The bridge between cognition and behavior in acquired brain injury: A graph theoretical approach

Patients with dementia with Lewy bodies display a signature alteration of their cognitive connectome

Cognition plays a central role in the diagnosis and characterization of dementia with Lewy bodies (DLB). However, the complex associations among cognitive deficits in different domains in DLB are largely unknown. To characterize these associations, we investigated and compared the cognitive connectome of DLB patients, healthy controls (HC), and Alzheimer's disease patients (AD). We obtained data from the National Alzheimer's Coordinating Center. We built cognitive connectomes for DLB (n = 104), HC (n = 3703), and AD (n = 1985) using correlations among 24 cognitive measures mapping multiple cognitive domains. Connectomes were compared using global and nodal graph measures of centrality, integration, and segregation. For global measures, DLB showed a higher global efficiency (integration) and lower transitivity (segregation) than HC and AD. For nodal measures, DLB showed higher global efficiency in most measures, higher participation (centrality) in free-recall memory, processing speed/attention, and executive measures, and lower local efficiency (segregation) than HC. Compared with AD, DLB showed lower nodal strength and local efficiency, especially in memory consolidation. The cognitive connectome of DLB shows a loss of segregation, leading to a loss of cognitive specialization. This study provides the data to advance the understanding of cognitive impairment and clinical phenotype in DLB, with implications for differential diagnosis.

A network approach to subjective cognitive decline: Exploring multivariate relationships in neuropsychological test performance across Alzheimer's disease risk states

Subjective cognitive decline (SCD) is characterized by subjective concerns of cognitive change despite test performance within normal range. Although those with SCD are at higher risk for developing further cognitive decline, we still lack methods using objective cognitive measures that reliably distinguish SCD from cognitively normal aging at the group level. Network analysis may help to address this by modeling cognitive performance as a web of intertwined cognitive abilities, providing insight into the multivariate associations determining cognitive status. Following previous network studies of mild cognitive impairment (MCI) and Alzheimer's dementia (AD), the current study centered upon the novel visualization and analysis of the SCD cognitive network compared to cognitively normal (CN) older adult, MCI, and AD group networks. Cross-sectional neuropsychological data from CIMA-Q and COMPASS-ND cohorts were used to construct Gaussian graphical models for CN (n = 122), SCD (n = 207), MCI (n = 210), and AD (n = 79) groups. Group networks were explored in terms of global network structure, prominent edge weights, and strength centrality indices. CN and SCD group networks were contrasted using the Network Comparison Test. Results indicate that CN and SCD groups did not differ in univariate cognitive performance or global network structure. However, measures of strength centrality, principally in executive functioning and processing speed, showed a CN-SCD-MCI gradient where subtle differences within the SCD network suggest that SCD is an intermediary between CN and MCI stages. Additional results may indicate a distinctiveness of network structure in AD, a reversal in network influence between age and general cognitive status as clinical impairment increases, and potential evidence for cognitive reserve. Together, these results provide evidence that network-specific metrics are sensitive to cognitive performance changes across the dementia risk spectrum and can help to objectively distinguish SCD group cognitive performance from that of the CN group.

The influence of working memory and processing speed on other aspects of cognitive functioning in de novo Parkinson's disease: Initial findings from network modelling and graph theory

Deficits in working memory (WM) and processing speed (PS) are thought to undermine other cognitive functions in de novo Parkinson's disease (dnPD). However, these interrelationships are only partially understood. This study investigated whether there are stronger relationships between verbal WM and verbal episodic memory encoding and retrieval, whether verbal WM and PS have a greater influence on other aspects of cognitive functioning, and whether the overall strength of interrelationships among several cognitive functions differs in dnPD compared to health. Data for 198 healthy controls (HCs) and 293 dnPD patients were analysed. Participants completed a neuropsychological battery probing verbal WM, PS, verbal episodic memory, semantic memory, language and visuospatial functioning. Deficit analysis, network modelling and graph theory were combined to compare the groups. Results suggested that verbal WM performance, while slightly impaired, was more strongly associated with measures of verbal episodic memory encoding and retrieval, as well as other measured cognitive functions in the dnPD network model compared to the HC network model. PS task performance was impaired and more strongly associated with other neuropsychological task scores in the dnPD model. Associations among task scores were stronger overall in the dnPD model. Together, these results provide further evidence that WM and PS are important influences on the other aspects of cognitive functioning measured in this study in dnPD. Moreover, they provide novel evidence that verbal WM and PS might bear greater influence on the other measured cognitive functions and that these functions are more strongly intertwined in dnPD compared to health.

Network neuropsychology: The map and the territory

In "network neuropsychology", network modelling and graph theory is applied to the neuropsychological test scores of patients with neurological disorders to investigate cognitive functioning. This review identifies the emerging literature on several disorders before focusing on the assumptions about cognition underlying the studies; specifically, that cognition can be thought of as a network of interrelated variables and that changes in these interrelationships, or cognitive rearrangement, can occur in neurological disorders. Next the review appraises how well network models can provide a "map" of this cognitive "territory". In particular, the review considers the lack of correspondence between the variables and properties of network models and cognitive functioning. The challenges of explicitly accounting for latent cognitive constructs and making inferences about cognition based on associative, as opposed to dissociative, methods are also discussed. It is concluded that the validity of network neuropsychological models is yet to be established and that cognitive theory and experiments, as well as network models, are needed to develop and interpret better maps.

Exploring the Complexity of Aphasia With Network Analysis

Purpose Aphasia is a complex, neurogenic language disorder, with different aphasia syndromes hallmarked by impairment in fluency, auditory comprehension, naming, and/or repetition. Broad, standardized assessments of language domains and specific language and cognitive assessments provide a holistic impairment profile of a person with aphasia. While many recognize the correlations between assessments, there remains a need to continue understanding the complexity of relationships between assessments for the purpose of better characterization of language impairment profiles of persons with aphasia. We explored the use of network analysis to identify the complex relationships between a variety of language assessments. Method We computed a regularized partial correlation network and a directed acyclic graph network to estimate the relations between different aphasia assessments in 128 persons with aphasia. Results Western Aphasia Battery-Revised Comprehension subtest was the most central assessment in the aphasia symptom network, whereas the Philadelphia Naming Test had the most putative causal influence on other assessments. Additionally, the language assessments segregated into three empirically derived communities denoting phonology, semantics, and syntax. Furthermore, several assessments, including the Philadelphia Naming Test, belonged to multiple communities, suggesting that certain assessments may capture multiple language impairments. Conclusion We discuss the implications of using a network analysis approach for clinical intervention and driving forward novel questions in the field of clinical aphasiology. Supplemental Material https://doi.org/10.23641/asha.16620229.

The Cognitive Connectome in Healthy Aging

Objectives: Cognitive aging has been extensively investigated using both univariate and multivariate analyses. Sophisticated multivariate approaches such as graph theory could potentially capture unknown complex associations between multiple cognitive variables. The aim of this study was to assess whether cognition is organized into a structure that could be called the “cognitive connectome,” and whether such connectome differs between age groups.

Methods: A total of 334 cognitively unimpaired individuals were stratified into early-middle-age (37–50 years, n = 110), late-middle-age (51–64 years, n = 106), and elderly (65–78 years, n = 118) groups. We built cognitive networks from 47 cognitive variables for each age group using graph theory and compared the groups using different global and nodal graph measures.

Results: We identified a cognitive connectome characterized by five modules: verbal memory, visual memory—visuospatial abilities, procedural memory, executive—premotor functions, and processing speed. The elderly group showed reduced transitivity and average strength as well as increased global efficiency compared with the early-middle-age group. The late-middle-age group showed reduced global and local efficiency and modularity compared with the early-middle-age group. Nodal analyses showed the important role of executive functions and processing speed in explaining the differences between age groups.

Conclusions: We identified a cognitive connectome that is rather stable during aging in cognitively healthy individuals, with the observed differences highlighting the important role of executive functions and processing speed. We translated the connectome concept from the neuroimaging field to cognitive data, demonstrating its potential to advance our understanding of the complexity of cognitive aging.

A Graph Theory Approach to Clarifying Aging and Disease Related Changes in Cognitive Networks

In accordance with the physiological networks that underlie it, human cognition is characterized by both the segregation and interdependence of a number of cognitive domains. Cognition itself, therefore, can be conceptualized as a network of functions. A network approach to cognition has previously revealed topological differences in cognitive profiles between healthy and disease populations. The present study, therefore, used graph theory to determine variation in cognitive profiles across healthy aging and cognitive impairment. A comprehensive neuropsychological test battery was administered to 415 participants. This included three groups of healthy adults aged 18-39 (n = 75), 40-64 (n = 75), and 65 and over (n = 70) and three patient groups with either amnestic (n = 75) or non-amnestic (n = 60) mild cognitive impairment or Alzheimer's type dementia (n = 60). For each group, cognitive networks were created reflective of test-to-test covariance, in which nodes represented cognitive tests and edges reflected statistical inter-nodal significance (p < 0.05). Network metrics were derived using the Brain Connectivity Toolbox. Network-wide clustering, local efficiency and global efficiency of nodes showed linear differences across the stages of aging, being significantly higher among older adults when compared with younger groups. Among patients, these metrics were significantly higher again when compared with healthy older controls. Conversely, average betweenness centralities were highest in middle-aged participants and lower among older adults and patients. In particular, compared with controls, patients demonstrated a distinct lack of centrality in the domains of semantic processing and abstract reasoning. Network composition in the amnestic mild cognitive impairment group was similar to the network of Alzheimer's dementia patients. Using graph theoretical methods, this study demonstrates that the composition of cognitive networks may be measurably altered by the aging process and differentially impacted by pathological cognitive impairment. Network alterations characteristic of Alzheimer's disease in particular may occur early and be distinct from alterations associated with differing types of cognitive impairment. A shift in centrality between domains may be particularly relevant in identifying cognitive profiles indicative of underlying disease. Such techniques may contribute to the future development of more sophisticated diagnostic tools for neurodegenerative disease.

Unfolding the Complex Dynamic Interplay Between Attentional Processes and Anxiety: A Commentary on Ghassemzadeh, Rothbart, and Posner

Theories about the involvement of attention in feelings of fear and anxiety have been debated in philosophical circles since long before the foundation of experimental psychology and cognitive neuroscience. In this issue, Ghassemzadeh, Rothbart, and Posner (2019) provide a much-needed historical and conceptual review of the relations between attention and anxiety disorders. Throughout their paper, they argue that insights from the study of brain networks of attention offer a particularly viable prospect for best clarifying the complex relations between attentional processes and anxiety. We fully share this view. Moreover, we believe that the computational and conceptual tools of network analysis (also known as graph theory) can enable researchers to move even closer to elucidating the complex dynamic interplay between those phenomena. In this commentary, we explain why and how to use network analysis for this purpose.

The bridge between cognition and behavior in acquired brain injury: A graph theoretical approach

BACKGROUND

The assumption is that executive dysfunctions (EF), associated with frontal lobe injury, are responsible for behavioral disturbances. Some studies do not find a relationship between EF and behavior following frontal lobe lesions. Our main goal of this study was to use a novel statistical method, graph theory, to analyze this relationship in different brain injury groups; frontal lobe damage, non-frontal lobe damage, and controls. Within the frontal group, we expect to find a pattern of executive nodes that are highly interconnected.

METHODS

For each group, we modeled the relationship between executive functions and behavior as a network of interdependent variables. The cognitive tests and the behavioral questionnaire are the "nodes" in the network, while the relationships between the nodes were modeled as the correlations between two nodes corrected for the correlation with all other nodes in the network. Sparse networks were estimated within each group using graphical LASSO. We analyzed the relative importance of the nodes within a network (centrality) and the clustering (modularity) of the different nodes.

RESULTS

Network analysis showed distinct patterns of relationships between EF and behavior in the three subgroups. The performance on the verbal learning test is the most central node in all the networks. In the frontal group, verbal memory forms a community with working memory and fluency. The behavioral nodes do not differentiate between groups or form clusters with cognitive nodes. No other communities were found for cognitive and behavioral nodes.

CONCLUSION

The cognitive phenotype of the frontal lobe damaged group, with its stability and proportion, might be theoretically interpreted as a potential "buffer" for possible cognitive executive deficits. This might explain some of the ambiguity found in the literature. This alternative approach on cognitive test scores provides a different and possibly complimentary perspective of the neuropsychology of brain-injured patients.