the dDBN method are discussed.

Directed transfer function analysis of fMRI data to investigate network dynamics

In this work, we have adapted the directed transfer function (DTF) to fMRI for the analysis of cortical network dynamics. While modern fMRI sequences are capable of sampling at second or sub-second rates, the underlying hemodynamic response limits the true temporal resolution to the order of 6-12 seconds. Therefore, DTF analysis of fMRI is appropriate for characterizing dynamics in brain response which evolves more slowly than the fMRI response, such as those during learning, fatigue and habituation. In such cases, the response to repeated trials will change with time and a summary measure from each trial can be used as input to the DTF analysis because these summary measures are of appropriate sampling rates and are not affected by the sluggishness of the hemodynamic response. As an example, we investigated the dynamic effects of muscle fatigue on the motor network. Specifically, DTF was used as a multivariate measure of the strength and direction of information flow between the various nodes of the network. We found that the primary motor area had a causal influence on the supplementary motor area, pre-motor area and cerebellum, and this influence initially increased with time and diminished towards the end of the experiment, probably as a result of fatigue.

Combining structural and functional neuroimaging data for studying brain connectivity: a review

Different brain areas are thought to be integrated into large-scale networks to support cognitive function. Recent approaches for investigating structural organization and functional coordination within these networks involve measures of connectivity among brain areas. We review studies combining in vivo structural and functional brain connectivity data, where (a) structural connectivity analysis, mostly based on diffusion tensor imaging is paired with voxel-wise analysis of functional neuroimaging data or (b) the measurement of functional connectivity based on covariance analysis is guided/aided by structural connectivity data. These studies provide insights into the relationships between brain structure and function. Promising trends involve (a) studies where both functional and anatomical connectivity data are collected using high-resolution neuroimaging methods and (b) the development of advanced quantitative models of integration.