Topological features of functional brain networks and subclinical impulsivity: an investigation in younger and older adults

Impulsive traits (i.e., the tendency to act without forethought regardless of negative outcomes) are frequently found in healthy populations. When exposed to risk factors, individuals may develop debilitating disorders of impulse control (addiction, substance abuse, gambling) characterized by behavioral and cognitive deficits, eventually leading to huge socioeconomic costs. With the far-reaching aim of preventing the onset of impulsive disorders, it is relevant to investigate the topological organization of functional brain networks associated with impulsivity in sub-clinical populations. Taking advantage of the open-source LEMON dataset, we investigated the topological features of resting-state functional brain networks associated with impulsivity in younger (n = 146, age: 20-35) and older (n = 61, age: 59-77) individuals, using a graph-theoretical approach. Specifically, we computed indices of segregation and integration at the level of specific circuits and nodes known to be involved in impulsivity (frontal, limbic, and striatal networks). In younger individuals, results revealed that impulsivity was associated with a more widespread, less clustered and less efficient functional organization, at all levels of analyses and in all selected networks. Conversely, impulsivity in older individuals was associated with reduced integration and increased segregation of striatal regions. Speculatively, such alterations of functional brain networks might underlie behavioral and cognitive abnormalities associated with impulsivity, a working hypothesis worth being tested in future research. Lastly, differences between younger and older individuals might reflect the implementation of age-specific adaptive strategies, possibly accounting for observed differences in behavioral manifestations. Potential interpretations, limitations and implications are discussed.

The relationship between disrupted anhedonia-related circuitry and suicidal ideation in major depressive disorder: A network-based analysis

BACKGROUND

Several epidemiological studies and psychological models have suggested that major depressive disorder (MDD) with anhedonia is associated with suicidal ideation (SI). However, little is known about whether the functional network pattern and intrinsic topologically disrupted in patients with anhedonia are related to SI.

METHODS

The resting-fMRI by applying network-based statistic (NBS) and graph-theory analyses was estimated in 273 patients with MDD (144 high anhedonia [HA], 129 low anhedonia [LA]) and 150 healthy controls. In addition, we quantified the SI scores of each patient. Finally, the mediation analysis assessed whether anhedonia symptoms could mediate the relationship between anhedonia-related network metrics and SI.

RESULT

The NBS analysis demonstrated that individuals with HA have a single abnormally increased functional connectivity component in a frontal-limbic circuit (termed the "anhedonia-related network", including the frontal cortex, striatum, anterior cingulate cortex and amygdala). The graph-theory analysis demonstrated that the anhedonia-related network showed a significantly disrupted topological organization (lower gamma and lambda), which the small-world property trend randomized. Furthermore, the anhedonia symptoms could mediate the relationship between the anhedonia-related network metrics (the mean functional connectivity values, the area under the curves values of gamma and nodal local efficiency in nucleus accumbens) and SI.

CONCLUSIONS

We found that disruption of the reward-related network in MDD leads to SI through anhedonia symptoms. These findings show the abnormal topological construction of functional brain network organization in anhedonia, shedding light on the neurological processes underlying SI in MDD patients with anhedonia symptoms.

Analysis of simultaneous visual and complex neural dynamics during cognitive learning to diagnose ASD

The interactions between gaze processing and neural activities mediate cognition. The present paper aims to identify the involvement of visual and neural dynamics in shaping the cognitive behavior in Autism Spectrum Disorder (ASD). Electroencephalogram (EEG) and Eye-tracker signals of ASD and Typically Developing (TD) are recorded while performing two difficulty levels of a maze-based experimental task. During task, the performance metrics, complex neural measures extracted from EEG data using Visibility Graph (VG) algorithm and visual measures extracted from eye-tracker data are analyzed and compared. For both task levels, the cognition processing is examined via performance metrics (reaction-time and poor accuracy), gaze measures (saccade, fixation duration and blinkrate) and VG-based metrics (average weighted degree, clustering coefficient, path length, global efficiency, mutual information). An engagement in cognitive processing in ASD is revealed statistically by high reaction time, poor accuracy, increased fixation duration, raised saccadic amplitude, higher blink rate, reduced average weighted degree, global efficiency, mutual information as well as higher eigenvector centrality and path length. Over the course of repetitive trials, the cognitive improvement is although poor in ASD compared to TDs, the reconfigurations of visual and neural network dynamics revealed activation of Cognitive Learning (CL) in ASD. Furthermore, the correlation of gaze-EEG measures reveal that independent brain region functioning is not impaired but declined mutual interaction of brain regions causes cognitive deficit in ASD. And correlation of EEG-gaze measures with clinical severity measured by Autism Diagnostic Observation Schedule(ADOS) suggest that visual-neural activities reveals social behavior/cognition in ASD. Thus, visual and neural dynamics together support the revelation of the cognitive behavior in ASD.

Preterm birth leads to impaired rich-club organization and fronto-paralimbic/limbic structural connectivity in newborns

Prematurity disrupts brain development during a critical period of brain growth and organization and is known to be associated with an increased risk of neurodevelopmental impairments. Investigating whole-brain structural connectivity alterations accompanying preterm birth may provide a better comprehension of the neurobiological mechanisms related to the later neurocognitive deficits observed in this population. Using a connectome approach, we aimed to study the impact of prematurity on neonatal whole-brain structural network organization at term-equivalent age. In this cohort study, twenty-four very preterm infants at term-equivalent age (VPT-TEA) and fourteen full-term (FT) newborns underwent a brain MRI exam at term age, comprising T2-weighted imaging and diffusion MRI, used to reconstruct brain connectomes by applying probabilistic constrained spherical deconvolution whole-brain tractography. The topological properties of brain networks were quantified through a graph-theoretical approach. Furthermore, edge-wise connectivity strength was compared between groups. Overall, VPT-TEA infants' brain networks evidenced increased segregation and decreased integration capacity, revealed by an increased clustering coefficient, increased modularity, increased characteristic path length, decreased global efficiency and diminished rich-club coefficient. Furthermore, in comparison to FT, VPT-TEA infants had decreased connectivity strength in various cortico-cortical, cortico-subcortical and intra-subcortical networks, the majority of them being intra-hemispheric fronto-paralimbic and fronto-limbic. Inter-hemispheric connectivity was also decreased in VPT-TEA infants, namely through connections linking to the left precuneus or left dorsal cingulate gyrus - two regions that were found to be hubs in FT but not in VPT-TEA infants. Moreover, posterior regions from Default-Mode-Network (DMN), namely precuneus and posterior cingulate gyrus, had decreased structural connectivity in VPT-TEA group. Our finding that VPT-TEA infants' brain networks displayed increased modularity, weakened rich-club connectivity and diminished global efficiency compared to FT infants suggests a delayed transition from a local architecture, focused on short-range connections, to a more distributed architecture with efficient long-range connections in those infants. The disruption of connectivity in fronto-paralimbic/limbic and posterior DMN regions might underlie the behavioral and social cognition difficulties previously reported in the preterm population.

Distance disintegration delineates the brain connectivity failure of Alzheimer's disease

Alzheimer's disease (AD) is associated with brain network dysfunction. Network-based investigations of brain connectivity have mainly focused on alterations in the strength of connectivity; however, the network breakdown in AD spectrum is a complex scenario in which multiple pathways of connectivity are affected. To integrate connectivity changes that occur under AD-related conditions, here we developed a novel metric that computes the connectivity distance between cortical regions at the voxel level (or nodes). We studied 114 individuals with mild cognitive impairment, 24 with AD, and 27 healthy controls. Results showed that areas of the default mode network, salience network, and frontoparietal network display a remarkable network separation, or greater connectivity distances, from the rest of the brain. Furthermore, this greater connectivity distance was associated with lower global cognition. Overall, the investigation of AD-related changes in paths and distances of connectivity provides a novel framework for characterizing subjects with cognitive impairment; a framework that integrates the overall network topology changes of the brain and avoids biases toward unreferenced connectivity effects.

Connectivity strength of the EEG functional network in schizophrenia and bipolar disorder

The application of graph theory measures in the study of functional brain networks allows for the description of their general properties and their alterations in mental illness. Among these measures, connectivity strength (CS) estimates the degree of functional connectivity of the whole network. Previous studies in schizophrenia patients have reported higher baseline CS values and modulation deficits in EEG spectral properties during cognitive activity. The specificity of these alterations and their relationships with pharmacological treatments remain unknown. Therefore, in the present study, we assessed functional CS on EEG-based brain networks in 79 schizophrenia and 29 bipolar patients in addition to 63 healthy controls. The subjects performed a P300 task during the EEG recordings from which the pre-stimulus and the task-related modulation CS values were computed in the global and theta bands. These values were compared between the groups and between the patients who had and had not received different treatments. The global band pre-stimulus CS was significantly higher in the schizophrenia group compared with the bipolar and control groups. Theta band CS modulation was decreased in schizophrenia and bipolar patients. Treatment with antipsychotics, lithium, benzodiazepines, and anticonvulsants did not significantly alter these CS values. The first-episode and chronic schizophrenia patients did not show significant differences in CS values. Higher global band pre-stimulus CS values were associated with worse general cognition in schizophrenia patients. These data support increased connectivity in the whole-brain network that is specific to schizophrenia and suggest a general hyper-synchronized basal state that might hamper cognition in this syndrome.

Visual search task immediate training effects on task-related functional connectivity

Brain plasticity occurs over the course of the human lifetime. Learning and training modify our neuronal synapses and adapt our brain activity, from priming effects in modal areas to higher-order changes in the association cortex. The current state of the art suggests that learning and training effects might induce large-scale brain connectivity changes. Here, we used task-fMRI data and graph-based approaches to study the immediate brain changes in functional connections associated with training on a visual search task, and the individual differences in learning were studied by means of brain-behavior correlations. In a previous work, we found that trained participants improved their response speed on a visual search task by 31%, whereas the control group hardly changed. In the present study, we showed that trained individuals changed regional connections (local links) in cortical areas devoted to the specific visual search processes and to areas that support information integration, and largely modified distributed connections (distant links) linking primary visual areas to specific attentional and cognitive control areas. In addition, we found that the individuals with the most enhanced connectivity in the dorsolateral prefrontal cortex performed the task faster after training. The observed behavioral and brain connectivity findings expand our understanding of large-scale dynamic readjustment of the human brain after learning experiences.

Deficits of entropy modulation in schizophrenia are predicted by functional connectivity strength in the theta band and structural clustering

Spectral entropy (SE) allows comparing task-related modulation of electroencephalogram (EEG) between patients and controls, i.e. spectral changes of the EEG associated to task performance. A SE modulation deficit has been replicated in different schizophrenia samples. To investigate the underpinnings of SE modulation deficits in schizophrenia, we applied graph-theory to EEG recordings during a P300 task and fractional anisotropy (FA) data from diffusion tensor imaging in 48 patients (23 first episodes) and 87 healthy controls. Functional connectivity was assessed from phase-locking values among sensors in the theta band, and structural connectivity was based on FA values for the tracts connecting pairs of regions. From those data, averaged clustering coefficient (CLC), characteristic path-length (PL) and connectivity strength (CS, also known as density) were calculated for both functional and structural networks. The corresponding functional modulation values were calculated as the difference in SE and CLC, PL and CS between the pre-stimulus and response windows during the task. The results revealed a higher functional CS in the pre-stimulus window in patients, predictive of smaller modulation of SE in this group. The amount of increase in theta CS from pre-stimulus to response related to SE modulation in patients and controls. Structural CLC was associated with SE modulation in the patients. SE modulation was predictive of negative symptoms, whereas CLC and PL modulation was associated with cognitive performance in the patients. These results support that a hyperactive functional connectivity and/or structural connective deficits in the patients hamper the dynamical modulation of connectivity underlying cognition.

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Functional connectivity strength and structural clustering properties were associated to the deficit in SE modulation in schizophrenia.

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Functional connectivity strength in the theta band was larger in the baseline in the patients.

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A hyperactive pre-stimulus state hampers the capacity for adequately modulating neural activity across the brain in schizophrenia.

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The possible basis for that problem may be investigated to identify therapeutic targets.

The structural connectivity of higher order association cortices reflects human functional brain networks

Human higher cognition arises from the main tertiary association cortices including the frontal, temporal and parietal lobes. Many studies have suggested that cortical functions must be shaped or emerge from the pattern of underlying physical (white matter) connectivity. Despite the importance of this hypothesis, there has not been a large-scale analysis of the white-matter connectivity within and between these associative cortices. Thus, we explored the pattern of intra- and inter-lobe white matter connectivity between multiple areas defined in each lobe. We defined 43 regions of interest on the lateral associative cortex cytoarchitectonically (6 regions of interest – ROIs in the frontal lobe and 17 ROIs in the parietal lobe) and anatomically (20 ROIs in the temporal lobe) on individuals' native space. The results demonstrated that intra-region connectivity for all 3 lobes was dense and graded generally. In contrary, the inter-lobe connectivity was relatively discrete and regionally specific such that only small sub-regions exhibited long-range connections to another lobe. The long-range connectivity was mediated by 6 major associative white matter tracts, consistent with the notion that these higher cognitive functions arises from brain-wide distributed connectivity. Using graph-theory network analysis we revealed five physically-connected sub-networks, which correspond directly to five known functional networks. This study provides strong and direct evidence that core functional brain networks mirror the brain's structural connectivity.