Causal Connectivity, Genidentity and the Ontology of Point-Eventism

There is growing evidence that the amygdala serves as the base for dealing with complex human social communication and emotion. Although amygdalar networks plays a central role in these functions, causality connectivity during the human lifespan between amygdalar subregions and their corresponding perception network (PerN), affiliation network (AffN) and aversion network (AveN) remain largely unclear. Granger causal analysis (GCA), an approach to assess directed functional interactions from time series data, was utilized to investigated effective connectivity between amygdalar subregions and their related networks as a function of age to reveal the maturation and degradation of neural circuits during development and ageing in the present study. For each human resting functional magnetic resonance imaging (fMRI) dataset, the amygdala was divided into three subareas, namely ventrolateral amygdala (VLA), medial amygdala (MedA) and dorsal amygdala (DorA), by using resting-state functional connectivity, from which the corresponding networks (PerN, AffN and AveN) were extracted. Subsequently, the GC interaction of the three amygdalar subregions and their associated networks during life were explored with a generalised linear model (GLM). We found that three causality flows significantly varied with age: the GC of VLA → PerN showed an inverted U-shaped trend with ageing; the GC of MedA→ AffN had a U-shaped trend with ageing; and the GC of DorA→ AveN decreased with ageing. Moreover, during ageing, the above GCs were significantly correlated with Social Responsiveness Scale (SRS) and State-Trait Anxiety Inventory (STAI) scores. In short, PerN, AffN and AveN associated with the amygdalar subregions separately presented different causality connectivity changes with ageing. These findings provide a strong constituent framework for normal and neurological diseases associated with social disorders to analyse the neural basis of social behaviour during life.

The representation of the flow of information between neurons in the brain based on their activity is termed the causal functional connectome. Such representation incorporates the dynamic nature of neuronal activity and causal interactions between them. In contrast to connectome, the causal functional connectome is not directly observed and needs to be inferred from neural time series. A popular statistical framework for inferring causal connectivity from observations is the directed probabilistic graphical modeling. Its common formulation is not suitable for neural time series since it was developed for variables with independent and identically distributed static samples. In this work, we propose to model and estimate the causal functional connectivity from neural time series using a novel approach that adapts directed probabilistic graphical modeling to the time series scenario. In particular, we develop the Time-Aware PC (TPC) algorithm for estimating the causal functional connectivity, which adapts the PC algorithm—a state-of-the-art method for statistical causal inference. We show that the model outcome of TPC has the properties of reflecting causality of neural interactions such as being non-parametric, exhibits the directed Markov property in a time-series setting, and is predictive of the consequence of counterfactual interventions on the time series. We demonstrate the utility of the methodology to obtain the causal functional connectome for several datasets including simulations, benchmark datasets, and recent multi-array electro-physiological recordings from the mouse visual cortex.

Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data

Mild cognitive impairment (MCI) was recognized as the prodromal stage of Alzheimer's disease (AD). Recent neuroimaging studies have shown that the cognitive and memory decline in AD and MCI patients is coupled with abnormal functions of focal brain regions and disrupted functional connectivity between distinct brain regions, as well as losses of small-world attributes. However, the causal interactions among the spatially isolated but function-related resting state networks (RSNs) are still largely unexplored in MCI patients. In this study, we first identified eight RSNs by independent components analysis (ICA) from resting state functional MRI data of 16 MCI patients and 18 age-matched healthy subjects respectively. Then, we performed a multivariate Granger causality analysis (mGCA) to evaluate the effective connectivity among the RSNs. We found that MCI patients exhibited decreased causal interactions among the RSNs in both intensity and quantity compared with normal controls. Results from mGCA indicated that the causal interactions involving the default mode network (DMN) became weaker in MCI patients, while stronger causal connectivity emerged related to the memory network and executive control network. Our findings suggested that the DMN played a less important role in MCI patients. Increased causal connectivity of the memory network and executive control network may elucidate the dysfunctional and compensatory processes in the brain networks of MCI patients. These preliminary findings may be helpful for further understanding the pathological mechanisms of MCI and provide a new clue to explore the neurophysiological mechanisms of MCI.

Recent neuroimaging studies have shown that the cognitive and memory decline in patients with Alzheimer's disease (AD) is coupled with abnormal functions of focal brain regions and disrupted functional connectivity between distinct brain regions, as well as losses in small-world attributes. However, the causal interactions among the spatially isolated, but functionally related, resting state networks (RSNs) are still largely unexplored. In this study, we first identified eight RSNs by independent components analysis from resting state functional MRI data of 18 patients with AD and 18 age-matched healthy subjects. We then performed a multivariate Granger causality analysis (mGCA) to evaluate the effective connectivity among the RSNs. We found that patients with AD exhibited decreased causal interactions among the RSNs in both intensity and quantity relative to normal controls. Results from mGCA indicated that the causal interactions involving the default mode network and auditory network were weaker in patients with AD, whereas stronger causal connectivity emerged in relation to the memory network and executive control network. Our findings suggest that the default mode network plays a less important role in patients with AD. Increased causal connectivity of the memory network and self-referential network may elucidate the dysfunctional and compensatory processes in the brain networks of patients with AD. These preliminary findings may provide a new pathway towards the determination of the neurophysiological mechanisms of AD.

The study of the functional connectivity of the human brain has been of significant interest in the research community. Causal connectivity refers to the understanding of the causal relationship between the brain regions. Estimation of causal interactions using fMRI data is a challenge for computational neuroimaging. In this work, we have estimated task-specific and disease-specific causal interactions between the brain regions using fMRI data. Granger causality is used to find the causal relationship between different brain regions. The quantification of causal configurations between the brain regions is achieved using transfer entropy. The obtained transfer entropy values are used as features for the classification of fMRI data. The performance of the proposed method has been validated on StarPlus and ADNI fMRI data. It achieves an average classification accuracy of 97.3% for cognitive state classification. The proposed technique achieves 99% accuracy for classification of Alzheimer’s disease and Control Normal subjects, 97% accuracy while classifying Alzheimer’s Disease and Mild Cognitive Impairment subjects, and 95% accuracy while classifying control normal and Mild cognitive impairment subjects. The proposed framework achieves an improvement of 2% and 3% for classification of task-specific and disease-specific fMRI data when compared to the existing methods.

The purpose of the present Ph.D. thesis is to develop and apply advanced algorithms for EEG/ERP signal analysis in order to study neurophysiological alterations associated with dyslexia. The used methods aim at a reliable analysis of synchronization, causal connectivity and complexity of EEG/ERP signals and are evaluated on both synthetic and real EEG/ERP signals of dyslexics and controls, acquired during Wechsler auditory test. First, the conventional components of ERP waveforms (peak amplitudes, latencies) are studied. Statistical analysis points out that dyslexics’ signals present significantly lower N100 amplitudes which are known to be associated with memory performance. An important parameter in dyslexia is the pre-attentive reaction time to auditory stimuli which is reflected through P50 latency and is found to be significantly prolonged at specific electrodes. Energy differentiations in time-frequency between the two groups (dyslexics and controls) are examined, enabling study of the temporal changes of ERP content. Various second order and adaptive time-frequency methods are comparatively assessed in terms of their accuracy in representing temporally changing spectra. Matching pursuit is proved to be quite effective in cross terms suppression and representation of energy peaks. Significant energy differentiations at delta (0-4 Hz), theta (5-7 Hz), alpha (8-13 Hz) and beta (14-30 Hz) frequency bands are detected, through a methodology of statistical evaluation based on normalization and multiple comparisons correction methods. The presence of significant energy differentiations may be the result of differing functional connectivity patterns between the two groups (controls, dyslexics). In order to study causal connectivity patterns, the multivariate autoregressive model is estimated using the Yule-Walker, Burg and Least Squares methods, with Burg and Least Squares proved to provide superior performance in terms of prediction error. A new measure for the estimation of direct causal interactions is proposed, which is based on the combination of the full frequency directed transfer function and the partial directed coherence, exhibiting spectral properties similar with those of the involved signals, and increased efficiency in suppressing false and non direct flows. Study of rest EEG connectivity patterns, by means of the new connectivity measure, revealed differentiations in specific activity flows between the two groups under study (controls and dyslexics). In order to calculate coupling measures of non-stationary signals, like ERP, the dynamic autoregressive model is used and its ability to accurately represent rapid changes of causal interactions is assessed using short window and adaptive Kalman filter approaches. The superiority of the Kalman filter approach in terms of the accuracy provided in the estimation of the model’s autoregressive parameters is demonstrated on both synthetic and real EEG/ERP signals. Furthermore, the predictability/complexity of EEG/ERP time-series of dyslexics versus controls was studied, using measures of spectral and approximate entropy. Spectral entropy and its modifications quantify the spectral complexity of time-series and are related with synchronization and dominance of specific frequency bands. In order to study the temporal evolution of signals’ spectral complexity, wavelet transform and optimal kernel approaches were used, and the superiority of the latter concerning its ability to discriminate the two groups was demonstrated. The representation through optimal kernel permits the adjustment to each analyzed signal, a property that is quite important in analyzing data characterized by intense variability. Finally, through approximate entropy, the presence of differentiations in predictability of EEG time series related with single electrodes or pairs of electrodes is studied, demonstrating that dyslexics’ signals are characterized by more predictable patterns.

Although numerous clinical neuroimaging studies have demonstrated that there are functional abnormalities of motor-related regions in patients with Parkinson's disease (PD) by resting-state functional magnetic resonance imaging (fMRI), little studies have explored the causal interactions within these motor-related regions. The present study aimed to examine Granger causality connectivity patterns within motor-related regions in PD patients. Resting-state fMRI was conducted to investigate the causal connectivity differences within motor-related regions between 17 PD patients and 17 matched healthy controls. Subsequently, the relationship between the Unified Parkinson's Disease Rating Scale scores and causal connectivity values within motor-related regions was examined in PD patients. An increased causal connectivity from the left premotor cortex (PMC) to right primary motor cortex (M1) was found in PD patients compared with that of healthy controls. Also, increased causal flow from the PMC to M1 was negatively correlated with motor scores. PD patients have abnormal causal connectivity in specific motor-related regions, which may reflect a compensatory role of motor deficits in PD patients.

According to an increasing number of authors, the best, if not the only, argument in favour of physicalism is the so‐called ‘overdetermination argument’. This argument, if sound, establishes that all the entities that enter into causal interactions with the physical world are physical. One key premise in the overdetermination argument is the principle of the causal closure of the physical world, said to be supported by contemporary physics. In this paper, I examine various ways in which physics may support the principle, either as a methodological guide or as depending on some other laws and principles of physics.

The current study investigated topographies and causality profiles of left hippocampus-related brain systems identified with resting state functional correlations in mild TBI as well as healthy individuals. We found a set of significantly connected brain regions (nodes) involved in the left hippocampal intrinsic network and altered causality profiles among the identified nodes. Functional connectivity analysis showed that the hippocampal network concerning episodic memory in mild TBI survivors (mTBI) became increasingly larger than that in normal controls, especially the medial frontal gyrus (MeFG), middle frontal gyrus (MFG), and temporo-parietal conjunction area. Granger causality analysis (GCA) illustrated increased bilateral causal connectivity and ipsilateral interaction in the right hemisphere, which was in contrast to intensive ipsilateral causal connection in the left hemisphere in normal controls. We postulated that the dysfunctioned hippocampus and ineffective compensation strategy resulted in a weakening episodic memory in mTBI patients.

Differential dose responses of transcranial focused ultrasound at brain regions indicate causal interactions

(1) Background: Alterations in brain functional connectivity (FC) precede clinical symptoms of Alzheimer’s disease (AD) by decades, presenting opportunities for early diagnosis. However, conventional FC analyses measure correlations between brain regions and do not provide insights into directional, causal interactions. Causal functional connectivity (CFC), which infers directed interactions between regions, addresses this limitation. This study aims to identify disrupted CFC networks in AD compared to cognitively normal (CN) individuals. (2) Methods: The recently developed Time-aware PC (TPC) algorithm was employed to infer directed CFC from functional magnetic resonance imaging (fMRI) data. These results were compared with traditional correlation-based FC obtained via sparse partial correlation. Network-based Statistics (NBS) for directed networks was used to identify altered CFC sub-networks, with corrections for multiple comparisons applied at the 5% significance level. (3) Results: Key causal networks, including the inferior frontal gyrus, superior temporal gyrus, middle temporal gyrus, and cerebellum, showed significantly reduced strength in AD compared to CN (p = 0.0299; NBS corrected). Instead of detecting disruptions at the level of individual edges, this study identifies network-level alterations, revealing systemic disruptions in brain connectivity. (4) Conclusions: This study demonstrates the utility of CFC analysis in uncovering network-level disruptions in AD. The identified disrupted networks align with published medical literature and provide a framework for future studies with larger datasets.

ObjectiveWhile effective connectivity (EC, causal interaction) between brain areas has been investigated in chronic users of cocaine as they view cocaine pictures cues, no study has examined EC while they take part in a resting-state scan. This resting-state fMRI study aims to investigate the causal interaction among brain areas in the mesocorticolimbic system (MCLS), which is involved in reward and motivation, in cocaine users (vs. controls).MethodTwenty cocaine users and 17 healthy controls finished a structural and a resting-state scan. Mean voxel-based time series data were obtained from brain regions of interest (ROIs) from the MCLS, and were input into a Bayesian search algorithm called IMaGES.ResultsThe causal interaction pattern was different between the two groups. The feed-forward pattern found in cocaine smokers, between 7 ROIs of the MCLS during resting-state [ventral tegmental area (VTA)→hippocampus (HIPP)→ventral striatum (VenStri)→orbital frontal cortex (OFC), medial frontal cortex (MFC), anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC)], was absent in controls. That is, the subcortical VenStri area had a causal influence on four cortical brain areas only in cocaine users.ConclusionsDuring the resting-state scan, the VTA of cocaine smokers abstinent for at least 72 hours, but not controls, begins causal connections to limbic, midbrain, and frontal regions in the MCLS in a feed-forward manner. Following replication, further studies may assess if changes over time in EC during resting-state predict cocaine treatment efficacy and outcome.

Although rivers are inherently dynamic systems that are susceptible to change, human impact on rivers in the last century is considered to have been so significant that it has caused an unprecedented intensity of geomorphological change in river channels and floodplains. As these changes often lead to deterioration of ecological conditions as well as increased flood risks, the approach to river management has changed over the past twenty years. There is an increasing emphasis on a holistic approach based on the understanding of river system processes, for which studies of geomorphological change in rivers represent a very important source of information. The aim of this review is to present the basic methods used in studies of geomorphological change in rivers, including the spatio-hierarchical delineation of the river system, data sources, and the most commonly analysed geomorphological characteristics and factors of change. The results of previous research are presented for the period of the last 150 years. The most important geomorphological changes include channel narrowing, incision, and reduction in the complexity of fluvial landforms and processes due to channelization and the construction of numerous barriers that disrupt the connectivity in water flow and sediment transport. Explaining the cumulative impacts and predicting future changes are the major research challenges. These challenges are related to the complexity of the river system, i.e. a large number of causal factors, connections, and interactions in the river system, and to the nonlinearity of the evolutionary trajectory of changes in rivers.

The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach tointroduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.