Effective Connectivity Analysis Research Articles

Perceptual learning of tone patterns changes the effective connectivity between Heschl's gyrus and planum temporale.

Learning of complex auditory sequences such as music can be thought of as optimizing an internal model of regularities through unpredicted events (or “prediction errors”). We used dynamic causal modeling (DCM) and parametric empirical Bayes on functional magnetic resonance imaging (fMRI) data to identify modulation of effective brain connectivity that takes place during perceptual learning of complex tone patterns. Our approach differs from previous studies in two aspects. First, we used a complex oddball paradigm based on tone patterns as opposed to simple deviant tones. Second, the use of fMRI allowed us to identify cortical regions with high spatial accuracy. These regions served as empirical regions‐of‐interest for the analysis of effective connectivity. Deviant patterns induced an increased blood oxygenation level‐dependent response, compared to standards, in early auditory (Heschl's gyrus [HG]) and association auditory areas (planum temporale [PT]) bilaterally. Within this network, we found a left‐lateralized increase in feedforward connectivity from HG to PT during deviant responses and an increase in excitation within left HG. In contrast to previous findings, we did not find frontal activity, nor did we find modulations of backward connections in response to oddball sounds. Our results suggest that complex auditory prediction errors are encoded by changes in feedforward and intrinsic connections, confined to superior temporal gyrus.

Brain circuits signaling the absence of emotion in body language

Adaptive social behavior and mental well-being depend on not only recognizing emotional expressions but also, inferring the absence of emotion. While the neurobiology underwriting the perception of emotions is well studied, the mechanisms for detecting a lack of emotional content in social signals remain largely unknown. Here, using cutting-edge analyses of effective brain connectivity, we uncover the brain networks differentiating neutral and emotional body language. The data indicate greater activation of the right amygdala and midline cerebellar vermis to nonemotional as opposed to emotional body language. Most important, the effective connectivity between the amygdala and insula predicts people's ability to recognize the absence of emotion. These conclusions extend substantially current concepts of emotion perception by suggesting engagement of limbic effective connectivity in recognizing the lack of emotion in body language reading. Furthermore, the outcome may advance the understanding of overly emotional interpretation of social signals in depression or schizophrenia by providing the missing link between body language reading and limbic pathways. The study thus opens an avenue for multidisciplinary research on social cognition and the underlying cerebrocerebellar networks, ranging from animal models to patients with neuropsychiatric conditions.

Disrupted pathways from frontal-parietal cortex to basal ganglia and cerebellum in patients with unmedicated obsessive compulsive disorder as observed by whole-brain resting-state effective connectivity analysis – a small sample pilot study

To date, a systematic characterization of abnormalities in resting-state effective connectivity (rsEC) in obsessive-compulsive disorder (OCD) is lacking. The present study aimed to systematically characterize whole-brain rsEC in OCD patients as compared to healthy controls. Using resting-state fMRI data of 50 unmedicated patients with OCD and 50 healthy participants, we constructed whole-brain rsEC networks using Granger causality analysis followed by univariate and multivariate comparisons between patients and controls. Similar analyses were performed for resting-state functional connectivity (rsFC) networks to examine how rsFC and rsEC differentially capture abnormal brain connectivity in OCD. Univariate comparisons identified 10 rsEC networks that were significantly disrupted in patients, and which were mainly associated with frontal-parietal cortex, basal ganglia, and cerebellum. Conversely, abnormal rsFC networks were widely distributed throughout the whole brain. Multivariate pattern analysis revealed a classification accuracy as high as 80.5% for distinguishing patients from controls using combined whole-brain rsEC and rsFC. The results of the present study suggest disrupted communication of information from frontal-parietal cortex to basal ganglia and cerebellum in OCD patients. Using combined whole-brain rsEC and rsFC, multivariate pattern analysis revealed a classification accuracy as high as 80.5% for distinguishing patients from controls. The alterations observed in OCD patients could aid in identifying treatment mechanisms for OCD.

Reduced effective connectivity between right parietal and inferior frontal cortex during audiospatial perception in neglect patients with a right-hemisphere lesion

A lesion to the right hemisphere of the brain in humans commonly leads to perceptual neglect of the left side of the sensorium. The clinical observation that lesions to disparate cortical and subcortical areas converge upon similar behavioural symptoms points to neglect as a dysconnection syndrome that may result from the disruption of a distributed network, rather than aberrant computations in any particular brain region. To test this hypothesis, we used Bayesian analysis of effective connectivity based on electroencephalographic recordings in ten patients (6 male, 4 female; age range 41–68) with left-sided neglect following a right-hemisphere lesion. In line with previous research, age-matched healthy controls showed a contralateral increase in connection strength between parietal and frontal cortex with respect to the laterality of audiospatial oddball stimuli. Neglect patients, however, showed a dysconnection between parietal and frontal cortex in the right hemisphere when oddballs appeared on their left side, but preserved connectivity in the left hemisphere when stimuli appeared on their right. This preserved fronto-parietal connectivity was associated with lower neglect severity. Moreover, we saw ipsilateral fronto-temporal connectivity increases for oddballs appearing on the neglected side, which might be a compensatory mechanism for residual left side awareness. No group differences were found in intrinsic (within-region) connectivity. While further validation is required in a bigger sample, our findings are in keeping with the idea that neglect results from the disruption of a distributed network, rather than a lesion to any single brain region. Significance statementLesions to the right hemisphere of the brain commonly lead to neglect syndrome, characterized by perceptual deficits where patients are unaware of the left side of their body and environment. Using analysis of non-invasive electrophysiological recordings, we provide evidence that patients with left-sided neglect have reduced connectivity between the right parietal and frontal cortex during audiospatial stimuli, but preserved connectivity between regions in the non-lesioned left hemisphere. Moreover, for these intact connections we observed an ipsilateral fronto-temporal increase in connectivity during oddballs appearing on the neglected side, which might be a compensatory mechanism for residual perception. Crucially, we found that patients with more severe neglect symptoms had reduced connectivity between parietal and frontal cortex in the left hemisphere. This suggests that neglect may be caused by the disruption of a distributed network in the brain, rather than a lesion to any particular brain region.

Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach.

Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN-2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.

Effective connectivity predicts cognitive empathy in cocaine addiction: a spectral dynamic causal modeling study.

Social cognition plays a crucial role in the development and treatment of cocaine dependence. However, studies investigating social cognition, such as empathy and its underlying neural basis, are lacking. To explore the neural interactions among reward and memory circuits, we applied effective connectivity analysis on resting-state fMRI data collected from cocaine-dependent subjects. The relationship between effective connectivity within these two important circuits and empathy ability - evaluated with the Interpersonal Reactivity Index (IRI) - was assessed by machine learning algorithm using multivariate regression analysis. In accordance with the neurocircuitry disruptions of cocaine addiction, the results showed that cocaine-dependent subjects relative to healthy controls had altered resting state effective connectivity between parts of the memory and reward systems. Furthermore, effective connectivity between the memory and reward system could predict the fantasy empathy (FE) subscale scores in cocaine dependence. Overall, our findings provide further evidence for the neural substrates of social cognition in cocaine-dependent patients. These new insights could be useful for the development of new treatment programs for this substance dependency disorder.

Cingulate cortex function and multi-modal connectivity mapped using intracranial stimulation

The cingulate cortex is part of the limbic system. Its function and connectivity are organized in a rostro-caudal and ventral-dorsal manner which was addressed by various other studies using rather coarse cortical parcellations. In this study, we aim at describing its function and connectivity using invasive recordings from patients explored for focal drug-resistant epilepsy.We included patients that underwent stereo-electroencephalographic recordings using intracranial electrodes in the University Emergency Hospital Bucharest between 2012 and 2019. We reviewed all high frequency stimulations (50 ​Hz) performed for functional mapping of the cingulate cortex. We used two methods to characterize brain connectivity. Effective connectivity was inferred based on the analysis of cortico-cortical potentials (CCEPs) evoked by single pulse electrical stimulation (SPES) (15 ​s inter-pulse interval). Functional connectivity was estimated using the non-linear regression method applied to 60 ​s spontaneous electrical brain signal intervals. The effective (stimulation-evoked) and functional (non-evoked) connectivity analyses highlight brain networks in a different way. While non-evoked connectivity evidences areas having related activity, often in close proximity to each other, evoked connectivity highlights spatially extended networks. To highlight in a comprehensive way the cingulate cortex’s network, we have performed a bi-modal connectivity analysis that combines the resting-state broadband h2 non-linear correlation with cortico-cortical evoked potentials. We co-registered the patient’s anatomy with the fsaverage FreeSurfer template to perform the automatic labeling based on HCP-MMP parcellation. At a group level, connectivity was estimated by averaging responses over stimulated/recorded or recorded sites in each pair of parcels. Finally, for multiple regions that evoked a clinical response during high frequency stimulation, we combined the connectivity of individual pairs using maximum intensity projection.Connectivity was assessed by applying SPES on 2094 contact pairs and recording CCEPs on 3580 contacts out of 8582 contacts of 660 electrodes implanted in 47 patients. Clinical responses elicited by high frequency stimulations in 107 sites (pairs of contacts) located in the cingulate cortex were divided in 10 groups: affective, motor behavior, motor elementary, versive, speech, vestibular, autonomic, somatosensory, visual and changes in body perception. Anterior cingulate cortex was shown to be connected to the mesial temporal, orbitofrontal and prefrontal cortex. In the middle cingulate cortex, we located affective, motor behavior in the anterior region, and elementary motor and somatosensory in the posterior part. This region is connected to the prefrontal, premotor and primary motor network. Finally, the posterior cingulate was shown to be connected with the visual areas, mesial and lateral parietal and temporal cortex.

Spatiotemporal dynamics of working memory under the influence of emotions based on EEG

Objective. Previous studies have reported that working memory (WM) may be affected by emotions and that the effect may exist in different stages of WM. However, at present it remains controversial whether emotions inhibit or facilitate WM, and how the mechanism of dynamic information transmission in the brain during WM is affected by emotions. Approach. In this study, we used a video database to induce three emotions (negative, neutral, and positive) and adopted a change detection paradigm based on electroencephalography. Event-related potential (ERP) analysis, event-related spectral perturbation analysis, source location analysis based on the dipole localization method and the distributed source localization method, and effective connectivity analysis were performed. Main results. Both behavioral and ERP results suggest that positive emotions have no significant effect on WM capacity, while negative emotions could facilitate WM capacity. Furthermore, the effective connectivity results based on two source location methods suggest that the long-range connectivity between the frontal and posterior areas can reflect the influence of positive and negative emotions on the WM network, in which the connectivity under the positive emotion condition occurs in the earlier period of WM maintenance, while the connectivity under the negative emotion condition occurs in the later period of WM maintenance. Significance. The consistency of the behavioral, ERP, and effective connectivity results suggests that under the negative emotion condition, the top-down attention modulation between the frontoparietal area and posterior area could promote the most relevant information storage during WM maintenance.

Selective auditory attention detection based on effective connectivity by single-trial EEG

Objective. Focusing attention on one speaker in an environment with lots of speakers is one of the important abilities of the human auditory system. The temporal dynamics of the attention process and how the brain precisely performs this task are yet unknown. This paper proposes a new method for the selective auditory attention detection (SAAD) from single-trial EEG signals using the brain effective connectivity and complex network analysis for two groups of listeners attending to the left or right ear. Approach. Here, the connectivity matrices of all subjects obtained from the Granger causality method are used to extract different features. Then, by employing the processes of feature selection and optimization, an optimized feature set is determined for the train of a classifier. Main results. Among different measures of brain connectivity (i.e. segregation, integration, and centrality), the evaluation results show that the optimized feature set obtained by the combination of the centrality measures contain the most discriminative features for the classification process. The proposed SAAD method as compared with state-of-the-art attention detection approaches from the literature yields the best performance in terms of various measures. Significance. The new SAAD approach is advantageous, in the sense that the detection of attention is performed from single-trial EEG signals of each subject, without reconstructing the speech stimuli. This means that the proposed method could be employed for real-time applications such as smart hearing aid devices or brain-computer interface (BCI) systems.

Resting-state networks in adolescents with poor behavior regulation. An analysis of effective cortical connectivity in eeg source space

Resting-state networks in adolescents with poor behavior regulation. An analysis of effective cortical connectivity in eeg source space

Reconstruction of a directed acyclic graph with intervention.

Identification of causal relations among variables is central to many scientific investigations, as in regulatory network analysis of gene interactions and brain network analysis of effective connectivity of causal relations between regions of interest. Statistically, causal relations are often modeled by a directed acyclic graph (DAG), and hence that reconstruction of a DAG's structure leads to the discovery of causal relations. Yet, reconstruction of a DAG's structure from observational data is impossible because a DAG Gaussian model is usually not identifiable with unequal error variances. In this article, we reconstruct a DAG's structure with the help of interventional data. Particularly, we construct a constrained likelihood to regularize intervention in addition to adjacency matrices to identify a DAG's structure, subject to an error variance constraint to further reinforce the model identifiability. Theoretically, we show that the proposed constrained likelihood leads to identifiable models, thus correct reconstruction of a DAG's structure through parameter estimation even with unequal error variances. Computationally, we design efficient algorithms for the proposed method. In simulations, we show that the proposed method enables to produce a higher accuracy of reconstruction with the help of interventional observations.

Ventral stream hierarchy underlying perceptual organization in adolescents with autism.

Object recognition relies on a hierarchically organized ventral visual stream, with both bottom-up and top-down processes. Here, we aimed at investigating the neural underpinnings of perceptual organization along the ventral visual stream in Autism Spectrum Disorders (ASD), and at determining whether this would be associated with decreased top-down processing in ASD. Nineteen typically developing (TD) adolescents and sixteen adolescents with ASD participated in an fMRI study where they had to detect visual objects. Five conditions displayed Gabor patterns (defined by texture and/or contour) with increasing levels of perceptual organization. In each condition, both groups showed similar abilities. In line with the expected cortical hierarchy, brain activity patterns revealed a progressive involvement of regions, from low-level occipital regions to higher-level frontal regions, when stimuli became more and more organized. The brain patterns were generally similar in both groups, but the ASD group showed greater activation than TD participants in the middle occipital gyrus and lateral occipital complex when perceiving fully organized everyday objects. Effective connectivity analyses suggested that top-down functional connections between the lower levels of the cortical hierarchy were less influenced by the meaning carried by the stimuli in the ASD group than in the TD group. We hypothesize that adolescents with ASD may have been less influenced by top-down processing when perceiving recognizable objects.

Dynamic effective connectivity

Metastability is a key source of itinerant dynamics in the brain; namely, spontaneous spatiotemporal reorganization of neuronal activity. This itinerancy has been the focus of numerous dynamic functional connectivity (DFC) analyses – developed to characterize the formation and dissolution of distributed functional patterns over time, using resting state fMRI. However, aside from technical and practical controversies, these approaches cannot recover the neuronal mechanisms that underwrite itinerant (e.g., metastable) dynamics—due to their descriptive, model-free nature. We argue that effective connectivity (EC) analyses are more apt for investigating the neuronal basis of metastability. To this end, we appeal to biologically-grounded models (i.e., dynamic causal modelling, DCM) and dynamical systems theory (i.e., heteroclinic sequential dynamics) to create a probabilistic, generative model of haemodynamic fluctuations. This model generates trajectories in the parametric space of EC modes (i.e., states of connectivity) that characterize functional brain architectures. In brief, it extends an established spectral DCM, to generate functional connectivity data features that change over time. This foundational paper tries to establish the model’s face validity by simulating non-stationary fMRI time series and recovering key model parameters (i.e., transition probabilities among connectivity states and the parametric nature of these states) using variational Bayes. These data are further characterized using Bayesian model comparison (within and between subjects). Finally, we consider practical issues that attend applications and extensions of this scheme. Importantly, the scheme operates within a generic Bayesian framework – that can be adapted to study metastability and itinerant dynamics in any non-stationary time series.

Disrupted pathways from limbic areas to thalamus in schizophrenia highlighted by whole-brain resting-state effective connectivity analysis

BackgroundNumerous neuroimaging studies have revealed that schizophrenia was characterized by wide-spread dysconnection among brain regions during rest measured by functional connectivity (FC). In contrast with FC, effective connectivity (EC) provides information about directionality of brain connections and is thus valuable in mechanistic investigation of schizophrenic brain. However, a systematic characterization of whole-brain resting-state EC (rsEC) and how it captures different information compared with resting-state FC (rsFC) in schizophrenia are still lacking. AimsTo systematically characterize the abnormalities of rsEC, compared with rsFC, in schizophrenia, and to test its discriminative power as a neuroimaging marker for schizophrenia diagnosis. MethodWhole-brain rsEC and rsFC networks were constructed using resting-state fMRI data and compared between 103 patients with schizophrenia and 110 healthy participants. Pattern classifications between patients and controls based on whole-brain rsEC and rsFC were further performed using multivariate pattern analysis. ResultsWe identified 17 rsEC significantly disrupted (mostly decreased) in patients, among which all were associated with the thalamus and 15 were from limbic areas (including hippocampus, parahippocampus and cingulate cortex) to the thalamus. In contrast, abnormal rsFC were widely distributed in the whole brain. The classification accuracies for distinguishing patients and controls using whole-brain rsEC and rsFC patterns were 78.6% and 82.7%, respectively, and was further improved to 84.5% when combining rsEC and rsFC. ConclusionsSchizophrenia is featured by disrupted ‘limbic areas-to-thalamus’ rsEC, in contrast with diffusively altered rsFC. Moreover, both rsEC and rsFC contain valuable and complementary information which may be used as diagnostic markers for schizophrenia.

A Data-Driven Measure of Effective Connectivity Based on Renyi's α-Entropy.

Transfer entropy (TE) is a model-free effective connectivity measure based on information theory. It has been increasingly used in neuroscience because of its ability to detect unknown non-linear interactions, which makes it well suited for exploratory brain effective connectivity analyses. Like all information theoretic quantities, TE is defined regarding the probability distributions of the system under study, which in practice are unknown and must be estimated from data. Commonly used methods for TE estimation rely on a local approximation of the probability distributions from nearest neighbor distances, or on symbolization schemes that then allow the probabilities to be estimated from the symbols' relative frequencies. However, probability estimation is a challenging problem, and avoiding this intermediate step in TE computation is desirable. In this work, we propose a novel TE estimator using functionals defined on positive definite and infinitely divisible kernels matrices that approximate Renyi's entropy measures of order α. Our data-driven approach estimates TE directly from data, sidestepping the need for probability distribution estimation. Also, the proposed estimator encompasses the well-known definition of TE as a sum of Shannon entropies in the limiting case when α → 1. We tested our proposal on a simulation framework consisting of two linear models, based on autoregressive approaches and a linear coupling function, respectively, and on the public electroencephalogram (EEG) database BCI Competition IV, obtained under a motor imagery paradigm. For the synthetic data, the proposed kernel-based TE estimation method satisfactorily identifies the causal interactions present in the data. Also, it displays robustness to varying noise levels and data sizes, and to the presence of multiple interaction delays in the same connected network. Obtained results for the motor imagery task show that our approach codes discriminant spatiotemporal patterns for the left and right-hand motor imagination tasks, with classification performances that compare favorably to the state-of-the-art.

Neural Correlates of Trust During an Automated System Monitoring Task: Preliminary Results of an Effective Connectivity Study

As autonomous systems become more prevalent and their inner workings become more opaque, we increasingly rely on trust to guide our interactions with them especially in complex or rapidly evolving situations. When our expectations of what automation is capable of do not match reality, the consequences can be sub-optimal to say the least. The degree to which our trust reflects actual capability is known as trust calibration. One of the approaches to studying this is neuroergonomics. By understanding the neural mechanisms involved in human-machine trust, we can design systems which promote trust calibration and possibly measure trust in real time. Our study used the Multi Attribute Task Battery to investigate neural correlates of trust in automation. We used EEG to record brain activity of participants as they watched four algorithms of varying reliability perform the SYSMON subtask on the MATB. Subjects reported their subjective trust level after each round. We subsequently conducted an effective connectivity analysis and identified the cingulate cortex as a node, and its asymmetry ratio and incoming information flow as possible indices of trust calibration. We hope our study will inform future work involving decision-making and real-time cognitive state detection.

Atypical Amygdala-Neocortex Interaction During Dynamic Facial Expression Processing in Autism Spectrum Disorder.

Atypical reciprocal social interactions involving emotional facial expressions are a core clinical feature of autism spectrum disorder (ASD). Previous functional magnetic resonance imaging (fMRI) studies have demonstrated that some social brain regions, including subcortical (e.g., amygdala) and neocortical regions (e.g., fusiform gyrus, FG) are less activated during the processing of facial expression stimuli in individuals with ASD. However, the functional networking patterns between the subcortical and cortical regions in processing emotional facial expressions remain unclear. We investigated this issue in ASD (n = 31) and typically developing (TD; n = 31) individuals using fMRI. Participants viewed dynamic facial expressions of anger and happiness and their corresponding mosaic images. Regional brain activity analysis revealed reduced activation of several social brain regions, including the amygdala, in the ASD group compared with the TD group in response to dynamic facial expressions vs. dynamic mosaics (p < 0.05, = 0.19). Dynamic causal modeling (DCM) analyses were then used to compare models with forward, backward, and bi-directional effective connectivity between the amygdala and neocortical networks. The results revealed that: (1) the model with effective connectivity from the amygdala to the neocortex best fit the data of both groups; and (2) the same model best accounted for group differences. Coupling parameter (i.e., effective connectivity) analyses showed that the modulatory effects of dynamic facial processing were substantially weaker in the ASD group than in the TD group. These findings suggest that atypical modulation from the amygdala to the neocortex underlies impairment in social interaction involving dynamic facial expressions in individuals with ASD.

Human visual processing for spatial 3-D imaging recognition: Cortical connectivity deficits in Parkinson's spatial perceptual visual pathway

Introduction Parkinson's disease (PD) is a Neurodegenerative disorder traditionally linked to motor impairment but recent findings have revealed, patients suffering from PD exhibit decline in visual-spatial skills [1] [2]. The aim of this study is to explore, neural structural connectivity between the superior parietal lobe (BA 5) and the primary visual cortex (BA 17, 18 & 19) in PD patients. Generally, this pathway modulates visual and spatial domains, hence if found to be impaired in PD patients could explain the dysfunction in visual-spatial skills. Methods In this research, we attempted to correlate the dorsal stream visual pathway in Parkinson's Patients. The study was carried out through ;“Diffusion Imaging Fibre Tractography;” which involves 120 DTI data sets from control and PD patients between 60 and 120 years with IRB approval. Observations It was observed that fibers in the females of the control group were higher (Fig 1) when compared to that of the males (Fig 2), and on completion, males displayed more prominent changes in numbers and volumes of where stream pathway;”(Fig 3). In males both right and left hemisphere were affected, whereas with female much difference is not noticed. The results are statistically significant at P Results Based on our observations, destruction in the where stream pathway were identified, and is believed as an underlying cause for deficiency in visual spatial recognition. However, for better understanding of this findings, functional and effective connectivity analysis must be recommended.

Structural alterations and their underlying substrates in correlations with “what” stream visual pathways in progression stages of Alzheimer's disease: Associative visual objective agnosia (AVOA)

Introduction Visual agnosia is a promptly noticed neurological deficit in Alzheimer's disease (AD), affects the inferior temporal lobe, which leads an inability to perceive objects. The interconnection between the two cortices in visual ventral stream, the visual cortex (BA 17, 18, 19) and inferior temporal lobe (BA 20) explain how we perceive and recognize the objects, called associative object visual perception. We Team Neuron, focused on correlating the neural structural connectivity with associative visual agnosia in Alzheimer's patients using ;“Diffusion Tensor Imaging Tractography;”. The study involves fifty DTI datasets, both the sexes of Control and disease progression stages of AD, with age ranges from 50 to 90 years. Results On observation, the females displayed with progressive increase in the (number and volume) fibers among the cognitive declining stages of AD. But, the males, noticed with a bi-modal variation (number and volume) of fibers, within the disease progression stages of AD. Conclusion Although the statistical analysis was insignificant, it was noticed that the males are predominantly affected than females. The current observations, propose an insight to understand the bi-modal distribution of fibers in the male and progressive increase of fibers in the female, from the control to disease progression stages of AD. However, these findings need to be confirmed with functional and effective connectivity analysis for further understandings.

Information-theoretic approach to detect directional information flow in EEG signals induced by TMS

Effective connectivity analysis has been widely applied to noninvasive recordings such as functional magnetic resonance imaging and electroencephalograms (EEGs). Previous studies have aimed to extract the causal relations between brain regions, but the validity of the derived connectivity has not yet been fully determined. This is because it is generally difficult to identify causality in the usual experimental framework based on observations alone. Transcranial magnetic stimulation (TMS) provides a framework in which a controllable perturbation is applied to a local brain region and the effect is examined by comparing the neural activity with and without this stimulation. This study evaluates two methods for effective connectivity analysis, symbolic transfer entropy (STE) and vector autoregression (VAR), by applying them to TMS-EEG data. In terms of the consistency of results from different experimental sessions, STE is found to yield robust results irrespective of sessions, whereas VAR produces less correlation between sessions. Furthermore, STE preferentially detects the directional information flow from the TMS target. Taken together, our results suggest that STE is a reliable method for detecting the effect of TMS, implying that it would also be useful for identifying neural activity during cognitive tasks and resting states.