Predictor Of Cognitive Decline Research Articles

CSF levels of proinflammatory and anti-inflammatory cytokines as predictors of cognitive decline across Alzheimer’s disease spectrum (N4.004)

CSF levels of proinflammatory and anti-inflammatory cytokines as predictors of cognitive decline across Alzheimer’s disease spectrum (N4.004)

Prediction of cognitive decline in older breast cancer survivors: the Thinking and Living with Cancer study.

Cancer survivors commonly report cognitive declines after cancer therapy. Due to the complex etiology of cancer-related cognitive decline (CRCD), predicting who will be at risk of CRCD remains a clinical challenge. We developed a model to predict breast cancer survivors who would experience CRCD after systematic treatment. We used the Thinking and Living with Cancer study, a large ongoing multisite prospective study of older breast cancer survivors with complete assessments pre-systemic therapy, 12 months and 24 months after initiation of systemic therapy. Cognition was measured using neuropsychological testing of attention, processing speed, and executive function (APE). CRCD was defined as a 0.25 SD (of observed changes from baseline to 12 months in matched controls) decline or greater in APE score from baseline to 12 months (transient) or persistent as a decline 0.25 SD or greater sustained to 24 months. We used machine learning approaches to predict CRCD using baseline demographics, tumor characteristics and treatment, genotypes, comorbidity, and self-reported physical, psychosocial, and cognitive function. Thirty-two percent of survivors had transient cognitive decline, and 41% of these women experienced persistent decline. Prediction of CRCD was good: yielding an area under the curve of 0.75 and 0.79 for transient and persistent decline, respectively. Variables most informative in predicting CRCD included apolipoprotein E4 positivity, tumor HER2 positivity, obesity, cardiovascular comorbidities, more prescription medications, and higher baseline APE score. Our proof-of-concept tool demonstrates our prediction models are potentially useful to predict risk of CRCD. Future research is needed to validate this approach for predicting CRCD in routine practice settings.

Resting EEG Periodic and Aperiodic Components Predict Cognitive Decline Over 10 Years.

Measures of intrinsic brain function at rest show promise as predictors of cognitive decline in humans, including EEG metrics such as individual α peak frequency (IAPF) and the aperiodic exponent, reflecting the strongest frequency of α oscillations and the relative balance of excitatory/inhibitory neural activity, respectively. Both IAPF and the aperiodic exponent decrease with age and have been associated with worse executive function and working memory. However, few studies have jointly examined their associations with cognitive function, and none have examined their association with longitudinal cognitive decline rather than cross-sectional impairment. In a preregistered secondary analysis of data from the longitudinal Midlife in the United States (MIDUS) study, we tested whether IAPF and aperiodic exponent measured at rest predict cognitive function (N = 235; age at EEG recording M = 55.10, SD = 10.71) over 10 years. The IAPF and the aperiodic exponent interacted to predict decline in overall cognitive ability, even after controlling for age, sex, education, and lag between data collection time points. Post hoc tests showed that "mismatched" IAPF and aperiodic exponents (e.g., higher exponent with lower IAPF) predicted greater cognitive decline compared to "matching" IAPF and aperiodic exponents (e.g., higher exponent with higher IAPF; lower IAPF with lower aperiodic exponent). These effects were largely driven by measures of executive function. Our findings provide the first evidence that IAPF and the aperiodic exponent are joint predictors of cognitive decline from midlife into old age and thus may offer a useful clinical tool for predicting cognitive risk in aging.

Prediction of cognitive decline in healthy aging based on neuropsychiatric symptoms and PET-biomarkers of Alzheimer's disease.

Neuropsychiatric symptoms (NPS) have been associated with a risk of accelerated cognitive decline or conversion to dementia of the Alzheimer's Disease (AD) type. Moreover, the NPS were also associated with higher AD biomarkers (brain tau and amyloid burden) even in non-demented patients. But the effect of the relationship between NPS and biomarkers on cognitive decline has not yet been studied. This work aims to assess the relationship between longitudinal cognitive changes and NPS, specifically depression and anxiety, in association with AD biomarkers in healthy middle-aged to older participants. The cohort consisted of 101 healthy participants aged 50-70years, 66 of whom had neuropsychological assessments of memory, executive functions, and global cognition at a 2-year follow-up. At baseline, NPS were assessed using the Beck Depression and Anxiety Inventories while brain tau and amyloid loads were measured using positron emission topography. For tau burden, THK5351 uptake is used as a proxy of tau and neuroinflammation. Participants, declining or remaining stable at follow-up, were categorized into groups for each cognitive domain. Group classification was investigated using binary logistic regressions based on combined AD biomarkers and the two NPS. The results showed that an association between anxiety and prefrontal amyloid burden significantly classified episodic memory decline, while the classification of global cognitive decline involved temporal and occipital amyloid burden but not NPS. Moreover, depression together with prefrontal and hippocampal tau burden were associated with a decline in memory. The classification of participants based on executive decline was related to depression and mainly prefrontal tau burden. These findings suggest that the combination of NPS and brain biomarkers of AD predicts the occurrence of cognitive decline in aging.

Subjective cognitive impairment and biomarkers of Alzheimer’s disease

AbstractBackgroundSubjective cognitive impairment (SCI) is common in older adults. It may represent the earliest clinical manifestation of cognitive decline and Alzheimer’s disease (AD).Objectives1) to evaluate associations between SCI and cerebrospinal fluid (CSF) biomarkers of AD; 2) to identify specific single questions or question combinations that are related to the presence of cerebral AD pathology; 3) to evaluate whether using SCI related scores or specific questions combined with other clinical features and biomarkers may improve prediction of cognitive decline.MethodWe conducted a longitudinal study in 164 non‐demented subjects participating on a biomarker study in a memory clinic setting, aged 53 to 88 years, with CDR scores of 0 (N = 84) and 0.5 (N = 80). SCI was assessed using the Cognitive Complaint Interview (CCI, Thomas‐Antérion et al., 2006), a validated 10 items questionnaire. Accordingly, SCI is defined as present when the subject answers “yes” to 3 or more items; and/or to item 5, and/or to items A, 4, 5, 7, 8. All participants underwent comprehensive neuropsychological evaluations and CSF biomarkers analysis. “Amyloid positivity" and “AD pathology” were defined according to the CSF Aβ1‐42 levels and pTau181/Aβ1‐42 ratio, respectively. Logistic regressions were used to address the associations of interest.ResultsParticipants with SCI had significantly lower Aβ1‐42 and higher Tau and pTau181 levels compared to those without SCI. SCI was associated with amyloid positivity while CCI total score and the question A (reporting changes in memory) were associated with both amyloid positivity and AD pathology in the whole cohort. When performing analysis separately in the CDR = 0 and CDR = 0.5 subgroups the associations remained significant in the CDR = 0.5 group. Only question 6 (difficulties in finding words) was associated with AD pathology in the CDR = 0 group. Results on the prediction of cognitive decline at follow‐up visits will be presented at the conference.ConclusionIn dementia‐free subjects from this memory clinic cohort SCI‐related scores and single questions indicate amyloid positivity and the presence of AD pathology depending on the absence/presence of mild cognitive impairment. Further studies are needed to evaluate the diagnostic and prognostic utility of cognitive complains in memory clinic patients.

Sensitivity of NIH ToolBox Motor tests to detect amyloid positivity in cognitively normal older adults

AbstractBackgroundChanges in motor functioning and particularly lower motor functioning such as balance and gait have been shown to be an early predictor of dementia (Kueper et al 2017). Based on these results, it has been suggested that balance and gait measures should be a regular component of a clinical dementia risk evaluation. The NIH Toolbox (NIHTB) has developed motor‐based tasks along with its cognitive battery. The aim of this study was to evaluate the utility of several of the NIHTB‐Motor task performance to predict amyloid status in cognitively normal older adults.MethodData from 45 cognitively normal adults age 65+ who were part of a larger study examining changes in driving behavior as predictors of cognitive decline were included in this study. Participants were divided into two groups based on PiB PET imaging and amyloid positivity determined using a centiloid scale: values ≥20 were considered positive, values ≤10 were negative; values between 10 and 20 were considered indeterminate and not included (n = 9)). Twenty nine participants were amyloid negative (OCN‐) and 16 were positive (OCN+). Data from the standing balance, grip strength and 2‐minute walking endurance tasks were analyzed using independent sample t‐tests.ResultThere were no differences in age or education between the two groups. However, there was a significant correlation between age and standing balance uncorrected score. As a result, fully corrected scores were used for this analysis. There was a significant difference between the OCN‐ and OCN+ for the fully corrected standing balance standard score (t = 1.7, p<0.04) such that the OCN+ group had lower balance scores compared to the OCN‐ group. No significance differences were found for grip strength or the 2‐minute walking endurance task.ConclusionThe Standing Balance task from the NIHTB may be a sensitive marker to identify individuals who are at higher risk of developing cognitive impairment and dementia. The NIHTB tasks were designed to be easy to administer in a relatively short amount of time with normative data, and these results suggest the Standard Balance task should be included as a routine measure of dementia risk.

Abstract 17121: Elevated Aortic Stiffness and Impairments in Cerebrovascular Regulation in Cancer Survivorship

Background: Approximately 46% of cancer survivors report cognitive impairments across many types of cancer, which has been associated with anti-cancer therapy and often results in reduced quality of life. Pre-clinical studies suggest that predictors of cognitive decline may involve vascular dysfunction. We aimed to characterize local (cerebrovascular reactivity and cerebral autoregulation) and global (aortic arch stiffness) vascular regulation in cancer survivorship. Methods: Cerebral autoregulation was determined using Mx, a moving 10s-windowed correlation between finger plethysmography-derived blood pressure and cerebral blood velocity of the middle cerebral artery. Higher Mx values represent pressure-passive cerebral blood flow, indicating poor cerebral autoregulation. In addition, cerebral blood velocity, blood pressure, and end-tidal CO 2 were collected during a rebreathing task. MAP was used to calculate an index of cerebrovascular conductance (CVCi). Cerebrovascular reactivity was calculated as the change in cerebral blood velocity or CVCi over the change in end-tidal CO 2 . Aortic arch stiffness, as pulse wave velocity (aaPWV), was calculated from ultrasound scans of the aortic valve and the descending aorta. Differences between groups were assessed with Mann-Whitney tests. Results: Eleven women were recruited (5 controls and 6 cancer survivors who had received treatment). Cancer types included melanoma, lymphoma, and breast cancer. Age and resting MAP were similar between groups. Mx values were higher in cancer survivors (-0.13 ± 0.14 vs. 0.14 ± 0.11, p < 0.05). Cerebrovascular reactivity, both absolute (2.8 ± 1.1 vs. 1.6 ± 0.8, p = 0.05) and as CVCi (16.8 ± 2.4 vs. 2.3 ± 9.4, p < 0.05), were lower in cancer survivors. aaPWV was higher in cancer survivors (2.7 ± 0.8 vs. 3.8 ± 0.6, p < 0.05). Across the entire study sample, aaPWV trended towards an association only with absolute cerebrovascular reactivity (Spearman r = -0.6, p = 0.054). Conclusion: Aortic stiffness was elevated and both cerebrovascular regulatory functions were impaired in cancer survivors compared to healthy controls. Present findings highlight the importance of monitoring cerebral and global vascular function in cancer survivors at high-risk for cognitive decline.

Comparison of the 10-, 14- and 20-Item CES-D Scores as Predictors of Cognitive Decline.

The association between depressive symptomatology and cognitive decline has been examined using the Centre for Epidemiologic Studies-Depression Scale (CES-D); however, concerns have been raised about this self-report measure. Here, we examined how the CES-D total score from the 14- and 10-item versions compared to the 20-item version in predicting progression to cognitive decline from a cognitively unimpaired baseline. Data from 1054 participants were analysed using ordinal logistic regression, alongside moderator and receiver-operating characteristics curve analyses. All baseline total scores significantly predicted progression to cognitive decline. The 14-item version was better than the 20-item version in predicting consensus diagnosis, as shown by their AICs, while also showing the highest accuracy when discriminating between participants by diagnosis at last visit. We did not find sex to moderate the relationship between CES-D score and cognitive decline. Current findings suggest the 10- and 14-item versions of the CES-D are comparable to the 20-item version, and that the 14-item version may be better at predicting longitudinal consensus diagnosis compared to the 20-item version.

Heart rate variability: a predictor of cognitive decline.

Heart rate variability: a predictor of cognitive decline.

Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG.

Early discrimination and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency. Our research is based on resting-state electroencephalography (EEG) and the current dataset includes 137 consensus-diagnosed, community-dwelling Black Americans (ages 60-90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time-varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity-based score. The leave-one-out cross-validation accuracy is 91.97% and 3-fold accuracy is 91.17%. The 9 to 18 months' progression trend prediction accuracy over an availability-limited subset sample is 84.61%. The EEG-based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.

Cocaine use is associated with cerebral white matter hyperintensities in HIV disease.

White matter hyperintensities (WMH), a marker of cerebral small vessel disease and predictor of cognitive decline, are observed at higher rates in persons with HIV (PWH). The use of cocaine, a potent central nervous system stimulant, is disproportionately common in PWH and may contribute to WMH. The sample included of 110 PWH on antiretroviral therapy. Fluid-attenuated inversion recovery (FLAIR) and T1-weighted anatomical MRI scans were collected, along with neuropsychological testing. FLAIR images were processed using the Lesion Segmentation Toolbox. A hierarchical regression model was run to investigate predictors of WMH burden [block 1: demographics; block 2: cerebrovascular disease (CVD) risk; block 3: lesion burden]. The sample was 20% female and 79% African American with a mean age of 45.37. All participants had persistent HIV viral suppression, and the median CD4+ T-cell count was 750. Nearly a third (29%) currently used cocaine regularly, with an average of 23.75 (SD = 20.95) days in the past 90. In the hierarchical linear regression model, cocaine use was a significant predictor of WMH burden (β = .28). WMH burden was significantly correlated with poorer cognitive function (r = -0.27). Finally, higher WMH burden was significantly associated with increased serum concentrations of interferon-γ-inducible protein 10 (IP-10) but lower concentrations of myeloperoxidase (MPO); however, these markers did not differ by COC status. WMH burden is associated with poorer cognitive performance in PWH. Cocaine use and CVD risk independently contribute to WMH, and addressing these conditions as part of HIV care may mitigate brain injury underlying neurocognitive impairment.

Alzheimer's disease diagnostic accuracy by fluid and neuroimaging ATN framework.

The ATN's different modalities (fluids and neuroimaging) for each of the Aβ (A), tau (T), and neurodegeneration (N) elements are used for the biological diagnosis of Alzheimer's disease (AD). We aim to identify which ATN category achieves the highest potential for diagnosis and predictive accuracy of longitudinal cognitive decline. Based on the availability of plasma ATN biomarkers (plasma-derived Aβ42/40 , p-tau181, NFL, respectively), CSF ATN biomarkers (CSF-derived Aβ42 /Aβ40 , p-tau181, NFL), and neuroimaging ATN biomarkers (18F-florbetapir (FBP) amyloid-PET, 18F-flortaucipir (FTP) tau-PET, and fluorodeoxyglucose (FDG)-PET), a total of 2340 participants were selected from ADNI. Our data analysis indicates that the area under curves (AUCs) of CSF-A, neuroimaging-T, and neuroimaging-N were ranked the top three ATN candidates for accurate diagnosis of AD. Moreover, neuroimaging ATN biomarkers display the best predictive ability for longitudinal cognitive decline among the three categories. To note, neuroimaging-T correlates well with cognitive performances in a negative correlation manner. Meanwhile, participants in the "N" element positive group, especially the CSF-N positive group, experience the fastest cognitive decline compared with other groups defined by ATN biomarkers. In addition, the voxel-wise analysis showed that CSF-A related to tau accumulation and FDG-PET indexes more strongly in subjects with MCI stage. According to our analysis of the data, the best three ATN candidates for a precise diagnosis of AD are CSF-A, neuroimaging-T, and neuroimaging-N. Collectively, our findings suggest that plasma, CSF, and neuroimaging biomarkers differ considerably within the ATN framework; the most accurate target biomarkers for diagnosing AD were the CSF-A, neuroimaging-T, and neuroimaging-N within each ATN modality. Moreover, neuroimaging-T and CSF-N both show excellent ability in the prediction of cognitive decline in two different dimensions.

Can Depressive Symptomatology at Diagnosis Predict Cognitive and Functional Decline Over 1 Year in Rural Canadian Patients With Dementia?

Depressive symptomatology is often associated with the onset of dementia, although the exact form and directionality of this association is still unclear. The aim of this study is to investigate whether depressive symptomatology at the time of dementia diagnosis was predictive of cognitive, functional, and behavioral decline over 1 year. In a Rural and Remote Memory Clinic, 375 patients consecutively diagnosed with mild cognitive impairment, Alzheimer disease, or non-Alzheimer disease dementia completed the Center for Epidemiological Studies Depression Scale at first visit and 1-year follow-up to assess depressive symptomatology. The same cohort was evaluated for cognitive, functional, and behavioral decline through the completion of 5 clinical tests performed at the first visit and at 1-year follow-up. Depressive symptomatology at time of dementia diagnosis did not predict cognitive or functional decline over 1 year, although increases in depressive symptomatology over 1 year significantly correlated with higher caregiver ratings of neuropsychiatric symptom severity and related distress over that time. Increasingly severe depressive symptomatology over 1 year correlated with greater caregiver distress. This study points the way for future studies delineating the relationship between depression, dementia progression, and caregiver distress.

The Early Detection of Neurodegenerative diseases initiative: an international and multidisciplinary effort for transforming the early detection of dementia‐causing diseases

AbstractBackgroundAround 50 million people have dementia worldwide, with nearly 10 million new cases every year. Diagnosis is complex and often relies on expensive and invasive measures, with most patients accessing medical support when they already experience symptoms.MethodThe Early Detection of Neurodegenerative diseases (EDoN) initiative, spearheaded by Alzheimer’s Research UK, brings together over 60 experts from 49 universities, research projects, patient cohorts and technology providers to create machine learning models to detect the earliest stages of dementia‐causing diseases. EDoN has reviewed behavioural and physiological modalities with the strongest association with pre‐clinical disease.ResultOver 140 modalities were identified from the review and were shortlisted to create the version 1 digital toolkit. This first version includes Mezurio and Longevity smartphone apps, a Fitbit charge 4 activity tracker and Dreem 3 sleep headband. This Toolkit was further refined through patient and public involvement studies and collects 26 measures related to 7 aspects of behaviour and physiology (cognition, neural activity, physical activity, heart rate, fine motor movement, sleep, language and speech). The Toolkit is now being used to collect digital data in four international cohorts (Boston University Alzheimer’s Disease Research Center ‐ BU ADRC; The predictors of COgnitive DECline in attenders of memory clinic using digital devices ‐ CODEC‐2; Western Australia Memory Study ‐ WAMS; Healthy Brain Aging ‐ HBA), alongside prospective and retrospective clinical data, to inform the development of machine learning models.ConclusionEDoN will build models with digital markers, validating them against other biomarkers to predict dementia subtypes and individualised disease trajectories. Based on the outputs of the initial models, EDoN will go through a series of iterations of cohort engagement, modality and tool refinement, and data collection. Workstreams are underway to inform data security, privacy, ethics and open policy research, as well as considering the integration of the final EDoN Toolkit into healthcare systems globally. EDoN aims to deliver a cost‐effective, low burden and population‐wide method for early detection of dementia‐causing diseases that will benefit the public, patients, carers, researchers and clinicians, as well as the broader healthcare system and the delivery of new therapies.

The Total CES‐D score from the 14‐item version as a predictor of cognitive decline

AbstractBackgroundDepressive symptomatology is associated with cognitive decline and greater risk for dementia. Studies have examined this association using self‐report measures such as the Centre for Epidemiologic Studies‐Depression Scale (CES‐D; Radloff, 1991). Although the CES‐D was originally designed as a 20‐item scale, certain items have been reported to misrepresent depressive symptoms due to sex and cultural differences, which are addressed in the 14‐item version of this measure (Carleton et al., 2013). The aim of this study was to examine how the CES‐D total score from the 14‐item version compared to the 20‐item version in predicting progression to cognitive decline from a cognitively unimpaired baseline, across genders.MethodData were extracted from the Wisconsin Registry for Alzheimer’s Prevention. A total of 1,055 participants were included who were cognitively unimpaired and stable at baseline and had at least one follow‐up assessment. Bivariate logistic regression analyses were conducted using follow‐up consensus diagnosis as outcome (cognitively unimpaired stable vs. cognitive unimpaired declining, mild cognitive impairment or dementia); baseline total CES‐D scores from either the 14‐item or 20‐item version were used as predictors in separate analyses; age at last follow‐up assessment, age difference between baseline and last follow‐up assessment, years of education, and polygenic risk score were covariates (see Table 1).ResultIn female participants, we observed that the logistic regression model with the total CES‐D score from the 14‐item version was statistically significant (χ2(5) = 54.674, p = .001) and explained 15.2% (Nagelkerke R2) of the variance in predicting cognitive decline; this result was comparable to that with the 20‐item version, which was also statistically significant (χ2(5) = 53.276, p = .001) and explained 14.8% of the variance. In male participants, the model with the total score from the 14‐item version was statistically significant (χ2(5) = 14.846, p = .011) and explained 9.9% of the variance, whereas the model with the total score from the 20‐item version was statistically significant (χ2(5) = 14.070, p = .015) and explained 9.4% of the variance.ConclusionOur findings suggest that the total CES‐D score from the 14‐item version is comparable to, if not slightly better than, the score from the 20‐item version in predicting progression to a clinical level of cognitive decline in both males and females.

Serum TCA cycle metabolites in Lewy bodies dementia and Alzheimer's disease: Network analysis and cognitive prognosis

Abnormalities in the Tri-Carboxylic-Acid (TCA) cycle have been documented in dementia. Through network analysis, TCA cycle metabolites could indirectly reflect known dementia-related abnormalities in biochemical pathways, and key metabolites might be associated with prognosis. This study analyzed TCA cycle metabolites as predictors of cognitive decline in a mild dementia cohort and explored potential interactions with the diagnosis of Lewy Body Dementia (LBD) or Alzheimer's Disease (AD) and APOE-ε4 genotype. We included 145 mild dementia patients (LBD = 59; AD = 86). Serum TCA cycle metabolites were analyzed at baseline, and partial correlation networks were conducted. Cognitive performance was measured annually over 5-years with the Mini-mental State Examination. Longitudinal mixed-effects Tobit models evaluated each baseline metabolite as a predictor of 5-years cognitive decline. APOE-ε4 and diagnosis interactions were explored. Results showed comparable metabolite concentrations in LBD and AD. Multiple testing corrected networks showed larger coefficients for a negative correlation between pyruvate – succinate and positive correlations between fumarate – malate and citrate – Isocitrate in both LBD and AD. In the total sample, adjusted mixed models showed significant associations between baseline citrate concentration and longitudinal MMSE scores. In APOE-ε4 carriers, baseline isocitrate predicted MMSE scores. We conclude that, in mild dementia, serum citrate concentrations could be associated with subsequent cognitive decline, as well as isocitrate concentrations in APOE-ε4 carriers. Downregulation of enzymatic activity in the first half of the TCA cycle (decarboxylating dehydrogenases), with upregulation in the latter half (dehydrogenases only), might be indirectly reflected in serum TCA cycle metabolites' networks.

Prediction of Cognitive Decline in Parkinson's Disease Using Clinical and DAT SPECT Imaging Features, and Hybrid Machine Learning Systems.

We aimed to predict Montreal Cognitive Assessment (MoCA) scores in Parkinson's disease patients at year 4 using handcrafted radiomics (RF), deep (DF), and clinical (CF) features at year 0 (baseline) applied to hybrid machine learning systems (HMLSs). 297 patients were selected from the Parkinson's Progressive Marker Initiative (PPMI) database. The standardized SERA radiomics software and a 3D encoder were employed to extract RFs and DFs from single-photon emission computed tomography (DAT-SPECT) images, respectively. The patients with MoCA scores over 26 were indicated as normal; otherwise, scores under 26 were indicated as abnormal. Moreover, we applied different combinations of feature sets to HMLSs, including the Analysis of Variance (ANOVA) feature selection, which was linked with eight classifiers, including Multi-Layer Perceptron (MLP), K-Neighbors Classifier (KNN), Extra Trees Classifier (ETC), and others. We employed 80% of the patients to select the best model in a 5-fold cross-validation process, and the remaining 20% were employed for hold-out testing. For the sole usage of RFs and DFs, ANOVA and MLP resulted in averaged accuracies of 59 ± 3% and 65 ± 4% for 5-fold cross-validation, respectively, with hold-out testing accuracies of 59 ± 1% and 56 ± 2%, respectively. For sole CFs, a higher performance of 77 ± 8% for 5-fold cross-validation and a hold-out testing performance of 82 + 2% were obtained from ANOVA and ETC. RF+DF obtained a performance of 64 ± 7%, with a hold-out testing performance of 59 ± 2% through ANOVA and XGBC. Usage of CF+RF, CF+DF, and RF+DF+CF enabled the highest averaged accuracies of 78 ± 7%, 78 ± 9%, and 76 ± 8% for 5-fold cross-validation, and hold-out testing accuracies of 81 ± 2%, 82 ± 2%, and 83 ± 4%, respectively. We demonstrated that CFs vitally contribute to predictive performance, and combining them with appropriate imaging features and HMLSs can result in the best prediction performance.

Baseline Differences in Driving Frequency as a Predictor of Cognitive Decline and Alzheimer's Disease.

To extend prior research by examining daily driving frequency as a predictor of cognitive decline and later diagnosis of Alzheimer's disease. 1,426 older adults completed batteries of questionnaires and neuropsychological tests at baseline and yearly follow-ups (M = 6.8, SD = 4.9). Linear mixed effects models were estimated to examine whether daily driving frequency at baseline was predictive of cognitive decline while accounting for IADLs, mobility, depression, and demographics. Cox regression was used to examine driving frequency as a predictor of Alzheimer's disease diagnosis. Less daily driving frequency was associated with greater decline in all cognitive domains over time except for working memory. Although driving frequency was associated with these changes in cognition, it did not uniquely predict the development of Alzheimer's disease when accounting for other factors (eg, other IADLs). Our findings extend prior research linking driving cessation to greater levels of cognitive decline. Future work might benefit from examining the utility of driving habits (especially changes in driving) as measures of everyday functioning in older adult evaluations.

5613510 EFFECTS OF REGIONAL BRAIN VOLUME ON COGNITION IN SICKLE CELL ANAEMIA: A DEVELOPMENTAL PERSPECTIVE

Cognitive decline, related at least in part to reduced processing speed and lower working memory, is a major problem in paediatric and adult patients with sickle cell anaemia (SCA)1,2 and affects quality of life. Multiple studies investigating the association between quantitative and qualitative neuroimaging findings and cognition have had mixed results. Hence, the aetiology of cognitive decline in this population is not clearly understood. Cerebral atrophy on magnetic resonance imaging is noted in the literature but few studies have investigated the association between brain volumes and cognition. White matter microstructural integrity and white matter hyperintensities have been associated with reduced processing speed index (PSI).3 However no study has investigated the association between white matter volumes and PSI in the SCA population. Total subcortical volumes have also been noted to be lower in SCA children and adults. Only one study has investigated the association between total subcortical volumes and working memory index (WMI) in adults.4 Moreover, several studies in the past have noted age related decline in total brain volume as well neurodevelopmental delay in grey matter volume growth.5,6 Most of the developmental trajectory data in the literature comes from longitudinal data sets with limited samples and without control groups. Hence, for the purpose of this study, we investigated the association between regional brain volumes (white matter volumes and total subcortical volumes) and cognition (processing speed index and working memory index) in 129 SCA patients aged 8 – 64 years (66 male) and 50 controls (21 males). Participants were enrolled in two studies - the Sleep Asthma Cohort (SAC) and the Prevention of Morbidity in SCA (POMS) trial. We hypothesized that white matter volumes would predict processing speed index and total subcortical volumes would predict working memory index in SCA patients as well as controls. We also hypothesized that the developmental trajectories for regional brain volumes and cognitive variables would differ between patients and controls. Separate multiple linear regression models for males and females revealed that WMV predicted processing speed index(p=.019) and total subcortical volumes predicted working memory index (p=.025) in male patients only. Age (p=.003) and socioeconomic status (p=.03) also independently predicted PSI in male patients. Individual subcortical regions predicting WMI were statistically significant for left and right thalamus only in male patients. Haemoglobin and age emerged as the best predictors of cognitive decline in patients. Comparison of developmental trajectories between patients and controls revealed that only processing speed was significantly delayed at 8 years. The rate of development for all variables was lower in SCA patients and age-related decline was accelerated in patients as compared to controls. We speculate that early cerebral haemodynamic compromise may contribute to long term cognitive decline in patients. We also highlight that male SCA patients are particularly vulnerable to SCA disease severity. Hence, timely intervention to stabilise haemoglobin levels, especially in males, should be considered. Limitations include lack of stratification by SCI and age (adults and children separately) and the possibility that we were underpowered to detect effects in females.

Prediction of cognitive decline in Parkinson’s disease (PD) patients with electroencephalography (EEG) connectivity characterized by time-between-phase-crossing (TBPC)

The aim of the study is to identify the dynamic change pattern of EEG to predict cognitive decline in patients with Parkinson’s disease. Here we demonstrate that the quantification of synchrony-pattern changes across the scalp, measured using electroencephalography (EEG), offers an alternative approach of observing an individual’s functional brain organization. This method, called “Time-Between-Phase-Crossing” (TBPC), is based on the same phenomenon as the phase-lag-index (PLI); it also considers intermittent changes in the signals of phase differences between pairs of EEG signals, but additionally analyzes dynamic connectivity changes. We used data from 75 non-demented Parkinson’s disease patients and 72 healthy controls, who were followed over a period of 3 years. Statistics were calculated using connectome-based modeling (CPM) and receiver operating characteristic (ROC). We show that TBPC profiles, via the use of intermittent changes in signals of analytic phase differences of pairs of EEG signals, can be used to predict cognitive decline in Parkinson’s disease (p < 0.05).