ObjectiveType 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and topological properties among whole brain networks and correlations with impaired cognition and distinguish T2DM from healthy controls (HC) to identify potential biomarkers for cognition abnormalities.MethodsA total of 80 T2DM and 55 well-matched HC were recruited in this study. Subjects’ clinical data, neuropsychological tests and resting-state (...) class='Hi'>functional magnetic resonance imaging data were acquired. Whole-brain network FC were mapped, the topological characteristics were analyzed using a graph-theoretic approach, the FC and topological characteristics of the network were compared between T2DM and HC using a general linear model, and correlations between networks and clinical and cognitive characteristics were identified. The support vector machine (SVM) model was used to identify differences between T2DM and HC.ResultsIn patients with T2DM, FC was higher in two core regions [precuneus/posterior cingulated cortex (PCC)_1 and later prefrontal cortex_1] in the default mode network and lower in bilateral superior parietal lobes (within dorsal attention network), and decreased between the right medial frontal cortex and left auditory cortex. The FC of the right frontal medial-left auditory cortex was positively correlated with the Montreal Cognitive Assessment scales and negatively correlated with the blood glucose levels. Long-range connectivity between bilateral auditory cortex was missing in the T2DM. The nodal degree centrality and efficiency of PCC were higher in T2DM than in HC (P < 0.005). The nodal degree centrality in the PCC in the SVM model was 97.56% accurate in distinguishing T2DM patients from HC, demonstrating the reliability of the prediction model.ConclusionFunctional abnormalities in the auditory cortex in T2DM may be related to cognitive impairment, such as memory and attention, and nodal degree centrality in the PCC might serve as a potential neuroimaging biomarker to predict and identify T2DM. (shrink)

Cognitive functions are directly related to interactions between the brain's functional networks. This functional organization changes in the autism spectrum disorder (ASD). However, the heterogeneous nature of autism brings inconsistency in the findings, and specific pattern of changes based on the cognitive theories of ASD still requires to be well-understood. In this study, we hypothesized that the theory of mind (ToM), and the weak central coherence theory must follow an alteration pattern in the network level of (...) functional interactions. The main aim is to understand this pattern by evaluating interactions between all the brain functional networks. Moreover, the association between the significantly altered interactions and cognitive dysfunctions in autism is also investigated. We used resting-state fMRI data of 106 subjects (5–14 years, 46 ASD: five female, 60 HC: 18 female) to define the brain functional networks. Functional networks were calculated by applying four parcellation masks and their interactions were estimated using Pearson's correlation between pairs of them. Subsequently, for each mask, a graph was formed based on the connectome of interactions. Then, the local and global parameters of the graph were calculated. Finally, statistical analysis was performed using a two-sample t-test to highlight the significant differences between autistic and healthy control groups. Our corrected results show significant changes in the interaction of default mode, sensorimotor, visuospatial, visual, and language networks with other functional networks that can support the main cognitive theories of autism. We hope this finding sheds light on a better understanding of the neural underpinning of autism. (shrink)

Current cognitive science models of perception and action assume that the objects that we move toward and perceive are represented as determinate in our experience of them. A proper phenomenology of perception and action, however, shows that we experience objects indeterminately when we are perceiving them or moving toward them. This indeterminacy, as it relates to simple movement and perception, is captured in the proposed phenomenologically based recurrent network models of brain function. These models provide a possible foundation (...) from which predicative structures may arise as an emergent phenomenon without the positing of a representing subject. These models go some way in addressing the dual constraints of phenomenological accuracy and neurophysiological plausibility that ought to guide all projects devoted to discovering the physical basis of human experience. (shrink)

Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On the (...) other hand, knowing a system's function and describing it with elegant mathematics tell one very little about what to expect of interneuronal behavior. Examples of simple networks based on neurophysiology are taken from the oculomotor literature to suggest how single-unit interpretability might decrease with increasing task complexity. It is argued that trying to explain how any real neural network works on a cell-by-cell, reductionist basis is futile and we may have to be content with trying to understand the brain at higher levels of organization. (shrink)

The human brain shows neuroplastic adaptations induced by motor skill training. However, the description of the plastic architecture of the whole-brain network in resting-state is still limited. In the present study, we aimed to detect how motor training affected the density distribution of whole-brain resting-state functional connectivity (FC) brain in fast-ball student-athletes using resting-state functional magnetic resonance imaging (fMRI) data of student-athletes (SA), and non-athlete healthy controls (NC). The voxel-wise data-driven graph theory approach, (...) namely global functional connectivity density (gFCD) mapping was applied. The results showed that the SA group exhibited significantly decreased gFCD in brain regions centered at left triangular part of inferior frontal gyrus (IFG), extending to opercular part of left IFG and middle frontal gyrus (MFG) compared with NC group. The findings supported the idea of an increased neural efficiency of athletes’ brain in the brain regions associated with attentional-motor modulation and executive control. Furthermore, the behavioral results demonstrated that in the SA group, faster executive control reaction time was associated with smaller gFCD values in left IFG. The findings implied that the motor skill training would decrease the numbers of FC in IFG to accelerate the execution control with high attentional demands, and focus the attention to the target detection the athletes are interested in. (shrink)

Background and PurposeTranscranial direct current stimulation is an emerging non-invasive neuromodulation technique for focal epilepsy. Because epilepsy is a disease affecting the brain network, our study was aimed to evaluate and predict the treatment outcome of cathodal tDCS by analyzing the ctDCS-induced functional network alterations.MethodsEither the active 5-day, −1.0 mA, 20-min ctDCS or sham ctDCS targeting at the most active interictal epileptiform discharge regions was applied to 27 subjects suffering from focal epilepsy. The functional networks (...) before and after ctDCS were compared employing graph theoretical analysis based on the functional magnetic resonance imaging data. A support vector machine prediction model was built to predict the treatment outcome of ctDCS using the graph theoretical measures as markers.ResultsOur results revealed that the mean clustering coefficient and the global efficiency decreased significantly, as well as the characteristic path length and the mean shortest path length at the stimulation sites in the fMRI functional networks increased significantly after ctDCS only for the patients with response to the active ctDCS. Our prediction model achieved the mean prediction accuracy of 68.3% after the nested cross validation. The mean area under the receiver operating curve was 0.75, which showed good prediction performance.ConclusionThe study demonstrated that the response to ctDCS was related to the topological alterations in the functional networks of epilepsy patients detected by fMRI. The graph theoretical measures were promising for clinical prediction of ctDCS treatment outcome. (shrink)

Lindquist et al. provide a convincing case against what they call the locationist account of emotion. Their quantitative approach elegantly illustrates the shortcomings of this still-entrenched viewpoint. Here, I discuss how a network perspective will advance our understanding of structure-function mappings in general, and the relationship between emotion and cognition in the brain.

Various methods have been employed to investigate different aspects of the brain activity modulation related to the performance of a cycling task. In our study we examined how functional connectivity and brain network efficiency varied during an endurance cycling task. To this purpose, we reconstructed EEG signals at source level: we computed current densities in 28 anatomical regions of interest (ROIs) through the eLORETA algorithm, then we calculated the Lagged Coherence of the 28 current density signals (...) to define the adjacency matrix. To quantify changes of functional network efficiency during an exhaustive cycling task, we computed three graph theoretical indices: local efficiency (LE), global efficiency (GE) and Density (D) in two different frequency bands, Alpha and Beta bands, that indicate alertness processes and motor binding/fatigue respectively. This analysis was conducted for six different task intervals: pre-cycling, initial, intermediate and final stages of cycling, active recovery and passive recovery. Fourteen participants performed an incremental cycling task with simultaneous EEG recording and RPE monitoring to detect participants’ exhaustion. LE remained constant during the endurance cycling task in both bands. Therefore, we speculate that fatigue processes did not affect segregated neural processing. We observed an increase of GE in the Alpha band only during cycling, that could be due to greater alertness processes and preparedness to stimuli during exercise. Conversely, although D did not change significantly over time in the Alpha band, its general reduction in the Beta bands during cycling could be interpreted within the framework of the neural efficiency hypothesis, which posits a reduced neural activity for expert/automated performances. We argue that the use of graph theoretical indices represents a clear methodological advancement in studying endurance performance. (shrink)

BackgroundThe neural activity and functional networks of emotion-based cognitive reappraisal have been widely investigated using electroencephalography and functional magnetic resonance imaging. However, single-mode neuroimaging techniques are limited in exploring the regulation process with high temporal and spatial resolution.ObjectivesWe proposed a source localization method with multimodal integration of EEG and fMRI and tested it in the source-level functional network analysis of emotion cognitive reappraisal.MethodsEEG and fMRI data were simultaneously recorded when 15 subjects were performing the emotional cognitive (...) reappraisal task. Fused priori weighted minimum norm estimation with sliding windows was proposed to trace the dynamics of EEG source activities, and the phase lag index was used to construct the functional brain network associated with the process of downregulating negative affect using the reappraisal strategy.ResultsThe functional networks were constructed with the measure of PLI, in which the important regions were indicated. In the gamma band source-level network analysis, the cuneus, the lateral orbitofrontal cortex, the superior parietal cortex, the postcentral gyrus, and the pars opercularis were identified as important regions in reappraisal with high betweenness centrality.ConclusionThe proposed multimodal integration method for source localization identified the key cortices involved in emotion regulation, and the network analysis demonstrated the important brain regions involved in the cognitive control of reappraisal. It shows promise in the utility in the clinical setting for affective disorders. (shrink)

BackgroundThe neural activity of irritable bowel syndrome patients in the resting state without any intervention has not been systematically studied. The purpose of this study was to compare the resting-state brain functions of IBS patients with healthy controls.MethodsThe published neuroimage studies were obtained from electronic databases including PubMed, EMBASE, PsycINFO, Web of Science Core, CNKI Database, Wanfang Database, VIP Database, and CBMdisc. Search dates were from inception to March 14th, 2022. The studies were identified by the preidentified inclusion and (...) exclusion criteria. Two independent reviewers compiled the studies and evaluated them for quality and bias.ResultsAltogether 22 fMRI studies were included in this review. The risk of bias of the included studies was generally low. The findings indicated that in IBS patients, increased or decreased brain areas were mostly associated with visceral sensations, emotional processing, and pain processing. According to brain network research, IBS may exhibit anomalies in the DMN, CEN, and emotional arousal networks. The fluctuations in emotion and symptoms in IBS patients were associated with alterations in the relevant brain regions.ConclusionThis study draws a preliminary conclusion that there are insufficient data to accurately distinguish the different neurological features of IBS in the resting state. Additional high-quality research undertaken by diverse geographic regions and teams is required to reach reliable results regarding resting-state changed brain regions in IBS. (shrink)

Multiple cortical areas of the human brain motor system interact coherently in the low frequency range (< 0.1 Hz), even in the absence of explicit tasks. Following stroke, cortical interactions are functionally disturbed. How these interactions are affected and how the functional organization is regained from rehabilitative treatments as people begin to recover motor behaviors has not been systematically studied. We recorded the intrinsic functional magnetic resonance imaging (fMRI) signals from 30 participants: 17 young healthy controls and (...) 13 aged stroke survivors. Stroke participants underwent mental practice (MP) or both mental practice and physical therapy (MP + PT) within 14-51 days following stroke. We investigated the network activity of five core areas in the motor-execution network, consisting of the left primary motor area (LM1), the right primary motor area (RM1), the left pre-motor cortex (LPMC), the right pre-motor cortex (RPMC) and the supplementary motor area (SMA). We discovered that (i) the network activity dominated in the frequency range 0.06 Hz – 0.08 Hz for all the regions, and for both able-bodied and stroke participants (ii) the causal information flow between the regions: LM1 and SMA, RPMC and SMA, RPMC and LM1, SMA and RM1, SMA and LPMC, was reduced significantly for stroke survivors (iii) the flow did not increase significantly after MP alone and (iv) the flow among the regions during MP+PT increased significantly. We also found that sensation and motor scores were significantly higher and correlated with directed functional connectivity measures when the stroke-survivors underwent MP+PT but not MP alone. The findings provide evidence that a combination of mental practice and physical therapy can be an effective means of treatment for stroke survivors to recover or regain the strength of motor behaviors, and that the spectra of causal information flow can be used as a reliable biomarker for evaluating rehabilitation in stroke survivors. (shrink)

AimsAlcohol consumption could lead to a series of health problems and social issues. In the current study, we investigated the resting-state functional brain networks of healthy volunteers before and after drinking through graph-theory analysis, aiming to ascertain the effects of acute alcohol intake on topology and information processing mode of the functional brain networks.Materials and methodsThirty-three healthy volunteers were enrolled in this experiment. Each volunteer accepted alcohol breathalyzer tests followed by resting-state magnetic resonance imaging at three (...) time points: before drinking, 0.5 h after drinking, and 1 h after drinking. The data obtained were grouped based on scanning time into control group, 0.5-h group and 1-h group, and post-drinking data were regrouped according to breath alcohol concentration (BrAC) into relative low BrAC group (A group; 0.5-h data, n = 17; 1-h data, n = 16) and relative high BrAC group (B group; 0.5-h data, n = 16; 1-h data, n = 17). The graph-theory approach was adopted to construct whole-brain functional networks and identify the differences of network topological properties among all the groups.ResultsThe network topology of most groups was altered after drinking, with the B group presenting the most alterations. For global network measures, B group exhibited increased global efficiency, Synchronization, and decreased local efficiency, clustering coefficient, normalized clustering coefficient, characteristic path length, normalized characteristic path length, as compared to control group. Regarding nodal network measures, nodal clustering coefficient and nodal local efficiency of some nodes were lower in B group than control group. These changes suggested that the network integration ability and synchrony improved, while the segregation ability diminished.ConclusionThis study revealed the effects of acute alcohol intake on the topology and information processing mode of resting-state functional brain networks, providing new perceptions and insights into the effects of alcohol on the brain. (shrink)

Neurofeedback is a complex learning scenario, as the task consists of trying out mental strategies while processing a feedback signal that signifies activation in the brain area to be self-regulated and acts as a potential reward signal. In an attempt to dissect these subcomponents, we obtained whole-brain networks associated with efficient self-regulation in two paradigms: parallel, where the task was performed concurrently, combining feedback with strategy execution; and serial, where the task was performed consecutively, separating feedback processing from (...) strategy execution. Twenty participants attempted to control their anterior midcingulate cortex using functional magnetic resonance imaging NF in 18 sessions over 2 weeks, using cognitive and emotional mental strategies. We analyzed whole-brain fMRI activations in the NF training runs with the largest aMCC activation for the serial and parallel paradigms. The equal length of the strategy execution and the feedback processing periods in the serial paradigm allows a description of the two task subcomponents with equal power. The resulting activation maps were spatially correlated with functionally annotated intrinsic connectivity brain maps. Brain activation in the parallel condition correlates with the basal ganglia network, the cingulo-opercular network, and the frontoparietal control network ; brain activation in the serial strategy execution condition with the default mode network, the FPCN, and the visual processing network; while brain activation in the serial feedback processing condition predominantly with the CON, the DMN, and the FPCN. Additional comparisons indicate that BG activation is characteristic to the parallel paradigm, while supramarginal gyrus and superior temporal gyrus activations are characteristic to the serial paradigm. The multifaceted view of the subcomponents allows describing the cognitive processes associated with strategy execution and feedback processing independently in the serial feedback task and as combined processes in the multitasking scenario of the conventional parallel feedback task. (shrink)

Classification learning is a preeminent human ability within the animal kingdom but the key mechanisms of brain networks regulating learning remain mostly elusive. Recent neuroimaging advancements have depicted human brain as a complex graph machinery where brain regions are nodes and coherent activities among them represent the functional connections. While long-term motor memories have been found to alter functional connectivity in the resting human brain, a graph topological investigation of the short-time effects of learning (...) are still not widely investigated. For instance, classification learning is known to orchestrate rapid modulation of diverse memory systems like short-term and visual working memories but how the brain functional connectome accommodates such modulations is unclear. We used publicly available repositories (openfmri.org) selecting three experiments, two focused on short-term classification learning along two consecutive runs where learning was promoted by trial-by-trial feedback errors, while a further experiment was used as supplementary control. We analyzed the functional connectivity extracted from BOLD fMRI signals, and estimated the graph information processing in the cerebral networks. The information processing capability, characterized by complex network statistics, significantly improved over runs, together with the subject classification accuracy. Instead, null-learning experiments, where feedbacks came with poor consistency, did not provoke any significant change in the functional connectivity over runs. We propose that learning induces fast modifications in the overall brain network dynamics, definitely ameliorating the short-term potential of the brain to process and integrate information, a dynamic consistently orchestrated by modulations of the functional connections among specific brain regions. (shrink)

IntroductionRace-walking is a sport that mimics normal walking and running. Previous studies on sports science mainly focused on the cardiovascular and musculoskeletal systems. However, there is still a lack of research on the central nervous system, especially the real-time changes in brain network characteristics during race-walking exercise. This study aimed to use a network perspective to investigate the effects of different exercise intensities on brain functional connectivity.Materials and methodsA total of 16 right-handed healthy young athletes (...) were recruited as participants in this study. The cerebral cortex concentration of oxyhemoglobin was measured by functional near-infrared spectroscopy in the bilateral prefrontal cortex (PFC), the motor cortex (MC) and occipital cortex (OC) during resting and race-walking states. Three specific periods as time windows corresponding to different exercise intensities were divided from the race-walking time of participants, including initial, intermediate and sprint stages. The brain activation and functional connectivity (FC) were calculated to describe the 0.01-0.1 Hz frequency-specific cortical activities.ResultsCompared to the resting state, FC changes mainly exist between MC and OC in the initial stage, while PFC was involved in FC changes in the intermediate stage, and FC changes in the sprint stage were widely present in PFC, MC and OC. In addition, from the initial-development to the sprint stage, the significant changes in FC were displayed in PFC and MC.ConclusionThis brain functional connectivity-based study confirmed that hemodynamic changes at different exercise intensities reflected different brain network-specific characteristics. During race-walking exercise, more extensive brain activation might increase information processing speed. Increased exercise intensity could facilitate the integration of neural signals such as proprioception, motor control and motor planning, which may be an important factor for athletes to maintain sustained motor coordination and activity control at high intensity. This study was beneficial to understanding the neural mechanisms of brain networks under different exercise intensities. (shrink)

The complex activity of the human brain makes it difficult to get a big picture of how the brain works and functions as the mind. We examine pertinent studies, as well as evolutionary and embryologic evidence to support our theoretical model consisting of separate but interactive layers of human neural activity. The most basic layer involves default mode network (DMN)activity and cardiorespiratory oscillations. We propose that these oscillations support other neural activity and cognitive processes. The second layer (...) involves limbic system activity accompanied by corresponding changes in cardiorespiratory oscillations. The third layer consists of corticothalamic processing and involves higher cortical functions including awareness, cognition,and consciousness. These layers interact to form the complex neural activity of the human brain. Examining the origins and relationships of various neural and physiologic oscillations may provide better understanding of human neurophysiology and consciousness. (shrink)

Functional near-infrared spectroscopy has attracted increasing attention in the field of brain–computer interfaces owing to their advantages such as non-invasiveness, user safety, affordability, and portability. However, fNIRS signals are highly subject-specific and have low test-retest reliability. Therefore, individual calibration sessions need to be employed before each use of fNIRS-based BCI to achieve a sufficiently high performance for practical BCI applications. In this study, we propose a novel deep convolutional neural network -based approach for implementing a subject-independent fNIRS-based (...) BCI. A total of 18 participants performed the fNIRS-based BCI experiments, where the main goal of the experiments was to distinguish a mental arithmetic task from an idle state task. Leave-one-subject-out cross-validation was employed to evaluate the average classification accuracy of the proposed subject-independent fNIRS-based BCI. As a result, the average classification accuracy of the proposed method was reported to be 71.20 ± 8.74%, which was higher than the threshold accuracy for effective BCI communication as well as that obtained using conventional shrinkage linear discriminant analysis. To achieve a classification accuracy comparable to that of the proposed subject-independent fNIRS-based BCI, 24 training trials were necessary for the traditional subject-dependent fNIRS-based BCI. It is expected that our CNN-based approach would reduce the necessity of long-term individual calibration sessions, thereby enhancing the practicality of fNIRS-based BCIs significantly. (shrink)

Network neuroscience provides a systems approach to the study of the brain and enables the examination of interactions measured at different temporal and spatial scales. We review current methods to quantify the structure of brain networks and compare that structure across different clinical cohorts, cognitive states, and subjects. We further introduce the emerging mathematical concept of multilayer networks and describe the advantages of this approach to model changing brain dynamics over time. We conclude by offering several (...) concrete examples of how multilayer network approaches to neuroimaging data provide novel insights into brain structure and evolving function. (shrink)

Objective: Cognitive impairment is a common neurological complication in patients with end-stage renal disease undergoing maintenance hemodialysis. Brain network analysis based on graph theory is a promising tool for studying CI. Therefore, the purpose of this study was to analyze the changes of functional brain networks in patients on MHD with and without CI by using graph theory and further explore the underlying neuropathological mechanism of CI in these patients.Methods: A total of 39 patients on MHD (...) and 25 healthy controls matched for age, sex, and years of education were enrolled in the study. Resting-state functional magnetic resonance imaging and T1-weighted high-resolution anatomical data were obtained, and functional brain networks for each subject were constructed. The brain network parameters at the global and regional levels were calculated, and a one-way analysis of covariance was used to compare the differences across the three groups. The associations between the changed graph-theory parameters and cognitive function scores in patients on MHD were evaluated using Spearman correlation analysis.Results: Compared with HCs, the global parameters [sigma, gamma, and local efficiency ] in both patient groups decreased significantly. The clustering coefficient in patients with CI was significantly lower than that in the other two groups. The regional parameters were significantly lower in the right superior frontal gyrus, dorsolateral and gyrus rectus of patients with CI than those of patients without CI; however the nodal local efficiency in the left amygdala was significantly increased. The global Cp and regional parameters in the three brain regions were significantly correlated with the cognitive function scores.Conclusion: This study confirmed that the topology of the functional brain network was disrupted in patients on MHD with and without CI and the disruption of brain network was more severe in patients with CI. The abnormal brain network parameters are closely related to cognitive function in patients on MHD. (shrink)

Micro-expressions can reflect an individual’s subjective emotions and true mental state, and they are widely used in the fields of mental health, justice, law enforcement, intelligence, and security. However, one of the major challenges of working with MEs is that their neural mechanism is not entirely understood. To the best of our knowledge, the present study is the first to use electroencephalography to investigate the reorganizations of functional brain networks involved in MEs. We aimed to reveal the underlying (...) neural mechanisms that can provide electrophysiological indicators for ME recognition. A real-time supervision and emotional expression suppression experimental paradigm was designed to collect video and EEG data of MEs and no expressions of 70 participants expressing positive emotions. Based on the graph theory, we analyzed the efficiency of functional brain network at the scalp level on both macro and micro scales. The results revealed that in the presence of MEs compared with NEs, the participants exhibited higher global efficiency and nodal efficiency in the frontal, occipital, and temporal regions. Additionally, using the random forest algorithm to select a subset of functional connectivity features as input, the support vector machine classifier achieved a classification accuracy for MEs and NEs of 0.81, with an area under the curve of 0.85. This finding demonstrates the possibility of using EEG to recognize MEs, with a wide range of application scenarios, such as persons wearing face masks or patients with expression disorders. (shrink)

Modeling emotion processes remains a conceptual and methodological challenge in affective sciences. In responding to the other target articles in this special section on “Emotion and the Brain” and the comments on our article, we address the issue of potentially separate brain networks subserving the functions of the different emotion components. In particular, we discuss the suggested role of component synchronization in producing information integration for the dynamic emergence of a coherent emotion process, as well as the links (...) between incentive salience and concern-relevance in the elicitation of emotion. (shrink)

The processing of emotional nonlinguistic information in speech is defined as emotional prosody. This auditory nonlinguistic information is essential in the decoding of social interactions and in our capacity to adapt and react adequately by taking into account contextual information. An integrated model is proposed at the functional and brain levels, encompassing 5 main systems that involve cortical and subcortical neural networks relevant for the processing of emotional prosody in its major dimensions, including perception and sound organization; related (...) action tendencies; and associated values that integrate complex social contexts and ambiguous situations. (shrink)

We examine recent work in cognitive neuroscience that investigates brain networks. Brain networks are characterized by the ways in which brain regions are functionally and anatomically connected to one another. Cognitive neuroscientists use various noninvasive techniques (e.g., fMRI) to investigate these networks. They represent them formally as graphs. And they use various graph theoretic techniques to analyze them further. We distinguish between knowledge of the graph theoretic structure of such networks (structural knowledge) and knowledge of what instantiates (...) that structure (nonstructural knowledge). And we argue that this work provides structural knowledge of brain networks. We explore the significance of this conclusion for the scientific realism debate. We argue that our conclusion should not be understood as an instance of a global structural realist claim regarding the structure of the unobservable part of the world, but instead, as a local structural realist attitude towards brain networks in particular. And we argue that various local approaches to the realism debate, i.e., approaches that restrict realist commitments to particular theories and/or entities, are problematic insofar as they don't allow for the possibility of such a local structural realist attitude. (shrink)

Our previous study has shown impaired blood oxygen level-dependent /functional magnetic resonance imaging activation of the visual attention network in strabismic amblyopia. However, there has been no comparison of resting state fMRI activation and functional connectivity in brain regions of interest along the visual attention network including visual cortex, intraparietal sulcus, and frontal eye fields during closed eye resting across the SA, or anisometropic amblyopes groups. Hence, we compared, gray matter volume, amplitude of low frequency (...) fluctuations, regional homogeneity, and FC in the left and right hemisphere ROIs of the visual attention network in SA, AA, and healthy controls. Correlation analyses of corrected visual acuity of amblyopic eye and MRI results were also performed and showed that the LogMAR cVA of the amblyopic eye positively correlated with right zALFF and zReHo FEF of SA and right IPS of AA only. GMV of both left and right hemisphere V1 areas was significantly greater but ALFF was significantly lower for SA compared to AA and HC groups. zALFF and zReHo analyses in the AA and SA groups indicated significantly higher activation than that in the HC group in the right FEF and IPS but lower than that in the HC group in the left FEF, and only the SA group showed lower activation in both V1 areas than the HC group. FC values of the right FEF–left V1, right FEF–right V1, and right FEF–right IPS pathways in the SA and AA groups were also significantly higher than those in the HC group whereas all other FC values were non-significant. Thus, this study indicates that even during resting-state the visual attention network function is impaired in SA and AA participants with only right hemisphere FEF showing significant activation in SA and IPS in AA suggesting that the slower saccade activation times characteristic of amblyopic eyes lead to the dominant eye controlling activation of the visual attention network. (shrink)