Cognitive network neuroscience

Stress from early life to adulthood: Is there a protective role of cognitive control?

Early life events can have long-lasting effects that may impact the quality of life into adulthood. The link between childhood adversities and adult mental and physical health is well documented, however, the underlying mechanisms remain poorly understood. Executive functions are assumed to be a key factor in successfully dealing with cognitive-emotional challenges thereby contributing to stress resilience and mental health across the lifespan. Here, we examined whether cognitive control moderates the link between early life adversity and depression. Data was available from a sample of 424 participants aged 20-70 years (Clinicaltrials.gov: NCT05155397). They performed in the Stroop task and behavior as well as frontal theta power were recorded. Negative childhood experiences were assessed with the Childhood Trauma Questionnaire (CTQ), chronic stress was measured with the Trier Inventory for Chronic Stress (TICS) and depression symptoms with Beck's Depression Inventory (BDI). The CTQ predicted symptoms of chronic stress and depression. Regression models pointed to the TICS as a crucial mediator in the relationship between CTQ and BDI. However, parameters of cognitive control demonstrated a rather weak effect as moderators. These results indicate that chronic stress is an important mediator linking childhood trauma to depression but suggest only a limited role for cognitive control.

Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM to a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders. Importantly, the generalizability of cross-sectional models was demonstrated in an external validation sample. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level.

A Hybrid Routing Pattern in Human Brain Structural Network Revealed By Evolutionary Computation

The human brain functional connectivity network (FCN) is constrained and shaped by the communication processes in the structural connectivity network (SCN). The underlying communication mechanism thus becomes a critical issue for understanding the formation and organization of the FCN. A number of communication models supported by different routing strategies have been proposed, with shortest path (SP), random diffusion (DIF), and spatial navigation (NAV) as the most typical, respectively requiring network global knowledge, local knowledge, and both for path seeking. Yet these models all assumed every brain region to use one routing strategy uniformly, ignoring convergent evidence that supports the regional heterogeneity in both terms of biological substrates and functional roles. In this regard, the current study developed a hybrid communication model that allowed each brain region to choose a routing strategy from SP, DIF, and NAV independently. A genetic algorithm was designed to uncover the underlying region-wise hybrid routing strategy (namely HYB). The HYB was found to outperform the three typical routing strategies in predicting FCN and facilitating robust communication. Analyses on HYB further revealed that brain regions in lower-order functional modules inclined to route signals using global knowledge, while those in higher-order functional modules preferred DIF that requires only local knowledge. Compared to regions that used global knowledge for routing, regions using DIF had denser structural connections, participated in more functional modules, but played a less dominant role within modules. Together, our findings further evidenced that hybrid routing underpins efficient SCN communication and locally heterogeneous structure-function coupling.

Updating functional brain units: Insights far beyond Luria

This paper reviews Luria's model of the three functional units of the brain. To meet this objective, several issues were reviewed: the theory of functional systems and the contributions of phylogenesis and embryogenesis to the brain's functional organization. This review revealed several facts. In the first place, the relationship/integration of basic homeostatic needs with complex forms of behavior. Secondly, the multi-scale hierarchical and distributed organization of the brain and interactions between cells and systems. Thirdly, the phylogenetic role of exaptation, especially in basal ganglia and cerebellum expansion. Finally, the tripartite embryogenetic organization of the brain: rhinic, limbic/paralimbic, and supralimbic zones. Obviously, these principles of brain organization are in contradiction with attempts to establish separate functional brain units. The proposed new model is made up of two large integrated complexes: a primordial-limbic complex (Luria's Unit I) and a telencephalic-cortical complex (Luria's Units II and III). As a result, five functional units were delineated: Unit I. Primordial or preferential (brainstem), for life-support, behavioral modulation, and waking regulation; Unit II. Limbic and paralimbic systems, for emotions and hedonic evaluation (danger and relevance detection and contribution to reward/motivational processing) and the creation of cognitive maps (contextual memory, navigation, and generativity [imagination]); Unit III. Telencephalic-cortical, for sensorimotor and cognitive processing (gnosis, praxis, language, calculation, etc.), semantic and episodic (contextual) memory processing, and multimodal conscious agency; Unit IV. Basal ganglia systems, for behavior selection and reinforcement (reward-oriented behavior); Unit V. Cerebellar systems, for the prediction/anticipation (orthometric supervision) of the outcome of an action. The proposed brain units are nothing more than abstractions within the brain's simultaneous and distributed physiological processes. As function transcends anatomy, the model necessarily involves transition and overlap between structures. Beyond the classic approaches, this review includes information on recent systemic perspectives on functional brain organization. The limitations of this review are discussed.

Integrated information decomposition unveils major structural traits of in silico and in vitro neuronal networks

The properties of complex networked systems arise from the interplay between the dynamics of their elements and the underlying topology. Thus, to understand their behavior, it is crucial to convene as much information as possible about their topological organization. However, in large systems, such as neuronal networks, the reconstruction of such topology is usually carried out from the information encoded in the dynamics on the network, such as spike train time series, and by measuring the transfer entropy between system elements. The topological information recovered by these methods does not necessarily capture the connectivity layout, but rather the causal flow of information between elements. New theoretical frameworks, such as Integrated Information Decomposition (Φ-ID), allow one to explore the modes in which information can flow between parts of a system, opening a rich landscape of interactions between network topology, dynamics, and information. Here, we apply Φ-ID on in silico and in vitro data to decompose the usual transfer entropy measure into different modes of information transfer, namely, synergistic, redundant, or unique. We demonstrate that the unique information transfer is the most relevant measure to uncover structural topological details from network activity data, while redundant information only introduces residual information for this application. Although the retrieved network connectivity is still functional, it captures more details of the underlying structural topology by avoiding to take into account emergent high-order interactions and information redundancy between elements, which are important for the functional behavior, but mask the detection of direct simple interactions between elements constituted by the structural network topology.

Global topology of human connectome is insensitive to early life environments - A prospective longitudinal study of the general population

The widely acknowledged detrimental impact of early adversity on child development has driven efforts to understand the underlying mechanisms that may mediate these effects within the developing brain. Recent efforts have begun to move beyond associating adversity with the morphology of individual brain regions towards determining if and how adversity might shape their interconnectivity. However, whether adversity effects a global shift in the organisation of whole-brain networks remains unclear. In this study, we assessed this possibility using parental questionnaire and diffusion imaging data from The Avon Longitudinal Study of Parents and Children (ALSPAC, N = 913), a prospective longitudinal study spanning more than 20 years. We tested whether a wide range of adversities-including experiences of abuse, domestic violence, physical and emotional cruelty, poverty, neglect, and parental separation-measured by questionnaire within the first seven years of life were significantly associated with the tractography-derived connectome in young adulthood. We tested this across multiple measures of organisation and using a computational model that simulated the wiring economy of the brain. We found no significant relationships between early exposure to any form of adversity and the global organisation of the structural connectome in young adulthood. We did detect local differences in the medial prefrontal cortex, as well as an association between weaker brain wiring constraints and greater externalising behaviour in adolescence. Our results indicate that further efforts are necessary to delimit the magnitude and functional implications of adversity-related differences in connectomic organization. RESEARCH HIGHLIGHTS: Diverse prospective measures of the early-life environment do not predict the organisation of the DTI tractography-derived connectome in young adulthood Wiring economy of the connectome is weakly associated with externalising in adolescence, but not internalising or cognitive ability Further work is needed to establish the scope and significance of global adversity-related differences in the structural connectome.

Genetic architecture of the structural connectome

Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.

Enhancing cognitive control with transcranial magnetic stimulation in subject-specific frontoparietal networks

BACKGROUND

Cognitive control processes, including those involving frontoparietal networks, are highly variable between individuals, posing challenges to basic and clinical sciences. While distinct frontoparietal networks have been associated with specific cognitive control functions such as switching, inhibition, and working memory updating functions, there have been few basic tests of the role of these networks at the individual level.

METHODS

To examine the role of cognitive control at the individual level, we conducted a within-subject excitatory transcranial magnetic stimulation (TMS) study in 19 healthy individuals that targeted intrinsic ("resting") frontoparietal networks. Person-specific intrinsic networks were identified with resting state functional magnetic resonance imaging scans to determine TMS targets. The participants performed three cognitive control tasks: an adapted Navon figure-ground task (requiring set switching), n-back (working memory), and Stroop color-word (inhibition).

OBJECTIVE

Hypothesis: We predicted that stimulating a network associated with externally oriented control [the "FPCN-B" (fronto-parietal control network)] would improve performance on the set switching and working memory task relative to a network associated with attention (the Dorsal Attention Network, DAN) and cranial vertex in a full within-subjects crossover design.

RESULTS

We found that set switching performance was enhanced by FPCN-B stimulation along with some evidence of enhancement in the higher-demand n-back conditions.

CONCLUSION

Higher task demands or proactive control might be a distinguishing role of the FPCN-B, and personalized intrinsic network targeting is feasible in TMS designs.

The cognitive improvement in patients with schizophrenia following low-intensity repetitive transcranial magnetic stimulation could last for 6 months: A randomized controlled trial

Cognitive impairments are a core symptom of schizophrenia. Although low-intensity repetitive transcranial magnetic stimulation (rTMS) also has cognitive improving effect like the commonly used high-intensity rTMS, it has not been applied in schizophrenia yet. To fill this gap, inpatients with schizophrenia were randomized to receive 20 sessions of daily adjunctive active low-intensity rTMS in 4 weeks, or sham treatment. At baseline, 4 weeks, and 6 months, the Positive and Negative Syndrome Scale (PANSS) was used to assess psychotic symptom severity, while the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and the Stroop Color and Word Test (SCWT) were used to assess cognitive functions. Compared to the sixty-nine patients receiving sham treatment, those fifty-nine patients receiving active rTMS performed better in all cognitive domains at post-treatment with small to large effect sizes. This superiority of active rTMS over sham treatment remained significant at 6-month follow-up, with small to large effect sizes, except for visuospatial function and delayed memory. The reduction in PANSS scores were not correlated with cognitive improvements. Our findings provide evidence for using low-intensity rTMS to ameliorate cognitive impairments in schizophrenia. More research are needed to determine the optimal intensity for each domain of cognitive functions.

The role of the thalamus in modular functional networks in temporal lobe epilepsy with cognitive impairment

OBJECTIVE

Cognitive deficit is common in patients with temporal lobe epilepsy (TLE). Here, we aimed to investigate the modular architecture of functional networks associated with distinct cognitive states in TLE patients together with the role of the thalamus in modular networks.

METHODS

Resting-state functional magnetic resonance imaging scans were acquired from 53 TLE patients and 37 matched healthy controls. All patients received the Montreal Cognitive Assessment test and accordingly were divided into TLE patients with normal cognition (TLE-CN, n = 35) and TLE patients with cognitive impairment (TLE-CI, n = 18) groups. The modular properties of functional networks were calculated and compared including global modularity Q, modular segregation index, intramodular connections, and intermodular connections. Thalamic subdivisions corresponding to the modular networks were generated by applying a 'winner-take-all' strategy before analyzing the modular properties (participation coefficient and within-module degree z-score) of each thalamic subdivision to assess the contribution of the thalamus to modular functional networks. Relationships between network properties and cognitive performance were then further explored.

RESULTS

Both TLE-CN and TLE-CI patients showed lower global modularity, as well as lower modular segregation index values for the ventral attention network and the default mode network. However, different patterns of intramodular and intermodular connections existed for different cognitive states. In addition, both TLE-CN and TLE-CI patients exhibited anomalous modular properties of functional thalamic subdivisions, with TLE-CI patients presenting a broader range of abnormalities. Cognitive performance in TLE-CI patients was not related to the modular properties of functional network but rather to the modular properties of functional thalamic subdivisions.

CONCLUSIONS

The thalamus plays a prominent role in modular networks and potentially represents a key neural mechanism underlying cognitive impairment in TLE.

Measuring semantic memory using associative and dissociative retrieval tasks

Recent theoretical advances highlighted the need for novel means of assessing semantic cognition. Here, we introduce the associative-dissociative retrieval task (ADT), positing a novel way to test inhibitory control over semantic memory retrieval by contrasting the efficacy of associative (automatic) and dissociative (controlled) retrieval on a standard set of verbal stimuli. All ADT measures achieved excellent reliability, homogeneity, and short-term temporal stability. Moreover, in-depth stimulus level analyses showed that the associative retrieval is easier for words evoking few but strong associates, yet such propensity hampers the inhibition. Finally, we provided critical support for the construct validity of the ADT measures, demonstrating reliable correlations with domain-specific measures of semantic memory functioning (semantic fluency and associative combination) but negligible correlations with domain-general capacities (processing speed and working memory). Together, we show that ADT provides simple yet potent and psychometrically sound measures of semantic memory retrieval and offers noteworthy advantages over the currently available assessment methods.

Evidence for increased parallel information transmission in human brain networks compared to macaques and male mice

Brain communication, defined as information transmission through white-matter connections, is at the foundation of the brain's computational capacities that subtend almost all aspects of behavior: from sensory perception shared across mammalian species, to complex cognitive functions in humans. How did communication strategies in macroscale brain networks adapt across evolution to accomplish increasingly complex functions? By applying a graph- and information-theory approach to assess information-related pathways in male mouse, macaque and human brains, we show a brain communication gap between selective information transmission in non-human mammals, where brain regions share information through single polysynaptic pathways, and parallel information transmission in humans, where regions share information through multiple parallel pathways. In humans, parallel transmission acts as a major connector between unimodal and transmodal systems. The layout of information-related pathways is unique to individuals across different mammalian species, pointing at the individual-level specificity of information routing architecture. Our work provides evidence that different communication patterns are tied to the evolution of mammalian brain networks.

Sex-dependent differences in connectivity patterns are related to episodic memory recall

Previous studies have shown that females typically outperform males on episodic memory tasks. In this study, we investigated if (1) there are differences between males and females in their connectome characteristics, (2) if these connectivity patterns are associated with memory performance, and (3) if these brain connectome characteristics contribute to the differences in episodic memory performance between sexes. In a sample of 655 healthy young subjects (n = 391 females; n = 264 males), we derived brain network characteristics from diffusion-weighted imaging (DWI) data using models of crossing fibers within each voxel of the brain and probabilistic tractography (graph strength, shortest path length, global efficiency, and weighted transitivity). Group differences were analysed with linear models and mediation analyses were used to explore how connectivity patterns might relate to sex-dependent differences in memory performance. Our results show significant sex-dependent differences in weighted transitivity (d = 0.42), with males showing higher values. Further, we observed a negative association between weighted transitivity and memory performance (r = -0.12). Finally, these distinct connectome characteristics partially mediated the observed differences in memory performance (effect size of the indirect effect r = 0.02). Our findings indicate a higher interconnectedness in females compared to males. Additionally, we demonstrate that the sex-dependent differences in episodic memory performance can be partially explained by the differences in this connectome measure. These results further underscore the importance of sex-dependent differences in brain connectivity and their impact on cognitive function.

Structural and functional connectivity alteration patterns of the cingulate gyrus in Type 2 diabetes

OBJECTIVE

We aimed to reveal the role of structural and functional alterations of cingulate gyrus in early cognitive impairment in Type 2 diabetes mellitus (T2DM) patients.

METHODS

Fifty-six T2DM patients and 60 healthy controls (HCs) underwent a neuropsychological assessment and sagittal three-dimensional T1-weighted and resting-state functional MRI. Differences in the cortical thickness of the cingulate cortex and the functional connectivity (FC) of the nine subregions of the cingulate gyrus and the whole brain were compared between T2DM patients and HCs. Correlation analysis was performed between cortex thickness and FC and the participants' clinical/cognitive variables.

RESULTS

The cortical thickness of the cingulate gyrus was not significantly different between T2DM patients and HCs. However, the T2DM patients showed significantly lower FC between the pregenual ACC (pACC) and the bilateral hippocampus, significantly higher FC between the pACC and bilateral lateral prefrontal cortex (LPFC) and left precentral gyrus, and significantly lower FC between the retrosplenial cortex (RSC) and right cerebellar Crus I. The FC between the pACC and the left hippocampus was negatively correlated with the FC between the pACC and LPFC (r = -0.306, p = 0.022).

INTERPRETATION

The pACC and the RSC show dysfunctional connectivity before the appearance of structural abnormalities in T2DM patients. Abnormal FC of the pACC with the bilateral hippocampus and LPFC may imply a neural compensatory mechanism for memory function. These findings provide valuable information and new directions for possible interventions for the T2DM-related cognitive impairment.

Individual-level functional connectivity predicts cognitive control efficiency

Cognitive control (CC) is essential for problem-solving in everyday life, and CC-related deficits occur alongside costly and debilitating disorders. The tri-partite model suggests that CC comprises multiple behaviors, including switching, inhibiting, and updating. Activity within the fronto-parietal control network B (FPCN-B), the dorsal attention network (DAN), the cingulo-opercular network (CON), and the lateral default-mode network (L-DMN) is related to switching and inhibiting behaviors. However, our understanding of how these brain regions interact to bring about cognitive switching and inhibiting in individuals is unclear. In the current study, subjects performed two in-scanner tasks that required switching and inhibiting. We used support vector regression (SVR) models containing individually-estimated functional connectivity between the FPCN-B, DAN, CON and L-DMN to predict switching and inhibiting behaviors. We observed that: inter-network connectivity can predict inhibiting and switching behaviors in individuals, and the L-DMN plays a role in switching and inhibiting behaviors. Therefore, individually estimated inter-network connections are markers of CC behaviors, and CC behaviors may arise due to interactions between a set of networks.

Brain-based graph-theoretical predictive modeling to map the trajectory of transdiagnostic symptoms of anhedonia, impulsivity, and hypomania from the human functional connectome

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM's mean square error (MSE) to that of a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region for information spread) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders, highlighting transdiagnostic generalization. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level. ClinicalTrials.gov identifier: NCT01976975.

Brain network communication: concepts, models and applications

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.

Network models to enhance the translational impact of cross-species studies

Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.

Disrupted dynamic network reconfiguration of the executive and reward networks in internet gaming disorder

BACKGROUND

Studies have shown that people with internet gaming disorder (IGD) exhibit impaired executive control of gaming cravings; however, the neural mechanisms underlying this process remain unknown. In addition, these conclusions were based on the hypothesis that brain networks are temporally static, neglecting dynamic changes in cognitive processes.

METHODS

Resting-state fMRI data were collected from 402 subjects [162 subjects with IGD and 240 recreational game users (RGUs)]. The community structure (recruitment and integration) of the executive control network (ECN) and the basal ganglia network (BGN), which represents the reward network, of patients with IGD and RGUs were compared. Mediation effects among the different networks were analyzed.

RESULTS

Compared to RGUs, subjects with IGD had a lower recruitment coefficient within the right ECN. Further analysis showed that only male subjects had a lower recruitment coefficient. Mediation analysis showed that the integration coefficient of the right ECN mediated the relationship between the recruitment coefficients of both the right ECN and the BGN in RGUs.

CONCLUSIONS

Male subjects with IGD had a lower recruitment coefficient than RGUs, which impairing their impulse control. The mediation results suggest that top-down executive control of the ECN is absent in subjects with IGD. Together, these findings could explain why subjects with IGD exhibit impaired executive control of gaming cravings; these results have important therapeutic implications for developing effective interventions for IGD.

The Co-Occurrence of Post-Traumatic Stress Disorder and Depression in Individuals with and without Traumatic Brain Injury: A Comprehensive Investigation

Post-traumatic stress disorder (PTSD) is a prevalent psychiatric disorder that often occurs following war trauma. Despite its high prevalence, there is still a lack of comprehensive understanding regarding the mechanisms underlying its progression and treatment resistance. Recent research has shed light on the biological basis of PTSD, with neuroimaging studies revealing altered brain connectivity patterns in affected individuals. In war contexts, traumatic brain injury (TBI) is a common occurrence and is associated with a high prevalence of PTSD. This study aimed to compare the severity of PTSD and depression in patients with and without a history of TBI to shed light on the impact of comorbid TBI on the presentation of PTSD symptoms. To achieve this goal, a cross-sectional study was conducted involving a sample of 60 outpatients who were diagnosed with both PTSD and Depressive Disorder. The inclusion criteria required participants to meet the diagnostic criteria for both disorders using validated tools. The severities of PTSD and depressive symptoms were assessed using scales that have been widely used and validated in previous research. By utilizing these standardized assessment tools, this study aimed to ensure the reliability and validity of the obtained data. The results of this study revealed that patients with comorbid PTSD and TBI exhibited a significantly higher severity of PTSD symptoms compared to those with PTSD only. Specifically, the comorbid group demonstrated higher ratings of symptom intensity across all symptom clusters. These findings are consistent with previous research that has highlighted the impact of comorbid TBI on the intensity and persistence of PTSD symptoms. When controlling for PTSD severity, no significant differences were observed in the severity of depressive symptoms between the two groups. This suggests that the increased depressive symptoms observed in the comorbid group may be primarily driven by the presence of more intense PTSD symptoms rather than TBI per se. The findings highlight the need for an accurate diagnosis of TBI in individuals with PTSD to guide appropriate treatment interventions. Further research is warranted to delve into the underlying mechanisms that contribute to the interaction between TBI and PTSD and to develop targeted interventions for individuals with comorbid PTSD and TBI.

The longitudinal relation between executive functioning and multilayer network topology in glioma patients

Many patients with glioma, primary brain tumors, suffer from poorly understood executive functioning deficits before and/or after tumor resection. We aimed to test whether frontoparietal network centrality of multilayer networks, allowing for integration across multiple frequencies, relates to and predicts executive functioning in glioma. Patients with glioma (n = 37) underwent resting-state magnetoencephalography and neuropsychological tests assessing word fluency, inhibition, and set shifting before (T1) and one year after tumor resection (T2). We constructed binary multilayer networks comprising six layers, with each layer representing frequency-specific functional connectivity between source-localized time series of 78 cortical regions. Average frontoparietal network multilayer eigenvector centrality, a measure for network integration, was calculated at both time points. Regression analyses were used to investigate associations with executive functioning. At T1, lower multilayer integration (p = 0.017) and epilepsy (p = 0.006) associated with poorer set shifting (adj. R2 = 0.269). Decreasing multilayer integration (p = 0.022) and not undergoing chemotherapy at T2 (p = 0.004) related to deteriorating set shifting over time (adj. R2 = 0.283). No significant associations were found for word fluency or inhibition, nor did T1 multilayer integration predict changes in executive functioning. As expected, our results establish multilayer integration of the frontoparietal network as a cross-sectional and longitudinal correlate of executive functioning in glioma patients. However, multilayer integration did not predict postoperative changes in executive functioning, which together with the fact that this correlate is also found in health and other diseases, limits its specific clinical relevance in glioma.

Estimating a brain network predictive of stress and genotype with supervised autoencoders

Targeted brain stimulation has the potential to treat mental illnesses. We develop an approach to help design protocols by identifying relevant multi-region electrical dynamics. Our approach models these dynamics as a superposition of latent networks, where the latent variables predict a relevant outcome. We use supervised autoencoders (SAEs) to improve predictive performance in this context, describe the conditions where SAEs improve predictions, and provide modelling constraints to ensure biological relevance. We experimentally validate our approach by finding a network associated with stress that aligns with a previous stimulation protocol and characterizing a genotype associated with bipolar disorder.

Neighborhood air pollution is negatively associated with neurocognitive maturation in early adolescence

The ability to maintain focus and process task-relevant information continues developing during adolescence, but the specific physical environmental factors that influence this development remain poorly characterized. One candidate factor is air pollution. Evidence suggests that small particulate matter and NO2 concentrations in the air may negatively impact cognitive development in childhood. We assessed the relationship between neighborhood air pollution and the changes in performance on the n-back task, a test of attention and working memory, in the Adolescent Brain Cognitive Development (ABCD) Study's baseline (ages 9-10) and two-year-follow-up releases (Y2, ages 11-12; n = 5,256). In the behavioral domain, multiple linear regression showed that developmental change in n-back task performance was negatively associated with neighborhood air pollution (β = -.044, t = -3.11, p = .002), adjusted for covariates capturing baseline cognitive performance of the child, their parental income and education, family conflicts, and their neighborhood's population density, crime rate, perceived safety, and Area Deprivation Index (ADI). The strength of the adjusted association for air pollution was similar to parental income, family conflict, and neighborhood ADI. In the neuroimaging domain, we evaluated a previously published youth cognitive composite Connectome-based Predictive Model (ccCPM), and again found that decreased developmental change in the strength of the ccCPM from pre- to early adolescence was associated with neighborhood air pollution (β = -.110, t = -2.69, p = .007), adjusted for the covariates mentioned above and head motion. Finally, we found that the developmental change in ccCPM strength was predictive of the developmental change in n-back performance (r = .157, p < .001), and there was an indirect-only mediation where the effect of air pollution on change in n-back performance was mediated by the change in the ccCPM strength (βindirect effect = -.013, p = .029). In conclusion, neighborhood air pollution is associated with lags in the maturation of youth cognitive performance and decreased strengthening of the brain networks supporting cognitive abilities over time.

Cholinergic-estrogen interaction is associated with the effect of education on attenuating cognitive sex differences in a Thai healthy population

The development of human brain is shaped by both genetic and environmental factors. Sex differences in cognitive function have been found in humans as a result of sexual dimorphism in neural information transmission. Numerous studies have reported the positive effects of education on cognitive functions. However, little work has investigated the effect of education on attenuating cognitive sex differences and the neural mechanisms behind it based on healthy population. In this study, the Wisconsin Card Sorting Test (WCST) was employed to examine sex differences in cognitive function in 135 Thai healthy subjects, and label-free quantitative proteomic method and bioinformatic analysis were used to study sex-specific neurotransmission-related protein expression profiles. The results showed sex differences in two WCST sub-scores: percentage of Total corrects and Total errors in the primary education group (Bayes factor>100) with males performed better, while such differences eliminated in secondary and tertiary education levels. Moreover, 11 differentially expressed proteins (DEPs) between men and women (FDR<0.1) were presented in both education groups, with majority of them upregulated in females. Half of those DEPs interacted directly with nAChR3, whereas the other DEPs were indirectly connected to the cholinergic pathways through interaction with estrogen. These findings provided a preliminary indication that a cholinergic-estrogen interaction relates to, and might underpin, the effect of education on attenuating cognitive sex differences in a Thai healthy population.

Persistent homology-based functional connectivity and its association with cognitive ability: Life-span study

Brain-segregation attributes in resting-state functional networks have been widely investigated to understand cognition and cognitive aging using various approaches [e.g., average connectivity within/between networks and brain system segregation (BSS)]. While these approaches have assumed that resting-state functional networks operate in a modular structure, a complementary perspective assumes that a core-periphery or rich club structure accounts for brain functions where the hubs are tightly interconnected to each other to allow for integrated processing. In this article, we apply a novel method, persistent homology (PH), to develop an alternative to standard functional connectivity by quantifying the pattern of information during the integrated processing. We also investigate whether PH-based functional connectivity explains cognitive performance and compare the amount of variability in explaining cognitive performance for three sets of independent variables: (1) PH-based functional connectivity, (2) graph theory-based measures, and (3) BSS. Resting-state functional connectivity data were extracted from 279 healthy participants, and cognitive ability scores were generated in four domains (fluid reasoning, episodic memory, vocabulary, and processing speed). The results first highlight the pattern of brain-information flow over whole brain regions (i.e., integrated processing) accounts for more variance of cognitive abilities than other methods. The results also show that fluid reasoning and vocabulary performance significantly decrease as the strength of the additional information flow on functional connectivity with the shortest path increases. While PH has been applied to functional connectivity analysis in recent studies, our results demonstrate potential utility of PH-based functional connectivity in understanding cognitive function.

The domain-separation language network dynamics in resting state support its flexible functional segregation and integration during language and speech processing

Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic "meta-network" framework to support flexible functional segregation and integration during language and speech processing.

Alpha and gamma EEG coherence during on-task and mind wandering states in schizophrenia

OBJECTIVE

Electroencephalographic (EEG) coherence is one of the most relevant physiological measures used to detect abnormalities in patients with schizophrenia. The present study applies a task-related EEG coherence approach to understand cognitive processing in patients with schizophrenia and healthy controls.

METHODS

EEG coherence for alpha and gamma frequency bands was analyzed in a group of patients with schizophrenia and a group of healthy controls during the performance of an ecological task of sustained attention. We compared EEG coherence when participants presented externally directed cognitive states (On-Task) and when they presented cognitive distraction episodes (Mind-Wandering).

RESULTS

Results reflect cortical differences between groups (higher coherence for schizophrenia in the frontocentral and fronto-temporal regions, and higher coherence for healthy-controls in the postero-central regions), especially in the On-Task condition for the alpha band, compared to Mind-Wandering episodes. Few individual differences in gamma coherence were found.

CONCLUSIONS

The current study provides evidence of neurophysiological differences underlying different cognitive states in schizophrenia and healthy controls.

SIGNIFICANCE

Differences between groups may reflect inhibitory processes necessary for the successful processing of information, especially in the alpha band, given its role in cortical inhibition processes. Patients may activate compensatory inhibitory mechanisms when performing the task, reflected in increased coherence in fronto-temporal regions.

Comparative efficacy of transcranial magnetic stimulation on different targets in Parkinson's disease: A Bayesian network meta-analysis

Background/Objective

The efficacy of transcranial magnetic stimulation (TMS) on Parkinson's disease (PD) varies across the stimulation targets. This study aims to estimate the effect of different TMS targets on motor symptoms in PD.

Methods

A Bayesian hierarchical model was built to assess the effects across different TMS targets, and the rank probabilities and the surface under the cumulative ranking curve (SUCRA) values were calculated to determine the ranks of each target. The primary outcome was the Unified Parkinson's Disease Rating Scale part-III. Inconsistency between direct and indirect comparisons was assessed using the node-splitting method.

Results

Thirty-six trials with 1,122 subjects were included for analysis. The pair-wise meta-analysis results showed that TMS could significantly improve motor symptoms in PD patients. Network meta-analysis results showed that the high-frequency stimulation over bilateral M1, bilateral DLPFC, and M1+DLPFC could significantly reduce the UPDRS-III scores compared with sham conditions. The high-frequency stimulation over both M1 and DLPFC had a more significant effect when compared with other parameters, and ranked first with the highest SCURA value. There was no significant inconsistency between direct and indirect comparisons.

Conclusion

Considering all settings reported in our research, high-frequency stimulation over bilateral M1 or bilateral DLPFC has a moderate beneficial effect on the improvement of motor symptoms in PD (high confidence rating). High-frequency stimulation over M1+DLPFC has a prominent beneficial effect and appears to be the most effective TMS parameter setting for ameliorating motor symptoms of PD patients (high confidence rating).

Investigating dynamic causal network with unified Granger causality analysis

BACKGROUND

Dynamic coupling phenomena characterize a widespread fundamental mechanism for the functional brain, which involves large-scale interactions at a multi-level. The Granger causality analysis (GCA) provides a data-driven procedure to investigate causal connections and has the potential to be a powerful dynamic capturing tool.

NEW METHOD

In this paper, distinct from the conventional two-stage scheme of most GCA methods, we suggest a unified GCA (uGCA) method incorporating a sliding window to further capture dynamic connections. And the uGCA method integrates all related procedures into the same space by a single mathematical theory, which involves a description length guided framework.

RESULTS

Through synthetic data experiments and real fMRI data experiments, we illustrated the effectiveness and priority of the proposed uGCA method.

COMPARISON WITH EXISTING METHODS

By varying the data length, we have demonstrated its superiority to conventional GCA in synthetic data experiments. We further illustrated the outstanding capability of their dynamic causal investigation in the fMRI data, involving serial mental arithmetic tasks under visual and auditory stimuli, respectively, one can evaluate the performance of different methods by accessing their network similarities among different stimuli. When varying windows size and step size of the sliding window, respectively, compared with conventional GCA, the uGCA identified higher network similarities while ensuring more robust performance.

CONCLUSIONS

The stability and effectiveness of uGCA will show it an advantage in the further research of multi-level dynamic coupling and characterizing.

The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging

The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.

Integrating media content analysis, reception analysis, and media effects studies

Every day, the world of media is at our fingertips, whether it is watching movies, listening to the radio, or browsing online media. On average, people spend over 8 h per day consuming messages from the mass media, amounting to a total lifetime dose of more than 20 years in which conceptual content stimulates our brains. Effects from this flood of information range from short-term attention bursts (e.g., by breaking news features or viral 'memes') to life-long memories (e.g., of one's favorite childhood movie), and from micro-level impacts on an individual's memory, attitudes, and behaviors to macro-level effects on nations or generations. The modern study of media's influence on society dates back to the 1940s. This body of mass communication scholarship has largely asked, "what is media's effect on the individual?" Around the time of the cognitive revolution, media psychologists began to ask, "what cognitive processes are involved in media processing?" More recently, neuroimaging researchers started using real-life media as stimuli to examine perception and cognition under more natural conditions. Such research asks: "what can media tell us about brain function?" With some exceptions, these bodies of scholarship often talk past each other. An integration offers new insights into the neurocognitive mechanisms through which media affect single individuals and entire audiences. However, this endeavor faces the same challenges as all interdisciplinary approaches: Researchers with different backgrounds have different levels of expertise, goals, and foci. For instance, neuroimaging researchers label media stimuli as "naturalistic" although they are in many ways rather artificial. Similarly, media experts are typically unfamiliar with the brain. Neither media creators nor neuroscientifically oriented researchers approach media effects from a social scientific perspective, which is the domain of yet another species. In this article, we provide an overview of approaches and traditions to studying media, and we review the emerging literature that aims to connect these streams. We introduce an organizing scheme that connects the causal paths from media content → brain responses → media effects and discuss network control theory as a promising framework to integrate media content, reception, and effects analyses.

Reconfigurations in brain networks upon awakening from slow wave sleep: Interventions and implications in neural communication

Sleep inertia is the brief period of impaired alertness and performance experienced immediately after waking. Little is known about the neural mechanisms underlying this phenomenon. A better understanding of the neural processes during sleep inertia may offer insight into the awakening process. We observed brain activity every 15 min for 1 hr following abrupt awakening from slow wave sleep during the biological night. Using 32-channel electroencephalography, a network science approach, and a within-subject design, we evaluated power, clustering coefficient, and path length across frequency bands under both a control and a polychromatic short-wavelength-enriched light intervention condition. We found that under control conditions, the awakening brain is typified by an immediate reduction in global theta, alpha, and beta power. Simultaneously, we observed a decrease in the clustering coefficient and an increase in path length within the delta band. Exposure to light immediately after awakening ameliorated changes in clustering. Our results suggest that long-range network communication within the brain is crucial to the awakening process and that the brain may prioritize these long-range connections during this transitional state. Our study highlights a novel neurophysiological signature of the awakening brain and provides a potential mechanism by which light improves performance after waking.

The topology, stability, and instability of learning-induced brain network repertoires in schizophrenia

There is a paucity of graph theoretic methods applied to task-based data in schizophrenia (SCZ). Tasks are useful for modulating brain network dynamics, and topology. Understanding how changes in task conditions impact inter-group differences in topology can elucidate unstable network characteristics in SCZ. Here, in a group of patients and healthy controls (n = 59 total, 32 SCZ), we used an associative learning task with four distinct conditions (Memory Formation, Post-Encoding Consolidation, Memory Retrieval, and Post-Retrieval Consolidation) to induce network dynamics. From the acquired fMRI time series data, betweenness centrality (BC), a metric of a node's integrative value was used to summarize network topology in each condition. Patients showed (a) differences in BC across multiple nodes and conditions; (b) decreased BC in more integrative nodes, but increased BC in less integrative nodes; (c) discordant node ranks in each of the conditions; and (d) complex patterns of stability and instability of node ranks across conditions. These analyses reveal that task conditions induce highly variegated patterns of network dys-organization in SCZ. We suggest that the dys-connection syndrome that is schizophrenia, is a contextually evoked process, and that the tools of network neuroscience should be oriented toward elucidating the limits of this dys-connection.

Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility

Probabilistic generative models are attractive for scientific modeling because their inferred parameters can be used to generate hypotheses and design experiments. This requires that the learned model provides an accurate representation of the input data and yields a latent space that effectively predicts outcomes relevant to the scientific question. Supervised Variational Autoencoders (SVAEs) have previously been used for this purpose, as a carefully designed decoder can be used as an interpretable generative model of the data, while the supervised objective ensures a predictive latent representation. Unfortunately, the supervised objective forces the encoder to learn a biased approximation to the generative posterior distribution, which renders the generative parameters unreliable when used in scientific models. This issue has remained undetected as reconstruction losses commonly used to evaluate model performance do not detect bias in the encoder. We address this previously-unreported issue by developing a second-order supervision framework (SOS-VAE) that updates the decoder parameters, rather than the encoder, to induce a predictive latent representation. This ensures that the encoder maintains a reliable posterior approximation and the decoder parameters can be effectively interpreted. We extend this technique to allow the user to trade-off the bias in the generative parameters for improved predictive performance, acting as an intermediate option between SVAEs and our new SOS-VAE. We also use this methodology to address missing data issues that often arise when combining recordings from multiple scientific experiments. We demonstrate the effectiveness of these developments using synthetic data and electrophysiological recordings with an emphasis on how our learned representations can be used to design scientific experiments.

Emotional states as distinct configurations of functional brain networks

The conceptualization of emotional states as patterns of interactions between large-scale brain networks has recently gained support. Yet, few studies have directly examined the brain’s network structure during emotional experiences. Here, we investigated the brain’s functional network organization during experiences of sadness, amusement, and neutral states elicited by movies, in addition to a resting-state. We tested the effects of the experienced emotion on individual variability in the brain’s functional connectome. Next, for each state, we defined a community structure of the brain and quantified its segregation and integration. We found that sadness, relative to amusement, was associated with higher modular integration and increased connectivity of cognitive control networks: the salience and fronto-parietal networks. Moreover, in both the functional connectome and the emotional report, the similarity between individuals was dependent on the sex. Our results suggest that the experience of emotion is linked to a reconfiguration of whole-brain distributed, not emotion-specific, functional networks and that the brain’s topological structure carries information about the subjective emotional experience.

Multi-modal and multi-subject modular organization of human brain networks

The human brain is a complex network of anatomically interconnected brain areas. Spontaneous neural activity is constrained by this architecture, giving rise to patterns of statistical dependencies between the activity of remote neural elements. The non-trivial relationship between structural and functional connectivity poses many unsolved challenges about cognition, disease, development, learning and aging. While numerous studies have focused on statistical relationships between edge weights in anatomical and functional networks, less is known about dependencies between their modules and communities. In this work, we investigate and characterize the relationship between anatomical and functional modular organization of the human brain, developing a novel multi-layer framework that expands the classical concept of multi-layer modularity. By simultaneously mapping anatomical and functional networks estimated from different subjects into communities, this approach allows us to carry out a multi-subject and multi-modal analysis of the brain's modular organization. Here, we investigate the relationship between anatomical and functional modules during resting state, finding unique and shared structures. The proposed framework constitutes a methodological advance in the context of multi-layer network analysis and paves the way to further investigate the relationship between structural and functional network organization in clinical cohorts, during cognitively demanding tasks, and in developmental or lifespan studies.

Functional Parcellation of Human Brain Using Localized Topo-Connectivity Mapping

The analysis of connectivity between parcellated regions of cortex provides insights into the functional architecture of the brain at a systems level. However, the derivation of functional structures from voxel-wise analyses at finer scales remains a challenge. We propose a novel method, called localized topo-connectivity mapping with singular-value-decomposition-informed filtering (or filtered LTM), to identify and characterize voxel-wise functional structures in the human brain from resting-state fMRI data. Here we describe its mathematical formulation and provide a proof-of-concept using simulated data that allow an intuitive interpretation of the results of filtered LTM. The algorithm has also been applied to 7T fMRI data acquired as part of the Human Connectome Project to generate group-average LTM images. Generally, most of the functional structures revealed by LTM images agree in the boundaries with anatomical structures identified by T1-weighted images and fractional anisotropy maps derived from diffusion MRI. In addition, the LTM images also reveal subtle functional variations that are not apparent in the anatomical structures. To assess the performance of LTM images, the subcortical region and occipital white matter were separately parcellated. Statistical tests were performed to demonstrate that the synchronies of fMRI signals in LTM-derived functional parcels are significantly larger than those with geometric perturbations. Overall, the filtered LTM approach can serve as a tool to investigate the functional organization of the brain at the scale of individual voxels as measured in fMRI.

The organization of individually mapped structural and functional semantic networks in aging adults

Language function in the brain, once thought to be highly localized, is now appreciated as relying on a connected but distributed network. The semantic system is of particular interest in the language domain because of its hypothesized integration of information across multiple cortical regions. Previous work in healthy individuals has focused on group-level functional connectivity (FC) analyses of the semantic system, which may obscure interindividual differences driving variance in performance. These studies also overlook the contributions of white matter networks to semantic function. Here, we identified semantic network nodes at the individual level with a semantic decision fMRI task in 53 typically aging adults, characterized network organization using structural connectivity (SC), and quantified the segregation and integration of the network using FC. Hub regions were identified in left inferior frontal gyrus. The individualized semantic network was composed of three interacting modules: (1) default-mode module characterized by bilateral medial prefrontal and posterior cingulate regions and also including right-hemisphere homotopes of language regions; (2) left frontal module extending dorsally from inferior frontal gyrus to pre-motor area; and (3) left temporoparietal module extending from temporal pole to inferior parietal lobule. FC within Module3 and integration of the entire network related to a semantic verbal fluency task, but not a matched phonological task. These results support and extend the tri-network semantic model (Xu in Front Psychol 8: 1538 1538, 2017) and the controlled semantic cognition model (Chiou in Cortex 103: 100 116, 2018) of semantic function.

A multi-scale probabilistic atlas of the human connectome

The human brain is a complex system that can be efficiently represented as a network of structural connectivity. Many imaging studies would benefit from such network information, which is not always available. In this work, we present a whole-brain multi-scale structural connectome atlas. This tool has been derived from a cohort of 66 healthy subjects imaged with optimal technology in the setting of the Human Connectome Project. From these data we created, using extensively validated diffusion-data processing, tractography and gray-matter parcellation tools, a multi-scale probabilistic atlas of the human connectome. In addition, we provide user-friendly and accessible code to match this atlas to individual brain imaging data to extract connection-specific quantitative information. This can be used to associate individual imaging findings, such as focal white-matter lesions or regional alterations, to specific connections and brain circuits. Accordingly, network-level consequences of regional changes can be analyzed even in absence of diffusion and tractography data. This method is expected to broaden the accessibility and lower the yield for connectome research.

Dynamic Functional Connectivity of Emotion Processing in Beta Band with Naturalistic Emotion Stimuli

While naturalistic stimuli, such as movies, better represent the complexity of the real world and are perhaps crucial to understanding the dynamics of emotion processing, there is limited research on emotions with naturalistic stimuli. There is a need to understand the temporal dynamics of emotion processing and their relationship to different dimensions of emotion experience. In addition, there is a need to understand the dynamics of functional connectivity underlying different emotional experiences that occur during or prior to such experiences. To address these questions, we recorded the EEG of participants and asked them to mark the temporal location of their emotional experience as they watched a video. We also obtained self-assessment ratings for emotional multimedia stimuli. We calculated dynamic functional the connectivity (DFC) patterns in all the frequency bands, including information about hubs in the network. The change in functional networks was quantified in terms of temporal variability, which was then used in regression analysis to evaluate whether temporal variability in DFC (tvDFC) could predict different dimensions of emotional experience. We observed that the connectivity patterns in the upper beta band could differentiate emotion categories better during or prior to the reported emotional experience. The temporal variability in functional connectivity dynamics is primarily related to emotional arousal followed by dominance. The hubs in the functional networks were found across the right frontal and bilateral parietal lobes, which have been reported to facilitate affect, interoception, action, and memory-related processing. Since our study was performed with naturalistic real-life resembling emotional videos, the study contributes significantly to understanding the dynamics of emotion processing. The results support constructivist theories of emotional experience and show that changes in dynamic functional connectivity can predict aspects of our emotional experience.

Using deep clustering to improve fMRI dynamic functional connectivity analysis

Dynamic functional connectivity (dFC) analysis of resting-state fMRI data is commonly performed by calculating sliding-window correlations (SWC), followed by k-means clustering in order to assign each window to a given state. Studies using synthetic data have shown that k-means performance is highly dependent on sliding window parameters and signal-to-noise ratio. Additionally, sources of heterogeneity between subjects may affect the accuracy of group-level clustering, thus affecting measurements of dFC state temporal properties such as dwell time and fractional occupancy. This may result in spurious conclusions regarding differences between groups (e.g. when comparing a clinical population to healthy controls). Therefore, is it important to quantify the ability of k-means to estimate dFC state temporal properties when applied to cohorts of multiple subjects, and to explore ways in which clustering performance can be maximised. Here, we explore the use of dimensionality reduction methods prior to clustering in order to map high-dimensional data to a lower dimensional space, providing salient features to the subsequent clustering step. We assess the use of deep autoencoders for dimensionality reduction prior to applying k-means clustering to the encoded data. We compare this deep clustering method to dimensionality reduction using principle component analysis (PCA), uniform manifold approximation and projection (UMAP), as well as applying k-means to the original feature space using either L1 or L2 distance. We provide extensive quantitative evaluation of clustering performance using synthetic datasets, representing data from multiple heterogeneous subjects. In synthetic data we find that deep clustering gives the best performance, while other approaches are often insufficient to capture temporal properties of dFC states. We then demonstrate the application of each method to real-world data from human subjects and show that the choice of dimensionality reduction method has a significant effect on group-level measurements of state temporal properties.

Disrupted dynamic network reconfiguration of the brain functional networks of individuals with autism spectrum disorder

Human and animal studies on brain functions in subjects with autism spectrum disorder have confirmed the aberrant organization of functional networks. However, little is known about the neural features underlying these impairments.

Using community structure analyses (recruitment and integration), the current study explored the functional network features of individuals with autism spectrum disorder from one database (101 individuals with autism spectrum disorder and 120 healthy controls) and tested the replicability in an independent database (50 individuals with autism spectrum disorder and 74 healthy controls). Additionally, the study divided subjects into different age groups and tested the features in different subgroups.

As for recruitment, subjects with autism spectrum disorder had lower coefficients in the default mode network and basal ganglia network than healthy controls. The integration results showed that subjects with autism spectrum disorder had a lower coefficient than healthy controls in the default mode network -medial frontal network and basal ganglia network -limbic networks. The results for the default mode network were mostly replicated in the independent database, but the results for the basal ganglia network were not. The results for different age groups were also analyzed, and the replicability was tested in different databases.

The lower recruitment in subjects with autism spectrum disorder suggests that they are less efficient at engaging these networks when performing relevant tasks. The lower integration results suggest impaired flexibility in cognitive functions in individuals with autism spectrum disorder. All these findings might explain why subjects with autism spectrum disorder show impaired brain networks and have important therapeutic implications for developing potentially effective interventions.

Mental Activity as the Bridge between Neural Biomarkers and Symptoms of Psychiatric Illness

The Research Domain Criteria (RDoC) initiative challenges researchers to build neurobehavioral models of psychiatric illness with the hope that such models identify better targets that will yield more effective treatment. However, a guide for building such models was not provided and symptom heterogeneity within Diagnostic Statistical Manual categories has hampered progress in identifying endophenotypes that underlie mental illness. We propose that the best chance to discover viable biomarkers and treatment targets for psychiatric illness is to investigate a triangle of relationships: severity of a specific psychiatric symptom that correlates to mental activity that correlates to a neural activity signature. We propose that this is the minimal model complexity required to advance the field of psychiatry. With an understanding of how neural activity relates to the experience of the patient, a genuine understanding for how treatment imparts its therapeutic effect is possible. After the discovery of this three-fold relationship, causal testing is required in which the neural activity pattern is directly enhanced or suppressed to provide causal, instead of just correlational, evidence for the biomarker. We suggest using non-invasive brain stimulation (NIBS) as these techniques provide tools to precisely manipulate spatial and temporal activity patterns. We detail how this approach enabled the discovery of two orthogonal electroencephalography (EEG) activity patterns associated with anhedonia and anxiosomatic symptoms in depression that can serve as future treatment targets. Altogether, we propose a systematic approach for building neurobehavioral models for dimensional psychiatry.

Driving brain state transitions in major depressive disorder through external stimulation

Major depressive disorder (MDD) as a dysfunction of neural circuits and brain networks has been established in modern neuroimaging sciences. However, the brain state transitions between MDD and health through external stimulation remain unclear, which limits translation to clinical contexts and demonstrable clinical utility. We propose a framework of the large-scale whole-brain network model for MDD linking the underlying anatomical connectivity with functional dynamics obtained from diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). Then, we further explored the optimal brain regions to promote the transition of brain states between MDD and health through external stimulation of the model. Based on the whole-brain model successfully fitting the brain state space in MDD and the health, we demonstrated that the transition from MDD to health is achieved by the excitatory activation of the limbic system and from health to MDD by the inhibitory stimulation of the reward circuit. Our finding provides novel biophysical evidence for the neural mechanism of MDD and its recovery and allows the discovery of new stimulation targets for MDD recovery.

Simulated Attack Reveals How Lesions Affect Network Properties in Poststroke Aphasia

Aphasia is a prevalent cognitive syndrome caused by stroke. The rarity of premorbid imaging and heterogeneity of lesion obscures the links between the local effects of the lesion, global anatomic network organization, and aphasia symptoms. We applied a simulated attack approach in humans to examine the effects of 39 stroke lesions (16 females) on anatomic network topology by simulating their effects in a control sample of 36 healthy (15 females) brain networks. We focused on measures of global network organization thought to support overall brain function and resilience in the whole brain and within the left hemisphere. After removing lesion volume from the network topology measures and behavioral scores [the Western Aphasia Battery Aphasia Quotient (WAB-AQ), four behavioral factor scores obtained from a neuropsychological battery, and a factor sum], we compared the behavioral variance accounted for by simulated poststroke connectomes to that observed in the randomly permuted data. Global measures of anatomic network topology in the whole brain and left hemisphere accounted for 10% variance or more of the WAB-AQ and the lexical factor score beyond lesion volume and null permutations. Streamline networks provided more reliable point estimates than FA networks. Edge weights and network efficiency were weighted most highly in predicting the WAB-AQ for FA networks. Overall, our results suggest that global network measures provide modest statistical value beyond lesion volume when predicting overall aphasia severity, but less value in predicting specific behaviors. Variability in estimates could be induced by premorbid ability, deafferentation and diaschisis, and neuroplasticity following stroke.SIGNIFICANCE STATEMENT Poststroke, the remaining neuroanatomy maintains cognition and supports recovery. However, studies often use small, cross-sectional samples that cannot fully model the interactions between lesions and other variables that affect networks in stroke. Alternate methods are required to account for these effects. "Simulated attack" models are computational approaches that apply virtual damage to the brain and measure their putative consequences. Using a simulated attack model, we estimated how simulated damage to anatomic networks could account for language performance. Overall, our results reveal that global network measures can provide modest statistical value predicting overall aphasia severity, but less value in predicting specific behaviors. These findings suggest that more theoretically precise network models could be necessary to robustly predict individual outcomes in aphasia.

Training the Conductor of the Brainwave Symphony: In Search of a Common Mechanism of Action for All Methods of Neurofeedback

There are several different methods of neurofeedback, most of which presume an operant conditioning model whereby the subject learns to control their brain activity in particular regions of the brain and/or at particular brainwave frequencies based on reinforcement. One method, however, called infra-low frequency [ILF] neurofeedback cannot be explained through this paradigm, yet it has profound effects on brain function. Like a conductor of a symphony, recent evidence demonstrates that the primary ILF (typically between 0.01–0.1 Hz), which correlates with the fluctuation of oxygenated and deoxygenated blood in the brain, regulates all of the classic brainwave bands (i.e. alpha, theta, delta, beta, gamma). The success of ILF neurofeedback suggests that all forms of neurofeedback may work through a similar mechanism that does not fit the operant conditioning paradigm. This chapter focuses on the possible mechanisms of action for ILF neurofeedback, which may be generalized, based on current evidence.

Social cognitive network neuroscience

Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks-collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach-which leverages methods from the field of network neuroscience and graph theory-can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.

Degeneracy measures in biologically plausible random Boolean networks

Background

Degeneracy—the ability of structurally different elements to perform similar functions—is a property of many biological systems. Highly degenerate systems show resilience to perturbations and damage because the system can compensate for compromised function due to reconfiguration of the underlying network dynamics. Degeneracy thus suggests how biological systems can thrive despite changes to internal and external demands. Although degeneracy is a feature of network topologies and seems to be implicated in a wide variety of biological processes, research on degeneracy in biological networks is mostly limited to weighted networks. In this study, we test an information theoretic definition of degeneracy on random Boolean networks, frequently used to model gene regulatory networks. Random Boolean networks are discrete dynamical systems with binary connectivity and thus, these networks are well-suited for tracing information flow and the causal effects. By generating networks with random binary wiring diagrams, we test the effects of systematic lesioning of connections and perturbations of the network nodes on the degeneracy measure.

Results

Our analysis shows that degeneracy, on average, is the highest in networks in which ~ 20% of the connections are lesioned while 50% of the nodes are perturbed. Moreover, our results for the networks with no lesions and the fully-lesioned networks are comparable to the degeneracy measures from weighted networks, thus we show that the degeneracy measure is applicable to different networks.

Conclusions

Such a generalized applicability implies that degeneracy measures may be a useful tool for investigating a wide range of biological networks and, therefore, can be used to make predictions about the variety of systems’ ability to recover function.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04601-5.

Eight-week multi-domain cognitive training does not impact large-scale resting-state brain networks in Parkinson's disease

There is meta-analytic evidence for the efficacy of cognitive training (CT) in Parkinson's disease (PD). We performed a randomized controlled trial where we found small positive effects of CT on executive function and processing speed in individuals with PD (n_total = 140). In this study, we assessed the effects of CT on brain network connectivity and topology in a subsample of the full study population (n_mri = 86). Participants were randomized into an online multi-domain CT and an active control condition and performed 24 sessions of either intervention in eight weeks. Resting-state functional MRI scans were acquired in addition to extensive clinical and neuropsychological assessments pre- and post-intervention. In line with our preregistered analysis plan (osf.io/3st82), we computed connectivity between 'cognitive' resting-state networks and computed topological outcomes at the whole-brain and sub-network level. We assessed group differences after the intervention with mixed-model analyses adjusting for baseline performance and analyzed the association between network and cognitive performance changes with repeated measures correlation analyses. The final analysis sample consisted of 71 participants (n CT = 37). After intervention there were no group differences on between-network connectivity and network topological outcomes. No associations between neural network and neuropsychological performance change were found. CT increased segregated network topology in a small sub-sample of cognitively intact participants. Post-hoc nodal analyses showed post-intervention enhanced connectivity of both the dorsal anterior cingulate cortex and dorsolateral prefrontal cortex in the CT group. The results suggest no large-scale brain network effects of eight-week computerized CT, but rather localized connectivity changes of key regions in cognitive function, that potentially reflect the specific effects of the intervention.