A new method for detecting causality in fMRI data of cognitive processing.
Londei Alessandro,D'Ausilio Alessandro,Basso Demis,Belardinelli Marta Olivetti
Cognitive processing
One of the most important achievements in understanding the brain is that the emergence of complex behavior is guided by the activity of brain networks. To fully apply this theoretical approach fully, a method is needed to extract both the location and time course of the activities from the currently employed techniques. The spatial resolution of fMRI received great attention, and various non-conventional methods of analysis have previously been proposed for the above-named purpose. Here, we briefly outline a new approach to data analysis, in order to extract both spatial and temporal activities from fMRI recordings, as well as the pattern of causality between areas. This paper presents a completely data-driven analysis method that applies both independent components analysis (ICA) and the Granger causality test (GCT), performed in two separate steps. First, ICA is used to extract the independent functional activities. Subsequently the GCT is applied to the independent component (IC) most correlated with the stimuli, to indicate its causal relation with other ICs. We therefore propose this method as a promising data-driven tool for the detection of cognitive causal relationships in neuroimaging data.
10.1007/s10339-005-0019-5
Changes of functional and effective connectivity in smoking replenishment on deprived heavy smokers: a resting-state FMRI study.
Ding Xiaoyu,Lee Seong-Whan
PloS one
Previous researches have explored the changes of functional connectivity caused by smoking with the aid of fMRI. This study considers not only functional connectivity but also effective connectivity regarding both brain networks and brain regions by using a novel analysis framework that combines independent component analysis (ICA) and Granger causality analysis (GCA). We conducted a resting-state fMRI experiment in which twenty-one heavy smokers were scanned in two sessions of different conditions: smoking abstinence followed by smoking satiety. In our framework, group ICA was firstly adopted to obtain the spatial patterns of the default-mode network (DMN), executive-control network (ECN), and salience network (SN). Their associated time courses were analyzed using GCA, showing that the effective connectivity from SN to DMN was reduced and that from ECN/DMN to SN was enhanced after smoking replenishment. A paired t-test on ICA spatial patterns revealed functional connectivity variation in regions such as the insula, parahippocampus, precuneus, anterior cingulate cortex, supplementary motor area, and ventromedial/dorsolateral prefrontal cortex. These regions were later selected as the regions of interest (ROIs), and their effective connectivity was investigated subsequently using GCA. In smoking abstinence, the insula showed the increased effective connectivity with the other ROIs; while in smoking satiety, the parahippocampus had the enhanced inter-area effective connectivity. These results demonstrate our hypothesis that for deprived heavy smokers, smoking replenishment takes effect on both functional and effective connectivity. Moreover, our analysis framework could be applied in a range of neuroscience studies.
10.1371/journal.pone.0059331
Aberrant executive control networks and default mode network in patients with right-sided temporal lobe epilepsy: a functional and effective connectivity study.
Zhang Zhao,Zhou Xia,Liu Jinping,Qin Lu,Ye Wei,Zheng Jinou
The International journal of neuroscience
We aimed to explore functional connectivity (FC) and effective connectivity (EC) of the executive control networks (ECNs) and the default mode network (DMN) in patients with right-sided TLE (rTLE) by applying independent component analysis (ICA) and Granger causal analysis (GCA). Twenty-seven patients with rTLE and 20 healthy controls (HCs) matched for age, gender underwent resting-state functional magnetic resonance imaging and Attention Network Test (ANT). The FC analysis showed compared to HCs, patients with rTLE demonstrated reduced FC strength in the right inferior parietal gyrus (IPG) and the right middle temporal gyrus (MTG). The left superior temporal gyrus (STG) displayed reduced FC values whereas the left thalamus revealed increased FC values in rTLE. ROI-wise GCA revealed that patients with rTLE displayed increased EC from the left thalamus to the left STG, and as well as enhanced EC from the right IPG to the right MTG compared to HCs. Voxel-wise GCA showed positive EC from the left thalamus to the left insula while the right middle occipital gyrus (MOG) exhibited increased EC to the right MTG in patients. The ANT results demonstrated executive dysfunction in patients compared to HCs. The increased FC in the left thalamus showed a negative association with ECF in patients. We speculated that recurrent seizures take effect on disruption among the brain networks, and self-modulation occurs simultaneously to compensate for cognitive decline. Our findings revealed new insights on the neuropathophysiological mechanisms of rTLE.
10.1080/00207454.2019.1702545
The salience network is responsible for switching between the default mode network and the central executive network: replication from DCM.
Goulden Nia,Khusnulina Aygul,Davis Nicholas J,Bracewell Robert M,Bokde Arun L,McNulty Jonathan P,Mullins Paul G
NeuroImage
With the advent of new analysis methods in neuroimaging that involve independent component analysis (ICA) and dynamic causal modelling (DCM), investigations have focused on measuring both the activity and connectivity of specific brain networks. In this study we combined DCM with spatial ICA to investigate network switching in the brain. Using time courses determined by ICA in our dynamic causal models, we focused on the dynamics of switching between the default mode network (DMN), the network which is active when the brain is not engaging in a specific task, and the central executive network (CEN), which is active when the brain is engaging in a task requiring attention. Previous work using Granger causality methods has shown that regions of the brain which respond to the degree of subjective salience of a stimulus, the salience network, are responsible for switching between the DMN and the CEN (Sridharan et al., 2008). In this work we apply DCM to ICA time courses representing these networks in resting state data. In order to test the repeatability of our work we applied this to two independent datasets. This work confirms that the salience network drives the switching between default mode and central executive networks and that our novel technique is repeatable.
10.1016/j.neuroimage.2014.05.052
Brain resting-state networks in adolescents with high-functioning autism: Analysis of spatial connectivity and temporal neurodynamics.
Brain and behavior
Introduction:Autism spectrum disorder (ASD) is mainly characterized by functional and communication impairments as well as restrictive and repetitive behavior. The leading hypothesis for the neural basis of autism postulates globally abnormal brain connectivity, which can be assessed using functional magnetic resonance imaging (fMRI). Even in the absence of a task, the brain exhibits a high degree of functional connectivity, known as intrinsic, or resting-state, connectivity. Global default connectivity in individuals with autism versus controls is not well characterized, especially for a high-functioning young population. The aim of this study is to test whether high-functioning adolescents with ASD (HFA) have an abnormal resting-state functional connectivity. Materials and Methods:We performed spatial and temporal analyses on resting-state networks (RSNs) in 13 HFA adolescents and 13 IQ- and age-matched controls. For the spatial analysis, we used probabilistic independent component analysis (ICA) and a permutation statistical method to reveal the RSN differences between the groups. For the temporal analysis, we applied Granger causality to find differences in temporal neurodynamics. Results:Controls and HFA display very similar patterns and strengths of resting-state connectivity. We do not find any significant differences between HFA adolescents and controls in the spatial resting-state connectivity. However, in the temporal dynamics of this connectivity, we did find differences in the causal effect properties of RSNs originating in temporal and prefrontal cortices. Conclusion:The results show a difference between HFA and controls in the temporal neurodynamics from the ventral attention network to the salience-executive network: a pathway involving cognitive, executive, and emotion-related cortices. We hypothesized that this weaker dynamic pathway is due to a subtle trigger challenging the cognitive state prior to the resting state.
10.1002/brb3.878
Abnormal large-scale resting-state functional networks in anti-N-methyl-D-aspartate receptor encephalitis.
Frontiers in neuroscience
Background:Patients with anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis often experience severe symptoms. Resting-state functional MRI (rs-fMRI) has revealed widespread impairment of functional networks in patients. However, the changes in information flow remain unclear. This study aims to investigate the intrinsic functional connectivity (FC) both within and between resting-state networks (RSNs), as well as the alterations in effective connectivity (EC) between these networks. Methods:Resting-state functional MRI (rs-fMRI) data were collected from 25 patients with anti-NMDAR encephalitis and 30 healthy controls (HCs) matched for age, sex, and educational level. Changes in the intrinsic functional connectivity (FC) within and between RSNs were analyzed using independent component analysis (ICA). The functional interaction between RSNs was identified by granger causality analysis (GCA). Results:Compared to HCs, patients with anti-NMDAR encephalitis exhibited lower performance on the Wisconsin Card Sorting Test (WCST), both in terms of correct numbers and correct categories. Additionally, these patients demonstrated decreased scores on the Montreal Cognitive Assessment (MoCA). Neuroimaging studies revealed abnormal intra-FC within the default mode network (DMN), increased intra-FC within the visual network (VN) and dorsal attention network (DAN), as well as increased inter-FC between VN and the frontoparietal network (FPN). Furthermore, aberrant effective connectivity (EC) was observed among the DMN, DAN, FPN, VN, and somatomotor network (SMN). Conclusion:Patients with anti-NMDAR encephalitis displayed noticeable deficits in both memory and executive function. Notably, these patients exhibited widespread impairments in intra-FC, inter-FC, and EC. These results may help to explain the pathophysiological mechanism of anti-NMDAR encephalitis.
10.3389/fnins.2024.1455131
Testing different ICA algorithms and connectivity analyses on MS patients.
Muthuraman M,Anjum T,Droby A,Fleischer V,Reitz S,Mideksa Kg,Schmidt G,Zipp F,Groppa S
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Multiple sclerosis (MS) is a progressive neurological disorder that affects the central nervous system. Functional magnetic resonance imaging (fMRI) has been employed to track the course and disease progression in patients with MS. The two main aims of this study were to apply in a data-driven approach the independent component analysis (ICA) in the spatial domain to depict the active sources and to look at the effective connectivity between the identified spatial sources. Several ICA algorithms have been proposed for fMRI data analysis. In this study, we aimed to test two well characterized algorithms, namely, the fast ICA and the complex infomax algorithms, followed by two effective connectivity algorithms, namely, Granger causality (GC) and generalized partial directed coherence (GPDC), to illustrate the connections between the spatial sources in patients with MS. The results obtained from the ICA analyses showed the involvement of the default mode network sources. The connectivity analyses depicted significant changes between the two applied algorithms. The significance of this study was to demonstrate the robustness of the analyzed algorithms in patients with MS and to validate them before applying them on larger datasets of patients with MS.
10.1109/EMBC.2015.7319349
Investigation of relationships between fMRI brain networks in the spectral domain using ICA and Granger causality reveals distinct differences between schizophrenia patients and healthy controls.
Demirci Oguz,Stevens Michael C,Andreasen Nancy C,Michael Andrew,Liu Jingyu,White Tonya,Pearlson Godfrey D,Clark Vincent P,Calhoun Vince D
NeuroImage
Functional network connectivity (FNC) is an approach that examines the relationships between brain networks (as opposed to functional connectivity (FC) that focuses upon the relationships between single voxels). FNC may help explain the complex relationships between distributed cerebral sites in the brain and possibly provide new understanding of neurological and psychiatric disorders such as schizophrenia. In this paper, we use independent component analysis (ICA) to extract the time courses of spatially independent components and then use these in Granger causality test (GCT) to investigate causal relationships between brain activation networks. We present results using both simulations and fMRI data of 155 subjects obtained during two different tasks. Unlike previous research, causal relationships are presented over different portions of the frequency spectrum in order to differentiate high and low-frequency effects and not merged in a scalar. The results obtained using Sternberg item recognition paradigm (SIRP) and auditory oddball (AOD) tasks showed FNC differentiations between schizophrenia and control groups, and explained how the two groups differed during these tasks. During the SIRP task, secondary visual and cerebellum activation networks served as hubs and included most complex relationships between the activated regions. Secondary visual and temporal lobe activations replaced these components during the AOD task.
10.1016/j.neuroimage.2009.02.014