Functional and Effective Connectivity Underlying Semantic Verbal Fluency.

Semantic verbal fluency (SVF) impairment is present in several neurological disorders. Although activation in SVF-related areas has been reported, how these regions are connected and their functional roles in the network remain divergent. We assessed SVF static and dynamic functional connectivity (FC) and effective connectivity in healthy participants using functional magnetic resonance imaging. We observed activation in the inferior frontal (IFG), middle temporal (pMTG) and angular gyri (AG), anterior cingulate (AC), insular cortex, and regions of the superior, middle, and medial frontal gyri (SFG, MFG, MidFG). Our static FC analysis showed a highly interconnected task and resting state network. Increased connectivity of AC with the pMTG and AG was observed for the task. The dynamic FC analysis provided circuits with connections similarly modulated across time and regions related to category identification, language comprehension, word selection and recovery, word generation, inhibition of speaking, speech planning, and articulatory planning of orofacial movements. Finally, the effective connectivity analysis provided a network that best explained our data, starting at the AG and going to the pMTG, from which there was a division between the ventral and dorsal streams. The SFG and MFG regions were connected and modulated by the MidFG, while the inferior regions formed the ventral stream. Therefore, we successfully assessed the SVF network, exploring regions associated with the entire processing, from category identification to word generation. The methodological approach can be helpful for further investigation of the SVF network in neurological disorders.

Identifying Effective Connectivity between Stochastic Neurons with Variable-Length Memory Using a Transfer Entropy Rate Estimator.

Information theory explains how systems encode and transmit information. This article examines the neuronal system, which processes information via neurons that react to stimuli and transmit electrical signals. Specifically, we focus on transfer entropy to measure the flow of information between sequences and explore its use in determining effective neuronal connectivity. We analyze the causal relationships between two discrete time series, X:=Xt:t∈Z and Y:=Yt:t∈Z, which take values in binary alphabets. When the bivariate process (X,Y) is a jointly stationary ergodic variable-length Markov chain with memory no larger than k, we demonstrate that the null hypothesis of the test-no causal influence-requires a zero transfer entropy rate. The plug-in estimator for this function is identified with the test statistic of the log-likelihood ratios. Since under the null hypothesis, this estimator follows an asymptotic chi-squared distribution, it facilitates the calculation of p-values when applied to empirical data. The efficacy of the hypothesis test is illustrated with data simulated from a neuronal network model, characterized by stochastic neurons with variable-length memory. The test results identify biologically relevant information, validating the underlying theory and highlighting the applicability of the method in understanding effective connectivity between neurons.

Brain connectivity changes associated with episodic recollection decline in aging: A review of fMRI studies.

With advancing age, individuals experience a gradual decline in recollection, the ability to retrieve personal experiences accompanied by details, such as temporal and spatial contextual information. Numerous studies have identified several brain regions that exhibit age-related activation differences during recollection tasks. More recently, an increasing number of studies have provided evidence regarding how brain connectivity among the regions supporting recollection contributes to the explanation of recollection deficits in aging. However, brain connectivity evidence has not been examined jointly to provide an integrative view of how these new findings have improved our knowledge of the neurofunctional changes underlying the recollection deficits associated with aging. Therefore, the aim of the present study was to examine functional magnetic resonance imaging (fMRI) studies that employed one of the numerous methods available for analyzing brain connectivity in older adults. Only studies that applied connectivity analysis to data recorded during episodic recollection tasks, either during encoding or retrieval, were assessed. First, the different brain connectivity analysis methods and the information conveyed were briefly described. Then, the brain connectivity findings from the different studies were described and discussed to provide an integrative point of view of how these findings explain the decline in recollection associated with aging. The studies reviewed provide evidence that the hippocampus consistently decreased its connectivity with the parahippocampal gyrus and the posterior cingulate cortex, essential regions of the recollection network, in older adults relative to young adults. In addition, older adults exhibited increased connectivity between the hippocampus and several widespread regions compared to young adults. The increased connectivity was interpreted as brain intensification recourse to overcome recollection decay. Additionally, suggestions for future research in the field are outlined.

Self-organization of in vitro neuronal assemblies drives to complex network topology.

Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, and tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of spike trains recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of modules. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.

Brain Structural-Functional Connectivity Relationship Underlying the Information Processing Speed.

Human cognition and behavior emerge from neuronal interactions on a brain structural architecture. The convergence (or divergence) between functional dynamics and structural connectivity (SC) and their relationship with cognition are still a pivotal question about the brain. We focused on the information processing speed (IPS), assessed by the Symbol Digit Modalities Test (SDMT), once delayed IPS underlies attention deficits in various clinical conditions. We hypothesize that the SC constrains but does not determine functional connectivity, and such a relationship is related to the cognitive performance. Blood oxygenation level-dependent and diffusion tensor images of healthy young volunteers were acquired in a 3T magnetic resonance imaging machine. Activation maps included the left and right middle frontal gyri, left superior parietal lobule, left precuneus, left inferior frontal gyrus (IFG), right cuneus, left lingual gyrus, and left declive. A network involving such regions and signal propagation from visual, through cognitive, up to motor regions was proposed. Random effects Bayesian model selection showed that the top-down connections have the highest expected and exceedance probabilities. Moreover, all pairs of task-related regions were connected by at least one tract, except for the left declive with the left IFG. The interactions between the right cuneus with left declive were related to the interindividual variability in SDMT performance. Altogether, our findings suggest that the IPS functional network is related to the highest SDMT scores when its effective endogenous connections are suppressed to the detriment of modulation caused by the experimental conditions, with the underlying structure providing low diffusion environments.

Brain functional and effective connectivity underlying the information processing speed assessed by the Symbol Digit Modalities Test.

Delayed Information Processing Speed (IPS) often underlies attention deficits and is particularly evident in patients with traumatic brain injury, Parkinson's disease, depression, dementia, and multiple sclerosis. Therefore, it is of interest to determine the brain network that is responsible for such essential cognitive function to understand IPS deficits and to develop effective rehabilitation programs. We assessed brain functional connectivity and effective connectivity during the performance of an adapted version of the Symbol Digit Modalities Test. Using dynamic causal modeling, we focused on obtaining a network model for IPS function in healthy subjects. Sixteen right-handed volunteers (seven women, age: 29.7 ±â€¯5.0 years) were included in the study after giving written consent for participating. Functional magnetic resonance images were acquired in a 3T scanner. According to our results, two systems interact during the IPS task performance. One is formed by frontoparietal and fronto-occipital networks, related to the control of goal-directed (top-down) selection for stimuli and response, while the second is composed of the temporoparietal and inferior frontal cortices, which are associated with stimulus-driven attention in the brain. Additionally, the default-mode network showed a significant correlation with networks positively associated with the task, mainly those related to visual detection and processing, indicating its relevant role in functional integration involving IPS. Therefore, an IPS-related network was proposed through a methodology that may be useful for future studies considering other cognitive functions and tasks, clinical groups, and longitudinal assessments.

Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data.

The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.

Dynamical predictions of insular hubs for social cognition and their application to stroke.

The insular cortex (IC) is considered a rich hub for context-sensitive emotions/social cognition. Patients with focal IC stroke provide unique opportunities to study socio-emotional processes. Nevertheless, Couto et al. (2013b) have recently reported controversial results regarding IC involvement in emotion and social cognition. Similarly, patients with similar lesions show high functional variability, ranging from almost totally preserved to strongly impaired behavior. Critical evidence suggests that the variability of these patients in the above domains can be explained by enhanced neuroplasticity, compensatory processes, and functional remapping after stroke. Therefore, socio-emotional processes would depend on long-distance connections between the IC and frontotemporal regions. We propose that predictive coding and effective connectivity represent a novel approach to explore functional connectivity and assess compensatory, contralateral, and subsidiary network differences among focal stroke patients. This approach would help explain why socio-emotional performance is so variable within this population.