EEG Functional Connectivity Differences Predict Future Conversion to Dementia in Mild Cognitive Impairment With Lewy Body or Alzheimer Disease.

BACKGROUND: Predicting which individuals may convert to dementia from mild cognitive impairment (MCI) remains difficult in clinical practice. Electroencephalography (EEG) is a widely available investigation but there is limited research exploring EEG connectivity differences in patients with MCI who convert to dementia. METHODS: Participants with a diagnosis of MCI due to Alzheimer's disease (MCI-AD) or Lewy body disease (MCI-LB) underwent resting state EEG recording. They were followed up annually with a review of the clinical diagnosis (n = 66). Participants with a diagnosis of dementia at year 1 or year 2 follow up were classed as converters (n = 23) and those with a diagnosis of MCI at year 2 were classed as stable (n = 43). We used phase lag index (PLI) to estimate functional connectivity as well as analysing dominant frequency (DF) and relative band power. The Network-based statistic (NBS) toolbox was used to assess differences in network topology. RESULTS: The converting group had reduced DF (U = 285.5, p = 0.005) and increased relative pre-alpha power (U = 702, p = 0.005) consistent with previous findings. PLI showed reduced average beta band synchrony in the converting group (U = 311, p = 0.014) as well as significant differences in alpha and beta network topology. Logistic regression models using regional beta PLI values revealed that right central to right lateral (Sens = 56.5%, Spec = 86.0%, -2LL = 72.48, p = 0.017) and left central to right lateral (Sens = 47.8%, Spec = 81.4%, -2LL = 71.37, p = 0.012) had the best classification accuracy and fit when adjusted for age and MMSE score. CONCLUSION: Patients with MCI who convert to dementia have significant differences in EEG frequency, average connectivity and network topology prior to the onset of dementia. The MCI group is clinically heterogeneous and have underlying physiological differences that may be driving the progression of cognitive symptoms. EEG connectivity could be useful to predict which patients with MCI-AD and MCI-LB convert to dementia, regardless of the neurodegenerative aetiology.

Instrumental variables in real-world clinical studies of dementia and neurodegenerative disease: Systematic review of the subject-matter argumentation, falsification test, and study design strategies to justify a valid instrument.

OBJECTIVES: We systematically reviewed how investigators argued for and justified the validity of their instrumental variables (IV) in clinical studies of dementia and neurodegenerative disease. METHODS: We included studies using IV analysis with observational data to investigate causal effects in clinical research studies of dementia and neurodegenerative disease. We reported the subject-matter argumentation, falsification test, and study design strategies used to satisfy the three assumptions of a valid IV: relevance, exclusion restriction, and exchangeability. RESULTS: Justification for the relevance assumption was performed in all 12 included studies, exclusion restriction in seven studies, and exchangeability in nine studies. Two subject-matter argumentation strategies emerged from seven studies on the relevance of their IV. All studies except one provided quantitative evidence for the strength of the association between the IV and exposure variable. Four argumentation strategies emerged for exclusion restriction from six studies. Four falsification tests were performed across three studies. Three argumentation strategies emerged for exchangeability across four studies. Nine falsification tests were performed across nine studies. Two notable study design strategies were reported. CONCLUSION: Our results reinforce IV analysis as a feasible option for clinical researchers in dementia and neurodegenerative disease by clarifying known strategies used to validate an IV.

Knee anatomic geometry accurately predicts risk of anterior cruciate ligament rupture.

BACKGROUND: Certain anatomical characteristics of the knee have potential relationships with the risk of anterior cruciate ligament (ACL) rupture. There remains a need for stronger evidence using arthroscopy as well as radiological imaging to accurately clarify these relationships. PURPOSE: To compare the anatomic geometry of the knee joint between patients with and without ACL ruptures. MATERIAL AND METHODS: Case-controlled study of patients with arthroscopically confirmed ACL ruptures (ACL group) compared to patients with arthroscopically confirmed normal ACLs (control group). Magnetic resonance imaging scans were assessed for a total of 14 quantitative radiological variables including medial and lateral tibial slope angles (bone and cartilage measurements), meniscal height, femoral width, intercondylar notch width and tibial depth. RESULTS: A total of 105 patients were included in the study, 55 in the ACL group and 50 in the control group. There was a significant difference (P < 0.05) of most of the radiological measurements between the two groups. There was also a significant difference (P < 0.05) when comparing the variables between sexes. Multiple logistic regression analysis produced a mathematical model utilizing all the radiological measurements with 92.6% classification accuracy in predicating an ACL rupture with the medial tibial slope angle being the strongest predicator variable (odds ratio = 8.97, P = 0.011). CONCLUSION: Greater postero-inferior directed slope measurements of bone and cartilage in both the lateral and medial compartments with a narrower intercondylar notch width increase the risk of ACL rupture. Mathematical modelling can accurately predict the risk of ACL rupture.