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.

Objective: Prior to evidence of episodic memory decline, a lengthy preclinical phase of Alzheimer’s disease (AD) exists characterized by the build-up of tau pathology within extrahippocampal structures. Semantic memory, also impaired in AD, has been linked to degradation within these earliest affected areas. This study aimed to assess the utility of performance discrepancies between letter and category verbal fluency tasks to detect neuronal loss in brain regions affected very early by AD. Method: Whole-brain voxel-based morphometry was used to assess the neural correlates of semantic processing in three patient groups: two groups of mild cognitive impairment (MCI) patients split into mildly (n = 58) and moderately (n = 53) affected and a mild AD dementia group (n = 71). Discrepancies between the level of impairment on the semantic category fluency test and nonsemantic letter fluency test were calculated for each participant and included in regression models measuring the relationship between semantic memory and whole-brain gray matter volume. Results: Patients at all disease stages demonstrated a loss of the normal semantic advantage in fluency tests, showing significantly greater impairments in category relative to letter fluency. Discrepancy scores in mild MCI correlated strongly with the structural integrity of the anterior medial temporal lobes. Correlations in more severely affected groups were weaker and more widespread. Conclusions: Semantic memory appears a useful indicator of even the earliest stages of medial temporal damage in AD. With advancing disease severity, the discrepancy index loses its focal anatomical association, reinforcing its value as an early marker of incipient decline.

Since their development, verbal fluency tests (VFTs) have been used extensively throughout research and in clinical settings to assess a variety of cognitive functions in diverse populations. In Alzheimer’s disease (AD), these tasks have proven particularly valuable in identifying the earliest forms of cognitive decline in semantic processing and have been shown to relate specifically to brain regions associated with the initial stages of pathological change. In recent years, researchers have developed more nuanced techniques to evaluate verbal fluency performance, extracting a wide range of cognitive metrics from these simple neuropsychological tests. Such novel techniques allow for a more detailed exploration of the cognitive processes underlying successful task performance beyond the raw test score. The versatility of VFTs and the richness of data they may provide, in light of their low cost and speed of administration, therefore, highlight their potential value both in future research as outcome measures for clinical trials and in a clinical setting as a screening measure for early detection of neurodegenerative diseases.

Background In accordance with the physiological networks which underlie it, human cognition is characterised by both segregation and interdependence of a number of cognitive domains. Cognition itself, therefore, constitutes a network, the organisation of which may be quantifiable using methods of graph theory. The present study aimed to exploit graph theory methods to assess changes in cognitive profiles throughout healthy ageing and further interrogate how such profiles may differ in the presence of pathology. Method A comprehensive neuropsychological test battery was performed in 6 participant groups; 3 groups of healthy adults split into the age ranges 18‐39 (N 75), 40‐64 (N 75) and 65 and over (N 70) and 3 patient groups including adults with amnestic mild cognitive impairment (MCI, N 75), non‐amnestic MCI (N 31) and Alzheimer’s type dementia (N 60). Partial correlations were performed on standardised scores between 16 cognitive tests. A binarised adjacency matrix was then created for each group in which significant correlations (p <.05) were retained as the connections or edges and each cognitive test represented a node. Network analysis measures were then applied to each matrix using the Brain Connectivity Toolbox. Result Network wide clustering and local efficiency of nodes showed an increase between the stages of normal ageing and this was considerably exacerbated by the presence of disease. Conversely, average betweenness centrality first showed an increase between the young and middle‐aged groups but was reduced compared to middle‐aged in the healthy older group. This reduction appeared to be again exacerbated within patients. These findings were particularly apparent between patients and healthy adults in tests of memory function. Conclusion While whole network approaches have become a staple of functional neuroimaging, such techniques have rarely been applied to the structure of cognition itself. Here, it has been demonstrated that the organisation of our cognitive networks is influenced not only by the ageing process but is further significantly altered by the presence of disease. Further investigation in this area may provide a novel approach for quantifying the effects of disease on cognitive function and could contribute to the development of sophisticated diagnostic tools in the future.

Background: Neuropsychiatric symptoms (NPS) in Lewy body diseases, including dementia with Lewy bodies (DLB) and Parkinson's disease (PD) dementia, occur frequently and at an early stage in disease progression. Such symptoms are significantly associated with quality of life, caregiver burden and functional limitations. Limited evidence exists, however, outlining the relationship between NPS and cognitive decline.Methods: Data from 217 participants were derived from three cohort studies. Patients diagnosed with either MCI with Lewy bodies (MCI-LB, n = 67), PD-MCI (n = 56) or Alzheimer's disease (MCI-AD, n = 39) and a group of healthy older adults (n = 55) completed comprehensive neuropsychological and neuropsychiatric assessment and were followed up longitudinally for a median of three years. NPS identified at baseline by the Neuropsychiatric Inventory (NPI) were included as predictors in linear regressions and linear mixed effects models to assess the relationship between NPS and performance on a range of cognitive tests both at baseline and longitudinally.Results: At baseline, NPS were most prevalent and severe among the MCI-LB group (49% with NPI score >4 in at least one symptom) and least prevalent in controls (7% with NPI score >4). Total NPI score was not related to baseline cognitive function in any domain in any group. Greater baseline total NPI score was associated with steeper rates of decline among PD-MCI patients on a measure of processing speed (p = .01). Among healthy adults, only sleep disturbance was associated with steeper rates of decline on global cognition (p = .005). None of the patient groups showed any association between individual NPS and decline on a measure of global cognitive function. Conclusions:The lack of a significant relationship found between NPS and either global cognitive function or decline in global cognition seen among MCI patients suggests that at this prodromal stage of disease, NPS appear to be of limited clinical impact and indicates that the mechanisms of cognitive and neuropsychiatric symptoms may differ in early Lewy body disease. Future work will investigate these mechanisms using multimodal imaging data from this cohort.