Cerebrospinal fluid Aβ42 moderates the relationship between brain functional network dynamics and cognitive intraindividual variability

Relationships between hourly cognitive variability and risk of Alzheimer's disease revealed with mixed-effects location scale models

OBJECTIVE

Observational studies on aging and Alzheimer's disease (AD) typically focus on mean-level changes in cognitive performance over relatively long periods of time (years or decades). Additionally, some studies have examined how trial-level fluctuations in speeded reaction time are related to both age and AD. The aim of the current project was to describe patterns of variability across repeated days of testing as a function of AD risk in cognitively normal older adults.

METHOD

The current project examined the performance of the Ambulatory Research in Cognition (ARC) smartphone application, a high-frequency remote cognitive assessment paradigm, that administers brief tests of episodic memory, spatial working memory, and processing speed. Bayesian mixed-effects location scale models were used to explore differences in mean cognitive performance and intraindividual variability across 28 repeated sessions over a 1-week assessment interval as function of age and genetic risk of AD, specifically the presence of at least one apolipoprotein E (APOE) ε4 allele.

RESULTS

Mean performance on processing speed and working memory was negatively related to age and APOE status. More importantly, e4 carriers exhibited increased session-level variability on a test of processing speed compared to noncarriers. Age and education did not consistently relate to cognitive variability, contrary to expectations.

CONCLUSION

Preclinical AD risk, defined as possessing at least one APOE ε4 allele, is not only associated with mean-level performance differences, but also with increases in variability across repeated testing occasions particularly on a test of processing speed. Thus, cognitive variability may serve as an additional and important indicator of AD risk. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Cognitive intra-individual variability in breast cancer survivors: A systematic review

Subjective and objective cognitive impairments in Breast Cancer Survivors (BCS) often do not correlate. One important contribution to the reported disparities may be the reliance on mean-based cognitive performance. Cognitive intra-individual variability (IIV) may provide important insights into these reported disparities. Cognitive IIV refers to the fluctuation in performance for an individual on either one cognitive task across a trial or dispersed across tasks within a neuropsychological test battery. The purpose of this systematic review was to search for and examine the literature on cognitive IIV in BCS. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach was used to search for all articles related to cognitive IIV in BCS. MEDLINE (via PubMed), Embase, and Scopus databases were searched using detailed search terms and strategies. Initially, 164 articles were retrieved but only 4 articles met the criteria for this systematic review. BCS differed from healthy controls in similar ways across the four studies, generally demonstrating similar performance but showing increased cognitive IIV for the more difficult tasks. Differences were enhanced later during chemotherapy. The four studies provide support for cognitive IIV as a useful measure to detect the subtle objective cognitive change often reported by BCS but frequently not detected by standard normed-based cognitive testing. Unexpectedly, measures of cognitive IIV were not consistently associated with self-reported measures of cognition.

Predicting survival in glioblastoma with multimodal neuroimaging and machine learning

PURPOSE

Glioblastoma (GBM) is the most common and aggressive malignant glioma, with an overall median survival of less than two years. The ability to predict survival before treatment in GBM patients would lead to improved disease management, clinical trial enrollment, and patient care.

METHODS

GBM patients (N = 133, mean age 60.8 years, median survival 14.1 months, 57.9% male) were retrospectively recruited from the neurosurgery brain tumor service at Washington University Medical Center. All patients completed structural neuroimaging and resting state functional MRI (RS-fMRI) before surgery. Demographics, measures of cortical thickness (CT), and resting state functional network connectivity (FC) were used to train a deep neural network to classify patients based on survival (< 1y, 1-2y, >2y). Permutation feature importance identified the strongest predictors of survival based on the trained models.

RESULTS

The models achieved a combined cross-validation and hold out accuracy of 90.6% in classifying survival (< 1y, 1-2y, >2y). The strongest demographic predictors were age at diagnosis and sex. The strongest CT predictors of survival included the superior temporal sulcus, parahippocampal gyrus, pericalcarine, pars triangularis, and middle temporal regions. The strongest FC features primarily involved dorsal and inferior somatomotor, visual, and cingulo-opercular networks.

CONCLUSION

We demonstrate that machine learning can accurately classify survival in GBM patients based on multimodal neuroimaging before any surgical or medical intervention. These results were achieved without information regarding presentation symptoms, treatments, postsurgical outcomes, or tumor genomic information. Our results suggest GBMs have a global effect on the brain's structural and functional organization, which is predictive of survival.

Brain network decoupling with increased serum neurofilament and reduced cognitive function in Alzheimer's disease

Neurofilament light chain, a putative measure of neuronal damage, is measurable in blood and CSF and is predictive of cognitive function in individuals with Alzheimer's disease. There has been limited prior work linking neurofilament light and functional connectivity, and no prior work has investigated neurofilament light associations with functional connectivity in autosomal dominant Alzheimer's disease. Here, we assessed relationships between blood neurofilament light, cognition, and functional connectivity in a cross-sectional sample of 106 autosomal dominant Alzheimer's disease mutation carriers and 76 non-carriers. We employed an innovative network-level enrichment analysis approach to assess connectome-wide associations with neurofilament light. Neurofilament light was positively correlated with deterioration of functional connectivity within the default mode network and negatively correlated with connectivity between default mode network and executive control networks, including the cingulo-opercular, salience, and dorsal attention networks. Further, reduced connectivity within the default mode network and between the default mode network and executive control networks was associated with reduced cognitive function. Hierarchical regression analysis revealed that neurofilament levels and functional connectivity within the default mode network and between the default mode network and the dorsal attention network explained significant variance in cognitive composite scores when controlling for age, sex, and education. A mediation analysis demonstrated that functional connectivity within the default mode network and between the default mode network and dorsal attention network partially mediated the relationship between blood neurofilament light levels and cognitive function. Our novel results indicate that blood estimates of neurofilament levels correspond to direct measurements of brain dysfunction, shedding new light on the underlying biological processes of Alzheimer's disease. Further, we demonstrate how variation within key brain systems can partially mediate the negative effects of heightened total serum neurofilament levels, suggesting potential regions for targeted interventions. Finally, our results lend further evidence that low-cost and minimally invasive blood measurements of neurofilament may be a useful marker of brain functional connectivity and cognitive decline in Alzheimer's disease.

Combined Cognitive Training and Vortioxetine Mitigates Age-Related Declines in Functional Brain Network Integrity

OBJECTIVE

Age-related cognitive decline is common and potentially modifiable with cognitive training. Combining cognitive training with pro-cognitive medication offers an opportunity to modify brain networks to mitigate age-related cognitive decline. We tested the hypothesis that the efficacy of cognitive training could be amplified by combining it with vortioxetine, a pro-cognitive and pro-neuroplastic multimodal antidepressant.

METHODS

We evaluated the effects of 6 months of computerized cognitive training plus vortioxetine (versus placebo) on resting state functional connectivity in older adults (age 65+) with age-related cognitive decline. We first evaluated the association of functional connectivity with age and cognitive performance (N = 66). Then we compared the effects of vortioxetine plus cognitive training versus placebo plus cognitive training on connectivity changes over the training period (n = 20).

RESULTS

At baseline, greater age was significantly associated with lower within-network strength and network segregation, and poorer cognitive function. Cognitive training plus vortioxetine over 6 months positively impacted the relationship between age to mean network segregation. These effects were not observed in the placebo group. In contrast, vortioxetine did not modify the relationship of age to change in mean within-network strength. Exploratory analyses identified the cingulo-opercular network as the network most affected by cognitive training plus vortioxetine.

CONCLUSION

This preliminary study provides evidence that combining cognitive training with pro-cognitive medication may modulate the effects of aging on functional brain networks. Results indicate that for older adults experiencing age-related cognitive decline, vortioxetine has a potentially beneficial effect on the correspondence between aging and functional brain network segregation. These results await replication in a larger sample.

Intraindividual Cognitive Variability and Magnetic Resonance Imaging in Aging American Indians: Data from the Strong Heart Study

BACKGROUND

American Indians have high prevalence of risk factors for Alzheimer's disease and related dementias (ADRD) compared to the general population, yet dementia onset and frequency in this population are understudied. Intraindividual cognitive variability (IICV), a measure of variability in neuropsychological test performance within a person at a single timepoint, may be a novel, noninvasive biomarker of neurodegeneration and early dementia.

OBJECTIVE

To characterize the cross-sectional associations between IICV and hippocampal, total brain volume, and white matter disease measured by magnetic resonance imaging (MRI) among older American Indians.

METHODS

IICV measures for memory, executive function, and processing speed, and multidomain cognition were calculated for 746 American Indians (aged 64-95) who underwent MRI. Regression models were used to examine the associations of IICV score with hippocampal volume, total brain volume, and graded white matter disease, adjusting for age, sex, education, body mass index, intracranial volume, diabetes, stroke, hypertension, hypercholesterolemia, alcohol use, and smoking.

RESULTS

Higher memory IICV measure was associated with lower hippocampal volume (Beta = -0.076; 95% CI -0.499, -0.023; p = 0.031). After adjustment for Bonferroni or IICV mean scores in the same tests, the associations were no longer significant. No IICV measures were associated with white matter disease or total brain volume.

CONCLUSION

These findings suggest that the IICV measures used in this research cannot be robustly associated with cross-sectional neuroimaging features; nonetheless, the results encourage future studies investigating the associations between IICV and other brain regions, as well as its utility in the prediction of neurodegeneration and dementia in American Indians.

Cognitive dispersion is elevated in amyloid-positive older adults and associated with regional hypoperfusion

OBJECTIVE

Cognitive dispersion across neuropsychological measures within a single testing session is a promising marker predictive of cognitive decline and development of Alzheimer's disease (AD). However, little is known regarding brain changes underlying cognitive dispersion, and the association of cognitive dispersion with in vivo AD biomarkers and regional cerebral blood flow (CBF) has received limited study. We therefore examined associations among cognitive dispersion, amyloid-beta (Aβ) positivity, and regional CBF among older adults free of dementia.

METHOD

One hundred and forty-eight Alzheimer's Disease Neuroimaging Initiative (ADNI) participants underwent neuropsychological testing and neuroimaging. Pulsed arterial spin labeling (ASL) magnetic resonance imaging (MRI) was acquired to quantify CBF. Florbetapir positron emission tomography (PET) imaging determined Aβ positivity.

RESULTS

Adjusting for age, gender, education, and mean cognitive performance, older adults who were Aβ+ showed higher cognitive dispersion relative to those who were Aβ-. Across the entire sample, higher cognitive dispersion was associated with reduced CBF in inferior parietal and temporal regions. Secondary analyses stratified by Aβ status demonstrated that higher cognitive dispersion was associated with reduced CBF among Aβ+ individuals but not among those who were Aβ-.

CONCLUSIONS

Cognitive dispersion may be sensitive to early Aβ accumulation and cerebrovascular changes adjusting for demographics and mean neuropsychological performance. Associations between cognitive dispersion and CBF were observed among Aβ+ individuals, suggesting that cognitive dispersion may be a marker of brain changes among individuals on the AD continuum. Future studies should examine whether cognitive dispersion predicts brain changes in diverse samples and among those with greater vascular risk burden.

Dynamic Functional Connectivity Alterations and Their Associated Gene Expression Pattern in Autism Spectrum Disorders

Autism spectrum disorders (ASDs) are a group of heterogeneous neurodevelopmental disorders that are highly heritable and are associated with impaired dynamic functional connectivity (DFC). However, the molecular mechanisms behind DFC alterations remain largely unknown. Eighty-eight patients with ASDs and 87 demographically matched typical controls (TCs) from the Autism Brain Imaging Data Exchange II database were included in this study. A seed-based sliding window approach was then performed to investigate the DFC changes in each of the 29 seeds in 10 classic resting-state functional networks and the whole brain. Subsequently, the relationships between DFC alterations in patients with ASDs and their symptom severity were assessed. Finally, transcription-neuroimaging association analyses were conducted to explore the molecular mechanisms of DFC disruptions in patients with ASDs. Compared with TCs, patients with ASDs showed significantly increased DFC between the right dorsolateral prefrontal cortex (DLPFC) and left fusiform/lingual gyrus, between the DLPFC and the superior temporal gyrus, between the right frontal eye field (FEF) and left middle frontal gyrus, between the FEF and the right angular gyrus, and between the left intraparietal sulcus and the right middle temporal gyrus. Moreover, significant relationships between DFC alterations and symptom severity were observed. Furthermore, the genes associated with DFC changes in ASDs were identified by performing gene-wise across-sample spatial correlation analysis between gene expression extracted from six donors’ brain of the Allen Human Brain Atlas and case-control DFC difference. In enrichment analysis, these genes were enriched for processes associated with synaptic signaling and voltage-gated ion channels and calcium pathways; also, these genes were highly expressed in autistic disorder, chronic alcoholic intoxication and several disorders related to depression. These results not only demonstrated higher DFC in patients with ASDs but also provided novel insight into the molecular mechanisms underlying these alterations.