Development of a machine learning algorithm to predict the residual cognitive reserve index

Elucidating the mechanisms by which late-life neurodegeneration causes cognitive decline requires understanding why some individuals are more resilient than others to the effects of brain change on cognition (cognitive reserve). Currently, there is no way of measuring cognitive reserve that is valid (e.g. capable of moderating brain-cognition associations), widely accessible (e.g. does not require neuroimaging and large sample sizes), and able to provide insight into resilience-promoting mechanisms. To address these limitations, this study sought to determine whether a machine learning approach to combining standard clinical variables could (i) predict a residual-based cognitive reserve criterion standard and (ii) prospectively moderate brain-cognition associations. In a training sample combining data from the University of California (UC) Davis and the Alzheimer's Disease Neuroimaging Initiative-2 (ADNI-2) cohort (N = 1665), we operationalized cognitive reserve using an MRI-based residual approach. An eXtreme Gradient Boosting machine learning algorithm was trained to predict this residual reserve index (RRI) using three models: Minimal (basic clinical data, such as age, education, anthropometrics, and blood pressure), Extended (Minimal model plus cognitive screening, word reading, and depression measures), and Full [Extended model plus Clinical Dementia Rating (CDR) and Everyday Cognition (ECog) scale]. External validation was performed in an independent sample of ADNI 1/3/GO participants (N = 1640), which examined whether the effects of brain change on cognitive change were moderated by the machine learning models' cognitive reserve estimates. The three machine learning models differed in their accuracy and validity. The Minimal model did not correlate strongly with the criterion standard (r = 0.23) and did not moderate the effects of brain change on cognitive change. In contrast, the Extended and Full models were modestly correlated with the criterion standard (r = 0.49 and 0.54, respectively) and prospectively moderated longitudinal brain-cognition associations, outperforming other cognitive reserve proxies (education, word reading). The primary difference between the Minimal model-which did not perform well as a measure of cognitive reserve-and the Extended and Full models-which demonstrated good accuracy and validity-is the lack of cognitive performance and informant-report data in the Minimal model. This suggests that basic clinical variables like anthropometrics, vital signs, and demographics are not sufficient for estimating cognitive reserve. Rather, the most accurate and valid estimates of cognitive reserve were obtained when cognitive performance data-ideally augmented by informant-reported functioning-was used. These results indicate that a dynamic and accessible proxy for cognitive reserve can be generated for individuals without neuroimaging data and gives some insight into factors that may promote resilience

Longitudinal declines in event-based, but not time-based, prospective memory among community-dwelling older adults.

Age-related deficits in prospective memory (PM) are well established, but it is not known whether PM is stable over time among older adults. In this study, 271 community-dwelling older adults underwent abaseline neuropsychological evaluation and up to three follow-up visits, approximately 2.4 years apart. Mixed effects linear longitudinal models revealed small, but significant linear declines and between-subjects variability in event-based PM performance. There were no changes in performance on measures of time-based PM, retrospective memory, or executive functions. Changes in event-based PM were not associated with age, retrospective memory, executive functions, or everyday functioning. Among older adults, event-based PM appears to be more susceptible to linear declines than does time-based PM, which future research might examine with regard to the possible underlying cognitive mechanisms of cue encoding, monitoring, detection, and retrieval processes

The McCusker subjective cognitive impairment inventory (McSCI): A novel measure of perceived cognitive decline

Background: Subjective cognitive decline (SCD), i.e. self/other-reported concerns on one’s cognitive functioning without objective evidence of significant decline, is an indicator of dementia risk. There is little consensus on reliability and validity of the available SCD measures. Therefore, introducing a novel and psychometrically sound measure of SCD is timely. Objective: The psychometric properties of a new SCD measure, the McCusker Subjective Cognitive Impairment Inventory–Self-Report (McSCI-S), are reported. Methods: Through review of previously published measures as well as our clinical and research data on people with SCD, we developed a 46-item self-report questionnaire to assess concerns on six cognitive domains, namely, memory, language, orientation, attention and concentration, visuoconstruction abilities and executive function. The McSCI-S was examined in a cohort of 526 participants using factor analysis, item response theory analysis and receiver operating characteristic (ROC) curve. Results: A unidimensional model provided acceptable fit (CFI = 0.94, TLI = 0.94, RMSEA [90% CI] = 0.052 [.049, 0.055], WRMR = 1.45). The McSCI-S internal consistency was excellent (.96). A cut-off score of ≥24 is proposed to identify participants with SCDs. Higher McSCI-S scores were associated with poorer general cognition, episodic verbal memory, executive function and greater memory complaints and depressive scores (P \u3c.001), controlling for age, sex and education. Conclusions: Excellent reliability and construct validity suggest the McSCI-S estimates SCDs with acceptable accuracy while capturing self-reported concerns for various cognitive domains. The psychometric analysis indicated that this measure can be used in cohort studies as well as on individual, clinical settings to assess SCDs

TDP-43 Proteinopathy and Motor Neuron Disease in Chronic Traumatic Encephalopathy

Epidemiological evidence suggests that the incidence of amyotrophic lateral sclerosis is increased in association with head injury. Repetitive head injury is also associated with the development of chronic traumatic encephalopathy (CTE), a tauopathy characterized by neurofibrillary tangles throughout the brain in the relative absence of β-amyloid deposits. We examined 12 cases of CTE and, in 10, found a widespread TAR DNA-binding protein of approximately 43 kd (TDP-43) proteinopathy affecting the frontal and temporal cortices, medial temporal lobe, basal ganglia, diencephalon, and brainstem. Three athletes with CTE also developed a progressive motor neuron disease with profound weakness, atrophy, spasticity, and fasciculations several years before death. In these 3 cases, there were abundant TDP-43–positive inclusions and neurites in the spinal cord in addition to tau neurofibrillary changes, motor neuron loss, and corticospinal tract degeneration. The TDP-43 proteinopathy associated with CTE is similar to that found in frontotemporal lobar degeneration with TDP-43 inclusions, in that widespread regions of the brain are affected. Akin to frontotemporal lobar degeneration with TDP-43 inclusions, in some individuals with CTE, the TDP-43 proteinopathy extends to involve the spinal cord and is associated with motor neuron disease. This is the first pathological evidence that repetitive head trauma experienced in collision sports might be associated with the development of a motor neuron disease

Practice Effects on Story Memory and List Learning Tests in the Neuropsychological Assessment of Older Adults

http://dx.doi.org/10.1371/journal.pone.016449

Publicly Available Data

Gavett BE, Zhao R, John SE, Bussell CA, Roberts JR, Yue C (2017) Phishing suspiciousness in older and younger adults: The role of executive functioning. PLoS ONE 12(2): e0171620. doi:10.1371/journal.pone.017162