Plasma Biomarkers as Predictors of Progression to Dementia in Individuals with Mild Cognitive Impairment.

Background: Blood-based biomarkers (BBMs) are of growing interest in the field of Alzheimer's disease (AD) and related dementias. Objective: This study aimed to assess the ability of plasma biomarkers to 1) predict disease progression from mild cognitive impairment (MCI) to dementia and 2) improve the predictive ability of magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) measures when combined. Methods: We used data from the Alzheimer's Disease Neuroimaging Initiative. Machine learning models were trained using the data from participants who remained cognitively stable (CN-s) and with Dementia diagnosis at 2-year follow-up visit. The models were used to predict progression to dementia in MCI individuals. We assessed the performance of models with plasma biomarkers against those with CSF and MRI measures, and also in combination with them. Results: Our models with plasma biomarkers classified CN-s individuals from AD with an AUC of 0.75±0.03 and could predict conversion to dementia in MCI individuals with an AUC of 0.64±0.03 (17.1% BP, base prevalence). Models with plasma biomarkers performed better when combined with CSF and MRI measures (CN versus AD: AUC of 0.89±0.02; MCI-to-AD: AUC of 0.76±0.03, 21.5% BP). Conclusions: Our results highlight the potential of plasma biomarkers in predicting conversion to dementia in MCI individuals. While plasma biomarkers could improve the predictive ability of CSF and MRI measures when combined, they also show the potential to predict non-progression to AD when considered alone. The predictive ability of plasma biomarkers is crucially linked to reducing the costly and effortful collection of CSF and MRI measures.

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals.

Importance: Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective: To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants: Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures: Individuals WODCI at baseline scan. Main Outcomes and Measures: Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid ß (Aß), and future cognitive decline were assessed. Results: In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aß positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance: The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

Association of Alcohol Consumption with Cognition in Older Population: The A4 Study.

BACKGROUND: Alcohol use disorders have been categorized as a 'strongly modifiable' risk factor for dementia. OBJECTIVE: To investigate the cross-sectional association between alcohol consumption and cognition in older adults and if it is different across sexes or depends on amyloid-ß (Aß) accumulation in the brain. METHODS: Cognitively unimpaired older adults (N = 4387) with objective and subjective cognitive assessments and amyloid positron emission tomography (PET) imaging were classified into four categories based on their average daily alcohol use. Multivariable linear regression was then used to test the main effects and interactions with sex and Aß levels. RESULTS: Individuals who reported no alcohol consumption had lower scores on the Preclinical Alzheimer Cognitive Composite (PACC) compared to those consuming one or two drinks/day. In sex-stratified analysis, the association between alcohol consumption and cognition was more prominent in females. Female participants who consumed two drinks/day had better performance on PACC and Cognitive Function Index (CFI) than those who reported no alcohol consumption. In an Aß-stratified sample, the association between alcohol consumption and cognition was present only in the Aß- subgroup. The interaction between Aß status and alcohol consumption on cognition was not significant. CONCLUSION: Low or moderate consumption of alcohol was associated with better objective cognitive performance and better subjective report of daily functioning in cognitively unimpaired individuals. The association was present only in Aß- individuals, suggesting that the pathophysiologic mechanism underlying the effect of alcohol on cognition is independent of Aß pathology. Further investigation is required with larger samples consuming three or more drinks/day.

Association of Stages of Objective Memory Impairment With Incident Symptomatic Cognitive Impairment in Cognitively Normal Individuals.

BACKGROUND AND OBJECTIVES: Increasing evidence indicates that a subset of cognitively normal individuals has subtle cognitive impairment at baseline. We sought to identify them using the Stages of Objective Memory Impairment (SOMI) system. Symptomatic cognitive impairment was operationalized by a Clinical Dementia Rating (CDR) ≥0.5. We hypothesized that incident impairment would be higher for participants with subtle retrieval impairment (SOMI-1), higher still for those with moderate retrieval impairment (SOMI-2), and highest for those with storage impairment (SOMI-3/4) after adjusting for demographics and APOE ε4 status. A secondary objective was to determine whether including biomarkers of ß-amyloid, tau pathology, and neurodegeneration in the models affect prediction. We hypothesized that even after adjusting for in vivo biomarkers, SOMI would remain a significant predictor of time to incident symptomatic cognitive impairment. METHODS: Among 969 cognitively normal participants, defined by a CDR = 0, from the Knight Alzheimer Disease Research Center, SOMI stage was determined from their baseline Free and Cued Selective Reminding Test scores, 555 had CSF and structural MRI measures and comprised the biomarker subgroup, and 144 of them were amyloid positive. Cox proportional hazards models tested associations of SOMI stages at baseline and biomarkers with time to incident cognitive impairment defined as the transition to CDR ≥0.5. RESULTS: Among all participants, the mean age was 69.35 years, 59.6% were female, and mean follow-up was 6.36 years. Participants in SOMI-1-4 had elevated hazard ratios for the transition from normal to impaired cognition in comparison with those who were SOMI-0 (no memory impairment). Individuals in SOMI-1 (mildly impaired retrieval) and SOMI-2 (moderately impaired retrieval) were at nearly double the risk of clinical progression compared with persons with no memory problems. When memory storage impairment emerges (SOMI-3/4), the hazard ratio for clinical progression increased approximately 3 times. SOMI stage remained an independent predictor of incident cognitive impairment after adjusting for all biomarkers. DISCUSSION: SOMI predicts the transition from normal cognition to incident symptomatic cognitive impairment (CDR ≥0.5). The results support the use of SOMI to identify those cognitively normal participants most likely to develop incident cognitive impairment who can then be referred for biomarker screening.

Tracking cognition with the T-MoCA in a racially/ethnically diverse older adult cohort.

Introduction: We investigated the utility of the Telephone-Montreal Cognitive Assessment (T-MoCA) to track cognition in a diverse sample from the Einstein Aging Study. Methods: Telephone and in-person MoCA data, collected annually, were used to evaluate longitudinal cognitive performance. Joint models of T-MoCA and in-person MoCA compared changes, variance, and test-retest reliability measured by intraclass correlation coefficient by racial/ethnic group. Results: There were no significant differences in baseline performance or longitudinal changes across three study waves for both MoCA formats. T-MoCA performance improved over waves 1-3 but declined afterward. Test-retest reliability was lower for the T-MoCA than for the in-person MoCA. In comparison with non-Hispanic Whites, non-Hispanic Blacks and Hispanics performed worse at baseline on both MoCA formats and showed lower correlations between T-MoCA and in-person versions. Conclusions: The T-MoCA provides valuable information on cognitive change, despite racial/ethnic disparities and practice effects. We discuss implications for health disparity populations. Highlights: We assessed the comparability of Telephone-Montreal Cognitive Assessment (T-MoCA) and in-person MoCA for tracking cognition.Changes within 3 years in T-MoCA were similar to that for the in-person MoCA.T-MoCA is subject to practice effects and shows difference in performance by race/ethnicity.Test-retest reliability of T-MoCA is lower than that for in-person MoCA.

Predictors of treatment-response to caffeine combination products, acetaminophen, acetylsalicylic acid (aspirin), and nonsteroidal anti-inflammatory drugs in acute treatment of episodic migraine.

OBJECTIVE: To identify predictors of acute treatment optimization for migraine with "over-the-counter" (OTC) or prescription nonsteroidal anti-inflammatory drugs (NSAIDs) as well as other widely used OTCs including acetaminophen, caffeine combination products (CCP), and acetylsalicylic acid (ASA, aspirin) among people with episodic migraine and to develop models that predict treatment response to each class of OTCs. BACKGROUND: Efficacy of acute OTC medications for migraine varies greatly. Identifying predictors of treatment response to particular classes of medication is a step toward evidence-based personalized therapy. METHODS: For this prediction model development study, we used data from 2224 participants from the American Migraine Prevalence and Prevention (AMPP) study who were aged ≥18 years, met criteria for migraine, had <15 monthly headache days, and reported being on monotherapy for acute migraine attacks with one of the following classes medications: CCP (N = 711), acetaminophen (N = 643), ASA (N = 110), and prescription or OTC NSAIDs (N = 760). The primary outcome measures of treatment optimization were adequate 2-h pain freedom (2hPF) and adequate 24-h pain relief (24hPR), which were defined by responses of half the time or more to the relevant items on the Migraine Treatment Optimization Questionnaire-6. RESULTS: The mean (SD) age of the participants was 46.2 (13.1) years, 79.4% (1765/2224) were female, 43.7% (972/2224) reported adequate 2hPF, and 46.1% (1025/2224) reported adequate 24hPR. Those taking CCP had better 2hPF and 24PR outcomes. For those taking NSAIDs, better outcomes were associated with lower average pain intensity (2hPF: odds ratio [OR] 0.89, 95% confidence interval [CI] 0.80-0.99; 24PR: OR 0.86, 95% CI 0.77-0.96), cutaneous allodynia (2hPF: OR 0.92, 95% CI 0.89-0.96; 24PR: OR 0.91, 95% CI 0.87-0.95), depressive symptoms (2hPF: OR 0.95, 95% CI 0.92-0.98; 24PR: OR 0.95, 95% CI 0.91-0.99), and Migraine Disability Assessment Scale (MIDAS) grade (2hPF: OR 0.76, 95% CI 0.64-0.90; 24PR: OR 0.79, 95% CI 0.65-0.95). Adequate 2hPF for those taking CCP was associated with male gender (OR 1.83, 95% CI 1.21-2.77), lower average pain intensity (OR 0.80, 95% CI 0.70-0.91), lower cutaneous allodynia (OR 0.94, 95% CI 0.90-0.97), and lower Migraine Symptom Severity Scale Score (MSSS; OR 0.91, 95% CI 0.86-0.97). Adequate 24hPR for those taking CCP was associated with lower average pain intensity (OR 0.85, 95% CI 0.75-0.96), lower cutaneous allodynia (OR 0.92, 95% CI 0.89-0.96), and lower MIDAS grade (OR 0.81, 95% CI 0.68-0.96). Participants who were married (OR 1.51, 95% CI 1.05-2.19), had lower average pain intensity (OR 0.79, 95% CI 0.70-0.89), lower MSSS (OR 0.93, 95% CI 0.88-0.99), less depression (OR 0.96, 95% CI 0.93-0.99), and lower MIDAS grade (OR 0.72, 95% CI 0.59-0.87) had adequate 2hPF after taking acetaminophen. Participants who were married (OR 1.50, 95% CI 1.02-2.21), had lower pain intensity (OR 0.78, 95% CI 0.69-0.88), less depression (OR 0.95, 95% CI 0.91-0.98) and lower MIDAS grade (OR 0.53, 95% CI 0.42-0.67) had higher 24hPR following use of acetaminophen. A lower MSSS was the only factor associated with higher 2hPF and 24PR after using ASA (OR 0.78, 95% CI 0.67-0.92 and OR 0.79, 95% CI 0.67-0.93). Predictive models had modest performance in identifying responders to each class of OTC. CONCLUSION: A large subgroup of people with migraine had an inadequate response to their usual acute OTC migraine treatment 2- and 24-h after dosing. These findings suggest a need to improve OTC treatment for some and to offer prescription acute medications for others. Predictive models identified several factors associated with better treatment-response in each OTC class. Selecting OTC treatment based on factors predictive of treatment optimization might improve patient outcomes.

Predictors of treatment-response to acute prescription medications in migraine: Results from the American Migraine Prevalence and Prevention (AMPP) Study.

OBJECTIVE: To identify predictors of acute treatment optimization with prescription drugs among people with episodic migraine. METHODS: A total of 2896 individuals from the American Migraine Prevalence and Prevention study were included in this study. The primary outcome measures of treatment optimization were 2-h pain freedom (2hPF) and 24-h pain relief (24hPR), which were defined by responses to the Migraine Treatment Optimization Questionnaire-6 (mTOQ-6). We identified predictors of 2hPF and 24hPR in response to triptans, butalbital combination medications (BCMs), and opioids. RESULTS: PARTICIPANTS: were on average 47.3 years old (SD=12.0), 85.6 % were female, and 88.4 % were white, 46.9 % of participants reported 2hPF and 49.5 % reported 24hPR with their "usual acute treatment". The odds of adequate 2hPF response were reduced in men and those with higher average headache pain intensity, higher migraine symptom severity scores, presence of cutaneous allodynia, and depression. Adequate 24hPR was associated with being married, but declined in those with higher-than-average average headache pain intensity and frequency, greater disability, presence of cutaneous allodynia, and depression. Among participants reporting acute monotherapy, individuals taking triptans were more likely to have adequate treatment response in comparison with those taking BCMs (2hPF: OR=1.86, 95 %CI 1.42-2.42; 24hPR: OR=2.26, 95 %CI 1.73-2.96) and opioids (2hPF: OR=2.39, 95 %CI 1.94-2.96; 24hPR: OR=2.78, 95 %CI 2.24-3.44). There was no significant difference in response to treatment between the subsample taking BCMs and opioid users. CONCLUSION: Almost half of study respondents were not optimized on their usual prescription acute migraine treatment(s). Predictive models identified several features associated with treatment optimization.

Association of Depressive Symptoms and Cognition in Older Adults Without Dementia Across Different Biomarker Profiles.

BACKGROUND: Depression is a late-life risk factor for cognitive decline. Evidence suggests an association between Alzheimer's disease (AD) associated pathologic changes and depressive symptoms. OBJECTIVE: To investigate the influence of AT(N) biomarker profile (amyloid-ß [A], p-tau [T], and neurodegeneration [N]) and gender on cross-sectional associations between subclinical depressive symptoms and cognitive function among older adults without dementia. METHODS: Participants included 868 individuals without dementia from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Depressive symptoms were measured using the Geriatric Depression Scale (GDS). ADNI neuropsychological composite scores assessed memory and executive function (EF). PET, cerebrospinal fluid, and MRI modalities classified the study sample into biomarker profiles: normal biomarkers (A-T-N-), AD continuum (A+T±N±), and suspect non-AD pathology (SNAP; A-T±N-or A-T-N±). Multivariate regression models were used to investigate associations between GDS and cognitive domains. RESULTS: GDS was negatively associated with memory (ß= -0.156, p < 0.001) and EF (ß= -0.147, p < 0.001) in the whole sample. When classified by biomarker profile, GDS was negatively associated with memory and EF in AD continuum (memory: ß= -0.174, p < 0.001; EF: ß= -0.129 p = 0.003) and SNAP (memory: ß= -0.172, p = 0.005; EF: ß= -0.197, p = 0.001) subgroups. When stratified by sex, GDS was negatively associated with memory (ß= -0.227, p < 0.001) and EF (ß= -0.205, p < 0.001) in men only. CONCLUSION: The association between subclinical depressive symptoms and cognitive function is highly influenced by the AT(N) biomarker profile.

Predictive models for determining treatment response to nonprescription acute medications in migraine: Results from the American Migraine Prevalence and Prevention Study.

OBJECTIVE: To identify predictors of acute treatment response for nonprescription (over-the-counter [OTC]) medications among people with migraine and develop improved models for predicting treatment response. BACKGROUND: Pain freedom and sustained pain relief are important priorities in the acute treatment of migraine. OTC medications are widely used for migraine; however, it is not clear which treatment works best for each patient without going through the trial and error process. METHODS: A prediction model development study was completed using the 2006 American Migraine Prevalence and Prevention Study survey, from participants who were aged ≥18, met criteria and headache day frequency for episodic migraine, did not take prescription medication for migraine, and used ≥1 of the following acute migraine medication classes: acetaminophen, aspirin, NSAIDs, or caffeine containing combination products (CCP). Two items from the Migraine Treatment Optimization Questionnaire were used to evaluate treatment response, adequate 2-h pain freedom (2hPF) and 24-h pain relief (24hPR), which were defined by a response to treatment ≥half the time at 2 h and 24 h post treatment, respectively. We identified predictors of adequate treatment response and developed models to predict probability of treatment response to each medication class. RESULTS: The sample included 3852 participants (3038 [79.0%] females) with an average age of 45.0 years (SD = 12.8). Only 1602/3852 (41.6%) and 1718/3852 (44.6%) of the participants reported adequate 2hPF and 24hPR, respectively. Adequate treatment-response was significantly predicted by lower average headache pain intensity, less cutaneous allodynia, and lower depressive symptom scores. Lower migraine symptom severity was predictive of adequate 2hPF and fewer monthly headache days was predictive of adequate 24hPR. Among participants reporting OTC monotherapy (n = 2168, 56.3%) individuals taking CCP were more likely to have adequate 2hPF (OR = 1.55, 95% CI 1.23-1.95) and 24hPR (OR = 1.79, 95% CI 1.18-1.88) in comparison with those taking acetaminophen. Predictive models were modestly predictive of responders to OTC medications (c-statistics = 0.65; 95% CI 0.62-0.68). CONCLUSION: These results show that response to acute migraine treatments is not optimized in the majority of people with migraine treating with OTC medications. Predictive models can improve our ability to choose the best therapeutic option for individuals with episodic migraine and increase the proportion of patients with optimized response to treatments.

Predicting Amyloid Positivity in Cognitively Unimpaired Older Adults: A Machine Learning Approach Using A4 Data.

BACKGROUND AND OBJECTIVES: To develop and test the performance of the Positive Aß Risk Score (PARS) for prediction of ß-amyloid (Aß) positivity in cognitively unimpaired individuals for use in clinical research. Detecting Aß positivity is essential for identifying at-risk individuals who are candidates for early intervention with amyloid targeted treatments. METHODS: We used data from 4,134 cognitively normal individuals from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) Study. The sample was divided into training and test sets. A modified version of AutoScore, a machine learning-based software tool, was used to develop a scoring system using the training set. Three risk scores were developed using candidate predictors in various combinations from the following categories: demographics (age, sex, education, race, family history, body mass index, marital status, and ethnicity), subjective measures (Alzheimer's Disease Cooperative Study Activities of Daily Living-Prevention Instrument, Geriatric Depression Scale, and Memory Complaint Questionnaire), objective measures (free recall, Mini-Mental State Examination, immediate recall, digit symbol substitution, and delayed logical memory scores), and APOE4 status. Performance of the risk scores was evaluated in the independent test set. RESULTS: PARS model 1 included age, body mass index (BMI), and family history and had an area under the curve (AUC) of 0.60 (95% CI 0.57-0.64). PARS model 2 included free recall in addition to the PARS model 1 variables and had an AUC of 0.61 (0.58-0.64). PARS model 3, which consisted of age, BMI, and APOE4 information, had an AUC of 0.73 (0.70-0.76). PARS model 3 showed the highest, but still moderate, performance metrics in comparison with other models with sensitivity of 72.0% (67.6%-76.4%), specificity of 62.1% (58.8%-65.4%), accuracy of 65.3% (62.7%-68.0%), and positive predictive value of 48.1% (44.1%-52.1%). DISCUSSION: PARS models are a set of simple and practical risk scores that may improve our ability to identify individuals more likely to be amyloid positive. The models can potentially be used to enrich trials and serve as a screening step in research settings. This approach can be followed by the use of additional variables for the development of improved risk scores. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that in cognitively unimpaired individuals PARS models predict Aß positivity with moderate accuracy.

Application of predictive models in boosting power of Alzheimer's disease clinical trials: A post hoc analysis of phase 3 solanezumab trials.

Background: The ideal participants for Alzheimer's disease (AD) clinical trials would show cognitive decline in the absence of treatment (i.e., placebo arm) and would also respond to the therapeutic intervention. Objective: To investigate if predictive models can be an effective tool for identifying and excluding people unlikely to show cognitive decline as an enrichment strategy in AD trials. Method: We used data from the placebo arms of two phase 3, double-blind trials, EXPEDITION and EXPEDITION2. Patients had 18 months of follow-up. Based on the longitudinal data from the placebo arm, we classified participants into two groups: one showed cognitive decline (any negative slope) and the other showed no cognitive decline (slope is zero or positive) on the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-cog). We used baseline data for EXPEDITION to train regression-based classifiers and machine learning classifiers to estimate probability of cognitive decline. Models were applied to EXPEDITION2 data to assess predicted performance in an independent sample. Features used in predictive models included baseline demographics, apolipoprotein E ε4 genotype, neuropsychological scores, functional scores, and volumetric magnetic resonance imaging. Result: In EXPEDITION, 46.3% of placebo-treated patients showed no cognitive decline and the proportion was similar in EXPEDITION2 (45.6%). Models had high sensitivity and modest specificity in both the training (EXPEDITION) and replication samples (EXPEDITION2) for detecting the stable group. Positive predictive value of models was higher than the base prevalence of cognitive decline, and negative predictive value of models were higher than the base rate of participants who had stable cognition. Conclusion: Excluding persons with AD unlikely to decline from the active and placebo arms of clinical trials using predictive models may boost the power of AD trials through selective inclusion of participants expected to decline.

Associations of Stages of Objective Memory Impairment With Amyloid PET and Structural MRI: The A4 Study.

BACKGROUND AND OBJECTIVES: The goal of this work was to investigate the neuroimaging correlates of the Stages of Objective Memory Impairment (SOMI) system operationalized with the Free and Cued Selective Reminding Test (FCSRT), a widely used episodic memory measure. METHODS: The FCSRT begins with a study phase in which items (e.g., grapes) are identified in response to unique semantic cues (e.g., fruit) that are used in the test phase to prompt recall of items not retrieved by free recall. There are 3 test trials of the 16 items (maximum 48). Data from 4,484 cognitively unimpaired participants from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) study were used. All participants had amyloid PET imaging, and a subset of 1,262 ß-amyloid (Aß)-positive had structural MRIs. We compared the Aß mean cortical standardized uptake value ratio (SUVR) and volumetric measures of hippocampus, parahippocampal gyrus, entorhinal cortex, and inferior temporal cortex between the 5 SOMI stages. RESULTS: Participants had a mean age of 71.3 (SD 4.6) years; 40.6% were male; and 34.6% were APOE ε4 positive. Half had no memory impairment; the other half had retrieval deficits, storage limitations, or both. Analysis of covariance in the entire sample while controlling for age, sex, education, and APOE ε4 showed that individuals in higher SOMI stages had higher global amyloid SUVR (p < 0.001). Both SOMI-4 and -3 subgroups had higher amyloid SUVR than SOMI-0 and SOMI-1 subgroups. Individuals in higher SOMI stages had smaller hippocampal volume (p = 0.003), entorhinal cortex (p < 0.05), and inferior temporal lobes (p < 0.05), but there was no difference between parahippocampal gyrus volume of different SOMI stages. Pairwise comparison of SOMI subgroups showed that the SOMI-4, -3, and -2 subgroups had smaller hippocampal volume than the SOMI-0 and -1 subgroup. The SOMI-4 subgroup had significantly smaller entorhinal cortex and smaller inferior temporal lobe compared to all other groups. DISCUSSION: Presence of Alzheimer disease pathology is closely related to memory impairment according to SOMI stages in the cognitively unimpaired sample of A4. Results from structural MRIs suggest that memory storage impairment (SOMI-3 and -4) is present when there is widespread medial temporal lobe atrophy. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov identifier: NCT02008357. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that, in normal older individuals, higher stages of memory impairment assessed with FCSRT were associated with higher amyloid imaging burden and lower volume of hippocampus, entorhinal cortex, and inferior temporal lobes.

Impact of sex and APOE ε4 on the association of cognition and hippocampal volume in clinically normal, amyloid positive adults.

INTRODUCTION: Cognitive decline follows pathological changes including neurodegeneration on the Alzheimer's disease continuum. However, it is unclear which cognitive domains first become affected by neurodegeneration in amyloid-positive individuals and if sex or apolipoprotein (APOE) ε4 status differences affect this relationship. METHODS: Data from 1233 cognitively unimpaired, amyloid-positive individuals 65 to 85 years of age were studied to assess the effect of hippocampal volume (HV) on cognition and to evaluate differences due to sex and APOE ε4 status. RESULTS: Lower HV was linked with worse performance on measures of memory (free recall, total recall, logical memory delayed recall, Mini-Mental State Examination [MMSE]), executive functioning (digit symbol substitution, DSS), and the Preclinical Alzheimer's Cognitive Composite (PACC). Among both women and APOE ε4+ individuals, all cognitive measures, except MMSE, were associated with HV. DSS and PACC had the largest effect sizes in differentiating early and intermediate stage neurodegeneration. DISCUSSION: Despite all cognitive measures being associated with HV, cognitive tests show differences in detecting early or late signs of neurodegeneration. Differences exist in association between cognition and neurodegeneration based on sex and APOE ε4 status.

Anterior cervical corpectomy and fusion with stand-alone cages in patients with multilevel degenerative cervical spine disease is safe.

BACKGROUND: In case of spinal cord compression behind the vertebral body, anterior cervical corpectomy and fusion (ACCF) proves to be a more feasible approach than cervical discectomy. The next step was the placement of an expandable titanium interbody in order to restore the vertebral height. The need for additional anterior plating with ACCF has been debatable and such technique has been evaluated by very few studies. The objective of the study is to evaluate radiographic and clinical outcomes in patients with multilevel degenerative cervical spine disease treated by stand-alone cages for anterior cervical corpectomy and fusion (ACCF). METHODS: Thirty-one patients (66.5 ± 9.75 years, range 53-85 years) were analyzed. Visual Analog Scale (VAS) and the 10-item Neck Disability Index (NDI) were assessed preoperatively and during follow-up on a regular basis after surgery and after one year at least. Assessment of radiographic fusion, subsidence, and lordosis measurement of Global cervical lordosis (GCL); fusion site lordosis (FSL); the anterior interbody space height (ant. DSH); the posterior interbody space height (post. DSH); the distance of the cage to the posterior wall of the vertebral body (CD) were done retrospectively. Mean clinical and radiographic follow-up was 20.0 ± 4.39 months. RESULTS: VAS-neck (p = 0.001) and VAS-arm (p < 0.001) improved from preoperatively to postoperatively. The NDI improved at the final follow-up (p < 0.001). Neither significant subsidence of the cages nor significant loss of lordotic correction were seen. All patients showed a radiographic union of the surgically addressed segments at the last follow up. CONCLUSIONS: Application of a stand-alone expandable cage in the cervical spine after one or two-level ACCF without additional posterior fixation or anterior plating is a safe procedure that results in fusion. Neither significant subsidence of the cages nor significant loss of lordotic correction were seen. TRIAL REGISTRATION: Retrospectively registered. According to the Decision of the ethics committee, Jena on 25th of July 2018, that this study doesn't need any registration.  https://www.laek-thueringen.de/aerzte/ethikkommission/registrierung/ .

Comparing Performance of Different Predictive Models in Estimating Disease Progression in Alzheimer Disease.

BACKGROUND: Automatic classification techniques provide tools to analyze complex data and predict disease progression. METHODS: A total of 305 cognitively normal; 475 patients with amnestic mild cognitive impairment (aMCI); and 162 patients with dementia were included in this study. We compared the performance of 3 different methods in predicting progression from aMCI to dementia: (1) index-based model; (2) logistic regression (LR); and (3) ensemble linear discriminant (ELD) machine learning models. LR and ELD models were trained using data from cognitively normal and dementia subgroups, and subsequently were applied to aMCI subgroup to predict their disease progression. RESULTS: Performance of ELD models were better than LR models in prediction of conversion from aMCI to Alzheimer dementia at all time frames. ELD models performed better when a larger number of features were used for prediction. CONCLUSION: Machine learning models have substantial potential to improve the predictive ability for cognitive outcomes.

Trans-Tubular Translaminar Microscopic-Assisted Nucleotomy for Lumbar Disc Herniations in the Hidden Zone.

STUDY DESIGN: A prospective cohort study in a high-flow spine center in Germany. OBJECTIVES: This study aimed to evaluate clinical outcomes and complications of the trans-tubular translaminar microscopic-assisted percutaneous nucleotomy in cases of cranially migrated lumbar disc herniations (LDH). METHODS: Between January 2013 and January 2018, 66 consecutive patients with cranio-laterally migrated LDH were operated upon. The following outcome measures were evaluated: (1) Visual Analog Scale (VAS) for leg and back pain; (2) Oswestry Disability Index (ODI) and Macnab´s criteria. All patients were operated upon with trans-tubular Translaminar Microscopic-assisted Percutaneous Nucleotomy (TL-MAPN). Perioperative radiographic and clinical evaluations were reported. The mean follow-up period was 32 months. RESULTS: The mean age was 59 years. L4/L5 was the commonest affected level (27 patients). The mean preoperative VAS for leg pain was 6.44 (±2.06), improved to 0,35 (±0.59) postoperatively. Dural injury occurred in 1 patient, treated with dural patch. Improved neurological function was reported in 41/44 Patients (neurological improvement rate of 93%) at the final follow up. There was a significant improvement in the mean ODI values, from 50.19 ± 4.92 preoperatively to 10.14 ± 2.22 postoperatively (P < 0.001). Sixty four out of 66 patients (96%) showed an excellent or good functional outcome according to Macnab´s criteria. No recurrent herniations were observed. CONCLUSION: The translaminar approach is a viable minimal invasive technique for cranially migrated LDH. The preservation of the flavum ligament is one of the main advantages of this technique. It is an effective, safe and reproducible minimally invasive surgical alternative in treatment of cranially migrated LDHs.

Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer's disease dementia.

We aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia. In a sample of people with MCI (n = 415) we assessed predictive performance of ATN classification using empirical knowledge-based cut-offs for each component of ATN and compared it to two data-driven approaches, logistic regression and RUSBoost machine learning classifiers, which used continuous clinical or biomarker scores. In data-driven approaches, we identified ATN features that distinguish normals from individuals with dementia and used them to classify persons with MCI into dementia-like and normal groups. Both data-driven classification methods performed better than the empirical cut-offs for ATN biomarkers in predicting conversion to dementia. Classifiers that used clinical features performed as well as classifiers that used ATN biomarkers for prediction of progression to dementia. We discuss that data-driven modeling approaches can improve our ability to predict disease progression and might have implications in future clinical trials.

Leveraging machine learning predictive biomarkers to augment the statistical power of clinical trials with baseline magnetic resonance imaging.

A key factor in designing randomized clinical trials is the sample size required to achieve a particular level of power to detect the benefit of a treatment. Sample size calculations depend upon the expected benefits of a treatment (effect size), the accuracy of measurement of the primary outcome, and the level of power specified by the investigators. In this study, we show that radiomic models, which leverage complex brain MRI patterns and machine learning, can be utilized in clinical trials with protocols that incorporate baseline MR imaging to significantly increase statistical power to detect treatment effects. Akin to the historical control paradigm, we propose to utilize a radiomic prediction model to generate a pseudo-control sample for each individual in the trial of interest. Because the variability of expected outcome across patients can mask our ability to detect treatment effects, we can increase the power to detect a treatment effect in a clinical trial by reducing that variability through using radiomic predictors as surrogates. We illustrate this method with simulations based on data from two cohorts in different neurologic diseases, Alzheimer's disease and glioblastoma multiforme. We present sample size requirements across a range of effect sizes using conventional analysis and models that include a radiomic predictor. For our Alzheimer's disease cohort, at an effect size of 0.35, total sample size requirements for 80% power declined from 246 to 212 for the endpoint cognitive decline. For our glioblastoma multiforme cohort, at an effect size of 1.65 with the endpoint survival time, total sample size requirements declined from 128 to 74. This methodology can decrease the required sample sizes by as much as 50%, depending on the strength of the radiomic predictor. The power of this method grows with increased accuracy of radiomic prediction, and furthermore, this method is most helpful when treatment effect sizes are small. Neuroimaging biomarkers are a powerful and increasingly common suite of tools that are, in many cases, highly predictive of disease outcomes. Here, we explore the possibility of using MRI-based radiomic biomarkers for the purpose of improving statistical power in clinical trials in the contexts of brain cancer and prodromal Alzheimer's disease. These methods can be applied to a broad range of neurologic diseases using a broad range of predictors of outcome to make clinical trials more efficient.

Radiofrequency ablation and balloon kyphoplasty for palliation of painful spinal metastases.

PURPOSE: This study was designed with an aim to assess the safety and early postoperative outcomes of the combined Radiofrequency ablation (RFA) and Balloon Kyphoplasty (BKP) used for the treatment of painful neoplastic spinal lesions palliatively. PATIENTS AND METHODS: Between December 2015 and December 2018, 60 patients (35 men and 25 women) with spinal metastases were operated using RFA and BKP at our institution. Transpedicular biopsy was performed in all cases. Patients' demographics, lesion characteristics, concurrent palliative therapies and complications were recorded. All patients were clinically (Pain score VAS 0-10) and radiologically evaluated pre- and postoperatively. Retrospective analysis of data for this cohort was performed. RESULTS: Seventy-five painful spinal metastases (46 in the lumbar spine and 29 in the thoracic region) in 60 patients were operated [transpedicular RFA alone in 5 lesions, and in combination with BKP in 70 lesions (93%)]. The mean pre-procedure and post-procedure VAS for back pain was 7.2/10 and 2.7/10, respectively (p value = 0.0001). No neurological complications related to RFA were found and no cement extravasation into the spinal canal was observed. In two patients, asymptomatic leaks into the needle track, in two patients into draining veins and in one patient into the disk space were detected. CONCLUSION: Combined RFA and BKP appears to be a safe, practical, effective and reproducible palliative treatment for painful spinal osteolytic metastasis. In carefully indicated cases, it relieves pain and maintains stability in a minimal invasive way without adding significant surgical trauma or complications.

White matter hyperintensities and cognition across different Alzheimer's biomarker profiles.

BACKGROUND/OBJECTIVES: To examine the association between white matter hyperintensities (WMH) and cognitive domains such as memory and executive function (EF) across different clinical and biomarker categories of Alzheimer's disease (AD). DESIGN: Cross-sectional study. SETTING: Alzheimer's Disease Neuroimaging Initiative. PARTICIPANTS: A total of 216 cognitively normal (CN) participants and 407 participants with mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) at baseline. MEASUREMENTS: Based on the 2018 research framework, participants were classified using AT(N) (amyloid-ß deposition [A], pathologic tau [T], and neurodegeneration [(N)]) biomarkers into one of three categories: biomarker negative [A - T- (N)-], amyloid negative but other biomarker positive [A - T ± (N)+ or A - T + (N)±] or amyloid positive [A + T ± (N)±]. Linear regression models were then used to examine the association between WMH and memory composite scores and EF composite scores. RESULTS: Higher WMH burden was associated with worse EF in both CN and MCI subgroups while a significant association between WMH and memory was only found in the MCI subgroup. Furthermore, WMH was associated with EF in the group with A - T ± (N)+ or A - T + (N)± biomarker category, but not for A - T - (N)- (normal biomarker) and A + T ± (N) ± (AD pathology). The association between higher WMH and worse memory was independent of amyloid levels in individuals with MCI with evidence of AD pathology. CONCLUSION: Vascular disease, as indexed by WMH, independent of AD pathology affects cognitive function in both CN and MCI subgroups. Future studies using the AT(N) research framework should consider white matter lesions as a key biomarker contributing to the clinical presentation of AD.